The ‘Four Economic Freedoms’ and Life Quality. General Tendencies and Some Hard Lessons for EU-27-Europe


The ‘Four Economic Freedoms’ and Life Quality. General Tendencies and Some Hard Lessons for EU-27-Europe
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Author: Tausch, Arno
Journal: Journal of Globalization Studies. Volume 3, Number 2 / November 2012

For many years, EU-27-Europe's political-economic strategy was characterized by an intensive opening to the forces of globalization and the realization of the ‘four freedoms’ of goods, capital, labour and services on the EU-27-European ‘internal market’. The present article is a 175 nation and 26 predictor variables study about the determinants of 12 sub-processes of life expectancy, infant mortality and other social cohesion variables in this context. Our detailed studies confirm a globalization critical paradigm and let us expect negative consequences for the overall EU-27-European health situation as a consequence of the current global economic crisis.

Keywords: life expectancy, infant mortality, international relations, international political economy, international migration.

‘Nobody can fall in love with the single market’, used to say Jacques Delors. That the single market is not loved, is normal and even reassuring. A market is an instrument, not an end in itself. When the market is regarded as a superior entity, as if it were always able to deliver efficiently and did not need appropriate regulation and rigorous supervision, dangers are likely to lie ahead, as shown by the financial crisis. It was forgotten by many that the market is a good servant but a bad master’ (Monti 2010).

1. Background

EU-27 Europe, confronted by global economic crisis and the Euro zone, perhaps more so than currently any other region in the world economy, seems to be affected by the ills of globalization, without reaping too many of its presumed benefits.1 What will be the effects of this on inequality, social cohesion, and life quality?

For many years, EU-27-Europe's political-economic strategy was characterized by an intensive opening to the forces of globalization and the realization of the ‘four freedoms’ of goods, capital, labour and services on the EU-27-European ‘internal market’. The present article is a 175 nation and 26 predictor variables study about the determinants of 12 sub-processes of life expectancy, infant mortality and other social cohesion variables development in this context. It is based on the maximum number of countries with available United Nations and other international data.

As this essay is being written, large-scale austerity programmes with expectedly very large-scale negative health policy and overall social effects are swept across Portugal, Ireland, Italy, Greece and Spain, often called nowadays the PIIGS-countries. Austerity is on the agenda in the entire EU-27. Globalization critical scholars from various disciplines increasingly link the growing lack of ‘social cohesion’ in EU-27-Europe and the world at large to what they perceive as ‘unfettered globalization’, while the very Article 2 of the founding EEC Treaty already solemnly enshrined the free movement of persons, services, goods and capital into the basic principles of the Union.2 Contemporary social scientists not only blame globalization as such, but the very European Union and the way it is constructed for rising inequality in EU-27-Europe with its adverse consequences for population health (Beckfield 2006). When discussing the quantitative determinants of life expectancy and under-five mortality in the countries of the world system, the medical profession in turn drew inspiration from the work of Richard G. Williamson, Carl Otto Schnell et al. (Wilkinson and Picket 2006; Schell et al. 2007) with their emphasis on the trade-off between inequality and deficits in health, but stopped short of researching into the global socio-economic determinants of inequality, which in turn lead to observable deficits in public health. Our article is deeply indebted to this research tradition in the public health profession, bridging the borders between the research traditions of economics and the social sciences, on the one hand, and public health – on the other hand. Limitations of space prohibit us from referring more profoundly to these recent advances of inequality-centred public health research. The current article, based in that ‘Williamson’ tradition, only would like to broaden the ongoing debate to include very plausible drivers of inequality, and how they in turn are linked to life expectancy and infant mortality and other social cohesion variables (see also Tausch 2010a, 2011, 2012).

2. Methods

The question of the geographic, demographic and other independent variables in development accounting found much attention in recent economic literature (Barro and Sala-i-Martin 2003; Durlauf, Kourtellos, and Tan 2008; Easterly 2000; Sala-i-Martin 1997: 178–183), while sociologists and political scientists generally featured their investigations on the ‘drivers’ and ‘bottlenecks’ of ‘health’ and ‘social development’ or ‘social cohesion’ on variables such as dependence of countries from the large transnational corporations (Jenkins, Scanlan, and Peterson 2007; on child hunger see Shandra, Nobles, London, and Williamson 2005). Our research about the determinants of ‘social cohesion’, that is life expectancy, infant mortality, economic inequality, and other ‘social quality’ variables is fully within this theoretical and empirical research tradition (just to mention some studies: Bornschier, Volker 1983 on income inequality; Sanderson 2010 on the influence of migration on the human development index). In particular, we will be interested in studying not only social effects of the globalization of goods and capital, but also of services and labour. While evidence on the social effects of such phenomena as penetration of the host economies of transnational foreign direct investments already abound, little has been said up to now on the predictive power of these freedoms of services and labour in comparison to the other two freedoms.

To start with, we have made our data and the codebooks completely and freely available on the Internet so that the global research community can have free access to our data and have an opportunity to check our results or to conduct new research (http://www.hichemkaroui.com/?p=2017). This mentioned internet site contains not only the Microsoft EXCEL data and lists of the sources but also a codebook with the variable definitions in PDF format. It should be emphasized that the number of countries under investigation is the maximum number of countries with available statistical data from reliable international sources.

