Create and document economic assumptions (#1161)

* Add function to store growth factors

* Add function to apply custom growthfactors

* Add page to docs

* Format

* Add changelog entry

Author: nikhilwoodruff

.gitignore

@@ -54,3 +54,4 @@ policyengine_uk/calibration/*.h5
 
 *.ipynb
 !docs/**/*.ipynb
+!uprating_growth_factors.csv

Makefile

@@ -14,6 +14,7 @@ format:
 test:
 	policyengine-core test policyengine_uk/tests/policy -c policyengine_uk
 	pytest policyengine_uk/tests/ -v
+	python policyengine_uk/data/economic_assumptions.py
 
 documentation:
 	jb clean docs/book

changelog_entry.yaml

@@ -0,0 +1,4 @@
+- bump: minor
+  changes:
+    added:
+    - Growth factor documentation.

docs/book/_config.yml

@@ -21,7 +21,6 @@ sphinx:
       - style.css
   extra_extensions:
     - "sphinx.ext.autodoc"
-    - "sphinxarg.ext"
     - "sphinx.ext.viewcode"
     - "sphinx.ext.napoleon"
     - "sphinx_math_dollar"

docs/book/_toc.yml

@@ -4,6 +4,9 @@ parts:
   - caption: Using the model
     chapters:
       - file: usage/getting-started
+  - caption: Economic assumptions
+    chapters:
+      - file: assumptions/growthfactors
   - caption: Programs
     chapters:
       - file: programs/gov/hmrc/child-benefit

docs/book/assumptions/growthfactors.ipynb

@@ -28,8 +28,9 @@ def __init__(
             self.benunit = benunit
             self.household = household
 
-        self.data_format = "time_period_arrays"
+        self.data_format = "arrays"
         self.time_period = fiscal_year
+        self.tables = (self.person, self.benunit, self.household)
 
     def save(self, file_path: str):
         with pd.HDFStore(file_path) as f:
@@ -43,9 +44,7 @@ def load(self):
         data = {}
         for df in (self.person, self.benunit, self.household):
             for col in df.columns:
-                data[col] = {
-                    self.time_period: df[col].values,
-                }
+                data[col] = df[col].values
 
         return data
 

policyengine_uk/data/dataset_schema.py

@@ -0,0 +1,77 @@
+import pandas as pd
+from pathlib import Path
+from policyengine_uk.data.dataset_schema import UKDataset
+
+START_YEAR = 2020
+END_YEAR = 2034
+
+
+def create_policyengine_uprating_factors_table():
+    from policyengine_uk.system import system
+
+    df = pd.DataFrame()
+
+    variable_names = []
+    years = []
+    index_values = []
+
+    parameter_by_variable = {}
+
+    for variable in system.variables.values():
+        if variable.uprating is not None:
+            parameter = system.parameters.get_child(variable.uprating)
+            parameter_by_variable[variable.name] = parameter.name
+            start_value = parameter(START_YEAR)
+            for year in range(START_YEAR, END_YEAR + 1):
+                variable_names.append(variable.name)
+                years.append(year)
+                growth = parameter(year) / start_value
+                index_values.append(round(growth, 3))
+
+    df["Variable"] = variable_names
+    df["Year"] = years
+    df["Value"] = index_values
+
+    # Convert to there is a column for each year
+    df = df.pivot(index="Variable", columns="Year", values="Value")
+    df = df.sort_values("Variable")
+
+    # Create a table with growth factors by year
+
+    df_growth = df.copy()
+    for year in range(END_YEAR, START_YEAR, -1):
+        df_growth[year] = round(df_growth[year] / df_growth[year - 1] - 1, 3)
+    df_growth[START_YEAR] = 0
+
+    file_path = Path(__file__).parent / "uprating_growth_factors.csv"
+    df_growth["Parameter"] = df.index.map(parameter_by_variable)
+    df_growth.to_csv(file_path)
+    return pd.read_csv(file_path)
+
+
+def apply_growth_factors(
+    dataset: UKDataset,
+    growth_factors: pd.DataFrame,
+    start_year: int,
+    end_year: int,
+):
+    dataset = dataset.copy()
+    for i in range(len(growth_factors)):
+        index = 1
+        variable = growth_factors["Variable"].values[i]
+        for year in range(start_year, end_year + 1):
+            growth_factor = growth_factors[str(year)].values[i]
+            index *= 1 + growth_factor
+
+        for table in dataset.tables:
+            if variable in table.columns:
+                table[variable] *= index
+
+    return dataset
+
+
+BASELINE_GROWFACTORS = create_policyengine_uprating_factors_table()
+
+
+if __name__ == "__main__":
+    create_policyengine_uprating_factors_table()

policyengine_uk/data/economic_assumptions.py

@@ -0,0 +1,76 @@
+Variable,2020,2021,2022,2023,2024,2025,2026,2027,2028,2029,2030,2031,2032,2033,2034,Parameter
+afcs,0,0.039,0.101,0.057,0.023,0.032,0.02,0.02,0.02,0.02,0.015,0.015,0.015,0.015,0.014,gov.obr.consumer_price_index
+afcs_reported,0,0.039,0.101,0.057,0.023,0.032,0.02,0.02,0.02,0.02,0.015,0.015,0.015,0.015,0.014,gov.obr.consumer_price_index
+alcohol_and_tobacco_consumption,0,0.039,0.101,0.057,0.023,0.032,0.02,0.02,0.02,0.02,0.015,0.015,0.015,0.015,0.014,gov.obr.consumer_price_index
+attendance_allowance_reported,0,0.039,0.101,0.057,0.023,0.032,0.02,0.02,0.02,0.02,0.015,0.015,0.015,0.015,0.014,gov.obr.consumer_price_index
+bsp_reported,0,0.039,0.101,0.057,0.023,0.032,0.02,0.02,0.02,0.02,0.015,0.015,0.015,0.015,0.014,gov.obr.consumer_price_index
+capital_gains,0,0.072,0.138,0.066,0.08,0.068,0.056,0.045,0.034,0.035,0.03,0.03,0.029,0.028,0.027,gov.obr.non_labour_income
+capital_gains_before_response,0,0.072,0.138,0.066,0.08,0.068,0.056,0.045,0.034,0.035,0.03,0.03,0.029,0.028,0.027,gov.obr.non_labour_income
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+employment_income,0,0.072,0.074,0.062,0.05,0.033,0.02,0.019,0.023,0.026,0.015,0.016,0.015,0.015,0.015,gov.obr.per_capita.employment_income
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+food_and_non_alcoholic_beverages_consumption,0,0.039,0.101,0.057,0.023,0.032,0.02,0.02,0.02,0.02,0.015,0.015,0.015,0.015,0.014,gov.obr.consumer_price_index
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