CC BY
44
Inequality, Migration, and ‘Smart’ Survival Performance
Arno Tausch
Innsbruck University, Austria;
Corvinus University, Budapest
ABSTRACT
In this article, we present a first empirical reflection on ‘smart survival’, its measurement and its possible ‘drivers’ and ‘bottlenecks’. The basic idea of ‘smart development’ was proposed by Dennis Meadows two decades ago and relates our whole concept of development to the natural resources needed to sustain it. We apply this reasoning to three central indicators of survival in public health research (female survival to age 65, infant mortality, and life expectancy). We relate these measures to the ecological footprint, needed by society to sustain the economic and social model, which permits their performance. Our study uses standard international aggregate statistical data on socio-economic development. We first show the OLS regression trade-offs between ecological footprints on our three outcome indicators of public health. The residuals from these regressions are our new empirical measures of smart survival. We then look at the cross-national drivers and bottlenecks of this ‘smart survival’. Our estimates underline the enormous importance of received worker remittances for smart survival. Inequality plays a certain role. Considering the ecological resources to sustain a societal model, migration is among the major determinants of public health outcomes.
BACKGROUND
In this article, we present a first empirical reflection on ‘smart development’, its measurement and its possible ‘drivers’ and ‘bottlenecks’. The very idea of ‘smart development’ was first proposed by
Social Evolution & History, Vol. 12, No. 2, September 2013 77-101 © 2013 ‘Uchitel’ Publishing House
Meadows (1992) and has not been really followed up to now in social science ever since. In the face of the huge usage of this term in the international media, such a statement is perhaps surprising, but our verdict corresponds to the clear bibliographical evidence on the base of such indices as ‘ISI Web of Knowledge’ or ‘Cambridge Scientific Abstracts/PROQUEST’.1
The basic idea, proposed by Meadows two decades ago, was that we should relate our whole concept of development, and not just economic growth, to the natural resources needed to sustain it. Arguably, ecological footprint today is the best single international yardstick for environmental destruction to be observed in a nation, and preferably should be used as the x-axis in any measure of the concept of ‘smart development’ (York et al. 2003). The y-axis then would be performance in public health, like life expectancy rates.
Following the path-breaking articles by R. G. Wilkinson and Picket (Wilkinson 1992, 1997; Wilkinson and Picket 2006), the income inequality has a very detrimental effect on life quality. But as we show in our article, ‘life quality’ or ‘survival’ also depends in a non-linear fashion on the environmental data. It would be senseless for a country to achieve, say, an average life expectancy of 85 years, even at moderate or low levels of social inequality at a very heavy ecological price of substantially further intensifying our ecological footprint here on earth (which measures how much land and water area human population requires to produce the resource it consumes and to absorb its carbon dioxide emissions, using prevailing technology).2 Ultimately, such an energy and resource intensive development will not be sustainable in the long run, and will backfire on life quality (human happiness) and life quantity (life expectancy).
But in a way, this exactly describes our alternatives today. Humanity already uses the equivalent of 1.5 planets to provide the resources we use and absorb our waste.3 If we continue what is called ‘progress’ in the 21st century not only life expectancy will have to be maximised and infant mortality will have to be minimised and human happiness would have to be further increased; all this ‘progress’ also would have to be achieved at the price of low and decreasing detrimental environmental consequences of our human life on our planet. ‘Smart development’ would combine a high life expectancy and a medium or low ecological footprint.
Arguably, the integration of the phenomenon of socioeconomic inequality, which dominated politics and economy of the industrialized western democracies throughout much of the late 19th and 20th century into current thinking about public health, has been a major scientific achievement. But in addition to fundamentally overlooking the environmental question, current thinking of the inequality-centred school of public health overlooks such important phenomena of the 21st century as migration, and the globalization of cultures and religions, brought along with global migration, which will all increasingly influence politics and economy of our globe and of course also potentially shape public health performance. Our article should serve exactly the public health research profession to face up to these new challenges of the 21st century.
