notebooks/AKY06.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Replicating Ashraf, Karlan and Yin (2006) \n",
    "\n",
    "> 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.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following is based on the [replication files](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/27854) for this paper (available from Harvard Dataverse).  This is written in a jupyter notebook using the [ipystata](Stata_in_jupyter.ipynb) python library.   The following reproduces only parts of the analysis from the do files:  [SEED.QJEtable.impact.do](AKY06\\SEED.QJEtable.impact.do) and [SEED.QJEtables.takeup.do](\\AKY06\\SEED.QJEtables.takeup.do)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ipystata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observations about the datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the original datasets. They have non-obvious names:\n",
    "\n",
    " - **seedanalysis_011204_1.dta**: -- Six month impacts\n",
    "\n",
    " - **seedanalysis_011204_080404_1.dta**:  -- Twelve month impacts\n",
    "\n",
    " - **seedanalysis_080404_1.dta**: -- not sure yet.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset has 3153 observations but the baseline survey appears to have been administered to only 1777 (if call==1).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "G:\\GC\\Dev-II\\notebooks\n",
      "\n",
      "      group |      Freq.     Percent        Cum.\n",
      "------------+-----------------------------------\n",
      "          C |        809       25.66       25.66\n",
      "          M |        776       24.61       50.27\n",
      "          T |      1,568       49.73      100.00\n",
      "------------+-----------------------------------\n",
      "      Total |      3,153      100.00\n",
      "\n",
      "      group |      Freq.     Percent        Cum.\n",
      "------------+-----------------------------------\n",
      "          C |        469       26.39       26.39\n",
      "          M |        466       26.22       52.62\n",
      "          T |        842       47.38      100.00\n",
      "------------+-----------------------------------\n",
      "      Total |      1,777      100.00\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "cd G:\\GC\\Dev-II\\notebooks\n",
    "use AKY06\\seedanalysis_011204_080404_1_v11.dta\n",
    "\n",
    "tab group\n",
    "tab group if treatment !=."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table 6: Impact "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OLS 6 months\n",
    "Note that the 6 months data is in the file:\n",
    "AKY06\\seedanalysis_011204_1.dta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Linear regression                               Number of obs     =      1,777\n",
      "                                                F(2, 1774)        =       2.90\n",
      "                                                Prob > F          =     0.0553\n",
      "                                                R-squared         =     0.0018\n",
      "                                                Root MSE          =     2293.2\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "             |               Robust\n",
      "   balchange |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "   treatment |   234.6785   101.7479     2.31   0.021     35.12004     434.237\n",
      "   marketing |   184.8506   146.9815     1.26   0.209    -103.4246    473.1257\n",
      "       _cons |   40.62573   61.67617     0.66   0.510    -80.33988    161.5913\n",
      "------------------------------------------------------------------------------\n",
      "\n",
      "Linear regression                               Number of obs     =      1,308\n",
      "                                                F(1, 1306)        =       0.10\n",
      "                                                Prob > F          =     0.7495\n",
      "                                                R-squared         =     0.0001\n",
      "                                                Root MSE          =     2550.2\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "             |               Robust\n",
      "   balchange |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "   treatment |   49.82794    156.027     0.32   0.750    -256.2631    355.9189\n",
      "       _cons |   225.4763   133.4046     1.69   0.091     -36.2344     487.187\n",
      "------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "use AKY06\\seedanalysis_011204_1_v11.dta\n",
    "xi: reg balchange treatment marketing, robust\n",
    "xi: reg balchange treatment if (treatment == 1 | marketing == 1), robust"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OLS 12 months\n",
    "Note that the 12 months data is in the file:\n",
    "AKY06\\seedanalysis_011204_080404_1.dta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Linear regression                               Number of obs     =      1,777\n",
      "                                                F(2, 1774)        =       1.43\n",
      "                                                Prob > F          =     0.2398\n",
      "                                                R-squared         =     0.0016\n",
      "                                                Root MSE          =       4528\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "             |               Robust\n",
      "   balchange |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "   treatment |   411.4664   244.0205     1.69   0.092    -67.13149    890.0644\n",
      "   marketing |    123.891   153.4397     0.81   0.420    -177.0506    424.8327\n",
      "       _cons |     65.183   124.2152     0.52   0.600    -178.4406    308.8066\n",
      "------------------------------------------------------------------------------\n",
      "\n",
      "Linear regression                               Number of obs     =      1,308\n",
      "                                                F(1, 1306)        =       1.58\n",
      "                                                Prob > F          =     0.2085\n",
      "                                                R-squared         =     0.0008\n",
      "                                                Root MSE          =     5025.5\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "             |               Robust\n",
      "   balchange |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "   treatment |   287.5754   228.5226     1.26   0.208    -160.7361    735.8869\n",
      "       _cons |    189.074   90.07236     2.10   0.036     12.37169    365.7764\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "use AKY06\\seedanalysis_011204_080404_1_v11.dta\n",
    "\n",
    "xi: reg balchange treatment marketing, robust\n",
    "\n",
    "xi: reg balchange treatment if (treatment == 1 | marketing == 1), robust"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Linear regression                               Number of obs     =      1,777\n",
      "                                                F(2, 1774)        =       1.43\n",
      "                                                Prob > F          =     0.2398\n",
      "                                                R-squared         =     0.0016\n",
      "                                                Root MSE          =       4528\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "             |               Robust\n",
      "   balchange |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "   treatment |   411.4664   244.0205     1.69   0.092    -67.13149    890.0644\n",
      "   marketing |    123.891   153.4397     0.81   0.420    -177.0506    424.8327\n",
      "       _cons |     65.183   124.2152     0.52   0.600    -178.4406    308.8066\n",
      "------------------------------------------------------------------------------\n",
      ". **economic impact #1: % of hh_inc \n",
      "\n",
      "    Variable |        Obs        Mean    Std. Dev.       Min        Max\n",
      "-------------+---------------------------------------------------------\n",
      "      hh_inc |      1,777    1.506908    1.953011          0    34.2623\n",
      ".02730535\n",
      "\n",
      "    Variable |        Obs        Mean    Std. Dev.       Min        Max\n",
      "-------------+---------------------------------------------------------\n",
      "      totbal |      1,777    50912.48    50489.83          0     223836\n",
      ".80818389\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "\n",
    "xi: reg balchange treatment marketing, robust\n",
    "    \n",
    "**economic impact #1: % of hh_inc \n",
    "summ hh_inc if e(sample)==1\n",
    "disp _b[treatment] / (_result(3)*10000)\n",
    "\n",
    "**economic impact #2: % of prior balance\n",
    "summ totbal if e(sample)==1\n",
    "disp _b[treatment] / (_result(3)/100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Probit regressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iteration 0:   log pseudolikelihood = -1072.4045\n",
      "Iteration 1:   log pseudolikelihood = -1064.6708\n",
      "Iteration 2:   log pseudolikelihood = -1064.6667\n",
      "\n",
      "Probit regression, reporting marginal effects           Number of obs =   1777\n",
      "                                                        Wald chi2(2)  =  15.34\n",
      "                                                        Prob > chi2   = 0.0005\n",
      "Log pseudolikelihood = -1064.6667                       Pseudo R2     = 0.0072\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "         |               Robust\n",
      "frac~_00 |      dF/dx   Std. Err.      z    P>|z|     x-bar  [    95% C.I.   ]\n",
      "---------+--------------------------------------------------------------------\n",
      "treatm~t*|   .1021901   .0266729     3.82   0.000   .473832   .049912  .154468\n",
      "market~g*|   .0482741   .0314653     1.56   0.119    .26224  -.013397  .109945\n",
      "---------+--------------------------------------------------------------------\n",
      "  obs. P |   .2915025\n",
      " pred. P |    .290007  (at x-bar)\n",
      "------------------------------------------------------------------------------\n",
      "(*) dF/dx is for discrete change of dummy variable from 0 to 1\n",
