IV Appendix

Eastern Europe in the World Economy

The Fall of the Berlin Wall makes it necessary to re-arrange existing data bases and research designs for the study of transnational capitalism. Data compiled by the author from recent issues of the Economist and Business Central Europe allow for a more detailed assessment of TNC dependence in East Central Europe and its effect on the economic well-being of countries, very recently integrated completely into the capitalist orbit:

Basic data: transformation countries

Country	Capital	Population	Chief executive	GDP growth	Inflation	Unemployment	GDP per cap	FDI stock	Exports bn $	Imports bn $
Yugoslavia	Belgrade	10,5	Milosevic	6	70	26	777	1,1	2,4	4,8
Uzbekistan	Tashkent	23,6	Karimov	2	40	0,6	611	0,423	3,7	3,8
Ukraine	Kiev	50,9	Kuchma	0	11	2,3	976	1,6	15,4	19,6
Turkmenistan	Dushanbe	6,1	Rakhmanov	3	46,3	2,9	179	0,087	0,746	0,81
Slovenia	Ljubljana	2	Drnovsek	4	8,5	14,4	9101	2,5	8,4	9,2
Slovakia	Bratislava	5,4	Dzurinda	5	7,5	11,6	3624	1,4	8,8	10,3
Russia	Moscow	147	Yeltsin	-5	40	10,9	3056	10,3	88,8	72,2
Romania	Bucharest	22,5	Radu	-5	60	8,8	1,549	2,8	8,4	10,4
Poland	Warsaw	38,7	Kwasniewski	5,2	11	10,5	3512	26,6	27,2	38,5
Albania	Tirana	3,2	Majko	9	21,9		708	0,339	0,226	0,901
Armenia	Yerevan	3,7	Kocharian	6	11	10,7	435	0,265	0,233	0,793
Azerbaijan	Baku	7,6	Aliyev	6,7	0,9	19,3	509	3,2	1,6	1,2
Belarus	Minsk	10,2	Lukashenko	5	50	2,8	1314	0,346	7,4	8,7
Bosnia H.	Sarajevo	4,1	rotating	30	3	40	1087		0,57	2,199
Bulgaria	Sofia	8,3	Kostov	4	25	13,7	1227	1	4,9	4,5
Moldova	Chisinau	4,3	Lucinschi	-2	13	1,6	504	0,342	0,823	1,142
Macedonia	Skopje	2	Gligorov	5	1,3	36	1663	0,124	1,2	1,6
Lithuania	Vilnius	3,7	Vagnorius	3	5,5	5,9	2581	1	4,2	5,3
Croatia	Zagreb	4,5	Tudjman	2,5	5,8	17,5	4267	2,1	4,2	9,4
Czech R	Prague	10,3	Zeman	-1,5	11	5,2	5050	7,6	22,5	27,1
Estonia	Tallinn	1,5	Siimann	5	11	10,5	3230	1,4	2,3	3,4
Georgia	Tbilisi	5,4	Shevardnadze	9	4	5,2	968	0,512	0,463	0,947
Hungary	Budapest	10,1	Orban	4,6	15	10,4	4462	22,5	19,6	21,4
Kazakhstan	Astana	15,7	Nazarbayev	1	10	4,1	1434	5,7	6,8	7,2
Kyrgyzstan	Bishkek	4,6	Akayev	4	12	3,2	366	0,309	0,631	0,646
Latvia	Riga	2,5	Kristopans	4	5,3	7	2211	1,9	1,8	2,7

Country	FDI penetration	Population	GDP in bn$	GDP growth	Inflation	Unemployment	GDP per cap	FDI stock	Exports bn $	Imports bn $	(M-X)/GDP
Albania	14,963	3,2	2,2656	9	21,9		708	0,339	0,226	0,901	29,79
Armenia	16,465	3,7	1,6095	6	11	10,7	435	0,265	0,233	0,793	34,79
Azerbaijan	82,722	7,6	3,8684	6,7	0,9	19,3	509	3,2	1,6	1,2	-10,34
Belarus	2,582	10,2	13,4028	5	50	2,8	1314	0,346	7,4	8,7	9,7
Bosnia H.	0	4,1	4,4567	30	3	40	1087		0,57	2,199	36,55
Bulgaria	9,819	8,3	10,1841	4	25	13,7	1227	1	4,9	4,5	-3,93
Croatia	10,937	4,5	19,2015	2,5	5,8	17,5	4267	2,1	4,2	9,4	27,08
Czech R	14,611	10,3	52,015	-1,5	11	5,2	5050	7,6	22,5	27,1	8,84
Estonia	28,896	1,5	4,845	5	11	10,5	3230	1,4	2,3	3,4	22,7
Georgia	9,795	5,4	5,2272	9	4	5,2	968	0,512	0,463	0,947	9,26
Hungary	49,927	10,1	45,0662	4,6	15	10,4	4462	22,5	19,6	21,4	3,99
Kazakhstan	25,318	15,7	22,5138	1	10	4,1	1434	5,7	6,8	7,2	1,78
Kyrgyzstan	18,354	4,6	1,6836	4	12	3,2	366	0,309	0,631	0,646	0,89
Latvia	34,374	2,5	5,5275	4	5,3	7	2211	1,9	1,8	2,7	16,28
Lithuania	10,472	3,7	9,5497	3	5,5	5,9	2581	1	4,2	5,3	11,52
Macedonia	3,728	2	3,326	5	1,3	36	1663	0,124	1,2	1,6	12,03
Moldova	15,781	4,3	2,1672	-2	13	1,6	504	0,342	0,823	1,142	14,72
Poland	19,571	38,7	135,914	5,2	11	10,5	3512	26,6	27,2	38,5	8,31
Romania	8,034	22,5	34,8525	-5	60	8,8	1549	2,8	8,4	10,4	5,74
Russia	2,293	147	449,232	-5	40	10,9	3056	10,3	88,8	72,2	-3,7
Slovakia	7,154	5,4	19,5696	5	7,5	11,6	3624	1,4	8,8	10,3	7,66
Slovenia	13,735	2	18,202	4	8,5	14,4	9101	2,5	8,4	9,2	4,4
Turkmenistan	7,968	6,1	1,0919	3	46,3	2,9	179	0,087	0,746	0,81	5,86
Ukraine	3,221	50,9	49,6784	0	11	2,3	976	1,6	15,4	19,6	8,45
Uzbekistan	2,934	23,6	14,4196	2	40	0,6	611	0,423	3,7	3,8	0,69
Yugoslavia	13,483	10,5	8,1585	6	70	26	777	1,1	2,4	4,8	29,42

From these data, it is possible to draw some generalized conclusions. First, inflation is a clearly decreasing function of economic growth in the transformation region:

Secondly, FDI’s flow into these regions, where GDP per capita is already higher. FDI stocks - in absolute terms - are a neat function of achieved GDP per capita:

Thirdly, FDI stocks largely determine exports; in a world economy, structured by oligopolies, market access for the periphery largely depends on FDI stock:

Fourthly, FDI stock per GDP, and thus MNC penetration, alleviates unemployment, but only to a certain degree:

Fifthly, economic growth is not affected by MNC penetration in a positive direction, expected by modernization theories:

The EU accession of Poland

The EU integration of Poland will become a critical test case for theories of peripheral capitalism. Ever since the reception of the original Polish debate by Wallerstein’s classic (1976), the case of Poland - once the biggest state in Europe, that went into decline after the onset of the expansion of world capitalism after the re-discovery of America in 1492 - has a theoretical significance that goes far beyond the borders of this country. The European Union, that Poland is about to enter, is a Union, characterized by distribution coalitions and market interventions. The basic data about EU finance recently were published by Le Monde and Bilan du Monde, published by the same newspaper:

EU-country	GNP in $	contr Eur budget in Euros	contribution, explained by GNP	paying too much or too little in Mill Euros	net contribution	net contr in % of gross contr.	GNP per capita	net contr in % of gross contr.
Germany	2319,5	21217,3	19976,9	1240,4	-10943,5	-51,6	28260	-51,6
France	1526	13185,9	13195,2	-9,3	-781,1	-5,9	26050	-5,9
UK	1220,2	8928,1	10581,7	-1653,6	-1798,8	-20,1	20710	-20,1
Italy	1165,4	8667,1	10113,4	-1446,3	-61,3	-0,7	20120	-0,7
Spain	570,1	5367,6	5025,7	341,9	5936	110,6	14510	110,6
NL	402,7	4837,6	3595	1242,6	-2276,2	-47,1	25820	-47,1
BLG	268,4	2971,4	2447,2	524,2	1079,5	36,3	26420	36,3
Sweden	232	2326	2136,1	189,9	-1129,5	-48,6	26220	-48,6
Austria	225,9	2110,4	2084	26,4	-723,6	-34,3	27980	-34,3
DK	171,4	1505,8	1618,2	-112,4	68,6	4,6	32500	4,6
Portugal	103,9	1077,8	1041,3	36,5	2721,8	252,5	10450	252,5
SF	123,8	1061,9	1211,4	-149,5	56,1	5,3	24080	5,3
Ireland	66,4	687	720,8	-33,8	2676,7	389,6	18280	389,6
Luxembourg	18,8	170,7	314	-143,3	725,5	425	45330	425
Greece	126,2	1178,4	1231,9	-53,5	4371,8	371	12010	371

