Working with GapMinder
¶

GapMinder
  • GapMinder is a teaching project that identifies systematic misconceptions about important global trends and proportions and uses reliable data to develop easy to understand teaching materials to rid people of their misconceptions.
  • Compiles and makes available many useful cross-country data sources
  • Free and easy to access (once you understand how)
  • Lot's of variables are available, from multiple sources covering the period after 1800.

Setup¶

Import Modules and set Paths¶

In [1]:
# Basic Packages
from __future__ import division
import os
from datetime import datetime

# Web & file access
import requests
import io

# Import display options for showing websites
from IPython.display import IFrame, HTML
In [2]:
# Data
import pandas as pd
import numpy as np
from pandas_datareader import data, wb

# GIS & maps
import geopandas as gpd
gp = gpd
import georasters as gr
import geoplot as gplt
import geoplot.crs as gcrs
import mapclassify as mc
import textwrap
In [3]:
# Plotting
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick

%pylab --no-import-all
%matplotlib inline

import seaborn as sns
sns.set(rc={'figure.figsize':(11.7,8.27)})
sns.set_context("talk")

import plotly.express as px
import plotly.graph_objects as go

from plotnine import ggplot, geom_point, aes, stat_smooth, facet_wrap
# Next line can import all of plotnine, but may overwrite things? Better import each function/object you need
#from plotnine import *

Using matplotlib backend: <object object at 0x152374fa0>
%pylab is deprecated, use %matplotlib inline and import the required libraries.
Populating the interactive namespace from numpy and matplotlib
In [4]:
# Data Munging
from itertools import product, combinations
import difflib
import pycountry
import geocoder
from geonamescache.mappers import country
mapper = country(from_key='name', to_key='iso3')
mapper2 = country(from_key='iso3', to_key='iso')
mapper3 = country(from_key='iso3', to_key='name')

# Regressions & Stats
from scipy.stats import norm
import statsmodels.formula.api as smf
from stargazer.stargazer import Stargazer, LineLocation
In [5]:
# Paths
pathout = './data/'

if not os.path.exists(pathout):
    os.mkdir(pathout)
    
pathgraphs = './graphs/'
if not os.path.exists(pathgraphs):
    os.mkdir(pathgraphs)
In [6]:
currentYear = datetime.now().year
year = min(2020, currentYear-2)

Getting data from GapMinder¶

There are two ways of getting data from GapMinder:

  1. Use GapMinder Data Website and select a series of interest and download it as a CSV or Excel file.

  2. Download the series of interest from GapMinder's Github reporsitories:

    • Systema Globalis (indicators inherited from Gapminder World, many are still updated)
    • Fast Track (indicators they compile manually)
    • World Development Indicators (WDI) (direct copy from World Bank repository)

Below we will access GapMinder's data via Github, since it is much easier and efficient.

Note: Although GapMinder provides WDI data, it is better to use the approach shown in the Working with World Development Indicators notebook.

GapMinder Data Website¶

So, we can get data from their GitHub site.

Two approaches:

  1. Clone repository and process (we could process everything to create one giant database)
  2. Access specific files we are interested in

