Ashraf, N., D. Karlan, and W. Yin. 2006. “Tying Odysseus to the Mast: Evidence From a Commitment Savings Product in the Philippines.” The Quarterly Journal of Economics 121 (2): 635–672. https://doi.org/10.1162/qjec.2006.121.2.635.
The following is based on the replication files for this paper (available from Harvard Dataverse). This is written in a jupyter notebook using the ipystata python library. The following reproduces only parts of the analysis from the do files: SEED.QJEtable.impact.do and SEED.QJEtables.takeup.do
In [4]:
import ipystata
These are the original datasets. They have non-obvious names:
seedanalysis_011204_1.dta: -- Six month impacts
seedanalysis_011204_080404_1.dta: -- Twelve month impacts
seedanalysis_080404_1.dta: -- not sure yet..
The dataset has 3153 observations but the baseline survey appears to have been administered to only 1777 (if call==1).
In [7]:
%%stata
cd G:\GC\Dev-II\notebooks
use AKY06\seedanalysis_011204_080404_1_v11.dta
tab group
tab group if treatment !=.
G:\GC\Dev-II\notebooks
group | Freq. Percent Cum.
------------+-----------------------------------
C | 809 25.66 25.66
M | 776 24.61 50.27
T | 1,568 49.73 100.00
------------+-----------------------------------
Total | 3,153 100.00
group | Freq. Percent Cum.
------------+-----------------------------------
C | 469 26.39 26.39
M | 466 26.22 52.62
T | 842 47.38 100.00
------------+-----------------------------------
Total | 1,777 100.00
In [8]:
%%stata
use AKY06\seedanalysis_011204_1_v11.dta
xi: reg balchange treatment marketing, robust
xi: reg balchange treatment if (treatment == 1 | marketing == 1), robust
Linear regression Number of obs = 1,777
F(2, 1774) = 2.90
Prob > F = 0.0553
R-squared = 0.0018
Root MSE = 2293.2
------------------------------------------------------------------------------
| Robust
balchange | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
treatment | 234.6785 101.7479 2.31 0.021 35.12004 434.237
marketing | 184.8506 146.9815 1.26 0.209 -103.4246 473.1257
_cons | 40.62573 61.67617 0.66 0.510 -80.33988 161.5913
------------------------------------------------------------------------------
Linear regression Number of obs = 1,308
F(1, 1306) = 0.10
Prob > F = 0.7495
R-squared = 0.0001
Root MSE = 2550.2
------------------------------------------------------------------------------
| Robust
balchange | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
treatment | 49.82794 156.027 0.32 0.750 -256.2631 355.9189
_cons | 225.4763 133.4046 1.69 0.091 -36.2344 487.187
------------------------------------------------------------------------------
In [4]:
%%stata
use AKY06\seedanalysis_011204_080404_1_v11.dta
xi: reg balchange treatment marketing, robust
xi: reg balchange treatment if (treatment == 1 | marketing == 1), robust
Linear regression Number of obs = 1,777
F(2, 1774) = 1.43
Prob > F = 0.2398
R-squared = 0.0016
Root MSE = 4528
------------------------------------------------------------------------------
| Robust
balchange | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
treatment | 411.4664 244.0205 1.69 0.092 -67.13149 890.0644
marketing | 123.891 153.4397 0.81 0.420 -177.0506 424.8327
_cons | 65.183 124.2152 0.52 0.600 -178.4406 308.8066
------------------------------------------------------------------------------
Linear regression Number of obs = 1,308
F(1, 1306) = 1.58
Prob > F = 0.2085
R-squared = 0.0008
Root MSE = 5025.5
------------------------------------------------------------------------------
| Robust
balchange | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
treatment | 287.5754 228.5226 1.26 0.208 -160.7361 735.8869
_cons | 189.074 90.07236 2.10 0.036 12.37169 365.7764
In the analysis that follows e(sample)==1 restricts the calculations to the sample that was used in the lsat regression and _result(3) refers to the mean of hh_inc
In [5]:
%%stata
xi: reg balchange treatment marketing, robust
**economic impact #1: % of hh_inc
summ hh_inc if e(sample)==1
disp _b[treatment] / (_result(3)*10000)
**economic impact #2: % of prior balance
summ totbal if e(sample)==1
disp _b[treatment] / (_result(3)/100)
Linear regression Number of obs = 1,777
F(2, 1774) = 1.43
Prob > F = 0.2398
R-squared = 0.0016
Root MSE = 4528
------------------------------------------------------------------------------
| Robust
balchange | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
treatment | 411.4664 244.0205 1.69 0.092 -67.13149 890.0644
marketing | 123.891 153.4397 0.81 0.420 -177.0506 424.8327
_cons | 65.183 124.2152 0.52 0.600 -178.4406 308.8066
------------------------------------------------------------------------------
. **economic impact #1: % of hh_inc
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
hh_inc | 1,777 1.506908 1.953011 0 34.2623
.02730535
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
totbal | 1,777 50912.48 50489.83 0 223836
.80818389
In [6]:
%%stata
xi: dprobit frac_change_00 treatment marketing, robust
xi: dprobit frac_change_00 treatment if (treatment == 1 | marketing == 1), robust
xi: dprobit frac_change_20 treatment marketing, robust
xi: dprobit frac_change_20 treatment if (treatment == 1 | marketing == 1), robust
Iteration 0: log pseudolikelihood = -1072.4045
Iteration 1: log pseudolikelihood = -1064.6708
Iteration 2: log pseudolikelihood = -1064.6667
Probit regression, reporting marginal effects Number of obs = 1777
Wald chi2(2) = 15.34
Prob > chi2 = 0.0005
Log pseudolikelihood = -1064.6667 Pseudo R2 = 0.0072
------------------------------------------------------------------------------
| Robust
frac~_00 | dF/dx Std. Err. z P>|z| x-bar [ 95% C.I. ]
---------+--------------------------------------------------------------------
treatm~t*| .1021901 .0266729 3.82 0.000 .473832 .049912 .154468
market~g*| .0482741 .0314653 1.56 0.119 .26224 -.013397 .109945
---------+--------------------------------------------------------------------
obs. P | .2915025
pred. P | .290007 (at x-bar)
------------------------------------------------------------------------------
(*) dF/dx is for discrete change of dummy variable from 0 to 1
z and P>|z| correspond to the test of the underlying coefficient being 0
