The detailed notes in this page explains the supporting data and evidence behind the facts we present in the book Factfulness, page by page, with links to the underlying sources.

VERSION 3 — Published: October 4, 2018  — This document is freely available under CC BY 4.0 LICENSE

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About this document

At the end of the book Factfulness (page 275 in the English edition) we are listing notes about the core facts in the book, but then we link to this document which is an extended version with much more details and links to hundreds of underlying sources.
We want all facts in the book Factfulness to be based on the most reliable data that exist. But nobody can be an expert in every field. It’s quite likely you know of a better source than the ones we use, or you may have evidence that conflicts with the sources we use. We want to constantly improve the fact–base of this book, and revise it in future editions. Please give us feedback or ask questions here: gapm.io/feedback

Thanks!

Anna Rosling Rönnlund & Ola Rosling
Co-authors of Factfulness and Co-founders of the Gapminder Foundation

Stockholm, Sweden, October 4, 2018

Current document status

This document is still work in progress. Hundreds of thousands of people are reading the book and they requests all kinds of clarifications and improved documentation. Please expect updates to these notes during the next couple of months.

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General Notes

A note about source acronyms

In this list, we use the same acronyms and identifiers for sources, e.g. UN-Pop[1], which are the same as you find in the Sources section at the end of the printed book.

Data for 2017

Throughout the book, where economic indicators do not extend to 2017, Gapminder has extended the series, mainly using forecasts from World Economic Outlook from IMF[1]. For extending demographic data, we have used the 2017 revision of World Population Prospects from UN Population Division; see UN-Pop[1]. See www.gapm.io/eext.

Country boundaries

Throughout the book, we refer to countries in the past as if they always had the boundaries they have today. For example, we talk about Bangladesh’s family sizes and life expectancy in 1942 as if it had been an independent country at that time, although in reality it was still under British rule as part of British India. See this page for more on the country borders we use in our charts.

Inside Cover

Inside front cover: World Health Chart 2017

When you open the book, you see a colorful chart: the World Health Chart 2017. This chart shows 182 states recognized by the UN, only excluding those with the smallest populations (such as the Vatican). Each bubble is a country. The size of the bubble represents the country’s population, and the color of the bubble its geographical region.

Gapminder has defined four regions, and color coded each of them: green for the Americas, blue for Africa, red for Asia and Australia, and yellow for Europe, including Greenland, Russia, and some parts of Central Asia. Read more about the four regions we use in our global graphs.

The x-axis shows GDP per capita (PPP in constant 2011 international $) on a logarithmic scale, which Gapminder[3] has divided into four income levels. The population data comes from UN-POP[1]. The GDP data comes from World Bank[1] and has been extended to 2017 by Gapminder[2] with forecasts from IMF[1]. The y-axis shows life expectancy at birth, based on data for 2016 from IHME[1] and extended to 2017 by Gapminder[4].

For the animated version of this chart, together with more information, visit gapminder.org/whc.

Inside cover at the end: People by region and income

The chart on the pastedown at the end of the book shows people by region and income. The maps show the UN-POP[1] population numbers from the medium fertility variant of the World Population Prospect 2017, rounded to full billions by Gapminder[3]. Household per capita are in PPP 2011 $/day using PPP from ICP[1]. Income and gini data for 2013 comes from PovCal, extended to 2017 and 2040 by Gapminder, with a method similar to what Hellebrandt and Mauro[1] describe in “The Future of Worldwide Income Distribution” from April 2015. Using IMF[1] forecasts extended by Gapminder[8] all the way to 2040.

The log normal distributions were calculated with the method provided by Bas van Leeuwen, as used in the van Zanden[1] paper “World Income Inequality 1820-2000”. Gapminder adjusted the assumption about log normal distribution and instead distributed people along a different shape that matches the PovCal survey based distributions much better.

The forecasted populations in different regions on different income levels in 2017 and 2040 are described in more detail here. Gapminder[12], see Income Mountains v. See Gapminder[12]. Read more here: www.gapminder.org/sources/income-mountains.

• PovcalNet “An Online Analysis Tool for Global Poverty Monitoring.” Founded by Martin Ravallion, at the World Bank. Accessed November 30, 2017. http://iresearch.worldbank.org/PovcalNet.

Author’s Note

Page ix. Gapminder

Gapminder is an economically independent, educational non-profit foundation with no political or religious affiliations. Gapminder is a fact tank, not a think tank. It was founded in 2005 by the three authors of Factfulness. Based in Stockholm, Sweden, Gapminder reports to the city council of Stockholm. For more information, see www.gapminder.org/about.

Introduction

Page 1. X-ray

The X-ray was taken by Staffan Bremmer at Sophiahemmet in Stockholm. The sword swallower is a friend of Hans’s, called Maryanne Magdalen. Her website is here: www.gapm.io/xsword. The original image that Hans saw was of Hjalmar Wickman, who later inspired Hans and taught him the art of sword swallowing.

Page 2. Hans’s sword swallowing

Watch Hans swallow his bayonet at the end of his second TED-talk at www.gapm.io/xtedros2.

Pages 3-5: The 13 Fact Questions

The 13 fact questions are listed in the beginning of the book Factfulness (pp. 1–8). Below are the underlying sources behind each of the fact questions, including links to further data and documentation.

Page 3. Fact question 1: Girls in school in low-income countries

Correct answer is C. 60 percent of the girls in low-income countries finish primary school. According to World Bank[3], the number was 63.2 percent in 2015, but we rounded it to 60 percent to avoid overstating progress. The definition of “low-income countries” is intentionally left out of the question, since we also aim to investigate how people interpret the term, as described in Chapter One. Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age and divided by the population at the entrance age for the last grade of primary education, which is roughly at age 11.

World Bank[2] defines 31 countries as low-income countries in 2017. The data is based on estimates from UNESCO[3] compiled primarily from household surveys conducted by USAID-DHS[1] and UNICEF-MICS. See gapm.io/q1.

Data Sources

• World Bank[2] “World Bank Country and Lending Groups.” Available at: www.gapm.io/xwb172
• World Bank[3] “Primary completion rate, female (% of relevant age group).” Available at: www.gapm.io/xwb173
• UNESCO[3] (United Nations Educational, Scientific and Cultural Organization) “Education: Out-of-school rate for children of primary school age, female.” Available at: www.gapm.io/xuisoutsf
• UNICEF-MICS Multiple Indicator Cluster Surveys. Funded by the United Nations Children’s Fund. Online at: www.mics.unicef.org
• USAID-DHS[1] Demographic and Health Surveys (DHS), funded by USAID. Online at: www.dhsprogram.com

Page 3. Fact Question 2: Where the Majority Lives

The correct answer is B. The majority of people live in middle-income countries. The definitions of the three income groups are intentionally left out of the question, since we also aim to investigate how people interpret the term. The World Bank[2] divides countries into income groups based on gross national income (GNI) per capita in current US $. According to the World Bank[4], the low-income countries represent 9 percent of the world population, the middle-income countries, 76 percent of the world population, and the high-income countries, 16 percent of the world population, as follows:

• Low income countries
  • Less than $1,005 GNI per capita
  • 31 countries
  • Total population: 0.7 billion people
• Middle income countries
  • $1,006 to $12,235 GNI per capita
  • 109 countries
  • Total population: 5.6 billion people
• High income countries
  • $12,236 or more GNI per capita
  • 78 countries
  • Total population: 1.2 billion people

See www.gapm.io/q2.

Data Sources

• World Bank[2] “World Bank Country and Lending Groups.” Accessed November 6, 2017. Available at: www.gapm.io/xwb172
• World Bank[4] “Population of Country Income Groups in 2015—Population, total.” Accessed November 7, 2017. Available at: www.gapm.io/xwb174

Page 3. Fact Question 3: Extreme Poverty

Correct answer is C. The share of people living on less than $1.9/day fell from 34 percent in 1993 to 10.7 percent in 2013, according to World Bank[5]. Despite the impression of precision given by the precise threshold of $1.9/day and the use of decimals, the uncertainties in these numbers are very large. Extreme poverty is very difficult to measure: the poorest people are mostly subsistence farmers with insufficient harvests or destitute slum dwellers, with unpredictable and constantly changing living conditions and few documented monetary transactions. The staff at PovcalNet tells us that the uncertainty of their estimates is probably in the range of half a billion people. But even if the exact levels are uncertain, the trend direction is not uncertain, because the method for estimating has not changed, and thus the sources of error are probably constant over time. We can trust that the level has fallen to at least half, if not one-third. See www.gapm.io/q3.

Page 3. Fact Question 4: Life Expectancy

Correct answer is C. The average global life expectancy for those born in 2016 was 72.48 years, according to IHME[1]. The UN-Pop[3] estimate is slightly lower, at 71.9 years. We rounded it to 70 to avoid overstating progress. The three answer alternatives were chosen by Gapminder after first having asked the question with an open answer field, letting respondents write any age they wanted. Most people wrote 50 or 60 years. See www.gapm.io/q4.

Page 4. Fact Question 5: Future Number of Children

Correct answer is C. The UN experts (i.e. the demographers of the UN Population Division) publish new official population forecasts every second year in their publication World Population Prospect. They work with multiple alternative scenarios. The one they think is most probable is called the ‘medium fertility variant’, which falls between the highest and lowest predictions of fertility and mortality decline worldwide. For the past ten years, the UN Population Division has published forecasts of this scenario predicting that the number of children in the year 2100 will not be higher than it is today. In their latest revision published in 2017, the UN Population Division estimates that there are 1.975 billion children (aged 0 to 14) in 2017 and forecast that the number will be 1.957 billion in 2100 (having peaked at 2.094 in the year 2057); see UN-Pop[2]. Nobody can know for sure, but the question is only asking which forecast is the most likely. See www.gapm.io/q5.

Page 4. Fact Question 6: Why is the Population Increasing?

Correct answer is B. In their forecasts, the UN experts (i.e. the demographers of the UN Population Division) calculate that 1 percent of population increase will come from 0.37 billion more children (age 0 to 14), 69 percent from 2.5 billion more adults (age 15 to 74), and 30 percent from 1.1 billion more very old people (age 75 and older). The data comes from UN-Pop[3]. See www.gapm.io/q6.

Page 4. Fact Question 7: Natural Disasters

Correct answer is C. Annual deaths from natural disasters have decreased by 75 percent over the past 100 years, according to EM-DAT. Since disasters vary from year to year, we compare ten-year averages. In chapter 4, where we further discuss the decline, we also use 25-year averages. In the last ten years (2007–2016), on average 80,386 people were killed by natural disasters per year. This is 25 percent of the number 100 years earlier (1907–1916), when it was 325,742 deaths per year.

The huge decline in disaster deaths would be even more striking if two other major global changes were also taken into account. First, the number of people has increased by four, which calls for counting disaster deaths per capita. 1907–1916, there were 181 disaster deaths per million people. 2007–2016, the number was 11. The relative number has dropped to 6 percent of what it was 100 years ago. Second, 100 years ago the communication technologies for reporting disasters were very primitive, compared to the monitoring of today, which means that many catastrophes must have gone unrecorded or been underreported.

The EM-DAT includes death toll estimates for 8,969 disasters recorded worldwide since 1900. All known emergency events have been categorized as follows: Animal accident, Complex disasters, Drought, Earthquake, Epidemic, Extreme temperature, Flood, Fog, Impact, Insect infestation, Landslide, Mass movement (dry), Storm, Volcanic activity, Wildfire. See www.gapm.io/q7.

Data Sources

• EM-DAT Centre for Research on the Epidemiology of Disasters (CRED). The International Disaster Database. Debarati Guha-Sapir, Université catholique de Louvain. Accessed November 5, 2017. Available at: www.emdat.be

Page 4. Fact Question 8: Where People Live

Correct answer is A. The world population in 2017 is 7.55 billion, according to UN-Pop[1]. That would usually be rounded to 8 billion, but we show 7 billion because we are rounding the population region by region. The populations of the four Gapminder regions were estimated based on national data from UN-Pop[1]: the Americas, 1.0 billion; Europe, 0.84 billion; Africa, 1.3 billion; Asia, 4.4 billion. See www.gapm.io/q8.

Page 4. Fact question 9: Vaccination

Correct answer is C. 88 percent of 1-year-old children in the world today are vaccinated against some disease, according to WHO[1]. We rounded it down to 80 percent to avoid overstating progress. The common vaccines that reached most 1-year-olds worldwide in 2016, sorted by coverage level, are as follows:

• BCG (Tuberculosis): 88%
• DTP3 (Diphtheria tetanus toxoid and pertussis): 86%
• MCV1 (Measles, 1st dose): 85%
• Pol3 (Polio): 85%
• HepB3 (Hepatitis B): 84%
• PAB (Neonatal tetanus): 84%
• Hib3 (Haemophilus influenzae type b): 70%
• MCV2 (Measles, 2nd dose): 64%
• PCV3 (Pneumococcal conjugate): 42%
• RotaC (Rotavirus): 15%

The estimate for immunization coverage of the vaccine against TB is based on Global Health Observatory data from WHO[10]. For vaccination data by category, see WHO[1]. See www.gapm.io/q9.

Page 5. Fact Question 10: Women’s Education

Correct answer is A. Worldwide, women aged 25 to 34 have an average of 9.09 years of schooling, and men have 10.21, according to IHME[2] estimates from 188 countries. Women aged 25 to 29 have an average of 8.79 years of schooling, and men 9.32 years, according to Barro and Lee (2013) estimates from 146 countries in 2010. As always, there is uncertainty in these kinds of estimates, but there is no reason to assume that the difference between genders is nearly as large as what people think. Gapminder first asked this question with an open answer field to see how responses were distributed, before deciding on three exact values for alternatives A, B and C, in order to make the skewed perception easier to compare to random results. See gapm.io/q10.

Page 5. Fact Question 11: Endangered Species

Correct answer is C. None of the three species are classified as more critically endangered today than they were in 1996. The data is based on the IUCN Red List of Threatened Species; see IUCN Red List[4] below, with tables on the numbers of threatened species between 1996 and 2017.

The tiger (Panthera tigris) was classified as Endangered (EN) in 1996, and it still is; see IUCN Red List[1]. According to a statement from WWF in 2016, the number of wild tigers are increasing: “After a century of decline, tiger numbers are on the rise. At least 3,890 tigers remain in the wild, but much more work is needed to protect this species that’s still vulnerable to extinction.” See also Platt (2016) in Scientific American reporting on the rising tiger numbers.

According to IUCN Red List[2], the giant panda was classified as Endangered (EN) in 1996, but in 2015, new assessments of increasing wild populations resulted in a change of classification to the less critical status of Vulnerable (VU).

The black rhino was classified as Critically Endangered (CR) and still is; see IUCN Red List[3]. The International Rhino Foundation states that wild populations are slowly increasing, estimating the population at 5,042–5,455 in their annual report for 2016. And the slow increase remains in March 2018, according to updates from the Rhino foundation. See www.gapm.io/q11.

Data Sources

• IUCN Red List[1] Goodrich, J., et al. “Panthera tigris (Tiger).” (2015) e.T15955A50659951. Available at: www.gapm.io/xiucnr1
• IUCN Red List[2] Swaisgood, R., et al. “Ailuropoda melanoleuca (Giant Panda).” (2016) e.T712A121745669. Available at: www.gapm.io/xiucn2
• IUCN Red List[3] Emslie, R. “Diceros bicornis (Black Rhinoceros, Hook-lipped Rhinoceros).” (2012) e.T6557A16980917. Available at: www.gapm.io/xiucn3
• IUCN Red List[4] Table 1: Numbers of threatened species by major groups of organisms (1996–2017). Available as PDF: www.gapm.io/xiucnr4

Page 5. Fact Question 12: Electricity

Correct answer is C. A majority of the world population, 85.3 percent, had some access to the electricity grid in their countries, according to GTF. We rounded this down to 80 percent to avoid overstating progress. The term “access” is defined differently in all their underlying sources. In some extreme cases, households may experience an average of 60 power outages per week and still be listed as “having access to electricity.” The question, accordingly, talks about “some” access. For the measures of access to electricity. GTF is a collaboration between the World Bank and the International Energy Agency; see www.gapm.io/xgtf. See www.gapm.io/q12.

Page 5. Fact Question 13: Climate Change

Correct answer is A. “Climate experts” refers to the 274 authors of the IPCC’s Fifth Assessment Report (AR5), published in 2014 by the Intergovernmental Panel on Climate Change; see IPCC[1]. Here is what they predict about the changes in the climate system, as summarized in IPCC[2]:

Surface temperature is projected to rise over the 21st century under all assessed emission scenarios. It is very likely that heat waves will occur more often and last longer, and that extreme precipitation events will become more intense and frequent in many regions. The ocean will continue to warm and acidify, and global mean sea level to rise.

See www.gapm.io/q13.

Data Sources

• IPCC[1] (Intergovernmental Panel on Climate Change) Fifth Assessment Report (AR5) Authors and Review Editors. May 2014. Download PDF: www.gapm.io/xipcca
• IPCC[2] Climate Change 2014—Synthesis Report Summary for Policymakers. “SPM 2.2 Projected changes in the climate system.” Accessed August 18 2018, p. 10. Download PDF: www.gapm.io/xipcc

Page 7. Fact questions online

In 2017, the Gapminder Test launched. It consists of 13 fact questions, all with an A, B, C alternative. The test is also freely available in multiple languages online. Here’s the Gapminder Test 2018 »

Pages 7–8 and Appendix. Poll results

In 2017, Gapminder worked with Ipsos-MORI and Novus to test 12,000 people in 14 countries. Their polls were conducted with online panels weighted to be representative of the adult populations. The average number of correct answers for the 12 fact questions—i.e. excluding question 13 on climate change—was 2.2, which we rounded to 2. The results of the online polls by question and country are set out in the appendix (p. 267, Factfulness). The results are also presented here: Gapminder Misconception Study 2017.

Pages 7–8. Vaccination

Vaccination data comes from WHO[1]. Even in Afghanistan, more than 60 percent of the one-year-olds today have received multiple vaccinations. None of these vaccines existed when Sweden was on Level 1 or 2, which is part of the reason lives were shorter in Sweden back then. See www.gapm.io/tvac.

Page 8. Public awareness of climate change

The first scientific hypothesis on man-made climate change due to CO2 emissions was published as early as 1896. The “few decades” mentioned here is the time elapsed since the formation of a broad consensus amongst scientist about fundamental facts in the matter, roughly in the 1980s, and the establishment of the Intergovernmental Panel on Climate Change in 1988.

Page 10. Hans testing students

The early results from testing students are described in Hans’s first TED talk called “The best stats you’ve ever seen.” The TED talk is available at www.gapm.io/xtedros.

Page 12. World Economic Forum lecture

For a video recording of Hans showing the audience results at the World Economic Forum in Davos 2015, see www.gapm.io/xwbros5m. Poll results from events, including the results from the experts at WEF, will soon be available at www.gapm.io/rrs.

Page 13. The Ten Instincts in Cognitive Science

Our thinking on the ten instincts was influenced by the work of a number of brilliant cognitive scientists. Here are some of the books that completely changed our thinking about the mind and about how we should teach facts about the world:

• Dan Ariely
  • Predictably Irrational (2008);
  • The Upside of Irrationality (2010);
  • The Honest Truth About Dishonesty (2012);
• Steven Pinker
  • How the Mind Works (1997);
  • The Stuff of Thought (2007);
  • The Blank Slate (2002);
  • The Better Angels of Our Nature (2011);
• Carol Tavris and Elliot Aronson
  • Mistakes Were Made (But Not by Me) (2007);
• Daniel Kahneman
  • Thinking, Fast and Slow (2011);
• Walter Mischel
  • The Marshmallow Test (2014);
• Philip E. Tetlock and Dan Gardner
  • Superforecasting (2015);
• Jonathan Gottschall
  • The Storytelling Animal (2012);
• Jonathan Haidt
  • The Happiness Hypothesis (2006);
  • The Righteous Mind (2012);
• Thomas Gilovich
  • How We Know What Isn’t So (1991)

Many of these authors study cognitive biases. At the moment of writing, English Wikipedia lists 186 studied biases. Psychologists struggle to capture these quirks in the mental machine in lab experiments so that they can be replicated or falsified. That’s not how we developed our list of misconceptions and instincts—they only describe our hypothesis on how the common ways of wrong thinking may work.

