. *** Code for Roodman Worms post #1 . *** requires public Worms and Worms at Work data sets (doi.org/10.7910/DVN/28038, doi.org/10.7910 > /DVN/ZNSY5O) and "outreg" Stata module by John Luke Gallup . . cd "C:\Users\David\Dropbox\Documents\Work\Library\Worms\Miguel & Kremer 2004\data" C:\Users\David\Dropbox\Documents\Work\Library\Worms\Miguel & Kremer 2004\data . . * load attendance data . use namelist if visit==981, clear . collapse (count) np=pupid (mean) wgrp sex elg98 stdgap yrbirth, by(sch98v1) . rename sch98v1 schid . . * add school-level variables . merge 1:1 schid using schoolvar, nogen keepusing(mk96_s distlake pup_pop latr_pup z_inf98 pop1_3k > m_updated pop1_36k_updated popT_3km_updated popT_36k_updated) // school data Result # of obs. ----------------------------------------- not matched 0 matched 75 ----------------------------------------- . replace mk96_s = mk96_s * 0.4357/0.8318 // Normalize 1996 mock tests to be in units of individual > std dev, equivalent to 1998, 1999 (75 real changes made) . . * add pupil questionnaire data . preserve . use pupq if pupdate_98_1!="" | schid_98_2<., clear // pupil questionnaire, for those with 1998 da > ta . ren schid_98_2 schid . gen preatt_98 = 1-absdays_98_6/20 // pre-program school attendance based on # days absent in prev > ious four weeks (152 missing values generated) . gen byte Ilivestock_98 = cows_98_23 | goats_98_24 | sheep_98_25 | pigs_98_26 if !missing(cows_98_ > 23, goats_98_24, sheep_98_25, pigs_98_26) // Household Has Livestock? (9 missing values generated) . gen byte Isoften_98 = 3.fallsick_98_37 // Child Sick Often? (9 missing values generated) . gen byte Iclean_98 = 1.clean_98_15 // Child clean? (1 missing value generated) . collapse preatt_98 havelatr_98_33 Ilivestock_98 waz_98 bloodst_98_58 Isoften_98 malaria_98_48 Icl > ean_98 pigs_98_26 cows_98_23 sheep_98_25 goats_98_24 (count) np38 = pupid, by(schid) . tempfile tempfile . save "`tempfile'" file C:\Users\David\AppData\Local\Temp\ST_09000004.tmp saved . restore . merge 1:1 schid using "`tempfile'", nogen Result # of obs. ----------------------------------------- not matched 0 matched 75 ----------------------------------------- . . * Replicate (revised) Table I--compare to Appendix I of PSDP-REP_ 2014-11.pdf at doi.org/10.7910/ > DVN/28038 . outreg, clear(TableI) . est clear . forvalues panel=1/3 { 2. local varlist: word `panel' of "sex - yrbirth" "preatt_98 - Iclean_98" "pup_pop - pop1 > _36k_updated" 3. local weight : word `panel' of np np38 1 4. foreach var of varlist `varlist' { 5. table wgrp [aw=`weight'], c(mean `var') 6. regress `var' ib3.wgrp [aw=`weight'] 7. outreg, keep(1.wgrp) se noautosumm ctitle("","Group 1 - Group 3") rtitle("`var > '") sdec(2) starlevels(10 5 1) nodisplay 8. outreg, keep(2.wgrp) se noautosumm ctitle("","Group 2 - Group 3") rtitle("`var > '") sdec(2) starlevels(10 5 1) nodisplay merge 9. outreg, replay(TableI) append store(TableI) nodisplay 10. qui regress `var' ib3.wgrp // for use by suest, don't cluster errors, and avoi > d weights since they differ by outcome var 11. est store `var' 12. } 13. } ---------------------- (mean) | wgrp | mean(sex) ----------+----------- 1 | .5330216 2 | .5095844 3 | .5221577 ---------------------- (sum of wgt is 3.4792e+04) Source | SS df MS Number of obs = 75 -------------+---------------------------------- F(2, 72) = 0.84 Model | .007012123 2 .003506062 Prob > F = 0.4365 Residual | .300999144 72 .004180544 R-squared = 0.0228 -------------+---------------------------------- Adj R-squared = -0.0044 Total | .308011268 74 .004162314 Root MSE = .06466 ------------------------------------------------------------------------------ sex | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- wgrp | 1 | .0108639 .0184507 0.59 0.558 -.0259169 .0476447 2 | -.0125733 .0183162 -0.69 0.495 -.049086 .0239394 | _cons | .5221577 .0131835 39.61 0.000 .4958767 .5484386 ------------------------------------------------------------------------------ warning: no existing table found with which to merge or append ----------------------- (mean) | wgrp | mean(elg98) ----------+------------ 1 | .885924 2 | .8919841 3 | .8842172 ----------------------- (sum of wgt is 3.4792e+04) Source | SS df MS Number of obs = 75 -------------+---------------------------------- F(2, 72) = 0.77 Model | .000841356 2 .000420678 Prob > F = 0.4653 Residual | .039174487 72 .00054409 R-squared = 0.0210 -------------+---------------------------------- Adj R-squared = -0.0062 Total | .040015843 74 .000540755 Root MSE = .02333 ------------------------------------------------------------------------------ elg98 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- wgrp | 1 | .0017069 .0066563 0.26 0.798 -.0115622 .0149759 2 | .0077669 .0066078 1.18 0.244 -.0054054 .0209393 | _cons | .8842172 .0047561 185.91 0.000 .8747361 .8936983 ------------------------------------------------------------------------------ ------------------------ (mean) | wgrp | mean(stdgap) ----------+------------- 1 | -1.972651 2 | -1.822477 3 | -1.96956 ------------------------ (sum of wgt is 3.4792e+04) Source | SS df MS Number of obs = 75 -------------+---------------------------------- F(2, 72) = 1.68 Model | .374551628 2 .187275814 Prob > F = 0.1935 Residual | 8.02368227 72 .111440031 R-squared = 0.0446 -------------+---------------------------------- Adj R-squared = 0.0181 Total | 8.3982339 74 .113489647 Root MSE = .33383 ------------------------------------------------------------------------------ stdgap | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- wgrp | 1 | -.0030915 .0952615 -0.03 0.974 -.1929918 .1868089 2 | .1470828 .0945671 1.56 0.124 -.0414332 .3355989 | _cons | -1.96956 .068067 -28.94 0.000 -2.105249 -1.833871 ------------------------------------------------------------------------------ ------------------------- (mean) | wgrp | mean(yrbirth) ----------+-------------- 1 | 1986.192 2 | 1986.542 3 | 1985.787 ------------------------- (sum of wgt is 3.4792e+04) Source | SS df MS Number of obs = 75 -------------+---------------------------------- F(2, 72) = 10.63 Model | 7.10907992 2 3.55453996 Prob > F = 0.0001 Residual | 24.0838656 72 .334498133 R-squared = 0.2279 -------------+---------------------------------- Adj R-squared = 0.2065 Total | 31.1929455 74 .42152629 Root MSE = .57836 ------------------------------------------------------------------------------ yrbirth | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- wgrp | 1 | .404556 .1650417 2.45 0.017 .0755514 .7335607 2 | .7551539 .1638386 4.61 0.000 .4285476 1.08176 | _cons | 1985.787 .1179269 1.7e+04 0.000 1985.552 1986.022 ------------------------------------------------------------------------------ -------------------------- (mean) | wgrp | mean(preat~98) ----------+--------------- 1 | .9727461 2 | .9632722 3 | .9694445 -------------------------- (sum of wgt is 1.3130e+04) Source | SS df MS Number of obs = 75 -------------+---------------------------------- F(2, 72) = 3.02 Model | .001159891 2 .000579946 Prob > F = 0.0548 Residual | .013807584 72 .000191772 R-squared = 0.0775 -------------+---------------------------------- Adj R-squared = 0.0519 Total | .014967475 74 .000202263 Root MSE = .01385 ------------------------------------------------------------------------------ preatt_98 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- wgrp | 1 | .0033016 .0038937 0.85 0.399 -.0044604 .0110637 2 | -.0061723 .003968 -1.56 0.124 -.0140824 .0017379 | _cons | .9694445 .0028043 345.69 0.000 .9638541 .9750348 ------------------------------------------------------------------------------ -------------------------- (mean) | wgrp | mean(havel~33) ----------+--------------- 1 | .822321 2 | .808343 3 | .818179 -------------------------- (sum of wgt is 1.3130e+04) Source | SS df MS Number of obs = 75 -------------+---------------------------------- F(2, 72) = 0.09 Model | .002577881 2 .00128894 Prob > F = 0.9169 Residual | 1.06897181 72 .014846831 R-squared = 0.0024 -------------+---------------------------------- Adj