Here we'll follow approach 2

Let's start by getting country names, codes, etc.¶

In [93]:
url = 'https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/'
file = 'ddf--entities--geo--country.csv'
countries_gm = pd.read_csv(url + file, 
                           encoding='utf-8', keep_default_na=False, na_values='')
countries_gm.head()
Out[93]:
country g77_and_oecd_countries income_3groups income_groups is--country iso3166_1_alpha2 iso3166_1_alpha3 iso3166_1_numeric iso3166_2 landlocked ... name un_sdg_ldc un_sdg_region un_state unhcr_region unicef_region unicode_region_subtag west_and_rest world_4region world_6region
0 abkh others NaN NaN True NaN NaN NaN NaN NaN ... Abkhazia NaN NaN False NaN NaN NaN NaN europe europe_central_asia
1 abw others high_income high_income True AW ABW 533.0 NaN coastline ... Aruba un_not_least_developed un_latin_america_and_the_caribbean False unhcr_americas NaN AW NaN americas america
2 afg g77 low_income low_income True AF AFG 4.0 NaN landlocked ... Afghanistan un_least_developed un_central_and_southern_asia True unhcr_asia_pacific sa AF rest asia south_asia
3 ago g77 middle_income lower_middle_income True AO AGO 24.0 NaN coastline ... Angola un_least_developed un_sub_saharan_africa True unhcr_southern_africa ssa AO rest africa sub_saharan_africa
4 aia others NaN NaN True AI AIA 660.0 NaN coastline ... Anguilla un_not_least_developed un_latin_america_and_the_caribbean False unhcr_americas NaN AI NaN americas america

5 rows × 23 columns

Now let's get Life-Expectancy Data¶

In [94]:
url = 'https://raw.githubusercontent.com/open-numbers/ddf--gapminder--gapminder_world/master/'
file = 'ddf--datapoints--life_expectancy_years--by--geo--time.csv'
life_exp = pd.read_csv(url + file, 
                           encoding='utf-8', keep_default_na=False, na_values='')
life_exp.head()
Out[94]:
geo life_expectancy_years time
0 afg 28.21 1800
1 afg 28.20 1801
2 afg 28.19 1802
3 afg 28.18 1803
4 afg 28.17 1804

Since it includes projections, let's drop values after {{year}}¶

In [95]:
life_exp = life_exp.loc[life_exp.time<=year].reset_index(drop=True)
life_exp.head()
Out[95]:
geo life_expectancy_years time
0 afg 28.21 1800
1 afg 28.20 1801
2 afg 28.19 1802
3 afg 28.18 1803
4 afg 28.17 1804

Let's get GDPpc¶

In [96]:
url = 'https://raw.githubusercontent.com/open-numbers/ddf--gapminder--gdp_per_capita_cppp/master/'
file = 'ddf--datapoints--income_per_person_gdppercapita_ppp_inflation_adjusted--by--geo--time.csv'
gdppc_gm = pd.read_csv(url + file, 
                           encoding='utf-8', keep_default_na=False, na_values='')
gdppc_gm.head()
Out[96]:
geo time income_per_person_gdppercapita_ppp_inflation_adjusted
0 afg 1800 683
1 afg 1801 683
2 afg 1802 683
3 afg 1803 683
4 afg 1804 683

Since it includes projections, let's drop values after {{year}}¶

In [11]:
gdppc_gm = gdppc_gm.loc[gdppc_gm.time<=year].reset_index(drop=True)
gdppc_gm.head()
Out[11]:
geo time income_per_person_gdppercapita_ppp_inflation_adjusted
0 afg 1800 683
1 afg 1801 683
2 afg 1802 683
3 afg 1803 683
4 afg 1804 683
... ... ... ...
43090 zwe 2016 3678
43091 zwe 2017 3796
43092 zwe 2018 3923
43093 zwe 2019 3630
43094 zwe 2020 3374

43095 rows × 3 columns

Let's get TFR¶

In [102]:
url = 'https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/countries-etc-datapoints/'
file = 'ddf--datapoints--children_per_woman_total_fertility--by--geo--time.csv'
tfr_gm = pd.read_csv(url + file, 
                           encoding='utf-8', keep_default_na=False, na_values='')
tfr_gm.head()
Out[102]:
geo time children_per_woman_total_fertility
0 abw 1800 5.64
1 abw 1801 5.64
2 abw 1802 5.64
3 abw 1803 5.64
4 abw 1804 5.64

Since it includes projections, let's drop values after {{year}}¶

In [98]:
tfr_gm = tfr_gm.loc[tfr_gm.time<=year].reset_index(drop=True)
tfr_gm.head()
Out[98]:
geo time children_per_woman_total_fertility
0 abw 1800 5.64
1 abw 1801 5.64
2 abw 1802 5.64
3 abw 1803 5.64
4 abw 1804 5.64