      "    z and P>|z| correspond to the test of the underlying coefficient being 0\n",
      "\n",
      "Iteration 0:   log pseudolikelihood = -812.57798\n",
      "Iteration 1:   log pseudolikelihood =  -810.3887\n",
      "Iteration 2:   log pseudolikelihood =  -810.3884\n",
      "\n",
      "Probit regression, reporting marginal effects           Number of obs =   1308\n",
      "                                                        Wald chi2(1)  =   4.35\n",
      "                                                        Prob > chi2   = 0.0369\n",
      "Log pseudolikelihood =  -810.3884                       Pseudo R2     = 0.0027\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "         |               Robust\n",
      "frac~_00 |      dF/dx   Std. Err.      z    P>|z|     x-bar  [    95% C.I.   ]\n",
      "---------+--------------------------------------------------------------------\n",
      "treatm~t*|   .0557175   .0263389     2.09   0.037   .643731   .004094  .107341\n",
      "---------+--------------------------------------------------------------------\n",
      "  obs. P |   .3126911\n",
      " pred. P |   .3121752  (at x-bar)\n",
      "------------------------------------------------------------------------------\n",
      "(*) dF/dx is for discrete change of dummy variable from 0 to 1\n",
      "    z and P>|z| correspond to the test of the underlying coefficient being 0\n",
      "\n",
      "Iteration 0:   log pseudolikelihood =  -784.0733\n",
      "Iteration 1:   log pseudolikelihood = -772.39775\n",
      "Iteration 2:   log pseudolikelihood = -772.35865\n",
      "Iteration 3:   log pseudolikelihood = -772.35865\n",
      "\n",
      "Probit regression, reporting marginal effects           Number of obs =   1777\n",
      "                                                        Wald chi2(2)  =  22.93\n",
      "                                                        Prob > chi2   = 0.0000\n",
      "Log pseudolikelihood = -772.35865                       Pseudo R2     = 0.0149\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "         |               Robust\n",
      "frac_~20 |      dF/dx   Std. Err.      z    P>|z|     x-bar  [    95% C.I.   ]\n",
      "---------+--------------------------------------------------------------------\n",
      "treatm~t*|   .1006771   .0223461     4.51   0.000   .473832   .056879  .144475\n",
      "market~g*|   .0405021   .0274247     1.53   0.126    .26224  -.013249  .094253\n",
      "---------+--------------------------------------------------------------------\n",
      "  obs. P |   .1609454\n",
      " pred. P |   .1572024  (at x-bar)\n",
      "------------------------------------------------------------------------------\n",
      "(*) dF/dx is for discrete change of dummy variable from 0 to 1\n",
      "    z and P>|z| correspond to the test of the underlying coefficient being 0\n",
      "\n",
      "Iteration 0:   log pseudolikelihood = -617.43102\n",
      "Iteration 1:   log pseudolikelihood =  -613.2009\n",
      "Iteration 2:   log pseudolikelihood = -613.19501\n",
      "\n",
      "Probit regression, reporting marginal effects           Number of obs =   1308\n",
      "                                                        Wald chi2(1)  =   8.31\n",
      "                                                        Prob > chi2   = 0.0039\n",
      "Log pseudolikelihood = -613.19501                       Pseudo R2     = 0.0069\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "         |               Robust\n",
      "frac_~20 |      dF/dx   Std. Err.      z    P>|z|     x-bar  [    95% C.I.   ]\n",
      "---------+--------------------------------------------------------------------\n",
      "treatm~t*|   .0636029   .0212161     2.88   0.004   .643731    .02202  .105186\n",
      "---------+--------------------------------------------------------------------\n",
      "  obs. P |   .1804281\n",
      " pred. P |   .1786767  (at x-bar)\n",
      "------------------------------------------------------------------------------\n",
      "(*) dF/dx is for discrete change of dummy variable from 0 to 1\n",
      "    z and P>|z| correspond to the test of the underlying coefficient being 0\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "xi: dprobit frac_change_00 treatment marketing, robust\n",
    "xi: dprobit frac_change_00 treatment if (treatment == 1 | marketing == 1), robust\n",
    "xi: dprobit frac_change_20 treatment marketing, robust\n",
    "xi: dprobit frac_change_20 treatment  if (treatment == 1 | marketing == 1), robust"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quantile Regressions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6 months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(fitting base model)\n",
      "\n",
      "Bootstrap replications (20)\n",
      "----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 \n",
      "....x.x....x..x.....x....\n",
      "\n",
      "Simultaneous quantile regression                    Number of obs =      1,777\n",
      "  bootstrap(20) SEs                                 .10 Pseudo R2 =     0.0089\n",
      "                                                    .20 Pseudo R2 =     0.0004\n",
      "                                                    .30 Pseudo R2 =     0.0029\n",
      "                                                    .40 Pseudo R2 =     0.0011\n",
      "                                                    .50 Pseudo R2 =     0.0012\n",
      "                                                    .60 Pseudo R2 =     0.0006\n",
      "                                                    .70 Pseudo R2 =     0.0002\n",
      "                                                    .80 Pseudo R2 =     0.0015\n",
      "                                                    .90 Pseudo R2 =     0.0051\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "             |              Bootstrap\n",
      "   balchange |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "q10          |\n",
      "   treatment |     317.49   96.41809     3.29   0.001      128.385     506.595\n",
      "   marketing |     327.15   106.2085     3.08   0.002      118.843     535.457\n",
      "       _cons |    -828.09   89.08566    -9.30   0.000    -1002.814   -653.3661\n",
      "-------------+----------------------------------------------------------------\n",
      "q20          |\n",
      "   treatment |         20   24.16008     0.83   0.408    -27.38521    67.38521\n",
      "   marketing |         20   24.86448     0.80   0.421    -28.76677    68.76677\n",
      "       _cons |       -300   22.99448   -13.05   0.000    -345.0991   -254.9009\n",
      "-------------+----------------------------------------------------------------\n",
      "q30          |\n",
      "   treatment |     107.03   15.97238     6.70   0.000     75.70331    138.3567\n",
      "   marketing |      100.9    31.1462     3.24   0.001     39.81289    161.9871\n",
      "       _cons |    -276.99   12.32701   -22.47   0.000     -301.167    -252.813\n",
      "-------------+----------------------------------------------------------------\n",
      "q40          |\n",
      "   treatment |   42.50999   18.72339     2.27   0.023     5.787762    79.23223\n",
      "   marketing |   29.60999   17.30305     1.71   0.087    -4.326526    63.54651\n",
      "       _cons |    -148.98   16.03131    -9.29   0.000    -180.4222   -117.5378\n",
      "-------------+----------------------------------------------------------------\n",
      "q50          |\n",
      "   treatment |         62   12.94691     4.79   0.000      36.6072     87.3928\n",
      "   marketing |      21.58   19.00313     1.14   0.256    -15.69088    58.85089\n",
      "       _cons |     -98.22   9.810769   -10.01   0.000    -117.4619   -78.97812\n",
      "-------------+----------------------------------------------------------------\n",
      "q60          |\n",
      "   treatment |      37.62    10.6566     3.53   0.000     16.71919    58.52081\n",
      "   marketing |      22.59   23.21491     0.97   0.331    -22.94144    68.12144\n",
      "       _cons |     -37.62    10.6566    -3.53   0.000    -58.52081   -16.71919\n",
      "-------------+----------------------------------------------------------------\n",
      "q70          |\n",
      "   treatment |       6.55   2.694475     2.43   0.015     1.265321    11.83468\n",
      "   marketing |   2.92e-13   1.094166     0.00   1.000     -2.14599     2.14599\n",
      "       _cons |  -2.97e-13   1.16e-13    -2.57   0.010    -5.24e-13   -7.00e-14\n",
      "-------------+----------------------------------------------------------------\n",
      "q80          |\n",
      "   treatment |      65.79   15.76751     4.17   0.000     34.86515    96.71485\n",
      "   marketing |       4.02   6.252162     0.64   0.520    -8.242379    16.28238\n",
      "       _cons |       6.85   1.559019     4.39   0.000     3.792292    9.907708\n",
      "-------------+----------------------------------------------------------------\n",
      "q90          |\n",
      "   treatment |     437.23   148.7802     2.94   0.003      145.427     729.033\n",
      "   marketing |     265.06   176.7912     1.50   0.134    -81.68094    611.8009\n",
      "       _cons |      107.4   52.46879     2.05   0.041     4.492847    210.3072\n",
      "------------------------------------------------------------------------------\n",
      "(fitting base model)\n",
      "\n",
      "Bootstrap replications (20)\n",
      "----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 \n",
      "xxxxxx.xx...xxx..xxxxxxxxx.x.x.xxxxxxxxxx..xxxxxxx.xxxxxxxxxx.xxxxxxxxx.xxx.xxx..xxxx..x.\n",
      "\n",
      "Simultaneous quantile regression                    Number of obs =      1,308\n",
      "  bootstrap(20) SEs                                 .10 Pseudo R2 =     0.0001\n",
      "                                                    .20 Pseudo R2 =    -0.0000\n",
      "                                                    .30 Pseudo R2 =     0.0000\n",
      "                                                    .40 Pseudo R2 =     0.0003\n",
      "                                                    .50 Pseudo R2 =     0.0006\n",
      "                                                    .60 Pseudo R2 =     0.0001\n",
