diff --git a/app/public/data/lanes.json b/app/public/data/lanes.json
index b4601d8..33b957d 100644
--- a/app/public/data/lanes.json
+++ b/app/public/data/lanes.json
@@ -357,6 +357,17 @@
    "running": false,
    "updated": "2026-08-21",
    "note": "5 model claims, 7 Chronicle facts (6 statistical + 1 non-simulated uprated EU-SILC survey input), 6 derived ratios dispositioned; 2 concept-mismatch attachments"
+  },
+  {
+   "id": "dwp-pension-participation",
+   "source": "DWP",
+   "area": "workplace pension participation",
+   "mode": 1,
+   "country": "UK",
+   "stage": "ingested",
+   "running": false,
+   "updated": "2026-08-25",
+   "note": "1377 claims \u2014 the first external validation of any pensions quantity in the UK scorecard (participation among auto-enrolment ELIGIBLE employees, GB, ASHE-derived)"
   }
  ]
 }
diff --git a/data/externals/dwp-pension-participation.json b/data/externals/dwp-pension-participation.json
new file mode 100644
index 0000000..57cc016
--- /dev/null
+++ b/data/externals/dwp-pension-participation.json
@@ -0,0 +1,24788 @@
+[
+ {
+  "source": "dwp-pension-participation",
+  "country": "UK",
+  "geography": "GB",
+  "program": "workplace_pension",
+  "metric": "participation_rate",
+  "unit_concept": "share",
+  "sector": "public",
+  "axis": "earnings_band",
+  "subgroup": "gbp_10k_20k",
+  "period": 2009,
+  "value": 0.78,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.3a:block1:\u00a310,000 - under \u00a320,000:public:2009"
+ },
+ {
+  "source": "dwp-pension-participation",
+  "country": "UK",
+  "geography": "GB",
+  "program": "workplace_pension",
+  "metric": "participation_rate",
+  "unit_concept": "share",
+  "sector": "public",
+  "axis": "earnings_band",
+  "subgroup": "gbp_10k_20k",
+  "period": 2010,
+  "value": 0.75,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.3a:block1:\u00a310,000 - under \u00a320,000:public:2010"
+ },
+ {
+  "source": "dwp-pension-participation",
+  "country": "UK",
+  "geography": "GB",
+  "program": "workplace_pension",
+  "metric": "participation_rate",
+  "unit_concept": "share",
+  "sector": "public",
+  "axis": "earnings_band",
+  "subgroup": "gbp_10k_20k",
+  "period": 2011,
+  "value": 0.76,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.3a:block1:\u00a310,000 - under \u00a320,000:public:2011"
+ },
+ {
+  "source": "dwp-pension-participation",
+  "country": "UK",
+  "geography": "GB",
+  "program": "workplace_pension",
+  "metric": "participation_rate",
+  "unit_concept": "share",
+  "sector": "public",
+  "axis": "earnings_band",
+  "subgroup": "gbp_10k_20k",
+  "period": 2012,
+  "value": 0.75,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.3a:block1:\u00a310,000 - under \u00a320,000:public:2012"
+ },
+ {
+  "source": "dwp-pension-participation",
+  "country": "UK",
+  "geography": "GB",
+  "program": "workplace_pension",
+  "metric": "participation_rate",
+  "unit_concept": "share",
+  "sector": "public",
+  "axis": "earnings_band",
+  "subgroup": "gbp_10k_20k",
+  "period": 2013,
+  "value": 0.79,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.3a:block1:\u00a310,000 - under \u00a320,000:public:2013"
+ },
+ {
+  "source": "dwp-pension-participation",
+  "country": "UK",
+  "geography": "GB",
+  "program": "workplace_pension",
+  "metric": "participation_rate",
+  "unit_concept": "share",
+  "sector": "public",
+  "axis": "earnings_band",
+  "subgroup": "gbp_10k_20k",
+  "period": 2014,
+  "value": 0.84,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.3a:block1:\u00a310,000 - under \u00a320,000:public:2014"
+ },
+ {
+  "source": "dwp-pension-participation",
+  "country": "UK",
+  "geography": "GB",
+  "program": "workplace_pension",
+  "metric": "participation_rate",
+  "unit_concept": "share",
+  "sector": "public",
+  "axis": "earnings_band",
+  "subgroup": "gbp_10k_20k",
+  "period": 2015,
+  "value": 0.84,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.3a:block1:\u00a310,000 - under \u00a320,000:public:2015"
+ },
+ {
+  "source": "dwp-pension-participation",
+  "country": "UK",
+  "geography": "GB",
+  "program": "workplace_pension",
+  "metric": "participation_rate",
+  "unit_concept": "share",
+  "sector": "public",
+  "axis": "earnings_band",
+  "subgroup": "gbp_10k_20k",
+  "period": 2016,
+  "value": 0.84,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.3a:block1:\u00a310,000 - under \u00a320,000:public:2016"
+ },
+ {
+  "source": "dwp-pension-participation",
+  "country": "UK",
+  "geography": "GB",
+  "program": "workplace_pension",
+  "metric": "participation_rate",
+  "unit_concept": "share",
+  "sector": "public",
+  "axis": "earnings_band",
+  "subgroup": "gbp_10k_20k",
+  "period": 2017,
+  "value": 0.87,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.3a:block1:\u00a310,000 - under \u00a320,000:public:2017"
+ },
+ {
+  "source": "dwp-pension-participation",
+  "country": "UK",
+  "geography": "GB",
+  "program": "workplace_pension",
+  "metric": "participation_rate",
+  "unit_concept": "share",
+  "sector": "public",
+  "axis": "earnings_band",
+  "subgroup": "gbp_10k_20k",
+  "period": 2018,
+  "value": 0.89,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.3a:block1:\u00a310,000 - under \u00a320,000:public:2018"
+ },
+ {
+  "source": "dwp-pension-participation",
+  "country": "UK",
+  "geography": "GB",
+  "program": "workplace_pension",
+  "metric": "participation_rate",
+  "unit_concept": "share",
+  "sector": "public",
+  "axis": "earnings_band",
+  "subgroup": "gbp_10k_20k",
+  "period": 2019,
+  "value": 0.87,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.3a:block1:\u00a310,000 - under \u00a320,000:public:2019"
+ },
+ {
+  "source": "dwp-pension-participation",
+  "country": "UK",
+  "geography": "GB",
+  "program": "workplace_pension",
+  "metric": "participation_rate",
+  "unit_concept": "share",
+  "sector": "public",
+  "axis": "earnings_band",
+  "subgroup": "gbp_10k_20k",
+  "period": 2020,
+  "value": 0.91,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.3a:block1:\u00a310,000 - under \u00a320,000:public:2020"
+ },
+ {
+  "source": "dwp-pension-participation",
+  "country": "UK",
+  "geography": "GB",
+  "program": "workplace_pension",
+  "metric": "participation_rate",
+  "unit_concept": "share",
+  "sector": "public",
+  "axis": "earnings_band",
+  "subgroup": "gbp_10k_20k",
+  "period": 2021,
+  "value": 0.88,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.3a:block1:\u00a310,000 - under \u00a320,000:public:2021"
+ },
+ {
+  "source": "dwp-pension-participation",
+  "country": "UK",
+  "geography": "GB",
+  "program": "workplace_pension",
+  "metric": "participation_rate",
+  "unit_concept": "share",
+  "sector": "public",
+  "axis": "earnings_band",
+  "subgroup": "gbp_10k_20k",
+  "period": 2022,
+  "value": 0.87,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.3a:block1:\u00a310,000 - under \u00a320,000:public:2022"
+ },
+ {
+  "source": "dwp-pension-participation",
+  "country": "UK",
+  "geography": "GB",
+  "program": "workplace_pension",
