Notes and sources: Data on children are taken from an OECD survey of the Netherlands and represent 13% as limited or simple, nearly an eighth, but in Britain the proportion is a sixth; the figure of a seventh is a geographically wider average (see Section 3.1). The next four rows are derived from British studies of poverty (Section 4.1), society (Section 5.1), wealth (Section 6.1) and psychiatric morbidity (Section 7.1) from surveys taken in the years shown. The final row is derived from dividing the 131 million people who voted in the US presidential election of 2008 by the population aged 15+, some 243 million (www.census.gov), equivalent to an 18+ estimate including all those not counted as resident in the US.
Sources: First column: this book. Second column: past evils, William Beveridge’s well-known labels and popular additions in brackets. Third column: political labels from socialists (Cockshott and Cottrell, 1983, pp 11, 12). Fourth column: philosophical labels from philosophers (Wolff and de-Shalit, 2007, pp 38-9). The most important are claimed to be 1, 2, 3, 4, 7 and 10, see pp 106 and 191, and for some extra ones, - 11-14, see p 198, footnote 9). Fifth column: public labels from newspapers (Mackay, 2008) and think-tank projects and surveys (Watts, 2008, p 6). Numbering is from original publications.
Notes: ‘None’ implies possessing no knowledge as far as can be measured. ‘Limited’ implies possessing very limited knowledge. ‘Barely’ stands for barely possessing adequate knowledge in the minds of the assessors. ‘Simple’ means understanding only simple concepts. ‘Effective’ is a little less damning. ‘Developed’ is better again; but only ‘advanced’ pupils are found to be capable, it is said, of the kind of thinking that might include ‘critical insight’. Source: OECD (2007) The Programme for International Student Assessment (PISA), OECD’s latest PISA study of learning skills among 15-year-olds, Paris: OECD, derived from figures in table 1, p 20)
Source: Data given in OECD (2007) The Programme for International Student Assessment (PISA), OECD’s latest PISA study of learning skills among 15-year-olds, Paris: OECD, derived from figures in table 1, p 20
Note - school leaving age in years, left hand axis and line marked by X's; university entry % by age 30, right hand axis and line marked by filled black circles. Sources: BBC (2007) ‘School leaving age set to be 18’, report, 12 January; Meikle, J. (2007) ‘Education dropouts at 16 will face sanctions’, The Guardian, 23 March; Timmins, N. (2001) The five giants: A biography of the welfare state (new edn), London: HarperCollins, pp 2, 73, 198 and 200); and latest official estimates, see Higher Education Funding Council for England website on widening participation of local areas: www.hefce.ac.uk/Widen/polar/
Notes: The prizes awarded in 2009 were so very unusual in distribution that they are not included here. For source of data see Figure 4 and below. Strictly speaking in statistical strictures too few women have been awarded the prize over the course of the last century for this simple goodness-of-fit test to be applied as in three categories fewer than five women would be expected to have been awarded a prize [[repetitive]]. The sum of the squared differences each divided by that expected number is 34.98. The numbers of degrees of freedom are sexes less 1 multiplied by subjects less 1, (2–1)*(6–1)=5. On five degrees of freedom a value of 20.515 is statistically significant at p=0.001. This is an approximate test as cell sizes are small. Nevertheless it would appear that sex and subject are far from independent (literature and peace above the average, physics, chemistry and economics below, while medicine awards prizes at the average rate). For an exact test see note 59 in chapter 3. The value of 20.515 can be found from standard tables such as those givene here: http://www.medcalc.be/manual/chi-square-table.php The standard function in excell gives a p value of 0.0000015 as shown above, you can alter that value by altering the data. Source: http://nobelprize.org/index.html See under Figure 4 for a list of prize winners who were women including in 2009, and Figure 6 for the counts including 2009.
Notes: Marie Curie is split between physics and chemistry; John Bardeen and Fred Sanger are counted only once. The 2009 prizes are not included above when five women in one year were prize winners. Source: http://nobelprize.org/index.html
Source: http://nobelprize.org/index.html; note that since the 1950s almost all the prizes for women have been in literature or peace, and only a few in medicine. Note that 2010 Nobel prizes have not been included. The cells shaded grey below are when fewer than five women were expected to receive a Nobel prize. There are so many that it is not possible to estimate and indirect test of independence.