Let's get CDR¶

In [97]:
url = 'https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/countries-etc-datapoints/'
file = 'ddf--datapoints--crude_death_rate_deaths_per_1000_population--by--geo--time.csv'
cdr_gm = pd.read_csv(url + file, 
                           encoding='utf-8', keep_default_na=False, na_values='')
cdr_gm = cdr_gm.loc[cdr_gm.time<=year].reset_index(drop=True)
cdr_gm.head()
Out[97]:
geo time crude_death_rate_deaths_per_1000_population
0 abw 1950 10.383
1 abw 1951 10.029
2 abw 1952 9.394
3 abw 1953 8.858
4 abw 1954 8.331

Let's get CBR¶

In [103]:
url = 'https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/countries-etc-datapoints/'
file = 'ddf--datapoints--crude_birth_rate_births_per_1000_population--by--geo--time.csv'
cbr_gm = pd.read_csv(url + file, 
                           encoding='utf-8', keep_default_na=False, na_values='')
cbr_gm = cbr_gm.loc[cbr_gm.time<=year].reset_index(drop=True)
cbr_gm.head()
Out[103]:
geo time crude_birth_rate_births_per_1000_population
0 abw 1800 39.51
1 abw 1801 39.51
2 abw 1802 39.51
3 abw 1803 39.51
4 abw 1804 39.51

Let's get Contraception use¶

In [99]:
url = 'https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/countries-etc-datapoints/'
file = 'ddf--datapoints--contraceptive_use_percent_of_women_ages_15_49--by--geo--time.csv'
contraception_gm = pd.read_csv(url + file, 
                           encoding='utf-8', keep_default_na=False, na_values='')
contraception_gm = contraception_gm.loc[contraception_gm.time<=year].reset_index(drop=True)
contraception_gm.head()
Out[99]:
geo time contraceptive_use_percent_of_women_ages_15_49
0 afg 2000 5.3
1 afg 2003 10.3
2 afg 2005 13.6
3 afg 2006 18.6
4 afg 2008 22.8

Let's get Food Supply¶

In [100]:
url = 'https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/countries-etc-datapoints/'
file = 'ddf--datapoints--food_supply_kilocalories_per_person_and_day--by--geo--time.csv'
food_gm = pd.read_csv(url + file, 
                           encoding='utf-8', keep_default_na=False, na_values='')
food_gm = food_gm.loc[food_gm.time<=year].reset_index(drop=True)
food_gm.head()
Out[100]:
geo time food_supply_kilocalories_per_person_and_day
0 afg 1961 2999
1 afg 1962 2917
2 afg 1963 2698
3 afg 1964 2953
4 afg 1965 2956

Let's get GDP per worker¶

In [101]:
url = 'https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/countries-etc-datapoints/'
file = 'ddf--datapoints--gdpperemployee_us_inflation_adjusted--by--geo--time.csv'
gdppc_pw_gm = pd.read_csv(url + file, 
                           encoding='utf-8', keep_default_na=False, na_values='')
gdppc_pw_gm = gdppc_pw_gm.loc[cbr_gm.time<=year].reset_index(drop=True)
gdppc_pw_gm.head()
Out[101]:
geo time gdpperemployee_us_inflation_adjusted
0 afg 1991 2393.89
1 afg 1992 2226.67
2 afg 1993 1529.35
3 afg 1994 1100.26
4 afg 1995 1550.83