The vast social science debate about migration as one of the possible future drivers of public health developments can only be briefly summarized here. The number of international migrants has increased more or less linearly over the past 40 years, from an estimated 76 million in 1965 to 188 million in 2005 (Taylor 2006). The flow of international migrant remittances has increased more rapidly than the number of international migrants, from an estimated US$ 2 billion in 1970 to US$ 216 in 2004. Nearly 70 % of all remittances go to LDCs. Worker remittances are especially affecting the less developed sending countries by the multiplier effect, well-known in economics since the days of the economist John Maynard Keynes (Taylor 1999). Countries with per capita income below US$ 1200 benefit most from remittances in the long run because they have the largest impact of remittances on savings (Ziesemer 2009). An important benefit of remittances is that less debt is incurred and less debt service is paid by countries than without remittances. Financial remittances are vital in improving the livelihoods of millions of people in developing countries (Human... 2009). There is a positive contribution of international remittances to household welfare, nutrition, health and living conditions in places of origin. An important function of remittances is to diversify sources of income and to cushion families against setbacks such as illness or larger shocks caused by economic downturns, political conflicts or climatic vagaries. In the comprehensive sociological literature, there have been already made attempts
to bring in migration as a determining variable of social well-being (Sanderson 2010). Contemporary levels of international migration in less-developed countries are raising new and important questions regarding the consequences of immigration for human welfare and well-being. This mentioned study assessed the impact of cumulative international migration flows on the human development index, the composite, well-known UNDP (United Nations Development Programme) measure of aggregate well-being.
In our own work, we also consider the potential negative effects of state sector intervention into the economy on social (here ecologically weighted public health) performance. In addition, we also look at the explanatory power of other standard international development predictors, well-known in the economic, political science and macro-sociological literature (Tausch et al. 2012).
METHODS
Confronting these multiple tasks to develop a timely understanding of the determinants of ecologically weighted public health performances, and keeping with a vast tradition in the social sciences, which relates development performance in a non-linear fashion to achieved income levels,4 we stipulate first that a is the constant in a standard, ordinary least square multiple regression equation, b1 and b2 are the unstandardised regression coefficients, and s denotes the error term. e is the well-known mathematical number 2.72 and rcis the well-known mathematical number 3.14... We should recall that (1/e2) corresponds to a numerical value of 0.14 and (ln (rc)) to a numerical value of 1.14...5 We have then accordingly:
Public health performance = a +- bi * ecological footprint^2 -+ b2 * ecological footprint(ln (rc)) + s (Equation 1)
In our essay, we use a recent standard international data set about globalization and development, which is freely available world-wide and which relies on well-established international data sources, such as the United Nations Development Programme, the World Bank, the International Monetary Fund, and the International Labour Organization, to test our propositions.6 We demon-strate7 the trade-off between ecological footprint and life quality, taking female survival rates to age 65, infant mortality and life expectancy as examples in Graph 1.
Graph 1a. Female survival rate to age 65 and ecological footprint
Data source: http://www.hichemkaroui.com/?p=2017#more-2017. Accessed on February 27, 2012.
180
0 5 10 15
Graph 1b. Infant mortality and ecological footprint
Data source: http://www.hichemkaroui.com/?p=2017#more-2017. Accessed on February 27, 2012.
Graph 1c. Life expectancy and ecological footprint
Data source: http://www.hichemkaroui.com/?p=2017#more-2017. Accessed on February 27, 2012.
Table 1 (see Appendix) shows the predicted values and the quality of our predictions (residuals) for female survival rates, infant mortality rates, and life expectancies (see Equation 1). By the residuals from our non-linear function, to be seen in Graphs 1a - 1c, we also present to our readers our new measures of ‘smart survival’. Good public health performance is also smart public health performance, if it is achieved at a low level of ecological footprint. Good or mediocre, let alone bad public health performance is un-smart public health performance, if it is achieved at a high or medium level of ecological footprint.
Analysing Table 1, our readers will find for example that the first country in the alphabet with complete data is Albania, which has an annual ecological footprint of 2.23 gha per capita. The female survival rate in a country with such a footprint level, corresponding to the international standard function, clearly visible in Graph 1a, would have to be expected at somewhere about 75 %. But in reality Albania's female survival rate to age 65 was 89.5 % in the first decade of the new millennium, and thus somewhere 14.7 % above the value, which would have been to be expected.
