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A further analysis of the IGM Experts forum data, focusing on individual characteristics. http://pstblog.com/2016/08/23/individual-chars
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# IGM Experts Panel: Individual Characteristics\n",
"\n",
"This is a second post exploring the data that I collected from the [IGM Experts Panel](http://www.igmchicago.org/igm-economic-experts-panel), which surveys a group of leading economists on a variety of policy questions. A CSV of all the data is available [here](https://www.dropbox.com/s/ouuqg7occ6o37ao/output_all.csv?dl=1), as are separate datasets of the [questions](https://www.dropbox.com/s/n407kk704fvdvno/igm_questions.csv?dl=1) and [responses](https://www.dropbox.com/s/lg3y056owry3inh/igm_responses.csv?dl=1).\n",
"\n",
"One of the interesting things that Gordon and Dahl looked at in their 2012 paper [[1, PDF]](http://econweb.ucsd.edu/~gdahl/papers/views-among-economists.pdf) was how individual characteristics of economists might influence their responses. They [compiled information](http://econweb.ucsd.edu/~gdahl/views-among-economists-code.html) on each economist including the institution of study, graduation year, current university, field of specialization, gender and NBER classification. Ten economists have been added to the group since 2012, so I did my best to gather this information from their CVs and the NBER database. The final CSV of individual characteristics is available [here](https://www.dropbox.com/s/nwxvfw3tqbcrq5m/indvars_2016.csv?dl=1).\n",
"\n",
"Below, I join this dataset of individual characteristics with the responses data from an earlier post, and look at some of the relationships graphically. I don't have background in economics or a very good understanding of regression analysis, so I stick to plotting trends rather than claiming statistical significance. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading the Data\n",
"\n",
"Below, I load the data and process it as described in an earlier [post](http://pstblog.com/2016/08/16/explore-igmforum). The main result is that I calculate a distance_median column, which is the distance from each response and the median response. "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"matplotlib.style.use('ggplot')\n",
"\n",
"#pd.set_option('max_colwidth', 30)\n",
"pd.set_option('max_colwidth', 400)\n",
"matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)\n",
"\n",
"df_responses = pd.read_csv('output_all.csv')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(8402, 18)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r_list = ['Strongly Disagree', 'Disagree', 'Uncertain', 'Agree', 'Strongly Agree']\n",
"\n",
"def indicator(x):\n",
"\tif x in r_list:\n",
"\t\treturn r_list.index(x)\n",
"\telse:\n",
"\t\treturn None\n",
"\n",
"df_responses['vote_num'] = df_responses['vote'].apply(indicator)\n",
"df_responses['median_num'] = df_responses['median_vote'].apply(indicator)\n",
"df_responses['vote_distance'] = abs(df_responses['median_num'] - df_responses['vote_num'])\n",
"\n",
"\n",
"# Construct a continuous column, incorporating confidence into vote_num\n",
"# Divide by 11 so 10 confidence of agree > 0 confidence of strongly agree\n",
"df_responses['incr_votenum'] = df_responses['vote_num'] + df_responses['confidence'] / 11.0\n",
"\n",
"# Median incr_votenum for each question:\n",
"df_responses['median_incrvotenum'] = df_responses.groupby(\n",
" ['qtitle','subquestion'])['incr_votenum'].transform('median')\n",
"\n",
"# Calculate distance from median for each econ vote, less biased by outliers. \n",
"df_responses['distance_median'] = abs(df_responses['median_incrvotenum'] - \\\n",
" df_responses['incr_votenum'])\n",
"\n",
"\n",
"df_responses.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading, Parsing the Individual Variables\n",
"\n",
"Next, I load and do some processing of the individual variables, getting them into the correct format to join with my responses dataset. I also load in the individual characteristics for the ten new economist, and concatenate those to the [original individual characteristics](http://econweb.ucsd.edu/~gdahl/views-among-economists-code.html) from Gordon and Dahl. \n",
"\n",
"The result of the join is a dataset with `8402` rows and `26` columns. "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>phdyear</th>\n",
" <th>phdfrom</th>\n",
" <th>currentuniv</th>\n",
" <th>ifield</th>\n",
" <th>gender</th>\n",
" <th>washington</th>\n",
" <th>nber</th>\n",
" <th>cohort</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Daron Acemoglu</td>\n",
" <td>92</td>\n",
" <td>Harvard</td>\n",
" <td>MIT</td>\n",
" <td>Labor</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>LS</td>\n",
" <td>15 - 30 Years</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Alberto Alesina</td>\n",