Note: The sixth who are poor on at least two criteria are shown in the areas with percentages labelled in them (5.5%+3.4%+1.8%+5.6%=16.3%, and 67%=100%–16%–6%–4%–7%). Source: Drawn from figures given in table 6 of the original study: Bradshaw, J. and Finch, N. (2003) ‘‘Overlaps in dimensions of poverty’, Journal of Social Policy, vol 32, no 4, pp 513-25.
Notes: For original data see Figure 7. Both data and fitted curves were read off the original graph. Columns: D is the number of poor law union areas recording a given number of paupers per 10,000 people (P). The Normal (N) and Binomial (B) are the two possible expected distributions as drawn on Karl Pearson’s original diagram along with the data. Cells are amalgamated (*) with expected values of 5 or less (marked by two curly brackets) resulting in 12 categories. The difference between expected and observed (B-D) is calculated, squared, divided by expected (B) and summed. The sum (1.25) is less than the 0.995 probability value (1.735) on a chi-squared distribution with 9 degrees of freedom (12–2 estimated parameters and less another 1 degree given a fixed n of 632 union areas). The probability that the data was drawn at random from the binomial distribution is less than 0.5%; the fit using Pearson’s own test is probably (with a more than 99.5% probability) too good to be true. Source: Derived from figures read of graphs as redrawn in Figure 7 from the original (Pearson, K (1895) ‘Contributions to the mathematical theory of evolution – II. Skew variation in homogeneous material’, Philosophical transactions of the Royal Society of London, Series A, Mathematical, vol 186, pp 343-414, Figure 17, plate 13)
Source: Figure redrawn from the original. Figures read of original graph shown below alongside a chi-squared test statistic Pearson, K (1895) ‘Contributions to the mathematical theory of evolution – II. Skew variation in homogeneous material’, Philosophical transactions of the Royal Society of London, Series A, Mathematical, vol 186, pp 343-414, Figure 17, plate 13)
Source: Adapted from David Gordon’s original and much replicated drawing. See publication details of various of the works (where earlier versions appear) at the Townsend Centre for International Poverty Research, University of Bristol (www.bris.ac.uk/poverty/).
Notes: X-axis shows a continuous log scale of annual income in comparable dollars, Y-axis shows millions of people living in families supported by such incomes. Source: Figures (in purchase power parity, US$) derived from estimates by Angus Maddison, from version produced in spreadsheets given in www.worldmapper.org, based in turn on UNDP income inequality estimates for each country.
Source: Figures (growth in the decade to year shown) derived from estimates by Angus Maddison, from versions provided in spreadsheets given in www.worldmapper.org (constant 1990 $ used here).
Source: Derived from ONS (2006) Social Trends (No 36), London: Palgrave Macmillan, table 5.15, p 78, mean of 1984, 1994 and 2004 surveys.
