Western countries: Income inequality and gross national income (GNI) by purchasing power parity (PPP) & average 2010–2013 GDP expenditure on health (GDPEH) and GDP military expenditure (sources World Bank): A military: health ratio [Source: World Bank, [37].
Abstract
Child mortality rates (CMR) indicate how a nation meets the needs of its children, so relative to their region, do some countries ‘neglect’ their children? Using William Penn (1693) statement ‘It's a reproach to religion and government to suffer so much poverty and excess’ to judge nations CMR from three world regions within the context of poverty, health and military gross domestic product (GDP) expenditure data. West (n= 21): USA, New Zealand and Canada are a reproach—Sweden, Japan Finland and Norway are commended. Asia (n= 17]: Pakistan, Myanmar and India are a reproach. Singapore and Thailand commended. Sub‐Saharan Africa (n= 33): Relative to their region, Madagascar and Namibia are commended. Twelve countries failed the United Nations (UN) target, including the relatively rich Nigeria and South Africa. Poverty and higher CMR are linked in all three regions. Relative poverty and military expenditures correlated in the West but not in the other regions. In the pursuit of social justice, societies need to be alerted to the extent of the impact of poverty on child mortality even though some countries will find this challenging.
Keywords
- child mortality
- poverty
- health
- military expenditure
1. Introduction
In terms of the well‐being of children, there are profound implications in the United Nations Children’s Fund (UNICEF)'s statement ‘
Poverty is the context in which child mortality rates (CMR) have been analysed in this chapter. Therefore, this study assesses the relationship between poverty and CMR in 71 countries from three world regions, the West, Asia and Sub‐Saharan Africa (SSA) and how successful they have been in reducing mortality rates over time relative and comparative to their region. There are, of course, many interrelated social policy factors that influence CMR reflecting differing political priorities so included is a comparison of health and military expenditure to reflect what in fiscal terms are competitive concerns [1–5].
Bringing together markedly different socio‐economic regions has its problems, but it has been argued that in a globalised world the concept of developed and underdeveloped nations is redundant and countries should be seen along a continuum of socio‐economic development [6]. This juxtaposition of three regions provides a comparative perspective of what is happening to children, in the context of poverty, within a regional perspective. Although the socio‐political and economic make up of these regions varies considerably, all 71 countries under review are signatory to the United Nations millennium goals aspiration of reducing under‐five CMR by 2% per annum [7, 8].
The importance of the poverty dimension originates from the seminal work of Wilkinson and Pickett who highlighted the significance of income inequality, a measure of relative poverty relevant to Western societies [4]. Income inequality is linked with a range of negative outcomes such as poorer employment, education, crime, housing and health outcomes as detailed in numerous Western ‘clinical’ studies [9–14]. International comparisons of CMR are problematic and more so when contrasting three world regions. However, as each nation is assessed, within its own region, against itself over time, it becomes is its own control, enabling us to judge how successful it has been in reducing CMR relative to its region [12–15].
In analysing mortality rates, it is easy to forget the emotional impact of the death of a child. This is epitomised in the lament of the octogenarian Elizabeth Barraclough who said ‘
2. Method
There are inherent methodological problems with international comparisons of mortality but the following method has sought to minimise them, as utilised in a number of comparative international studies covering healthcare, suicide, child‐abuse‐related deaths, cancer and neurological disease [15–18]. Nonetheless, there can be limitations linked to the accuracy of mortality data in less industrialised nations [1, 8, 19].
2.1. Mortality data
Two types of mortality data have been used: confirmed and estimated figures. The World Health Organisation (WHO) provides
UN Millennium Goals Indicators (UNMGI) and the UN Statistics Division provide
As CMR varies on an annual basis, a 3‐year baseline average (1988–1990) is contrasted with a 3‐year index average (2008–2010) and a percentage of change calculated. As also indicated in the tables, WHO data for China were available until 1994, based upon a 10% sample of population (running into the tens of millions), but UNICEF data are used for 2008‐2010. Index data for Canada and New Zealand is only available from 2007–2009 and Germany, Portugal and Spain have slightly later baseline years of 1990–1992 and is noted in the table.
