Never Let a Disaster Go To Waste: Using Natural Experiments to

Understand Vulnerability and Resilience

Michael E. LoevinsohnApplied Ecology Research

IDS: Centre for Development Impact/Climate Change & Development Program

October 13, 2016

Setting • Extreme events, of various kinds, likely to

increase – magnitude, frequency, impact – though unequally

• Will affect all aspects of wellbeing and environmental integrity

• We are often surprised by them but learn much less than we could from the experience

• Adaptation and resilience extolled and promoted yet variously understood

UNISDR definition:

Resilience – the capacity of a system, community or society hypothetically exposed to hazards to adapt so as maintain an acceptable level of functioning and structure

Limited basis to guide and assess progress towards resilience because exposure considered hypothetically

Natural experiments

• Are uniquely suited to understand and assess impact of extreme events

• Have often revealed unexpected aspects of vulnerability and resilience

• Can serve as a baseline to guide and assess progress towards resilience and adaptation – hopefully before the next extreme event occurs

Characteristics of natural experiments

• “Observational studies of sharp, well-defined but unplanned changes” (M. Susser)

• The “intervention” – of a kind or on a scale that could not ethically or feasibly be implemented deliberately

• Defines before/after; creates exposed/ unexposed, or differentially exposed groups

• Framed, more than designed

Natural experiments

•Have been used by a range of disciplines, often yielding crucial insights•Yet often seen as lacking in rigour (vs. RCT)

– Require careful assessment of data quality and confounding factors

– Analysis must take account of spatial/ temporal autocorrelations

Natural experiments

Greater potential (and need) for corroboration of hypotheses than e.g. RCT Potentially low-cost by using existing data NEs are public events, shared experience

• Can elicit broader input to assess internal/ external validity, and progress on adaptation and resilience

Three examples

Unanticipated effects of a policy“Intervention”: Sharply increased insecticide use by Filipino rice farmers with subsidized credit - 1973Exposure measures: patterns of use by year/ season and toxicity class from surveysOutcome measures: death rates from civil registries for rural men (exposed), children/women/ urban residents (unexposed)

Outcomes: 27%↑death rate for rural men over 12 years after credit; greater in months of heavy use and for causes associated/confused with acute insecticide poisoning; not seen in the unexposed Source: Loevinsohn Lancet 1987; Loevinsohn and Rola 1998

Evidence of adaptation: •Farmers reduced dosages and sprays•Policy actors responded with significant delay

partial bans on the most toxic chemicals introduced Farmer Field Schools

Impact of a CC proxy on malaria“Intervention”: Sharp increase in (min) temp. and rain in Rwandan highlands with 1987 El NiñoExposure measures: weather variables from a well-run weather stationOutcome measures: malaria incidence at nearby well-run rural clinic, by age, altitude of residenceOutcomes: Steepest increase in 1987 at highest altitude (5x) and for young w/o immunity (12x); incidence over 15 years best explained by min T, consistent with temperature limiting malaria’s rangeSource: Loevinsohn Lancet 1994

Evidence of adaptation:

•Unclear. Current strong El Niño has been associated with sharp increase in malaria – and popular criticism of Gov’t response

•Discussions underway with National Malaria Program on using NEs, at different scales, in operational research

Worked example

The 2001-03 famine and HIV dynamics in Malawi

Context•Increasingly vulnerable rural population: reliant on maize in one season, casual labour and the market•Two poor harvests 2001-02 and poor administrative decisions → maize price surge•Unequally felt: especially rural, farmers, women•Accounts/ethnography described people pushed by hunger into situations of infection risk

Survival sex – exchanged for food or work Distress migration to towns/cities and less affected

rural areasSource: Loevinsohn PLOS ONE 2015

Hypotheses

Greater involvement in survival sex will increase rural HIV incidence, especially in women, in proportion to local extent/ duration of hunger

• Compounded by nutrition effects on immune function; changes in sexual networks

Distress migration will lead to changes in the distribution of HIV and people in both source and destination, if migration more than temporary

