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How to split disbursements across SDG goals

Calculate per-goal or per-target disbursement totals from a get_tossd frame, weighted so activities tagged with multiple SDG codes count fractionally across each goal.

Steps

  1. Query data using the "analysis" column preset. Both sdg_totals and explode_sdg read sdg_codes_raw, which is included in the "analysis" preset. The "minimal" preset omits it.

    import tossd_reader as tossd

df = tossd.get_tossd(years=2024, columns="analysis", units="usd_million")

  2. Explode SDG codes for row-level analysis. The sdg_codes_raw column packs one or more semicolon-delimited codes per activity, combining goals (4) and targets (4.2). The TOSSD Reporting Instructions cap dissemination at the first ten SDG targets or goals reported per activity, so activities with more tags show only the first ten. explode_sdg gives each code its own row and assigns an equal weight sdg_weight of 1/n across the n reported codes. Pass value= to generate a precomputed {value}_weighted column alongside the untouched original.

    ex = df.tossd.explode_sdg(value="usd_disbursement")
ex[
    ["sdg_code", "sdg_goal", "sdg_is_target", "sdg_weight", "usd_disbursement_weighted"]
].head()

      sdg_code  sdg_goal  sdg_is_target  sdg_weight  usd_disbursement_weighted
0      4.2         4           True        0.25                   0.002704
1       13        13          False        0.25                   0.002704
2       10        10          False        0.25                   0.002704
3        1         1          False        0.25                   0.002704
4        5         5          False        0.50                   0.008104

    The sdg_goal column extracts the integer goal number, grouping target-level tags like 4.2 and goal-level tags like 13 under their primary goal. Summing usd_disbursement_weighted restores each activity's original disbursement total.

  3. Calculate weighted disbursements per goal with df.tossd.sdg_totals(). This method runs the explode-and-weight process internally, allocating a multi-tagged activity's disbursement proportionally across its goals. It excludes is_aggregate rows by default (include_aggregates=False).

    df.tossd.sdg_totals(top=5)

       sdg_goal  usd_disbursement  share_pct  rank
0        17      50855.509935  18.453846     1
1         3      26973.450607   9.787807     2
2        13      24788.626611   8.995003     3
3         1      21036.438058   7.633454     4
4         9      20760.196998   7.533215     5

    The share_pct column reports each goal's share of the weighted total (0-100, unrounded) and sums to 100 across the full result before applying any top= truncation. That weighted total equals the SDG-tagged subset of the included rows' usd_disbursement, as activities with no SDG tag (an empty or null sdg_codes_raw) contribute zero to goal totals.

    Pass level="code" to obtain target-level detail.

    df.tossd.sdg_totals(level="code", top=5)

      sdg_code  usd_disbursement  share_pct  rank
0       17      29297.097592  10.630984     1
1       13      12618.315801   4.578785     2
2     17.3      12317.547051   4.469646     3
3        9       9459.240859   3.432457     4
4       16       8412.056263   3.052467     5

    The publisher formats some goal-level tags with a trailing .0 token ("17.0", "16.0", "1.0", "9.0" in the 2024 file). Setting level="code" preserves each distinct token as its own row. Setting level="goal" (the default) folds these into their respective goals alongside standard goal-level tags.

Verify it worked

Compare the SDG-tagged total to the non-aggregate total in df.

tagged = df.tossd.sdg_totals()["usd_disbursement"].sum()
non_aggregate = df.tossd.exclude_aggregates()["usd_disbursement"].sum()
print(round(tagged / non_aggregate * 100, 1))

69.2

In this dataset, 69.2% of non-aggregate disbursements carry at least one SDG tag. The remainder contain no sdg_codes_raw entry.

Troubleshooting

ValueError naming sdg_codes_raw. Both sdg_totals and explode_sdg require sdg_codes_raw. Re-query with columns="analysis" or add sdg_codes_raw to your explicit column list.

tossd.get_tossd(years=2024, columns="minimal").tossd.sdg_totals()

ValueError: sdg_totals() needs column(s) sdg_codes_raw, not present in df. Re-query with columns='analysis', or add sdg_codes_raw to your columns= list.

ValueError naming sdg_code, sdg_goal, sdg_is_target, or sdg_weight. Both explode_sdg and sdg_totals reject a DataFrame that already contains these columns to prevent duplicate weighting. Pass the original get_tossd frame.

See also