" <td>86</td>\n",
" <td>Harvard</td>\n",
" <td>Harvard</td>\n",
" <td>International</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>POL</td>\n",
" <td>30+ Years</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Joseph Altonji</td>\n",
" <td>81</td>\n",
" <td>Princeton</td>\n",
" <td>Yale</td>\n",
" <td>Labor</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>LS</td>\n",
" <td>30+ Years</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Alan Auerbach</td>\n",
" <td>78</td>\n",
" <td>Harvard</td>\n",
" <td>Berkeley</td>\n",
" <td>Public Finance</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>PE</td>\n",
" <td>30+ Years</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>David Autor</td>\n",
" <td>99</td>\n",
" <td>Harvard</td>\n",
" <td>MIT</td>\n",
" <td>Labor</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>LS</td>\n",
" <td>15 - 30 Years</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name phdyear phdfrom currentuniv ifield gender \\\n",
"0 Daron Acemoglu 92 Harvard MIT Labor 1 \n",
"1 Alberto Alesina 86 Harvard Harvard International 1 \n",
"2 Joseph Altonji 81 Princeton Yale Labor 1 \n",
"3 Alan Auerbach 78 Harvard Berkeley Public Finance 1 \n",
"4 David Autor 99 Harvard MIT Labor 1 \n",
"\n",
" washington nber cohort \n",
"0 0 LS 15 - 30 Years \n",
"1 0 POL 30+ Years \n",
"2 0 LS 30+ Years \n",
"3 1 PE 30+ Years \n",
"4 0 LS 15 - 30 Years "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"df_indvars = pd.read_csv('individual-vars.csv')\n",
"\n",
"df_indvars['name'] = df_indvars['name'].str.split(', ').map(lambda row: row[1] + ' ' + row[0])\n",
"\n",
"institutions = ['Berkeley', 'Chicago', 'Harvard', 'MIT', 'Princeton', 'Stanford', 'Yale']\n",
"\n",
"def inst_ind(element):\n",
" return institutions[int(element) - 1]\n",
"\n",
"df_indvars['phdfrom'] = df_indvars['phdfrom'].apply(inst_ind)\n",
"df_indvars['currentuniv'] = df_indvars['currentuniv'].apply(inst_ind)\n",
"\n",
"\n",
"def age_cohort(element):\n",
" cur_year = 116\n",
" cohort = cur_year - int(element)\n",
" if cohort <= 15:\n",
" return '0 - 15 Years'\n",
" elif cohort >= 30:\n",
" return '30+ Years'\n",
" else:\n",
" return '15 - 30 Years'\n",
"\n",
"df_indvars['cohort'] = df_indvars['phdyear'].apply(age_cohort)\n",
"\n",
"ifields = {8:'Industrial Org', 7:'Public Finance', 6:'Labor', 5:'Finance', 4:'Macro', 3:'International'}\n",
"\n",
"def ifield_ind(element):\n",
" return ifields[int(element)]\n",
"\n",
"df_indvars['ifield'] = df_indvars['ifield'].apply(ifield_ind)\n",
"# Their coding error? Alesina is listed as MAC in appendix, but has ifield of 3?\n",
"# df_indvars[] change it?\n",
"\n",
"# Load individual variables from 10 new economists\n",
"# Self coded based on personal websites, NBER information\n",
"# http://www.nber.org/programs/\n",
"df_indvarsnew = pd.read_csv('individual_vars_new.csv')\n",
"\n",
"# Add cohort column\n",
"df_indvarsnew['cohort'] = df_indvarsnew['phdyear'].apply(age_cohort)\n",
"\n",
"# Concat both old and new individual vars datasets\n",
"df_indvarsall = pd.concat([df_indvars, df_indvarsnew], ignore_index=True)\n",
"\n",
"#df_indvarsall.to_csv('indvars_2016.csv', encoding='utf-8', index=False)\n",
"\n",
"df_indvarsall.head()\n",
"\n",
"# Gordon Online Appendix and Data:\n",
"# http://econweb.ucsd.edu/~gdahl/views-among-economists-code.html\n",
"# http://econweb.ucsd.edu/~gdahl/papers/views-among-economists-online-appendix.pdf\n",
"# Notes: PhD From and Current University categories are BER=Berkeley; CHI=Chicago, Rochester; HAR=Harvard, \n",
"# Cambridge, LSE, Wisconsin; MIT=MIT, Oxford; PRI=Princeton; STA=Stanford; YAL=Yale.\n",
"# Field categories are defined by primary NBER affiliation: MAC=macro (EFG, ME, POL); INT=international (IFM, ITI);\n",
"# FIN=finance (AP, CF); LAB=labor (LS, ED, AG, DAE, DEV); PF=public finance (PF, EEE); \n",
"# IO=industrial organization (IO, LE). Three panel members are not in the NBER; \n",
"# Ray Fair and James Stock are assigned to MAC, Eric Maskin is assigned to FIN. \n",
"# Female is an indicator equal to 1 for women. Wash is an indicator for experience serving in Washington."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(8402, 26)\n"
]
}
],
"source": [
"# Inner join, on name column\n",
"df_all = pd.merge(df_responses, df_indvarsall, on=['name'], how='inner')\n",
"\n",
"print df_all.shape\n",
"# Remove middle initials for Brunnermeier, Kaplan, to get right shape of (8402, 26)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Confidence by Education\n",
"\n",
"One of the interesting things Gordon and Dahl found was that economists that were educated at MIT and the University of Chicago seemed to be more confident. I find less evidence of this in the newer data, although this is just a boxplot, not a regression analysis. "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"4\" halign=\"left\">confidence</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>std</th>\n",
