Dear Stata List,
As a follow up to my last email, I am still grappling with the balancing
property using the pscore module. Further to Ricardo's suggestion, I used
fewer variables to estimate the propensity score. However, no matter what
combination I use, I still get the error. I wonder if any one else has has
this problem and what they did to overcome it. I will grateful for your
help.
My command is :
-pscore waterpipe v151 v152 v012 ageresp2 aware ///
v512 v715 eduhusb eduhusb2 v730 partnerage2 v025 ///
sh42 , pscore(mypscore) blockid(myblock) comsup numblo(5) level(0.005)
logit-
where my variables are
v151 sex of household head
v152 age of household head
v012 current age - respondent
ageresp2 square of current age-respondent
aware awareness (listens to radio/ watches tv/ readsnewspaper)
v512 years since first marriage
v715 husbands education-single yrs
eduhusb square of husbands education-single yrs
eduhusb2 cube of husbands education-single yrs
v730 partners age
partnerage2 sqaure of partmers age
v025 type of place of residence
sh42 does this household own this
house or any other house?
The output is :
****************************************************
Algorithm to estimate the propensity score
****************************************************
The treatment is waterpipe
waterpipe | Freq. Percent Cum.
------------+-----------------------------------
0 | 15,396 61.91 61.91
1 | 9,473 38.09 100.00
------------+-----------------------------------
Total | 24,869 100.00
Estimation of the propensity score
Iteration 0: log likelihood = -16525.718
Iteration 1: log likelihood = -13670.121
Iteration 2: log likelihood = -13630.618
Iteration 3: log likelihood = -13630.359
Iteration 4: log likelihood = -13630.359
Logistic regression Number of obs =
24869
LR chi2(13) =
5790.72
Prob > chi2 =
0.0000
Log likelihood = -13630.359 Pseudo R2 =
0.1752
------------------------------------------------------------------------------
waterpipe | Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------+----------------------------------------------------------------
v151 | -.0863459 .0610051 -1.42 0.157 -.2059138
.033222
v152 | -.0029664 .0010262 -2.89 0.004 -.0049776
-.0009551
v012 | .0485913 .0219485 2.21 0.027 .005573
.0916097
ageresp2 | .0000214 .0003888 0.06 0.956 -.0007406
.0007833
aware | .7133884 .0343954 20.74 0.000 .6459746
.7808022
v512 | -.0718782 .0052339 -13.73 0.000 -.0821364
-.06162
v715 | .0415108 .0208768 1.99 0.047 .000593
.0824286
eduhusb | -.0047051 .0033843 -1.39 0.164 -.0113382
.001928
eduhusb2 | .000216 .0001408 1.53 0.125 -.00006
.000492
v730 | .0366385 .010276 3.57 0.000 .016498
.0567791
partnerage2 | -.0004604 .0001197 -3.84 0.000 -.000695
-.0002257
v025 | -1.659859 .0351148 -47.27 0.000 -1.728683
-1.591036
sh42 | -.2596864 .0528507 -4.91 0.000 -.3632719
-.156101
_cons | .8748132 .3151323 2.78 0.006 .2571653
1.492461
------------------------------------------------------------------------------
Note: the common support option has been selected
The region of common support is [.06287784, .90866427]
Description of the estimated propensity score
in region of common support
Estimated propensity score
-------------------------------------------------------------
Percentiles Smallest
1% .120273 .0628778
5% .1411216 .063518
10% .1534986 .0638564 Obs 24823
25% .1842821 .0689311 Sum of Wgt. 24823
50% .3147234 Mean .3815467
Largest Std. Dev. .2302978
75% .5411638 .9032886
90% .7707648 .9035941 Variance .0530371
95% .8032801 .9039739 Skewness .7839778
99% .851611 .9086643 Kurtosis 2.176904
******************************************************
Step 1: Identification of the optimal number of blocks
Use option detail if you want more detailed output
******************************************************
The final number of blocks is 13
