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**Abstract**

Public health research debates for two decades the effects of inequality on pub-
lic health. More recent research also considered the additional effects of inter-
national trade and world economic openness. These investigations analyse
public health outcomes in such terms as infant mortality rates, life expectan-
cies, etc. But with the growing environmental crisis, ideas to weigh economic
or social or public health progress by the 'environmental input' necessary to
achieve it are increasingly gaining acceptance. We might call such a weighting
of infant mortality rates, or life expectancies by the 'environmental input' nec-
essary to achieve them 'smart public health'. Which factors of social organiza-
tion now contribute then to a responsible use of the resources of our planet
Earth to achieve 'smart public health'?

We use standard OLS non-linear regressions of ecological footprints per capita and their square on combined public health performances. The residuals from this regression are our new measure of 'smart public health'.

Our research results suggest that not inequality, but migration is a very
important determinant of 'smart public health'. Migration sending countries
find it relatively easy to enjoy combined good public health performances at
a relatively small environmental price. Other drivers of 'smart public health'
are the share of a country's population in world population, and the UNDP
education index. The main bottleneck of 'smart public health' is constituted by
the crowding-out effect of public education expenditures on smart health per-
formance.

In contrast to earlier research, we come to the conclusion that migration
sending countries reap substantial benefits from receiving worker remittances,
while inequality and globalization indicators hardly affect the smart public
health performance of the sample countries (all countries with available data).

**Keywords:** Index Numbers and Aggregation, public health, infant mortality, female survival probability of surviving to age 65, UNDP human development index (HDI), average life expectancy (years), life satisfaction, international migration, remittances.
## Objectives

This article is motivated by the fact that public health research debates for two decades now the effects of inequality on public health.<fnref n="1" /> More recent research also considered the additional effects of international trade and world economic openness. But these investigations analyse public health outcomes in such terms as simple, unweighted infant mortality rates, life expectancies, etc. With the growing environmental crisis, ideas to weigh economic or social or public health progress by the ‘environmental input’ necessary to achieve it are increasingly gaining acceptance. Such a research question is typically motivated by economics: to achieve a maximum of results under the constraint of existing scarce resources. Under such given constraints, is the price mechanism, free flows of globalization, and the absence of government intervention much better suited to achieve good results for public health than government interventions to redress inequalities?

We might call such a weighting of infant mortality rates, or life expectancies by the ‘environmental input’ necessary to achieve them ‘smart public health’. Which factors of social organization do now contribute then to a responsible use of the resources of our planet Earth to achieve ‘smart public health’?

The essence of the by now dominant paradigm in public health about a strong correlation between high inequality and low life quality seems to suggest that inequality negatively determines a number of public health variables, like physical health, mental health, drug abuse, and teenage births (Pickett and Wilkinson 2007). Recent contributions, further elaborating the approach, initiated by R. G. Wilkinson, highlighted, for example, the role played by international trade and world economic openness in determining public health outcomes. But in large sections of the economics profession, such as paradigm, critical of inequality and globalization, will not go uncontested.<fnref n="2" />

<footnote n="1">¹ The Equality Trust (homepage on the Internet), 32–36 Loman Street, London SE1 0EH. London (UK) (cited 30 May 2011) Why More Equality? URL: http://www equalitytrust.org.uk/research/ why-more-equality.</footnote>

<footnote n="2">² The flagship article of the school of thought, featuring the trade-offs between inequality and public health outcomes is undoubtedly Wilkinson's 'For Debate – Income Distribution and Life Expectancy' (1992). This article was followed according to the Web of Science's Documentation system (accessed on May 20th, 2014 at Vienna University Library) by 463 studies. One of the central public health profession articles linking trade, world economic openness and globalization to public health outcomes is Blouin, Chopra, and van der Hoeven's, 'Trade and Social Determinants of Health' (2009). This study initiated 18 follow-up studies to this day. By contrast, let us just recall here that major sections of Economics hold a sceptical or even very sceptical view about efforts to change existing income distribution patterns and inequality structures by government intervention. Perhaps, the most uncompromising attack in this direction was published by Economics Nobel laureate von Hayek in 1960 in his *The Constitution of Liberty*. His attack on egalitarianism is a true classic of Economics (90 editions were published between 1959 and 2010 in 9 languages and held by</footnote>
This question is already intriguing enough by itself and is being dealt with
today by a growing number of studies, focusing on the environmental price of
human progress. Even more intriguing, however, is the question, which factors
of social organization contribute to a responsible use of the resources of our
planet Earth. In this essay, we will present the first systematic study on how
outward migration – or rather, more concretely, received worker remittances
per Gross Domestic Product (GDP) – helps the nations of our globe to enjoy
a good overall public health system at a relatively small environmental price
(henceforth called ‘smart public health’). According to our study, it is not ine-
quality or globalization, which primarily determines this ‘smart public health’,
but the existence of a system of the economic freedom to migrate, measured by
worker remittances. This is potentially an important new start in the entire de-
bate about the societal drivers and bottlenecks of global public health perform-
ance, dominated in recent years by the thought that inequality is mainly to
blame for the cross-nationally observed public health shortcomings.

The indicators of public health, which we use in this essay, are derived
from standard recent international data<fnref n="3" /> on infant mortality, female survival
probability of surviving to age 65, the United Nations Development Programme
(UNDP) Human Development Index (HDI), average life expectancy (years)
and life satisfaction (0–10).

2,201 libraries worldwide according to Worldcat Identites; see http://www.worldcat.org/identities/ lccn-n80-126331 [Date accessed: 20.05.2014]). Major sections of Economics also would believe that world economic openness is good for the poor, on all fronts and not just by promoting economic growth. Efforts to hinder the process of globalization will be to the detriment of economic well- being. One of the most important studies in this direction is Dollar and Kraay's 'Growth Is Good for the Poor' (2002), which led to 276 follow-up studies and which showed that average incomes of the poorest quintile rise proportionately with average incomes in a sample of 92 countries over the last four decades. Dollar and Kraay state that the share of income of the poorest quintile does not vary systematically with average income. It also does not vary with many of the policies and institutions that explain growth rates of average incomes, nor does it vary with measures of policies intended to benefit the poorest in society. This evidence emphasizes the importance of economic growth for poverty reduction. Another influential study in this direction was published by Dreher (2006). His work led to 109 follow-up studies, showing that an index of globalization covering its three main dimensions: economic integration, social integration, and political integration is well associated with good economic outcomes. Dreher used panel data for 123 countries in 1970–2000 and analysed empirically whether the overall index of globalization as well as sub-indexes constructed to measure single dimensions affect economic growth. As the results claim to show, globalization indeed promotes growth. The dimensions most robustly related with growth refer to actual economic flows and restrictions in developed countries. Although less robustly, information flows also promote growth whereas political integration has no effect. While our analysis does not necessarily side with these arguments, it is necessary to emphasize that there is an urgent need in public health for further solid empirical studies on these subjects and realizing that large sections of the science, claiming that it developed the greatest professional competence for issues such as inequality and globalization, start from a consensus, which is completely different from the one, emerging in public health.

<footnote n="3">³ All the original variables see at URL: http://www.hichemkaroui.com/?p=2017 (date accessed: 20.05.2014).</footnote>
The very idea of 'smart development' was first proposed by Dennis Meadows and has not been really followed up to now in social science ever since (Meadows 1992). 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'<fnref n="4" /> or 'Cambridge Scientific Abstracts/Proquest'.<fnref n="5" />

To present a theory or competing theories of ‘smart public health’ is virtually impossible, because there has been no measurement, let alone accounting of its cross-national successes and failures in the literature up to now. We really had to start research into this issue from ‘scratch’.

Of particular interest in the context of our research is the effect of migration. As it is well-known, migration is part and parcel of what social sciences but also international politics and international law nowadays call the ‘four freedoms’ of ‘capitalism’ (i.e. ‘market economies’), besides the freedom of goods, services, and capital. A particular earlier flagship survey of the hitherto existing migration theories came to the pessimistic conclusion that migration theories up to that time were either advanced to explain the initiation of international migration or put forth to account for the persistence of migration across space and time (Massey et al. 1993). Massey et al. suggested that, because they are specified at such different levels of analysis, the theories are not inherently logically inconsistent. As Taylor pointed out in his later, summarizing policy statement on the state of migration theory for the United Nations in 2006, indeed it would be foolish to exclude migration from any future discourse about global development, but that existing hard-core evidence on how migration really affects the development process is limited (Taylor 1999, 2006).

This is all the more surprising, since 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. 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 billion in 2004. Nearly 70 % of all remittances go to less-developed countries (LDC). Remittances were equivalent to 78 % of the total value of exports in El Salvador and 108 % in Nicaragua. Worker remittances are especially affecting the less developed sending countries by the multiplier effect, well-known in economics: $1 of remittances from international migrants may create $2–$3 or more of new income in migrant-sending areas. One person's spending is another person's income. Even if all income in remittance-receiving households is spent on consumption, remittances may stimulate investments by the other households whose incomes go up (Taylor 2006: 9). This optimistic view about worker re-

<footnote n="4">⁴ URL: http://wokinfo.com/ (Date accessed: 20.05.2014).</footnote>

<footnote n="5">⁵ URL: http://www.csa.com/ (Date accessed: 20.05.2014).</footnote>
mittances is also supported in the well-received comparative international study by Ziesemer (2009).

Migration is thus seen in many social scientific approaches as a win-win situation (United Nations 2009; Williamson 2002). For several observers, among them Hatton and Williamson (2009), the ‘current hysteria’ about inward migration in many industrialized countries has no real basis. For them, the Third World has been undergoing an emigration life cycle since the 1960s, and, except for Africa, emigration rates have remained about equal or were even declining since a peak in the late 1980s and the early 1990s. The current economic crisis will serve only to accelerate those trends. Sanderson (2010) was one of the first consistent research attempts to bring in migration as a determining variable of social well-being. 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. However, there is little systematic cross-national evidence of how international migration affects human development levels in migrant-receiving countries in the less-developed world. The Sanderson paper addressed this gap in the literature by assessing the impact of cumulative international migration flows on the human development index, the composite, well-known UNDP measure of aggregate well-being. A series of panel data models are estimated using a sample of less-developed countries for the period, 1970–2005. The results indicate that higher levels of international migration are associated with lower scores on the human development index, net of controls, but that the effect of international migration is relatively small.

## Methods

To estimate the effects of migration on ‘smart public health’, we used a freely-available new cross-national comparative data set, which is publicly available on the Internet without any restrictions.<fnref n="6" /> This electronic data set offers Microsoft EXCEL data and a list of the international standard sources, and a codebook in PDF format. It also offers an EXCEL file with the combined UNDP type development performance index, on which this study rests.

Each of these indicators (infant mortality, female survival probability of surviving to age 65, the United Nations Development Programme (UNDP) Human Development Index (HDI), average life expectancy (years) and life satisfaction (0–10) was standardized according to the well-known practice of the United Nations Human Development Programme on a scale, ranging from 0 (worst value) to 1 (best value) according to the formula:

$$Z_{ij} = (X_{ij} - X_{j}^{\min}) / (X_{j}^{\max} - X_{j}^{\min}), \quad (\text{Eq. 1})$$

<footnote n="6">⁶ URL: http://www.hichemkaroui.com/?p=2017 (Date accessed: 20.05.2014).</footnote>
where $X_{ij}$ is indicator j of country i and $Z_{ij}$ its normalized counterpart and $X^{min}$ and $X^{max}$ are sample minimum and maximum values of indicator j. Our final index of public health performance is based on the simple means of the standardized component indices:

$$Index_i = \sum_{j=1}^{J} w_j Z_{ij}, \qquad (\text{Eq. 2})$$

where $w_j$ are weights assigned to each of the J indicators, in this case equal weights, $w = 1$, is employed. Our performance scale of public health is then compared with the environmental destruction, which a society causes in maintaining its development level. We rely here on data about ecological footprint, which measures how much land and water area a human population requires to produce the resource it consumes and to absorb its carbon dioxide emissions, using prevailing technology.<fnref n="7" /> Ecological Footprint is usually measured in global hectares. Existing time series nowadays allow us to grasp the extent of the accelerating environmental constraints, facing our globe.<fnref n="8" />

The standardized residual (SR) values of Table 1 – our final performance scale of ‘smart public health’, measuring how much of infant mortality reduction, female survival to age 65, a good Human Development Index, a high average life expectancy and a good life satisfaction are achieved at a minimum ecological footprint and are computed as observed minus predicted development outcomes, Z, divided by the square root of the residual mean square, $\sigma$.

$$SR_i = (Z_i - \hat{Z}_i) / \hat{\sigma}. \qquad (\text{Eq. 3})$$

High positive outliers imply a very high smart public health performance, while countries below the fitted trend line are the countries with a low smart public health performance. Having established a residual-based smart public health indicator family, we now can look more realistically at the cross-national determinants of smart public health performance (see Table 1). We are aware about the limitations of our approach but we think that our estimates cover the wide range of existing international data in the field. Even with different components of our indicator, the results would not dramatically differ.

<table><caption>**Table 1.** Performance of countries in respect with smart public health</caption><thead><tr><th>Country</th><th>Smart public health</th><th>Rank</th><th>Country</th><th>Smart public health</th><th>Rank</th></tr></thead><tbody><tr><td>1</td><td>2</td><td>3</td><td>4</td><td>5</td><td>6</td></tr><tr><td>Jamaica</td><td>1.780</td><td>1</td><td>France</td><td>0.132</td><td>71</td></tr><tr><td>Philippines</td><td>1.745</td><td>2</td><td>Belgium</td><td>0.119</td><td>72</td></tr></tbody></table>

<footnote n="7">⁷ URL: http://www.footprintnetwork.org/en/index.php/GFN/page/glossary/ (Date accessed: 20.05.2014).</footnote>