Several developing countries by far outperform richer countries in achieving good or medium public health results at a low or
moderate ecological footprint rates per capita, while many rich countries - among them several established Western welfare states with low socio-economic inequality rates - perform relatively bad public health results, and consume a considerable amount of energy and resources to achieve their survival performances. The real ‘superstars’ of ‘smart survival performance’ regarding infant mortality in comparison to ecological footprint are countries like Sri Lanka, the Philippines, and Jamaica. Similar trends and country results hold also for our other indicators in question.
What determines these performances? Is it inequality? Many of the countries with a good performance on our smart survival scales are developing countries with high degrees of inequality, like the Philippines, Colombia or Peru.
To further allow our readers a deeper understanding of the mathematical functions used in our research, we elaborated Table 2 (see Appendix), which shows the mathematical properties of the trade-offs between ecological footprint and life quality, each time applying Equation 1. Table 2 is the appropriate compendium of the mathematical functions of our study, determining the shape of Graphs 1a - 1c and also the results of Table 1.
Graph 2 presents the synopsis of the mathematical functions used in our study.
public health The three main functions
performance
ecological footprint per capita
Graph 2. The main public health functions
Apart from the quintile share of income inequality, which is the difference in the absolute incomes of the richest 20 % and poorest 20 % in society, we used standard development predictors in our equations, often used in international development accounting. The following ones achieved significant results:
1. Membership in the Organi- 4. Public education expendi-
zation of Islamic Coopera- ture per GNP (Blankenau
tion (De Soysa and Ragnhild and Sympson 2004; Ram
2007). 1986; Sylwester 2000).
2. Military expenditures per 5. UNDP education index
GDP (Auvinen and Nafziger 6. Worker remittance inflows 1999; Heo 1998). as % of GDP (Acosta et al.
3. Muslim population share per 2008).
total population (Acemoglu
et al. 2002; Ram 1997).
In our calculations, we first tested the stepwise standard OLS multiple regression results of these variables on our smart survival performance indicators.8 The insignificant predictors were weeded out; and the final models included only the significant predictors, and are based on standard stepwise OLS forward regressions.
RESULTS
Our calculations9 about the comparative effects of standard econometric, public health, and social science predictors of global social and economic performance show that inequality, as correctly predicted by R. G. Wilkinson and his school of public health research still has detrimental effects, but that the effects are not as huge as expected, once we properly control for the other intervening variables.10
The full statistical results of our research are presented in Tables 3-5 in Appendix.
CONCLUSIONS AND IMPLICATIONS
Considering the fact that high infant mortality rates are socially and politically undesirable results, we arrive at the following generalized interpretations implicit from Tables 3-5. All these results have considerable implications for risk assessment in international health policy.
There are very clear-cut results for the socio-cultural phenomena of migration: received worker remittances and the share of Muslims per total population are positive and significant drivers of the performance-related indicators.11 The Muslim population shares have a net and significant positive effect on smart life expectancy and also smart female survival rates, irrespective of the effects of the other intervening variables.12 This result supports a social scientific research tradition, which recognizes the development potentials of Islamic civilizations. At the same time our research is aware about the hitherto existing growth and energy savings constraints in many Muslim countries, especially in the Arab world, brought about by the rentier character of these states and their dependence on the hitherto existing oil wealth and the lack of democracy in the region, which existed for many decades, and which might be changing now (see also the optimistic study by Noland and Pack 2007). Interestingly enough, the real net effect of Islamic civilization, measured by Muslim population shares per total population, is positive, while membership in the Organization of Islamic Cooperation (OIC), an organization of existing states in the existing world system, has significant negative effects on smart female survival and smart life expectancy. To be exact, we do not say that membership in the Organization of Islamic Cooperation (OIC) as such has a statistically significant negative effect on female survival and life expectancy. The effect is rather on smart female survival and smart life expectancy; considering the level of ecological footprint at given technologies and political patterns in a given country with given levels of female survival and life expectancy. An important intervening variable is the hitherto existing energyintensive development paths in many OIC member countries and the necessity of a ‘greening’ of the member countries of the OIC (on energy policy in the Arab world see Reiche 2010). Put in other words - to achieve a reasonable life expectancy and good other survival data, OIC nations need a lot of energy.