This number of blocks ensures that the mean propensity score
is not different for treated and controls in each blocks
**********************************************************
Step 2: Test of balancing property of the propensity score
Use option detail if you want more detailed output
**********************************************************
Variable v730 is not balanced in block 9
Variable eduhusb is not balanced in block 12
Variable eduhusb2 is not balanced in block 12
Variable v715 is not balanced in block 13
Variable eduhusb is not balanced in block 13
Variable eduhusb2 is not balanced in block 13
The balancing property is not satisfied
Try a different specification of the propensity score
Inferior |
of block | waterpipe
of pscore | 0 1 | Total
-----------+----------------------+----------
0 | 45 3 | 48
.1 | 1,811 247 | 2,058
.15 | 2,560 461 | 3,021
.175 | 1,998 485 | 2,483
.2 | 1,659 479 | 2,138
.25 | 1,348 497 | 1,845
.3 | 1,955 947 | 2,902
.35 | 1,379 878 | 2,257
.4 | 847 692 | 1,539
.5 | 306 417 | 723
.6 | 318 678 | 996
.7 | 849 2,631 | 3,480
.8 | 275 1,058 | 1,333
-----------+----------------------+----------
Total | 15,350 9,473 | 24,823
Note: the common support option has been selected
*******************************************
End of the algorithm to estimate the pscore
*******************************************
Sincerely,
Gauri
>From: "Gauri Khanna" <[email protected]>
>Reply-To: [email protected]
>To: [email protected]
>Subject: Re: st: Pscore and balancing
>Date: Wed, 11 Apr 2007 16:45:11 +0000
>
>Dear Ricardo,
>
>Thank you for your prompt response:
>
>1. So that I know what you mean by "more parsimonious", you are suggesting
>to start off with fewer variables than my current set of 73 to generate the
>pscore? Am I right?
>
>The reason I say this is because in Becker and Ichino's article in Stata
>Journal, Vol 2, No 4 the exact opposite is stated... if the balancing
>property is not satisfied a "less parsimonious" specification is needed.
>
>2. A related question, if I start off with fewer variables is there a
>criteria that one uses to pick and choose?
>
>(To be on the safe side I looked up the dictionary meaning of parsimonious
>and it means frugal.)
>
>
>I repeat the other two questions that perhaps you or anyone else might know
>
>1. Firstly, why does the number of observations get reduced to
>>>13821 from 24,672 in the logit estimation?
>
>4. browsed at the pscore and blockid variables generated "pscorepump" and
>"myblock" and found pscorepump==. for myblock==0. I don't understand what
>this means. There are atleast 10,000 missing values for pscorepump.
>
>I appreciate your continued advice.
>
>
>Regards,
>
>Gauri
>
>PS. Thanks for the reference of Deheja and Wahba (i had their working paper
>of this and I will see the final version also)
>
>
>>From: "ricardo sierra" <[email protected]>
>>Reply-To: [email protected]
>>To: [email protected]
>>Subject: Re: st: Pscore and balancing
>>Date: Wed, 11 Apr 2007 11:39:29 -0400
>>
>>Is there a way you can start with a more parsimonious logit
>>specification to estimate de score ??
>>
>>** This is the 1st step proposed in Dehejia and Wahba (2002)
>>
>>
>>On 4/11/07, Gauri Khanna <[email protected]> wrote:
>>>Dear Stata List,
>>>
>>>I am using Stata Version 9.2. I am generating propensity scores using the
>>>pscore module (authors Becker & Ichino 2002) and it is updated. I am
>>>stuck
>>>with the balancing property which remains unsatisfied. I have 73
>>>variables,
>>>mostly categorical or dummy, for generating pscore. I have copied the
>>>output
>>>below for reference. I have also copied a description of my variables, in
>>>case you would like to scan through them. I looked at the statalist
>>>archives
>>>but did not find a response to my question.
