Ein neues, einfacheres Verfahren zur Messung von Geschlechtergleichberechtigung in Ländern

David Geary, der ja bekanntlich ein wunderbares Buch zur Evolutionsbiologie geschrieben hat und schon andere Studien zum Gender Equality Paradox und zu der Frage, ob Frauen im mathematischen Bereich benachteiligt werden, geschrieben hat hat mit seinem Kollegen Stoet einen weiteren sehr interessanten Artikel geschrieben, in dem er eine neue Methode zur Messung von Gleichberechtigung vorstellt:

The Global Gender Gap Index is one of the best-known measures of national gender inequality, used by both academics and policy makers. We argue that that this measure has a number of problems and introduce a simpler measure of national levels of gender inequality. Our proposed measure is based on sex differences in the opportunity to lead a long healthy and satisfied life that is grounded on educational opportunities. The measure better captures variation in gender inequality than other measures, with inclusion of outcomes that can be favorable or unfavorable to either sex, not simply unfavorable to women. We focus on some of the most basic measures available for 134 countries from 2012–2016 (i.e., disadvantages in children’s basic education, life satisfaction, and healthy life span) and we relate these to various measures, including the United Nations‘ Human Development Index. We found that low levels of human development are typically associated with disadvantages for girls and women, while medium and high levels of development are typically associated with disadvantages for boys and men. Countries with the highest levels of human development are closest to gender parity, albeit typically with a slight advantage for women. We argue that the disparities, when they are found, are related to the sexual division of labor (i.e., traditional gender roles) in poorly developed countries as well as the underinvestment in preventative health care in more developed nations.

Also:

• Unsere vorgeschlagene Maßnahme basiert auf geschlechtsspezifischen Unterschieden in der Möglichkeit, ein langes, gesundes und zufriedenes Leben zu führen, das auf Bildungschancen beruht:
• Die Maßnahme erfasst die Unterschiede in der Ungleichheit der Geschlechter besser als andere Maßnahmen, mit Einbeziehung von Ergebnissen, die für beide Geschlechter vorteilhaft oder ungünstig sein können, nicht nur für Frauen.
• Wir konzentrieren uns auf einige der grundlegendsten Maßnahmen, die für 134 Länder von 2012-2016 verfügbar sind (d.h. Nachteile in der Grundbildung von Kindern, Lebenszufriedenheit und gesunde Lebenserwartung), und wir beziehen diese auf verschiedene Maßnahmen, einschließlich des Human Development Index der Vereinten Nationen.
• Wir haben festgestellt, dass ein niedriges Niveau der menschlichen Entwicklung typischerweise mit Nachteilen für Mädchen und Frauen verbunden ist, während ein mittleres und hohes Entwicklungsniveau typischerweise mit Nachteilen für Jungen und Männer verbunden ist.

Das ist in der Tat ein sehr neuer Ansatz, der dementsprechend auch ganz andere Ergebnisse bringt. Insbesondere erkennt er an, dass es auch Nachteile für Männer geben kann, was zwar im Gender Pay Gap theoretisch möglich ist (verschiedene Städte im Osten haben ja beispielsweise einen „negativen Gender Pay Gap“, also Frauen verdienen mehr als Männer), aber eben so wenig anerkannt ist.

Der Index baut auf der Möglichkeit auf ein zufriedenes Leben zu führen, bei dem man gleiche Möglichkeiten hat etwas zu lernen. Da werden natürlich viele Gegner anführen, dass das ja gar nichts darüber aussagt, wer eigentlich die „Macht“ hat und wer mehr „Ressourcen“ erhält.