The statistical design of our study is based on the usual, SPSS XVIII ordinary least square standard regression analysis, starting with ‘kitchen sink type’ regressions with all the predictors of our investigation in the tradition of Durlauf et al., and Barro (Barro and Sala-i-Martin 2003; Durlauf, Kourtellos, and Tan 2008; Sala-i-Martin 1997), and then using the most significant predictors in these stepwise regressions as explanatory variables in the final forward regression procedures. As it is known, there are two common strategies in model building: a general-to-simple and a simple-to-general. In maintaining simplicity, model builders begin with a small model specification and gradually build up the model by adding more explanatory variables. This approach, due to the omitted variable bias, is not recommended. Increased computational power has led to the fact that the alternative – the general-to-simple model strategy – is increasingly attractive. This approach has been automated in some statistical packages to ease their implementation. For instance in a ‘kitchen sink’ regression, first every relevant variable is included, until the insignificant ones remain in the ‘kitchen sink’ to be thrown away.

According to this type of research (Easterly 2000; Durlauf, Kourtellos, and Tan 2008), we currently are facing around 100 popular independent variables in the current econometric literature on the determinants of economic growth. Our investigation duly acknowledges many of the key determinants of development, usually associated with economic growth, all mentioned in the relevant economic literature, like current shares of the country's inhabitants in total world population; the Heritage Foundation Economic Freedom Score; absolute geographical latitude; long-term annual population growth rate, 1975–2005 (per cent); the trade-off between development level and development performance, otherwise also known in economics as ‘conditional convergence’ (ln GDP per capita; ln GDP per capita ^2); the simple ‘Huntingtonian’ fact of whether a country is a Muslim country, to be measured by the Organization of Islamic Conference (OIC) Membership or by Muslim population share; data on population density; public education expenditure per GDP; and the UNDP education index, combining the enrolment rates at the primary, secondary and tertiary education levels. We also take into account UNDP figures on military expenditures per GDP and the openly available US-CIA data on military personnel rates, which are key variables of contemporary political science, international relations theory, and peace research. In our analysis, we also show the theoretical and practical (political) potential of the following two drivers of development, which are somewhat a ‘terra incognita Australis’in the hitherto existing macro-sociological debate: migration and European (Monetary) Union membership.

The present article will thus duly confront the underlying, pro-globalist logic of the ‘Lisbon strategy’ or ‘Lisbon agenda’ or ‘Europe 2020 agenda’ as the unhappy successor of the failed 2000–2010 strategy with what we perceive as the essence of a globalization-critical argument. As we already mentioned, and as we should highlight here especially, recent comparative sociological literature (see Tausch 2010b, 2010c, 2010d for a survey of the available literature) investigated the implications of foreign capital penetration for patterns of development. The choice of a country to be included in the final analysis (175 countries3) was determined by the availability of a fairly good data series for these independent variables (if not mentioned otherwise, UNDP data for the middle of the first decade of the new millennium). In the final regressions, we applied the ‘list wise deletion of missing values’ routine (i.e. only entering countries with complete data into the statistical analysis).

Our main independent variables were:

1. Share of women in government, all levels.

2. Per cent of world population.

3. 2000 Economic Freedom Score.

4. Absolute latitude.

5. Annual population growth rate, 1975–2005 (%).

6. Comparative price levels (US=1.00).

7. Foreign savings rate.

8. FPZ (free production zones) employment as % of total population.

9. ln GDP per capita.

10. ln GDP per capita ^2.

11. Membership in the Islamic Conference.

12. Military expenditures per GDP.

13. Military personnel rate ln (MPR+1).

14. MNC outward investments (stock) per GDP.

15. MNC PEN – stock of Inward FDI per GDP.

16. MNC PEN: DYN MNC PEN 1995–2005.

17. Openness-Index, 1990 (export-share per GDP + import-share per GDP).

18. Population density.

19. Public education expenditure per GNP.

20. UNDP education index.

21. Worker remittance inflows as a share of GDP.

22. Immigration – Share of population 2005 (%).

23. Muslim population share per total population.

24. Net international migration rate, 2005–2010.

25. Years of membership in the EU, 2010.

26. Years of membership in EMU, 2010.

The main dependent variables for this analysis also correspond to standard knowledge in comparative political science and sociology. An ever-growing number of more recently published investigations not only look into the effects of MNC penetration on economic growth, but into the more social and ecological conditions in general.4

The list of the twelve dependent variables which were entered into the final multiple stepwise regressions, consists of the following key variables:

1. Closing economic gender gap.

2. Closing of global gender gap overall score 2009.

3. Closing political gender gap.

4. Female survival probability of surviving to age 65 female.

5. Gender empowerment index value.

6. Happy life years.

7. Human development index (HDI) value 2004.

8. Infant mortality 2005.

9. Life Expectancy (years).

10. Life Satisfaction (0–10).

11. Quintile share income difference between richest and poorest 20 %.

12. Unemployment rate.

Our investigation thus not only brings together the most important possible determinants of global development, it also presents a fairly good collection of dependent variables in the literature to date on the dimensions of life quality.

3. Results

In interpreting the results, we concentrate on the indicators of the economic freedoms of trade, capital, services and labour.

1) The global contradictions of world economic openness, and hence, the freedom of trade, are on the social level. In accordance with the mainstream of dependency theory and globalization critical writing, a negative social trade-off of world economic openness has to be expected. World economic openness does have a significant negative effect on the Human Development Index, regarded by many as the master variable for the social situation in a country.

