diff --git a/app/App.css b/app/App.css
index 2a0bc55..2112ee9 100644
--- a/app/App.css
+++ b/app/App.css
@@ -34,7 +34,7 @@
   background: rgba(15, 118, 110, 0.95);
   backdrop-filter: blur(12px);
   -webkit-backdrop-filter: blur(12px);
-  padding: var(--pe-space-md) var(--pe-space-xl);
+  padding: var(--pe-space-md) 0;
   width: 100%;
   position: sticky;
   top: 0;
@@ -45,6 +45,7 @@
 .title-row-inner {
   max-width: 1400px;
   margin: 0 auto;
+  padding: 0 var(--pe-space-xl);
   display: flex;
   justify-content: space-between;
   align-items: center;
@@ -65,7 +66,6 @@
   font-size: 1.05rem;
   line-height: 1.7;
   color: var(--pe-text-secondary);
-  max-width: 820px;
   margin-bottom: var(--pe-space-xl);
   animation: fadeIn 0.4s ease-out;
 }
@@ -491,7 +491,11 @@
   }
 
   .title-row {
-    padding: var(--pe-space-sm) var(--pe-space-md);
+    padding: var(--pe-space-sm) 0;
+  }
+
+  .title-row-inner {
+    padding: 0 var(--pe-space-md);
   }
 
   .title-row h1 {
diff --git a/app/page.jsx b/app/page.jsx
index 62762fc..3af961a 100644
--- a/app/page.jsx
+++ b/app/page.jsx
@@ -77,12 +77,19 @@ function Dashboard() {
     <div className="app">
       <header className="title-row">
         <div className="title-row-inner">
-          <h1>Spring Statement 2026</h1>
+          <h1>Spring Statement 2026 analysis</h1>
         </div>
       </header>
       <main className="main-content">
         <p className="dashboard-intro">
-          PolicyEngine analysis of the OBR&apos;s March 2026 economic forecast
+          PolicyEngine analysis of the{" "}
+          <a
+            href="https://obr.uk/economic-and-fiscal-outlooks/"
+            target="_blank"
+            rel="noreferrer"
+          >
+            OBR&apos;s March 2026 economic forecast
+          </a>{" "}
           revisions and their projected impact on UK household incomes. The
           Spring Statement contained no new policy measures — all changes result
           from updated economic assumptions.
diff --git a/public/data/detailed_budgetary_impact.csv b/public/data/detailed_budgetary_impact.csv
index eddd4ef..7afe079 100644
--- a/public/data/detailed_budgetary_impact.csv
+++ b/public/data/detailed_budgetary_impact.csv
@@ -1,51 +1,51 @@
 year,program,baseline,reform,difference
-2026,income_tax,303272.73899443523,303272.73899443523,0.0
-2026,national_insurance,56035.53018029651,56035.53018029651,0.0
-2026,vat,200684.99023889532,200684.99023889532,0.0
-2026,council_tax,60975.72982729077,60975.72982729077,0.0
-2026,fuel_duty,34765.63809750822,34765.63809750822,0.0
-2026,tax_credits,5406.764615242458,5406.764615242458,-0.0
-2026,universal_credit,80875.61182102392,80875.61182102392,-0.0
-2026,child_benefit,15677.099071694834,15677.099071694834,-0.0
-2026,state_pension,122276.98239907347,122276.98239907347,-0.0
-2026,pension_credit,5989.291994108764,5989.291994108764,-0.0
-2027,income_tax,318842.63694264094,318842.63694264094,0.0
-2027,national_insurance,58063.57606287163,58063.57606287163,0.0
-2027,vat,205496.36494742037,205496.36494742037,0.0
-2027,council_tax,64659.05849905342,64659.05849905342,0.0
-2027,fuel_duty,39462.0899228921,39462.0899228921,0.0
-2027,tax_credits,5558.882313274557,5558.882313274557,-0.0
-2027,universal_credit,83077.66709902306,83077.66709902306,-0.0
-2027,child_benefit,16125.300974580407,16125.300974580407,-0.0
-2027,state_pension,126142.7111801254,126142.7111801254,-0.0
-2027,pension_credit,6103.4966800892735,6103.4966800892735,-0.0
-2028,income_tax,332888.4167182199,332888.4167182199,0.0
-2028,national_insurance,58846.53796278711,58846.53796278711,0.0
-2028,vat,210527.23408144133,210527.23408144133,0.0
-2028,council_tax,68507.11337248725,68507.11337248725,0.0
-2028,fuel_duty,41970.256974364114,41970.256974364114,0.0
-2028,tax_credits,5681.531851997269,5681.531851997269,-0.0
-2028,universal_credit,85396.48175922343,85396.48175922343,-0.0
-2028,child_benefit,16516.8669624525,16516.8669624525,-0.0
-2028,state_pension,129329.04617681382,129329.04617681382,-0.0
-2028,pension_credit,6190.7437699792235,6190.7437699792235,-0.0
-2029,income_tax,347810.65010074497,347810.65010074497,0.0
-2029,national_insurance,60484.88450799108,60484.88450799108,0.0
-2029,vat,215767.21074488971,215767.21074488971,0.0
-2029,council_tax,72505.6181612071,72505.6181612071,0.0
-2029,fuel_duty,44280.64615361491,44280.64615361491,0.0
-2029,tax_credits,5811.8286665137675,5811.8286665137675,-0.0
-2029,universal_credit,87999.0821609842,87999.0821609842,-0.0
-2029,child_benefit,16927.9919284299,16927.9919284299,-0.0
-2029,state_pension,132659.48127452147,132659.48127452147,-0.0
-2029,pension_credit,6263.407463412997,6263.407463412997,-0.0
-2030,income_tax,364245.2533321321,364245.2533321321,0.0
-2030,national_insurance,62468.09505541894,62468.09505541894,0.0
-2030,vat,221072.93211309024,221072.93211309024,0.0
-2030,council_tax,76714.3507657324,76714.3507657324,0.0
-2030,fuel_duty,46687.89005494893,46687.89005494893,0.0
-2030,tax_credits,5937.397368991846,5937.397368991846,-0.0
-2030,universal_credit,89904.66771697627,89904.66771697627,-0.0
-2030,child_benefit,17351.062528444258,17351.062528444258,-0.0
-2030,state_pension,136099.19445440217,136099.19445440217,-0.0
-2030,pension_credit,5732.043683078642,5732.043683078642,-0.0
+2026,income_tax,304730.8439144392,304744.0698935997,13.225979160522462
+2026,national_insurance,55737.99965794466,56141.3191489743,403.3194910296402
+2026,vat,200835.8714179344,200835.8714179344,0.0
+2026,council_tax,60664.11190772149,60664.11190772149,0.0
