Small Area Estimation of Child MalnutritionAssessing the Omission of Maternal Anthropometrics

Ericka G. Rascon-Ramirez

ISER, University of Essex

What is SAE in the Public Policy context?

Set of statistical methods for obtaining small areas indicators (at locality, town or LSOA level) not represented by household surveys.

Assessment of the SAE method of Elbers, Lanjouw and Lanjouw (2003) to generate child malnutrition indicators at the local level when maternal anthropometrics is omitted in the modelling stage.

Objective of the study

Applications of SAE: ELL Method for Poverty and Nutrition Mapping

Around 45 Poverty Maps and 5 Nutrition Maps

in the world.

Figure: Extreme Poverty and Stunting at the municipality level in Mexico (2005).

ELL: Using a household survey and census or administrative data, they derive statistical properties of estimators of welfare indicators to be imputed at small area levels not representative in surveys.

Brief Description of the ELL Approach

• Stage Zero: Comparability between census and survey variables.

• Stage One: Modelling of welfare indicator in the survey according to the representativity.

• Stage Two: Computation of welfare indicator in census records.

Stages of ELL Methodology

In the survey, we run a GLS model using ONLY comparable variables between census and survey:

Where denotes child’s height, is a matrix of household and individual characteristics, and xxx is the error component.

Stage One: Child’s Anthropometric Model

Stage Two: Computation of welfare indicator

In the census, using the best prediction model, we obtain the welfare indicator as follows:

Drawn parameters: and

Having R replications at the individual level, we use their average for constructing the welfare indicator at the local level.

Drawbacks of ELL methodology

• Area Homogeneity (Conditional Independence). The conditional distribution of the welfare indicator y given X covariates in the small area A is the same as in the larger geographical region G. (Deaton and Tarozzi, RES 2009)

• Omitted Variable Bias. The use of ONLY comparable variables between census and survey restricts the inclusion of relevant variables. (Focus of this study)

Methodological Exercise

Assessing the Omission of Maternal Height using Monte Carlo

Simulations

Let’s assume the true DGP of the variable of interest follows this structure:

Where is maternal height and is not available in census records. The bias of the final estimate of will depend on the influence of on its variance and/or the correlation with other covariates.

Monte Carlo Exercises: Child’s DGP

Contribution of Maternal Height: 25 % of Child’s

Contribution of Maternal Height: 75 % of Child’s

Note: Mean of MSE when the prediction of the model is 45%.

Assessing the Omission of Maternal HeightMean of Bias of Child Malnutrition at EA

When a “relevant” variable has been omitted as a consequence of census-survey comparability:

Obtain SAE of the “relevant” omitted variable at the individual level using ELL.

Having the relevant variable in both sources, use it as a covariate for the final model.

Following the ELL approach, obtain the final SAE of child height.

Study’s Contribution: Two-Step Small Area Estimation

Empirical Evidence

Two-Step SAE for obtaining malnutrition indicators at the municipality level in Mexico

(Chiapas and Hidalgo)

Empirical Exercise: Two Step SAE

Slight differences between both approaches:

Figure: Height (z-scores) for Mexican Children under 5.

Empirical Exercise: Two Step SAE

Relevant differences between both approaches:

Figure: : Height (z-scores) for Mexican Children under 5.

Methodological Exercise: Higher contribution of the omitted variable (with low correlation with other covariates) may bias the final malnutrition estimate.

Empirical Application: Empirical evidence support a two-step SAE for obtaining less biased estimates for highly heterogeneous communities.

Conclusions