Merge¶

In [19]:
df = countries_gm.merge(life_exp, left_on='country', right_on='geo', how='right')
print(df.shape)
df = df.merge(gdppc_gm, on=['geo', 'time'], how='inner')
print(df.shape)
df = df.merge(tfr_gm, on=['geo', 'time'], how='left')
df = df.merge(cbr_gm, on=['geo', 'time'], how='left')
df = df.merge(cdr_gm, on=['geo', 'time'], how='left')
df = df.merge(contraception_gm, on=['geo', 'time'], how='left')
df = df.merge(food_gm, on=['geo', 'time'], how='left')
df = df.merge(gdppc_pw_gm, on=['geo', 'time'], how='left')
df['year'] = df['time']
df

(43444, 26)
(40303, 27)
Out[19]:
country g77_and_oecd_countries income_3groups income_groups is--country iso3166_1_alpha2 iso3166_1_alpha3 iso3166_1_numeric iso3166_2 landlocked ... life_expectancy_years time income_per_person_gdppercapita_ppp_inflation_adjusted children_per_woman_total_fertility crude_birth_rate_births_per_1000_population crude_death_rate_deaths_per_1000_population contraceptive_use_percent_of_women_ages_15_49 food_supply_kilocalories_per_person_and_day gdpperemployee_us_inflation_adjusted year
0 afg g77 low_income low_income True AF AFG 4.0 NaN landlocked ... 28.21 1800 683 7.00 48.14 NaN NaN NaN NaN 1800
1 afg g77 low_income low_income True AF AFG 4.0 NaN landlocked ... 28.20 1801 683 7.00 48.14 NaN NaN NaN NaN 1801
2 afg g77 low_income low_income True AF AFG 4.0 NaN landlocked ... 28.19 1802 683 7.00 48.14 NaN NaN NaN NaN 1802
3 afg g77 low_income low_income True AF AFG 4.0 NaN landlocked ... 28.18 1803 683 7.00 48.14 NaN NaN NaN NaN 1803
4 afg g77 low_income low_income True AF AFG 4.0 NaN landlocked ... 28.17 1804 683 7.00 48.14 NaN NaN NaN NaN 1804
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
40298 ssd NaN low_income low_income True SS SSD 728.0 NaN landlocked ... 56.70 2011 4190 5.29 37.91 11.600 NaN NaN 2139.73 2011
40299 ssd NaN low_income low_income True SS SSD 728.0 NaN landlocked ... 56.80 2012 2196 5.20 37.51 11.104 NaN NaN 2047.46 2012
40300 ssd NaN low_income low_income True SS SSD 728.0 NaN landlocked ... 57.20 2013 2426 5.11 37.13 11.136 NaN NaN 2258.47 2013
40301 ssd NaN low_income low_income True SS SSD 728.0 NaN landlocked ... 57.60 2014 2461 5.02 36.76 11.493 NaN NaN 2282.65 2014
40302 ssd NaN low_income low_income True SS SSD 728.0 NaN landlocked ... 58.00 2015 2162 4.94 36.40 11.072 NaN NaN 1998.36 2015

40303 rows × 34 columns

Let's get country groups etc from WDI as before¶

Steps¶

In [20]:
wbcountries = wb.get_countries()
wbcountries = wbcountries.loc[wbcountries.region.isin(['Aggregates'])==False].reset_index(drop=True)
wbcountries['name'] = wbcountries.name.str.strip()
wbcountries['incomeLevel'] = wbcountries['incomeLevel'].str.title()
wbcountries.loc[wbcountries.iso3c=='VEN', 'incomeLevel'] = 'Upper Middle Income'
In [21]:
df['iso3c'] = df['country'].str.upper()
wdi = wbcountries.merge(df, on='iso3c', suffixes=['', '_GM'])
wdi.head()
Out[21]:
iso3c iso2c name region adminregion incomeLevel lendingType capitalCity longitude latitude ... life_expectancy_years time income_per_person_gdppercapita_ppp_inflation_adjusted children_per_woman_total_fertility crude_birth_rate_births_per_1000_population crude_death_rate_deaths_per_1000_population contraceptive_use_percent_of_women_ages_15_49 food_supply_kilocalories_per_person_and_day gdpperemployee_us_inflation_adjusted year
0 AFG AF Afghanistan South Asia South Asia Low Income IDA Kabul 69.1761 34.5228 ... 28.21 1800 683 7.0 48.14 NaN NaN NaN NaN 1800
1 AFG AF Afghanistan South Asia South Asia Low Income IDA Kabul 69.1761 34.5228 ... 28.20 1801 683 7.0 48.14 NaN NaN NaN NaN 1801
2 AFG AF Afghanistan South Asia South Asia Low Income IDA Kabul 69.1761 34.5228 ... 28.19 1802 683 7.0 48.14 NaN NaN NaN NaN 1802
3 AFG AF Afghanistan South Asia South Asia Low Income IDA Kabul 69.1761 34.5228 ... 28.18 1803 683 7.0 48.14 NaN NaN NaN NaN 1803
4 AFG AF Afghanistan South Asia South Asia Low Income IDA Kabul 69.1761 34.5228 ... 28.17 1804 683 7.0 48.14 NaN NaN NaN NaN 1804

