Nested loops

[Varsha Vaishnav]

 global est_1 edu_1 atten_1 cm_1 nutri_1 elec_1 toilet_1 water_1 house_1 fuel_1 asset_1

This 4th line of the following loop generates the variable at the national level. I want the variable at the state level. For that, I'm changing the 3rd line in this way:

 bysort region_01: sum c_censured_vector_`j'_`k' [iw = weight] if sample_`j'==1

How should I modify the 4th line to get the variable at the state level?

 foreach j of numlist 1 {

 foreach k of numlist 20 33 50 {

 sum c_censured_vector_`j'_`k' [iw = weight] if sample_`j'==1

 gen MPI_`j'_`k' = r(mean)

 lab var MPI_`j'_`k' "MPI with k=`k'"

 }

 }

Also, can someone explain to me the first line of the loop? 'j' is just 1 in the global macro, it's not variable, so I'm getting a little confused with the way it's written.

[Daniel Schaefer]

global est_1 edu_1 atten_1 cm_1 nutri_1 elec_1 toilet_1 water_1 house_1 fuel_1 asset_1
foreach j of numlist 1 {
     foreach k of numlist 20 33 50 {
        *sum c_censured_vector_`j'_`k' [iw = weight] if sample_`j'==1
        bysort region_01: sum c_censured_vector_`j'_`k' [iw = weight] if sample_`j'==1
        gen MPI_`j'_`k' = r(mean)
        lab var MPI_`j'_`k' "MPI with k=`k'"
     }
}

It's difficult to tell what this code is doing without any context or example data. That being said, it looks like you are correct; the outer loop is entirely extraneous and this could be improved by removing it entirely. If you'd like help collecting state level data, please explain what you are trying to do in detail, explain why the current code doesn't work, and provide example data using the -dataex- command. It may also be helpful for you to read through the guidelines in the FAQ at the top of the page.

[Daniel Schaefer]

A few other notes. First, this line also appears unnecessary.

global est_1 edu_1 atten_1 cm_1 nutri_1 elec_1 toilet_1 water_1 house_1 fuel_1 asset_1

As to your first question, it looks like you might want something like this:

egen MPI_`j'_`k' = mean(c_censured_vector_`j'_`k' [iw = weight]) if sample_`j'==1, by(region_01)

But of course I haven't tested this because you didn't provide example data. Check out #6 in this thread.

[Varsha Vaishnav]

This is the dataset.