+  "metric": "participation_rate",
+  "unit_concept": "share",
+  "sector": "public",
+  "axis": "earnings_band",
+  "subgroup": "gbp_10k_20k",
+  "period": 2023,
+  "value": 0.87,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.3a:block1:\u00a310,000 - under \u00a320,000:public:2023"
+ },
+ {
+  "source": "dwp-pension-participation",
+  "country": "UK",
+  "geography": "GB",
+  "program": "workplace_pension",
+  "metric": "participation_rate",
+  "unit_concept": "share",
+  "sector": "public",
+  "axis": "earnings_band",
+  "subgroup": "gbp_10k_20k",
+  "period": 2024,
+  "value": 0.86,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.3a:block1:\u00a310,000 - under \u00a320,000:public:2024"
+ },
+ {
+  "source": "dwp-pension-participation",
+  "country": "UK",
+  "geography": "GB",
+  "program": "workplace_pension",
+  "metric": "participation_rate",
+  "unit_concept": "share",
+  "sector": "public",
+  "axis": "earnings_band",
+  "subgroup": "gbp_10k_20k",
+  "period": 2025,
+  "value": 0.88,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.3a:block1:\u00a310,000 - under \u00a320,000:public:2025"
+ },
+ {
+  "source": "dwp-pension-participation",
+  "country": "UK",
+  "geography": "GB",
+  "program": "workplace_pension",
+  "metric": "participation_rate",
+  "unit_concept": "share",
+  "sector": "public",
+  "axis": "earnings_band",
+  "subgroup": "gbp_20k_30k",
+  "period": 2009,
+  "value": 0.84,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.3a:block1:\u00a320,000 - under \u00a330,000:public:2009"
+ },
+ {
+  "source": "dwp-pension-participation",
+  "country": "UK",
+  "geography": "GB",
+  "program": "workplace_pension",
+  "metric": "participation_rate",
+  "unit_concept": "share",
+  "sector": "public",
+  "axis": "earnings_band",
+  "subgroup": "gbp_20k_30k",
+  "period": 2010,
+  "value": 0.8200000000000001,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.3a:block1:\u00a320,000 - under \u00a330,000:public:2010"
+ },
+ {
+  "source": "dwp-pension-participation",
+  "country": "UK",
+  "geography": "GB",
+  "program": "workplace_pension",
+  "metric": "participation_rate",
+  "unit_concept": "share",
+  "sector": "public",
+  "axis": "earnings_band",
+  "subgroup": "gbp_20k_30k",
+  "period": 2011,
+  "value": 0.8200000000000001,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.3a:block1:\u00a320,000 - under \u00a330,000:public:2011"
+ },
+ {
+  "source": "dwp-pension-participation",
+  "country": "UK",
+  "geography": "GB",
+  "program": "workplace_pension",
+  "metric": "participation_rate",
+  "unit_concept": "share",
+  "sector": "public",
+  "axis": "earnings_band",
+  "subgroup": "gbp_20k_30k",
+  "period": 2012,
+  "value": 0.8200000000000001,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.3a:block1:\u00a320,000 - under \u00a330,000:public:2012"
+ },
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+  "period": 2021,
+  "value": 0.91,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.9a:block1:Scotland:all:2021"
+ },
+ {
+  "source": "dwp-pension-participation",
+  "country": "UK",
+  "geography": "GB",
+  "program": "workplace_pension",
+  "metric": "participation_rate",
+  "unit_concept": "share",
+  "sector": "all",
+  "axis": "region",
+  "subgroup": "Scotland",
+  "period": 2022,
+  "value": 0.91,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.9a:block1:Scotland:all:2022"
+ },
+ {
+  "source": "dwp-pension-participation",
+  "country": "UK",
+  "geography": "GB",
+  "program": "workplace_pension",
+  "metric": "participation_rate",
+  "unit_concept": "share",
+  "sector": "all",
+  "axis": "region",
+  "subgroup": "Scotland",
+  "period": 2023,
+  "value": 0.91,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.9a:block1:Scotland:all:2023"
+ },
+ {
+  "source": "dwp-pension-participation",
+  "country": "UK",
+  "geography": "GB",
+  "program": "workplace_pension",
+  "metric": "participation_rate",
+  "unit_concept": "share",
+  "sector": "all",
+  "axis": "region",
+  "subgroup": "Scotland",
+  "period": 2024,
+  "value": 0.93,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.9a:block1:Scotland:all:2024"
+ },
+ {
+  "source": "dwp-pension-participation",
+  "country": "UK",
+  "geography": "GB",
+  "program": "workplace_pension",
+  "metric": "participation_rate",
+  "unit_concept": "share",
+  "sector": "all",
+  "axis": "region",
+  "subgroup": "Scotland",
+  "period": 2025,
+  "value": 0.93,
+  "denominator": "Employees ELIGIBLE for automatic enrolment \u2014 an earnings-trigger and age-range definition that has moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every row rather than left in a methodology note.",
+  "survey_axis": "DWP estimates derived from ONS ASHE, an employer survey of JOBS in Great Britain. PolicyEngine-UK's certified world is FRS-based, a household survey, so a PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries.",
+  "status": "ok",
+  "edition": "Workplace pension participation and savings trends: 2009 to 2025",
+  "source_column": "1.9a:block1:Scotland:all:2025"
+ }
+]
diff --git a/data/lanes.json b/data/lanes.json
index b4601d8..33b957d 100644
--- a/data/lanes.json
+++ b/data/lanes.json
@@ -357,6 +357,17 @@
    "running": false,
    "updated": "2026-08-21",
    "note": "5 model claims, 7 Chronicle facts (6 statistical + 1 non-simulated uprated EU-SILC survey input), 6 derived ratios dispositioned; 2 concept-mismatch attachments"
+  },
+  {
+   "id": "dwp-pension-participation",
+   "source": "DWP",
+   "area": "workplace pension participation",
+   "mode": 1,
+   "country": "UK",
+   "stage": "ingested",
+   "running": false,
+   "updated": "2026-08-25",
+   "note": "1377 claims \u2014 the first external validation of any pensions quantity in the UK scorecard (participation among auto-enrolment ELIGIBLE employees, GB, ASHE-derived)"
   }
  ]
 }
diff --git a/scorecard_db/build_db.py b/scorecard_db/build_db.py
index 8998ab8..1b1668b 100644
--- a/scorecard_db/build_db.py
+++ b/scorecard_db/build_db.py
@@ -19,6 +19,7 @@
     campaign_us  staged day-1/day-2 campaign results (claim matching)
     uk_externals five UK primary-source families + Chronicle staging
     uk_deductions FRR family
+    dwp_pensions  workplace pension participation (ASHE, GB)
     produce_uk + campaign_uk  archive-resolved UK reckoner attaches
     be_jrc      JRC EUROMOD-BE model claims + honest demo attachments;
                 final so its 2026-08-21 lane update cannot be regressed by
@@ -47,6 +48,7 @@
     ingest_solo,
     ingest_uk_deductions,
     ingest_uk_externals,
+    ingest_dwp_pensions,
     ingest_urban,
     produce_campaign_uk,
 )
@@ -94,6 +96,9 @@ def build(db_path: Path) -> dict:
         ("campaign_us", lambda: ingest_campaign.ingest(db_path)),
         ("uk_externals", lambda: ingest_uk_externals.ingest(db_path)),
         ("uk_deductions", lambda: ingest_uk_deductions.ingest(db_path)),
+        # DWP workplace pension participation (#98): the first external
+        # validation of any pensions quantity in the UK scorecard.