" <th>count</th>\n",
" <th>median</th>\n",
" <th>mean</th>\n",
" </tr>\n",
" <tr>\n",
" <th>phdfrom</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>LSE</th>\n",
" <td>1.964802</td>\n",
" <td>93</td>\n",
" <td>6</td>\n",
" <td>6.290323</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MIT</th>\n",
" <td>2.494196</td>\n",
" <td>2414</td>\n",
" <td>6</td>\n",
" <td>6.063380</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Princeton</th>\n",
" <td>2.404093</td>\n",
" <td>591</td>\n",
" <td>7</td>\n",
" <td>6.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Illinois</th>\n",
" <td>2.839723</td>\n",
" <td>112</td>\n",
" <td>6</td>\n",
" <td>5.910714</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Stanford</th>\n",
" <td>2.430876</td>\n",
" <td>959</td>\n",
" <td>6</td>\n",
" <td>5.851929</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Harvard</th>\n",
" <td>2.384803</td>\n",
" <td>2082</td>\n",
" <td>6</td>\n",
" <td>5.850144</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yale</th>\n",
" <td>2.399019</td>\n",
" <td>358</td>\n",
" <td>5</td>\n",
" <td>5.410615</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chicago</th>\n",
" <td>2.538058</td>\n",
" <td>387</td>\n",
" <td>6</td>\n",
" <td>5.395349</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Berkeley</th>\n",
" <td>2.310260</td>\n",
" <td>28</td>\n",
" <td>5</td>\n",
" <td>5.178571</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" confidence \n",
" std count median mean\n",
"phdfrom \n",
"LSE 1.964802 93 6 6.290323\n",
"MIT 2.494196 2414 6 6.063380\n",
"Princeton 2.404093 591 7 6.000000\n",
"Illinois 2.839723 112 6 5.910714\n",
"Stanford 2.430876 959 6 5.851929\n",
"Harvard 2.384803 2082 6 5.850144\n",
"Yale 2.399019 358 5 5.410615\n",
"Chicago 2.538058 387 6 5.395349\n",
"Berkeley 2.310260 28 5 5.178571"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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U+cjYzp075fF49Nhjj2nTpk1q0aKFrr/+ekVERARzfAAAACe0Kh8Z8/l8+vXX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\nZQwAAMAg1qa0DPnJb3N+1iZ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PPvpIBQUFkg413tzc3HLbSIcmJyUlRZmZmXr99dcl6ai2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\njAEAABj0/8PduGHBp80yAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10703f150>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_all.boxplot(column='confidence', by='phdfrom', whis=[5.0,95.0])\n",
"\n",
"df_all.groupby('phdfrom').agg({'confidence':{'mean': 'mean', 'median':'median',\n",
" 'std': 'std', 'count':'count'}}).sort_values(\n",
" by=('confidence','mean'),\n",
" ascending=False)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Distance from Median by Education\n",
"\n",
"Another interesting thing to look at is whether any institutions produce graduates with views further from the median view. There definitely are differences in the means and medians below, but I don't think they would rise to the level of significance because the standard deviations overlap. \n",
"\n",
"It's also important to note that the vote counts by institution vary from `2414` (MIT) to `28` (Berkeley), so these responses don't represent the institution as a whole. "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"4\" halign=\"left\">distance_median</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>std</th>\n",
" <th>count</th>\n",
" <th>median</th>\n",
" <th>mean</th>\n",
" </tr>\n",
" <tr>\n",
" <th>phdfrom</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>MIT</th>\n",
" <td>0.559621</td>\n",
" <td>2414</td>\n",
" <td>0.545455</td>\n",
" <td>0.655363</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Harvard</th>\n",
" <td>0.580161</td>\n",
" <td>2082</td>\n",
" <td>0.454545</td>\n",
" <td>0.638023</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chicago</th>\n",
" <td>0.524551</td>\n",
" <td>387</td>\n",
" <td>0.454545</td>\n",
" <td>0.633427</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LSE</th>\n",
" <td>0.489293</td>\n",
" <td>93</td>\n",
" <td>0.545455</td>\n",
" <td>0.621701</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Stanford</th>\n",
" <td>0.559780</td>\n",
" <td>959</td>\n",
" <td>0.409091</td>\n",
" <td>0.621149</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yale</th>\n",
" <td>0.522308</td>\n",
" <td>358</td>\n",
" <td>0.409091</td>\n",
" <td>0.574022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Princeton</th>\n",
" <td>0.501498</td>\n",
" <td>591</td>\n",
" <td>0.363636</td>\n",
" <td>0.556145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Illinois</th>\n",
" <td>0.523816</td>\n",
" <td>112</td>\n",
" <td>0.363636</td>\n",
" <td>0.551542</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Berkeley</th>\n",
