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Home » Topics » Unmet Need » Getting Unmet Need & Demand Satisfied Estimates to Match StatCompiler for 5 Different Surveys (Getting Unmet Need & Demand Satisfied Estimates to Match StatCompiler for 5 Different Surveys)
Getting Unmet Need & Demand Satisfied Estimates to Match StatCompiler for 5 Different Surveys [message #16217] Mon, 26 November 2018 17:28 Go to previous message
cgreenba is currently offline  cgreenba
Messages: 16
Registered: October 2017
Member
Dear all,

I am working on a report looking at demand satisfied among youth across different DHS surveys and am having a difficult time matching the new definition of unmet need and demand satisfied numbers that I am getting in Stata with the estimates in StatCompiler for a few of the surveys that I have included in my analysis: the 2011-12 Honduras DHS, the 2006 Nepal DHS, the 2006 Niger DHS, the 2010 Rwanda DHS, and the 2011 Uganda DHS. For the surveys that did not have the new unmet need variable (v626a), I used either the general or survey specific recoding of unmet need do file produced by Sarah Bradley - attached below - but am still getting different estimates.

Here are the estimates that I get in Stata:
Honduras 2011-12 - Unmet need (all women) 15-19: 4.8%
Honduras 2011-12 - Unmet need (all women) 20-24: 7.6%
Honduras 2011-12 - Unmet need (all women) 15-24: 6.1%
Honduras 2011-12 - Demand satisfied by modern methods (all women) 15-19: 66.8%
Honduras 2011-12 - Demand satisfied by modern methods (all women) 20-24: 75.0%
Honduras 2011-12 - Demand satisfied by modern methods (all women) 15-24: 72.2%
Nepal 2006 - Unmet need (all women) 15-19: 37.8%
Nepal 2006 - Unmet need (all women) 20-24: 33.2%
Nepal 2006 - Unmet need (all women) 15-24: 34.7%
Nepal 2006 - Demand satisfied by modern methods (all women) 15-19: 25.7%
Nepal 2006 - Demand satisfied by modern methods (all women) 20-24: 43.4%
Nepal 2006 - Demand satisfied by modern methods (all women) 15-24: 38.4%
Niger 2006 - Unmet need (all women) 15-19: 6.9%
Niger 2006 - Unmet need (all women) 20-24: 15.4%
Niger 2006 - Unmet need (all women) 15-24: 11.1%
Niger 2006 - Demand satisfied by modern methods (all women) 15-19: 10.6%
Niger 2006 - Demand satisfied by modern methods (all women) 20-24: 16.7%
Niger 2006 - Demand satisfied by modern methods (all women) 15-24: 15.0%
Rwanda 2010 - Unmet need (all women) 15-19: 1.0%
Rwanda 2010 - Unmet need (all women) 20-24: 7.3%
Rwanda 2010 - Unmet need (all women) 15-24: 4.0%
Rwanda 2010 - Demand satisfied by modern methods (all women) 15-19: 62.3%
Rwanda 2010 - Demand satisfied by modern methods (all women) 20-24: 69.1%
Rwanda 2010 - Demand satisfied by modern methods (all women) 15-24: 68.3%
Uganda 2011 - Unmet need (all women) 15-19: 7.8%
Uganda 2011 - Unmet need (all women) 20-24: 25.3%
Uganda 2011 - Unmet need (all women) 15-24: 15.5%
Uganda 2011 - Demand satisfied by modern methods (all women) 15-19: 41.5%
Uganda 2011 - Demand satisfied by modern methods (all women) 20-24: 41.8%
Uganda 2011 - Demand satisfied by modern methods (all women) 15-24: 41.7%