A reasoning, symbolized by a line of perfect social justice would now show, that the poorer nations proportionately to their low per capita income receive back a greater share of their contributions to the common budget (the straight line); while in reality, the line corresponds to an ‘U’, with Luxembourg, Ireland, Greece, and Portugal disproportionately gaining from the Union budget, in a set-up that benefits the poor and the super-rich tax heaven at the expense of the ‘EU middle class’:

Based on the special issue of the Austrian Political Science Journal (OeZP, 4, 1998), and the UNDP data set (1998), it is also possible to draw conclusions about the most probable length of negotiations to be expected for the Polish case. The share of agriculture in total employment should provide, paradoxically enough, no major stumbling bloc, if the former negotiations are to be any guideline for the present enlargement process:

However, differences in real per capita incomes are a much more serious stumbling bloc, and would make us expect, that Poland will be able to finish its accession by 2005, 11 years after its bid for membership. Countries below the 5000 $ real purchasing power parity benchmark (in 1987 dollars) see their negotiation process increased dramatically in length, while only the very rich countries (> 15000 $) had a shorter negotiation process:

Quite a similar scenario emerges from a comparison of the length of negotiations and the Human Development Index at the time of accession:

The Social Sector and Development

Country Code	top/bottom	social sec	educ exp	health exp	top 20%
Canada	7,1	21,7	7,6	7,4	40,2
USA	8,9	10,5	5,3	6,2	41,9
Japan	4,3	11,5	4,7	5,2	37,5
NL	4,5		5,9	6,8	36,9
NOR	5,9	19,6	8,4	7,6	36,7
SF	6	30,5	7,3	7	37,6
France	7,5		5,7	7,3	41,9
Iceland			5,8	6,9	44,4
Swed	4,6	38,3	8,3	6,2	36,9
Spain	4,4		4,6	5,7	36,6
Australia	9,6		5,5	5,8	42,2
Belgium	4,6		5,2	7,3	36
Austria		24,5	5,8	6	45,1
NZ	8,8	20,2	7,1	5,9	44,7
CH	8,6	14	5,2	6,8	44,6
UK	9,6		5,2	5,9	44,3
DK	7,1	29,5	7,4	5,5	38,6
GER	5,8	24,5	4,7	6	40,3
IRE		19,4	6,2	5,1	39,4
ITA	6		5,4	6,2	41
GRE			3,1	4,3	44,5
ISR	6,6	11,8	5,8	4,2	39,6
LUX			4,1	6,3	41,8
MAL			4,6	4,169	44
POR		9	5	4,1	49,1
HUNG	3,2	17,3	7	6	36,6
LATV		9,1	6,7	2,58	36,7
POL	3,9	17	5,6	5,1	36,6
RUS	11,4		4	3,9	53,8
BELR		12	6,6	3,2	32,9
BULG	4,7	19,8	5,9	5,4	39,3
EST			5,8	4,59	46,3
KAZ			7,7	4,4	40,4
ROM		16,9	3,6	3,9	34,8
UKR			7,8	3,3	35,4
LITH			5,5	3,6	42,1
ARM			7,3	4,2	48,4
UZB			9,5	5,9	50,1
AZER			7,7	4,3	48,6
KYR			6,9	5	49,1
GEO			5,2	4,5	45,6
ALB			3,4	4	48,7
TAJ			8,6	6	50,3
Hong Kong	8,7		2,8	1,1	47
Cyprus			4	1,788	44,5
Barbados			7		41,9
Bahamas			3,6	3	49,4
S-Korea	5,7	2,3	4,2	2,7	45,2
Argentina		4,5	3,1	2,5	45,2
Costa Rica	12,7		4,4	5,67	50,7
Uruguay		14,8	2,8	2,5	52,5
Chile	18,3		2,9	3,4	61
Singapore	9,6	7,2	3	1,1	48,9
Trinidad			4		50
Bahrein			4,8	3,276	51,1
Antigua			2,7		44,8
Panama	29,9		5,5	5,76	59,8
Venezuela	10,3		5,3	2	58,4
Saint Kitts			3,3		40,4
Fiji			5,6	2,322	48,5
Mexico	13,6		4,9	1,6	55,3
Colombia	15,5	2,4	3,1	1,8	55,8
Thailand	8,3	0,1	4	1,1	52,7
Malaysia	11,7	2,3	5,5	1,3	53,7
Mauritius		3,6	3,7	1,98	47,6
Brazil	32,1		4,6	2,8	67,5
Seychelles			8,5	2	47,8
Dominica			5,8		39,2
Belize			5,7	3,809	49,4
Algeria	6,7		8,1	5,4	46,1
Botswana	16,4		8,3	2	50,7
Saint Vincent			6,7	3,25	46,3
Suriname			7,3		46,6
Saint Lucia			9,9		46,4
Grenada			6,7	2,5	43,4
Tunesia	7,8	4,1	6,1	3,3	46,3
Oman			3,8	3,84	50,8
Turkey		4,9	3,4	1,5	56,5
Paraguay			2,6	1,2	50,2
Jamaica	8,1		4,7	2,254	47,5
Dominican R	13,2		1,6	2,1	55,7
Sri Lanka	4,4	2,5	3,3	1,8	39,3
Peru	10,5		3,8	1,9	50,4
Philippines	7,4	1,2	2,9	1	47,8
South Africa	19,2		7	3,2	63,3
Indonesia	4,9		2,2	0,7	40,7
Guyana			7,8		46,4
Egypt			5	1	41,1
Maldives			6,6	2	49,9
China	6,5		2	2,1	43,9
Swaziland			6		50,1
Bolivia	8,6	1,6	6,6	2,4	48,2
Guatemala	30		1,5	2,1	63
Mongolia			8,5	1	48,9
Honduras	23,5		4,1	2,9	57,4
El Salvador			1,6	2,6	47,8
Namibia			9,4	4	49,7
Nicaragua	13,2		6,8	6,7	55,2
Gabon			4,5		50,2
Cape Verde			4,2		48,5
Morocco	7	1,8	5,8	0,9	46,3
Zimbabwe	15,6		9,1	3,2	62,3
Congo			8,6		50
Papua NG			5,4	2,8	49
Cameroon			3,1	1	49,7
Kenya	18,2	0,7	5,4	2,7	62,1
Ghana	6,3	0,1	3,1	1,7	42,2
Lesotho	20,7		6	4,16	60,1
Equatorial G			1,8		46,5
Pakistan	4,7		2,7	1,8	39,7
India	4,7	0,3	3,7	1,3	42,6
Zambia	8,9		2,6	2,2	50,4
Nigeria	9,6		0,708	1,2	49,3
Comoros			4,1	3,3	50,3
Togo		1,1	6,7	2,5	46,8
Bangladesh	4,1		2,3	1,4	37,9
Tanzania	26,1		5	3,2	45,4
Cote d'Ivoire	6,5		6,636	1,7	44,1
C African R			2,8	2,6	48,8
Mauritania	13,2	0,9	5	1,336	46,5
Madagascar			2,9	1,3	50
Nepal	4,3		2,9	2,2	39,5
Rwanda	4		3,8	1,9	39,1
Benin			3,1	2,8	49,2
Malawi			3,3	2,9	55,2
Guinea-Biss.	28		3,2	0,725	58,9
Gambia			2,7	2,142	50,2
Chad			2,3	4,7	48,4
Burundi		0,4	3,7	1,7	48,6
Mozambique			6,2	4,4	49
Burkina F.		0,6	2,7	7	48,9
Mali			2,8	2,8	49,3
Sierra L.			1,4	1,7	54,5
Niger		0,4	2,3004	3,4	44,1
Country Code	top/bottom	social sec	educ exp	health exp	top 20%

Samples and data on a world level

List of variables
The models based on 134 countries

The sample:

Canada

USA

Japan

NL

NOR

SF

France

Iceland

Swed

Spain

Australia

Belgium

Austria

NZ

CH

UK

DK

GER

IRE

ITA

GRE

ISR

LUX

MAL

POR

HUNG

LATV

POL

RUS

BELR

BULG

EST

KAZ

ROM

UKR

LITH

ARM

UZB

AZER

KYR

GEO

ALB

TAJ

Hong Kong

Cyprus

Barbados

Bahamas

S-Korea

Argentina

Costa Rica

Uruguay

Chile

Singapore

Trinidad

Bahrein

Antigua

Panama

Venezuela

Saint Kitts

Fiji

Mexico

Colombia

Thailand

Malaysia

Mauritius

Brazil

Seychelles

Dominica

Belize

Algeria

Botswana

Saint Vincent

Suriname

Saint Lucia

Grenada

Tunesia

Oman

Turkey

Paraguay

Jamaica

Dominican R

Sri Lanka

Peru

Philippines

South Africa

Indonesia

Guyana

Egypt

Maldives

China

Swaziland

Bolivia

Guatemala

Mongolia

Honduras

El Salvador

Namibia

Nicaragua

Gabon

Cape Verde

Morocco

Zimbabwe

Congo

Papua NG

Cameroon

Kenya

Ghana

Lesotho

Equatorial G

Pakistan

India

Zambia

Nigeria

Comoros

Togo

Bangladesh

Tanzania

Cote d'Ivoire

C African R

Mauritania

Madagascar

Nepal

Rwanda

Benin

Malawi

Guinea-Biss.