<footnote n="8">⁸ URL: http://www.happyplanetindex.org/learn/download-report.html (Date accessed: 20.05.2014).</footnote>
<table><thead><tr><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th></tr></thead><tbody><tr><td>Cuba</td><td>1.707</td><td>3</td><td>Turkey</td><td>0.100</td><td>73</td></tr><tr><td>Sri Lanka</td><td>1.699</td><td>4</td><td>Poland</td><td>0.058</td><td>74</td></tr><tr><td>Costa Rica</td><td>1.670</td><td>5</td><td>Ukraine</td><td>0.043</td><td>75</td></tr><tr><td>Vietnam</td><td>1.650</td><td>6</td><td>Bolivia</td><td>0.037</td><td>76</td></tr><tr><td>Dominican Republic</td><td>1.488</td><td>7</td><td>Spain</td><td>0.029</td><td>77</td></tr><tr><td>Indonesia</td><td>1.480</td><td>8</td><td>Australia</td><td>0.025</td><td>78</td></tr><tr><td>Colombia</td><td>1.404</td><td>9</td><td>Iceland</td><td>0.024</td><td>79</td></tr><tr><td>Moldova</td><td>1.211</td><td>10</td><td>Hungary</td><td>0.015</td><td>80</td></tr><tr><td>Guatemala</td><td>1.204</td><td>11</td><td>Norway</td><td>0.001</td><td>81</td></tr><tr><td>El Salvador</td><td>1.180</td><td>12</td><td>United Arab Emirates</td><td>-0.003</td><td>82</td></tr><tr><td>Morocco</td><td>1.164</td><td>13</td><td>Iran</td><td>-0.012</td><td>83</td></tr><tr><td>Georgia</td><td>1.162</td><td>14</td><td>Paraguay</td><td>-0.014</td><td>84</td></tr><tr><td>Tunisia</td><td>1.143</td><td>15</td><td>United Kingdom</td><td>-0.017</td><td>85</td></tr><tr><td>Armenia</td><td>1.129</td><td>16</td><td>Ireland</td><td>-0.044</td><td>86</td></tr><tr><td>Tajikistan</td><td>1.110</td><td>17</td><td>Canada</td><td>-0.066</td><td>87</td></tr><tr><td>Peru</td><td>1.105</td><td>18</td><td>Denmark</td><td>-0.085</td><td>88</td></tr><tr><td>Argentina</td><td>1.084</td><td>19</td><td>Portugal</td><td>-0.088</td><td>89</td></tr><tr><td>Egypt</td><td>1.053</td><td>20</td><td>Latvia</td><td>-0.094</td><td>90</td></tr><tr><td>Jordan</td><td>1.033</td><td>21</td><td>Hong Kong, China</td><td>-0.106</td><td>91</td></tr><tr><td>China</td><td>0.893</td><td>22</td><td>New Zealand</td><td>-0.114</td><td>92</td></tr><tr><td>Ecuador</td><td>0.874</td><td>23</td><td>Cambodia</td><td>-0.117</td><td>93</td></tr><tr><td>Albania</td><td>0.870</td><td>24</td><td>Azerbaijan</td><td>-0.152</td><td>94</td></tr><tr><td>Honduras</td><td>0.866</td><td>25</td><td>Congo</td><td>-0.242</td><td>95</td></tr><tr><td>Malaysia</td><td>0.853</td><td>26</td><td>Bosnia &amp; Herzegovina</td><td>-0.270</td><td>96</td></tr><tr><td>Bangladesh</td><td>0.846</td><td>27</td><td>Greece</td><td>-0.273</td><td>97</td></tr><tr><td>Algeria</td><td>0.840</td><td>28</td><td>Guyana</td><td>-0.297</td><td>98</td></tr><tr><td>Syria</td><td>0.798</td><td>29</td><td>Kuwait</td><td>-0.328</td><td>99</td></tr><tr><td>Kyrgyzstan</td><td>0.789</td><td>30</td><td>Czech Republic</td><td>-0.337</td><td>100</td></tr><tr><td>Brazil</td><td>0.783</td><td>31</td><td>Lebanon</td><td>-0.339</td><td>101</td></tr><tr><td>Nicaragua</td><td>0.756</td><td>32</td><td>Senegal</td><td>-0.378</td><td>102</td></tr><tr><td>India</td><td>0.754</td><td>33</td><td>Togo</td><td>-0.423</td><td>103</td></tr><tr><td>Trinidad and Tobago</td><td>0.750</td><td>34</td><td>Madagascar</td><td>-0.445</td><td>104</td></tr></tbody></table>
<table><thead><tr><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th></tr></thead><tbody><tr><td>Belize</td><td>0.732</td><td>35</td><td>Belarus</td><td>-0.489</td><td>105</td></tr><tr><td>Saudi Arabia</td><td>0.718</td><td>36</td><td>Ghana</td><td>-0.560</td><td>106</td></tr><tr><td>Luxembourg</td><td>0.713</td><td>37</td><td>Uruguay</td><td>-0.574</td><td>107</td></tr><tr><td>Chile</td><td>0.697</td><td>38</td><td>Russia</td><td>-0.645</td><td>108</td></tr><tr><td>Thailand</td><td>0.670</td><td>39</td><td>Malawi</td><td>-0.646</td><td>109</td></tr><tr><td>Bhutan</td><td>0.619</td><td>40</td><td>Mauritania</td><td>-0.733</td><td>110</td></tr><tr><td>Nepal</td><td>0.583</td><td>41</td><td>Macedonia</td><td>-0.760</td><td>111</td></tr><tr><td>Pakistan</td><td>0.567</td><td>42</td><td>Kazakhstan</td><td>-0.797</td><td>112</td></tr><tr><td>Panama</td><td>0.555</td><td>43</td><td>Djibouti</td><td>-0.853</td><td>113</td></tr><tr><td>Laos</td><td>0.519</td><td>44</td><td>Benin</td><td>-0.921</td><td>114</td></tr><tr><td>Venezuela</td><td>0.484</td><td>45</td><td>Kenya</td><td>-0.966</td><td>115</td></tr><tr><td>Croatia</td><td>0.480</td><td>46</td><td>Mongolia</td><td>-1.043</td><td>116</td></tr><tr><td>Malta</td><td>0.470</td><td>47</td><td>Guinea</td><td>-1.047</td><td>117</td></tr><tr><td>Netherlands</td><td>0.439</td><td>48</td><td>Estonia</td><td>-1.091</td><td>118</td></tr><tr><td>Mexico</td><td>0.407</td><td>49</td><td>South Africa</td><td>-1.156</td><td>119</td></tr><tr><td>Bulgaria</td><td>0.398</td><td>50</td><td>Cameroon</td><td>-1.220</td><td>120</td></tr><tr><td>Singapore</td><td>0.386</td><td>51</td><td>Congo (Dem. Rep. of)</td><td>-1.249</td><td>121</td></tr><tr><td>Germany</td><td>0.385</td><td>52</td><td>Uganda</td><td>-1.262</td><td>122</td></tr><tr><td>Korea (Republic of)</td><td>0.376</td><td>53</td><td>Rwanda</td><td>-1.277</td><td>123</td></tr><tr><td>Haiti</td><td>0.349</td><td>54</td><td>Tanzania</td><td>-1.455</td><td>124</td></tr><tr><td>Uzbekistan</td><td>0.315</td><td>55</td><td>Nigeria</td><td>-1.463</td><td>125</td></tr><tr><td>Slovakia</td><td>0.312</td><td>56</td><td>Burundi</td><td>-1.480</td><td>126</td></tr><tr><td>Switzerland</td><td>0.288</td><td>57</td><td>Sudan</td><td>-1.516</td><td>127</td></tr><tr><td>United States</td><td>0.281</td><td>58</td><td>Zambia</td><td>-1.545</td><td>128</td></tr><tr><td>Myanmar</td><td>0.274</td><td>59</td><td>Mozambique</td><td>-1.545</td><td>129</td></tr><tr><td>Romania</td><td>0.274</td><td>60</td><td>Ethiopia</td><td>-1.593</td><td>130</td></tr><tr><td>Sweden</td><td>0.273</td><td>61</td><td>Chad</td><td>-1.749</td><td>131</td></tr><tr><td>Austria</td><td>0.242</td><td>62</td><td>Angola</td><td>-1.811</td><td>132</td></tr><tr><td>Lithuania</td><td>0.229</td><td>63</td><td>Mali</td><td>-1.889</td><td>133</td></tr><tr><td>Cyprus</td><td>0.214</td><td>64</td><td>Zimbabwe</td><td>-1.956</td><td>134</td></tr><tr><td>Finland</td><td>0.196</td><td>65</td><td>Sierra Leone</td><td>-2.032</td><td>135</td></tr><tr><td>Japan</td><td>0.193</td><td>66</td><td>Niger</td><td>-2.104</td><td>136</td></tr><tr><td>Italy</td><td>0.173</td><td>67</td><td>Burkina Faso</td><td>-2.120</td><td>137</td></tr><tr><td>Yemen</td><td>0.145</td><td>68</td><td>Central African Rep.</td><td>-2.382</td><td>138</td></tr></tbody></table>
<table><thead><tr><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th></tr></thead><tbody><tr><td>Slovenia</td><td>0.138</td><td>69</td><td>Namibia</td><td>-2.457</td><td>139</td></tr><tr><td>Israel</td><td>0.135</td><td>70</td><td>Botswana</td><td>-3.052</td><td>140</td></tr></tbody></table>

*Notes*: Public health performance is measured by a combined UNDP-type index of infant mortality, female survival probability of surviving to age 65, the United Nations Development Programme (UNDP) Human Development Index (HDI), average life expectancy (years) and life satisfaction (0–10). The data for the standardized performance indicators are given in the data sheet ‘Smart development Heshmati Tausch Final UNDP type indicators 2011’, the energy efficiency indicators are found in the file ‘Data for energy efficiency analysis May 2011’ (see Tausch 2010). The codebook of this data file lists the data definitions and sources.

Our standard comparative cross-national data operationalize standard economic, sociological and political science knowledge in international development accounting. We compare the predictive power of all these standard predictors, using standard ordinary least squares (OLS) stepwise regression procedures, based on IBM SPSS XVIII, weeding out the relevant from the irrelevant predictors of smart public health. The final model is based on standard forward OLS multiple regression with the most significant predictors from the prior, preliminary weeding out exercise.

The independent variables, used in our research to explain performance along this new international scale of smart public health in the first decade of the new Millennium, range from standard social science cross national development accounting explanatory variables, measuring the dimensions of feminism, demography, economic freedom, geography, dependency and world systems theories, to migration, convergence effects of poorer countries growing more rapidly than richer countries, Muslim population shares and membership of a country in the Organization of Islamic Cooperation, military expenditures and military personnel rates, human capital formation, and participation in European economic and monetary integration, thus reflecting contemporary social science and public health research practice of cross-national development accounting.<fnref n="9" />

The independent variables are (arranged in alphabetical order) as follows (Table 2).

<footnote n="9">⁹ For a recent exhaustive argumentation about drivers and bottlenecks of global development see Tausch et al. (2012).</footnote>
<table><caption>**Table 2.** The potential societal drivers and bottlenecks of smart public health</caption><tr><td><ul><li>% women in government, all levels</li><li>% world population</li><li>2000 Economic Freedom Score</li><li>Absolute latitude</li><li>Annual population growth rate, 1975–2005 (%)</li><li>Comparative price levels (US = 1.00)</li><li>Foreign savings rate</li><li>FPZ (free production zones) employment as % of total population</li><li>Immigration – Share of population 2005 (%)</li><li>In GDP per capita</li><li>In GDP per capita ^2</li><li>Membership in the Organization of Islamic Cooperation (OIC); Muslim population share per total population</li></ul></td><td><ul><li>Military expenditures per GDP</li><li>Military personnel rate ln (MPR+1)</li><li>MNC outward investments (stock) per GDP; MNC PEN – stock of Inward FDI per GDP; MNC PEN: DYN MNC PEN 1995–2005</li><li>Net international migration rate, 2005–2010</li><li>Openness-Index, 1990 (export-share per GDP + import-share per GDP)</li><li>Population density</li><li>Public education expenditure per GNP; UNDP education index</li><li>Worker remittance inflows as % of GDP</li><li>Years of membership in EMU, 2010, Years of membership in the EU, 2010</li></ul></td></tr></table>

The choice of a country to be included in the final analysis (175 countries) 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, in total 115).

The statistical design of our study is thus based on the usual, SPSS XVIII ordinary least square standard regression analysis of the ‘kitchen sink type’ of economic growth and economic, social and political performance.

## Results

Table 2 shows the estimation results for the drivers and bottlenecks of ‘smart public health’. Which are the countries best combining the task of a maximum of ‘public health’ with a minimum of ecological footprint per capita? Our model explains 29.9 % of the total variance of ‘smart public health’, and is based on the analysis of the 115 countries with complete data; the F-value is 13.183 and the error *p* of the entire equation is 0.000, and constitutes the best available estimate from our independent variables. The constant, which is significant, has a value of –1.657. The drivers of ‘smart public health’ are the share of a country's population in world population, indicating the relative size of a nation, the UNDP education index, measuring the levels of education in a given country, and worker remittance inflows as percent of GDP. The main bottleneck of
'smart public health' is constituted by the crowding-out effect of public education expenditures on human development.

<table><caption>**Table 3.** OLS regression results of drivers and bottlenecks of smart public health (dependent variable is SR)</caption><thead><tr><th>Independent Variable</th><th>B (unstandardized regression coefficient)</th><th>Standard error</th><th>Beta</th><th>t-value (Student's test)</th><th>Error probability</th></tr></thead><tbody><tr><td>Constant</td><td>-1.657</td><td>0.348</td><td></td><td>-4.760</td><td>0.000</td></tr><tr><td>% world population</td><td>0.055</td><td>0.029</td><td>0.152</td><td>1.894</td><td>0.061</td></tr><tr><td>Public education expenditure per GNP</td><td>-0.097</td><td>0.042</td><td>-0.196</td><td>-2.283</td><td>0.024</td></tr><tr><td>UNDP education index</td><td>2.437</td><td>0.430</td><td>0.478</td><td>5.666</td><td>0.000</td></tr><tr><td>Worker remittance inflows as % of GDP</td><td>0.044</td><td>0.010</td><td>0.352</td><td>4.461</td><td>0.000</td></tr><tr><td>Memorandum item: statistical properties of the equation</td><td>Adj. R<sup>2</sup></td><td>Df.</td><td>F</td><td>Error prob. of the entire equation</td><td></td></tr><tr><td></td><td>29.900</td><td>114</td><td>13.183</td><td>0.000</td><td></td></tr></tbody></table>

## Conclusion

Our residuals-based reformulation of smart public health realistically captures the trade-off between Global Ecological Footprint per capita and development performance and offers us a better idea about smart public health performance at different stages of socio-economic development.<fnref n="10" /> Our results show that traditional indicators of economic globalization and also inequality have little influence on combined smart public health performance, but that hitherto neglected elements of social science theories, such as migration, gain in importance. Also such factors as the demographic weight of a country and scale effects of public health provision, and education cannot be overlooked. In contrast to most of the current thinking on the issue, we can show that levels of public education expenditures crowd out health performance, while levels of achieved education, measured by the UNDP education index, have a beneficial

<footnote n="10">¹⁰ The inclusion of the UNDP-standardized equality score (= performance in avoiding a high ratio of income differences between the richest 20 % and the poorest 20 %) only has a minor effect on our results: for the 106 countries with complete data. The equality score achieves an error probability of 13.5 %. As expected, equality has a positive effect on smart public health, but the effect is far smaller than existing approaches would suggest.</footnote>
effect on ‘smart public health’. True enough, we have to state that worker re-
mittances redistribute global well-being and the achievement of good public
health outcomes at relatively low ecological resource use to the countries of the
‘global South’ and away from the rich democracies of the OECD.

We are aware that our answers to the questions raised in this article might
be incomplete. But we hope to have provided at least some preliminary guiding
posts for further research on this important subject how the four economic free-
doms affect smart public health and to have shown that primarily not inequal-
ity, but migration matters for public health. If we have expressed this perspec-
tive sufficiently clear, then our essay already achieved its aim. Further research
might concentrate on such issues as ‘smart infant mortality reduction’ or ‘smart
life expectancy’.

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9

Is Geography ‘Dead’ or ‘Destiny’
in a Globalizing World?

A Network Analysis
and Latent Space Modeling Approach
of the World Trade Network

Anthony Howell

**Abstract**

Drawing on advancements made in network analysis, statistical modeling and computer science, this paper employs latent space modeling techniques to explore the role of geography in the global trade economy. Latent space models postulate that the probability of a link between pairs of actors depends on the distance between them in unobserved Euclidean social space and on observed covariates. Using probabilistic models, I investigate the effect that distance has on influencing trade ties in social space, while also controlling for several co-variates, including region-based homophily (a proxy for regionalization), transitivity and country wealth. The findings are posited within the 'Geography is Dead' thesis and reveal that the distance-destroying result attributed to globalization may be over-estimated in the global trade economy.