Aus der Studie:

Apart from political agendas, research on gender inequality has also almost exclusively focused on issues highlighted in the women’s rights movement. Issues disadvantaging more men than women have been understudied (for a review, see [3]) and are not heavily weighted (if at all) in widely used measures of gender inequality, such as the highly cited Global Gender Gap Index (GGGI)[4]. Further, the GGGI truncates all values such that no country can, by definition, be more favorable for women than for men (for details see below). As a result, existing measures do not fully capture patterns of wellbeing and disadvantage at a national level. This is an important oversight, as there are issues that disproportionately affect boys and men. Among the many examples are harsher punishments for the same crimes [5] and an overrepresentation (93% worldwide) in the prison population [6]; compulsory military service (in living history or currently [3]); the large majority of homeless people without shelter are men [7]; higher levels of drug and alcohol abuse [8]; higher suicide rates [9]; more occupational deaths [10]; underperformance in schools [2]; and, men are more often victims of physical assault in general (see [3], p.30-33) and within schools, thus limiting educational opportunities [11]. Men are also overrepresented in occupations that are risky (e.g., exposure to toxins [12]) and physically taxing, such as front-line military duty, firefighting, mining, construction, or sewage cleaning.

In many countries, the retirement age is higher for men than women (although there are a few in which women’s effective age of labor market exit is later, including Spain, Finland, and France), but even when it is equal, men often have fewer retirement years due to a shorter healthy life expectancy [13].

 

Da legt er vieles dar, was im Maskulismus so diskutiert wird. Und er stellt zu recht dar, dass ein Index, der Gleichberechtigung erfassen soll, zunächst erst einmal auch die Möglichkeit haben muss, diese für beide Geschlechter zu erfassen, wenn es ein vernünftiges Bild geben soll. Wer nur die Nachteile, aber nicht die Vorteile eines Geschlechts erfasst, der verkennt, dass sich daraus durchaus ein ausgewogenes Bild ergeben kann.

Finally, polygyny is tolerated in nearly half of all nations and is reported as being negative for women and it often is [14]; for a nuanced discussion see [15]. Polygyny, however, also means that more men than women in these nations are excluded from marriage, a family, and the opportunity to reproduce (given that polygyny leads to an unequal distribution of available partners). In other words, polygyny can be viewed as disadvantageous for most men (irrespective of the fact of whether it is disadvantageous for most women, as noted).

In der Tat kann etwas auch für beide Geschlechter nachteilig sein und die Nachteile können verschiedener Art sein.

No existing composite measure of gender inequality fully captures the hardships that are disproportionately experienced by men and thus do not fully capture the extent to which any nation is promoting the wellbeing of all of its citizens. This is a major challenge, especially for the Global Gender Gap Index, which caps all disadvantages such that, by its definition, men can never be more disadvantaged than women. In other words, measures such as the GGGI will by design fail to measure any disadvantages experienced by boys and men.

Was ja auch ein häufiger Kritikpunkt ist. 

Er führt dazu weiter aus:

The Global Gender Gap Index (GGGI)

The GGGI [4], first published in 2006, is now one of the most established indices of national gender inequality across the world. All included nations are ranked based on four subindices, namely women’s 1) economic participation and opportunity, 2) educational attainment, 3) health and survival, and 4) political empowerment. These four subindices are each based on several variables, each weighted differently. The scores for each subindex range, theoretically, from 0 to 1, whereby 1 indicates that women have parity (or that men fall behind, given that values higher than 1 are capped).

There are several difficulties with the way the GGGI is composed. For one, there is no defensible rationale for truncating scores on an ‘equality’ measure when they disadvantage boys or men. Further, certain subindices may result more from choice than from a disadvantage. For example, fewer young adult men than women enroll in tertiary education in most developed nations. Although this may represent a disadvantage for men, it may also simply reflect a preference for a less academically oriented pathway into vocational occupations [16]. Another example is the earnings gap between men and women, which may well reflect a strategic and desired division of labor within families, rather than a disadvantage to women [17]. This may similarly affect the desire to engage in high-level politics, which require exceptional demands on the work-life balance, and which therefore may be less desirable to many woman [18]. Altogether, these differences in occupational preferences and strategic divisions of labor in family life may skew quantifications of true gender inequality. We are not arguing here that the GGGI is definitely skewed because of this, but merely that there is currently no way of knowing whether inequalities in outcomes are the result of inequalities of opportunities; therefore, not using these variables may resolve this potential skew in the GGGI.

