Jenn Denno Cissé is a PhD student at Cornell’s Dyson School. Follow her on Twitter @jenncisse.
Quite a bit of confusion, or at least disagreement, appears to exist within the development community as to the definition of resilience and how to measure it. Chris Barrett and Mark Constas answer these questions in their new PNAS paper:
The conceptualization we advance for “development resilience” focuses on the stochastic dynamics of individual and collective human well-being, in particular the capacity to avoid and escape from unacceptable standards of living—“poverty,” for short—over time and in the face of myriad stressors and shocks. (p. 14625)
The paper uses a probabilistic approach, adapted from the risk and econometric modeling literatures, to identify an individual’s (or household’s) probability of sustainably achieving an adequate level of well-being:
This approach to resilience uniquely manages to bring together many (all?) of the characteristics that the development community feels are most necessary in a resilience model (see Régibeau & Rockett 2011, Holling 1996, and Folke 2006, among others). Specifically, the Barrett & Constas approach:
- explicitly brings in both observed and non-observed shocks (realized catastrophic events for which a household or community is at risk, such as drought or illness) and stressors (the ex ante exposure to risk which may have its own negative consequences);
- allows poverty (or, conversely, well-being) dynamics to vary depending on the initial level of poverty faced by a household or individual – for some those pathways may lock them in poverty (i.e., a poverty trap); and
- highlights the inter-temporal nature of well-being dynamics, which need not be linear.
This resilience approach responds nicely to some of the critiques of resilience raised in the first post of this series. For example, it is impossible to sacrifice well-being for resilience since resilience is measured in terms of (the probability of maintaining high levels of) well-being. There may, however, be tradeoffs or even complementarities between different types of well-being (e.g. nutrition vs. income, assets vs. educational attainment, etc.); the extent of those trade-offs and complementarities is an empirical question.
With regards to power relations and social institutions, Barrett & Constas acknowledge that these “human institutions” may directly or indirectly impact resilience (expected well-being) and their approach provides a methodology for understanding these impacts.
The CEF [conditional expectation function] arises from individual and collective choices subject to constraints imposed by human institutions (e.g., laws and norms), resource availability (e.g., money, time), and nature. Choices made by agents successfully maximizing their well-being may be optimal, in the usual economic sense that no greater expected well-being is feasible given the available choices, and nonetheless lead, sooner or later, to an undesirable outcome because no nonpoor outcomes are both feasible and sustainable. (p. 14626)
As we can see in the above quote, the paper speaks to the spirit of this blog by recognizing that the poor and non-resilient may be behaving rationally, maximizing their own inter-temporal well-being, with regards to the choices available to them. The paper highlights how improved understanding of poverty dynamics can be used to better target and design development interventions. I will discuss these implications and the empirical process of identifying individual/household resilience in the next post in this series.