* Example generated by -dataex-. To install: ssc install dataex
clear
input long hh_id double ind_id float sample_weight byte(subsample region_01) float(edu_1 cm_1 nutri_1 elec_1 toilet_1 water_1 house_1 fuel_1)
280690010 28069001002  .060845 . 2 1 1 0 1 1 1 1 1
281740024 28174002402 1.877836 . 2 1 1 1 1 1 1 1 1
280690004 28069000402  .060845 . 2 1 1 1 1 0 1 1 1
280590014 28059001401 2.506085 . 2 0 1 1 1 0 1 1 0
280680015 28068001509  .259337 . 2 1 1 1 1 0 1 1 1
280030005 28003000506 2.506085 . 2 1 1 1 1 0 0 0 0
281510058 28151005801  .060845 . 2 1 1 . 1 1 1 1 1
280700050 28070005003  .259337 . 2 1 1 1 1 1 1 1 1
280760041 28076004102 1.877836 . 2 1 1 1 1 1 1 1 1
281000012 28100001204 2.506085 . 2 1 1 0 1 0 0 1 0
280840016 28084001608 2.506085 . 2 1 1 . 1 1 1 1 0
281130007 28113000701  .259337 . 2 1 1 1 1 1 1 1 1
281740023 28174002302 1.877836 . 2 1 1 1 1 1 1 0 0
280050014 28005001402 1.877836 . 2 1 1 0 1 1 1 1 1
280470002 28047000201 2.506085 . 2 1 1 1 1 1 1 1 1
281740022 28174002202 1.877836 . 2 1 1 1 1 1 1 1 1
280310011 28031001106  .259337 . 2 1 1 0 1 1 1 1 1
281380007 28138000703 2.506085 . 2 1 1 0 1 0 1 0 0
280750004 28075000403 1.877836 . 2 1 1 1 1 0 0 0 0
280760060 28076006001 1.877836 . 2 1 1 0 1 0 1 1 1
281740021 28174002103 1.877836 . 2 1 1 1 1 0 1 1 1
281920018 28192001803 2.506085 . 2 0 1 0 1 0 1 0 0
280950016 28095001602 2.506085 . 2 1 1 0 1 0 1 0 0
281220030 28122003003  .060845 . 2 0 1 0 1 0 1 1 1
281130031 28113003111  .259337 . 2 1 1 0 1 1 1 1 1
281740027 28174002704 1.877836 . 2 1 1 1 1 1 1 1 1
280880024 28088002401 1.877836 . 2 1 1 1 1 1 1 1 1
281870012 28187001203 2.506085 . 2 1 1 1 1 1 1 1 0
281950013 28195001303 2.506085 . 2 1 1 0 1 0 1 0 0
281550034 28155003403 1.877836 . 2 1 1 0 1 0 1 1 0
281230016 28123001603 2.506085 . 2 1 0 1 1 1 1 1 1
281740028 28174002803 1.877836 . 2 1 1 0 1 0 1 1 1
281740021 28174002101 1.877836 . 2 1 1 1 1 0 1 1 1
280680011 28068001103  .259337 . 2 1 1 0 1 1 1 1 1
281740029 28174002901 1.877836 . 2 1 1 1 1 1 1 1 1
280880022 28088002202 1.877836 . 2 1 1 1 1 1 1 1 1
281740029 28174002902 1.877836 . 2 1 1 1 1 1 1 1 1
281060006 28106000606  .259337 . 2 1 1 1 1 1 1 1 1
280630008 28063000806  .060845 . 2 1 1 1 1 1 1 1 1
281850053 28185005304  .060845 . 2 1 1 1 1 1 1 1 1
281250009 28125000902 2.506085 . 2 1 1 1 1 0 1 0 0
281630005 28163000501  .060845 . 2 0 1 1 1 0 1 1 1
280660027 28066002701 2.506085 . 2 1 1 0 1 0 1 0 0
281280007 28128000702 2.506085 . 2 1 1 1 1 1 1 1 0
281740020 28174002003 1.877836 . 2 1 1 1 1 1 1 1 1
280180006 28018000604  .060845 . 2 1 1 0 1 0 1 1 1
281290037 28129003703 1.877836 . 2 0 1 0 1 0 0 1 0
281740030 28174003004 1.877836 . 2 1 1 1 1 1 1 1 0
280900019 28090001901 2.506085 . 2 1 1 1 1 0 1 0 1
281590014 28159001401 2.506085 . 2 0 1 0 0 0 1 0 0
281490040 28149004001  .060845 . 2 0 1 1 0 1 1 1 1
280370010 28037001003 2.506085 . 2 0 1 1 1 0 1 0 0
280500043 28050004302  .259337 . 2 1 1 0 1 0 1 0 1
281870001 28187000102 2.506085 . 2 1 1 0 1 0 1 1 0
280920044 28092004401 1.877836 . 2 0 1 1 1 0 1 1 0
281740020 28174002002 1.877836 . 2 1 1 1 1 1 1 1 1
281580010 28158001001  .259337 . 2 0 1 . 1 0 1 1 0
280100006 28010000601 2.506085 . 2 0 1 1 1 0 1 1 0
281940004 28194000401  .060845 . 2 1 1 . 1 1 1 1 1
281740030 28174003005 1.877836 . 2 1 1 1 1 1 1 1 0
280790009 28079000902 2.506085 . 2 1 1 0 1 1 1 0 0
281830025 28183002503  .060845 . 2 1 1 1 1 1 1 1 1
281290045 28129004504 1.877836 . 2 1 1 0 1 0 1 0 0
281940010 28194001004  .060845 . 2 1 1 0 1 0 1 1 1
281270044 28127004403 1.877836 . 2 1 1 0 1 0 1 1 0
281170050 28117005004  .060845 . 2 0 1 1 1 0 1 1 1
281690019 28169001902  .259337 . 2 1 1 1 1 1 1 1 1
280910013 28091001308  .259337 . 2 1 1 1 1 0 1 1 1
281550056 28155005607 1.877836 . 2 1 . . 1 0 0 0 0
280020024 28002002402  .060845 . 2 1 1 1 1 1 1 1 1
280050003 28005000305 1.877836 . 2 1 1 1 1 1 1 1 1
281170022 28117002201  .060845 . 2 1 0 . 1 1 1 1 1
280810014 28081001402 2.506085 . 2 1 1 0 1 0 1 0 0
281660016 28166001601 2.506085 . 2 1 1 0 1 1 0 1 0
281590016 28159001604 2.506085 . 2 1 1 0 1 0 1 0 0
281840001 28184000101 2.506085 . 2 1 1 1 1 1 1 0 0
281740018 28174001801 1.877836 . 2 1 1 1 1 1 1 0 1
280970047 28097004703  .259337 . 2 1 1 1 1 0 1 1 1
281830004 28183000408  .060845 . 2 1 1 0 1 1 1 1 1
281630045 28163004504  .060845 . 2 1 1 0 1 1 1 1 1
280610008 28061000809  .060845 . 2 1 1 1 1 0 1 1 1
280440035 28044003504  .259337 . 2 1 1 1 1 0 0 1 1
280650021 28065002104 2.506085 . 2 1 1 0 1 0 1 1 0
280810027 28081002704 2.506085 . 2 1 1 1 1 0 1 0 1
280910004 28091000405  .259337 . 2 1 0 0 1 1 1 1 1
281740017 28174001701 1.877836 . 2 1 1 1 1 1 1 1 1
280040008 28004000804 2.506085 . 2 1 1 1 1 1 1 1 1
281740035 28174003502 1.877836 . 2 1 1 1 1 1 1 1 1
280030013 28003001301 2.506085 . 2 1 1 0 1 1 0 0 0
280260018 28026001801 2.506085 . 2 0 1 1 0 0 1 0 0
281700032 28170003206  .060845 . 2 1 1 0 1 0 1 1 1
280340017 28034001701 2.506085 . 2 0 1 0 1 0 1 0 0
281630040 28163004002  .060845 . 2 0 1 0 0 0 1 1 1
280700014 28070001401  .259337 . 2 1 1 1 1 1 1 1 1
281320006 28132000603 1.877836 . 2 1 1 0 1 1 1 1 1
280190022 28019002204 2.506085 . 2 0 1 1 1 0 1 1 0
281630047 28163004703  .060845 . 2 0 1 1 0 0 1 1 0
281680040 28168004002  .060845 . 2 1 1 1 1 0 1 1 1