Es wird in der Tat nur eine Ungleichheit an sich festgehalten, wodurch diese zustanden kommt wird hingegen nicht ermittelt. Damit wird suggeriert, dass es an Diskriminierung liegen kann, obwohl es eine bewusste Entscheidung entweder aufgrund Arbeitsteilung oder aufgrund einer anderen Work-Life-Balance sein kann.

The weighting of GGGI subindices is another issue, as is the degree to which the chosen variables are relevant for the majority of the population. More specifically, the subindex „health and survival“ is the combination of the sex ratio at birth (which may indicate sex-specific abortions) and healthy life expectancy. The underlying argument is that sex-specific abortions of girls indicate a negative attitude toward women. This may in fact be the case (but for a philosophical argument see [19]), but is a very indirect measure, and not a good indicator of the health and survival of living persons. We believe that weighing this much heavier (weight 0.693) than healthy life expectancy (weight 0.307) undervalues the health and survival of actually living persons. Given that men typically have a shorter life expectancy, this again skews the GGGI toward overestimating female disadvantage. At the very least, if birth ratios are considered, they should be an independent index.

Auch ein interessanter Punkt: Abtreibung von Mädchen wird sehr hart bewertet, weil es eine gewisse Mißachtung dieser aufzeigt (auch wenn dies gar nicht der Fall sein muss, sondern lediglich etwa hohe Brautgaben abschrecken). Aber das frühere Sterben der Männer wird gar nicht gewertet, lediglich der Punkt, dass Frauen nicht länger leben als diese gibt einen Bonus, allerdings einen kleiner gewichteten. Dabei wäre Lebenserwartung ja durchaus ein sehr interessanter Faktor für die allgemeine Zufriedenheit.

Weiter aus der Studie:

An index that captured core aspects of life that are common to all people and can be measured with a few readily available indices would help to address these issues

We propose that these core aspects of life are reasonably well captured by people’s opportunity to live a long and healthy satisfied life that is grounded on educational opportunities in childhood. Accordingly, our Basic Index of Gender Inequality (BIGI) is the ratio of women to men on three core dimensions of life;

1) Educational opportunities in childhood;

2) Healthy life expectancy (the number of years one can expect to live in good health); and,

3) Overall life satisfaction.

We believe these 3 components complement each other in important ways; leaving one out misses an important aspect of what defines a good life. For example, a person may have a satisfied and long life, but without educational opportunities, such a person might not have had a chance to develop his or her talents. Or, a person may have a satisfied life following a good education, but dies prematurely. And finally, one may be educated and live long, but without much life satisfaction. We believe that the three components together capture the core of what defines a healthy and long satisfied life that is grounded on equal educational opportunities. We believe that these are the minimal components needed for living a fulfilled life, and that our indicators indirectly reflect other aspects of life (e.g., a decent standard of living, which will be reflected in healthy life span and life satisfaction)

The use of overall life satisfaction is a key feature of BIGI. The idea is that while it is very difficult to determine the degree to which men and women are disadvantaged in any particular aspect of life, an overall assessment of life satisfaction likely reflects the combination of advantages and disadvantages they have experienced, whatever they might be [20].

Also ein ganz anderer Ansatz: Welche Chancen auf eine gute Ausbildung hat man? Wie lange lebt man üblicherweise gesund? Wer ist eigentlich zufriedener mit seinem Leben?

Zu den weiteren Methoden:

We calculated the BIGI score using the following steps.

For each country, we calculated the ratio of women to men for healthy life span and for overall life satisfaction. Thus, men and women scoring equally results in a value of 1, women scoring lower than men in a value below 1, and women scoring higher than men in a value above 1.