Iteration 0: log pseudolikelihood = -812.57798
Iteration 1: log pseudolikelihood = -810.3887
Iteration 2: log pseudolikelihood = -810.3884
Probit regression, reporting marginal effects Number of obs = 1308
Wald chi2(1) = 4.35
Prob > chi2 = 0.0369
Log pseudolikelihood = -810.3884 Pseudo R2 = 0.0027
------------------------------------------------------------------------------
| Robust
frac~_00 | dF/dx Std. Err. z P>|z| x-bar [ 95% C.I. ]
---------+--------------------------------------------------------------------
treatm~t*| .0557175 .0263389 2.09 0.037 .643731 .004094 .107341
---------+--------------------------------------------------------------------
obs. P | .3126911
pred. P | .3121752 (at x-bar)
------------------------------------------------------------------------------
(*) dF/dx is for discrete change of dummy variable from 0 to 1
z and P>|z| correspond to the test of the underlying coefficient being 0
Iteration 0: log pseudolikelihood = -784.0733
Iteration 1: log pseudolikelihood = -772.39775
Iteration 2: log pseudolikelihood = -772.35865
Iteration 3: log pseudolikelihood = -772.35865
Probit regression, reporting marginal effects Number of obs = 1777
Wald chi2(2) = 22.93
Prob > chi2 = 0.0000
Log pseudolikelihood = -772.35865 Pseudo R2 = 0.0149
------------------------------------------------------------------------------
| Robust
frac_~20 | dF/dx Std. Err. z P>|z| x-bar [ 95% C.I. ]
---------+--------------------------------------------------------------------
treatm~t*| .1006771 .0223461 4.51 0.000 .473832 .056879 .144475
market~g*| .0405021 .0274247 1.53 0.126 .26224 -.013249 .094253
---------+--------------------------------------------------------------------
obs. P | .1609454
pred. P | .1572024 (at x-bar)
------------------------------------------------------------------------------
(*) dF/dx is for discrete change of dummy variable from 0 to 1
z and P>|z| correspond to the test of the underlying coefficient being 0
Iteration 0: log pseudolikelihood = -617.43102
Iteration 1: log pseudolikelihood = -613.2009
Iteration 2: log pseudolikelihood = -613.19501
Probit regression, reporting marginal effects Number of obs = 1308
Wald chi2(1) = 8.31
Prob > chi2 = 0.0039
Log pseudolikelihood = -613.19501 Pseudo R2 = 0.0069
------------------------------------------------------------------------------
| Robust
frac_~20 | dF/dx Std. Err. z P>|z| x-bar [ 95% C.I. ]
---------+--------------------------------------------------------------------
treatm~t*| .0636029 .0212161 2.88 0.004 .643731 .02202 .105186
---------+--------------------------------------------------------------------
obs. P | .1804281
pred. P | .1786767 (at x-bar)
------------------------------------------------------------------------------
(*) dF/dx is for discrete change of dummy variable from 0 to 1
z and P>|z| correspond to the test of the underlying coefficient being 0
In [7]:
%%stata
use AKY06\seedanalysis_011204_080404_1_v11.dta
sqreg balchange treatment marketing, q(.1 .2 .3 .4 .5 .6 .7 .8 .9)
sqreg balchange treatment if (treatment == 1 | marketing == 1), q(.1 .2 .3 .4 .5 .6 .7 .8 .9)
(fitting base model)
Bootstrap replications (20)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
....x.x....x..x.....x....
Simultaneous quantile regression Number of obs = 1,777
bootstrap(20) SEs .10 Pseudo R2 = 0.0089
.20 Pseudo R2 = 0.0004
.30 Pseudo R2 = 0.0029
.40 Pseudo R2 = 0.0011
.50 Pseudo R2 = 0.0012
.60 Pseudo R2 = 0.0006
.70 Pseudo R2 = 0.0002
.80 Pseudo R2 = 0.0015
.90 Pseudo R2 = 0.0051
------------------------------------------------------------------------------
| Bootstrap
balchange | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
q10 |
treatment | 317.49 96.41809 3.29 0.001 128.385 506.595
marketing | 327.15 106.2085 3.08 0.002 118.843 535.457
_cons | -828.09 89.08566 -9.30 0.000 -1002.814 -653.3661
-------------+----------------------------------------------------------------
q20 |
treatment | 20 24.16008 0.83 0.408 -27.38521 67.38521
marketing | 20 24.86448 0.80 0.421 -28.76677 68.76677
_cons | -300 22.99448 -13.05 0.000 -345.0991 -254.9009
-------------+----------------------------------------------------------------
q30 |
treatment | 107.03 15.97238 6.70 0.000 75.70331 138.3567
marketing | 100.9 31.1462 3.24 0.001 39.81289 161.9871
_cons | -276.99 12.32701 -22.47 0.000 -301.167 -252.813
-------------+----------------------------------------------------------------
q40 |
treatment | 42.50999 18.72339 2.27 0.023 5.787762 79.23223
marketing | 29.60999 17.30305 1.71 0.087 -4.326526 63.54651
_cons | -148.98 16.03131 -9.29 0.000 -180.4222 -117.5378
-------------+----------------------------------------------------------------
q50 |
treatment | 62 12.94691 4.79 0.000 36.6072 87.3928
marketing | 21.58 19.00313 1.14 0.256 -15.69088 58.85089
_cons | -98.22 9.810769 -10.01 0.000 -117.4619 -78.97812
-------------+----------------------------------------------------------------
q60 |
treatment | 37.62 10.6566 3.53 0.000 16.71919 58.52081
marketing | 22.59 23.21491 0.97 0.331 -22.94144 68.12144
_cons | -37.62 10.6566 -3.53 0.000 -58.52081 -16.71919
-------------+----------------------------------------------------------------
q70 |
treatment | 6.55 2.694475 2.43 0.015 1.265321 11.83468
marketing | 2.92e-13 1.094166 0.00 1.000 -2.14599 2.14599
_cons | -2.97e-13 1.16e-13 -2.57 0.010 -5.24e-13 -7.00e-14
-------------+----------------------------------------------------------------
q80 |
treatment | 65.79 15.76751 4.17 0.000 34.86515 96.71485
marketing | 4.02 6.252162 0.64 0.520 -8.242379 16.28238
_cons | 6.85 1.559019 4.39 0.000 3.792292 9.907708
-------------+----------------------------------------------------------------
q90 |
treatment | 437.23 148.7802 2.94 0.003 145.427 729.033
marketing | 265.06 176.7912 1.50 0.134 -81.68094 611.8009
_cons | 107.4 52.46879 2.05 0.041 4.492847 210.3072
------------------------------------------------------------------------------
(fitting base model)
Bootstrap replications (20)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
xxxxxx.xx...xxx..xxxxxxxxx.x.x.xxxxxxxxxx..xxxxxxx.xxxxxxxxxx.xxxxxxxxx.xxx.xxx..xxxx..x.