**Keywords:** network analysis, latent space model, world trade network, geography, regionalization, globalization.

**1. Introduction**

Since Toffler (1970) first argued that place is no longer an important determinant due to the evolution of transport and communication systems, numerous scholars have speculated the ‘death of geography’, giving rise to a heated debate (Ohmae 1990, 1995; Friedmann 1995). O’Brien (1992) proclaimed that the globalization era equates to ‘the end of geography’, because geographical location no longer matters for economic development due to the increasing rate of globalization. In this context, globalization is defined as ‘the deepening integration of global economic activity facilitated by the rapid development of information and communications technology and the underlying trend towards liberalization in trade and investment’ (Staples 2007: 99).
Despite the ‘geography is dead’ claims, many notable (economic) geographers emphasize the critical role of geography in trade, as well as in innovation, knowledge and development (Krugman 1993; Yeung 1998; Massey 1984, 1999; Morgan 2004). It is well known that the effects of globalization are not distributed uniformly throughout the global economy, and there are place- and region-based variations that require a geographical lens in order to understand issues of unequal development (Warwick 2005). Moreover, the growing forms of regionalization shed further evidence that geography does matter for trade and economic development. Regionalization is defined here as a process, ‘whereby economic interaction, such as flows of goods and capital, increase faster among countries within a particular geographical area than between those countries and others outside the area’ (Moore 2007: 36).

In the present paper, I apply latent space modeling – developed by Hoff et al. (2002) – to test the ‘geography is dead’ thesis. Hoff et al. (2002) postulate that the probability of a link between pairs of actors depends on the distance between them in unobserved Euclidean social space and on observed covariates. Using the latent space modeling approach, I investigate the effect that distance has on trade ties in latent space, while also controlling for several covariates, including region-based homophily (a proxy for regionalization), transitivity and country wealth.

Stochastic models can be used to identify the specific processes that have led the network to its particular configuration. Both the gravity model and the exponential random graph model (ERGM) are possible approaches to test the relationship between geography and trade. Aside from weak theoretical backing, another main shortcoming with these approaches is that they assume independence among all trade linkages between country pairs. In reality, it is very likely that there is inherent dependency between ties (Shortreed et al. 2006). For example, if South Africa and Brazil are trade partners, and China and Brazil are trade partners, then it is more likely that South Africa and China are trade partners then it is if these previous trade relationships did not exist.

By implementing proxies to take into account second- and third-order dependencies in the network, the latent space model is one method to deal with this dependency.

This paper attempts to add to the growing literature on the World Trade Network (WTN), as well as to test the ‘death of geography’ thesis, by statistically analyzing the role of geography and trade integration using latent space stochastic models. To carry out these objectives, I estimate several simple latent space models to capture the relationship between distance and the likelihood of two countries establishing a trade partnership in the WTN, while also taking into account higher order dependencies in the network. Results from the analysis support regionalization, in favor of the ‘geography is destiny’ thesis (Dieter 2007), implying that proponents of the ‘geography is dead’ overestimate the distance-destroying effects of globalization on the global trade economy.
The outline of this paper is as follows. In the subsequent section, I provide a brief background on relevant network analysis studies. In Section 3, I discuss issues related to building, specifying and representing the trade network. In Section 4, I provide an overview of the main network statistics and network properties commonly used to infer patterns in the trade network. Specifically, I consider connectivity, centrality, clustering and hierarchy, as well as homophily and transitivity. In Section 5, I specify several latent space models and test the principles of propinquity, homophily and transitivity. Lastly, Section 6 concludes with some final remarks.

## 2. Background

Due to advancements in physics and computer science, network analysis is increasingly relied upon to study the world trade network and is a powerful tool that can be used to reveal topological properties, as well as the underlying structure of the trade network (Fagiolo et al. 2009; Reyes et al. 2007, 2010). For instance, network analysis applications of the world trade network (WTN) have most notably addressed two major questions: (1) does the trade network follow a core-periphery structure (Clark 2008, 2010; Kali and Reyes 2007); and (2) do global elites tend to trade among themselves and what are the effects of international trade on economic growth (Bhattacharya et al. 2008; Serrano 2008; Fagiolo et al. 2009).

Although comparatively underdeveloped, network analysis has also been employed to investigate the role of geography in the global trade economy. Kim and Shin (2002) argue that network analysis can naturally be extended from dependency/world-systems theory to test the globalization vs. regionalization thesis that indirectly tests the role of geography by determining whether countries in the network are globalizing or regionalizing (Aggarwal and Koo 2005; Kim and Shin 2002; He and Deem 2010).

Findings from network analysis contribute to the debate over whether regionalization is a stepping stone or stumbling block to globalization (Bhagwati et al. 1999). On the one hand, some scholars believe that regionalization is a transitory step that some countries pursue to become more competitive on the global market, eventually promoting globalization and rendering geography unimportant. On the other hand, other scholars suggest that regionalization impedes globalization by hurting the welfare of non-member countries and leading to inefficient production strategies that may work at the regional scale but not at the global scale.

For instance, Kastelle et al. (2006) provides evidence that the ‘movement of trade, capital and people is a geographically heterogeneous and historically episodic process and can be interpreted to support regionalization rather than globalization’. The authors' finding is significant because it highlights the power of geography to influence trade outcomes; even in an ever-increasing global-
ized world, countries still pursue regional trade integration policies with nearby
countries.

Conversely, Kim and Shin (2002) argue that globalization and regionaliza-
tion are not necessarily competitive, but complementary processes. From 1959–
1996, the authors show that the WTN became globalized (overall network den-
sity increased significantly), while it also became regionalized (intraregional
density also significantly increased). Based on their findings, the authors sug-
gest that regionalization does not jeopardize globalization; rather the two proc-
esses are complimentary and can coincide with one another.

While the authors' findings have far reaching implications into the effects of
regionalization and globalization on the global economy, the findings are predi-
cated merely on descriptive statistics, in this case, a network statistic called node
degree. Node degree measures the probability of a randomly chosen vertex to
have *k*-connections to other vertices and provides a summary of a node's overall
activity.¹¹ The problem with this network statistic, like any other descriptive
statistic, is that no statistical model is used to control for other potential inter-
mediating variables that may influence the outcome of a trade tie being estab-
lished.

Most of the literature on the WTN only examines the network's summary
statistics to track topological changes, and few attempts are made to statistically
analyze the trade network using stochastic models (notable exceptions are Gar-
laschelli and Loffredo 2005; Garlaschelli et al. 2007). Fitting statistical models
to networks, in general, is still in its infancy stages due to the complexity of
modeling networks and the high level of computation that is required (Hunter
and Handcock 2005). It is not surprising, therefore, that the WTN literature
has only recently begun to be modeled; despite the complicated nature of
the WTN, pertinent topological properties of the global trade system can and
should be extracted through modeling the system as a network (Serrano
2008).

**3. The Network Data: Specification and Representation**

Bilateral trade data are extracted from the United Nations COMTRADE data-
base. Data for GDP per capita and the trade/GDP share are extracted from Penn
World Table 6.2 (for a country listing, see Appendix). In the trade network,
countries represent nodes and the links between two countries are their shared
imports and exports. If a trade tie is not present, then yᵢⱼ = 0. The data offer in-
formation on both exports and imports, however, I use only import data be-
cause previous scholars suggest that these figures are more accurate than export
figures (Kim and Shin 2002).

<footnote n="1">¹ Node degree is discussed in greater detail in Section 4.</footnote>
A network can be set up as some combination of binary/weighted, di-rected/undirected and static/longitudinal. For the purposes of this research, I build a binary, undirected and static network. These specifications are chosen for the following reasons: (1) Squartini et al. (2011) specify various combina-tions of the network and find that the projections made by the binary matrix are maximally informative and should be the focus of subsequent models of trade; (2) the number of in and out ties are highly correlated, and in accordance with Fagiolo et al. (2009) and Serrano and Boguna (2003), the WTN is sufficiently symmetric to use an undirected analysis; and (3) while the descriptive statistics may change as new countries are incorporated into the network and trade rela-tionships are established and/or strengthened, it is likely that the underlying processes that generate the network are likely to be stable over time (Schiavo et al. 2010). To avoid the complexities of using longitudinal data, it is suffice to select a stochastic model for a single year, 2008, to examine the statistical properties of the WTN.

## Network Representation

Graph theory, advanced by Harary and his collaborators (Harary 1959; Harary et al. 1965), is used to inform much of what we know about how networks work. A graph is a network model consisting of dichotomous (binary) relations. The network can be represented with the following graph notation:

$$G = (V, E) \qquad (\text{Eq. 1})$$

where $V$ is a vertex set, $V = \{v_1, ..., v_2\}$, and in the undirected graph, $E \subseteq \{ (v_i, v_j) : v_i, v_j \in V \}$. In the undirected case, if country $i$ exports to country $j$ or country $j$ exports to country $i$, then $y_{ij} = 1$. Countries represent vertices, and edges between any two countries $(v_i, v_j)$ exist if at least one million U.S. dollars in trade is transacted during the year in observation. The one million U.S. dollar threshold is common in the WTN literature (Kim and Shin 2002) and is selected in order to focus on significant trade relationships that shape the network.

I set $Y$ to be the adjacency matrix for the random graph $G$. $Y_{ij}$ is a binary random variable which indicates the state of the $i, j$ edge. The Pr($Y_{ij} = y_{ij}$) is the probability of the $Y_{ij}$ edge state. I can express $y_{ij}$ in terms of the WTN as a dichotomous outcome:

$$y_{ij} = \begin{cases} 1 & \text{if } (v_i, v_j) \text{ trade volume} \geq \$1 \text{ million US.} \\ 0 & \text{otherwise} \end{cases} \qquad (\text{Eq. 2})$$

The density of a network is the proportion of present ties to the maximum possible lines in a graph. A $gXg$ nodal graph can be computed as:

$$\Delta = \frac{\sum_{i,j} y_{ij}}{g(g-1)}. \qquad (\text{Eq. 3})$$

The density for the WTN in 2008 is .59, which means based on the number of nodes, trade ties represent approximately 59 per cent of the total possible.
There are 7,177 mutual ties in 2008, but 2,799 asymmetric trade ties. Germany, the U.S., and China are the biggest traders averaging around US $8 billion to each of its trading partners. Almost 40 per cent of countries export something to almost every other country, and every country exports to at least 20 other countries, indicating that the trade network is very concentrated.

<table><caption>**Table 1. Network statistics for 2008**</caption><thead><tr><th></th><th>2008</th></tr></thead><tbody><tr><td>Countries Reporting Trade</td><td>190</td></tr><tr><td>Graph Density</td><td>.59</td></tr><tr><td>Total number of dyad trade ties</td><td>7 177</td></tr><tr><td>Total number of asymmetric trade ties</td><td>2 799</td></tr><tr><td>Countries making up 50% of exports</td><td>9</td></tr></tbody></table>

*Source:* Author's calculations using COMTRADE database on reported trade 2008.

## 4. Network Summary Measures: Definitions and Descriptive Statistics

Each network statistic attempts to explore the underlying structure of the network along one of the four major dimensions: connectivity, assortativity, clustering and centrality. Within each dimension, various node level statistics can be employed to quantify individual positions in the network and describe the local neighborhood. For example, node degree (ND) and node strength (NS) are network statistics used to measure node connectivity. ND is used when dealing with a binary network, and is the fractional count of trading partners a country has relative to all possible trade links in the network. NS is used when dealing with weighted networks, and measures the intensity of these trade links.

Both statistics calculate the number of direct ties coming in and going out of a node and represent how connected a country is within a trade network. High degree positions are influential in the network, and at the same time, may be vulnerable to other actors' influence. These statistical measures are used in the empirical studies to offer evidence for or against increasing globalization. If the statistics increase in value, they show the globe is becoming smaller or more integrated over time.

The average nearest neighbor degree (ANND) and average nearest neighbor strength (ANNS) are the most common network statistics to test assortativity. They measure the number of trading partners and the intensity (volume of trade) of a given country's trading partners. For example, if country A has 20 trading partners and each of those 20 countries trades with 20 other countries, ANND/ANNS gives ND/NS statistics for each of country A's trading partners. These two statistics are commonly employed to assess whether certain group-
ings of countries tend to trade with well- or less-connected countries. For ex-
ample, ANND/ANNS can be used to test whether a ‘rich club phenomenon’ has
emerged in the WTN.

The binary clustering coefficient (BCC) and the core clustering coefficient (CCC) are statistics for clustering. The BCC is a ratio that counts the number of triangles that exist compared to the total number of triangles that are possible in the network. CCC measures the trade intensity of these triangles. These statistics offer a perspective on the multi-lateralism vs. bilateralism debate. Clearly, if the statistics increase over time, the WTN is strengthening multi-lateral ties, whereas if the statistic is decreasing, it is associated with a rise in bilateralism.

Lastly, the centrality dimension has probably received the most attention in
the network analysis because of its explanatory power of describing the hierar-
chy that exists within the network. The betweenness (BET) and the random
walk betweenness centrality (RWBC) measures are the most commonly em-
ployed statistic for the centrality dimension and are based on reach and flow
mediation. Both statistics quantify the ability of the ego-node to influence other
vertices in the network. The higher is the measure for a country, the higher is
the degree of influence that country has on the WTN. Most often, this measure
has been found to show a core-periphery hierarchy in the WTN, thus strength-
ening the position of world-systems perspective.

In addition to network statistics, homophily is an important feature in this
study of social networks and helps to explain why we observe a particular type
of network. The principle of homophily is predicated on the fact that people with
similar characteristics will have a higher rate of contact between them than dis-
similar people (Louch 2000; McPherson et al. 2001). One can scale this principle
up to include, organizations, countries, regions, and so forth. In the present con-
text, I am interested in whether homophily by region exists. That is, do regions
delineated by geographical proximity and historical reference tend to trade more
among themselves relative to ‘outsiders’ in other regions that do not share a simi-
lar degree of cultural and historical shared experience? While there are many
different ways to delineate regions, the most basic source of homophily is space
(McPherson et al. 2001), so it makes intuitive sense to group countries based on
geographic proximity (refer back to the Appendix for a country listing by region).

Transitivity is another main feature found within networks. Transitivity is a statistics that measures the degree of network integration. Balance theory predicts that people should adjust their relations until the network becomes stabilized around a pattern where all dyadic ties are largely transitive, that is triadic. This social phenomenon tends to be explained in terms of triadic relationships and by the adage ‘a friend of a friend is a friend’ (Krivitsky et al. 2009). Balance theory predicts that if ties exist between country A and country B and country B and country C, then country A and country C have a strong propensity to develop a tie. A triangle is defined to be any set f(i; j); (j; k); (k; i)g of three edges (Morris et al. 2008).
Descriptive Network Statistics: Connectivity, Centrality and Homophily

Mathematically, the node degree measures the probability of a randomly chosen vertex to have *k*-connections to other vertices and provides a summary of a node's overall activity. The number of incoming ties is called in-degree, expressed as the sum of incoming ties over the number of actors in the network minus 1. In-degree ties will equal out-degree ones, expressed as

$$ \frac{C_i(n_i)=\sum_{j=1}^{g} x_{ji}}{(g-1)}. \qquad (\text{Eq. 4}) $$

Histograms of the node degree show that the distribution of trading partners is right-skewed, meaning that most countries in the network have a small number of trading partners but a smaller number of countries, referred to as ‘hubs’, have a comparatively larger number of trading partners (see Fig. 1).

<figure><img src="image_138.png" /><figcaption>Fig. 1. Node degree distribution for the world trade network</figcaption></figure>
Along the second dimension, centrality measures the quantity of walks that pass through the ego-node, that is betweenness. Betweenness (BET) is the tendency for an ego-node to reside on the shortest paths between third parties, that is, to serve as a bridge between two other nodes.