The significant effects for worker remittances (see unstandardised regression coefficients, see Tables 3-5) on smart survival are dramatic, and all in the desired direction, with one per cent increase in received worker remittances moving up smart female survival rates by 0.5 per cent, and resulting in a reduction of
unsmart infant mortality rates by 1.3 points. Also, a 1 % increase in received worker remittances increases smart life expectancy by 0.3 years. Reaping the benefits from one of the four freedoms of the ‘capitalist’ order - migration - has absolutely beneficial effects on our environmentally weighted survival performance scales.
Large sections of current economic theory are vindicated by the positive significant effects of human capital formation (operationalized here by the UNDP education index) on smart survival. High military expenditures per GDP and high public education expenditures per GDP crowd out smart survival (see especially Blankenau and Simpson 2004).
There are two significant empirical effects to be recorded for the original Wilkinson approach: the significant negative effect of inequality on smart female survival and on smart life expectancy. Thus, the Wilkinson research agenda still finds its proper place also in the coming new and necessary debates about ‘smart development’, but certainly, the weight of other variables also has to be properly taken into account, such as
• membership in the Organization of Islamic Cooperation;
• military expenditures per GDP;
• Muslim population share per total population;
• public education expenditure per GNP;
• UNDP education index;
• worker remittance inflows as % of GDP.
A particularly promising area of future scholarship on the subject could be the question, as to whether the ‘social capital’ of voluntary organizations, as already specified in a very influential study (see Kawachi et al. 1997) is responsible for the explanation of the some 60 % to 70 % of the variance of smart survival rates, still unaccounted for by our models. At any rate, we hope that we have contributed a novel perspective to the paths of inequality oriented survival rate indicator performance research in public health.
NOTES
1 Accessed via Vienna University Library, April 24th, 2012.
2 URL: http://www.footprintnetwork.org/en/index.php/gfn/page/footprint_basics_ overview/ [accessed February 27, 2012].
3 URL: http://www.footprintnetwork.org/en/index.php/GFN/page/world_foot print/ [accessed February 27, 2012].
4 For a survey of the literature, see, among others Tausch and Prager 1993. Following an essay by Goldstein (1985) there were many empirical attempts to capture this trade-off. The empirical function we use in this essay has been taken from (Tausch and Prager 1993).
5 All these numbers are well-known constants from general mathematical systems theory. See also Bronstein and Semendjajew 1972.
6 URL: http://www.hichemkaroui.com/?p=2017 [accessed February 27, 2012].
7 Statistical software used: SPSS/IBM XVIII [http://www-01.ibm.com/soft ware/analytics/spss/] [accessed February 27, 2012].
8 See URL: http://www.hichemkaroui.com/?p=2017 [accessed February 27, 2012] for the data definitions and sources.
9 Standard econometric development accounting is to be found, among others, in Barro and Sala-i-Martin 2003.
10 Prior stepwise regression procedure with the most important predictors, commonly used today in econometrics and political science. The significant predictors were retained for the final results, reported here, which are based on forward regression and the standard default SPSS XVIII multiple regression options.
11 This is especially relevant for researchers in Europe. In the widely received work by Sarrazin (2010), it is maintained that Muslim diasporas are to be blamed for a great number of social and economic problems in countries like Germany. Our empirical results, by contrast, suggest that the social cohesion of Muslim life in the Diasporas is a positive asset for smart survival rates.
12 A good reason, why Muslim population shares wield such effects on our variable, is the phenomenon of social cohesion and social trust in these societies (see Tausch and Heshmati 2009). What has been described in classic Arab literature as ‘Asabiyya’ (social trust, social cohesion, social capital) is of course not new for the public health profession (see Kawachi et al. 1997).