Simultaneous quantile regression Number of obs = 1,308
bootstrap(20) SEs .10 Pseudo R2 = 0.0001
.20 Pseudo R2 = -0.0000
.30 Pseudo R2 = 0.0000
.40 Pseudo R2 = 0.0003
.50 Pseudo R2 = 0.0006
.60 Pseudo R2 = 0.0001
.70 Pseudo R2 = 0.0001
.80 Pseudo R2 = 0.0012
.90 Pseudo R2 = 0.0006
------------------------------------------------------------------------------
| Bootstrap
balchange | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
q10 |
treatment | -9.660004 105.8526 -0.09 0.927 -217.3198 197.9998
_cons | -500.94 105.6118 -4.74 0.000 -708.1273 -293.7527
-------------+----------------------------------------------------------------
q20 |
treatment | 0 14.62935 0.00 1.000 -28.6996 28.6996
_cons | -280 10.26516 -27.28 0.000 -300.138 -259.862
-------------+----------------------------------------------------------------
q30 |
treatment | 6.12999 32.19983 0.19 0.849 -57.03907 69.29905
_cons | -176.09 21.26357 -8.28 0.000 -217.8045 -134.3755
-------------+----------------------------------------------------------------
q40 |
treatment | 12.9 14.01399 0.92 0.357 -14.59238 40.39239
_cons | -119.37 9.211683 -12.96 0.000 -137.4413 -101.2987
-------------+----------------------------------------------------------------
q50 |
treatment | 40.42 16.54314 2.44 0.015 7.965957 72.87404
_cons | -76.64 12.82142 -5.98 0.000 -101.7928 -51.48717
-------------+----------------------------------------------------------------
q60 |
treatment | 15.03 12.593 1.19 0.233 -9.674723 39.73472
_cons | -15.03 12.593 -1.19 0.233 -39.73472 9.674723
-------------+----------------------------------------------------------------
q70 |
treatment | 6.55 3.169129 2.07 0.039 .3328592 12.76714
_cons | 1.04e-13 .3108714 0.00 1.000 -.609862 .609862
-------------+----------------------------------------------------------------
q80 |
treatment | 61.77 17.17601 3.60 0.000 28.07442 95.46558
_cons | 10.87 4.421678 2.46 0.014 2.19563 19.54437
-------------+----------------------------------------------------------------
q90 |
treatment | 172.17 192.5683 0.89 0.371 -205.6071 549.9471
_cons | 372.46 164.7767 2.26 0.024 49.20397 695.716
In [8]:
%%stata
use AKY06\seedanalysis_011204_080404_v11.dta, clear
sqreg balchange treatment marketing, q(.1 .2 .3 .4 .5 .6 .7 .8 .9)
sqreg balchange treatment if (treatment == 1 | marketing == 1), q(.1 .2 .3 .4 .5 .6 .7 .8 .9)
file AKY06\seedanalysis_011204_080404_v11.dta not found
r(601);
(fitting base model)
Bootstrap replications (20)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
....x..x...x.....xx.....xx.
Simultaneous quantile regression Number of obs = 1,777
bootstrap(20) SEs .10 Pseudo R2 = 0.0089
.20 Pseudo R2 = 0.0004
.30 Pseudo R2 = 0.0029
.40 Pseudo R2 = 0.0011
.50 Pseudo R2 = 0.0012
.60 Pseudo R2 = 0.0006
.70 Pseudo R2 = 0.0002
.80 Pseudo R2 = 0.0015
.90 Pseudo R2 = 0.0051
------------------------------------------------------------------------------
| Bootstrap
balchange | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
q10 |
treatment | 317.49 81.95606 3.87 0.000 156.7494 478.2306
marketing | 327.15 75.29605 4.34 0.000 179.4717 474.8283
_cons | -828.09 79.19658 -10.46 0.000 -983.4184 -672.7616
-------------+----------------------------------------------------------------
q20 |
treatment | 20 35.28501 0.57 0.571 -49.20457 89.20457
marketing | 20 38.20277 0.52 0.601 -54.92716 94.92716
_cons | -300 35.68941 -8.41 0.000 -369.9977 -230.0023
-------------+----------------------------------------------------------------
q30 |
treatment | 107.03 18.42875 5.81 0.000 70.88564 143.1743
marketing | 100.9 19.00898 5.31 0.000 63.61764 138.1823
_cons | -276.99 15.21966 -18.20 0.000 -306.8403 -247.1396
-------------+----------------------------------------------------------------
q40 |
treatment | 42.50999 9.363725 4.54 0.000 24.1449 60.87509
marketing | 29.60999 11.60699 2.55 0.011 6.845176 52.37481
_cons | -148.98 9.653964 -15.43 0.000 -167.9143 -130.0457
-------------+----------------------------------------------------------------
q50 |
treatment | 62 15.82094 3.92 0.000 30.97037 93.02963
marketing | 21.58 20.5556 1.05 0.294 -18.73574 61.89574
_cons | -98.22 15.6571 -6.27 0.000 -128.9283 -67.51171
-------------+----------------------------------------------------------------
q60 |
treatment | 37.62 13.34395 2.82 0.005 11.44848 63.79152
marketing | 22.59 19.36071 1.17 0.243 -15.38219 60.56219
_cons | -37.62 13.34395 -2.82 0.005 -63.79152 -11.44848
-------------+----------------------------------------------------------------
q70 |
treatment | 6.55 3.06789 2.14 0.033 .5329406 12.56706
marketing | 2.92e-13 1.276839 0.00 1.000 -2.504266 2.504266
_cons | -2.97e-13 1.10e-13 -2.69 0.007 -5.14e-13 -8.06e-14
-------------+----------------------------------------------------------------
q80 |
treatment | 65.79 20.26571 3.25 0.001 26.04281 105.5372
marketing | 4.02 6.550422 0.61 0.539 -8.827356 16.86736
_cons | 6.85 3.14329 2.18 0.029 .6850583 13.01494
-------------+----------------------------------------------------------------
q90 |
treatment | 437.23 99.8697 4.38 0.000 241.3553 633.1047
marketing | 265.06 211.3404 1.25 0.210 -149.4423 679.5623
_cons | 107.4 56.54647 1.90 0.058 -3.504705 218.3047
------------------------------------------------------------------------------
(fitting base model)
Bootstrap replications (20)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
xxxxxxx.xx.xxxxxxxxx.xxxx.xxxxxxxxxxx.xxxxxx..xxxxxxxxxxxx.xxx.xxxxxxxxx.xxxxxxxx..xxxx.xxx.xxxxxxxxxxx.xx.xxx.xxxx.xxx.xxx.