The instinct to generalize appear in The Stuff of Thought (2007) by Steven Pinker. In his book on language and the human mind, Pinker explains how we generalize to make sense of the world. With metaphors and other linguistic tools, we use language to best accommodate our minds, for example by grouping objects and stuff of thought into categories.

Page 14. Cognitive biases

The idea of explaining cognitive biases using the Müller-Lyer illusion comes from Thinking, Fast and Slow by Daniel Kahneman (2011).

Chapter One: The Gap Instinct

Page 19. Child mortality in 1995 and 2017

Child mortality data used in the 1995 lecture came from UNICEF[1]. In this book we have updated the examples, using 2017 estimations from UN-IGME—a data collaboration between UNICEF, WHO, UN Population Division and the World Bank. The numbers used in the evening lecture back in 1995 were only slightly different: Saudi Arabia in 1960 was 292 and in 1993, 38; Malaysia in 1960 was 105 and in 1993, 17; Brazil in 1960 was 181 and in 1993, 63; Tanzania in 1960 was 249 and in 1993, 16.

The data from UNICEF[1] is based on The State of the World’s Children 1995, published by Oxford U P, available to download as PDF here. Data for 2017 is derived from “Child Mortality Estimates” from www.childmortality.org. UN-IGME—United Nations Inter-agency Group for Child Mortality Estimation—is a data collaboration between UNICEF, WHO, UN Population Division and the World Bank.

Page 21. Improvements in Sweden and Saudi Arabia

Sweden had a child mortality rate of 249 in 1869. It dropped below 35 in 1946, a process that took 77 years. Saudi Arabia moved from 242 in 1960 to 35 in 1993, roughly the same difference in 33 years. For detailed documentation behind our data on child mortality, see www.gapm.io/itfr.

Page 25. Graph: The world in 1965

Each circle, or bubble, represents a country. The area of the circle is proportional to the population of the country, using data from UN-POP[1]. On the x-axis is the total fertility rate, using data from UN-POP[3]. The scale is reversed putting large families to the left and small to the right. This is to show progress as a movement from left to right, which is more intuitive. The y-axis shows the child survival rate in percent. These numbers are more commonly expressed as the indicator child mortality rate in deaths before 5 years of age per 1,000 live births. Instead of deaths per thousand we changed the rate to percent (deaths per 100) because it is more broadly understood, and we also show survival instead of mortality so that the positive direction is upward, which intuitively is more positive. Data comes from UN-IGME.

The two boxes are not showing any official thresholds. They are there to visually emphasize the divided world that existed in 1965, in which 125 countries with 68 percent of the world’s population were in the “developing” box. Only 44 countries, with 30 percent of the world’s population, were in the “developed” box. An interactive version of the chart will soon be freely available at www.gapm.io/voutdwv.

Page 26. Graph: The world in 2017

Each circle, or bubble, represents a country. The area of the circle is proportional to the population of the country, using data from UN-POP[1]. On the x-axis is the total fertility rate, using data from UN-POP[3]. The scale is reversed, putting large families to the left and small to the right. This is to show progress as a movement from left to right, which is more intuitive. The y-axis shows the child survival rate in percent. These numbers are more commonly expressed as the child mortality rate in deaths before 5 years of age, per 1,000 live births. We changed the rate to percent, because it is more broadly understood, and we also show survival instead of mortality so that the positive direction is upward, which intuitively is more positive. The UN-IGME data for the child mortality rate ends in 2016. Gapminder[6] extended the series by using the percentage change expected by UN WPP 2017 medium fertility variant from World Population Prospects 2017.

The two boxes are not showing any official thresholds. They are there for comparison with the world in 1965. In 2017, only 13 countries with 6.4 percent of the world population are still in the “developing” box. Those are as follows: Angola, Burkina Faso, Burundi, Chad, DR Congo, East Timor, Gambia, Mali, Mozambique, Niger, Nigeria, Somalia and Uganda. 37 countries with 8.4% of the world population are between the boxes. 134 countries are in the “developed” box. For an interactive version of the chart, see gapm.io/voutdwv.

Page 28. Danish TV interview

The Danish TV show is called Deadline and the interviewing journalist was Adam Holm. The interview with English subtitles can be seen here: http://gapm.io/imright.

Page 29. Primary school completion rate

The primary school completion rate for girls is below 35 percent in just three countries. But for all three, the uncertainty is high and the numbers are outdated: Afghanistan (1993), 15 percent; South Sudan (2011), 18 percent; Chad (2011), 30 percent. Three other countries—Somalia, Syria, and Libya—have no official number. The girls in these six countries suffer under severe gender inequality, but in total they make up only 2 percent of all girls of primary school age in the world. The data is based on UN-Pop[4], “Annual population by age—Female, medium fertility variant,” available at www.gapm.io/xpopage. Note that in these countries, many boys are also missing school. See gapm.io/twmedu.

Page 30. Polls: Imagining the worst

Gapminder has asked the public in the United States and Sweden how they imagine life in “low-income countries” or “developing countries.” They systematically guessed numbers that would have been correct 30 or 40 years ago. On average, respondents believed life expectancy is roughly 45 years, while World Bank[7] says 62 years. They believed that roughly 20 percent of people in low-income countries have access to an improved water source—the correct number, according to World Bank[8], is 66 percent. Further, they believed 40 percent of children are vaccinated—but data says 78 percent; see World Bank[9] based on WHO[1]. They believed roughly 70 percent are undernourished but it is only 26 percent; see World Bank[10] based on FAO[1]. All data from low-income countries have large uncertainties; still, the uncertainties are smaller than the public’s misconceptions. See www.gapm.io/rdev.

How many live in low-income countries?

Gapminder polled the public, asking the question with an open-answer field so respondents were not limited by our three predefined alternatives. In the US, 61 percent of respondents entered a value above 50 percent, guessing that a majority of people live in low-income countries. The average guesses were 57 percent in the US and 61 percent in Sweden, see Novus[3]. Gapminder also asked the same question but with “low-income countries” replaced by “developing countries”. The results were the same, as if the terms were synonyms.

Data Sources

• UN-Pop[4] “Annual population by age—Female, medium fertility variant,” available at www.gapm.io/xpopage

• World Bank[7] “Life expectancy at birth, total (years).” United Nations Statistical Division. Population and Vital Statistics Reports (various years). Accessed November 8, 2017. Available at: www.gapm.io/xwb177

• World Bank[8] “Improved water source (% of population with access).” WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply and Sanitation. Accessed November 8, 2017. Available at: www.gapm.io/xwb178

• World Bank[9] “Immunization, measles (% of population with access).” Accessed November 8, 2017. Available at: www.gapm.io/xwb179

• World Bank[10] “Prevalence of undernourishment (% of population).” Food and Agriculture Organisation. Accessed November 8, 2017. Available at: www.gapm.io/xwb1710

Page 32. Where 75% of humanity lives

See: Fact Question 2: Where the Majority Lives

Page 32. Graph: Four income levels

The numbers are rounded to billion people to make it easier to remember. Incomes are in price adjusted PPP 2011 dollars by ICP. Gapminder[8] estimates that the number of people on each income level in 2017 are as follows:

• Level 1 has 0.75 billion people living on less than $2 per day
• Level 2 has 3.3 billion people living on incomes between $2 to $8 per day
• Level 3 has 2.5 billion people living on $8 to $32 per day
• Level 4 has 0.9 billion people living on more than $32 per day

The detailed estimates are based on the World Bank’s PovcalNet for 2013 and forecasts from IMF[1]. Povcal[1] is the dataset that the World Bank uses to estimate the official rate of extreme poverty worldwide. Data was collected through household income surveys from across the world. National currencies are converted to comparable dollars adjusted for differences between countries in cost of living. The threshold of $2 per day is almost identical to the World Bank’s $1.9 per day. Gapminder rounded that up to $2 per day to make it easier to remember, and to avoid the false precision in poverty estimates that are very rough. See www.gapm.io/fwlevels

Page 33. Doubling scales

Throughout the book, when talking about personal income levels and countries’ average incomes, we use a doubling scale. This is because the impact of another dollar is completely different on different income levels. Doubling (or logarithmic) scales are also used for measuring the size of earthquakes, populations, sound levels, pH levels, and in many other situations when comparing numbers across a large range and where small differences between small numbers are as important as big differences between big numbers. It’s not a mistake, it’s not cheating. It’s representing the world as it really is: in this case, a world where it is not size of the pay rise that matters, but the size of the pay rise in relation to what you had before. Logarithmic scales are based on the theory of diminishing marginal value, first proposed by Daniel Bernoulli in 1738, as Kahneman (2011) describes in his book Thinking, Fast and Slow (p. 274). Doubling scales are explained with more examples in Factfulness on page 98. See www.gapm.io/esca.

Page 34: People on Level 1

Approximately 0.75 billion people live on Level 1 with less than $2 per day, according to Gapminder[8]. The threshold of $2 per day is almost identical to the World Bank’s $1.9 per day. Gapminder rounded that up to $2 per day to make it easier to remember, and to avoid the false precision in poverty estimates that are very rough.

Page 34. Average family size on Level 1

On Level 1, in most places, families have on average more than five children, and on average one child dies. See: Graph: Family size by income

Page 34. Children working in the household

In Somalia, Ethiopia, and Rwanda, where a majority live in extreme poverty, most girls aged 5 to 14 spend at least 2 hours every day doing household chores like fetching water, gathering firewood and cooking. But where water is far away or firewood is scarce some children spend the whole day, everyday, fulfilling these tasks. Read more in the UNICEF[2] publication: “Narrowing the Gaps—The Power of Investing in the Poorest Children.”

Page 34. Air pollution

UNICEF writes that pneumonia remains the leading infectious cause of death among children under-5 years, and primarily for children under age 2. Pneumonia killed 2,500 children per day in 2015, mainly in extreme poverty. In countries like Tanzania, where half of the population live in extreme poverty, roughly 95 percent of household energy come from burning fuel, according to WHO’s estimates from 2013 on household air pollution around the world. http://gamapserver.who.int/gho/interactive_charts/phe/iap_exposure/atlas.html

In extreme poverty, almost every home uses coal and biomass (wood, animal dung and crop waste) for cooking and heating. Over 4 million people die every year from household air pollution and over 50 percent of deaths from pneumonia among children under age 5 are caused by the smoke particles in the household; see WHO at http://www.who.int/mediacentre/factsheets/fs292/en/ and http://apps.who.int/gho/data/node.wrapper.ENVHEALTHHAP?lang=en&menu=hide. UNICEF on how pneumonia affects children’s health: www.gapm.io/xunicef6.

Page 35: Cost of illness on Level 2

Life is uncertain on Level 1 and 2. Many (maybe half) of those leaving the extreme poverty of Level 1 fall back again within a year or two. In his great book One Illness Away: Why People Become Poor and How They Escape Poverty (2010), Anirudh Krishna describes the effect of disease on poor families who are making progress. Illness often forces them back into destitute, since they have no safety nets or health insurances whatsoever. Level 2 roughly corresponds to the World Bank income group called “Lower middle income”, where people pay on average 55 percent of their health expenditure with cash, see World Bank[11].

Voices of the Poor is a three volume publication from the World Bank[12] of interviews with poor people across 47 countries. Many describe the unfortunate events of illness that lead them back into extreme poverty again soon after escaping.

Page 35. Work in a garment factory

For example in Cambodia, the minimum salary in the garment industry in 2017 was $5 per day, see “Cambodia raises 2017 minimum wage for textile industry workers” reported by Reuters.

Page 36. Fridges and food on Level 2

Many people on Level 2 also have some kind of freezer, but with the unstable electricity grid it’s usually not until Level 3 that it’s worth mentioning. The increased variation of dishes can be seen by looking into the refrigerators of people on different incomes on Dollar Street.

Page 36. Traffic accidents on Level 3

Road injuries is one of the leading causes of disability among working age people on Level 3, based on IHME[3] “Road injuries as a percentage of all disability.” GBD Compare, available at: www.gapm.io/x-ihaj

Page 37. Education on Level 4

The average length of schooling is above 12 years in all countries on Level 4, including many of the gulf states that only recently reached Level 4, like Saudi Arabia and the UAE. The only exceptions on Level 4 are the three small gulf states: Bahrain, Oman, and Qatar. See IHME[2] and Barro-Lee.

Page 37. Travel and vacation on Level 4

More than half the citizens in countries on Level 4 traveled abroad in 2014, and large amount of of those trips were for leisure; see The TripBarometer 2015, by TripAdvisor, based on an online survey conducted from January 16 to February 2 in 2015 by Ipsos MORI. On Level 4, the average number of tourist departures abroad in 2015 was roughly 600 per 1,000 citizens, which is 6 times higher than the average for Level 3, based on World Bank[13] and UN-POP[1].

Page 37. Books on Level 4

On Level 4, the number of new book titles published each year is roughly 1 per 1000 citizens. That is roughly five times higher than the average on Level 3, based on Wikipedia[1] divided by UN-POP[1].

Data Sources

• ICP[1] (International Comparison Program) “Purchasing Power Parity $ 2011.” www.gapm.io/x-icpp

Page 38. Historic poverty rate

For the estimate of 85 percent on Level 1 in 1800, see the note to page 52, Extreme poverty graph.

Page 38. Incomes in Western Europe and the US in the 1950s

Historic records of GDP per capita, adjusted for inflation and price differences, puts the majority of Western Europe and the US in the 1950s on level 2 and 3. That is where the majority of the world population is today. For more on the historic income distribution of Europe and the US, see Gapminder[8].

Page 38. Outdated terminology and the World Bank

For one of these lectures, filmed June 8, 2015, see World Bank[14]. The outdated terminology of a divided worldview is specifically challenged beginning at 01:30:31. Five months later, the World Bank announced in a public blog post (World Bank[15]) that they had taken the challenge. With references to Hans’s latest presentation, and to an interview with Bill Gates in the New York Times, the World Bank finally announced that they planned to phase out the use of the term “developing world”. They illustrated their decision with a version of the two boxes bubble graph used in this book.

Page 38. “Developing countries” in other organizations

Large parts of the UN still use the term “developing countries”, but there is no common definition. The UN Statistics Division (2017) uses it for so-called “statistical convenience”, finding it convenient to classify as many as 144 countries as developing (including Qatar and Singapore, two of the healthiest and richest countries on the planet). The World Economic Forum re-posted the World Bank announcement of phasing out the division in “developed” and ”developing”, but continues to use the term. From only a few years back the term appears in several headlines on their website: “84% of refugees live in developing countries” (June 20, 2017), ”5 ways to make global trade work for developing countries” (September, 2016), and “Digital can lift the developing world out of poverty” (July 10, 2017). This illustrates one aspect of the problematic definitions. In the first article, the number is taken from a report issued by UNHCR where “developing countries” refers to the outdated list of the UN Statistics Division. In the second and third, “developing countries” and “developing world” actually refer to the UN list of Least Developed Countries, which is updated every third year, taking into account per capita income, human assets and economic vulnerability.

Page 44. Poverty and extreme poverty

The term “extreme poverty” has a set technical meaning: it means you have a daily income of less than $1.9 per day. The term “poverty” in many countries on Level 4 is a relative term, and the “poverty line” may refer to the threshold for eligibility for social welfare or the official statistical measure of poverty in that country. In Scandinavia, the official poverty lines are 20 times higher than the poverty lines in the poorest countries, like Malawi, even after adjusting for the large differences in purchasing power; see World Bank[17]. The latest US census estimates that 13 percent of the population lives below its poverty line, putting it at approximately $20/day. In Sweden, the official numbers of “poor” are defined relative to the median income of the country, by counting all individuals with incomes less than 60 percent of the median income. The social and economic challenges of being among the poorest in a rich country should not be neglected, but it is not the same thing as being in extreme poverty. In extreme poverty, you can’t even afford a daily meal of staple grain porridge. You can’t get poorer without dying. See World Bank[5]. See www.gapm.io/tepov.

Page 45. Levels of poverty

Voices of the Poor is a three volume publication from World Bank[12] of interviews with poor people across 47 countries. The interviews shed light on the existing differences between levels of poverty. It is clear that those who live in poverty are themselves well-aware of these differences.

Page 40. Overlapping spreads

The overlapping spreads or ranges of numbers (p. 40) are commonly referred to as ‘overlapping bell curves’.

Pages 40–41. Math scores

Part of the example is borrowed from Denise Cummins (2014).

Pages 41. Graphs: Incomes in Mexico & US

The graphs showing people distributed by income, comparing Mexico and the United States in 2016, are based on the same data as the four income levels, slightly adjusted to align with the shape of the distributions from the latest available national income surveys.

The number of people on different incomes are based on latest available income distribution data from the World Bank’s Povcal[1], which was extended to 2013 by Gapminder[10] using the indicator Household per capita income and converted into constant international dollars (PPP 2011), on a logarithmic scale. By adjusting the exchange rates to consider price differences with PPP, the incomes are more comparable across countries. The shapes of the curves were adjusted to better follow the distributions, as reported by the latest available national income survey for each country; see ENIGH for Mexico, and US-CPS for the United States.

The right chart uses a doubling scale for income, just like all other charts displaying income the book. This image shows what the shapes would look like if they were drawn on a linear scale. For more details see: gapm.io/fbincmlinlog

Data Sources

• ENIGH (Encuesta Nacional de Ingresos y Gastos de los Hogares) 2016. Tabulados básicos 2017: Table 2.3. Available at: www.beta.inegi.org.mx/proyectos/enchogares/regulares/enigh/nc/2016
• US-CPS (US Census Bureau) Current Population Survey (CPS) 2017: Family Income in 2016. Table: FINC0_01. Accessed online from: www.gapm.io/xuscps17
• PovcalNet “An Online Analysis Tool for Global Poverty Monitoring.” Founded by Martin Ravallion, at the World Bank. Accessed November 30, 2017. http://iresearch.worldbank.org/PovcalNet.

Page 42. In apartheid South Africa

At present, the average white household in South Africa can spend roughly five times more money than the average black household. During apartheid in the 1970s, white people earned on average 12 times more than black people, according data from IRR. In the United States as of 2016, white households earned on average 1.6 times more than black households, $65,041 and $39,490 respectively; see US-CPS below. In this article, BBC reports on historic race specific employment and incomes in South Africa, using data from IRR.

Data Sources

• BBC Justice Malala. “Viewpoint: Does race matter in South Africa?” 29 August 2012. Available at: www.gapm.io/xsa

• IRR (South African Institute of Race Relations) South Africa Survey Online 2009/2010. Employment and incomes.
• STAT-SA (Statistics South Africa). Living Conditions of Households in South Africa 2014/2015. Table 2.2. Available to download as PDF

Page 43. Brazilian income inequality

Brazil’s trend line for the richest share of income, comes from World Bank[16]. The distribution of Brazilian population over different incomes, is base don data from PovcalNet and slightly adjusted to better align with CETAD.

Data Sources

• CETAD (Centro de Estudos Tributários e Aduaneiros). “Distribuição da Renda por Centis Ano MARÇO 2017.” Ministério da Fazenda, Brazil, 2017. Available at: www.gapm.io/xbra17
• PovcalNet “An Online Analysis Tool for Global Poverty Monitoring.” Founded by Martin Ravallion, at the World Bank. Accessed November 30, 2017. http://iresearch.worldbank.org/PovcalNet.

Chapter Two: The Negativity Instinct

Page 48. Living conditions in 1950s Sweden

Hans grew up in Uppsala, Sweden, in the working class slum suburb of Eriksberg, next to the Ekeby brick factory. The sewage problem in the early 1950s was just as bad as it is in industrial slums on level 2 across the world today. Not until the 1970s did this part of Uppsala get improved sanitation. See the page on Eriksbergs Upsala-Ekeby from Uppsala country, which can be downloaded here, via Uppsala kommun, Kulturmiljöutredning.

Page 49. Terrorism on the rise

The data about fatalities from terrorism comes from GTD (2017) and Gapminder[3] for data on terror deaths per income level. In 1999, terrorism worldwide reached its lowest annual death toll in two decades, with only 2,200 killed worldwide. It then began to increase over the next 12 years. By 2014, terrorism multiplied tenfold to 32,765 deaths, but has slowly begun to decline during the past two years. For more on terrorism, see note to page 118. For the poll about fear of terrorism, see Gallup[4]. See www.gapm.io/tter.