281740037 28174003702 1.877836 . 2 1 1 1 1 0 1 1 1
280560013 28056001303 2.506085 . 2 1 1 1 1 0 0 0 0
281710036 28171003602 1.877836 . 2 0 0 1 1 1 1 1 1
281350045 28135004502 1.877836 . 2 1 1 1 1 1 0 1 0
280690038 28069003803  .060845 . 2 0 1 . 1 0 1 1 0
281220031 28122003108  .060845 . 2 1 1 0 1 0 1 1 1
280300009 28030000901 2.506085 . 2 1 1 1 1 0 1 1 1
281390028 28139002810 2.506085 . 2 1 1 0 1 0 1 0 0
281740037 28174003704 1.877836 . 2 1 1 1 1 0 1 1 1
281540040 28154004002  .259337 . 2 1 1 1 1 1 1 1 1
281410008 28141000806 1.877836 . 2 1 1 0 1 0 1 1 1
281220038 28122003801  .060845 . 2 1 1 1 1 0 1 1 1
280600020 28060002003 2.506085 . 2 1 1 0 1 0 0 1 0
280320061 28032006102  .060845 . 2 1 1 1 1 1 1 1 1
281740013 28174001301 1.877836 . 2 1 1 1 1 1 1 1 1
280240044 28024004410  .259337 . 2 1 1 1 1 1 1 1 1
281350011 28135001101 1.877836 . 2 1 1 1 1 1 1 1 1
280320022 28032002204  .060845 . 2 1 1 0 1 0 1 1 1
280790020 28079002005 2.506085 . 2 1 1 1 1 0 1 0 0
280150001 28015000102 2.506085 . 2 1 1 0 1 0 1 1 0
281740012 28174001201 1.877836 . 2 1 . . 1 1 1 1 1
280860011 28086001105 1.877836 . 2 1 1 0 1 1 1 0 0
281700036 28170003604  .060845 . 2 1 1 0 1 1 1 0 1
280680007 28068000707  .259337 . 2 1 1 1 1 1 1 1 1
281630024 28163002401  .060845 . 2 0 1 1 1 1 1 1 1
280910005 28091000506  .259337 . 2 1 1 0 1 0 1 1 1
280500046 28050004607  .259337 . 2 1 1 0 1 1 1 0 1
281400035 28140003510  .259337 . 2 1 1 0 1 0 1 1 1
280870021 28087002106  .259337 . 2 1 1 1 1 1 1 1 1
280040029 28004002901 2.506085 . 2 1 1 1 1 1 1 1 1
280800003 28080000302 2.506085 . 2 0 1 1 1 0 1 1 0
281690004 28169000402  .259337 . 2 1 1 1 1 1 1 1 1
281320028 28132002802 1.877836 . 2 1 1 1 1 1 0 1 1
281810004 28181000403 2.506085 . 2 1 1 0 0 1 1 0 0
281740042 28174004201 1.877836 . 2 1 1 1 1 1 1 1 1
280360027 28036002705  .060845 . 2 1 1 0 1 1 1 1 1
280970060 28097006012  .259337 . 2 1 1 0 1 0 1 1 1
281410037 28141003707 1.877836 . 2 1 1 1 1 1 1 1 1
280060021 28006002104 2.506085 . 2 1 1 0 1 0 1 1 0
280250005 28025000502  .060845 . 2 1 1 1 1 0 1 1 0
280060023 28006002303 2.506085 . 2 1 1 0 1 0 1 1 0
280650001 28065000103 2.506085 . 2 1 1 0 1 1 1 0 1
281570021 28157002102 2.506085 . 2 0 1 0 1 0 0 1 0
280040022 28004002204 2.506085 . 2 1 1 1 1 1 1 1 1
280140007 28014000701 1.877836 . 2 1 1 0 1 1 1 1 1
281940052 28194005201  .060845 . 2 1 1 0 1 0 1 1 1
280320047 28032004705  .060845 . 2 1 1 1 1 1 1 1 1
281480004 28148000402 2.506085 . 2 0 1 1 0 0 0 0 0
280250046 28025004604  .060845 . 2 1 1 1 1 1 1 1 1
281490010 28149001001  .060845 . 2 1 1 0 1 1 1 1 1
281170045 28117004503  .060845 . 2 1 1 0 1 1 1 1 1
280140022 28014002204 1.877836 . 2 1 1 1 1 1 1 1 1
280610051 28061005101  .060845 . 2 1 1 . 1 1 1 1 1
280030029 28003002901 2.506085 . 2 1 0 1 1 0 1 1 1
280640008 28064000803 2.506085 . 2 0 0 0 0 0 1 1 0
280830031 28083003101  .060845 . 2 1 1 0 1 1 1 1 1
281410043 28141004303 1.877836 . 2 1 1 0 1 1 1 1 1
280650020 28065002003 2.506085 . 2 1 1 . 0 0 1 0 0
281590009 28159000903 2.506085 . 2 1 1 0 1 0 1 0 0
281110022 28111002201 1.877836 . 2 1 1 1 1 1 0 1 0
281740045 28174004501 1.877836 . 2 1 1 0 1 0 1 0 0
281740006 28174000601 1.877836 . 2 1 1 0 1 1 1 1 1
281740045 28174004502 1.877836 . 2 1 1 0 1 0 1 0 0
280550021 28055002104 2.506085 . 2 0 1 1 1 0 1 0 0
281130031 28113003106  .259337 . 2 1 1 0 1 1 1 1 1
280690009 28069000904  .060845 . 2 1 1 0 1 1 1 1 1
281030003 28103000301  .060845 . 2 1 1 1 1 0 0 0 1
281740047 28174004704 1.877836 . 2 1 1 1 1 1 1 1 0
280440037 28044003704  .259337 . 2 1 1 1 1 1 1 1 1
281740004 28174000406 1.877836 . 2 1 1 1 1 1 1 1 1
280370007 28037000702 2.506085 . 2 1 1 1 1 1 1 1 1
281180001 28118000103 2.506085 . 2 1 1 1 1 0 1 0 0
281240015 28124001501  .259337 . 2 1 1 1 1 0 1 1 1
280650013 28065001302 2.506085 . 2 1 1 0 1 0 0 1 1
280180036 28018003601  .060845 . 2 1 1 0 1 0 1 1 1
280860003 28086000303 1.877836 . 2 1 1 1 1 0 1 1 1
281640010 28164001001 2.506085 . 2 0 1 1 0 0 1 0 0
280430020 28043002004 2.506085 . 2 0 1 1 1 0 1 1 1
280250010 28025001003  .060845 . 2 1 1 0 1 1 1 1 1
281830016 28183001603  .060845 . 2 1 1 1 1 1 1 1 1
280030001 28003000101 2.506085 . 2 1 0 0 1 0 0 0 0
281280014 28128001401 2.506085 . 2 1 1 0 1 1 1 1 1
281740004 28174000402 1.877836 . 2 1 1 1 1 1 1 1 1
281240008 28124000801  .259337 . 2 1 1 1 1 0 1 1 1
281560024 28156002402 2.506085 . 2 1 1 1 0 0 0 0 0
280310006 28031000602  .259337 . 2 1 1 1 1 1 1 1 1
281280033 28128003302 2.506085 . 2 1 1 1 1 1 1 0 0
280420008 28042000801 2.506085 . 2 1 1 0 1 0 1 0 0
280640023 28064002303 2.506085 . 2 1 1 0 1 0 1 0 0
280290033 28029003302 2.506085 . 2 1 1 0 1 0 1 0 0
281770002 28177000205 2.506085 . 2 1 1 0 1 0 0 1 0
281740001 28174000104 1.877836 . 2 1 1 1 1 1 1 1 1
281350026 28135002601 1.877836 . 2 1 1 1 1 1 1 1 1
280040028 28004002801 2.506085 . 2 1 1 1 1 1 0 1 1
280910032 28091003205  .259337 . 2 1 1 1 1 0 0 1 1
280930010 28093001001 1.877836 . 2 1 1 1 1 0 1 0 0
281900021 28190002102 1.877836 . 2 1 1 1 1 1 1 1 1
280780032 28078003202  .060845 . 2 1 1 0 1 0 1 1 0
280610017 28061001702  .060845 . 2 1 1 1 1 0 1 1 1
280020043 28002004303  .060845 . 2 1 1 1 1 1 1 1 1
280030030 28003003003 2.506085 . 2 1 1 1 1 0 1 0 0
280990007 28099000702 2.506085 . 2 1 1 0 1 0 1 0 0