2.2. Poverty data
There is a long‐standing debate about definitions of poverty, crucially between ‘relative’ poverty in Western countries and ‘absolute’ poverty in the developing world [22–25]. Recently, the World Bank highlights that whilst there is no internationally agreed definition of poverty, in effect each country determines a ‘relevant welfare measure’ juxtaposed against a selected poverty line for that country to report poverty in relation to its total population [26]. The Western concept of relative poverty is usually proportionate to national average income, so a family income 60% below the average is designated as in relative poverty [26–28].
For Western countries, a ratio of income inequality is used, that is, the gap between the top and bottom 20% of incomes used by Wilkinson & Pickett [4], alongside gross national income (GNI) data [29] as indicated in the tables. The benefit of using this ratio is that it is country specific, thereby reflecting the relative positions of poorer families within that society but avoiding the blurring of average incomes. As previously noted, income inequalities have been found to be associated with a wide range of poorer outcomes in education, crime, unemployment and health [2, 4, 30–33].
As no comparable income inequality data exist for Asian countries, GNI figures by purchasing power parity (PPP) have been used [34]. PPP is the estimated value of the local currency converted into US dollars sufficient to obtain basic foodstuffs but does not demonstrate the income gaps that exist in that society. Absolute poverty relates to an individual surviving on $1–2 a day [24, 25]. GNI is the total national income divided by total population, adjusted for PPP and so provides a global indication of parity of income to show relative gaps between the West and other regions [29]. The problem of an average income figure is that it obscures variations between groups. For example, the UK's average income is £28,000, yet 60% of the population receive under £18,000 p.a. indicating the mode income is far lower than the average [35].
Recent World Bank data has been published that includes 30 of the 33 SSA countries (Anglo, Congo (Kinshasa) and Somalia were not available) and so matching GNI data are reported for 2010 [28]. SSA data are available for 2015, but over 5 years, there was virtually no difference between the countries ranking, hence CMR and 2010 GNI are also correlated to explore any link between CMR and poverty.
2.3. Socio‐economic, health and military expenditure
The different socio‐economic backgrounds of these regions are recognised but to an extent both Asian and SSA societies from the former British Empire have faced similar postcolonial struggles [36]. Comparisons of countries since their independence
Although Angola, China, Nigeria, Somalia, South Africa and Yemen are considered developing countries, they are among the world's top 20 producers of minerals and oil [34]. It is also noted that 14 of the 33 SSA countries have endured serious civil conflict over the period under review.
An important policy priority context is what percentage countries spend of their national wealth (gross domestic product, GDP) on health and military. World Bank data are extrapolated as a percentage of GDP for health and military expenditure from which a military to health expenditure ratio is calculated [37]. This ratio reflects national priorities and is likely to be influenced by local/regional political history as regimes change over time and respond to their sense of threat from their regional perspective. This is exemplified by the long-standing tension between India and Pakistan, Greece and turkey. Hence the military and health ratios can be sen as broad indicators of policy proirities.