R-squared = -0.0253 Total | 1.07154969 74 .014480401 Root MSE = .12185 ------------------------------------------------------------------------------ havelatr_~33 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- wgrp | 1 | .0041424 .0342603 0.12 0.904 -.0641543 .0724391 2 | -.0098352 .034914 -0.28 0.779 -.079435 .0597646 | _cons | .8181786 .0246749 33.16 0.000 .7689901 .8673671 ------------------------------------------------------------------------------ -------------------------- (mean) | wgrp | mean(Ilive~98) ----------+--------------- 1 | .659097 2 | .672535 3 | .664068 -------------------------- (sum of wgt is 1.3130e+04) Source | SS df MS Number of obs = 75 -------------+---------------------------------- F(2, 72) = 0.10 Model | .002318088 2 .001159044 Prob > F = 0.9061 Residual | .845057643 72 .011736912 R-squared = 0.0027 -------------+---------------------------------- Adj R-squared = -0.0250 Total | .847375731 74 .011451023 Root MSE = .10834 ------------------------------------------------------------------------------ Ilivestoc~98 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- wgrp | 1 | -.0049706 .0304615 -0.16 0.871 -.0656945 .0557533 2 | .0084672 .0310427 0.27 0.786 -.0534153 .0703498 | _cons | .664068 .0219389 30.27 0.000 .6203335 .7078025 ------------------------------------------------------------------------------ ------------------------ (mean) | wgrp | mean(waz_98) ----------+------------- 1 | -1.39012 2 | -1.402161 3 | -1.439656 ------------------------ (sum of wgt is 1.3130e+04) Source | SS df MS Number of obs = 75 -------------+---------------------------------- F(2, 72) = 0.64 Model | .033326941 2 .016663471 Prob > F = 0.5326 Residual | 1.88769898 72 .026218041 R-squared = 0.0173 -------------+---------------------------------- Adj R-squared = -0.0099 Total | 1.92102592 74 .02595981 Root MSE = .16192 ------------------------------------------------------------------------------ waz_98 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- wgrp | 1 | .0495361 .0455276 1.09 0.280 -.0412215 .1402936 2 | .037495 .0463963 0.81 0.422 -.0549943 .1299843 | _cons | -1.439656 .0327898 -43.91 0.000 -1.505021 -1.37429 ------------------------------------------------------------------------------ -------------------------- (mean) | wgrp | mean(blood~58) ----------+--------------- 1 | .263557 2 | .220657 3 | .193488 -------------------------- (sum of wgt is 1.3130e+04) Source | SS df MS Number of obs = 75 -------------+---------------------------------- F(2, 72) = 2.37 Model | .063485915 2 .031742957 Prob > F = 0.1003 Residual | .962329712 72 .01336569 R-squared = 0.0619 -------------+---------------------------------- Adj R-squared = 0.0358 Total | 1.02581563 74 .013862373 Root MSE = .11561 ------------------------------------------------------------------------------ bloodst_9~58 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- wgrp | 1 | .070069 .0325065 2.16 0.034 .0052685 .1348695 2 | .0271693 .0331267 0.82 0.415 -.0388676 .0932063 | _cons | .1934879 .0234118 8.26 0.000 .1468174 .2401585 ------------------------------------------------------------------------------ -------------------------- (mean) | wgrp | mean(Isoft~98) ----------+--------------- 1 | .097607 2 | .10446 3 | .081049 -------------------------- (sum of wgt is 1.3130e+04) Source | SS df MS Number of obs = 75 -------------+---------------------------------- F(2, 72) = 1.86 Model | .007079175 2 .003539587 Prob > F = 0.1627 Residual | .136837976 72 .001900527 R-squared = 0.0492 -------------+---------------------------------- Adj R-squared = 0.0228 Total | .143917151 74 .001944826 Root MSE = .0436 ------------------------------------------------------------------------------ Isoften_98 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- wgrp | 1 | .0165577 .0122578 1.35 0.181 -.0078777 .0409931 2 | .0234107 .0124917 1.87 0.065 -.001491 .0483123 | _cons | .0810494 .0088283 9.18 0.000 .0634506 .0986483 ------------------------------------------------------------------------------ -------------------------- (mean) | wgrp | mean(malar~48) ----------+--------------- 1 | .367683 2 | .384038 3 | .401812 -------------------------- (sum of wgt is 1.3130e+04) Source | SS df MS Number of obs = 75 -------------+---------------------------------- F(2, 72) = 0.76 Model | .01473294 2 .00736647 Prob > F = 0.4706 Residual | .696257134 72 .009670238 R-squared = 0.0207 -------------+---------------------------------- Adj R-squared = -0.0065 Total | .710990075 74 .009607974 Root MSE = .09834 ------------------------------------------------------------------------------ malaria_9~48 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- wgrp | 1 | -.0341286 .0276499 -1.23 0.221 -.0892475 .0209904 2 | -.017774 .0281774 -0.63 0.530 -.0739446 .0383967 | _cons | .4018115 .0199139 20.18 0.000 .3621138 .4415092 ------------------------------------------------------------------------------ -------------------------- (mean) | wgrp | mean(Iclea~98) ----------+--------------- 1 | .603285 2 | .657277 3 | .669712 -------------------------- (sum of wgt is 1.3130e+04) Source | SS df MS Number of obs = 75 -------------+---------------------------------- F(2, 72) = 2.43 Model | .063784678 2 .031892339 Prob > F = 0.0949 Residual | .943615485 72 .013105771 R-squared = 0.0633 -------------+---------------------------------- Adj R-squared = 0.0373 Total | 1.00740016 74 .013613516 Root MSE = .11448 ------------------------------------------------------------------------------ Iclean_98 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- wgrp | 1 | -.0664265 .0321889 -2.06 0.043 -.1305938 -.0022592 2 | -.0124349 .032803 -0.38 0.706 -.0778266 .0529568 | _cons | .6697119 .023183 28.89 0.000 .6234974 .7159264 ------------------------------------------------------------------------------ ------------------------- (mean) | wgrp | mean(pup_pop) ----------+-------------- 1 | 392.7200012 2 | 403.7999878 3 | 375.8800049 ------------------------- (sum of wgt is 7.5000e+01) Source | SS df MS Number of obs = 75 -------------+---------------------------------- F(2, 72) = 0.12 Model | 9882.32 2 4941.16 Prob > F = 0.8880 Residual | 2990497.68 72 41534.69 R-squared = 0.0033 -------------+---------------------------------- Adj R-squared = -0.0244 Total | 3000380 74 40545.6757 Root MSE = 203.8 ------------------------------------------------------------------------------ pup_pop | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- wgrp | 1 | 16.84 57.64352 0.29 0.771 -98.07025 131.7503 2 | 27.92 57.64352 0.48 0.630 -86.99025 142.8303 | _cons | 375.88 40.76012 9.22 0.000 294.6262 457.1338 ------------------------------------------------------------------------------ ------------------------- (mean) | wgrp | mean(z_inf98) ----------+-------------- 1 | .368895 2 | .366519 3 | .360046 ------------------------- (sum of wgt is 7.5000e+01) Source | SS df MS Number of obs = 75 -------------+---------------------------------- F(2, 72) = 0.05 Model | .001048818 2 .000524409 Prob > F = 0.9534 Residual | .790732524 72 .010982396 R-squared = 0.0013 -------------+---------------------------------- Adj R-squared = -0.0264 Total | .791781342 74 .010699748 Root MSE = .1048 ------------------------------------------------------------------------------ z_inf98 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- wgrp | 1 | .0088493 .029641 0.30 0.766 -.050239 .0679377 2 | .0064732 .029641 0.22 0.828 -.0526152 .0655615 | _cons | .360046 .0209594 17.18 0.000 .3182642 .4018277 ------------------------------------------------------------------------------ -------------------------- (mean) | wgrp | mean(distlake) ----------+--------------- 1 | 10.03356 2 | 9.922958 3 | 9.45548 -------------------------- (sum of wgt is 7.4000e+01) Source | SS df MS Number of obs = 74 -------------+---------------------------------- F(2, 71) = 0.05 Model | 4.69353615 2 2.34676807 Prob > F = 0.9494 Residual | 3204.41479 71 45.1326026 R-squared = 0.0015 -------------+---------------------------------- Adj R-squared = -0.0267 Total | 3209.10832 73 43.960388 Root MSE = 6.7181 ------------------------------------------------------------------------------ distlake | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- wgrp | 1 | .57808 1.90016 0.30 0.762 -3.210731 4.366892 2 | .4674786 1.919851 0.24 0.808 -3.360596 4.295553 | _cons | 9.45548 1.343616 7.04 0.000 6.776386 12.13457 ------------------------------------------------------------------------------ -------------------------- (mean) | wgrp | mean(latr_pup) ----------+--------------- 1 | .0074168 2 | .0061782 3 | .0065807 -------------------------- (sum of wgt is 7.0000e+01) Source | SS df MS Number of obs = 70 -------------+---------------------------------- F(2, 67) = 0.78 Model | .000018548 2 9.2739e-06 Prob > F = 0.4614 Residual | .000794177 67 .000011853 R-squared = 0.0228 -------------+---------------------------------- Adj R-squared = -0.0063 Total | .000812725 69 .000011779 Root MSE = .00344 ------------------------------------------------------------------------------ latr_pup | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- wgrp | 1 | .0008362 .0009939 0.84 0.403 -.0011476 .00282 2 | -.0004025 .0010162 -0.40 0.693 -.0024308 .0016259 | _cons | .0065807 .0007028 9.36 0.000 .0051779 .0079834 ------------------------------------------------------------------------------ ------------------------ (mean) | wgrp | mean(mk96_s) ----------+------------- 1 | -.1029716 2 | .0918428 3 | .0111289 ------------------------ (sum of wgt is 7.5000e+01) Source | SS df MS Number of obs = 75 -------------+---------------------------------- F(2, 72) = 1.27 Model | .479052529 2 .239526264 Prob > F = 0.2868 Residual | 13.5685559 72 .188452165 R-squared = 0.0341 -------------+---------------------------------- Adj R-squared = 0.0073 Total | 14.0476084 74 .189832547 Root MSE = .43411 ------------------------------------------------------------------------------ mk96_s | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- wgrp | 1 | -.1141005 .1227851 -0.93 0.356 -.358868 .1306671 2 | .0807139 .1227851 0.66 0.513 -.1640537 .3254815 | _cons | .0111289 .0868222 0.13 0.898 -.1619479 .1842057 ------------------------------------------------------------------------------ -------------------------- (mean) | wgrp | mean(popT_3~d) ----------+--------------- 1 | 1272.716 2 | 1369.087 3 | 1151.869 -------------------------- (sum of wgt is 7.5000e+01) Source | SS df MS Number of obs = 75 -------------+---------------------------------- F(2, 72) = 0.55 Model | 592290.514 2 296145.257 Prob > F = 0.5810 Residual | 38974198.8 72 541308.317 R-squared = 0.0150 -------------+---------------------------------- Adj R-squared = -0.0124 Total | 39566489.4 74 534682.289 Root MSE = 735.74 ------------------------------------------------------------------------------ popT_3km_u~d | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- wgrp | 1 | 120.8462 208.0977 0.58 0.563 -293.989 535.6814 2 | 217.2178 208.0977 1.04 0.300 -197.6174 632.053 | _cons | 1151.869 147.1473 7.83 0.000 858.5365 1445.202 ------------------------------------------------------------------------------ -------------------------- (mean) | wgrp | mean(pop1_3~d) ----------+--------------- 1 | 430.386 2 | 433.1711 3 | 344.5075 -------------------------- (sum of wgt is 7.5000e+01) Source | SS df MS Number of obs = 75 -------------+---------------------------------- F(2, 72) = 0.38 Model | 127034.137 2 63517.0686 Prob > F = 0.6876 Residual | 12144691 72 168676.265 R-squared = 0.0104 -------------+---------------------------------- Adj R-squared = -0.0171 Total | 12271725.2 74 165834.124 Root MSE = 410.7 ------------------------------------------------------------------------------ pop1_3km_u~d | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- wgrp | 1 | 85.8785 116.1641 0.74 0.462 -145.6904 317.4474 2 | 88.66354 116.1641 0.76 0.448 -142.9054 320.2325 | _cons | 344.5075 82.14043 4.19 0.000 180.7636 508.2515 ------------------------------------------------------------------------------ -------------------------- (mean) | wgrp | mean(popT_3~d) ----------+--------------- 1 | 3431.257 2 | 3259.787 3 | 3502.097 -------------------------- (sum of wgt is 7.5000e+01) Source | SS df MS Number of obs = 75 -------------+---------------------------------- F(2, 72) = 0.23 Model | 776122.937 2 388061.469 Prob > F = 0.7937 Residual | 120558315 72 1674421.04 R-squared = 0.0064 -------------+---------------------------------- Adj R-squared = -0.0212 Total | 121334438 74 1639654.56 Root MSE = 1294 ------------------------------------------------------------------------------ popT_36k_u~d | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- wgrp | 1 | -70.84051 365.9968 -0.19 0.847 -800.4419 658.7608 2 | -242.3105 365.9968 -0.66 0.510 -971.9119 487.2908 | _cons | 3502.098 258.7988 13.53 0.000 2986.191 4018.004 ------------------------------------------------------------------------------ -------------------------- (mean) | wgrp | mean(pop1_3~d) ----------+--------------- 1 | 1157.599 2 | 1042.968 3 | 1297.344 -------------------------- (sum of wgt is 7.5000e+01) Source | SS df MS Number of obs = 75 -------------+---------------------------------- F(2, 72) = 0.82 Model | 811462.173 2 405731.086 Prob > F = 0.4457 Residual | 35742084.1 72 496417.835 R-squared = 0.0222 -------------+---------------------------------- Adj R-squared = -0.0050 Total | 36553546.3 74 493966.842 Root MSE = 704.57 ------------------------------------------------------------------------------ pop1_36k_u~d | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- wgrp | 1 | -139.7451 199.2823 -0.70 0.485 -537.0071 257.5169 2 | -254.3752 199.2823 -1.28 0.206 -651.6371 142.8868 | _cons | 1297.344 140.9139 9.21 0.000 1016.437 1578.25 ------------------------------------------------------------------------------ . outreg, replay(TableI) // replicated Table I ---------------------------------------------------------- Group 1 - Group 3 Group 2 - Group 3 ---------------------------------------------------------- sex 0.01 -0.01 (0.02) (0.02) elg98 0.00 0.01 (0.01) (0.01) stdgap -0.00 0.15 (0.10) (0.09) yrbirth 0.40 0.76 (0.17)** (0.16)*** preatt_98 0.00 -0.01 (0.00) (0.00) havelatr_98_33 0.00 -0.01 (0.03) (0.03) Ilivestock_98 -0.00 0.01 (0.03) (0.03) waz_98 0.05 0.04 (0.05) (0.05) bloodst_98_58 0.07 0.03 (0.03)** (0.03) Isoften_98 0.02 0.02 (0.01) (0.01)* malaria_98_48 -0.03 -0.02 (0.03) (0.03) Iclean_98 -0.07 -0.01 (0.03)** (0.03) pup_pop 16.84 27.92 (57.64) (57.64) z_inf98 0.01 0.01 (0.03) (0.03) distlake 0.58 0.47 (1.90) (1.92) latr_pup 0.00 -0.00 (0.00) (0.00) mk96_s -0.11 0.08 (0.12) (0.12) popT_3km_updated 120.85 217.22 (208.10) (208.10) pop1_3km_updated 85.88 88.66 (116.16) (116.16) popT_36k_updated -70.84 -242.31 (366.00) (366.00) pop1_36k_updated -139.75 -254.38 (199.28) (199.28) ---------------------------------------------------------- * p<0.1; ** p<0.05; *** p<0.01 . . * SUR-based test for balance, with small-sample adjustment . suest * Simultaneous results for sex, elg98, stdgap, yrbirth, preatt_98, havelatr_98_33, Ilivestock_98, waz > _98, bloodst_98_58, Isoften_98, malaria_98_48, Iclean_98, pup_pop, z_inf98, distlake, latr_pup, m > k96_s, popT_3km_updated, pop1_3km_updated, popT_36k_updated, pop1_36k_updated Number of obs = 75 ---------------------------------------------------------------------------------------- | Robust | Coef. Std. Err. z P>|z| [95% Conf. Interval] -----------------------+---------------------------------------------------------------- sex_mean | wgrp | 1 | .0119001 .0075231 1.58 0.114 -.0028449 .0266452 2 | -.0086579 .0211286 -0.41 0.682 -.0500692 .0327534 | _cons | .5194335 .0045517 114.12 0.000 .5105122 .5283547 -----------------------+---------------------------------------------------------------- sex_lnvar | _cons | -5.489288 .7751683 -7.08 0.000 -7.00859 -3.969986 -----------------------+---------------------------------------------------------------- elg98_mean | wgrp | 1 | .004683 .006625 0.71 0.480 -.0083018 .0176679 2 | .015127 .0080912 1.87 0.062 -.0007315 .0309856 | _cons | .882622 .0042495 207.70 0.000 .8742932 .8909509 -----------------------+---------------------------------------------------------------- elg98_lnvar | _cons | -7.153688 .2272506 -31.48 0.000 -7.599091 -6.708285 -----------------------+---------------------------------------------------------------- stdgap_mean | wgrp | 1 | -.0244995 .0861754 -0.28 0.776 -.1934003 .1444013 2 | .090571 .1038706 0.87 0.383 -.1130116 .2941537 | _cons | -1.974586 .0657008 -30.05 0.000 -2.103357 -1.845815 -----------------------+---------------------------------------------------------------- stdgap_lnvar | _cons | -2.128296 .1705609 -12.48 0.000 -2.462589 -1.794003 -----------------------+---------------------------------------------------------------- yrbirth_mean | wgrp | 1 | .477832 .150134 3.18 0.001 .1835748 .7720893 2 | .8040967 .1790493 4.49 0.000 .4531665 1.155027 | _cons | 1985.757 .1017916 2.0e+04 0.000 1985.557 1985.956 -----------------------+---------------------------------------------------------------- yrbirth_lnvar | _cons | -.9705235 .2109358 -4.60 0.000 -1.38395 -.5570968 -----------------------+---------------------------------------------------------------- preatt_98_mean | wgrp | 1 | .0043232 .0034802 1.24 0.214 -.0024979 .0111443 2 | -.0076675 .004086 -1.88 0.061 -.0156759 .0003408 | _cons | .9695311 .0018491 524.32 0.000 .9659069 .9731553 -----------------------+---------------------------------------------------------------- preatt_98_lnvar | _cons | -8.433579 .235687 -35.78 0.000 -8.895517 -7.971641 -----------------------+---------------------------------------------------------------- havelatr_98_33_mean | wgrp | 1 | .009084 .0344945 0.26 0.792 -.0585239 .0766919 2 | -.0140568 .0422877 -0.33 0.740 -.0969391 .0688255 | _cons | .8096977 .0300423 26.95 0.000 .7508159 .8685795 -----------------------+---------------------------------------------------------------- havelatr_98_33_lnvar | _cons | -4.029854 .1992106 -20.23 0.000 -4.4203 -3.639409 -----------------------+---------------------------------------------------------------- Ilivestock_98_mean | wgrp | 1 | -.0265008 .0314217 -0.84 0.399 -.0880862 .0350847 2 | -.0009923 .0303346 -0.03 0.974 -.060447 .0584625 | _cons | .6748721 .0210193 32.11 0.000 .633675 .7160692 -----------------------+---------------------------------------------------------------- Ilivestock_98_lnvar | _cons | -4.377758 .1272062 -34.41 0.000 -4.627078 -4.128439 -----------------------+---------------------------------------------------------------- waz_98_mean | wgrp | 1 | .0510803 .0480236 1.06 0.287 -.0430444 .1452049 2 | .0139198 .0538456 0.26 0.796 -.0916157 .1194554 | _cons | -1.440187 .0402088 -35.82 0.000 -1.518995 -1.361379 -----------------------+---------------------------------------------------------------- waz_98_lnvar | _cons | -3.482255 .1935243 -17.99 0.000 -3.861555 -3.102954 -----------------------+---------------------------------------------------------------- bloodst_98_58_mean | wgrp | 1 | .0548993 .0391849 1.40 0.161 -.0219017 .1317003 2 | .0242108 .0373435 0.65 0.517 -.0489811 .0974026 | _cons | .2043866 .0310192 6.59 0.000 .1435901 .2651832 -----------------------+---------------------------------------------------------------- bloodst_98_58_lnvar | _cons | -4.083176 .2554784 -15.98 0.000 -4.583905 -3.582448 -----------------------+---------------------------------------------------------------- Isoften_98_mean | wgrp | 1 | .0142766 .0130126 1.10 0.273 -.0112277 .0397809 2 | .029677 .0118512 2.50 0.012 .0064491 .0529049 | _cons | .0777073 .0080282 9.68 0.000 .0619722 .0934423 -----------------------+---------------------------------------------------------------- Isoften_98_lnvar | _cons | -6.165253 .1574901 -39.15 0.000 -6.473928 -5.856578 -----------------------+---------------------------------------------------------------- malaria_98_48_mean | wgrp | 1 | -.0394347 .0278135 -1.42 0.156 -.0939482 .0150787 2 | .0106874 .0322903 0.33 0.741 -.0526004 .0739752 | _cons | .4012834 .0212322 18.90 0.000 .3596691 .4428978 -----------------------+---------------------------------------------------------------- malaria_98_48_lnvar | _cons | -4.448611 .1814009 -24.52 0.000 -4.80415 -4.093072 -----------------------+---------------------------------------------------------------- Iclean_98_mean | wgrp | 1 | -.0645173 .0341719 -1.89 0.059 -.131493 .0024584 2 | .0064785 .0345558 0.19 0.851 -.0612495 .0742066 | _cons | .6523937 .0236947 27.53 0.000 .6059529 .6988345 -----------------------+---------------------------------------------------------------- Iclean_98_lnvar | _cons | -4.172095 .19201 -21.73 0.000 -4.548428 -3.795762 -----------------------+---------------------------------------------------------------- pup_pop_mean | wgrp | 1 | 16.84 43.50858 0.39 0.699 -68.43524 102.1152 2 | 27.92 62.24006 0.45 0.654 -94.06827 149.9083 | _cons | 375.88 30.28804 12.41 0.000 316.5165 435.2435 -----------------------+---------------------------------------------------------------- pup_pop_lnvar | _cons | 10.63428 .2939112 36.18 0.000 10.05823 11.21034 -----------------------+---------------------------------------------------------------- z_inf98_mean | wgrp | 1 | .0088493 .0298141 0.30 0.767 -.0495853 .0672839 2 | .0064732 .0290906 0.22 0.824 -.0505433 .0634896 | _cons | .360046 .0212808 16.92 0.000 .3183363 .4017556 -----------------------+---------------------------------------------------------------- z_inf98_lnvar | _cons | -4.511462 .1026616 -43.94 0.000 -4.712675 -4.310249 -----------------------+---------------------------------------------------------------- distlake_mean | wgrp | 1 | .57808 1.65516 0.35 0.727 -2.665974 3.822134 2 | .4674786 2.06388 0.23 0.821 -3.577652 4.512609 | _cons | 9.45548 1.262462 7.49 0.000 6.9811 11.92986 -----------------------+---------------------------------------------------------------- distlake_lnvar | _cons | 3.809605 .1232039 30.92 0.000 3.56813 4.05108 -----------------------+---------------------------------------------------------------- latr_pup_mean | wgrp | 1 | .0008362 .0009558 0.87 0.382 -.0010371 .0027094 2 | -.0004025 .0010123 -0.40 0.691 -.0023866 .0015817 | _cons | .0065807 .00067 9.82 0.000 .0052675 .0078938 -----------------------+---------------------------------------------------------------- latr_pup_lnvar | _cons | -11.3429 .1467929 -77.27 0.000 -11.63061 -11.05519 -----------------------+---------------------------------------------------------------- mk96_s_mean | wgrp | 1 | -.1141005 .110072 -1.04 0.300 -.3298376 .1016367 2 | .0807139 .1280058 0.63 0.528 -.1701729 .3316008 | _cons | .0111289 .0806119 0.14 0.890 -.1468676 .1691253 -----------------------+---------------------------------------------------------------- mk96_s_lnvar | _cons | -1.668911 .2048305 -8.15 0.000 -2.070371 -1.267451 -----------------------+---------------------------------------------------------------- popT_3km_updated_mean | wgrp | 1 | 120.8462 202.8445 0.60 0.551 -276.7217 518.4141 2 | 217.2178 198.8498 1.09 0.275 -172.5206 606.9562 | _cons | 1151.869 132.2335 8.71 0.000 892.6963 1411.042 -----------------------+---------------------------------------------------------------- popT_3km_updated_lnvar | _cons | 13.20174 .1560561 84.60 0.000 12.89588 13.50761 -----------------------+---------------------------------------------------------------- pop1_3km_updated_mean | wgrp | 1 | 85.8785 110.8018 0.78 0.438 -131.2891 303.0461 2 | 88.66354 113.2664 0.78 0.434 -133.3346 310.6617 | _cons | 344.5075 73.56789 4.68 0.000 200.3171 488.698 -----------------------+---------------------------------------------------------------- pop1_3km_updated_lnvar | _cons | 12.03574 .1700088 70.79 0.000 11.70253 12.36895 -----------------------+---------------------------------------------------------------- popT_36k_updated_mean | wgrp | 1 | -70.84051 389.1476 -0.18 0.856 -833.5557 691.8747 2 | -242.3105 