      "                                                    .70 Pseudo R2 =     0.0001\n",
      "                                                    .80 Pseudo R2 =     0.0012\n",
      "                                                    .90 Pseudo R2 =     0.0006\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "             |              Bootstrap\n",
      "   balchange |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "q10          |\n",
      "   treatment |  -9.660004   105.8526    -0.09   0.927    -217.3198    197.9998\n",
      "       _cons |    -500.94   105.6118    -4.74   0.000    -708.1273   -293.7527\n",
      "-------------+----------------------------------------------------------------\n",
      "q20          |\n",
      "   treatment |          0   14.62935     0.00   1.000     -28.6996     28.6996\n",
      "       _cons |       -280   10.26516   -27.28   0.000     -300.138    -259.862\n",
      "-------------+----------------------------------------------------------------\n",
      "q30          |\n",
      "   treatment |    6.12999   32.19983     0.19   0.849    -57.03907    69.29905\n",
      "       _cons |    -176.09   21.26357    -8.28   0.000    -217.8045   -134.3755\n",
      "-------------+----------------------------------------------------------------\n",
      "q40          |\n",
      "   treatment |       12.9   14.01399     0.92   0.357    -14.59238    40.39239\n",
      "       _cons |    -119.37   9.211683   -12.96   0.000    -137.4413   -101.2987\n",
      "-------------+----------------------------------------------------------------\n",
      "q50          |\n",
      "   treatment |      40.42   16.54314     2.44   0.015     7.965957    72.87404\n",
      "       _cons |     -76.64   12.82142    -5.98   0.000    -101.7928   -51.48717\n",
      "-------------+----------------------------------------------------------------\n",
      "q60          |\n",
      "   treatment |      15.03     12.593     1.19   0.233    -9.674723    39.73472\n",
      "       _cons |     -15.03     12.593    -1.19   0.233    -39.73472    9.674723\n",
      "-------------+----------------------------------------------------------------\n",
      "q70          |\n",
      "   treatment |       6.55   3.169129     2.07   0.039     .3328592    12.76714\n",
      "       _cons |   1.04e-13   .3108714     0.00   1.000     -.609862     .609862\n",
      "-------------+----------------------------------------------------------------\n",
      "q80          |\n",
      "   treatment |      61.77   17.17601     3.60   0.000     28.07442    95.46558\n",
      "       _cons |      10.87   4.421678     2.46   0.014      2.19563    19.54437\n",
      "-------------+----------------------------------------------------------------\n",
      "q90          |\n",
      "   treatment |     172.17   192.5683     0.89   0.371    -205.6071    549.9471\n",
      "       _cons |     372.46   164.7767     2.26   0.024     49.20397     695.716\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "use AKY06\\seedanalysis_011204_080404_1_v11.dta\n",
    "sqreg balchange treatment marketing, q(.1 .2 .3 .4 .5 .6 .7 .8 .9)\n",
    "\n",
    "sqreg balchange treatment if (treatment == 1 | marketing == 1), q(.1 .2 .3 .4 .5 .6 .7 .8 .9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 12 months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "file AKY06\\seedanalysis_011204_080404_v11.dta not found\n",
      "r(601);\n",
      "(fitting base model)\n",
      "\n",
      "Bootstrap replications (20)\n",
      "----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 \n",
      "....x..x...x.....xx.....xx.\n",
      "\n",
      "Simultaneous quantile regression                    Number of obs =      1,777\n",
      "  bootstrap(20) SEs                                 .10 Pseudo R2 =     0.0089\n",
      "                                                    .20 Pseudo R2 =     0.0004\n",
      "                                                    .30 Pseudo R2 =     0.0029\n",
      "                                                    .40 Pseudo R2 =     0.0011\n",
      "                                                    .50 Pseudo R2 =     0.0012\n",
      "                                                    .60 Pseudo R2 =     0.0006\n",
      "                                                    .70 Pseudo R2 =     0.0002\n",
      "                                                    .80 Pseudo R2 =     0.0015\n",
      "                                                    .90 Pseudo R2 =     0.0051\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "             |              Bootstrap\n",
      "   balchange |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "q10          |\n",
      "   treatment |     317.49   81.95606     3.87   0.000     156.7494    478.2306\n",
      "   marketing |     327.15   75.29605     4.34   0.000     179.4717    474.8283\n",
      "       _cons |    -828.09   79.19658   -10.46   0.000    -983.4184   -672.7616\n",
      "-------------+----------------------------------------------------------------\n",
      "q20          |\n",
      "   treatment |         20   35.28501     0.57   0.571    -49.20457    89.20457\n",
      "   marketing |         20   38.20277     0.52   0.601    -54.92716    94.92716\n",
      "       _cons |       -300   35.68941    -8.41   0.000    -369.9977   -230.0023\n",
      "-------------+----------------------------------------------------------------\n",
      "q30          |\n",
      "   treatment |     107.03   18.42875     5.81   0.000     70.88564    143.1743\n",
      "   marketing |      100.9   19.00898     5.31   0.000     63.61764    138.1823\n",
      "       _cons |    -276.99   15.21966   -18.20   0.000    -306.8403   -247.1396\n",
      "-------------+----------------------------------------------------------------\n",
      "q40          |\n",
      "   treatment |   42.50999   9.363725     4.54   0.000      24.1449    60.87509\n",
      "   marketing |   29.60999   11.60699     2.55   0.011     6.845176    52.37481\n",
      "       _cons |    -148.98   9.653964   -15.43   0.000    -167.9143   -130.0457\n",
      "-------------+----------------------------------------------------------------\n",
      "q50          |\n",
      "   treatment |         62   15.82094     3.92   0.000     30.97037    93.02963\n",
      "   marketing |      21.58    20.5556     1.05   0.294    -18.73574    61.89574\n",
      "       _cons |     -98.22    15.6571    -6.27   0.000    -128.9283   -67.51171\n",
      "-------------+----------------------------------------------------------------\n",
      "q60          |\n",
      "   treatment |      37.62   13.34395     2.82   0.005     11.44848    63.79152\n",
      "   marketing |      22.59   19.36071     1.17   0.243    -15.38219    60.56219\n",
      "       _cons |     -37.62   13.34395    -2.82   0.005    -63.79152   -11.44848\n",
      "-------------+----------------------------------------------------------------\n",
      "q70          |\n",
      "   treatment |       6.55    3.06789     2.14   0.033     .5329406    12.56706\n",
      "   marketing |   2.92e-13   1.276839     0.00   1.000    -2.504266    2.504266\n",
      "       _cons |  -2.97e-13   1.10e-13    -2.69   0.007    -5.14e-13   -8.06e-14\n",
      "-------------+----------------------------------------------------------------\n",
      "q80          |\n",
      "   treatment |      65.79   20.26571     3.25   0.001     26.04281    105.5372\n",
      "   marketing |       4.02   6.550422     0.61   0.539    -8.827356    16.86736\n",
      "       _cons |       6.85    3.14329     2.18   0.029     .6850583    13.01494\n",
      "-------------+----------------------------------------------------------------\n",
      "q90          |\n",
      "   treatment |     437.23    99.8697     4.38   0.000     241.3553    633.1047\n",
      "   marketing |     265.06   211.3404     1.25   0.210    -149.4423    679.5623\n",
      "       _cons |      107.4   56.54647     1.90   0.058    -3.504705    218.3047\n",
      "------------------------------------------------------------------------------\n",
      "(fitting base model)\n",
      "\n",
      "Bootstrap replications (20)\n",
      "----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 \n",
      "xxxxxxx.xx.xxxxxxxxx.xxxx.xxxxxxxxxxx.xxxxxx..xxxxxxxxxxxx.xxx.xxxxxxxxx.xxxxxxxx..xxxx.xxx.xxxxxxxxxxx.xx.xxx.xxxx.xxx.xxx.\n",
      "\n",
      "Simultaneous quantile regression                    Number of obs =      1,308\n",
      "  bootstrap(20) SEs                                 .10 Pseudo R2 =     0.0001\n",
      "                                                    .20 Pseudo R2 =    -0.0000\n",
      "                                                    .30 Pseudo R2 =     0.0000\n",
      "                                                    .40 Pseudo R2 =     0.0003\n",
      "                                                    .50 Pseudo R2 =     0.0006\n",
      "                                                    .60 Pseudo R2 =     0.0001\n",
      "                                                    .70 Pseudo R2 =     0.0001\n",
      "                                                    .80 Pseudo R2 =     0.0012\n",
      "                                                    .90 Pseudo R2 =     0.0006\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "             |              Bootstrap\n",
      "   balchange |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "q10          |\n",
      "   treatment |  -9.660004   64.97214    -0.15   0.882    -137.1212    117.8012\n",
      "       _cons |    -500.94   54.30732    -9.22   0.000    -607.4791   -394.4009\n",
      "-------------+----------------------------------------------------------------\n",
      "q20          |\n",
      "   treatment |          0   22.41162     0.00   1.000    -43.96671    43.96671\n",
      "       _cons |       -280   4.786988   -58.49   0.000     -289.391    -270.609\n",
      "-------------+----------------------------------------------------------------\n",
      "q30          |\n",
      "   treatment |    6.12999   33.11795     0.19   0.853    -58.84021    71.10019\n",
      "       _cons |    -176.09   21.44269    -8.21   0.000    -218.1559   -134.0241\n",
      "-------------+----------------------------------------------------------------\n",