" <td>0.536900</td>\n",
" <td>28</td>\n",
" <td>0.272727</td>\n",
" <td>0.431818</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" distance_median \n",
" std count median mean\n",
"phdfrom \n",
"MIT 0.559621 2414 0.545455 0.655363\n",
"Harvard 0.580161 2082 0.454545 0.638023\n",
"Chicago 0.524551 387 0.454545 0.633427\n",
"LSE 0.489293 93 0.545455 0.621701\n",
"Stanford 0.559780 959 0.409091 0.621149\n",
"Yale 0.522308 358 0.409091 0.574022\n",
"Princeton 0.501498 591 0.363636 0.556145\n",
"Illinois 0.523816 112 0.363636 0.551542\n",
"Berkeley 0.536900 28 0.272727 0.431818"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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4pCJ0/pekzMxMTZ8+Xe3bt8/T8T8/w4cP14wZM1S1atVCl6Xzvzm250/nf7vb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\nBmZEREREEvEPM2eik8h71s4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x107cca490>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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u1FyAvNn279+Pv/71r/j+++9x+PBhpKSk4OTJk0hOTuZj1gVy59q+fTtGjhyJjIwMrF+/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\nwDCKcAAAAMCw/wfoJL/TY2LhMwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x107cca950>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_all.boxplot(column='distance_median', by='phdfrom', whis=[5.0,95.0])\n",
"\n",
"df_all.hist(column='distance_median', by='phdfrom', bins=20, figsize=(12,12))\n",
"\n",
"df_all.groupby('phdfrom').agg({'distance_median':{'mean': 'mean', 'median':'median',\n",
" 'std': 'std', 'count':'count'}}).sort_values(\n",
" by=('distance_median','mean'),\n",
" ascending=False)\n",
"\n",
"#Only 28 votes from Berkeley\n",
"#len(df_all[(df_all['phdfrom'] == 'Berkeley') & (df_all['vote'].isin(r_list))])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Washington Insiders\n",
"\n",
"Spending time in Washington doesn't seem to have much of an effect on confidence. Those that haven't spent time in Washington maybe have a little bit longer tail when it comes to controversial responses, but overall both groups are very similar. "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x107292790>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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AsIgwBgAAYBFhDAAAwCLCGAAAgEWEMQAAAIsIYwAAABYRxgAAACwijAEAAFhE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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x108a1bcd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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97W9/06effqrHHnus2b8zAJMZAGAYxn/+8x8jODjYWLlyZb3nBw4caPTs2dMwDMOYP3++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\n0oxNAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10392f8d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_all.boxplot(column='confidence', by='washington', whis=[5.0,95.0])\n",
"\n",
"df_all.boxplot(column='distance_median', by='washington', whis=[5.0,95.0])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How do responses differ by field of study?\n",
"\n",
"Gordon and Dahl also used the NBER classification of each economist to look at how responses changed by field of study. I do something similar below, looking at confidence and distance_median grouped by field of study. The Finance and Public Economics groups seem a little more confident. The International and Labor Economics groups seem to have a little higher distance_median, but it's unlikely any of this rises to the level of significance. "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1039ccd50>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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GDRqkAwcOqHXr1nr77bfDTt+VdqToZI4e/fw5EydOVH5+vm644QZJ0oABA3TbbbdpwYIF\noef8/ve/V82aNTVp0iSNHz9eVapUUVpamq655ppf9No333yzqlevrsmTJ+uRRx5RjRo11KVLFw0a\nNEiS9MADD6hBgwaaMWOG7r77blWrVk3nnHOOhgwZcsJtA6jcfM4JBlU8/fTTWrNmjWrVqqWpU6dK\nOnKdoMcff1y7d+9WSkqK7rjjDlWvXv2UFBxpgUAgdOoExyIfM/IxIx8z8jEjHzPyMYumfE44ZqxX\nr166//77w+5744031Lp1a02fPl1+v1+vv/56hRVY0X4+gwvhyMeMfMzIx4x8zMjHjHzMoimfEzZj\nzZs3V2JiYth9q1ev1oUXXijpyBWvP/3004qpDgAAoJIr02zK/fv3h9aAS05ODlvuAwAAACfvhGPG\npCMXNJw0aVJozNiNN96o2bNnhx6/6aab9Pzzz5f6vYFAIOxQYXp6enlrBgAAiDrz588Pfe33+0Nj\n2so0mzI5OVnZ2dmh/9aqVeu4zy35Ykdt3769LC9bIZKSkpSTk2O7DNdKTU3Vtm3bbJfhWuw/ZuRj\nRj5m5GNGPmZuy6dBgwbHPSB1UqcpHccJu5L1eeedp6VLl0qSli5dqg4dOpS/SgAAAA864ZGx6dOn\na8OGDcrJydHQoUOVnp6uq666So899pg+/PBDnXbaabrjjjtORa0AAACVzkmNGYs0TlNGD/IxIx8z\n8jEjHzPyMSMfM7fl06BBg+M+xtqUAAAAFnm+GRs//uQWAgZKw/5jRj5m5GNGPmbkYxZN+Xj+NGVq\nagNt2+aeetzGbYd53Yb9x4x8zMjHjHzMyMfMbflwmhIAAMClaMYAAAAsohkDAACwiGYMAADAotgx\nY8aMOdUv6qYB4XFxcerc+bDtMlwrPj5eBQUFtstwLfYfM/IxIx8z8jEjHzO35ZOUlHTcxzw/m5LZ\ngmbkY0Y+ZuRjRj5m5GNGPmZuy4fZlAAAAC5FMwYAAGARzRgAAIBFNGMAAAAWeb4Zi6a1q+A+7D9m\n5GNGPmbkY0Y+ZtGUj+dnU7pt7Sq3cdtsFLdh/zEjHzPyMSMfM/Ixc1s+zKYEAABwKZoxAAAAi2jG\nAAAALKIZAwAAsIi1KV22dpXbsDalGfuPGfmYkY8Z+ZiRj5nb8mFtSgNmC5qRjxn5mJGPGfmYkY8Z\n+Zi5LR9mUwIAALgUzRgAAIBFNGMAAAAW0YwBAABY5PlmLJrWroL7sP+YkY8Z+ZiRjxn5mEVTPp6f\nTem2tavcxm2zUdyG/ceMfMzIx4x8zMjHzG35MJsSAADApWjGAAAALKIZAwAAsIhmDAAAwCLWpnTZ\n2lVuw9qUZuw/ZuRjRj5m5GNGPmZuy4e1KQ2YLWhGPmbkY0Y+ZuRjRj5m5GPmtnyYTQkAAOBSNGMA\nAAAW0YwBAABYRDMGAABgkeebsWhauwruw/5jRj5m5GNGPmbkYxZN+Xh+NqXb1q5yG7fNRnEb9h8z\n8jEjHzPyMSMfM7flY5pNWaU8G168eLGWLFkiSbrooovUt2/f8mwOAADAc8p8mvL777/Xv/71L02c\nOFFTpkzRmjVrtHPnzkjWBgAAUOmVuRnbtm2bmjZtqqpVqyomJkYtWrTQJ598EsnaAAAAKr0yN2ON\nGjVSZmamDh48qPz8fK1du1Z79uyJZG0AAACVXpnHjKWmpurKK6/UuHHjlJCQoCZNmigm5tjeLhAI\nKBAIhG6np6cb12c61e6/v8hV9ZxKNWvWjNi2Dhw4ELFtRRMv7z8ng3zMyMessubTuHENZWf7IrKt\n1NTjDwo/WcnJjrZuPRiBatzFjfvP/PnzQ1/7/X75/X5JEZxN+corr6hu3bq65JJLTvhcN82mZLag\nGfmYkY8Z+ZiRj1llzSdSs/wilY/bZh1Gitv2nwpbm/Lo0ZCsrCz95z//0QUXXFCezQEAAHhOuS5t\n8eijj+rgwYOKjY3VH/7wB1WvXj1SdQEAAHhCuZqxhx56KFJ1AAAAeJLnl0MCAACwyfPNWDStXWUD\n+ZiRjxn5mJGPGfmgPKJp/2Ftyko6iyRSyMeMfMzIx4x8zCprPsymPDXc9nNV2GxKAAAAlA/NGAAA\ngEU0YwAAABbRjAEAAFjk+WZs1Kh82yW4GvmYkY8Z+ZiRjxn5oDyiaf/x/GxKt61d5TbkY0Y+ZuRj\nRj5mlTUfZlOeGm7bf5hNCQAA4FI0YwAAABbRjAEAAFhEMwYAAGCR55uxaFq7ygbyMSMfM/IxIx8z\n8kF5RNP+4/nZlJV1FkmkkI8Z+ZiRjxn5mFXWfJhNeWq47ediNiUAAIBL0YwBAABYRDMGAABgEc0Y\nAACARZ5vxqJp7SobyMeMfMzIx4x8zMgH5RFN+4/nZ1O6be0qtyEfM/IxIx8z8jGrrPkwm/LUcNv+\nw2xKAAAAl6IZAwAAsIhmDAAAwCKaMQAAAIs834xF09pVNpCPGfmYkY8Z+ZiRD8ojmvYfz8+mrKyz\nSCKFfMzIx4x8zMjHrLLmw2zKU8NtPxezKQEAAFyKZgwAAMAimjEAAACLaMYAAAAs8nwzFk1rV9lA\nPmbkY0Y+ZuRjRj4oj2jafzw/m9Jta1e5DfmYkY8Z+ZiRj1llzYfZlKeG2/YfZlMCAAC4FM0YAACA\nRTRjAAAAFtGMAQAAWOT5Ziya1q6ygXzMyMeMfMzIx4x8UB7RtP94fjZlZZ1FEinkY0Y+ZuRjRj5m\nlTUfZlOeGm77uUyzKauUZ8OLFi3Shx9+KJ/Pp8aNG2vYsGGqUqVcmwQAAPCUMp+m3Lt3r959911N\nmjRJU6dOVXFxsVatWhXJ2gAAACq9co0ZCwaDysvLU3FxsfLz81W7du1I1QUAAOAJZT6nWKdOHfXr\n10/Dhg1TfHy8zj33XJ177rmRrA0AAKDSK3MzdujQIa1evVpPPfWUqlevrkcffVQrV67UBRdcEPa8\nQCCgQCAQup2enq6kpKSyV/yTxo1rKDvbV+7tSEcG+ZVXcrKjrVsPRqCayCAfM/IxIx8z8jEjnxOL\nxO/BuLi4iGxHikw9bnP//UWu+7nmz58f+trv98vv90sqRzO2bt06paSkqEaNGpKkzp0766uvvjqm\nGSv5YkdFYvZHdnaS62ajuGkNLPIxIx8z8jEjHzPyOZHI/FyRW3vRXWs4RsrIkZHpNyIlKSlJ6enp\npT5W5jFj9erV0zfffKOCggI5jqN169YpNTW1zEUCAAB4UZmPjDVt2lRdunTRyJEjFRsbqyZNmuji\niy+OZG0AAACVXrkuCta/f3/1798/UrUAAAB4jueXQwIAALCJZgwAAFQ60bQ2Jc0YAACodCZOjLdd\nwkmjGQMAALCIZgwAAMAimjEAAACLaMYAAAAsohkDAACVzqhR+bZLOGk0YwAAoNK5774C2yWcNJox\nAAAAi2jGAAAALKIZAwAAsIhmDAAAwCKaMQAAUOmwNiUAAIBFrE0JAACAk0IzBgAAYBHNGAAAgEU0\nYwAAABbRjAEAgEqHtSkBAAAsYm1KAAAAnBSaMQAAAItoxgAAACyiGQMAALCIZgwAAFQ6rE0JAABg\nEWtTAgAA4KTQjAEAAFhEMwYAAGARzRgAAIBFNGMAAKDSYW1KAAAAi1ibEgAAACeFZgwAAMAimjEA\nAACLaMYAAAAsohkDAACVDmtTAgAAWBRNa1NWKes3bt++XY8//rh8Pp8cx9HOnTs1YMAA9e3bN5L1\nAQAAVGplbsYaNGigyZMnS5KCwaCGDh2qTp06RawwAAAAL4jIacp169apfv36qlevXiQ2BwAA4BkR\nacY++ugjdevWLRKbAgAA8JQyn6Y8qqioSKtXr9bvfve7Uh8PBAIKBAKh2+np6UpKSirvy8qRT0ot\n92YkSeWvRnIk5SQdiMCWIicSOcfFxUVkO1Jk6okk8jEjHzPyMSOf4+P3l1njxjWUne2LyLZSUxuU\nexvJyY62bj0YgWqk+fPnh772+/3y+/2SItCMff7550pLS1PNmjVLfbzkix2Vk5NT3pdVTTnatm17\nubeTlJQUkXpSUxtoW07564mcyPxckconUvVEDvmYkY8Z+ZiRjwm/v8yys5Ncl0+k9uf09PRSHyv3\nacqVK1dyihIAAKCMytWM5efna926dercuXOk6gEAAPCUcp2mjI+P13PPPRepWgAAADyHK/ADAABY\nRDMGAABgEc0YAACARTRjAAAAFtGMAQAAWEQzBgAAYBHNGAAAgEU0YwAAABbRjAEAAFhEMwYAAGAR\nzRgAAIBFNGMAAAAW0YwBAABYRDMGAABgEc0YAACARTRjAAAAFtGMAQAAWEQzBgAAYBHNGAAAgEU0\nYwAAABbRjAEAAFhEMwYAAGARzRgAAIBFNGMAAAAW0YwBAABYRDMGAABgEc0YAACARTRjAAAAFtGM\nAQAAWEQzBgAAYBHNGAAAgEU0YwAAABbRjAEAAFhEMwYAAGARzRgAAIBFNGMAAAAW0YwBAABYRDMG\nAABgEc0YAACARVXK8825ubl65pln9P3338vn82no0KE6++yzI1UbAABApVeuZmz27Nlq166d7rzz\nThUXFys/Pz9SdQEAAHhCmU9T5ubmKjMzU7169ZIkxcbGqnr16hErDAAAwAvKfGRs165dSkpK0lNP\nPaUtW7YoLS1NN954o+Li4iJZHwAAQKVW5mYsGAzqu+++080336yzzjpLL7zwgt544w2lp6eHPS8Q\nCCgQCIRup6enKykpqewVlxCJ7cTFxbmqnkhx5JNSI7OtSPxUjqScpAMR2FJkkI8Z+ZiRjxn5nBi/\nv46vMu8/8+fPD33t9/vl9/sllaMZq1OnjurWrauzzjpLktSlSxe98cYbxzyv5IsdlZOTU9aXLSEp\nIttJSorMdiJVT6TUlKNt27aXezuRyic1tYG25ZS/nkghHzPyMSMfM/I5EX5/mVTW/ScpKemYA1ZH\nlXnMWHJysurWravt248UuG7dOjVs2LCsmwMAAPCkcs2mvPHGG/Xkk0+qqKhI9evX17BhwyJVFwAA\ngCeUqxlr0qSJJkyYEKlaAAAAPIcr8AMAAFhEMwYAAGARzRgAAIBFNGMAAAAW0YwBAABYRDMGAABg\nEc0YAACARTRjAAAAFtGMAQAAWEQzBgAAYBHNGAAAgEU0YwAAABbRjAEAAFhEMwYAAGARzRgAAIBF\nNGMAAAAW0YwBAABYRDMGAABgEc0YAACARTRjAAAAFtGMAQAAWEQzBgAAYBHNGAAAgEU0YwAAABbR\njAEAAFhEMwYAAGARzRgAAIBFNGMAAAAW0YwBAABYRDMGAABgEc0YAACARTRjAAAAFtGMAQAAWEQz\nBgAAYBHNGAAAgEU0YwAAABbRjAEAAFhEMwYAAGARzRgAAIBFVcrzzcOHD1f16tXl8/kUGxurCRMm\nRKouAAAATyhXM+bz+TR69GjVqFEjUvUAAAB4SrlOUzqOI8dxIlULAACA55T7yNi4ceMUExOjiy66\nSBdffHGk6gIAAPCEcjVjY8eOVe3atXXgwAGNHTtWDRs2VPPmzcOeEwgEFAgEQrfT09OVlJRUnpcN\nSU1tEJHtSOWvJznZidjPFSmRqCcuLi5iPxf5mJGPmdvy4fPHjHzMyMessn7+zJ8/P/S13++X3++X\nVM5mrHbt2pKkmjVrqlOnTtq4ceMxzVjJFzsqJyenPC8rSdq2rfzbkI78D7Ft2/aIbCsCP1YEJUUk\n56SkyGwnUvVEDvmYkY8Jnz9m5GNGPidSOT9/kpKSlJ6eXupjZR4zlp+fr7y8PElSXl6e/vvf/6pR\no0Zl3RwAAIAnlfnI2P79+zVlyhT5fD4VFxere/fuatOmTSRrAwAAqPTK3IylpKRoypQpkawFAADA\nc7gCPwAAgEWeb8ZGjcq3XQIAj+Lzx4x