361.1986 -0.67 0.502 -950.2467 465.6257 | _cons | 3502.098 293.9392 11.91 0.000 2925.987 4078.208 -----------------------+---------------------------------------------------------------- popT_36k_updated_lnvar | _cons | 14.33098 .1346589 106.42 0.000 14.06705 14.5949 -----------------------+---------------------------------------------------------------- pop1_36k_updated_mean | wgrp | 1 | -139.7451 212.28 -0.66 0.510 -555.8063 276.3161 2 | -254.3752 193.0961 -1.32 0.188 -632.8365 124.0862 | _cons | 1297.344 156.169 8.31 0.000 991.2579 1603.429 -----------------------+---------------------------------------------------------------- pop1_36k_updated_lnvar | _cons | 13.11517 .138243 94.87 0.000 12.84422 13.38612 ---------------------------------------------------------------------------------------- . test 1.wgrp 2.wgrp ( 1) [sex_mean]1.wgrp = 0 ( 2) [elg98_mean]1.wgrp = 0 ( 3) [stdgap_mean]1.wgrp = 0 ( 4) [yrbirth_mean]1.wgrp = 0 ( 5) [preatt_98_mean]1.wgrp = 0 ( 6) [havelatr_98_33_mean]1.wgrp = 0 ( 7) [Ilivestock_98_mean]1.wgrp = 0 ( 8) [waz_98_mean]1.wgrp = 0 ( 9) [bloodst_98_58_mean]1.wgrp = 0 (10) [Isoften_98_mean]1.wgrp = 0 (11) [malaria_98_48_mean]1.wgrp = 0 (12) [Iclean_98_mean]1.wgrp = 0 (13) [pup_pop_mean]1.wgrp = 0 (14) [z_inf98_mean]1.wgrp = 0 (15) [distlake_mean]1.wgrp = 0 (16) [latr_pup_mean]1.wgrp = 0 (17) [mk96_s_mean]1.wgrp = 0 (18) [popT_3km_updated_mean]1.wgrp = 0 (19) [pop1_3km_updated_mean]1.wgrp = 0 (20) [popT_36k_updated_mean]1.wgrp = 0 (21) [pop1_36k_updated_mean]1.wgrp = 0 (22) [sex_mean]2.wgrp = 0 (23) [elg98_mean]2.wgrp = 0 (24) [stdgap_mean]2.wgrp = 0 (25) [yrbirth_mean]2.wgrp = 0 (26) [preatt_98_mean]2.wgrp = 0 (27) [havelatr_98_33_mean]2.wgrp = 0 (28) [Ilivestock_98_mean]2.wgrp = 0 (29) [waz_98_mean]2.wgrp = 0 (30) [bloodst_98_58_mean]2.wgrp = 0 (31) [Isoften_98_mean]2.wgrp = 0 (32) [malaria_98_48_mean]2.wgrp = 0 (33) [Iclean_98_mean]2.wgrp = 0 (34) [pup_pop_mean]2.wgrp = 0 (35) [z_inf98_mean]2.wgrp = 0 (36) [distlake_mean]2.wgrp = 0 (37) [latr_pup_mean]2.wgrp = 0 (38) [mk96_s_mean]2.wgrp = 0 (39) [popT_3km_updated_mean]2.wgrp = 0 (40) [pop1_3km_updated_mean]2.wgrp = 0 (41) [popT_36k_updated_mean]2.wgrp = 0 (42) [pop1_36k_updated_mean]2.wgrp = 0 chi2( 42) = 153.00 Prob > chi2 = 0.0000 . * "confirmed with formal tests that these differences...are indeed unexpected if the groups were > statistically the same" . di "F(" r(df) "," e(N)-1 ") = " r(chi2)/r(df) * (e(N)-r(df))/e(N) "; p = " Ftail(r(df), e(N)-1, r > (chi2)/r(df) * (e(N)-r(df))/e(N)) F(42,74) = 1.6029065; p = .03798284 . . * OLS/F test for balance . replace distlake = 3.75 if schid==133 // Roodman calculation for this missing data point (1 real change made) . xi i.wgrp, noomit . * Do with and without latrines/pupil because of missing data for 5 obs . * "confirmed with formal tests that these differences...are indeed unexpected if the groups were > statistically the same" . reg _Iwgrp_1 latr_pup sex elg98 stdgap yrbirth preatt_98 havelatr_98_33 Ilivestock_98 waz_98 bloo > dst_98_58 Isoften_98 malaria_98_48 Iclean_98 pup_pop z_inf98 distlake mk96_s popT_3km_updated pop > 1_3km_updated popT_36k_updated pop1_36k_updated if wgrp!=2 Source | SS df MS Number of obs = 48 -------------+---------------------------------- F(21, 26) = 1.86 Model | 7.20682406 21 .343182098 Prob > F = 0.0667 Residual | 4.79317594 26 .184352921 R-squared = 0.6006 -------------+---------------------------------- Adj R-squared = 0.2780 Total | 12 47 .255319149 Root MSE = .42936 ---------------------------------------------------------------------------------- _Iwgrp_1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -----------------+---------------------------------------------------------------- latr_pup | 27.75818 25.96627 1.07 0.295 -25.61625 81.13261 sex | -2.645643 3.704107 -0.71 0.481 -10.25954 4.968258 elg98 | -6.701122 5.31946 -1.26 0.219 -17.63543 4.233185 stdgap | -.8143818 .3996203 -2.04 0.052 -1.635813 .0070496 yrbirth | .6327076 .2257768 2.80 0.009 .1686168 1.096798 preatt_98 | 6.095031 7.565675 0.81 0.428 -9.456436 21.6465 havelatr_98_33 | -.1659757 .9288616 -0.18 0.860 -2.075278 1.743327 Ilivestock_98 | -.0740107 .8051941 -0.09 0.927 -1.729111 1.581089 waz_98 | -.1496872 .5440648 -0.28 0.785 -1.268028 .9686541 bloodst_98_58 | 1.888792 1.156912 1.63 0.115 -.4892757 4.26686 Isoften_98 | .3838188 2.290627 0.17 0.868 -4.324632 5.09227 malaria_98_48 | -.5457537 1.018573 -0.54 0.597 -2.63946 1.547952 Iclean_98 | -.7739403 .8718493 -0.89 0.383 -2.566052 1.018172 pup_pop | .0007867 .0006607 1.19 0.245 -.0005713 .0021448 z_inf98 | .9757951 1.331279 0.73 0.470 -1.760689 3.712279 distlake | .0425967 .0219137 1.94 0.063 -.0024477 .087641 mk96_s | .0368152 .2627659 0.14 0.890 -.5033079 .5769382 popT_3km_updated | .0001452 .0001573 0.92 0.364 -.0001781 .0004684 pop1_3km_updated | -.0001081 .0002667 -0.41 0.689 -.0006563 .0004402 popT_36k_updated | .0001043 .0000861 1.21 0.237 -.0000727 .0002812 pop1_36k_updated | -.0000328 .0001462 -0.22 0.824 -.0003333 .0002677 _cons | -1257.777 446.9985 -2.81 0.009 -2176.596 -338.9588 ---------------------------------------------------------------------------------- . reg _Iwgrp_1 sex elg98 stdgap yrbirth preatt_98 havelatr_98_33 Ilivestock_98 waz_98 bloo > dst_98_58 Isoften_98 malaria_98_48 Iclean_98 pup_pop z_inf98 distlake mk96_s popT_3km_updated pop > 1_3km_updated popT_36k_updated pop1_36k_updated if wgrp!=2 Source | SS df MS Number of obs = 50 -------------+---------------------------------- F(20, 29) = 1.64 Model | 6.62759979 20 .331379989 Prob > F = 0.1106 Residual | 5.87240021 29 .202496559 R-squared = 0.5302 -------------+---------------------------------- Adj R-squared = 0.2062 Total | 12.5 49 .255102041 Root MSE = .45 ---------------------------------------------------------------------------------- _Iwgrp_1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -----------------+---------------------------------------------------------------- sex | -.7030373 3.724795 -0.19 0.852 -8.321099 6.915024 elg98 | -4.464656 5.470037 -0.82 0.421 -15.65214 6.722826 stdgap | -.988341 .4098369 -2.41 0.022 -1.826552 -.1501304 yrbirth | .6974072 .2348211 2.97 0.006 .217144 1.17767 preatt_98 | 4.115407 7.881846 0.52 0.606 -12.00478 20.23559 havelatr_98_33 | .5921562 .9066054 0.65 0.519 -1.26206 2.446373 Ilivestock_98 | -.0824384 .8316777 -0.10 0.922 -1.78341 1.618533 waz_98 | .5594089 .4624826 1.21 0.236 -.3864742 1.505292 bloodst_98_58 | .6021914 1.050756 0.57 0.571 -1.546846 2.751229 Isoften_98 | .2858605 2.394222 0.12 0.906 -4.610873 5.182594 malaria_98_48 | -.0972292 1.044711 -0.09 0.926 -2.233904 2.039446 Iclean_98 | -.6085395 .909349 -0.67 0.509 -2.468367 1.251288 pup_pop | .0006991 .0006033 1.16 0.256 -.0005347 .0019329 z_inf98 | 1.090773 1.358142 0.80 0.428 -1.686938 3.868485 distlake | .0307642 .0217267 1.42 0.167 -.0136719 .0752002 mk96_s | -.1415864 .2488002 -0.57 0.574 -.65044 .3672671 popT_3km_updated | 5.65e-06 .0001515 0.04 0.971 -.0003042 .0003155 pop1_3km_updated | .0000591 .0002566 0.23 0.820 -.0004657 .0005839 popT_36k_updated | .0000945 .0000896 1.05 0.301 -.0000888 .0002777 pop1_36k_updated | -.0000961 .0001481 -0.65 0.522 -.0003991 .0002069 _cons | -1386.787 464.8583 -2.98 0.006 -2337.529 -436.0451 ---------------------------------------------------------------------------------- . reg _Iwgrp_2 latr_pup sex elg98 stdgap yrbirth preatt_98 havelatr_98_33 Ilivestock_98 waz_98 bloo > dst_98_58 Isoften_98 malaria_98_48 Iclean_98 pup_pop z_inf98 distlake mk96_s popT_3km_updated pop > 1_3km_updated popT_36k_updated pop1_36k_updated if wgrp!