+        ("dwp_pensions", lambda: ingest_dwp_pensions.ingest(db_path)),
         ("produce_uk", lambda: produce_campaign_uk.produce(db_path)),
         (
             "campaign_uk",
diff --git a/scorecard_db/ingest_dwp_pensions.py b/scorecard_db/ingest_dwp_pensions.py
new file mode 100644
index 0000000..16865a0
--- /dev/null
+++ b/scorecard_db/ingest_dwp_pensions.py
@@ -0,0 +1,198 @@
+"""Ingest DWP workplace pension participation (#98).
+
+PolicyEngine-UK models pensions — contributions, their relief, the age
+limit, the salary-sacrifice interaction — and until now not one of the
+15,858 UK external claims said anything about pensions. This is the
+external side: 1,377 participation rates by earnings band, age band and
+region, split public/private/overall, 2009-2025.
+
+Two things ride on every claim because leaving them in a methodology
+note would make the rows uninterpretable:
+
+  denominator   The rate is a share of employees ELIGIBLE for automatic
+                enrolment — an earnings-trigger and age-range definition
+                that has moved over the series. A participation rate
+                whose denominator is unstated says nothing.
+  survey_axis   DWP derives these from ONS ASHE, an employer survey of
+                jobs; the certified PE-UK world is FRS-based. A
+                PE-vs-DWP gap is a survey-population difference before
+                it is an engine question — the same axis the LPC lane
+                (#88) carries.
+
+Geography is GB, not UK: ASHE excludes Northern Ireland, and the
+registry keeps the two apart for the same reason #91 keeps IFS's
+coverage-restricted analyses apart.
+
+    PYTHONPATH=. python -m scorecard_db.ingest_dwp_pensions data/scorecard.db
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+from .db import LANE_SQL, SCORES_SQL, ScorecardDB
+from .harvest import REPO, finish, require_fields
+from .models import ExternalScore, Metric, TimeBasis, UnitConcept
+from .relationships import uk_relationship
+from .uk_aliases import canon
+
+EXTERNALS = REPO / "data" / "externals"
+
+ADAPTER_SOURCE = "dwp-pension-participation"
+SOURCE = "dwp_pensions"
+LANE_ID = "dwp-pension-participation"
+LANE_UPDATED = "2026-08-25"
+FEED_UPDATED = "2026-08-19"
+LANE_FEED_META = {
+    "source": "DWP",
+    "area": "workplace pension participation",
+    "mode": 1,
+    "country": "UK",
+}
+
+PUBLICATION = {
+    "title": "DWP Workplace pension participation and savings trends: 2009 to 2025",
+    "url": (
+        "https://www.gov.uk/government/statistics/"
+        "workplace-pension-participation-and-savings-trends-2009-to-2025"
+    ),
+    "date": "2026-07-30",
+    "country": "UK",
+}
+
+_KNOWN_FIELDS = frozenset(
+    {
+        "axis",
+        "country",
+        "denominator",
+        "edition",
+        "geography",
+        "metric",
+        "period",
+        "program",
+        "sector",
+        "source",
+        "source_column",
+        "status",
+        "subgroup",
+        "survey_axis",
+        "unit_concept",
+        "value",
+    }
+)
+
+_METRICS = {
+    "participation_rate": (Metric.PARTICIPATION_RATE, UnitConcept.SHARE, "share")
+}
+# the adapter's axis name -> the uk_aliases axis its values are closed on
+_AXES = {"earnings_band": "earnings_band", "age_band": "age_band", "region": "region"}
+
+
+def _load() -> list[dict]:
+    path = EXTERNALS / f"{ADAPTER_SOURCE}.json"
+    if not path.exists():
+        raise FileNotFoundError(
+            f"{path} missing — run sources/dwp-pension-participation/adapter.py first"
+        )
+    rows = json.loads(path.read_text())
+    for row in rows:
+        require_fields(row, _KNOWN_FIELDS, SOURCE)
+        if row["source"] != ADAPTER_SOURCE:
+            raise ValueError(f"{SOURCE}: row source {row['source']!r} is wrong")
+        if row["country"] != "UK":
+            raise ValueError(f"{SOURCE}: row country {row['country']!r} is not 'UK'")
+    return rows
+
+
+def stage() -> tuple[list[ExternalScore], dict]:
+    scores: list[ExternalScore] = []
+    counts: dict[str, int] = {}
+    for row in _load():
+        metric, unit, value_kind = _METRICS[row["metric"]]
+        canon(SOURCE, "unit", row["unit_concept"])
+        axis = row["axis"]
+        if axis not in _AXES:
+            raise ValueError(f"{SOURCE}: unregistered axis {axis!r}")
+        value = float(row["value"])
+        if not 0.0 <= value <= 1.0:
+            raise ValueError(
+                f"{SOURCE}: participation rate {value} is not a share — the "
+                "publication gives fractions, and a percent slipping in here "
+                "would be a hundredfold error nobody could see downstream"
+            )
+        cond = {
+            "country": "UK",
+            # GB, not UK: ASHE excludes Northern Ireland
+            "geography": canon(SOURCE, "geography", row["geography"]),
+            "program": canon(SOURCE, "program", row["program"]),
+            "sector": canon(SOURCE, "sector", row["sector"]),
+            # which axis this row cuts on, so an earnings band and an age
+            # band never collide on one subgroup key
+            "axis": axis,
+            "subgroup": canon(SOURCE, _AXES[axis], row["subgroup"]),
+            # the two facts that make the row interpretable at all
+            "denominator": row["denominator"],
+            "survey_axis": row["survey_axis"],
+            "edition": row["edition"],
+        }
+        scores.append(
+            ExternalScore(
+                source=SOURCE,
+                metric=metric,
+                unit_concept=unit,
+                period=int(row["period"]),
+                # ASHE April reference year, carried as a calendar year
+                time_basis=TimeBasis.ANNUAL,
+                value=value,
+                conditions=cond,
+                calibration_relationship=uk_relationship(SOURCE, metric)[0],
+                source_model="dwp_ashe",
+                source_column=row["source_column"],
+                publication=PUBLICATION,
+                value_kind=value_kind,
+                status=row["status"],
+            )
+        )
+        counts[axis] = counts.get(axis, 0) + 1
+    return finish(scores, SOURCE), counts
+
+
+_EXPECTED = {"earnings_band": 357, "age_band": 459, "region": 561}
+
+
+def ingest(db_path: Path) -> dict:
+    scores, counts = stage()
+    if counts != _EXPECTED:
+        raise ValueError(f"claim accounting drifted: {counts} != {_EXPECTED}")
+    db = ScorecardDB(db_path)
+    rows = [ScorecardDB.score_row(s) for s in scores]
+
+    from .baselines import register_baselines_txn
+    from .ingest_harvest import sync_lane_feed
+
+    with db.conn:
+        db.conn.execute("DELETE FROM external_scores WHERE source = ?", (SOURCE,))
+        db.conn.executemany(SCORES_SQL, rows)
+        register_baselines_txn(db)
+        detail = (
+            f"{len(rows)} claims — the first external validation of any "
+            "pensions quantity in the UK scorecard (participation among "
+            "auto-enrolment ELIGIBLE employees, GB, ASHE-derived)"
+        )
+        db.conn.execute(LANE_SQL, (LANE_ID, "ingested", detail, LANE_UPDATED))
+    sync_lane_feed(
+        db,
+        REPO / "data" / "lanes.json",
+        FEED_UPDATED,
+        lanes={LANE_ID: LANE_FEED_META},
+    )
+    db.close()
+    return {"claims": len(rows), "by_axis": dict(sorted(counts.items()))}
+
+
+if __name__ == "__main__":
+    import sys
+
+    out = Path(sys.argv[1] if len(sys.argv) > 1 else "data/scorecard.db")
+    print(json.dumps(ingest(out), indent=1))
diff --git a/scorecard_db/relationships.py b/scorecard_db/relationships.py
index ba14ca6..181b184 100644
--- a/scorecard_db/relationships.py
+++ b/scorecard_db/relationships.py
@@ -222,6 +222,20 @@ def effective_relationship(program, metric):
     "nothing in pe-uk-data consumes them.",
 )
 
+# DWP workplace pension participation (#98). ASHE-derived, like the LPC
+# lane, so the survey difference is the first divergence axis.