Betweenness relies on the concept of geodesic distance, which is the shortest path between two nodes, *i* and *j*. Betweenness can be quantified and expressed as:

$$C_b(n_i) = \frac{\sum_{j<k} \frac{g_{ik}(n_i)}{g_{jk}}}{\frac{(g-1)(g-2)}{2}} \quad (\text{Eq. 5})$$

$g_{jk}$ is the number of *j*, *k* geodesics (the shortest path between *j*, *k*) and $g_{\downarrow ik}(n_{\downarrow i})$ is the number of *j*, *k* geodesics that include *i*. High betweenness positions are associated with the term 'broker'. In the network literature, a 'broker' is an actor that mediates between third parties who are not directly tied. Both the node degree and betweenness measures are standardized and are compared to the theoretical maximum number of edges possible for that graph, values ranging from 0 to 1.

Another centrality measure that is less commonly explored in the world trade network is the eigenvalue centrality (EC). This measure quantifies the position of the actor in terms of the sum of the centralities of its neighbors, attenuated by a scaling constant ($\lambda$). Eigenvector centrality can be expressed numerically as:

$$C_D(n_i) = \frac{1}{\lambda} \sum_{j=1}^{g} x_{ij} C_D(n_j) \quad (\text{Eq. 6})$$

Actors with high eigenvector centrality are those with many central neighbors. This centrality measure is often overlooked by the previous articles on the WTN, which is bizarre considering this statistics is ideally suited to test core-periphery relations, a major focus point for the WTN analyses in the past.

Table 2 reports the statistics for a selective number of measures, including connectivity (ND) and centrality (BET, EC) by region. The findings reveal the most connected countries within regions, as well as compare the degree of influence across regions. For example, NAFTA and East Asian countries are the most connected and central/influential regions in the global economy. Despite the high connectivity and centrality scores for the United Kingdom, Germany and France, the EU consists of many small Eastern European countries not very well connected, thereby lowering overall average scores for the EU. SAA and the Arab league are the least connected and least central regions in the global economy.
<table><caption>**Table 2.** Connectivity and Centrality Measures by Region and Select Countries</caption><thead><tr><th>Region</th><th>ND</th><th>BET</th><th>EC</th></tr></thead><tbody><tr><td>1</td><td>2</td><td>3</td><td>4</td></tr><tr><td>NAFTA (n = 3)</td><td>279.3</td><td>218.9</td><td>.107</td></tr><tr><td>USA</td><td>346</td><td>439.39</td><td>.121</td></tr><tr><td>CAN</td><td>284</td><td>186.2</td><td>.11</td></tr><tr><td>MEX</td><td>208</td><td>31.2</td><td>.09</td></tr><tr><td>EU 2 (n = 40)</td><td>210.3</td><td>103.8</td><td>.084</td></tr><tr><td>UKG</td><td>344</td><td>522.9</td><td>.12</td></tr><tr><td>GFR</td><td>340</td><td>376.9</td><td>.121</td></tr><tr><td>FRN</td><td>338</td><td>304.3</td><td>.11</td></tr><tr><td>East Asia (n = 5)</td><td>246</td><td>177.7</td><td>.094</td></tr><tr><td>JPN</td><td>342</td><td>477.7</td><td>.12</td></tr><tr><td>China</td><td>332</td><td>270.8</td><td>.121</td></tr><tr><td>ROK</td><td>310</td><td>177.21</td><td>.11</td></tr><tr><td>ECE (n = 11)</td><td>156.2</td><td>27.4</td><td>.079</td></tr><tr><td>RUS</td><td>278</td><td>104</td><td>.11</td></tr><tr><td>UKR</td><td>276</td><td>109</td><td>.12</td></tr><tr><td>BLR</td><td>194</td><td>39.5</td><td>.082</td></tr><tr><td>ASEAN (n = 10)</td><td>191.4</td><td>78.5</td><td>.079</td></tr><tr><td>THI</td><td>304</td><td>233.7</td><td>.113</td></tr><tr><td>MAL</td><td>298</td><td>170.4</td><td>.113</td></tr><tr><td>INS</td><td>292</td><td>144.4</td><td>.112</td></tr><tr><td>SAA (n = 9)</td><td>136.7</td><td>45.6</td><td>.058</td></tr><tr><td>IND</td><td>314</td><td>222.7</td><td>.116</td></tr><tr><td>PAK</td><td>262</td><td>107.6</td><td>.102</td></tr><tr><td>BNG</td><td>196</td><td>57.68</td><td>.082</td></tr><tr><td>Arab League (n = 17)</td><td>155.4</td><td>24.4</td><td>.068</td></tr><tr><td>SAU</td><td>234</td><td>90.2</td><td>.093</td></tr><tr><td>ISR</td><td>232</td><td>65.3</td><td>.095</td></tr><tr><td>UAE</td><td>218</td><td>62.1</td><td>.09</td></tr><tr><td>Pacific Islands (n = 13)</td><td>80</td><td>38.04</td><td>.033</td></tr><tr><td>AUI</td><td>294</td><td>246.7</td><td>.11</td></tr><tr><td>AUS</td><td>266</td><td>114.12</td><td>.106</td></tr><tr><td>NEW</td><td>220</td><td>120.7</td><td>.09</td></tr><tr><td>Latin America (31)</td><td>132.9</td><td>21.2</td><td>.059</td></tr><tr><td>BRA</td><td>294</td><td>175.2</td><td>.11</td></tr><tr><td>ARG</td><td>244</td><td>65.3</td><td>.101</td></tr><tr><td>RUM</td><td>242</td><td>69.4</td><td>.1</td></tr><tr><td>African Union (50)</td><td>106.3</td><td>11.2</td><td>.049</td></tr><tr><td>SAF</td><td>280</td><td>131.2</td><td>.109</td></tr><tr><td>EGY</td><td>228</td><td>49.5</td><td>.096</td></tr></tbody></table>
Within East Asia, China has only 10 fewer trading partners than Japan (i.e. connectivity), yet its BET centrality score is almost half as big as Japan's. This distinction between connectivity and centrality is a key feature of network analysis. It reveals that although China is increasing the number of its trading partners and becoming better connected with the global economy, its actual influence in the network in terms of trade remains limited relative to Japan. Japan, along with the UK, and the USA have the highest BET centrality score, representing the brokers in the network; China, on the other hand, is plotted much lower than any of these three countries (see Fig. 2).

To gain a better understanding on whether homophily by region is present in the WTN, I present the mixing matrix for each region (Table 3). The mixing matrix presents the count of trade relationships cross-tabulated by the region of the two countries involved. If a strong presence of homophily is present, then there would be large values along the diagonal relative to off-diagonal values. Based on the fact that the diagonal values in the matrix do not tend to be higher than the off-diagonal values, countries do not appear to be overwhelmingly trading within their particular region; homophily by region does not appear to be a major factor.

<figure><img src="image_139.png" /><figcaption>Fig. 2. Centrality score by country</figcaption></figure>
<table><caption>**Table 3.** Mixing matrix by region</caption><thead><tr><th></th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th><th>10</th><th>11</th></tr></thead><tbody><tr><th>1</th><td>3</td><td>104</td><td>3</td><td>12</td><td>21</td><td>23</td><td>18</td><td>40</td><td>19</td><td>81</td><td>92</td></tr><tr><th>2</th><td>104</td><td>590</td><td>37</td><td>144</td><td>302</td><td>250</td><td>168</td><td>388</td><td>141</td><td>599</td><td>893</td></tr><tr><th>3</th><td>3</td><td>37</td><td>NA</td><td>5</td><td>11</td><td>10</td><td>7</td><td>16</td><td>7</td><td>27</td><td>43</td></tr><tr><th>4</th><td>12</td><td>144</td><td>5</td><td>7</td><td>38</td><td>37</td><td>25</td><td>52</td><td>23</td><td>99</td><td>123</td></tr><tr><th>5</th><td>21</td><td>302</td><td>11</td><td>38</td><td>47</td><td>48</td><td>43</td><td>71</td><td>27</td><td>98</td><td>106</td></tr><tr><th>6</th><td>23</td><td>250</td><td>10</td><td>37</td><td>48</td><td>40</td><td>51</td><td>93</td><td>43</td><td>128</td><td>194</td></tr><tr><th>7</th><td>18</td><td>168</td><td>7</td><td>25</td><td>43</td><td>51</td><td>16</td><td>62</td><td>29</td><td>62</td><td>118</td></tr><tr><th>8</th><td>40</td><td>388</td><td>16</td><td>52</td><td>71</td><td>93</td><td>62</td><td>83</td><td>44</td><td>142</td><td>247</td></tr><tr><th>9</th><td>19</td><td>141</td><td>7</td><td>23</td><td>27</td><td>43</td><td>29</td><td>44</td><td>13</td><td>80</td><td>81</td></tr><tr><th>10</th><td>81</td><td>599</td><td>27</td><td>99</td><td>98</td><td>128</td><td>62</td><td>142</td><td>80</td><td>243</td><td>259</td></tr><tr><th>11</th><td>92</td><td>893</td><td>43</td><td>123</td><td>106</td><td>194</td><td>118</td><td>247</td><td>81</td><td>259</td><td>251</td></tr></tbody></table>

There are two caveats to this interpretation. First, marginal totals can be misleading and do not statistically test for the presence of homophily (this will be carried out in the modeling section below). The trade network is also very complex and strict interpretations of homophily are not always straight forward. For example, the largest value in the matrix is between Europe (region 2) and Africa (region 11). Due to the colonization era, African and European countries still maintain a strong, client-like relationship in many cases. Second, there are likely some misleading results due to the way countries are grouped. While there is no ‘right’ way to group countries into regions, defining China (region 4) as its own region has some drawbacks in certain cases, since its value along the diagonal is 0, and the data only cover international trade. Therefore, it is not possible to see China’s intra-trade relationships and how it compares to other countries’ international trade within a particular region.

The number of triangles found in the network area is a proxy for measuring the transitivity. Of the 7,177 ties in the network, the number of triangles is surprisingly large – 157,645. This number is far larger than what would be expected by chance and offers initial evidence that the trade network has a high degree of transitivity. This is significant because it reveals the dyadic trade dependencies among countries supporting the use of a latent space modeling approach.

**5. Latent Space and Latent Position Model: Is Geography Dead?**

Latent space models have replaced block-modeling as the primary approach to study issues of propinquity, the tendency of spatially proximate vertices to be tied. In other words, latent space models are used to determine the role of geography in the international trade context, and can help examine whether the trade network is globalizing or regionalizing. If proponents of globalization who suggest ‘geography is dead’ are correct in their assertion, then the results of the latent space model will confirm that distance does not play a significant
role in influencing the probability that a trade tie is established between country *i* and country *j*.

In order to test the role of geography in determining the probability two countries (*i*, *j*) form a trade relationship, I specify several latent space models. Based on the presence of homophily indicated by the descriptive statistics, there is evidence that propinquity – the probability of a link between two actors is a function of the distance between them in an unobserved latent space – exists in the trade network.

The latent position model assumes a conditional independence approach to modeling. Let {$z_i$} be the positions of the actors in the social space $R^k$ and {$x_{i,j}$} denote the observed characteristics that are dyad-specific. That is the presence or absence of a trade tie between two countries is independent of all other ties in the system, given the unobserved positions in social space of the two individuals:

$$P(Y|Z, X, \theta) = \prod P(y_{i,j} | z_i, z_j, x_i, x_j, \theta), \quad (\text{Eq. 7})$$

where X and $x_i$ and $x_{i,j}$ are observed characteristics that are pair-specific and vector-valued and $\theta$ and Z are parameters and positions to be estimated (Hoff et al. 2002). I use logistic regression to parameterize Eq. 3.

$$\eta_{i,j} = \log \text{odds}(y_{i,j} = 1 | z_i, z_j, x_i, x_j, \alpha, \beta). \quad (\text{Eq. 8})$$

$$\eta_{i,j} = \alpha + \beta'x_{i,j} - |z_i - z_j|, \quad (\text{Eq. 9})$$

where the log odds ratio for two actors *j* and *k*, equidistant from *i*, is $B^*(x_{i,j} - x_{i,k})$. I estimate $\eta_{i,j}$ using the log-likelihood of a conditional independence model, expressed as

$$\log P(Y|\eta) = \sum_{i \neq j} \{\eta_{i,j} y_{i,j} - \log(1 + e^{\eta_{i,j}})\}, \quad (\text{Eq. 10})$$

where $\eta$ is a function of parameters and unknown positions. As such, I use maximum-likelihood to estimate $\eta$. Model degeneracy is a serious problem that frequently occurs when dealing with networks. If a model is degenerate then the terms in the model are grossly unsuitable at describing the underlying processes that form the observed network. That is, even under the maximum likelihood coefficients in the model, the observed network is so unlikely to occur that the model cannot even be properly estimated (Goodreau et al. 2008). To check for issues of degeneracy, I carry out an MCMC estimation procedure for each model that I estimate. The results show that the model statistics do not diverge from the mean, meaning that the models are not degenerate and the maximum likelihood estimates are reliable.

I specify several simple latent space models to test the role of distance and region-based homophily. Table 4 reports the coefficients generated from the latent space models. Model 1 only examines the role of distance in establishing a trade partner. The coefficient on EDGES is highly significant and positive, indicating that larger distances increase the likelihood of two countries estab-
lishing a tie. This finding is bizarre and at odds with predictions made by gravity models that predict trade decreases as a function of distance. In Models 2 and 3, I give additional measures to control for underlying structures within the network that may affect whether a trade tie is established.

<table><caption>**Table 4.** Latent space models (d = 2)</caption><thead><tr><th></th><th>Model 1</th><th>Model 2</th><th>Model 3</th><th>Model 4</th></tr></thead><tbody><tr><td>Edges</td><td>2.56***</td><td>-5.73***</td><td>-6.21***</td><td>-6.13***</td></tr><tr><td>Latentcov (homoregion)</td><td></td><td>26.25***</td><td>27.20***</td><td>27.81***</td></tr><tr><td>Triangle</td><td></td><td></td><td>1.84***</td><td>2.02***</td></tr><tr><td>Nodecov.GDP</td><td></td><td></td><td></td><td>1.42***</td></tr></tbody></table>

A good model is the one that accounts for a country's tendency for assortative mixing, which is based on the notion of homophily (Goodreau *et al.* 2008). In the present context, I want to account for assortative mixing that may occur for countries that belong to a particular region. If assortative mixing is present, then countries within the same region have a greater probability of forming a tie relative to countries in other regions.

Model 2 introduces *Homoregion*, a covariate that accounts for homophily. In this model, I find the sign of the EDGES coefficient switches from negative to positive, confirming the conventional relationship between trade and distance. In other words, the likelihood of two countries forming a tie decreases as distance between countries in latent space increases. The coefficient on *Homo-region* is very large and statistically significant. This finding indicates that countries classified into the same regional grouping will be more likely to form a trade tie within their own region than with countries from other regional groupings, in support of the regionalization thesis. Model 3 adds *Triangle* to take into account the transitive nature of the network. The significant, positive coefficient for *Triangle* confirms that if two countries *i*, *j*, have a mutual trading partner, *m*, then the likelihood that countries *i*, *j* begin to trade increases.

In addition to controlling for network statistics, Model 4 adds real per capita GDP, *Nodecov.GDP* as an additional covariate to control for the effect of wealth on countries forming a tie. The positive, statistically significant coefficient produced by the wealth covariate reveals the hierarchical structure of the network, meaning that rich countries tend to trade disproportionately among themselves.