For children’s education, we performed a more complicated calculation. First, we calculated three education ratios, namely for primary education enrollment (i.e., ratio of girls to boys enrolled), for secondary education enrollment, and for literacy rates. Of these three ratios, we took for each country the value that deviated most from 1, that is, from parity. In this way, we ensured that the lack of opportunity in any aspect of education is not obscured by the other education indicators. For example, a country in which the ratio of literate females to males is 0.7, the ratio in primary school enrollment is 0.8, and the ratio secondary education is only 0.5, we would take the 0.5 value to express the level of educational gender inequality in that country. (Note that rarely, not all three educational variables were available; in those cases, we ignored the missing data and choose the available value that deviated most from parity).

Das ist schon deswegen ein sehr interessanter Ansatz, weil er tatsächlich auch berücksichtigt, dass Männer schlechter abschneiden können.

Taking the most extreme disadvantage in children’s education (of literacy rate, primary school enrollment, and secondary school enrollment) provides a more sensitive indicator of disparities than does averaging the three. This seems justified, because illiteracy, for instance, can potentially be a better indication of basic educational opportunities than enrollment in primary or secondary education (e.g., children might be officially enrolled but rarely show up); this is confirmed by the fact that the gender gap in primary education enrollment does not correlate strongly with the gender gap in the literacy rate (i.e., for 2016, Spearman’s rho, r_s = .20, for 2015, r_s = .30), as one would expect if primary education enrollment in and of itself was not sufficient to learn how to read. Next, we calculated for each country the average of the three ratios (i.e., healthy life span, educational opportunities, and overall life satisfaction). In order to have 0 representing parity, we subtracted the resulting average from 1. As a result, BIGI values below 0 represent a disadvantage for boys and men, while values above 0 a disadvantage for girls and women (S1 Table).

Finally, because there might be small fluctuations from year to year due to „noise“, we decided to collate the data of a 5-year period (2012–2016).

Note that for calculating the ratio of women to men, we adjusted the ratios to ensure symmetry, as is commonly done ([25], p.3). The adjustment prevents overestimation of disadvantages for men. For example, a rate of 0.8 for women and 0.9 for men (sex ratio 0.8/0.9 = 0.89) differs 0.11 from 1, whereas a rate of 0.9 for women and 0.8 for men (ratio 0.9/0.8 = 1.13) differs 0.13 from 1. The adjusted ratio (dividing the smaller by the larger value) differs 0.11 from 1 irrespective of gender.

Dabei ist dann dieses herausgekommen:

Unterschiede in der Gleichberechtigung Männer Frauen

Für die Schwarzen Flächen gab es keine Daten, Geld ist eine geringe Ungleichheit, um so roter es wird, um so größer ist die Ungleichheit, wobei, wenn ich das richtig sehe, nicht zwischen Ungleichheiten zu Lasten von Männern oder Frauen unterschieden wird, was natürlich sehr interessant gewesen wäre. Deutschland schneidet sehr gut ab, Afrika relativ schlecht. Das Saudi Arabien aber innerhalb der Gleichheit so gut abschneidet, wenn ich das richtig sehe, klingt auch nicht so durchdacht. Wobei es dort vielleicht vielen schlecht geht, was die Geschlechter relativ gleich macht. Wie gleichberechtigt Kenia ist wäre auch interessant, es sticht in Afrika etwas heraus

Die Berechnung anhand eines Beispiels, den USA, erklärt:

We use the United States to illustrate the calculation of the BIGI scores (S1 Table). We start with the BIGI 2016. Note, all ratios in the calculation below are adjusted as described above (i.e., smaller value divided by larger value).

