2.2. Quantitative methods informed by critical realism

basic reproduction (food, adequate shelter, keeping kids in school which limits and savings needed to invest in assets, training, or petty business capital. They thus own levels of endowments (labour capacity, assets, savings) and work as labour or rent borrow assets. They also often have negative endowments and liabilities (debt, ill health, heavy caring responsibilities) as well as experiencing exclusion or adverse inclusion in institutional relationships (in families, markets and social and political institutions). We probe for participants’ perspectives of each wellbeing category (see Annex A).

2.2. Quantitative methods informed by critical realism

CPAN studies typically begin with a quantitative investigation into drivers of different poverty trajectories, relying on panel data (see Box 1 above). This panel data is constructed by repeatedly surveying the same households over time, and thus allows poverty trajectories to be identified. National panel data analysis enables us to identify changes in poverty status for the same household over the panel periods and examines the surveyed factors associations with this change. It is thus a diachronic change based statistical analysis.

While panel studies had a powerful impact on our understanding of trends in poverty dynamics, the quality of the causal analysis can be insufficient revealing factors statistically significant associations with poverty dynamics (e.g., land ownership, off farm income, education) but without further information one can only guess at the reasons why it associated and how unsurveyed variables might be involved in the explanation of such dynamics (Scott and Marrioti, 2014). This encouraged a mixed methods approach which involved diachronic qualitative field research and analysis.

A second challenge was that in practice in earlier studies, the combination of national data given legitimacy by international institutions with the strict positivism of these quantitative studies could lead to statistically significant associations represented as causal ‘facts’ and enable politicised readings of panel data gaps vacuums to a-political causation (e.g. a drought or large families) rather than to the insufficiency of effectively resourced and implemented policies (da Corta et al., 2018).

The reaction to these challenges was to both link panel data analysis to intensive change oriented qualitative field research and analysis (and iterate between the two). Then as Olsen and Morgan (2005) have argued, positivist epistemological assumptions regarding closed systems and constant conjuncture can be relaxed sufficiently and iterated carefully with findings from interpretive epistemologies and realist appraisals. CPAN panel quantitative analysis is also generally exploratory and cautious rather than determinative, aiming to avoid the pretention of absolute truth through statistical associations and instead used as a way to explore e statistical trends and non-intuitive findings that, together with strong

qualitative analysis on multiple levels, can help underpin causal analysis. The data is understood not as facts but as ‘ficts’ ready to be interpreted in the context of a larger analysis of historical and relational qualitative data (Olsen and Morgan, 2005).