Gambia

Chad

Burundi

Mozambique

Burkina F.

Mali

Sierra L.

Niger

% labor force participation ratio (UNDP, 1996)

% of the labor force in agriculture (UNDP, 1996; Fischer Weltalmanach, 1996)

% of the labor force in industry (see: labor force agriculture)

absolute GNP (UNDP, 1996)

absolute income growth (growth in the sense of Arrighi’s and Amin’s theory) 1960-1990.

agricultural share in GDP (UNDP, 1996; Fischer Weltalmanach, 1996)

average population growth (UNDP, 1996)

CPM poverty (UNDP, 1996) (combines lack of access to health personnel during birth, underweight children, female illiteracy, and real per capita income in purchasing power parity rates) (for the substitution of missing values, see below)

early death at age 40 in % of total population (UNDP, 1998)

economic growth 80 - 93, pc. and year (UNDP, 1996)

EU membership years (Fischer Weltalmanach, 1995, 1996)

FDI per GDP (UNCTAD, 1996; Business Central Europe, 1996)

human development index (UNDP)

income distribution: share of top 20% in total incomes (World Bank WDR 1996; UNDP 1996; Moaddel 1994; the few missing cases were substituted with the means from linear trendline estimates, based on the countries with complete data, using mean years of schooling and population growth as predictor variables)(see below)

infant mortality rates, 1993 (UNDP, 1996)

inflation 93 (UNDP, 1996)

main telephone lines per 100 population (UNDP, 1996)

mean years of schooling, population aged >25y

military expenditures per GDP (UNDP, 1996)

state sector size (gov. expenditures per GDP; UNDP 1996; Weltalmanach, 1995, 1996; World Resources Institute)

structural heterogeneity (labor force share in agriculture divided by product share of agriculture; see labor force data)

total fertility rate (UNDP, 1996)

UN membership years (Weltalmanach, 1996, 1995)

violation of political rights (1, democracy, to 7, dictatorship) (Stiftung Entwicklung und Frieden, 1996, based on Freedom House)

violations of civil rights (see political rights)

war years (from D. Smith, 1997)

years of Communist Rule (Autorenkollektiv; Weltalmanach)

The reduced sample of OECD and underdeveloped countries, excluding most of the former communist nations:

Barb

Hong

Urug

Trinid

Baham

Korea

Costa

Sing

Arg

Ven

Domi

Kuw

Mex

Maurit

Malay

Gren

Antig

Colom

Seych

Sur

UAE

Pan

Jamai

Braz

Fiji

Saint L

TRK

Thai

Saint V

Saint K

Syria

Belize

Saudi

South A

Sri L

Liby

Ecu

Para

Phil

Tune

Oman

Peru

Domin

Jord

China

Iran

Botsw

Guya

Alger

Indon

Gabon

El Sal

Mald

Guat

Hond

Swaz

Solom

Moroc

Lesoth

Zimbab

Boliv

Egypt

Sao T

Congo

Kenya

Madag

Papua

Zambia

Ghana

Pak

Camer

India

Cote

Haiti

Tanz

Comor

Zaire

Lao

Nigeria

Togo

Ugan

Bangl

Rwanda

Ethi

Malaw

Burun

CAfricR

Mozam

Bhut

Angol

Maure

Benin

GuiBis

Chad

Somal

Gamb

Mali

Niger

Burki

Sierr

Jap

Can

Norw

Switz

Swe

USA

Austral

Fran

NL

UK

Ger

DK

SF

Aus

Blg

NZ

Israel

Ire

Ital

Spa

Gree

Hun

Port

The general model of world development, based on 123 nations (Table 2.13)

Indicators of globalisation:

aid dependency (UNDP, 1994) net per capita aid in $. Donor countries are listed according to their donations per capita and are recorded as ( - )

export processing zones (Bailey et al., 1993). The variable codes the number of export processing zones per country at the beginning of the 1990s

migration dependency (UNDP, 1993) net worker remittances per GNP/GDP at the beginning of the 1990s

MNC penetration index: penetration by transnational capital, weighted by population and capital stock, mid - 1970s (Bornschier/Heintz, 1979, based on OECD)

share of outward FDI stock in gross domestic product in 1985 (UNCTAD, 1996. For a very small number of countries, regional averages had to be taken to substitute for the few, missing values)

terms of trade index 1987 - 90 (UNDP, 1994, Weltalmanach, 1994, based on UN)

trade dependency index: exports plus imports as % of GDP 1990 (UNDP, 1993/94)

vulnerability of a nation in terms of the expansion of the new international division of labor, 1990 (share of women in the national labor force) (UNDP, 1993/94)

Indicators for the institutional environment:

ethno - linguistic fractionalization index, mid - 1960s (Bornschier/Heintz, 1979, based on Taylor/Hudson). Ethnic discrimination is thought to be the purest form of a 'distribution coalition'

government consumption per GDP, 1990 (UNDP, 1993/94)

government expenditures per GNP, 1991 (UNDP, 1993/94; UNICEF, Regional Monitoring Report, 1, 11, 1993, see Cornia, 1993; World Resources Research Institute)

public investment in the preceding Kondratieff cycle (Bornschier/Heintz)

total area in thousands of square kilometers (World Bank, WDR, 1994. This variable measures the role, that territory and resources could play for attracting foreign capital)

violation of civil rights, 1991 (Stiftung, 1993/94, based on Freedom House, combining freedom of religion, the press, freedom of assembly and association, freedom of trade unions, the right to property and equality before the law)

violation of political rights index, 1991 (Stiftung, 1993/94, based on Freedom House, combining free elections, role of the elected parliament in political decision making, party competition, protection of minorities)

world political threats to a country, to be measured by the percentage of armed forces per population. Some neo - liberals maintain that world political threats increase the growth potential of a nation. Due to the skewness of the indicator, the natural logarithm ln (MPR+1) has to be taken (calculated from UNDP, 1993/94; see also: Weede, 1985)

years of membership in the United Nations (coded from Weltalmanach, 1995)

Indicators for the social policy approach:

human development index (UNDP, 1994) as an indicator for the quality of past social policy

increase/decrease of fertility rates 1960 - 90 (UNDP, 1993/94)

life expectancy at birth as an indicator for the quality of past social policy, 1990 (UNDP, 1993/94 and World Bank, WDR, 1994)

share of women in the membership of national legislature (lower house) (UNDP, 1993/94)

social security benefits expenditure as % of GDP in the era of the evolving contemporary Kondratieff cycle, 1985 - 90 (UNDP, 1994). Social security benefits expenditures include here the compensations for the loss of income for the sick and the temporarily disabled; payments to the elderly, the permanently disabled and the unemployed; they also include family, maternity and child allowances and the cost of welfare services. The UNDP data collection is based on ILO sources

total fertility rate (most recent estimate) (UNDP, 1993/94)

total number of inhabitants, divided by the surface area of a country (population density). Due to the skewness of the indicator, the squared root (population density^.50) had to be taken (calculated from UNDP, 1993/94)

violation of civil rights, 1991 (Stiftung, 1993/94, based on Freedom House)

violation of political rights index, 1991 (Stiftung, 1993/94, based on Freedom House)

The dependent variables of the general models

absolute income growth (growth in the sense of Arrighi’s and Amin’s theory) 1960-1990. This measure is the simple difference between real purchasing power parity rate per capita incomes in US $ 1990 and 1960 (UNDP, 1993 ff.)