1. The healthy life span ratio for women (71 years) to men (68 years) in the US in 2016 is 1.0423 ([4], p.55).
2. For 2016, enrollment sex ratio in primary education and literacy are listed as 1.00 ([4], p.50-51), whereas the sex ratio in secondary education enrollment ([4], p.52) as 1.0326, and literacy rate as 1.00. For the education component of BIGI we take the value that deviates most from parity, in this case 1.0326.
3. From the Gallup World Poll data 2016, we took the ratio of women’s life satisfaction in the USA (6.9094) and men’s life satisfaction (6.6947), which was 1.0311.
4. The average of the above (healthy life span ratio, 1.0423, education score, 1.0326, and the life satisfaction score, 1.0311) is 1.0353.
5. In order to have 0 as representing parity, we subtracted the results from 1, that is 1–1.0353 = -0.0353. In other words, in the US in 2016, the BIGI deviation from parity was 3.5% (in favor of women, because the value is below zero).
6. We calculated the US’s BIGI scores for the years 2012–2016 the same way (-0.0357, -0.0419, -0.0246, -0.0271,-0.0353, respectively), resulting in the BIGI average for 2012–2016 of -0.0329.

Die USA haben also oben eine orange Färbung, weil sie nachteilig für Männer sind.

Die Werte für Deutschland:

Rank:  26

Rank nach Gleichheit über alle Faktoren: 20

Gesamtergebnis: -0.01299 (also leicht nachteilig für Männer)

AADP: 0.02945

Ausbildung: 0.02468 (Etwas besser bei Männern

Gesunde Lebenserwartung: -0.05147 besser für Frauen

Zufriendenheit mit dem Leben: -0.01219 besser für Frauen

Menschlicher Entwicklungsindex: 0.92225

Hier die Tabelle zu den ermittelten Werten aller Länder:

Table S1

Rank	Rank in overall parity	country	BIGI	AADP	Basic Education	Healthy Life Span	Life Satisfaction	Human Development Index
1	12	Bahrain	-0.00794	0.00794	-0.01264	0.00000	-0.01117	0.82050
2	24	Great Britain	-0.01455	0.01567	-0.01214	-0.03318	0.00169	0.90500
3	27	Netherlands	-0.01585	0.01585	-0.01109	-0.02748	-0.00898	0.92300
4	35	Iceland	-0.01768	0.01681	-0.01101	-0.02437	-0.01506	0.91550
5	32	New Zealand	-0.01709	0.01709	-0.01033	-0.02725	-0.01368	0.91150
6	30	Serbia	-0.01670	0.01851	-0.00487	-0.04794	0.00273	0.77200
7	39	Norway	-0.01950	0.01865	-0.00417	-0.03303	-0.01875	0.94600
8	34	Ireland	-0.01744	0.02011	0.00401	-0.04635	-0.00997	0.91375
9	40	Ecuador	-0.02012	0.02012	-0.00663	-0.05348	-0.00025	0.73500
10	31	Lebanon	-0.01697	0.02016	0.00479	-0.04472	-0.01098	0.76375
11	10	Malta	-0.00732	0.02170	-0.01064	-0.03288	0.02157	0.84600
12	16	Belgium	-0.01118	0.02190	0.01609	-0.04925	-0.00038	0.89250
13	2	Israel	0.00063	0.02230	0.02111	-0.03251	0.01328	0.89575
14	45	United Arab Emirates	-0.02244	0.02244	-0.03587	0.00000	-0.03145	0.83425
15	26	Sweden	-0.01575	0.02253	0.01017	-0.03792	-0.01950	0.90800
16	46	Denmark	-0.02347	0.02347	-0.03058	-0.03604	-0.00380	0.92450
17	23	Canada	-0.01417	0.02380	0.01445	-0.03777	-0.01919	0.91500
18	11	Switzerland	-0.00794	0.02391	0.02404	-0.04011	-0.00757	0.93675
19	49	Australia	-0.02420	0.02420	-0.01385	-0.04032	-0.01844	0.93625
20	36	Czech Rep.	-0.01833	0.02466	0.00325	-0.06448	0.00625	0.87225
21	52	Cyprus	-0.02550	0.02550	-0.01317	-0.03495	-0.02838	0.85225
22	43	France	-0.02177	0.02567	-0.00430	-0.06686	0.00584	0.89200
23	4	Azerbaijan	-0.00267	0.02639	0.02781	-0.04359	0.00777	0.75350
24	1	Italy	0.00021	0.02695	0.00178	-0.04011	0.03897	0.88025
25	87	Belize	-0.04794	0.02939	0.00323	-0.08355	-0.00140	0.70575
26	20	Germany	-0.01299	0.02945	0.02468	-0.05147	-0.01219	0.92225
27	58	Spain	-0.02990	0.02990	-0.00438	-0.05832	-0.02702	0.87925
28	21	Montenegro	-0.01312	0.03023	0.03238	-0.03980	-0.01852	0.80325
29	33	Austria	-0.01739	0.03057	0.01978	-0.05998	-0.01196	0.89100
30	42	Luxembourg	-0.02113	0.03091	-0.03208	-0.04599	0.01467	0.89450
31	18	Albania	-0.01289	0.03223	0.02901	-0.01818	-0.04950	0.76150
32	66	Malaysia	-0.03595	0.03242	-0.00505	-0.05152	-0.04071	0.78450
33	9	Macedonia	0.00683	0.03244	0.02806	-0.03841	0.03085	0.74450
34	61	United States of America	-0.03294	0.03294	-0.02006	-0.04757	-0.03118	0.91725
35	62	Jordan	-0.03303	0.03303	-0.00468	-0.02173	-0.07268	0.73900
36	8	China	0.00626	0.03352	0.05967	-0.03504	-0.00585	0.72700
37	63	Chile	-0.03321	0.03356	-0.03905	-0.06111	0.00052	0.84100
38	38	Greece	-0.01916	0.03414	0.02247	-0.04909	-0.03086	0.86325
39	37	Peru	0.01863	0.03438	0.07510	-0.02362	0.00442	0.73575
40	60	Moldova (Republic of)	-0.03240	0.03452	0.00318	-0.09538	-0.00501	0.69550
41	65	Mexico	-0.03545	0.03545	-0.04241	-0.05797	-0.00595	0.75675
42	13	Madagascar	0.00997	0.03580	0.06866	-0.03652	-0.00223	0.51000
43	68	Paraguay	-0.03600	0.03600	-0.05271	-0.05400	-0.00128	0.68800
44	44	Slovenia	-0.02206	0.03621	-0.01060	-0.07680	0.02122	0.88600
