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While working on the final report, we noticed that compared to the KIR, some indicators had different values. Corrections were made for the final report. The indicator value in the final report is the same as in STATcompiler.

use ZZIR62FL.DTA rename b*_0* b*_* gen age_2nd_birth = . forvalues i = 1/20 { replace age_2nd_birth = int((b3_`i' - v011)/12) if bord_`i' == 2 } tab age_2nd_birth

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1. Demoralize the household weight HV005 or the individual weight V005 in DHS does not need the population size of the indicator to be calculated. For de-normalizing HV005, it is always multiplying the weight by the total number of households of the country at the time of the survey and dividing by the total number of households interviewed in the survey. For de-normalizing V005, it is always multiplying the weight by the total number of women 15-49 of the country at the time of the survey and dividing by the total number women 15-49 interviewed in the survey. The population size of the indicators to be calculated using household weight or individual weight have nothing to do with the de-normalization.

2. For surveys older than 2010, the stratification variable HV022 or V022 might not be coded correctly in the DHS data, it is safe to use HV024 crossing HV025 or V024 crossing V025 to get the sampling stratum, because in most of the DHS surveys, stratification is the crossing of region by urban rural.

My research project will try to estimate the impact of foreign land acquistions on children malnutrition in Tanzania. As such, I am using only children datasets and I pooled together the children datasets for 1996, 1999, 2004-2005, 2010 and 2015-2016.

Reading several threads, I understood that I have to:

1) de-normalize the sample weights as I am using several rounds of survey. This can be done by making sure that the weights for each round sum up to one and then multiplying those weight by the eligible population, i.e. kids under five (which I found here https://esa.un.org/unpd/wpp/Download/Standard/Population/). Is there someone who can confirm this procedure?

2) setting my dataset as survey data using the "svyset" command in Stata. Here is the point where I am stuck.

I have read other threads where they indicate the code for correctly using "svyset" for different surveys round, but my problem is that I can't identify the strata variables for 1996, 1999 and 2004-2005 datasets.

For the 2010 and 2015-2016 datasets, the strata variable is v022 and the stratification is well explained in the final report. In those datasets stratification is done at the regional and urban/rural levels, so for each region there are two strata (urban and rural). So, in 2010 there are 26 regions in Tanzania and V022 lists 52 strata, while in 2015 there are 30 regions and v022 lists 59 strata (one region is considered totally urban here).

In 2004-2005, problems start. Even though the sampling frame is the same as the 2010 survey (the 2002 census), the variable v022 lists 221 strata so I don't think that this variable is correctly identifying strata. However, I read in detail the final report and I found no information on the stratification. The same is true for the 1996 and 1999 surveys, where variable v022 lists 177 and 84 strata, respectively. However, the final report for the 1996 rounds says "The list of PSUs for the 1996 TDHS survey was stratified by each of the 20 regions (for the mainland) and within each region by urban and rural areas".

Therefore, I am not sure how to identify strata for those three surveys and I would greatly appreciate any help that you can give me! I apologize if the questions are silly but I have never worked with survey data and I am trying to understand how DHS works.

Thanks a lot.

Best,

Mariachiara

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May I ask, how do you calculate, for example, age at second birth? (not second to last)

thank you!

best,

g]]>

I am using the TZPR7HFL.DTA dataset for Stata, variable hv121 to represent attending, hv122 for the education level, and hv105 for age.

I am also using:

weight --> HV005/1,000,000

cluster --> HV021 Primary sampling unit

strata --> HV023 Stratification used in sample design

More or less (with minor background formatting/variable generation), I'm doing the following:

gen net attend = 0

replace netattend = 100 if attending==1 & inrange(age,7,13) & level==1

svyset cluster [pweight=hhweight], strata(strata)

svy, subpop(if inrange(age,7,13)) : mean netattend

estat size, obs

Thanks!]]>

I have recieved the e-mails from Dr. Kia Reinis,

thank you very much!

Sincerely,

Fredrik]]>

Thank you!]]>