- household livelihood characteristics (agriculture, livestock, non-farm enterprises)

- financial characteristics (receiving transfer income, savings, etc.)

- dwelling characteristics (ownership, improved roof/walls/floor)

- household mobile phone ownership

- geographic characteristics (rural / urban status, district of residence)

The first characteristic, i.e. whether the individual is a member of the original household or a tracking target, is particularly important in cases where the implementation of the tracking methodology is of questionable quality. The described methodology thus far assumes perfect implementation of the survey plan. However, in some cases, the field teams may not search for all eligible tracking cases. In an ideal case, it would be possible to know which cases were attempted and construct a separate model at this step to incorporate those probabilities of selection. For example, perhaps the tracking team decided only to look for those split-off members that they thought would be easy to locate. If the analyst knows which households were considered easy and therefore actually targeted, whether or not they were located, the attrition calculations can be done separately for those targeted and those not targeted, as the most important determining factor of attrition in those cases would be the classification as easy. If the explicit division was not recorded, but the tracking team can inform the analyst roughly which criteria they used in making the determination about whether the split-off members would be easy to find, the designation can be modeled and the division predicted. In the absence of any information, the split-off indicator variable is the best way to capture this element of attrition.

The estimated logistic model is used to obtain a predicted probability of response for each household member in the round 2 survey.<sup>9</sup> These response probabilities are then aggregated to the household level (by calculating the mean). Then, using the household-level predicted response probabilities as the ranking variable, all households are ranked into 10 equal groups (deciles). An attrition adjustment factor ( _ac_ ) is then defined as the reciprocal of the mean empirical response rate for the household-level propensity score decile.

Incorporating the attrition correction, the adjusted weights would be:

# _7) Trimming_

Complex weight calculations have the potential to produce outlier weights, which increase the standard errors of estimates. A common practice is therefore to ‘trim’ the weights at this stage to eliminate the outlier weights (see Little et al, 1997). Trimming introduces a small amount of bias into the estimates, but allows estimates to be more efficient. Common values for trimming range between one and five percentage points at the top and bottom of the distribution, and the LSMS-ISA weights are trimmed below the 2<sup>nd</sup> percentile and above the 98<sup>th</sup> percentile. Those weights identified as outliers on the top of the distribution are replaced with the highest value of a weight below the cut-off. For example, with a two-percent trim, weights in the 99<sup>th</sup> and 100<sup>th</sup> percentiles would be replaced with the highest weight

> 9 In some cases, values of round 1 variables may be missing. These values must be imputed using multivariate regression or logistic regression techniques.  There are many suitable imputation command packages available in statistical analysis software, such as the multiple imputation ( _mi_ ) commands in Stata or ( _proc mi_ ) in SAS. Note that the resulting values from these imputations do not necessarily need to become part of the published dataset, and in fact the LSMS-ISA surveys leave missing values as missing to be treated at the discretion of the end user. The imputations are necessary only for the weight calculations so that there are no missing values in the propensity score model and values can be predicted for all observations.

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