+2026,fuel_duty,35463.747557224386,35463.747557224386,0.0
+2026,tax_credits,5290.852230760112,5286.707752740712,4.144478019400597
+2026,universal_credit,80892.74900119173,80867.7426383464,25.006362845321654
+2026,child_benefit,15611.400307538195,15603.84313430796,7.557173230236053
+2026,state_pension,122078.41468818983,122176.21637289779,-97.80168470796204
+2026,pension_credit,5972.874071519186,5954.92955415779,17.944517361395835
+2027,income_tax,320908.0067779534,320934.23671441426,26.22993646081543
+2027,national_insurance,58186.76532734835,57479.012494316165,-707.7528330321884
+2027,vat,205610.5477711693,205610.5477711693,0.0
+2027,council_tax,64519.435719095694,64519.435719095694,0.0
+2027,fuel_duty,40246.61442554191,40246.61442554191,0.0
+2027,tax_credits,5436.194798181148,5416.586102475047,19.60869570610237
+2027,universal_credit,82995.18245214972,82906.04219420836,89.14025794136047
+2027,child_benefit,16057.736020704735,16021.75957636995,35.97644433478546
+2027,state_pension,125938.71122687338,126143.831024713,-205.11979783961488
+2027,pension_credit,6085.320196301552,6024.0809320465605,61.23926425499153
+2028,income_tax,335134.91884455556,335164.9201421303,30.001297574768067
+2028,national_insurance,58915.746722880016,58806.409548955045,-109.3371739249649
+2028,vat,210561.63934467576,210561.63934467576,0.0
+2028,council_tax,68699.73700763391,68699.73700763391,0.0
+2028,fuel_duty,42787.86606888584,42787.86606888584,0.0
+2028,tax_credits,5555.893679060627,5534.008332033964,21.88534702666378
+2028,universal_credit,85295.01359726118,85198.50800779137,96.50558946980286
+2028,child_benefit,16447.663123018487,16407.538359263017,40.124763755470276
+2028,state_pension,129121.12650330196,129331.85101443302,-210.72451113105774
+2028,pension_credit,6174.526047702704,6108.649815220868,65.87623248183633
+2029,income_tax,350155.8273071976,350188.269292015,32.44198481738281
+2029,national_insurance,61384.56714483321,61774.167857809356,389.60071297615053
+2029,vat,215717.8752039614,215717.8752039614,0.0
+2029,council_tax,72862.3997553772,72862.3997553772,0.0
+2029,fuel_duty,45125.56573567095,45125.56573567095,0.0
+2029,tax_credits,5682.515596333218,5656.567814368341,25.947781964878082
+2029,universal_credit,87912.83786355042,87801.65622456431,111.18163898609924
+2029,child_benefit,16857.063952252585,16809.306644069908,47.75730818267822
+2029,state_pension,132446.50504959468,132663.80369090423,-217.2986413095703
+2029,pension_credit,6239.735857206298,6165.61580016285,74.12005704344749
+2030,income_tax,367081.0083849407,367114.8418567441,33.833471803405764
+2030,national_insurance,62611.38354587946,63411.14844326598,799.7648973865204
+2030,vat,221022.3838654562,221022.3838654562,0.0
+2030,council_tax,77210.71335799992,77210.71335799992,0.0
+2030,fuel_duty,47578.74239954073,47578.74239954073,0.0
+2030,tax_credits,5803.93303676049,5773.654437884992,30.278598875496865
+2030,universal_credit,89749.45900276749,89492.85746436992,256.60153839756777
+2030,child_benefit,17278.36202601888,17222.61199670249,55.75002931639099
+2030,state_pension,135881.71015803205,136105.63123261754,-223.92107458551024
+2030,pension_credit,5770.899498894896,5687.848066462216,83.05143243268108
diff --git a/public/data/distributional_impact.csv b/public/data/distributional_impact.csv
index 1849005..948bde7 100644
--- a/public/data/distributional_impact.csv
+++ b/public/data/distributional_impact.csv
@@ -1,56 +1,56 @@
-year,decile,absolute_change,relative_change
-2026,1st,0.0,0.0
-2026,2nd,0.0,0.0
-2026,3rd,0.0,0.0
-2026,4th,0.0,0.0
-2026,5th,0.0,0.0
-2026,6th,0.0,0.0
-2026,7th,0.0,0.0
-2026,8th,0.0,0.0
-2026,9th,0.0,0.0
-2026,10th,0.0,0.0
-2026,All,0.0,0.0
-2027,1st,0.0,0.0
-2027,2nd,0.0,0.0
-2027,3rd,0.0,0.0
-2027,4th,0.0,0.0
-2027,5th,0.0,0.0
-2027,6th,0.0,0.0
-2027,7th,0.0,0.0
-2027,8th,0.0,0.0
-2027,9th,0.0,0.0
-2027,10th,0.0,0.0
-2027,All,0.0,0.0
-2028,1st,0.0,0.0
-2028,2nd,0.0,0.0
-2028,3rd,0.0,0.0
-2028,4th,0.0,0.0
-2028,5th,0.0,0.0
-2028,6th,0.0,0.0
-2028,7th,0.0,0.0
-2028,8th,0.0,0.0
-2028,9th,0.0,0.0
-2028,10th,0.0,0.0
-2028,All,0.0,0.0
-2029,1st,0.0,0.0
-2029,2nd,0.0,0.0
-2029,3rd,0.0,0.0
-2029,4th,0.0,0.0
-2029,5th,0.0,0.0
-2029,6th,0.0,0.0
-2029,7th,0.0,0.0
-2029,8th,0.0,0.0
-2029,9th,0.0,0.0
-2029,10th,0.0,0.0
-2029,All,0.0,0.0
-2030,1st,0.0,0.0
-2030,2nd,0.0,0.0
-2030,3rd,0.0,0.0
-2030,4th,0.0,0.0
-2030,5th,0.0,0.0
-2030,6th,0.0,0.0
-2030,7th,0.0,0.0
-2030,8th,0.0,0.0
-2030,9th,0.0,0.0
-2030,10th,0.0,0.0
-2030,All,0.0,0.0
+year,decile,absolute_change_nominal,absolute_change_real,relative_change_nominal,relative_change_real
+2026,1st,0.8,0.8,0.004,0.004
+2026,2nd,0.91,0.91,0.003,0.003
+2026,3rd,-0.4,-0.4,-0.0011,-0.0011
+2026,4th,-0.71,-0.71,-0.0017,-0.0017
+2026,5th,-0.31,-0.31,-0.0007,-0.0007
+2026,6th,-0.24,-0.24,-0.0005,-0.0005
+2026,7th,0.12,0.12,0.0002,0.0002
+2026,8th,2.27,2.27,0.0034,0.0034
+2026,9th,6.03,6.03,0.0083,0.0083
+2026,10th,-133.24,-133.24,-0.1061,-0.1061
+2026,All,-12.6,-12.6,-0.0233,-0.0233
+2027,1st,-1.86,2.15,-0.0091,0.0108
+2027,2nd,-4.87,1.32,-0.0156,0.0043
+2027,3rd,-7.93,-0.46,-0.0212,-0.0012
+2027,4th,-8.27,0.37,-0.0191,0.0009
+2027,5th,-8.51,0.76,-0.0183,0.0017
+2027,6th,-5.5,4.99,-0.0104,0.0096
+2027,7th,-8.21,2.99,-0.0145,0.0054
+2027,8th,-2.83,10.32,-0.0042,0.0157
+2027,9th,2.31,16.71,0.0031,0.0231
+2027,10th,215.04,235.77,0.1685,0.1885
+2027,All,17.32,27.72,0.0315,0.0515
+2028,1st,-2.34,9.48,-0.0114,0.048
+2028,2nd,-6.29,11.92,-0.02,0.0394
+2028,3rd,-9.98,12.22,-0.0261,0.0333
+2028,4th,-9.5,16.04,-0.0215,0.0379
