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ReplicationCodePython.ipynb
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ReplicationCodePython.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Difference in difference analysis to estimate impact of CICIG on Guatemala's homicide rates\n",
"\n",
"This is a replication of the quantitative analysis made by CrisisGroup in the article [\"Saving Guatemala’s Fight Against Crime and Impunity\"](https://www.crisisgroup.org/latin-america-caribbean/central-america/guatemala/70-saving-guatemalas-fight-against-crime-and-impunity). The analysis uses synthetics controls estimated with an [entropy balancing method](https://web.stanford.edu/~jhain/Paper/PA2012.pdf) to estimate a counterfactual from nearby latinamerican countries. \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib as mlp\n",
"from matplotlib import pyplot as plt\n",
"import numpy as np \n",
"import seaborn as sbs\n",
"from statsmodels import api as stm\n",
"sbs.set(style=\"whitegrid\")\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def tryFunc(functionToTry, onError = np.NaN, showErrors = False):\n",
" def wrapper(inputValue):\n",
" try:\n",
" return functionToTry(inputValue)\n",
" except:\n",
" if showErrors:\n",
" print(\"Bad value: \", inputValue)\n",
" return onError\n",
" return wrapper\n",
"\n",
"toFloat = lambda x: float(x)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"gt1 = pd.read_excel(\"./guatemala1.xlsx\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Series Name</th>\n",
" <th>Series Code</th>\n",
" <th>Country Name</th>\n",
" <th>Country Code</th>\n",
" <th>YR1990</th>\n",
" <th>YR2000</th>\n",
" <th>YR2001</th>\n",
" <th>YR2002</th>\n",
" <th>YR2003</th>\n",
" <th>YR2004</th>\n",
" <th>...</th>\n",
" <th>YR2008</th>\n",
" <th>YR2009</th>\n",
" <th>YR2010</th>\n",
" <th>YR2011</th>\n",
" <th>YR2012</th>\n",
" <th>YR2013</th>\n",
" <th>YR2014</th>\n",
" <th>YR2015</th>\n",
" <th>YR2016</th>\n",
" <th>YR2017</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>homicide</td>\n",
" <td>VC.IHR.PSRC.P5</td>\n",
" <td>Costa Rica</td>\n",
" <td>CRI</td>\n",
" <td>..</td>\n",
" <td>6.3</td>\n",
" <td>6.4</td>\n",
" <td>6.3</td>\n",
" <td>7.3</td>\n",
" <td>6.7</td>\n",
" <td>...</td>\n",
" <td>11.6</td>\n",
" <td>11.7</td>\n",
" <td>11.6</td>\n",
" <td>10.3</td>\n",
" <td>8.7</td>\n",
" <td>8.7</td>\n",
" <td>10</td>\n",
" <td>11.8</td>\n",
" <td>..</td>\n",
" <td>..</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>homicide</td>\n",
" <td>VC.IHR.PSRC.P5</td>\n",
" <td>Cuba</td>\n",
" <td>CUB</td>\n",
" <td>..</td>\n",
" <td>..</td>\n",
" <td>5.4</td>\n",
" <td>5.9</td>\n",
" <td>5.7</td>\n",
" <td>5.9</td>\n",
" <td>...</td>\n",
" <td>4.6</td>\n",
" <td>5</td>\n",
" <td>4.5</td>\n",
" <td>4.7</td>\n",
" <td>..</td>\n",
" <td>..</td>\n",
" <td>..</td>\n",
" <td>..</td>\n",
" <td>..</td>\n",
" <td>..</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>homicide</td>\n",
" <td>VC.IHR.PSRC.P5</td>\n",
" <td>El Salvador</td>\n",
" <td>SLV</td>\n",
" <td>..</td>\n",
" <td>40.3</td>\n",
" <td>37.8</td>\n",
" <td>37.9</td>\n",
" <td>37.2</td>\n",
" <td>46.8</td>\n",
" <td>...</td>\n",
" <td>52.9</td>\n",
" <td>72.8</td>\n",
" <td>66</td>\n",
" <td>72.2</td>\n",
" <td>42.7</td>\n",
" <td>41.3</td>\n",
" <td>64.2</td>\n",
" <td>108.6</td>\n",
" <td>..</td>\n",