>>>
>>>This is the command that I use: "waterpump, a binary variable, is my
>>>treatment"
>>>
>>>-pscore waterpump v136 v151 v152 hv217 v012 v109 v110 v112 v115 v119 sh39
>>>v131 v137 ///
>>>v208 v218 v404 v416 husbwifelive v512 v627 v628 v629 ///
>>>v715 v705 v717 v718 v714 v731 v730 b9 s509 s510 s511a s511b s511d s512a
>>>///
>>>s512b s513 sh29 sh32a sh32b sh32c sh32d sh32e sh32f sh32x sh34 sh35 ///
>>>sh36 sh37 sh42 sh43 sh46 sh49 sh47a sh47b sh47c sh47d sh47e sh47f ///
>>>sh47g sh47h sh47j sh47k sh47l sh47p sh47q sh47r sh47s sh47t ownerTV ///
>>>hv211 v024, pscore(pscorepump) blockid(myblock) numblo(5) level(0.005)
>>>logit-
>>>
>>>1. Firstly, why does the number of observations get reduced to 13821 from
>>>24,672 in the logit estimation?
>>>(When I used the -comsup-option, the no. of observations in the table
>>>"inferior of block pscore" then the no. of observations gets reduced to
>>>13000 odd from 24,762 which is expected. But in the output below, I have
>>>not
>>>run -comsup- so the no. of observations remain 24,762)
>>>
>>>2. Is there an efficienty way of trying to meet the balancing property?
>>>Since I have so many variables this might be a near impossible task.
>>>
>>>3. I understand that one of the ways to meet the balancing property is to
>>>use higher order terms and interactions. Given that I have many dummy
>>>variables or categorical variables this suggestion is not very useful.
>>>
>>>4. I browsed at the pscore and blockid variables generated "pscorepump"
>>>and
>>>"myblock" and found pscorepump==. for myblock==0. I don't understand
>>>what
>>>this means. There are atleast 10,000 missing values for pscorepump.
>>>
>>>Given that I have generate propensity scores several times over for
>>>different treatment groups I would be extremely grateful if someone could
>>>please advise me.
>>>
>>>Sincerely,
>>>
>>>Gauri
>>>
>>>****************************************************
>>>Algorithm to estimate the propensity score
>>>****************************************************
>>>
>>>
>>>The treatment is waterpump
>>>
>>> waterpump | Freq. Percent Cum.
>>>------------+-----------------------------------
>>> 0 | 15,885 64.15 64.15
>>> 1 | 8,877 35.85 100.00
>>>------------+-----------------------------------
>>> Total | 24,762 100.00
>>>
>>>
>>>
>>>Estimation of the propensity score
>>>
>>>Iteration 0: log likelihood = -9109.675
>>>Iteration 1: log likelihood = -8131.2148
>>>Iteration 2: log likelihood = -8090.9179
>>>Iteration 3: log likelihood = -8089.9872
>>>Iteration 4: log likelihood = -8089.9861
>>>Iteration 5: log likelihood = -8089.9861
>>>
>>>Logistic regression Number of obs =
>>>13821
>>> LR chi2(73) =
>>>2039.38
>>> Prob > chi2 =
>>>0.0000
>>>Log likelihood = -8089.9861 Pseudo R2 =
>>>0.1119
>>>
>>>------------------------------------------------------------------------------
>>> waterpump | Coef. Std. Err. z P>|z| [95% Conf.
>>>Interval]
>>>-------------+----------------------------------------------------------------
>>> v136 | .0165368 .0096416 1.72 0.086 -.0023604
>>>.035434
>>> v151 | -.0437896 .0882641 -0.50 0.620 -.2167841
>>>.129205
>>> v152 | .0003202 .0016804 0.19 0.849 -.0029733
>>>.0036138
>>> hv217 | -.0211372 .024753 -0.85 0.393 -.0696522
>>>.0273777
