PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of bmcphBioMed Centralsearchsubmit a manuscriptregisterthis articleBMC Public Health
 
BMC Public Health. 2012; 12: 286.
Published online Apr 18, 2012. doi:  10.1186/1471-2458-12-286
PMCID: PMC3349504
Hierarchical cluster analysis of labour market regulations and population health: a taxonomy of low- and middle-income countries
Carles Muntaner,1,2 Haejoo Chung,corresponding author3 Joan Benach,4 and Edwin Ng1
1Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
2Bloomberg Faculty of Nursing, University of Toronto, Toronto, Canada
3Department of Healthcare Management, Korea University, Seoul, Republic of Korea
4Health Inequalities Research Group (GREDS), Employment Conditions Network (EMCONET), CIBER Epidemiología y Salud Pública (CIBERESP), Department of Experimental Sciences and Health, Pompeu Fabra University, Barcelona, Catalonia, Spain
corresponding authorCorresponding author.
Carles Muntaner: carles.muntaner/at/utoronto.ca; Haejoo Chung: hpolicy/at/korea.ac.kr; Joan Benach: joan.benach/at/upf.edu; Edwin Ng: edwin.ng/at/utoronto.ca
Received May 11, 2011; Accepted April 18, 2012.
Abstract
Background
An important contribution of the social determinants of health perspective has been to inquire about non-medical determinants of population health. Among these, labour market regulations are of vital significance. In this study, we investigate the labour market regulations among low- and middle-income countries (LMICs) and propose a labour market taxonomy to further understand population health in a global context.
Methods
Using Gross National Product per capita, we classify 113 countries into either low-income (n = 71) or middle-income (n = 42) strata. Principal component analysis of three standardized indicators of labour market inequality and poverty is used to construct 2 factor scores. Factor score reliability is evaluated with Cronbach's alpha. Using these scores, we conduct a hierarchical cluster analysis to produce a labour market taxonomy, conduct zero-order correlations, and create box plots to test their associations with adult mortality, healthy life expectancy, infant mortality, maternal mortality, neonatal mortality, under-5 mortality, and years of life lost to communicable and non-communicable diseases. Labour market and health data are retrieved from the International Labour Organization's Key Indicators of Labour Markets and World Health Organization's Statistical Information System.
Results
Six labour market clusters emerged: Residual (n = 16), Emerging (n = 16), Informal (n = 10), Post-Communist (n = 18), Less Successful Informal (n = 22), and Insecure (n = 31). Primary findings indicate: (i) labour market poverty and population health is correlated in both LMICs; (ii) association between labour market inequality and health indicators is significant only in low-income countries; (iii) Emerging (e.g., East Asian and Eastern European countries) and Insecure (e.g., sub-Saharan African nations) clusters are the most advantaged and disadvantaged, respectively, with the remaining clusters experiencing levels of population health consistent with their labour market characteristics.
Conclusions
The labour market regulations of LMICs appear to be important social determinant of population health. This study demonstrates the heuristic value of understanding the labour markets of LMICs and their health effects using exploratory taxonomy approaches.
Articles from BMC Public Health are provided here courtesy of
BioMed Central