Notes: (a) Excess deaths under age 65 of those living in the worst-off 30% of areas by population compared to the national average rate. (b) Reduction in under age 65 death rates of those 10% living in the best-off areas as compared to the national average. (c) Minimum proportion of Conservative voters who would have to move parliamentary constituency if an identical proportion were to vote for that party in every constituency in the general election held that year (in Figure 13 an average of the two 1974 figures is graphed). (d) Share of national income received by the best-off 1% of the population before tax. (e) Share of that income enjoyed by the best-off 1% post-tax. The Pearson product-moment correlation coefficients (r) shown above are calculated using the ‘PEARSON’ function in Excel. The p values are calculated by creating a z-score using the Excel function f=‘SQRT(n-3)*FISHER(r)’ where n is 24, which is the number of observations above. The Excel function ‘2*NORMDIST(f,0,1,TRUE)’ returns the p value. All this assumes, among many things, that the sample pairs are independently distributed, which coming from the same pair of years they are not, but the method remains a useful way of deciding whether to take a high correlation seriously. However, what you take seriously is ultimately up to you. Karl Pearson’s friends measured skull attributes, attempting to correlate them with everything including penis length! See Gladwell (2007) ‘What IQ doesn’t tell you about race’, New Yorker, 17 December. Sources: Columns 1 and 2, Figure 12; Column 3, Figure 13; Columns 4 and 5, Figure 14 (a) and (b) Dorling and Thomas (2009) ‘Geographical inequalities in health over the last century’, in H. Graham (ed) Health inequalities, Oxford: Oxford University Press, pp 66-83, derived from Table 4.3, with interpolation between five year rates in some circumstances. &quot;[c] initially inDorling (2006) ’Class alignment’, Renewal: The Journal of Labour Politics,vol 41, no 1, p 849.&quot; (d) and (e) Atkinson, A.B. (2003) ‘Top incomes in the United Kingdom over the twentieth century’, Nuffield College Working Papers, Oxford (http://ideas.repec.org/p/nuf/esohwp/_043.html), figures 2 and 3; from 1922 to 1935 the 0.1% rate was used to estimate the 1% when the 1% rate was missing, and for 2005 the data source was Brewer, M., Sibieta, L. and Wren-Lewis, L. (2008) Racing away? Income inequality and the evolution of high incomes, London: Institute for Fiscal Studies, p 11; the final post-tax rate of 12.9% is derived from 8.6%+4.3%, the pre-tax rate scaled from 2001
Note: The line marked by white squares shows how much lower the age-sex standardised under age 65 mortality rate of the best-off 10% by area is as compared to the average. The line marked by dark diamonds shows how much higher that of the worst-off 30% is than the average. Source: Dorling and Thomas (2009) ‘Geographical inequalities in health over the last century’, in H. Graham (ed) Health inequalities, Oxford: Oxford University Press, pp 66-83, derived from Table 4.3, with interpolation between five year rates in some circumstances.
Note: An average of the two 1974 figures is shown here; actual figures are graphed in the source and also used for the correlations reported here (they were 8.01% in the February election and 10.74% in the October election of that year, see Table 5). Source: Drawn initially in Dorling (2006) ’Class alignment’, Renewal: The Journal of Labour Politics, vol 41, no 1, p 849, showing the spatial segregation index.
Note: Lower line is post-tax share. Source: Atkinson, A.B. (2003) ‘Top incomes in the United Kingdom over the twentieth century’, Nuffield College Working Papers, Oxford (http://ideas.repec.org/p/nuf/esohwp/_043.html), figures 2 and 3; from 1922 to 1935 the 0.1% rate was used to estimate the 1% when the 1% rate was missing, and for 2005 the data source was Brewer, M., Sibieta, L. and Wren-Lewis, L. (2008) Racing away? Income inequality and the evolution of high incomes, London: Institute for Fiscal Studies, p 11; the final post-tax rate of 12.9% is derived from 8.6%+4.3%, the pre-tax rate scaled from 2001 (see Table 5, figures and formulae included in the cells below).
Source: Dorling (2009) Migration: A long run perspective, London: IPPR, Figure 8. The graph shows how many more people entered than left these countries, as a proportion of births, including official projections up to 2080.
