Employmentweb_low

AppeNdIx
Bank's Atlas method (adjusted by exchange rate). Figure A1 shows three clear groups of
countries and the cut-point log10 of Gnppc (Babones, 2005). then we re-categorised
countries based on our knowledge and perception of their position in the world-system. For
example, oil-rich countries were categorised as core countries based on income level.
However, their function as a provider of oil does not qualify them as core countries, which
usually occupy high value-added sectors such as finance and banking (Arrighi, 2003). we have
also categorised east-Asian countries into the semi-periphery, because of their relative
dependence on Us hegemony and integrated industrialisation with Japan and the Us.
Figure a1. Three positions in the World-System (year 2000, smoothing=0.15).
structure of the world-Economy analytical tool
Salvatore babones
University of pittsburgh
* All values are expressed as 1995 US dollars
Year:
Sample:
Smoothing kernel: Income series:
Weighting:
Countries by National Income Level Troughs in Distribution
Log10
Value
Trough 1 midpoint 3,275 $1,884
Trough 2 midpoint 3,925 $8,414
Peaks in Distribution
Log10
Value
peak 1 midpoint 2,675 $473
peak 2 midpoint 3,425 $2,661
peak 3 midpoint 4,375 $23,714
population in bin (millions)*
log10 of Income per Capita
COUNTRIES included: 102
POPULATION (millions): 5,024
Journal of World-Systems Research, xI, 1, July 2005
http://jwsr.ucr.edu/
ISSN1076-156x
© 2005 Salvatore J. babones
Factor scores and cluster analyses
For semi-peripheral and peripheral countries, we constructed 2 labpov and labeq
factor scores, using the variables listed under each factor name in table A1. Factor
analyses were conducted using a principal components method, and the reliability of
the score was measured by cronbach's alpha. Finally, factor scores were constructed
using the regression method. Using this factor score, we conducted a series of
hierarchical cluster analyses to generate clusters of countries. this was achieved
using ward's method of measuring squared euclidean distance.
For core countries, we used 3 standardised variables listed in table A1 to generate
clusters using ward's methods. Analyses were conducted using stAtA version 10.0.
descriptive analyses of labour market variables and bivariate association
with various health outcomes
we calculated descriptive statistics of standardized labour market variables and
factors by position in the world system and later by labour market clusters. the
bivariate association of labour market variables and factors with various health
outcomes was also calculated to analyse the relationship between labour market
characteristics and health outcomes.
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