clear *
cls
import excel "https://www.beerinstitute.org/wp-content/uploads/2021/12/2021-September-The-Brewers-Almanac-Beer-Institute-2021.xlsx", ///
sheet("Beer Shipments by State") cellrange(A3:MV55) clear
keep A CX-MV
qui foreach v of var CX-MV {
loc year: di `v'[1]
loc month: di `v'[2]
rename `v' shipments`year'_`month'
}
drop in 1/2
br* Example generated by -dataex-. To install: ssc install dataex clear input str3(country_i country_j) int year byte(gatt pta) "AUS" "BRA" 1986 0 0 "AUS" "BRA" 1987 0 0 "AUS" "BRA" 1988 0 1 "AUS" "BRA" 1989 0 1 "AUS" "BRA" 1990 0 1 "AUS" "BRA" 1991 0 1 "AUS" "BRA" 1992 1 1 "AUS" "BRA" 1993 1 1 "AUS" "BRA" 1994 1 1 "BRA" "AUS" 1986 0 0 "BRA" "AUS" 1987 0 0 "BRA" "AUS" 1988 1 0 "BRA" "AUS" 1989 1 0 "BRA" "AUS" 1990 1 1 "BRA" "AUS" 1991 1 1 "BRA" "AUS" 1992 1 1 "BRA" "AUS" 1993 1 1 "BRA" "AUS" 1994 1 1 end
| dependent variable: loan growth | (1) | (2) | (3) |
| VARIABLES | 2019 | 2020 | 2021 |
| did_estimator_2019 | -0.0487*** | ||
| (0.0161) | |||
| post2019 | 0.0186* | ||
| (0.0112) | |||
| GDP Growth | 0.234*** | 0.230*** | 0.237*** |
| (0.0765) | (0.0763) | (0.0756) | |
| UNEMPLOYMENT | 2.644*** | 2.491*** | 3.071*** |
| (0.343) | (0.351) | (0.360) | |
| INFLATION | 2.148*** | 3.240*** | 1.828*** |
| (0.293) | (0.609) | (0.288) | |
| INTEREST RATES | 17.55*** | 16.18*** | 17.33*** |
| (1.024) | (0.751) | (0.696) | |
| did_estimator_2020 | -0.0353*** | ||
| (0.0119) | |||
| post2020 | -0.0140 | ||
| (0.0111) | |||
| did_estimator_2021 | -0.0224* | ||
| (0.0121) | |||
| post2021 | 0.0416*** | ||
| (0.00482) | |||
| Constant | -0.418*** | -0.389*** | -0.434*** |
| (0.0213) | (0.0127) | (0.0129) | |
| Observations | 2,227 | 2,227 | 2,227 |
| R-squared | 0.535 | 0.535 | 0.543 |
| Number of ID | 373 | 373 | 373 |
clear all input state year gdp post2003 treatmentGroup treated 1 2001 100 0 1 0 1 2002 115 0 1 0 1 2003 95 1 1 1 1 2004 87 1 1 1 1 2005 73 1 1 1 2 2001 113 0 0 0 2 2002 117 0 0 0 2 2003 121 1 0 0 2 2004 125 1 0 0 2 2005 129 1 0 0 3 2001 47 0 0 0 3 2002 53 0 0 0 3 2003 59 1 0 0 3 2004 62 1 0 0 3 2005 66 1 0 0 end *Set panel xtset state year, yearly
*xthdidregress using default VCE
xthdidregress twfe (gdp) (treated), group(state) hettype(time)
note: variable _did_cohort, containing cohort indicators formed by treatment variable treated and group variable state, was added to the dataset.
Computing ATETs using margins ...
Treatment and time information
Time variable: year
Time interval: 2001 to 2005
Control: _did_cohort = 0
Treatment: _did_cohort > 0
-------------------------------
| _did_cohort
------------------+------------
Number of cohorts | 2
------------------+------------
Number of obs |
Never treated | 10
2003 | 5
-------------------------------
Heterogeneous-treatment-effects regression Number of obs = 15
Number of panels = 3
Estimator: Two-way fixed effects
Panel variable: state
Treatment level: state
Control group: Never treated
Heterogeneity: Time
(Std. err. adjusted for 3 clusters in state)
------------------------------------------------------------------------------
| Robust
Time | ATET std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
year |
2003 | -20 1.984313 -10.08 0.010 -28.53781 -11.46219
2004 | -31.5 1.322876 -23.81 0.002 -37.19187 -25.80813
2005 | -49.5 1.322876 -37.42 0.001 -55.19187 -43.80813
------------------------------------------------------------------------------*xthdidregress explicitly specifying default VCE
xthdidregress twfe (gdp) (treated), group(state) hettype(time) vce(cluster state)
note: variable _did_cohort, containing cohort indicators formed by treatment variable treated and group variable state, was added to the dataset.
