Dot11 Media — MP Interests Transparency Leaderboard

UK MPs' Interests — Methodology

How the composite score is calculated, why equal weighting is used, and what the data cannot tell you. ← Back to leaderboard

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What this tool measures

This tool ranks all 650 Members of the House of Commons by declared financial interests. Every data point originates from the Register of Members' Financial Interests — a statutory disclosure requirement under parliamentary rules. MPs declare their own entries. We count them. We do not infer, impute, or editorially select.

A high composite score means an MP has declared broad and significant outside financial engagement across multiple categories. It does not indicate impropriety. MPs with extensive professional backgrounds before entering Parliament will naturally score higher than those who entered with fewer outside interests.

Data sources

Primary: mySociety bulk CSV reformats of the House of Commons Register of Members' Financial Interests, published at pages.mysociety.org/parl_register_interests under Creative Commons Attribution 4.0 International. Updated weekly.

Secondary: UK Parliament Members API at members-api.parliament.uk. Used to confirm current serving status and retrieve constituency data. No API key required.

All data is published under the Open Parliament Licence v3.0. Required attribution: "Contains Parliamentary information licensed under the Open Parliament Licence v3.0."

Data refresh schedule
Parliament publishes updated interest data every Monday. Our pipeline runs every Wednesday by 6am GMT, aiming to pick up the latest mySociety reformatted CSV, if mySociety have been able to publish their updated dataset by the time our refresh takes place. The data on d11m.org/mp-stats is current to the most recent weekly register update shared by mySociety.

Composite score — normalisation and weighting

Normalisation

Each of the twelve metrics is normalised to a 0–100 scale using min-max normalisation across the population of currently serving MPs. The formula is:

normalised value = ( raw value − minimum ) ÷ ( maximum − minimum ) × 100

The composite score is then the unweighted arithmetic mean of all twelve normalised values — each metric contributes 1/12 to the final score.

This normalisation method is consistent with established practice in composite index construction, including the United Nations Human Development Index, the Green Economy Index, the Green Growth Index, and the Sustainable Society Index. It is the default approach recommended by the OECD Handbook on Composite Indicators.

Why equal weighting — the empirical case

Equal weighting is sometimes challenged on intuitive grounds: surely a paid directorship in a company lobbying Parliament carries more significance than a rental property? This section explains why the literature supports equal weighting as the most defensible choice, and what the evidence actually shows.

The core argument: any non-equal weighting scheme requires an empirical or normative justification for every weight differential. In the absence of a validated empirical basis — which would require a peer-reviewed study correlating specific interest categories with measurable legislative outcomes — equal weighting is the only approach that cannot be accused of editorial bias.

Three peer-reviewed studies establish the methodological basis for this decision:

Wehner (2006)[1] — The closest domain match. Wehner constructs a composite index of parliamentary budget institutions across 36 countries, normalised to 0–100, and explicitly states that "differential weighting is not implied by the theoretical approach." Equal weights are used throughout. This is a published composite index of parliamentary behaviour, in our domain, in a peer-reviewed political science journal, making the same methodological choice for the same reason.

Godlewska & Sidorczuk-Pietraszko (2019)[2] — These researchers went further: they calculated entropy-derived optimal weights mathematically, attempting to find what the data itself implied the weights should be. The empirical result was that the derived weights were "practically equal across all variables." Their conclusion confirms the recommendation of researchers who apply equal weights to aggregate indicators for well-being and sustainable development: the intuition that some dimensions must matter more rarely survives contact with the data.

Becker et al. (2022)[3] — Documents that composite indicators are widely used in policymaking and advocacy by international organisations, and that min-max normalisation to the 0–100 interval is the standard aggregation pattern. Confirms equal weighting as the established method when no empirical basis for differential weights exists.

Phase 4 upgrade path

The most empirically grounded alternative is parliamentary exposure weighting: metrics weighted by the degree to which an MP holds legislative influence over the domain of their declared interest — derived from committee membership overlap. Green & Homroy (2021) [4] provide the precedent for this approach, demonstrating that corporate connections measurably affect committee participation patterns. This requires Phase 4 committee membership data and is the planned upgrade when that data is available.

Why these metrics matter

The strongest objection to the composite score is not methodological — it is substantive. Why does declared outside financial engagement matter?

Green & Homroy (2021) [4] provide peer-reviewed causal evidence. Using a 2002 amendment to UK parliamentary disclosure regulations as a natural experiment, they demonstrate that MPs with corporate connections are measurably more likely to join select committees where legislation is drafted, and attend more committee meetings than MPs without corporate connections. Firms respond by rebalancing political activities toward sitting MPs rather than ex-politicians.