2.4. Statistical analysis
Spearman rank order (Rho) correlations have been used to determine any association between regional CMR and poverty, that is, GNI, military and health data. Standard deviations (SD) of CMR in each of the regions have been calculated and 1 SD above or below the regional average is the measure used to assess whether a nation merits a
3. Identifying countries of reproach and commendation
3.1. The West
Country | Income inequality | GNI $ average per person | %GDPEH 2010–2013 | % GDP military expenditure | Military:health ratio |
---|---|---|---|---|---|
1. USA | 8.5 | 45,640 | 17.1 | 3.3 | 1:5.2 |
2. Portugal | 8.0 | 24,080 | 10.2 | 1.9 | 1:5.4 |
7.2 | 35,860 | 9.2 | 1.9 | 1:4.8 | |
4. Australia | 7.0 | 38510 | 9.0 | 2.0 | 1:4.5 |
5. New Zealand | 6.8 | n/a | 10.0 | 1.2 | 1:8.3 |
6 Italy | 6.7 | 31,870 | 9.3 | 1.3 | 1:7.2 |
7. Greece | 6.2 | 28,800 | 9.6 | 2.6 | 1:3.7 |
8. Ireland | 6.1 | n/a | 8.9 | 0.4 | 1:22.3 |
9. Switzerland | 5.7 | 47,100 | 11.2 | 0.7 | 1:16.0 |
10. Canada | 5.6 | 37,280 | 11.0 | 1.0 | 1:11.0 |
11. Spain | 5.6 | 31,490 | 9.3 | 1.2 | 1:7.8 |
12. France | 5.6 | 33,950 | 11.6 | 2.1 | 1:5.5 |
13. Netherlands | 5.3 | 37,940 | 12.4 | 1.2 | 1:10.3 |
14. Germany | 5.2 | 36,850 | 11.3 | 1.2 | 1:9.4 |
15. Austria | 4.8 | 31,900 | 11.0 | 0.7 | 1:15.7 |
16. Belgium | 4.5 | 36,610 | 10.8 | 0.9 | 1:12.0 |
17. Denmark | 4.3 | 32678 | 10.9 | 1.2 | 1:9.1 |
18. Sweden | 4.0 | 38,050 | 9.6 | 1.1 | 1:8.7 |
19. Norway | 3.9 | 39,869 | 9.4 | 1.5 | 1:6.3 |
20. Finland | 3.7 | n/a | 9.1 | 1.3 | 1:7.0 |
21. Japan | 3.4 | 33,440 | 10.1 | 1.0 | 1:10.1 |
Mean average | 5.6 | 35,662 | 10.5 | 1.4 | 1:7.5 |
The average health expenditure in the West is 10.5% of GDP; therefore, out of every $100 of a nation's wealth, $10.50 is, on average, spent on health. Figures range from 17.1% in the USA and 11.6% in France down to 9% in Australia and 8.9% in Ireland. The average military expenditure in the West is 1.4% of GDP. Figures range from 3.3% in the USA followed by Greece at 2.6%, down to 0.7% in Austria and 0.4% in Ireland.
Military: Health ratios are narrowest in Australia 1:4.5, the UK 1:1.4.8 and the USA 1:5.2, the West's average being 1:7.5; ratios are widest in Ireland 1:22.3, Switzerland 1:16.0 and the Netherlands 1:10.3; reflecting different political priorities, which according to Nye Bevan is the essence of politics (Foot, 1978)
Country by CMR rank (latest years) | CMR baseline pm (1988–1990) [20] | CMR index pm (2008–2010 unless stated) [20] | % of change |
---|---|---|---|
2. New Zealand (2007–2009) | |||
4. UK | 1929 | 1113 | |
5. Australia | 1886 | 1030 | |
6. Ireland | 1659 | 947 | |
7. Switzerland | 1783 | 944 | |
8. Austria | 1944 | 939 | |
9. Netherlands | 1729 | 906 | |
10. Belgium | 2013 | 886 | |
11. France | 1740 | 876 | |
12. Germany | 1611 | 838 | |
13. Italy | 1895 | 822 | |
14. Spain | 1790 | 820 | |
15. Denmark | 1993 | 813 | |
16. Greece | 2039 | 792 | |
17. Portugal | 3019 | 782 | |
2005 | 691 | ||
20. Finland | |||
The USA, New Zealand and Canada merit a relative