      "q40          |\n",
      "   treatment |       12.9   12.24727     1.05   0.292    -11.12647    36.92648\n",
      "       _cons |    -119.37   8.031628   -14.86   0.000    -135.1263   -103.6137\n",
      "-------------+----------------------------------------------------------------\n",
      "q50          |\n",
      "   treatment |      40.42   15.55394     2.60   0.009     9.906553    70.93344\n",
      "       _cons |     -76.64   10.85084    -7.06   0.000    -97.92698   -55.35301\n",
      "-------------+----------------------------------------------------------------\n",
      "q60          |\n",
      "   treatment |      15.03   12.87917     1.17   0.243    -10.23613    40.29613\n",
      "       _cons |     -15.03   12.87917    -1.17   0.243    -40.29613    10.23613\n",
      "-------------+----------------------------------------------------------------\n",
      "q70          |\n",
      "   treatment |       6.55   2.234516     2.93   0.003     2.166367    10.93363\n",
      "       _cons |   1.04e-13   .2258429     0.00   1.000    -.4430545    .4430545\n",
      "-------------+----------------------------------------------------------------\n",
      "q80          |\n",
      "   treatment |      61.77   22.97972     2.69   0.007     16.68879    106.8512\n",
      "       _cons |      10.87   8.091951     1.34   0.179    -5.004645    26.74464\n",
      "-------------+----------------------------------------------------------------\n",
      "q90          |\n",
      "   treatment |     172.17   245.2228     0.70   0.483    -308.9036    653.2436\n",
      "       _cons |     372.46   225.9348     1.65   0.099    -70.77494    815.6949\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "use AKY06\\seedanalysis_011204_080404_v11.dta, clear\n",
    "sqreg balchange treatment marketing, q(.1 .2 .3 .4 .5 .6 .7 .8 .9)\n",
    "sqreg balchange treatment if (treatment == 1 | marketing == 1), q(.1 .2 .3 .4 .5 .6 .7 .8 .9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table VIII: ITT effect of subgroups\n",
    "### 12 months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Linear regression                               Number of obs     =      1,777\n",
      "                                                F(4, 1772)        =       1.98\n",
      "                                                Prob > F          =     0.0948\n",
      "                                                R-squared         =     0.0022\n",
      "                                                Root MSE          =     4529.2\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "             |               Robust\n",
      "   balchange |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "   treatment |   680.2893     420.26     1.62   0.106     -143.968    1504.547\n",
      "   marketing |   137.2044   150.0914     0.91   0.361    -157.1704    431.5792\n",
      "      female |   192.9631   135.0963     1.43   0.153    -72.00175    457.9279\n",
      "female_treat |  -443.4216    483.559    -0.92   0.359    -1391.828    504.9845\n",
      "       _cons |  -53.72176   93.64086    -0.57   0.566    -237.3799    129.9364\n",
      "------------------------------------------------------------------------------\n",
      "\n",
      "Linear regression                               Number of obs     =      1,777\n",
      "                                                F(4, 1772)        =       8.72\n",
      "                                                Prob > F          =     0.0000\n",
      "                                                R-squared         =     0.0040\n",
      "                                                Root MSE          =     4524.9\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "             |               Robust\n",
      "   balchange |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "   treatment |   676.3477   327.5403     2.06   0.039     33.94163    1318.754\n",
      "   marketing |   122.4113   152.3797     0.80   0.422    -176.4515    421.2741\n",
      "      active |   637.8621   204.6197     3.12   0.002     236.5407    1039.183\n",
      "active_treat |  -738.1954   393.8328    -1.87   0.061    -1510.621    34.23035\n",
      "       _cons |   -164.665   81.52621    -2.02   0.044    -324.5626   -4.767302\n",
      "------------------------------------------------------------------------------\n",
      "\n",
      "Linear regression                               Number of obs     =      1,777\n",
      "                                                F(4, 1772)        =       1.57\n",
      "                                                Prob > F          =     0.1785\n",
      "                                                R-squared         =     0.0018\n",
      "                                                Root MSE          =       4530\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "             |               Robust\n",
      "   balchange |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "   treatment |   247.7803   362.0502     0.68   0.494    -462.3101    957.8708\n",
      "   marketing |   122.8684   154.1362     0.80   0.425    -179.4395    425.1764\n",
      "        edhi |  -145.0309   166.6162    -0.87   0.384    -471.8158     181.754\n",
      "  edhi_treat |   279.7698   448.2779     0.62   0.533    -599.4393    1158.979\n",
      "       _cons |   148.0578   200.4277     0.74   0.460    -245.0418    541.1574\n",
      "------------------------------------------------------------------------------\n",
      "\n",
      "Linear regression                               Number of obs     =      1,777\n",
      "                                                F(4, 1772)        =       2.05\n",
      "                                                Prob > F          =     0.0849\n",
      "                                                R-squared         =     0.0019\n",
      "                                                Root MSE          =     4529.9\n",
      "\n",
      "---------------------------------------------------------------------------------\n",
      "                |               Robust\n",
      "      balchange |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "----------------+----------------------------------------------------------------\n",
      "      treatment |   464.2607   271.0703     1.71   0.087    -67.39051     995.912\n",
      "      marketing |   131.9819   150.2833     0.88   0.380    -162.7692     426.733\n",
      "      hi_hh_inc |   193.5094    153.943     1.26   0.209    -108.4197    495.4384\n",
      "hi_hh_inc_treat |  -106.6214   444.0923    -0.24   0.810    -977.6211    764.3784\n",
      "          _cons |  -32.60318   84.76645    -0.38   0.701    -198.8559    133.6496\n",
      "---------------------------------------------------------------------------------\n",
      "\n",
      "Linear regression                               Number of obs     =      1,774\n",
      "                                                F(4, 1769)        =       0.89\n",
      "                                                Prob > F          =     0.4683\n",
      "                                                R-squared         =     0.0018\n",
      "                                                Root MSE          =     4533.8\n",
      "\n",
      "--------------------------------------------------------------------------------------\n",
      "                     |               Robust\n",
      "           balchange |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "---------------------+----------------------------------------------------------------\n",
      "           treatment |   344.6337     290.47     1.19   0.236    -225.0667    914.3342\n",
      "           marketing |    126.032   153.4896     0.82   0.412     -175.008     427.072\n",
      "      hyper_mon_new2 |  -28.40704   132.3356    -0.21   0.830    -287.9576    231.1436\n",
      "hyper_mon_new2_treat |   243.8663   470.7961     0.52   0.605    -679.5089    1167.242\n",
      "               _cons |   72.63304   142.1695     0.51   0.609    -206.2049     351.471\n",
      "--------------------------------------------------------------------------------------\n",
      "\n",
      "Linear regression                               Number of obs     =      1,774\n",
      "                                                F(4, 1769)        =       2.09\n",
      "                                                Prob > F          =     0.0800\n",
      "                                                R-squared         =     0.0023\n",
      "                                                Root MSE          =     4532.8\n",
      "\n",
      "--------------------------------------------------------------------------------------\n",
      "                     |               Robust\n",
      "           balchange |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "---------------------+----------------------------------------------------------------\n",
      "           treatment |   516.7935    261.952     1.97   0.049     3.025574    1030.561\n",
      "           marketing |    127.571   151.9509     0.84   0.401    -170.4513    425.5932\n",
      "      silly_mon_new2 |   284.8332   353.4208     0.81   0.420    -408.3331    977.9995\n",
      "silly_mon_new2_treat |  -633.5807   448.5194    -1.41   0.158    -1513.264     246.103\n",
      "               _cons |   15.99006   80.69286     0.20   0.843    -142.2733    174.2534\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "xi: reg balchange treatment marketing female female_treat, robust \n",
    "xi: reg balchange treatment marketing active active_treat, robust\n",
    "xi: reg balchange treatment marketing edhi edhi_treat, robust\n",
    "xi: reg balchange treatment marketing hi_hh_inc hi_hh_inc_treat, robust\n",
    "xi: reg balchange treatment marketing hyper_mon_new2 hyper_mon_new2_treat, robust\n",
    "xi: reg balchange treatment marketing silly_mon_new2 silly_mon_new2_treat, robust"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TAKEUP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(2344 missing values generated)\n",
      "(2344 real changes made)\n",
      "(2839 real changes made)\n",
      "(2193 real changes made)\n",