281260029 28126002902  .259337 . 2 1 1 0 1 0 1 1 1
280390014 28039001403 2.506085 . 2 1 1 0 1 1 1 1 1
280790020 28079002003 2.506085 . 2 1 1 1 1 0 1 0 0
280130022 28013002201  .259337 . 2 1 1 1 1 0 1 1 1
281880009 28188000904 2.506085 . 2 0 1 1 1 0 0 0 0
280260024 28026002403 2.506085 . 2 0 1 0 1 0 1 1 0
281430021 28143002101  .060845 . 2 1 1 0 1 1 1 1 1
280380060 28038006003 1.877836 . 2 1 1 1 1 1 1 1 1
281740001 28174000101 1.877836 . 2 1 1 1 1 1 1 1 1
280640019 28064001901 2.506085 . 2 1 1 0 1 1 1 1 0
280010010 28001001004 2.506085 . 2 1 1 0 1 0 0 0 0
280830002 28083000201  .060845 . 2 1 1 1 1 1 1 1 1
281110007 28111000704 1.877836 . 2 1 1 0 1 1 1 1 1
280030016 28003001604 2.506085 . 2 1 1 0 1 1 1 0 1
280270005 28027000501 2.506085 . 2 1 1 1 1 1 1 1 1
280610010 28061001005  .060845 . 2 1 1 1 1 1 1 1 1
280830031 28083003107  .060845 . 2 1 1 0 1 1 1 1 1
281170036 28117003603  .060845 . 2 1 1 0 1 1 1 1 1
280760032 28076003203 1.877836 . 2 1 1 0 1 0 1 1 1
281350041 28135004103 1.877836 . 2 1 1 0 1 1 1 1 0
280410014 28041001401 2.506085 . 2 0 1 0 1 0 1 0 0
280870017 28087001706  .259337 . 2 1 1 1 1 1 1 1 1
280690029 28069002901  .060845 . 2 0 1 0 1 1 1 1 0
281680009 28168000901  .060845 . 2 1 1 1 1 0 1 1 1
281480015 28148001501 2.506085 . 2 1 1 0 1 0 1 0 0
280120017 28012001701  .259337 . 2 1 1 0 1 1 1 1 1
281240026 28124002602  .259337 . 2 0 1 0 1 1 1 1 1
281060011 28106001108  .259337 . 2 1 1 0 1 1 1 1 1
281540034 28154003402  .259337 . 2 1 1 1 1 1 0 1 1
281480010 28148001003 2.506085 . 2 0 1 0 1 0 0 0 0
280060028 28006002805 2.506085 . 2 1 1 1 1 1 1 1 1
280250032 28025003205  .060845 . 2 1 1 0 1 1 1 1 1
280070020 28007002005 2.506085 . 2 1 1 0 1 0 1 1 0
280320001 28032000101  .060845 . 2 1 1 0 1 1 1 1 1
281340001 28134000101 2.506085 . 2 0 1 1 1 0 1 1 0
281730035 28173003501 1.877836 . 2 1 1 1 1 1 1 1 1
281620014 28162001404 2.506085 . 2 1 1 0 1 0 1 0 0
280020058 28002005807  .060845 . 2 1 1 0 1 1 1 1 1
280200015 28020001503 2.506085 . 2 1 1 1 1 0 0 1 0
280230004 28023000403 2.506085 . 2 0 1 0 1 0 1 1 0
281240047 28124004703  .259337 . 2 1 1 0 1 1 1 1 1
280630005 28063000502  .060845 . 2 1 1 1 1 1 1 1 1
280530016 28053001601 2.506085 . 2 0 1 1 1 0 1 0 0
280670006 28067000606  .060845 . 2 1 1 1 1 0 1 0 1
280370023 28037002302 2.506085 . 2 0 1 1 0 0 0 0 0
280160002 28016000201  .259337 . 2 1 1 1 1 1 1 1 1
281250005 28125000502 2.506085 . 2 1 1 0 1 0 1 0 0
281460003 28146000301 2.506085 . 2 1 1 1 1 0 0 1 0
281430025 28143002501  .060845 . 2 1 1 1 1 1 1 1 1
280020017 28002001701  .060845 . 2 1 1 1 1 0 1 1 1
281530013 28153001303 2.506085 . 2 0 1 0 1 0 1 1 0
280610033 28061003305  .060845 . 2 1 1 1 1 0 1 1 1
281750019 28175001904  .259337 . 2 1 1 1 1 1 1 1 1
280710003 28071000302 2.506085 . 2 1 1 1 1 0 0 0 0
281600023 28160002305 2.506085 . 2 1 1 0 1 0 1 1 0
280610049 28061004901  .060845 . 2 1 1 1 1 1 1 1 1
281420005 28142000504  .259337 . 2 1 1 . 1 1 1 1 1
280670037 28067003702  .060845 . 2 1 1 1 1 1 1 1 1
281170044 28117004405  .060845 . 2 1 1 1 1 1 1 1 1
280750040 28075004004 1.877836 . 2 1 1 1 0 0 0 0 0
280920045 28092004501 1.877836 . 2 1 1 0 1 0 0 1 0
281200020 28120002006 2.506085 . 2 1 1 0 1 0 1 0 0
281740057 28174005703 1.877836 . 2 1 1 1 0 0 0 0 0
281540028 28154002803  .259337 . 2 1 1 1 1 1 1 1 1
280970053 28097005301  .259337 . 2 1 1 0 1 0 1 1 1
281730029 28173002901 1.877836 . 2 1 1 1 1 0 1 1 0
280380015 28038001502 1.877836 . 2 1 1 1 1 1 1 1 1
280940001 28094000103 2.506085 . 2 1 1 0 1 0 1 1 0
280100016 28010001605 2.506085 . 2 1 1 0 1 0 1 1 1
281130040 28113004003  .259337 . 2 1 1 1 1 0 1 1 1
281730027 28173002705 1.877836 . 2 1 1 1 1 1 1 1 1
280760003 28076000304 1.877836 . 2 1 1 0 1 0 1 1 1
280730029 28073002901 2.506085 . 2 0 1 0 1 0 1 0 0
281090055 28109005503  .259337 . 2 1 1 1 1 0 1 1 1
281940037 28194003703  .060845 . 2 1 1 0 1 0 1 1 1
280930014 28093001401 1.877836 . 2 0 1 1 1 0 1 1 1
281740057 28174005705 1.877836 . 2 1 1 1 0 0 0 0 0
281940011 28194001105  .060845 . 2 1 1 1 1 1 1 1 1
280360010 28036001003  .060845 . 2 1 1 1 1 0 1 1 1
281740057 28174005707 1.877836 . 2 1 1 1 0 0 0 0 0
281810014 28181001402 2.506085 . 2 1 1 0 1 1 1 0 0
281950021 28195002102 2.506085 . 2 0 1 1 1 0 1 1 0
281740061 28174006101 1.877836 . 2 1 1 0 1 0 1 0 0
280960022 28096002203  .259337 . 2 1 1 0 1 1 1 1 1
280810018 28081001801 2.506085 . 2 1 1 0 1 1 1 0 0
281440033 28144003306  .259337 . 2 1 1 1 1 1 1 1 1
281740061 28174006102 1.877836 . 2 1 1 0 1 0 1 0 0
281110029 28111002902 1.877836 . 2 0 1 0 1 1 1 1 0
281740061 28174006104 1.877836 . 2 1 1 0 1 0 1 0 0
280980022 28098002210 2.506085 . 2 1 1 0 1 0 1 1 0
281030059 28103005903  .060845 . 2 0 1 0 1 0 0 0 1
280090011 28009001105 2.506085 . 2 1 1 0 1 0 1 1 0
280690045 28069004501  .060845 . 2 1 1 1 1 1 1 1 1
281470007 28147000707 1.877836 . 2 1 1 1 1 0 1 0 0
280340010 28034001003 2.506085 . 2 1 1 1 1 0 1 0 1
280320033 28032003303  .060845 . 2 1 1 0 1 0 1 1 1
281410029 28141002903 1.877836 . 2 1 1 1 1 1 1 1 1
280640008 28064000801 2.506085 . 2 0 0 0 0 0 1 1 0
281030031 28103003103  .060845 . 2 1 1 1 1 0 1 0 1
280900025 28090002501 2.506085 . 2 0 1 0 1 1 1 1 1
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180320035 18032003505 1.052729 . 4 1 1 1 0 0 0 0 0