2) The significant influence of comparative price levels on our chosen development indicators is equally clear. High comparative price levels, and hence, implicitly, a high level of services of general interest,are a good and sound precondition of the leveling of the income differences between rich and poor (Yotopoulos 1996; Yotopoulos and Sawada 2005). Opening the borders of the European Union countries to unfettered competition in the services sector, and thus lowering the prices of non-tradables, that is services, would doubtlessly undermine the ‘European social model’. At the same time, it has to be remarked critically that the Lisbon strategy, 2000–2010 precisely aimed at this low comparative price levels, and the stark competition in the services sector.5 Euphemistically, Eurostat calls a low comparative price level an indicator of economic reform. But pushing down comparative price levels to dimensions of Moldova, let alone Bangladesh, will not be a constructive approach to economic reform in the EU-27.

3) The freedom of capital has the following results. The apprehensions of globalization critical research are fully vindicated by the significant effects of the foreign savings rate. High foreign savings are indeed a driver of unemployment, and income inequality. The New International Division of Labour (NIDL)-model, featured the critical theories of globalization since the 1970s, most prominently in the works by Froebel, Heinrichs, and Kreye (Froebel 1980), which best can be measured by the indicator free production zones employment as a share of total population, and thus the existence of run-away processes of capital, has no significant effects on our variables. For Volker Bornschier's globalization critical paradigm (Bornschier and Chase-Dunn 1985), an important control variable was MNC headquarter status. MNC penetration's negative impacts on the social development of the host countries of foreign direct investments are – the theory says – mitigated by the positive effects of MNC headquarter status. But this predictor also has no significant effect on our indicators.But MNC penetration increases income polarization and infant mortality. Increases in MNC penetration over time had no significant effects on our variables.

4) The freedom of labour considerably affects social outcomes. Worker remittances have a significant positive effect on Life expectancy (years), closing the political gender gap, Life Satisfaction (0–10), closing the overall gender gap, and Happy Life Years. The consensus of a large and ever-growing tradition of research would tend to see the effects of international migration on the recipient countries in very positive terms (United Nations 2009; Williamson 2002). However, not all of the optimistic forecasts of this liberal school of thought can be maintained empirically or at least on a 1:1 basis. We can assume from the effects of worker remittances that the import of labour to the world economy has – ceteris paribus – detrimental effects on life quality (Happy Planet Index, life expectancy, life satisfaction, Happy Life Years), and gender relations (closing the political gender gap; closing the overall gender gap). Also, the percentage of the population with what today is called an ‘immigration background’ has – ceteris paribus – a negative effect on some other key indicators of gender justice. Ceteris paribus, there hold nevertheless some other important effects as well, which tend to confirm the migration policy liberal Consensus, inherent in the UNDP Human Development Report 2009 (United Nations 2009). A large share of people with migration background per total population seems to coincide with a weakening of the role of traditional, local, native elites, and income inequality tends to be lower, when the share of population with a migration background is higher per total population in a given country. In the comparative social sciences, stock data – like the already given share of population with an immigration background – need not necessarily and always coincide with the patterns of associations of flow data in the empirical analysis. Net international migration rates, 2005–2010, which is a typical migration flow measure, relating to current and contemporary migration flows, are in turn significantly and positively influencing the ratio of the closing the political gender gap. There are clear beneficial effects of Muslim traditions on reducing the quintile share income difference between richest and poorest 20 per cent, and also the Human Development Index cannot be negated. Muslim societies, by contrast, are poor performers along the following indicators: closing overall gender gap; closing the economic gender gap; closing the political gender gap; gender empowerment index; unemployment rate. OIC membership is detrimental to closing the political gender gap, and Muslim population shares negatively affect the closing the economic gender gap, the gender empowerment index, the closing overall gender gap, and they increase the unemployment rate.6 However, there is a positive effect on longevity and the Human Development Index.

4. Conclusions – Disconnecting from the ‘Bad Master’ (Mario Monti)?

The EU-27-European debate hardly ever evaluated, from a rational, quantitative and comparative perspective, the current global balance sheet of advantages and disadvantages of the four freedoms of goods, capital, labor and services for ‘social cohesion’. As we had shown in the quotation at the beginning of this analysis, Mario Monti, a former EU-Commissioner, now recognizes that when the market is regarded as a superior entity, as if it were always able to deliver efficiently and did not need appropriate regulation and rigorous supervision. And dangers are likely to lie ahead, as shown by the current financial crisis. It was – Monti says correctly – forgotten by many that the market ‘is a good servant but a bad master’. As correctly predicted by the dependency literature in the tradition of Osvaldo Sunkel, social polarization dramatically increases by a development model, based on a very high foreign capital penetration (Sunkel 1973). In our opinion, EU-27-European policy-making finally should dare to take the globalization-critical organizations of contemporary global and EU-27-European ‘civil society’ seriously (Brand et al. 2008). We also came to the conclusion that the understanding of globalization critical research of migration up to now has been rather deficient. For the public health profession, the implication is clear: variables, measuring the ‘four freedoms’, have to be controlled in future, if we want to draw meaningful conclusions about the drivers of inequality. In addition, we have shown empirically the detrimental effects of the ‘four freedoms’ not on income inequality alone, but on other measures of inequality, such as the gender gap.