8zMin8vB8M3bffQW2SwDgUXz+mJGPGflUHp5vxgAAAGyi\nGQMAALCIZgwAAMAimjEAAACLPN+MjR8fZ7sEAB7F548Z+ZiRT+Xh+WZs4sR42yUA8Cg+f8zIx4x8\nKg/PN2MAAAA20YwBAABYRDMGAABgEc0YAACARZ5vxljbC4AtfP6YkY8Z+VQenm/GWNsLgC18/piR\njxn5VB6eb8YAAABsohkDAACwiGYMAADAIpoxAAAAizzfjLG2FwBb+PwxIx8z8qk8PN+MsbYXAFv4\n/DEjHzMH3TeKAAAgAElEQVTyqTw834wBAADYRDMGAABgEc0YAACARTRjAAAAFnm+GWNtLwC28Plj\nRj5m5FN5eL4ZY20vALbw+WNGPmbkU3l4vhkDAACwiWYMAADAIpoxAAAAi2jGAAAALPJ8M8baXgBs\n4fPHjHzMyKfy8HwzxtpeAGzh88eMfMzIp/LwfDMGAABgE80YAACARTRjAAAAFtGMAQAAWOT5Zoy1\nvQDYwuePGfmYkU/l4flmjLW9ANjC548Z+ZiRT+VR7mYsGAxq5MiRmjRpUiTqAQAA8JRyN2OLFy9W\nampqJGoBAADwnHI1Y3v27NHatWt10UUXRaoeAAAATylXMzZnzhwNGjRIPp8vUvUAAAB4SpWyfuOa\nNWtUq1YtNWnSRIFAQI7jlPq8QCCgQCAQup2enq6kpKSyvmzETZpUTSNH2q6iYqSmNojQlsr/fiUn\nO6563yXyORHyqXiV+fMnEsjHrDLnU1k/f+bPnx/62u/3y+/3S5J8zvG6qBN4+eWXtWLFCsXGxqqg\noECHDx9W586dddttt53we7dv316Wl6wQqakNtG2be+pxG/IxIx8z8jEjHzPyMSMfM7fl06DB8RvM\nMh8ZGzhwoAYOHChJ2rBhg/7xj3+cVCMGAACA//H8dcYAAABsKvORsZJatmypli1bRmJTAAAAnsKR\nMQAAAItix4wZM+ZUv2hOTs6pfsnjiouLU+fOh22X4VrkY0Y+ZuRjRj5m5GNGPmZuy8c0I7PMsynL\nw02zKZOSklzVHLoN+ZiRjxn5mJGPGfmYkY+Z2/IxzabkNCUAAIBFNGMAAAAW0YwBAABYRDMGAABg\nkeebsfHj42yX4GrkY0Y+ZuRjRj5m5GNGPmbRlI/nZ1O6be0qtyEfM/IxIx8z8jEjHzPyMXNbPsym\nBAAAcCmaMQAAAItoxgAAACyiGQMAALCItSldtnaV25CPGfmYkY8Z+ZiRjxn5mLktH9amNHDb2lVu\nQz5m5GNGPmbkY0Y+ZuRj5rZ8mE0JAADgUjRjAAAAFtGMAQAAWEQzBgAAYJHnm7FoWrvKBvIxIx8z\n8jEjHzPyMSMfs2jKx/OzKd22dpXbkI8Z+ZiRjxn5mJGPGfmYuS0fZlMCAAC4FM0YAACARTRjAAAA\nFtGMAQAAWMTalC5bu8ptyMeMfMzIx4x8zMjHjHzM3JYPa1MauG3tKrchHzPyMSMfM/IxIx8z8jFz\nWz7MpgQAAHApmjEAAACLaMYAAAAsohkDAACwyPPNWDStXWUD+ZiRjxn5mJGPGfmYkY9ZNOXj+dmU\nblu7ym3Ix4x8zMjHjHzMyMeMfMzclg+zKQEAAFyKZgwAAMAimjEAAACLaMYAAAAsYm1Kl61d5Tbk\nY0Y+ZuRjRj5m5GNGPmZuy4e1KQ3ctnaV25CPGfmYkY8Z+ZiRjxn5mLktH9Nsyipl3WhhYaFGjx6t\noqIiFRcXq0uXLurfv39ZNwcAAOBJZW7GqlatqtGjRys+Pl7BYFB/+ctf1K5dOzVt2jSS9QEAAFRq\n5RrAHx8fL+nIUbLi4uKIFAQAAOAlZT4yJknBYFCjRo3Szp07demll3JUDAAA4BeKyAD+3NxcTZky\nRTfffLMaNmwY9lggEFAgEAjdTk9Pd9WAukmTqmnkSPfMtnAb8jEjHzPyMSMfMy/nU7NmzYht68CB\nAxHbVjRx2/6TlJSk+fPnh277/X75/X5JEZxNmZGRoYSEBPXr1++Ez3XTbEq3rV3lNuRjRj5m5GNG\nPmbkY+a22YJu47b9p0LWpjxw4IByc3MlSQUFBVq3bp3xhQAAAHCsMo8Zy87O1syZMxUMBuU4jrp2\n7ar27dtHsjYAAIBKr8zNWOPGjTVp0qRI1gIAAOA5rE0JAABgEWtTumztKrchHzPyMSMfM/IxIx+z\n+Ph4FRQU2C7Dtdy2/7A2pQGzUczIx4x8zMjHjHzMyMeMfMzclk+FzKYEAABA+dGMAQAAWEQzBgAA\nYBHNGAAAgEWeb8bGj4+zXYKrkY8Z+ZiRjxn5mJEPyiOa9h/Pz6Z029pVbkM+ZuRjRj5m5GNGPmZu\nmy3oNm7bf5hNCQAA4FI0YwAAABbRjAEAAFhEMwYAAGARa1O6bO0qtyEfM/IxIx8z8jEjHzPWpjRz\n2/7D2pQGzEYxIx8z8jEjHzPyMSMfM/Ixc1s+zKYEAABwKZoxAAAAi2jGAAAALKIZAwAAsMjzzVg0\nrV1lA/mYkY8Z+ZiRjxn5oDyiaf/x/GxKt61d5TbkY0Y+ZuRjRj5m5GPmttmCbuO2/YfZlAAAAC5F\nMwYAAGARzRgAAIBFNGMAAAAWsTaly9auchvyMSMfM/IxIx8z8jFjbUozt+0/rE1pwGwUM/IxIx8z\n8jEjHzPyMSMfM7flw2xKAAAAl6IZAwAAsIhmDAAAwCKaMQAAAIs834xF09pVNpCPGfmYkY8Z+ZiR\nD8ojmvYfz8+mdNvaVW5DPmbkY0Y+ZuRjRj5mbpst6DZu23+YTQkAAOBSNGMAAAAW0YwBAABYRDMG\nAABgEWtTumztKrchHzPyMSMfM/IxIx8z1qY0c9v+w9qUBsxGMSMfM/IxIx8z8jEjHzPyMXNbPqbZ\nlFXKutE9e/ZoxowZ2r9/v3w+ny666CL17du3rJsDAADwpDI3Y7GxsRo8eLCaNGmivLw8jRw5Um3a\ntFFqamok6wMAAKWYOXOmbrjhBttlIALKPIA/OTlZTZo0kSQlJCQoNTVVe/fujVRdAADAYNGiRbZL\nQIREZDblrl27tGXLFp199tmR2BwAAIBnlPk05VF5eXmaNm2ahgwZooSEhGMeDwQCCgQCodvp6enG\nGQWn2qRJ1TRypO0q7KhZs2bEtnXgwIGIbSuaeHn/ORnkY0Y+ZuRzrJkzZ4aOiK1atUrp6emSpH79\n+mn48OE2S3MdN+4/8+fPD33t9/vl9/sllXM2ZXFxsSZOnKh27dr9osH7bppN6ba1q9zGbbNR3Ib9\nx4x8zMjHjHzM0tPTw365I5zb9p8KW5vy6aefVsOGDZlFCQAAUEZlPk2ZmZmpFStWqHHjxhoxYoR8\nPp+uv/56tW3bNpL1AQCAUvTr1892CYiQMjdjzZs317x58yJZCwAAOEnDhw9nGEklwdqUAAAAFnm+\nGRs1Kt92CYhi7D9m5GNGPmbkg/KIpv2HtSmZLWhEPmbkY0Y+ZuRjRj5m5GPmtnwqbDYlAAAAyodm\nDAAAwCKaMQAAAItoxgAAACzyfDM2fnyc7RIQxdh/zMjHjHzMyAflEU37j+dnU7pt7Sq3cdtsFLdh\n/zEjHzPyMSMfMz6fzdy2/zCbEgAAwKVoxgAAACyiGQMAALCIZgwAAMAizzdj0bR2FdyH/ceMfMzI\nx4x8UB7RtP94fjYls1HMyMeMfMzIx4x8zMjHjHzM3JYPsykBAABcimYMAADAIpoxAAAAi2jGAAAA\nLPJ8MxZNa1fBfdh/zMjHjHzMyAflEU37j+dnU7pt7Sq3cdtsFLdh/zEjHzPyMSMfMz6fzdy2/zCb\nEgAAwKVoxgAAACyiGQMAALCIZgwAAMAizzdj0bR2FdyH/ceMfMzIx4x8UB7RtP94fjYls1HMyMeM\nfMzIx4x8zMjHjHzM3JYPsykBAABcimYMAADAIpoxAAAAi2jGAAAALPJ8MxZNa1fBfdh/zMjHjHzM\nyAflEU37j+dnU7pt7Sq3cdtsFLdh/zEjHzPyMSMfMz6fzdy2/zCbEgAAwKVoxgAAACyiGQMAALCI\nZgwAAMAizzdj0bR2FdyH/ceMfMzIx4x8UB7RtP94fjYls1HMyMeMfMzIx4x8zMjHjHzM3JaPaTZl\nlfJs+Omnn9aaNWtUq1YtTZ06tTybAgAA8KRynabs1auX7r///kjVAheqWbOm7RIAAKW46667bJeA\nCClXM9a8eXMlJiZGqhYAAHCS3n33XdslIEI8P4AfAADApnKNGTsZgUBAgUAgdDs9Pd04iM2GpKQk\n2yW4loX5HVGH/ceMfMzIx4x8jm/r1q22S3A9t+0/8+fPD33t9/vl9/slnYIjY36/X+np6aF/blMy\nGByLfMzIx4x8zMjHjHzMyMfMjfmU7IeONmJSBJoxx3E4egIAAFBG5TpNOX36dG3YsEE5OTkaOnSo\n0tPT1atXr0jVBgAAUOmVqxn705/+FKk6rCl5mBDHIh8z8jEjHzPyMSMfM/Ixi6Z8rFyBHwAAAEdw\naQsAAACLaMYAAAAsohkDAACwiGYMAADAIpoxAAAQ9RzH0fLly5WRkSFJysrK0saNGy1XdXIqfDkk\nN3IcRytWrNCuXbt03XXXKSsrS9nZ2WratKnt0qx7/vnnj7mvevXqOuuss9SxY0cLFbnHwYMHjY/X\nqFHjFFXiXsFgUHfeeacef/xx26Ugiq1evVobNmyQJLVs2VIdOnSwXJE7LF68WD179lS1atX0zDPP\naPPmzRo4cKDatGljuzRXmDVrlnw+nwKBgK677jolJCToueee