value in the 98<sup>th</sup> percentile. Similarly, weights in the 1<sup>st</sup> and 2<sup>nd</sup> percentile would be replaced with the lowest value in the 3<sup>rd</sup> percentile.

After trimming W3, we have the adjusted weights W4.

# _8) Post-stratification_

To reduce the overall standard errors and weight the population totals up to the known population figures, a post-stratification correction (Wps) is applied. This correction also reduces overall standard errors (see Little et al, 1997). The level of disaggregation for the post-stratification correction should be at the lowest reliable level available from an auxiliary data source, regardless of the level of representativeness for which the survey was designed. For example, if reliable census projections are available for the subregional level, these should be used even if the sample was designed only to be representative at the regional level. Note that close attention should be paid to the reliability of the outside data source underlying the post-stratification calculations. With proper design and implementation, the survey population estimates should be close to the actual population estimates. Post-stratification should therefore be seen more as a fine-tuning adjustment rather than a major realignment. If the weights are adjusted using poor quality auxiliary information, there is the possibility of reducing precision or introducing bias into the estimates.

In order to calculate the post-stratification adjustment for the LSMS-ISA surveys, census projections of household populations at the regional–urban/rural level are generally used. These projections are provided by the national statistics bureaus. The adjustment is done at the household level since households are the units of analysis which are sampled and for which the weights are calculated. If the census projections give the population totals in terms of individuals, the average household size is calculated from the dataset for each category in the disaggregated data and the totals calculated in terms of households. Within each category the weighted total number of observations is calculated and the census projection is divided by this number for the corresponding category. This correction is then applied to all the observations in the category. For example, if the weighted total of households in the capital city is calculated from the survey to be 157,304, but the census projections give a total of 193,484, the poststratification adjustment factor would be 1.23. The weight of each household in the capital city would then be multiplied by 1.23.

The final panel weight would then be:

# **Weight Calculations for Rounds Subsequent to Round 2**

The weights for subsequent rounds of panel data follow the same steps as round 2 with one important difference. In round 2, each household member was classifiable into one of four categories: a reinterviewed household member (observed in round 1), a new household member (new in round 2), a round 1 member who has died, or an attriter (observed in round 1 but not re-interviewed in round 2). In round 3, there would be an additional category of returning household member, which would apply to those members that were present in round 1, not found and thus classified as attriters in round 2, and then present in round 3. In the course of the calculation of round 2 weights, an attrition adjustment was applied to compensate for the departure of these members. As these members have now returned, simply using the weights from the previous round as the base weights for the panel calculations would overestimate the 9