Here are the estimates that StatCompiler lists:
Honduras 2011-12 - Unmet need (all women) 15-19: 6.5%
Honduras 2011-12 - Unmet need (all women) 20-24: 9.7%
Honduras 2011-12 - Unmet need (all women) 15-24: 7.9%
Honduras 2011-12 - Demand satisfied by modern methods (all women) 15-19: 62.0%
Honduras 2011-12 - Demand satisfied by modern methods (all women) 20-24: 72.1%
Honduras 2011-12 - Demand satisfied by modern methods (all women) 15-24: 68.6%
Nepal 2006 - Unmet need (all women) 15-19: 12.2%
Nepal 2006 - Unmet need (all women) 20-24: 26.7%
Nepal 2006 - Unmet need (all women) 15-24: 18.7%
Nepal 2006 - Demand satisfied by modern methods (all women) 15-19: 25.9%
Nepal 2006 - Demand satisfied by modern methods (all women) 20-24: 43.6%
Nepal 2006 - Demand satisfied by modern methods (all women) 15-24: 38.5%
Niger 2006 - Unmet need (all women) 15-19: 6.9%
Niger 2006 - Unmet need (all women) 20-24: 15.2%
Niger 2006 - Unmet need (all women) 15-24: 11.0%
Niger 2006 - Demand satisfied by modern methods (all women) 15-19: 22.4%
Niger 2006 - Demand satisfied by modern methods (all women) 20-24: 35.0%
Niger 2006 - Demand satisfied by modern methods (all women) 15-24: 31.6%
Rwanda 2010 - Unmet need (all women) 15-19: 2.2%
Rwanda 2010 - Unmet need (all women) 20-24: 10.3%
Rwanda 2010 - Unmet need (all women) 15-24: 6.1%
Rwanda 2010 - Demand satisfied by modern methods (all women) 15-19: 45.3%
Rwanda 2010 - Demand satisfied by modern methods (all women) 20-24: 62.1%
Rwanda 2010 - Demand satisfied by modern methods (all women) 15-24: 59.8%
Uganda 2011 - Unmet need (all women) 15-19: 10.5%
Uganda 2011 - Unmet need (all women) 20-24: 27.5%
Uganda 2011 - Unmet need (all women) 15-24: 18.1%
Uganda 2011 - Demand satisfied by modern methods (all women) 15-19: 34.9%
Uganda 2011 - Demand satisfied by modern methods (all women) 20-24: 39.9%
Uganda 2011 - Demand satisfied by modern methods (all women) 15-24: 38.4%

Is anyone aware of why I might be seeing these discrepancies? Is StatCompiler using a different sample, even in the indicator marked all women? Do I need to used a different weighting scheme other than the sample weights provided? I tried to match using the earlier definition of unmet need at as well, but that did not work. Any help or information would be greatly appreciated!

Here is my code in case it helps:
gen indiv_weight = v005/1000000

gen modern_contraception=0
replace modern_contraception=1 if v313==3
replace modern_contraception=. if v313==.

gen any_contraception=0
replace any_contraception=1 if v313==3 | v313==1 | v313==2
replace any_contraception=. if v313==.

gen unmet_need=0
replace unmet_need=1 if v626a==1 | v626a==2
replace unmet_need=. if v626a==.
* OR: gen umet_need=unmettot


gen demand_satisfied=.
replace demand_satisfied=0 if unmet_need==1
replace demand_satisfied=0 if v313==0 | v313==1 | v313==2
replace demand_satisfied=1 if v313==3
replace demand_satisfied=. if unmet_need==0 & any_contraception==0 | unmet_need==. | modern_contraception==.

proportion unmet_need if v013==1 | v013==2 [pweight = indiv_weight]
proportion unmet_need if v013==1 [pweight = indiv_weight]
proportion unmet_need if v013==2 [pweight = indiv_weight]
proportion demand_satisfied if v013==1 | v013==2 [pweight = indiv_weight]
proportion demand_satisfied if v013==1 [pweight = indiv_weight]
proportion demand_satisfied if v013==2 [pweight = indiv_weight]

Thank you so much!

Best regards,
Charlotte
 
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