## 6. Conclusion

The findings presented in this paper suggest that regionalization is a particularly important strategy pursued by countries in the global economy. The integration of regional blocs, along with the proliferation of regional trade agreements (RTAs) promote regionalization and have emerged as individual countries attempt to mitigate the new economic and security vulnerabilities (unregulated capital flows, human and drug trafficking, transnational terrorist net-
works, disease, etc.) brought about by globalizing forces that undermine indi-
vidual states' territorial sovereignty. The process of regionalization signals that
'geography is destiny' (Dieter 2007: 11), as opposed to 'geography is dead'.

The results of the descriptive analyses in this report agree with other previ-
ous works. The WTN network has a high density, the node degree has a high
right-skew, trade partners of well-connected countries are less interconnected relative to those of poorly connected ones, and countries holding many trade partners are on average connected with countries holding relatively few countries. The latent space model tests directly the role of space in determining the likelihood of whether or not a tie will be established. When controlling for regional homophily and other covariates, the Euclidean distance – calculated in social space – is returned negative, significant, and large in magnitude. This result supports findings in the gravitas literature on trade and reaffirms that the probability that trade ties are established decreases as distance increases. Lastly, the latent space models add an additional dimension of analysis of the WTN by controlling for network dependencies, and reveals that region-based homophily – the proxy for regionalization – has a large and significant influence on trade outcomes, even more so than a country's wealth.

Despite the complicated nature of the WTN, pertinent topological proper-
ties of the global trade system are extracted through modeling the system as
a network, and are used to show the significance of geography in influencing
trade outcomes. Understanding the structure of the global trade network has
implications for research across numerous social science disciplines trying to
examine the effects of geography on economic integration and internationaliza-
tion. Future areas of research can extend the latent space model applied in this
paper to examine the evolutionary role of geography over time. Although evi-
dence reported in this paper suggests that geography maintains a crucial role in
the trade network, it is indeterminate whether geography's impact on trade ties
is increasing or decreasing over time.

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Appendix

190 countries are placed into 11 regions. These regions are based on present-day trading blocs and/or geographical location. Several regions combine two or more economic trading blocks that span a certain geographic region. For example, the EU, EFTA and Central European FTA member countries are all categorized as one European region based on their geographical proximity. Similarly, UNASUL, Caribbean Community and the Central American Integration System member countries are all categorized as Latin America.

Regional Groupings

NAFTA (Region 1)

CAN

MEX

USA

Europe (Region 2)

ALG

AND

ANG

ARG

ARM

AUL

AUS

AZE

BAH

BAR

BEL

BEN

BFO

BHM

BHU

BLR

BLZ

BNG

BOL

BOS

BOT

BRA

BRU

BUI

BUL

CAM

CAN

CAO

CAP

CDI

CEN

CHA

CHL

CHN

COL

COM

CON

COS

CRO

CUB

East Asia (Region 3)

(Also Region 4)

JPN

MON

PRK

ROK

TAW

CHN

Eurasian Economic

Community

(Region 5)

ARM

AZE

BLR

GRG

KYR

KZK

RUS

TAJ

TKM

UKR

UZB

ASEAN (Region 6)

BRU

CAM

DRV

INS

LAO

MAL

DRV

MYA

PHI

SIN

THI

South Asia Association

(Region 7)

AFG

BHU

BNG

IND

MAD

NEP

PAK

SOL

SRI
<table><tbody><tr><td><strong>Arab League (Region 8)</strong></td><td>COL</td><td>ETH</td></tr><tr><td>BAH</td><td>COS</td><td>GAB</td></tr><tr><td>EQG</td><td>CUB</td><td>GAM</td></tr><tr><td>IRN</td><td>DOM</td><td>GHA</td></tr><tr><td>IRQ</td><td>ECU</td><td>GNB</td></tr><tr><td>ISR</td><td>GRN</td><td>KEN</td></tr><tr><td>JOR</td><td>GUA</td><td>LBR</td></tr><tr><td>KUW</td><td>GUI</td><td>LES</td></tr><tr><td>LEB</td><td>GUY</td><td>LIB</td></tr><tr><td>MOR</td><td>HAI</td><td>LIE</td></tr><tr><td>OMA</td><td>HON</td><td>MAG</td></tr><tr><td>PAL</td><td>JAM</td><td>MAS</td></tr><tr><td>QAT</td><td>MSI</td><td>MAW</td></tr><tr><td>SAU</td><td>NIC</td><td>MLI</td></tr><tr><td>SUD</td><td>PAN</td><td>MZM</td></tr><tr><td>SYR</td><td>RUM</td><td>NAM</td></tr><tr><td>UAE</td><td>SAL</td><td>NIG</td></tr><tr><td>YEM</td><td>SKN</td><td>NIR</td></tr><tr><td><strong>Pacific Islands (Region 9)</strong></td><td>SLU</td><td>PAR</td></tr><tr><td>AAB</td><td>SUR</td><td>PER</td></tr><tr><td>AUL</td><td>SVG</td><td>RWA</td></tr><tr><td>AUS</td><td>TRI</td><td>SAF</td></tr><tr><td>DMA</td><td>URU</td><td>SEN</td></tr><tr><td>FJI</td><td>VEN</td><td>SEY</td></tr><tr><td>FSM</td><td><strong>African Union (Region 11)</strong></td><td>SIE</td></tr><tr><td>KBI</td><td>ANG</td><td>SOM</td></tr><tr><td>NAU</td><td>BEN</td><td>STP</td></tr><tr><td>NEW</td><td>BFO</td><td>SWA</td></tr><tr><td>PNG</td><td>BOT</td><td>TAZ</td></tr><tr><td>TON</td><td>BUI</td><td>TOG</td></tr><tr><td>TUV</td><td>CAO</td><td>TUN</td></tr><tr><td>VAN</td><td>CAP</td><td>UGA</td></tr><tr><td><strong>Latin America (Region 10)</strong></td><td>CDI</td><td>ZAM</td></tr><tr><td>ARG</td><td>CEN</td><td>ZIM</td></tr><tr><td>BAR</td><td>CHA</td><td></td></tr><tr><td>BHM</td><td>COM</td><td></td></tr><tr><td>BLZ</td><td>CON</td><td></td></tr><tr><td>BOL</td><td>DJI</td><td></td></tr><tr><td>BRA</td><td>DRC</td><td></td></tr><tr><td>BRA</td><td>EGY</td><td></td></tr><tr><td>CHL</td><td>ERI</td><td></td></tr></tbody></table>
10

The British-Italian Performance
in the Mediterranean from the Artillery
Perspective

Kent R. Crawford and Nicholas W. Mitiukov

**Abstract**

The Italian defeat in the Second World War was a consequence of its failure to dominate the central Mediterranean. In the numerous works that address the issue, scholars tend to see reasons of a political nature, shortcomings in the organization and planning by the Italian Navy, indecisive political leadership, and so on. But all agree that technically the Italian fleet was superior to British ships deployed in the Mediterranean area, and in this regard the defeat of the Italians is seen as paradoxical. In this paper, the authors explore the theory that the poor showing of the Italian navy may have resulted from the errors of the political, military and technical leadership in the prewar period, in particular in the performance of the Italian naval artillery.

**Keywords:** Mediterranean history, naval history, Italy, Great Britain, historical reconstruction.

**Introduction**

In recent years the phrase ‘technical innovation’ has been widely used in the literature or for various purposes. But nevertheless, there is an undeniable connection between social evolution and technical innovation. Probably, one can find the most original presentation of this relationship in *Theory of Cultural Circles* by Fritz Graebner (Methode der Ethnologie; see Graebner 1911). When analyzing ancient and medieval data, it is easy to notice that progress in technical areas, especially in military technology, could give the possessors of these innovations a decisive advantage over other nations, contribute to the conquest and exploration of new territories, and insure a certain domestic tranquility. Also there is no doubt that the level of innovation is ‘cumulative’; each subsequent military innovation is much more effective than the previous one. Nor is there any doubt that a continual innovation became the key to survival. For instance, in the twelfth century, Saladin's Moslems perfected the horse archer regarding it as the ultimate weapon. Thereafter, the innovation in the military

History & Mathematics: Trends and Cycles 2014 300–313
sphere virtually ceased, and the Muslim states thus missed out on the developments in gunpowder weapons.

The twentieth-century data brought about some changes in this coherent theory. It turned out that the nature of technical innovations varies, which is most clearly demonstrated by the evolution of naval artillery. Because of the high cost, hardly anyone, even the richest state, could afford to evolve artillery by trial and error or aesthetics, but had to develop and strictly adhere to a certain doctrine, referred to in the literature as 'technology policy'. For example, advances in development of propellant powders and artillery materiel in the beginning of the twentieth century was applied to create a new generation of artillery for high muzzle velocities and therefore, better ballistic performance. And it became possible to design artillery with the performance of the previous generation, but much easier and simpler to produce, and hence less expensive. In this case, there seems to be a continuing conformity with the 'cultural circles', though this is an illusion. It was just the high cost and complexity of the twentieth-century artillery technology that turned out to be a trap in innovation. If the underlying facts and theories for a particular technology policy decision subsequently prove to have been incorrect, the resulting material must still be used until it can be replaced, with corresponding performance issues. Making a wrong choice in policy has serious and costly consequences.

To illustrate the effect of such a highly subjective characteristic as 'technology policy' at the macro-level, one needs merely to look at the combat operations in the Mediterranean theater of the Second World War. The most characteristic feature are the operation in 1940–1943, the period of active participation in the war at sea in Italy. In the late 1920s and 1930s, Italy had been virtually isolated from the influence of external military innovation, primarily German and American. And in point of fact, the Italian ordnance technology had its origins in the British firms such as Armstrong/Elswick and Vickers in the late nineteenth century, which continued through and immediately following the Great War, after which the source 'dried up' and left the Italians to their own devices. So, in a general sense, their ordnance of the Second World War predominately reflected the Italian policy decisions.

Perhaps, more than any other form of combat, naval warfare is dominated by technology. For example, the naval engagements of the Russo-Japanese War (1904–1905) may be viewed in terms of French and French influenced technology versus British technology. In a like manner, the combat in the 1940–1943 period compares the products of Italian versus British technology. At stake was control of the central Mediterranean, necessary to provide the ability to supply and reinforce the military forces in North Africa.

**Historical Note**

The naval war in the Mediterranean Sea from 1940 to 1943 is still the subject of much controversy, revolved not so much about what actually happened, but
rather *why* the campaign proceeded the way it did, and the various engagements so unsatisfactory for the Italian *Regia Marina*. There are many theories, and this paper and model explore one of them.

**The Regia Marina.** The strategic situation of the Italian military in 1940 was the one of dominating the central Mediterranean, from roughly Algiers to Tripoli. And this was improved considerably in the spring of 1941 with the conquest of Greece and Crete, which extended their presence as far as Ben-ghazi, with a combination of air power and naval power.

The main power of the *Regia Marina* was four extensively re-built and thoroughly modernized fast battleships from the Great War. More properly, these should probably be considered as battle-cruisers due to their high speed and relatively light armor protection. Four very powerful modern fast battleships of the *Littorio* class, two of which would join the fleet in 1940, and the other two scheduled for completion in 1942. In the event, only *Roma* would be completed on time, with *Impero* still fitting out slowly in September 1943 (Gardiner 1980).

These were backed by seven heavy cruisers armed with 8-in (203-mm) guns, completed between 1928 and 1933, and twelve light cruisers armed with 6-in (152-mm) completed between 1931 and 1937. Twelve extremely fast un-armored scout cruisers were laid down in 1939, but only three were completed before the Italian surrender.

Sixty well armed destroyers, launched between 1925 and 1943, and sixty five torpedo boats, which could also be classified as destroyer escorts or cor-vettes, were launched between 1937 and 1943.

The *Regia Marina* had adopted a high velocity / heavy projectile combination for their naval guns, first used with the 12-in (304.8-mm) guns of 1909. This would provide good range and good armor penetration. With the modern guns, it was quickly apparent that the dispersion pattern was too great, so the muzzle velocity was reduced without completely fixing the problem. The Table below gives the original MV and the reduced MV, with comments as applicable.

<table><caption>**Table 1.** Ballistic information of Italian guns</caption><thead><tr><td>Gun</td><td>Original MV (m/s)</td><td>Reduced MV (m/s)</td><td>Notes</td></tr></thead><tbody><tr><td>381 mm / 50</td><td>870</td><td>850</td><td></td></tr><tr><td>203.2 mm / 50</td><td>905</td><td>840</td><td>With new lighter shell</td></tr><tr><td>203.2 mm / 53</td><td>960</td><td>900</td><td>With original heavy shell</td></tr><tr><td>152.4 mm / 53</td><td>1000</td><td>850</td><td></td></tr><tr><td>120 mm / 50</td><td>950</td><td>920</td><td></td></tr></tbody></table>

The 320-mm / 43.8 (bored out 304.8-mm / 46) were quite good. With a muzzle velocity of 830 m/s, their shooting was good and the initial patterns were very tight; so tight, in fact, that they were adjusted to be larger. The 6-in / 55 (152.4-mm)
had a muzzle velocity of 910 m/s, which was left unchanged. The new 135-mm / 45 (5.3-in) had a muzzle velocity of 825 m/s and shot well. Doctrine called for firing by turret with a several second interval. For a three turreted ship, the order would be A, C, B. For a four turreted ship it would be A, D, and C and B together. Cruisers with twin turret mounts would be A, D, C, B.

Fire Control was one of the RM's strong points, and their equipment and system were excellent. While they were late developing radar, they had fully developed the concept of the Fire Control Central, which featured the Director, computing machinery, inclinometers, follow-the-pointer gear, and range finders, all of a very high quality. They had also developed the concept of scarometry, by means of which the fall of shot was ranged with a stereoscopic range finder and the results compared to the calculated gun range. This would measure the variance and provide the correction. Problems with Italian gunnery cannot be blamed on their fire control suite (O'Hara 2009).

**The Royal Navy.** Great Britain had a vastly larger navy than Italy. But they also had many commitments for their finite resources, which included keeping a viable force at each end of the Mediterranean, so they were perpetually numerically inferior to the *Regia Marina*.

The main strength of the RN in 1940 consisted of the five *Queen Elizabeth* class battleships, four of which had fought at Jutland in 1916. Two of them had been reconstructed and modernized, while the other two had not been, and remained little improved. The fifth, the famous *Warspite*, which had been reconstructed to a slightly lesser extent than the other two, engaged Italian warships on several occasions. There were also the four surviving *Revenge* class, two of which had been at Jutland. They had not been modernized, and were decidedly inferior at modern battle ranges. The two ‘Treaty’ battleships completed the battle line. The new fast battleship *King George V* joined the fleet in 1940. There were additionally the battle-cruisers *Hood, Renown* and *Repulse*, the last two very lightly protected and under-armed. However, *Renown* had been thoroughly reconstructed and modernized, and was often attached to Force H out of Gibraltar (Gardiner 1980).

The Royal Navy also had many modern heavy and light cruisers and destroyers. Losses had been heavy, especially around Crete. Thus the make-up of these light forces changed frequently throughout the period.

British naval guns were of good quality. The performance was moderate, so they were often theoretically out ranged by their opponents, though not so in reality. In fact, *Warspite* scored one of the two longest range hits during the war. Their projectiles, however, were first rate, and always seem to have performed well. Doctrine was for fairly tight patterns with ‘half’ salvos.

The RN's fire control was one of their strong suits. They were well ahead in the development of radar, and all the cruisers and capital ships had elaborate equipments for solving the gunnery problem. All the un-reconstructed ships
were fitted with the Dreyer Fire Control Table Mk. V, or as modified over the inter-war years. The new ships and those that had been reconstructed were fitted with the Admiralty Fire Control Table (AFCT), which was a post-Great War development, as did most of the modern cruisers.