Source: ONS (2008) Wealth and assets survey: Initial reports, London: ONS, Table 3
Note: The first two columns of data make up most of domestic household borrowing in normal years (data column 4). 2008 was not a normal year. Data column 3 is the sum of columns 4-9. Statistical tests can be applied as described under Table 5. They show that each of the coefficients in the last row of this table could be reporting random variations from variables with true correlation of zero, with a probability of 10% or much more (thus not unlikely). As of February 2010 third quarter mortgage debt flow was US$-370 billion, consumer credit US$-82 billion, total debt increase had fallen uner a trillion dollars for the first time since 2000 excluding the financial sectors. Including these sectors, total US debt fell to the third quarter by US$276 billion. It has never fallen in this series before, and has fallen in every quarter of 2009 so far reported. Business total debt fell for the first time since 1992, but by much more than it did then: some US$284 billion by the third quarter of 2009. Only local, state and federal governments kept up borrowing levels to try to deal with the crisis. Source: US Federal Reserve: Debt growth, borrowing and debt outstanding tables (www.federalreserve.gov/releases/Z1/Current/, latest figures as of March 2009 shown above)
Note: The bars show the ratio of debt to annual disposable income with axis to the right. The line shows the percentage change in that ratio over the coming five years with axis to the left (31% = [127.2–96.8]/96.8). Source: Foster, J.B. (2006) ‘The household debt bubble’, Monthly Review, vol 58, no 1 (www.monthlyreview.org/0506jbf.htm), Table 1: disposable income is the income after paying taxes. Derived from: Board of Governors of the Federal Reserve System, Flows of Funds Accounts of the United States, Historical Series and Annual Flows and Outstandings, Fourth Quarter 2005, 9 March 2006, available at www.federalreserve.gov/releases/Z1/Current/, table 2 (p 8 of ‘complete file’).
Note: Low emitting and polluting quintiles are labelled 1, the highest are labelled 5. The proportion living in poverty is derived from breadline surveys. The areas used are local government wards, amalgamated by emission and inhalation rates and defined on the boundaries of those existing in 1981. Source: Mitchell, G. and Dorling, D. (2003) ‘An environmental justice analysis of British air quality’, Environment and Planning A, vol 35, pp 909-29, Figure 9: Poverty rate by NOx emission and ambient air quality for 10,444 British wards in 1999
Source: Redrawn from figures given in McMahon, W. (2008), Centre for Crime and Justice Studies, ‘Graph of the month, London, personal communication, originally appearing as a graph in the Journal of Social Policy, and in a presentation on ‘Welfare fraud, welfare fiction’ by Greg Marston, Social Policy Unit, University of Queensland (www.bsl.org.au/pdfs/Greg_Marston_Welfare_fraud&fiction_29Nov07_.pdf).
Source: Derived from data provided by the Federal Reserve Board on required debt payments on mortgage and consumer debt, car lease payments, rental payments, insurance and property tax payments (www.federalreserve.gov/releases/housedebt/). For a series of just mortgage and consumer debt see Chart 1 in Foster, J.B. (2006) ‘The household debt bubble’, Monthly Review, vol 58, no 1 (www.monthlyreview.org/0506jbf.htm). Data plotted here is the Total FOR series. FOR = Financial Obligations Ratios
Notes: * Country of study not obvious from article title or journal. It is assumed that the final study was located in Seattle assuming the reference in the source is to: Vander Stoep, A. et al (2005) ‘Universal emotional health screening at the middle school transition’, Journal of Emotional and Behavioural Disorders, vol 13, no 4, pp 213-23. In the graph the two results from one study (4 and 5) are excluded because the figures reported in the original article are for 15- to 16-year-olds only, not 15-24 as reported above, and rely on 12-month recall under a diagnosis method which reports higher rates in general: composite international diagnostic interview (see Kessler, R.C. and Walters, E.E.  ‘Epidemiology of DSM-III-R major depression and minor depression among adolescents and young adults in the National Comorbidity Survey’, Depression and Anxiety, vol 7, pp 3-14). If rates were not reported for girls or boys separately, or at all, in the original source they are also not shown here.
Note: Each circle represents a study; the area of the circle is drawn in proportion to study size. See Table 7 for the details of when and where each study took place. Source: Re-analysis of Costello, E.J. et al (2006) ‘Is there an epidemic of child or adolescent depression?’, Journal of Child Psychology and Psychiatry, vol 47, no 12, pp 1263-71. The data shown above are for those studies where the children lived in the US, the US territory of Puerto Rico, or Canada (excluding one study which used different diagnosis methods to the other 16, see notes to Table 7). Those included are study numbers: 2, 6, 8, 9, 10, 17, 18, 20, 22, 26, 29, 30, 31, 34, 35 and 41.
Note: Each line refers to the cohort born in the decade it is labelled by. X-axis gives age of cohort members. Y-axis gives how many more times a man of that age born in that decade is likely to die in a year as compared to a women living in the same set of countries born at the same time and of the same age. Mor detail is shown here than in the figure in the book, including projections of future ratios Source: Original figure given in Rigby, J.E. and Dorling, D. (2007) ‘Mortality in relation to sex in the affluent world’, Journal of Epidemiology and Community Health, vol 61, no 2, pp 159-64, sample size one billion people.
Note: This particular divide is the social, economic and political divide in England. Below the line people live about a year longer on average; identical houses cost much more; people in similar situations are more likely to vote Conservative below than above the line, and much more besides. For a more detailed description of the line and exactly where it is estimated to run see: www.sasi.group.shef.ac.uk/maps/nsdivide Source: Drawn by the author with help from John Pritchard and derived from many sources.
Notes: Right-hand axis, net US$ billion additional borrowed in year shown by the bars in the graph. Left-hand axis: percentage change in that amount. Final percentage change unknown but to be based on a denominator of ‘just’ –46 US$ billion (the only negative bar). It is shown plummeting down off the scale. The 2009 mortgage debt to Quarter 3 was –370 US$ billion.
Notes: The NHS uses financial years when reporting on prescribing rates because costs are still mainly counted in terms of money rather than human misery. The measure shown is what is called standardised defined daily doses per 1,000 people aged 15+ Source: NHS Quality Improvement Scotland (2007) NHS quality improvement Scotland: Clinical indicators 2007, Glasgow: NHS Quality Improvement Scotland, Table 1.1, p 12
Statistical clues to social injustice
Statistical clues to social injustice Danny Dorling University of Sheffield Radical Statistics Annual Conference London, February 27 th 2010
Proportions that suffer injustices of different kinds in affluent nations Fraction Subject How Labelled Description of group who suffer the injustice % Year A seventh Children Delinquent Found limited or simple at learning 13 2006 A sixth People Debarred Excluded from society in at least two ways 16 1999/2001 A fifth Adults Debtors Admit not managing to get by financially (if asked) 21 1984-2004 A quarter Households Discarded Have no car where car use has become assumed 26 2006/7 A third Families Depressed Member suffers depression or chronic anxiety disorder 33 2000 A half Citizens Disenfranchised Adults who did not or could not vote in the latest US presidential elections 46 2008
Injustices, social evils, political, philosophical and public labels combined 2010 Injustice 1942 Past 1983 Political 2007 Philosophical 2008 Public Elitism Ignorance 6. Differences in skills and ability 4. Threats to sense, using imagination, and thought 5. Threats to experiencing and expressing emotions freely 3. A decline in values, lack of tolerance, compassion and respect 4. Problems concerning young people, family breakdown and poor parenting Exclusion Want 1. The exploitation of those who work 3. Threats to bodily integrity 9. Threats to play, ability to relax, take Sabbaths and holidays 1. Problems caused by individualism, consumerism, decline