04QJE2yXdkKebMai+Q2raIWFhdq+\nfbu6dOkiSfrkk0+UkpKiLVu2KBAIaMiQIXYLtGjkyJHy+Xylrjjh8/k0Y8YMC1W5S0xMjBo0aKCs\nrCzVq1fPdjmulJubq/nz5yszM1PSkWbjuuuuU/Xq1S1X5g4vv/yyNm7cqAsuuECS9M477+jrr7/W\nwIEDLVdm34cffqi+ffvq888/16FDh3TbbbdpxowZNGM/2bhxoyZNmqQRI0ZIOvIHclFRkeWqTo4n\nm7FofsMq2tatWzV27FjFxBw5g33JJZfowQcf1NixY3XXXXdZrs6umTNn2i4hKhw6dEh33nmnmjZt\nqvj4+ND9I0eOtFiVezz11FNq3Lix7rjjDknS8uXL9dRTT+nuu++2XJk7rFmzRpMnTw59BvXs2VMj\nRoygGZNCfwiuXbtWPXr0UKNGjViOsITY2FgFg0H5fD5J0oEDB0Jfu50nm7FofsMq2sGDB5WXlxf6\nKz0/P18HDx5UTEyMqlatark69zh48KB27NihgoKC0H0tW7a0WJF7DBgwwHYJrrZz586wxqt///66\n5557LFbkPrm5uaHT/rm5uZarcY+0tDSNGzdOu3bt0sCBA3X48GF+d5Vw2WWXacqUKdq/f79eeeUV\nffzxx/rtb39ru6yT4slmLJrfsIp25ZVX6p577pHf75fjOPryyy919dVXKy8vT61bt7ZdnissWbJE\nixcv1t69e9WkSRN9/fXXOuecczR69GjbpblCy5YtlZ2drW+//VaS1LRpU9WqVctyVe4RFxenzMxM\nNW/eXJKUmZmpuLg4y1W5x1VXXaURI0aEfQb97ne/s12WK/zxj3/U5s2bVb9+fcXHxysnJ0fDhg2z\nXZZrdO/eXWlpaVq3bp0k6Z577lHDhg0tV3VyPLsc0rZt20JvWKtWraLmDatIjuNoz549io2NDc1A\nOeuss1SnTh3LlbnLXXfdpQkTJuj+++/XlClTtG3bNr3yyiucZvrJRx99pJdeeil0pPDLL7/UoEGD\nQuMQvW7z5s2aOXNm6IhPYmKihg8frl/96leWK7Ov5GdQyWY+OTnZcmXu8J///EetWrUKnbk4dOiQ\nAoGAOnXqZLkyd/j666/VqFEjVatWTdKRo6rbtm3T2WefbbmyE/PkkbGjb9ivf/1rSUfesG+++SYq\n3rCK5PP5NGHCBD366KOenzlpEhcXFzqSUVhYqNTUVG3fvt1yVe7x+uuva8KECaGjYQcOHNDYsWNp\nxnRktun27ds1ZcqUUDPGwP3/KfkZxAzKYy1YsCCs8UpMTFRGRgbN2E9mzZqlSZMmhW4nJCQcc59b\nefI6Y7NmzVJCQkLo9tE3DNKZZ54ZNddlsaVOnTo6dOiQOnbsqHHjxmny5Mk67bTTbJflGsFgMOy0\nZI0aNRQMBi1W5B4xMTF66623JB1pwmjEjsVn0PGVdiKruLjYQiXu5DhO2Bi6mJiYqMnHk0fGovkN\nq2gbN27U/fffr5SUFMXHx4eymjp1qu3SXOPoYOv09HRt2LBBubm5atu2reWq3KNt27Z65JFH1K1b\nN0lHTlu2a9fOclXu0bp1a7311lvq2rVr2B+FXKfuiI0bN+qBBx7QaaedxmfQz6SlpWnOnDm69NJL\nJUnvvfee0tLSLFflHvXr19fixYt1ySWXSJLef/99paSkWK7q5HhyzNjUqVPVsmXLsDds/fr1oUtd\neNnu3btLvZ8jP+GCwaCys7PDjvhwXa3/+eSTT0LX0WrRogWnUUoYPnz4Mfdxnbr/4TPo+PLy8rRw\n4cLQeOdzzz1X11xzTVhT72X79+/X7NmztX79evl8PrVq1UpDhgyJiglEnmzGovkNq2jr16/XDz/8\nIElq2LChWrVqZbki93nnnXeUkZGhWrVqhY6w8pd7uOzsbH3zzTfy+XwMwMYvEs2DsIGy8mQzhmPt\n3btXU6dOVdWqVUOHvTdt2qSCggLdc889zKgs4fbbb9f48eOVlJRkuxRXWrJkiTIyMtSqVavQpQmu\nvfZa9e7d23ZprvDuu++qe/fuSkxMlHTkmnWrVq0KnXryuhEjRmjSpEmhP3SCwaDuvffeqBiEXVFe\neOEFDRkyRBMnTiz1umJcUPmIAwcO6J///Kd2794dNvQoGi7/4ckxY9H8hlWU5557Tpdccol69uwZ\ndv+yZcs0a9YsTuGWUK9ePQZeG7z11luaPHlyqFnNycnRAw88QDP2kyVLloRmcktHxootWbKEZuwn\njOk9Vo8ePSRJv/nNbyxX4m6TJ09W8+bN1bp169AKDtHCk81YNL9hFeWHH34o9SrgF154oV577TUL\nFblXSkqKxowZo/bt24etStCvXz+LVblHUlJS6BSTJFWrVo2jiCUEg8GwhiMYDLIcWwnRPAi7ohw9\nW8EqH2b5+fn6/e9/b7uMMvFkMxbNb1hFOd7Z6mAwyGUJfqZevXqqV6+eioqK+CVawqJFiyRJp59+\nuu677z516NBBPp9Pq1evVuPGjS1X5x5t27bVY489pj59+kiSPvjgA2bjlnDLLbdo9uzZeu2110Jj\nem+99VbbZbnCZ599pnnz5mn37t1hTf2cOXNsl+YK5513ntasWaP27dvbLuUX8+SYsVdffVXnnHNO\nVL5hFeWFF15QXl6ehgwZEpqZk5eXpzlz5qhq1aq66aabLFfoPnl5eZLETKafLFiwwPh4//79T1El\n7hYMBvXPf/4zbEbcRRddxFF6nNDtt9+uu+++W40bN2ZNylLccMMNys/PV5UqVVSlSpWoalY92YxF\n8xtWUYqKivTyyy9r2bJloUs0ZGVl6cILL9TAgQNVpYonD6KWauvWrZoxY4YOHjwo6chpudtuu02N\nGjWyXBkQvd58801deeWVev7550t9nD8IpYceekh/+ctfaNwrIU82Yzi+goIC7dixQ5JCi9Ei3AMP\nPKDf/va3oct+BAIBvfLKKxo3bpzlytzhwIEDevPNN/XDDz+ooKAgdD8LqR/x448/6uWXX9YPP/yg\nwsLC0P1ev87Y6tWr1aFDBy1durTUx38+uciLNm7cqHnz5qlly5aMVz2OgwcPaseOHWGfPdEw1s6z\nhzui9Q2raHFxcYzvOYH8/Pyw66/5/X7l5+dbrMhdnnjiCXXt2lVr1qzRLbfcoqVLl6pmzZq2y3KN\np556Sunp6ZozZ47uu+8+ffjhh8cds+klR9eipOk6vldffVUJCQkqLCxkvGoplixZosWLF2vv3r1q\n0qSJvv76a51zzjlR8YegJ5uxaH7DYF9KSooyMjJC081XrFjh+dleJeXk5Kh3795avHixWrZsqZYt\nW+ree++1XZZrFBQUqHXr1nIcR6eddprS09M1cuRIDRgwwHZpVp3oOmJcS0vat2+fHn30UdtluNbi\nxYs1YcIE3X///Ro9erS2bdumV155xXZZJ8WTzVg0v2Gwb+jQoZo/f37oQ7F58+YaOnSo5arc4+j4\nwtq1a2vNmjWqXbt2aHwdpKpVqyoYDOqMM87Qu+++qzp16oQmg3jZ119/rXr16qlbt25q2rSp7XJc\nqV27dvriiy/Upk0b26W4UlxcnOLi4iRJhYWFSk1N1fbt2y1XdXI82YxF8xtWUTZt2mR8nMVo/6dG\njRoMJja45pprlJubq0GDBmn27NnKzc3V4MGDbZflGkOGDFFBQYFuvPFGzZs3T+vXry91vUqv+dvf\n/qb//ve/WrlypVauXKn27durW7duTIwp4f3339c//vEPValSRbGxsZLk+clnJdWpU0eHDh1Sx44d\nNW7cOCUmJkbNmqaeHMA/ZcoUDRs2TG+//bYCgYASExNVXFzs6VMpDz30kPFxTuGyJEl5vP3227r8\n8sttl4EoUVhYqFWrVmnu3Lnq379/2IoFwMnYsGGDcnNz1bZt26i4GoAnm7GSou0Ngz2bNm1SWlqa\nNmzYUOrjTAA5vqFDh+rpp5+2XYZVjIk6scLCQq1Zs0arVq3S7t27dd5556l3796sjVvC6tWrQ59B\nfr9f5513nuWK3CUYDCo7OzvsYuVHL9f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UCgoEAtrY2NDCwoLpklBndMYAAGhgxWJRiURC\nkhQMBjU2Nma4ItQbnTEAABpYS8vffRPGk82JDfwAADSwcDisixcvSpJs25ZlWfJ4PLJtWy6XS9ls\n1nCFOCnCGAAAgEGMKQEAAAwijAEAABhEGAMAADCIMAYAAGAQYQyA48Tjcb1580aSlEqlFIlE9OjR\nI+XzeT18+PBY93j27JlmZ2eP/DwWi+n169d1qRcA/guHvgJwnIMDMPP5vF69eqV0Oq0LFy5IkmZm\nZo59n4OHLgOASXTGADjWly9f5Pf7a0EMAJyIzhgAx4nFYhocHNTTp09VrVY1PDysUCik7u5uzc7O\nam5uTpK0u7urJ0+e6O3bt/J6vRoYGFB/f/+/3vPFixfK5XIqlUq6devWaX4dAGccnTEAjnTlyhXd\nvXtXN27cUDab1Z07d3753LZtTU1N6dq1a5qfn9f4+LiWl5e1ubl56F7FYlGZTEYPHjxQOp3W9+/f\ntbOzc1pfBcAZRxgD0JTev3+vvb093b59W263W36/Xzdv3tTLly8PrV1dXVVPT486OzvV0tKicDjM\nfjIAp4YxJYCmtLW1pZ2dHUUikdp71WpVXV1dh9bu7u7K5/PVrj0ejy5fvnwqdQIAYQxAU/L5fPL7\n/Uomk/+7tq2tTZ8+fapdl8tl7e3t/cnyAKCGMSWApnT9+nV5vV4tLS3JsixVq1V9/PhRhULh0Nre\n3l6tr6/r3bt32t/fVy6Xk23bBqoGcBbRGQPgOMfZz+V2uzU6OqpsNqv79+9rf39f7e3tGhoaOrS2\no6ND0WhUyWRS5XJZoVDol7ElAPxJLpuffwAAAMYwpgQAADCIMAYAAGAQYQwAAMAgwhgAAIBBhDEA\nAACDCGMAAAAGEcYAAAAMIowBAAAY9Bc4BJaY9xGHQwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x107473e10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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qH8EsgJeWbHCDl2a1uIHPj1nbtm3dLgGotlg5wsxL/SGYBeCCl2YEMzM+P2b1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\nBAszIiIiIpVgYUZERESkEv8H0BTstTGQ5BgAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10747c450>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# http://econweb.ucsd.edu/~gdahl/papers/views-among-economists-online-appendix.pdf\n",
"# ifield is grouped nber classifications: \n",
"# Field categories are defined by primary NBER affiliation: MAC=macro (EFG, ME, POL); INT=international (IFM, ITI);\n",
"# FIN=finance (AP, CF); LAB=labor (LS, ED, AG, DAE, DEV); PF=public finance (PF, EEE); \n",
"# IO=industrial organization (IO, LE). Three panel members are not in the NBER; \n",
"# Ray Fair and James Stock are assigned to MAC, Eric Maskin is assigned to FIN. \n",
"\n",
"df_all.boxplot(column='confidence', by='ifield', rot=90, whis=[5.0,95.0])\n",
"\n",
"df_all.boxplot(column='distance_median', by='ifield', rot=90, whis=[5.0,95.0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Responses by Age Cohort\n",
"\n",
"One final thing to do is look at these characteristics by age cohort, defined as years since their PhD. Confidence doesn't seem to be too different by age cohort, but it does seem like there are more outlier responses in the older groups. Perhaps the older economists have earned the right to say controversial things? "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"4\" halign=\"left\">distance_median</th>\n",
" <th colspan=\"3\" halign=\"left\">confidence</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>std</th>\n",
" <th>count</th>\n",
" <th>median</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>median</th>\n",
" <th>mean</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cohort</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>30+ Years</th>\n",
" <td>0.561107</td>\n",
" <td>3230</td>\n",
" <td>0.454545</td>\n",
" <td>0.633971</td>\n",
" <td>2.413021</td>\n",
" <td>6</td>\n",
" <td>6.013622</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15 - 30 Years</th>\n",
" <td>0.554432</td>\n",
" <td>3510</td>\n",
" <td>0.454545</td>\n",
" <td>0.632673</td>\n",
" <td>2.503507</td>\n",
" <td>6</td>\n",
" <td>5.789744</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0 - 15 Years</th>\n",
" <td>0.524708</td>\n",
" <td>284</td>\n",
" <td>0.272727</td>\n",
" <td>0.523528</td>\n",
" <td>2.111850</td>\n",
" <td>6</td>\n",
" <td>5.795775</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" distance_median confidence \\\n",
" std count median mean std median \n",
"cohort \n",
"30+ Years 0.561107 3230 0.454545 0.633971 2.413021 6 \n",
"15 - 30 Years 0.554432 3510 0.454545 0.632673 2.503507 6 \n",
"0 - 15 Years 0.524708 284 0.272727 0.523528 2.111850 6 \n",
"\n",
" \n",
" mean \n",
"cohort \n",
"30+ Years 6.013622 \n",
"15 - 30 Years 5.789744 \n",
"0 - 15 Years 5.795775 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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Tp05Vdna2HnroIUnSfffdp2HDhundd9/1LvPggw8qLCxMiYmJmjx5soKCglSvXj316tXr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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x107d4b050>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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PPii3XWXn7Xz/uwKwDsEMgCTpiiuukNvtVlpamjp06OB9f/369ef8XLNmzTR06FANHTpU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\nBgAAYBP/HwrDUwsw0WMiAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1094ce990>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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yB58KN4PB4P52WV1djdDQUACXv0n+cLysqqqqdgcRdDqdLQ4PpqWl+RLFb5inY0rKo6Qs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\nzaEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x107d4bad0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_all.boxplot(column='confidence', by='cohort', whis=[5.0,95.0])\n",
"\n",
"df_all.boxplot(column='distance_median', by='cohort', whis=[5.0,95.0])\n",
"\n",
"df_all.hist(column='distance_median', by='cohort', bins=20)\n",
"\n",
"df_all.groupby('cohort').agg({'distance_median':{'mean': 'mean', 'median':'median',\n",
" 'std': 'std', 'count':'count'},\n",
" 'confidence': {'mean': 'mean', 'median':'median',\n",
" 'std': 'std'}}).sort_values(by=('distance_median','mean'),\n",
" ascending=False)\n",
"\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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