" <td>..</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 23 columns</p>\n",
"</div>"
],
"text/plain": [
" Series Name Series Code Country Name Country Code YR1990 YR2000 YR2001 \\\n",
"0 homicide VC.IHR.PSRC.P5 Costa Rica CRI .. 6.3 6.4 \n",
"1 homicide VC.IHR.PSRC.P5 Cuba CUB .. .. 5.4 \n",
"2 homicide VC.IHR.PSRC.P5 El Salvador SLV .. 40.3 37.8 \n",
"\n",
" YR2002 YR2003 YR2004 ... YR2008 YR2009 YR2010 YR2011 YR2012 YR2013 \\\n",
"0 6.3 7.3 6.7 ... 11.6 11.7 11.6 10.3 8.7 8.7 \n",
"1 5.9 5.7 5.9 ... 4.6 5 4.5 4.7 .. .. \n",
"2 37.9 37.2 46.8 ... 52.9 72.8 66 72.2 42.7 41.3 \n",
"\n",
" YR2014 YR2015 YR2016 YR2017 \n",
"0 10 11.8 .. .. \n",
"1 .. .. .. .. \n",
"2 64.2 108.6 .. .. \n",
"\n",
"[3 rows x 23 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gt1.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"gtpop = pd.read_excel(\"./gtm_pop.xlsx\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>GTM_population</th>\n",
" <th>GTM_pop100k</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2007</td>\n",
" <td>13700286</td>\n",
" <td>137.00286</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2008</td>\n",
" <td>14006366</td>\n",
" <td>140.06366</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2009</td>\n",
" <td>14316208</td>\n",
" <td>143.16208</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year GTM_population GTM_pop100k\n",
"0 2007 13700286 137.00286\n",
"1 2008 14006366 140.06366\n",
"2 2009 14316208 143.16208"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gtpop.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 209,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# Ignore Cuba and Haiti\n",
"gt1 = gt1[gt1[\"Country Code\"].isin([\"CUB\", \"HTI\"]) == False]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>Series Name</th>\n",
" <th>Series Code</th>\n",
" <th>Country Name</th>\n",
" <th>Country Code</th>\n",
" <th>YR1990</th>\n",
" <th>YR2000</th>\n",
" <th>YR2001</th>\n",
" <th>YR2002</th>\n",
" <th>YR2003</th>\n",
" <th>YR2004</th>\n",
" <th>...</th>\n",
" <th>YR2008</th>\n",
" <th>YR2009</th>\n",
" <th>YR2010</th>\n",
" <th>YR2011</th>\n",
" <th>YR2012</th>\n",
" <th>YR2013</th>\n",
" <th>YR2014</th>\n",
" <th>YR2015</th>\n",
" <th>YR2016</th>\n",
" <th>YR2017</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>homicide</td>\n",
" <td>VC.IHR.PSRC.P5</td>\n",
" <td>Costa Rica</td>\n",
" <td>CRI</td>\n",
" <td>..</td>\n",
" <td>6.3</td>\n",
" <td>6.4</td>\n",
" <td>6.3</td>\n",
" <td>7.3</td>\n",
" <td>6.7</td>\n",
" <td>...</td>\n",
" <td>11.6</td>\n",
" <td>11.7</td>\n",
" <td>11.6</td>\n",
" <td>10.3</td>\n",
" <td>8.7</td>\n",
" <td>8.7</td>\n",
" <td>10</td>\n",
" <td>11.8</td>\n",
" <td>..</td>\n",
" <td>..</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>homicide</td>\n",
" <td>VC.IHR.PSRC.P5</td>\n",
" <td>El Salvador</td>\n",
" <td>SLV</td>\n",
" <td>..</td>\n",
" <td>40.3</td>\n",
" <td>37.8</td>\n",
" <td>37.9</td>\n",
" <td>37.2</td>\n",
" <td>46.8</td>\n",
" <td>...</td>\n",
" <td>52.9</td>\n",
" <td>72.8</td>\n",
" <td>66</td>\n",
" <td>72.2</td>\n",
" <td>42.7</td>\n",
" <td>41.3</td>\n",
" <td>64.2</td>\n",
" <td>108.6</td>\n",
" <td>..</td>\n",
" <td>..</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>homicide</td>\n",
" <td>VC.IHR.PSRC.P5</td>\n",
" <td>Dominican Republic</td>\n",
" <td>DOM</td>\n",
" <td>..</td>\n",
