def METHOD_NAME(PIP_PATH: str):
    # Load PIP data
    tb_pip = pd.read_csv(PIP_PATH)
    # Rename country and year vars
    rename_list = {"Entity": "country", "Year": "year"}
    tb_pip = tb_pip.rename(columns=rename_list)
    # Drop welfare variables not used in the explorers
    drop_list = ["above", "poverty_severity", "watts", "stacked"]
    for var in drop_list:
        tb_pip = tb_pip[tb_pip.columns.drop(list(tb_pip.filter(like=var)))]
    # Additional variables to drop from PIP
    drop_list = [
        "survey_year",
        "survey_comparability",
        "comparable_spell",
        "distribution_type",
        "estimation_type",
        "cpi",
        "ppp",
        "reporting_gdp",
        "reporting_pce",
        "mld",
        "polarization",
        "reporting_level",
        "welfare_type",
    ]
    tb_pip = tb_pip.drop(columns=drop_list)
    # Rename regional aggregations
    regions_dict = {
        "Sub-Saharan Africa": "Sub-Saharan Africa (PIP)",
        "Europe and Central Asia": "Europe and Central Asia (PIP)",
        "High income countries": "High income countries (PIP)",
        "Latin America and the Caribbean": "Latin America and the Caribbean (PIP)",
        "South Asia": "South Asia (PIP)",
        "Middle East and North Africa": "Middle East and North Africa (PIP)",
        "East Asia and Pacific": "East Asia and Pacific (PIP)",
    }
    tb_pip["country"] = tb_pip["country"].replace(regions_dict)
    # Calculate bottom 50% share
    tb_pip["bottom50_share"] = (
        tb_pip["decile1_share"]
        + tb_pip["decile2_share"]
        + tb_pip["decile3_share"]
        + tb_pip["decile4_share"]
        + tb_pip["decile5_share"]
    )
    return tb_pip