+2028,5th,-11.09,16.33,-0.0234,0.0359
+2028,6th,-7.13,24.0,-0.0132,0.0462
+2028,7th,-11.27,21.93,-0.0196,0.0398
+2028,8th,-6.48,32.48,-0.0096,0.0498
+2028,9th,-6.72,36.32,-0.009,0.0504
+2028,10th,45.12,118.05,0.0345,0.0939
+2028,All,-2.41,29.54,-0.0043,0.0551
+2029,1st,-3.38,16.21,-0.0162,0.0827
+2029,2nd,-8.95,21.44,-0.0279,0.071
+2029,3rd,-13.32,23.49,-0.0344,0.0645
+2029,4th,-11.85,30.33,-0.0266,0.0723
+2029,5th,-13.98,31.81,-0.0289,0.0699
+2029,6th,-9.44,42.08,-0.0172,0.0817
+2029,7th,-13.57,41.4,-0.0233,0.0756
+2029,8th,-7.53,56.65,-0.011,0.0879
+2029,9th,-9.85,61.77,-0.0129,0.086
+2029,10th,-106.31,23.86,-0.0798,0.019
+2029,All,-19.66,34.26,-0.0347,0.0642
+2030,1st,-11.24,8.73,-0.0536,0.0451
+2030,2nd,-17.71,13.23,-0.0545,0.0441
+2030,3rd,-21.49,16.01,-0.0546,0.0441
+2030,4th,-19.37,23.19,-0.043,0.0557
+2030,5th,-21.09,25.36,-0.0429,0.0558
+2030,6th,-14.06,38.49,-0.0249,0.0738
+2030,7th,-18.37,36.55,-0.0313,0.0674
+2030,8th,-8.13,55.96,-0.0117,0.0871
+2030,9th,-5.89,64.71,-0.0076,0.0911
+2030,10th,-244.06,-101.39,-0.1793,-0.0807
+2030,All,-38.21,17.25,-0.0663,0.0324
diff --git a/public/data/economic_forecast.json b/public/data/economic_forecast.json
index bc81904..3b1ba5a 100644
--- a/public/data/economic_forecast.json
+++ b/public/data/economic_forecast.json
@@ -127,38 +127,6 @@
       "change": 0.0
     }
   ],
-  "per_capita_gdp": [
-    {
-      "year": 2026,
-      "previous": 3.3,
-      "updated": 2.9,
-      "change": -0.4
-    },
-    {
-      "year": 2027,
-      "previous": 3.3,
-      "updated": 3.2,
-      "change": -0.1
-    },
-    {
-      "year": 2028,
-      "previous": 3.0,
-      "updated": 3.1,
-      "change": 0.1
-    },
-    {
-      "year": 2029,
-      "previous": 2.9,
-      "updated": 3.0,
-      "change": 0.1
-    },
-    {
-      "year": 2030,
-      "previous": 3.1,
-      "updated": 3.1,
-      "change": 0.0
-    }
-  ],
   "social_rent": [
     {
       "year": 2026,
diff --git a/public/data/household_comparison.json b/public/data/household_comparison.json
index 2a205fb..78f2f0e 100644
--- a/public/data/household_comparison.json
+++ b/public/data/household_comparison.json
@@ -1,38 +1,272 @@
 [
   {
+    "year": 2026,
     "group": "Single adult, no children",
-    "baseline_hnet": 29438,
-    "reformed_hnet": 29430,
-    "change": -8
+    "baseline_hnet": 27647,
+    "reformed_hnet_nominal": 27648,
+    "reformed_hnet_real": 27648,
+    "change_nominal": 0,
+    "change_real": 0
   },
   {
+    "year": 2026,
     "group": "Couple, no children",
-    "baseline_hnet": 64188,
-    "reformed_hnet": 64180,
-    "change": -8
+    "baseline_hnet": 70150,
+    "reformed_hnet_nominal": 70082,
+    "reformed_hnet_real": 70082,
+    "change_nominal": -68,
+    "change_real": -68
   },
   {
+    "year": 2026,
     "group": "Single parent",
-    "baseline_hnet": 39886,
-    "reformed_hnet": 39855,
-    "change": -31
+    "baseline_hnet": 37993,
+    "reformed_hnet_nominal": 37987,
+    "reformed_hnet_real": 37987,
+    "change_nominal": -6,
+    "change_real": -6
   },
   {
+    "year": 2026,
     "group": "Couple with children",
-    "baseline_hnet": 74966,
-    "reformed_hnet": 74932,
-    "change": -34
+    "baseline_hnet": 75762,
+    "reformed_hnet_nominal": 75784,
+    "reformed_hnet_real": 75784,
+    "change_nominal": 22,
+    "change_real": 22
   },
   {
+    "year": 2026,
     "group": "Single pensioner",
-    "baseline_hnet": 27033,
-    "reformed_hnet": 27027,
-    "change": -6
+    "baseline_hnet": 25853,
+    "reformed_hnet_nominal": 25856,
+    "reformed_hnet_real": 25856,
+    "change_nominal": 3,
+    "change_real": 3
   },
   {
+    "year": 2026,
     "group": "Pensioner couple",
-    "baseline_hnet": 54118,
-    "reformed_hnet": 54121,
-    "change": 3
+    "baseline_hnet": 52677,
+    "reformed_hnet_nominal": 52686,
+    "reformed_hnet_real": 52686,
+    "change_nominal": 9,
+    "change_real": 9
+  },
+  {
+    "year": 2027,
+    "group": "Single adult, no children",
+    "baseline_hnet": 28050,
+    "reformed_hnet_nominal": 28043,
+    "reformed_hnet_real": 27493,
+    "change_nominal": -7,
+    "change_real": -1
+  },
+  {
+    "year": 2027,
+    "group": "Couple, no children",
+    "baseline_hnet": 71313,
+    "reformed_hnet_nominal": 71388,
+    "reformed_hnet_real": 69989,
+    "change_nominal": 75,
+    "change_real": 87
+  },
+  {
+    "year": 2027,
+    "group": "Single parent",
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+    "change_real": 61
   }
 ]
\ No newline at end of file
diff --git a/public/data/household_scatter.csv b/public/data/household_scatter.csv
index a5a3f5e..4066869 100644
--- a/public/data/household_scatter.csv
+++ b/public/data/household_scatter.csv
@@ -1,2001 +1,2001 @@
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+28200.273,-5.7753906,-5.7753906,1
+20022.332,0.0,0.0,1
+54189.65,0.0,0.0,6
+27997.846,-1.578125,-1.578125,1
+21691.46,0.0,0.0,3
+83634.914,-1.5703125,-1.5703125,7
+21326.0,4.451172,4.451172,2
+383.18945,-2.0800781,-2.0800781,1
+102073.54,0.0,0.0,10
+22523.436,7.3242188,7.3242188,3
+28585.64,0.0,0.0,2
+7649.6807,0.0,0.0,1
+10305.517,9.160156,9.160156,1
+49897.48,0.0,0.0,7
+48768.723,0.0,0.0,6
+28929.787,-3.4394531,-3.4394531,5
+10079.656,-5.7753906,-5.7753906,1
+124302.805,12.359375,12.359375,10
+29833.818,0.0,0.0,5
+86293.45,1.671875,1.671875,5
+38280.977,0.0,0.0,1
+100989.984,0.0,0.0,10
+49171.777,16.570312,16.570312,2
+55982.664,-0.0390625,-0.0390625,7
+60054.164,7.328125,7.328125,8
+12079.743,0.0,0.0,1
