Quantitative and qualitative methods for poverty analysis

The major differences between quantitative and qualitative poverty analysis methods are outlined in detail in Kanbur (2003). Key fundamental differences include data collection methods, type of data collected and methods of analysis. Quantitative analysts tend to rely on

deductive methods and general random sampling to capture the big picture. In contrast, qualitative researchers rely on inductive methods (Kanbur and Shaffer, 2007) and are more concerned with returning the research findings to the population under study and to using the research experience to directly empower the poor.

The standard static poverty and poverty dynamic measures are inherently quantitative based on monetary indicators of poverty  usually income or expenditure, such that a person with a higher income or expenditure is deemed to enjoy a higher standard of living. A cut-off level of income or expenditure is typically chosen as the poverty line, below which one is considered to be poor. The strengths of quantitative methods include ease of aggregation, they provide results whose reliability are measurable, and allow simulation of different policy options. These measures rely on rigorous statistical methods for inference that can be used to examine a variety of poverty issues: time series comparison to identify trends, cross-section comparisons at different levels, correlations which identify associations and raise questions of causality and covariant changes, estimation of prevalence and distribution of poverty within population areas, triangulation and linkages with qualitative data. Other advantages of these measures include the credibility of numbers in influencing policy-makers and the utility to policy-makers of being able to put numbers on trends and other comparisons.

Despite widespread use, flow-based quantitative approaches for poverty analysis suffer from two fundamental conceptual problems. The first is the identification problem of what weights to attach to aspects of individual welfare that are not revealed by market behaviour. The second is the referencing problem of determining the appropriate level of welfare below which one is considered to be poor (i.e. the poverty line). It can be argued that while the poverty line used in this approach is a numerical parameter calculated using statistical methods, it is subjectively chosen, and the same value judgements can be used to choose other poverty lines. In practice, these problems are dealt with by making assumptions based upon the caloric energy requirements of 2250 per adult equivalent per day. Also, these measures can only provide partial information on poverty and often miss out many of the other wider aspects of well-being. While it is not possible to capture all of the different dimensions of poverty in conventional household surveys, there have been efforts to include information on some of the key non-monetary indicators of poverty (such as education, anthropometric status, morbidity and mortality) (Baulch and Masset, 2003).

In recent years, the use of qualitative approaches in poverty appraisal including poverty trends and dynamics has been increasing. These are mainly in the form of

participatory poverty assessments (PPAs). In general, PPAs can be classified as contextual methods of analysis including data collection methods that aim to understand poverty dimensions within social, cultural, economic and political environment of a locality or of group of people. Participatory poverty assessment methods are diverse and often act as complimentary to conventional quantitative approaches. These approaches are generally subjective and often context specific. The commonly used PPA methodologies include focus group discussions (FGDs), timelines, trend analysis, gender analysis, social mapping, seasonal calendar, wealth ranking, or a combination of these methods. These tools are often adopted in a sequence, and as such can be tailored to fit a particular context and the specific aspect of interest. The main strengths of participatory approaches include a richer definition of poverty, more insights into causal processes, and more accuracy and depth of information on certain aspects of poverty. The major limitations that have been cited include lack of generalizability, difficulties in verifying information, subjectiveness and context specificity.

New participatory methods of poverty and poverty dynamics analyses that rely on community-based focus group discussions to make interpersonal comparisons of welfare have been developed over the past few years. In principle, it is possible to triangulate welfare assessments using focal groups formed from random samples within the geographic primary sampling units of quantitative surveys (Kanbur, 2003). The Stages-of-Progress (SOP) method, for example, relies on community FGDs to delineate locally applicable ‗Stages of Progress‘ that poor households typically follow as they make their way out of poverty (Krishna, 2006). These stages are used to create a ‗ladder‘ by which households‘ well-being is measured at different points in time. De Weerdt (2010) uses a combination of qualitative and quantitative data to explore the growth trajectories of households in Kagera region of Tanzania between 1993 and 2004. The qualitative component comprised of an FGD based on a six-step ‗ladder of life‘ — from poorest (bottom) to richest (top) — to assess the position of individuals on the ‗ladder of life‘ in 1993 and 2004, in what they refer to as ‗peer-assessment‘.

Other qualitative approaches use self-rated welfare. Pradhan and Ravallion (2000), for example, show how qualitative perceptions of the adequacy of consumption and services can be used to derive social subjective poverty lines using data from Jamaica and Nepal.

Ravallion and Lokshin (2002) use a 9-step ladder (from poor to rich) to study the determinants of peoples‘ perception of their economic welfare among Russian adults in a panel study. Though the association between subjective assessments of economic welfare and standard income-based measures was highly significant, large discrepancies were found.

About 60% of the poorest eighth of adults in terms of current household income relative to the poverty line in their sample did not place themselves on either the poorest or second poorest rungs of the subjective ladder. Their ladder question, however, seemed to be better at distinguishing the rich from middle-income groups than it was at identifying the poor. While income was a significant predictor of subjective economic welfare, subjective economic welfare was influenced by other factors including health, education, employment, assets, relative income in the area of residence and expectations about future welfare.

Self-rated welfare has been criticized for biases that arise as a result of mood variability¹¹, and thus responses can vary according to the time of the interview (Ravallion and Lokshin 2001). Secondly, since these measures are subjective, different people can have different personal notions of what a high or low level of subjective welfare means.

Other studies have found participatory approaches such as wealth rankings to result in similar rankings as monetary ones. Scoones (1995) found wealth rankings to be highly correlated with livestock ownership, farm asset holdings, crop harvests and crop sales among farming households in southern Zimbabwe. The study concludes that wealth ranking provides an adequate indicator of relative wealth and can be a useful complementary method to be employed alongside survey assessments. Likewise, Kozel and Parker (1999) found similarities in the characteristics of better-off and worse-off households using participatory approaches and those obtained through survey exercises in rural India. Wealthier households had more agriculture land, more education, higher paid jobs, and better access to basic services.

The potential benefits of using mixed quantitative and qualitative methods for poverty analysis have been a subject of debate in recent years. Carvalho and White (1997) outline three major ways of combining these methods for poverty measurement and analysis. The first is through integration where quantitative information is used to focus on particular groups of interest for qualitative study and use of qualitative work to design quantitative survey instruments. The second involves using one approach to examine, explain, confirm, refute and or enrich information from the other. Third, the findings from the two approaches can be merged into one set of policy recommendations. Altogether, these options involve sequential and simultaneous mixing. In sequential mixing, the qualitative methods are largely used before or after the quantitative methods or surveys. Simultaneous mixing involves integrating certain qualitative methods into standard quantitative surveys. There are many

11 For example, two happy people may have very different variances in their happiness over time.

opportunities for mixing, but to realize the potential benefits of mixed methods, it is desirable to have qualitative and quantitative data for the same households or communities.

2.3 The Setting and study sites