5 rows × 44 columns

Regression Analysis with¶

statsmodels
In [22]:
url = 'https://www.statsmodels.org/stable/index.html'
IFrame(url, width=800, height=400)
Out[22]:

Linear Regressions using OLS¶

It is very easy to run a regression in statsmodels.

We only need

  • Data in a pandas dataframe
  • An equation we want to estimate

Equations are strings of the form

'dependent_variable ~ indep_var_1 + function(indep_var2) + C(indep_var3)'

where:

  • dependent_variable is the outcome variable of interest
  • indep_var_1 is the first independent variable
  • function(indep_var2) is a function of another independent variable (if needed)
  • C(indep_var3) defines fixed-effects/dummies based on categories given in indep_var3

Simple Regression of Log[Life Expectancy] and Log[GDP pc]¶

In [23]:
wdi['ln_life_exp'] = wdi['life_expectancy_years'].apply(np.log)
wdi['ln_gdp_pc'] = wdi['income_per_person_gdppercapita_ppp_inflation_adjusted'].apply(np.log)
wdi['tfr'] = wdi['children_per_woman_total_fertility']
wdi['life_exp'] = wdi['life_expectancy_years']
wdi['gdp_pc'] = wdi['income_per_person_gdppercapita_ppp_inflation_adjusted']
In [24]:
year = wdi['year'].max()
In [25]:
yvar = 'ln_life_exp'
xvar = 'ln_gdp_pc'
zvar = 'tfr'
In [26]:
dffig = wdi.loc[wdi.year==year]\
            .dropna(subset=[xvar, yvar, zvar])\
            .sort_values(by='region').reset_index(drop=True)
dffig.head()
Out[26]:
iso3c iso2c name region adminregion incomeLevel lendingType capitalCity longitude latitude ... crude_death_rate_deaths_per_1000_population contraceptive_use_percent_of_women_ages_15_49 food_supply_kilocalories_per_person_and_day gdpperemployee_us_inflation_adjusted year ln_life_exp ln_gdp_pc tfr life_exp gdp_pc
0 PRK KP Korea, Dem. People's Rep. East Asia & Pacific East Asia & Pacific (excluding high income) Low Income Not classified Pyongyang 125.754 39.03190 ... 8.088 NaN 2061.0 1110.23 2015 4.268298 7.625107 1.92 71.4 2049
1 FSM FM Micronesia, Fed. Sts. East Asia & Pacific East Asia & Pacific (excluding high income) Lower Middle Income IDA Palikir 158.185 6.91771 ... 5.008 NaN NaN NaN 2015 4.204693 8.145550 3.19 67.0 3448
2 MNG MN Mongolia East Asia & Pacific East Asia & Pacific (excluding high income) Lower Middle Income IBRD Ulaanbaatar 106.937 47.91290 ... 6.158 NaN 2483.0 9538.68 2015 4.178992 9.306559 2.79 65.3 11010
3 THA TH Thailand East Asia & Pacific East Asia & Pacific (excluding high income) Upper Middle Income IBRD Bangkok 100.521 13.73080 ... 6.724 NaN 2782.0 10198.24 2015 4.318821 9.698000 1.50 75.1 16285
4 LAO LA Lao PDR East Asia & Pacific East Asia & Pacific (excluding high income) Lower Middle Income IDA Vientiane 102.177 18.58260 ... 7.513 NaN 2738.0 2981.26 2015 4.195697 8.786304 2.76 66.4 6544