Simultaneous quantile regression Number of obs = 1,308
bootstrap(20) SEs .10 Pseudo R2 = 0.0001
.20 Pseudo R2 = -0.0000
.30 Pseudo R2 = 0.0000
.40 Pseudo R2 = 0.0003
.50 Pseudo R2 = 0.0006
.60 Pseudo R2 = 0.0001
.70 Pseudo R2 = 0.0001
.80 Pseudo R2 = 0.0012
.90 Pseudo R2 = 0.0006
------------------------------------------------------------------------------
| Bootstrap
balchange | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
q10 |
treatment | -9.660004 64.97214 -0.15 0.882 -137.1212 117.8012
_cons | -500.94 54.30732 -9.22 0.000 -607.4791 -394.4009
-------------+----------------------------------------------------------------
q20 |
treatment | 0 22.41162 0.00 1.000 -43.96671 43.96671
_cons | -280 4.786988 -58.49 0.000 -289.391 -270.609
-------------+----------------------------------------------------------------
q30 |
treatment | 6.12999 33.11795 0.19 0.853 -58.84021 71.10019
_cons | -176.09 21.44269 -8.21 0.000 -218.1559 -134.0241
-------------+----------------------------------------------------------------
q40 |
treatment | 12.9 12.24727 1.05 0.292 -11.12647 36.92648
_cons | -119.37 8.031628 -14.86 0.000 -135.1263 -103.6137
-------------+----------------------------------------------------------------
q50 |
treatment | 40.42 15.55394 2.60 0.009 9.906553 70.93344
_cons | -76.64 10.85084 -7.06 0.000 -97.92698 -55.35301
-------------+----------------------------------------------------------------
q60 |
treatment | 15.03 12.87917 1.17 0.243 -10.23613 40.29613
_cons | -15.03 12.87917 -1.17 0.243 -40.29613 10.23613
-------------+----------------------------------------------------------------
q70 |
treatment | 6.55 2.234516 2.93 0.003 2.166367 10.93363
_cons | 1.04e-13 .2258429 0.00 1.000 -.4430545 .4430545
-------------+----------------------------------------------------------------
q80 |
treatment | 61.77 22.97972 2.69 0.007 16.68879 106.8512
_cons | 10.87 8.091951 1.34 0.179 -5.004645 26.74464
-------------+----------------------------------------------------------------
q90 |
treatment | 172.17 245.2228 0.70 0.483 -308.9036 653.2436
_cons | 372.46 225.9348 1.65 0.099 -70.77494 815.6949
In [9]:
%%stata
xi: reg balchange treatment marketing female female_treat, robust
xi: reg balchange treatment marketing active active_treat, robust
xi: reg balchange treatment marketing edhi edhi_treat, robust
xi: reg balchange treatment marketing hi_hh_inc hi_hh_inc_treat, robust
xi: reg balchange treatment marketing hyper_mon_new2 hyper_mon_new2_treat, robust
xi: reg balchange treatment marketing silly_mon_new2 silly_mon_new2_treat, robust
Linear regression Number of obs = 1,777
F(4, 1772) = 1.98
Prob > F = 0.0948
R-squared = 0.0022
Root MSE = 4529.2
------------------------------------------------------------------------------
| Robust
balchange | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
treatment | 680.2893 420.26 1.62 0.106 -143.968 1504.547
marketing | 137.2044 150.0914 0.91 0.361 -157.1704 431.5792
female | 192.9631 135.0963 1.43 0.153 -72.00175 457.9279
female_treat | -443.4216 483.559 -0.92 0.359 -1391.828 504.9845
_cons | -53.72176 93.64086 -0.57 0.566 -237.3799 129.9364
------------------------------------------------------------------------------
Linear regression Number of obs = 1,777
F(4, 1772) = 8.72
Prob > F = 0.0000
R-squared = 0.0040
Root MSE = 4524.9
------------------------------------------------------------------------------
| Robust
balchange | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
treatment | 676.3477 327.5403 2.06 0.039 33.94163 1318.754
marketing | 122.4113 152.3797 0.80 0.422 -176.4515 421.2741
active | 637.8621 204.6197 3.12 0.002 236.5407 1039.183
active_treat | -738.1954 393.8328 -1.87 0.061 -1510.621 34.23035
_cons | -164.665 81.52621 -2.02 0.044 -324.5626 -4.767302
------------------------------------------------------------------------------
Linear regression Number of obs = 1,777
F(4, 1772) = 1.57
Prob > F = 0.1785
R-squared = 0.0018
Root MSE = 4530
------------------------------------------------------------------------------
| Robust
balchange | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
treatment | 247.7803 362.0502 0.68 0.494 -462.3101 957.8708
marketing | 122.8684 154.1362 0.80 0.425 -179.4395 425.1764
edhi | -145.0309 166.6162 -0.87 0.384 -471.8158 181.754
edhi_treat | 279.7698 448.2779 0.62 0.533 -599.4393 1158.979
_cons | 148.0578 200.4277 0.74 0.460 -245.0418 541.1574
------------------------------------------------------------------------------
Linear regression Number of obs = 1,777
F(4, 1772) = 2.05
Prob > F = 0.0849
R-squared = 0.0019
Root MSE = 4529.9
---------------------------------------------------------------------------------
| Robust
balchange | Coef. Std. Err. t P>|t| [95% Conf. Interval]
----------------+----------------------------------------------------------------
treatment | 464.2607 271.0703 1.71 0.087 -67.39051 995.912
marketing | 131.9819 150.2833 0.88 0.380 -162.7692 426.733
hi_hh_inc | 193.5094 153.943 1.26 0.209 -108.4197 495.4384
hi_hh_inc_treat | -106.6214 444.0923 -0.24 0.810 -977.6211 764.3784
_cons | -32.60318 84.76645 -0.38 0.701 -198.8559 133.6496
---------------------------------------------------------------------------------
Linear regression Number of obs = 1,774
F(4, 1769) = 0.89
Prob > F = 0.4683
R-squared = 0.0018
Root MSE = 4533.8
--------------------------------------------------------------------------------------
| Robust
balchange | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------------------+----------------------------------------------------------------
treatment | 344.6337 290.47 1.19 0.236 -225.0667 914.3342
marketing | 126.032 153.4896 0.82 0.412 -175.008 427.072
hyper_mon_new2 | -28.40704 132.3356 -0.21 0.830 -287.9576 231.1436
hyper_mon_new2_treat | 243.8663 470.7961 0.52 0.605 -679.5089 1167.242
_cons | 72.63304 142.1695 0.51 0.609 -206.2049 351.471
--------------------------------------------------------------------------------------
Linear regression Number of obs = 1,774
F(4, 1769) = 2.09
Prob > F = 0.0800
R-squared = 0.0023
Root MSE = 4532.8
--------------------------------------------------------------------------------------
| Robust
balchange | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------------------+----------------------------------------------------------------
treatment | 516.7935 261.952 1.97 0.049 3.025574 1030.561
marketing | 127.571 151.9509 0.84 0.401 -170.4513 425.5932
silly_mon_new2 | 284.8332 353.4208 0.81 0.420 -408.3331 977.9995
silly_mon_new2_treat | -633.5807 448.5194 -1.41 0.158 -1513.264 246.103
_cons | 15.99006 80.69286 0.20 0.843 -142.2733 174.2534
There is a lot more to the impact results in the original do file.... but for now the analysis here turns to the takeup questions:
In [15]:
%%stata
use AKY06\seedanalysis_011204_080404_1_v11.dta, clear
gen control = 1 if group == "C"
replace control = 0 if control ==.