+44916.65,0.0,0.0,5
+23065.14,-0.6796875,-0.6796875,1
+-174.60938,0.0,0.0,1
+10618.452,0.0,0.0,1
+74360.96,0.0,0.0,8
+25611.863,7.3242188,7.3242188,2
+26741.39,7.328125,7.328125,4
+75139.82,13.96875,13.96875,6
+27494.283,0.0,0.0,6
+24542.111,9.15625,9.15625,2
+65271.523,911.85156,911.85156,10
+-7820.168,7.3271484,7.3271484,1
+31484.504,-5.203125,-5.203125,6
+15025.359,2.734375,2.734375,1
+26331.75,12.855469,12.855469,2
+25846.79,-4.0742188,-4.0742188,2
+68241.26,8.9921875,8.9921875,9
+80509.32,6.7734375,6.7734375,4
+90982.555,5.4921875,5.4921875,10
+88841.63,-0.4453125,-0.4453125,9
+59301.87,-0.50390625,-0.50390625,9
+107802.7,11.2109375,11.2109375,9
+74163.445,14.6484375,14.6484375,9
+50891.902,14.652344,14.652344,7
+46397.637,6.7734375,6.7734375,1
+82583.164,12.828125,12.828125,9
+132736.97,0.0,0.0,9
+65937.68,0.0,0.0,9
+107565.125,0.0,0.0,10
+35596.95,-6.0585938,-6.0585938,3
+25673.963,0.0,0.0,4
+9887.0,-5.7773438,-5.7773438,1
+25503.709,0.0,0.0,4
+142513.28,4.65625,4.65625,8
+78020.77,0.0,0.0,2
+15891.302,2.734375,2.734375,1
+83479.7,0.0,0.0,10
+62806.785,0.0,0.0,7
+20223.59,9.15625,9.15625,4
+24403.84,-8.818359,-8.818359,1
+87196.59,13.9296875,13.9296875,6
+23167.166,-5.1621094,-5.1621094,3
+100679.72,-6.109375,-6.109375,7
+93778.91,6.78125,6.78125,9
+58005.957,0.0,0.0,8
+42840.18,0.0,0.0,5
+35284.938,-5.7734375,-5.7734375,1
+28788.549,0.0,0.0,3
+54770.652,-13.21875,-13.21875,5
+26059.25,-4.466797,-4.466797,4
+81737.586,-16.328125,-16.328125,6
+30499.848,0.0,0.0,1
+66304.01,0.0,0.0,9
+28906.121,9.15625,9.15625,3
+82644.65,0.0,0.0,8
+71117.93,4.71875,4.71875,10
+26542.824,7.326172,7.326172,1
+87265.0,-1.7890625,-1.7890625,9
+18605.172,7.3242188,7.3242188,2
+34164.86,-1.5742188,-1.5742188,1
+35731.285,0.0,0.0,4
+96181.32,6.7734375,6.7734375,3
+61484.3,-4.3125,-4.3125,7
+69246.93,14.1015625,14.1015625,5
+59064.98,-0.6796875,-0.6796875,5
+44951.434,-9.2734375,-9.2734375,3
+19157.363,7.328125,7.328125,1
+88466.61,0.0,0.0,9
+-191042.4,0.0,0.0,3
+121343.77,3.65625,3.65625,9
+95705.62,5.4921875,5.4921875,10
+50638.656,-12.925781,-12.925781,8
+71288.81,-4.3515625,-4.3515625,4
+-555.3806,0.0,0.0,-1
+98335.86,0.0,0.0,7
+6572.1104,9.158203,9.158203,1
+93817.87,0.0,0.0,9
+34582.453,7.3242188,7.3242188,4
+54049.516,-15.3359375,-15.3359375,6
+67639.67,237.74219,237.74219,6
+56053.984,14.480469,14.480469,7
+55240.727,-0.671875,-0.671875,6
+23065.059,-1.7929688,-1.7929688,4
+125832.5,-13.375,-13.375,7
+52696.395,-0.00390625,-0.00390625,5
+102851.81,-1.8125,-1.8125,9
+64669.234,3.6601562,3.6601562,10
+49470.535,1.0820312,1.0820312,1
+49676.137,0.0,0.0,2
+56766.363,-0.6796875,-0.6796875,7
+61891.58,-0.640625,-0.640625,8
+48246.54,-1.125,-1.125,3
+43614.066,7.3242188,7.3242188,2
+48954.766,-2.1484375,-2.1484375,4
+68817.086,14.2578125,14.2578125,8
+28566.17,0.0,0.0,6
+46010.375,-0.73046875,-0.73046875,4
+54549.42,4.7070312,4.7070312,2
+73614.6,0.0,0.0,10
+102638.805,11.6171875,11.6171875,9
+51128.63,0.0,0.0,7
+79219.05,7.875,7.875,7
+49298.438,0.0,0.0,9
+31884.254,-5.15625,-5.15625,2
+36852.418,14.6484375,14.6484375,2
+42274.254,7.3242188,7.3242188,9
+44964.19,-0.55078125,-0.55078125,1
+117164.38,0.0,0.0,10
+112115.29,6.7265625,6.7265625,2
+97098.66,0.0,0.0,10
+93071.72,-6.015625,-6.015625,6
+60400.805,0.0,0.0,9
+49926.0,-14.8671875,-14.8671875,6
+24727.885,-7.423828,-7.423828,4
+43308.406,-8.125,-8.125,4
diff --git a/public/data/household_stats.json b/public/data/household_stats.json
index d3ffea0..f91c494 100644
--- a/public/data/household_stats.json
+++ b/public/data/household_stats.json
@@ -1,38 +1,212 @@
 [
   {
+    "year": 2026,
     "group": "Single adult, no children",
-    "mean_hnet": 29438,
-    "median_hnet": 24215,
-    "weighted_n": 8135470
+    "mean_hnet": 27647,
+    "median_hnet": 23876,
+    "weighted_n": 5487196
   },
   {
+    "year": 2026,
     "group": "Couple, no children",
-    "mean_hnet": 64188,
-    "median_hnet": 54442,
-    "weighted_n": 10806929
+    "mean_hnet": 70150,
+    "median_hnet": 59006,
+    "weighted_n": 8763130
   },
   {
+    "year": 2026,
     "group": "Single parent",
-    "mean_hnet": 39886,
-    "median_hnet": 36933,
-    "weighted_n": 2516884
+    "mean_hnet": 37993,
+    "median_hnet": 35597,
+    "weighted_n": 2034406
   },
   {
+    "year": 2026,
     "group": "Couple with children",
-    "mean_hnet": 74966,
-    "median_hnet": 63045,
-    "weighted_n": 6611670
+    "mean_hnet": 75762,
+    "median_hnet": 64338,
+    "weighted_n": 6704035
   },
   {
+    "year": 2026,
     "group": "Single pensioner",
-    "mean_hnet": 27033,
-    "median_hnet": 23103,
-    "weighted_n": 4061703
+    "mean_hnet": 25853,
+    "median_hnet": 22400,
+    "weighted_n": 3891606
   },
   {
+    "year": 2026,
     "group": "Pensioner couple",
-    "mean_hnet": 54118,
-    "median_hnet": 49209,
-    "weighted_n": 3383456
+    "mean_hnet": 52677,
+    "median_hnet": 47523,
+    "weighted_n": 4773454
+  },
+  {
+    "year": 2027,
+    "group": "Single adult, no children",
+    "mean_hnet": 28050,
+    "median_hnet": 24238,
+    "weighted_n": 5507498
+  },
+  {
+    "year": 2027,
+    "group": "Couple, no children",
+    "mean_hnet": 71313,
+    "median_hnet": 59824,
+    "weighted_n": 8795553
+  },
+  {
+    "year": 2027,
+    "group": "Single parent",
+    "mean_hnet": 38534,
+    "median_hnet": 36165,
+    "weighted_n": 2041933
+  },
+  {
+    "year": 2027,
+    "group": "Couple with children",
+    "mean_hnet": 76982,
+    "median_hnet": 65498,
+    "weighted_n": 6728840
+  },
+  {
+    "year": 2027,