+_DWP_PENSIONS_HELD = (
+    CR.HELD_OUT,
+    "DWP's workplace pension participation estimates are derived from ONS "
+    "ASHE, an employer survey of jobs, where the certified "
+    "policyengine-uk world is FRS-based; no pe-uk-data target and no "
+    "policyengine-uk parameter is fitted to a participation rate "
+    "(consumption surfaces read 2026-08-25 at the certified pins). Note "
+    "the engine models pension contributions and their relief but has no "
+    "pension commencement lump sum at all, so the pensions question this "
+    "scorecard can ask is narrower than the pensions system.",
+)
+
 _UKMOD_HELD = (
     CR.HELD_OUT,
     "UKMOD is a peer microsimulation, not a calibration source; no PE UK "
@@ -276,6 +290,8 @@ def uk_relationship(source, metric, program=None, kind=None):
         if program in OBR_CONSUMED_WELFARE_PROGRAMS:
             return _OBR_CONSUMED
         return _OBR_UNCONSUMED
+    if source == "dwp_pensions":
+        return _DWP_PENSIONS_HELD
     if source == "ukmod":
         return _UKMOD_HELD
     if source == "hm_treasury":
diff --git a/scorecard_db/uk_aliases.py b/scorecard_db/uk_aliases.py
index 33d5a49..8217624 100644
--- a/scorecard_db/uk_aliases.py
+++ b/scorecard_db/uk_aliases.py
@@ -55,6 +55,21 @@ def _alias(source: str, axis: str, source_value: str, canonical: str) -> None:
         ("dwp_takeup:housing_benefit_pensioners", "obr:housing_benefit_on_jsa"),
         ("dwp_takeup:housing_benefit_pensioners", "ukmod:housing_benefit"),
         ("dwp_takeup:benefit_units", "ukmod:families"),
+        # DWP's auto-enrolment ELIGIBILITY earnings bands are defined by
+        # the enrolment trigger, not by an income distribution, so they
+        # are not UKMOD's quantiles nor anyone's deciles.
+        ("dwp_pensions:gbp_10k_20k", "ukmod:q1"),
+        ("dwp_pensions:gbp_70k_plus", "ukmod:q5"),
+        # DEFERRED, deliberately: DWP's age bands are also not LPC's
+        # minimum-wage age bands (one is an auto-enrolment range, the
+        # other a wage-rate category), but the `lpc` vocabulary is
+        # registered on the #93 branch and not here. Asserting a pair
+        # against a source nobody has registered is the same overclaiming
+        # the review caught on #92 — the ledger would read as if the
+        # distinction had been checked when there is nothing on the other
+        # side to check against. The pairs go in once #93 lands; until
+        # then the distinction lives in prose, in
+        # sources/dwp-pension-participation/source.json.
     }
 )
 
@@ -299,6 +314,48 @@ def _alias(source: str, axis: str, source_value: str, canonical: str) -> None:
     _identity(_src, "unit", ["gbp", "households"])
 
 
+# --- DWP workplace pension participation (#98) -------------------------------
+# Geography is GB, not UK: ASHE excludes Northern Ireland, and the two
+# are kept apart for the same reason #91 keeps IFS's coverage-restricted
+# analyses apart from the UK. The earnings and age bands are the
+# publication's own and are closed per source — DWP's auto-enrolment
+# eligibility bands are not UKMOD's income groups and not LPC's age
+# bands, even where the numbers coincide.
+_identity("dwp_pensions", "geography", ["GB"])
+_identity("dwp_pensions", "program", ["workplace_pension"])
+_identity("dwp_pensions", "sector", ["public", "private", "all"])
+_identity(
+    "dwp_pensions",
+    "earnings_band",
+    [
+        "gbp_10k_20k",
+        "gbp_20k_30k",
+        "gbp_30k_40k",
+        "gbp_40k_50k",
+        "gbp_50k_60k",
+        "gbp_60k_70k",
+        "gbp_70k_plus",
+    ],
+)
+_identity(
+    "dwp_pensions",
+    "age_band",
+    [
+        "age_22_25",
+        "age_26_30",
+        "age_31_35",
+        "age_36_40",
+        "age_41_45",
+        "age_46_50",
+        "age_51_55",
+        "age_56_60",
+        "age_61_65",
+    ],
+)
+_identity("dwp_pensions", "region", _UK_REGIONS)
+_identity("dwp_pensions", "unit", ["share"])
+
+
 def canon(source: str, axis: str, value: str) -> str:
     """Canonical value for (source, axis, source_value); unknown raises."""
     try:
diff --git a/sources/dwp-pension-participation/adapter.py b/sources/dwp-pension-participation/adapter.py
new file mode 100644
index 0000000..ee5f69a
--- /dev/null
+++ b/sources/dwp-pension-participation/adapter.py
@@ -0,0 +1,286 @@
+"""Adapter: DWP workplace pension participation (#98).
+
+Input:  raw/dwp_workplace_pension_participation_2009_2025.xlsx — the DWP
+        "Workplace pension participation and savings trends: 2009 to
+        2025" data tables (published 2026-07-30; see raw/README.md).
+Output: data/externals/dwp-pension-participation.json — tidy rows.
+
+PolicyEngine-UK models pensions — contributions, relief, the age limit,
+the salary-sacrifice interaction — and until now not one of the 15,858
+UK external claims said anything about pensions at all. This is the
+external side.
+
+The measure is PARTICIPATION: the share of ELIGIBLE employees saving
+into a workplace pension, by earnings band, age band and region, split
+Public / Private / Overall, 2009-2025.
+
+Three things the publication forces, each handled explicitly:
+
+1. **Eligibility is part of the identity, not a footnote.** The
+   denominator is employees ELIGIBLE for automatic enrolment — which is
+   itself defined by an earnings trigger and an age range that have
+   moved over the series. A participation rate whose denominator is
+   unstated is uninterpretable, so every row carries the population it
+   is a share of.
+
+2. **The sheets hold SIX tables side by side and only the first is
+   labelled.** Row 6 of each sheet describes block 1 ("Percentage of
+   eligible employees participating") and is blank above the other five.