      "(762 missing values generated)\n",
      "(762 real changes made)\n",
      "pop: characters , removed; replaced as int\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "\n",
    "use AKY06\\seedanalysis_011204_080404_1_v11.dta, clear\n",
    "gen control = 1 if group == \"C\"\n",
    "replace control = 0 if control ==.\n",
    "macro define GB \"GBloan GBloandefault\"\n",
    "macro define fullset \"married edhi numhh unemployed age $GB hh_inc hh_inc2\"\n",
    "macro define fullset_noGB \"married edhi numhh unemployed age hh_inc hh_inc2\"\n",
    "\n",
    "gen dormant_new = 1- active\n",
    "drop dormant\n",
    "rename dormant_new dormant\n",
    "replace totbal = totbal/100\n",
    "replace newtotbal = newtotbal/100\n",
    "gen dist_GB = dbutuan if butuan ==1\n",
    "replace dist_GB = dampayon if ampayon == 1\n",
    "destring pop, ignore(\",\") replace\n",
    "gen brgy_penetration = no_clients /pop\n",
    "bysort brgy: egen sd_totbal = sd(totbal)\n",
    "bysort brgy: egen mean_totbal = mean(totbal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "            |  Summary of Client savings balance\n",
      "     Active |             (hundreds)\n",
      "    account |        Mean   Std. Dev.       Freq.\n",
      "------------+------------------------------------\n",
      "          0 |   483.12239   498.52538        1145\n",
      "          1 |    556.2335    513.2936         632\n",
      "------------+------------------------------------\n",
      "      Total |   509.12477   504.89831        1777\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "tab active if call==1,summ(totbal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table 1: Savings Goals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                    Savings goal |      Freq.     Percent        Cum.\n",
      "---------------------------------+-----------------------------------\n",
      "Agricultural Financing/Investing |          4        2.04        2.04\n",
      "            Capital for Business |         20       10.20       12.24\n",
      "Christmas/Birthday/Celebration/G |         95       48.47       60.71\n",
      "                       Education |         41       20.92       81.63\n",
      "House/Lot construction and purch |         20       10.20       91.84\n",
      "                         Medical |          1        0.51       92.35\n",
      "  Personal Needs/Future Expenses |          3        1.53       93.88\n",
      "Purchase or Maintenance of Machi |          8        4.08       97.96\n",
      "                 Vacation/Travel |          4        2.04      100.00\n",
      "---------------------------------+-----------------------------------\n",
      "                           Total |        196      100.00\n",
      "\n",
      "    Type of |\n",
      "    savings |\n",
      "       goal |      Freq.     Percent        Cum.\n",
      "------------+-----------------------------------\n",
      "     amount |         62       30.69       30.69\n",
      "       date |        140       69.31      100.00\n",
      "------------+-----------------------------------\n",
      "      Total |        202      100.00\n",
      "\n",
      "     Bought |\n",
      "  ganansiya |\n",
      "        box |      Freq.     Percent        Cum.\n",
      "------------+-----------------------------------\n",
      "          0 |         35       17.33       17.33\n",
      "          1 |        167       82.67      100.00\n",
      "------------+-----------------------------------\n",
      "      Total |        202      100.00\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "tab goal_category if seedtakeup==1\n",
    "tab goal_type if seedtakeup==1\n",
    "tab box if seedtakeup==1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table II: Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                 Result of call |      Freq.     Percent        Cum.\n",
      "--------------------------------+-----------------------------------\n",
      "                      completed |      1,777      100.00      100.00\n",
      "--------------------------------+-----------------------------------\n",
      "                          Total |      1,777      100.00\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "tab call"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Panel A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Summary statistics: mean, se(mean)\n",
      "  by categories of: group \n",
      "\n",
      " group |    totbal    active\n",
      "-------+--------------------\n",
      "     C |  530.7378  .3603412\n",
      "       |  23.38712  .0221926\n",
      "-------+--------------------\n",
      "     M |  499.0084  .3626609\n",
      "       |  23.50222  .0222951\n",
      "-------+--------------------\n",
      "     T |   502.685  .3491686\n",
      "       |  17.33115  .0164382\n",
      "-------+--------------------\n",
      " Total |  509.1248  .3556556\n",
      "       |  11.97734  .0113593\n",
      "----------------------------\n",
      "\n",
      "      Source |       SS           df       MS      Number of obs   =     1,777\n",
      "-------------+----------------------------------   F(2, 1774)      =      0.59\n",
      "       Model |  301690.551         2  150845.275   Prob > F        =    0.5536\n",
      "    Residual |   452440317     1,774  255039.638   R-squared       =    0.0007\n",
      "-------------+----------------------------------   Adj R-squared   =   -0.0005\n",
      "       Total |   452742008     1,776  254922.302   Root MSE        =    505.01\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "      totbal |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "   treatment |   3.676564   29.15807     0.13   0.900    -53.51121    60.86434\n",
      "     control |   31.72941   33.03165     0.96   0.337    -33.05563    96.51446\n",
      "       _cons |   499.0084   23.39434    21.33   0.000      453.125    544.8918\n",
      "------------------------------------------------------------------------------\n",
      "\n",
      "      Source |       SS           df       MS      Number of obs   =     1,777\n",
      "-------------+----------------------------------   F(2, 1774)      =      0.15\n",
      "       Model |   .06859733         2  .034298665   Prob > F        =    0.8612\n",
      "    Residual |  407.157064     1,774  .229513565   R-squared       =    0.0002\n",
      "-------------+----------------------------------   Adj R-squared   =   -0.0010\n",
      "       Total |  407.225661     1,776  .229293728   Root MSE        =    .47908\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "      active |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "   treatment |  -.0134923   .0276604    -0.49   0.626    -.0677428    .0407582\n",
      "     control |  -.0023198   .0313351    -0.07   0.941    -.0637773    .0591377\n",
      "       _cons |   .3626609   .0221928    16.34   0.000     .3191342    .4061876\n",
      "------------------------------------------------------------------------------\n",
      "\n",
      "Summary statistics: mean, se(mean)\n",
      "  by categories of: group \n",
      "\n",
      " group |   dist_GB  brgy_p~n  sd_tot~l  mean_t~l       pop\n",
      "-------+--------------------------------------------------\n",
      "     C |  21.86567  .0219365  487.1321  473.3466  5853.949\n",
      "       |  .8419475  .0004179  3.507831   3.74361  213.4831\n",
      "-------+--------------------------------------------------\n",
      "     M |  23.22961  .0216379  491.3003  476.9744  5708.208\n",
      "       |  .8874667  .0004076  3.449334  3.716019  203.4358\n",
      "-------+--------------------------------------------------\n",
      "     T |  22.70784  .0219269  488.0445  476.0261    5729.5\n",
      "       |  .6718419   .000292  2.442733  2.599624    152.98\n",
      "-------+--------------------------------------------------\n",
      " Total |   22.6224  .0218536  488.6575  475.5676  5756.762\n",
      "       |  .4525451  .0002066  1.735807  1.854791  106.1334\n",
      "----------------------------------------------------------\n",
      "\n",
      "      Source |       SS           df       MS      Number of obs   =     1,777\n",
      "-------------+----------------------------------   F(2, 1774)      =      0.61\n",
      "       Model |  446.531678         2  223.265839   Prob > F        =    0.5417\n",
      "    Residual |  645883.097     1,774  364.082918   R-squared       =    0.0007\n",
      "-------------+----------------------------------   Adj R-squared   =   -0.0004\n",
      "       Total |  646329.629     1,776   363.92434   Root MSE        =    19.081\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "     dist_GB |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "   treatment |  -.5217753   1.101679    -0.47   0.636      -2.6825     1.63895\n",
      "     control |  -1.363942   1.248034    -1.09   0.275    -3.811715    1.083831\n",
      "       _cons |   23.22961   .8839083    26.28   0.000       21.496    24.96322\n",
      "------------------------------------------------------------------------------\n",
      "\n",
      "      Source |       SS           df       MS      Number of obs   =     1,777\n",
      "-------------+----------------------------------   F(2, 1774)      =      0.19\n",
      "       Model |   .00002944         2   .00001472   Prob > F        =    0.8238\n",
      "    Residual |  .134701232     1,774  .000075931   R-squared       =    0.0002\n",
      "-------------+----------------------------------   Adj R-squared   =   -0.0009\n",
      "       Total |  .134730672     1,776  .000075862   Root MSE        =    .00871\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "brgy_penet~n |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "   treatment |    .000289   .0005031     0.57   0.566    -.0006977    .0012758\n",
      "     control |   .0002987   .0005699     0.52   0.600    -.0008192    .0014165\n",
      "       _cons |   .0216379   .0004037    53.60   0.000     .0208462    .0224296\n",
      "------------------------------------------------------------------------------\n",
      "\n",
      "      Source |       SS           df       MS      Number of obs   =     1,777\n",
      "-------------+----------------------------------   F(2, 1774)      =      0.44\n",