>>> v012 | -.0370832 .0079621 -4.66 0.000 -.0526886
>>>-.0214777
>>> v109 | .0142181 .0726133 0.20 0.845 -.1281014
>>>.1565376
>>> v110 | -.1876761 .0590738 -3.18 0.001 -.3034587
>>>-.0718935
>>> v112 | -.0562225 .0476427 -1.18 0.238 -.1496003
>>>.0371554
>>> v115 | -.0058888 .0009844 -5.98 0.000 -.0078182
>>>-.0039594
>>> v119 | -.9156391 .1534767 -5.97 0.000 -1.216448
>>>-.6148303
>>> sh39 | -.0093317 .0046107 -2.02 0.043 -.0183686
>>>-.0002948
>>> v131 | -.0449353 .017664 -2.54 0.011 -.0795562
>>>-.0103145
>>> v137 | -.0812052 .0256932 -3.16 0.002 -.131563
>>>-.0308473
>>> v208 | .0150714 .0351368 0.43 0.668 -.0537955
>>>.0839383
>>> v218 | -.0535865 .0225205 -2.38 0.017 -.0977259
>>>-.0094471
>>> v404 | .2572917 .0560506 4.59 0.000 .1474345
>>>.3671488
>>> v416 | -.0089503 .0161706 -0.55 0.580 -.0406442
>>>.0227436
>>>husbwifelive | .1250677 .0961814 1.30 0.193 -.0634444
>>>.3135797
>>> v512 | .0439059 .0087173 5.04 0.000 .0268204
>>>.0609914
>>> v627 | -.0036405 .0259638 -0.14 0.888 -.0545286
>>>.0472475
>>> v628 | .0055433 .0290203 0.19 0.849 -.0513354
>>>.062422
>>> v629 | -.0025496 .0170102 -0.15 0.881 -.0358891
>>>.0307898
>>> v715 | .0156505 .0050324 3.11 0.002 .0057872
>>>.0255139
>>> v705 | .0009952 .0026297 0.38 0.705 -.0041589
>>>.0061492
>>> v717 | .0157639 .0075627 2.08 0.037 .0009413
>>>.0305866
>>> v718 | -.054141 .0170187 -3.18 0.001 -.0874971
>>>-.020785
>>> v714 | .5117575 .2638649 1.94 0.052 -.0054081
>>>1.028923
>>> v731 | -.2636456 .1454615 -1.81 0.070 -.5487448
>>>.0214537
>>> v730 | .0071485 .0034344 2.08 0.037 .0004172
>>>.0138798
>>> b9 | -.1381251 .1456387 -0.95 0.343 -.4235717
>>>.1473215
>>> s509 | -.0037555 .0020511 -1.83 0.067 -.0077756
>>>.0002645
>>> s510 | .0039359 .0022827 1.72 0.085 -.0005381
>>>.0084099
>>> s511a | .0046628 .0172106 0.27 0.786 -.0290693
>>>.038395
>>> s511b | .0244404 .0220627 1.11 0.268 -.0188018
>>>.0676825
>>> s511d | -.0662242 .0226893 -2.92 0.004 -.1106944
>>>-.021754
>>> s512a | .0551096 .0352604 1.56 0.118 -.0139995
>>>.1242188
>>> s512b | .1820999 .0517648 3.52 0.000 .0806427
>>>.283557
>>> s513 | -.1124802 .0398112 -2.83 0.005 -.1905087
>>>-.0344516
>>> sh29 | .0133232 .0017694 7.53 0.000 .0098552
>>>.0167912
>>> sh32a | -.0457174 .1929909 -0.24 0.813 -.4239726
>>>.3325379
>>> sh32b | -.4238643 .2675577 -1.58 0.113 -.9482677
>>>.1005391
>>> sh32c | .0914454 .2024985 0.45 0.652 -.3054444
>>>.4883352
>>> sh32d | -.7686991 .1828472 -4.20 0.000 -1.127073
>>>-.4103252
>>> sh32e | -.3027793 .6182508 -0.49 0.624 -1.514529
>>>.9089701
>>> sh32f | .3425503 .1947313 1.76 0.079 -.039116
>>>.7242167
>>> sh32x | .0599032 .3102354 0.19 0.847 -.548147
>>>.6679534
>>> sh34 | -.4306421 .1426025 -3.02 0.003 -.7101378
>>>-.1511464
>>> sh35 | -.0315757 .0168604 -1.87 0.061 -.0646214
>>>.00147
>>> sh36 | -.1901594 .0456417 -4.17 0.000 -.2796155
>>>-.1007033
>>> sh37 | .0214189 .0073974 2.90 0.004 .0069202
>>>.0359176
>>> sh42 | .2409778 .0744005 3.24 0.001 .0951556
>>>.3868001
>>> sh43 | -.1217373 .0462229 -2.63 0.008 -.2123325
>>>-.0311421
>>> sh46 | .02258 .0465304 0.49 0.627 -.0686178
>>>.1137779
>>> sh49 | .0316219 .0306231 1.03 0.302 -.0283983
>>>.0916421
>>> sh47a | -.281183 .0458982 -6.13 0.000 -.3711419
>>>-.1912241
>>> sh47b | -.3105555 .067574 -4.60 0.000 -.4429981