180730034 18073003402 1.052729 . 4 1 1 0 0 0 0 0 0
180330014 18033001403 1.052729 . 4 1 . . 1 0 1 0 0
180760059 18076005905  .504068 . 4 1 1 1 1 1 1 1 1
180790039 18079003903 1.052729 . 4 1 1 1 0 0 1 0 0
180530002 18053000201 1.052729 . 4 1 1 0 0 0 0 0 0
180530002 18053000204 1.052729 . 4 1 1 0 0 0 0 0 0
180270035 18027003502 1.052729 . 4 0 1 0 0 0 1 0 0
180750013 18075001301  .504068 . 4 1 1 0 1 0 1 1 0
180660030 18066003007 1.052729 . 4 1 1 1 1 0 1 1 1
180840006 18084000604 1.052729 . 4 1 1 0 0 0 0 0 0
180820039 18082003902 1.052729 . 4 1 1 0 1 1 1 1 1
180530009 18053000906 1.052729 . 4 0 1 1 0 0 0 0 0
180230019 18023001904 1.052729 . 4 1 1 0 0 1 1 1 0
180480015 18048001502  .504068 . 4 1 1 1 1 1 1 1 1
180300001 18030000103 1.052729 . 4 0 1 0 0 . 0 0 0
180350053 18035005301 1.052729 . 4 1 1 1 0 0 1 0 0
180810010 18081001004 1.052729 . 4 0 . . 0 0 1 0 0
180480007 18048000705  .504068 . 4 1 1 0 1 1 1 1 1
180530002 18053000206 1.052729 . 4 1 1 0 0 0 0 0 0
180530009 18053000904 1.052729 . 4 0 1 1 0 0 0 0 0
180120015 18012001501 1.052729 . 4 1 1 0 0 1 1 0 0
180220012 18022001209 1.052729 . 4 1 1 0 0 0 1 0 0
180440014 18044001405 1.052729 . 4 0 1 . 0 0 0 0 0
180490021 18049002102 1.052729 . 4 1 1 0 0 1 0 1 0
180760014 18076001402  .504068 . 4 1 1 0 1 1 1 1 1
180030001 18003000106 1.052729 . 4 1 1 0 0 0 1 0 0
180600002 18060000204 1.052729 . 4 1 1 1 1 1 1 0 1
180050004 18005000403 1.052729 . 4 1 1 1 1 1 1 0 0
180450004 18045000405 1.052729 . 4 1 1 1 1 0 1 1 1
180460022 18046002201 1.052729 . 4 1 1 0 0 0 1 0 0
180040035 18004003503 1.052729 . 4 1 1 1 1 0 1 0 0
180730046 18073004609 1.052729 . 4 1 0 1 0 0 0 0 0
180330013 18033001301 1.052729 . 4 0 1 0 0 0 0 0 0
180620035 18062003501 1.052729 . 4 0 1 0 0 0 1 0 0
180660018 18066001801 1.052729 . 4 1 1 1 1 1 0 1 1
180480005 18048000503  .504068 . 4 1 1 1 1 1 1 1 1
180860036 18086003601 1.052729 . 4 1 1 0 0 0 1 0 0
180160019 18016001903 1.052729 . 4 1 1 1 0 0 1 0 0
180470042 18047004203 1.052729 . 4 1 1 1 1 0 1 0 0
180730016 18073001604 1.052729 . 4 1 1 1 0 0 0 0 0
180530003 18053000301 1.052729 . 4 1 1 1 0 0 0 0 0
180060036 18006003602  .504068 . 4 1 1 0 0 0 0 0 0
180730027 18073002707 1.052729 . 4 0 1 0 0 0 0 0 0
180330011 18033001103 1.052729 . 4 1 1 0 0 0 0 1 0
180310004 18031000404  .504068 . 4 1 1 1 1 1 1 1 1
180170001 18017000103 1.052729 . 4 1 1 1 0 0 1 0 0
180190015 18019001501 1.052729 . 4 0 1 0 0 0 1 0 0
180180039 18018003903 1.052729 . 4 1 1 1 1 0 1 0 1
180260005 18026000501 1.052729 . 4 0 1 1 0 0 1 0 0
180470041 18047004103 1.052729 . 4 0 1 0 0 0 1 0 0
180860031 18086003101 1.052729 . 4 1 0 0 1 0 1 0 0
180650003 18065000301 1.052729 . 4 1 1 1 0 0 0 0 0
180290051 18029005102 1.052729 . 4 0 1 0 0 1 1 0 0
180300001 18030000101 1.052729 . 4 0 1 0 0 . 0 0 0
180510008 18051000801 1.052729 . 4 0 1 0 0 1 1 0 0
180330019 18033001905 1.052729 . 4 0 1 0 0 0 0 0 0
180730015 18073001502 1.052729 . 4 1 1 0 0 0 0 0 0
180290058 18029005806 1.052729 . 4 1 1 0 0 1 1 0 0
180460027 18046002709 1.052729 . 4 1 1 0 0 0 1 0 0
180470008 18047000805 1.052729 . 4 0 0 0 0 0 1 0 0
180640031 18064003101  .504068 . 4 1 1 . 1 1 1 1 1
180290053 18029005305 1.052729 . 4 1 1 1 0 1 1 1 0
180770056 18077005603  .504068 . 4 1 1 0 1 0 1 0 1
180360001 18036000104  .504068 . 4 1 1 1 1 1 1 1 1
180810005 18081000504 1.052729 . 4 0 1 0 0 0 1 0 0
180170024 18017002403 1.052729 . 4 1 1 0 0 0 1 0 0
180090034 18009003402 1.052729 . 4 1 1 0 0 1 1 0 0
180160001 18016000101 1.052729 . 4 1 1 0 1 1 0 0 0
180510048 18051004805 1.052729 . 4 0 1 0 0 1 1 0 0
180730016 18073001606 1.052729 . 4 1 1 1 0 0 0 0 0
180530002 18053000203 1.052729 . 4 1 1 0 0 0 0 0 0
180170013 18017001304 1.052729 . 4 1 1 1 0 1 1 1 0
180600039 18060003905 1.052729 . 4 1 1 0 0 0 1 0 0
180610048 18061004806  .504068 . 4 1 1 1 1 0 1 1 1
180460030 18046003005 1.052729 . 4 0 1 1 0 0 1 0 0
180200019 18020001902 1.052729 . 4 1 1 0 0 0 0 0 0
180530003 18053000307 1.052729 . 4 1 1 1 0 0 0 0 0
180770017 18077001704  .504068 . 4 1 1 1 1 0 1 0 1
180210037 18021003702 1.052729 . 4 1 1 0 0 1 1 0 0
180470029 18047002904 1.052729 . 4 1 1 0 1 0 0 1 0
180190027 18019002710 1.052729 . 4 1 1 0 0 0 0 0 0
180870025 18087002504  .504068 . 4 1 1 1 1 1 1 0 1
180140041 18014004103 1.052729 . 4 0 1 1 0 0 0 0 0
180860036 18086003608 1.052729 . 4 1 1 0 0 0 1 0 0
180140044 18014004404 1.052729 . 4 1 1 1 0 0 1 0 0
180490032 18049003201 1.052729 . 4 1 1 1 0 0 0 0 0
180360019 18036001905  .504068 . 4 1 1 1 1 0 1 0 0
180430029 18043002904 1.052729 . 4 0 0 1 0 0 1 0 0
180210012 18021001204 1.052729 . 4 1 1 1 0 1 0 1 0
180470039 18047003901 1.052729 . 4 . 1 0 0 0 0 0 0
180410007 18041000703  .504068 . 4 1 1 1 1 0 1 1 1
180470029 18047002905 1.052729 . 4 1 1 0 1 0 0 1 0
180440012 18044001201 1.052729 . 4 0 1 1 0 0 0 0 0
180160054 18016005402 1.052729 . 4 1 1 1 0 0 1 0 0
180060004 18006000404  .504068 . 4 1 1 0 0 0 1 0 0
180510003 18051000302 1.052729 . 4 1 1 0 0 0 1 0 0
180560036 18056003601 1.052729 . 4 0 1 0 1 1 1 0 0
180100031 18010003103  .504068 . 4 1 1 0 1 1 1 0 1
180760037 18076003701  .504068 . 4 1 1 0 1 1 1 1 1
180630018 18063001802  .504068 . 4 1 1 1 1 1 1 1 1
180530004 18053000404 1.052729 . 4 0 0 0 0 0 0 0 0
180090026 18009002603 1.052729 . 4 1 1 1 1 0 0 0 0
180760047 18076004704  .504068 . 4 1 1 1 1 1 1 1 1