+   Rather than guess what the unlabelled blocks measure, this adapter
+   reads block 1 and TALLIES the other five as deliberately unread. A
+   number whose meaning is inferred from position is not a claim.
+
+3. **The source is ASHE**, an employer survey of jobs — the same survey
+   behind the Low Pay Commission lane (#88). So the same divergence axis
+   applies: PE-UK's certified world is FRS-based, a household survey,
+   and a PE-vs-DWP participation gap is a survey-population difference
+   before it is an engine question. That axis rides on every row.
+"""
+
+import hashlib
+import json
+import re
+import zipfile
+import xml.etree.ElementTree as ET
+from pathlib import Path
+
+HERE = Path(__file__).resolve().parent
+RAW = HERE / "raw"
+OUT_DIR = HERE.parent.parent / "data" / "externals"
+OUT_DIR.mkdir(parents=True, exist_ok=True)
+
+SOURCE_ID = "dwp-pension-participation"
+EDITION = "Workplace pension participation and savings trends: 2009 to 2025"
+
+MNS = "{http://schemas.openxmlformats.org/spreadsheetml/2006/main}"
+RNS = "{http://schemas.openxmlformats.org/officeDocument/2006/relationships}"
+
+WORKBOOK = "dwp_workplace_pension_participation_2009_2025.xlsx"
+SHA256 = {
+    WORKBOOK: "4d4bd871b23f9ef763101b4491bce437f3c02e622ce9394c857c0346fb326e41"
+}
+
+# Block 1 is the only labelled table on each sheet; its descriptor sits
+# at row 6 column 1 and is anchored so a re-shuffled workbook fails.
+BLOCK1_ANCHOR = "Percentage of eligible employees participating"
+BLOCK_WIDTH = 18  # label column + 17 years
+HEADER_ROW = 8
+BLOCKS_PER_SHEET = 6
+
+SECTORS = {"Public": "public", "Private": "private", "Overall": "all"}
+
+# sheet -> (axis name, {published label: canonical value})
+SHEETS = {
+    "1.3a": (
+        "earnings_band",
+        {
+            "£10,000 - under £20,000": "gbp_10k_20k",
+            "£20,000 - under £30,000": "gbp_20k_30k",
+            "£30,000 - under £40,000": "gbp_30k_40k",
+            "£40,000 - under £50,000": "gbp_40k_50k",
+            "£50,000 - under £60,000": "gbp_50k_60k",
+            "£60,000 - under £70,000": "gbp_60k_70k",
+            "£70,000+": "gbp_70k_plus",
+        },
+    ),
+    "1.4": (
+        "age_band",
+        {
+            "22 to 25": "age_22_25",
+            "26 to 30": "age_26_30",
+            "31 to 35": "age_31_35",
+            "36 to 40": "age_36_40",
+            "41 to 45": "age_41_45",
+            "46 to 50": "age_46_50",
+            "51 to 55": "age_51_55",
+            "56 to 60": "age_56_60",
+            "61 to 65": "age_61_65",
+        },
+    ),
+    "1.9a": (
+        "region",
+        {
+            "North East": "North East",
+            "North West": "North West",
+            "Yorkshire & The Humber": "Yorkshire and the Humber",
+            "East Midlands": "East Midlands",
+            "West Midlands": "West Midlands",
+            "South West": "South West",
+            "East": "East",
+            "London": "London",
+            "South East": "South East",
+            "Wales": "Wales",
+            "Scotland": "Scotland",
+        },
+    ),
+}
+
+DENOMINATOR = (
+    "Employees ELIGIBLE for automatic enrolment — an earnings-trigger and "
+    "age-range definition that has moved over the series. A participation "
+    "rate whose denominator is unstated is uninterpretable, so it is carried "
+    "on every row rather than left in a methodology note."
+)
+SURVEY_AXIS = (
+    "DWP estimates derived from ONS ASHE, an employer survey of JOBS in "
+    "Great Britain. PolicyEngine-UK's certified world is FRS-based, a "
+    "household survey, so a PE-vs-DWP participation gap is a "
+    "survey-population difference before it is an engine question — the same "
+    "axis the Low Pay Commission lane (#88) carries."
+)
+GEOGRAPHY = "GB"
+
+
+def _check_sha(fname):
+    digest = hashlib.sha256((RAW / fname).read_bytes()).hexdigest()
+    if digest != SHA256[fname]:
+        raise RuntimeError(
+            f"{fname}: sha256 {digest} != pinned {SHA256[fname]} — the raw "
+            "file drifted from the bytes this adapter was written against"
+        )
+
+
+def _grid(path, sheet_name):
+    z = zipfile.ZipFile(path)
+    ss = [
+        "".join(t.text or "" for t in si.iter(MNS + "t"))
+        for si in ET.fromstring(z.read("xl/sharedStrings.xml")).iter(MNS + "si")
+    ]
+    wb = ET.fromstring(z.read("xl/workbook.xml"))
+    rels = {
+        r.get("Id"): r.get("Target")
+        for r in ET.fromstring(z.read("xl/_rels/workbook.xml.rels"))
+    }
+    for s in wb.iter(MNS + "sheet"):
+        if s.get("name") != sheet_name:
+            continue
+        root = ET.fromstring(z.read("xl/" + rels[s.get(RNS + "id")].lstrip("/")))
+        grid = {}
+        for row in root.iter(MNS + "row"):
+            for c in row.iter(MNS + "c"):
+                v = c.find(MNS + "v")
+                if v is None:
+                    continue
+                m = re.match(r"([A-Z]+)(\d+)", c.get("r"))
+                col = 0
+                for ch in m.group(1):
+                    col = col * 26 + (ord(ch) - 64)
+                grid[(int(m.group(2)), col)] = (
+                    ss[int(v.text)] if c.get("t") == "s" else v.text
+                )
+        return grid
+    raise KeyError(f"no sheet {sheet_name!r}")
+
+
+def _closed(registry, key, what):
+    try:
+        return registry[key]
+    except KeyError:
+        raise RuntimeError(
+            f"unregistered {what}: {key!r} — the published table changed. "
+            "Register it deliberately, never pass it through"
+        ) from None
+
+
+def parse_sheet(sheet, out):
+    """Block 1 of one sheet: sector x category x year participation rates."""
+    grid = _grid(RAW / WORKBOOK, sheet)
+    descriptor = str(grid.get((6, 1), "")).strip()
+    if not descriptor.startswith(BLOCK1_ANCHOR):
+        raise RuntimeError(
+            f"{sheet}: block-1 anchor failed ({descriptor!r}); the workbook "
+            "was re-shuffled and the unlabelled blocks make position-based "
+            "reading unsafe"
+        )
+    axis, categories = SHEETS[sheet]
+
+    # years live on the header row within block 1 only
+    years = {
+        c: int(str(v).strip())
+        for (r, c), v in grid.items()
+        if r == HEADER_ROW and 2 <= c <= BLOCK_WIDTH and str(v).strip().isdigit()
+    }
+    if not years:
+        raise RuntimeError(f"{sheet}: no year headers in block 1")
+
+    sector = None
+    n = 0
+    for r in sorted({r for (r, c) in grid if c == 1 and r >= HEADER_ROW}):
+        label = str(grid.get((r, 1), "")).strip()
+        if label in SECTORS:
+            sector = SECTORS[label]
+            continue
+        if not label or sector is None:
+            continue
+        category = _closed(categories, label, f"{sheet} {axis}")
+        for c, year in sorted(years.items()):
+            if (r, c) not in grid:
+                continue
+            out.append(
+                {
+                    "source": SOURCE_ID,
+                    "country": "UK",
+                    "geography": GEOGRAPHY,
+                    "program": "workplace_pension",
+                    "metric": "participation_rate",
+                    "unit_concept": "share",
+                    "sector": sector,
+                    "axis": axis,
+                    "subgroup": category,
+                    "period": year,
+                    "value": float(grid[(r, c)]),
+                    "denominator": DENOMINATOR,
+                    "survey_axis": SURVEY_AXIS,
+                    "status": "ok",
+                    "edition": EDITION,
+                    "source_column": f"{sheet}:block1:{label}:{sector}:{year}",
+                }
+            )
+            n += 1
+    return n
+
+
+def build():
+    for fname in SHA256:
+        _check_sha(fname)
+    rows = []
+    counts = {sheet: parse_sheet(sheet, rows) for sheet in SHEETS}
+
+    # What is deliberately NOT read, tallied rather than implied.