5 rows × 49 columns

In [27]:
mod = smf.ols(formula='ln_life_exp ~ ln_gdp_pc', data=dffig, missing='drop').fit()
In [28]:
mod.summary2()
Out[28]:
Model: OLS Adj. R-squared: 0.647
Dependent Variable: ln_life_exp AIC: -470.7658
Date: 2024-02-22 09:01 BIC: -464.3359
No. Observations: 184 Log-Likelihood: 237.38
Df Model: 1 F-statistic: 336.1
Df Residuals: 182 Prob (F-statistic): 3.29e-43
R-squared: 0.649 Scale: 0.0044842
Coef. Std.Err. t P>|t| [0.025 0.975]
Intercept 3.5419 0.0398 89.0717 0.0000 3.4635 3.6204
ln_gdp_pc 0.0781 0.0043 18.3335 0.0000 0.0697 0.0865
Omnibus: 61.936 Durbin-Watson: 1.817
Prob(Omnibus): 0.000 Jarque-Bera (JB): 167.770
Skew: -1.422 Prob(JB): 0.000
Kurtosis: 6.714 Condition No.: 76

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

Plot Data and OLS Regression Predictions¶

In [29]:
pred_ols = mod.get_prediction()
iv_l = pred_ols.summary_frame()["mean_ci_lower"]
iv_u = pred_ols.summary_frame()["mean_ci_upper"]

fig, ax = plt.subplots(figsize=(8, 6))
ax.plot(dffig[xvar], dffig[yvar], "o", label="data")
ax.plot(dffig[xvar], mod.fittedvalues, "r--.", label="OLS")
ax.plot(dffig[xvar], iv_u, "r--")
ax.plot(dffig[xvar], iv_l, "r--")
ax.legend(loc="best")
Out[29]:
<matplotlib.legend.Legend at 0x163c25430>
No description has been provided for this image
In [30]:
fig
Out[30]:
No description has been provided for this image

Simple Regression of Log[Life Expectancy] and Log[GDP pc] for WB region dummies¶

In [31]:
mod2 = smf.ols(formula='ln_life_exp ~ ln_gdp_pc + C(region)', data=dffig, missing='drop').fit()
In [32]:
mod2.summary2()
Out[32]:
Model: OLS Adj. R-squared: 0.745
Dependent Variable: ln_life_exp AIC: -524.8326
Date: 2024-02-22 09:01 BIC: -499.1131
No. Observations: 184 Log-Likelihood: 270.42
Df Model: 7 F-statistic: 77.35
Df Residuals: 176 Prob (F-statistic): 2.26e-50
R-squared: 0.755 Scale: 0.0032382
Coef. Std.Err. t P>|t| [0.025 0.975]
Intercept 3.7983 0.0474 80.1247 0.0000 3.7047 3.8919
C(region)[T.Europe & Central Asia] 0.0208 0.0147 1.4109 0.1600 -0.0083 0.0499
C(region)[T.Latin America & Caribbean ] 0.0151 0.0152 0.9891 0.3240 -0.0150 0.0451
C(region)[T.Middle East & North Africa] 0.0294 0.0169 1.7354 0.0844 -0.0040 0.0627
C(region)[T.North America] 0.0255 0.0426 0.5985 0.5503 -0.0586 0.1097
C(region)[T.South Asia] -0.0052 0.0231 -0.2255 0.8219 -0.0509 0.0405
C(region)[T.Sub-Saharan Africa ] -0.0911 0.0149 -6.1297 0.0000 -0.1205 -0.0618
ln_gdp_pc 0.0518 0.0050 10.2953 0.0000 0.0419 0.0617
Omnibus: 38.972 Durbin-Watson: 2.290
Prob(Omnibus): 0.000 Jarque-Bera (JB): 79.400
Skew: -0.986 Prob(JB): 0.000
Kurtosis: 5.543 Condition No.: 111