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APPENDIX
Table 1
Smart survival
Ecological footprint (gha/cap) Predicted female survival rate Residual: female survival rate Predicted infant mortality Residual: infant mortality Predicted life expectancy Residual: life expectancy
Albania 2.230 74.782 14.718 36.246 -20.246 68.333 7.867
Algeria 1.660 68.859 10.041 49.031 -15.031 64.839 6.861
Angola 0.910 56.288 -22.388 76.283 77.717 57.584 -15.884
Argentina 2.460 76.673 8.927 32.176 -17.176 69.466 5.334
Armenia 1.440 65.921 15.979 55.388 -29.388 63.128 8.572
Australia 7.810 91.217 0.983 1.875 3.125 79.561 1.339
Austria 4.980 87.985 3.915 8.145 -4.145 76.676 2.724
Azerbaijan 2.160 74.157 1.843 37.591 36.409 67.961 -0.861
Bangladesh 0.570 46.513 16.687 97.533 -43.533 52.025 11.075
Belarus 3.850 84.466 -3.166 15.525 -5.525 74.301 -5.601
Belgium 5.130 88.327 2.673 7.439 -3.439 76.923 1.877
Belize 2.560 77.426 9.374 30.557 -15.557 69.920 5.980
Benin 1.010 58.484 -2.784 71.516 17.484 58.841 -3.441
Bhutan 1.000 58.274 9.326 71.971 -6.971 58.721 5.979
Bolivia 2.120 73.789 -4.789 38.384 13.616 67.742 -3.042
Tausch / Inequality, Migration, and ‘Smart’ Survival Performance 91
Bosnia and Herzegovina 2.920 79.843 25.374 71.393 3.107
Botswana 3.600 83.411 -51.511 17.762 69.238 73.623 -25.523
Brazil 2.360 75.879 2.621 33.883 -2.883 68.989 2.711
Bulgaria 2.710 78.486 6.814 28.282 -16.282 70.563 2.137
Burkina Faso 2.000 72.633 -18.133 40.878 55.122 67.057 -15.657
Burundi 0.840 54.604 -13.504 79.941 34.059 56.623 -8.123
Cambodia 0.940 56.971 0.829 74.800 23.200 57.975 0.025
Cameroon 1.270 63.299 -20.799 61.070 25.930 61.610 -11.810
Canada 7.070 90.918 0.082 2.318 2.682 79.105 1.195
Central African Republic 1.580 67.843 -35.743 51.228 63.772 64.246 -20.546
Chad 1.700 69.347 -18.847 47.976 76.024 65.124 -14.724
Chile 3.000 80.324 8.276 24.344 -16.344 71.689 6.611
China 2.110 73.696 7.204 38.585 -15.585 67.687 4.813
Colombia 1.790 70.398 11.402 45.703 -28.703 65.740 6.560
Congo 0.540 45.398 0.502 99.960 -18.960 51.393 2.607
Congo (Democratic Republic of the) 0.610 47.917 -9.117 94.478 34.522 52.820 -7.020
Costa Rica 2.270 75.128 13.472 35.500 -24.500 68.539 9.961
Croatia 3.200 81.450 7.050 21.937 -15.937 72.388 2.912
Cuba 1.760 70.055 16.745 46.446 -40.446 65.539 12.161
Cyprus 4.500 86.709 5.591 10.800 -6.800 75.788 3.212
92 Social Evolution & History / September 2013
Czech Republic 5.360 88.805 0.195 6.457 -3.457 77.275 -1.375
Denmark 8.040 91.249 -3.849 1.869 2.131 79.669 -1.769
Djibouti 1.490 66.630 -16.230 53.853 34.147 63.540 -9.640
Dominican Republic 1.490 66.630 10.070 53.853 -27.853 63.540 7.960
Ecuador 2.200 74.517 9.483 36.816 -14.816 68.175 6.525
Egypt 1.670 68.982 11.218 48.764 -20.764 64.911 5.789
El Salvador 1.620 68.358 10.142 50.114 -27.114 64.546 6.754
Estonia 6.390 90.346 -6.046 3.377 2.623 78.524 -7.324
Ethiopia 1.350 64.576 -17.676 58.301 50.699 62.349 -10.549
Finland 5.250 88.583 3.217 6.912 -3.912 77.111 1.789
France 4.930 87.865 4.335 8.393 -4.393 76.591 3.609
Georgia 1.080 59.894 23.106 68.454 -27.454 59.650 11.050