Computing ATETs using margins ...
Treatment and time information
Time variable: year
Time interval: 2001 to 2005
Control: _did_cohort = 0
Treatment: _did_cohort > 0
-------------------------------
| _did_cohort
------------------+------------
Number of cohorts | 2
------------------+------------
Number of obs |
Never treated | 10
2003 | 5
-------------------------------
Heterogeneous-treatment-effects regression Number of obs = 15
Number of panels = 3
Estimator: Two-way fixed effects
Panel variable: state
Treatment level: state
Control group: Never treated
Heterogeneity: Time
(Std. err. adjusted for 3 clusters in state)
------------------------------------------------------------------------------
| Robust
Time | ATET std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
year |
2003 | -20 1.984313 -10.08 0.010 -28.53781 -11.46219
2004 | -31.5 1.322876 -23.81 0.002 -37.19187 -25.80813
2005 | -49.5 1.322876 -37.42 0.001 -55.19187 -43.80813
------------------------------------------------------------------------------*regress
reg gdp treated#i(2003/2005).year post2003 treatmentGroup i.state i.year, vce(cluster state)
note: 3.state omitted because of collinearity.
note: 2005.year omitted because of collinearity.
Linear regression Number of obs = 15
F(2, 2) = .
Prob > F = .
R-squared = 0.9967
Root MSE = 2.7544
(Std. err. adjusted for 3 clusters in state)
--------------------------------------------------------------------------------
| Robust
gdp | Coefficient std. err. t P>|t| [95% conf. interval]
---------------+----------------------------------------------------------------
treated#year |
1 2003 | -20 2.173707 -9.20 0.012 -29.3527 -10.6473
1 2004 | -31.5 1.449138 -21.74 0.002 -37.73514 -25.26486
1 2005 | -49.5 1.449138 -34.16 0.001 -55.73514 -43.26486
|
post2003 | 19.16667 3.392803 5.65 0.030 4.568614 33.76472
treatmentGroup | 56.8 1.014396 55.99 0.000 52.4354 61.1646
|
state |
2 | 63.6 2.70e-14 2.4e+15 0.000 63.6 63.6
3 | 0 (omitted)
|
year |
2002 | 8.333333 5.660781 1.47 0.279 -16.02304 32.68971
2003 | -7.5 .7245688 -10.35 0.009 -10.61757 -4.382432
2004 | -4 2.36e-08 -1.7e+08 0.000 -4 -4
2005 | 0 (omitted)
|
_cons | 46.53333 3.165456 14.70 0.005 32.91348 60.15319
--------------------------------------------------------------------------------| Earning | employment | Establishment | |
| Treat | 27,425.41 | 19,458.09 | 3,829.57 |
| (10,636.87) | (35,378.12) | (4,147.39) | |
| Control | 33,612.52 | 22,614.83 | 2,640.76 |
| (14,158.23) | (40,015.26) | (3,051.18) |
| Earn (OLS) | Earn ( IV I) | (Earn IV II) | Emp (OLS) | Emp (IV I) | Emp(IV II) | Est (OLS) | (Est IV I) | Est (IV II) |
| -.157*** | -1.116*** | -1.080*** | -.031** | -.365** | -.307** | .044*** | .256** | .240** |
| 0.038 | 0.336 | 0.415 | 0.014 | 0.173 | 0.142 | 0.009 | 0.113 | 0.118 |
* Example generated by -dataex-. For more info, type help dataex clear input str3 code str29 value "A " "test1test1test1test1。" "A " "test2test2test2test2。" "A " "test3test3test3test3。" "B " "test1test1test1test1。" "B " "test2test2test2test2。" "B " "test3test3test3test3。" "B " "test4test4test4test4。" "C " "test1test1test1test1。" "C " "test2test2test2test2。" end
| year | etype | category | value | avg_freq |
| 1996 | 1 | 1 | 25526966.0 | 8 |
| 1997 | 1 | 1 | 3266403.0 | 8 |