in community life 2. Excessive use of drugs and alcohol, both as consequence and causes 5. Inequality and poverty, corrosive evil in an affluent society Prejudice Idleness (sexism and racism) 3. Unemployment 5. The economic subordination of women 6. Threats to being able to use practical reason (to be able to contribute) 7. Threats to affiliation, to belonging, having mutual respect 7. Violence and crime, child abuse and exploitation 8. Gender inequality, inequalities embedded in current thinking 9. Intolerance resulting from the beliefs of many religions, and similar ideas 10. Problems of attitudes to social diversity and immigration Greed Squalor 2. The inheritance of wealth by a minority 10. Threats to having control over one’s environment (to having rights) 8. Threats to other species, lack of concern sure to backfire 6. Problems caused by big business, apathy and a democratic deficit 12. Environmental issues, selfishness and insularity Despair Disease 4. Infirmity and the problems of old age 1. Threats to life 2. Threats to bodily health 11. Health problems, especially lack of care for older people
Children by ability in the Netherlands, according to the OECD, 2006 (%)
Distribution of children by ability, according to the OECD, 2006 (%)
School-leaving age (years) and university entry (%), Britain, 1876-2013 School leaving age University entry % years
Pearson goodness-of-fit test of Nobel prize by sex and subject, 1901–2008 Medicine Physics Chemistry Literature Peace Economics Total Observed men 184 180 149 94 84 62 753 women 8 1.5 2.5 11 12 0 35 total 192 181.5 151.5 105 96 62 788 Expected men 183.5 173.4 144.8 100.3 91.7 59.2 753 women 8.5 8.1 6.7 4.7 4.3 2.8 35 total 192.0 181.5 151.5 105.0 96.0 62.0 788.0 (O-E) men 0.5 6.6 4.2 -6.3 -7.7 2.8 0.0 women -0.5 -6.6 -4.2 6.3 7.7 -2.8 0.0 total 0.0 0.0 0.0 0.0 0.0 0.0 0.0 (O-E)²/E men 0.0015 0.2482 0.1235 0.4001 0.6524 0.1280 1.55 women 0.0327 5.3407 2.6579 8.6087 14.0354 2.7538 33.43 total 0.03 5.59 2.78 9.01 14.69 2.88 34.98
Male and female Nobel (and economics) laureates, by subject, 1901-2008
Female Nobel laureates (%), by decade, worldwide, 1901-2009
Proportion of households poor by different measures (%), Britain, 1999
Outstanding consumer debt as a proportion of disposable income, US, 1975-2005
Poverty, car exhaust emissions and pollution inhaled in Britain, by area, 1999
Social security and taxation prosecutions, Australia, counts, 1989-2003
Debt payments as a percentage of disposable income, US, 1980-2008
Studies of adolescent depression available for meta-analysis, 1973-2006 Study number Place Year of birth Age at interview Obs Rates (%) Age<13 Rates aged 13-18 (%) Original Study, first author and citation Both Girls Boys 1 *Isle of Wight 54–55 14–15 483 3.2 Rutter (Graham & Rutter, 1973) 2 USA 65–74 10–20 776 2.5 3.7 7.6 1.6 Cohen (Velez, Johnson, & Cohen, 1989) 3 USA 65–74 10–20 776 3.1 Cohen (Velez et al., 1989) 4 USA 66–75 15–24 1769 7 12.4 1.5 Kessler(Kessler & Walters, 1998) 5 USA 66–75 15–24 1769 13 21.5 4.4 Kessler (Kessler & Walters, 1998) 6 Canada 66–79 6–16 2852 .6 1.8 2.3 1.2 Fleming (Fleming, Offord, & Boyle, 1989) 7 Puerto Rico 68–80 4–16 386 Bird (Bird et al., 1988) 8 *USA 69–74 14–18 1710 2.9 3.8 2 Lewinsohn (Lewinsohn et al., 1993) 9 USA 69–74 14–18 1710 3.2 3.7 2.6 Lewinsohn (Lewinsohn, Rohde, & Seeley, 1998) 10 *USA 71–72 13 792 1.8 2.1 2.2 2.1 Anderson (Anderson, Williams, McGee, & Silva, 1987) 11 NZ 