adjustment 1965/80 - 80/93(calculated from UNDP, 1993/94/96). Adjustment is calculated by a simple, standard linear regression model of growth 1965-80 predicting growth in the period 1980/93. The residuals from this regression are the ‘adjustment scores’

capability poverty measure (CPM - value) (UNDP, 1996. The measure weights unattended births, underweight children, and female illiteracy. It is regarded by the UNDP as a direct measure of absolute poverty)

deforestation rate (UNDP, 1993 - 95; World Bank, 1995; World Resources Research Institute). The index measures annual rates of deforestation in the 1980s in %. Because of missing data, a number of countries had to be coded by the LDC average or the OECD average

destabilization index (coded according to Weltalmanach, 1995. The indicator codes all those countries as '1' (destabilized), that have entries about serious armed internal or external conflict in 1994. The rest is coded as '0'

employment (UNDP, 1994) labor force as % of total population

ethno - warfare (Gurr, 1994) magnitude of ethno - political conflict. The scores are country sums of the squared roots of the deaths (in 10s of thousands) from ethno - political conflict 1993 - 94 plus refugees (in 100s of thousands). Countries with no entries according to Gurr's main research results, 1994, are coded as '0'

forest area per total land area (UNDP, 1993 - 95). In contrast to the above indicator, that measures flows, this measure rather captures stocks of already existent forest destruction. Agricultural land per total land area has to be taken into consideration as an independent variable, because else the regression equations would be biased by a desert - factor

gender empowerment measure (UNDP, 1995. The index weights seats held by women in parliament, the percentage share of women and managers, the share of women in the professional and technical workforce, and the share of women in total earned income

gender - related development index (GDI) (UNDP, 1995) - dimension female life chances. This index was developed by the UNDP especially for the 1995 women's conference in Beijing. The index weights the share of earned income for females and males, the gender - specific life expectancies, the gender - specific adult literacy rates, and the gross primary, secondary and tertiary enrollment ratios. It ranges theoretically from 0.0 to 0.999, with Sweden (0.919) at the top of the international scale, and Afghanistan (0.169) at the bottom. Since there unfortunately no were no data for Dominica, Grenada, Antigua, Seychelles, Saint Lucia, Saint Vincent, Saint Kits, Belize, South Africa, Oman, Jordan, Gabon, Solomon Islands, Sao Tome, Congo, Rwanda, Bhutan, Angola, Mauritania, Somalia, Gambia, Germany and Israel, we had to substitute these missing values with averages for the socio - economic groups concerned: a) the industrialized democracies (gender development index average 0.87) b) developing countries with a Human Development Index above 0.6 (in our 123 nations analysis: Barbados to Tunisia, gender development index average 0.721) c) developing countries with a Human Development Index from 0.599 to 0.389 (Oman to Egypt, gender development index average 0,542) d) developing countries with a Human Development Index under 0.388 (except for the very least developed countries; Kenya to Sierra Leone; gender development index average 0.33) and e) the very least developed countries Benin, Guinea Bissau, Chad, Mali, Niger, Burkina Faso and Sierra Leone with a gender development index of 0.2. This procedure can be regarded only as a first approximation and should be substituted in future research

GNP per capita growth (1965 - 1980 and 1980 - 90/1980 - 92, 1980 - 93) (UNDP, 1993/94/95/96; Fischer Weltalmanach) - dimension growth

greenhouse index, 1989 (greenhouse index per 10 million people, UNDP, 1994. The greenhouse index measures the net emissions of three major greenhouse gases: carbon dioxide, methane and chlorofluorocarbons. The index weights each gas according to it’s heattrapping quality in carbon dioxide equivalents and expresses them in metric tons of carbon per capita) - dimension environmental quality/degradation

human development index (HDI) (UNDP, 1994)

income distribution (Moaddel, 1994. The measure focuses on the share of the top 20% in total incomes in over 80 countries. Wherever possible, Moaddel’s data were updated by World Bank WDR, 1994, 1995 and 1996). For the calculation of the relationship with income distribution in Table 2.13, a reduced sample of the n = 123 world sample with complete data was taken

increase in life expectancy 1960 - 90 (calculated from UNDP, 1993/94 via a regression procedure, predicting 1990 life expectancy on 1960 life expectancy, and then taking the residuals as growth rates) - dimension redistribution and human development
mean years of schooling of the population aged 25 and > (UNDP, 1993 and 1994)

mobility in the international system (calculated from UNDP, 1993 ff.) This indicator is the difference in relative positions of a country on the PPP $ per capita income scale in 1990 (USA = 100) vis-à-vis 1960 (USA = 100)

violation of civil rights, 1991 (Stiftung, 1993/94, based on Freedom House) - dimension democracy

violation of political rights index, 1991 (Stiftung, 1993/94, based on Freedom House) - dimension democracy

mobility in the international system (calculated from UNDP, 1993 ff.) This indicator is the difference in relative positions of a country on the PPP $ per capita income scale in 1990 (USA = 100) vis-à-vis 1960 (USA = 100)

The models based on 49 Polish voivodships

variables taken from the Polish Central Statistical Office and from the daily Polish Press:

big estates (> 25 ha) per total land

percent employment in agriculture

percent employment in foreign capital enterprises per total employment

percent employment in mixed enterprises per total employment

external migration per total population

female excess infant mortality rates (based on regression residuals of male on female infant mortality rates in the different voivodships of the country). Voivodships are said to have an excess female infant mortality, when the regression residual is positive

human development index

industrial employment

industrial waste

private land per total land

real GDP per capita

religious practice

urbanization

voting data on a voivodship level for all the elections and referenda since 1993

wage level

Excess mortality in the Polish provinces

Original data: Stat. Yearbooks, Poland

Column (1) male infant mortality rate per voivodship

Column (2) female infant mortality rate per voivodship

Column (3) female infant mortality, predicted by linear ordinary least square regression on male infant mortality rates, all per voivodship

Column (4) implicit ‘excess female infant mortality’: real female infant mortality minus predicted female infant mortality rate per voivodship

1	2	3	4	voivodship (abr.)
1485	1195	1206,349862	-11,3498622	Warszawa
1672	1076	1292,0205	-216,0204998	bialpodlas
1335	1404	1137,630099	266,3699006	bialystock
1496	1287	1211,389311	75,61068852	bielski
1984	1838	1434,957606	403,0423935	Bydg.
2021	1670	1451,908481	218,0915187	chelm
2011	1112	1447,327164	-335,3271638	ciech
1444	1121	1187,56646	-66,56646037	czesto
1334	963	1137,171968	-174,1719677	elblas
1572	1064	1246,207325	-182,2073246	gdan
1991	1433	1438,164529	-5,164528718	gorzo
1629	1290	1272,320834	17,67916551	jelen
1926	1692	1408,385965	283,6140352	kalis
1715	1473	1311,720165	161,2798348	kato
1335	1065	1137,630099	-72,6300994	kiele
1579	1195	1249,414247	-54,41424689	konin
1521	1127	1222,842605	-95,84260528	kosza
1620	1099	1268,197649	-169,1976487	krak
1350	1342	1144,502076	197,4979243	krosn
1733	1288	1319,966537	-31,9665367	legni
1752	1315	1328,67104	-13,67103999	leszcz
1509	1259	1217,345024	41,65497575	lubel
1506	1523	1215,970629	307,029371	lomzy
1830	1331	1364,405317	-33,40531665	lodz
1361	1135	1149,541525	-14,54152495	nowosad
1399	912	1166,950532	-254,9505315	olszt
1741	1683	1323,631591	359,3684093	opol
1678	1195	1294,76929	-99,76929034	ostrol
1329	1533	1134,881309	398,1186911	pilsk
1362	1226	1149,999657	76,00034329	piotrk
1794	1413	1347,912574	65,08742643	plock
1290	1228	1117,014171	110,9858294	pozn
1568	1278	1244,374798	33,62520238	przem
1330	1354	1135,339441	218,6605594	radom
1443	1225	1187,108329	37,89167138	rzesz
1379	1148	1157,787896	-9,78789649	siedle
1335	984	1137,630099	-153,6300994	sierad
1374	899	1155,497238	-256,4972377	skiernie
1835	1234	1366,695975	-132,6959754	slupsk
1730	995	1318,592141	-323,5921414	suwal
1688	1055	1299,350608	-244,3506079	szczec
1472	1146	1200,394149	-54,39414943	tarnbrzes
1699	1074	1304,390057	-230,3900571	tarnow
2054	1392	1467,026829	-75,02682909	torun
1726	1274	1316,759614	-42,75961444	walbrz
1557	1797	1239,335348	557,6646517	wlocl
1751	1188	1328,212908	-140,2129082	wroc
1346	696	1142,669549	-446,6695487	zamoj
1432	1294	1182,068879	111,9311207	zielono
male inf mor	fem inf mor	mor trend va	excess

The CPM poverty measure

This measure was developed by the United Nations UNDP in 1996. It is a composite measure of the basic-human needs oriented variety, and combines the percentages of people, not having access to such basic services as water, health etc. For the industrialized countries and for several developing countries, we had to estimate the measure:

Country	CPM	CPM est.	CPM	HDIestimate (2) Human Development Index
Canada		3,1966	3,7081	0,951
USA		4,0884	3,8742	0,94
Japan		4,2505	4,6991	0,938
NL		4,2505	4,5817	0,938
NOR		4,3316	4,1137	0,937
SF		4,4937	5,0614	0,935
France		4,4937	4,4803	0,935
Iceland		4,6559	5,0202	0,933
Swed		4,6559	4,5536	0,933
Spain		4,6559	7,405	0,933
Australia		4,9802	4,3397	0,929
Belgium		4,9802	4,7236	0,929
Austria		5,0613	4,747	0,928
NZ		5,1423	5,013	0,927
CH		5,2234	4,7909	0,926
UK		5,3855	4,7406	0,924
DK		5,3855	4,5233	0,924
GER		5,7098	5,1277	0,92
IRE		5,7909	6,2979	0,919
ITA		6,1963	6,8216	0,914
GRE		6,6016	8,4514	0,909
ISR		6,6827	6,4936	0,908
LUX		7,7367	4,8605	0,895
MAL		8,4663	9,3748	0,886
POR		9,1149	9,4562	0,878
HUNG		10,98	9,4282	0,855
LATV		13,817	12,502	0,82
POL		13,898	11,756	0,819
RUS		15,114	13,69	0,804
BELR		16,493	15,324	0,787
BULG		17,628	15,209	0,773
EST		19,573	14,928	0,749
KAZ		20,303	23,522	0,74
ROM		20,465	17,206	0,738
UKR		22,006	20,053	0,719
LITH		22,006	17,376	0,719
ARM		25,167	25,244	0,68
UZB		25,248	26,644	0,679
AZER		26,383	29,225	0,665
KYR		26,546	27,687	0,663
GEO		28,005	27,14	0,645
ALB		28,978	21,748	0,633
TAJ		30,356	27,943	0,616
Hong Kong	7,3	7,3	7,1448	0,909
Cyprus		6,6016	7,8503	0,909
Barbados	6,5	6,5	7,0111	0,906
Bahamas		7,7367	7,9895	0,895
S-Korea	8,6	8,6	8,8131	0,886
Argentina	6,3	6,3	8,0552	0,885
Costa Rica	6,1	6,1	9,6924	0,884
Uruguay	4,7	4,7	8,6287	0,883
Chile	2,8	2,8	8,8302	0,882
Singapore	7,7	7,7	8,3455	0,881
Trinidad	4,1	4,1	8,7269	0,872
Bahrein		10,088	10,724	0,866
Antigua		10,088	11,529	0,866
Panama	7,2	7,2	10,105	0,859
Venezuela	15,2	15,2	10,372	0,859
Saint Kitts		10,736	11,362	0,858
Fiji		11,142	12,332	0,853
Mexico	16,9	16,9	12,08	0,845
Colombia	13,4	13,4	10,655	0,84
Thailand	21,1	21,1	12,956	0,832
Malaysia	20,6	20,6	12,274	0,826
Mauritius	20,6	20,6	13,384	0,825
Brazil	10	10	14,856	0,796
Seychelles		16,087	15,009	0,792
Dominica		18,357	19,182	0,764
Belize		19,168	19,495	0,754
Algeria	49,5	49,5	21,793	0,746
Botswana	30,4	30,4	19,385	0,741
Saint Vincent		20,465	20,07	0,738
Suriname		20,546	20,117	0,737
Saint Lucia		20,87	17,046	0,733
Grenada		21,195	19,921	0,729
Tunesia	29,9	29,9	22,378	0,727
Oman		22,249	29,365	0,716
Turkey	21,2	21,2	20,723	0,711
Paraguay	15,9	15,9	19,03	0,704
Jamaica	12,3	12,3	17,669	0,702
Dominican R	12,4	12,4	19,94	0,701
Sri Lanka	19,3	19,3	17,262	0,698
Peru	25,7	25,7	17,833	0,694
Philippines	28,8	28,8	16,952	0,665
South Africa	30,4	30,4	23,281	0,649
Indonesia	42,3	42,3	23,051	0,641
Guyana	18,4	18,4	22,464	0,633
Egypt	43,7	43,7	30,621	0,611
Maldives	35,5	35,5	25,969	0,61
China	17,5	17,5	26,665	0,609
Swaziland	25,1	25,1	28,872	0,586
Bolivia	31,6	31,6	27,204	0,584
Guatemala	45	45	30,719	0,58
Mongolia	12,6	12,6	26,838	0,578
Honduras	22,4	22,4	27,847	0,576
El Salvador	25,6	25,6	28,921	0,576
Namibia		33,842	28,537	0,573
Nicaragua	24,3	24,3	28,136	0,568
Gabon	28,4	28,4	34,113	0,557
Cape Verde		36,599	37,151	0,539
Morocco	49,7	49,7	39,607	0,534
Zimbabwe	22,3	22,3	30,894	0,534
Congo	41,7	41,7	39,614	0,517
Papua NG	49,8	49,8	43,181	0,504
Cameroon	33,5	33,5	45,718	0,481
Kenya	33,8	33,8	37,191	0,473
Ghana	39,3	39,3	39,726	0,467
Lesotho	38,6	38,6	32,042	0,464
Equatorial G		42,922	50,647	0,461
Pakistan	60,8	60,8	53,794	0,442
India	61,5	61,5	40,727	0,436
Zambia	35,1	35,1	40,739	0,411
Nigeria	51,6	51,6	56,109	0,4
Comoros		47,949	50,953	0,399
Togo	45,4	45,4	44,502	0,385
Bangladesh	76,9	76,9	55,623	0,365
Tanzania	39,4	39,4	36,285	0,364
Cote d'Ivoire	46,7	46,7	59,75	0,357
C African R	46	46	52,278	0,355
Mauritania	60,8	60,8	67,819	0,353
Madagascar	36,7	36,7	39,654	0,349
Nepal	77,3	77,3	51,856	0,332
Rwanda	51,5	51,5	48,731	0,332
Benin	51,9	51,9	72,718	0,327
Malawi	44,1	44,1	44,769	0,321
Guinea-Biss.	56,6	56,6	62,385	0,297
Gambia	38	38	70,538	0,292
Chad	61,2	61,2	51,878	0,291
Burundi	66,1	66,1	55,192	0,282
Mozambique	66,9	66,9	57,975	0,261
Burkina F.	59,7	59,7	71,822	0,225
Mali	59,4	59,4	56,775	0,223
Sierra L.	62,3	62,3	82,333	0,219
Niger	71,7	71,7	74,866	0,204

CPM measure: UNDP, 1996

CPM (1): = 80,29 - (81,073 * HDI)

CPM (2): = combined EXCEL 7.0 variation estimate from the predictor variables:

% labor force participation rate

absolute GNP

agricultural share

FDI per GDP

human development index

ln PPP (purchasing power)

