Beta Convergence Of Economic Growth-Evidence from 1990-2014

Setup

# Load computational modules import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import plotly.express as px import plotly.graph_objects as go import statsmodels.api as sm import statsmodels.formula.api as smf

Import data

df = pd.read_csv('/work/data/dat.csv') df

idint64

1 - 108

countryobject

Albania0.9%

Algeria0.9%

106 others98.1%

0

1

Albania

1

Albania

2

1

Albania

3

1

Albania

4

1

Albania

5

1

Albania

6

1

Albania

7

1

Albania

8

1

Albania

9

1

Albania

data_definitions = pd.read_csv('/work/data/dat-definitions.csv') data_definitions

var_nameobject

country3.6%

year3.6%

26 others92.9%

var_defobject

Standardized country name (from PWT)3.6%

Year3.6%

26 others92.9%

0

country

Standardized country name (from PWT)

1

year

Year

2

Y

GDP

3

K

Physical Capital

4

pop

Population

5

L

Labor Force

6

s

Years of Schooling

7

alpha_it

Variable Capital Share

8

GDPpc

GDP per capita

9

lp

Labor Productivity

10

h

Human Capital Index

11

kl

Capital per Worker

12

kp

Capital Productivity

13

ky

Capital-Output Ratio

14

TFP

Aggregate Efficiency

15

log_GDPpc_raw

Log of GDP per capita

16

log_lp_raw

Log of Labor Productivity

17

log_ky_raw

Log of Capital-Output Ratio

18

log_h_raw

Log of Human Capital

19

log_tfp_raw

Log of Total Factor Productivity

20

log_GDPpc

Trend (HP400) of log of GDP per capita

21

log_lp

Trend (HP400) of log of labor productivity

22

log_ky

Trend (HP400) of log of Capital-Output Ratio

23

log_h

Trend (HP400) of log of Human Capital

24

log_tfp

Trend (HP400) of log of Aggregate Efficiency

25

region

Regional group (Classification of the UN)

26

hi1990

High income country (as of 1990, World Bank classification)

27

isocode

ISO code from the PWT9.0

Transform data

df1 = df[['country', 'year', 'region', 'hi1990', 'isocode','log_GDPpc']] df1

countryobject

Albania0.9%

Algeria0.9%

106 others98.1%

yearint64

1990 - 2014

0

Albania

1990

1

Albania

1991

2

Albania

1992

3

Albania

1993

4

Albania

1994

5

Albania

1995

6

Albania

1996

7

Albania

1997

8

Albania

1998

9

Albania

1999

10

Albania

2000

11

Albania

2001

12

Albania

2002

13

Albania

2003

14

Albania

2004

15

Albania

2005

16

Albania

2006

17

Albania

2007

18

Albania

2008

19

Albania

2009

20

Albania

2010

21

Albania

2011

22

Albania

2012

23

Albania

2013

24

Albania

2014

dfw = df1.pivot_table( index=['country','region','hi1990','isocode'], columns='year', values='log_GDPpc').reset_index(drop=False) # Make sure the column names are strings dfw.columns = dfw.columns.astype(str) dfw

countryobject

Albania0.9%

Algeria0.9%

106 others98.1%

regionobject

Europe27.8%

Asia25.9%

3 others46.3%

0

Albania

Europe

1

Algeria

Africa

2

Argentina

Americas

3

Armenia

Asia

4

Australia

Oceania

5

Austria

Europe

6

Bangladesh

Asia

7

Belgium

Europe

8

Benin

Africa

9

Bolivia

Americas

dfw['growth']=dfw['2014']-dfw['1990'] dfw

countryobject

Albania0.9%

Algeria0.9%

106 others98.1%

regionobject

Europe27.8%

Asia25.9%

3 others46.3%

0

Albania

Europe

1

Algeria

Africa

2

Argentina

Americas

3

Armenia

Asia

4

Australia

Oceania

5

Austria

Europe

6

Bangladesh

Asia

7

Belgium

Europe

8

Benin

Africa

9

Bolivia

Americas

Describe data

df1.describe().round(2)

yearfloat64

log_GDPpcfloat64

count

2700

mean

2002

8.87

std

7.21

1.24

min

1990

6.13

25%

1996

7.81

50%

2002

8.99

75%

2008

9.95

max

2014

11.37

dfw.describe().round(2)

1990float64

1991float64

count

108

mean

8.54

8.56

std

1.19

min

6.16

6.2

25%

7.57

7.59

50%

8.68

8.71

75%

9.6

9.63

max

10.49

10.51

Data Visualization

px.line(df1, x='year', y='log_GDPpc', color='country', facet_col= 'region')

px.line(df1, x='year', y='log_GDPpc', color='country', facet_col= 'hi1990')

px.choropleth( df1, locations="isocode", color="log_GDPpc", hover_name="country", animation_frame="year", color_continuous_scale='RdBu_r', projection="natural earth")

px.choropleth( dfw, locations="isocode", color="growth", hover_name="country", color_continuous_scale='RdBu_r', projection="natural earth")

px.box( df1, x="log_GDPpc", color="hi1990", hover_name= 'country', animation_frame = 'year' )

px.box( df1, x="log_GDPpc", color="region", hover_name= 'country', animation_frame = 'year' )

px.box( dfw, x="growth", color="region", hover_name= 'country' )

Regression analysis

px.scatter( dfw, x='1990', y='growth', color="hi1990", hover_name="country", trendline="ols")

px.scatter( dfw, x='1990', y='growth', color="region", hover_name="country", trendline="ols")

Beta Convergence Of Economic Growth-Evidence from 1990-2014

Setup

Import data

Transform data

Describe data

Data Visualization

Regression analysis

Conclusion

1. There is sign of beta convergence in High-Income countries

2. There is sign of beta convergence in Asia & Europe& America

5. There is sign of divergence in Africa

3. Europe has relatively faster speed of convergence

4. Economic development disparities in Low-Income countries

Reference

Beta Convergence Of Economic Growth-Evidence from 1990-2014

Setup

Import data

Transform data

Describe data

Data Visualization

Regression analysis

Conclusion

1. There is sign of beta convergence in High-Income countries

2. There is sign of beta convergence in Asia &amp; Europe&amp; America

5. There is sign of divergence in Africa

3. Europe has relatively faster speed of convergence

4. Economic development disparities in Low-Income countries

Reference

2. There is sign of beta convergence in Asia & Europe& America