Notes

1 http://www.ft.com/indepth/euro-in-crisis (this and all other internet downloads: August 30, 2011).

2 http://europa.eu/legislation_summaries/institutional_affairs/treaties/treaties_eec_en.htm.

3 Albania; Algeria; Angola; Antigua and Barbuda; Argentina; Armenia; Australia; Austria; Azerbaijan; Bahamas; Bahrain; Bangladesh; Barbados; Belarus; Belgium; Belize; Benin; Bhutan; Bolivia; Bosnia and Herzegovina; Botswana; Brazil; Brunei Darussalam; Bulgaria; Burkina Faso; Burundi; Cambodia; Cameroon; Canada; Cape Verde; Central African Republic; Chad; Chile; China; Colombia; Comoros; Congo; Congo (Democratic Republic of the); Costa Rica; Côte d'Ivoire; Croatia; Cuba; Cyprus; Czech Republic; Denmark; Djibouti; Dominica; Dominican Republic; Ecuador; Egypt; El Salvador; Equatorial Guinea; Eritrea; Estonia; Ethiopia; Fiji; Finland; France; Gabon; Gambia; Georgia; Germany; Ghana; Greece; Grenada; Guatemala; Guinea; Guinea-Bissau; Guyana; Haiti; Honduras; Hong Kong, China (SAR); Hungary; Iceland; India; Indonesia; Iran (Islamic Republic of); Ireland; Israel; Italy; Jamaica; Japan; Jordan; Kazakhstan; Kenya; Korea (Republic of); Kuwait; Kyrgyzstan; Lao People's Democratic Republic; Latvia; Lebanon; Lesotho; Libyan Arab Jamahiriya; Lithuania; Luxembourg; Macedonia (TFYR); Madagascar; Malawi; Malaysia; Maldives; Mali; Malta; Mauritania; Mauritius; Mexico; Moldova; Mongolia; Morocco; Mozambique; Myanmar; Namibia; Nepal; Netherlands; New Zealand; Nicaragua; Niger; Nigeria; Norway; Oman; Pakistan; Panama; Papua New Guinea; Paraguay; Peru; Philippines; Poland; Portugal; Qatar; Romania; Russian Federation; Rwanda; Saint Kitts and Nevis; Saint Lucia; Saint Vincent and the Grenadines; Samoa; Sao Tome and Principe; Saudi Arabia; Senegal; Seychelles; Sierra Leone; Singapore; Slovakia; Slovenia; Solomon Islands; South Africa; Spain; Sri Lanka; Sudan; Suriname; Swaziland; Sweden; Switzerland; Syrian Arab Republic; Tajikistan; Tanzania (United Republic of); Thailand; Timor-Leste; Togo; Trinidad and Tobago; Tunisia; Turkey; Turkmenistan; Uganda; Ukraine; United Arab Emirates; United Kingdom; United States; Uruguay; Uzbekistan; Vanuatu; Venezuela (Bolivarian Republic of); Vietnam; Yemen; Zambia; and Zimbabwe.

4 Although we presume many of the indicators to be known, we refer our readers especially to the brief summary of the ever more important Happy Planet Index Indicator, relating happy life years to ecological footprint, available at http://www.happyplanetindex.org/list.htm, and the very comprehensive Yale/Columbia environmental data series, available at http://sedac.ciesin.columbia.edu/es/esi/ and http://epi.yale.edu/Home, which up to now have been practically neglected by cross-national comparative dependency and world systems research. The important new grammar of the footprint discourse can be found at http://www.footprintnetwork.org/en/index.php/GFN/page/glossary/.

5 URL: http://epp.eurostat.ec.europa.eu/portal/page/portal/structural_indicators/indicators/economic reform. Comparative price levels are measured as comparative price levels of final consumption by private households including indirect taxes (EU-27=100). Comparative price levels are the ratio between Purchasing Power Parities (PPPs) and market exchange rate for each country. PPPs are currency conversion rates that convert economic indicators expressed in national currencies to a common currency, called Purchasing Power Standard (PPS), which equalizes the purchasing power of different national currencies and thus allows meaningful comparison. The ratio is shown in relation to the EU average (EU27 = 100). If the index of the comparative price levels shown for a country is higher/lower than 100, the country concerned is relatively expensive/cheap as compared with the EU average. Comparative price levels are often brought into connection with the work of economists Balassa (1964) and Samuelson (1964), who rationalized a systematic deviation of current exchange rates from purchasing power parity (PPP) levels: At the going exchange rate aggregate price levels are higher in richer than in poorer economies. The simplest version of the Balassa-Samuelson hypothesis is stated within a framework of two countries, two homogenous goods (one tradable, one non-tradable), and one factor of production (labor). If – as is commonly assumed – the biggest differences in (labor) productivity across countries are in the tradable rather than non-tradable production, it follows that aggregate price levels are highest in the country with the highest labor productivity in the tradable sector or rather with the highest per worker income (Balassa 1964; Samuelson 1964).