180470020 18047002002 1.052729 . 4 1 1 1 0 0 1 0 0
180460049 18046004905 1.052729 . 4 1 1 0 1 0 1 0 0
180110007 18011000703 1.052729 . 4 1 1 1 1 0 0 0 0
180290057 18029005704 1.052729 . 4 1 1 0 0 1 0 0 0
180400030 18040003001 1.052729 . 4 1 1 0 0 0 1 0 0
180710008 18071000808 1.052729 . 4 1 1 0 0 0 1 0 0
180450029 18045002905 1.052729 . 4 1 1 . 1 1 1 1 1
180460058 18046005802 1.052729 . 4 0 1 1 0 0 1 0 0
180430017 18043001706 1.052729 . 4 1 1 1 0 0 1 0 0
180070045 18007004502  .504068 . 4 1 1 1 1 1 1 1 1
180520004 18052000401 1.052729 . 4 1 1 0 1 1 1 1 1
180730008 18073000801 1.052729 . 4 0 1 0 0 0 0 0 0
180540011 18054001106 1.052729 . 4 1 0 1 0 0 0 0 0
180300020 18030002002 1.052729 . 4 0 1 1 0 0 0 0 0
180860012 18086001205 1.052729 . 4 1 1 1 0 0 1 0 0
180400008 18040000801 1.052729 . 4 1 1 0 1 0 1 0 0
180460024 18046002403 1.052729 . 4 0 1 0 0 0 1 0 0
180730013 18073001308 1.052729 . 4 1 1 0 0 0 0 0 0
180290043 18029004307 1.052729 . 4 1 1 0 0 1 0 0 0
180620012 18062001202 1.052729 . 4 0 1 1 0 0 0 0 0
180560037 18056003702 1.052729 . 4 1 1 1 1 1 1 0 0
180120028 18012002802 1.052729 . 4 1 1 0 1 0 1 0 0
180680057 18068005706  .504068 . 4 1 1 1 1 1 1 0 0
180090023 18009002302 1.052729 . 4 0 1 0 0 0 0 0 0
180470036 18047003602 1.052729 . 4 1 1 1 0 0 0 0 0
180190012 18019001205 1.052729 . 4 1 1 0 0 0 1 1 0
180280029 18028002906 1.052729 . 4 0 1 0 0 0 1 0 0
180350029 18035002902 1.052729 . 4 0 1 0 0 0 1 0 0
180330006 18033000601 1.052729 . 4 0 1 0 0 0 0 0 0
180530006 18053000602 1.052729 . 4 0 1 0 0 0 0 0 0
180700019 18070001902 1.052729 . 4 0 1 1 0 0 1 0 0
180630031 18063003105  .504068 . 4 1 1 1 1 1 1 1 1
180050033 18005003304 1.052729 . 4 1 1 1 0 1 0 0 0
180330006 18033000602 1.052729 . 4 0 1 0 0 0 0 0 0
180530006 18053000605 1.052729 . 4 0 1 0 0 0 0 0 0
180330007 18033000706 1.052729 . 4 0 1 0 0 0 1 0 0
180280019 18028001905 1.052729 . 4 0 1 0 0 0 1 0 0
180600021 18060002103 1.052729 . 4 1 1 0 0 0 1 0 0
180530007 18053000702 1.052729 . 4 0 1 1 0 0 0 0 0
180470033 18047003304 1.052729 . 4 0 0 1 0 0 0 0 0
180150001 18015000102 1.052729 . 4 1 1 0 0 0 1 0 0
180470033 18047003303 1.052729 . 4 0 0 1 0 0 0 0 0
180290034 18029003404 1.052729 . 4 0 1 1 0 0 1 0 0
180330007 18033000704 1.052729 . 4 0 1 0 0 0 1 0 0
180530014 18053001403 1.052729 . 4 1 1 0 0 0 0 0 0
180760006 18076000603  .504068 . 4 1 1 1 1 1 1 1 1
180610033 18061003301  .504068 . 4 1 1 0 1 1 1 1 1
180100018 18010001802  .504068 . 4 1 1 . 1 1 1 1 1
180750038 18075003803  .504068 . 4 1 1 0 1 1 1 1 1
180500022 18050002201  .504068 . 4 1 1 1 1 0 1 0 1
180230008 18023000802 1.052729 . 4 1 1 1 0 0 1 0 0
180470033 18047003302 1.052729 . 4 0 0 1 0 0 0 0 0
180140035 18014003502 1.052729 . 4 0 1 1 0 0 0 0 0
180130027 18013002704 1.052729 . 4 1 1 0 1 0 1 0 0
180330006 18033000603 1.052729 . 4 0 1 0 0 0 0 0 0
180860017 18086001702 1.052729 . 4 1 1 1 0 0 1 0 0
180620030 18062003002 1.052729 . 4 1 1 1 1 0 1 0 0
180540031 18054003101 1.052729 . 4 0 1 0 0 0 0 0 0
180740042 18074004203 1.052729 . 4 0 1 0 0 1 1 0 0
180600033 18060003304 1.052729 . 4 1 1 0 1 1 1 0 1
180660042 18066004203 1.052729 . 4 1 1 1 1 0 1 1 1
180730010 18073001005 1.052729 . 4 1 1 0 0 1 1 0 0
180270008 18027000804 1.052729 . 4 1 1 1 0 1 1 0 0
180580007 18058000701  .504068 . 4 1 1 0 1 0 1 0 1
180790017 18079001701 1.052729 . 4 0 1 1 0 0 0 0 0
180140008 18014000802 1.052729 . 4 0 1 1 0 0 1 0 0
180490020 18049002004 1.052729 . 4 1 1 0 0 0 0 0 0
180320032 18032003201 1.052729 . 4 0 1 0 0 1 1 0 0
180790035 18079003507 1.052729 . 4 1 1 1 0 0 0 0 0
180450009 18045000901 1.052729 . 4 1 1 1 0 0 1 0 0
180380024 18038002401  .504068 . 4 1 1 0 1 0 0 0 1
180460057 18046005703 1.052729 . 4 0 1 0 0 0 1 0 0
180420027 18042002703 1.052729 . 4 0 1 0 0 0 1 0 0
180710015 18071001504 1.052729 . 4 1 1 0 0 0 1 0 0
180670009 18067000904 1.052729 . 4 1 1 1 0 1 1 0 0
180870007 18087000704  .504068 . 4 1 1 0 0 0 1 0 0
180260008 18026000801 1.052729 . 4 1 1 1 0 0 0 0 0
180740031 18074003105 1.052729 . 4 1 1 0 0 0 1 0 0
180520015 18052001503 1.052729 . 4 1 1 1 1 0 1 1 1
180690017 18069001704  .504068 . 4 0 1 0 1 0 1 0 0
180320031 18032003102 1.052729 . 4 0 1 0 0 0 1 0 0
180260026 18026002603 1.052729 . 4 1 1 0 0 0 1 0 0
180510044 18051004401 1.052729 . 4 1 1 0 1 1 1 0 1
180790028 18079002801 1.052729 . 4 1 1 1 0 0 0 0 0
180490022 18049002202 1.052729 . 4 1 1 0 0 0 0 0 0
180830019 18083001904  .504068 . 4 1 1 0 1 0 1 0 1
180180030 18018003001 1.052729 . 4 0 1 1 0 0 1 0 0
180560033 18056003304 1.052729 . 4 0 1 0 1 1 1 0 0
180780011 18078001101  .504068 . 4 1 1 1 1 1 1 1 1
180300012 18030001201 1.052729 . 4 1 0 1 0 0 0 0 0
180110037 18011003702 1.052729 . 4 1 1 . 0 0 0 0 0
180860001 18086000110 1.052729 . 4 1 0 0 0 0 1 0 0
180830005 18083000505  .504068 . 4 1 1 1 1 1 1 1 1
180520013 18052001303 1.052729 . 4 1 1 0 0 0 0 0 0
180530005 18053000501 1.052729 . 4 0 1 0 0 0 0 0 0
180870028 18087002803  .504068 . 4 1 1 1 1 1 1 1 1
180490026 18049002602 1.052729 . 4 1 1 1 1 0 0 0 0
180080022 18008002204  .504068 . 4 1 1 1 1 1 1 1 1
180630010 18063001003  .504068 . 4 1 1 1 1 0 1 0 0
end
label values region_01 reg
label def reg 2 "Andhra Pradesh", modify
label def reg 3 "Arunachal Pradesh", modify
label def reg 4 "Assam", modify