macro define GB "GBloan GBloandefault"
macro define fullset "married edhi numhh unemployed age $GB hh_inc hh_inc2"
macro define fullset_noGB "married edhi numhh unemployed age hh_inc hh_inc2"
gen dormant_new = 1- active
drop dormant
rename dormant_new dormant
replace totbal = totbal/100
replace newtotbal = newtotbal/100
gen dist_GB = dbutuan if butuan ==1
replace dist_GB = dampayon if ampayon == 1
destring pop, ignore(",") replace
gen brgy_penetration = no_clients /pop
bysort brgy: egen sd_totbal = sd(totbal)
bysort brgy: egen mean_totbal = mean(totbal)
(2344 missing values generated)
(2344 real changes made)
(2839 real changes made)
(2193 real changes made)
(762 missing values generated)
(762 real changes made)
pop: characters , removed; replaced as int
In [7]:
%%stata
tab active if call==1,summ(totbal)
| Summary of Client savings balance
Active | (hundreds)
account | Mean Std. Dev. Freq.
------------+------------------------------------
0 | 483.12239 498.52538 1145
1 | 556.2335 513.2936 632
------------+------------------------------------
Total | 509.12477 504.89831 1777
In [8]:
%%stata
tab goal_category if seedtakeup==1
tab goal_type if seedtakeup==1
tab box if seedtakeup==1
Savings goal | Freq. Percent Cum.
---------------------------------+-----------------------------------
Agricultural Financing/Investing | 4 2.04 2.04
Capital for Business | 20 10.20 12.24
Christmas/Birthday/Celebration/G | 95 48.47 60.71
Education | 41 20.92 81.63
House/Lot construction and purch | 20 10.20 91.84
Medical | 1 0.51 92.35
Personal Needs/Future Expenses | 3 1.53 93.88
Purchase or Maintenance of Machi | 8 4.08 97.96
Vacation/Travel | 4 2.04 100.00
---------------------------------+-----------------------------------
Total | 196 100.00
Type of |
savings |
goal | Freq. Percent Cum.
------------+-----------------------------------
amount | 62 30.69 30.69
date | 140 69.31 100.00
------------+-----------------------------------
Total | 202 100.00
Bought |
ganansiya |
box | Freq. Percent Cum.
------------+-----------------------------------
0 | 35 17.33 17.33
1 | 167 82.67 100.00
------------+-----------------------------------
Total | 202 100.00
In [24]:
%%stata
tab call
Result of call | Freq. Percent Cum.
--------------------------------+-----------------------------------
completed | 1,777 100.00 100.00
--------------------------------+-----------------------------------
Total | 1,777 100.00
In [22]:
%%stata
tabstat totbal active if call==1, by(group) stats(mean sem)
reg totbal treatment control if call ==1
reg active treatment control if call ==1
sort brgy
tabstat dist_GB brgy_penetration sd_totbal mean_totbal pop if call==1, by(group) stats(mean sem)
reg dist_GB treatment control if call ==1
reg brgy_penetration treatment control if call ==1
reg sd_totbal treatment control if call ==1
reg mean_totbal treatment control if call ==1
reg pop treatment control if call ==1
Summary statistics: mean, se(mean)
by categories of: group
group | totbal active
-------+--------------------
C | 530.7378 .3603412
| 23.38712 .0221926
-------+--------------------
M | 499.0084 .3626609
| 23.50222 .0222951
-------+--------------------
T | 502.685 .3491686
| 17.33115 .0164382
-------+--------------------
Total | 509.1248 .3556556
| 11.97734 .0113593
----------------------------
Source | SS df MS Number of obs = 1,777
-------------+---------------------------------- F(2, 1774) = 0.59
Model | 301690.551 2 150845.275 Prob > F = 0.5536
Residual | 452440317 1,774 255039.638 R-squared = 0.0007
-------------+---------------------------------- Adj R-squared = -0.0005
Total | 452742008 1,776 254922.302 Root MSE = 505.01
------------------------------------------------------------------------------
totbal | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
treatment | 3.676564 29.15807 0.13 0.900 -53.51121 60.86434
control | 31.72941 33.03165 0.96 0.337 -33.05563 96.51446
_cons | 499.0084 23.39434 21.33 0.000 453.125 544.8918
------------------------------------------------------------------------------
Source | SS df MS Number of obs = 1,777
-------------+---------------------------------- F(2, 1774) = 0.15
Model | .06859733 2 .034298665 Prob > F = 0.8612
Residual | 407.157064 1,774 .229513565 R-squared = 0.0002
-------------+---------------------------------- Adj R-squared = -0.0010
Total | 407.225661 1,776 .229293728 Root MSE = .47908
------------------------------------------------------------------------------
active | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
treatment | -.0134923 .0276604 -0.49 0.626 -.0677428 .0407582
control | -.0023198 .0313351 -0.07 0.941 -.0637773 .0591377
_cons | .3626609 .0221928 16.34 0.000 .3191342 .4061876
------------------------------------------------------------------------------
Summary statistics: mean, se(mean)
by categories of: group
group | dist_GB brgy_p~n sd_tot~l mean_t~l pop
-------+--------------------------------------------------
C | 21.86567 .0219365 487.1321 473.3466 5853.949
| .8419475 .0004179 3.507831 3.74361 213.4831
-------+--------------------------------------------------
M | 23.22961 .0216379 491.3003 476.9744 5708.208
| .8874667 .0004076 3.449334 3.716019 203.4358
-------+--------------------------------------------------
T | 22.70784 .0219269 488.0445 476.0261 5729.5
| .6718419 .000292 2.442733 2.599624 152.98
-------+--------------------------------------------------
Total | 22.6224 .0218536 488.6575 475.5676 5756.762
| .4525451 .0002066 1.735807 1.854791 106.1334
----------------------------------------------------------
Source | SS df MS Number of obs = 1,777
-------------+---------------------------------- F(2, 1774) = 0.61
Model | 446.531678 2 223.265839 Prob > F = 0.5417
Residual | 645883.097 1,774 364.082918 R-squared = 0.0007
-------------+---------------------------------- Adj R-squared = -0.0004
Total | 646329.629 1,776 363.92434 Root MSE = 19.081
------------------------------------------------------------------------------
dist_GB | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