Germany 4.230 85.857 5.143 12.589 -8.589 75.214 3.886
Ghana 1.490 66.630 -10.130 53.853 14.147 63.540 -4.440
Greece 5.860 89.666 1.634 4.714 -0.714 77.944 0.956
Guatemala 1.510 66.906 10.694 53.255 -21.255 63.700 6.000
Guinea 1.270 63.299 -7.599 61.070 36.930 61.610 -6.810
Guyana 2.630 77.931 -11.131 29.473 17.527 70.226 -5.026
Haiti 0.530 45.013 12.487 100.797 -16.797 51.175 8.325
Honduras 1.770 70.170 6.430 46.196 -15.196 65.606 3.794
Tausch / Inequality, Migration, and ‘Smart’ Survival Performance 93
Hong Kong, China (SAR) 5.680 89.383 4.217 5.284 77.718 4.182
Hungary 3.550 83.185 1.215 18.242 -11.242 73.479 -0.579
Iceland 7.400 91.090 1.310 2.036 -0.036 79.329 2.171
India 0.890 55.820 10.280 77.299 -21.299 57.317 6.383
Indonesia 0.950 57.194 18.606 74.316 -46.316 58.102 11.598
Iran 2.680 78.280 0.020 28.723 2.277 70.438 -0.238
Ireland 6.260 90.200 -0.200 3.661 1.339 78.393 0.007
Israel 4.850 87.667 4.633 8.803 -3.803 76.451 3.849
Italy 4.760 87.436 5.064 9.284 -5.284 76.289 4.011
Jamaica 1.090 60.088 18.212 68.033 -51.033 59.762 12.438
Japan 4.890 87.767 6.033 8.596 -5.596 76.522 5.779
Jordan 1.710 69.467 8.733 47.717 -25.717 65.194 6.706
Kazakhstan 3.370 82.328 -8.628 20.066 42.934 72.937 -7.037
Kenya 1.070 59.699 -17.199 68.879 10.121 59.538 -7.438
Korea (Republic of) 3.740 84.016 6.784 16.477 -11.477 74.011 3.889
Kuwait 8.890 91.153 -2.253 2.323 6.677 79.953 -2.653
Kyrgyzstan 1.100 60.281 14.119 67.616 -9.616 59.872 5.728
Laos 1.060 59.501 4.199 69.308 -7.308 59.424 3.776
Latvia 3.490 82.907 1.893 18.833 -9.833 73.303 -1.303
Lebanon 3.080 80.787 -0.187 23.353 3.647 71.976 -0.476
Lithuania 3.200 81.450 4.150 21.937 -14.937 72.388 0.112
94 Social Evolution & History / September 2013
Luxembourg 10.190 90.438 0.362 4.262 -0.262 80.078 -1.678
Macedonia 4.610 87.027 -2.727 10.135 4.865 76.006 -2.206
Madagascar 1.080 59.894 -1.794 68.454 5.546 59.650 -1.250
Malawi 0.470 42.553 -8.853 106.151 -27.151 49.784 -3.484
Malaysia 2.420 76.360 6.740 32.847 -22.847 69.278 4.422
Mali 1.620 68.358 -14.258 50.114 69.886 64.546 -11.446
Malta 3.790 84.223 6.177 16.038 -11.038 74.144 4.956
Mauritania 1.900 71.605 -2.205 43.097 34.903 66.450 -3.250
Mexico 3.380 82.377 2.123 19.961 2.039 72.968 2.632
Moldova 1.230 62.628 12.872 62.525 -48.525 61.223 7.177
Mongolia 3.500 82.954 -14.954 18.733 20.267 73.332 -7.432
Morocco 1.130 60.847 18.553 66.388 -30.388 60.197 10.203
Mozambique 0.930 56.746 -21.446 75.289 24.711 57.846 -15.046
Myanmar 1.110 60.471 3.629 67.203 7.797 59.981 0.819
Namibia 3.710 83.890 -41.990 16.746 29.254 73.929 -22.329
Nepal 0.760 52.504 8.796 84.506 -28.506 55.426 7.174
Netherlands 4.390 86.375 4.025 11.501 -7.501 75.561 3.639
New Zealand 7.700 91.192 -1.192 1.898 3.102 79.504 0.297
Nicaragua 2.050 73.124 4.176 39.817 -9.817 67.348 4.552
Niger 1.640 68.610 -14.210 49.569 100.431 64.693 -8.893
Nigeria 1.340 64.421 -23.821 58.638 41.362 62.259 -15.759
Norway 6.920 90.818 0.882 2.494 0.506 78.992 0.808
Tausch / Inequality, Migration, and ‘Smart’ Survival Performance 95
