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?

• Identifiers Expenditure• Household roster Durable goods• Education Household enterprise• Health Other income • Time Use Transfers/donations• Labour Credit• Housing Subjective wellbeing• Food consumption Child anthropometry• Food security Outmigration• Deaths Land use, Farm Inputs

Data files

Household Individual 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

Country Total Fertility

Under-five mortality per (10000 live births)

Mean number of people per household

% with electricity

Colombia 2010

2.1 22 3.8 95.2

Malawi 2010

5.7 127 4.6 8.7

Peru 2012 2.6 25 3.9 89.2

Child anthropometryCountry % stunted

Rural Urban Total

Colombia 20.8 12.0 15.3

Malawi 46.9 37.3 45.9

Peru 32.2 10.5 18.0

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

0-5 6-11 12-17 18-23 24-29 30-35 36-41 42-47 48-53 54-59

-2.5

-2

-1.5

-1

-0.5

0

Colombia 2010 Malawi 2010 Peru 2012

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