Methods

Joint analysis of 3 data sets:•Antenatal HIV surveillance data from 1999 (before the crisis) and 2003 (after the worst)•Humanitarian survey from late 2002: estimated proportion of rural HHs/district likely in need of food assistance in Dec 02-Mar 03•Integrated HH Survey 2004-05: migration by age, sex and destination

Multilevel, random intercept models relate• Surveillance site factors (rural/non-rural,

rural hunger in the district) and• Individual level factors (age, occupation,

education)• Interactions of site and individual factors to Change in woman’s probability of being

HIV positive across the famine

Similar model relates change in woman’s Prob. of being a farmer to site and the other individualfactors

Results confirmed by MCMC simulation

Tests for unmeasured site-specific effects and spatial autocorrelation that might affect the interpretation – negative

Change in HIV vs. rural hunger Rural antenatal sites

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Rural population in need of food aid (%)

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Change in HIV vs. rural hunger Non-rural antenatal sites

■ cities ▲ towns

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Rural population in need of food aid (%)

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Surprising, but consistent with migration effects• HIV prevalence was lower in villages than in

towns/cities• Rural migrants, when they became

pregnant, would “dilute” prevalence at surveillance sites in towns/cities

The greater hunger in surrounding rural area, the more women were pushed into migration, hence town/city prevalence falls with increasing rural hunger

Analysis of interactions shows almost entire non-rural decline associated with “farmers” (consistent with selective migration)

Corroboration: Proportion of farmers at rural antenatal sites falls with rural hunger

0.5

0.75

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1.25

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10 20 30 40 50

Rural population in need of food aid (%)

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Corroboration: Proportion of farmers at non-rural sites rises with rural hunger

Nsanje

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0.751

1.251.5

1.75

0 10 20 30 40 50

Rural population in need of food aid (%)

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Analysis of interactions shows changes in “farmer” proportion concentrated in younger women (<25 years) with low levels of schooling

Corroborated by IHS 2004-05 survey•Marked rise in rural-nonrural migration 1-2 years earlier•Particularly marked for women < 25 years•Similar pattern in rural-rural migration

Rural – nonrural migration, from IHS

Critical findings

•The famine’s effects apparently more severe, widespread than thought•Hypotheses drawing on contemporary accounts and ethnography supported quantitatively

•Study offers a well-corroborated explanation for decline in urban prevalence in Malawi

• AIDS researchers interpreted this decline as evidence of sexual behaviour change

• They did not consider possible impact of the famine and role of migration

• Similar demographic/epidemiological conditions existed elsewhere in the region affected by harvest failures in 2001-02 and HIV declines

• Very different policy implications from these two explanations

Evidence of adaptation

•Farmers were increasingly growing cassava – a more robust crop than maize •Districts where more HHs grew cassava had oLower levels of hungeroLower peak maize prices in local markets

o Lower changes in HIV•Post famine: cassava expansion continued•Gov’t: expanded agricultural input subsidy program

How can such a study aid assessment of resilience and adaptation?

•Provides a basis to assess the impact of national programs or local efforts

– Reflected in the relationship between exposure and outcome

– Local successes or failures reflected in outliers

Enables sharper questions, drawing in a wider public, following up the hypotheses and evidence they drew on

In the midst of a recurring food crisis in Malawi:

• Is the response of institutions – at all levels – to poor harvests (preventing, warning, mitigating) better than it was in 2001-03?

• Have the structural features of economy and livelihood altered so that people have more diverse, robust options to fall back on when maize fails?

Using natural experiments

• Require understanding of central questions being asked locally

• Familiarity with potential data sources• Retrospective NEs are opportunistic, akin to

prospecting• May make donors uneasy

• Prospective NEs are also possible• over a wide area, extreme events are likely to occur

somewhere; can be planned for

• Many opportunities going begging

War: Developmental effects of hunger in utero

“Intervention”: Nazi-imposed famine in NW Holland 1944-45 (Hongerwinter)Exposure measures: extent, duration of maternal under-nutrition from civil recordsOutcome measures: routine tests on army recruits; follow-up clinical studies over >50 yearsOutcomes: increased adult obesity, diabetes, cognitive and psychiatric effects depending on when in gestation food restriction occurredSources: Stein et al. 1972; Lumey, Stein and Susser 2011 and many others