+    "group": "Single pensioner",
+    "mean_hnet": 26280,
+    "median_hnet": 22718,
+    "weighted_n": 3906005
+  },
+  {
+    "year": 2027,
+    "group": "Pensioner couple",
+    "mean_hnet": 53563,
+    "median_hnet": 48505,
+    "weighted_n": 4791116
+  },
+  {
+    "year": 2028,
+    "group": "Single adult, no children",
+    "mean_hnet": 28510,
+    "median_hnet": 24532,
+    "weighted_n": 5529528
+  },
+  {
+    "year": 2028,
+    "group": "Couple, no children",
+    "mean_hnet": 72625,
+    "median_hnet": 60898,
+    "weighted_n": 8830735
+  },
+  {
+    "year": 2028,
+    "group": "Single parent",
+    "mean_hnet": 39125,
+    "median_hnet": 36492,
+    "weighted_n": 2050101
+  },
+  {
+    "year": 2028,
+    "group": "Couple with children",
+    "mean_hnet": 78200,
+    "median_hnet": 66446,
+    "weighted_n": 6755755
+  },
+  {
+    "year": 2028,
+    "group": "Single pensioner",
+    "mean_hnet": 26665,
+    "median_hnet": 22976,
+    "weighted_n": 3921629
+  },
+  {
+    "year": 2028,
+    "group": "Pensioner couple",
+    "mean_hnet": 54396,
+    "median_hnet": 49344,
+    "weighted_n": 4810280
+  },
+  {
+    "year": 2029,
+    "group": "Single adult, no children",
+    "mean_hnet": 28974,
+    "median_hnet": 24878,
+    "weighted_n": 5553858
+  },
+  {
+    "year": 2029,
+    "group": "Couple, no children",
+    "mean_hnet": 73700,
+    "median_hnet": 61662,
+    "weighted_n": 8869591
+  },
+  {
+    "year": 2029,
+    "group": "Single parent",
+    "mean_hnet": 39720,
+    "median_hnet": 37036,
+    "weighted_n": 2059121
+  },
+  {
+    "year": 2029,
+    "group": "Couple with children",
+    "mean_hnet": 79390,
+    "median_hnet": 67388,
+    "weighted_n": 6785480
+  },
+  {
+    "year": 2029,
+    "group": "Single pensioner",
+    "mean_hnet": 27058,
+    "median_hnet": 23223,
+    "weighted_n": 3938884
+  },
+  {
+    "year": 2029,
+    "group": "Pensioner couple",
+    "mean_hnet": 55211,
+    "median_hnet": 50027,
+    "weighted_n": 4831445
+  },
+  {
+    "year": 2030,
+    "group": "Single adult, no children",
+    "mean_hnet": 29479,
+    "median_hnet": 25242,
+    "weighted_n": 5578851
+  },
+  {
+    "year": 2030,
+    "group": "Couple, no children",
+    "mean_hnet": 75192,
+    "median_hnet": 62714,
+    "weighted_n": 8909504
+  },
+  {
+    "year": 2030,
+    "group": "Single parent",
+    "mean_hnet": 40215,
+    "median_hnet": 37382,
+    "weighted_n": 2068387
+  },
+  {
+    "year": 2030,
+    "group": "Couple with children",
+    "mean_hnet": 80721,
+    "median_hnet": 68462,
+    "weighted_n": 6816015
+  },
+  {
+    "year": 2030,
+    "group": "Single pensioner",
+    "mean_hnet": 27396,
+    "median_hnet": 23490,
+    "weighted_n": 3956609
+  },
+  {
+    "year": 2030,
+    "group": "Pensioner couple",
+    "mean_hnet": 56021,
+    "median_hnet": 50725,
+    "weighted_n": 4853187
   }
 ]
\ No newline at end of file
diff --git a/public/data/inequality.csv b/public/data/inequality.csv
index 26d4759..fa5495f 100644
--- a/public/data/inequality.csv
+++ b/public/data/inequality.csv
@@ -1,16 +1,16 @@
-year,metric,baseline,reform
-2026,gini,0.28264225403383014,0.28264225403383014
-2026,top_10_pct_share,0.24338873248534484,0.24338873248534484
-2026,top_1_pct_share,0.06421276046945473,0.06421276046945473
-2027,gini,0.2839566166296492,0.2839566166296492
-2027,top_10_pct_share,0.24112361610547406,0.24112361610547406
-2027,top_1_pct_share,0.06498068132903656,0.06498068132903656
-2028,gini,0.28544831196233966,0.28544831196233966
-2028,top_10_pct_share,0.24133922403379313,0.24133922403379313
-2028,top_1_pct_share,0.06508929940412486,0.06508929940412486
-2029,gini,0.28668073147264467,0.28668073147264467
-2029,top_10_pct_share,0.24241208304900427,0.24241208304900427
-2029,top_1_pct_share,0.0658162585346852,0.0658162585346852
-2030,gini,0.2884374666524972,0.2884374666524972
-2030,top_10_pct_share,0.24347194378147255,0.24347194378147255
-2030,top_1_pct_share,0.06643822861764408,0.06643822861764408
+year,metric,baseline,reform_nominal,reform_real
+2026,gini,0.28291195949308817,0.282743616786075,0.282743616786075
+2026,top_10_pct_share,0.24028316663722432,0.24014494548121476,0.24014494548121476
+2026,top_1_pct_share,0.06448224582651775,0.06423838226543441,0.06423838226543441
+2027,gini,0.2842003744929869,0.2845518649059099,0.28455186463868415
+2027,top_10_pct_share,0.24025349142671878,0.2405346533086001,0.24053465415330955
+2027,top_1_pct_share,0.06382009081093477,0.06412096507564989,0.06412096527303157
+2028,gini,0.28588488792559646,0.2860221529330154,0.2860221528580134
+2028,top_10_pct_share,0.2425271346494153,0.24252668996899426,0.24252668967508842
+2028,top_1_pct_share,0.06496273311571045,0.06503612888528176,0.0650361290247345
+2029,gini,0.2868648845495297,0.2867903987629185,0.28679040006718093
+2029,top_10_pct_share,0.24169740521303365,0.24152175080891358,0.24152175146097346
+2029,top_1_pct_share,0.06443141788046869,0.06437224754283671,0.06437224828905778
+2030,gini,0.2891819010569601,0.2890009249391396,0.2890009244725892
+2030,top_10_pct_share,0.2432481679345802,0.24287518823126414,0.24287518854102044
+2030,top_1_pct_share,0.06536563048246652,0.06506871296817356,0.06506871276297052
diff --git a/public/data/intra_decile.csv b/public/data/intra_decile.csv
index f75698d..3fd99fc 100644
--- a/public/data/intra_decile.csv
+++ b/public/data/intra_decile.csv
@@ -1,276 +1,276 @@
-year,decile,outcome,share
-2026,1st,Lose more than 5%,0.0
-2026,1st,Lose less than 5%,0.0
-2026,1st,No change,1.0