45	70	Brazil	-0.03678	0.03678	-0.03711	-0.06858	-0.00464	0.74725
46	50	Romania	-0.02455	0.03692	-0.01062	-0.08159	0.01856	0.79775
47	59	Kuwait	-0.03106	0.03694	-0.04861	0.00882	-0.05339	0.79550
48	6	Singapore	0.00315	0.03701	0.05710	-0.04451	-0.00942	0.92275
49	5	Indonesia	-0.00309	0.03777	0.05202	-0.03468	-0.02660	0.68350
50	72	Finland	-0.03802	0.03802	-0.00628	-0.06502	-0.04276	0.89125
51	25	Kyrgyz Republic	-0.01509	0.03857	0.02218	-0.08049	0.01304	0.65725
52	29	Georgia	-0.01654	0.03866	0.03072	-0.08279	0.00246	0.76275
53	73	Bangladesh	-0.03892	0.03892	-0.03523	-0.00904	-0.07251	0.57225
54	41	Slovak Republic	-0.02104	0.03991	0.00314	-0.09143	0.02516	0.84150
55	76	Tajikistan	0.03958	0.04158	0.10556	-0.00299	0.01618	0.62275
56	22	Bosnia and Herzegovina	-0.01387	0.04215	0.03922	-0.05714	-0.03010	0.74350
57	47	Hungary	-0.02361	0.04249	0.01302	-0.09915	0.01529	0.83200
58	80	Japan	-0.04286	0.04286	-0.01000	-0.06940	-0.04917	0.89950
59	19	Bulgaria	-0.01290	0.04362	0.02541	-0.08478	0.02068	0.78850
60	81	Costa Rica	-0.04373	0.04373	-0.04921	-0.04225	-0.03972	0.77025
61	67	Kazakhstan	-0.03598	0.04513	0.00042	-0.12167	0.01330	0.78950
62	56	Croatia	-0.02832	0.04519	-0.04455	-0.06571	0.02531	0.82175
63	85	Honduras	-0.04623	0.04623	-0.08593	-0.04644	-0.00631	0.62000
64	78	Korea (Republic of)	-0.04113	0.04664	0.00827	-0.07243	-0.05922	0.89675
65	82	Latvia	-0.04533	0.04759	-0.01947	-0.11991	0.00338	0.82350
66	86	Estonia	-0.04631	0.04791	-0.01740	-0.12394	0.00240	0.86100
67	54	El Salvador	-0.02645	0.04903	0.03387	-0.08932	-0.02390	0.67725
68	89	Panama	-0.04907	0.04907	-0.07370	-0.05781	-0.01569	0.78150
69	28	Bolivia (Plurinational State of)	0.01648	0.04956	0.06422	-0.04963	0.03483	0.66800
70	14	Zimbabwe	-0.01028	0.04996	0.06027	-0.07089	-0.01872	0.50225
71	91	Poland	-0.05090	0.05090	-0.01735	-0.09907	-0.03628	0.84875
72	3	Saudi Arabia	-0.00155	0.05144	0.07483	-0.03087	-0.04862	0.84075
73	53	Viet Nam	-0.02598	0.05251	0.03979	-0.09025	-0.02748	0.67600
74	75	Botswana	-0.03953	0.05468	-0.13115	-0.01016	0.02272	0.69650
75	77	Lithuania	-0.03962	0.05484	0.01067	-0.14168	0.01217	0.84225
76	69	Rwanda	0.03671	0.05536	0.09620	-0.02934	0.04055	0.49100
77	97	Colombia	-0.05558	0.05558	-0.07398	-0.06360	-0.02917	0.72075
78	48	Mauritius	-0.02381	0.05565	0.05216	-0.07756	-0.03725	0.77350
79	55	Burundi	0.02732	0.05646	0.12470	-0.03787	-0.00681	0.40300
80	15	Kenya	0.01113	0.05649	0.10143	-0.03056	-0.03748	0.54800
81	100	Sri Lanka	-0.05761	0.05650	-0.05267	-0.06873	-0.04809	0.76175
82	98	South Africa	-0.05560	0.05654	-0.10774	-0.06047	0.00141	0.66075
83	7	Turkey	-0.00616	0.05673	0.07585	-0.05373	-0.04059	0.76100
84	99	Portugal	-0.05676	0.05676	-0.08245	-0.06027	-0.02754	0.83700
85	101	Jamaica	-0.05821	0.05908	-0.09979	-0.06667	-0.01078	0.72825
86	104	Argentina	-0.05960	0.05960	-0.08308	-0.07246	-0.02325	0.82525
87	105	Thailand	-0.06116	0.06116	-0.07061	-0.08641	-0.02645	0.73700
88	83	Belarus	-0.04567	0.06261	-0.01056	-0.15196	0.02530	0.79650
89	74	Russia	-0.03949	0.06416	0.01739	-0.15548	0.01963	0.80275