Data Sources

• GTD (Global Terrorism Database) (2017) Accessed December 2, 2017. Online at: www.gapm.io/xgtdb17

Page 49. The State of World Fisheries

Fishing vessels are becoming larger and can go out further into the deep seas to find the remaining stocks of fish. In their report The State of World Fisheries and Aquaculture 2016, FAO[2] conducted an analysis of assessed commercial fish stocks. The study showed that the share of fish stocks within biologically sustainable levels decreased from 90 percent in 1974, to 68.6 percent in 2013.

In The Plundered Planet (2010), Collier shows how the real price of a natural resource is calculated—the use by one generation of humans, based on the reproduction rate of the resource. This is a way of determining how much fish each generation can consume.

UNEP[1] reports more than 500 recorded dead zones in polluted coastal areas around the world. According to IUCN Red List[4], the number of threatened species in 2015 was 23,250 and increased to 24,307 in 2016. See www.gapm.io/tnplu.

Data Sources

• Collier, Paul The Plundered Planet: Why We Must—and How We Can—Manage Nature for Global Prosperity. See page 160. New York: Oxford University Press, 2010.
• FAO[2] (Food and Agriculture Organization of the United Nations) The State of World Fisheries and Aquaculture 2016. Rome: 2016. See, specifically, page 5. Available to download as PDF: www.gapm.io/xfaofi
• UNEP[1] (United Nations Environment Programme) Towards a Pollution-Free Planet. Nairobi: 2017. Available to download as PDF: www.gapm.io/xpolfr17

Page 49. Sea levels

Hans presented the new forecasts of rising sea levels at the launch of the Fifth Assessment Report of IPCC[1] in 2013. The video clip is called “Hans Rosling – 200 years of global change“.

Page 50. Graph: Better, worse, or about the same?

This poll was initially conducted by YouGov. The results were so extraordinary that Gapminder decided to see if they could be replicated with a different polling company. In 2017, the same question was asked with Ipsos-MORI and the results were similar: This barchart mixes the results from the two online polls. YouGov polled 18,000 people in 17 countries. The two countries where most people were optimistic about the world were China and Indonesia in which 41% and 23% said the world is getting better. But we have decided to remove these two outliers because the proportion of people with access to the internet is not large enough to represent the whole population. It’s quite likely that people with internet access have a different perception of the world than the rest of the population. This is not to discard the plausible and interesting hypothesis that Asians may be more positive than westerners. See www.gapm.io/rbetter.

Data Sources

• YouGov[1] November–December 2015. Poll results. Available to download as PDF

Page 50. When to trust the data

In this chapter we introduce the idea that you should never trust the data 100 percent. For Gapminder’s guidelines on reasonable doubt for dif­ferent kinds of data, see www.gapm.io/doubt.

Page 52. Graph: Extreme poverty rate

The levels of extreme poverty historically can not be known exactly. Adjusting for changes in prices, currencies, food, employment and technology is very difficult. In Factfulness we use numbers from Gapminder[9]. The numbers before 1980 are based on two sources. First: Bourguignon and Morrisson (2002) estimate that in 1820, the share of people below $2 per day (in constant 1985 PPP dollars) was 94.4 percent, and the share of people below $1 per day was 83.9 percent. To express this in 2011 PPP dollar prices is not trivial. The two alternative rates from Bourguignon and Morrisson, Max Roser at OurWorldInData[1] use the higher estimate when showing a single line for the global trend of extreme poverty rate; Max Roser uses the higher estimate, while we have decided to go for a lower estimate. This is because the second source, van Zanden[1], indicates a lower rate. The paper World Income Inequality 1820-2000 uses historic GDP per capita from Maddison[1] to estimate what income levels people lived on. For the distribution of incomes within countries they use historic records of the differences in heights of people (such as military data archives). Insufficient food during childhood stops growth and leads to a shorter adult person. By estimating the childhood stunting they can guess the share of people missing food, hence living in extreme poverty. Based on those estimations they assess that 73 percent of people lived below $2 per day, and 39 percent below $1 per day (in constant 1990 PPP dollars). But they couldn’t construct height and GDP data for all countries and roughly 25% of humanity are missing from this estimate. The share missing were probably mostly the poorest, who didn’t even have organized military archives, hence we can add them to the extremely poor, and get 82 percent of humanity in extreme poverty in 1820. We then pull this number back 20 years and assume even more people were poor in 1800. We land on 85 percent on Level 1 at the start of the trend in 1800. After 1980 the data comes from PovcalNet and is described in the note to Fact Question 3. The official World Bank estimate of extreme poverty in year 2013 is 10.7 percent which Gapminder[9] has extended to 2017, by assuming income distributions being constant and IMF[1] GDP per capita forecasts are applicable on household incomes form PovcalNet.

Data Sources

• OurWorldInData[1] Roser, Max, and Esteban Ortiz-Ospina. “Declining global poverty: share of people living in extreme poverty, 1820–2015, Global Extreme Poverty.” Accessed November 20, 2017. Available at: www.ourworldindata.org/extreme-poverty

Page 52. 19th century living conditions

United Kingdom probably had among the world’s longest lives in 1800, on average 36 years according to Livi-Bacci (1989), A Concise History of World Population. Swedes and almost everyone else lived to 32 years or less; see Gapminder[4]. British children began to work at an early age, on average at 10, which varied between regions. Young children were extremely valuable in British coal mines because they were small. Until 1842, many children, some as young as five years old, died while working their 10 hour shifts as trappers. For more on child labor in industrial England, see this article by Griffin (2014).

Page 53. Dip in extreme poverty: China, India and Latin America

The numbers in the text on the reductions in extreme poverty in China, India, Latin America and elsewhere come from World Bank[5] extended to 2017 by Gapminder[9], assuming IMF forecasts are accurate. See www.gapm.io/vepovt.

Page 53. Life expectancy and data doubt

Life expectancy data is from IHME[1] (Institute for Health Metrics and Evaluation). In 2016, only the Central African Republic and Lesotho had a life expectancy as low as 50 years. But uncertainties are huge, especially on Level 1 and 2. Learn how much data doubt you should have at www.gapm.io/blexd.

Page 53. Fact Question 4: Life Expectancy

See the detailed note to Fact Question 4.

Page 54. Historic child mortality

As populations didn’t grow much before 1800 despite the large number of babies per woman, child mortality (death rate before age 5) must have been almost 50 percent on average throughout human history, and then almost half of those surviving their first five years, died before finishing their parenthood. Livi-Bacci (1989, p. 17) suggests that 55 percent of children died when life expectancy was 20 years, and 40 percent died when life expectancy was 50 years.

Page 54–55. Deaths from starvation in Ethiopia

This number is an average of two sources. FRD, page 87 says: “By the end of 1973, famine had claimed the lives of about 300,000 peasants of Tigray and Welo, and thousands more had sought relief in Ethiopian towns and villages.“ EM-DAT has a record of 100,000 deaths from the famine in Ethiopia in 1973.

Page 55. Graph: Average life expectancy from 1800 to today

The famine in India between 1876 and 1878 began with a drought in 1875. This led to food shortage and disease during several years, causing up to 5 million deaths according to the sources listed in the Wikipedia article about the Great Famine. As a result, the Indian life expectancy dropped to roughly 19 years, according to economic historian Mattias Lindgren, see Gapminder[4]. In his book America’s Forgotten Pandemic (1989), Alfred W. Crosby estimates that the Spanish flu caused 50 million deaths. The number is confirmed by Johnson and Mueller (2002) and CDC[1]. The world population in 1918 was 1.84 billion, which means this pandemic wiped out 2.7 percent of the entire global population.

Page 56. World Food Programme.

The World Food Programme was established in 1961 as an experiment to provide food through the UN. The WFP started its life saving mission in September 1962 when an earthquake killed 12,000 people in Iran, leaving thousands without homes and food.

Page 56. Swedes living on Level 4

82 percent of Swedes live on Level 4 according to PovcalNet, meaning they have an income above $32 per day (in price adjusted dollars, PPP 2011).

Page 58. Not uncommon for children to drown

With “not uncommon”, we mean that the drowning percentage of all deaths is higher on Level 3 than on other Levels. This is further explained on page 73 and note.

Page 58. Citizens of Lesotho

The citizens of Lesotho are usually referred to as the Basotho. Many Basotho also live outside Lesotho, but here we refer to those actually living in Lesotho.

Page 58. Life expectancy in Lesotho and uncertainty of data

In 2016, only two countries had a life expectancy as low as 50 years according to IHME[1]— The Central African Republic at 50.2 years and Lesotho at 50.3 years. The uncertainties are huge for all health estimates on Level 1. According to the IHME models, the confidence interval for these numbers are ±2.5 years. That is only expressing the uncertainty within the model. When IHME change their model and improve their estimation calculations, the country estimates may change outside these uncertainty intervals. Between the previous two revisions, estimates for 24 percent of all countries changed beyond the confidence interval. In the Global Burden of Disease Study 2013, for example, Botswana’s life expectancy for 2013 was estimated between 62.6 and 68.7 years. In the Global Burden of Disease 2015 revision, the lower estimate for 2013 was adjusted downwards by 5.7 years, down to 56.9. This was mainly due to an improved modelling of the HIV pandemic. This is just one example of the actual size of uncertainty of health estimates on Level 1 and 2. There are no examples of such large  adjustments for estimates when it comes to countries on Level 3 and 4, where data is more certain.

Page 58. Literacy in Sweden and India

In Sweden 100 years ago and India today, “literacy” may only mean basic recognition of letters and the ability to parse text slowly. The figures do not imply an ability to understand advanced written messages. The literacy rate for India is from India Census 2011. Historic literacy numbers for Sweden are from van Zanden[2] and OurWorldInData[2], visually interpolated by Gapminder.

The most recent census data on literacy in India puts the overall literacy rate at 74 percent of the population aged 7 and above. While literacy differed between states—for example, 64 percent in Bihar and 94 percent in Kerala—the numbers show a 10 percent increase since the last census that was published 10 years earlier. Assuming that the literacy rate continues to increase, it is probable that the majority of India has a literacy rate of at least 80 percent in 2017.

Literacy in Sweden began to slowly increase in 1765, as the church decided to penalize those who didn’t participate in the catechetical exams, conducted in households to measure people’s ability to read and write (these paintings depict the so called “house interrogations” or “husförhör” in Swedish). Despite increasing rates of literacy, many Swedes still couldn’t read and write a century later. Not until 1842, with the Swedish school reform making it obligatory for children to attend school, literacy numbers climbed further. By 1900, the literacy rate was at about 87 percent.

Data Sources

• OurWorldInData[2] Roser, Max, and Esteban Ortiz-Ospina. “When did literacy start growing in Europe?” Accessed November 20, 2017. Available at: www.ourworldindata.org/literacy

Page 59. Vaccination on Level 1

In Afghanistan in 2016, more than 60 percent of the 1-year olds have received most of the vaccines as listed by WHO[1]: BCG (Tuberculosis): 74 percent, DTP3 (Diphtheria tetanus toxoid and pertussis): 65 percent, HepB3 (Hepatitis B): 65 percent, Hib3 (Haemophilus influenzae type b): 65 percent, MCV1 (Measles, 1st dose): 62 percent, PAB (Neonatal tetanus): 65 percent, PCV3 (Pneumococcal conjugate): 65 percent and Pol3 (Polio): 60 percent. Only MCV2 (the second shot against measles which brings immunity from 95 to 99.99 percent) is lower, at 39 percent, which is the case in many countries on Level 1. None of these vaccines existed when Sweden was on Level 1 or 2, which is part of the reason lives were shorter in Sweden back then.

32 improvements

Below are the 32 improvement graphs that are printed in the book Factfulness, on pages 60 to 63 in the English edition, with detailed documentation on how the many sources were used.

Page 60. Graph: Legal slavery

To count the number of countries where forced labor is legal, we have used different indicators. Legal slavery in a country means that there is no law or constitution prohibiting forced labor or serfdom, and the country hasn’t signed the UN conventions banning forced labor. If state owned companies or the state itself is accused of practicing forced labor and ILO is refused the possibilities to investigate it, the country is considered as practising legal slavery, regardless of its official legislation. When a country passes a law to make slavery illegal, it doesn’t mean that the practice has stopped. It is a big difference between abolishing slavery in the law and doing so in practice. As with all laws, the enactment relies on law enforcement—that is, police intervention. Our counts of legal shifts in countries in different years should not be interpreted as expressing the extent to which slavery is practiced. Many countries are still not taking sufficient action to end the remaining slavery, as is the case in North Korea, Turkmenistan and Tajikistan. The dates collected of slavery abolishments concern passed laws, constitutions or signatures of UN conventions for all 195 states recognised by the UN, based on three UN treaties:

• The 1926 Slavery Convention
• The 1930 Forced Labour Convention (No. 29), see www.gapm.io/xiloflc.
• The 1957 Abolition of Forced Labour Convention (No. 105), see www.gapm.io/xilola.

The dates when countries passed domestic laws and constitutions, explicitly banning slavery or forced labor, come from the Slavery in Domestic Legislation database, compiled by Jean Allain and Dr. Marie Lynch at Queen’s University Belfast. Most historic dates of abolishment before 1950 are based on Wikipedia[1], see www.gapm.io/xwiki1. In 2018 all governments have a legal document banning forced labor, but some of them seem to be practicing forced labor themselves. An organisation called www.antislavery.org and others report about forced labor commanded by state owned cotton industries in Turkmenistan and Uzbekistan, most commonly during cotton harvest in October every year. There are, in addition, plenty of reports of forced labour in North Korean work camps. In each of these cases, ILO is not fully content with the countries’ abilities to collaborate in investigating these claims. Therefore we decided to mark these countries as not having made slavery illegal yet, even if all of these countries have signed UN conventions and banned it by their constitution. After gathering the earliest date of abolishment for all countries, we could count the number of countries that are yet to abolish slavery and forced labor for the years 1800–2018. The UN treaty collection is available at the UN treaties website. For detailed documentation and data behind this graph, visit www.gapm.io/doc_lslave.

Page 60. Graph: Oil spills

Not only has the amount of oil spilled been falling. The number of accidents dropped from an average of 24.5 per year in the 1970s to 1.7 per year between 2010 to 2016. The oil spill statistics comes from ITOPF (International Tanker Owners Pollution Federation); the publication can be downloaded via www.gapm.io/xitopf.

Page 60. Graph: Child mortality

The trend shows ten-year averages from Gapminder[6] that ends with data from UN-IGME, published in 2017 for the period 1990–2016. Estimates before 1990 come from hundreds of historical sources, primarily www.mortality.org and Mitchell’s books. They have been compiled into one coherent trend line. In summary:

• 1800–1950. Gapminder’s historic estimates were compiled and documented by Mattias Lindgren, mainly from the Child Mortality Estimates database; see www.mortality.org. The historic estimates of infant mortality rate come from Brian R. Mitchell’s volume International Historical Statistics, which were converted to child mortality through regression. For detailed documentation on the data and sources, see Version 7 at the bottom of the page at www.gapm.io/du5mr.
• 1950–2016. UN-IGME is a data collaboration project between UNICEF, WHO, UN Population Division and the World Bank. They released new estimates of child mortality for countries and a global estimate on October 17, 2017, which is available at www.childmortality.org. In this dataset almost all countries have estimates between 1970 and 2016, while roughly half the countries also reach back to 1950.
• 1950–2017. UN-Pop[1], World Population Prospects 2017, provides annual data of the child mortality rate for all countries in the interpolated demographic indicators. The data is available for download.

Our county estimates are based on these sources, and our global trend for child mortality rate is using the UN-IGME data for the period 1970 to 2016. All other years is a weighted mean of countries data. The proper way to calculate the global child mortality, would require estimating the total number of child births and child deaths each year. But we don’t have good estimates of the number of births, so instead we have used a proxy: the fertility rate multiplied by population. This method gets us very close to the properly calculated UN-IGME numbers. For 1990, UN-IGME has 93.4, and our weighted average is 96.6. We have linked our weighted average for the world, to the UN-IGME series, by using the rate of change before 1990, and apply that backwards in time, so the whole series is moved down to meet UN-IGME in 1990. See www.gapm.io/du5mr for detailed documentation.

Page 60. Graph: Death penalty

The reason to abolish death penalty is not only the terrible risk of wrongful execution. Death penalty breaches two essential human rights: the right to life and the right to live free from torture. Both rights are protected under the Universal Declaration of Human Rights, adopted by the United Nations in 1948, and should be respected by all 193 members states. Amnesty International keeps track of countries that have abolished the death penalty after 1990; see www.gapm.io/xamndp17. To qualify as a country, it requires that the use of death penalty is prohibited as punishment for all crimes. Abolishment prior to 1990 in this trend comes from Wikipedia[2], see www.gapm.io/xwiki2, and The Better Angels of Our Nature, Pinker (2011).

Page 60. Graph: Leaded gasoline

Tetraethyl lead began to be added in gasoline for increased vehicle performance and fuel economy in the 1920s, see www.gapm.io/xwiki11. That was a stupid idea, since lead is a toxicant that affects multiple body systems and is particularly harmful to young children: See WHO’s Fact sheet, Updated August 2017, called “Lead poisoning and health”.

The first country to ban lead in gasoline was Japan in 1986. Since then 192 countries have followed. The only three countries where leaded gasoline is still not completely phased out are Iraq, Yemen and Algeria, according to UNEP[3].

Page 61. Graph: Child labor

The current ILO (International Labour Organization) definition of “child labor” excludes light part-time work, and includes only the so called “worst forms of child labour”; see ILO[6]. Gapminder[42] combines data from three different ILO reports, using three age intervals; ILO[7,8,9]. The actual degree of child labor is uncertain, but the trend of decline is evident in all sources measured consistently over time. Data from ILO[8] covers the years 2000–2012. It overlaps in time with ILO[7] but reports data for a wider age-interval spanning the years 5–17 years. The overlapping years in ILO[8] were used to align with ILO[7]. The ILO[9] data is from the ILO Programme on Estimates and Projections on the Elimination of Child Labour and covers the period 1950–1995. The age interval reported here is 10–14 years. For 1950, 27.6 percent is the estimate of children were involved in child labor. This is probably a low estimate, considering that a majority of children didn’t go to school in the 1950s— only one third did in China and India, according to Barro-Lee. We don’t know if these children out of school were used for labor under bad conditions, so we decided to use this official ILO estimate, even though it reports on an older age group. While the global decline of child labor is certain, the actual levels at all times are not. Since writing this book, new numbers supporting our general estimates have been published on OurWorldInData[3].

Data Sources

• OurWorldInData[3] Roser, Max, and Esteban Ortiz-Ospina. “Child Labor.” Accessed November 20, 2017. Available at: www.ourworldindata.org/child-labor

Page 61. Graph: Deaths from disaster

Annual deaths from natural disasters have decreased by 75 percent over the past 100 years, according to the International Disaster Database; see EM-DAT. Since disasters vary from year to year, here we compare ten-year averages. In chapter four where we further discuss the decline, we also use 25-year averages. In the last ten years (2007–2016), on average 80,386 people were killed by natural disasters per year. This is 25 percent of the number 100 years earlier (1907–1916), when it was 325,742 deaths per year. The huge decline in disaster deaths would be even more striking if two other major global changes are taken into account. First that the world’s population has increased by four, which calls for counting disaster deaths per capita. In 1907–1916 there were 181 disaster deaths per million people. In 2007–2016 the number was 11. The relative number has dropped to 6 percent of what it was 100 years ago. Second, 100 years ago the communication technologies for reporting disasters and their victims were very primitive, compared to the monitoring of today. Many catastrophes must have gone unrecorded ur been underreported. The EM-DAT data include death toll estimates for 8969 disasters recorded worldwide since 1900, including all known emergency events categorized as: Animal accident, Complex Disasters, Drought, Earthquake, Epidemic, Extreme temperature, Flood, Fog, Impact, Insect infestation, Landslide, Mass movement (dry), Storm, Volcanic activity, Wildfire.

Page 61. Graph: Nuclear arms

The number of warheads peaked in the mid 1980s and has steadily decreased since. SIPRI (Stockholm International Peace Research Institute) estimates the total number in 2017 at 14,935—see www.gapm.io/xsipri17. The Nuclear Notebook gives a detailed breakdown: “As of mid-2017, we estimate that there are nearly 15,000 nuclear weapons located at some 107 sites in 14 countries. Roughly, 9,400 of these weapons are in military arsenals; the remaining weapons are retired and awaiting dismantlement. Approximately 4,150 are operationally available, and some 1,800 are on high alert and ready for use on short notice. By far, the largest concentrations of nuclear weapons reside in Russia and the United States, which possess 93 percent of the total global inventory.” The Nuclear Notebook can be found at www.gapm.io/xnuno.