| 1998 | 1 | 1 | 1870628.0 | 8 |
| year | etype | category | value | avg_freq | smoothed | parameter |
| 1996 | 1 | 1 | 25526966 | 8 | 14396684 | 0.0001305 |
| 1997 | 1 | 1 | 3266403 | 8 | 14398137 | 0.0001305 |
| 1998 | 1 | 1 | 1870628 | 8 | 14396684 | 0.0001305 |
* Example generated by -dataex-. For more info, type help dataex clear input int(origin_id destination_id) long(dest_nb_workers_pre origin_nb_workers_pre) float time_pre 11 1 11568 16923 12.509804 155 1 11568 10075 25.47222 27 1 11568 29488 19.314816 119 1 11568 9281 26.979166 108 1 11568 35021 18.75926 161 1 11568 9769 25.40476 107 1 11568 33708 18.383333 2 1 11568 25126 21.73684 136 1 11568 31699 19.12745 87 1 11568 18090 30.285715 79 1 11568 12699 32.433334 39 1 11568 33048 15.880953 101 1 11568 20942 13.071428 143 1 11568 11053 25.142857 36 1 11568 30324 11.254386 186 1 11568 8450 24.03333 134 1 11568 8053 21.666666 3 1 11568 13570 20.203703 68 1 11568 10037 33.696968 124 1 11568 12811 41.85417 125 1 11568 37678 21.75 60 1 11568 22955 29.40476 32 1 11568 15031 7.15 168 1 11568 13771 21.4375 29 1 11568 48756 13.308642 24 1 11568 20245 21.526316 129 1 11568 18135 19.210976 138 1 11568 38623 15.458427 113 1 11568 21499 20.17857 95 1 11568 26565 21.145834 16 1 11568 27891 14.953704 91 1 11568 20309 26.083334 83 1 11568 10129 31.59524 117 1 11568 6483 32.9 22 1 11568 22016 15.50926 23 1 11568 15647 19.61111 70 1 11568 13890 27.6875 130 1 11568 24611 23.58974 170 1 11568 11920 29.020834 76 1 11568 19319 30.79487 102 1 11568 31378 12.261905 55 1 11568 11421 33.729168 96 1 11568 28450 18.833334 140 1 11568 19779 30.641666 120 1 11568 7435 30.574074 57 1 11568 18720 31.02778 15 1 11568 22508 12.287828 43 1 11568 18596 21.927084 183 1 11568 7033 22.366667 158 1 11568 28382 28.38 133 1 11568 13059 21.61111 152 1 11568 11078 26.291666 181 1 11568 7346 24.583334 12 1 11568 34221 12.904762 88 1 11568 33010 24.78125 35 1 11568 28795 10.73077 180 1 11568 7636 28.166666 157 1 11568 23929 26.64 150 1 11568 7652 28.25904 72 1 11568 19315 29.960945 44 1 11568 11533 20.72222 182 1 11568 7307 33.5 42 1 11568 36102 22.61111 82 1 11568 11991 29.703703 33 1 11568 18661 9.25 46 1 11568 18178 11.766666 38 1 11568 23380 16.461538 89 1 11568 21118 27.166666 105 1 11568 17771 9.8125 144 1 11568 5836 26 78 1 11568 19862 36.075 146 1 11568 8054 29.805555 148 1 11568 7251 29.72222 166 1 11568 10311 18.78833 71 1 11568 14571 30.020834 126 1 11568 12807 18.958334 154 1 11568 17941 26.51282 18 1 11568 23908 5.236111 75 1 11568 9317 32.633335 8 1 11568 11364 15.242424 128 1 11568 30389 18.759226 7 1 11568 10443 19.020834 25 1 11568 6855 13.962963 178 1 11568 12847 24.469696 97 1 11568 18639 17.656862 1 1 11568 11568 0 156 1 11568 23595 24.46667 141 1 11568 13920 26.39394 153 1 11568 9940 17.13889 167 1 11568 39253 19.731884 14 1 11568 19615 5.569445 175 1 11568 8301 37.416668 122 1 11568 10140 32.541668 99 1 11568 23426 14.309524 41 1 11568 21956 18.255556 114 1 11568 9324 14.166667 110 1 11568 22293 22.19697 34 1 11568 8062 9.595238 5 1 11568 7015 19.875 151 1 11568 6818 25.90476 end
* Example generated by -dataex-. To install: ssc install dataex clear input long date float term double sprtrn float quint 12425 .013541103 .005951362 1 12425 .01908227 .005951362 2 12425 .02498053 .005951362 3 12425 .030166345 .005951362 4 12425 .05550182 .005951362 5 12425 .03056108 .005951362 . end format %td date