=1 Source | SS df MS Number of obs = 46 -------------+---------------------------------- F(21, 24) = 3.13 Model | 8.4052906 21 .400251933 Prob > F = 0.0041 Residual | 3.07297027 24 .128040428 R-squared = 0.7323 -------------+---------------------------------- Adj R-squared = 0.4980 Total | 11.4782609 45 .255072464 Root MSE = .35783 ---------------------------------------------------------------------------------- _Iwgrp_2 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -----------------+---------------------------------------------------------------- latr_pup | -7.61537 26.8556 -0.28 0.779 -63.0426 47.81186 sex | 1.953833 1.150842 1.70 0.102 -.4213876 4.329053 elg98 | 1.296408 4.285943 0.30 0.765 -7.549343 10.14216 stdgap | -.6555325 .3151202 -2.08 0.048 -1.305909 -.0051564 yrbirth | .6291285 .1526291 4.12 0.000 .3141175 .9441396 preatt_98 | -14.39682 5.476583 -2.63 0.015 -25.69993 -3.093705 havelatr_98_33 | -1.869353 1.053901 -1.77 0.089 -4.044498 .3057926 Ilivestock_98 | .096769 .6715247 0.14 0.887 -1.28919 1.482728 waz_98 | .6774535 .5450105 1.24 0.226 -.4473928 1.8023 bloodst_98_58 | .785968 .985307 0.80 0.433 -1.247606 2.819542 Isoften_98 | 5.140763 2.240844 2.29 0.031 .5158892 9.765637 malaria_98_48 | -1.310581 .8263006 -1.59 0.126 -3.015982 .3948195 Iclean_98 | .3014 .8132464 0.37 0.714 -1.377058 1.979858 pup_pop | -.0007537 .0004681 -1.61 0.120 -.0017198 .0002125 z_inf98 | 2.875711 1.02119 2.82 0.010 .7680778 4.983344 distlake | .0553074 .0167624 3.30 0.003 .0207116 .0899032 mk96_s | .6709592 .2081116 3.22 0.004 .2414379 1.100481 popT_3km_updated | .0001405 .0001531 0.92 0.368 -.0001755 .0004565 pop1_3km_updated | .0003482 .0002449 1.42 0.168 -.0001573 .0008537 popT_36k_updated | .0001295 .0001017 1.27 0.215 -.0000804 .0003394 pop1_36k_updated | -.0000492 .0001608 -0.31 0.762 -.0003811 .0002826 _cons | -1238.33 303.2958 -4.08 0.000 -1864.302 -612.3585 ---------------------------------------------------------------------------------- . reg _Iwgrp_2 sex elg98 stdgap yrbirth preatt_98 havelatr_98_33 Ilivestock_98 waz_98 bloo > dst_98_58 Isoften_98 malaria_98_48 Iclean_98 pup_pop z_inf98 distlake mk96_s popT_3km_updated pop > 1_3km_updated popT_36k_updated pop1_36k_updated if wgrp!=1 Source | SS df MS Number of obs = 50 -------------+---------------------------------- F(20, 29) = 2.21 Model | 7.55370803 20 .377685401 Prob > F = 0.0249 Residual | 4.94629197 29 .170561792 R-squared = 0.6043 -------------+---------------------------------- Adj R-squared = 0.3314 Total | 12.5 49 .255102041 Root MSE = .41299 ---------------------------------------------------------------------------------- _Iwgrp_2 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -----------------+---------------------------------------------------------------- sex | 1.439734 1.277798 1.13 0.269 -1.173655 4.053123 elg98 | -.826902 3.693803 -0.22 0.824 -8.381578 6.727774 stdgap | -.6404412 .3131363 -2.05 0.050 -1.280877 -5.49e-06 yrbirth | .4921262 .1460893 3.37 0.002 .19334 .7909123 preatt_98 | -9.978211 5.760948 -1.73 0.094 -21.76067 1.80425 havelatr_98_33 | .065079 .8119967 0.08 0.937 -1.595641 1.725799 Ilivestock_98 | .7429861 .7033778 1.06 0.300 -.6955831 2.181555 waz_98 | .460239 .4983619 0.92 0.363 -.5590255 1.479504 bloodst_98_58 | .0472067 .9421013 0.05 0.960 -1.879607 1.97402 Isoften_98 | 3.239605 1.909401 1.70 0.100 -.665559 7.14477 malaria_98_48 | .025592 .7927642 0.03 0.974 -1.595793 1.646977 Iclean_98 | .3655408 .7110453 0.51 0.611 -1.08871 1.819792 pup_pop | .0001759 .0003457 0.51 0.615 -.0005311 .000883 z_inf98 | 2.057019 1.091611 1.88 0.070 -.1755762 4.289615 distlake | .031911 .0170179 1.88 0.071 -.0028944 .0667165 mk96_s | .374471 .1923769 1.95 0.061 -.018984 .7679259 popT_3km_updated | .0001575 .0001475 1.07 0.294 -.0001442 .0004591 pop1_3km_updated | .0001328 .0002332 0.57 0.574 -.0003443 .0006098 popT_36k_updated | .000052 .0001123 0.46 0.647 -.0001777 .0002817 pop1_36k_updated | -.0000845 .0001706 -0.50 0.624 -.0004335 .0002644 _cons | -970.4671 291.1969 -3.33 0.002 -1566.032 -374.9025 ---------------------------------------------------------------------------------- . . * this form is most relevant for Baird et al. 2016, which defines treatment = groups 1 & 2 . reg _Iwgrp_3 latr_pup sex elg98 stdgap yrbirth preatt_98 havelatr_98_33 Ilivestock_98 waz_98 bloo > dst_98_58 Isoften_98 malaria_98_48 Iclean_98 pup_pop z_inf98 distlake mk96_s popT_3km_updated pop > 1_3km_updated popT_36k_updated pop1_36k_updated Source | SS df MS Number of obs = 70 -------------+---------------------------------- F(21, 48) = 2.35 Model | 7.98703802 21 .380335144 Prob > F = 0.0075 Residual | 7.78439055 48 .162174803 R-squared = 0.5064 -------------+---------------------------------- Adj R-squared = 0.2905 Total | 15.7714286 69 .228571429 Root MSE = .40271 ---------------------------------------------------------------------------------- _Iwgrp_3 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -----------------+---------------------------------------------------------------- latr_pup | -.6469576 19.53878 -0.03 0.974 -39.93231 38.63839 sex | -.6632022 1.06609 -0.62 0.537 -2.806719 1.480315 elg98 | 3.921513 3.43432 1.14 0.259 -2.98365 10.82668 stdgap | .7110028 .2373722 3.00 0.004 .2337339 1.188272 yrbirth | -.6673119 .1237894 -5.39 0.000 -.9162071 -.4184167 preatt_98 | 2.458873 3.914869 0.63 0.533 -5.412499 10.33025 havelatr_98_33 | .5746447 .7222393 0.80 0.430 -.8775146 2.026804 Ilivestock_98 | -.3891042 .5121148 -0.76 0.451 -1.41878 .6405717 waz_98 | -.0737528 .4406372 -0.17 0.868 -.9597132 .8122077 bloodst_98_58 | -.9350869 .7032229 -1.33 0.190 -2.349011 .4788376 Isoften_98 | -1.289032 1.500486 -0.86 0.395 -4.305962 1.727898 malaria_98_48 | .3396307 .6200575 0.55 0.586 -.9070784 1.58634 Iclean_98 | .3045177 .5770174 0.53 0.600 -.8556535 1.464689 pup_pop | -.0001094 .0003845 -0.28 0.777 -.0008826 .0006637 z_inf98 | -1.639795 .7974888 -2.06 0.045 -3.243253 -.0363359 distlake | -.0383228 .0138157 -2.77 0.008 -.0661011 -.0105446 mk96_s | -.2896851 .1714219 -1.69 0.098 -.6343519 .0549817 popT_3km_updated | -.0001106 .000114 -0.97 0.337 -.0003399 .0001187 pop1_3km_updated | -4.49e-06 .0001793 -0.03 0.980 -.000365 .000356 popT_36k_updated | -.0000716 .0000723 -0.99 0.327 -.000217 .0000739 pop1_36k_updated | .0000355 .0001207 0.29 0.770 -.0002071 .0002781 _cons | 1322.633 245.404 5.39 0.000 829.2148 1816.05 ---------------------------------------------------------------------------------- . reg _Iwgrp_3 sex elg98 stdgap yrbirth preatt_98 havelatr_98_33 Ilivestock_98 waz_98 bloo > dst_98_58 Isoften_98 malaria_98_48 Iclean_98 pup_pop z_inf98 distlake mk96_s popT_3km_updated pop > 1_3km_updated popT_36k_updated pop1_36k_updated Source | SS df MS Number of obs = 75 -------------+---------------------------------- F(20, 54) = 1.97 Model | 7.03455445 20 .351727723 Prob > F = 0.0250 Residual | 9.63211221 54 .178372448 R-squared = 0.4221 -------------+---------------------------------- Adj R-squared = 0.2080 Total | 16.6666667 74 .225225225 Root MSE = .42234 ---------------------------------------------------------------------------------- _Iwgrp_3 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -----------------+---------------------------------------------------------------- sex | -.6945369 1.101939 -0.63 0.531 -2.903792 1.514718 elg98 | 2.550315 2.81628 0.91 0.369 -3.095986 8.196616 stdgap | .7369056 .2319489 3.18 0.002 .271876 1.201935 yrbirth | -.5645725 .1197558 -4.71 0.000 -.8046685 -.3244766 preatt_98 | 1.588217 3.902075 0.41 0.686 -6.234973 9.411406 havelatr_98_33 | -.2520036 .5995259 -0.42 0.676 -1.453981 .9499734 Ilivestock_98 | -.2546574 .5264533 -0.48 0.631 -1.310133 .8008179 waz_98 | -.4952835 .3872329 -1.28 0.206 -1.271639 .2810718 bloodst_98_58 | -.2027653 .674633 -0.30 0.765 -1.555323 1.149792 Isoften_98 | -1.478553 1.374557 -1.08 0.287 -4.234374 1.277267 malaria_98_48 | .0062025 .6213226 0.01 0.992 -1.239474 1.251879 Iclean_98 | .1004588 .5617507 0.18 0.859 -1.025784 1.226701 pup_pop | -.0003476 .000304 -1.14 0.258 -.000957 .0002619 z_inf98 | -1.24461 .7724427 -1.61 0.113 -2.793264 .3040445 distlake | -.0297155 .0136292 -2.18 0.034 -.0570403 -.0023907 mk96_s | -.1076388 .1534392 -0.70 0.486 -.415266 .1999884 popT_3km_updated | -.0000491 .0001093 -0.45 0.655 -.0002682 .00017 pop1_3km_updated | -.0000579 .0001664 -0.35 0.729 -.0003916 .0002757 popT_36k_updated | -.0000457 .0000743 -0.62 0.541 -.0001946 .0001032 pop1_36k_updated | .000086 .0001226 