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Fix SC CCAP copay for large families
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+70
-24
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+70
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‎policyengine_us/parameters/gov/states/sc/dss/ccap/copay/max_paid_family_size.yaml‎

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@@ -0,0 +1,13 @@
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description: South Carolina sets this maximum family size with positive copay tiers under the Child Care Scholarship Program fee scale.
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values:
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2024-10-01: 14
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metadata:
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unit: person
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period: year
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label: South Carolina CCAP copay maximum paid-tier family size
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reference:
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- title: SC CCAP Fee Scale 2024-2025, Appendix 2
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href: https://www.scchildcare.org/media/ubhdm1at/1-13-2025_policy-manual.pdf#page=182
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- title: SC CCAP Fee Scale 2025-2026
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href: https://www.scchildcare.org/media/ih2mrjw5/fee-scale-2025-2026.pdf#page=1

policyengine_us/variables/gov/states/sc/dss/ccap/sc_ccap_copay.py renamed to policyengine_us/variables/gov/states/sc/dss/ccap/copay/sc_ccap_copay.py

Lines changed: 57 additions & 24 deletions
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@@ -1,4 +1,6 @@
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from policyengine_us.model_api import *
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from policyengine_us.variables.gov.hhs.hhs_smi import smi
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from policyengine_us.variables.gov.hhs.tax_unit_fpg import fpg
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35

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class sc_ccap_copay(Variable):
@@ -15,22 +17,16 @@ class sc_ccap_copay(Variable):
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)
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def formula(spm_unit, period, parameters):
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from policyengine_us.variables.gov.hhs.hhs_smi import smi
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2020
p = parameters(period).gov.states.sc.dss.ccap.copay
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monthly_income = spm_unit("sc_ccap_countable_income", period)
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tier_count = len(p.smi_tier_ratios.thresholds)
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# Cap family size at max_family_size for threshold computation.
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# Pre-2024: 16 (all sizes have paid tiers).
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# Post-2024: 14 (sizes 15-16 are $0-only).
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size = spm_unit("spm_unit_size", period.this_year)
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max_size = int(p.max_family_size)
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capped_size = min_(size, max_size)
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above_max = size > max_size
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fee_scale_size = min_(size, max_size)
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state = spm_unit.household("state_code_str", period)
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monthly_smi = smi(capped_size, state, period, parameters) / MONTHS_IN_YEAR
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state_group = spm_unit.household("state_group_str", period)
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# Family-level copay exemptions (Section 3.4.2, p.108).
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# Head Start copay waiver is per-child, handled below.
@@ -46,34 +42,71 @@ def formula(spm_unit, period, parameters):
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# Pre-2024-10-01: tiers at fixed SMI ratios (45/55/65/75% mark
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# boundaries between 5 tiers).
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# Post-2024-10-01: equal-width bands from 150% FPL to 85% SMI.
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tier = np.zeros_like(monthly_income, dtype=int)
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below_fpl_threshold = np.zeros_like(monthly_income, dtype=bool)
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zero_only_row = np.zeros_like(monthly_income, dtype=bool)
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if p.fpg_exempt_in_effect:
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p_fpg = parameters(period).gov.hhs.fpg
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state_group = spm_unit.household("state_group_str", period)
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capped_fpg = (
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p_fpg.first_person[state_group]
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+ p_fpg.additional_person[state_group] * (capped_size - 1)
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) / MONTHS_IN_YEAR
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lower = np.floor(capped_fpg * p.fpg_exempt_rate + 0.5)
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below_fpl_threshold = monthly_income <= lower
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max_paid_size = int(p.max_paid_family_size)
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zero_only_row = fee_scale_size > max_paid_size
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# Rows 15-16 are $0-only on the published fee scale. For rows with
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# paid tiers, fall back to the largest published paid-tier size
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# whose derived 150% FPL and 85% SMI bounds still form a
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# nonnegative band.
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paid_band_size = np.ones_like(size, dtype=int)
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for candidate in range(1, max_paid_size + 1):
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lower_candidate = np.floor(
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fpg(candidate, state_group, period, parameters)
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/ MONTHS_IN_YEAR
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* p.fpg_exempt_rate
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+ 0.5
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)
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upper_candidate = np.floor(
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smi(candidate, state, period, parameters)
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/ MONTHS_IN_YEAR
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* p.smi_tier_ratios.calc(tier_count)
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+ 0.5
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)
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valid_candidate = (fee_scale_size >= candidate) & (
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lower_candidate <= upper_candidate
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)
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paid_band_size = where(
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valid_candidate,
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candidate,
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paid_band_size,
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)
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monthly_fpg = (
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fpg(paid_band_size, state_group, period, parameters) / MONTHS_IN_YEAR
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)
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monthly_smi = (
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smi(paid_band_size, state, period, parameters) / MONTHS_IN_YEAR
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)
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lower = np.floor(monthly_fpg * p.fpg_exempt_rate + 0.5)
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upper = np.floor(monthly_smi * p.smi_tier_ratios.calc(tier_count) + 0.5)
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band_width = np.ceil((upper - lower) / tier_count)
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tier = np.zeros_like(monthly_income, dtype=int)
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below_fpl_threshold = (~zero_only_row) & (monthly_income <= lower)
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for i in range(1, tier_count):
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threshold = lower + band_width * i
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tier = tier + (monthly_income > threshold).astype(int)
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tier = tier + ((~zero_only_row) & (monthly_income > threshold)).astype(
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int
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)
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else:
65-
below_fpl_threshold = False
66-
tier = np.zeros_like(monthly_income, dtype=int)
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monthly_smi = (
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smi(fee_scale_size, state, period, parameters) / MONTHS_IN_YEAR
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)
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for i in range(1, tier_count):
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ratio = p.smi_tier_ratios.calc(i)
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threshold = np.floor(monthly_smi * ratio + 0.5)
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tier = tier + (monthly_income > threshold).astype(int)
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exempt = (
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protective | is_tanf | below_fpl_threshold | has_disabled_child | above_max
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)
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exempt = protective | is_tanf | below_fpl_threshold | has_disabled_child
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76-
weekly_copay_per_child = p.weekly_amounts.calc(tier + 1)
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weekly_copay_per_child = where(
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zero_only_row,
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0,
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p.weekly_amounts.calc(tier + 1),
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)
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# Head Start children have no copay (Section 2.15); only count
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# non-Head-Start eligible children for the copay calculation.

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