tsset date repeated time values in sample
xtreg
xtreg outcome ib9.time_indicator##ib0.treatment [w = weight], fe robust cluster(state)
coefplot drop(`vars') vertical yline(0)
clear set obs 10 gen year = _n + 2010 gen value = 2 twoway (line value year if year >= 2013.5 & year <= 2015.5), xlabel(2011(1)2020)
clear all
tempfile locations
tempname locat
postfile `locat' str32(x1 x2 x3 x4) using `locations', replace
sysuse auto
gen population = 1
gen domestico = 1 if foreign == 0
gen externo = 1 if foreign == 1
gen alto = 1 if gear_ratio <= 2
gen bajo = 1 if gear_ratio > 2
gen cost_h = 1 if price >= 10000
gen cost_l = 1 if price < 10000
local place "domestico externo"
local gear_l "alto bajo"
local cost "cost_h cost_l"
foreach p of local place{
foreach g of local gear_l{
foreach c of local cost{
summ population if `c'== 1 & `g' == 1 & `p' == 1
local r = r(N)
dis "`p'" "`g'" "`c'" "`r'"
post `locat' ("`c'")("`g'")("`p'")("`r'")
}
}
}unknown function () post: above message corresponds to expression 1, variable x1
| ID | Var 1 | Var 2 | Var 3 | Var 4 | Var 5 |
| 1 | Yes | . | No | No | N0 |
| 2 | Yes | . | . | . | . |
| 3 | Yes | . | No | . | . |
| Group | t=0 | t=1 |
| Control | 2016 | 2019 |
| Treated 1 | 2017 | 2020 |
| Treated 2 | 2018 | 2021 |
clear set obs 9999 egen id = seq(), from(1) to(9999) block(1) gen outcome = 1 + rnormal() generate year=2016 if id<=3333 replace year=2017 if id>3333 & id <=6666 replace year=2018 if id>6666 & id <=9999 generate t=0 save samplet0, replace replace year=2019 if id<=3333 replace year=2020 if id>3333 & id <=6666 replace year=2021 if id>6666 & id <=9999 replace t=1 append using samplet0 generate treated1=0 replace treated1=1 if year==2020 generate treated2=0 replace treated2=1 if year==2021 reg outcome t##treated1##treated2
xtoprobit A170 LnNSDPpcC X003 X011 marriage employ gender dharm1 dharm2 dharm3 health2 health3 edu2 edu3 edu4 class1 class2 So2 No2 BOD formalscindex2 formalscindex3 informalSc2 informalSc3 , vce(robust)
margins, dydx(*)
clear use Dataset_Regressions.dta xtset pidp year keep if year==2015 | year==2016 *Remove observations which have missing values drop if treated2016_basecase==. | visit_gp==. | age==. | agesq==. | furthereduc==. | female==. | married==. | homeowner==. | child==. | extraminch==. | permanent==. | commute==. | smoker==. | numcigs==. *Any individual that only appears once must be dropped because otherwise regression may capture effects of leaving the labour market bysort pidp: drop if _N==1 *Check to ensure that the size of treatment and control groups is the same in both the pre and post intervention periods tab year treated2016_basecase *Ordered logistic regression for GP visits eststo drop * *Regression eststo: xtologit visit_gp i.post2016##i.treated2016_basecase c.age i.furthereduc i.female *Marginal effects eststo: margins, dydx(treated2016_basecase) at(treated2016_basecase=1) at(post2016=1)
Fitting comparison model:
Iteration 0: log likelihood = -2355.6421
Iteration 1: log likelihood = -2337.7227
Iteration 2: log likelihood = -2337.6938
Iteration 3: log likelihood = -2337.6938
Refining starting values:
Grid node 0: log likelihood = -2286.9491
Fitting full model:
Iteration 0: log likelihood = -2286.9491
Iteration 1: log likelihood = -2209.1284
Iteration 2: log likelihood = -2204.8774
Iteration 3: log likelihood = -2204.8211
Iteration 4: log likelihood = -2204.8211
Random-effects ordered logistic regression Number of obs = 1,690
Group variable: pidp Number of groups = 845
Random effects u_i ~ Gaussian Obs per group:
min = 2
avg = 2.0
max = 2
Integration method: mvaghermite Integration pts. = 12
Wald chi2(6) = 28.67
Log likelihood = -2204.8211 Prob > chi2 = 0.0001
-----------------------------------------------------------------------------------------------
visit_gp | Coefficient Std. err. z P>|z| [95% conf. interval]
------------------------------+----------------------------------------------------------------
|
post2016 |
Post Intervention | -.1433743 .1195572 -1.20 0.230 -.3777021 .0909535
|
treated2016_basecase |
Treated | .3348412 .2164677 1.55 0.122 -.0894277 .7591101
|
post2016#treated2016_basecase |
Post Intervention#Treated | -.3885202 .2085959 -1.86 0.063 -.7973605 .0203202
|
age | -.007706 .0080193 -0.96 0.337 -.0234236 .0080116
|
furthereduc |
Achieved Further Education | .244885 .1809999 1.35 0.176 -.1098683 .5996383
|
female |
female | -.6936186 .1887752 -3.67 0.000 -1.063611 -.3236261
------------------------------+----------------------------------------------------------------
/cut1 | -5.400528 .4615378 -6.305126 -4.495931
/cut2 | -3.87221 .4405636 -4.735699 -3.008721
/cut3 | -1.822361 .42432 -2.654013 -.9907095
/cut4 | 1.126816 .4201844 .3032702 1.950363
------------------------------+----------------------------------------------------------------
/sigma2_u | 4.43263 .5114511 3.535469 5.557453
-----------------------------------------------------------------------------------------------
LR test vs. ologit model: chibar2(01) = 265.75 Prob >= chibar2 = 0.0000
(est1 stored)
. *Marginal effects
. eststo: margins, dydx(treated2016_basecase) at(treated2016_basecase=1) at(post2016=1)
Average marginal effects Number of obs = 1,690
Model VCE: OIM
dy/dx wrt: 1.treated2016_basecase
1._predict: Pr(1.visit_gp), predict(pr outcome(1))
2._predict: Pr(2.visit_gp), predict(pr outcome(2))
3._predict: Pr(3.visit_gp), predict(pr outcome(3))
4._predict: Pr(4.visit_gp), predict(pr outcome(4))
5._predict: Pr(5.visit_gp), predict(pr outcome(5))
1._at: treated2016_basecase = 1
2._at: post2016 = 1
-----------------------------------------------------------------------------------------
| Delta-method
| dy/dx std. err. z P>|z| [95% conf. interval]
------------------------+----------------------------------------------------------------
0.treated2016_basecase | (base outcome)
------------------------+----------------------------------------------------------------
1.treated2016_basecase |
_predict#_at |
1 1 | -.0040163 .0062129 -0.65 0.518 -.0161933 .0081607
1 2 | .0019696 .0078703 0.25 0.802 -.0134559 .0173951
2 1 | -.0052691 .0074747 -0.70 0.481 -.0199193 .0093811
2 2 | .002248 .0089558 0.25 0.802 -.0153049 .019801
3 1 | -.0087369 .0112432 -0.78 0.437 -.0307732 .0132995
3 2 | .0031155 .0123593 0.25 0.801 -.0211083 .0273393
4 1 | .0007934 .003308 0.24 0.810 -.0056903 .007277
4 2 | -.0013864 .0056086 -0.25 0.805 -.012379 .0096062
5 1 | .0172289 .0218619 0.79 0.431 -.0256197 .0600775