+    z = zipfile.ZipFile(RAW / WORKBOOK)
+    wb = ET.fromstring(z.read("xl/workbook.xml"))
+    all_sheets = [s.get("name") for s in wb.iter(MNS + "sheet")]
+    unread = {
+        "unlabelled_side_by_side_blocks": {
+            "blocks": (BLOCKS_PER_SHEET - 1) * len(SHEETS),
+            "reason": (
+                "Each sheet holds six tables side by side and only the first "
+                "carries a descriptor (row 6). The other five are unlabelled, "
+                "and a number whose meaning is inferred from its column "
+                "position is not a claim. They are read when DWP labels them "
+                "or the methodology note is transcribed deliberately."
+            ),
+        },
+        "sheets_not_read": {
+            "sheets": len(all_sheets) - len(SHEETS) - 4,  # less Cover/Contents/Guidance/Notes
+            "reason": (
+                "The other data sheets — by sector, employer size, gender, "
+                "working pattern, industry, occupation, and the savings-level "
+                "tables — are each their own population decision rather than "
+                "something to sweep in because the file was already open."
+            ),
+        },
+    }
+
+    out = OUT_DIR / f"{SOURCE_ID}.json"
+    out.write_text(json.dumps(rows, indent=1) + "\n")
+    print(f"wrote {len(rows)} rows -> {out}")
+    for k, v in counts.items():
+        print(f"  {k}: {v}")
+    for k, v in unread.items():
+        print(f"  NOT READ {k}: {v.get('blocks', v.get('sheets'))}")
+    return rows, counts, unread
+
+
+if __name__ == "__main__":
+    build()
diff --git a/sources/dwp-pension-participation/raw/README.md b/sources/dwp-pension-participation/raw/README.md
new file mode 100644
index 0000000..de8a3ca
--- /dev/null
+++ b/sources/dwp-pension-participation/raw/README.md
@@ -0,0 +1,51 @@
+# dwp-pension-participation raw artifacts
+
+Fetched 2026-08-25 from GOV.UK via the content API attachment list for
+`/government/statistics/workplace-pension-participation-and-savings-trends-2009-to-2025`
+(first published 2026-07-30 — the current edition).
+
+| file | source URL | sha256 |
+|---|---|---|
+| dwp_workplace_pension_participation_2009_2025.xlsx | https://assets.publishing.service.gov.uk/media/6a68b23acc35a9471da4c1cf/workplace-pension-participation-2009-2025.xlsx | 4d4bd871b23f9ef763101b4491bce437f3c02e622ce9394c857c0346fb326e41 |
+
+The release also offers the same tables as ODS. The XLSX is vendored
+because the repo's other spreadsheet adapters already read the
+OpenXML shared-strings layout; the two are the same data.
+
+## What is read
+
+Block 1 of three sheets — the only block on each that carries a
+descriptor:
+
+- `1.3a` participation by **earnings band**
+- `1.4` participation by **age band**
+- `1.9a` participation by **region**
+
+each split Public / Private / Overall, 2009-2025.
+
+## What was surveyed and deliberately NOT kept
+
+- **The five unlabelled side-by-side blocks on each sheet.** Every sheet
+  states it "contains six tables presented next to each other", and row
+  6 describes only the first ("Percentage of eligible employees
+  participating"). The other five have no descriptor. A number whose
+  meaning is inferred from its column position is not a claim, so they
+  are tallied as unread rather than guessed at. They are readable once
+  DWP labels them or the methodology note is transcribed deliberately.
+- **The other 21 data sheets** — by sector, employer size, gender,
+  working pattern, industry, occupation, and the savings-level tables.
+  Each is its own population decision.
+
+## Why the survey matters
+
+DWP derives these from **ONS ASHE**, an employer survey of jobs in Great
+Britain — the same survey behind the Low Pay Commission lane (#88), and
+not the FRS the certified policyengine-uk world is built on. That
+difference is the first divergence axis for any counterpart, and it is
+carried on every row rather than left here.
+
+## Geography
+
+Great Britain, not the UK: ASHE excludes Northern Ireland. Rows carry
+`GB`, and the repo's registry keeps that distinct from `UK` for the same
+reason #91 keeps IFS's coverage-restricted analyses distinct.
diff --git a/sources/dwp-pension-participation/raw/dwp_workplace_pension_participation_2009_2025.xlsx b/sources/dwp-pension-participation/raw/dwp_workplace_pension_participation_2009_2025.xlsx
new file mode 100644
index 0000000..4e261aa
Binary files /dev/null and b/sources/dwp-pension-participation/raw/dwp_workplace_pension_participation_2009_2025.xlsx differ
diff --git a/sources/dwp-pension-participation/source.json b/sources/dwp-pension-participation/source.json
new file mode 100644
index 0000000..7187663
--- /dev/null
+++ b/sources/dwp-pension-participation/source.json
@@ -0,0 +1,28 @@
+{
+  "id": "dwp-pension-participation",
+  "name": "DWP \u2014 Workplace pension participation and savings trends (2009 to 2025)",
+  "organization": "Department for Work and Pensions",
+  "country": "UK",
+  "claim_class": "population_statistic",
+  "url": "https://www.gov.uk/government/statistics/workplace-pension-participation-and-savings-trends-2009-to-2025",
+  "data_urls": [
+    "https://assets.publishing.service.gov.uk/media/6a68b23acc35a9471da4c1cf/workplace-pension-participation-2009-2025.xlsx"
+  ],
+  "published": "2026-07-30",
+  "fetched": "2026-08-25",
+  "model": {
+    "name": "DWP estimates derived from ONS ASHE",
+    "open_source": false,
+    "method": "Participation is the share of employees ELIGIBLE for automatic enrolment who are saving into a workplace pension, estimated from the Annual Survey of Hours and Earnings (an employer survey of jobs), Great Britain, April reference period.",
+    "method_basis": "DWP background information and methodology note accompanying the release"
+  },
+  "metrics": [
+    "participation_rate"
+  ],
+  "denominator_note": "ELIGIBILITY IS PART OF THE IDENTITY. The denominator is employees eligible for automatic enrolment, defined by an earnings trigger and an age range that have both moved over the series. A participation rate whose denominator is unstated is uninterpretable, so it is carried on every claim rather than left in a methodology note.",
+  "period": "ASHE April reference years 2009 to 2025, carried as calendar years",
+  "geographies": "Great Britain \u2014 ASHE excludes Northern Ireland, so rows carry GB and not UK",
+  "coverage_note": "What is READ: block 1 of sheets 1.3a (earnings band), 1.4 (age band) and 1.9a (region), each split Public / Private / Overall. What is deliberately NOT read and tallied by the build: the five unlabelled side-by-side blocks on each of those sheets, because every sheet holds six tables next to each other and only the first carries a descriptor \u2014 a number whose meaning is inferred from its column position is not a claim; and the other 21 data sheets, each of which is its own population decision.",
+  "diagnosis_upstream": "Built on ASHE, an employer survey of jobs, where the certified policyengine-uk world is FRS-based, a household survey. A PE-vs-DWP participation gap is a survey-population difference before it is an engine question \u2014 the same axis the Low Pay Commission lane (#88) carries \u2014 and it must be sized before any engine defect is named, and then only through the citable-known-issue gate. Note also that PE-UK models pension CONTRIBUTIONS and their relief but has no representation of the pension commencement lump sum (verified at the certified pin, #98), so the pensions question this scorecard can ask is narrower than the pensions system.",
+  "identity_note": "DWP's auto-enrolment earnings bands are defined by the enrolment trigger, not by an income distribution, so they are recorded DISTINCT from UKMOD's quantiles in uk_aliases. The same is true of DWP's age bands against the Low Pay Commission's minimum-wage age bands \u2014 one is an enrolment range, the other a wage-rate category \u2014 but that pair is DEFERRED from the ledger until the LPC vocabulary lands (issue #88, PR #93): asserting distinctness against a source nobody has registered would read as if it had been checked when there is nothing on the other side to check against."