[Varsha Vaishnav]

Using the above dataset, can you please explain the importance of the outer loop in the following codes?

 global est_1 edu_1 atten_1 cm_1 nutri_1 elec_1 toilet_1 water_1 house_1 fuel_1 asset_1

 foreach j of numlist 1 {

 gen sample_`j' = (edu_`j'!=. & atten_`j'!=. & cm_`j'!=. & nutri_`j'!=. & elec_`j'!=. & toilet_`j'!=. & water_`j'!=. & house_`j'!=. & fuel_`j'!=. & asset_`j'!=.)

 replace sample_`j' = . if subsample==0

 }

 foreach j of numlist 1 {

 foreach var in ${est_`j'} {

 gen g0`j'_`var' = 1 if `var'==0

 replace g0`j'_`var' = 0 if `var'==1

 }

 }

And, If I remove the outer loop then the code you provided me to generate the state-level variable doesn't run.

[Daniel Schaefer]

A much smaller example dataset would have been preferable. It looks to me like in each case the outer loop creates a constant local macro named `j' with its value equal to 1. The correct way to do this is with the following line:

local j = 1

Of course, you could also replace `j' with 1 by hand and achieve the same effect.

And, If I remove the outer loop then the code you provided me to generate the state-level variable doesn't run.

You will either need to create the local macro `j' or replace `j' with 1 to get that line to work as expected without the outer loop. I would strongly recommend reading more about macros and loops in Stata before you continue.

[Daniel Schaefer]

Or, better yet, run this code and see what the outer loop is doing for yourself:

foreach j of numlist 1 {
    display "j equals `j'"
}

foreach k of numlist 20 33 50 {
    display "k equals `k'"
}

[Varsha Vaishnav]

As you said, I removed the outer loop and now the codes are running without any error.

Here is the smaller example dataset.

