Do you have any resources on how to calculate these indicators particularly in Stata? Or any advice on how to use the weights for this data?

]]>

Not sure if these suggestions will help, but thought I'd mention a couple of tricky parts.

First, did you divide all the weights by 1000000? It shouldn't make much of a difference, but I think it could lead to mathematically slightly different answers if Stata is trying to deal with really big weight numbers. Also, how are you actually weighting these? (I don't really know exactly how you should, other than it might involve the all woman factor, but seems like you'd have to re-calculate the denominator and numerator by summing across woman-years times weight instead of just the woman-years).

Second, getting the timing so that women-years are properly balanced between age-groups is tricky. The DHS method seems to truncate age to age-in-round-years, so maybe you are rounding differently than they do.

Third, there is another approach to calculating TFRs (and age-specific rates) that is a "person period" approach. You can find a discussion here http://paa2012.princeton.edu/papers/122446 but I don't think the package is totally ready yet (once it is, that will be handy). This will almost certainly not return the DHS numbers, and the interpretation is probably slightly different, but it's not clear to me that the DHS numbers are in any sense "better" than what you'd get from this method.

In general, it seems to be pretty hard to get exactly the numbers that the DHS gets. If yours are not meaningfully different (economically/epidemilogically/etc) from the published ones, it might just be an alogrythm thing about how Stata computes things and uses weights.

If you post a .do file, I might be able to take a look. I've found the process difficult myself, and maybe I could learn something from how you are trying to do it. Hope something here was helpful.

]]>

The module and the paper presenting it can be downloaded from the Demographi Research journal's website (http://www.demographic-research.org/volumes/vol28/38/).

Best regards,

Bruno]]>

Bruno]]>

All the best,

Anne]]>

However, these programs are old and have limitations, so user beware. They should though give you enough information to reproduce the results in the reports.]]>

have you used the individual recode data file (KEIR52FL)?

This is the result I get when running tfr2 with that file - and they match perfectly the rates published in the Kenya report (p.47).

Best,

Bruno

. tfr2

weight variable is v005

Preparing table of events and exposure for 3 year(s) preceding the survey

Period covered: 12/2005 to 11/2008

Central date is 2007.4636

Number of cases (women): 8421

Number of person-years (weighted): 23658.801

Number of events (weighted): 3609.3125

ASFRs - TFR

events Coef. Std. Err. z P>z [95% Conf. Interval]

Rate_1519 .1029641 .0043925 23.44 0.000 .0943549 .1115733

Rate_2024 .2378978 .007007 33.95 0.000 .2241644 .2516313

Rate_2529 .2155345 .0071216 30.26 0.000 .2015764 .2294926

Rate_3034 .1751397 .0073817 23.73 0.000 .1606718 .1896076

Rate_3539 .1178257 .0068037 17.32 0.000 .1044907 .1311607

Rate_4044 .0504861 .004728 10.68 0.000 .0412194 .0597528

Rate_4549 .011841 .0031293 3.78 0.000 .0057076 .0179743

TFR 4.558445 .0793782 57.43 0.000 4.402866 4.714023

]]>

Thanks you.]]>

Thank you!]]>

tfr2 computes rates for the 3 preceding years by default.

Just typing tfr2 will thus compute the rates for the three years preceding the survey

Actually, it is a shortcut for:

tfr2 [pw=v005], dates(v008) bvar(b3*) wb(v011) len(3) ageg(5) awf(awfactt)

In Pakistan, you should be careful to use the correct all-women factors for sub-populations.

For instance, to compute rates for rural and urban areas, you can use the following command

by v025, sort: tfr2, awf(awfactu)

If you want to compute these rates for a 5- year period, it would become

by v025, sort: tfr2, len(5) awf(awfactu)

Best, Bruno

]]>

You can download it directly from Stata by typing

ssc install tfr2

Best regards,

Bruno]]>

Did you use the Individual Recode data file ?

Bruno]]>

. tfr2

weight variable is v005

Preparing table of events and exposure for 3 year(s) preceding the survey

Period covered: 8/2008 to 7/2011

Central date is 2010.1332

Number of cases (women): 28609

Number of person-years (weighted): 85763.883

Number of events (weighted): 42221.082

ASFRs - TFR

------------------------------------------------------------ ------------------

events | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+---------------------------------------------- ------------------

Rate_1519 | .2282156 .0083323 27.39 0.000 .2118846 .2445466

Rate_2024 | .3761918 .0061126 61.54 0.000 .3642114 .3881722

Rate_2529 | .4757862 .0054558 87.21 0.000 .465093 .4864794

Rate_3034 | .4690061 .0053467 87.72 0.000 .4585268 .4794854

Rate_3539 | .5036195 .0052258 96.37 0.000 .493377 .5138619

Rate_4044 | .5500663 .0065024 84.59 0.000 .5373218 .5628108

Rate_4549 | .6933619 .0089963 77.07 0.000 .6757293 .7109944

TFR | 16.48124 .0888354 185.53 0.000 16.30712 16.65535

------------------------------------------------------------ ------------------

I used individual data sets

]]>

Best regards,

Bruno]]>

. use "C:\12.DATA\DHS\IR\UGIR60FL.DTA", clear

. tfr2

weight variable is v005

Preparing table of events and exposure for 3 year(s) preceding the survey

Period covered: 8/2008 to 7/2011

Central date is 2010.1378

Number of cases (women): 8634

Number of person-years (weighted): 23918.096

Number of events (weighted): 4916.5352

ASFRs - TFR

events Coef. Std. Err. z P>z [95% Conf. Interval]

Rate_1519 .1344827 .0048509 27.72 0.000 .1249751 .1439904

Rate_2024 .3132275 .008087 38.73 0.000 .2973772 .3290778

Rate_2529 .2914675 .0082319 35.41 0.000 .2753333 .3076017

Rate_3034 .2323487 .0085758 27.09 0.000 .2155404 .249157

Rate_3539 .1716094 .0077221 22.22 0.000 .1564743 .1867444

Rate_4044 .0742423 .0063143 11.76 0.000 .0618665 .0866181

Rate_4549 .0230467 .0043584 5.29 0.000 .0145044 .031589

TFR 6.202124 .0933726 66.42 0.000 6.019117 6.385131

]]>