Skip to content

Update example for Project 4 #1

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
skjorlien wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Update example for Project 4 #1

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion example_costs.org
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -135,7 +135,7 @@ income effect associated with changing prices.
import matplotlib.pyplot as plt
%matplotlib inline

my_j = 'Millet' # Interesting Ugandan staple
my_j = 'Millet Flour' # Interesting Ugandan staple

P = np.geomspace(.01,10,50)*pbar[my_j]

Expand Down
61 changes: 33 additions & 28 deletions example_nutrition.org
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@ After having estimated a demand system using data from our favorite country, we

If you don't already have the latest version of the =CFEDemands= package
installed, grab it, along with some dependencies:
#+begin_src jupyter-python
#+begin_src jupyter-python :tangle no
#!pip install -r requirements.txt
#+end_src

Expand All @@ -34,12 +34,13 @@ import cfe.regression as rgsn

Here are addresses of google sheets for different dataframes for the
case of Uganda:
#+begin_src jupyter-python
InputFiles = {'Expenditures':('1yVLriVpo7KGUXvR3hq_n53XpXlD5NmLaH1oOMZyV0gQ','Expenditures (2019-20)'),
'Prices':('1yVLriVpo7KGUXvR3hq_n53XpXlD5NmLaH1oOMZyV0gQ','Prices'),
'HH Characteristics':('1yVLriVpo7KGUXvR3hq_n53XpXlD5NmLaH1oOMZyV0gQ','HH Characteristics'),
'FCT':('1yVLriVpo7KGUXvR3hq_n53XpXlD5NmLaH1oOMZyV0gQ','FCT'),
'RDI':('1yVLriVpo7KGUXvR3hq_n53XpXlD5NmLaH1oOMZyV0gQ','RDI'),}
#+begin_src jupyter-python
uganda_file = "1yFWlP5N7Aowaj6t2roRSFFUC50aFD-RLBGfzGtqLl0w"
InputFiles = {'Expenditures':(uganda_file,'Food Expenditures (2019-20)'),
'Prices':(uganda_file,'Food Prices (2019-20)'),
'HH Characteristics':(uganda_file,'Household Characteristics'),
'FCT':(uganda_file,'FCT'),
'RDI':(uganda_file,'RDI'),}
#+end_src

*** Prices, FCT, RDI
Expand All @@ -50,21 +51,15 @@ import pandas as pd

# Get prices
p = read_sheets(InputFiles['Prices'][0],
sheet=InputFiles['Prices'][1])

p = p.set_index(['t','m'])
p.columns.name = 'j'
sheet=InputFiles['Prices'][1]).set_index(['t', 'm', 'j', 'u'])

p = p.apply(lambda x: pd.to_numeric(x,errors='coerce'))
p = p.replace(0,np.nan)
p = p.xs('Kg', level="u").squeeze().unstack("j")

fct = read_sheets(InputFiles['FCT'][0],
sheet=InputFiles['FCT'][1])

fct['j'] = fct['index']
fct = fct.set_index('j')
fct.columns.name = 'n'

fct = fct.apply(lambda x: pd.to_numeric(x,errors='coerce'))
fct.drop(columns=['index'], inplace=True)

################## RDI, if available (consider using US) #####################
rdi = read_sheets(InputFiles['RDI'][0],
Expand All @@ -84,8 +79,11 @@ Choose reference prices. Here we'll choose a particular year, and average price
#+begin_src jupyter-python
# Reference prices chosen from a particular time; average across place.
# These are prices per kilogram:
pbar = p.xs('2019-20',level='t').mean()
pbar = pbar[r.beta.index] # Only use prices for goods we can estimate
pbar = p[r.beta.index] # Only use prices for goods we can estimate
pbar = pbar.mean(axis=0)

# Replace missing prices with 1 (this is because they're best measured in expenditures)
pbar = pbar.replace(np.nan,1)
#+end_src

*** Budgets
Expand All @@ -110,13 +108,11 @@ qhat = (xhat.unstack('j')/pbar).dropna(how='all')

# Drop missing columns
qhat = qhat.loc[:,qhat.count()>0]

qhat
#+end_src

Finally, define a function to change a single price in the vector $p$:
#+begin_src jupyter-python :results silent
def my_prices(p0,p=pbar,j='Millet'):
def my_prices(p0,j,p=pbar):
"""
Change price of jth good to p0, holding other prices fixed.
"""
Expand All @@ -129,25 +125,24 @@ def my_prices(p0,p=pbar,j='Millet'):
*** Demand functions
#+begin_src jupyter-python
import matplotlib.pyplot as plt
%matplotlib inline
# %matplotlib inline

use = 'Millet' # Good we want demand curve for
use = 'Matoke' # Good we want demand curve for

# Vary prices from 50% to 200% of reference.
scale = np.linspace(.5,2,20)

# Demand for Millet for household at median budget
plt.plot([r.demands(xref,my_prices(pbar[use]*s,pbar))[use] for s in scale],scale)
plt.plot([r.demands(xref,my_prices(pbar[use]*s,use,pbar))[use] for s in scale],scale)

# Demand for Millet for household at 25% percentile
plt.plot([r.demands(xbar.quantile(0.25),my_prices(pbar[use]*s,pbar))[use] for s in scale],scale)
plt.plot([r.demands(xbar.quantile(0.25),my_prices(pbar[use]*s,use,pbar))[use] for s in scale],scale)

# Demand for Millet for household at 75% percentile
plt.plot([r.demands(xbar.quantile(0.75),my_prices(pbar[use]*s,pbar))[use] for s in scale],scale)
plt.plot([r.demands(xbar.quantile(0.75),my_prices(pbar[use]*s,use,pbar))[use] for s in scale],scale)

plt.ylabel(f"Price (relative to base of {pbar[use]:.2f})")
plt.xlabel(f"Quantities of {use} Demanded")

#+end_src
*** Engel Curves

Expand Down Expand Up @@ -256,6 +251,16 @@ ax.set_ylabel('log nutrient')
For the average household in our data, the number of
different kinds of people can be computed by averaging over households:
#+begin_src jupyter-python :results silent
def clean_rdi_demogs(colname):
sex, age = colname.split()

sex = "Females" if sex == "F" else "Males"
age = "51-99" if age == "51+" else age

return f"{sex} {age}"

rdi.columns = [clean_rdi_demogs(x) for x in rdi.columns]

# In first round, averaged over households and villages
dbar = r.d[rdi.columns].mean()
#+end_src
Expand Down