The Royal Navy was not ‘flashy’ in its ships and guns. Rather, they sought consistency and high quality throughout. It should be remembered that the brand new, untried and not even properly worked up *Prince of Wales* scored against *Bismarck* in spite of equipment breakdowns and other problems with the gun turrets.

None of the engagements between the heavy ships was fought to a conclusion, and all were at long range. Neither side has a significant advantage in fire control. This is to say that the RN advantage of radar would have been offset by the RM's scartometry. The RM enjoyed, on the whole, the advantage of more powerful guns. This, however, meant nothing in view of the overly large dispersion patterns.

## Research

Marc Antonio Bragadin's *The Italian Navy in World War II* (Bragadin 1997) is bewildering. Their 'greatest' victory was Pantellaria, in which a British destroyer and several transports were sunk. But given the correlation of the forces involved, they should have exterminated the entire convoy to the last vessel! And the 'super fast' Italian ships would never catch the much slower British vessels; *Bartilomeo Colleoni*, supposedly capable of 40 kts, was savaged by HMAS *Sydney*, which on her best day made only 32 kts.

How could it be that with the larger fleet, magnificent artillery and well-trained crews the Italian Fleet suffered one shattering defeat after another? Let us try to look at the problem through the prism of naval guns.

For the purposes of comparison, we shall select three artillery systems that were nearly analogous between the two navies: the 381-mm (15") main guns of the battleships, 203-mm (8") guns of the heavy cruisers, and the 152-mm (6") of the light cruisers. The performance of each is summarized below.

<table><caption>**Table 2.** Characteristics of British and Italian guns (Campbell 1985)</caption><thead><tr><td>Caliber</td><td>Model</td><td>Shell weight, kg</td><td>Muzzle velocity, m/s</td><td>Form factor to the Law of 1943</td></tr></thead><tbody><tr><td>152/50</td><td>Mk XXIII</td><td>50.8</td><td>841</td><td>1.08</td></tr><tr><td>203/50</td><td>Mk VIII</td><td>116.1</td><td>855</td><td>1.03</td></tr><tr><td>381/42</td><td>Mk I</td><td>871.0</td><td>752</td><td>1.27</td></tr><tr><td>152/53</td><td>Model 1926</td><td>47.5</td><td>1000</td><td>1.09</td></tr><tr><td>203/53</td><td>Model 1927</td><td>125.3</td><td>955</td><td>1.09</td></tr><tr><td>381/50</td><td>Model 1934</td><td>885.0</td><td>850</td><td>0.89</td></tr></tbody></table>
The technique and functions for ballistic calculations was presented in sufficient detail on the pages of *Warship International* in the article by William Jurens (1984). Many of the functions are of an empirical character, and thus differ a little bit for each country. So in Russia the definitions of a standard atmosphere were set forth in the Russian State Standard 4401–78, which defined the character of temperature variations, density, viscosity, and air pressure at altitude functions. These are the functions used for this analysis. And for the laws of resistance the following were applied:

- Law of Siacci (for shells of a form similar to the standard Type 1);

- The Law of 1930 (similar to Type 8);

- The Law of 1943 (similar to Type 7).

In this case for the definition of the form factor of a shell, the Law of 1943 was employed. From Table 2, it is evident that the British and Italians have used shells with almost identical ballistic properties. However, here there is nothing unusual, as the British influence on Italian ordnance was really significant. Up to the end of WWI, the guns of the Italian fleet were made under license to designs from the firms of Armstrong (EOC) and Vickers. And as a matter of fact, subsequent gun developments were modern versions of those designs. This connection, by the way, shows rather exponential comparison of the form factors for shells of the main guns of the leading maritime states. For example, for guns of about 127-mm (5") which were introduced into the inventories during the 1920–1930s, as the main guns for destroyers, the values are as follows (using the Law of Siacci):

<table><caption>**Table 3.** Characteristics of destroyers' guns of the world</caption><thead><tr><th>System</th><th>State</th><th>Muzzle velocity, m/s</th><th>Shell weight, kg</th><th>Range for elevation, m</th><th>Form factor to the Siacci's Law</th></tr></thead><tbody><tr><td>120/45 Mk I, Mk II</td><td>Britain</td><td>814</td><td>22.70</td><td>14,450 (30)</td><td>0.82</td></tr><tr><td>130/40 Model 1924</td><td>France</td><td>725</td><td>34.85</td><td>18,700 (35)</td><td>0.60</td></tr><tr><td>127/45 SK C/34</td><td>Germany</td><td>830</td><td>28.00</td><td>17,400 (30)</td><td>0.66</td></tr><tr><td>120/50 Model 1926</td><td>Italy</td><td>950</td><td>23.15</td><td>22,000 (45)</td><td>0.62</td></tr><tr><td>120/45 Type 3</td><td>Japan</td><td>825</td><td>20.41</td><td>16,000 (33)</td><td>0.66</td></tr><tr><td>130/50 B 13</td><td>USSR</td><td>870</td><td>33.40</td><td>25,730 (45)</td><td>0.52</td></tr><tr><td>127/38 Mk 12</td><td>USA</td><td>762</td><td>25.04</td><td>15,300 (35)</td><td>0.73</td></tr></tbody></table>

From the above table, taken from Tony DiGiulan's contributions to the *Warships*1 website (www.warships1.com), the ballistics of guns of the main European states and Japan were at approximately the same level. It is interesting to note, however, that the Soviet shell had the best ballistic form. But this should not be surprising, as the attention given to ballistics in the USSR, which resulted in the M.1928 pattern projectiles, is well known now. Stalin even took a personal interest in the development program, which produced gun systems equal or superior to all foreign designs in all main parameters save one – barrel
life. This unfortunately cancelled out all of their virtues, as the Effective Full Charge life of the gun was equal to the capacity of the magazine!

The American and British guns have the worst ballistics form, but this can-
not be the only criterion, since doctrine required the more universal application
of both anti-surface and anti-air capabilities.

But to return to the Anglo-Italian conflict in the Mediterranean, it is well
known that the hit probability is determined in large part by the angle in de-
scent of a shell, known as the Danger Space. Steve McLaughlin (2001) defined
this relationship as:

$$
\text{Danger space} = \text{Target width} + \text{Target height} / \text{Tangent of Angle of Descent}.
$$

It follows, therefore, that the lower the angle of descent, the greater the hit
probability, which is the rationale behind the use of high velocity guns. Fig. 1
reflects this parameter of the major British and Italian guns.

As is depicted in Fig. 1, at all battle ranges the angle of descent of the Ital-
ian shells is less than that of their British opposite number. Indeed, at ranges up
to 16,000 m, the angle of descent of the Italian 203-mm shell is less than that of
the British 381-mm!

<figure><img src="image_140.png" /><figcaption>Fig. 1. Comparison of angle of incidences of shells<fnref n="1" /></figcaption></figure>

If comparison were only limited to the size of the danger space, than the Italians
should have enjoyed a considerable advantage. This makes the results of the gun

<footnote n="1">¹ For all figures: circle are 152-mm guns, square are 203-mm, rhombus are 381-mm guns; white for British systems, black for Italian systems.</footnote>
battles quite paradoxical. Therefore, as a second step we must try to estimate the values of the ballistic corrections. A technique for obtaining such values would be to determine the effect of corrections in an elevation angle: the variation of an elevation angle is applied, which affects the range. Thus, for each degree of deviation either way, the shell either falls short or flies over by a certain number of meters. Other corrections produce a similar result. The unique exception is a variation of the atmospheric density and pressure, the values of which are generally included in the Range Tables. The given technique was approved by the authors on the basis of Range Tables (see TS-146 1971) for the 122-mm Soviet howitzer, model 1938, and has given satisfactory convergence.

1) Correction of elevation angle – its physical sense is sensitivity of the gun to the roll of the ships (see Fig. 2). Though Fire Control Suites were common before the War, the very sensitive instruments that appeared only afterwards had effect as if the ship were on an even keel, the consequences of roll being eliminated insofar as the guns were concerned. But in the absence of such systems, the divergence between the British and Italian guns is most obvious in the performance of the 381-mm guns. Dispersion of the Italian shells was almost 1.5–2 times greater! This means that in the presence of virtually any wave activity at sea (which is almost always), the British would have on average twice as many hits as would the Italians!

<figure><img src="image_141.png" /><figcaption>**Fig. 2.** The correction on an elevation angle</figcaption></figure>
2) Correction for the mass of the shell – sensitivity of the gun to the ‘know-how’ of shells (see Fig. 3). As is known, the more developed manufacturing processes warrant obtaining smaller tolerances. Thus, dispersion due to variation of the mass of the shell is lower, as the shells are more uniform. However, as Jack Greene and Alessandro Massignani (see Greene and Massignani 1998) have pointed out in their *The Naval War in the Mediterranean 1940–1943*, manufacturing tolerances in the production of the Italian shells were overly large on the one hand, as was the weight control of the propellant used in bagged charges.

The Table below shows the changes in range caused by a mere one per cent variance in shell weight and propellant charge weight.

<table><caption>**Table 4.** Changes in range caused by a per cent variance in shell weight and propellant charge weight</caption><thead><tr><th>Condition</th><th>Shell weight (kg.)</th><th>MV (m/s)</th><th>Range at 15-deg. elevation (meters)</th></tr></thead><tbody><tr><td>Range with 0% increase</td><td>885</td><td>870</td><td>26,420</td></tr><tr><td>1% increase in charge</td><td>885</td><td>874.34</td><td>26,640</td></tr><tr><td>1% decrease in charge</td><td>885</td><td>865.64</td><td>26,201</td></tr><tr><td>1% increase in shell wt.</td><td>893.85</td><td>865.68</td><td>26,289</td></tr><tr><td>1% decrease in shell wt.</td><td>876.15</td><td>874.38</td><td>26,552</td></tr><tr><td>1% increase in both</td><td>893.85</td><td>870</td><td>26,507</td></tr><tr><td>1% decrease in both</td><td>876.15</td><td>870</td><td>26,332</td></tr><tr><td>1% increase in charge &amp; 1% decrease in shell wt.</td><td>876.15</td><td>878.74</td><td>26,772</td></tr><tr><td>1% decrease in charge &amp; 1% increase in shell wt.</td><td>893.85</td><td>861.34</td><td>26,070</td></tr></tbody></table>

So even though it may have been possible for the Italians to have adjusted for the variations in shell weight, which were often labeled on the projectile and allowed for in the Range Tables, the variation in the propellant charges could not. Thus the Italians were laboring under an additional burden with regard to dispersion.

3) Correction for atmospheric pressure (see Fig. 4). In this area, the change in condition would affect both sides, with neither obtaining a material advantage. Thus, the value of this correction is not so great, as atmospheric pressure varies rather slowly, which allows for its rather exact measure.
<figure><img src="image_142.png" /><figcaption>**Fig. 3a.** The correction on a mass for 152-mm shells</figcaption></figure>

<figure><img src="image_143.png" /><figcaption>**Fig. 3b.** The correction on a mass for 203-mm shells</figcaption></figure>
<figure><img src="image_144.png" /><figcaption>Fig. 3c. The correction on a mass for 381-mm shells</figcaption></figure>

<figure><img src="image_145.png" /><figcaption>Fig. 4. The correction on atmospheric pressure</figcaption></figure>
4) The correction for atmospheric density actually displays sensitivity of the gun to meteorological conditions, as the presence of rain or snow results in increased density of the air (see Fig. 5). This correction, as opposed to atmospheric pressure, is rather difficult to take into account. Sudden rain or snow showers (the latter not common in the Mediterranean), or fog, would have a detrimental effect on ballistic performance. But in this regard, the opponents approximately correspond to each other, with neither obtaining an advantage.

5) Corrections in initial (muzzle) velocity caused by variations in the condition of the charges (see Fig. 6). These include charge temperature. Within a range of tolerance, accounted for in the Range Tables, a higher temperature would result in a higher initial velocity, and a lower temperature – in a lower velocity. Other factors are not so predictable. The very conditions of storage can negatively affect the charges, and could result in a breakdown of the chemical components, while excess moisture would reduce burning efficiency. In our opinion, the Italians had a slight advantage in this area.

<figure><img src="image_146.png" /><figcaption>Fig. 5. A correction for air density</figcaption></figure>
<figure><img src="image_147.png" /><figcaption>**Fig. 6.** The correction on initial velocity</figcaption></figure>

On the face of it, the British Royal Navy has an advantage over the Italians in
only one area of correction, but it is the most important and significant. What
does this mean? In the theoretical sense, the smaller danger space of the lower
velocity British guns would imply that only the most careful preparations and
calculations would counter the Italian advantage in hit probability. However,
the ballistic effects of roll are less for the British than for the Italians, and there-
fore correspondingly easier to correct. The worse is the sea state, the greater is
the British advantage in this respect. It is interesting that, empirically, the Ital-
ian gunnery performance should have improved as a result of their reducing the
muzzle velocity of their guns. The effect would have been to decrease the dan-
ger space, on the one hand, but to enjoy a corresponding decrease in the disper-
sion caused by the roll of the ship, on the other.

**Conclusion**

It is interesting to note that both the Royal Navy and the *Regia Marina* came to
similar conclusions based on the empirical evidence of combat, and over the
course of the war demonstrated an inclination to favor reduced muzzle velocities. Such reductions could be ten per cent or more, giving, for example, the Italian 152-mm cannon a new muzzle velocity of 850 m/s from its original of 1000 m/s.

We are aware of the fact that such measures can only attempt a 'cure for the disease', but in any case do not answer the question of the 'severity of the disease'. The British guns, of course, demonstrated much less sensitivity to roll, but also a marked inferiority in other areas of performance, that they stood to gain little or nothing from further reductions in muzzle velocity.

Thus, the choice of any one or two specifications as a marker for the evolution of a 'cultural community' can give the illusion of progress, but paradoxically lead to misunderstanding of the deep historical processes that affect the synergistic relationship of many parameters (Crawford et al. 2005).

## References

**Bragadin M. A. 1997. *The Italian Navy in WW II.* 2 vols. Ekaterinburg: Zerkalo. In *Russian* (Брагадин М. А. Итальянский флот во Второй мировой войне. В 2-х т. Екатеринбург: Зеркало).

**Campbell J. 1985. *Naval Weapons of World War Two*. Annapolis, MD: Naval Institute Press.

**Crawford K. R., Mitiukov N. W., and Mokrousov S. A. 2005. On Confrontation between England and Italy in the Mediterranean during the WW II. *Istoriya korablya* 2: 50–54. In *Russian* (Крауфорд К. Р., Митюков Н. В.. Мокроусов С. А. К вопросу об англо-итальянском противостоянии на Средиземном море периода Второй мировой войны. *История корабля* 2: 50–54).

