Add Childcare Cost Simulation (CAP data)

jb/child_allowance.py

@@ -11,7 +11,8 @@
 
 # Read in census data and specify columns for use
 raw = pd.read_csv(
-    "https://github.com/UBICenter/child-allowance/blob/master/jb/data/cps_00003.csv.gz",
+    "https://github.com/UBICenter/child-allowance/blob/master/jb/data/cps_00003.csv.gz?raw=true",
+    compression="gzip",
     usecols=[
         "YEAR",
         "MONTH",

jb/child_allowance_costs.py

@@ -0,0 +1,111 @@
+# Roadmap:
+# Pull in the edited dataset
+# Merge on State index using person-level child age indicator
+# Create two rows per index (low and high quality)
+# Calculate state-based outcomes.
+
+
+# Preamble and read data
+import microdf as mdf
+import pandas as pd
+import numpy as np
+import us
+
+# Read in census data and specify columns for use
+person_raw = pd.read_csv(
+    "https://github.com/UBICenter/child-allowance/blob/master/jb/data/cps_00003.csv.gz?raw=true",
+    compression="gzip",
+    usecols=[
+        "YEAR",
+        "STATEFIP",
+        "AGE",
+        "SEX",
+        "SPMWT",
+        "SPMFTOTVAL",
+        "SPMTOTRES",
+        "SPMCHXPNS",
+        "SPMTHRESH",
+        "SPMFAMUNIT",
+        "ASECWT",
+    ],
+)
+person = person_raw.copy(deep=True)
+
+# Define child age identifiers
+person["person"] = 1
+person["child_6"] = person.age < 6
+person["infant"] = person.age < 1
+person["toddler"] = person.age.between(1, 2)
+person["preschool"] = person.age.between(3, 5)
+
+# Age categories for merge
+person["age_cat"] = "over_5"
+person.loc[(person.age < 1), "age_cat"] = "infant"
+person.loc[(person.age.between(1, 2)), "age_cat"] = "toddler"
+person.loc[(person.age.between(3, 5)), "age_cat"] = "preschool"
+
+# Create State categories
+person["state"] = (
+    pd.Series(person.statefip)
+    .apply(lambda x: us.states.lookup(str(x).zfill(2)).name)
+    .tolist()
+)
+
+# Read in cost data
+costs = pd.read_csv(
+    "https://github.com/UBICenter/child-allowance/blob/master/jb/data/CCare_cost.csv"
+)
+
+# Merge datasets to calculate per-child cost
+# Creates two rows per person (one base_quality
+# and one high_quality with different costs)
+person_costs = person.merge(
+    costs[
+        [
+            "state",
+            "high_quality",
+            "age_cat",
+            "cost",
+        ]
+    ],
+    how="left",
+    on=["state", "age_cat"],
+)
+
+# Set over_5 cost of childcare to 0
+person_costs.loc[(person_costs.age_cat == "over_5"), "cost"] = 0
+
+# Define data collected at the SPM unit level
+SPMU_COLS = [
+    "spmfamunit",
+    "spmwt",
+    "spmftotval",
+    "spmtotres",
+    "spmchxpns",
+    "spmthresh",
+    "year",
+]
+
+SPMU_AGG_COLS = ["child_6", "infant", "toddler", "preschool", "person", "cost"]
+spmu_quality = person_costs.groupby(SPMU_COLS + ["high_quality"])[
+    SPMU_AGG_COLS
+].sum()
+spmu_quality.columns = ["spmu_" + i for i in SPMU_AGG_COLS]
+spmu_quality.reset_index(inplace=True)
+
+# Calculate total cost of transfers, and total number of children
+program_cost_high = mdf.weighted_sum(
+    spmu_quality[spmu_quality.high_quality], "cost", "spmwt"
+)
+program_cost_high = mdf.weighted_sum(
+    spmu_quality[~spmu_quality.high_quality], "cost", "spmwt"
+)
+
+# Program costs also group by age category (sum inf + todd + preschool)
+mdf.weighted_sum.groupby
+
+# New microdf groupby argument - have one program cost dataframe - won't need to filter just groupby
+
+# Groupby age_cat and highquality to get the weighted sums we are interested in.
+
+# Get total cost for Max.