We find strong support for the idea that labour market clusters in LMICs are associated 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. Regarding our original three research questions, we respond in turn. First, how do LMICs cluster together across labour market regulations, measured with inequality and poverty indicators? Based on labour market inequality and poverty indicators, LMICs clustered into six labour market groups: Residual, Emerging, Informal, Post-Communist, Less Successful Informal, and Insecure. Second, what is the strength of association between labour market regulations and population health? Labour market inequality and poverty and population health were strongly correlated in low-income countries, but only labour market poverty and health was significant in middle-income nations. Improving material living conditions in LMICs are crucial to enhancing population health through strengthening labour market regulations (e.g., decreasing levels of child labour and poor workers). Third, are more egalitarian labour market clusters associated with better population health outcomes? More egalitarian clusters exhibited better health outcomes compared to their cluster counterparts and health distributions were graded across labour market clusters.
Given the dearth of research on labour markets and population health, this study's most key contribution is the development of labour market taxonomies in LMICs. Middle-income clusters consisted of relatively advanced industrialized East Asian and Eastern European countries, less industrialized countries of Latin America, and marginally industrialized countries. Noticeably, East Asian countries and East European countries clustered together to form the Residual group. While the former failed to develop or implement strong labour market regulations and encouraged instead the private sector to meet the citizenry's welfare needs [56
], the latter have succeeded toward strengthening labour markets through redistributive and conservative welfare systems [15
]. Common to all Residual countries includes the extent of growing industrialization, and more importantly, the incorporation of rural farmers into the urban working class [51
Emerging labour markets, or Cluster 2 of middle-low countries, represents the Latin America's upward industrialization (e.g., import substitution type of developmental strategy)[51
]. Emerging markets offer an interesting contrast to the experience of East Asian nations. On one hand, East Asian "tigers" are ideologically important to the US because of their proximity to the former Soviet Union border. In contrast, Latin America's political environment is comparably more independent to advance the interests of workers and buffers the effects of imperialism. This has contributed to the rise of center-left and left-leaning governments in Latin America that often cultivate their support from urban formal sector workers, who in return, enjoy relatively more generous welfare benefits from governments compared to East Asian countries [51
The Informal cluster of middle-income countries represents a mixture of three different types of countries: first are Middle Eastern countries such as Bahrain, Oman, and Saudi Arabia, which predominantly rely on export of petroleum as their primary economic activity. The second are industrialized countries in Africa such as Botswana and Tunisia. El Salvador, Belize, and Turkey form a third sub-cluster, relying primarily on tourism and agriculture for economic growth. Despite variations in GDP per capita and geographical location, Informal labour markets share common industrial specializations, which limit the ability of workers to organize and increase the availability of informal contracts.
Low-income countries represent another level of labour market instability altogether. These countries are similarly impoverished yet critical variations exist. For example, Cluster 1 within low-income countries consists of post-communist republics that seceded from former countries with vestigial forms of welfare states. Failed African states and other similarly unstable countries represent the Insecure cluster where labour contracts are notoriously difficult to enforce [36
] and health indicators are predictably worse. Poor levels of population health are primarily attributed to general economic and political disequilibrium [36
], rather than to the character of labour market conditions or regulations.
This study's analytic methods and findings largely complement other comparative research on global regimes, labour markets, working conditions, and welfare outcomes. Specifically, our work builds on Gough and Wood's comparative welfare regimes framework [36
] and Rosskam's research using the Work Security Index (WSI) [60
]. Guided by Gough and Wood's distinction between informal security and security regimes, we used national income and labour market characteristics to distinguish transitional (e.g., middle-income) and developing (e.g., low-income) countries and to investigate cross-national distributions of population health. By shifting the research focus from developed contexts to developing ones, we advance the analysis of labour market institutions to include informal regulations as important determinants. Our mapping of labour market clusters largely mirror Gough and Wood's cluster analysis of welfare regimes. For example, Gough and Woods's "Actual or Potential Welfare State Regimes" represents their most advantaged cluster (e.g., Thailand, several Eastern European and Latin American countries) because these countries are characterized with high state commitments and high welfare outcomes. Our most advantaged labour market cluster, Residual, includes these same countries and consistently ranked as the healthiest cluster. At the other extreme, our Insecure cluster resembles Gough and Wood's "Externally Dependent Insecurity Regime" given that both consist of sub-Saharan Africa countries with predatory forms of capitalism, high dependencies on foreign aid, and very poor welfare and health outcomes.
This study also augments Rosskam's recent work using the WSI, which was developed by the ILO's Socio-Economic Security Programme [61
] as a benchmarking system to compare industrialized and industrializing countries on the extent governments protect working populations' health, safety, and well-being [62
]. Findings using the WSI cross-validate our methods and results in two important ways. First, Rosskam [60
] found that women workers are most disadvantaged with respect o social and economic insecurities and inequalities. This finding substantiates the gendered dimensions of work and provides support to our use of 'estimated earned income ratio between male and female workers' and 'labour force participation gap between female and male workers' to construct our labour market inequality factor score. Second, the most critical cases of worker insecurities are found in the most economically deprived countries in Africa (e.g., Guinea-Bissau, Mauritania, Rwanda), Asia (e.g., Indonesia, Nepal, China, India) and Eastern Europe (e.g., Albania, Armenia, Bulgaria) [60
] p. 276. Figures , , , and confirm this finding, in that, the worst population health distributions are found in low-income countries, and in Post-Communist (e.g., Albania, Armenia, Bulgaria, China, Indonesia), Less Successful Informal (e.g., India, Mauritania), and Insecure (e.g., Guinea-Bissau, Nepal, Rwanda) clusters.
Given the exploratory nature of our study, several limitations warrant further attention. First, our interpretation adopts a "top-down", macro approach to understanding the impact of labour markets on population health rather than a "bottom-up", micro approach, which represents the more common method within social and health policy literatures. We acknowledge that "bottom-up" effects such as community or labour organizing has the potential to influence macro structural changes (e.g., increasing worker's bargaining power, voting for pro-labour political parties). Though the social mechanisms responsible for population health are non-recursive and reciprocal, we did not test alternative pathways. Second, an alleged weakness of taxonomy construction is its lack of predictive power. To assess the usefulness our taxonomy, we compared our labour market clusters against Gough and Wood's [36
] global welfare regimes and found high agreement between both classifications (e.g., informal security regimes mapped onto middle-income countries and insecurity regimes mirrored low-income nations). Cluster techniques have been criticized for its macro-level focus at the expense of overlooking inequalities within-countries [64
]; however, we counter that identifying labour market clusters remains instructive to bringing to light the political and economic contexts of global health [37
]. Third, our data represents a limited time period from 2000 to 2004. This is potentially problematic because the health impact of labour market policies and regulations is time-dependent. Our results should be interpreted as heuristic and as a proxy for long-term labour market effects. Future studies should take advantage of time-series data (e.g., measurements equally spaced through time) and methods (e.g., time-domain, frequency domain) to make valid inferences on the health impact of labour markets over time [65
]. Forth, our 6 labour market clusters shows much heterogeneity, resulting in part from the limitations associated with quantitative and macro-comparative approaches. Some countries do not entirely conform to the explanations provided for a given cluster (e.g., the Philippines and El Salvador are not Post Communist or oil rich countries, respectively). Since our empiricist approach reduces data at the expense of finer distinctions, future work should elaborate on country clusters using methods that can account for historical, political, and economic factors.