treatment | -.5217753 1.101679 -0.47 0.636 -2.6825 1.63895
control | -1.363942 1.248034 -1.09 0.275 -3.811715 1.083831
_cons | 23.22961 .8839083 26.28 0.000 21.496 24.96322
------------------------------------------------------------------------------
Source | SS df MS Number of obs = 1,777
-------------+---------------------------------- F(2, 1774) = 0.19
Model | .00002944 2 .00001472 Prob > F = 0.8238
Residual | .134701232 1,774 .000075931 R-squared = 0.0002
-------------+---------------------------------- Adj R-squared = -0.0009
Total | .134730672 1,776 .000075862 Root MSE = .00871
------------------------------------------------------------------------------
brgy_penet~n | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
treatment | .000289 .0005031 0.57 0.566 -.0006977 .0012758
control | .0002987 .0005699 0.52 0.600 -.0008192 .0014165
_cons | .0216379 .0004037 53.60 0.000 .0208462 .0224296
------------------------------------------------------------------------------
Source | SS df MS Number of obs = 1,777
-------------+---------------------------------- F(2, 1774) = 0.44
Model | 4662.35264 2 2331.17632 Prob > F = 0.6473
Residual | 9504305.63 1,774 5357.55672 R-squared = 0.0005
-------------+---------------------------------- Adj R-squared = -0.0006
Total | 9508967.98 1,776 5354.14864 Root MSE = 73.195
------------------------------------------------------------------------------
sd_totbal | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
treatment | -3.255741 4.226085 -0.77 0.441 -11.54437 5.032889
control | -4.168199 4.787511 -0.87 0.384 -13.55796 5.221557
_cons | 491.3003 3.390708 144.90 0.000 484.6501 497.9505
------------------------------------------------------------------------------
Source | SS df MS Number of obs = 1,777
-------------+---------------------------------- F(2, 1774) = 0.28
Model | 3412.62158 2 1706.31079 Prob > F = 0.7567
Residual | 10853846.7 1,774 6118.29015 R-squared = 0.0003
-------------+---------------------------------- Adj R-squared = -0.0008
Total | 10857259.4 1,776 6113.32171 Root MSE = 78.219
------------------------------------------------------------------------------
mean_totbal | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
treatment | -.9482933 4.516166 -0.21 0.834 -9.805859 7.909273
control | -3.627729 5.116129 -0.71 0.478 -13.662 6.406545
_cons | 476.9744 3.623448 131.64 0.000 469.8677 484.081
------------------------------------------------------------------------------
Source | SS df MS Number of obs = 1,777
-------------+---------------------------------- F(2, 1774) = 0.15
Model | 6154208.23 2 3077104.11 Prob > F = 0.8576
Residual | 3.5543e+10 1,774 20035747.1 R-squared = 0.0002
-------------+---------------------------------- Adj R-squared = -0.0010
Total | 3.5550e+10 1,776 20016649.5 Root MSE = 4476.1
------------------------------------------------------------------------------
pop | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
treatment | 21.29185 258.4388 0.08 0.934 -485.5846 528.1683
control | 145.7407 292.7718 0.50 0.619 -428.4732 719.9546
_cons | 5708.208 207.3528 27.53 0.000 5301.527 6114.89
In [17]:
%%stata
tabstat yearsed female age impatient_mon01 hyper_mon_new2 if call==1, by(group) stats(mean sem)
reg yearsed treatment control if call ==1
reg female treatment control if call ==1
reg age treatment control if call ==1
reg impatient_mon01 treatment control if call ==1
reg hyper_mon_new2 treatment control if call ==1
Summary statistics: mean, se(mean)
by categories of: group
group | yearsed female age impa~n01 h~n_new2
-------+--------------------------------------------------
C | 18.19403 .6162047 42.05117 .8081023 .2622601
| .1373271 .0224796 .5943071 .0393999 .0203327
-------+--------------------------------------------------
M | 17.91845 .5472103 42.87124 .8903226 .2758621
| .144761 .0230834 .658222 .0409272 .0207714
-------+--------------------------------------------------
T | 18.22209 .5985748 42.10808 .8690476 .2782402
| .1052178 .016903 .4578191 .0296984 .015462
-------+--------------------------------------------------
Total | 18.13506 .589758 42.29319 .8585118 .2733935
| .072415 .0116717 .318447 .0205285 .010585
----------------------------------------------------------
Source | SS df MS Number of obs = 1777
-------------+------------------------------ F( 2, 1774) = 1.60
Model | 29.8721265 2 14.9360633 Prob > F = 0.2014
Residual | 16519.7137 1774 9.31212722 R-squared = 0.0018
-------------+------------------------------ Adj R-squared = 0.0007
Total | 16549.5858 1776 9.31846048 Root MSE = 3.0516
------------------------------------------------------------------------------
yearsed | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
treatment | .3036353 .1761892 1.72 0.085 -.041925 .6491956
control | .2755749 .1995956 1.38 0.168 -.1158923 .6670421
_cons | 17.91845 .1413616 126.76 0.000 17.6412 18.19571
------------------------------------------------------------------------------
Source | SS df MS Number of obs = 1777
-------------+------------------------------ F( 2, 1774) = 2.56
Model | 1.23708842 2 .618544211 Prob > F = 0.0776
Residual | 428.696508 1774 .241655303 R-squared = 0.0029
-------------+------------------------------ Adj R-squared = 0.0018
Total | 429.933596 1776 .242079727 Root MSE = .49158
------------------------------------------------------------------------------
female | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
treatment | .0513645 .0283827 1.81 0.071 -.0043024 .1070315
control | .0689944 .0321532 2.15 0.032 .0059322 .1320566
_cons | .5472103 .0227722 24.03 0.000 .5025471 .5918735
------------------------------------------------------------------------------
Source | SS df MS Number of obs = 1777
-------------+------------------------------ F( 2, 1774) = 0.59
Model | 212.035992 2 106.017996 Prob > F = 0.5555
Residual | 319828.212 1774 180.286478 R-squared = 0.0007
-------------+------------------------------ Adj R-squared = -0.0005
