Democracy Status

Polity

Here is democracy status from the Polity dataset. Their DEMOC and AUTOC columns contain the level of insititutional leanings of a country, POLITY column is one subtracted from the other, it ranges btw -10 and +10, the latter being full democracy.

import pandas as pd
df = pd.read_excel('../../mbl/2020/p4v2018.xls')
df = df[df.year > 1950]
df.loc[df.polity < -10, 'polity'] = np.nan
df['polity'] = df['polity'].interpolate(method='linear')
polity = df.groupby('year')['polity'].mean()
polity.plot()
plt.savefig('polity-all.png')

BTI

Data comes from the BTI Transformation Index that evaluates aspects of governance for selected countries, recently updated for 2020.

import pandas as pd
df = pd.read_stata('../../mbl/2020/BTI 2006-2020.dta')
df1 = df[['country','dem_stat','year']]
df1 = df1[df1.country.isin(['Russia','Turkey','Iran','China','Ukraine'])]
df1=df1.pivot(index='year', columns='country', values='dem_stat')
df1.plot()
plt.savefig('bti-compare.png')

import pandas as pd
df = pd.read_stata('../../mbl/2020/BTI 2006-2020.dta')
g = df.groupby('year').dem_stat.mean()
g.plot()
plt.savefig('bti-all.png')

Democracy Barometer

Data

import zipfile
with zipfile.ZipFile('db.zip', 'r') as z:
   df = pd.read_csv(z.open('DB_data_2020_all_data_Standardised.csv'),sep=';') 
   df = df[['Year','DQ']].groupby('Year').mean()   
   print (df.tail(5))
   df.plot()

plt.savefig('db-all.png')

            DQ
Year          
2013  3.870913
2014  3.855549
2015  3.857140
2016  3.864331
2017  3.821315

The Fragile States Index

The Fragile States Index (FSI) produced by The Fund for Peace (FFP), attemps to highlight pressures that states experience, through its various measures and a final score (lower is better) that shows how fragile a state is.

import pandas as pd
df = pd.read_csv('fsi.csv')
df['Year2'] = df.apply(lambda x: pd.to_datetime(x['Year']).year, axis=1)
df = df[df.Country.isin(['United States','France','Turkey'])]

c = 'P2: Public Services'
df2 = df[['Country','Year2',c]]
df2=df2.pivot(index='Year2', columns='Country', values=c)
df2.plot(title=c)
plt.savefig('fsi1.png')

c = 'P1: State Legitimacy'
df2 = df[['Country','Year2',c]]
df2=df2.pivot(index='Year2', columns='Country', values=c)
df2.plot(title=c)
plt.savefig('fsi2.png')

Code

Coups

Worldwide annual counts of military interventions. Based on Cline Center Coup D’état Project Dataset

import pandas as pd
df = pd.read_csv('Coup_Data_v2.0.0.csv')
df = df[df['event_type'] == 'coup']
g = df.groupby('year').size()
print (g.tail(5))

year
2013    1
2014    3
2015    2
2017    1
2019    3
dtype: int64

g.plot()
plt.savefig('coups.png')