The relationship between Years of Schooling and GDP

Setup

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 #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

df = pd.read_csv("https://raw.githubusercontent.com/quarcs-lab/mendez2020-convergence-clubs-code-data/master/assets/dat.csv")

df

idint64

1 - 108

countryobject

Zambia0.9%

Italy0.9%

106 others98.1%

2699

108

Zambia

1199

48

Italy

1324

53

Kyrgyz Republic

1299

52

Kenya

1274

51

Kazakhstan

2649

106

Vietnam

1249

50

Japan

1224

49

Jamaica

1174

47

Israel

1374

55

Lithuania

Dataset definitions

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

Descriptive statistics

df.describe().round(2)

idfloat64

yearfloat64

count

2700

mean

54.5

2002

std

31.18

7.21

min

1

1990

25%

27.75

1996

50%

54.5

2002

75%

81.25

2008

max

108

2014

Prepare data

select

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

Data Visualization

px.treemap(df1.query("year==1990"),color="log_GDPpc", values ="pop", path=("region","country"), hover_name="country", height=500)

px.choropleth( df1.query("year==1990"), locations="isocode", animation_frame="year", color="s", hover_name="country", projection="natural earth")

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

px.scatter(df1, animation_frame="year", x="s", y="log_lp", range_x= [0, 16], range_y= [7, 12.2], color="region", size="pop", size_max=60, hover_name="country", labels=dict(s="Years of Schooling", log_lp="Labor productivity(in Logs)", region="Continent", pop= "Population") )

px.scatter( df1, animation_frame="year", x="s", y="log_GDPpc", hover_name="country", color="hi1990", trendline="ols",)

References

Plotly Express：https://deepnote.com/@carlos-mendez/PYTHON-Plotly-Express-3e900d54-8143-405b-8be2-266157d4ec75