>>>-.1781129
>>> sh47c | -.2774826 .0570406 -4.86 0.000 -.3892801
>>>-.1656852
>>> sh47d | .2857967 .0507951 5.63 0.000 .1862401
>>>.3853534
>>> sh47e | -.0465287 .0600335 -0.78 0.438 -.1641923
>>>.0711348
>>> sh47f | -.0569313 .0474101 -1.20 0.230 -.1498534
>>>.0359908
>>> sh47g | .1779822 .0602837 2.95 0.003 .0598282
>>>.2961362
>>> sh47h | .453847 .0426894 10.63 0.000 .3701772
>>>.5375167
>>> sh47j | -.0186251 .0684948 -0.27 0.786 -.1528723
>>>.1156222
>>> sh47k | .2616956 .2048625 1.28 0.201 -.1398276
>>>.6632188
>>> sh47l | .0638542 .1556463 0.41 0.682 -.241207
>>>.3689154
>>> sh47p | .1524665 .3714589 0.41 0.681 -.5755795
>>>.8805126
>>> sh47q | .4475498 .0971597 4.61 0.000 .2571203
>>>.6379794
>>> sh47r | .382921 .0751051 5.10 0.000 .2357178
>>>.5301243
>>> sh47s | .666872 .1650351 4.04 0.000 .3434092
>>>.9903348
>>> sh47t | -.3674771 .1922123 -1.91 0.056 -.7442063
>>>.0092521
>>> ownerTV | .126107 .0726295 1.74 0.083 -.0162441
>>>.2684582
>>> hv211 | .2028699 .1075005 1.89 0.059 -.0078272
>>>.413567
>>> v024 | .0062517 .0025395 2.46 0.014 .0012744
>>>.0112291
>>> _cons | .0984652 .4290789 0.23 0.818 -.742514
>>>.9394444
>>>------------------------------------------------------------------------------
>>>
>>>
>>>
>>>Description of the estimated propensity score
>>>
>>> Estimated propensity score
>>>-------------------------------------------------------------
>>> Percentiles Smallest
>>>1% .0435489 .0014547
>>>5% .0808509 .0104286
>>>10% .1245135 .0129766 Obs 13821
>>>25% .2320692 .0131112 Sum of Wgt. 13821
>>>
>>>50% .3675371 Mean .3703061
>>> Largest Std. Dev. .1787873
>>>75% .5033935 .9397132
>>>90% .6070773 .9507915 Variance .0319649
>>>95% .6681031 .9670365 Skewness .1283541
>>>99% .7641454 .9676308 Kurtosis 2.319066
>>>
>>>
>>>
>>>******************************************************
>>>Step 1: Identification of the optimal number of blocks
>>>Use option detail if you want more detailed output
>>>******************************************************
>>>
>>>
>>>The final number of blocks is 14
>>>
>>>This number of blocks ensures that the mean propensity score
>>>is not different for treated and controls in each blocks
>>>
>>>
>>>
>>>**********************************************************
>>>Step 2: Test of balancing property of the propensity score
>>>Use option detail if you want more detailed output
>>>**********************************************************
>>>
>>>Variable v717 is not balanced in block 1
>>>
>>>Variable sh47h is not balanced in block 1
>>>
>>>Variable v115 is not balanced in block 2
>>>
>>>Variable v705 is not balanced in block 2
>>>
>>>Variable s511a is not balanced in block 2
>>>
>>>Variable sh32x is not balanced in block 2
>>>
>>>Variable sh43 is not balanced in block 2
>>>
>>>Variable sh47r is not balanced in block 2
>>>
>>>Variable sh42 is not balanced in block 3
>>>
>>>Variable v119 is not balanced in block 4
>>>
>>>Variable sh32a is not balanced in block 4
>>>
>>>Variable sh34 is not balanced in block 4
>>>
>>>Variable sh47d is not balanced in block 4
>>>
>>>Variable sh47g is not balanced in block 4
>>>
>>>Variable sh32f is not balanced in block 5
>>>
>>>Variable sh32a is not balanced in block 6
>>>
>>>Variable sh32f is not balanced in block 6