Total | 320040.248 1776 180.202842 Root MSE = 13.427
------------------------------------------------------------------------------
age | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
treatment | -.7631686 .7752405 -0.98 0.325 -2.283649 .7573122
control | -.8200719 .8782295 -0.93 0.351 -2.542545 .9024014
_cons | 42.87124 .6219975 68.93 0.000 41.65132 44.09117
------------------------------------------------------------------------------
Source | SS df MS Number of obs = 1774
-------------+------------------------------ F( 2, 1771) = 1.17
Model | 1.75557046 2 .877785228 Prob > F = 0.3093
Residual | 1323.7309 1771 .747448278 R-squared = 0.0013
-------------+------------------------------ Adj R-squared = 0.0002
Total | 1325.48647 1773 .747595302 Root MSE = .86455
------------------------------------------------------------------------------
impatien~n01 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
treatment | -.021275 .0499723 -0.43 0.670 -.1192859 .076736
control | -.0822202 .0565785 -1.45 0.146 -.1931878 .0287473
_cons | .8903226 .0400926 22.21 0.000 .8116888 .9689564
------------------------------------------------------------------------------
Source | SS df MS Number of obs = 1774
-------------+------------------------------ F( 2, 1771) = 0.20
Model | .080716446 2 .040358223 Prob > F = 0.8164
Residual | 352.323455 1771 .198940404 R-squared = 0.0002
-------------+------------------------------ Adj R-squared = -0.0009
Total | 352.404171 1773 .198761518 Root MSE = .44603
------------------------------------------------------------------------------
hyper_mon~w2 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
treatment | .0023781 .0257935 0.09 0.927 -.0482107 .052967
control | -.0136019 .029205 -0.47 0.641 -.0708818 .0436779
_cons | .2758621 .0207063 13.32 0.000 .2352507 .3164734
0 = patient, 1 = somewhat impatient, 2=most impatient
In [23]:
%%stata
tab impatient_mon01 impatient_mon67 if call==1
Impatient |
Now w/r/t | Impatient Later w/r/t money
money | 0 1 2 | Total
-----------+---------------------------------+----------
0 | 606 126 73 | 805
1 | 206 146 59 | 411
2 | 154 93 299 | 546
-----------+---------------------------------+----------
Total | 966 365 431 | 1,762
In [24]:
%%stata
gen female_hyper_mon_new2 = female * hyper_mon_new2
(1379 missing values generated)
In [27]:
%%stata
xi: dprobit seedtakeup hyper_mon_new2 female impatient_200p250_01 impatient_250p200_01 impatient_200p250_67 impatient_250p200_67 fem_married $fullset fem_frac_veryown_inc_0_25 fem_frac_veryown_inc_25_50 fem_frac_veryown_inc_50_75 fem_frac_veryown_inc_75_100 frac_veryown_inc_0_25 frac_veryown_inc_25_50 frac_veryown_inc_50_75 frac_veryown_inc_75_100 active if group=="T" & call==1 & reached==1, robust
> _50 fem_frac_veryown_inc_50_75 fem_frac_veryown_inc_75_100 frac_veryown_inc_0_25 frac_veryown_inc_25_50 frac_veryown_inc_50_75 frac_veryown_inc_75_100 active if group=="T" & call==1 & reached==1, rob
> ust
Iteration 0: log pseudolikelihood = -425.65043
Iteration 1: log pseudolikelihood = -408.47662
Iteration 2: log pseudolikelihood = -407.69152
Iteration 3: log pseudolikelihood = -407.141
Iteration 4: log pseudolikelihood = -407.06088
Iteration 5: log pseudolikelihood = -407.05886
Iteration 6: log pseudolikelihood = -407.05886
Probit regression, reporting marginal effects Number of obs = 715
Wald chi2(25) = 35.68
Prob > chi2 = 0.0767
Log pseudolikelihood = -407.05886 Pseudo R2 = 0.0437
------------------------------------------------------------------------------
| Robust
seedta~p | dF/dx Std. Err. z P>|z| x-bar [ 95% C.I. ]
---------+--------------------------------------------------------------------
h~n_new2*| .1248659 .0667069 1.94 0.053 .278322 -.005877 .255609
female*| .098883 .1372349 0.70 0.482 .6 -.170093 .367858
im~50_01*| -.0300081 .0496469 -0.59 0.552 .241958 -.127314 .067298
i~200_01*| .0764479 .0718961 1.07 0.285 .444755 -.064466 .217362
im~50_67*| .0968518 .0646814 1.56 0.119 .206993 -.029922 .223625
i~200_67*| .0150482 .063883 0.24 0.814 .545455 -.11016 .140257
fem_ma~d*| -.1132497 .0905445 -1.22 0.222 .423776 -.290714 .064214
married*| .0489946 .0770504 0.62 0.536 .765035 -.102021 .200011
edhi*| .0834128 .0383418 2.14 0.032 .59021 .008264 .158561
numhh | .0001891 .0076993 0.02 0.980 5.58462 -.014901 .01528
unempl~d*| .0404135 .1091283 0.38 0.703 .033566 -.173474 .254301
age | -.0019155 .0013788 -1.39 0.165 42.6531 -.004618 .000787
GBloan*| -.0141822 .0363595 -0.39 0.697 .502098 -.085445 .057081
GBloan~t*| -.0317615 .072292 -0.43 0.670 .055944 -.173451 .109928
hh_inc | .0486918 .0307147 1.56 0.118 1.55682 -.011508 .108892
hh_inc2 | -.007847 .004361 -1.76 0.078 5.82637 -.016394 .0007
fem_f~25*| .0153704 .1822196 0.09 0.932 .051748 -.341773 .372514
fem~5_50*| .0480785 .16932 0.29 0.770 .14965 -.283783 .37994
fem_f~75*| .1349672 .1818598 0.79 0.431 .114685 -.221472 .491406
fe~5_100*| .0182208 .1546866 0.12 0.905 .174825 -.284959 .321401
frac_~25*| -.0106804 .1540169 -0.07 0.945 .095105 -.312548 .291187
fra~5_50*| -.0472313 .1407564 -0.33 0.745 .218182 -.323109 .228646
frac_~75*| -.0340687 .1388783 -0.24 0.810 .202797 -.306265 .238128
fr~5_100*| .0247978 .142089 0.18 0.861 .355245 -.253692 .303287
active*| -.0360562 .0341531 -1.04 0.296 .353846 -.102995 .030883
---------+--------------------------------------------------------------------
obs. P | .2825175
pred. P | .2647361 (at x-bar)
------------------------------------------------------------------------------
(*) dF/dx is for discrete change of dummy variable from 0 to 1
z and P>|z| correspond to the test of the underlying coefficient being 0
In [29]:
%%stata
tab seedtakeup if call==1
SEED take |
up | Freq. Percent Cum.
------------+-----------------------------------
0 | 1,575 88.63 88.63
1 | 202 11.37 100.00
------------+-----------------------------------
Total | 1,777 100.00
In [ ]:
%%stata
tab impatient_mon01
tab active impatient_mon01,row col
In [11]:
%%stata --graph
tab zerototbal if call==1
summ howlongopen
hist howlongopen
zerototbal | Freq. Percent Cum.