ln PPP^2

mean years adult education

military expenditures per GDP

population growth

state sector size per GDP

UN accession

UN member years

years of Communism

Income inequality benefiting the top 20% of the population

country code	(1) top 20%	Y^top20%	CPM-measure	estimate top20%	LFPR	original data		estimate = 0,7752POPDYN^2+7,099POPDYN+36,93	estimate=0,1424MEANEDUC^2+0,9301MEANEDUC+48,031
Canada	40,2	43,017	3,1966	42	53	40,2	Canada	45,834	38,183
USA	41,9	40,853	4,0884	40,7	50	41,9	USA	43,801	37,669
Japan	37,5	41,741	4,2505	42,1	52	37,5	Japan	42,691	41,467
NL	36,9	41,782	4,2505	41,8	46	36,9	NL	42,691	40,81
NOR	36,7	39,13	4,3316	39,7	50	36,7	NOR	40,91	38,436
SF	37,6	40,224	4,4937	40,4	52	37,6	SF	39,646	41,251
France	41,9	40,437	4,4937	40,1	44	41,9	France	41,519	38,687
Iceland	44,4	42,363	4,6559	44,4	56		Iceland	44,333	44,535
Swed	36,9	39,788	4,6559	40,2	54	36,9	Swed	40,286	40,128
Spain	36,6	45,512	4,6559	44,9	41	36,6	Spain	42,113	47,669
Australia	42,2	42,998	4,9802	42,5	50	42,2	Australia	46,304	38,687
Belgium	36	39,635	4,9802	39,8	41	36	Belgium	38,99	40,585
Austria	45,1	39,581	5,0613	39,6	46	45,1	Austria	38,99	40,128
NZ	44,7	42,676	5,1423	43	48	44,7	NZ	44,333	41,68
CH	44,6	39,561	5,2234	40,9	53	44,6	CH	42,113	39,659
UK	44,3	38,667	5,3855	39,2	50	44,3	UK	38,99	39,42
DK	38,6	38,617	5,3855	40,3	57	38,6	DK	39,646	41,032
GER	40,3	38,847	5,7098	39,3	50	40,3	GER	38,99	39,659
IRE	39,4	42,433	5,7909	43,3	37	39,4	IRE	41,519	45,029
ITA	41	41,473	6,1963	43,3	43	41	ITA	39,646	46,997
GRE	44,5	43,768	6,6016	44,5	42		GRE	41,519	47,564
ISR	39,6	46,718	6,6827	46,7	39	39,6	ISR	50,73	42,703
LUX	41,8	38,981	7,7367	41,8	43		LUX	41,519	42,097
MAL	44	44,056	8,4663	44	37		MAL	39,646	48,406
POR	49,1	42,591	9,1149	43,6	49	49,1	POR	38,99	48,151
HUNG	36,6	42,536	10,98	40,6	46	36,6	HUNG	37,632	43,47
LATV	36,7	42,235	13,817	42,9	55	36,7	LATV	40,91	44,868
POL	36,6	44,891	13,898	44,1	49	36,6	POL	42,113	46,083
RUS	53,8	40,892	15,114	42,9	52	53,8	RUS	40,91	44,868
BELR	32,9	42,352	16,493	44,5	52	32,9	BELR	41,519	47,564
BULG	39,3	42,135	17,628	43,6	51	39,3	BULG	39,646	47,564
EST	46,3	42,436	19,573	43,2	54	46,3	EST	41,519	44,868
KAZ	40,4	44,584	20,303	47,7	47	40,4	KAZ	46,304	49,122
ROM	34,8	41,905	20,465	44,5	46	34,8	ROM	41,519	47,456
UKR	35,4	39,413	22,006	44,7	50	35,4	UKR	40,91	48,485
LITH	42,1	41,177	22,006	43,8	52	42,1	LITH	42,691	44,868
ARM	48,4	44,297	25,167	48,4	48		ARM	47,62	49,122
UZB	50,1	50,69	25,248	50,1	39		UZB	50,998	49,122
AZER	48,6	45,762	26,383	48,6	42		AZER	48,027	49,122
KYR	49,1	46,102	26,546	49,1	41		KYR	49,157	49,122
GEO	45,6	38,659	28,005	45,6	49		GEO	42,113	49,122
ALB	48,7	44,097	28,978	48,7	48		ALB	49,157	48,324
TAJ	50,3	51,642	30,356	50,3	36		TAJ	51,487	49,122
Hong Kong	47	48,134	7,3	47,5	51	47	Hong Kong	47,62	47,346
Cyprus	44,5	42,198	6,6016	44,5	48		Cyprus	41,519	47,564
Barbados	41,9	42,718	6,5	41,9	51		Barbados	39,646	44,191
Bahamas	49,4	49,869	7,7367	49,4	50		Bahamas	50,446	48,324
S-Korea	45,2	46,366	8,6	45,6	46	45,2	S-Korea	46,758	44,365
Argentina	45,2	48,031	6,3	45,2	38		Argentina	45,834	44,535
Costa Rica	50,7	56,154	6,1	50	38	50,7	Costa Rica	51,25	48,706
Uruguay	52,5	46,392	4,7	43,9	44	52,5	Uruguay	41,519	46,222
Chile	61	48,223	2,8	46,9	38	61	Chile	47,197	46,622
Singapore	48,9	47,739	7,7	47,9	49	48,9	Singapore	46,304	49,473
Trinidad	50	47,09	4,1	45,3	39	50	Trinidad	44,849	45,796
Bahrein	51,1	53,355	10,088	51,1	45		Bahrein	52,712	49,397
Antigua	44,8	49,022	10,088	44,8	47		Antigua	40,286	49,296
Panama	59,8	53,499	7,2	48,8	39	59,8	Panama	49,833	47,771
Venezuela	58,4	53,927	15,2	49,8	37	58,4	Venezuela	51,487	48,06
Saint Kitts	40,4	39,889	10,736	40,4	43		Saint Kitts	32,392	48,485
Fiji	48,5	51,822	11,142	48,5	34		Fiji	48,027	49,071
Mexico	55,3	54,457	16,9	49,8	37	55,3	Mexico	50,446	49,169
Colombia	55,8	50,626	13,4	48,1	40	55,8	Colombia	49,157	46,997
Thailand	52,7	52,592	21,1	49,5	57	52,7	Thailand	49,502	49,492
Malaysia	53,7	49,597	20,6	49,5	39	53,7	Malaysia	50,147	48,774
Mauritius	47,6	45,419	20,6	47,6	41		Mauritius	45,834	49,451
Brazil	67,5	53,965	10	49,5	44	67,5	Brazil	49,502	49,473
Seychelles	47,8	49,778	16,087	47,8	40		Seychelles	46,304	49,296
Dominica	39,2	38,888	18,357	39,2	40		Dominica	29,056	49,257
Belize	49,4	50,258	19,168	49,4	31		Belize	49,502	49,296
Algeria	46,1	53,085	49,5	50,1	28	46,1	Algeria	50,73	49,519
Botswana	50,7	55,891	30,4	50,7	44		Botswana	51,915	49,466
Saint Vincent	46,3	46,004	20,465	46,3	40		Saint Vincent	43,254	49,296
Suriname	46,6	48,05	20,546	46,6	34		Suriname	43,801	49,425
Saint Lucia	46,4	46,057	20,87	46,4	40		Saint Lucia	43,254	49,492
Grenada	43,4	44,005	21,195	43,4	40		Grenada	37,632	49,257
Tunesia	46,3	50,712	29,9	49,1	35	46,3	Tunesia	48,796	49,356
Oman	50,8	49,863	22,249	50,8	26		Oman	52,825	48,753
Turkey	56,5	51,169	21,2	49,5	45	56,5	Turkey	49,502	49,534
Paraguay	50,2	52,186	15,9	50,2	37		Paraguay	51,25	49,169
Jamaica	47,5	46,827	12,3	46,6	49	47,5	Jamaica	44,333	48,961
Dominican R	55,7	51,957	12,4	49,8	40	55,7	Dominican R	50,147	49,397
Sri Lanka	39,3	46,122	19,3	47,3	40	39,3	Sri Lanka	47,197	47,346
Peru	50,4	52,509	25,7	49,1	35	50,4	Peru	50,147	48,06
Philippines	47,8	50,707	28,8	48,5	40	47,8	Philippines	50,147	46,875
South Africa	63,3	53,172	30,4	49,7	39	63,3	South Africa	49,833	49,492
Indonesia	40,7	48,848	42,3	48,9	44	40,7	Indonesia	48,419	49,451
Guyana	46,4	45,443	18,4	46,4	40		Guyana	43,801	49,071
Egypt	41,1	48,016	43,7	49,5	35	41,1	Egypt	49,502	49,54
Maldives	49,9	51,957	35,5	49,9	41		Maldives	50,446	49,333
China	43,9	43,84	17,5	48,2	59	43,9	China	47,197	49,122
Swaziland	50,1	52,566	25,1	50,1	34		Swaziland	50,73	49,509
Bolivia	48,2	50,823	31,6	49,3	40	48,2	Bolivia	49,157	49,473
Guatemala	63	48,296	45	50,2	35	63	Guatemala	50,998	49,451
Mongolia	48,9	47,02	12,6	48,9	47		Mongolia	50,446	47,346
Honduras	57,4	52,435	22,4	50,6	34	57,4	Honduras	51,709	49,473
El Salvador	47,8	49,871	25,6	49,3	36	47,8	El Salvador	49,157	49,425
Namibia	49,7	49,878	33,842	49,7	42		Namibia	50,147	49,201
Nicaragua	55,2	50,09	24,3	50,4	34	55,2	Nicaragua	51,487	49,333
Gabon	50,2	51,928	28,4	50,2	49		Gabon	50,998	49,487
Cape Verde	48,5	48,55	36,599	48,5	37		Cape Verde	47,62	49,388
Morocco	46,3	45,76	49,7	49,7	38	46,3	Morocco	49,833	49,54
Zimbabwe	62,3	52,146	22,3	50,6	46	62,3	Zimbabwe	51,709	49,546
Congo	50	50,986	41,7	50	42		Congo	50,73	49,356
Papua NG	49	49,424	49,8	49	49		Papua NG	49,157	48,819
Cameroon	49,7	49,261	33,5	49,7	40		Cameroon	50,147	49,155
Kenya	62,1	53,827	33,8	50,9	48	62,1	Kenya	52,44	49,417
Ghana	42,2	45,87	39,3	50	47	42,2	Ghana	50,446	49,542
Lesotho	60,1	54,46	38,6	49,7	40	60,1	Lesotho	49,833	49,542
Equatorial G	46,5	45,894	42,922	46,5	45		Equatorial G	44,333	48,684
Pakistan	39,7	47,617	60,8	50,3	35	39,7	Pakistan	51,25	49,284
India	42,6	47,435	61,5	49,1	43	42,6	India	48,796	49,443
Zambia	50,4	54,205	35,1	50,6	42	50,4	Zambia	51,709	49,504
Nigeria	49,3	50,226	51,6	49,8	40	49,3	Nigeria	50,73	48,942
Comoros	50,3	51,003	47,949	50,3	44		Comoros	51,709	48,819
Togo	46,8	49,051	45,4	50,1	42	46,8	Togo	50,998	49,155
Bangladesh	37,9	47,028	76,9	49,6	49	37,9	Bangladesh	49,833	49,322
Tanzania	45,4	50,616	39,4	50,4	52	45,4	Tanzania	51,487	49,322
Cote d'Ivoire	44,1	48,888	46,7	51,1	37	44,1	Cote d'Ivoire	52,825	49,284
C African R	48,8	47,019	46	48,8	49		C African R	48,796	48,882
Mauritania	46,5	47,027	60,8	48,9	46	46,5	Mauritania	49,502	48,38
Madagascar	50	52,65	36,7	50,2	48	50	Madagascar	50,998	49,388
Nepal	39,5	43,846	77,3	49,4	47	39,5	Nepal	49,502	49,356
Rwanda	39,1	52,465	51,5	50,2	52	39,1	Rwanda	51,487	48,882
Benin	49,2	44,568	51,9	49,2	46		Benin	49,833	48,612
Malawi	55,2	54,178	44,1	50,7	45	55,2	Malawi	52,105	49,201
Guinea-Biss.	58,9	47,857	56,6	48,2	48	58,9	Guinea-Biss.	48,027	48,38
Gambia	50,2	51,014	38	50,2	50		Gambia	51,915	48,538
Chad	48,4	47,11	61,2	48,4	49		Chad	48,419	48,297
Burundi	48,6	45,859	66,1	48,6	54		Burundi	48,796	48,38
Mozambique	49	48,447	66,9	49	53		Mozambique	48,796	49,155
Burkina F.	48,9	45,148	59,7	48,9	54		Burkina F.	49,502	48,211
Mali	49,3	46,03	59,4	49,3	50		Mali	50,147	48,38
Sierra L.	54,5	47,377	62,3	48,4	37	54,5	Sierra L.	48,027	48,753
Niger	44,1	49,569	71,7	50	49	44,1	Niger	51,709	48,211