      "       Model |  4662.35264         2  2331.17632   Prob > F        =    0.6473\n",
      "    Residual |  9504305.63     1,774  5357.55672   R-squared       =    0.0005\n",
      "-------------+----------------------------------   Adj R-squared   =   -0.0006\n",
      "       Total |  9508967.98     1,776  5354.14864   Root MSE        =    73.195\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "   sd_totbal |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "   treatment |  -3.255741   4.226085    -0.77   0.441    -11.54437    5.032889\n",
      "     control |  -4.168199   4.787511    -0.87   0.384    -13.55796    5.221557\n",
      "       _cons |   491.3003   3.390708   144.90   0.000     484.6501    497.9505\n",
      "------------------------------------------------------------------------------\n",
      "\n",
      "      Source |       SS           df       MS      Number of obs   =     1,777\n",
      "-------------+----------------------------------   F(2, 1774)      =      0.28\n",
      "       Model |  3412.62158         2  1706.31079   Prob > F        =    0.7567\n",
      "    Residual |  10853846.7     1,774  6118.29015   R-squared       =    0.0003\n",
      "-------------+----------------------------------   Adj R-squared   =   -0.0008\n",
      "       Total |  10857259.4     1,776  6113.32171   Root MSE        =    78.219\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      " mean_totbal |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "   treatment |  -.9482933   4.516166    -0.21   0.834    -9.805859    7.909273\n",
      "     control |  -3.627729   5.116129    -0.71   0.478      -13.662    6.406545\n",
      "       _cons |   476.9744   3.623448   131.64   0.000     469.8677     484.081\n",
      "------------------------------------------------------------------------------\n",
      "\n",
      "      Source |       SS           df       MS      Number of obs   =     1,777\n",
      "-------------+----------------------------------   F(2, 1774)      =      0.15\n",
      "       Model |  6154208.23         2  3077104.11   Prob > F        =    0.8576\n",
      "    Residual |  3.5543e+10     1,774  20035747.1   R-squared       =    0.0002\n",
      "-------------+----------------------------------   Adj R-squared   =   -0.0010\n",
      "       Total |  3.5550e+10     1,776  20016649.5   Root MSE        =    4476.1\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "         pop |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "   treatment |   21.29185   258.4388     0.08   0.934    -485.5846    528.1683\n",
      "     control |   145.7407   292.7718     0.50   0.619    -428.4732    719.9546\n",
      "       _cons |   5708.208   207.3528    27.53   0.000     5301.527     6114.89\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "tabstat totbal active if call==1, by(group) stats(mean sem)\n",
    "reg totbal treatment control if call ==1\n",
    "reg active treatment control if call ==1\n",
    "sort brgy\n",
    "tabstat dist_GB brgy_penetration sd_totbal mean_totbal pop if call==1, by(group) stats(mean sem)\n",
    "reg dist_GB treatment control if call ==1\n",
    "reg brgy_penetration treatment control if call ==1\n",
    "reg sd_totbal treatment control if call ==1\n",
    "reg mean_totbal treatment control if call ==1\n",
    "reg pop treatment control if call ==1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Panel B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Summary statistics: mean, se(mean)\n",
      "  by categories of: group \n",
      "\n",
      " group |   yearsed    female       age  impa~n01  h~n_new2\n",
      "-------+--------------------------------------------------\n",
      "     C |  18.19403  .6162047  42.05117  .8081023  .2622601\n",
      "       |  .1373271  .0224796  .5943071  .0393999  .0203327\n",
      "-------+--------------------------------------------------\n",
      "     M |  17.91845  .5472103  42.87124  .8903226  .2758621\n",
      "       |   .144761  .0230834   .658222  .0409272  .0207714\n",
      "-------+--------------------------------------------------\n",
      "     T |  18.22209  .5985748  42.10808  .8690476  .2782402\n",
      "       |  .1052178   .016903  .4578191  .0296984   .015462\n",
      "-------+--------------------------------------------------\n",
      " Total |  18.13506   .589758  42.29319  .8585118  .2733935\n",
      "       |   .072415  .0116717   .318447  .0205285   .010585\n",
      "----------------------------------------------------------\n",
      "\n",
      "      Source |       SS       df       MS              Number of obs =    1777\n",
      "-------------+------------------------------           F(  2,  1774) =    1.60\n",
      "       Model |  29.8721265     2  14.9360633           Prob > F      =  0.2014\n",
      "    Residual |  16519.7137  1774  9.31212722           R-squared     =  0.0018\n",
      "-------------+------------------------------           Adj R-squared =  0.0007\n",
      "       Total |  16549.5858  1776  9.31846048           Root MSE      =  3.0516\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "     yearsed |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "   treatment |   .3036353   .1761892     1.72   0.085     -.041925    .6491956\n",
      "     control |   .2755749   .1995956     1.38   0.168    -.1158923    .6670421\n",
      "       _cons |   17.91845   .1413616   126.76   0.000      17.6412    18.19571\n",
      "------------------------------------------------------------------------------\n",
      "\n",
      "      Source |       SS       df       MS              Number of obs =    1777\n",
      "-------------+------------------------------           F(  2,  1774) =    2.56\n",
      "       Model |  1.23708842     2  .618544211           Prob > F      =  0.0776\n",
      "    Residual |  428.696508  1774  .241655303           R-squared     =  0.0029\n",
      "-------------+------------------------------           Adj R-squared =  0.0018\n",
      "       Total |  429.933596  1776  .242079727           Root MSE      =  .49158\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "      female |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "   treatment |   .0513645   .0283827     1.81   0.071    -.0043024    .1070315\n",
      "     control |   .0689944   .0321532     2.15   0.032     .0059322    .1320566\n",
      "       _cons |   .5472103   .0227722    24.03   0.000     .5025471    .5918735\n",
      "------------------------------------------------------------------------------\n",
      "\n",
      "      Source |       SS       df       MS              Number of obs =    1777\n",
      "-------------+------------------------------           F(  2,  1774) =    0.59\n",
      "       Model |  212.035992     2  106.017996           Prob > F      =  0.5555\n",
      "    Residual |  319828.212  1774  180.286478           R-squared     =  0.0007\n",
      "-------------+------------------------------           Adj R-squared = -0.0005\n",
      "       Total |  320040.248  1776  180.202842           Root MSE      =  13.427\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "         age |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "   treatment |  -.7631686   .7752405    -0.98   0.325    -2.283649    .7573122\n",
      "     control |  -.8200719   .8782295    -0.93   0.351    -2.542545    .9024014\n",
      "       _cons |   42.87124   .6219975    68.93   0.000     41.65132    44.09117\n",
      "------------------------------------------------------------------------------\n",
      "\n",
      "      Source |       SS       df       MS              Number of obs =    1774\n",
      "-------------+------------------------------           F(  2,  1771) =    1.17\n",
      "       Model |  1.75557046     2  .877785228           Prob > F      =  0.3093\n",
      "    Residual |   1323.7309  1771  .747448278           R-squared     =  0.0013\n",
      "-------------+------------------------------           Adj R-squared =  0.0002\n",
      "       Total |  1325.48647  1773  .747595302           Root MSE      =  .86455\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "impatien~n01 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "   treatment |   -.021275   .0499723    -0.43   0.670    -.1192859     .076736\n",
      "     control |  -.0822202   .0565785    -1.45   0.146    -.1931878    .0287473\n",
      "       _cons |   .8903226   .0400926    22.21   0.000     .8116888    .9689564\n",
      "------------------------------------------------------------------------------\n",
      "\n",
      "      Source |       SS       df       MS              Number of obs =    1774\n",
      "-------------+------------------------------           F(  2,  1771) =    0.20\n",
      "       Model |  .080716446     2  .040358223           Prob > F      =  0.8164\n",
      "    Residual |  352.323455  1771  .198940404           R-squared     =  0.0002\n",
      "-------------+------------------------------           Adj R-squared = -0.0009\n",
      "       Total |  352.404171  1773  .198761518           Root MSE      =  .44603\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "hyper_mon~w2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]\n",
      "-------------+----------------------------------------------------------------\n",
      "   treatment |   .0023781   .0257935     0.09   0.927    -.0482107     .052967\n",
      "     control |  -.0136019    .029205    -0.47   0.641    -.0708818    .0436779\n",
      "       _cons |   .2758621   .0207063    13.32   0.000     .2352507    .3164734\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "tabstat yearsed female age impatient_mon01 hyper_mon_new2 if call==1, by(group) stats(mean sem) \n",
    "reg yearsed treatment control if call ==1\n",
    "reg female treatment control if call ==1\n",
    "reg age treatment control if call ==1\n",
    "reg impatient_mon01 treatment control if call ==1\n",
    "reg hyper_mon_new2 treatment control if call ==1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table III: Time preference questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0 = patient, 1 = somewhat impatient, 2=most impatient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Impatient |\n",
      " Now w/r/t |   Impatient Later w/r/t money\n",