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

Simple Regression of Log[Life Expectancy] and Log[GDP pc] and TFR, accounting for WB region dummies¶

In [33]:
mod3 = smf.ols(formula='ln_life_exp ~ ln_gdp_pc + tfr + C(region)', data=dffig, missing='drop').fit()
In [34]:
mod3.summary2()
Out[34]:
Model: OLS Adj. R-squared: 0.754
Dependent Variable: ln_life_exp AIC: -530.3147
Date: 2024-02-22 09:01 BIC: -501.3803
No. Observations: 184 Log-Likelihood: 274.16
Df Model: 8 F-statistic: 71.00
Df Residuals: 175 Prob (F-statistic): 6.11e-51
R-squared: 0.764 Scale: 0.0031269
Coef. Std.Err. t P>|t| [0.025 0.975]
Intercept 3.9325 0.0682 57.6766 0.0000 3.7979 4.0670
C(region)[T.Europe & Central Asia] 0.0161 0.0146 1.1047 0.2708 -0.0127 0.0449
C(region)[T.Latin America & Caribbean ] 0.0115 0.0150 0.7662 0.4446 -0.0181 0.0412
C(region)[T.Middle East & North Africa] 0.0355 0.0168 2.1151 0.0358 0.0024 0.0686
C(region)[T.North America] 0.0279 0.0419 0.6669 0.5057 -0.0548 0.1107
C(region)[T.South Asia] -0.0095 0.0228 -0.4171 0.6771 -0.0545 0.0355
C(region)[T.Sub-Saharan Africa ] -0.0687 0.0168 -4.0911 0.0001 -0.1019 -0.0356
ln_gdp_pc 0.0419 0.0061 6.8217 0.0000 0.0298 0.0541
tfr -0.0168 0.0062 -2.6950 0.0077 -0.0290 -0.0045
Omnibus: 56.848 Durbin-Watson: 2.341
Prob(Omnibus): 0.000 Jarque-Bera (JB): 168.323
Skew: -1.255 Prob(JB): 0.000
Kurtosis: 6.956 Condition No.: 164

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

Producing a nice table with stargazer¶

In [35]:
url = 'https://nbviewer.org/github/mwburke/stargazer/blob/master/examples.ipynb'
IFrame(url, width=800, height=400)
Out[35]:

Add the estimated models to Stargazer¶

In [36]:
stargazer = Stargazer([mod, mod2, mod3])
In [37]:
stargazer.significant_digits(2)
stargazer.show_degrees_of_freedom(False)
#stargazer.dep_var_name = ''
stargazer.dependent_variable = ' Log[Life Expectancy (' + str(year) + ')]'
stargazer.custom_columns(['Simple', 'WB Regs', 'TFR'], [1, 1, 1])
#stargazer.show_model_numbers(False)
stargazer.rename_covariates({'ln_gdp_pc':' Log[GDP per capita (' + str(year) + ')]',
                             'tfr':'Total Fertility Rate (' + str(year) + ')'})
stargazer.add_line('WB Region FE', ['No', 'Yes', 'Yes'], LineLocation.FOOTER_TOP)
stargazer.covariate_order(['ln_gdp_pc', 'tfr'])
stargazer.cov_spacing = 2
In [38]:
stargazer
Out[38]:
Dependent variable: Log[Life Expectancy (2015)]
SimpleWB RegsTFR
(1)(2)(3)
Log[GDP per capita (2015)]0.08***0.05***0.04***
(0.00)(0.01)(0.01)
Total Fertility Rate (2015)-0.02***
(0.01)
WB Region FENoYesYes
Observations184184184
R20.650.750.76
Adjusted R20.650.740.75
Residual Std. Error0.070.060.06
F Statistic336.12***77.35***71.00***
Note: *p<0.1; **p<0.05; ***p<0.01

