Inequality, Migration, and ‘Smart’ Survival Performance


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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 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 socio-economic 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 e denotes the error term. e is the well-known mathematical number 2.72 and p is the well-known mathematical number 3.14… We should recall that (1/e2) corresponds to a numerical value of 0.14 and (ln (p)) to a numerical value of 1.14...5 We have then accordingly:

Public health performance = a +- b1 * ecological footprint(1/e2) -+ b2 * ecological footprint(ln (p)) + e (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 demonstrate7 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.

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.

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 Organization of Islamic Cooperation (De Soysa and Ragnhild 2007).

2. Military expenditures per GDP (Auvinen and Nafziger 1999; Heo 1998).

3. Muslim population share per total population (Acemoglu et al. 2002; Ram 1997).

4. Public education expenditure per GNP (Blankenau and Sympson 2004; Ram 1986; Sylwester 2000).

5. UNDP education index

6. Worker remittance inflows as % of GDP (Acosta et al. 2008).

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 energy-intensive 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

Bosnia and Herzegovina

2.920
79.843
25.374

71.393

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

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

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

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

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

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
Table 2
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 capita (1/e2)

176.706
28.925
1.051
6.109
0.000

footprint per capita (ln (p))

–2.494
1.081
–0.397
–2.307
0.023

statistical parameters of the equation

adj R^2
47.60 %



n =
139



F =
63.696



error p =
.000



Infant mortality

Constant

451.730
60.074

7.520
0.000

footprint per capita (1/e2)

–385.382
60.060
–1.085
–6.417
0.000

footprint per capita (ln (p))

5.622
2.244
0.424
2.505
0.013

statistical parameters of the equation

adj R^2
49.20 %



n =
138



F =
67.307



error p =
.000



Life expectancy

Constant

–38.934
16.951

–2.297
0.023

footprint per capita (1/e2)

98.794
16.943
0.981
5.831
0.000

footprint per capita (ln (p))

–1.140
0.633
–0.303
–1.799
0.074

statistical parameters of the equation

adj R^2
49.30 %



n =
140



F =
68.458



error p =
.000



Table 3
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. R^2 = 0.453; n = 88; F = 11.311; error p = .000.

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. R^2 = 0.316; n = 88; F = 6.745; error p = .000.

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. R^2 = 0.411; n = 88; F = 9.684; error p = .000.