The rank scale of inequality in the world system

Brazil	67,5
South Africa	63,3
Guatemala	63
Zimbabwe	62,3
Kenya	62,1
Chile	61
Lesotho	60,1
Panama	59,8
Guinea-Biss.	58,9
Venezuela	58,4
Honduras	57,4
Turkey	56,5
Colombia	55,8
Dominican R	55,7
Mexico	55,3
Nicaragua	55,2
Malawi	55,2
Sierra L.	54,5
RUS	53,8
Malaysia	53,7
Thailand	52,7
Uruguay	52,5
Bahrein	51,1
Oman	50,8
Costa Rica	50,7
Botswana	50,7
Peru	50,4
Zambia	50,4
TAJ	50,3
Comoros	50,3
Paraguay	50,2
Gabon	50,2
Gambia	50,2
UZB	50,1
Swaziland	50,1
Trinidad	50
Congo	50
Madagascar	50
Maldives	49,9
Namibia	49,7
Cameroon	49,7
Bahamas	49,4
Belize	49,4
Nigeria	49,3
Mali	49,3
Benin	49,2
POR	49,1
KYR	49,1
Papua NG	49
Mozambique	49
Singapore	48,9
Mongolia	48,9
Burkina F.	48,9
C African R	48,8
ALB	48,7
AZER	48,6
Burundi	48,6
Fiji	48,5
Cape Verde	48,5
ARM	48,4
Chad	48,4
Bolivia	48,2
Seychelles	47,8
Philippines	47,8
El Salvador	47,8
Mauritius	47,6
Jamaica	47,5
Hong Kong	47
Togo	46,8
Suriname	46,6
Equatorial G	46,5
Mauritania	46,5
Saint Lucia	46,4
Guyana	46,4
EST	46,3
Saint Vincent	46,3
Tunesia	46,3
Morocco	46,3
Algeria	46,1
GEO	45,6
Tanzania	45,4
S-Korea	45,2
Argentina	45,2
Austria	45,1
Antigua	44,8
NZ	44,7
CH	44,6
GRE	44,5
Cyprus	44,5
Iceland	44,4
UK	44,3
Cote d'Ivoire	44,1
Niger	44,1
MAL	44
China	43,9
Grenada	43,4
India	42,6
Australia	42,2
Ghana	42,2
LITH	42,1
USA	41,9
France	41,9
Barbados	41,9
LUX	41,8
Egypt	41,1
ITA	41
Indonesia	40,7
KAZ	40,4
Saint Kitts	40,4
GER	40,3
Canada	40,2
Pakistan	39,7
ISR	39,6
Nepal	39,5
IRE	39,4
BULG	39,3
Sri Lanka	39,3
Dominica	39,2
Rwanda	39,1
DK	38,6
Bangladesh	37,9
SF	37,6
Japan	37,5
NL	36,9
Swed	36,9
NOR	36,7
LATV	36,7
Spain	36,6
HUNG	36,6
POL	36,6
Belgium	36
UKR	35,4
ROM	34,8
BELR	32,9

(^Y) is an EXCEL 5.0 variation trendline estimate of inequality, based on the following variables:

early death

e-function

pi-function

mean y scho

absoluteGNP

FDI per GDP

Years of Comm

population gr

state sector

%labor force

ln PPP

ln PPP^2

agr.shareGP

MILEX

HDI

estimate top 20% is based on the averages of the two far-right hand columns above. POPDYN is the population growth rate, MEANEDU are the average years of education of the population above 25 years of age. The results are rounded at 1 decimal point.

Kondratieff cycle data:

For the calculations of Kondratieff cycles, I used J. Goldstein’s classic (1988), plus the following data (estimates) for world (and or OECD) growth 1975 - 1997, based on IMF, IFRI, UN ECE, and Fischer Weltalmanach:

year	growth rate	5-year aver.	9-year aver.
1741	-4,8
1742	10,1
1743	-4,6	2,16
1744	14,3	4
1745	-4,2	2,8	1,81111111
1746	4,4	5,34	3,73333333
1747	4,1	0,26	2,61111111
1748	8,1	3,6	3,12222222
1749	-11,1	2,72	1,94444444
1750	12,5	1,9	2,02222222
1751	0	1,02	1,94444444
1752	0	2,54	0,3
1753	3,7	0,78	-0,15555556
1754	-3,5	-1,36	1,07777778
1755	3,7	-0,56	-0,74444444
1756	-10,7	-1,3	-0,74444444
1757	4	-1,38	-0,21111111
1758	0	-2,12	-0,62222222
1759	-3,9	0,98	-0,66666667
1760	0	0,18	-0,17777778
1761	4,8	-0,6	1,01111111
1762	0	1,8	2,21111111
1763	-3,9	1,8	2,21111111
1764	8,1	3,8	2,28888889
1765	0	3,8	3,02222222
1766	14,8	3,94	2,14444444
1767	0	3,64	2,14444444
1768	-3,2	3,02	3,65555556
1769	6,6	0,06	2,1
1770	-3,1	2	1,41111111
1771	0	1,46	0,13333333
1772	9,7	-1,1	0,5
1773	-5,9	0,18	1,2
1774	-6,2	0,84	0,81111111
1775	3,3	-0,48	0,5
1776	3,3	1,32	0,5
1777	3,1	1,38	-0,92222222
1778	3,1	0,72	1,88888889
1779	-5,9	-0,56	2,27777778
1780	0	2,7	2,22222222
1781	-3,1	1,54	3,95555556
1782	19,4	3,28	4,11111111
1783	-2,7	7,06	4,01111111
1784	2,8	8,58	4,43333333
1785	18,9	5,14	6,61111111
1786	4,5	5,26	6,75555556
1787	2,2	8,62	4,81111111
1788	-2,1	4,48	5,92222222
1789	19,6	3,96	4,1
1790	-1,8	4,98	2,43333333
1791	1,9	2,68	2,56666667
1792	7,3	-0,46	3,11111111
1793	-13,6	1,04	2,42222222
1794	3,9	2,08	1,25555556
1795	5,7	-1,04	3,48888889
1796	7,1	3,5	3,9
1797	-8,3	6,38	2,5
1798	9,1	6,36	4,63333333
1799	18,3	3,88	4,34444444
1800	5,6	6,66	4
1801	-5,3	5,1	3,5
1802	5,6	1,96	4,55555556
1803	1,3	1,36	3,95555556
1804	2,6	2,66	1,38888889
1805	2,6	2,28	1,04444444
1806	1,2	1,06	2,57777778
1807	3,7	1,04	2,57777778
1808	-4,8	2,22	1,72222222
1809	2,5	3,1	1,55555556
1810	8,5	1,08	1,38888889
1811	5,6	2,26	2,48888889
1812	-6,4	1,98	2,07777778
1813	1,1	2,5	2,61111111
1814	1,1	1,38	4,55555556
1815	11,1	2,66	2,68888889
1816	0	6,44	2,06666667
1817	0	4,56	2,77777778
1818	20	2,34	3,66666667
1819	-8,3	2,34	4,46666667
1820	0	4,16	3,23333333
1821	0	1,82	4,08888889
1822	9,1	3,48	3,28888889
1823	8,3	5,02	2,77777778
1824	0	3,58	4,44444444
1825	7,7	4,84	3,75555556
1826	-7,2	4,52	5,23333333
1827	15,4	3,28	4,22222222
1828	6,7	4,4	2,64444444
1829	-6,2	5,84	4,03333333
1830	13,3	1,58	3,8
1831	0	2,74	5,18888889
1832	-5,9	5,1	4,58888889
1833	12,5	3,5	2,83333333
1834	5,6	5,5	4,63333333
1835	5,3	4,86	4,16666667
1836	10	4,36	4,16666667
1837	-9,1	5,06	4,82222222
1838	10	4	2,96666667
1839	9,1	2	2,83333333
1840	0	2,98	3,63333333
1841	0	1,86	3,34444444
1842	-4,2	2,54	4,35555556
1843	4,4	4,02	2,85555556
1844	12,5	4,02	3,03333333
1845	7,4	4,16	3,03333333
1846	0	5,42	3,03333333
1847	-3,5	2,92	4,57777778
1848	10,7	1,44	5,06666667
1849	0	3,38	4,56666667
1850	0	5,84	4,3
1851	9,7	5,3	4,82222222
1852	8,8	6,3	6,22222222
1853	8	7,24	4,8
1854	5	7,12	4,8
1855	4,7	4,94	5,51111111
1856	9,1	3,34	5,1
1857	-2,1	3,62	4,12222222
1858	0	3,88	1,97777778
1859	6,4	2,06	2,36666667
1860	6	0,22	2,5
1861	0	1,92	1,68888889
1862	-11,3	1,82	3,53333333
1863	8,5	0,98	3,35555556
1864	5,9	3,88	3,54444444
1865	1,8	5,82	3,37777778
1866	14,5	5,74	3,06666667
1867	-1,6	5,46	5,3
1868	8,1	4,54	5,55555556
1869	4,5	3,4	4,76666667
1870	-2,8	5,88	4,56666667
1871	8,8	4,02	2,67777778
1872	10,8	3,12	3,27777778
1873	-1,2	3,18	2,51111111
1874	0	2,18	2,27777778
1875	-2,5	0,26	2,84444444
1876	3,8	0,98	2,76666667
1877	1,2	1,44	2,15555556
1878	2,4	3,56	3,06666667
1879	2,3	3,86	3,48888889
1880	8,1	5,02	3,46666667
1881	5,3	5,3	2,93333333
1882	7	4,3	3,43333333
1883	3,8	2,48	4,05555556
1884	-2,7	2,56	4,26666667
1885	-1	2,76	4,33333333
1886	5,7	2,84	4,31111111
1887	8	5,12	3,68888889
1888	4,2	6,34	3,42222222
1889	8,7	5,48	3,35555556
1890	5,1	4,16	3,85555556
1891	1,4	2,66	4,27777778
1892	1,4	1,62	3,66666667
1893	-3,3	2,5	3,73333333
1894	3,5	2,72	3,78888889
1895	9,5	3,4	4,04444444
1896	2,5	5,9	3,94444444
1897	4,8	6,68	4,22222222
1898	9,2	4,88	5,47777778
1899	7,4	5,16	5,33333333
1900	0,5	5,8	4,32222222
1901	3,9	4,4	5,17777778
1902	8	3	5,15555556
1903	2,2	4,94	4,45555556
1904	0,4	5,08	2,72222222
1905	10,2	4,06	3,74444444
1906	4,6	1,98	4,1
1907	2,9	3,84	3,4
1908	-8,2	3,22	4,13333333
1909	9,7	2,64	4,62222222
1910	7,1	3,82	2,5
1911	1,7	6,42	2,33333333
1912	8,8	2,7	2,85555556
1913	4,8	1,9	3,85555556
1914	-8,9	3,08	2,12222222
1915	3,1	1,48	0,41111111
1916	7,6	-0,66	0,86666667
1917	0,8	-0,54	-1,67777778
1918	-5,9	-1,7764E-16	0,28888889
1919	-8,3	-4,34	1,85555556
1920	5,8	0	2,15555556
1921	-14,1	2,22	2,21111111
1922	22,5	5,04	2,48888889
1923	5,2	5,5	3,87777778
1924	5,8	8,98	5,31111111
1925	8,1	5,8	5,47777778
1926	3,3	5,68	5,67777778
1927	6,6	5,98	1,71111111
1928	4,6	1,9	-0,46666667
1929	7,3	-1,4	0,28888889
1930	-12,3	-5,6	0,63333333
1931	-13,2	-4	1,56666667
1932	-14,4	-3,22	2,24444444
1933	12,6	1,58	2,85555556
1934	11,2	6,76	0,88888889
1935	11,7	11,66	4,14444444
1936	12,7	7,06	6,43333333
1937	10,1	8,22	10,1888889
1938	-10,4	7,36	10,6444444
1939	17	8,7	10,9444444
1940	7,4	10,02	9,07777778
1941	19,4	14,88	4,25555556
1942	16,7	10,46	1,47777778
1943	13,9	2,84	4,03333333
1944	-5,1	-4,02	2,97777778
1945	-30,7	-4,84	2,37777778
1946	-14,9	-6,12	1,74444444
1947	12,6	-4,7	0,84444444
1948	7,5	4,18	-0,43333333
1949	2	8,88	0,95555556
1950	13,7	6,84	4,36666667
1951	8,6	6,82	7,34444444
1952	2,4	6,42	6,48888889
1953	7,4	6,06	6,02222222
1954	0	5,32	5,51111111
1955	11,9	5,5	5,24444444
1956	4,9	3,5	5,02222222
1957	3,3	5,76	5,15555556
1958	-2,6	4,7	5,06666667
1959	11,3	4,44	5,63333333
1960	6,6	5,1	5,22222222
1961	3,6	6,64	5,5
1962	6,6	6,02	6,01111111
1963	5,1	6,18	6,46666667
1964	8,2	7,04	5,91111111
1965	7,4	6,02	6
1966	7,9	6,26	5,83333333
1967	1,5	6,1	5,32222222
1968	6,3	5,04	5,62222222
1969	7,4	3,86	5,72222222
1970	2,1	5,12	4,98888889
1971	2	5,68	3,37777778
1972	7,8	4,36	3,74444444
1973	9,1	2,62	3,45555556
1974	0,8	3,18	3,08888889
1975	-6,6	2,36	3,18888889
1976	4,8	1,36	3,01111111
1977	3,7	1,8	2,31111111
1978	4,1	3,2	1,26666667
1979	3	2,54	1,48888889
1980	0,4	1,74	2,76666667
1981	1,5	1,48	2,61111111
1982	-0,3	1,86	2,48888889
1983	2,8	2,46	2,41111111
1984	4,9	2,68	2,55555556
1985	3,4	3,42	2,92222222
1986	2,6	3,72	3,05555556
1987	3,4	3,48	3,28888889
1988	4,3	3,34	3,28888889
1989	3,7	3,18	3,04444444
1990	2,7	3,06	3,12222222
1991	1,8	2,74	3,24444444
1992	2,8	2,82	3,31111111
1993	2,7	3,02	3,32222222
1994	4,1	3,46
1995	3,7	3,78
1996	4
1997	4,4
1998
1999

1975	-6,6 (-0,7)
1976	4,8
1977	3,7
1978	4,1
1979	3
1980	0,4
1981	1,5
1982	-0,3
1983	2,8
1984	4,9
1985	3,4
1986	2,6
1987	3,4
1988	4,3
1989	3,7
1990	2,7
1991	1,8
1992	2,8
1993	2,7
1994	4,1
1995	3,7
1996	4
1997	4,4
1998
1999

The graphical results for these data were already published in Tausch (1998) at:

/archive

Books -> Website documents

The geography of the development marathon

Some further details on the geography of the development marathon should be considered:

Map 1: development reversals. GDP per capita lowest values, 1960-95

Legend: Lowest value for GDP per capita in 1987 constant real values, 1960-95 in the year ... The darker, the later that date. Only countries, marked by white color, had their lowest real income back in 1960. All the others suffered a setback since 1960; the darker, the later the date of that setback. The dark zones of our map are zones of late impoverishment in the world economy.

The UNDP data series also permits the analysis of the question, in which year a country achieved its highest per capita income in real terms. A very fortunate and small number of countries achieved their highest GNP per capita in 1995, while others have to realize that they were better off ten, fifteen, or even twenty years from now. Their racing along the last 10, 15, or even 20 last kilometers was totally in vain, and they have to start again by kilometer 10 or 20. Will they ever mile number 26? In terms of the marathon race, they stumbled on their way, while approaching the finish, or they even took a wrong direction:

Map 2: development stability. GDP per capita highest values, 1960-95

Legend: Highest value for GDP per capita in 1987 constant real values, 1960-95 in the year ... The darker, the later that date. Only countries, marked by dark color, had its highest real income in 1995. All the others suffered a setback; the lighter, the earlier the date of that severe setback, that could not be made good until 1995.

Map 3: the stability and strength of the growth engine. Development stability and avoidance of development reversals combined

Legend: Highest value for GDP per capita in 1987 constant real values minus lowest value for GDP per capita in 1987 constant real values. The darker, the longer the period of uninterrupted ascending growth. Countries, marked by white or light colors, had their lowest real income often well in the 1980s and beyond and their highest real income often in the 1960s or early 1970s. The lighter the shade of the country, the greater the setback since 1960.

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