" <td>14.1</td>\n",
" <td>12.6</td>\n",
" <td>14.5</td>\n",
" <td>21.2</td>\n",
" <td>24.6</td>\n",
" <td>...</td>\n",
" <td>24.8</td>\n",
" <td>24.3</td>\n",
" <td>25</td>\n",
" <td>25.1</td>\n",
" <td>22.3</td>\n",
" <td>..</td>\n",
" <td>17.4</td>\n",
" <td>..</td>\n",
" <td>..</td>\n",
" <td>..</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>homicide</td>\n",
" <td>VC.IHR.PSRC.P5</td>\n",
" <td>Honduras</td>\n",
" <td>HND</td>\n",
" <td>..</td>\n",
" <td>50.9</td>\n",
" <td>54.7</td>\n",
" <td>55.7</td>\n",
" <td>61.5</td>\n",
" <td>53.9</td>\n",
" <td>...</td>\n",
" <td>61.4</td>\n",
" <td>71.5</td>\n",
" <td>83.1</td>\n",
" <td>93.2</td>\n",
" <td>92.7</td>\n",
" <td>81.9</td>\n",
" <td>74.6</td>\n",
" <td>63.8</td>\n",
" <td>..</td>\n",
" <td>..</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>homicide</td>\n",
" <td>VC.IHR.PSRC.P5</td>\n",
" <td>Guatemala</td>\n",
" <td>GTM</td>\n",
" <td>..</td>\n",
" <td>24.8</td>\n",
" <td>27</td>\n",
" <td>29.6</td>\n",
" <td>33.7</td>\n",
" <td>35</td>\n",
" <td>...</td>\n",
" <td>44.6</td>\n",
" <td>45.1</td>\n",
" <td>40.5</td>\n",
" <td>37.7</td>\n",
" <td>33.5</td>\n",
" <td>33.5</td>\n",
" <td>31.2</td>\n",
" <td>..</td>\n",
" <td>..</td>\n",
" <td>..</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 23 columns</p>\n",
"</div>"
],
"text/plain": [
" Series Name Series Code Country Name Country Code YR1990 YR2000 \\\n",
"0 homicide VC.IHR.PSRC.P5 Costa Rica CRI .. 6.3 \n",
"2 homicide VC.IHR.PSRC.P5 El Salvador SLV .. 40.3 \n",
"3 homicide VC.IHR.PSRC.P5 Dominican Republic DOM .. 14.1 \n",
"4 homicide VC.IHR.PSRC.P5 Honduras HND .. 50.9 \n",
"5 homicide VC.IHR.PSRC.P5 Guatemala GTM .. 24.8 \n",
"\n",
" YR2001 YR2002 YR2003 YR2004 ... YR2008 YR2009 YR2010 YR2011 YR2012 \\\n",
"0 6.4 6.3 7.3 6.7 ... 11.6 11.7 11.6 10.3 8.7 \n",
"2 37.8 37.9 37.2 46.8 ... 52.9 72.8 66 72.2 42.7 \n",
"3 12.6 14.5 21.2 24.6 ... 24.8 24.3 25 25.1 22.3 \n",
"4 54.7 55.7 61.5 53.9 ... 61.4 71.5 83.1 93.2 92.7 \n",
"5 27 29.6 33.7 35 ... 44.6 45.1 40.5 37.7 33.5 \n",
"\n",
" YR2013 YR2014 YR2015 YR2016 YR2017 \n",
"0 8.7 10 11.8 .. .. \n",
"2 41.3 64.2 108.6 .. .. \n",
"3 .. 17.4 .. .. .. \n",
"4 81.9 74.6 63.8 .. .. \n",
"5 33.5 31.2 .. .. .. \n",
"\n",
"[5 rows x 23 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gt1.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"CPIA_public_sector 10\n",
"under5_mortality_rate 10\n",
"homicide 10\n",
"poverty_headcount_320 10\n",
"gdp_per_capita_ppp_2011 10\n",
"household_consumption 10\n",
"CPIA_accountability_corruption 10\n",
"youth_literacy_rate 10\n",
"adult_literacy_rate 10\n",
"Name: Series Name, dtype: int64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The available indicators\n",
"gt1[\"Series Name\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"cols = gt1.columns[gt1.columns.map(lambda x: x.startswith(\"YR\")) == True]\n",
"gt2 = gt1.set_index([\"Country Code\", \"Series Name\"])[cols].stack().map(tryFunc(toFloat)).unstack(1).rename(index=lambda x: int(x[2:6]) if str.startswith(x, \"YR\") else x)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Series Name</th>\n",
" <th>Country Code</th>\n",
" <th>level_1</th>\n",
" <th>CPIA_accountability_corruption</th>\n",
" <th>CPIA_public_sector</th>\n",
" <th>adult_literacy_rate</th>\n",
" <th>gdp_per_capita_ppp_2011</th>\n",