90	88	Trinidad and Tobago	-0.04859	0.06463	-0.05030	-0.03800	-0.10558	0.77750
91	106	Armenia	-0.06523	0.06523	-0.10006	-0.08600	-0.00962	0.73975
92	107	Dominican Rep.	-0.06627	0.06627	-0.12793	-0.03097	-0.03991	0.71525
93	103	Qatar	-0.05937	0.06628	-0.10421	0.02353	-0.07111	0.85200
94	108	Venezuela (Bolivarian Republic of)	-0.06653	0.06653	-0.09542	-0.07570	-0.02848	0.76925
95	109	Nicaragua	-0.06813	0.06813	-0.13631	-0.05758	-0.01050	0.63825
96	71	Syria	0.03680	0.06987	0.12976	-0.06117	0.01868	0.57475
97	51	Ukraine	-0.02481	0.07017	-0.01458	-0.12789	0.06805	0.74525
98	92	Tanzania (United Republic of)	0.05197	0.07155	0.15517	-0.02937	0.03010	0.51875
99	96	Ghana	0.05442	0.07192	0.15320	-0.02625	0.03631	0.57500
100	111	Uruguay	-0.07366	0.07366	-0.10006	-0.07714	-0.04378	0.79200
101	17	Guatemala	0.01220	0.07688	0.13361	-0.07196	-0.02505	0.62550
102	112	Mongolia	-0.07693	0.07693	-0.06632	-0.11113	-0.05333	0.72925
103	93	Tunisia	0.05382	0.07701	0.18283	-0.03936	0.00885	0.72250
104	79	Algeria	0.04284	0.08338	0.18834	-0.01587	-0.04591	0.74150
105	116	Suriname	-0.08442	0.08552	-0.17081	-0.08162	0.00412	0.72225
106	84	Uganda	0.04568	0.08607	0.19763	-0.05435	-0.00624	0.48550
107	110	Malawi	0.07135	0.09029	0.22548	-0.02840	0.01698	0.46850
108	90	Cambodia	0.04941	0.09393	0.18443	-0.06679	0.03058	0.55500
109	57	Iran (Islamic Republic of)	-0.02898	0.09678	0.10170	-0.03137	-0.15729	0.77175
110	94	Cameroon	0.05383	0.09809	0.22685	-0.01020	-0.05722	0.51000
111	64	Egypt	0.03333	0.09841	0.19761	-0.04158	-0.05604	0.68650
112	121	Philippines	-0.09874	0.09874	-0.15951	-0.08839	-0.04832	0.67700
113	115	Zambia	0.08292	0.10469	0.25035	-0.03230	0.03142	0.57250
114	117	India	0.08477	0.10761	0.28583	-0.03425	0.00275	0.61125
115	120	Nigeria	0.09076	0.11014	0.30240	-0.00426	-0.02377	0.52175
116	113	Angola	0.07718	0.11298	0.27046	-0.05968	-0.00879	0.52850
117	122	Bhutan	0.10976	0.11433	0.31798	-0.02150	0.00352	0.59900
118	119	Mauritania	0.09051	0.12053	0.29360	-0.04503	0.02294	0.50900
119	102	Namibia	-0.05866	0.12256	-0.21754	-0.04777	0.10235	0.63350
120	95	Morocco	0.05386	0.12388	0.26661	-0.02888	-0.07616	0.64150
121	114	Nepal	0.08147	0.13153	0.31949	-0.02000	-0.05509	0.55225
122	123	Senegal	0.11005	0.13227	0.36348	-0.02967	-0.00366	0.48550
123	127	Burkina Faso	0.12362	0.13767	0.39169	-0.02107	0.00024	0.39750
124	124	Pakistan	0.11617	0.14280	0.38846	-0.00338	-0.03656	0.54450
125	125	Ethiopia	0.11647	0.14572	0.37882	-0.04388	0.01447	0.43775
126	128	Mozambique	0.12896	0.15070	0.41463	-0.01286	0.02461	0.41150
127	129	Lesotho	-0.15264	0.15520	-0.36738	-0.06563	-0.03260	0.49175
128	126	Cote d’Ivoire	0.11995	0.15663	0.41494	-0.03804	-0.01689	0.46275
129	132	Mali	0.16047	0.16047	0.45581	0.00148	0.02414	0.43275
130	118	Yemen	0.09023	0.16653	0.38513	-0.02546	-0.08900	0.49475
131	130	Guinea	0.15346	0.17123	0.48838	-0.02000	-0.00532	0.41150
132	131	Liberia	0.15764	0.17410	0.49762	-0.01887	-0.00582	0.42450
133	133	Benin	0.18726	0.18961	0.49987	-0.00980	0.05915	0.47675
134	134	Chad	0.23114	0.24009	0.66015	-0.01343	0.04670	0.39175