-2026,1st,Gain less than 5%,0.0
-2026,1st,Gain more than 5%,0.0
-2026,2nd,Lose more than 5%,0.0
-2026,2nd,Lose less than 5%,0.0
-2026,2nd,No change,1.0
-2026,2nd,Gain less than 5%,0.0
-2026,2nd,Gain more than 5%,0.0
-2026,3rd,Lose more than 5%,0.0
-2026,3rd,Lose less than 5%,0.0
-2026,3rd,No change,1.0
-2026,3rd,Gain less than 5%,0.0
-2026,3rd,Gain more than 5%,0.0
-2026,4th,Lose more than 5%,0.0
-2026,4th,Lose less than 5%,0.0
-2026,4th,No change,1.0
-2026,4th,Gain less than 5%,0.0
-2026,4th,Gain more than 5%,0.0
-2026,5th,Lose more than 5%,0.0
-2026,5th,Lose less than 5%,0.0
-2026,5th,No change,1.0
-2026,5th,Gain less than 5%,0.0
-2026,5th,Gain more than 5%,0.0
-2026,6th,Lose more than 5%,0.0
-2026,6th,Lose less than 5%,0.0
-2026,6th,No change,1.0
-2026,6th,Gain less than 5%,0.0
-2026,6th,Gain more than 5%,0.0
-2026,7th,Lose more than 5%,0.0
-2026,7th,Lose less than 5%,0.0
-2026,7th,No change,1.0
-2026,7th,Gain less than 5%,0.0
-2026,7th,Gain more than 5%,0.0
-2026,8th,Lose more than 5%,0.0
-2026,8th,Lose less than 5%,0.0
-2026,8th,No change,1.0
-2026,8th,Gain less than 5%,0.0
-2026,8th,Gain more than 5%,0.0
-2026,9th,Lose more than 5%,0.0
-2026,9th,Lose less than 5%,0.0
-2026,9th,No change,1.0
-2026,9th,Gain less than 5%,0.0
-2026,9th,Gain more than 5%,0.0
-2026,10th,Lose more than 5%,0.0
-2026,10th,Lose less than 5%,0.0
-2026,10th,No change,1.0
-2026,10th,Gain less than 5%,0.0
-2026,10th,Gain more than 5%,0.0
-2026,All,Lose more than 5%,0.0
-2026,All,Lose less than 5%,0.0
-2026,All,No change,1.0
-2026,All,Gain less than 5%,0.0
-2026,All,Gain more than 5%,0.0
-2027,1st,Lose more than 5%,0.0
-2027,1st,Lose less than 5%,0.0
-2027,1st,No change,1.0
-2027,1st,Gain less than 5%,0.0
-2027,1st,Gain more than 5%,0.0
-2027,2nd,Lose more than 5%,0.0
-2027,2nd,Lose less than 5%,0.0
-2027,2nd,No change,1.0
-2027,2nd,Gain less than 5%,0.0
-2027,2nd,Gain more than 5%,0.0
-2027,3rd,Lose more than 5%,0.0
-2027,3rd,Lose less than 5%,0.0
-2027,3rd,No change,1.0
-2027,3rd,Gain less than 5%,0.0
-2027,3rd,Gain more than 5%,0.0
-2027,4th,Lose more than 5%,0.0
-2027,4th,Lose less than 5%,0.0
-2027,4th,No change,1.0
-2027,4th,Gain less than 5%,0.0
-2027,4th,Gain more than 5%,0.0
-2027,5th,Lose more than 5%,0.0
-2027,5th,Lose less than 5%,0.0
-2027,5th,No change,1.0
-2027,5th,Gain less than 5%,0.0
-2027,5th,Gain more than 5%,0.0
-2027,6th,Lose more than 5%,0.0
-2027,6th,Lose less than 5%,0.0
-2027,6th,No change,1.0
-2027,6th,Gain less than 5%,0.0
-2027,6th,Gain more than 5%,0.0
-2027,7th,Lose more than 5%,0.0
-2027,7th,Lose less than 5%,0.0
-2027,7th,No change,1.0
-2027,7th,Gain less than 5%,0.0
-2027,7th,Gain more than 5%,0.0
-2027,8th,Lose more than 5%,0.0
-2027,8th,Lose less than 5%,0.0
-2027,8th,No change,1.0
-2027,8th,Gain less than 5%,0.0
-2027,8th,Gain more than 5%,0.0
-2027,9th,Lose more than 5%,0.0
-2027,9th,Lose less than 5%,0.0
-2027,9th,No change,1.0
-2027,9th,Gain less than 5%,0.0
-2027,9th,Gain more than 5%,0.0
-2027,10th,Lose more than 5%,0.0
-2027,10th,Lose less than 5%,0.0
-2027,10th,No change,1.0
-2027,10th,Gain less than 5%,0.0
-2027,10th,Gain more than 5%,0.0
-2027,All,Lose more than 5%,0.0
-2027,All,Lose less than 5%,0.0
-2027,All,No change,1.0
-2027,All,Gain less than 5%,0.0
-2027,All,Gain more than 5%,0.0
-2028,1st,Lose more than 5%,0.0
-2028,1st,Lose less than 5%,0.0
-2028,1st,No change,1.0
-2028,1st,Gain less than 5%,0.0
-2028,1st,Gain more than 5%,0.0
-2028,2nd,Lose more than 5%,0.0
-2028,2nd,Lose less than 5%,0.0
-2028,2nd,No change,1.0
-2028,2nd,Gain less than 5%,0.0
-2028,2nd,Gain more than 5%,0.0
-2028,3rd,Lose more than 5%,0.0
-2028,3rd,Lose less than 5%,0.0
-2028,3rd,No change,1.0
-2028,3rd,Gain less than 5%,0.0
-2028,3rd,Gain more than 5%,0.0
-2028,4th,Lose more than 5%,0.0
-2028,4th,Lose less than 5%,0.0
-2028,4th,No change,1.0
-2028,4th,Gain less than 5%,0.0
-2028,4th,Gain more than 5%,0.0
-2028,5th,Lose more than 5%,0.0
-2028,5th,Lose less than 5%,0.0
-2028,5th,No change,1.0
-2028,5th,Gain less than 5%,0.0
-2028,5th,Gain more than 5%,0.0
-2028,6th,Lose more than 5%,0.0
-2028,6th,Lose less than 5%,0.0
-2028,6th,No change,1.0
-2028,6th,Gain less than 5%,0.0
-2028,6th,Gain more than 5%,0.0
-2028,7th,Lose more than 5%,0.0
-2028,7th,Lose less than 5%,0.0
-2028,7th,No change,1.0
-2028,7th,Gain less than 5%,0.0
-2028,7th,Gain more than 5%,0.0
-2028,8th,Lose more than 5%,0.0
-2028,8th,Lose less than 5%,0.0
-2028,8th,No change,1.0
-2028,8th,Gain less than 5%,0.0
-2028,8th,Gain more than 5%,0.0
-2028,9th,Lose more than 5%,0.0
-2028,9th,Lose less than 5%,0.0
-2028,9th,No change,1.0
-2028,9th,Gain less than 5%,0.0
-2028,9th,Gain more than 5%,0.0
-2028,10th,Lose more than 5%,0.0
-2028,10th,Lose less than 5%,0.0
-2028,10th,No change,1.0
-2028,10th,Gain less than 5%,0.0
-2028,10th,Gain more than 5%,0.0
-2028,All,Lose more than 5%,0.0
-2028,All,Lose less than 5%,0.0
-2028,All,No change,1.0
-2028,All,Gain less than 5%,0.0
-2028,All,Gain more than 5%,0.0
-2029,1st,Lose more than 5%,0.0
-2029,1st,Lose less than 5%,0.0
-2029,1st,No change,1.0
-2029,1st,Gain less than 5%,0.0
-2029,1st,Gain more than 5%,0.0
-2029,2nd,Lose more than 5%,0.0
-2029,2nd,Lose less than 5%,0.0
-2029,2nd,No change,1.0
-2029,2nd,Gain less than 5%,0.0
-2029,2nd,Gain more than 5%,0.0
-2029,3rd,Lose more than 5%,0.0
-2029,3rd,Lose less than 5%,0.0
-2029,3rd,No change,1.0
-2029,3rd,Gain less than 5%,0.0
-2029,3rd,Gain more than 5%,0.0
-2029,4th,Lose more than 5%,0.0
-2029,4th,Lose less than 5%,0.0
-2029,4th,No change,1.0
-2029,4th,Gain less than 5%,0.0
