def METHOD_NAME(dest_dir: str) -> None:
    # Load WID explorer step
    ds_wid: Dataset = paths.load_dependency("world_inequality_database")
    tb_wid = ds_wid["world_inequality_database"].reset_index()
    # Load LIS explorer step
    ds_lis: Dataset = paths.load_dependency("luxembourg_income_study")
    tb_lis = ds_lis["luxembourg_income_study"].reset_index()
    # Load PIP data
    tb_pip = add_pip_data(PIP_PATH)
    # Merge explorer datasets and assign a short name
    tb_explorer = pd.merge(tb_wid, tb_lis, on=["country", "year"], how="outer", validate="one_to_one")
    tb_explorer = pd.merge(
        tb_explorer,
        tb_pip,
        on=["country", "year"],
        how="outer",
        validate="one_to_one",
    )
    # Verify index and sort
    tb_explorer = tb_explorer.set_index(["country", "year"], verify_integrity=True).sort_index()
    tb_explorer.metadata.short_name = "poverty_inequality"
    # Add a table that only includes PIP inequality data (NOTE: this will be removed when PIP steps are created)
    tb_pip_inequality = create_pip_inequality_table(tb_pip)
    # Create Table from tb_pip_inequality
    tb_pip_inequality = Table(tb_pip_inequality, short_name="pip_inequality", underscore=True)
    # Create explorer dataset with merged table in csv format
    ds_explorer = create_dataset(dest_dir, tables=[tb_explorer, tb_pip_inequality], formats=["csv"])
    ds_explorer.save()