      "     money |         0          1          2 |     Total\n",
      "-----------+---------------------------------+----------\n",
      "         0 |       606        126         73 |       805 \n",
      "         1 |       206        146         59 |       411 \n",
      "         2 |       154         93        299 |       546 \n",
      "-----------+---------------------------------+----------\n",
      "     Total |       966        365        431 |     1,762 \n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "tab impatient_mon01 impatient_mon67 if call==1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table V: Determinants of Takeup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(1379 missing values generated)\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "gen female_hyper_mon_new2 = female * hyper_mon_new2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "> _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\n",
      "> ust\n",
      "\n",
      "Iteration 0:   log pseudolikelihood = -425.65043\n",
      "Iteration 1:   log pseudolikelihood = -408.47662\n",
      "Iteration 2:   log pseudolikelihood = -407.69152\n",
      "Iteration 3:   log pseudolikelihood =   -407.141\n",
      "Iteration 4:   log pseudolikelihood = -407.06088\n",
      "Iteration 5:   log pseudolikelihood = -407.05886\n",
      "Iteration 6:   log pseudolikelihood = -407.05886\n",
      "\n",
      "Probit regression, reporting marginal effects           Number of obs =    715\n",
      "                                                        Wald chi2(25) =  35.68\n",
      "                                                        Prob > chi2   = 0.0767\n",
      "Log pseudolikelihood = -407.05886                       Pseudo R2     = 0.0437\n",
      "\n",
      "------------------------------------------------------------------------------\n",
      "         |               Robust\n",
      "seedta~p |      dF/dx   Std. Err.      z    P>|z|     x-bar  [    95% C.I.   ]\n",
      "---------+--------------------------------------------------------------------\n",
      "h~n_new2*|   .1248659   .0667069     1.94   0.053   .278322  -.005877  .255609\n",
      "  female*|    .098883   .1372349     0.70   0.482        .6  -.170093  .367858\n",
      "im~50_01*|  -.0300081   .0496469    -0.59   0.552   .241958  -.127314  .067298\n",
      "i~200_01*|   .0764479   .0718961     1.07   0.285   .444755  -.064466  .217362\n",
      "im~50_67*|   .0968518   .0646814     1.56   0.119   .206993  -.029922  .223625\n",
      "i~200_67*|   .0150482    .063883     0.24   0.814   .545455   -.11016  .140257\n",
      "fem_ma~d*|  -.1132497   .0905445    -1.22   0.222   .423776  -.290714  .064214\n",
      " married*|   .0489946   .0770504     0.62   0.536   .765035  -.102021  .200011\n",
      "    edhi*|   .0834128   .0383418     2.14   0.032    .59021   .008264  .158561\n",
      "   numhh |   .0001891   .0076993     0.02   0.980   5.58462  -.014901   .01528\n",
      "unempl~d*|   .0404135   .1091283     0.38   0.703   .033566  -.173474  .254301\n",
      "     age |  -.0019155   .0013788    -1.39   0.165   42.6531  -.004618  .000787\n",
      "  GBloan*|  -.0141822   .0363595    -0.39   0.697   .502098  -.085445  .057081\n",
      "GBloan~t*|  -.0317615    .072292    -0.43   0.670   .055944  -.173451  .109928\n",
      "  hh_inc |   .0486918   .0307147     1.56   0.118   1.55682  -.011508  .108892\n",
      " hh_inc2 |   -.007847    .004361    -1.76   0.078   5.82637  -.016394    .0007\n",
      "fem_f~25*|   .0153704   .1822196     0.09   0.932   .051748  -.341773  .372514\n",
      "fem~5_50*|   .0480785     .16932     0.29   0.770    .14965  -.283783   .37994\n",
      "fem_f~75*|   .1349672   .1818598     0.79   0.431   .114685  -.221472  .491406\n",
      "fe~5_100*|   .0182208   .1546866     0.12   0.905   .174825  -.284959  .321401\n",
      "frac_~25*|  -.0106804   .1540169    -0.07   0.945   .095105  -.312548  .291187\n",
      "fra~5_50*|  -.0472313   .1407564    -0.33   0.745   .218182  -.323109  .228646\n",
      "frac_~75*|  -.0340687   .1388783    -0.24   0.810   .202797  -.306265  .238128\n",
      "fr~5_100*|   .0247978    .142089     0.18   0.861   .355245  -.253692  .303287\n",
      "  active*|  -.0360562   .0341531    -1.04   0.296   .353846  -.102995  .030883\n",
      "---------+--------------------------------------------------------------------\n",
      "  obs. P |   .2825175\n",
      " pred. P |   .2647361  (at x-bar)\n",
      "------------------------------------------------------------------------------\n",
      "(*) dF/dx is for discrete change of dummy variable from 0 to 1\n",
      "    z and P>|z| correspond to the test of the underlying coefficient being 0\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "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 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More Exploratory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  SEED take |\n",
      "         up |      Freq.     Percent        Cum.\n",
      "------------+-----------------------------------\n",
      "          0 |      1,575       88.63       88.63\n",
      "          1 |        202       11.37      100.00\n",
      "------------+-----------------------------------\n",
      "      Total |      1,777      100.00\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "tab seedtakeup if call==1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "%%stata\n",
    "tab impatient_mon01\n",
    "tab active impatient_mon01,row col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " zerototbal |      Freq.     Percent        Cum.\n",
      "------------+-----------------------------------\n",
      "          0 |      1,623       91.33       91.33\n",
      "          1 |        154        8.67      100.00\n",
      "------------+-----------------------------------\n",
      "      Total |      1,777      100.00\n",
      "\n",
      "    Variable |       Obs        Mean    Std. Dev.       Min        Max\n",
      "-------------+--------------------------------------------------------\n",
      " howlongopen |       212    340.9764    48.90105         84        404\n",
      "(bin=14, start=84, width=22.857143)\n"
     ]
    },
    {
     "data": {
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6TEU02nAnFo023EfBKBgFo2AUjIIRCK5fmnd9tOE+CsgFrz59piIabbgTi0Yb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\nl+ZB0KlDdUMCXb807/pow32ooNGG+ygYBaNgFIyCQQuuX5p3fYg0gK5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Th+qGBLp+ad71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\ng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XURSsP9ZIcZAxKACGJHsLY1DJg1n//86hMcXWyGmJS47tWnz0gIJs6QuPzT51cIBBOH\nqIcZjqXhDpOCATR7IYhKnkpc9wqhHc3Bn199wlCP3TFQr0HshZkGBVg8CEdEGU6uT0fRKBpFo4j2\naLThPopG0SgaRdRF8JZfJ4SBDsw6T376/AoVLU9kwA4S17369BmCYI1ItPbuOpgtaM1QqDi0rQlr\ns6K1a4mx99Wnz0imUe6pxGbsHsGlngHDMfCG+zoIAx2Q4BhMxeT4dBSNolE0iuiDRhvuo2gUjaJR\nRF20DrnBB203f/oMb24yoAh+foXcpkRqoWIRhDa+URvoa+DqUBvlUHGYYqheFDVYrMAhiOYpJENw\negrWzUAxB2E4AwMDasMdIYVVPZIgsqUMDAzILWkc6hHiKI5cAxNFUUyyT0fRKBpFo4huaLThPopG\n0SgaRdRFiJYfUrMPgmBSSG1NeJMaszkIa/vCGt8w7cgqYWoYGBhQGqBQcbhFMFsQToKJIJsGQVC9\nCHs/w61mQDYBimAtWrhdnxCGIzsJgmCGMzAgN9xh6olzDMxGBgbc5iOphxmO4XKsUiT6dBSNolE0\niuiIRhvuo2gUjaJRRF0Ea/lha95Bh36RpKAiyK1YBIIaBW/OQhUjtVYhInGJiQwMyMZCm5hwjZgt\nVIhGBuLshYsgWYFAmEbBWs8IESQEMwpJFtMEJARVj+QYqO+IVA81HJvLX8FlEUEK1Y5VPVQxdntH\n0SgaRaOI5mi04T6KRtEoGkXURbCWH6ItiEAYLVpYGxSb4lfwliK8EQld3QHXDrErcTm0XY4mjmim\nYzTcSbQXZiBW9RiegmnHpvgVXBahnlTHkKQeqhjejkdDGI4nzaejaBSNolFETzTacB9Fo2gUjSLq\nImjLD2tLEaPlh0/xK0z10PY3bB0IhGvWeRLWPIW2y9Hb9zCVcAWk2otXPYZiqGOwtn1fYVGPz/BX\nWNRDzSdOPdRwQgCi+DNcPVbDUU0eRaNoFI0ieqPRhvsoGkWjaBRRF5HU8sOn+BUW9ShtVogshA0Z\nZoawIeIo7Wa6NtzxKX5FBfUogYCJUNVDDScEIIo/w9VjNRzV5FE0ikbRKKI3Gm24j6JRNIpGEXUR\nSS0/fIpfwZrjDGadJz99fvXp8yu4CYnrXsFkoW1xyCh74jrs7VoSG+4Qk5Hsxace6iSEp6AOYEhc\n9+rTZ0yEoR6f4a+wOAZqPi71qOYTMBwD4VOPavIoGkWjaBTRG4023EfRKBpFo4i6iKSWH7QNiquN\ni9FmhTXQzTpPQi2CGQVtmicuh4rDltNAEFQW3nAn1V6ImVT1FKnqEY4hST3U5bgUYyCoeuJ8OopG\n0SgaRXRFow33UTSKRtEooi4ireUHbWUiiSAhqFGojU6IoFnzmk4TBgZY8/QzQrwjkYGBAUn886tP\nmA13Uu2FihDpKUwRJAQ1ClmWRMfAGu541cOditdwTISuHRnh9dcoGkWjaBTRHI023EfRKBpFo4i6\niMSWH6xJjake1uJEHTuHiZuYmTEwMCA1Z1HE0U2D2QJvuMNF0FXCzEG1l2qegilmIFI9NsfAG+7E\nqV8DFUIOKxiCGYXo55Do01E0ikbRKKIjGm24j6JRNIpGEXURyS0/WFuTAblliVUQgmCGMDAgN8Rx\ni7/6hGgZI4tjtQKrIBmeggkyIJuDMJyBgQFVPUIKq3okQURrm4GBAdVJcPXIgq+QxJEa6J/hnmJA\nCRaoIJoJEATzFIrLR9EoGkWjiG5otOE+ikbRKBpF1EXktPwQLUs0gNJghSFYK5wBeVz5E9LQMob5\ncC1ILdTPr0iwl4qeSmwmTT0DhmNgDXczM4in0AA2R8K0YAOoYUKOT0fRKBpFo4g+aLThPopG0Sga\nRdRF5Lb8YG1rGMCmBopgzVCU8ePPcKsxWroIw1EbqZ9ffUJIwQBWe6EmU+qpxHWv4OrRHf/51ScM\n9VjNhHs/cd0r9OY+VvUwhG44A8QEVESuT0fRKBpFo4j2aLThPopG0SgaRaOIrgja1MbacCcKoTTc\nR9EoGkWjaOSg0Yb7KBpFo2gUjSIqI2jTHPvINOXNbspNGEWjaBSNoiGJRhvuo2gUjaJRNIqojGBL\nSrCtzIEtxMciRSwabbiPolE0ikYoGm24j6JRNIpG0SiiNkKsJsexfZb8dTKfRxvuo2gUjaIRi0Yb\n7qNoFI2iUTSKaIDgbXQsAOsSGuLRaMN9FI2iUTRC0WjDfRSNolE0ikYRrdByjNY7BStk4Gi04T6K\nRtEoGqFotOE+ikbRKBpFo2gUjaJRNIpG0RBAow33UTSKRtEoGkWjaBSNolE0ioYAGm24j6JRNIpG\n0SgaRaNoFI2iUTQE0GjDfRSNolE0ikbRKBpFo2gUjaIhgEYb7qNoFI2iUTSKRtEoGkWjaBQNATTa\ncB9Fo2gUjaJRNIpG0SgaRaNoCKDRhvsoGkWjaBSNolE0ikbRKBpFQwCNNtxH0SgaRaNoFI2iUTSK\nRtEoGgJotOE+ikbRKBpFo2gUjaJRNIpG0RBAow33UTSKRtEoGkWjaBSNolE0ioYAGm24j6JRNIpG\n0SgaRaNoFI2iUTQE0GjDfRSNolE0ikbRKBpFo2gUjaIhgEYb7qNoFI2iUTSKRtEoGkWjaBQNATTa\ncB9Fo2gUjaJRNIpG0SgaRaNoCKDRhvsoGkWjaBSNolE0ikbRKBpFQwCNNtxH0SgaRaNoFI2iUTSK\nRtEoGgJotOE+ikbRKBpFo2gUjaJRNIpG0RBAow33UTSKRtEoGkWjaBSNolE0ioYAGm24j6JRNIpG\n0SgaRaNoFI2iUTQE0GjDfRSNolE0ikbRKBpFo2gUjaIhgEYb7qNoFI2iUTSKRtEoGkWjaBQNATTa\ncB9Fo2gUjaJRNIpG0SgaRaNoCKDRhvsoGkWjaBSNolE0ikbRKBpFQwCNNtxH0SgaRaNoFI2iUTSK\nRtEoGgJotOE+ikbRKBpFo2gUjaJRNIpG0RBAow33UTSKRtEoGkWjaBSNolE0ioYAGm24j6JRNIpG\n0SgaRaNoFI2iUTQE0GjDfRSNolE0ikbRKBpFo2gUjaIhgEYb7qNoFI2iUTSKRtEoGkWjaBQNATTa\ncB9Fo2gUjaJRNIpG0SgaRaNoCKDRhvsoGkWjaBSNolE0ikbRKBpFQwCNNtxH0SgaRaNoFI2iUTSK\nRtEoGgJotOE+ikbRKBpFo2gUjaJRNIpG0RBAow33UTSKRtEoGkWjaBSNolE0ioYAAgAKFho7IIxm\nWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 500
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%stata --graph\n",
    "tab zerototbal if call==1\n",
    "summ howlongopen\n",
    "hist howlongopen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     Active |     Summary of nonseedbalchange\n",
      "    account |        Mean   Std. Dev.       Freq.\n",
