mpitb 0.2

A toolbox for multidimensional poverty index (MPI) estimation and analysis

📘 MPIToolbox User Guide

Attribution

This work builds upon code originally developed by Nicolai Suppa (2022), licensed under the MIT License. This guide introduces the MPIToolbox Python class, designed to estimate, compare, and summarize multidimensional poverty indices (MPI) using flexible specifications.

📚 Additional Resources

👉 See the MPI Results Utilities Guide

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🔧 Initialization

from mpitb.core import MPIToolbox
mpi = MPIToolbox()

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🗂️ 1. Define Specifications

mpi.set(
    name="trial01",
    description="Preferred trial specification",
    dimensions=[
        (["d_cm", "d_nutr"], "hl"),
        (["d_satt", "d_educ"], "ed"),
        (["d_elct", "d_wtr", "d_sani", "d_hsg", "d_ckfl", "d_asst"], "ls")
    ],
    replace=True
)

• name: Unique name for the specification
• dimensions: A list of (indicators, dimension name)
• replace=True: Overwrite existing spec

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⚖️ 2. Set Weights

mpi.setwgts("trial01", "health50", dimw=[0.5, 0.25, 0.25])
mpi.setwgts("trial01", "ind_equal", indw=[0.1]*10)

You can define:

• Dimension-level weights (dimw)
• Indicator-level weights (indw)

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📏 3. Estimate MPI

mpi.est(
    df=df,
    name="trial01",
    klist=[33],
    weights="equal",
    svy=True,
    lframe="myresults",
    over=["region", "area"]
)

Output:

• Estimation records are stored in mpi.results["myresults"]
• Use replace=True to overwrite an existing frame

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📊 4. Change-over-Time Estimation

mpi.est_cot(
    df=df,
    name="trial01",
    yearvar="t",
    klist=[33],
    cotmeasures=["M0", "H", "A", "hd", "hdk"],
    wgts="equal",
    cotframe="mycot",
    replace=True,
    raw=True,
    ann=True,
    total=True,
    insequence=False,
    svy=True
)

• cotmeasures: include M0, H, A, and optional hd, hdk
• raw and ann: toggle raw/annual change
• total or insequence: whether to compute period vs. year-to-year changes

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📁 5. Extract and Summarize Results

Use functions from mpi_results_utils.py (see companion guide):

from mpi_results_utils import extract_core_measures, extract_cot_summary, pivot_cot_summary

summary = extract_core_measures(df_myresults)
cot_table = extract_cot_summary(mpi.results, "mycot", measure="H", k=[33])
pivot = pivot_cot_summary(mpi.results, "mycot", measure=["H", "M0"], k=[33, 50])

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📌 6. Store Custom Results

mpi.stores(
    frame="myresults",
    loa="nat",
    measure="M0",
    spec="trial01",
    k=33,
    estimate=0.115,
    ts=True
)

You can manually add structured metadata to mpi.results["myresults"].

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🛠️ Other Tools

• mpi.svyset(...): Define survey weight/PSU/stratum variables.
• mpi.get_equal_weights(name): View equal weighting structure.
• mpi.show(name): Print full spec summary.
• mpi.rframe(...): Register a new result frame structure.

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🧪 Example Quick Start

mpi = MPIToolbox()
mpi.svyset(psu="psu", weight="weight", strata="stratum")
mpi.set(name="trial01", description="baseline", dimensions=[
    (["d_cm", "d_nutr"], "hl"),
    (["d_satt", "d_educ"], "ed"),
    (["d_elct", "d_wtr", "d_sani", "d_hsg", "d_ckfl", "d_asst"], "ls")
])
mpi.setwgts("trial01", "equal", dimw=[1/3, 1/3, 1/3])
mpi.est(df=df, name="trial01", klist=[33], weights="equal", svy=True, lframe="results")

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