Using quantitative and qualitative data

Some forms of social exclusion are relatively easy to measure, while others are quite difficult. Many forms of social exclusion are represented by clear divisions between groups, but multiple identities can blur group boundaries, and some excluded groups can be ‘invisible’. For example, it can be difficult to collect information on mobile populations, and some sensitive information such as HIV status can be difficult to collect using traditional methods such as surveys. This has implications for how data is collected and reported.

Gacitua-Mario, E., & Woden, Q. (2001). Measurement and Meaning: Combining Quantitative and Qualitative Methods for the Analysis of Poverty and Social Exclusion in Latin America (Technical Paper No. 518). Washington, DC: World Bank.
What policy implications do integrated poverty analyses, incorporating quantitative and qualitative methods, present to decision-makers in Latin America? This paper examines three case studies, from Argentina, Chile and Uruguay, to survey the recommendations produced by research combining quantitative and qualitative approaches. It argues that governments need to improve service provision to marginal communities by expanding public information campaigns and developing their collection of data on poverty in these areas.
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Adato, M., Carter, M. R., & May, J. (2006). Exploring Poverty Traps and Social Exclusion Using Qualitative and Quantitative Data. Journal of Development Studies, 42(2), 226-247.
Are poverty traps inevitable in a polarised society like South Africa? This article investigates social capital and blockages to upward mobility using quantitative and qualitative data from the 1990s. Large numbers of South Africans are indeed trapped in poverty. Social relationships are most helpful for non-poor households. For the poor, social capital at best helps stabilise livelihoods at low levels and does little to promote upward mobility. Poverty alleviation therefore requires more proactive efforts to ensure that households have a minimum bundle of assets and access to the markets needed to increase them.
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Popay, J. et al. (2008) Defining and Measuring Social Exclusion. In Understanding and Tackling Social Exclusion, Final Report to the WHO Commission on Social Determinants of Health from the Social Exclusion Knowledge Network, Part 2
Part II of this report focuses on issues related to the definition and measurement of social exclusion. Chapter 2 describes the general approach to the concept of social exclusion adopted by the SEKN and presents the conceptual model we have developed. The global salience of the concept of social exclusion is considered, as is the relationship between social exclusion, population health and health inequalities. Chapter 3 presents a series of thematic case studies to explore the nature, scale and impact of exclusionary processes, before describing some of the formal approaches to measurement which are available or being developed.
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Stewart, F., Brown, G., & Mancini, L. (2005). Why Horizontal Inequalities Matter: Some Implications for Measurement (Working Paper No. 19). Oxford: Centre of Research on Inequality, Human Security, and Ethnicity.
Why do inequalities between groups matter as well as inequalities between individuals? What is the best way to measure such horizontal inequalities? This paper argues that horizontal inequalities (HI) matter for the wellbeing of individuals within groups, and for their impact on wider growth and conflict. Most discussion of inequality concerns Vertical Inequality (VI) between individuals, and is generally confined to a few economic variables such as income or consumption. Horizontal inequalities (HI) are inequalities between groups, and have been largely ignored by policy makers.  Group inequality is important because it can affect happiness, efficiency and political stability. It is difficult to assess HI because group identities are fluid, multiple, and may be endogenous. However, felt differences are important and clear enough in many societies to measure HIs if the contingent nature of group definitions is taken into account. Three alternative HI measures were reviewed and compared using data over time for Indonesia, South Africa and the USA; the coefficient of variation among groups (GCOV), the Group Gini (GGINI), and Group Theil (GTHEIL) indices.
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