Pakistan 0.820 54.098 12.502 81.041 -2.041 56.334 8.266
Panama 3.190 81.396 4.504 22.052 -3.052 72.354 2.746
Paraguay 3.220 81.557 -3.857 21.709 -1.709 72.454 -1.154
Peru 1.570 67.712 9.788 51.512 -28.512 64.169 6.531
Philippines 0.870 55.342 23.958 78.338 -53.338 57.044 13.956
Poland 3.960 84.893 3.107 14.621 -8.621 74.579 0.621
Portugal 4.440 86.529 4.371 11.178 -7.178 75.665 2.035
Romania 2.870 79.532 4.168 26.039 -10.039 71.202 0.698
Russia 3.750 84.058 -8.058 16.389 -2.389 74.037 -9.037
Rwanda 0.790 53.316 -18.816 82.742 35.259 55.888 -10.688
Saudi Arabia 2.620 77.860 4.140 29.626 -8.626 70.183 2.017
Senegal 1.360 64.731 4.970 57.967 19.033 62.438 -0.138
Sierra Leone 0.770 52.778 -15.178 83.911 81.090 55.582 -13.782
Singapore 4.160 85.618 5.182 13.091 -10.091 75.056 4.344
Slovakia 3.290 81.924 5.376 20.928 -13.928 72.683 1.517
Slovenia 4.460 86.590 3.510 11.051 -8.051 75.707 1.693
South Africa 2.080 73.413 -27.413 39.196 15.804 67.519 -16.719
Spain 5.740 89.481 4.019 5.087 -1.087 77.795 2.705
Sri Lanka 1.020 58.691 22.609 71.066 -59.066 58.960 12.640
Sudan 2.440 76.518 -21.218 32.510 29.490 69.373 -11.973
Sweden 5.100 88.261 4.039 7.576 -4.576 76.875 3.625
Switzerland 5.000 88.032 4.568 8.048 -4.048 76.710 4.590
96 Social Evolution & History / September 2013
Syria 2.080 73.413 10.187 39.196 -25.196 67.519 6.081
Tajikistan 0.700 50.783 21.217 88.247 -29.247 54.447 11.853
Tanzania 1.140 61.032 -20.032 65.986 10.014 60.304 -9.304
Thailand 2.130 73.882 1.618 38.184 -20.184 67.797 1.803
Togo 0.820 54.098 7.102 81.041 -3.041 56.334 1.466
Trinidad and Tobago 2.130 73.882 -1.782 38.184 -21.184 67.797 1.403
Tunisia 1.760 70.055 15.245 46.446 -26.446 65.539 7.961
Turkey 2.710 78.486 3.814 28.282 -2.282 70.563 0.837
Uganda 1.370 64.883 -28.283 57.636 21.364 62.526 -12.826
Ukraine 2.690 78.349 1.151 28.575 -15.575 70.480 -2.780
United Arab Emirates 9.460 90.916 -0.716 3.004 4.996 80.049 -1.749
United Kingdom 5.330 88.746 0.854 6.579 -1.579 77.231 1.769
United States 9.420 90.937 -3.937 2.947 3.053 80.045 -2.145
Uruguay 5.480 89.033 -1.933 5.992 8.008 77.448 -1.548
Uzbekistan 1.810 70.624 2.676 45.215 11.785 65.873 0.927
Venezuela 2.810 79.149 3.451 26.859 -8.859 70.968 2.232
Vietnam 1.260 63.133 19.567 61.429 -45.429 61.514 12.186
Yemen 0.910 56.288 5.412 76.283 -0.283 57.584 3.916
Zambia 0.770 52.778 -30.878 83.911 18.090 55.582 -15.082
Zimbabwe 1.120 60.660 -42.660 66.793 14.207 60.090 -19.190
Tausch / Inequality, Migration, and ‘Smart’ Survival Performance 97
The trade-off between ecological footprint and life quality
Life quality indicator (dependent variable) Independent variables Regression coefficient B Standard error Beta T Error probability
Female survival Constant -115.938 28.930 -4.007 0.000
footprint per capita11 eJI 176.706 28.925 1.051 6.109 0.000
footprint per capita(ln(It)) -2.494 1.081 -0.397 -2.307 0.023
statistical parameters of the equation adi RA2 47.60 %
n = 139
F = 63.696
error p = .000
Infant mortality Constant 451.730 60.074 7.520 0.000
footprint per capita11 ejl -385.382 60.060 -1.085 -6.417 0.000