-2029,4th,Gain more than 5%,0.0
-2029,5th,Lose more than 5%,0.0
-2029,5th,Lose less than 5%,0.0
-2029,5th,No change,1.0
-2029,5th,Gain less than 5%,0.0
-2029,5th,Gain more than 5%,0.0
-2029,6th,Lose more than 5%,0.0
-2029,6th,Lose less than 5%,0.0
-2029,6th,No change,1.0
-2029,6th,Gain less than 5%,0.0
-2029,6th,Gain more than 5%,0.0
-2029,7th,Lose more than 5%,0.0
-2029,7th,Lose less than 5%,0.0
-2029,7th,No change,1.0
-2029,7th,Gain less than 5%,0.0
-2029,7th,Gain more than 5%,0.0
-2029,8th,Lose more than 5%,0.0
-2029,8th,Lose less than 5%,0.0
-2029,8th,No change,1.0
-2029,8th,Gain less than 5%,0.0
-2029,8th,Gain more than 5%,0.0
-2029,9th,Lose more than 5%,0.0
-2029,9th,Lose less than 5%,0.0
-2029,9th,No change,1.0
-2029,9th,Gain less than 5%,0.0
-2029,9th,Gain more than 5%,0.0
-2029,10th,Lose more than 5%,0.0
-2029,10th,Lose less than 5%,0.0
-2029,10th,No change,1.0
-2029,10th,Gain less than 5%,0.0
-2029,10th,Gain more than 5%,0.0
-2029,All,Lose more than 5%,0.0
-2029,All,Lose less than 5%,0.0
-2029,All,No change,1.0
-2029,All,Gain less than 5%,0.0
-2029,All,Gain more than 5%,0.0
-2030,1st,Lose more than 5%,0.0
-2030,1st,Lose less than 5%,0.0
-2030,1st,No change,1.0
-2030,1st,Gain less than 5%,0.0
-2030,1st,Gain more than 5%,0.0
-2030,2nd,Lose more than 5%,0.0
-2030,2nd,Lose less than 5%,0.0
-2030,2nd,No change,1.0
-2030,2nd,Gain less than 5%,0.0
-2030,2nd,Gain more than 5%,0.0
-2030,3rd,Lose more than 5%,0.0
-2030,3rd,Lose less than 5%,0.0
-2030,3rd,No change,1.0
-2030,3rd,Gain less than 5%,0.0
-2030,3rd,Gain more than 5%,0.0
-2030,4th,Lose more than 5%,0.0
-2030,4th,Lose less than 5%,0.0
-2030,4th,No change,1.0
-2030,4th,Gain less than 5%,0.0
-2030,4th,Gain more than 5%,0.0
-2030,5th,Lose more than 5%,0.0
-2030,5th,Lose less than 5%,0.0
-2030,5th,No change,1.0
-2030,5th,Gain less than 5%,0.0
-2030,5th,Gain more than 5%,0.0
-2030,6th,Lose more than 5%,0.0
-2030,6th,Lose less than 5%,0.0
-2030,6th,No change,1.0
-2030,6th,Gain less than 5%,0.0
-2030,6th,Gain more than 5%,0.0
-2030,7th,Lose more than 5%,0.0
-2030,7th,Lose less than 5%,0.0
-2030,7th,No change,1.0
-2030,7th,Gain less than 5%,0.0
-2030,7th,Gain more than 5%,0.0
-2030,8th,Lose more than 5%,0.0
-2030,8th,Lose less than 5%,0.0
-2030,8th,No change,1.0
-2030,8th,Gain less than 5%,0.0
-2030,8th,Gain more than 5%,0.0
-2030,9th,Lose more than 5%,0.0
-2030,9th,Lose less than 5%,0.0
-2030,9th,No change,1.0
-2030,9th,Gain less than 5%,0.0
-2030,9th,Gain more than 5%,0.0
-2030,10th,Lose more than 5%,0.0
-2030,10th,Lose less than 5%,0.0
-2030,10th,No change,1.0
-2030,10th,Gain less than 5%,0.0
-2030,10th,Gain more than 5%,0.0
-2030,All,Lose more than 5%,0.0
-2030,All,Lose less than 5%,0.0
-2030,All,No change,1.0
-2030,All,Gain less than 5%,0.0
-2030,All,Gain more than 5%,0.0
+year,decile,outcome,share_nominal,share_real
+2026,1st,Lose more than 5%,0.003588456128435063,0.003588456128435063
+2026,1st,Lose less than 5%,0.0040980518038138385,0.0040980518038138385
+2026,1st,No change,0.9477600002451908,0.9477600002451908
+2026,1st,Gain less than 5%,0.04316734269823038,0.04316734269823038
+2026,1st,Gain more than 5%,0.0013861491243300102,0.0013861491243300102
+2026,2nd,Lose more than 5%,0.0014519814345184962,0.0014519814345184962
+2026,2nd,Lose less than 5%,0.00152994179735113,0.00152994179735113
+2026,2nd,No change,0.9763020334870605,0.9763020334870605
+2026,2nd,Gain less than 5%,0.019278491166723834,0.019278491166723834
+2026,2nd,Gain more than 5%,0.0014375521143460287,0.0014375521143460287
+2026,3rd,Lose more than 5%,1.134064713466854e-05,1.134064713466854e-05
+2026,3rd,Lose less than 5%,0.0015791426335324097,0.0015791426335324097
+2026,3rd,No change,0.9851807796384523,0.9851807796384523
+2026,3rd,Gain less than 5%,0.010861547930898451,0.010861547930898451
+2026,3rd,Gain more than 5%,0.0023671891499821365,0.0023671891499821365
+2026,4th,Lose more than 5%,1.352600574963601e-05,1.352600574963601e-05
+2026,4th,Lose less than 5%,0.0003390258063541088,0.0003390258063541088
+2026,4th,No change,0.997294759099829,0.997294759099829
+2026,4th,Gain less than 5%,0.0015196415025011908,0.0015196415025011908
+2026,4th,Gain more than 5%,0.0008330475855660725,0.0008330475855660725
+2026,5th,Lose more than 5%,0.0051356091973205975,0.0051356091973205975
+2026,5th,Lose less than 5%,0.0009182962414090315,0.0009182962414090315
+2026,5th,No change,0.9916329829786502,0.9916329829786502
+2026,5th,Gain less than 5%,0.0022977545785939444,0.0022977545785939444
+2026,5th,Gain more than 5%,1.5357004026221944e-05,1.5357004026221944e-05
+2026,6th,Lose more than 5%,0.0001034603029005416,0.0001034603029005416
+2026,6th,Lose less than 5%,0.0003948357108072325,0.0003948357108072325
+2026,6th,No change,0.9970526853233909,0.9970526853233909
+2026,6th,Gain less than 5%,0.0022032321426259088,0.0022032321426259088
+2026,6th,Gain more than 5%,0.0002457865202754973,0.0002457865202754973
+2026,7th,Lose more than 5%,0.006701531753188732,0.006701531753188732
+2026,7th,Lose less than 5%,0.00020084146290064484,0.00020084146290064484
+2026,7th,No change,0.9872816377986482,0.9872816377986482
+2026,7th,Gain less than 5%,0.005649270609415134,0.005649270609415134
+2026,7th,Gain more than 5%,0.0001667183758472417,0.0001667183758472417
+2026,8th,Lose more than 5%,7.885230298477537e-05,7.885230298477537e-05
+2026,8th,Lose less than 5%,1.600165143458112e-05,1.600165143458112e-05
+2026,8th,No change,0.9897843353128988,0.9897843353128988
+2026,8th,Gain less than 5%,0.009524639355917736,0.009524639355917736