" <th>homicide</th>\n",
" <th>household_consumption</th>\n",
" <th>poverty_headcount_320</th>\n",
" <th>under5_mortality_rate</th>\n",
" <th>youth_literacy_rate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>COL</td>\n",
" <td>1990</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>7533.518385</td>\n",
" <td>NaN</td>\n",
" <td>2894.417281</td>\n",
" <td>NaN</td>\n",
" <td>35.1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>COL</td>\n",
" <td>2000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8308.222950</td>\n",
" <td>65.7</td>\n",
" <td>3101.881202</td>\n",
" <td>28.8</td>\n",
" <td>25.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>COL</td>\n",
" <td>2001</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8327.070759</td>\n",
" <td>67.9</td>\n",
" <td>3105.015716</td>\n",
" <td>38.2</td>\n",
" <td>24.3</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>COL</td>\n",
" <td>2002</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8415.759119</td>\n",
" <td>68.3</td>\n",
" <td>3122.136969</td>\n",
" <td>29.8</td>\n",
" <td>23.6</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>COL</td>\n",
" <td>2003</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8625.246570</td>\n",
" <td>53.4</td>\n",
" <td>3170.331340</td>\n",
" <td>27.7</td>\n",
" <td>22.9</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Series Name Country Code level_1 CPIA_accountability_corruption \\\n",
"0 COL 1990 NaN \n",
"1 COL 2000 NaN \n",
"2 COL 2001 NaN \n",
"3 COL 2002 NaN \n",
"4 COL 2003 NaN \n",
"\n",
"Series Name CPIA_public_sector adult_literacy_rate gdp_per_capita_ppp_2011 \\\n",
"0 NaN NaN 7533.518385 \n",
"1 NaN NaN 8308.222950 \n",
"2 NaN NaN 8327.070759 \n",
"3 NaN NaN 8415.759119 \n",
"4 NaN NaN 8625.246570 \n",
"\n",
"Series Name homicide household_consumption poverty_headcount_320 \\\n",
"0 NaN 2894.417281 NaN \n",
"1 65.7 3101.881202 28.8 \n",
"2 67.9 3105.015716 38.2 \n",
"3 68.3 3122.136969 29.8 \n",
"4 53.4 3170.331340 27.7 \n",
"\n",
"Series Name under5_mortality_rate youth_literacy_rate \n",
"0 35.1 NaN \n",
"1 25.0 NaN \n",
"2 24.3 NaN \n",
"3 23.6 NaN \n",
"4 22.9 NaN "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gt2.head().reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Series Name</th>\n",
" <th>adult_literacy_rate</th>\n",
" <th>gdp_per_capita_ppp_2011</th>\n",
" <th>homicide</th>\n",
" <th>household_consumption</th>\n",
" <th>poverty_headcount_320</th>\n",
" <th>under5_mortality_rate</th>\n",
" <th>youth_literacy_rate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>20.000000</td>\n",
" <td>80.000000</td>\n",
" <td>69.000000</td>\n",
" <td>79.000000</td>\n",
" <td>56.000000</td>\n",
" <td>80.00000</td>\n",
" <td>20.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>87.666931</td>\n",
" <td>8599.024509</td>\n",
" <td>28.108696</td>\n",
" <td>3245.349387</td>\n",
" <td>24.321429</td>\n",
" <td>30.63875</td>\n",
" <td>94.577347</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>6.772482</td>\n",
" <td>3851.423356</td>\n",
" <td>19.192383</td>\n",
" <td>1502.428044</td>\n",
" <td>10.644806</td>\n",
" <td>13.38700</td>\n",
" <td>4.636641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>69.101730</td>\n",
" <td>3003.519024</td>\n",
" <td>6.300000</td>\n",
" <td>1102.633868</td>\n",
" <td>9.900000</td>\n",
" <td>10.30000</td>\n",