Page 61. Graph: Smallpox

Smallpox had been one of the major killers of humans. In 18th century Europe, for example, it caused about 7% of all deaths, according to Max Roser (2018). Vaccines were invented already in 1796, but not until 1980 was the nasty viruses defeated thanks to massive global vaccination campaigns lead by the World Health Organization. The last known case was recorded in Somalia in 1977. Smallpox was the first (and so far the only) disease eradicated by global vaccination programs. The graph shows the years when the last case was recorded in each country, based on data that was kindly shared with us by Katie Hampson, at Wellcome Trust Boyd Orr Centre for Population and Ecosystem at University of Glasgow, Medical, Veterinary & Life Sciences. The data was published in the paper Towards the endgame and beyond: complexities and challenges for the elimination of infectious diseases by Klepac, et al (2013).

Data Sources

• Max Roser (2018) “Eradication of Diseases” Smallpox. Published online at OurWorldInData.org. Retrieved from: www.ourworldindata.org/eradication-of-diseases
• Klepac, Petra, et al “Towards the endgame and beyond: complexities and challenges for the elimination of infectious diseases.” Figure 1. Phil. Trans. R. Soc. B June 24, 2013. (doi: 10.1098/rstb.2012.0137) Available at: www.rstb.royalsocietypublishing.org/content/368/1623/20120137

Page 61. Graph: Ozone depletion

The data show the impressive decline in the use of gases that are causing a hole in the ozone layer of the Earth’s atmosphere. The ozone layer prevents harmful UVB wavelengths of ultraviolet light from reaching the Earth’s surface, where it causes skin cancer, sunburn, and cataracts, all of which were projected to increase dramatically as a result of thinning ozone. The UVB light is also harmful to plants and animals. When scientists presented evidence of the gases causing the ozone hole, the world reacted fast. All countries agreed to stop using these gases, and ratified the Montreal Protocol in 1987. Since then, humanity has almost completely stopped consuming Ozone Depleting Substances (ODS). In 2017 the hole finally showed signs of shrinking as reported by MIT which may be caused by warmer temperature according to NASA. The trend line shows ODS Consumption in ODP Tonnes. The unit ODP tonnes is not an actual weight measurement, but represent the destructive effects of a substance compared to a reference substance. Data is compiled and available form UNEP[4], and the substances included are Chlorofluorocarbons (CFCs), Halons, Other Fully Halogenated CFCs, Carbon Tetrachloride, Methyl Chloroform, Hydrochlorofluorocarbons (HCFCs), Hydrobromofluorocarbons (HBFCs), Bromochloromethane and Methyl Bromide.

Page 62. Graph: New movies

The Internet Movie Database (IMBd) is maintained by film enthusiasts across the world, and has a nearly complete coverage of all feature films, making 3.5 million movies searchable and filterable by year. Many early cinematic recordings were done in the last years of the 19th century, but they dare often experimental and short we don’t consider them feature films. We have chosen 1906 as the year of the first feature, referring to “The Story of the Kelly Gang”, a 70 minute long narrative film; see Wikipedia[3].

Page 62. Graph: Protected nature

This trend is based on UNEP[5]  which keeps track of protected areas as defined by IUCN[1,2] (International Union for Conservation of Nature). The trend after 1990 is based on the figure on page 30 in UNEP[6]. Between 1911 and 1990 the trend comes from Abouchakra et al (2016), which is based on UNEP[5] data from february 2012. Between 1900 and 1911 the data was aggregated from the historic records in UNEP[5] by Gapminder[31]. To see The World Database on Protected Areas (WDPA), visit www.protectedplanet.net. For categories of the IUCN protected areas, see www.gapm.io/x-protareacat, and the IUCN definition, see www.gapm.io/xprarde. For more about the data behind this graph, visit www.gapm.io/protnat.

Page 62. Graph: Women’s right to vote

In 1893 New Zealand took the step, and then all other countries followed—Australia in 1902, Azerbaijan in 1918, Sweden in 1919 (but women voted for the first time in 1921) and Syria in 1949, to mention a few—except one. Saudi Arabia started letting women vote in 2015 (and since 2017 women are also allowed to drive cars). As of 2017, The Vatican State is the only country left where voting requires male gender. One could argue that there is no popular vote at all in the Vatican, since the head of state is chosen by the cardinals, but nevertheless, it’s the only state where no woman’s voice is heard in election. The data from Gapminder[20] is mainly based on the Wikipedia[4] page about the women’s suffrage movement.

Page 62. Graph: New music

The first time music was “recorded” was for experimental purposes in 1857. Since then, music recording have increased both in terms of quality and quantity. The curve shows the number of songs on Spotify, based on the recording date as stated by the ISRC (International Standard Recording Code) which is a global identifier for sound recordings that let music artists track and charge based on how often their recordings are played in different channels. Of course, not all music recordings are available through Spotify, but the shape of the curve shows the mind blowing increase in cultural expression and consumption.

Page 62. Graph: Science

The final data-point in this curve, 2.6 million articles, is an estimate from Elsevier Publishing, that published 400,000 peer reviewed articles in 2015, with the help of 700,000 peer-reviewers from a scientific community of 7.8 million active researchers worldwide. The first peer-reviewed scientific journal was Philosophical Transactions published by the Royal Society in London. The first issue was published in March 1665 and during its first year in existence 119 scientific articles were printed (only counting articles that are more like modern day scientific articles, excluding book reviews and extracts from letters). Another scientific journal, Journal des Sçavans, was established in France in the same year, but without peer-reviewing. See “Elsevier Publishing—A Look at the Numbers, and More.” at www.gapm.io/xelsevier.

Page 62. Graph: Harvest

Today, each acre of cropland produces on average 3.6 times more food compared to 50 years ago. See the crop statistics from FAO[4] (Food and Agriculture Organization of the United Nations) available at www.gapm.io/xcer.

Page 62. Graph: Democracy

Putting countries into two groups as being a “democracy” or not is highly problematic. We use Max Roser’s data as compiled at OurWorldInData[4]. Roser has adopted the definitions of the Polity IV dataset but give the numbers in terms of inhabitants, not number of countries. The Polity IV dataset puts countries on a democracy scale, and defines non-democratic regimes as autocracies (e.g. China), closed anocracy (e.g. Morocco), open anocracy (e.g. Russia) or colonial regimes. In this graph, we only show democracies, hence disregarding all types of non-democratic regimes. For an alternate detailed list of development of democracy, see Mathew White’s “Chronological List of Democracies”.

Data Sources

• OurWorldInData[4] Roser, Max. “Share of World Population Living in Democracies.” Published online at OurWorldInData.org. Accessed November 26, 2017. Available at: www.ourworldindata.org/democracy

Page 62. Graph: Literacy

This trend from Gapminder[21] for the years 1978–2016, show the latest data from UNESCO[2]. Literacy is hard to define and measure. UNESCO[2] collected data from national surveys and censuses, all using slightly different definitions and methods for measuring literacy. The numbers between 1820 and 1960 are from van Zanden[3], who gathered the data from multiple sources, described in the chapter Education since 1820 by Bas van Leeuwen and Jieli van Leeuwen-Li (see p. 94). The exact levels are even more uncertain than after 1970. We removed dips and peaks in the trend line from van Zanden[3] as they might give an impression of non-existing accuracy. Visit Literacy rate—v1 for detailed documentation of the data sources behind this graph.

Data Sources

• Gapminder[21] Literacy rate—v1, based on UNESCO[2] and van Zanden. Available online: www.gapm.io/dliterae
• UNESCO[2] (United Nations Educational, Scientific and Cultural Organization) “Education: Literacy rate.” Last modified July 2017. Accessed November 5, 2017. Available at: www.gapm.io/xuislit
• van Zanden[3] van Zanden, Jan Luiten, et al., eds. How Was Life? Global Well-Being Since 1820. Paris: OECD Publishing, 2014. DOI: https://doi.org/10.1787/9789264214262-en Book chapter is online at: www.gapm.io/x-zanoecd

Page 63. Graph: Child cancer survival

This is not a global average for survival of all children with cancer. The data is for children treated in the US, which is representative for the cancer treatment given to children on Level 4, in Europe, Japan and elsewhere. That’s what we mean by “best treatment”. The trend shows that cancer research has been successful and the state of the art treatment is improving, but we should keep in mind that most people don’t have access to this treatment. The first part of the trend line comes from NCI[1] (National Cancer Institute), see www.gapm.io/xccs17. The 2010 estimate is from NCI[2], see www.gapm.io/xccs10.

Page 63. Graph: Girls in school

This trend is based on UNESCO[3], and shows the number of girls of official primary school age who are enrolled in primary or secondary school, expressed as a percentage of the population of official primary school age girls. The age interval for primary education varies between countries, but it is often between 6 to 11 years of age. Girls of primary school age who are still in pre-primary education are excluded and considered out of school. UNESCO[4] expresses the numbers in “out-of-school” children. We have inverted the numbers to show children not out-of-school: the in-school rate. See also the note to Fact question 1: Girls in school.

Data Sources

• UNESCO[3] (United Nations Educational, Scientific and Cultural Organization) “Education: Out-of-school rate for children of primary school age, female.” Accessed November 26, 2017. Available at: www.gapm.io/xuisoutsf
• UNESCO[4] “Rate of out-of-school children.” Accessed November 29, 2017. Available at: www.gapm.io/xoos

Page 63. Graph: Monitored species

The IUCN (International Union for Conservation of Nature) Red List[4] now includes an impressive 87,967 wildlife lkölspecies (animals, fungi and plants), each with an expert assessments of threat-status. Out of these, 25,062 (approximately 28%) today fall within one of the the three “threatened” categories: Critically Endangered (CR), Endangered (EN), or Vulnerable (VU). Despite the fact that the status of many of these species is not improving, we consider it a great improvement that they are at least monitored. Data after 2000 come from Red List[4]. The estimates for the following years are handpicked from the following publications and pages: 1986, 1990 and 1996 come from previous paper editions of the list: Page iv, Red List 1986 edition; Page ix, Red List 1990 edition; and page 4, Red List 1996 edition. The first systematic approach to register and monitor threatened species was the 1959 “Threatened Mammals Card Index”. It compiled data about 34 mammal species and was managed by The Species Survival Commission under Leofric Boyle, according to the about page at Red List. For the IUCN Red List of threatened species (1996–2017), see www.gapm.io/xiucnr4. See also the note to Fact question 11: Endangered species.

Page 63. Graph: Electricity coverage

Data comes from GTF, the Global Tracking Framework, a collaboration between the World Bank and the International Energy Agency. The term “access” is defined differently in all their underlying sources. In some extreme cases, households may experience an average of 60 power outages per week and still be listed as “having access to electricity.” The graph, accordingly, shows people with “some” access. For the GTF measures of electricity access, see http://gtf.esmap.org/results. See also the note to Fact question 12: Electricity.

Page 63. Graph: Mobile phones

Statistics showing the global increase of mobile penetration often uses data from ITU from the International Telecommunication Union, ITU[1], counting the number of subscriptions, not subscribers. In the world in 2015 there were 7.2 billion SIM cards and 7.5 billion people, but the rate is misleading since many people have multiple SIM cards. GSMA publishes estimates of the number of unique subscribers, and their data series start with the 2010 numbers. Gapminder has extended the series, combining these two measures by calculating the rate of subscriptions per subscriber for the overlapping year 2010, and then assume that the same rate is applicable from the beginning of the ITU[1] subscription series in 1980 (when ITU reports 23,482 subscriptions worldwide). The ITU series beginning in 1980 is retrieved from the World Bank[18].

Page 63. Graph: Internet

Data for 2005 to 2017 is taken from ITU[2] divided by UN-POP[1]. Data before 2005 comes from previous editions of the ITU report via World Bank[19]. These are combined in Gapminder[22]. The first data point, zero internet users in 1980, is based on the Internet System Consortium (ISC), which count internet hosts historically. The first record is from August 1981 with 213 hosting servers, why we assume that the year before, the number of users was practically zero.

Page 63. Graph: Immunization

Data from WHO[1] gives the immunization coverage of all different common vaccines. Gapminder[23] has combined these to one single indicator: the share of children who has received at least one vaccine. This indicates at least some basic form of access to modern health service and scientific medicine. Read more in the note to Fact question 9: Vaccination.

Page 64. Graph: Guitars per capita

This documentation will be added in the next version of this document. For more information about this chart, see www.gapm.io/tcminsg.

Page 66. Causes of death in human history

The main causes of deaths have been the same throughout human history: bacteria, viruses, starvation, and violence. The common lethal bacterias probably killed people at a constant rate until modern sanitation was invented. Roughly 1% of childbirths resulted in the death of the mother, due to complications or infections. As women gave birth to an average of 6 children, this means that at least 6% of women died giving birth; see Livi-Bacci[1], Paine & Boldsen[1], and Gapminder[25]. Of these newborns, somewhere between 30% and 50% died before age 1, and another 10% before age 5; see Lewis[1]. Most children died from diarrhea, pneumonia, measles, malaria, or some bacterial infection. The 60% who survived childhood, died before completing parenthood. The general risk of interpersonal violence and accidents were constantly higher, but the other main killers—violence, pandemics and starvation—killed at rates that varied enormously between years and places. During most years, there was probably enough food, it was peace and people were somewhat healthy. But then, every 50 years came some pandemic of cholera, smallpox, measles or malaria, a bad harvest, or a war, killing 10% or more of those who had survived childhood; see Paine and Boldsen (p. 176).

Page 66. Ancient graveyards and burial sites

Paleodemography is an academic research field that attempts to reconstruct the mortality, fertility and age compositions of prehistoric populations based on skeletal samples from archeological sites. Experts in paleodemography generally expect that at least 30% of all deaths happened before adulthood in all archeological populations. Lewis (2006) has a list of sites with many recorded non-adult skeletons, and argues that “large numbers of non-adult remains have been recovered from cemetery sites, and continue to be housed in museums and universities ready for study.” (from The Bioarchaeology of Children, p. 20) In general however, experts are surprised at not finding as many infants as they could expect, not anything near 30%. The percentage of infants varies enormously between sites, and occasionally, among hundreds of adults in certain sites, not a single child skeletal is found. Some studies have tried to find out why infants are underrepresented in archeological findings. In a study from Manifold (2014), researchers examined 790 child and adolescent skeletons to assess if local soil properties could make them more easily decomposed.

Sources

• Lewis (2006) Mary E. Lewis The Bioarchaeology of Children: Perspectives from Biological and Forensic Anthropology “2- Fragile bones and shallow graves.” Cambridge U P. See p. 20 and Table 2.1. DOI: https://doi.org/10.1017/CBO9780511542473.002
• Manifold (2014) “Skeletal preservation of children’s remains in the archaeological record.” HOMO-Journal of Comparative Human Biology 66 6: pp. 520-548. Not open access. Abstract online at: http://www.sciencedirect.com/science/article/pii/S0018442X15000748.
  DOI: https://doi.org/10.1016/j.jchb.2015.04.003

Page 66. Historic child murders

All groups of hunter-gatherers and hunter-horticulturalists have not been equally brutal. At some archaeological sites, the violent death rates are lower than in most violent cities today, at 1%. But that is uncommon. Instead, violence among hunter-gatherers were probably at least 10 times more common. For 39 hunter-gatherers populations where archaeologists and anthropologists have assessed the causes of death, they have found flint arrowheads in chests and holes in skulls. And on average, the estimated murder rate for these sites was at 16%; see Pinker[3] and OurWorldInData[7]. Skeletons found in excavations of prehistoric massacres show that children were treated just as brutally as adults. Infanticide is found in many human groups with food scarcity across the world; see Pinker[4]. The hunter-gatherer groups studied by Gurven and Kaplan (2007) showed that deaths under age-15 were on average caused by violence.

In violent communities, children are not spared. Members of hunter-gatherer groups generally experienced lots of violence, as described in Gurven and Kaplan (2007), Diamond (2012), Pinker (2011), and OurWorldInData[5]. This doesn’t mean all tribes of hunter-gatherers are the same. In situations of extreme poverty all across the world, many cultures have accepted the practice of infanticide, the killing of one’s own children to reduce the number of mouths to feed in difficult times. This terrifying way of losing a child is just as painful as other ways, as consistently documented in traditional societies by anthropologists interviewing parents who had to kill a newborn—Pinker (2011, p. 417).

Data Sources

• OurWorldInData[5] Roser, Max. “Ethnographic and Archaeological Evidence on Violent Deaths.” Accessed November 26, 2017. Available at: www.ourworldindata.org/ethnographic-and-archaeological-evidence-on-violent-deaths

Page 70. Educating girls

The data on girls’ and boys’ education comes from UNESCO[5]. Schultz (2002) describes clearly and in more detail how educating girls has proven to be one of the world’s best-ever ideas. See also the note to Fact question 1: Girls in school »

Page 73. Drownings

The data on drownings today comes from IHME[4,5]: see www.hmeuw.org/49kq and www.ihmeuw.org/49ks respectively. Up until 1900, more than 20 percent of the victims of drownings were children below the age of ten. The Swedish Life Saving Society started lobbying for obligatory swimming practice in all schools, which together with other preventive actions reduced the number; see Sundin et al. (2005).

Page 75. Graph: Catching up with Sweden

Use the animated version of the World Health Chart to see how almost all countries are now catching up with Sweden—or select another country to compare—at www.gapminder.org/whc

Chapter Three: The Straight Line Instinct

Page 75. Ebola

The data is from WHO[3], the Ebola Response Team’s research article evaluating the first 9 months of the outbreak, with future projections. The material Gapminder produced to try to communicate the urgency of the situation is at ww.gapm.io/vebol. Watch the epidemic explained in Hans Rosling’s Factpod #6 and #8 on YouTube.

Data Sources

• WHO[3] WHO Ebola Response Team “Ebola Virus Disease in West Africa—The First 9 Months of the Epidemic and Forward Projections.” New England Journal of Medicine 371 (October 6, 2014): 1481–95. Available at: www.gapm.io/xeboresp DOI: 10.1056/NEJMoa1411100

Page 76. Lord Krishna’s chessboard

The Indian legend, depicting the effect of doubling, is called the Legend of the Ambalappuzha Paal Payasam, named after the temple where it supposedly happened. In calculating the volume of rice to cover India, the total number of grains Lord Krishna is owed were divided by India’s total land area. The grains’ estimated weight and volume—300 grains of rice equals 6 grams (thanks, gapm.io/xrice)—were converted into density per square meter, using this grain converter: gapm.io/xcalc.

Page 77. The world population is not just increasing

Roughly a billion people will be added over the next 13 years, based on the 2017 revision of World Population Prospects from the population indicator medium fertility variant; see UN Population Data below, UN-Pop[1]. The data is freely available online.

Page 78. Graph: Future number of children

The graph to Fact Question 5 shows three forecasts. The dashed line at the bottom of the graph, alternative C, shows the official UN forecast. The trend up to 2005 is the UN numbers for the global child population, ages 0–14. Two billion children is a rounded number. The precise UN numbers are 1.95 billion for 2017 and 1.97 billion for 2100. See also the note to Fact Question 5.

Page 79. Teacher’s conference in Norway

9% or 7 out of 81 teachers at the conference in Gardermoen, Norway picked the correct line on the graph. The polling devices used to test the audience were from TurningPoint. In 2013, when Hans asked the question to the teachers, the forecast had been published two months earlier. But the trend line wasn’t news—the line from two years earlier looked almost identical. In fact, the official forecasts have stayed the same in the past four revisions of the publication, 2010, 2012, 2015, and 2017. Official projections from UN have been freely available to the public for the past 8 years, showing that the number of children in the world has stopped increasing. See also: How Reliable is the World Population Forecast? on Gapminder.

Page 79. Experts at the World Economic Forum

For a video recording of Hans showing the audience results at the World Economic Forum in Davos 2015, see www.gapm.io/xwbros5m.

Page 79. The accuracy of UN Population forecasts

UN Population forecasts are based on UN-Pop[1,2,5]; see below for the UN Population data. Like forecasting the weather, it is almost impossible to perfectly predict the future population. But the demography experts at the UN Population Division have been very accurate in their forecasts for many decades, even before modern computer modeling was possible. Their forecasts of the future number of children have stayed the same in the past four revisions of the publication. The official UN projection is alternative C. 2 billion children is a rounded number. The precise UN numbers are 1.95 billion as of 2017 and 1.97 billion for 2100.

The Accuracy of Past Projections is a study from Bongaarts and Bulato (2000) that measure past projections, and find that the average error of UN world population forecasts are modest, at 2.8%, for an average length of projection of 17 years (see, for example, p. 50). Also Keilman (2001) studied the quality of UN population projections, and analyzed forecasts prepared by the UN between 1950 to 1993. For a video of Hans Rosling comparing these historic forecast based on Keilman’s study, see: How Reliable is the World Population Forecast? The projections are available at the UN website, as well as the uncertainty intervals of their medium forecasts.

UN Population Data

• UN-Pop[1] UN Population Division. World Population Prospects 2017. United Nations, Department of Economic and Social Affairs.
  • Population indicator: Medium fertility variant. 1950–2100.
  • Downloaded as Excel file: WPP2017_POP_F01_1_TOTAL_POPULATION_BOTH_SEXES.xlsx
    Database available at: https://esa.un.org/unpd/wpp
• UN-Pop[2] UN Population Division. World Population Prospects 2017
  • Annual age composition of world population, medium fertility variant.
  • Downloaded as Excel file: WPP2017_INT_F03_1_POPULATION_BY_AGE_ANNUAL_BOTH_SEXES.xlsx Database available at: https://esa.un.org/unpd/wpp
• UN-Pop[5] UN Population Division. World Population Prospects 2017
  • World Population Probabilistic Projections.
  • Accessed November 29, 2017. Available at: www.gapm.io/xpopproj

Other Sources

• Bongaarts and Bulato (2000) Beyond Six Billion: Forecasting the World’s Population (2000) “Chapter 4: The Accuracy of Past Projections.” 37–52. Available online at: www.gapm.io/xbonpop
• Keilman (2001) Keilman, Nico. “Data quality and accuracy of United Nations population projections, 1950–95.” Population Studies 55, no. 2 (2001): 149–64. Posted December 9, 2010. Available at: www.gapm.io/xpaccur. DOI: https://doi.org/10.1080/00324720127686

Page 80. Historic world population and three big cities

To compare the population in prehistory with the current population of three major cities, we use data from the UN Statistics Division published in the Demographic Yearbook–2015. The population was 6.5 million in Rio de Janeiro; 8.1 million in London; and 8.3 million in Bangkok; see Table 8, “Population of capital cities and cities of 100 000 or more inhabitants: latest available year, 1996-2015,” at www.gapm.io/xpop.

Page 80. Graph: World population from 8000 BC to today

The graph showing the world population from 8000 bc to today uses data from hundreds of dif­ferent sources, compiled by the economic historian Mattias Lindgren. The sources listed under the chart are only the main sources. The next billion were added to the world population in 130 years after 1800:

Year	Estimate
1800	946,764,816
1812	1,000,325,622
1929	2,017,045,912

UN Population Division prepared historic world population estimates that can be found online in the UN publication, The World at Six Billion. For estimates beginning in year zero, see Table 1, page 5, available to download as PDF. Caldwell and Schindlmayr (2002) provides population estimates before 1950 in Historical Population Estimates: Unraveling the Consensus published in the Population and Development Review (pp. 183–204). For more about Gapminder’s population data, see www.gapm.io/spop.

Page 84. Graph: Babies per woman from 1800 to today

We use the term “babies per woman” for the statistical indicator “total fertility rate” (TFR). Fertility rates decline during times of hardship. The dips and humps before 1965 shown on the graph were caused by the Second World War and famines. Fertility climbs to rates higher than usual when the crisis is over. Globally, the average fertility rate was at 5.05 children in 1950, according to UN-Pop[3]. As of 2017, the estimated fertility rate was 2.48 children, according to UN-Pop. Replacement rate with the current mortality worldwide is 2.3.

The long trend displayed on the graph shows Mattias Lindgren’s work that compiled multiple historical sources for the years before 1950. It is aligned with UN-Pop[3] estimates for post-1950; see Gapminder[7]. The dashed line after 2017 shows the UN-Pop[1] medium fertility projection, which is expected to reach 1.96 in 2099.

For more about famines before 1965, see, for example, the Great Chinese Famine, “the single biggest famine event in history in terms of absolute numbers of death” on Wikipedia. The most accurate number is probably estimated at 40 million people who died during the Chinese famine; see necrometrics for a documentation of sources. The age structure of the Chinese population still bear marks of fewer number of children born during the famine. See www.gapm.io/tbab.

Page 85. Fertility transitions

The change from large to small families in a society is called fertility transitions, and refers to long-term decline of fertility in a population. One of the most experienced demographers at UN Population Division explained it to Hans Rosling like this:

“Fertility runs the show. It’s reduced fertility rate that will slow down population growth and reduced child mortality rate is one of those factors, because when 1 in 5 children is dying, a country has a very fast population growth, but mortality is still too high to allow for a strong demand for family planning. Reduced child mortality in isolation does not automatically reduce fertility, it is more of a prerequisite for lowered fertility; basic education, a move out of extreme poverty, changing values towards rights for women and access to contraceptives are the other important determinants. In fact, these major aspects of modernization must all come together for each of them to happen.”

The contributing factors to lowered fertility in a population has a technical term: fertility determinants. And as the UN expert pointed out, decreasing rates in child mortality is only one of those. The full complexity is analyzed by Karen Oppenheim Mason, who in her interdisciplinary article “Explaining Fertility Transitions” reviews the main theories of fertility to assess why they present conflicting views of what makes fertility drop in a population. Oppenheim Mason argues that it is due to the following wrong assumptions:

1. Assuming that fertility transitions have the same cause
2. Ignoring mortality decline as a precondition for fertility decline
3. Assuming that societies had a so-called “natural fertility” before the fertility transition
4. Using decadal time scales to compare cause and effect

Listed first, the major problem of fertility theories is to think that all fertility transitions share one and the same cause—when in reality, a combination of conditions motivates people to prevent most births. To understand fertility decline, a number of causes must be considered. According to Oppenheim Mason, the main determinants make up:

• Mortality decline;
• Change of perception;
• Access to modern contraceptives and safe abortion;
• Costs of having many children outweighs benefits of many children.

While the above determinants are necessary to make fertility rates drop, they are not sufficient on their own, Oppenheim Mason says. And as Oppenheim Mason importantly points out, the idea of a “natural fertility” rules out the fact that many societies do have family planning strategies even though they have large families and high fertility. Changing fertility means changes in family norms, gender roles, sexual behaviour, as well as in education and economy. But the strength of cultural differences is easily overstated, and gender roles change quite fast across all cultures as people get richer, and their way of living is modernized. In cultures with extended families, including three generations, norms may survive a bit longer and the outdated expectations on women may take longer to transform. Multiple factors behind falling fertility rates are also taken into account by John Bryant, who argues that transition happens as:

• Socio-economic circumstances change the motivation to have children;
• Women gain access to contraceptives; and
• New ideas spread through society.

Bryant notes that countries slow in initiating fertility drop still manage to catch up fast, and the societal improvements needed for transition become less over time. But there are two caveats of examples that break the pattern of the fertility slide. The first is pointed out by Caldwell (2008) in “Three fertility Compromises and Two transitions” (pp. 427-446), who writes that a few countries dropped their fertility rates long before mortality. Hans Rosling wrote that “Caldwell review how low fertility in historical Europe was achieved reduced access to sex by late or no marriage and strong repression on sex outside marriage.” The last caveat is that on the higher incomes on Level 4, fertility begins to increase again. See, for example, this study in Nature, “Advances in development reverse fertility declines,” from Myrskylä M, et al. (2008).

Page 86. The inevitable fill-up

This counterintuitive phenomena is what population experts usually refer to as the demographic momentum, a term used to describe the delayed stopping of population increase. The fill-up effect is almost identical to the demographic momentum, but Gapminder has simplified the process by only comparing the size of cohorts.

Future changes in population can be attributed to three factors: fertility, mortality, and momentum. To find out how a population will change, demographers make up a set of variations. By keeping fertility at replacement-level or letting mortality be constant, they can analyze the effects of different fertility and mortality scenarios. In that way, demographers can compare the outcome with their main scenario, and then attribute the difference to the factor they kept constant.

If you find it hard to understand the fill-up in the text and graphs in this book, we find it easier to explain with animations or with our own hands—see www.gapm.io/vidfu. For more technical descriptions, see from UN-Pop[6, 7]. See also www.gapm.io/efill.

Data Sources

• UN-Pop[7] Andreev, K., V. Kantorová, and J. Bongaarts. “Demographic components of future population growth.” Technical paper no. 2013/3. United Nations DESA Population Division, 2013. Available for PDF download at: www.gapm.io/xpopfut2

Pages 87–88. The Old Balance

Gapminder presents the old balance as a family with 6 children, out of which 4 die. This iconic family represents a theoretical average, measured over the millennia with wide ranges in mortality and fertility. In the long time-frame, however, the average family probably had no more than two surviving children, since population growth on average stayed stable until 1800.

Nobody knows the average fertility rate before 1800. In theory, the maximum number of children per woman would be between 10 and 15 children if all fertile women in a healthy population had sex often and continuously with no attempts to limit fertility. In reality, it is almost always lower than that, and six children per woman is a likely average. But fertility varies between groups and individual women and, as Gurven and Kaplan writes, it “ranges widely from below 4 to as high as 8 children per woman.” (2007, p. 347)

Age specific mortality rates of the first millennials of human history are equally uncertain, but we can assume that over these huge time-horizons, mortality rate was high enough to keep population sizes down; see Livi-Bacci (1989). Mortality was unpredictable. Sudden catastrophes, caused by famines, wars or pandemics, killed many and became much more common with agriculture and crowded settlements, or through contact with other populations that brought germs with them. For regular years, between these bad periods, estimates vary from 32% to 45% of deaths in children under-5 years, which is a similar rate to that of the hunter-gatherer societies studied in modern times. Gurven and Kaplan (2007) writes that:

“The regular mortality rates differ among populations and among periods, especially in risks of violent death. However, those differences are small in a comparative cross-species perspective, and the similarity in mortality profiles of traditional peoples living in varying environments is impressive.”

Before 1800, when agriculture made people crowd in settlements and diseases from domesticated animals killed many, child mortality in regular years was probably above 35%. The main sources behind our assumptions about fertility and mortality in pre-1800 families are Livi-Bacci (1989), Paine and Boldsen (2002), and Gurven and Kaplan (2007).

Page 87. The new balance

The population curve is flat when the fill-up is completed and childbearing is on the level of replacement. The technical term called replacement-level fertility is a measure of children per woman (total fertility rate) that is needed for the next generation of a population to replace itself. About 2.1 children per woman is often assumed to be the global level of replacement, but this is only the case in periods of good health and well-being in a population. For a country to achieve fertility at replacement-level, it requires that no more than 5% of children die. If four out of six people die before reaching childbearing age, replacement fertility would be 6 children per woman. See www.gapm.io/eonb.

Page 90. Graph: Average family size by income

The graph presenting family size by income uses the most recent available data from World Bank[5] estimating that 10.7 percent of the population lived in extreme poverty in 2013. Gapminder[9] has extended the data to 2017, using IMF’s GDP per capita growth, and estimates that 0.75 billion people of the world population live on less than $2 a day; that is 10 percent of 7.55 billion people in the world. UN and the World Bank has set the threshold of extreme poverty at $1.9. Since these numbers are very rough estimates, Gapminder has rounded up the threshold to $2 a day.

Our estimates for families on dif­ferent income levels are based on household data compiled by Countdown to 2030 and GDL[1,2], combining hundreds of households surveys from UNICEF-MICS, USAID-DHS[1], IPUMS, and others. Instead of using national averages, household data allow us to include families on Level 1, as well as the poorest families living in countries on Level 2 and 3. The income levels of households in these datasets are estimated from their material assets, for instance by the number of people per sleeping room, floor-material and means of transport. For more about the data and methods behind Gapminder’s four income levels, see gapm.io/elev.

Page 92. Two public health miracles

Life expectancy suffered an extreme dip in Bangladesh 1971 because of the Bangladesh war of independence. In 1972, the total fertility rate was 6.93 children per woman and life expectancy was 47 years according to UN, or 52 years according to IHME. In Bangladesh today, the total fertility rate is 2.07 children per woman and life expectancy is 72.8 years according to UN, or 72.7 according to IHME. Since 1972, the under-five mortality rate per 1,000 live births (U5mr) has reduced significantly. It fell from 221.7 children in 1972 to 34.2 in 2016. In other words, the child survival rate in 1972 was 778 surviving children per 1,000 which means 77.8 percent. Today, the number is 966 out of 1,000 which means that 96.6 percent of children in Bangladesh survive. The child mortality rate was 313 children per 1,000 live births in Egypt 1960. Child mortality rates in Egypt and Bangladesh are based on data from UN-IGME. At www.gapm.io/du5mr we describe in full how we have combined child mortality data from multiple sources.

The construction of the High Aswan Dam began in 1960 to control flooding along the Nile. The dam was completed in 1970 and installed 1971; see gapm.io/xdam. In our animated World Health Chart you can see the progress of Egypt, Bangladesh, or most other countries—search by country and click Play at www.gapminder.org/whc.

Page 92. Child survival

See the talk at TEDxChange where Hans describes how reducing child mortality is both a moral and environmental imperative, here: www.gapm.io/vu5mrmoral.

Pages 93–97. Graphs: 14 Straight lines, S-bends, slides, and humps

These 14 graphs of differently shaped lines are all derived from plotting two indicators against each other and then drawing a line in the middle—just like the World Health Chart you see in the beginning of the book. We removed the background bubbles in the small images, because it got too cluttered. Most of these charts use national averages, aggregated by the national income level; see Gapminder[3]. A few (the straight line on recreational spending, the S-bend on vaccinations and fridges, and the slide on fertility) use household data. Our estimates of typical families on different income levels are not based on country averages, because that would severely underestimate the number of the poorest and mask the wide range of differences for countries on levels 1–3. Very few countries follow these lines exactly, but the lines show the general pattern of all countries over several decades. In each example, there are huge differences between countries on every level. You can explore the actual plotted bubbles behind these lines here: www.gapm.io/flinex.

Page 97. E.coli Bacteria

The generation time of E.coli bacteria, or the time required for the bacteria to double in number, is 15–20 minutes in a laboratory “but in the intestinal tract, the coliform’s generation time is estimated to be 12-24 hours,” as described in this bacteriology textbook: gapm.io/xecol.

Pages 98-99. CO2 emissions from transportation

According to data from EPA (United States Environmental Protection Agency), transportation is the source of almost 28.5 percent of 2016 CO2 emissions; see Total U.S. Greenhouse Gas Emissions by Economic Sector in 2016.

Page 99. What part of the line are you seeing?

Many lines that are not straight can look straight if you zoom in enough—even a circle. This idea was inspired by Jordan Ellenberg, How Not to Be Wrong: The Power of Mathematical Thinking (2014). See gapm.io/fline.

Chapter Four: The Fear Instinct

Page 105. Fear in polls

In the passage about the most common fears, we refer to those self-reported among adults. The polls, conducted in the United States and the United Kingdom respectively, reported similar results. In the Gallup[2] survey, top US fears were snakes; public speaking; and heights, followed by entrapment; spiders and insects; needles; ice; flying; and dogs. In the poll from YouGov[2], the leading fears in the United Kingdom were heights; snakes; and public speaking; followed by spiders; entrapment; mice; needles; and airplanes. Read more about the fear instinct at www.gapminder.org/factfulness.org/fear.

Page 107. Disaster data

The International Disasters Database (see CRED) estimates that the earthquake in Nepal 2015 killed 9,034, injured 200,000 and affected 5.6 million people. The government of Nepal estimates a slightly higher death toll of 10,000 deaths; see PDNA. To avoid underestimating the suffering, we have used the higher number from PDNA. Numbers for the 2003 heat wave in Europe are from UNISDR, estimating the total death toll for Western Europe to be 46,730 fatalities. All other disaster data used here comes from EM-DAT. Nowadays, Bangladesh has a very advanced flood-monitoring website; see http://www.ffwc.gov.bd. See gapm.io/tdis.

Page 110. ReliefWeb

A specialized digital service of the UN Office for the Coordination of Humanitarian Affairs (OCHA), the ReliefWeb is a global humanitarian information platform. On their FAQ page they explain that their main function is to provide humanitarian workers with key information on global crises and disasters. The ReliefWeb has a yearly budget of $3.8 million, which is funded by the OCHA and include contributions from Sweden, Japan, the United States, the United Kingdom, Denmark and others. In detailed reports, the ReliefWeb describes how the money was used, stating that “the Red Crescent is also working with the United Nations’ World Food Programme to distribute 112.5 tonnes of biscuits to 30,000 families in Cox’s Bazar.”

Page 111. Child deaths from diarrhea

Our calculations of child deaths from diarrhea caused by contaminated drinking water are based on numbers from IHME[11] and WHO[4]. See www.gapm.io/tsan.

Page 112. Plane accidents

The data on fatalities in recent years is from IATA. The data on passenger miles is from the UN agency that managed to reduce the number of accidents, based on ICAO[1,2,3]. Gapminder[16] on airplane fatalities—v1 is based on IATA, ICAO[3], US Air Carrier Safety Data and Revenue Passenger-Miles from BTS[1,2] and The Annual Reports of the U.S. Scheduled Airline Industry from ATAA. See www.gapm.io/ttranspa and www.gapm.io/dpland.

Data Sources

• ATAA, Air Transport Association of America
• Gapminder[16] Air plane fatalities—v1. Indicator Population—v5—all countries—1800–2100, based on IATA, ICAO[3], BTS[1,2] & ATAA. Soon available online at: www.gapm.io/dpland
• IATA Accident Overview
• ICAO[1,2,3] Convention on International Civil Aviation, Annex 13 in 1955, and the Air Transport Monitor

Page 113. Deaths in wars

The figure of 65 million World War II deaths includes all deaths and comes from White[1,2]. Estimates of fatalities in Syria are from UCDP[2].

Page 114. Graph: Battle deaths

The data sources for battle deaths—Correlates of War Project, Gleditsch, UCDP[1] and PRIO—include reported deaths of civilians and soldiers during battle, but not indirect deaths like those from starvation. We strongly recommend watching this interactive data-driven documentary, which puts all known wars in perspective—at www.fallen.io. To interactively compare fatalities in wars since 1990, go to http://ucdp.uu.se. Measuring battle deaths is not trivial, as a war zone is no place for careful data collection. These studies publish numbers estimated by combining official sources and media reports from conflicts. But this method of estimating conflict fatalities has been disputed by several other researchers. Obermeyer and Murray showed in 2008 (www.gapm.io/xobewar08) that the number of battle deaths in recent wars seems much higher if estimated with a different method, based on sample surveys of the local population in war-torn areas, who report how many family members they lost in conflict, with the so called sibling method. These authors claim explicitly that “there’s no evidence to support a recent decline in war deaths” since the Vietnam War. But the representativeness of the sample is not a trivial problem in these extreme events, and the number easily gets exaggerated when a local death toll is multiplied to a broader population. As there is little chance that new primary data will show up about past conflicts, the chances of new reliable estimates from other sources are small. The methodological discussion seems to have ended with a response in 2012 by Lacina & Gleditsch (see www.gapm.io/xgledwar12), making the case that their data-sources are indeed bias in an unknown way and that the bias may not be the same over the decades. Maybe the tendency is to under-report in some wars in some decade and then over-report in others. But still, even if they take into account the experts highest levels of doubts and use the widest reasonable uncertainty estimates and they try their hardest to generate an increasing impression of fatalities, by drawing a trendline from the lowest estimates of past conflicts to the highest estimates of the recent conflicts, even such line would be steadily falling. See www.gapm.io/twar.

Fear of Contamination

Page 114. Fukushima

The data on Fukushima is from the National Police Agency of Japan and Ichiseki (2013). According to police records, the Tōhoku earthquake and tsunami caused 15,894 confirmed deaths, and 2,546 people are still missing (as of December 2017). We rounded this to 18,000 deaths. Tanigawa et al. (2012) concluded that 61 very old people in critical health conditions died during the hasty evacuation. About 1,600 further deaths were indirectly caused by other kinds of problems for mainly elderly evacuees, reports Ichiseki. Nobody was reported dying from the nuclear leak, and WHO concludes that it might be possible to detect a small increase of mortality, but that it is expected to occur in a very limited group of people.

According to Pew[1], in 2012, 76 percent of people in Japan believed that food from Fukushima was dangerous. The contamination of the very word Fukushima is discussed in the book “Hazards, Risks, and Disasters in Society” by John Shroder (2014).

Page 114. Chernobyl

The discussion of health investigations after Chernobyl is based on WHO[5]. In 2016, the World Health Organization published “1986-2016: Chernobyl at 30 – an update” and they report that they couldn’t confirm any large increase in cancers, like thyroid cancer, that many had expected in the population in general. Instead, they conclude: “The psycho-social impact of disasters and emergencies has been well documented. It has been reported to be the Chernobyl accident main public health impact that affected the largest number of people. A similar effect is now reported in the aftermath of the Fukushima disaster, “ reporting that “anxiety levels that were twice as high than non-exposed population, and were more likely to report multiple unexplained physical symptoms and subjective poor health.” Read more about WHO’s summarizing reports on the healths effects of the Chernobyl accident, here: http://gapm.io/xwhoc30.

Data about nuclear warheads are from the website Nuclear Notebook. See www.gapm.io/tnuc.

Page 115. DDT

Paul Hermann Müller won the Nobel Prize in Physiology and Medicine in 1948 for “his discovery of the high efficiency of DDT as a contact poison against several arthropods.” Hungary was the first country to ban DDT, in 1968, followed by Sweden in 1969. The United States banned it three years later; see CDC[2]. At the Stockholm Convention on Persistent Organic Pollutants (POPs) in 2004, the UN quickly helped to set up an international treaty against various pesticides, including DDT, which has entered into force in 158 countries; see http://www.pops.int.

Since the 1970s, CDC (Centers for Disease Control and Prevention) and EPA (US Environmental Protection Agency) have issued directives on how to avoid the dangers of DDT to humans; see Toxicological profile for DDT, DDE and DDD and the EPA Pesticide information. CDC correctly label direct DDT exposure to humans as unhealthy, but also state that the “health effects from DDT at low environmental doses are unknown.” Today, the World Health Organization promotes the use of DDT to save lives in poor settings by killing malaria mosquitoes, within strict safety guidelines: “The use of DDT in malaria vector control” and DDT in Indoor Residual Spraying from WHO[6,7].

The CDC produced this 497 page document: Toxicological profile for DDT, DDE and DDD. We refer to the CDC as the source for the DDT report, even if it is signed by the Agency for Toxic Substances and Disease Registry (ATSDR). ATSDR is a component of CDC’s CIOs (Center, Institute, and Offices) and one of the CDC’s independent expert groups within the U.S. Department of Health and Human Services; see the CDC’s organizational structure.

Page 116. Chemophobia

Gordon Gribble (2013) tracks the origin of chemophobia back to the publication of Silent Spring (1962) by Rachel Carson, and chemical accidents in the decades that followed. He argues that the exaggerated and irrational fear of chemicals today leads to wrong usage of common resources. According to Gribble, “Regulatory authorities should focus not on pesticides, antibiotics and dioxins, but rather on pathogens, bacteria and fungi, which cause millions of cases of food-borne infections that result in hospitalization or death each year.” See www.gapm.io/ffea.

Page 116. Refusing vaccination

In the US, 4 percent of parents think that vaccines are not important, according to Gallup[3]. In 2016, Larson et al. found that, across 67 countries, an average of 13 percent of people were skeptical about vaccination in general. There were huge variations between countries: from more than 35 percent in France and Bosnia and Herzegovina to 0 percent in Saudi Arabia and Bangladesh. In 1990, measles was the cause of 7 percent of all child deaths. Today, thanks to vaccination, it is only 1 percent. Deaths from measles mainly happen on Level 1 and Level 2, where children only recently started to get vaccinated; see IHME[7] and WHO[1]. See www.gapm.io/tvac.

The environmental movement

Carson’s book Silent Spring is definitely one of the most influential popular science books ever. It sparked a global movement with global impact. Thanks to this movement the number of countries signing international Environmental Treaties is still climbing, as you can see in UNCTAD’s treaty collection visualized at OurWorldInData[9].

Page 118. Terrorism

The data about fatalities from terrorism comes from GTD, the Global Terrorism Database 2017—see www.gapm.io/xgtdb17. The data on terror deaths per income level comes from Gapminder[3]. For the poll about fear of terrorism, see Gallup[4]. See www.gapm.io/tter.

Page 121. Alcohol deaths

Our calculations on deaths involving alcohol draw on IHME[9], NHTSA (2017), FBI, and BJS. Data on traffic fatalities come from NHTSA (2017). In 2016, drunk drivers in the United States killed 948 pedestrians and cyclists, 1,550 passengers in the vehicles they were driving, and 1,520 in other vehicles—a total of 4,018 deaths, of which 1,233 were children. For murder and nonnegligent manslaughter, US statistics are not as transparent with information on blood-alcohol concentrations in offenders. The total number for 2016 was 17,250 homicide victims, according to FBI. To guess how many of those involved a drunk murder, we went to the Bureau of Justice Statistics, BJS[1], Alcohol and Crime: Data From 2002 to 2008, which reported a rate between 19–37 percent. We used the lower estimate of 20 percent, estimating that 3,450 homicides occured when the murderer was under the influence. One caveat here is that many of these deaths could theoretically have happened without the perpetrator being drunk. Domestic violence often involves alcohol (roughly 50 percent), but after the perpetrator (almost always the man) stops drinking, the violence may continue in half of the cases, according to Klostermann (2006).

The total estimate is that alcohol was in the drivers and murderers who killed 7,468 people in the United States 2016. Compared to a terrorism average of 155 per year (which includes 9/11). That would give us a US figure of roughly 9,000 deaths a year. That difference in risks between terrorism and alcoholism figures are is similar comparable across most European countries on Level 4, which all have quite terrible drinking habits: the risk that your loved one will be killed by a drunk person is 50 48 times higher than the risk they will be killed by a terrorist. See www.gapm.io/alcterex.

Page 122. Risks of dying

The percentages we quote take the death tolls on Level 4 for the past ten years divided by the number of all deaths on Level 4 over that period, and are based on the following data sources: EM-DAT for natural disasters, IATA for plane crashes, IHME[10] for murders, UCDP[1] for wars, and GTD for terrorism. A more relevant risk calculation should not just divide by the number of all deaths, but rather should take into account exposure to the situations in which these kinds of deaths can occur. See www.gapm.io/ffear.

Page 122. “How Many Deaths Make a Natural Disaster Newsworthy?”

To compare dif­ferent kinds of disaster deaths, read “Not All Deaths Are Equal: How Many Deaths Make a Natural Disaster Newsworthy?” from OurWorldInData[8]. Gapminder is currently compiling data about the skewed media coverage of dif­ferent kinds of deaths and dif­ferent kinds of environmental problems. When ready, it will be published here: www.gapm.io/fndr.

Data Sources

• EM-DAT Centre for Research on the Epidemiology of Disasters (CRED) The International Disaster Database. Debarati Guha-Sapir. Accessed November 5, 2017. Online at: www.emdat.be
• IATA (International Air Transport Association) “Accident Overview.” Table. Fact Sheet Safety. December 2017. Available at: www.gapm.io/xiata
• GTD (Global Terrorism Database) (2017) Accessed December 2, 2017. Online at: www.gapm.io/xgtdb17
• OurWorldInData[8] Tzvetkova, Sandra “Not All Deaths Are Equal: How Many Deaths Make a Natural Disaster Newsworthy?” Accessed July 19, 2017. Available at: www.gapm.io/xowd8
• UCDP[1] (Uppsala Conflict Data Program) Battle-Related Deaths Dataset, 1989 to 2016, dyadic, version 17.1. See Allansson et al. (2017). Available at: www.ucdp.uu.se/downloads

Chapter Five: The Size Instinct

(More notes coming soon for this chapter.)

Page 124–125. Nacala child mortality calculation

The births and population data used for these calculations is based on the Mozambique census of 1970, the Nacala hospital’s own records, and UN-IGME of 2017. The deaths in the hospital are based on Hans’s own notes. The official child mortality estimate for the whole country was probably different back then. For this calculation example, we use the latest available estimate for that year from UN-IGME of 2017. Mozambique was the poorest country in the world in 1979, according to the World Bank’s latest available estimates; see www.gapminder.org/whc. According to WHO[8], the number of doctors per 100,000 people in 1980 was 2.2 in Sweden, and 0.0255 in Mozambique. After the liberation in Mozambique, extremely few doctors remained in the remote districts; see World Bank[24].

Page 127. Saving lives

The list of the low-cost, high-impact interventions that save the most lives comes from UNICEF[2], which also set out the essential basic health care to which all citizens should have access before public health budgets start being spent on more advanced care. The publication from UNICEF[2] is called “Narrowing the Gaps—The Power of Investing in the Poorest Children” (2017), and is available for PDF download here: www.gapm.io/xunicef2.

Page 128. Wrong proportions: Perils of Perception

The examples of proportions that people tend to overestimate come from Ipsos MORI[2,3] and reveal misconceptions across 33 countries. The survey is called Perils of Perception (2015) and presents numbers like these highlights from the UK poll: For the wealth that the top 1 percent owns, the average guess was 59 percent, when the correct figure is 23 percent; on immigration, they guessed that 25 percent of the population are immigrants, when the the correct figure is 13 percent.

Innumeracy (1988) by John Allen Paulos is full of fascinating examples of disproportionality, asking, for example, how much the level of the Red Sea would rise if you added all the human blood in the world. See www.gapm.io/fsize.

Page 129. Educated mothers and child survival

The discussion on how educated mothers lead to higher child survival is based on a study of data from 175 countries between 1970 and 2009, by Lozano, Murray et al. (2010). See www.gapm.io/tcare.

Page 130. 4.2 million

The data on infant deaths in recent years comes from UN-IGME. The data on births and infant deaths in 1950 comes from UN-Pop[3].

Page 131. Bach Mai Hospital

The bombing of the Bach Mai hospital in Hanoi, Vietnam, in 1972 and the various reports of events are described in more detail in this passage in the book Hanoi: Biography of a City by William Stewart Logan.

Page 132. The Vietnam War

The figures of war deaths in Vietnam between 1965 and 1975 are based on American War and Military Operations Casualties: Lists and Statistics, and various sources compiled by Necrometrics. WHPSI, The World Handbook of Political and Social Indicators by Charles Lewis Taylor, estimates that a total of 1,520,453 Vietnamese people were killed. For the total death toll, see the American Phase (unstarred) indicating the median totals to about 1,700,000 deaths and the total of medians to 1,300,000 deaths. Data on US war casualties is available to download as pdf.

Page 132–133. Bears and axes

This striking comparison was brought to the public’s awareness by a man named Hans Hansson. He wrote to his local newspaper about the absurd neglect of domestic violence against women and went on to start a network for men to help them break their violent behavior. Read an interview with him in English here.

In a dissertation from Karolinska Institutet, Shilan Caman (2017) studied domestic violence in Sweden, summarizing on page 82 that:

“Enligt Brå hade cirka 17 kvinnor dödats varje år av en man i en nära relation under perioden 1990 – 2005. Detta var en minskning jämfört med perioden innan [6]. I en rapport från 2015 redovisar Brå en fortsatt minskning. Under perioden 2008 – 2013 var antalet fall 13 kvinnor per år. Sedan början på 2000-talet har minskningen varit 20 procent.” [According to Brå, about 17 women was killed every year by a man in a close relationship during the period of 1990–2005. This was a decrease in comparison to the period before. In a report from 2015, Brå presents a continuing decrease. For 2008–2013, the number was 13 women per year. The decrease has been 20 percent since the beginning of the 2000s.]

Caman’s study called Intimate Partner Homicide Rates and Characteristics is in English and can be downloaded as PDF.

Page 133. The Spanish flu

In his book America’s Forgotten Pandemic, Crosby (1989) estimated that the Spanish flu caused 50 million deaths. The number is confirmed by Johnson and Mueller (2002) and CDC[1]. The world population in 1918 was 1.84 billion, which means this pandemic wiped out 2.7 percent of the entire global population.

Page 133. Tuberculosis (TB) and the swine flu

The data on swine flu comes from WHO[17] and the data on tuberculosis are based on Global Health Observatory data and www.gapm.io/xmdrtb from WHO[10,11]. For the risk for multidrug resistant TB strains, see WHO[17]. See www.gapm.io/bswin.

Page 134. Energy sources

The data comparing energy sources is from Energy Transitions: Global and National Perspectives by Smil (2016). Smil describes the slow transition away from fossil fuels and also debunks myths about food production, innovation, population, and mega-risks. See www.gapm.io/tene.

Page 136. The world’s PIN Code

These maps show the official UN forecasts, rounded to billions, from the 2017 Revision of World Population Prospects from UN-Pop[1]. The world population was 7.55 billion in 2017 according to their estimates. That would usually be rounded to 8 billion, but we show 7 billion because we are rounding the population region by region. The populations of the four Gapminder regions were estimated based on national data: the Americas, 1.0 billion; Europe, 0.84 billion; Africa, 1.3 billion; and Asia, 4.4 billion. See also the note to Fact Question 8.

Page 138: Graphs: West and Rest

On these graphs, where the world population is divided into west and rest, we refer to what we think the student in the European classroom had in mind — the west consisting predominantly of Western Europe, United States, Canada, and Australia. Gapminder’s rough estimates of the number of people on different incomes for 2017 are based on World Bank official data from PovCalNet, and extended into the future with IMF forecasts. To see how the majority of people are catching up with the west, see the color-chart at the end of the book, showing the number of people on each income level separated by the four regions, or use this interactive version to animate the income mountains: www.gapm.io/incm.

Page 138. Future consumers

Two great books on this are The Post-American World by Fareed Zakaria (2008) and The World Is Flat by Thomas L. Friedman (2005).

Page 139: Child mortality rate

The number of infant deaths come from UN-IGME. Estimates on the number of births come from UN-POP[3].

Page 139. CO2 per capita

The data on CO2 per capita for China, the United States, Germany, and India come from CDIAC. See www.gapm.io/tco2.

Chapter Six: The Generalization Instinct

Page 151. The cost of diabetes

Diabetes is one of those diseases that push people back into poverty, when a household might have to spend their entire budget on expensive insulin. See the global report on diabetes from WHO.

Page 155. Comparing stoves

See the similarities between the cooking pots in China and Nigeria, and more stoves, on Dollar Street.

Page 159. Graph: Differences within Africa

The distance between Liberia and Kenya, using Monrovia to Mombasa as points of reference, is about 3,555 miles. For London and Tehran the estimated number is 2,734 miles. For an interactive version of the graph on page 159, showing differences in health and wealth in Africa, see www.gapm.io/edafr.

Page 160. Contraceptives in Sweden

The individual and national gains of making birth control easily available is described in “Children of the Pill: The Effect of Subsidizing Oral Contraceptives on Children’s Health and Wellbeing” (2012). The study, conducted by Andreas Madestam and Emilia Simeonova, looks at the long-term effects of Sweden’s subsidized birth-control policy in several municipalities between 1989 and 1998. Madestam and Simeonova report that women who decided to have children and were eligible for the subsidy achieved better health, economy and education for the next generation than women who decided to have children but were not eligible for improved access to birth-control.

Page 160. Family planning: Needs met and unmet needs

The data on the use of contraceptives is based on UNFPA[1] and UN-Pop[9]. UNFPA is the part of the United Nations that deals with sexual and reproductive health. The data show the unmet need for family planning and are based on estimates from the UN data set, World Contraceptive Use 2017. The data set provides estimates for all women of reproductive age, 15–49 years, who are married or in a union. See, for example, contraceptive prevalence rate for the select countries, along with more details. You can explore the data in the interactive chart further down the page.

While we report the percentages of women who say their needs for contraceptives are met, UN publishes the reverse numbers, as rates of unmet need for family planning. It is defined as the share of women who don’t want to become pregnant, or want to postpone pregnancy, but are not using any method of contraception. The data set, World Contraceptive Use 2017, is available here: www.gapm.io/xcontr. See www.gapm.io/twmc.

Page 160. Everything is made from chemicals

People with chemophobia divide the world into “natural” (safe) and “chemical” (industrial and harmful). The world’s largest database of defined chemical compounds sees it differently. CAS contains 132 million organic and synthetic chemicals and their properties. It shows that toxicity is not related to who produces the compound. Cobratoxin (CAS registry number 12584-83-7), for example, which is produced by nature, paralyzes your nervous system until you can’t breathe; see Del Brutto (2012) retrieved via Wikipedia[8]. See www.gapm.io/tind.

Page 161. The Salhi family on Dollar Street

See more about the Salhi family at www.gapm.io/dssah. If you think we have too few homes from Tunisia or elsewhere on www.gapm.io/dstun, feel free to contribute. Read more about how you can do it at: www.gapminder.org/dollar-street/about.

Page 163: The recovery position

Before the 1950s, war surgery guidelines didn’t have a specific body position recommended for the care of trauma patients. When the so-called NATO coma position had been successfully used by the US in the Korean War, however, it began to be promoted for saving unconscious soldiers; see Högberg and Bergström (1997). The recovery position was not standardized until decades later, in the early 1990s, when it emerged in general first aid handbooks. See Wikipedia[10] for more on the history of the recovery position.

Page 163. Hong Kong report on SIDS

The number of sudden infant deaths fell during the 1990s due to successful intervention campaigns telling mothers not to leave their babies in the prone position. But these campaigns only helped bring down the rates of sudden infant deaths to the same low levels where they used to be, before the prone position was promoted. The same pattern evolved in the US, Norway and Sweden. The conclusion that it was public health policy on the prone position that caused the increase in SIDS in Sweden is described by Gilbert et al. (2005) and Högberg and Bergström (1997), who brought this to our attention with their article “Läkarråd ökade risken för plötslig spädbarnsdöd” [“Physicians’ advice increased the risk of sudden infant death syndrome”].

The report from Hong Kong is from Davies (1985). The study was the first to suggest that the practice of placing babies in prone position could be the cause of sudden infant deaths; see Cot death in Hong Kong: a rare problem? and this follow-up report from N.N. Lee (1989). For historical data on sudden infant deaths in Norway, see, for example, Irgens et. al (1995), available to download as PDF here. For more about international trends in SIDS.

Chapter Seven: The Destiny Instinct

Page 169. The sense of superiority

For more on the sense of superiority over other groups, see Haidt, The Righteous Mind: Why Good People Are Divided by Politics and Religion (2012). See www.gapm.io/fdes.

Pages 170. World Health Chart over 200 years

To see the World Health Chart in motion over 200 years, visit www.gapminder.org/whc and click Play. See also the note to World Health Chart 2017

Page 170. Life expectancy 1970–2016: North Africa and Western Europe

In the book we say that life expectancy in Africa today is 65 years. Estimates from UN says the number for Africa is 66 years. IHME don’t present an aggregate for Africa, so Gapminder estimated the average lifespan to be 65.5 for 2017, weighted by population, using select GBD tables published in Lancet article 2017, available to download via IHME. Since population weight seems to slightly increase life expectancy, Gapminder rounded it downwards, to 65 years. Total Europe (as used in the rest of the book) has 78 years life expectancy, while Western Europe (or EU) has 82. So this comparison is assuming we use EU or Western Europe.

In 1970, the average lifespan in Sweden was 74.5 years. In their estimates for 2016, IHME combines North Africa with the Middle East, including Afghanistan, and puts life expectancy to 73.16 years, while UN-Pop[1] estimates 71.8 years. To compare historical rates in Sweden with North Africa today, we used data from IHME, GBD (2016). Sweden in 1967 and 1970 had 74.5 years and 75.9 years in 1980, with the weighted average of 74.2 years. The five countries in North Africa with life expectancies above the world average of 72 years are as follow:

• Algeria, 77.4 years
• Egypt, 72.1 years
• Libya, 75.05 years
• Morocco, 75 years
• Tunisia, 77.46 years

 

The five countries have lower Income than Sweden used to have, as you can see in the interactive graph: http://bit.ly/2IxvIAI

To estimate the average lifespan for the countries and regions mentioned above, we looked at GBD, the table showing life expectancy every five years from 1970 to 2016. For sub-Saharan Africa, life expectancy is 62.87 according to IHME, while UN estimates 59.7. IHME numbers are generally higher. The reasons why are explained by Lancet. See Gapminder[4] for detailed documentation on life expectancy for countries and regions.

Page 170. Speed of improvement over the decades

The average speed of improvement in Africa and Europe lead to the rough assumption that Africa develops at the speed that others did in terms of infrastructure:

• Electrification
  • SSA: 1991–2014: 19–37%
  • Europe, UK: 1865–1880: 24.4–77%
• Education
  • SSA:  1971–2014- : 46–68%
• Sanitation
  • SSA: 1990–2015 : 23–29%
• Water
  • SSA: 1990–2015 : 46–67%

This article discusses the challenges of making electricity available and how to achieve energy access to the world’s poorest populations, comparing historical rates: Electricity for All: What Universal Energy Access Will Take. See the speed of improvement over the past decades here: www.gapm.io/edafr2.

Page 170. Child mortality rates for Sub-Saharan Africa and Sweden

See last two sheets “speed of u5mr drop.xlsx” The under-5 mortality rates for all countries were turned into five year averages (pre 1800, 10 year averages) to get rid of temporary enormous drops and data artefacts. We calculated all 57 year periods for all countries and SAA’s countries dif during the period 1960–2017. This was compared with all Sweden’s difs for all 57 years periods of Swedish history. Sweden was never reducing child deaths per 1000 faster than all the SAA countries during 1960–2017. Counted in terms = (u5mr2017 – u5mr1960). Sweden had a slow steady increase over 200 years, which SAA have achieved, with vaccines etc, in just 57 years.

Page 171. Progress in China, Bangladesh, and Vietnam

The Population Bomb by Paul and Anne Ehrlich (1968) contributed to a widespread idea that Asia and Africa would never be able to feed their growing populations. A total of 3.5 million people died in the so-called Bengal famine in 1942–1943: 2 million in Bangladesh and 1.5 million in India, based on the territorial boundaries today. The data on deaths from famines is from the International Disaster Database, EM-DAT. The Peace Research Institute Oslo (PRIO) produces maps of conflicts and poverty: www.gapm.io/mpoco. For global textile production, see www.gapm.io/tmante.

Page 171. Mozambique and India

Compare progress in India and Mozambique in this animated chart.

Page 171. Estimates of extreme poverty

Roughly half a billion people in Africa today live in extreme poverty, as cited in Factfullness, p. 171. The more precise estimate from Gapminder[9] is 0.41 billion people, based on PovalCal[1] and IMF[1]. Keep in mind that there are huge uncertainties in extreme poverty, as described in the note Fact Question 3: Extreme Poverty.

Paul Collier writes in The Bottom Billion (2007) about the future prospects for the world’s poorest people, living in conflict areas on meager soil. To see where these people are, the thing to measure is child mortality, which is the most reliable indication of extreme poverty. By combining these four detailed maps, we can locate the poorest on earth with the most unfortunate future. Our rough estimate of people in extreme poverty close to conflicts is based on ODI (Overseas Development Institute) from 2015, preliminary results by Andreas Forø Tollefsen and Gudrun Østby of the number of people who live close to conflict worldwide (743 millions in 2016); download PDF here. Data on where most people live is from United Nations Population Division; where there are high rates of child mortality from IHME[6]; where soils give low yield from FAO[4]; and where there are conflicts from UCDP[2]. As long as there’s conflict, most people stuck in poverty will have a hard time getting out.

Page 172. IMF forecasts

Our comments on the IMF’s forecasting track record are based on the World Economic Outlook; see IMF[2]. IMF forecasts are used for speculations about future growth in the stock and finance industry, as BBC News points out. Here is one example of such speculations.

See www.gapm.io/eecof. The following graphs are from IMF forecast track record, based on the World Economic Outlook from previous years IMF[2-10]:

Advanced economies

Sub-Saharan Africa

Where growth happened

Data Sources

• IHME[1] (Institute for Health Metrics and Evaluation) Global Burden of Disease Study 2016 (GBD 2016) All-cause Under-5 Mortality, Adult Mortality, and Life Expectancy 1970-2016. Table 13A. Select Lancet article tables (2017). Available to download from GHDx via the “Files” tab: www.gapm.io/xihlex
• UN-Pop[1] (UN Population Division) Population, medium fertility variant. World Population Prospects 2017. Online at: www.esa.un.org/unpd/wpp

Page 172. 80 years ago in Sweden and US

In the US in the 1930s, the Farm Security Administration (FSA) sent photographers across the nation to understand what it looked like, resulting in famous black and white images from the rural poor. In Sweden in 1938, Ludvig “Lubbe” Nordström gave eye witness reports to radio listeners on the national radio report series called Dirt Sweden. It is described in the article “Culture, Health, and Religion at the Millennium: Sweden Unparadised” by Demker et al. (2014). These human faces to mobilized support for massive development investments across these countries, leading to the Swedish welfare system being constructed and the New Deal in the US.

Fertility and Religion

Page 174. Fertility in Iran

Professor Hossein Malek-Afzali at Tehran University of Medical Science was Hans’s host in Iran, who showed him the infertility clinic and taught him about Iran’s family planning and sexual education programs. We use the term “babies per woman” for the statistical indicator “total fertility rate.” In 1997, the full drop in fertility rate had not yet been achieved. Still, in the data that Hans had back then, Iran had the fastest drop. Today’s UN data rather put Oman as the fastest with Gapminder data compared. (Except for Oman, depending on which years you compare, and how you deal with Gapminder’s estimates of temporary dips. When removing those (by 5 year averages) the lead table looks like the following: Drop size within any time frame: Armenia: 4.06; Iran: 3.47; China: 3.46; Oman: 3.45; North Korea: 3.34; Russia: 3.11; Cuba: 2.45.

These improvements in Iran, especially for women, also reflect achievements in regards to health and education. Women in Iran today have an average 10 years of school, compared to 2 years in 1970, according to HME[2]. During the same years, life expectancy increased from 56 years to 76 during the same period, based on HME[2]. Comparing these numbers back then with Iran today, the differences are as wide as they probably could be. In Afghanistan, women today have 1 year of school, which is half of Iran’s level in 1970. At present, Iran has a lower fertility rate than the US and Sweden. As of 2017, the total fertility rate is estimated to be at 1.636 in Iran; 1.876 in the US; and 1.909 in Sweden, based on total fertility (TFR) data from UN’s World Population Prospects 2017.  To compare Iran—the world champion in family planning—against other countries over time, see www.gapm.io/vm2.

Page 175–176. Graphs: Three groups based on religion

In a sense, it is ridiculous to show only three kinds of religions. The variations of people’s spiritual beliefs are probably just as many as there are people. If you search the internet for family trees of world religions you will see how they all branch from a few common beliefs, splitting into a myriad of different interpretations, practices and belief systems that keeps splitting over the decades. It is unusual that religious groups emerge, just like branches on a tree.

Globally, roughly 1.1 billion people do not affiliate with any of the world’s largest religions, such as atheists, agnostics, or those without any particular religion. The number of people who affiliate with the world’s large religions represent about 2.1 billion Christians, 1.6 billion Muslims and about 2.6 billion other large religions (1 billion Hindus, 0.5 billion Buddhists). The numbers come from PEW[2] and the ARDA[1], where you can find percentages of people following each religion.

Page 175. Classifying major religions

When classifying countries based on religion, we no longer use the term ‘majority religion’. We only use this term when talking about the 40 countries back in 1960 (p. 175 in Factfulness) for comparison. Estimates of Religious Composition by Country by Pew Research Center show percentages of those affiliated with any religion (or not) by country as of 2010 and in the future. In most countries, more than 50% of the population adhere to one of the large religions. However, in many countries there is no clear majority. In Nigeria, for example, 49 percent of the population was Christian and 48 percent Muslim in 2010, according to our data on religion; see Pew[2,3].

We have split 81 such countries into three separate bubbles in the relevant charts, using Pew[2] and USAID-DHS[2]. To estimate each religious group’s fertility rate, and roughly estimating each religious group’s per capita income, we have used data from GDL[1,2], OECD[3] and other sources. See www.gapm.io/ereltfr.

Page 175. High income means low fertility

Fertility rates for global religions are based on estimates from PEW[3], calculating fertility across all income levels. The average fertility rate for global religions are based on population growth projections, 2010–2050 from PEW[3], calculating fertility rate of each religion, across all income levels. According to their estimates, the highest fertility rate is among Muslims, with a rate at 3.1 children per woman; second highest is Christian fertility, with a rate at 2.7; Hindu fertility is at 2.4, which is closer to the global average; Jewish fertility at 2.3.  Total Fertility Rate by Religion, 2010-2015 or download the complete report here.

Page 176. Swedish values and RFSU

The earliest condoms found are from Egypt 13000 years BC. In the nineteenth century they became better and less expensive thanks to new rubber technology. Condoms were allowed in Sweden, but could only be sold by medical staff and up to 1939 it was prison sentence if anyone tried to educate about them, as it was considered demoralizing and “could harm the family and societies and stimulate prostitution”. The radical journalist Elise Ottesen–Jensen changed all that by educating women and distributing pesars. She mobilized brave women and men and brave union leaders to finally create the Swedish Association for Sexuality Education: RFSU. It wasn’t until the 1970s when the laws changed.

Page 177. Asian values

In “Explaining Fertility Transitions” (1997), Karen Oppenheim Mason discusses changing family norms. Of course there are many differences between cultures and how fast their values change when families change to a modern lifestyle with more resources. Europe never had such oppressive traditions against women like for example the foot bindings of East Asia.

Karen Oppenheim Mason discusses the changing family norms in her cross-disciplinary article “Explaining Fertility Transitions”. The strength of cultural differences is easily overstated though. Gender roles change quite fast in all cultures as people get richer and their way of living is modernized. In cultures with an emphasis on extended families, values may change a bit more slowly. See www.gapm.io/twmi.

Page 177. Asian University for Women in Bangladesh

See www.auw.edu.bd.

Page 179. Nature reserves

The data on protected nature is based on data from The World Database on Protected Areas (WDPA) and Protected Planet Report 2016 by UNEP–WCMC and IUCN; see UNEP[5,6]. The trend for 1900–1911 was aggregated by Gapminder[31] from the historic records of WDPA, which keeps track of protected areas following the IUCN Definition 2008 and the Protected Area Categories; see IUCN[1,2]. The trend for 1911–1990 comes from Abouchakra et al. (2016) Looking Ahead: The 50 Trends That Matter, based on WDPA data from February 2012. The trend after 1990 is based on UNEP[6] Protected Planet Report 2016 (fig. 4.1, p. 30), freely available for download at www.gapm.io/xprotp16.

Page 179. Sri Lanka’s earliest protected forest

“Conservation of nature and culture are ancient traditions in Sri Lanka” If the king King Pandukabaya, hundred years before King Devanampiyatissa is known to have given certain lands royal protection, according to IUCN[3]. In 1820 Charles Waterton pioneered natural conservation by  building a nine-foot-high wall around three miles of his estate in, to protect birds, wildlife and plants. See Wikipedia[25] Yorkshire.

Page 180. Outdated chimpanzee questions

In the 1990s, students at Karolinska Institutet did not know that many European countries had worse health outcomes than many Asian countries. These are the results that Hans show in his first TED talk—Rosling (2006). Thirteen years later, when we wanted to check whether people’s knowledge had improved, we could no longer use the original questions since the European countries had managed to caught up. This is shown in the animated graph here—at www.gapm.io/vm3.

Page 180. Swedish school system

This article explains in English what the head of OECD PISA study has to say about the experiment; see The Local (2017), “Deregulation and freedom of choice have hurt Sweden’s schools.”

Page 181. Attitudes toward same-sex marriage

The data on attitudes toward same-sex marriage in the United States is from Gallup[5] — US Support for Gay Marriage Edges to New High.

Chapter Eight: The Single Perspective Instinct

Page 187. Expert forecasts: The single perspective in other books

People with extraordinary expertise in one field score just as badly on our fact questions as everyone else. This wouldn’t surprise Philip E. Tetlock and Dan Gardner, the authors of Superforecasting (2015). In this book they describe a systematic way to test people’s ability to predict the future, and they find that one thing that can really impair good judgment is narrow expertise. Even forecasts by so-called experts in the media are often overly dramatic. When checking against actual outcomes, Tetlock and Gardner show that the experts not only score worse than the public but also worse than random. The personality traits that often come with good judgment are humility, curiosity, and a willingness to learn from mistakes. The Good Judgement Project, a forecasting services firm co-created by Tetlock, runs a public tournament called Good Judgement Open. You can practice your forecasting here: www.gjopen.com.

Page 188. Lindau Nobel laureate meeting

This is a great annual gathering of brilliant young researchers who, thanks to this wonderful organization, get the chance to learn from Nobel laureates once a year. We are not criticizing that! We are just using their really low score on the vaccination question to make the case that expert knowledge doesn’t guarantee general knowledge. Read more about the presentation on the Lindau website: www.gapm.io/xlindau64.

Page 188. Poll results from groups of professionals

Poll results for the groups of professionals mentioned here, and others, will soon be available at www.gapm.io/rrs.

Page 189. Plundered natural resources

For discussions about the commons and how to avoid exploitation, see The Plundered Planet: Why We Must—and How We Can—Manage Nature for Global Prosperity, by Paul Collier (2010). The numbers of threatened species 1996–2017 come from IUCN Red List[4] and can be downloaded as PDF.

Pages 192–198. Medicine Is Not the Single Solution

Page 192. Eradicating one disease at a time

Halfdan Mahler, the Director-General of WHO from 1973 to 1988, describes in an interview from 2008 how they came to promote primary health care to all instead of eradicating one disease at a time. It was declared “a most important world-wide social goal” at the International Conference on Primary Health Care in Alma Ata 1978. The publication is available to download as PDF: www.gapm.io/xalmaata.

Page 193. Falling profits of Big Pharma

In a study published in Health Affairs, Berndt et al. (2015) analyzed the falling profits of Big Pharma, concluding that: “If this level of diminished returns persists, we believe that the rewards for innovation will not be sufficient for pharmaceutical manufacturers to maintain the historical rates of investments needed to sustain biomedical innovation.” Forbes (2016) refers to this study, discussing the presumed end of the so-called Golden Era of pharmaceutical profits.

Page 193. Education needs electricity

The link between education and electricity has been evaluated by UNDESA (United Nations Department of Economic and Social Affairs). You can download the report as PDF here: www.gapm.io/xdessel.

Page 197. Chart: Cuba

Hans was showing child survival rates on his bubble charts back then. We later decided to show life expectancy so that people wouldn’t think we were focusing only on children. Life expectancy and child survival go hand in hand.

The charts hide a few richer countries to save space to the right, and the most unhealthy countries, at the bottom.

Next to Cuba, the new data puts Costa Rica and Panama on the same income, but with higher life expectancy. That wasn’t the case when Hans was showing the graph in Cuba. The Cuban experience is described at greater length in Hans’s memoirs; see Rosling[8].

Pages 198–199. Chart: US Health Spendings

The United States spends much more on health care than other capitalist countries on Level 4—the European Union, Japan, and South Korea. The health spending data comes from WHO’s Global Health Expenditure Database, via World Bank[24]. This comparison between US spending and spending in other countries comes from OECD[1], a study named “Why Is Health Spending in the United States So High?”

Compared to other OECD countries, the US spends more on all parts of the system—hospitals, ambulatory care, pharmaceuticals, and public health and administration—which indicate that spending in the US health-care system is generally much higher across the board. The component that stands out is ambulatory care providers—that is, physicians, specialists, dentists, etc. The other most exaggerated costs are public health and administration of the US health system, which is 60 percent higher than in the rest of the OECD countries.

The study continues to ask: Is the US providing too much health care? In some common practices, the answer is clearly yes—when comparing the cost of regular health delivery in US and France, the US health costs are 50 percent higher, which is in large part due to unnecessary doctor’s appointments, along with costly diagnostic equipment and over-testing. These frequent doctor consultations and elective health interventions, as we see, do not lead to healthier or longer lives on average. In fact, it is the opposite. In other countries, that same amount of time with doctors is spent on patients with worse conditions and payed through public insurance. A few sub-systems are performing well, in terms of patient content and cancer rates, as can be seen in the 32 improvements charts, where the outcomes have been marvelous for those who got treatment. But the US health system do not provide incentives for doctors to spend time with the patients most in need of care. See www.gapm.io/theasp.

There are two small countries that are excluded from the comparison above. The Marshall Islands is reported to have similar health expenditure per capita as the US, but we don’t mention it because of its incomparable size. Maldives is also very small, and it’s under the horizontal line, and can’t be seen in the chart.

Data Sources

• OECD[1] (Organisation for Economic Co-operation and Development). “Why is Health Spending in the United States so High?” Chart 4: Health spending per capita by category of care, US and selected OECD countries, 2009. Health at a Glance 2011: OECD Indicators. Available as PDF: www.gapm.io/x-ushealth.
• World Bank[24] “Health expenditure, total (% of GDP).” World Health Organization Global Health Expenditure Database, 2017. www.gapm.io/xwb1724

Page 201. Democracy Is Not the Single Solution

Paul Collier’s books are just as disturbing as they are fact-based. See his Wars, Guns and Votes: Democracy in Dangerous Places (2011) for more on how democracy can destabilize countries rather than make them safer. Unfortunately, this seems to hold true for countries on Level 1, where democracy is promoted by the international community, but leads to destabilization rather than safety. The promotion of so-called “democracy” is well-intended. But in practice, it often involves no more than an “election”, and the winner is usually the strongest man with control of the weapons in the violent fight preceding the election. Reality is definitely not as simple as that, but we’re describing the disturbing tendency. And to those who lose their loved ones, suffer starvation, violence, miss school, and remain stuck in poverty during endless civil conflicts, it is difficult to convince them about the benefits of democracy, if that’s what triggered the conflict.

Collier quotes a threshold of $2700 in GDP per capita per year: over this threshold, democracy lead to more stability, but below it, war was a more likely outcome. That threshold is exactly where the line goes between Level 1 and 2 in Gapminder’s income levels. More disturbing problems with democracy is discussed in a great book by Fareed Zakarias, The Future of Freedom: Illiberal Democracy at Home and Abroad. In it, Zakarias talks about legislation that has become a question for the angry crowds, who wants tougher punishment for criminals without considering the predicted costs. Read it! We must remind ourselves, in line with the wise words of Winston Churchill, that democracy, despite all its problems, in the long run, still is the least bad system we know. See www.gapminder.org/topics/government/democracy.

Page 201. Democracy and progress

Democracy is not a prerequisite for social and economic progress. Mostly non-democracies grow fast. South Korea, for instance, moved from Level 1 to Level 3, all the time as a military dictatorship. This discussion is based on IMF[1], the World Economic Outlook 2017 and the Democracy Index 2016 from The Economist[2]. This index gives countries “democracy” ratings on a scale of 1 to 10, with the lowest score of 1.8 going to North Korea and the highest score of 9.93 to Norway. These are the 10 countries with the fastest economic growth over the past five years and their democracy scores (fastest first): Turkmenistan, 1.83; Ethiopia, 3.6; China, 3.14; Mongolia, 6.62; Ireland, 9.15; Uzbekistan, 1.95; Myanmar, 4.2; Laos, 2.37; Panama, 7.13; Georgia, 5.93. Only one out of the ten fastest-growing economies scores well on democracy.

Chapter Nine: The Blame Instinct

Page 204. Neglected diseases

The World Health Organization has a list of neglected tropical diseases (WHO[15]) that primarily affect people living on income Level 1 and markedly neglected in research and development (R&D). The illnesses on this list are not profitable to the pharmaceutical industry—only until recently, Ebola was on this list. See also this article by Von Philipsborn (2015) in Glob Health Action that evaluate poverty in relation to neglect in R&D.

Page 207. Systems thinking

Peter Senge developed the idea of systems thinking within corporate organizations as a way of stopping people from blaming one another and helping them to understand the mechanisms that are causing problems. But his ideas apply to all kinds of human organizations where blaming individuals blocks understanding. See Senge, The Fifth Discipline: The Art & Practice of the Learning Organization (1990). See www.gapm.io/fblame.

Page 208. How UNICEF gets the lowest prices

The most amazing side of UNICEF is its streamlined logistics and supply chain. If you want to place a bid, the supplies and services UNICEF is looking for right now are posted on UNGM’s tender notices page. You can read more about its procurement process at UNICEF[5]. The family business called Rivopharm is here: http://www.rivopharm.com.

Page 210. Gapminder’s Ignorance Project

In 2013, BBC and CNN reported on the results from Gapminder’s Ignorance Project and posted our fact questions on their websites. Here are the US results on CNN and the British results on BBC (2013).

Page 211. Poll results

The results from different events are from Gapminder[27]. These include two media conferences, and the factual filmmakers are people from National Geographic and the Discovery Channel. As it says in the book, journalists and documentarians are not lying—most of the journalists and filmmakers who inform us about the world have themselves a skewed worldview (see also “Who Should You Blame?” on page 221 in the book).

Page 212. Why refugees don’t fly

Hans presents this in Factpod #16, “Why Boat Refugees Don’t Fly!”. see Gapminder[42]. While in the Geneva Convention says: “” , which in practice doesn’t happen in EU, as this EU(2001) COUNCIL DIRECTIVE 2001/51/EC commands: “Member States shall take the necessary measures to oblige carriers which are unable to effect the return of a third-country national whose entry is refused to find means of onward transportation immediately and to bear the cost thereof ”which recognizes the right of persons to seek asylum from persecution in other countries”. See www.gapminder.org/topics/people/refugees

Sweden didn’t confiscate the boats belonging to those who smuggled refugees from Denmark during the Second World War—see the BBC documentary “How the Danish Jews Escaped the Holocaust” at www.gapm.io/xbbcesc17. According to Goldberger (1987) 7,220 Danish Jews were saved by these boats. Today, the EU Council[1] Directive 2002/90/EC defines “smuggler” as anyone facilitating illegal immigration, and the EU Council[2] framework decision allows “confiscation of the means of transport used to commit the offence.” The Geneva Conventions, however, state that many of these refugees have the right to asylum; see UNHCR. See www.gapm.io/p16 and www.gapm.io/tpref.

Page 214. CO2 emissions by income

Researchers are trying to figure out how to adjust emissions quotas for changing population sizes; see Shengmin et al. (2011) and Raupach et al. (2014). See gapm.io/eco2a.

On Level 4, transportation is behind one third of all CO2 emissions—which also double with income, based on US EPA data (27 percent for the US). See Global, Regional, and National Fossil-Fuel CO2 Emissions. For more on CO2 emissions at dif­ferent incomes, visit gapm.io/tco2i.

Page 216. Syphilis

If you think you are not living in the best of times, search for images of syphilis and you will soon feel blessed. We got the many names of this disgusting disease from Quétel (1990) via the University of Glasgow Library.

Page 216. 1 billion people and Mao

1 billion is a rounded-down approximation of the number of people whose lives were affected by Chairman Mao, who ruled China from 1949 until his death in 1976. The number of victims is uncertain. A list of sources and evaluation of their claims can be found at Necrometrics.

Page 216. Falling birth rates and powerful leaders

The Chinese population was 0.55 billion in 1949 and increased by another 0.7 billion 1949–1976. From 1970 to 1976, the total fertility rate declined by half—when Deng Xiaoping enacted China’s one child policy in 1979, population growth was already on the fall.

Data about the population of China is derived from UN-Pop[1] and Sheng Luo (1988), which reconstructed population trends in China by year. The study is available here. To see the population of China move over time, click here and press play.

This interactive chart shows how all countries’ birth rates have fallen since 1800: www.gapm.io/vm4. To see the population of China move over time, click here and press play.

Page 217. Family planning: Contraceptive usage by Catholics

The data on contraceptive use in Catholic-majority countries—60 percent—come from the ARDA, using data from their National Religion Dataset, aggregate weighted by total population. The global rates for the rest of the world come from UN-Pop[9], “Model-based estimates and projections: Countries.”

Data Sources

• ARDA (Association of Religion Data Archives) World Religion Dataset: National Religion Dataset. Available at: http://www.thearda.com/Archive/Files/Descriptions/WRDNATL.asp
• UN-Pop[9] World Contraceptive Use 2017. Data: Model-based estimates and projections: Countries. Contraceptive Prevalence: Any modern method, Percentage of married or in-union women aged 15 to 49 years. Online at: www.un.org/en/development/desa/population/theme/family-planning/cp_model.shtml

Page 218. Access to safe abortion

The UNFPA[2] report about Access to Safe Abortion present these findings:

“Whether legally restricted or not, abortions continue to occur with abortion rates being higher where it is restricted than where it is permitted on request or under broad grounds (….) Where abortion is legally highly restricted, the incidence of unsafe abortion and related mortality is high. Legal restrictions also result in major inequity in access to safe providers, as women in urban areas and those who can afford to pay can access physicians or travel abroad to procure abortion.”

The WHO guidelines on Access to Safe Abortion say: “Restriction in access to safe abortion services results in both unsafe abortions and unwanted births. Almost all deaths and morbidity from unsafe abortion occur in countries where abortion is severely restricted in law and/or in practice.” See WHO[2] and UNFPA[2].

Page 218. Institutions

Institutions are best understood through the work performed by the people maintaining them. All prospering countries have the same basic institutions. In their book Poor Economics, Banerjee and Duflo (2011) describe the very basic institutions needed to make the escape out of poverty easier. See www.gapm.io/tgovin.

Page 219. The governmental employees who saved the world from Ebola

Dr. Mosoka Fallah is one of the Ebola contact tracers who Hans worked with in Monrovia. Listen to Dr. Fallah’s own words about the government’s employees and their commitment to their society when it needed them most, and watch him describe how to maintain trust within the community while hunting the infection, in his TEDx Monrovia talk.

Page 219. Thank you, industrial Revolution

See the magic washing machine in action in this TED talk.

See also: Fact Question 12: Electricity

Chapter Ten: The Urgency Instinct

Page 227. The Urgency Instinct in other books

The instinct to think—and act—in oppositional pairs are deeply rooted in our evolutionary survival. For the gazelle, watching out for a possible predator, the difference between “yes” and “no” could be a matter of life and death. Humans, as well, are notoriously bad at keeping a reasonable range of options at hand. Like the gazelle, we tend to instinctively pick between opposites and choose the one that would best benefit our survival. A evolutionary remnant that once saved our lives, black-and-white-thinking nowadays blocks us from weighting the good and bad, yes and no, and select something more useful, such as a “maybe”. See Superforecasting by Tetlock and Gardner (2015) for more about our tendency toward urgent decision-making.

Page 225. Roadblocks

Roadblocks seldom work to stop spreading an outbreak, as David L. Heymann (2014) points out in Nature about his experiences of Ebola outbreaks in the past: “Attempts to block the disease at Africa’s porous borders did not stop past outbreaks, and will not work now. A cordon sanitaire was established by the DRC government around Kikwit in 1995 and enforced through military roadblocks. But contacts under fever surveillance travelled outside the cordon in dugout canoes. The military patrolled roads, but not forest paths leading to the Kwilu River.”

Page 223. Konzo

To understand the lives of the villagers and their children suffering from konzo, watch the film by Thorkild Tylleskär (1995), recorded in the Bandundu Province, in present-day Democratic Republic of Congo on www.gapm.io/x2.

Page 227. Now or never

Learn to defend yourself against common sales tricks in Robert Cialdini’s Influence (2001).

Page 232. The melting ice cap

The website Greenland Today shows the melting at the North Pole, using satellite images to keep track of the ice cap every day: https://nsidc.org/greenland-today.

Page 232. Fresh numbers for GDP and CO2

The OECD regularly publishes data for its 35 rich member countries. As of December 2017, the most recent number for GDP growth is from six weeks ago. The most recent number for CO2 emissions is from three years ago; see OECD[2] Air and GHG emissions: Carbon dioxide. Since 2014, Sweden tracks quarterly greenhouse gas emissions—CO2 emissions data that is not older than three months can be found at the website for Sweden’s System of Environmental and Economic Accounts; see SCB (Statistiska Centralbyrån).

Page 233: Climate refugees

Many studies claim to show that the number of refugees will increase dramatically because of climate change. The UK Government Office for Science showed in their study Migration and Global Environmental Change (Foresight, 2011) fundamental weaknesses in the common assumptions underlying these claims. First it found that most of the frequently quoted studies refer back to just two original sources, one estimating that climate change will create 10 million refugees and the other anticipating 150 million; see Box 1.2: “Existing estimates of ‘numbers of environmental migrants’ tend to be based on one or two sources.” And second, it found that these original sources underestimate people living on Levels 1 and 2 and their ability to cope with change. They describe migration as their only option in the face of climate change. For a fact-based picture of global migration and the refugee situation, see UNHCR Population Statistics. Important books on global migration are Alexander Betts’s and Paul Collier’s Refuge (2017) and Collier’s Exodus (2013).

Page 233. Climate reductionism and catastrophizing

The bad habit of reducing all problems to one single problem—the climate—is sometimes called climate reductionism. The term sometimes refer to the monocausality explanation that “all climate change has a single cause, often CO2”. To confront such simplicity is not to deny climate change. It is to have realistic expectations about how people will cope with it in relation to other challenges and problems. Climate change is among other problems, and it is there are many examples in world history of humans adapting to new circumstances.

The idea of climate change as a single scenario is refuted by Smil (2008). In his book Global Catastrophes and Trends, Smil uses global warming as a good example of a process that is too complex to happen as a single event. An assumption that is questioned by Johan Rockström and others who insist that this line of thinking may be a case of the straight line instinct. Smil argues that climate change will probably not lead to a catastrophic event destroying everything at ones, but neither will it be a slow transition that we won’t notice. The future effects of climate change will have aspect of both fast and gradual transition. Fascinating examples of pivotal moments in human history, in regards to food and energy consumption, is also given by Ruth DeFries (2014). In The Big Ratchet, DeFries convincingly shows how the current human-produced system is unsustainable, with fertilizers and depletion of natural resources, without claiming that this must necessarily lead to a sudden catastrophic event.

Page 234. Ebola

The WHO[13] Ebola situation reports lists all situation reports produced to track the Ebola pandemic since 2014. They still show suspect cases, and the CDC[3] continues to use the high estimates, which include suspected and unconfirmed cases.

Pages 237–240. The Five Global Risks

Page 237. The risk of global pandemic

A small version of the Spanish flu is more likely to occur than a large one; see Smil (2008). While we should work against the obscene overuse of antibiotics in the meat industry—see WHO[14] Antimicrobial resistance—we must also be careful not to make the same mistake as we did with DDT and become overprotective. Antibiotics could save even more lives if they were less expensive. See www.gapm.io/tgerm.

Page 238. The risk of financial collapse

During the past ten years, “the external environment is volatile, with capital markets increasingly characterized by more extreme events”, observe Dobbs et al. in No Ordinary Disruption (2016), illustrated by the peaks in the trend line on page 88. In his article “How Should We Prevent the Next Financial Crisis?” (2015) Ricardo Hausmann suggests that since the financial system constantly changes, it is difficult to learn from past mistakes—our next financial crisis will simply not look like any other that has happened before. Many countries have regulated their financial systems to prevent future crises by restricting loans and risk-taking. While this strategy is good for some countries, preventative measures do not benefit all. A number of countries in Latin America for example, Hausmann writes, have instead made their economies too secure. You can read the article by Hausmann in full at www.gapm.io/xecc. See www.gapm.io/dysec.

Page 239. The risk of World War III

Smil (2008), in Global Catastrophes and Trends: The Next Fifty Years, was already discussing ten years ago how six unfolding trends of the new world order were slowly leading to intensified conflicts between parts of the world: Europe’s place, Japan’s decline, Islam’s choice, Russia’s way, China’s rise, and the United States’ retreat.

In Energy Transitions: Global and National Perspectives, Smil (2012) mentions three challenging identity shifts of the world order, which could lead to a third world war: first, Europe’s place in the world; second, Japan’s decline; and third, Islam’s choice, Russians way, China’s rise and the United States’ retreat. All summarized in the chapter Dominance and Decline, in which these shifts require new identities. Most concerning are the huge disparities within China and the pride of Europe. Sweden has never been invaded by great Britain, but why not next? Only 35 years ago, the United Kingdom attacked the Falkland Island. If you read the book All the Countries We’ve Ever Invaded by Stuart Laycock (2012), it’s easier to imagine that Europe might not be the victim of a third world war and more likely the perpetrator and a threat to world peace. See www.gapminder.org/topic/military/war. See www.gapm.io/dysso.

Page 239. The risk of climate change and plundering of natural resources

In Energy Transitions, Smil (2010) argues that a transition to non-fossils will be very hard to achieve for humans. Smil’s argument is difficult to accept, but he makes the case that a fast transition away from carbon is very unlikely to happen at high speed. The actions of ecosystems are hard to predict, but human actions are not. If nothing terrible happens, it is easy to imagine how humanity continues to consume resources until it’s too late. See www.gapminder.org/topic/nature. See www.gapm.io/dysna.

This passage also draws on The Plundered Planet by Paul Collier (2010), OurWorldInData[7] and the thinking of economist Elinor Ostrom. In her work Governing the Commons (1990) Ostrom explores the way in which humans across the world have managed to regulate their common resources to avoid overexploitation. Read more about Ostrom and her design principles of common pool resource administration.

In The Plundered Planet, Collier describes all kinds of natural resources and how humans are continuously plundering them, usually to the point that the process stops further exploitation, but at that point the breeding ground may already have become overused.

Data Sources

• OurWorldInData[7] Number of parties in multilateral environmental agreements based on UNCTAD United Nations Treaty Collection. Published online at: www.ourworldindata.org/grapher/number-of-parties-env-agreements

Page 240. The risk of extreme poverty

The passage draws on World Bank[26], ODI, PRIO, Paul Collier’s The Bottom Billion (2007), and the BBC documentary “Don’t Panic—End Poverty” from Gapminder[11].

While extreme poverty has fallen, the number of extremely poor people living in conflict has been stable or even increased, based on preliminary data from PRIO. If current wars continue, the vast majority of extremely poor children will soon live behind military lines. This poses a cultural challenge to the international aid community. The International Dialogue on Peacebuilding and Statebuilding had its fifth global meeting in Stockholm, 2016. The meeting, following the Stockholm Declaration against war and conflict, discussed how the aid community is now preparing for the risk of extreme poverty in conflict prone countries. See www.gapm.io/tepov.

The other risks are not ongoing. Extreme poverty is. 78% are small scale farmers in extremely poor areas. In extreme poverty, every second person is a child, see World Bank[26], they are from ethnic minorities; see ODI. While the global rate of poor has fallen, the number of poor in conflict has been stable or even increased, based on preliminary data from PRIO. One can only speculate about the future, but if the current wars continue, soon a vast majority of all these children will need to be helped behind a military defend line. This poses a cultural challenge to the international aid community, as Collier describes in The Bottom Million.

• Poverty leads to civil war, and civil war leads to poverty.
• Conflicts in Afghanistan and central Africa mean that all other sustainability projects in those places are on hold.
• Terrorists hide in the few remaining areas of extreme poverty.
• When rhinos are stuck in the middle of a civil war, it’s much more difficult to save them.
• There’s no innovation needed to end poverty. The quicker we act, the smaller the problem (as people in extreme poverty have large families and their numbers keep increasing. )
• The hardest to help will be those stuck behind violent and chaotic armed gangs in weakly governed states.
• To escape poverty, they will need a stabilizing military presence of some kind. They will need police officers with guns and government authority to defend innocent citizens against violence and to allow teachers to educate the next generation in peace.

For a longer list of major risks, see Global Catastrophes and Trends: The Next Fifty Years by Smil (2008). For those who find numbers calming, this is where you will find the big picture of the proportional risks and uncertainties of all kinds of possible fatal discontinuities. See www.gapm.io/furgr.

Data Sources

• ODI (Overseas Development Institute) Greenhill, Romilly, Paddy Carter, Chris Hoy, and Marcus Manuel (2015) “Financing the future: how international public finance should fund a global social compact to eradicate poverty.” Download as PDF: www.gapm.io/xodi
• PRIO “The Battle Deaths Dataset version 3.1.” Updated in 2006; 1946–2008. See Gleditsch and Lacina (2005). Accessed November 12, 2017. Available at: www.gapm.io/xpriod
• World Bank[26] Newhouse, David, Pablo Suarez-Becerra and Martin C. Evans (2016) “New Estimates of Extreme Poverty for Children” Policy Research Working Paper. Download PDF: www.gapm.io/xwb1726

Chapter Eleven: Factfulness in Practice

Page 247. Teachers

Visit www.gapminder.org/teach to find our free teaching materials and join the community of teachers promoting a fact-based worldview in their classrooms.

Business

The world has emerged into a continuous in which services and industries move more freely. In The world is flat, Thomas L. Friedman (2005) describes how most of the things we take for granted about business, worklife, and the future, are no longer true. At the same time, globalization has only started, as described so well by Fareed Zakaria (2008), who calls it The Post-American World in his book with the same title. Zakaria makes these important points:

• Outsourcing to level 2  – same quality for less than half the price. Textiles industry Europe > Bangladesh and Cambodia > Kenya and other African countries as Bangladesh and Cambodia become wealthier and approach Level 3.
• Ghana, Nigeria, and Kenya are some of the best investment opportunities that can be found today.

These two books opened my eyes and made me brave, because I realized that others were seeing what I was seeing. Farred invited me to his studio where I showed how China will catch up also, and how large parts of rural China are lagging dangerously behind.

Page 251. Diversified economies

MIT has produced a free-of-charge tool (https://atlas.media.mit.edu/en/) to help countries work out how best to diversify, given its existing industries and skills; see www.gapm.io/x4 or read Hausmann et al. (2013).

Page 252. Speling miskates

This typo on page 252 is intentional, inspired by the fact that oriental rugs should always contain at least one deliberate mistake. At least one knot must always be wrong in every rug. It is to remind us that we are humans and we should not pretend we are perfect. Deliberately, we have no source behind this fact.

Page 252. Constructive news

Here are two very dif­ferent approaches for fixing the news problem: https://constructiveinstitute.org and https://www.wikitribune.com/.

Page 253. Local ignorance and data

Don’t miss Alan Smith’s TEDx talk “Why you should love statistics” where he shows great examples of local misconceptions in the UK. Gapminder is starting to develop localized visualizations, like these about Stockholm: see www.gapm.io/gswe1. Each bubble represents a small area of the city. The differences in income are disturbingly large, but just like the global picture, the areas in Stockholm are NOT divided into two groups: poor vs. rich, as it’s usually sounds like in the Swedish news. Push play and see how most of Stockholm is getting richer and more educated, despite the common feeling of decline.

A Final Note

Free global development data

Open access to data and research made this book possible. In 1999, the World Bank produced, on a CD-ROM, the most comprehensive set of global statistics ever: “World Development Indicators.” We uploaded the content to our website in our animated bubble graphs to make it easier for people to use. The World Bank got a bit angry, but our argument was that taxpayers had already paid for this official data to be collected; we were just making sure they could reach what they already owned. And we asked, “Don’t you believe in free access to information in order for global market forces to work as they should?” In 2010, the World Bank decided to release all of its data for free (and thanked us for insisting). We presented at the ceremony for their new Open Data platform in May 2010, and since then the World Bank has become the main access point for reliable global statistics; see www.gapm.io/x6.

This was all possible thanks to Tim Berners-Lee and other early visionaries of the free internet. Sometime after he had invented the World Wide Web, Tim Berners-Lee contacted us, asking to borrow a slide show that showed how a web of linked data sources could flourish (using an image of pretty flowers). We share all of our content for free, so of course we said yes. Tim used this “flower-powerpoint” in his 2009 TED talk—see www.gapm.io/x6—to help people see the beauty of “The Next Web,” and he uses Gapminder as an example of what happens when data from multiple sources come together; see Berners-Lee (2009). His vision is so bold, we have thus far seen only the early shoots!

Unfortunately, this book uses almost no data from the International Energy Agency (www.iea.org), which, together with OECD, still puts price tags on lots of taxpayers’ data. That probably will—and has to—change soon, as energy statistics are way too important to remain so inaccessible.