3.2. ASIA
Country and GNI rank | $ GNI average per person | GDPEH % 2014 | % GDP military | Health:military ratio |
---|---|---|---|---|
1. Singapore | 49,780 | 4.6 | 3.2 | 1:1.4 |
2. Hong Kong | 44,540 | n/a | n/a | n/a |
3. Japan | 33,440 | 10.5 | 1.0 | 1:10.5 |
4. Korea South | 27,240 | 7.2 | 2.6 | 1:2.8 |
5. Malaysia | 13,710 | 4.0 | 1.5 | 1:2.7 |
6. Thailand | 7640 | 4.6 | 1.5 | 1:3.1 |
7. China | 6890 | 5.6 | n/a | n/a |
8. Sri Lanka | 4720 | 3.2 | 2.2 | 1:1.5 |
9. Indonesia | 3720 | 3.1 | 0.9 | 1:3.4 |
10. Philippines | 3540 | 4.4 | 1.3 | 1:3.4 |
11. India | 3280 | 4.0 | 2.4 | 1:1.7 |
12. Vietnam | 2790 | 6.0 | 2.4 | 1:2.5 |
13. Pakistan | 2680 | 2.8 | 3.6 | 1:0.8 |
14. Cambodia | 1820 | 7.5 | n/a | n/a |
15. Bangladesh | 1550 | 3.7 | n/a | n/a |
16. Nepal | 1180 | 6.0 | 1.5 | 1:4.0 |
17. Myanmar | n/a | 1.8 | 3.4 | 1:0.5 |
The average expenditure on the military is 2.6% for Asian countries. Figures range from 3.6% in Pakistan, followed by 3.4% in Myanmar and 3.2% in Singapore, down to 1% in Japan and 0.9% in Indonesia. The narrowest military to health ratios are in Myanmar (1:0.5) and Pakistan (1:08), as they spent more on their military than health expenditure.
Country by CMR Rank | CMR baseline pm (1988–1990) | CMR index pm (2008–2010) | % of change |
---|---|---|---|
1.Pakistan | 124,000 | 87,000 | −30# |
112,000 | 66,000 | −41 | |
3. India | 115,000 | 63,000 | −45 |
4. Cambodia | 121,000 | 51,000 | −58 |
5. Nepal | 141,000 | 50,000 | −64 |
6. Bangladesh | 143,000 | 48,000 | −66 |
7. Indonesia | 85,000 | 35,000 | −59 |
8. Philippines | 59,000 | 29,000 | −51 |
9. Vietnam | 51,000 | 23,000 | −55 |
10. China (WHO 1994) | 9390 | n/a | n/a |
10. China | 48,000 | 18,000 | −62 |
11. Sri Lanka | 32,000 | 17,000 | −47 |
12. Thailand | 32,000 | 13,000 | −59 |
13. Malaysia | 18,000 | 6000 | −67 |
14. Korea South 2007–2009 (WHO) | 1220 | 840 | −31# |
1550 | 808 | −48 | |
16. Japan (WHO) | 1218 | 663 | −46 |
17. Singapore (WHO) | 1598 | 552 | −67 |
China's (WHO) data from 1994, based upon urban and rural 10% samples, averaged CMR of 9394 pm. Yet, UNICEF data estimate a total mortality rate of 48,000 pm in 1990 reducing by 62% to 18,000 pm by 2010. CMR in Thailand is 1 SD below the non‐industrialised Asian mean of 39,000 pm and merits a
3.3. Sub‐Saharan Africa
SSA country | GNI by PPP $average p.p | % GDP on health | % GDP military4 | Military: health ratio |
---|---|---|---|---|
1 Gabon | 16,350 | 3.8 | 1.2 | 1:3.2 |
2. Botswana | ||||
3. South Africa | ||||
4. Namibia | 9380 | 7.7 | 4.8 | 1:1.6 |
5.. Swaziland | 7450 | 8.4 | 1.8 | 1:4.7 |
6. Nigeria C | 5380 | 3.9 | 0.4 | 1:9.8 |
7. Ghana C | 3850 | 5.4 | 0.5 | 1:10.8 |
8. Sudan C | 3810 | 6.5 | 2.1 | 1:3.1 |
9. Yemen C | 3650 | 5.4 | n/a | n/a |
10. Zambia | 3580 | 5.0 | 1.7 | 1:2.9 |
11. Lesotho | 3280 | 11.5 | 0.7 | 1:16.4 |
12. Cote d’ Ivory C | 2890 | 5.7 | 1.5 | 1:3.8 |
13. Kenya | 2820 | 4.5 | 1.5 | 1:3.0 |
14. Cameroon | 2780 | 5.1 | 1.0 | 1:5.1 |
15. Senegal | 2,210 | 4.2 | 1.6 | 1:2.6 |
18. Uganda | 1680 | 9.8 | 1.3 | 1:7.5 |
19. Zimbabwe | 1610 | n/a | 2.7 | n/a |