      "------------+------------------------------------\n",
      "          0 |   105.73035   4714.6168        1145\n",
      "          1 |   370.66207   2928.8638         632\n",
      "------------+------------------------------------\n",
      "      Total |    199.9548   4169.1327        1777\n",
      "\n",
      "                      nonseedbalchange\n",
      "-------------------------------------------------------------\n",
      "      Percentiles      Smallest\n",
      " 1%     -1506.14       -4541.77\n",
      " 5%      -947.57       -2168.88\n",
      "10%      -500.94        -2138.5       Obs                3152\n",
      "25%     -255.515       -2114.25       Sum of Wgt.        3152\n",
      "\n",
      "50%       -81.16                      Mean           133.8924\n",
      "                        Largest       Std. Dev.      3287.174\n",
      "75%            0       30921.14\n",
      "90%       115.06       52460.47       Variance       1.08e+07\n",
      "95%       870.87       100067.1       Skewness       24.70557\n",
      "99%      5866.63         114001       Kurtosis       757.9191\n",
      "\n",
      "    Active |     active_treat\n",
      "   account |         0          1 |     Total\n",
      "-----------+----------------------+----------\n",
      "         0 |     1,145          0 |     1,145 \n",
      "         1 |       338        294 |       632 \n",
      "-----------+----------------------+----------\n",
      "     Total |     1,483        294 |     1,777 \n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "tab active if call==1,summ( nonseedbalchange)\n",
    "summ nonseedbalchange,detail\n",
    "tab active active_treat if call==1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(bin=32, start=-2168.8799, width=145.59624)\n",
      "  201\n",
      "\n",
      "savedathome |      Freq.     Percent        Cum.\n",
      "------------+-----------------------------------\n",
      "          0 |      1,426       80.25       80.25\n",
      "          1 |        351       19.75      100.00\n",
      "------------+-----------------------------------\n",
      "      Total |      1,777      100.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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04U4sGm24j4JRMAoGFbh+ad710Yb7KBgFo2AUDG7w6tNnKqLRhjuxaLThPgpG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\nFIyCUTAK/v//P9pwHyg02nAfBaNgSIDrl+Zdp2oD9PqoaSSi65fmXR9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Th+ooR9cvzaM6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\nHsngkqYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 500
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%stata --graph\n",
    "hist balchange_total if balchange <2500\n",
    "count if balchange_total==0\n",
    "tab savedathome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     Active |      Summary of time_acct_tot\n",
      "    account |        Mean   Std. Dev.       Freq.\n",
      "------------+------------------------------------\n",
      "          0 |   19.213974   650.15954        1145\n",
      "          1 |   15.822785   397.77864         632\n",
      "------------+------------------------------------\n",
      "      Total |   18.007878     573.153        1777\n",
      "\n",
      "            |   Summary of total household debt\n",
      "            |  (amount owed on dwelling, loans,\n",
      "     Active |     agricultural assets and re\n",
      "    account |        Mean   Std. Dev.       Freq.\n",
      "------------+------------------------------------\n",
      "          0 |   18896.648    75069.07        2125\n",
      "          1 |   12595.804   48363.039        1028\n",
      "------------+------------------------------------\n",
      "      Total |   16842.329    67588.91        3153\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "tab active if call==1, summ(time_acct_tot)\n",
    "tab active,summ (housedebt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    Active |     SEED take up\n",
      "   account |         0          1 |     Total\n",
      "-----------+----------------------+----------\n",
      "         0 |     1,009        136 |     1,145 \n",
      "         1 |       566         66 |       632 \n",
      "-----------+----------------------+----------\n",
      "     Total |     1,575        202 |     1,777 \n",
      "\n",
      "\n",
      "                Means, Standard Deviations and Frequencies\n",
      "                  of Change in total savings held at bank\n",
      "\n",
      "    Active |    SEED take up\n",
      "   account |         0          1 |     Total\n",
      "-----------+----------------------+----------\n",
      "         0 | 94.116509  908.01279 | 190.78891\n",
      "           | 4834.5791  7223.3173 | 5178.9086\n",
      "           |      1009        136 |      1145\n",
      "-----------+----------------------+----------\n",
      "         1 | 379.61849  1313.6483 | 477.15959\n",
      "           | 2968.6202  3238.7227 | 3008.8488\n",
      "           |       566         66 |       632\n",
      "-----------+----------------------+----------\n",
      "     Total | 196.71595  1040.5472 | 292.63824\n",
      "           | 4260.3395  6202.5993 | 4529.0155\n",
      "           |      1575        202 |      1777\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "tab active seedtakeup if call==1\n",
    "tab active seedtakeup if call==1,summ(balchange)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                Means, Standard Deviations and Frequencies\n",
      "                  of Change in total savings held at bank\n",
      "\n",
      "           |   Active account\n",
      "     group |         0          1 |     Total\n",
      "-----------+----------------------+----------\n",
      "         C | -81.54299  301.87018 | 43.575771\n",
      "           | 1244.8123  3496.8758 | 2248.5158\n",
      "           |       545        264 |       809\n",
      "-----------+----------------------+----------\n",
      "         M |-18.270314  478.89511 | 150.86844\n",
      "           | 1339.7333  2709.9326 |  1931.619\n",
      "           |       512        264 |       776\n",
      "-----------+----------------------+----------\n",
      "         T | 225.59401  386.63116 | 276.94514\n",
      "           | 5360.6638  1981.0306 | 4563.1934\n",
      "           |      1068        500 |      1568\n",
      "-----------+----------------------+----------\n",
      "     Total | 88.065447  388.55799 | 186.03764\n",
      "           | 3909.5162  2631.1481 | 3546.1206\n",
      "           |      2125       1028 |      3153\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "tab group active,summ(balchange)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decile plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This isn't quite the Decile plots on page 661 but close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
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     "text": [
      "\n",
      "                Means, Standard Deviations and Frequencies\n",
      "                  of Change in total savings held at bank\n",
      "\n",
      "        10 |\n",
      " quantiles |\n",
      "        of |             group\n",
      "balchange  |         C          M          T |     Total\n",
      "-----------+---------------------------------+----------\n",
      "         1 |-1120.4232 -994.66139 -986.56178 |-1035.6207\n",
      "           | 449.23924  353.19363  346.42548 | 390.14235\n",
      "           |        63         43         73 |       179\n",
      "-----------+---------------------------------+----------\n",
      "         2 | -336.8518 -340.81136  -369.2864 |-352.69417\n",
      "           | 83.117129  82.913023  90.475814 | 87.534972\n",
      "           |        72         59        111 |       242\n",
      "-----------+---------------------------------+----------\n",
      "         3 |-240.94806    -232.93 -225.79961 |-231.92044\n",
      "           | 29.195496  31.587574  28.978035 | 30.185034\n",
      "           |        31         32         51 |       114\n",
      "-----------+---------------------------------+----------\n",
      "         4 |-146.92694 -145.79947 -152.07352 |-148.62831\n",
      "           | 20.957726  21.480777  20.236842 | 20.920627\n",
      "           |        49         57         71 |       177\n",
      "-----------+---------------------------------+----------\n",
      "         5 |-95.722433 -94.705294 -94.852584 |-94.991977\n",
      "           | 15.786802  16.028026  16.594359 | 16.179985\n",
      "           |        37         51         89 |       177\n",
      "-----------+---------------------------------+----------\n",
      "         6 |-17.134444 -16.453053 -14.004251 |-15.546676\n",
      "           | 22.666709  21.527046  19.618369 | 21.028635\n",
      "           |       108         95        167 |       370\n",
      "-----------+---------------------------------+----------\n",
      "         8 | 10.502083  9.0546808  9.6755881 | 9.7399386\n",
      "           | 5.5756922  5.8252881  5.5605988 | 5.6349909\n",
      "           |        48         47         68 |       163\n",
      "-----------+---------------------------------+----------\n",
      "         9 |   127.853  93.227714   102.9846 | 105.25742\n",
      "           |  86.67977  69.590768    70.4752 | 73.637829\n",
      "           |        30         35        113 |       178\n",
      "-----------+---------------------------------+----------\n",
      "        10 | 4552.6512  3605.4385  5405.5254 | 4778.1628\n",
      "           | 9386.6839  4902.8496  17029.412 | 13541.666\n",
      "           |        31         47         99 |       177\n",
      "-----------+---------------------------------+----------\n",
      "     Total | 65.183004  189.07403  476.64944 | 292.63824\n",
      "           |  2690.654   1944.996  6093.2376 | 4529.0155\n",
      "           |       469        466        842 |      1777\n"
     ]
    }
   ],
   "source": [
    "%%stata\n",
    "xtile decile = balchange if call==1, n(10)\n",
    "tab decile group if call==1, summ(balchange)"
   ]
  },
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   "source": []
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