from IPython.display import YouTubeVideo YouTubeVideo('jDlHNdIeEJk')

import numpy as np import pandas as pd #import geopandas as gpd import matplotlib.pyplot as plt import seaborn as sns #import statsmodels.formula.api as smf #import statsmodels.base.model as smclass #import linearmodels as plm # Set diplay options to two decimals pd.set_option('display.float_format', '{:.2f}'.format)

# import data with multi-index df1 = pd.read_stata('https://www.rug.nl/ggdc/docs/pwt91.dta') df1 = df1.set_index(["country", "year"]) df1

## consumption per capita df1["cPop"] = df1["rconna"]/df1["pop"] ## labor productivity (Output per worker) df1["lp"] = df1["rgdpna"]/df1["emp"] ## labor force participation rate df1["ePop"] = df1["emp"]/df1["pop"] ## non-savings rate df1["ns"] = df1["cPop"]/(df1["lp"]*df1["ePop"])

# plot the evolution of consumption per capita of Japan df_cPop_Japan = df1.loc["Japan","cPop"] df_cPop_Japan.plot()

# plot the evolution of the non-savings share of Japan df_ns_Japan = df1.loc["Japan","ns"] df_ns_Japan.plot()

# plot the evolution of the labor force participation of Japan df_ePop_Japan = df1.loc["Japan","ePop"] df_ePop_Japan.plot()

# plot the evolution of the labor productivity of Japan df_lp_Japan = df1.loc["Japan","lp"] df_lp_Japan.plot()

# plot the evolution of consumption per capita of China df_cPop_China = df1.loc["China","cPop"] df_cPop_China.plot()

# plot the evolution of the non-savings share of China df_ns_China = df1.loc["China","ns"] df_ns_China.plot()

# plot the evolution of the labor force participation of China df_ePop_China = df1.loc["China","ePop"] df_ePop_China.plot()

# plot the evolution of the labor productivity of China df_lp_China = df1.loc["China","lp"] df_lp_China.plot()