0.70 0.486 -.0001599 .0003319 _cons | 1120.48 237.7145 4.71 0.000 643.8914 1597.069 ---------------------------------------------------------------------------------- . . * Although included above, rerun school-reported attendance regression to show imbalance . regress preatt_98 ib3.wgrp [aw=np38] // "when Worms compares groups 1 and 2 to 3, it does not fin > d especially significant differences" (sum of wgt is 1.3130e+04) Source | SS df MS Number of obs = 75 -------------+---------------------------------- F(2, 72) = 3.02 Model | .001159891 2 .000579946 Prob > F = 0.0548 Residual | .013807584 72 .000191772 R-squared = 0.0775 -------------+---------------------------------- Adj R-squared = 0.0519 Total | .014967475 74 .000202263 Root MSE = .01385 ------------------------------------------------------------------------------ preatt_98 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- wgrp | 1 | .0033016 .0038937 0.85 0.399 -.0044604 .0110637 2 | -.0061723 .003968 -1.56 0.124 -.0140824 .0017379 | _cons | .9694445 .0028043 345.69 0.000 .9638541 .9750348 ------------------------------------------------------------------------------ . test 1.wgrp = 2.wgrp // "the distance from group 1 to 2...is large enough to be statistically sig > nificant" ( 1) 1.wgrp - 2.wgrp = 0 F( 1, 72) = 5.91 Prob > F = 0.0175 . regress preatt_98 ib3.wgrp [aw=np38] // "...as is that from group 1 to 2 and 3 averaged together" (sum of wgt is 1.3130e+04) Source | SS df MS Number of obs = 75 -------------+---------------------------------- F(2, 72) = 3.02 Model | .001159891 2 .000579946 Prob > F = 0.0548 Residual | .013807584 72 .000191772 R-squared = 0.0775 -------------+---------------------------------- Adj R-squared = 0.0519 Total | .014967475 74 .000202263 Root MSE = .01385 ------------------------------------------------------------------------------ preatt_98 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- wgrp | 1 | .0033016 .0038937 0.85 0.399 -.0044604 .0110637 2 | -.0061723 .003968 -1.56 0.124 -.0140824 .0017379 | _cons | .9694445 .0028043 345.69 0.000 .9638541 .9750348 ------------------------------------------------------------------------------ . . * Association between school- and researcher-recorded pre-treatment attendance, at school level . use namelist if inlist(visit, 981, 982), clear // first two surprise visits in 1998, presumably f > irst quarter, pre-treatment . collapse prs, by(pupid) . merge m:1 pupid using pupq, keep(match) Result # of obs. ----------------------------------------- not matched 0 matched 15,053 (_merge==3) ----------------------------------------- . gen preatt_98 = (20-absdays_98_6)/20 // school-reported attendance rate over last 4 weeks (2,532 missing values generated) . collapse prs preatt_98 (count) np=pupid, by(schid_98_2) . regress prs preatt_98 [aw=np] // "Each 1% increase in a school's self-reported attendance...predi > cted a 3% increase in researcher-recorded attendance" (sum of wgt is 4.0960e+03) Source | SS df MS Number of obs = 27 -------------+---------------------------------- F(1, 25) = 8.40 Model | .039930733 1 .039930733 Prob > F = 0.0077 Residual | .118897919 25 .004755917 R-squared = 0.2514 -------------+---------------------------------- Adj R-squared = 0.2215 Total | .158828652 26 .006108794 Root MSE = .06896 ------------------------------------------------------------------------------ prs | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- preatt_98 | 3.042666 1.050069 2.90 0.008 .8800088 5.205323 _cons | -2.05599 1.016334 -2.02 0.054 -4.149168 .0371887 ------------------------------------------------------------------------------ . ren schid_98_2 schid . merge m:1 schid using schoolvar, nogen Result # of obs. ----------------------------------------- not matched 1 from master 1 from using 0 matched 75 ----------------------------------------- . regress prs i.wgrp [aw=np] // "those more-accurate numbers suggest little imbalance across the th > ree groups" (sum of wgt is 4.0960e+03) Source | SS df MS Number of obs = 27 -------------+---------------------------------- F(2, 24) = 0.88 Model | .010828379 2 .00541419 Prob > F = 0.4286 Residual | .148000273 24 .006166678 R-squared = 0.0682 -------------+---------------------------------- Adj R-squared = -0.0095 Total | .158828652 26 .006108794 Root MSE = .07853 ------------------------------------------------------------------------------ prs | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- wgrp | 2 | -.0480895 .0369507 -1.30 0.205 -.1243519 .028173 3 | -.0227661 .041908 -0.54 0.592 -.10926 .0637277 | _cons | .9177422 .0298206 30.78 0.000 .8561956 .9792888 ------------------------------------------------------------------------------ . . * Miguel & Kremer, Table 9, col. 1, as updated (PSDP-REP_ 2014-11.pdf at doi.org/10.7910/DVN/2803 > 8) with and without school-recorded attendance . use namelist if visit>981 & elg98<. & (std98v1>=0 & std98v1<=8 | std98v1==55), clear . replace schid = sch98v1 (266,896 real changes made) . merge m:1 schid using schoolvar, nogen Result # of obs. ----------------------------------------- not matched 0 matched 493,575 ----------------------------------------- . merge m:1 pupid using pupq , nogen keep(master match) Result # of obs. ----------------------------------------- not matched 268,215 from master 268,215 from using 0 matched 225,360 ----------------------------------------- . merge m:m pupid using comply , nogen keep(master match) // note: duplicates in namelist.dta & c > omply.dta duplicate geometrically in merge (label sch_label already defined) Result # of obs. ----------------------------------------- not matched 9,600 from master 9,600 from using 0 matched 483,975 ----------------------------------------- . gen byte Y98 = visit>980 & visit<990 . replace mk96_s = mk96 * 0.4357/0.8318 // Normalize and adjust mock scores to individual units (493,575 real changes made) . gen byte yr = 1 + (visit > 992) . gen byte t_any = yr >= wgrp . gen p1 = cond(std98v1==5 | std98v1==6, z9899_56, cond(std98v1==7 | std98v1==8, z9899_78, z9899_34 > )) // standard-specific zonal infection rate. note: assigns z9899_34 to those with missing or oth > er grades . collapse prs t_any elg98 p1 mk96_s Y98 sap? std98v1 Isem? schid absdays_98_6 (sum) obs, by > (pupid yr) . regress prs t_any elg98 p1 mk96_s c.Y98##(sap?) i.std98v1 Isem? [aw=obs], cluster(schid) (sum of wgt is 2.0758e+05) Linear regression Number of obs = 56,496 F(25, 74) = 498.24 Prob > F = 0.0000 R-squared = 0.2189 Root MSE = .27865 (Std. Err. adjusted for 75 clusters in schid) ------------------------------------------------------------------------------ | Robust prs | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- t_any | .0575647 .0139747 4.12 0.000 .0297196 .0854099 elg98 | .0633746 .0069183 9.16 0.000 .0495895 .0771597 p1 | -.0259527 .056066 -0.46 0.645 -.1376666 .0857613 mk96_s | .0716202 .0212853 3.36 0.001 .0292082 .1140322 Y98 | .1699589 .0599672 2.83 0.006 .0504715 .2894462 1.sap1 | .086083 .0225098 3.82 0.000 .0412313 .1309348 1.sap2 | .1163295 .026757 4.35 0.000 .0630151 .169644 1.sap3 | .0725969 .0333028 2.18 0.032 .0062397 .138954 1.sap4 | .0439537 .0433136 1.01 0.314 -.0423506 .1302579 | sap1#c.Y98 | 1 | -.3187499 .0382203 -8.34 0.000 -.3949054 -.2425943 | sap2#c.Y98 | 1 | -.356457 .0323702 -11.01 0.000 -.420956 -.2919579 | sap3#c.Y98 | 1 | -.3117544 .0362269 -8.61 0.000 -.3839382 -.2395706 | sap4#c.Y98 | 1 | -.2295292 .0571277 -4.02 0.000 -.3433587 -.1156997 | std98v1 | 1 | -.0157288 .0210655 -0.75 0.458 -.0577027 .0262451 2 | -.0134638 .0225292 -0.60 0.552 -.0583542 .0314266 3 | .0102193 .0218665 0.47 0.642 -.0333507 .0537893 4 | .0838309 .0193535 4.33 0.000 .0452681 .1223936 5 | .0798564 .0212282 3.76 0.000 .0375583 .1221545 6 | .0903541 .0216645 4.17 0.000 .0471866 .1335217 7 | .1032812 .0244891 4.22 0.000 .0544857 .1520767 8 | .2061236 .0301909 6.83 0.000 .1459668 .2662803 55 | -.5558578 .0179007 -31.05 0.000 -.5915258 -.5201898 | Isem1 | .3308328 .048539 6.82 0.000 .2341167 .4275488 Isem2 | .4069148 .088497 4.60 0.000 .2305807 .5832489 Isem3 | .2329967 .0656344 3.55 0.001 .1022172 .3637761 _cons | .4388829 .057228 7.67 0.000 .3248537 .5529121 ------------------------------------------------------------------------------ . . * "controlling for school-recorded attendance hardly perturbs the widely cited impact estimates > for researcher-recorded attendance" . gen preatt_98 = 1 - absdays_98_6/20 // school-recorded attendance, early 1998 (40,772 missing values generated) . regress prs t_any elg98 p1 mk96_s c.Y98##(sap?) i.std98v1 Isem? [aw=obs] if preatt_98<. > , cluster(schid) (sum of wgt is 9.0411e+04) Linear regression Number of obs = 22,701 F(23, 74) = 68.21 Prob > F = 0.0000 R-squared = 0.1272 Root MSE = .26161 (Std. Err. adjusted for 75 clusters in schid) ------------------------------------------------------------------------------ | Robust prs | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- t_any | .0383071 .0108486 3.53 0.001 .0166908 .0599234 elg98 | .0707548 .0067081 10.55 0.000 .0573887 .0841209 p1 | .0187327 .0541079 0.35 0.730 -.0890797 .126545 mk96_s | .0517225 .0150583 3.43 0.001 .0217182 .0817267 Y98 | .4351087 .0904692 4.81 0.000 .2548449 .6153724 1.sap1 | .1823363 .0433356 4.21 0.000 .0959883 .2686843 1.sap2 | .2063701 .0413078 5.00 0.000 .1240624 .2886777 1.sap3 | .2025897 .0463582 4.37 0.000 .1102189 .2949605 1.sap4 | .1657851 .0511751 3.24 0.002 .0638164 .2677538 | sap1#c.Y98 | 1 | -.500029 .0619592 -8.07 0.000 -.6234854 -.3765726 | sap2#c.Y98 | 1 | -.5080971 .0557686 -9.11 0.000 -.6192184 -.3969758 | sap3#c.Y98 | 1 | -.4966655 .0548559 -9.05 0.000 -.6059683 -.3873627 | sap4#c.Y98 | 1 | -.4158994 .0643643 -6.46 0.000 -.544148 -.2876508 | std98v1 | 3 | -.1057383 .0659749 -1.60 0.113 -.2371963 .0257196 4 | -.0368005 .066673 -0.55 0.583 -.1696494 .0960484 5 | -.0294377 .0664643 -0.44 0.659 -.1618707 .1029953 6 | -.0271308 .0648219 -0.42 0.677 -.1562912 .1020296 7 | .0009644 .0653126 0.01 0.988 -.1291738 .1311027 8 | .0594679 .0659339 0.90 0.370 -.0719082 .1908441 55 | -.6180598 .0802575 -7.70 0.000 -.7779764 -.4581432 | Isem1 | .4739983 .0585574 8.09 0.000 .3573201 .5906765 Isem2 | .0077672 .0919927 0.08 0.933 -.1755323 .1910667 Isem3 | .5394134 .1046172 5.16 0.000 .3309592 .7478676 _cons | .3378688 .0858024 3.94 0.000 .1669037 .5088338 ------------------------------------------------------------------------------ . regress prs t_any preatt_98 elg98 p1 mk96_s c.Y98##(sap?) i.std98v1 Isem? [aw=obs] if preatt_98<. > , cluster(schid) (sum of wgt is 9.0411e+04) Linear regression Number of obs = 22,701 F(24, 74) = 67.26 Prob > F = 0.0000 R-squared = 0.1302 Root MSE = .26116 (Std. Err. adjusted for 75 clusters in schid) ------------------------------------------------------------------------------ | Robust prs | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- t_any | .0376298 .0107649 3.50 0.001 .0161804 .0590793 preatt_98 | .2154909 .0419185 5.14 0.000 .1319665 .2990152 elg98 | .070446 .0066469 10.60 0.000 .0572017 .0836903 p1 | .0127902 .0533985 0.24 0.811 -.0936087 .1191891 mk96_s | .0517796 .0151263 3.42 0.001 .0216397 .0819195 Y98 | .4394248 .0896264 4.90 0.000 .2608403 .6180093 1.sap1 | .1839138 .0429358 4.28 0.000 .0983623 .2694652 1.sap2 | .2056635 .0412259 4.99 0.000 .1235191 .287808 1.sap3 | .2018137 .0460635 4.38 0.000 .1100303 .2935972 1.sap4 | .1671432 .0505798 3.30 0.001 .0663607 .2679257 | sap1#c.Y98 | 1 | -.4986656 .0618043 -8.07 0.000 -.6218133 -.3755178 | sap2#c.Y98 | 1 | -.5062709 .0557709 -9.08 0.000 -.6173969 -.395145 | sap3#c.Y98 | 1 | -.495092 .0546964 -9.05 0.000 -.6040769 -.3861071 | sap4#c.Y98 | 1 | -.4147561 .0641752 -6.46 0.000 -.542628 -.2868842 | std98v1 | 3 | -.0978936 .0660668 -1.48 0.143 -.2295347 .0337474 4 | -.0289994 .0667895 -0.43 0.665 -.1620803 .1040815 5 | -.0220001 .0665926 -0.33 0.742 -.1546887 .1106885 6 | -.020251 .0649699 -0.31 0.756 -.1497063 .1092043 7 | .0053082 .0655165 0.08 0.936 -.1252362 .1358527 8 | .0630322 .06609 0.95 0.343 -.0686549 .1947194 55 | -.6140567 .0800072 -7.68 0.000 -.7734746 -.4546389 | Isem1 | .4656978 .0571803 8.14 0.000 .3517635 .579632 Isem2 | .0073186 .0925299 0.08 0.937 -.1770511 .1916884 Isem3 | .5402677 .1042279 5.18 0.000 .332589 .7479463 _cons | .1251609 .0919962 1.36 0.178 -.0581456 .3084674 ------------------------------------------------------------------------------ . . * "The regression that anchors GiveWell's cost-effectiveness analysis, which puts the impact of 2 > .4 extra years of deworming on later wage earnings at 31% (p = 0.002)---also estimates that being > in the cost-sharing treatment arm for a year cut wage earnings by 14%" . cd "C:\Users\David\Dropbox\Documents\Work\Library\Worms\Baird et al 2016\data" C:\Users\David\Dropbox\Documents\Work\Library\Worms\Baird et al 2016\data . use Baird-etal-QJE-2016_data_primary, clear . regress ln_emp_salary_total treatment /// // Baird et al., Table IV, Panel B, row 1, col. 1, expo > nentiated coefficients for interpretation > cost_sharing saturation_dm demeaned_popT_6k /// > zoneidI2-zoneidI8 pup_pop wave2 month_interviewI2-month_interviewI12 /// > std98_base_I2 std98_base_I3 std98_base_I4 std98_base_I5 std98_base_I6 female_baseline avg > test96 [aw=weight], cluster(psdpsch98) eform(ExpCoef) (sum of wgt is 2.4313e+03) Linear regression Number of obs = 710 F(31, 72) = 8.52 Prob > F = 0.0000 R-squared = 0.1963 Root MSE = .80944 (Std. Err. adjusted for 73 clusters in psdpsch98) ------------------------------------------------------------------------------------ | Robust ln_emp_salary_to~l | ExpCoef Std. Err. t P>|t| [95% Conf. Interval] -------------------+---------------------------------------------------------------- treatment | 1.308079 .1115133 3.15 0.002 1.103644 1.550382 cost_sharing | .8579837 .0742864 -1.77 0.081 .7219718 1.019619 saturation_dm | 3.128544 2.718164 1.31 0.193 .5535474 17.68193 demeaned_popT_6k | 1.036256 .0272964 1.35 0.181 .9832459 1.092125 zoneidI2 | .8449889 .1258028 -1.13 0.262 .6279965 1.136959 zoneidI3 | .8345164 .1267857 -1.19 0.238 .6164586 1.129707 zoneidI4 | .8544992 .1249344 -1.08 0.286 .6384579 1.143644 zoneidI5 | .8991896 .169789 -0.56 0.575 .6171297 1.310165 zoneidI6 | 1.138151 .1575057 0.94 0.353 .8637558 1.499715 zoneidI7 | .8986211 .1304176 -0.74 0.464 .6728675 1.200117 zoneidI8 | .9363229 .1730004 -0.36 0.723 .6478353 1.353277 pup_pop | .9856012 .2458675 -0.06 0.954 .5994206 1.620581 wave2 | 1.205561 .1201076 1.88 0.065 .9884079 1.470422 month_interviewI2 | .6521004 .1305906 -2.13 0.036 .4374596 .9720551 month_interviewI3 | .8165709 .1573191 -1.05 0.296 .5561602 1.198913 month_interviewI4 | .7673741 .1936651 -1.05 0.298 .4639963 1.269111 month_interviewI5 | .9082182 .1579471 -0.55 0.582 .6421384 1.284552 month_interviewI6 | .9698214 .1651384 -0.18 0.858 .6906759 1.361787 month_interviewI7 | 1.340134 .2135368 1.84 0.070 .9754379 1.841182 month_interviewI8 | 1.071654 .14701 0.50 0.615 .8152491 1.4087 month_interviewI9 | 1.095837 .1597787 0.63 0.532 .8194362 1.46547 month_interviewI10 | .983504 .1261791 -0.13 0.897 .7615604 1.270129 month_interviewI11 | .8455121 .1322512 -1.07 0.287 .6190177 1.154879 month_interviewI12 | 1.176352 .2225788 0.86 0.394 .8067291 1.715326 std98_base_I2 | 1.046607 .1617067 0.29 0.769 .7691665 1.424121 std98_base_I3 | 1.383617 .2086797 2.15 0.035 1.024334 1.868916 std98_base_I4 | 1.736213 .2978199 3.22 0.002 1.233381 2.444043 std98_base_I5 | 1.792212 .2659409 3.93 0.000 1.333286 2.409104 std98_base_I6 | 2.046555 .3539954 4.14 0.000 1.449684 2.889172 female_baseline | .6441851 .0582435 -4.86 0.000 .5379408 .7714128 avgtest96 | 1.03545 .1291557 0.28 0.781 .8074971 1.327754 _cons | 1723.132 362.1538 35.46 0.000 1133.346 2619.841 ------------------------------------------------------------------------------------ . test _b[treatment]/2.41 = -_b[cost_sharing]/.8 // "The hypothesis that the two implied rates of i > mpact are equal...fits the data" ( 1) .4149378*treatment + 1.25*cost_sharing = 0 F( 1, 72) = 0.60 Prob > F = 0.4405 . end of do-file