>>>
>>>Variable sh47h is not balanced in block 6
>>>
>>>Variable sh32a is not balanced in block 9
>>>
>>>Variable sh32f is not balanced in block 9
>>>
>>>Variable sh47g is not balanced in block 11
>>>
>>>Variable sh47j is not balanced in block 11
>>>
>>>Variable v115 is not balanced in block 12
>>>
>>>Variable sh47q is not balanced in block 12
>>>
>>>Variable s512a is not balanced in block 13
>>>
>>>Variable s512b is not balanced in block 13
>>>
>>>Variable sh29 is not balanced in block 13
>>>
>>>Variable sh47c is not balanced in block 13
>>>
>>>The balancing property is not satisfied
>>>
>>>Try a different specification of the propensity score
>>>
>>> Inferior |
>>> of block | waterpump
>>>of pscore | 0 1 | Total
>>>-----------+----------------------+----------
>>> 0 | 7,398 3,765 | 11,163
>>> .05 | 370 18 | 388
>>> .075 | 340 34 | 374
>>> .1 | 696 107 | 803
>>> .15 | 754 174 | 928
>>> .2 | 912 251 | 1,163
>>> .25 | 895 339 | 1,234
>>> .3 | 1,745 890 | 2,635
>>> .4 | 1,383 1,160 | 2,543
>>> .5 | 517 610 | 1,127
>>> .55 | 359 546 | 905
>>> .6 | 391 668 | 1,059
>>> .7 | 109 268 | 377
>>> .8 | 16 47 | 63
>>>-----------+----------------------+----------
>>> Total | 15,885 8,877 | 24,762
>>>
>>>
>>>
>>>*******************************************
>>>End of the algorithm to estimate the pscore
>>>*******************************************
>>>
>>>DESCRIPTION OF VARIABLES:
>>>
>>>des v136 v151 v152 hv217 v012 v109 v110 v112 v115 v119 sh39 v131 v137
>>>v208
>>>v218 v404 v416 husbwifelive v512 v627 v628 v629 v715 v705 v717 v718 v714
>>>v731 v730 b9 s509 s510 s511a s511b s511d s512a s512b s513 sh29 sh32a
>>>sh32b
>>>sh32c sh32d sh32e sh32f sh32x sh34 sh35 sh36 sh37 sh42 sh43 sh46 sh49
>>>sh47a
>>>sh47b sh47c sh47d sh47e sh47f sh47g sh47h sh47j sh47k sh47l sh47p sh47q
>>>sh47r sh47s sh47t ownerTV hv211 v024
>>>
>>> storage display value
>>>variable name type format label variable label
>>>-------------------------------------------------------------------------------
>>>v136 byte %8.0g number of household members
>>>v151 byte %8.0g v151 sex of household head
>>>v152 byte %8.0g age of household head
>>>hv217 byte %8.0g hv217 relationship structure
>>>v012 byte %8.0g current age - respondent
>>>v109 byte %8.0g v109 reads newspaper once a week
>>>v110 byte %8.0g v110 watches tv every week
>>>v112 byte %8.0g v112 listens to radio every week
>>>v115 int %8.0g v115 time to get to water source
>>>v119 byte %8.0g v119 has electricity
>>>sh39 byte %8.0g sh39 religion of household head
>>>v131 byte %8.0g v131 ethnicity (scheduled caste
>>>or
>>> tribe)
>>>v137 byte %8.0g number of children 5 and
>>>under
>>>v208 byte %8.0g births in last five years
>>>v218 byte %8.0g number of living children
>>>v404 byte %8.0g v404 currently breastfeeding
>>>v416 byte %8.0g v416 heard of oral rehydration
>>>husbwifelive float %9.0g
>>>v512 byte %8.0g years since first marriage
>>>v627 byte %8.0g v627 ideal number of boys
>>>v628 byte %8.0g v628 ideal number of girls
>>>v629 byte %8.0g v629 ideal number of either sex
>>>v715 byte %8.0g v715 husbands education-single
>>>yrs
>>>v705 byte %8.0g v705 partner's occupation
>>>v717 byte %8.0g v717 respondent's occupation
>>>v718 byte %8.0g v718 current type of employment