footprint per capita(ln(It)) 5.622 2.244 0.424 2.505 0.013
statistical parameters of the equation adi RA2 49.20 %
n = 138
F = 67.307
error p = .000
Life expectancy Constant -38.934 16.951 -2.297 0.023
footprint per capita11 ejl 98.794 16.943 0.981 5.831 0.000
footprint per capita(ln(lt)) -1.140 0.633 -0.303 -1.799 0.074
statistical parameters of the equation adi RA2 49.30 %
n = 140
F = 68.458
error p = .000
98 Social Evolution & History / September 2013
Explaining the residuals from ecological footprint and female survival rate (ecologically efficient female survival rate, smart female survival)
Regression coefficient B Standard error Beta T Error probability
Constant -16.116 6.560 -2.457 0.016
Membership in the Organization of Islamic Cooperation -24.527 7.524 -0.827 -3.260 0.002
Military expenditures per GDP -1.138 0.495 -0.195 -2.300 0.024
Public education expenditure per GNP -1.741 0.611 -0.253 -2.847 0.006
UNDP education index 34.479 7.151 0.485 4.822 0.000
Worker remittance inflows as % of GDP 0.525 0.176 0.259 2.987 0.004
Muslim population share per total population 0.368 0.092 1.055 3.991 0.000
Quintile share income difference between the richest and the poorest 20 % -0.396 0.131 -0.256 -3.033 0.003
Note: adj. RA2 = 0.453; n= 88; F = 11.311; error p = .000.
Tausch / Inequality, Migration, and ‘Smart’ Survival Performance 99
Explaining the residuals from ecological footprint and infant mortality
Regression coefficient B Standard error Beta T Error probability
Constant 37.623 13.603 2.766 0.007
Membership in the Organization of Islamic Cooperation 30.806 15.603 0.560 1.974 0.052
Military expenditures per GDP 1.473 1.026 0.136 1.436 0.155
Public education expenditure per GNP 1.836 1.268 0.144 1.449 0.151
UNDP education index -63.311 14.829 -0.481 -4.269 0.000
Worker remittance inflows as % of GDP -1.286 0.365 -0.342 -3.527 0.001
Muslim population share per total population -0.358 0.191 -0.553 -1.870 0.065
Quintile share income difference between the richest and the poorest 20 % 0.322 0.271 0.112 1.189 0.238
Note: adj. RA2 = 0.316; n = 88; F = 6.745; error p = .000.
100 Social Evolution & History / September 2013
Explaining the residuals from ecological footprint and life expectancy (ecologically efficient life expectancy; smart life expectancy)
Regression coefficient B Standard error Beta T Error probability
Constant -9.764 3.976 -2.456 0.016
Membership in the Organization of Islamic Cooperation -14.447 4.560 -0.834 -3.168 0.002
Military expenditures per GDP -0.722 0.300 -0.212 -2.408 0.018
Public education expenditure per GNP -0.884 0.371 -0.220 -2.385 0.019
UNDP education index 19.967 4.334 0.481 4.607 0.000
Worker remittance inflows as % of GDP 0.330 0.107 0.278 3.092 0.003
Muslim population share per total population 0.205 0.056 1.004 3.660 0.000
Quintile share income difference between the richest and the poorest 20 % -0.196 0.079 -0.217 -2.482 0.015
Note: adj. RA2 = 0.411; n= 88; F = 9.684; error p = .000.
Tausch / Inequality, Migration, and ‘Smart’ Survival Performance 101
