Resilience Against Chronic Poverty:
Some Reflections and An Agenda
Christopher B. Barrett
Cornell University
Seminar to Harvard University Sustainability Science Program
October 24, 2012
Motivation
Resilience has quickly become a buzzword in the development
and humanitarian communities.
Two big drivers:
1) Perceived increasing risk – climate, mkts, macroeconomy,
violence, etc. – in both frequency and intensity
2) Recurring crises lay bare the longstanding difficulty of
reconciling humanitarian response to disasters with
longer-term development efforts. Many recent calls for
renewed efforts to “build resilience” quite explicitly aim to
align humanitarian and development objectives.
But we lack a theory-measurement-and-evidence-based
understanding of what resilience is, how to measure it, and
how to effectively promote it so as to reduce chronic poverty.
Motivation
Shocks that disrupt lives and livelihoods: the single greatest
cause of descents into chronic poverty (Krishna, etc.)
Uninsured risk of catastrophic loss (stressors): a key structural
reason for poverty traps (Carter&Barrett; Santos&Barrett)
We seek to bridge the ecological/engineering literatures on
resilience with the social science literature on poverty traps to:
- advance a theory of resilience against chronic poverty
- tease out measurement principles appropriate to the theory
- build toward a body of empirical evidence on resilience
Focus
Resilience of whom to what?
Subject of interest – quality of life, roughly Sen’s ‘capabilities’.
This implies a focus on individuals’ (and groups’) well-being
within a system, not the state of a system itself. System has
instrumental rather than intrinsic importance.
Focus further on minimizing the human experience of
chronic poverty. We therefore focus on places with high
rates of chronic poverty.
Do not focus on a specific source of risk b/c problem is
uninsured exposure to a wide array of stressors and shocks to
which resilience implies adaptability while staying non-poor.
Toward a theory
We need to adapt ecological/engineering theory to the
development/humanitarian response context.
As used in ecology or engineering – e.g., “the ability of the
system to maintain its identity in the face of internal change
and external shocks and disturbances” (Cumming et al. 2005
Ecosystems, p. 976) – resilience is not necessarily desirable
for populations trapped in chronic poverty. Their objective
may be escape from – not persistence in -- their present state
of existence.
To be useful for development policy, we need resilience to be a
normative property, to be orderable – and preferably
decomposable (in FGT sense) – in order to offer a useful
metric to gauge performance and guide policy/programming.
Toward a theory
Figure 1: Nonlinear expected well-being dynamics with multiple stable states
Humanitarian emergency zone
E[future]
capabilities
Death
Death T1
Chronic
poverty zone
Non-poor zone
T2
Current
capabilities
Noncontroversially: NPZ >> CPZ >> HEZ
Those in CPZ or HEZ are chronically poor in expectation
The CEF reflects indiv/collective behaviors (agency/power) w/n system
Toward a theory
Figure 1: Nonlinear expected well-being dynamics with multiple stable states
The humanitarian ambition is to
keep people from falling into HEZ
… offers foundation of a rightsbased approach to resilience.
E[future]
capabilities
Humanitarian emergency zone
The development ambition is to
move people into the non-poor
zone and keep them there.
Death
Death
Chronic
poverty zone
Non-poor zone
Current
capabilities
For the current non-poor, seek ‘resilience’ against shocks in the ecological
sense: no shift to either of the lower, less desirable zones.
But for the current poor, those in HEZ/CPZ, the objective is productive
disruption, to shift states.
Asymmetry is therefore a fundamental property of resilience against
chronic poverty. Thus stability ≠ resilience.
Toward a theory
A Utopian, asymmetric vision of well-being dynamics:
Figure 2: Desired expected well-being dynamics with multiple stable states
E[future]
capabilities
Humanitarian emergency zone
Egalitarian option
Death
Death
Chronic
poverty zone
Non-poor zone
Current
capabilities
‘Egalitarian option’: engineering concept applies - return to initial state.
‘Random walk w/safety net option’: implies perfect downward resilience
at NPZ/CPZ boundary … but zero resilience upward or w/n NPZ.
Toward a theory
Explicitly incorporate risk, move from CEF to CTD:
Figure 3: Nonlinear well-being dynamics with conditional transition distributions
Humanitarian emergency zone
Future
capabilities
Death
Death
Chronic
poverty zone
Non-poor zone
Current
capabilities
Note: The shape of the CTD affects the shape of the CEF
Transitory shocks (- or +) can have persistent effects
Risk may be partly endogenous to system state
Toward a theory
Feedback between sub-systems can be crucial
If we represent the preceding conditional transitions as:
Wt+1=g(Wt|Rt,εt)
where W is welfare, R is the state of the natural resource, and ε
is an exogenous stochastic driver
Then simply introducing feedback between R and W
(e.g., range conditions depend on herd size/stocking rate)
Rt+1=h(Rt|Wt,εt)
or allowing for drift in ε (e.g., due to climate change)
means the underlying CTD changes over time.
Then the resilience of the underlying resource base becomes
instrumentally important to resilience against chronic poverty.
Programming
implications
Objective: min likelihood people fall into HEZ/CPZ
Three options:
1) Shift people’s current state – i.e., move initial state
rightward. Ex: asset transfers: cash, education, land.
2) Alter CTDs directly (and thereby ∆ system too). Ex: social
protection - EGS, insurance, improved police protection,
drought-resistant animal/plant genetics.
3) Change the underlying system structure – institutions/
technologies – induces ∆ in behaviors and CTDs. Prob:
multi-scalar reinforcement – ‘fractal poverty traps’
Systems modeling becomes important to reveal the structure –
and possible intervention points – behind univariate dynamics.
Programming
implications
The importance of social institutional arrangements
“A tale of two widows”
And would the widower’s
dynamic = the widow’s?
Toward measurement
Define resilience as a – or perhaps function (e.g., discountweighted avg probability) of the – sequence of period-specific
Pr(well-beingt)<poverty line
Pr(well-being)
< Pov. Line
1
0
Chronically poor just >T1
Marginally poor just <T2
Begins in non-poor zone just >T2
Time
Big issues:
- defining the poverty line?
- units of observation – individuals? households? aggregates?
- frequency of longitudinal observation (retrospective/prospective)?
- how to estimate probabilities? Objective/subjective?
- how to allow x-sectional heterogeneity in CTDs/CEFs?
- how to triangulate with subjective and qualitative measures?
Build empirical evidence
Need to study interventions aimed at improving
resilience and replicate across contexts:
Candidates to discuss:
i. Index-based livestock insurance for pastoralists
ii. Safety nets (NREGS in India, PSNP in Ethiopia)
iii. Soil health interventions (e.g., fertilizers, NRM) in African
smallholder agriculture
iv. Accelerated disaster response interventions (e.g., LRP of food aid vs.
traditional, transoceanic deliveries)
Develop longitudinal data on individuals and households integrating
qualitative and quantitative measures in sentinel sites. Where
ethical/feasible, use RCTs or exploit natural/policy discontinuities to
identify causal effects.
Use results to develop clear policy/programming guidance. Ex: HSNP vs.
IBLI in Kenya; post-drought herd restocking; livestock gift programs
Summary
Resilience is a popular buzzword now. But little precision in its
use, either theoretically or empirically.
Aim to help facilitate rigorous, precise use of the concept to
help identify how best to reduce chronic poverty.
This will require advances in theory, measurement and
empirical work in many different contexts and over time.
Much to do in all of these areas … a massive research agenda.
Thank you
Thank you for your time, interest and comments!