------------+-----------------------------------
0 | 1,623 91.33 91.33
1 | 154 8.67 100.00
------------+-----------------------------------
Total | 1,777 100.00
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
howlongopen | 212 340.9764 48.90105 84 404
(bin=14, start=84, width=22.857143)
Out[11]:
In [12]:
%%stata
tab active if call==1,summ( nonseedbalchange)
summ nonseedbalchange,detail
tab active active_treat if call==1
Active | Summary of nonseedbalchange
account | Mean Std. Dev. Freq.
------------+------------------------------------
0 | 105.73035 4714.6168 1145
1 | 370.66207 2928.8638 632
------------+------------------------------------
Total | 199.9548 4169.1327 1777
nonseedbalchange
-------------------------------------------------------------
Percentiles Smallest
1% -1506.14 -4541.77
5% -947.57 -2168.88
10% -500.94 -2138.5 Obs 3152
25% -255.515 -2114.25 Sum of Wgt. 3152
50% -81.16 Mean 133.8924
Largest Std. Dev. 3287.174
75% 0 30921.14
90% 115.06 52460.47 Variance 1.08e+07
95% 870.87 100067.1 Skewness 24.70557
99% 5866.63 114001 Kurtosis 757.9191
Active | active_treat
account | 0 1 | Total
-----------+----------------------+----------
0 | 1,145 0 | 1,145
1 | 338 294 | 632
-----------+----------------------+----------
Total | 1,483 294 | 1,777
In [13]:
%%stata --graph
hist balchange_total if balchange <2500
count if balchange_total==0
tab savedathome
(bin=32, start=-2168.8799, width=145.59624)
201
savedathome | Freq. Percent Cum.
------------+-----------------------------------
0 | 1,426 80.25 80.25
1 | 351 19.75 100.00
------------+-----------------------------------
Total | 1,777 100.00
Out[13]:
In [14]:
%%stata
tab active if call==1, summ(time_acct_tot)
tab active,summ (housedebt)
Active | Summary of time_acct_tot
account | Mean Std. Dev. Freq.
------------+------------------------------------
0 | 19.213974 650.15954 1145
1 | 15.822785 397.77864 632
------------+------------------------------------
Total | 18.007878 573.153 1777
| Summary of total household debt
| (amount owed on dwelling, loans,
Active | agricultural assets and re
account | Mean Std. Dev. Freq.
------------+------------------------------------
0 | 18896.648 75069.07 2125
1 | 12595.804 48363.039 1028
------------+------------------------------------
Total | 16842.329 67588.91 3153
In [32]:
%%stata
tab active seedtakeup if call==1
tab active seedtakeup if call==1,summ(balchange)
Active | SEED take up
account | 0 1 | Total
-----------+----------------------+----------
0 | 1,009 136 | 1,145
1 | 566 66 | 632
-----------+----------------------+----------
Total | 1,575 202 | 1,777
Means, Standard Deviations and Frequencies
of Change in total savings held at bank
Active | SEED take up
account | 0 1 | Total
-----------+----------------------+----------
0 | 94.116509 908.01279 | 190.78891
| 4834.5791 7223.3173 | 5178.9086
| 1009 136 | 1145
-----------+----------------------+----------
1 | 379.61849 1313.6483 | 477.15959
| 2968.6202 3238.7227 | 3008.8488
| 566 66 | 632
-----------+----------------------+----------
Total | 196.71595 1040.5472 | 292.63824
| 4260.3395 6202.5993 | 4529.0155
| 1575 202 | 1777
In [33]:
%%stata
tab group active,summ(balchange)
Means, Standard Deviations and Frequencies
of Change in total savings held at bank
| Active account
group | 0 1 | Total
-----------+----------------------+----------
C | -81.54299 301.87018 | 43.575771
| 1244.8123 3496.8758 | 2248.5158
| 545 264 | 809
-----------+----------------------+----------
M |-18.270314 478.89511 | 150.86844
| 1339.7333 2709.9326 | 1931.619
| 512 264 | 776
-----------+----------------------+----------
T | 225.59401 386.63116 | 276.94514
| 5360.6638 1981.0306 | 4563.1934
| 1068 500 | 1568
-----------+----------------------+----------
Total | 88.065447 388.55799 | 186.03764
| 3909.5162 2631.1481 | 3546.1206
| 2125 1028 | 3153
This isn't quite the Decile plots on page 661 but close
In [34]:
%%stata
xtile decile = balchange if call==1, n(10)
tab decile group if call==1, summ(balchange)
Means, Standard Deviations and Frequencies
of Change in total savings held at bank
10 |
quantiles |
of | group
balchange | C M T | Total
-----------+---------------------------------+----------
1 |-1120.4232 -994.66139 -986.56178 |-1035.6207
| 449.23924 353.19363 346.42548 | 390.14235
| 63 43 73 | 179
-----------+---------------------------------+----------
2 | -336.8518 -340.81136 -369.2864 |-352.69417
| 83.117129 82.913023 90.475814 | 87.534972
| 72 59 111 | 242
-----------+---------------------------------+----------
3 |-240.94806 -232.93 -225.79961 |-231.92044
| 29.195496 31.587574 28.978035 | 30.185034
| 31 32 51 | 114
-----------+---------------------------------+----------
4 |-146.92694 -145.79947 -152.07352 |-148.62831
| 20.957726 21.480777 20.236842 | 20.920627
| 49 57 71 | 177
-----------+---------------------------------+----------
5 |-95.722433 -94.705294 -94.852584 |-94.991977
| 15.786802 16.028026 16.594359 | 16.179985
| 37 51 89 | 177
-----------+---------------------------------+----------
6 |-17.134444 -16.453053 -14.004251 |-15.546676
| 22.666709 21.527046 19.618369 | 21.028635
| 108 95 167 | 370
-----------+---------------------------------+----------
8 | 10.502083 9.0546808 9.6755881 | 9.7399386
| 5.5756922 5.8252881 5.5605988 | 5.6349909
| 48 47 68 | 163
-----------+---------------------------------+----------
9 | 127.853 93.227714 102.9846 | 105.25742
| 86.67977 69.590768 70.4752 | 73.637829
| 30 35 113 | 178
-----------+---------------------------------+----------
10 | 4552.6512 3605.4385 5405.5254 | 4778.1628
| 9386.6839 4902.8496 17029.412 | 13541.666
| 31 47 99 | 177
-----------+---------------------------------+----------
Total | 65.183004 189.07403 476.64944 | 292.63824
| 2690.654 1944.996 6093.2376 | 4529.0155
| 469 466 842 | 1777
In [ ]:
Content source: jhconning/Dev-II
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