71–72 15 792 2.8 4.4 1.2 Hankin (Hankin et al., 1998) 12 USA 71 8 70 1.5 Kashani (Kashani, Orvaschel, Rosenberg, & Reid, 1989) 13 USA 76 12 70 1.5 Kashani (Kashani et al., 1989) 14 USA 80 17 70 5.7 Kashani (Kashani et al., 1989) 15 USA 73–77 7–11 300 .8 Costello (Costello et al., 1988b) 16 USA 73–77 12–17 300 5.4 Costello (Costello, Angold, & Keeler, 1999) 17 USA 73–81 9–17 336 3.4 6 5.6 6.4 Angold (Angold et al., 2002) 18 USA 73–81 9–17 542 .7 2.1 3.4 .8 Angold (Angold et al., 2002) 19 USA 74–82 9–17 1285 Shaffer (Shaffer et al., 1996) 20 USA 74–83 8–16 2762 .4 2.4 2.1 Simonoff (Simonoff et al., 1997) 21 Netherlands 75–80 13–18 780 3.6 Verhulst (Verhulst, van der Ende, Ferdinand, & Kasius, 1997) 22 Canada 75–82 12–19 1847 2.6 12.5 6.2 Cairney (Cairney, 1998) 23 NZ 77 15 986 6.3 9.2 3.3 Fergusson (Fergusson, Horwood, & Lynskey, 1993) 24 NZ 77 18 1011 18.2 26.5 9.7 Fergusson (Fergusson & Horwood, 2001) 25 Switzerland 78–82 15–19 203 .6 5.3 9.8 1.1 Steinhausen (Steinhausen & Winkler Metzke, 2003) 26 USA 78–83 12–17 4023 13.9 7.4 Kilpatrick (Kilpatrick et al., 2003) 27 Switzerland 78–87 7–16 1964 .3 2.4 .0 Steinhausen(Steinhausen & Winkler Metzke, 2003) 28 Sweden 79–81 16–17 231 1.4 2.2 .6 Olsson (Olsson & von Knorring, 1999) 29 *USA-Anglo 79–82 12–15 558 4.3 4.5 4.0 Doi (Doi, Roberts, Takeuchi, & Suzuki, 2001) 30 *USA-African-American 79–82 12–15 665 6.1 6.5 5.7 Doi (Doi et al., 2001) 31 *USA-Mexican-American 79–82 12–15 429 9.0 11.4 6.3 Doi (Doi et al., 2001) 32 *Japan 79–82 12–15 494 1.3 .9 1.8 Doi (Doi et al., 2001) 33 Germany 80–83 14–16, 16–19 1395 8.0 10.2 5.8 Oldehinkel (Oldehinkel, Wittchen, & Schuster, 1999) 34 USA 80–84 9–16 4984 1.9 3.1 4.2 1.9 Costello (Costello et al., 1996) 35 USA 80–84 9–16 1691 1.1 3.0 4.2 1.9 Costello (Costello et al., 1996) 36 Finland 81 8–9 278 3.2 Almqvist (Almqvist, Kumpulainen et al., 1999) 37 Finland 81 8–9 278 5.9 Almqvist (Almqvist, Kumpulainen et al., 1999) 38 Finland 81 8–9 255 7.8 Almqvist (Almqvist, Puura et al., 1999) 39 Finland 81 8–9 180 4.7 Almqvist (Almqvist, Puura et al., 1999) 40 Australia 81–92 6–17 3597 2.3 4.0 4.7 3.4 Sawyer(Sawyer et al., 2001) 41 Puerto Rico 82–96 4–17 1886 2.1 5.8 9.7 2.0 Canino (Canino et al., 2004) (and personal communication) 42 GB 84–94 5–15 10438 .3 2.5 Ford (Ford, Goodman, & Meltzer, 2003) 43 Brazil 86–89 11–14 625 .2 1.9 Fleitlich-Bilyk(Fleitlich-Bilyk & Goodman, 2004) 44 Brazil 90–93 7–10 625 .2 Fleitlich-Bilyk (Fleitlich-Bilyk & Goodman, 2004) 45 *USA 90–93 11–12 508 3.0 Van der Stoep (personal communication) Study number Place Year of birth Age at interview Obs Rates (%) Age<13 Original Study, first author and citation Both Girls Boys
Adolescent girls assessed as depressed (%), North America, 1984-2001
Male/female mortality ratio by age in the rich world, 1850–1999
The fractal nature of geographical divides, North–South/West–East, Britain, 2010
The crash: US mortgage debt, 1977-2009 (% change and US$ billion)
The rate of prescribing anti-depressants by the NHS in Scotland, 1992-2006
Credits <ul><li>Slideshow created by Benjamin Hennig </li></ul><ul><li>The tables and figures from this presentation will soon be available from: http://www.policypress.co.uk/ </li></ul><ul><li>(search for ‘injustice’ in the search box) </li></ul><ul><li>The fully animated version of this slideshow is available as a PowerPoint document on </li></ul><ul><li>http://sheffield.ac.uk/geography/staff/dorling_danny/lectures.html </li></ul>