To show the table¶

HTML(stargazer.render_html())
In [39]:
HTML(stargazer.render_html())
Out[39]:
Dependent variable: Log[Life Expectancy (2015)]
SimpleWB RegsTFR
(1)(2)(3)
Log[GDP per capita (2015)]0.08***0.05***0.04***
(0.00)(0.01)(0.01)
Total Fertility Rate (2015)-0.02***
(0.01)
WB Region FENoYesYes
Observations184184184
R20.650.750.76
Adjusted R20.650.740.75
Residual Std. Error0.070.060.06
F Statistic336.12***77.35***71.00***
Note: *p<0.1; **p<0.05; ***p<0.01

To export the table to another file¶

In [40]:
file_name = "gapminder_table.html" #Include directory path if needed
html_file = open(pathgraphs + file_name, "w" ) #This will overwrite an existing file
html_file.write( stargazer.render_html() )
html_file.close()
In [112]:
url = pathgraphs + 'table.html'
url = 'https://smu-econ-growth.github.io/EconGrowthUG-Slides-Working-with-GapMinder/gapminder_table.html'
IFrame(url, width=500, height=400)
Out[112]:

Plotting GapMinder data¶

Many options¶

  • Since the data is a pandas dataframe, we could just use its functions as we did previously
  • Use the seaborn package
  • Use the plotly package
  • Use the plotnine package

Plots with¶

seaborn
In [42]:
url = 'https://seaborn.pydata.org/examples/index.html'
IFrame(url, width=800, height=400)
Out[42]:

Let's create a Scatterplot with varying point sizes and hues that plots the latitude and Log[GDP per capita] of each country and uses its log-population and the WB region in the last available year as the size and hue.

Using relplot¶

In [43]:
sns.set(rc={'figure.figsize':(11.7,8.27)})
sns.set_context("talk")

g = sns.relplot(x="ln_gdp_pc", 
                y="ln_life_exp", 
                data=dffig,
                hue="region",
                hue_order = dffig.region.drop_duplicates().sort_values(),
                style="region",
                style_order = dffig.region.drop_duplicates().sort_values(),
                size="tfr",
                sizes=(10, 400), 
                alpha=.5, 
                height=6,
                aspect=2,
                palette="muted",
               )
g.set_axis_labels('Log[GDP per capita (' + str(year) + ')]', 'Log[Life Expectancy (' + str(year) + ')]')
Out[43]:
<seaborn.axisgrid.FacetGrid at 0x15cfbf820>
No description has been provided for this image
In [44]:
g.fig
Out[44]:
No description has been provided for this image

Using scatterplot¶

In [45]:
sns.set(rc={'figure.figsize':(11.7,8.27)})
sns.set_context("talk")

fig, ax = plt.subplots()
sns.scatterplot(x="ln_gdp_pc", 
                y="ln_life_exp", 
                data=dffig,
                hue="region",
                hue_order = dffig.region.drop_duplicates().sort_values(),
                style="region",
                style_order = dffig.region.drop_duplicates().sort_values(),
                size="tfr",
                sizes=(10, 400), 
                alpha=.5, 
                palette="muted",
                ax=ax
               )
ax.set_xlabel('Latitude')
ax.set_ylabel('Log[GDP per capita (' + str(year) + ')]')
ax.legend(fontsize=10)
Out[45]:
<matplotlib.legend.Legend at 0x15cd69640>
No description has been provided for this image
In [46]:
fig
Out[46]: