US Nested Genpop

You can insert this screener into your survey using

s.standard_screener("US", "NestedGenPop")

Or if you want to edit it, just copy and paste the code below:

age = s.numeric_question(
    question="How old are you?",
    min_max=(18, 65),
    recodes={
        "18-24": "18-24",
        "25-34": "25-34",
        "35-44": "35-44",
        "45-54": "45-54",
        "55-64": "55-64",
        "65+": "65+",
    },
)

gender = s.select_question("What is your gender?", ["Male", "Female", "Non-Binary", "Prefer not to say"])

ethnicity = s.select_question(
    "What is your ethnicity?",
    [
        "White",
        "Black",
        "Other",
    ],
)

hispanic = s.select_question("Would you describe yourself as Hispanic?", ["Yes", "No"])

income = s.numeric_question(
    "What is your annual income?",
    min_max=(0, 500000),
    recodes={
        "0-20000": "Less than $20,000",
        "20000-34999": "$20,000 to $34,999",
        "35000-49999": "$35,000 to $49,999",
        "50000-74999": "$50,000 to $74,999",
        "75000-99999": "$75,000 to $99,999",
        "100000-149999": "$100,000 to $149,999",
        "150000+": "$150,000 or more",
    },
)

s.select_question(
    "What is your highest level of education?",
    [
        "Less than high school degree",
        "High school graduate",
        "Some college",
        "Bachelor's degree",
        "Master's degree",
        "Post-graduate degree",
    ],
)

region = s.select_question(
    "Which region of the country do you come from?", options=["Northeast", "Midwest", "South", "West"]
)

# Age quotas
s.set_quota(
    name="Age",
    quotas=[
        s.quota("18-24", criteria=(18 <= age <= 24), quota=0.12),
        s.quota("25-34", criteria=(25 <= age <= 34), quota=0.18),
        s.quota("35-44", criteria=(35 <= age <= 44), quota=0.17),
        s.quota("45-54", criteria=(45 <= age <= 54), quota=0.16),
        s.quota("55-64", criteria=(55 <= age <= 64), quota=0.17),
        s.quota("65-99", criteria=(65 <= age <= 99), quota=0.2),
    ],
)

# Ethnicity x Hispanic quotas
s.set_quota(
    name="Ethnicity_Hispanic",
    quotas=[
        s.quota("White_Hispanic_Yes", criteria=(ethnicity == "White") & (hispanic == "Yes"), quota=0.18),
        s.quota("White_Hispanic_No", criteria=(ethnicity == "White") & (hispanic == "No"), quota=0.58),
        s.quota("Black_Hispanic_Yes", criteria=(ethnicity == "Black") & (hispanic == "Yes"), quota=0.01),
        s.quota("Black_Hispanic_No", criteria=(ethnicity == "Black") & (hispanic == "No"), quota=0.13),
        s.quota("Other_Hispanic_No", criteria=(ethnicity == "Other") & (hispanic == "No"), quota=0.10),
    ],
)

# Hispanic quotas (flat)
s.set_quota(
    name="Hispanic",
    quotas=[
        s.quota("Yes", criteria=(hispanic == "Yes"), quota=0.19),
        s.quota("No", criteria=(hispanic == "No"), quota=0.81),
    ],
)

# Gender x Age quotas

s.set_quota(
    name="Age and Gender",
    quotas=[
        s.quota(
            f"{gender_val}_{min_age}-{max_age}",
            criteria=(gender == gender_val) & (age >= min_age) & (age <= max_age),
            quota=quota_val,
        )
        for gender_val, age_quotas in {
            "Male": {
                (18, 24): 0.06,
                (25, 34): 0.09,
                (35, 44): 0.085,
                (45, 54): 0.08,
                (55, 64): 0.085,
                (65, 99): 0.09,
            },
            "Female": {
                (18, 24): 0.06,
                (25, 34): 0.09,
                (35, 44): 0.085,
                (45, 54): 0.08,
                (55, 64): 0.085,
                (65, 99): 0.11,
            },
        }.items()
        for (min_age, max_age), quota_val in age_quotas.items()
    ],
)

# Income x Gender quotas
s.set_quota(
    name="Income_Gender",
    quotas=[
        s.quota(
            f"{income_range}_{gender_val}",
            criteria=gender_quotas["criteria"] and (gender == gender_val),  # type: ignore
            quota=gender_quotas[gender_val],
        )
        for gender_val in ["Male", "Female"]
        for income_range, gender_quotas in {
            "0-24999": {"Male": 0.08, "Female": 0.09, "criteria": (income < 25000)},
            "25000-49999": {"Male": 0.09, "Female": 0.10, "criteria": (income >= 25000) & (income < 50000)},
            "50000-99999": {"Male": 0.14, "Female": 0.14, "criteria": (income >= 50000) & (income < 100000)},
            "100000-149999": {"Male": 0.08, "Female": 0.08, "criteria": (income >= 100000) & (income < 150000)},
            "150000-500000": {"Male": 0.10, "Female": 0.10, "criteria": (income >= 150000)},
        }.items()
    ],
)

# Region quotas
s.set_quota(
    name="Region",
    quotas=[
        s.quota("Northeast", criteria=(region == "Northeast"), quota=0.18),
        s.quota("Midwest", criteria=(region == "Midwest"), quota=0.23),
        s.quota("South", criteria=(region == "South"), quota=0.37),
        s.quota("West", criteria=(region == "West"), quota=0.22),
    ],
)