20. Gambia | 1600 | 6.0 | 0.8# | 1:7.5 |
22. Rwanda C | 1,540 | 11.1 | 1.2 | 1:9.3 |
23. Madagascar C | ||||
24. Ethiopia C | 1370 | 5.1 | 0.7 | 1:7.3 |
25. Guinea | 1140 | 4.7 | n/a | n/a |
26. Mozambique | 1060 | 6.8 | 1.0 | 1:6.8 |
27. Niger | 880 | 6.5 | n/a | n/a |
28. Malawi | 760 | n/a | 0.7 | n/a |
29. Liberia C | 710 | 10.0 | 0.7 | 1:14.3 |
31. Tanzania | ||||
32. Angola | ||||
33. Somalia | ||||
SSA average |
The average military expenditure in SSA countries is 1.5% of GDP. Again, there are marked variations ranging from 4.8% in Namibia and 3.5% in the Democratic Republic of Congo to 0.5% in Ghana and 0.4% in Nigeria. The average military to health ratio is 1:4.4. The narrowest is a 1:1 ratio in the Democratic Republic of Congo; Lesotho has the highest ratio of 1:16.
SSA country | CMR Baseline pm | CMR index pm | % of change | Lowest GNI: rank |
---|---|---|---|---|
188,000 | ||||
176,000 | ||||
174,000 | − | |||
173,000 | − | |||
170,000 | − | |||
161,000 | − | |||
7. Nigeria C | 213,000 | 143,000 | −33 # | 25 |
8. Niger | 311,000 | 143,000 | −54 | 4 |
9. Cameroon | 137,000 | 136,000 | −1 # | 17 |
10. Mozambique | 219,000 | 135,000 | −38 # | 5 |
11. Guinea | 229,000 | 130,000 | −43 | 6 |
12. Cote d’ Ivory C | 151,000 | 123,000 | −19 # | 19 |
13. Zambia | 183,000 | 111,000 | −39 # | 21 |
14. Ethiopia C | 184,000 | 106,000 | −42 | 7= |
15. Sudan C | 125,000 | 103,000 | −18 # | 23 |
16. Liberia C | 227,000 | 103,000 | −55 | 2 |
17. Uganda | 175,000 | 99,000 | −43 | 13 |
18. Gambia | 165,000 | 98,000 | −41 | 11 |
19. Congo (Kinshasa) C | 116,000 | 93,000 | −20 # | n/a |
20. Malawi | 222,000 | 92,000 | −59 | 3 |
21. Rwanda C | 163,000 | 91,000 | −44 | 9 |
22. Lesotho | 89,000 | 85,000 | −4 # | 20 |
23. Kenya | 99,000 | 85,000 | −14 # | 18 |
24. Zimbabwe | 78,000 | 80,000 | +3 # | 12 |
25. Swaziland | 96,000 | 78,000 | −19 # | 26 |
26. Yemen C | 128,000 | 77,000 | −46 | 22 |
27. Senegal | 139,000 | 75,000 | −46 | 15= |
28. Ghana C | 122,000 | 74,000 | ||
29. Gabon | 93,000 | 74,000 | −20 # | 30 |
30. Madagascar C | 159,000 | 62,000 | − | |
31. Botswana | 59,000 | 48,000 | − | |
32. Namibia | 73,000 | 40,000 | 27 | |
33. South Africa ( | 11,245 | |||
− | ||||
− | ||||
− | ||||
1893 | − |
Countries with the lowest regional CMR include Namibia at 40,000 pm, Botswana at 48,000 pm and Madagascar at 62,000 pm, all of whom are 1 SD below the mean meriting a relative
The average reduction in CMR was 33% and 16 SSA countries reduced their CMR by more than 35%; 12 achieving the millennium goal. Therefore, 21 (including South Africa) SSA countries failed to meet the UN target of a 2% reduction in CMR per annum, though five countries came close with falls of more than 30%. Fourteen SSA countries have been in civil conflict situations in the last 20 years; paradoxically Ethiopia, Liberia, Madagascar, Rwanda and Yemen managed to reduce their CMR by more than 40% over the review period. Compared to Nigeria who had the sixth highest income and equal seventh highest CMR, surely meriting a reproach.