6 Thus the current results are replicating Norris and Inglehart 2004.

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Appendix 1

Multiple regressions – the globalization critical paradigm Predictors:

% women in government, all levels

% world population

2000 Economic Freedom Score

Absolute latitude

Annual population growth rate, 1975–2005 (per cent)

comparative price levels (US=1.00)

foreign savings rate

FPZ (free production zones) employment as per cent of total population

ln GDP per capita

ln GDP per capita ^2

Membership in the Islamic Conference

military expenditures per GDP

military personnel rate ln (MPR+1)

MNC outward investments (stock) per GDP

MNC PEN – stock of Inward FDI per GDP

MNC PEN: DYN MNC PEN 1995–2005

Openness-Index, 1990 (export-share per GDP + import-share per GDP)

population density

public education expenditure per GNP

UNDP education index

worker remittance inflows as % of GDP

Immigration – Share of population 2005 (%)

Muslim population share per total population

net international migration rate, 2005–2010

Years of membership in the EU, 2010

years of membership in EMU, 2010

The reported equations were chosen from the following dependent variables:

closing economic gender gap

closing of global gender gap overall score 2009

closing political gender gap

female survival probability of surviving to age 65 female

gender empowerment index value

Happy life years

Human development index (HDI) value 2004

Infant mortality 2005

Life Expectancy (years)

Life Satisfaction (0–10)

quintile share income difference between richest and poorest 20 %

unemployment rate


Dependent variable

results from multiple regression

statistical properties

statistical properties

statistical properties

statistical properties

statistical properties

1. Closing overall gender gap

Independent Variable

B

standard error



error probability


Constant

0.516

0.216



0.019


% women in government, all levels

0.002

0.001



0.009


ln GDP per capita

0.015

0.050



0.767


ln GDP per capita ^2

0.001

0.003



0.841


military personnel rate ln (MPR+1)

0.014

0.007



0.037


worker remittance inflows as % of GDP

0.001

0.001



0.012


Muslim population share per total population

–0.001

0.000



0.000


memorandum item: statistical properties of the equation

adj R^2

df

F

error prob.




58.700

109.000

26.796

.000


2. Female survival probability of surviving to age 65 female

Independent Variable

B

standard error



error probability


Constant

–178.454

61.114



0.004


quintile share income difference between richest and poorest 20 %

0.618

0.122



0.000


ln GDP per capita

49.109

14.514



0.001


ln GDP per capita ^2

–2.304

0.820



0.006


public education expenditure per GNP

–1.406

0.546



0.011


UNDP education index

19.464

8.692



0.027


memorandum item: statistical properties of the equation

adj R^2

df

F

error prob.




66.800

114.000

46.815

.000


3. Gender empowerment index

Independent Variable

B

standard error



error probability


Constant

3.834

0.636



0.000


% women in government, all levels

0.005

0.001



0.001


ln GDP per capita

–0.845

0.143



0.000


ln GDP per capita ^2

0.053

0.008



0.000


Muslim population share per total population

–0.002

0.000



0.000


memorandum item: statistical properties of the equation

adj R^2

df

F

error prob.




81.500

70.000

77.895

.000


4. Closing the economic gender gap

Independent Variable

B

standard error



error probability


Constant

1.246

0.469



0.009


% women in government, all levels

0.005

0.001



0.001


ln GDP per capita

–0.136

0.108



0.214


ln GDP per capita ^2

0.007

0.006



0.245


Muslim population share per total population

–0.002

0.000



0.000


memorandum item: statistical properties of the equation

adj R^2

df

F

error prob.




40.100

122.000

21.380

.000


5. Closing the political gender gap

Independent Variable

B

standard error



error probability


Constant

1.473

0.599



0.016


ln GDP per capita

–0.343

0.142



0.018


ln GDP per capita ^2

0.022

0.008



0.009


Membership in the Islamic Conference

–0.044

0.026



0.087


worker remittance inflows as % of GDP

0.003

0.002



0.054


Immigration – Share of population 2005 (%)

–0.003

0.002



0.026


net international migration rate, 2005–2010

0.066

0.038



0.083


memorandum item: statistical properties of the equation

adj R^2

df

F

error prob.




24.900

113.000

7.243

.000


6. Happy Life Years

Independent Variable

B

standard error



error probability


Constant

–87.614

35.855



0.016


ln GDP per capita

19.100

8.451



0.026


ln GDP per capita ^2

–0.460

0.490



0.350


military expenditures per GDP

0.754

0.318



0.020


worker remittance inflows as % of GDP

0.257

0.112

0.118

2.295

0.024


memorandum item: statistical properties of the equation

adj R^2

df

F

error prob.




77.100

102.000

86.653

.000


7. Life Satisfaction
(0
10)

Independent Variable

B

standard error



error probability


Constant

–6.732

3.790



0.079


Absolute latitude

–0.017

0.006



0.005


ln GDP per capita

1.816

0.899



0.046


ln GDP per capita ^2

–0.036

0.053



0.498


worker remittance inflows as % of GDP

0.035

0.011



0.001


memorandum item: statistical properties of the equation

adj R^2

df

F

error prob.




69.400

113.000

64.990

.000


8. Infant mortality 2005

Independent Variable

B

standard error



error probability


Constant

576.606

83.129



0.000


2000 Economic Freedom Score

–0.777

0.179



0.000


ln GDP per capita

–87.974

19.619



0.000


ln GDP per capita ^2

4.453

1.120



0.000


MNC PEN – stock of Inward FDI per GDP

0.364

0.085



0.000


UNDP education index

–94.037

12.705



0.000


memorandum item: statistical properties of the equation

adj R^2

df

F

error prob.