**Gardiner R. (Ed.) 1980. *Conway's All the World's Fighting Ships, 1922–1946*. New York: Mayflower Books.

**Graebner F. 1911. *Methode der Ethnologie*. Heidelberg: Carl Winter.

**Greene J., and Massignani A. 1998. *The Naval War in the Mediterranean, 1940–1943*. London: Chatham Publishing.

**Jurens W. R. 1984. Exterior Ballistics with Microcomputers. *Warship International* 1: 49–72.**

**McLaughlin S. 2001. Predreadnoughts vs. Dreadnought: Action off Cape Sarych, 18 November 1914. In Preston A. *Warship 2001–2002*, pp. 117–140. London: Conway Maritime Press.**

**O'Hara, V. P. 2009. *Struggle for the Middle Sea*. Annapolis, MD: Naval Institute Press.

**TS-146. 1971. *Range Tables TS-146*. Moscow: Voenizdat. In *Russian* (Таблицы стрельбы ТС-146. М.: Воениздат).**
# 11
## The Shield of Islam? Islamic Factor of HIV Prevalence in Africa*

Alisa R. Shishkina, Leonid M. Issaev, Konstantin M. Truevtsev, and Andrey V. Korotayev

### Abstract

HIV first appeared in West-Central Africa, then spread to the South, East and West and, at the same time, had hardly reached North Africa. A possible explanation of this pattern can be the role of Islam that pays particular attention to the prevention of extramarital sexual relations. In addition, one can mention that the circumcised men suffer from HIV significantly less frequently than the non-circumcised. Against such background, we had certain grounds to expect that Islamic societies would have lower levels of HIV prevalence than non-Islamic. Our cross-cultural tests have supported this hypothesis. The data have been analyzed with power-law regression. We have found a significant ($p < .001$) and really strong ($r = -.747$) negative power-law correlation between percentage of Muslims and the HIV prevalence in African countries. Of course, one should take into account that the stigma attached to HIV is also much higher among Muslims and so, the Muslims tend to be tested, identified and monitored at lower numbers than those from other religious and cultural backgrounds, which implies that further in-depth research is necessary in order to detect the real relationship between variables in question.

**Keywords:** HIV prevalence, Africa, power-law correlation, Muslims, extra-marital sexual relations, circumcision.

The HIV (Human Immunodeficiency Virus) first appeared in West-Central Africa, then spread to the South, East and West and, at the same time, had hardly reached North Africa. In the last decade of the twentieth century an acquired immunodeficiency syndrome (AIDS) caused by the HIV became a critical issue of socio-economic and demographic development of the Third World, and, especially, in Africa. By 2000, three quarters of 22 million of deaths from AIDS in the world were reported from Africa (United Nations 2001). The origin of HIV is still a matter of scientific debate, although there are many suppo-

<footnote>* This article is an output of a research project implemented as part of the Basic Research Program at the National Research University Higher School of Economics (HSE) in 2014.</footnote>
sitions about its West-Central African origins (Garenne and Zwang 2008; Preston and Alcabes 2001). Examining the emergence and diffusion of HIV in Central Africa, one should pay particular attention to ‘the historical association between soldiers, prostitute contact and the spread of sexually-transmitted diseases in countries which have recently experienced civil war’ (Cliff and Shallman-Raynor 1992). Some geneticist associate the emergence of circumstances facilitating the HIV diffusion with the advent of colonialism and the growth of large colonial African cities leading to social changes, including a higher degree of disorder in sexual relations, the spread of prostitution, and the concomitant emergence of a high frequency of genital ulcer disease (such as syphilis) in the population of nascent colonial cities (De Sousa et al. 2010).

The epidemic started in Africa around the late 1970s and early 1980s. According to one version, some African monkeys are carriers of the virus, but they do not become sick with AIDS. As a result of contacts between people and infected monkeys or their bites the virus could get into the human body. Virologists have found that a simian immunodeficiency virus (SIV) which is observed among West African monkeys is genetically similar to a weakly contagious form of the AIDS and could be considered as its precursor (Moore 2004). HIV-1 and HIV-2 viruses are believed to have originated in West and Central Africa and transferred (a process known as zoonosis) from monkeys to humans. HIV-1 first appeared in southern Cameroon through the evolution of simian immunodeficiency virus (SIVcpz) which infects wild chimpanzees (HIV-1 descends from SIVcpz endemic in the chimpanzee subspecies *Pan troglodytes troglodytes*) (Gao, Bailes et al. 1999; Keele and van Heuverswyn et al. 2006). Next of kin HIV-2 is SIVsmm, a virus of sooty mangabeys (*Cercopcebus atys*), Old World monkeys in West Africa (from southern Senegal to western Ivory Coast) (Reeves and Doms 2002).

Thus, there are all grounds to make a conclusion about cross-species (chimpanzee-to-human) transmissions of AIDS in African region. Paul M. Sharp and his associates mention that M, N, and O – different groups of HIV-1 virus – represent three distinct cross-species transmissions of HIV-1cpz, evidently pointing to Western Equatorial Africa as the territory where they occurred (Sharp, Bailes et al. 2001). Sharp insists that the common ancestor of HIV-1 group M existed before 1940 already infecting humans at that point. The transmission of the virus became possible only in the latter half of the twentieth century due to the changes in population structure and behavior in Africa during the twentieth century and perhaps medical interventions that provided the opportunity for rapid human-to-human spread of the virus (Chitnis and Rawls et al. 2000).

The first epidemic of HIV/AIDS is believed to have occurred in Kinshasa, the Democratic Republic of Congo, in the 1970s. Later, HIV has been carried to Eastern Africa (Kenya, Uganda, Tanzania, etc.) from its eastern equatorial
origin and has gained rapid transmission rates due to labor migration, sexually transmitted diseases (Iliffe 2006) and the prevalence of sex workers as well (Piot *et al.* 1987). The spread of HIV into Western Equatorial Africa continued further in the 1980s, although it did not reach the proportions of East African states. In this decade HIV had been identified in all countries of West Africa. In the middle of the 1980s the virus spread into the rural areas of South Africa through traders, migrants, soldiers or mostly by truck drivers (New Scientist 1987). By the end of the decade Malawi, Zambia, Mozambique, Botswana and Zimbabwe were enveloped by the epidemic that acquired devastating nature in the general population. It is thought that HIV in South Africa had been mostly homosexually transmitted (Hiza 1988). HIV prevalence in South African states continued to increase in the 1990s at the same time as in some parts of East Africa it stabilized or even declined (Mayanja 1999). Note, however, that the HIV diffusion in the northern direction was much less successful. As a result the HIV diffusion pattern looks now as an inversed pattern of the diffusion of Islam in the African continent (see Figs 1 and 2).

<figure><img src="image_148.png" /><figcaption>**Fig. 1.** Share of Muslims in total population of African countries (%)}</figcaption></figure>
<figure><img src="image_149.png" /><figcaption>**Fig. 2.** HIV prevalence in African countries in 2009</figcaption></figure>

There are certain features in Islam (which we well spell out in the final part of the present article) that would inhibit the diffusion of the AIDS epidemic; hence, there are certain grounds to expect that Islamic societies would have lower levels of HIV prevalence than non-Islamic. Our cross-national tests have supported this hypothesis.

The data on prevalence of HIV have been taken from the World Develop-
ment Indicators database (Washington: World Bank, 2012); the data on per-
centage of Muslims in the population of respective countries are from the Pew
Research Center (The Future of the Global Muslim Population. Pew Research
Center, 2011. URL: http://pewresearch.org/pubs/1872/muslim-population-projec
tions-worldwide-fast-growth). The data have been analyzed through a power-
law regression analysis.

The cross-country association between share of Muslims in total popula-
tion of African countries and HIV prevalence in 2009 is shown in Fig. 3.
<figure><img src="image_150.png" /><figcaption>**Fig. 3.** Cross-sectional relationship between share of Muslims in total population of African countries and HIV prevalence in 2009 (double logarithmic scale with fitted power-law regression line)</figcaption></figure>

The power-law correlation between the two variables has turned out to be in the predicted direction, rather strong ($r = -0.64$; $R^2 = 0.412$) and significant beyond any doubt ($p \ll 0.0001$). It was obtained with the least squares method through a power-law regression analysis; the best fit is produced by the following power-law equation:

$$H = 8.03 \times M^{-0.454},$$

where $H$ is HIV prevalence in a country, $M$ is share of Muslims in the population of a respective country.

Thus, we believe that one of the possible explanations of the pattern of HIV diffusion in Africa can be connected with the role of Islam, which pays particular attention to the prevention of extramarital sexual relations. Unlike Christianity, which also decries the extramarital sexual relations, Islam not only condemns them, but punishes. This is primarily due to the close relationship between legal and religious precepts of Islam, a religious basis of Islamic law. This is a fact confirmed by the analysis of Muslim law as a system of existing legal norms (Sykiyainen 2007a, 2007b). First of all, we speak about common origins of all regulations of Islam. Thus, al-Qur'an and al-Sunnah are recognized as the main sources of Islamic law; they are based on the divine revelation and consolidate basic foundations of faith, rules of worship and morality, generally determining the content of Islamic law in the legal sense.
Its focus on the realization of the ideals of Islam as a religious system, the inclusion of a number of religious cult rules explain why Islamic law is often rightly called the quintessence, the main part of Islam, the most clear expression of Muslim ideology (David 1967). In particular, the ‘concept of interest’ coming out of focus of law to defend the five core values, among which the first is given to religion, is important to understand not only the general ideological framework, but also a number of legal features of Islamic law (Zayyed 1966–1967). Students of Islamic Law generally point its two characteristic and interdependent features: the religious origin (‘the divine nature’), and the close relationship of legal orders with Muslim dogma, morals and religious law of Islam in general. Famous modern specialists in the Islamic Law, such as Muhammad Moussa Youssuf and Subhi Mahmasani note that Islamic law is religious in origin and faithful people consider it as a divine revelation (Moussa 1952). On the base of universal nature of Islam and its regulatory requirements, it is concluded that Islam is both ‘faith and state’, and Islamic Law (*fiqh*) is not only a law itself, but a religion as well (Mahmasani 1952). A similar point of view is expressed by many famous specialists in the Islamic Law. Thus, Joseph Schacht notes that Islamic law is characterized by the dualism of religion and state (Schacht 1966). According to R. Charle, Islamic law is primarily a religion, and then – a state and culture (Charle 1959). Islam is a religion of law, and Islamic law nature is not rational, but religious (divine), – emphasizes Rene David (David 1967). This is not applicable to Christianity where the Canon law could not become an imperative norm and, respectively, a part of current legislation.

Thus, we can summarize that the essential feature of Islamic law is in close dependence of its norms implementation on religious consciousness. At the same time, this approach helps to identify another important feature of socio-psychological mechanism of Islamic law implementation which explains the high efficiency of its regulatory effect on the behavior of Muslims who, in practice, in many cases refer to the norms of Islamic law as religious precepts. This is not typical for Canon law. As a consequence, Christian Canon law, in contrast to the Muslim law, could not be a part of the current legislation, including that in African countries.

Islam not only condemns extramarital sexual relations (*zina*), but considers it as a serious crime (*hadd*) like, for example, crimes against the *Ummah* and infringement of the Allah rights (al-Qur’ān 4: 19–22; 7: 32; 25: 68–69; 17: 32; al-Bukhārī 2475, 6878; Muslim 57/100, 1676/25; Ibn Mājah 4019; al-Manhaj 2/40). This is due to the fact that this kind of law breaking impinges on the basis of the Islamic *Ummah* – the family, and therefore should be punished to the fullest extent of the law. It is no accident that adultery and extramarital sexual relations are governed by the criminal law, not the Muslim family law in Africa (Al-Riahi 2011; Sykiyainen 2007a, 2007b).

Obviously, we should remember regarding this point that there exist some kind of disjuncture between religious law and beliefs and practices (see, e.g., Korotayev 2000, 2004). We can suppose that the Muslims who follow the reli-
gious doctrine, and also pronouncedly worship and confirm to the rules actually
have lower risk of HIV infection.

In addition, one can mention that the circumcised men suffer from HIV
significantly less frequently than the non-circumcised, whereas the circumci-
sion is practiced very consistently by the African Muslims (but, of course, not
only African). Thus, some of epidemiological studies have shown that in high-
risk sub-Saharan Africa the male circumcision that is performed for different
reasons including religious, ritual, cultural, and medical, is often associated
with a reduced risk of HIV infection (Bloemenkamp and Farley 2002). Although
in some cases there in no sufficient evidence, the proposition about protective
effect of male circumcision against HIV infection becomes more and more
popular day by day, and it is regarded as one of the more powerful reducers of
infection risk (Harmon 2011; Short 2006; Bonner 2007).

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## Contributors

**David G. Anderson** is an archaeologist in the department of anthropology at the University of Tennessee, Knoxville, who specializes in Southeastern archaeology. His professional interests include exploring the development of cultural complexity in Eastern North America, maintaining and improving the nation's CRM program, teaching and writing about archaeology, and developing technical and popular syntheses of archaeological research. He is the project director of the on-line Paleoiindian Database of the Americas. Anderson has conducted field work and led field projects in many locations in the Eastern United States, as well as projects in the Southwest and the Caribbean. His work is documented in over 200 publications, some 30 technical monographs, and 7 books. Anderson has received a number of professional awards from his colleagues, including the Excellence in Cultural Resource Management Research Award from the Society for American Archaeology (1999), the Dissertation Prize from the Society for American Archaeology (1991), and the first C. B. Moore Award for Excellence in Southeastern Archaeological Studies by the Lower Mississippi Valley Survey / Southeastern Archaeological Conference (1990). In 2006 he was elected President-elect of the Southeastern Archaeological Conference and served as President of that organization from 2008 to 2010. He has served as President of the South Carolina Council of Professional Archaeologists (1992–1993), The Tennessee Council for Professional Archaeology (2006–2007), and the Archaeological Society of South Carolina (1998–1999), and as an officer or board member in a number of state, regional, and national professional organizations.

**David C. Baker** is an Assistant Professor in Big History at the University of Amsterdam and an editor for the Paris Basin in the cliodynamics SESHAT project. He did his PhD under David Christian in Sydney, and works frequently on projects in population dynamics, cultural evolution, and the wider application of Universal Darwinism to complexity studies. He is currently script-writing for a Big History Youtube series that is funded by the Gates Big History Project and is going to be released on the channel Crashcourse later in 2014. E-mail: david.baker1@students.mq.edu.au.

**Kent Rand Crawford**, Master in History, Doctor of Science, Professor of Natural History. He educated in the United States, he spent much of the past ten years traveling and visiting colleagues in the United Kingdom, Spain and Russia. Now retired, he lives and works in Costa Rica, where he continues his forty plus years study of Naval Ordnance. He is the author about 50 scholarly publications, including such monographs as *Identification of the Parameters of Naval Artillery* (in co-authorship with N. W. Mitiukov, Prague, 2013).

**Sergey Gavrilets** is a Distinguished Professor at the Department of Ecology and Evolutionary Biology and the Department of Mathematics at the University of Tennessee, Knoxville, as well as an Associate Director for Scientific Activities at the National Institute for Mathematical and Biological Synthesis (NIMBioS) in Knoxville. He obtained his PhD in Physics and Mathematics from the Moscow State University in the USSR in 1987. He subsequently worked at the Vavilov Institute of General Genet-
ics in Moscow, the Institute National de la Recherche Agronomique in Toulouse, France, and the University of California at Davis, USA, before moving to Knoxville in 1995. He has received the President's Award from the American Society of Naturalists (1999) and the Guggenheim Fellowship (2008). He has published a monograph on the mathematical theory of speciation (in 2004) and over 100 papers. His current research interests are in speciation, human origins, and human social and societal evolution. E-mail: gavrila@utk.edu.