5 2 | -.0059467 .0235787 -0.25 0.801 -.05216 .0402666
-----------------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
(est2 stored)clear input long pidp int year float(post2016 treated2016_basecase female furthereduc age) 68017687 2015 0 1 1 0 36 68017687 2016 1 1 1 1 37 68116287 2015 0 0 1 0 33 68116287 2016 1 0 1 0 34 68150967 2015 0 0 0 0 54 68150967 2016 1 0 0 0 55 68150975 2015 0 0 0 0 27 68150975 2016 1 0 0 0 29 68155047 2015 0 0 1 0 59 68155047 2016 1 0 1 0 60 68166607 2015 0 1 1 1 60 68166607 2016 1 1 1 1 61 68197895 2015 0 1 0 0 24 68197895 2016 1 1 0 0 25 68244807 2015 0 0 1 0 38 68244807 2016 1 0 1 0 38 68273371 2015 0 1 1 0 49 68273371 2016 1 1 1 0 50 68293099 2015 0 0 0 1 25 68293099 2016 1 0 0 1 26 68317567 2015 0 1 0 0 36 68317567 2016 1 1 0 0 37 68347487 2015 0 1 0 0 58 68347487 2016 1 1 0 0 59 68351571 2015 0 0 0 1 41 68351571 2016 1 0 0 1 42 68356331 2015 0 0 0 0 32 68356331 2016 1 0 0 0 33 68439289 2015 0 0 1 0 53 68439289 2016 1 0 1 0 54 68469887 2015 0 0 1 1 48 68469887 2016 1 0 1 1 49 68474647 2015 0 1 1 0 41 68474647 2016 1 1 1 0 42 68569173 2015 0 0 1 1 24 68569173 2016 1 0 1 1 25 68573253 2015 0 0 1 1 25 68573253 2016 1 0 1 1 26 68622207 2015 0 0 1 0 57 68622207 2016 1 0 1 0 58 68636487 2015 0 0 1 0 43 68636487 2016 1 0 1 0 44 68757527 2015 0 0 0 1 53 68757527 2016 1 0 0 1 55 69007091 2015 0 0 0 1 44 69007091 2016 1 0 0 1 45 69078487 2015 0 0 1 1 36 69078487 2016 1 0 1 1 37 69224007 2015 0 0 0 1 37 69224007 2016 1 0 0 1 39 69264127 2015 0 0 1 0 39 69264127 2016 1 0 1 0 40 69269571 2015 0 0 0 0 55 69269571 2016 1 0 0 0 56 69280447 2015 0 1 0 1 62 69280447 2016 1 1 0 1 63 69625205 2015 0 0 1 1 45 69625205 2016 1 0 1 1 46 69831249 2015 0 1 1 0 47 69831249 2016 1 1 1 0 48 70069925 2015 0 0 1 0 37 70069925 2016 1 0 1 0 38 70625485 2015 0 0 1 0 45 70625485 2016 1 0 1 0 46 70786645 2015 0 1 1 0 54 70786645 2016 1 1 1 0 55 71148405 2015 0 1 0 0 50 71148405 2016 1 1 0 0 51 71148409 2015 0 0 1 0 48 71148409 2016 1 0 1 0 49 71434005 2015 0 1 1 1 39 71434005 2016 1 1 1 1 40 71461209 2015 0 1 1 1 26 71461209 2016 1 1 1 1 27 77031164 2015 0 1 0 0 29 77031164 2016 1 1 0 0 30 81959729 2015 0 0 0 0 48 81959729 2016 1 0 0 0 49 82171885 2015 0 1 1 0 48 82171885 2016 1 1 1 0 49 88785569 2015 0 0 0 1 57 88785569 2016 1 0 0 1 58 88916809 2015 0 1 1 1 38 88916809 2016 1 1 1 1 39 89099729 2015 0 1 1 0 33 89099729 2016 1 1 1 0 34 89300329 2015 0 0 1 1 51 89300329 2016 1 0 1 1 52 89358125 2015 0 0 0 0 52 89358125 2016 1 0 0 0 53 95278889 2015 0 0 0 1 39 95278889 2016 1 0 0 1 40 95416925 2015 0 0 1 0 41 95416925 2016 1 0 1 0 42 95575369 2015 0 0 0 0 43 95575369 2016 1 0 0 0 44 96029605 2015 0 0 0 1 46 96029605 2016 1 0 0 1 47 96029609 2015 0 0 1 0 47 96029609 2016 1 0 1 0 48 end label values year b_pregno label values post2016 post2016 label def post2016 0 "Pre Intervention", modify label def post2016 1 "Post Intervention", modify label values treated2016_basecase treated2016_basecase label def treated2016_basecase 0 "Control", modify label def treated2016_basecase 1 "Treated", modify label values female female label def female 0 "male", modify label def female 1 "female", modify label values furthereduc furthereduc label def furthereduc 0 "No Further Education", modify label def furthereduc 1 "Achieved Further Education", modify