79.400

152.000

117.921

.000


9. Life expectancy (years)

Independent Variable

B

standard error



error probability


Constant

–63.159

27.661



0.025


ln GDP per capita

23.657

6.558



0.000


ln GDP per capita ^2

–0.914

0.383



0.019


worker remittance inflows as % of GDP

0.324

0.073



0.000


quintile share income difference between richest and poorest 20 %

0.187

0.063



0.004


public education expenditure per GNP

–0.654

0.274



0.019


memorandum item: statistical properties of the equation

adj R^2

df

F

error prob.




74.800

105.000

63.293

.000


10. Quintile share income difference between richest and poorest
20 per cent

Independent Variable

B

standard error



error probability


Constant

9.775

55.216



0.860


Annual population growth rate, 1975–2005 (%)

4.236

1.146



0.000


comparative price levels (US=1.00)

8.866

5.337



0.099


foreign savings rate

0.203

0.074



0.007


ln GDP per capita

–3.599

13.441



0.789


ln GDP per capita ^2

0.394

0.836



0.638


MNC PEN – stock of Inward FDI per GDP

0.139

0.055



0.013


Immigration – Share of population 2005 (%)

0.250

0.122



0.044


Muslim population share per total population

–0.082

0.025



0.001


memorandum item: statistical properties of the equation

adj R^2

df

F

error prob.




25.500

119.000

6.098

.000


11. Unemployment rate

Independent Variable

B

standard error



error probability


Constant

–90.856

27.685



0.001


quintile share income difference between richest and poorest 20 %

0.161

0.061



0.009


foreign savings rate

0.154

0.044



0.001


ln GDP per capita

20.749

6.541



0.002


ln GDP per capita ^2

–1.141

0.381



0.004


public education expenditure per GNP

0.548

0.275



0.049


Muslim population share per total population

0.053

0.016



0.001


memorandum item: statistical properties of the equation

adj R^2

df

F

error prob.




35.400

103.000

10.416

.000


12. Human Development Index

Independent Variable

B

standard error



error probability


Constant

–0.575

0.202



0.005


ln GDP per capita

0.127

0.048



0.009


ln GDP per capita ^2

–0.002

0.003



0.396


Openness-Index, 1990 (export-share per GDP + import-share per GDP)

0.000

0.000



0.005


public education expenditure per GNP

–0.005

0.002



0.016


UNDP education index

0.503

0.031



0.000


Muslim population share per total population

0.000

0.000



0.001


memorandum item: statistical properties of the equation

adj R^2

df

F

error prob.




95.000

133.000

425.085

.000


Appendix 2

Abbreviations and terms, used in this article

Absolute latitude is the absolute geographical position of a country, away from the earth’s equator.

Comparative price levels (US=1.00) were calculated from the UNDP Human Development Report 2000. The GDP at current international exchange rate is simply divided by the GDP at real purchasing power parity for each country of the world. The U.S.A. are the international standard, with the US achieving the value of 1.0. For Eurostat, comparative price levels are comparative levels of final consumption by private households including indirect taxes (EU-27=100). If the index of the comparative price levels shown for a country is higher/ lower than 100, the country concerned is relatively expensive/cheap as compared with the EU average. Dependency theories and world systems theories assume that low comparative price levels are an indicator of ‘unequal exchange’ between the countries of the center and the periphery and that comparative price levels should be high, while neoliberal theories and the EU Comission aim at low comparative price levels (low comparative price levels as an indicator of economic reform).

DYN is a measure of dynamic change over time.

Economic Freedom (Score for 2000) is the key international indicator for economic liberalism and was published, among others, by the Heritage Foundation, the CATO Institute and other leading global liberal think-tanks. The basic assumption of the indicator is that economic freedom is the fundamental right of every human to control his or her own labor and property. In an economically free society, the assumption is that individuals are free to work, produce, consume, and invest in any way they please, with that freedom both protected by the state and unconstrained by the state. In economically free societies, the indicator assumption is that governments allow labor, capital and goods to move freely, and refrain from coercion or constraint of liberty beyond the extent necessary to protect and maintain liberty itself. The index measures ten components of economic freedom, assigning a grade in each using a scale from 0 to 100, where 100 represents the maximum freedom. The ten component scores are then averaged to give an overall economic freedom score for each country. The ten components of economic freedom are: Business Freedom, Trade Freedom, Fiscal Freedom, Government Spending, Monetary Freedom, Investment Freedom, Financial Freedom, Property rights, Freedom from Corruption, and Labor Freedom. We time-lagged the index somewhat to allow the study of the more long-term effects.

EU-2020 strategy: The European Commission sets out a vision of ‘Europe's social market economy’ for the 21st century. The strategy pretends to show how the EU can ‘come out stronger from the [current world economic] crisis and how it can be turned into a smart, sustainable and inclusive economy delivering high levels of employment, productivity and social cohesion’. (http://ec.europa.eu/eu2020/index_en.htm). The strategy rests on five pillars – social security, labour, education, research and development, and environmental protection.

Foreign savings rate, calculated from the UNDP Human Development Report 2000 for the year 1998. We time-lagged the indicator to evaluate the more long-term effects of the variable. For dependency authors, especially Paul Israel Singer, foreign savings show the weight that foreign savings, mostly from the centers and richer semi-peripheries, have in the accumulation process of the host countries in the periphery and semi-periphery. It is calculated by the difference between the share of investments per GDP and the share of savings per GDP.