* Example generated by -dataex-. To install: ssc install dataex
clear
input long psu float(strata sample_weight sample edu_1 atten_1 cm_1 nutri_1 elec_1 toilet_1 water_1 house_1 fuel_1 asset_1 c_censured_vector_33)
28069 28711  .060845 1 1 1 1 0 1 1 1 1 1 0         0
28174 28001 1.877836 1 1 1 1 1 1 1 1 1 1 1         0
28069 28711  .060845 1 1 1 1 1 1 0 1 1 1 1         0
28059 28002 2.506085 1 0 1 1 1 1 0 1 1 0 1         0
28068 28721  .259337 1 1 1 1 1 1 0 1 1 1 0         0
28003 28002 2.506085 1 1 1 1 1 1 0 0 0 0 0         0
28151 28711  .060845 0 1 1 1 . 1 1 1 1 1 1         .
28070 28721  .259337 1 1 1 1 1 1 1 1 1 1 1         0
28076 28001 1.877836 1 1 1 1 1 1 1 1 1 1 1         0
28100 28002 2.506085 1 1 1 1 0 1 0 0 1 0 0  .3888889
28084 28002 2.506085 0 1 1 1 . 1 1 1 1 0 0         .
28113 28721  .259337 1 1 0 1 1 1 1 1 1 1 0         0
28174 28001 1.877836 1 1 1 1 1 1 1 1 0 0 0         0
28005 28001 1.877836 1 1 1 1 0 1 1 1 1 1 1         0
28047 28002 2.506085 1 1 1 1 1 1 1 1 1 1 1         0
28174 28001 1.877836 1 1 1 1 1 1 1 1 1 1 1         0
28031 28721  .259337 1 1 1 1 0 1 1 1 1 1 0         0
28138 28002 2.506085 1 1 1 1 0 1 0 1 0 0 1  .3333333
28075 28001 1.877836 1 1 1 1 1 1 0 0 0 0 1         0
28076 28001 1.877836 1 1 1 1 0 1 0 1 1 1 1         0
28174 28001 1.877836 1 1 1 1 1 1 0 1 1 1 1         0
28192 28002 2.506085 1 0 1 1 0 1 0 1 0 0 0  .5555556
28095 28002 2.506085 1 1 1 1 0 1 0 1 0 0 1  .3333333
28122 28711  .060845 1 0 1 1 0 1 0 1 1 1 0 .44444445
28113 28721  .259337 1 1 1 1 0 1 1 1 1 1 1         0
28174 28001 1.877836 1 1 1 1 1 1 1 1 1 1 1         0
28088 28001 1.877836 1 1 1 1 1 1 1 1 1 1 1         0
28187 28002 2.506085 1 1 1 1 1 1 1 1 1 0 1         0
28195 28002 2.506085 1 1 1 1 0 1 0 1 0 0 0  .3888889
28155 28001 1.877836 1 1 0 1 0 1 0 1 1 0 1 .44444445
28123 28002 2.506085 1 1 1 0 1 1 1 1 1 1 1         0
28174 28001 1.877836 1 1 1 1 0 1 0 1 1 1 0         0
28174 28001 1.877836 1 1 1 1 1 1 0 1 1 1 1         0
28068 28721  .259337 1 1 1 1 0 1 1 1 1 1 1         0
28174 28001 1.877836 1 1 1 1 1 1 1 1 1 1 1         0
28088 28001 1.877836 1 1 1 1 1 1 1 1 1 1 0         0
28174 28001 1.877836 1 1 1 1 1 1 1 1 1 1 1         0
28106 28721  .259337 1 1 1 1 1 1 1 1 1 1 1         0
28063 28711  .060845 1 1 1 1 1 1 1 1 1 1 1         0
28185 28711  .060845 1 1 1 1 1 1 1 1 1 1 1         0
28125 28002 2.506085 1 1 0 1 1 1 0 1 0 0 0  .3888889
28163 28711  .060845 1 0 1 1 1 1 0 1 1 1 0         0
28066 28002 2.506085 1 1 1 1 0 1 0 1 0 0 0  .3888889
28128 28002 2.506085 1 1 1 1 1 1 1 1 1 0 1         0
28174 28001 1.877836 1 1 1 1 1 1 1 1 1 1 1         0
28018 28711  .060845 1 1 1 1 0 1 0 1 1 1 1         0
28129 28001 1.877836 1 0 1 1 0 1 0 0 1 0 1        .5
28174 28001 1.877836 1 1 1 1 1 1 1 1 1 0 0         0
28090 28002 2.506085 1 1 1 1 1 1 0 1 0 1 1         0
28159 28002 2.506085 1 0 1 1 0 0 0 1 0 0 0  .6111112
12045 12002  .081897 1 1 1 1 0 1 1 1 0 0 1         0
12045 12002  .081897 1 1 1 1 1 1 1 1 0 0 0         0
12040 12002  .081897 1 0 0 1 0 1 0 0 0 0 0  .7777779
12051 12001  .067889 1 1 0 1 1 1 1 1 0 1 1         0
12021 12002  .081897 1 0 0 1 0 1 1 1 0 0 0  .6666667
12045 12002  .081897 1 1 1 1 0 1 1 1 0 0 1         0
12046 12001  .067889 1 1 1 1 0 1 0 1 0 0 0  .3888889
12034 12002  .081897 1 1 0 1 0 0 0 1 0 0 0  .6111112
12002 12002  .081897 1 0 1 0 1 1 0 1 0 0 0  .5555556
12023 12001  .067889 1 1 1 1 1 1 0 1 0 1 1         0
12031 12001  .067889 1 0 0 1 1 1 1 1 0 0 1 .44444445
12048 12001  .067889 1 1 1 1 0 1 1 1 0 0 1         0
12015 12002  .081897 1 0 0 1 1 1 1 1 0 0 1 .44444445
12060 12002  .081897 1 1 0 1 0 0 0 1 0 0 0  .6111112
12017 12002  .081897 1 1 0 1 1 1 1 0 0 0 0  .3888889
12019 12002  .081897 1 0 0 1 0 0 0 1 0 0 1  .7222223
12047 12002  .081897 1 1 1 1 1 1 0 1 0 0 1         0
12032 12002  .081897 1 0 0 1 1 1 0 0 0 0 0  .6111112
12046 12001  .067889 1 1 1 1 0 1 0 1 0 0 0  .3888889
12033 12002  .081897 1 0 0 1 0 0 0 1 0 0 0  .7777779
12043 12001  .067889 1 1 1 1 1 1 1 1 0 1 1         0
12034 12002  .081897 1 1 0 1 0 0 0 1 0 0 1  .5555556
12057 12001  .067889 1 1 1 1 1 0 0 1 0 0 0         0
12051 12001  .067889 1 1 1 0 1 1 1 1 1 1 1         0
12047 12002  .081897 1 1 1 1 0 0 0 1 0 0 1  .3888889
12041 12002  .081897 1 0 1 1 0 0 1 0 0 0 0  .6111112
12030 12002  .081897 1 1 1 1 1 0 0 0 0 0 0  .3333333
12049 12002  .081897 1 1 1 1 1 1 0 0 0 0 0         0
12010 12002  .081897 1 1 1 1 0 1 0 1 0 0 0  .3888889
12032 12002  .081897 1 0 0 1 0 1 0 0 0 0 0  .7777779
12048 12001  .067889 1 0 1 1 1 1 0 1 0 1 0  .3333333
12051 12001  .067889 0 0 0 . . 1 0 1 0 0 1         .
12019 12002  .081897 1 1 1 1 0 0 0 1 0 0 1  .3888889
12034 12002  .081897 1 1 1 1 0 0 0 0 0 0 0        .5
12056 12002  .081897 1 0 1 1 1 0 0 0 0 0 0        .5
12053 12002  .081897 1 0 0 1 1 1 0 1 0 0 1        .5
12048 12001  .067889 1 1 1 1 0 1 1 1 1 1 1         0
12021 12002  .081897 1 1 1 1 1 1 1 1 0 0 1         0
12021 12002  .081897 1 1 1 1 1 1 1 1 0 0 1         0
12010 12002  .081897 1 1 1 1 1 1 1 0 1 1 1         0
12052 12002  .081897 1 0 1 1 0 1 0 1 0 0 1        .5
12004 12002  .081897 1 1 0 1 0 0 0 0 0 0 0  .6666667
12009 12002  .081897 1 0 0 1 0 1 0 1 0 0 0  .7222223
12009 12002  .081897 1 0 0 1 0 1 0 1 0 0 0  .7222223
12005 12001  .067889 1 1 0 1 1 1 1 1 1 1 1         0
12058 12001  .067889 1 1 1 1 1 1 1 1 1 1 0         0
12013 12002  .081897 1 1 1 1 1 1 0 1 0 0 0         0
12008 12002  .081897 1 1 1 1 1 1 0 1 0 0 0         0
12009 12002  .081897 1 1 1 0 1 1 0 1 0 0 0  .3888889
12006 12001  .067889 1 1 1 1 1 1 1 1 1 1 1         0
end

 sum  c_censured_vector_33 [iw = sample_weight] if sample==1

  gen  a = r(mean)

The above codes generate 'a' at the national level. I want to create it at the state level (region_01 in the dataset), and then I want to make a table that shows the estimates along with their standard errors. How can I do that?

[Varsha Vaishnav]

Also, can you please explain how I can generate the variable 'a' when I'm simply weighting the data? I mean, without the svy option, what should be the code?

[Daniel Schaefer]

The above codes generate 'a' at the national level. I want to create it at the state level (region_01 in the dataset)

If you take a look at the resources I've already provided you should be able to find the answer yourself.

Also, can you please explain how I can generate the variable 'a' when I'm simply weighting the data? I mean, without the svy option, what should be the code?

Why would you ever try to implement this yourself? Just use svy.