Setup

import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from scipy import stats import inequality from inequality.gini import Gini_Spatial import plotly.express as px import plotly.graph_objects as go #chart_studio.tools.set_credentials_file(username='econdata777', api_key='ADDhere') import statsmodels.api as sm import statsmodels.formula.api as smf import warnings #warnings.filterwarnings('ignore')

Import data

df1 = pd.read_csv("https://raw.githubusercontent.com/quarcs-lab/mendez2020-convergence-clubs-code-data/master/assets/dat.csv") #df1 = pd.read_csv("https://raw.githubusercontent.com/quarcs-lab/mendez2020-convergence-clubs-code-data/master/assets/dat.csv", parse_dates =['year'])

df1

idint64

1 - 108

countryobject

Zambia0.9%

Italy0.9%

106 others98.1%

2699

108

Zambia

1199

Italy

1324

Kyrgyz Republic

1299

Kenya

1274

Kazakhstan

2649

106

Vietnam

1249

Japan

1224

Jamaica

1174

Israel

1374

Lithuania

#df1.head()

df1.columns

df1.dtypes

df1['country'].unique()

Dataset definitions

# Import definitions of dataset df1_def = pd.read_csv("https://raw.githubusercontent.com/quarcs-lab/mendez2020-convergence-clubs-code-data/master//assets/dat-definitions.csv") df1_def

var_nameobject

country3.6%

year3.6%

26 others92.9%

var_defobject

Standardized country name (from PWT)3.6%

Year3.6%

26 others92.9%

country

Standardized country name (from PWT)

year

Year

GDP

Physical Capital

pop

Population

Labor Force

Years of Schooling

alpha_it

Variable Capital Share

GDPpc

GDP per capita

Labor Productivity

Descriptive statistics

df1.describe().round(2)

idfloat64

yearfloat64

count

2700

mean

54.5

2002

std

31.18

7.21

min

1990

25%

27.75

1996

50%

54.5

2002

75%

81.25

2008

max

108

2014

select

df1.GDPpc

pivot_table

# Pivot panel data from long form to wide form df1_gdp_wide = df1.pivot_table( index = ['country', 'region', 'hi1990'], columns = 'year', values = 'GDPpc').reset_index(drop=False) # Make sure the column names are strings df1_gdp_wide.columns = df1_gdp_wide.columns.astype(str)

df1_gdp_wide

countryobject

Albania0.9%

Algeria0.9%

106 others98.1%

regionobject

Europe27.8%

Asia25.9%

3 others46.3%

Albania

Europe

Algeria

Africa

Argentina

Americas