by Hans Rosling
Fact Question 13: Correct answer is A. “Climate experts” refers to the 274 authors of the IPCC[1] Fifth Assessment Report (AR5), published in 2014 by the Intergovernmental Panel on Climate Change (IPCC), who write, “Surface temperature is projected to rise over the 21st century under all assessed emission scenarios”; see IPCC[2]. See gapm.io/q13.
Illusions. The idea of explaining cognitive biases using the Müller-Lyer illusion comes from Thinking, Fast and Slow, by Daniel Kahneman (2011).
The ten instincts and cognitive psychology. Our thinking on the ten instincts was influenced by the work of a number of brilliant cognitive scientists. Some of the books that completely changed our thinking about the mind and about how we should teach facts about the world are: Dan Ariely, Predictably Irrational (2008), The Upside of Irrationality (2010), and The Honest Truth About Dishonesty (2012); Steven Pinker, How the Mind Works (1997), The Stuff of Thought (2007), The Blank Slate (2002), and 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) and The Righteous Mind (2012); and Thomas Gilovich, How We Know What Isn’t So (1991).
Chapter One: The Gap Instinct
Child mortality. The child mortality data used in the 1995 lecture came from UNICEF[1]. In this book we have updated the examples and use the new mortality data from UN-IGME.
Bubble charts. The bubble charts on family size and child survival rates in 1965 and 2017 use data from UN-Pop[1,3,4] and UN-IGME. An interactive version of the chart is freely available here: gapm.io/voutdwv.
Low-income countries. 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. See gapm.io/rdev.
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 But for all three, the uncertainty is high and the numbers are outdated: Afghanistan (1993), 15 percent; South Sudan (2011), 18 percent; Chad (2011), 30 percent.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, based on UN-Pop[4]. Note that in these countries, many boys are also missing school. See gapm.io/twmedu.
Income levels. The numbers of people on the four income levels have been defined by Gapminder[8] based on data from PovcalNet and forecasts from IMF[1]. Incomes are adjusted for Purchasing Power Parity $ 2011 from ICP. See gapm.io/fwlevels.
The graphs showing people distributed by income, comparing incomes in Mexico and the United States in 2016, are based on the same data, slightly adjusted to align with the shape of the distributions from the latest available national income surveys. Brazil’s numbers come from World Bank[16], PovcalNet, slightly adjusted to better align with CETAD. See gapm.io/ffinex.
Throughout the book, when talking about personal income levels and countries’ average incomes, we use a doubling scale. Doubling (or logarithmic) scales are used in many situations when comparing numbers across a large range, or when small differences between small numbers are as important as big differences between big numbers. It’s a useful scale when it is not the size of the pay rise that matters, but the size of the rise in relation to what you had before. See gapm.io/esca.
“Developing countries.” Here is the World Bank announcing its plan to phase out the use of the term “developing world,” five months after I explicitly challenged its outdated terminology: https://blogs.worldbank.org/opendata/should-we-continue-use-term-developing-world. See World Bank[15].
Large parts of the UN still use the term “developing countries”, but there’s no common definition. The UN Statistic Division (2017) uses it for something it calls “statistical convenience”. and finds it convenient to classify as many as 184 countries as developing (including Qatar and Singapore, two of the healthiest and richest countries on the planet).
Math scores. Part of the example is borrowed from Denise Cummins (2014).
Extreme poverty. The term “extreme poverty” has a set technical meaning: it means you have a daily income of less than $1.9/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. The social and economic challenges of being among the poorest in a rich country should not be neglected (see World Bank[5]), but it is not the same thing as being extremely poor. See gapm.io/tepov.
Chapter Two: The Negativity Instinct
The environment. The statements about overfishing and the deterioration of the seas are based on UNEP[1] and FAO[2], Paul Collier, The Plundered Planet (2010), p. 160, and data for endangered species comes from IUCN Red List[4]. See gapm.io/tnplu.
Bar chart: Better, worse, or about the same? The bar chart mixes results from YouGov[1] and Ipsos MORI[1], as an identical question was asked in different countries. See gapm.io/rbetter.
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 gapm.io/doubt.
Graph: Extreme poverty trend. Historians have tried to estimate the extreme poverty rate in 1820 using different methods, and their results differ widely. Gapminder[9] roughly estimates that 85 percent of the world population lived on Level 1 in 1800. The post-1980 data comes from PovcalNet. Gapminder[9] has extended the trend to 2017 using PovcalNet and IMF[1] forecasts. The numbers in the text on the reductions in extreme poverty in China, India, Latin America, and elsewhere come from World Bank[5]. See gapm.io/vepovt.
Life expectancy. Life expectancy data is from IHME[1]. In 2016, only the Central African Republic and Lesotho had a life expectancy as low as 50 years. However, uncertainties are huge, especially on Levels 1 and 2. Learn how much data doubt you should have at gapm.io/blexd.
Deaths from starvation in Ethiopia. This number is an average of two sources, FRD and EM-DAT.
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.
Literacy. Historic literacy numbers for Sweden are from van Zanden[2] and OurWorldInData[2]. The literacy rate for India is from India Census 2011. Both in India today and in Sweden 100 years ago, “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. See gapm.io/tlit.
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 gapm.io/tvac.
32 improvements. The data behind each of the 32 line charts on pages 60–63, together with detailed documentation of how the many sources were used, can be found here: gapm.io/ffimp.
Guitars per capita. For more information about this chart, see gapm.io/tcminsg.
Historic child murders. 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 hu
nter-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; see Pinker (2011), pp. 417.
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.
Drownings. The data on drownings today comes from IHME[4,5]. 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).
Catching up. 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
Ebola. The data on Ebola is from WHO[3]. The material Gapminder produced to try to communicate the urgency of the situation is at gapm.io/vebol.
Population forecasts. Population forecasts are based on UN-Pop[1,2,5]. 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 editions of the publication. Two billion children is a rounded number. The precise UN numbers are 1.95 billion for 2017 and 1.97 billion for 2100. For more on the quality of UN forecasts, see Nico Keilman (2010) and Bongaarts and Bulatao (2000). See gapm.io/epopf.
Historic population data. The line 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. See gapm.io/spop.
Babies per woman. We use the term “babies per woman” for the statistical indicator “total fertility rate.” We use UN-Pop[3] for post-1950 data and Gapminder[7], based on Mattias Lindgren’s work, for the years before 1950. The dashed line after 2017 shows the UN medium fertility projection, expected to reach 1.96 in 2099. See gapm.io/tbab.
The fill-up. If you find it hard to understand the fill-up in the text and static images in this book, we find it easier to explain with animations, or with our own hands; see gapm.io/vidfu. (This phenomenon is also called the demographic momentum. For technical descriptions see UN-Pop[6, 7]). See gapm.io/efill.
Historic babies per woman and child mortality. 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). Nobody knows the fertility rate before 1800, but six is a commonly used and likely average. See gapm.io/eonb.
Chart: Average family size by income. 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. See Gapminder[30].
Changing the typical family size. For more on how societies transition from large to small families, see Rosling et al. (1992), Oppenheim Mason (1997), Bryant (2007), and Caldwell (2008). Babies per woman seems to start to increase again when people reach really high incomes on Level 4; see Myrskylä et al. (2009). This video shows how saving lives leads to fewer people: gapm.io/esclfp.
Straight lines, S-bends, slides, and humps. Most of these charts use national income data; 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. In each example, there are huge differences between countries on every level. Very few countries follow these lines exactly, but the lines show the general pattern of all countries over several decades. You can explore the actual plotted bubbles behind these lines at gapm.io/flinex.
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 Ellenberg (2014), How Not to Be Wrong: The Power of Mathematical Thinking. See gapm.io/fline.
Chapter Four: The Fear Instinct
Natural disasters. The numbers for the Nepal earthquake are from PDNA. Numbers for the 2003 heat wave in Europe are from UNISDR. All other disaster data is from EM-DAT. Nowadays, Bangladesh has a very cool flood-monitoring website; see http://www.ffwc.gov.bd. See gapm.io/tdis.
Child deaths from diarrhea. Our calculations of child deaths from diarrhea from contaminated drinking water are based on numbers from IHME[11] and WHO[4]. See gapm.io/tsan.
Plane accidents. The data on fatalities in recent years is from IATA and the data on passenger miles is from the UN agency that managed to reduce the number of accidents, see ICAO [1,2,3]. See gapm.io/ttranspa.
Deaths in wars. The figure of 65 million World War II deaths includes all deaths and comes from White[1,2]. The data sources for battle deaths (Correlates of War Project, Gleditsch, PRIO and UCDP[1]) include reported deaths of civilians and soldiers during battle, but not indirect deaths like those from starvation. Estimates of fatalities in Syria are from UCDP[2]. We strongly recommend watching this interactive data-driven documentary, which puts all known wars in perspective: www.fallen.io. To interactively compare fatalities in wars since 1990, go to http://ucdp.uu.se. See gapm.io/twar.
Fear of nuclear. 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). 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. According to Pew[1], in 2012, 76 percent of people in Japan believed that food from Fukushima was dangerous. The discussion of health investigations after Chernobyl is based on WHO[5]. Data about nuclear warheads is from the website Nuclear Notebook. See gapm.io/tnuc.
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. See gapm.io/ffea.
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 gapm.io/tvac.
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]. An international treaty against various pesticides, including DDT, has since entered into force in 158 countries; see http://www.pops.int. Since the 1970s, CDC[4] and EPA have issued directives on how to avoid the dangers of DDT to humans. Today, the World Health Organization promotes the use of DDT to save lives in poor settings by killing malaria mosquitoes, within strict safety guidelines; see WHO[6, 7].
Terrorism. The data about fatalities from terrorism comes from the
Global Terrorism Database; see GTD. The data on terror deaths per income level comes from Gapminder[3]. See Gallup[4] for the poll about fear of terrorism. See gapm.io/tter.
Alcohol deaths. Our calculations on deaths involving alcohol draw on IHME[9], NHTSA (2017), FBI, and BJS. See gapm.io/alcterex.
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 gapm.io/ffear.
Comparing disasters. To compare different kinds of disaster deaths, see “Not All Deaths Are Equal: How Many Deaths Make a Natural Disaster Newsworthy?” online at 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: gapm.io/fndr.
Chapter Five: The Size Instinct
Nacala child deaths 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.
Wrong proportions. The examples of proportions that people tend to overestimate come from Ipsos MORI[2,3], which reveal misconceptions across 33 countries. Paulos, Innumeracy (1988), 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 gapm.io/fsize.