+}
diff --git a/tests/test_dwp_pensions.py b/tests/test_dwp_pensions.py
new file mode 100644
index 0000000..a56ed93
--- /dev/null
+++ b/tests/test_dwp_pensions.py
@@ -0,0 +1,227 @@
+"""DWP workplace pension participation (#98).
+
+PolicyEngine-UK models pensions and until this lane not one of the
+15,858 UK claims said anything about them. What is pinned here is what
+makes each rate interpretable — its denominator and its survey — and the
+refusal to read the parts of the workbook whose meaning is unlabelled.
+"""
+
+import hashlib
+import json
+from pathlib import Path
+
+import pytest
+
+ROOT = Path(__file__).resolve().parent.parent
+LANE = ROOT / "sources" / "dwp-pension-participation"
+RAW = LANE / "raw" / "dwp_workplace_pension_participation_2009_2025.xlsx"
+EXTERNAL = ROOT / "data" / "externals" / "dwp-pension-participation.json"
+SHA = "4d4bd871b23f9ef763101b4491bce437f3c02e622ce9394c857c0346fb326e41"
+
+
+@pytest.fixture(scope="module")
+def rows():
+    return json.loads(EXTERNAL.read_text())
+
+
+@pytest.fixture(scope="module")
+def adapter():
+    import importlib.util
+
+    spec = importlib.util.spec_from_file_location(
+        "dwp_pens_adapter", LANE / "adapter.py"
+    )
+    mod = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(mod)
+    return mod
+
+
+def test_raw_artifact_is_the_pinned_bytes():
+    assert RAW.exists()
+    assert hashlib.sha256(RAW.read_bytes()).hexdigest() == SHA
+
+
+def test_row_counts(rows):
+    assert len(rows) == 1377
+    by = {}
+    for r in rows:
+        by[r["axis"]] = by.get(r["axis"], 0) + 1
+    assert by == {"earnings_band": 357, "age_band": 459, "region": 561}
+
+
+# --- what makes a rate interpretable ----------------------------------------
+
+
+def test_every_rate_carries_its_denominator(rows):
+    """Eligibility is part of the identity. The denominator is employees
+    ELIGIBLE for automatic enrolment — an earnings trigger and age range
+    that have both moved over the series."""
+    for r in rows:
+        assert "ELIGIBLE for automatic enrolment" in r["denominator"]
+        assert "moved over the series" in r["denominator"]
+
+
+def test_every_rate_carries_the_survey_axis(rows):
+    """ASHE is an employer survey of jobs; PE-UK is FRS-based. That
+    difference is the first divergence axis, same as the LPC lane."""
+    for r in rows:
+        assert "ASHE" in r["survey_axis"]
+        assert "FRS-based" in r["survey_axis"]
+
+
+def test_rates_are_shares_not_percents(rows):
+    assert all(0.0 <= r["value"] <= 1.0 for r in rows)
+
+
+def test_a_percent_slipping_in_is_refused(monkeypatch):
+    """A hundredfold error nobody could see downstream."""
+    from scorecard_db import ingest_dwp_pensions as dp
+
+    raw = json.loads(EXTERNAL.read_text())
+    bad = [dict(r) for r in raw]
+    bad[0]["value"] = 78.0
+    monkeypatch.setattr(dp, "_load", lambda: bad)
+    with pytest.raises(ValueError, match="not a share"):
+        dp.stage()
+
+
+# --- geography and identity -------------------------------------------------
+
+
+def test_geography_is_gb_not_uk(rows):
+    """ASHE excludes Northern Ireland."""
+    assert {r["geography"] for r in rows} == {"GB"}
+    readme = (LANE / "raw" / "README.md").read_text()
+    assert "excludes Northern Ireland" in readme
+
+
+def test_the_axis_rides_on_the_claim(rows):
+    """Otherwise an earnings band and an age band collide on one
+    subgroup key."""
+    from scorecard_db.ingest_dwp_pensions import stage
+
+    scores, _ = stage()
+    assert len({s.claim_id() for s in scores}) == len(scores) == 1377
+    axes = {s.conditions["axis"] for s in scores}
+    assert axes == {"earnings_band", "age_band", "region"}
+
+
+def test_eligibility_bands_are_not_income_quantiles():
+    """DWP's bands are defined by the auto-enrolment trigger, not by an
+    income distribution."""
+    from scorecard_db.uk_aliases import DISTINCT
+
+    assert ("dwp_pensions:gbp_10k_20k", "ukmod:q1") in DISTINCT
+    assert ("dwp_pensions:gbp_70k_plus", "ukmod:q5") in DISTINCT
+
+
+def test_the_lpc_age_band_distinction_is_deferred_not_asserted():
+    """DWP's age bands are also not LPC's minimum-wage age bands — one is
+    an enrolment range, the other a wage-rate category — but the `lpc`
+    vocabulary is registered on #93's branch, not here. Asserting a pair
+    against a source nobody has registered is the same overclaiming the
+    review caught on #92: the ledger would read as if the distinction had
+    been checked when there is nothing on the other side. So it is
+    deferred in a comment and stated in prose instead."""
+    from scorecard_db.uk_aliases import DISTINCT, known
+
+    assert not known("lpc", "subgroup"), "lpc is registered here after all"
+    assert not any("lpc" in a + b for a, b in DISTINCT)
+    src = (ROOT / "scorecard_db" / "uk_aliases.py").read_text()
+    assert "DEFERRED, deliberately" in src
+    note = json.loads((LANE / "source.json").read_text())["identity_note"]
+    assert "DEFERRED from the ledger" in note
+
+
+def test_unregistered_values_raise():
+    from scorecard_db.uk_aliases import canon
+
+    with pytest.raises(ValueError, match="unregistered"):
+        canon("dwp_pensions", "earnings_band", "£80,000+")
+
+
+# --- what is deliberately not read ------------------------------------------
+
+
+def test_the_unlabelled_blocks_are_not_guessed_at(tmp_path, monkeypatch, adapter):
+    """Each sheet holds six tables side by side and only the first has a
+    descriptor. A number whose meaning is inferred from its column
+    position is not a claim."""
