Paper ideas using the household surveys: a social science perspective Nyovani Madise

Outline Data Research questions Types of analytical approaches to address the research questions

What modules of data do we have from household surveys? IdentifiersExpenditure Household rosterDurable goods EducationHousehold enterprise HealthOther income Time UseTransfers/donations LabourCredit HousingSubjective wellbeing Food consumptionChild anthropometry Food securityOutmigration DeathsLand use, Farm Inputs

Data files HouseholdIndividual Under-five children file Community- level GIS External – contextual files

Research questions What are the direct and indirect contributions of ecosystem services to food security and nutritional status for the rural poor? – What are the intra-household and community level differences in access to and use of ecosystem services for food security and health outcomes?

Research questions To what extent are coping strategies to food insecurity dependent on ecosystem services over multiple spatial and temporal scales?

Research question How are the levels of direct and indirect contributions of ecosystem services to local food security and nutritional health outcomes for the rural poor likely to change under future land use and climate change scenarios? -FEEDME Models, requiring locally generated food balance sheets

Types of data analyses Data cleaning, data checks- validity Descriptive analyses – E.g. describing ES and food security of 3 sites – Describing dietary diversity – Comparison of poverty levels by country, community Analytical – Analysing relationship between variables

Types of analytical modelling approaches (Potential) causation – cause and effect Changes in outcomes of interest over time Correlation/linkages between variables I.e. Predictor and outcome variables, where “predictors” can be at different scales (individual, household, community, national, etc) – Find patterns – data mining

Logical process in correlation/causation analysis Distal Predictor Variables Proximate determinants Outcomes Time

A more complex logical process

Examples of outcomes measures of wealth/poverty (e.g. using expenditure module, assets, subjective ranking) Subjective wellbeing Relative wealth (e.g. using monetary and non- monetary quintile classification) Gender differentials Child nutritional status Mother’s nutritional status Food security etc

Predictor variables Plenty! Individual-level (person characteristics, experiences (e.g. illness)) Household level data e.g. expenditure, educational level of household-head, Community-level variables –E.g. ES data, weather, food prices, existence of markets, good roads, socio-cultural factors (e.g. dominant ethnicity) Regional and National indicators

Examples of papers Quantifying the degree of food insecurity in the 3 sites – Characteristics of those most a risk (distance to forest, water reservoir etc) – Differential effect? Relationship between wellbeing and ES in the 3 settings – Extent to which household wealth/food consumption/child health are linked to ES net of other influences

Examples of papers Pathways through which ES affects nutritional status among the rural poor – Is the relationship the same in the 3 countries? – Different communities? Multiple dimensions of poverty – Money metrics – Assets – Capabilities – Social capital (communal facilities)

Methods - analytical Regression methods (linear, logistic regression, probit etc) Multilevel statistical modelling (acknowledging the hierarchical structure of the data) Structural Equation Modelling (SEM) Methods borrowed from market research (e.g. segmentation into typologies of households, communities by some key variable)

Typical SEM

Some Indicators: Colombia, Peru, Malawi CountryTotal Fertility Under-five mortality per (10000 live births) Mean number of people per household % with electricity Colombia Malawi Peru

Child anthropometry Country% stunted RuralUrbanTotal Colombia Malawi Peru

Colombia Weight-for-age z-scores

Peru, weight-for-age z-scores

Malawi, Weight-for-age z-scores

Childhood stunting by age, Colombia, Malawi, Peru

Percent children 0-59 months who were stunted, Colombia 2010