FPZ (free production zones) – FPZ employment as % of total population is the indicator, best suited to measure the effects of the ‘NIDL’ (new international division of labour). An important sub-school of dependency and world systems research, most prominently represented by Froebel/Heinrichs and Kreye (1980), predicted the unfettered rise of the model of ‘export processing zones’, especially in China and Southeast Asia. More recent studies highlighted the fact that these Export Processing Zones (EPZ) – or ‘Free Production Zones’ already account for some 80 per cent of the merchandise exports of countries like China, Kenya, the Philippines, Malaysia, Mauritius, Mexico, Senegal, Tunisia, Vietnam. 3500 EPZs in 130 countries of the world now employ 66 Million people, among these 40 million employees in China.

Gender Empowerment Measure (GEM) is a composite UNDP indicator that captures gender inequality in three key areas: the extent of women's political participation and decision-making, economic participation and decision making-power and the power exerted by women over economic resources. In the calculation of the index, female and male shares of parliamentary seats (1st component), female and male shares of positions as legislators, senior officials and managers (the first half of the 2nd component) and Female and male shares of professional and technical positions
(the second half of the 2nd component) and female estimated earned real income expressed in international purchasing power parities (the 3rd component) enter into the calculation.

Growth of MNC penetration over time (Dynamic Effects from Multinational Corporations Penetration, DYN MNC PEN), from 1995 to 2005, based on UNCTAD sources. Several global sociologists, like Volker Bornschier and associate authors, expected short-term dynamic effects from such MNC penetration increases.

Happy life years (from Happy Planet Index). Happy Planet Index Organization used data from the life satisfaction question: All things considered, how satisfied are you with your life as a whole these days? Responses were made on a numeric scales from 0 to 10, where 0 is dissatisfied and
10 is satisfied. In HPI 2.0, the HPI Organization takes advantage of new data collected by Gallup. Its World Poll has, in the last two years, included the question on life satisfaction and asked it in 112 countries included in this report. Gallup’s intention is to continue polling the countries of the world on a regular basis so as to monitor how life develops. To augment these 112 countries, authors also included data from the two most recent waves of the World Values Survey (WVS, from 2000 and 2005). This survey asks the exact same question regarding life satisfaction, albeit with a slightly different response scale (1–10 as opposed to 0–10). The two waves cover 84 countries. HPI used well-documented econometric and politometric methods to augment the data base, where Gallup public opinion survey data were missing and were World Values Survey data were available.

Happy Planet Index, HPI. The HPI is a measure that shows the ecological efficiency with which human well-being is delivered around the world. It is the first ever index to combine environmental impact with well-being to measure the environmental efficiency with which country by country, people live long and happy lives. It shows the relative efficiency with which nations convert the planet's natural resources (footprint per capita) into long and happy lives for their citizens. The nations that top the Index are nоt the happiest places in the world, but the nations that score well show that achieving, long, happy lives without over-stretching the planet's resources is possible.

Immigration. Share of population 2005 (%) was also directly taken from the UNDP HDR 2009 statistics facility.

Infant mortality 2005 (UNDP). Infant mortality rates are calculated per 1,000 live births. Data were taken from the UNDP's statistics facility on the internet, and are from the UNDP HDR 2007/2008 edition.

ISI Web of Knowledge. Bibliographical data base, run by the Institute for the Study of Scientific Information (nowadays also increasingly referred to by the term ‘Web of Science’) is an online academic citation index provided by the company Thomson Reuters.

KOF business cycle research institute at the ETH Zurich.

Kuznets curve. Economics Nobel laureate Simon Smith Kuznets (April 30, 1901 – July 8, 1985) ventured the hypothesis that along the path of development, a nation will experience first a rising, and later on, falling rate of economic inequalities.

Life Expectancy (years) (Happy Planet Index). We used the HPI data series for life expectancy. Average life expectancy at birth was taken by the HPI Organization from 2007/08 UNDP Human Development report, which provides figures for the year 2005.

Life Satisfaction (0–10) (Happy Planet Index). See Happy Life Years.

ln natural logarithm.

Matthews effect. Most economists assume that economic growth at middle income levels is most rapid. There is a well-established tradition to control for these effects by introducing a double-logarithmic function (ln GDP per capita; (ln GDP per capita)^2) in all cross-national multiple regression equations, explaining economic growth rates. This curve-linear function of growth, being regressed on the natural logarithm of development level and its square, is sometimes called the ‘Matthew effect’ following Matthew (chaper 13, verse 12) in the Christian Bible ('For whosoever hath, to him shall be given, and he shall have more abundance: but whosoever hath not, from him shall be taken away even that he hath').

Membership in the Islamic Conference is a very clear and simple measurement concept for the hypothesis by the U.S. political scientist Samuel Phillips Huntington (April 18, 1927 – December 24, 2008) that – inter alia – Islam will be a development blockade in the 21st Century. Our indicator is simply a dummy-variable (1 for membership, 0 for non-membership), based on the Website of the Organization of Islamic Conference (download 2009).

MILEX military expenditures per GDP.

Military expenditures per GDP were taken from the UNDP Human Development Report Office Statistics facility, HDR 2007–2008, and were time-lagged to take into account the very-long-term effects of military spending rates. The time point chosen was the beginning of the 1990s.