+    monkeypatch.setattr(adapter, "OUT_DIR", tmp_path)
+    _, counts, unread = adapter.build()
+    assert counts == {"1.3a": 357, "1.4": 459, "1.9a": 561}
+    assert unread["unlabelled_side_by_side_blocks"]["blocks"] == 15
+    assert unread["sheets_not_read"]["sheets"] > 0
+    for entry in unread.values():
+        assert len(entry["reason"]) > 80
+
+
+def test_a_reshuffled_workbook_raises(tmp_path, monkeypatch, adapter):
+    monkeypatch.setattr(adapter, "OUT_DIR", tmp_path)
+    monkeypatch.setattr(adapter, "BLOCK1_ANCHOR", "Something DWP never printed")
+    with pytest.raises(RuntimeError, match="block-1 anchor failed"):
+        adapter.build()
+
+
+def test_an_unregistered_category_raises(tmp_path, monkeypatch, adapter):
+    monkeypatch.setattr(adapter, "OUT_DIR", tmp_path)
+    sheets = dict(adapter.SHEETS)
+    sheets["1.4"] = ("age_band", {"22 to 25": "age_22_25"})
+    monkeypatch.setattr(adapter, "SHEETS", sheets)
+    with pytest.raises(RuntimeError, match="unregistered 1.4 age_band"):
+        adapter.build()
+
+
+# --- ingest ------------------------------------------------------------------
+
+
+def test_every_claim_is_held_out():
+    from scorecard_db.ingest_dwp_pensions import stage
+    from scorecard_db.models import CalibrationRelationship
+
+    scores, _ = stage()
+    assert all(
+        s.calibration_relationship is CalibrationRelationship.HELD_OUT for s in scores
+    )
+
+
+def test_the_relationship_names_the_narrower_question():
+    """The engine models contributions and relief but has no pension
+    commencement lump sum at all (#98), so the pensions question this
+    scorecard can ask is narrower than the pensions system."""
+    from scorecard_db.models import Metric
+    from scorecard_db.relationships import uk_relationship
+
+    _, evidence = uk_relationship("dwp_pensions", Metric.PARTICIPATION_RATE)
+    assert "no pension commencement lump sum" in evidence
+    assert "2026-08-25" in evidence
+
+
+def test_full_ingest_round_trip(tmp_path):
+    import sqlite3
+
+    from scorecard_db import ScorecardDB
+    from scorecard_db.ingest_dwp_pensions import ingest
+
+    db_path = tmp_path / "t.db"
+    ScorecardDB(db_path).close()
+    assert ingest(db_path)["claims"] == 1377
+    conn = sqlite3.connect(db_path)
+    conn.row_factory = sqlite3.Row
+    n = conn.execute(
+        "SELECT COUNT(*) FROM external_scores WHERE source='dwp_pensions'"
+    ).fetchone()[0]
+    assert n == 1377
+    lane = conn.execute(
+        "SELECT * FROM lanes WHERE lane='dwp-pension-participation'"
+    ).fetchone()
+    conn.close()
+    assert lane["stage"] == "ingested"
+
+
+def test_build_db_registers_the_step():
+    import inspect
+
+    from scorecard_db import build_db
+
+    assert "ingest_dwp_pensions.ingest" in inspect.getsource(build_db)
diff --git a/tests/test_uk_aliases_ledger.py b/tests/test_uk_aliases_ledger.py
new file mode 100644
index 0000000..2266b0c
--- /dev/null
+++ b/tests/test_uk_aliases_ledger.py
@@ -0,0 +1,104 @@
+"""The DISTINCT ledger only ever contains checkable assertions.
+
+`DISTINCT` is a plain frozenset of `"source:value"` strings, so nothing
+stops it naming a source that has no registered vocabulary. A pair like
+that reads as if a distinction had been checked when there is nothing on
+the other side to check against — and, because the pair is just a
+string, every test asserting it exists still passes.
+
+That defect was caught by review on #92 (ONS quintiles claimed distinct
+from HBAI, which registers no quantile vocabulary at all) and it recurred
+on the pensions branch (DWP age bands claimed distinct from LPC, whose
+vocabulary lives on another branch). Two occurrences is a class, so it
+gets a guard rather than a third correction.
+"""
+
+import re
+from pathlib import Path
+
+import pytest
+
+from scorecard_db.uk_aliases import DISTINCT, known
+
+ROOT = Path(__file__).resolve().parent.parent
+SOURCE = (ROOT / "scorecard_db" / "uk_aliases.py").read_text()
+
+
+def _registered_sources():
+    """Every source with at least one registered value, read from the
+    registry itself rather than a hardcoded list."""
+    sources = set()
+    for axis_call in re.finditer(
+        r'_(?:identity|alias)\(\s*\n?\s*"([a-z_0-9]+)"', SOURCE
+    ):
+        sources.add(axis_call.group(1))
+    for pair in re.findall(r"for _src in \(([^)]*)\)", SOURCE):
+        sources.update(re.findall(r'"([a-z_0-9]+)"', pair))
+    return sources
+
+
+def test_every_distinct_pair_names_two_registered_sources():
+    """An assertion against a source nobody registered is not a
+    distinction — it is a sentence."""
+    registered = _registered_sources()
+    dangling = sorted(
+        {
+            side.split(":", 1)[0]
+            for pair in DISTINCT
+            for side in pair
+            if ":" in side and side.split(":", 1)[0] not in registered
+        }
+    )
+    assert not dangling, (
+        f"DISTINCT names sources with no registered vocabulary: {dangling}. "
+        "Either register them, or defer the pair with a comment saying which "
+        "branch or issue brings the other side — do not assert against "
+        "nothing."
+    )
+
+
+def test_every_distinct_value_exists_on_its_own_side():
+    """The stricter form: not just the source, the VALUE. A pair naming
+    `ukmod:q9` would be equally unfalsifiable."""
+    missing = []
+    for pair in DISTINCT:
+        for side in pair:
+            if ":" not in side:
+                continue
+            src, value = side.split(":", 1)
+            axes = (
+                "program",
+                "subgroup",
+                "geography",
+                "income_group",
+                "quantile",
+                "unit",
+                "benefit",
+                "earnings_band",
+                "age_band",
+                "region",
+                "income_concept",
+                "statistic",
+                "rate_scope",
+                "poverty_line",
+                "housing_costs",
+                "sector",
+            )
+            if not any(value in known(src, axis) for axis in axes):
+                missing.append(side)
+    assert not missing, (
+        f"DISTINCT names values that are not registered anywhere: "
+        f"{sorted(set(missing))}"
+    )
+
+
+def test_the_guard_is_not_passing_trivially():
+    """A guard that would pass on an empty set is not a guard. The
+    long-standing base pairs must be present, so the checks above are
+    actually checking something."""
+    assert ("dwp_takeup:benefit_units", "ukmod:families") in DISTINCT
+    assert (
+        "dwp_takeup:housing_benefit_pensioners",
+        "ukmod:housing_benefit",
+    ) in DISTINCT
+    assert len(DISTINCT) >= 4