Out of the 33 SSA counties, 21 (including South Africa) failed to meet the UN target of a 2% per annum reduction in CMR, although 5 came close with falls of more than 30%.
Perhaps the biggest surprise relates to figures from South Africa. Under the apartheid regime in 1990, WHO data yielded CMR of 6431 pm, but this might be a serious underestimation as child mortality in rural areas could have gone unreported. The first available WHO data for the post‐apartheid regime (2002–2004) records a rate of 10,410 pm, equivalent to a 62% increase. Taking only post‐apartheid WHO data, the latest index years 2007–2009 figure of 11,245 pm points to a rise in CMR of 8% over 7 years. However, South Africa's annual figures vary widely from year to year, for example, in 2009, the WHO reported rate fell to 9158 pm. This variation is also reflected in the UN Statistics Division data where for the baseline years (1988–1990) CMR is estimated at 61,000 pm, 59,600 pm and 58,500 pm, respectively, averaging 59,700 pm. For the years 2008–2010, CMR estimates went from 69,300 pm, down to 53,200 pm and 47,500 pm, averaging 56,700 pm—a 5% reduction, yet well below the millennium target.
When looking at SSA nations, those with the highest GNI figures such as Cameroon and Nigeria, against expectations, had higher CMR, whilst poorer countries such as Madagascar and Zimbabwe had lower CMR, suggesting major differences in policy in these societies in relation to child health. To explain this more fully would require country‐specific research. Remembering that GNI is adjusted for PPP in comparative terms, we in the West probably cannot conceive what such low levels of effective income mean for these societies. Again, perhaps counter intuitively, there was no correlation between the health, military expenditures and CMR.
4. Discussion
These limitations mean that these results cannot be definitive. Rather, they are indicative of changes found in other studies of non‐Western societies such as Islamic, Latin American and former Warsaw Pact countries related to suicide and child‐abuse‐related deaths, where data accuracy has been found to be problematic because of cultural and political taboos [15–17]. Nonetheless, despite these limitations, this first‐ever comparative study of societies’ response to children in three world regions provides significant indicators of those meriting a relative regional
4.1. The West
Most Western governments can be congratulated on the impressive reduction in mortality rates, but the
Relative poverty and higher CMR are significantly correlated, which is seen in the fact that the five Western countries with the highest CMR occupied the six widest income inequalities positions. Conversely, countries with the narrowest income inequalities have the lowest CMR, that is, Sweden, Finland, Japan and Norway, meriting their
4.2. Asia
There is a very strong correlation between CMR and relative poverty in Asian countries. Whilst
4.3. Sub‐Saharan Africa
Even acknowledging the incredible poverty of Africa compared with much of Asia and the West
4.4. Governments
What also has to be recognised is that globally
When exploring the percentage of GDP on military expenditure, it was significant that the higher military expenditure in the West was statistically linked with worse income inequality, but not in the other regions. However, when considering the comparison of health and military expenditure ratios in Asia and Africa, we are ill‐equipped to comment, in part because of unavailable data and the various countries perceived security threats. However, we recall the valedictory address of President Eisenhower, America's top general and commander‐in‐chief of the Allied war in Europe, who warned of the inherent socio‐economic‐political dangers of the ‘military industrial complex’.