" <td>82.220260</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>86.137218</td>\n",
" <td>6070.409423</td>\n",
" <td>10.400000</td>\n",
" <td>2175.455903</td>\n",
" <td>16.525000</td>\n",
" <td>22.07500</td>\n",
" <td>94.767635</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>90.405135</td>\n",
" <td>8238.534249</td>\n",
" <td>24.800000</td>\n",
" <td>3105.015716</td>\n",
" <td>23.150000</td>\n",
" <td>27.80000</td>\n",
" <td>96.346065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>91.999635</td>\n",
" <td>11090.964672</td>\n",
" <td>43.600000</td>\n",
" <td>4265.429084</td>\n",
" <td>28.500000</td>\n",
" <td>37.32500</td>\n",
" <td>97.619985</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>94.868190</td>\n",
" <td>16149.480338</td>\n",
" <td>68.300000</td>\n",
" <td>6815.420781</td>\n",
" <td>64.300000</td>\n",
" <td>81.80000</td>\n",
" <td>97.991310</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Series Name adult_literacy_rate gdp_per_capita_ppp_2011 homicide \\\n",
"count 20.000000 80.000000 69.000000 \n",
"mean 87.666931 8599.024509 28.108696 \n",
"std 6.772482 3851.423356 19.192383 \n",
"min 69.101730 3003.519024 6.300000 \n",
"25% 86.137218 6070.409423 10.400000 \n",
"50% 90.405135 8238.534249 24.800000 \n",
"75% 91.999635 11090.964672 43.600000 \n",
"max 94.868190 16149.480338 68.300000 \n",
"\n",
"Series Name household_consumption poverty_headcount_320 \\\n",
"count 79.000000 56.000000 \n",
"mean 3245.349387 24.321429 \n",
"std 1502.428044 10.644806 \n",
"min 1102.633868 9.900000 \n",
"25% 2175.455903 16.525000 \n",
"50% 3105.015716 23.150000 \n",
"75% 4265.429084 28.500000 \n",
"max 6815.420781 64.300000 \n",
"\n",
"Series Name under5_mortality_rate youth_literacy_rate \n",
"count 80.00000 20.000000 \n",
"mean 30.63875 94.577347 \n",
"std 13.38700 4.636641 \n",
"min 10.30000 82.220260 \n",
"25% 22.07500 94.767635 \n",
"50% 27.80000 96.346065 \n",
"75% 37.32500 97.619985 \n",
"max 81.80000 97.991310 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This gives the same that stata\n",
"del gt2[\"CPIA_accountability_corruption\"]\n",
"del gt2[\"CPIA_public_sector\"]\n",
"gt2[gt2.index.get_level_values(1) < 2007].describe()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
"To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
" \n"
]
}
],
"source": [
"cols = [\"gdp_per_capita_ppp_2011\", \"homicide\", \"household_consumption\", \"poverty_headcount_320\"]\n",
"gt2[\"intercept\"] = 1\n",
"def trends(df):\n",
" results = []\n",
" for col in cols:\n",
" results.append(np.linalg.lstsq(df[df[col].isna() == False].reset_index()[[\"intercept\", \"level_1\"]], df[df[col].isna() == False][col])[0][1])\n",
" return pd.Series(index = cols, data = results)\n",
"trends = gt2[(gt2.index.get_level_values(1) < 2007 )].groupby(\"Country Code\").apply(trends)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>gdp_per_capita_ppp_2011</th>\n",
" <th>homicide</th>\n",
" <th>household_consumption</th>\n",
" <th>poverty_headcount_320</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Country Code</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>COL</th>\n",
" <td>121.033285</td>\n",
" <td>-5.971429</td>\n",
" <td>31.783762</td>\n",
" <td>-1.597143</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CRI</th>\n",
" <td>225.242755</td>\n",
" <td>0.314286</td>\n",
" <td>72.978773</td>\n",