>>>v714 byte %8.0g v714 respondent currently
>>>working
>>>v731 byte %8.0g v731 worked in last 12 months
>>>v730 byte %8.0g v730 partners age
>>>b9 byte %8.0g b9 who child lives with
>>>s509 byte %8.0g s509 how much education should
>>>be
>>> given to girls
>>>s510 byte %8.0g s510 how much education should
>>>be
>>> given to boys
>>>s511a byte %8.0g s511a who decides what to cook
>>>s511b byte %8.0g s511b who decides on obtaining
>>>health
>>> care
>>>s511d byte %8.0g s511d who decides about
>>>respondent
>>> staying with family
>>>s512a byte %8.0g s512a permission needed to go to
>>> market
>>>s512b byte %8.0g s512b permission needed to visit
>>> relatives or friends
>>>s513 byte %8.0g s513 allowed to have money set
>>>aside
>>>sh29 byte %8.0g sh29 where do household members
>>>go
>>> for treatment
>>>sh32a byte %8.0g sh32a strain by cloth to purify
>>>water
>>>sh32b byte %8.0g sh32b use alum to purify water
>>>sh32c byte %8.0g sh32c use water filter to purify
>>>water
>>>sh32d byte %8.0g sh32d boil water to purify
>>>sh32e byte %8.0g sh32e use electronic purifier to
>>> purify water
>>>sh32f byte %8.0g sh32f use nothing to purify water
>>>sh32x byte %8.0g sh32x use other method to purify
>>>water
>>>sh34 byte %8.0g sh34 main source of lighting
>>>sh35 byte %8.0g sh35 number of rooms
>>>sh36 byte %8.0g sh36 separate room used as a
>>>kitchen
>>>sh37 byte %8.0g sh37 main cooking fuel
>>>sh42 byte %8.0g sh42 does this household own
>>>this
>>> house or any other house?
>>>sh43 byte %8.0g sh43 does this household own any
>>> agricultural land?
>>>sh46 byte %8.0g sh46 household owns livestock
>>>sh49 byte %8.0g sh49 type of house
>>>sh47a byte %8.0g sh47a mattress
>>>sh47b byte %8.0g sh47b pressure cooker
>>>sh47c byte %8.0g sh47c chair
>>>sh47d byte %8.0g sh47d cot or bed
>>>sh47e byte %8.0g sh47e table
>>>sh47f byte %8.0g sh47f clock or watch
>>>sh47g byte %8.0g sh47g fan
>>>sh47h byte %8.0g sh47h bicycle
>>>sh47j byte %8.0g sh47j sewing maching
>>>sh47k byte %8.0g sh47k telephone
>>>sh47l byte %8.0g sh47l refrigerator
>>>sh47p byte %8.0g sh47p car
>>>sh47q byte %8.0g sh47q water pump
>>>sh47r byte %8.0g sh47r bullock cart
>>>sh47s byte %8.0g sh47s thresher
>>>sh47t byte %8.0g sh47t tractor
>>>ownerTV float %9.0g
>>>hv211 byte %8.0g hv211 has motorcycle
>>>v024 byte %8.0g v024 region
>>>
>>>.
>>>
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>>>*
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>>>
>>*
>>* For searches and help try:
>>* http://www.stata.com/support/faqs/res/findit.html
>>* http://www.stata.com/support/statalist/faq
>>* http://www.ats.ucla.edu/stat/stata/
>
>_________________________________________________________________
>Express yourself instantly with MSN Messenger! Download today it's FREE!
>http://messenger.msn.click-url.com/go/onm00200471ave/direct/01/
>
>*
>* For searches and help try:
>* http://www.stata.com/support/faqs/res/findit.html
>* http://www.stata.com/support/statalist/faq
>* http://www.ats.ucla.edu/stat/stata/
_________________________________________________________________
Express yourself instantly with MSN Messenger! Download today it's FREE!
http://messenger.msn.click-url.com/go/onm00200471ave/direct/01/
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