This is not assertion. It is a peer-reviewed finding, published in Economica, showing that the category of financial relationships this tool measures has a demonstrated relationship with UK parliamentary behaviour. Our tool makes those interests visible and comparable. The causal pathway from declared interest to legislative behaviour is established in the literature we cite.

The 12 metrics

Each metric is computed from the register category indicated. Raw values are normalised to 0–100 before scoring.

ID Metric What it measures Source
M-01 Total interests declared Count of all register entries across all categories. Category: Overall
M-02 Distinct outside employers Count of unique payer names in Category 1. Conservative measure — variant spellings count as separate entries. Category 1
M-03 Category spread Number of distinct register categories in which the MP has at least one entry. Measures breadth of outside engagement. Overall
M-04 Total declared payment value £ Sum of all declared monetary values in Categories 1.1 and 1.2. Excludes interests with no stated value. Categories 1.1, 1.2
M-05 Implied hourly rate £ Total declared pay divided by total declared hours (Category 1.1 only, rows with both figures present). Indicative — both figures are MP self-declarations. Category 1.1
M-06 Single largest donor value £ Maximum declared donation value across Categories 2 and 3. Categories 2, 3
M-07 Overseas visits count Count of Category 4 entries. Each visit is one row regardless of how many donors contributed to it. Category 4
M-08 Distinct countries visited Count of unique primary destination countries in Category 4. Secondary destinations not counted. Category 4
M-09 Foreign gifts count Count of Category 5 entries. Category 5 is structurally distinct from UK sources (Category 3) — not an editorial judgement. Category 5
M-10 Directorships held Count of Category 1 rows where the MP is declared as a paid director. Green & Homroy (2021) establish that directorships of this type measurably affect committee participation. [4] Category 1
M-11 Family members in lobbying Count of Category 10 entries. Category 10 covers family members employed in lobbying roles. Structural conflict indicator — no editorial judgement on individual entries. Category 10
M-12 Properties with rental income Count of Category 6 entries where registrable rental income is declared. Portfolio size may be undercounted where multiple properties appear as a single entry. Category 6

Known limitations

These limitations are disclosed, not hidden. Understanding them is part of using the tool correctly.

  • All monetary values and hours worked are self-declared by MPs. Parliament does not independently audit them. Treat M-04, M-05, and M-06 as reflecting what was declared, not what was paid.
  • Donor and employer names are not deduplicated. Variant spellings of the same entity count as separate entries. M-02 and M-06 systematically undercount MPs with interests in large organisations that use multiple name variants. Donor deduplication is planned for a future version.
  • Interests without a stated monetary value are excluded from value-based metrics. This systematically understates financial exposure for MPs with significant in-kind interests.
  • The twelve-month retention rule means recently expired interests remain in the register. MPs who left Parliament mid-term may appear in historical period views.
  • The implied hourly rate (M-05) is indicative, not definitive. Both the declared pay figure and the declared hours figure are self-reported. Treat M-05 as a directional signal, not a precise measurement.
  • Only the primary country field is used for overseas visits (M-08). Visits with multiple destinations may be undercounted.
  • Category 9 (family members employed in Parliament) is not a standalone metric. It is counted in M-01 and M-03 only.
  • The composite score has not been peer-reviewed or validated against external outcome measures. It measures declared engagement with outside financial interests. It does not measure misconduct, political influence, or performance.

The honest statement

The MP Interests Transparency Leaderboard applies a composite scoring methodology consistent with established international practice, including the UN Human Development Index and peer-reviewed legislative transparency indices. [1][2][3]

Every data point originates from the UK Parliament's own statutory register, under the Open Parliament Licence. The normalisation method — min-max scaled to 0–100, aggregated by unweighted arithmetic mean — is the standard approach recommended for composite indices when no empirical basis exists for differential weighting. [1][2]

The category of financial interests this tool measures has been shown in peer-reviewed research to have a demonstrable relationship with UK parliamentary behaviour. [4] All limitations are disclosed above. The score measures declared engagement, not misconduct. Parliament declared the data. We counted it.

References

  1. [1] Wehner J. Assessing the Power of the Purse: An Index of Legislative Budget Institutions. Political Studies. 2006;54(4):767–785. https://doi.org/10.1111/j.1467-9248.2006.00628.x
  2. [2] Godlewska J, Sidorczuk-Pietraszko E. Taxonomic Assessment of Transition to the Green Economy in Polish Regions. Sustainability. 2019;11(18):5098. https://doi.org/10.3390/su11185098
  3. [3] Becker WE, Caperna G, Del Sorbo M, et al. COINr: An R package for developing composite indicators. Journal of Open Source Software. 2022;7(78):4567. https://doi.org/10.21105/joss.04567
  4. [4] Green C, Homroy S. Incorporated in Westminster: Channels and Returns to Political Connection in the United Kingdom. Economica. 2021;89(354):377–408. https://doi.org/10.1111/ecca.12402