**Leonid E. Grinin** is Research Professor and the Director of the Volgograd Center for Social Research, as well as the Deputy Director of the Eurasian Center for Big History & System Forecasting, Senior Research Professor at the Institute for Oriental Studies of the Russian Academy of Sciences in Moscow and Leading Research Fellow of the Laboratory for Destabilization Risk Monitoring of the National Research University Higher School of Economics. He is the Editor-in-Chief of the journal *Age of Globalization* (in Russian), as well as a co-editor of the international journals *Social Evolution & History* and the *Journal of Globalization Studies*. Dr. Grinin is the author of more than 400 scholarly publications in Russian and English, including twenty six monographs. These monographs include *Philosophy, Sociology, and the Theory of History* (2007, in Russian); *Productive Forces and Historical Process* (2006, in Russian); *State and Historical Process* (3 vols, 2009–2010, in Russian); *Social Macroevolution: World System Transformations* (2009, in Russian; with A. Korotayev); *Macroevolution in Biological and Social Systems* (2008, in Russian; with A. Markov and A. Korotayev); *Global Crisis in Retrospective: A Brief History of Upswings and Crises* (2010, in Russian; with A. Korotayev); *The Evolution of Statehood: From Early State to Global Society* (2011); *The Cycles of Development of Modern World System* (2011, in Russian; with A. Korotayev and S. Tsirel); *From Confucius to Comte: The Formation of the Theory, Methodology and Philosophy of History* (2012, in Russian); *Macrohistory and Globalization* (2012); *Cycles, Crises, and Traps of the Modern World-System* (2012, in Russian; with A. Korotayev).

**Tony Harper** is an independent researcher with interests in the pattern of urbanization over time and the implications of that pattern. He was a teacher at the faculty of New Trier High School for thirty-three years, during the last eleven of which he developed a strong interest in the application of math models to the study of human history. Tony Harper was twice invited by Andrey Korotayev to take part in the conference 'Hierarchy and Power in the History of Civilizations', has recently presented at the Big History conference at Dominican University in San Rafael, California, and is continuing to do research in the relationship between urbanization and deurbanization. E-mail: ajdharper@gmail.com.

**Almas Heshmati** is currently Professor of Economics at Jönköping University, Sweden, and Sogang University, South Korea. He held similar positions at the Korea University, Seoul National University, and University of Kurdistan Hawler. His research interests include applied microeconomics, globalization, development strategy, efficiency, productivity and growth with application to manufacturing and services. In addition to more than 120 scientific journal articles he has published books on the EU Lisbon Process, global inequality, East Asian manufacturing, Chinese economy, technology transfer, information technology, water resources, landmines, power generation, development economics and economic growth. He is also Kurdish Board member.
ber of the World Kurdish Congress (South Korea). E-mail: almas.heshmati@gmail.com.

**Anthony Howell** is currently a Doctoral Candidate at the Geography Department at UCLA and holds advanced degrees in both Statistics (UCLA – 2012) and Geography (Michigan State University – 2009). Trained as a statistician, urban-economic geographer and China specialist, Anthony's research combines applied statistical approaches with the aid of GIS techniques and mapping of spatial relationships to develop new empirically-driven theoretical frameworks that are capable of informing social and economic policy. His dissertation research, supported by a 2012–2013 Fulbright scholarship, is a microeconomic analysis that presents new, critical information highlighting the short- and long-run impacts of Chinese industrial policy on innovation, knowledge spillovers and firm performance. Prior to enrolling at UCLA, Anthony Howell attended Michigan State University for both undergraduate and graduate studies. At MSU, he participated in 9 study/research abroad programs that provided diverse opportunities to study Spanish and Chinese languages, volunteer with orphans and the disabled, intern, and carry out research projects in countries ranging from Mexico to Ireland, Czech Republic, United Arab Emirates and China. In summer 2006, Anthony Howell participated in an undergraduate research opportunity that took him for the first time to China, where he examined rural-urban migration patterns at the Chinese Academy of Sciences in Beijing. The culmination of his experiences in China were life-changing, both personally and professionally. Since 2006, Anthony has accumulated two-years of experience living in China, dedicating much of his time to language acquisition, and researching issues related to migration, inequality and regional growth.

**Leonid M. Issaev** is a Senior Lecturer of the General Politics Department at the National Research University Higher School of Economics, Researcher at the Laboratory for Sociopolitical Destabilization Risks Monitoring (National Research University Higher School of Economics), and Member of Scientific Council of the Russian Association of Political Science. He graduated from the Faculty of Applied Political Science, the National Research University ‘Higher School of Economics’ and the University of Cairo. He had internship at the headquarters of the League of Arab States in Cairo. He is an author of more than 60 scientific publications, including the monograph *Egyptian Turmoil of the 21st Century* (Moscow: Librokom, 2012, co-authored with A. R. Shishkina), *Syria and Yemen: Unfinished Revolutions* (Moscow: Librokom, 2012, in collaboration with A. R. Shishkina), *System Monitoring of Global and Regional Risks: The Arab World After the Arab Spring* (Moscow: Librokom/URSS, 2013; ed., together with A. V. Korotayev and A. R. Shishkina), and *System Monitoring of Global and Regional Risks. Central Asia: New Challenges* (Moscow: Librokom/URSS, 2013, ed., together with B. A. Akayeva, A. V. Korotayev and A. R. Shishkina).

**Andrey V. Korotayev** is Head of the Laboratory for Destabilization Risk Monitoring of the National Research University Higher School of Economics, Senior Research Professor of the International Laboratory for Political Demography of the Russian Presidential Academy of National Economy and Public Administration, Senior Research Professor of the Oriental Institute and Institute for African Studies, Russian Academy of Sciences, as well as a Professor at the Faculty of Global Studies of the Moscow State University. He is the author of over 300 scholarly publications, including such
monographs as *Ancient Yemen* (1995), *World Religions and Social Evolution of the Old World Oikumene Civilizations: A Cross-Cultural Perspective* (2004), *Introduction to Social Macrodynamics: Compact Macromodels of the World System Growth* (2006), and *Introduction to Social Macrodynamics: Secular Cycles and MILLENNIAL TRENDS* (2006). At present, together with Askar Akaev and Sergey Malkov, he coordinates the Russian Academy of Sciences Presidium Project ‘Complex System Analysis and Mathematical Modeling of Global Dynamics’. He is a laureate of the Russian Science Support Foundation in ‘The Best Economists of the Russian Academy of Sciences’ Nomination (2006).

**Sergey Yu. Malkov** is a Senior Research Fellow of the Institute of Economics of the Russian Academy of Sciences and a Professor of the Department of Applied Mathematics of the Russian State Social University. He is an author of more than 200 scientific publications, including such monographs as, for example, *Modeling of Socio-Political and Economic Dynamics* (Moscow: Russian State Social University, 2004) and *Social Self-Organization and Historical Process. Possibilities of Mathematical Modeling* (Moscow: Librokom/URSS, 2009). E-mail: S@Malkov.org.

**Alexander V. Markov** is Acting Head of the Department of Biological Evolution at Moscow State University and Senior Research Fellow of the Institute for Paleontology of the Russian Academy of Sciences. He is the author of more than 140 scientific publications in zoology, paleontology, evolution theory, historical dynamics of biodiversity, and in other fields of evolutionary biology, including monographs: *Morphology, Systematics and Phylogeny of Sea Urchins of the Schizasteridae Family* (1994); *Quantitative Laws of Macroevolution: Experience of Systematic Approach Use for the Analysis of Supraspecific Taxons* (1998; with E. B. Neymark); *Macroevolution in Biological and Social Systems* (2008; with Leonid Grinin, Andrey Korotayev), *Hyperbolic Growth in Biological and Social Systems* (2009; with Andrey Korotayev). Dr. Markov is a member of the Editorial Board of *Journal of General Biology*, an author of numerous popular science publications, the founder and author of the research and education portal ‘Problems of Evolution’. E-mail: http://www.evolbiol.ru.

**Nicholas W. Mitiukov** is Professor in Engineering, Docent, Corresponding Member of Real Academia de la Mar (Spain), Corresponding Member of the Academy of Military Science (Russia), Professor of Izhevsk State Technical University, Professor of Kama Institute of Humanitarian and Engineering Technologies. He is the author of over 500 academic publications, including such monographs as *The Spanish-American War in the Pacific Ocean* (in 3 volumes, St. Petersburg, 2005–2007), *Simulation Modeling in Military History* (Moscow, 2007, 2nd ed., Moscow, 2010), *Ballistics of Arrows according to the Archeological Data* (in co-authorship with A. V. Korobeinikov, Izhevsk, 2007, 2nd ed., Moscow, 2014), *Identification of the Parameters of Naval Artillery* (in co-authorship with K. R. Crawford, Prague, 2013).

**Sergey A. Nefedov** is Senior Fellow at the Institute of History and Archaeology, Ural Branch of the Russian Academy of Sciences, Professor of Ural Federal University. He is the author of over 200 scholarly publications, including such monographs as *Demographic and Structural Analysis of the Socio-Economic History of Russia* (2005), *Factor Analysis of the Historical Process. History of the Orient* (2008), *Secular Cycles* (2009, with P. Turchin), *History of Russia. Factor Analysis* (in 2 vols, 2010–2011). Sergey Nefedov is the winner of the ‘Social Thought’ (2008, 2011), besides he
received the award named after the academician Rychkova (2011). In 2009–2011, he gave an interdisciplinary research program ‘Russian Historical Dynamics: Factors, Models, Predictions’. E-mail: hist1@yandex.ru.

**Arno Tausch** is an Austrian citizen and in his academic functions he is Adjunct Professor (Universitätsdozent) of Political Science at Innsbruck University, Department of Political Science. Currently, he is also Associate Professor of Economics, Corvinus University, Budapest, and Lecturer of International Development, Vienna University. He authored or co-authored 82 articles in peer-reviewed journals and 16 books in English, 2 in French, and 8 books in German. His publications include a number of essays for leading economic and foreign policy think tanks in nine countries, and for the Jean Monnet Institutes of the European Union in three European Union countries. E-mail: arno.tausch@yahoo.de.

**William R. Thompson** is Distinguished Professor and Donald A. Rogers Professor of Political Science at Indiana University. His recent books include *The Arc of War: Origins, Escalation and Transformation* (2011, with Jack S. Levy), *How Rivalries End* (2013, with Karen Rasler and Sumit Ganguly), and *Transition Scenarios: China and the United States in the Twenty-first Century* (2013, with David P. Rapkin). E-mail: wthompso@indiana.edu.

**Konstantin M. Truevtsev**, Associate Professor of the General Politics Department at the National Research University Higher School of Economics, Senior Fellow at the Laboratory for Sociopolitical Destabilization Risks Monitoring (National Research University Higher School of Economics). He is an author of more than 60 scientific publications, including the monographs *Political System of Modern Russia* (Moscow: HSE, 2004); *2011 – a New Wave of the Democratisation?* (Moscow: HSE, 2011). E-mail: hrc@mail.ru.

**Kentaro Sakuwa** is working on his PhD dissertation in the Political Science Department at Indiana University.

**Alisa R. Shishkina** is a Junior Research Fellow at the Laboratory for Sociopolitical Destabilization Risks Monitoring, National Research University Higher School of Economics. Alisa Shishkina is an author of more than 20 scientific works, including monographs *Egyptian Turmoil of the XXI Century* (Moscow: Librokom, 2012, co-authored with L. M. Issaev), *Syria and Yemen: Unfinished Revolutions* (Moscow: Librokom, 2012, in collaboration with L. M. Issaev), *The Arab World in the Digital Age: Social Media as a Form of Political Activity* (Moscow: Librokom/URSS, 2014, co-authored with L. M. Issaev), *System Monitoring of Global and Regional Risks: The Arab World after the Arab Spring* (Moscow: Librokom/URSS, 2013, ed., together with A. V. Korotayev and L. M. Issaev), and *System Monitoring Global and Regional Risks. Central Asia: New Challenges* (Moscow: Librokom/URSS, 2013, ed., together with B. A. Akayeva, A. V. Korotayev, and L. M. Issaev). Her sphere of scientific interests includes human rights, democratic transition, political processes in the modern world, sociocultural development, media studies, and the risks of social and political instability. E-mail: alisa.shishkina@gmail.com.

**Peter Turchin** was trained as a theoretical biologist, but during the last fifteen years he has been working in the field of historical social science that he and his colleagues call *Cliodynamics* (http://cliodynamics.info/). His research interests lie at the intersec-
tion of sociocultural evolution, historical macrosociology, economic history and cli-
ometrics, mathematical modeling of long-term social processes, and the construction
and analysis of historical databases. More specifically, he investigates two broad and
interrelated questions: What general mechanisms explain the collapse of historical
empires? And how did large-scale states and empires evolve in the first place? What
are the social forces that hold together huge human conglomerates, and under what
conditions they fail? Turchin uses the theoretical framework of cultural multilevel se-
lection to address these questions. Currently his main research effort is directed at co-
ordinating SESHAT – a massive historical database of cultural evolution that will be
used in empirical tests of theoretical predictions coming from various social evolution
theories. Turchin has published over 100 articles in peer-reviewed journals, including
over ten in *Nature*, *Science*, and *PNAS*. His publications are frequently cited and in
2004 he was designated as ‘Highly cited researcher’ by ISIHighlyCited.com. Turchin
has authored five books. The most recent include *Secular Cycles* (with Sergey Nefe-
dov, Princeton, 2009), *War and Peace and War* (Plume, 2005), and *Historical Dy-
namics: Why States Rise and Fall* (Princeton 2003). Turchin is Editor-in-Chief of
*Cliodynamics: The Journal of Theoretical and Mathematical History*. His blog is on
the Social Evolution Forum. Web page (with a link to publications and CV):
http://cliodynamics.info/Turchin.html.
**History and Mathematics**

***Guidelines for Contributors***

*Preparation of manuscripts.* Articles should generally be no longer than 100,000 symbols. The paper's abstract should not exceed 150 words. A separate sheet should give the author's brief CV (up to 250 word). A list of keywords should be supplied. Figures of good quality should be submitted as separate files.

*Bibliographical references* should be given in parentheses in standard author-date form in the body of the text: (Duffy, Morrison, and Macdonald 2002; Crumley 1987: 164–165; 1995: 4; 2001; Claessen 1985: 196–198, 201, 207; 2000, 2002; Chu *et al.* 2003: 29).

*Examples of references in text:*

'In a larger population there will be proportionally more people lucky or smart enough to come up with new ideas' (Kremer 1993: 685); according to Rothmaler (1976: 127–129).

A complete list of references cited, arranged alphabetically by author's surname, should be typed at the end of the article along the following lines:

A) Journal articles:

**Schaffer W. M. 1985.** *Order and Chaos in Ecological Systems*. *Ecology* 11(1): 103–109.

B) Edited volumes:

**Frank A. G., and Gills B. K. (Eds.) 1993.** *The World System: Five Hundred Years of Five Thousand?* London: Routledge.

C) Contributions to edited volumes:

**Humphrey N. K. 1976.** *The Social Function of Intellect. Growing Points in Ethology* / Ed. by P. P. G. Bateson and R. A. Hinde, pp. 303–317. Cambridge: Cambridge University Press.

D) Monographs:

**Berryman A. A. 1981.** *Population Systems: A General Introduction*. New York, NY: Plenum Press.

E) Internet resource:

a) If the site does not mention the publication date:

**Chase-Dunn C., and Hall T. D. 1999.** *The Chesapeake World-System*. URL: http://www.wsarch.ucr.edu/archive/papers/c-d&hall/asa99b/asa99b.htm.

b) If possible, put the publication date:

**U.S. Bureau of the Census. 2008.** *World Population Information*. URL: http://www.census.gov/ipc/www/world.html. Date accessed: 24.02.2008.

*Quotations*. Single inverted commas should be used except for quotations within quotations, which should have double inverted commas. Quotations of more than 60 words should be set off from the text with an extra line of space above and below, and typed without inverted commas.

The articles are to be sent to the following email addresses:

Leonid Grinin lgrinin@mail.ru

Andrey Korotayev akorotayev@mail.ru

‘History and Mathematics’ homepage on our website: http://www.sociostudies.org/almanac/ham/