Military personnel rate measures a country's army personnel per 1000 population, and due to the skewness of the indicator, there is a strong and well-founded research tradition, founded by the eminent German sociologist Erich Weede, to calculate the natural logarithm of the original number plus the number 1 (ln (MPR+1)). The statistical source of our data was the official website of the United States Central Intelligence Agency. The data refer to the first decade of the new Millenium.

MNC headquarter status (MNC HEADQU), measured in our analysis by the time-lagged indicator MNC outward investments (stock) per GDP by around 1995. It is thus an indicator of the power or weakness of the ‘national’ capital in question on the world markets. Bornschier and his school expected that a high headquarter status mitigates against the long-term negative effects of MNC penetration (the value of the stock of cumulated foreign direct investment per GDP of the host country).

MNC Multinational Corporations, mostly used in connection with MNC outward investments (stock) per GDP.

MNC PEN – stock of Inward FDI per GDP, a measure of the penetration of a country by multinational corporations.

MNC penetration (MNC PEN) is the key variable of most quantitative dependency and world systems theories, and it measures the weight that cumulated foreign capital investments had in the host countries, i.e. the percentages of the cumulated stocks of multinational corporation investments per total host country GDP. We time-lagged our indicator and used the values for the year 1995, to take the long-term societal consequences of foreign direct investment penetration into account. The Swiss sociologist Volker Bornschier and his school predicted a strong long-term negative determination of development by a high MNC penetration, due to the negative consequences that monopolies have on the long term development trajectory of countries.

Multiple Regression see regression equation.

Muslim population share per total population was taken from the University of Sydney's Nationmaster statistics facility.

NIDL New International Division of Labour.

OIC The Organisation of Islamic Cooperation (OIC), which currently has 57 member states. It changed its name from the Organisation of the Islamic Conference on 28 June 2011.

Openness-Index is time-lagged for the year 1990, and measures the very long-term two-decade effects of export-shares per GDP + import-shares per GDP. It was taken from the UNDP Human Development Report Office statistics facility, HDR 2007/2008. The countries with the greatest openness in 1990 were the small states and territories including Hong Kong, China (SAR), Bahrain, Luxembourg, Malta, Antigua and Barbuda, Slovenia, Panama, Croatia, Swaziland, and Saint Lucia.

Population density was taken from US CIA World Factbook. It measures population density per square kilometre by around the first decade in the new Millennium.

Public education expenditure per GDP for the middle of the first decade of the new millenium was taken from the UNDP's Human Development Report Office statistics facility on the internet (UNDP HDR 2000), and refers to the time-lagged data for 1995–1997 to measure the long-term effects of public education expenditures.

Quintile share income difference between richest and poorest 20 per cent (Eurostat indicator). The income quintile share ratio or the S80/S20 ratio is a measure of the inequality of income distribution. It is calculated as the ratio of total income received by the 20 % of the population with the highest income (the top quintile) to that received by the 20 % of the population with the lowest income (the bottom quintile). Our global data were taken from the UNDP's statistics facility on the internet, and refer to the UNDP HDR 2006. Data show the ratio of the income or expenditure share of the richest 20 % to that of the poorest 20 %, and are based on relevant World Bank data.

Share of women in government, all levels is one of the UNDP's long-term lead indicators of the institutionalization of political feminism. We time-lagged the variable and measured it by around 1998. It was documented in the NDP HDR 2000. The idea of the indicator is to capture the real advance of women not only at the level of the top political administration of a given country, but at the general level of the central government, i.e. taking the important decision-making ministerial bureaucracies into account as well.

Share of world population was calculated from UNDP HDR 2007/2008 statistics, and reflects the enormous differences in size of the nations of the earth, and the demographic weight of a nation in world society.

Social security expenditure per GDP is average 1990s (International Labor Organization, ILO). The social security expenditure ratio is generally to be considered as the best single indicator of the existence of a tight social net.

UNDP's education index is a compound measure of performance of the education system on the primary, secondary and tertiary level, measured for the middle of the new decade of the millenium, and taken from the UNDP HDR 2007/2008. It is comprised of adult literacy rates and the combined gross enrolment ratio for primary, secondary and tertiary schooling, weighted to give adult literacy more significance in the statistic.

Unemployment rate (United Nations Statistics, data refer to around 2003/2004). The adult unemployment rate refers to the proportion of the adult (aged 15 years and older) labour force that is unemployed, unless otherwise specified. The unemployed are persons who are currently without work, who are currently available for work, and who are seeking or have sought work recently. The base for these statistics is the labour force (that is, the economically active portion of the population), not the total population.

UTIP University of Texas Inequality Project.

Worker remittance inflows as % of GDP was directly taken from the UNDP HDR 2009 statistics facility.

World Economic Forum Gender Gap Indices 2007 were designed to measure gender-based gaps in access to resources and opportunities in individual countries rather than the actual levels of the available resources and opportunities in those countries. The Global Gender Gap Report 2007 by the World Economic Forum measures the size of the gender gap in four critical areas of inequality between men and women: 1) Economic participation and opportunity – outcomes on salaries, participation levels and access to high-skilled employment; 2) Educational attainment – outcomes on access to basic and higher level education; 3) Political empowerment – outcomes on representation in decision-making structures; 4) Health and survival – outcomes on life expectancy and sex ratio.