When discussing CMR, in one sense, using rates distances us, but rates are statistics, numbers are real children. One practical feature must be the accumulative societal impact of high child mortality as bereavement itself is damaging to family health [49, 50]. Losing a child must be one of the worst and bitter tragedies for any parent in whatever world region and should be a focus of future research. So what do these rates mean in terms of relative excess’ deaths of children? The USA and UK, who claim to be the mature and greatest democracies, have somewhat distorted political priorities, not only warned of by President Eisenhower, but former Chief of Staff General Colin Powell, who complained the US military was out of tilt and distorting the US economy [51]. Yet the US and the UK have the higher military to health expenditure ratios, reflecting their priorities. Does this influence the conjugation that if the UK and USA had the same current CMR of Portugal, who had been the highest Western country in 1989–1991, then there would be 850 fewer dead children in Britain and 13,591 fewer American grieving parents, more than four times the worst ever terrorist atrocity. Indeed, both countries’ CMR substantially exceeded that of Hong Kong, Singapore and South Korea. It might be argued that for every bullet, plane and tank manufactured, potentially it is taking the sustenance from children in need, not only in the West but also in the other two regions.
One excuse for SSA is they only have imperfect or new democracies, which we do not accept as apartheid ended 26 years ago and forthcoming research over a similar period of the former Warsaw Pact countries, shows that eight of them now have lower CMR than the USA, so with such considerable improvement, we should have greater expectations for post‐apartheid Africa.
In all three regions, there seem to be questionable priorities: countries with narrow ratios, narrower than the average, such as Greece, Australia, UK and the USA, all less than 1:5.5, should be challenged as to the rationale in relation to their CMR. In Asia, except Japan, they have a far ‘worse’ health to military ratio, averaging 1:2.3. Again, surely this should raise questions as to their priorities—especially Myanmar and Pakistan, who spend more on military than on health and who have the highest CMR in Asia. In view of India's economic success, their ratio of 1:1.7 in part may be a reaction to their neighbour's military expenditure, but again should be challenged.
Finally, on the ‘government’ side of Penn's dictum, Sub‐Sahara‐Africa military: health ratios vary considerably, averaging 1:4.4. However, for 12 of the 27 SSA countries for whom we have data are below the ‘average’ and in view of CMR toll is of itself a reproach. For in the last analysis, every gun, tank and plane manufactured is competing for feeding and providing adequate healthcare for their children. This is reported with great sadness, but we must never be afraid to report what we find even if it can inadvertently be re‐framed as racist, as politicians over the centuries have used patriotism as the last refuge for the political scoundrel.
4.5. Religion
William Penn (1693) condemned both government and religion [38], however. Christianity and Islam, the main religions in the West, Asia and Africa, hold strong socially positive messages about the care of children. Both condemn child neglect and abuse in the strongest terms. For example, Jesus of Nazareth, also revered in Islam, denounces those who actively or passively neglect and abuse children:
From the Qur’ an, there are clear obligations concerning how to treat and give priority to children, for example; ‘
Therefore, these two faiths come together to reinforce Penn's (1693) message that ‘
5. Implications
The professions and those academics in universities concerned with children should not be afraid to highlight the United Nations declaration of the Rights of Children. At times of limited resources for services and research, some organisations may be afraid of their staff appearing ‘political’ or to offend powerful vested interests. Yet the inherent independence of the professional and academic ethos means we have a responsibility to ‘tell it as it is’ based upon the best available evidence. Therefore, whilst some countries can be commended for the progress they have made, these results should be a spur to the other to match the best or better in their regions. Here is evidence that is a challenge to all societies to honour its obligation to children in the constant pursuit of social justice, especially those societies of
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