+2026,8th,Gain more than 5%,0.0005961713767640827,0.0005961713767640827
+2026,9th,Lose more than 5%,0.0,0.0
+2026,9th,Lose less than 5%,0.0,0.0
+2026,9th,No change,0.9861350878257589,0.9861350878257589
+2026,9th,Gain less than 5%,0.013862308570134794,0.013862308570134794
+2026,9th,Gain more than 5%,2.6036041063458904e-06,2.6036041063458904e-06
+2026,10th,Lose more than 5%,0.005089949299462445,0.005089949299462445
+2026,10th,Lose less than 5%,0.0,0.0
+2026,10th,No change,0.956282643195625,0.956282643195625
+2026,10th,Gain less than 5%,0.03236474862711772,0.03236474862711772
+2026,10th,Gain more than 5%,0.006262658877794868,0.006262658877794868
+2026,All,Lose more than 5%,0.0022174707071694957,0.0022174707071694957
+2026,All,Lose less than 5%,0.0009076137107602977,0.0009076137107602977
+2026,All,No change,0.9814706944905505,0.9814706944905505
+2026,All,Gain less than 5%,0.014072897718215908,0.014072897718215908
+2026,All,Gain more than 5%,0.0013313233733038506,0.0013313233733038506
+2027,1st,Lose more than 5%,0.0039047779593303724,0.0057258286492013815
+2027,1st,Lose less than 5%,0.2910341384393943,0.23733195943658875
+2027,1st,No change,0.5225717572364299,0.568529057712456
+2027,1st,Gain less than 5%,0.1811943936550268,0.18714116424432745
+2027,1st,Gain more than 5%,0.0012949327098185877,0.0012719899574263403
+2027,2nd,Lose more than 5%,0.0014665986514748879,0.0022436083443566138
+2027,2nd,Lose less than 5%,0.37947893500922103,0.3230994951667567
+2027,2nd,No change,0.44241518271432184,0.4666583936742274
+2027,2nd,Gain less than 5%,0.17519080978879928,0.2067887275747674
+2027,2nd,Gain more than 5%,0.0014484738361829942,0.001209775239891897
+2027,3rd,Lose more than 5%,1.1377997708996649e-05,2.3585260728679517e-05
+2027,3rd,Lose less than 5%,0.32037457302116984,0.24771554057680137
+2027,3rd,No change,0.5684511027483009,0.6166056948564966
+2027,3rd,Gain less than 5%,0.10859031160768223,0.13308381087247886
+2027,3rd,Gain more than 5%,0.002572634625138189,0.0025713684334945416
+2027,4th,Lose more than 5%,0.0002463074954584472,0.0002794035605248458
+2027,4th,Lose less than 5%,0.2681531522530694,0.1896287249502328
+2027,4th,No change,0.6518901302254578,0.6875026836333288
+2027,4th,Gain less than 5%,0.07910469603871159,0.12198347386861084
+2027,4th,Gain more than 5%,0.0006057139873026753,0.0006057139873026753
+2027,5th,Lose more than 5%,0.005143827901174483,0.005151733439141025
+2027,5th,Lose less than 5%,0.2235366319021381,0.14092562012737125
+2027,5th,No change,0.7288277378236272,0.737206439086952
+2027,5th,Gain less than 5%,0.04248061406204653,0.116705019035522
+2027,5th,Gain more than 5%,1.1188311013705845e-05,1.1188311013705845e-05
+2027,6th,Lose more than 5%,0.00010187716925605217,0.0008535618099962039
+2027,6th,Lose less than 5%,0.12199957542685184,0.10152670149824512
+2027,6th,No change,0.8433435425804002,0.7781435955080515
+2027,6th,Gain less than 5%,0.03429005089595294,0.11924904912468628
+2027,6th,Gain more than 5%,0.00026495392753897744,0.00022709205902102464
+2027,7th,Lose more than 5%,0.00020482778459988786,0.00021411832067491204
+2027,7th,Lose less than 5%,0.14020264919276448,0.10912460529653056
+2027,7th,No change,0.8381395583200397,0.8320379978899822
+2027,7th,Gain less than 5%,0.02128546277818536,0.05845577656840188
+2027,7th,Gain more than 5%,0.00016750192441054355,0.00016750192441054355
+2027,8th,Lose more than 5%,0.0064861918409962315,0.007050409279490119
+2027,8th,Lose less than 5%,0.06420511190322528,0.035931594408148027
+2027,8th,No change,0.9167701951809388,0.8821598829435128
+2027,8th,Gain less than 5%,0.01194587718532085,0.07482970691782424
+2027,8th,Gain more than 5%,0.0005926238895187958,2.8406451024908463e-05
+2027,9th,Lose more than 5%,0.0,0.0019110891744820763
+2027,9th,Lose less than 5%,0.02213026137145353,0.019089794988703335
+2027,9th,No change,0.9708726052694878,0.9331466221803508
+2027,9th,Gain less than 5%,0.006994520858798827,0.04585249365646372
+2027,9th,Gain more than 5%,2.6125002598258946e-06,0.0
+2027,10th,Lose more than 5%,0.0002697160729275004,0.0002697160729275004
+2027,10th,Lose less than 5%,0.029889163707232897,0.0036780404955479313
+2027,10th,No change,0.951680591728465,0.9539270376495244
+2027,10th,Gain less than 5%,0.013175523493108847,0.03714020078373443
+2027,10th,Gain more than 5%,0.004985004998265785,0.004985004998265785
+2027,All,Lose more than 5%,0.0017835502872926858,0.002372305391152336
+2027,All,Lose less than 5%,0.18610041922265208,0.1408052076944926
+2027,All,No change,0.743496240382747,0.7455917405134882
+2027,All,Gain less than 5%,0.06742522603636332,0.11012294226468171
+2027,All,Gain more than 5%,0.0011945640709450079,0.0011078041361851421
+2028,1st,Lose more than 5%,0.00412615585662092,0.006823840175410107
+2028,1st,Lose less than 5%,0.29299780165107403,0.15791862468768514
+2028,1st,No change,0.5177895473827401,0.36566002693828414
+2028,1st,Gain less than 5%,0.18379174683628424,0.46926117203938333
+2028,1st,Gain more than 5%,0.0012947482732808325,0.00033633615923731925
+2028,2nd,Lose more than 5%,0.001672931242976737,0.0030820419605822155
+2028,2nd,Lose less than 5%,0.39543962306691377,0.16612980879001832
+2028,2nd,No change,0.42651593403508303,0.4286267060708422
+2028,2nd,Gain less than 5%,0.1749127173157391,0.40174405968542004
+2028,2nd,Gain more than 5%,0.0014587943392874113,0.00041738349313730124
+2028,3rd,Lose more than 5%,2.614464940352919e-05,0.0018686849472051561
+2028,3rd,Lose less than 5%,0.3355061279825,0.11974786317098139
+2028,3rd,No change,0.5563156035673287,0.44557522031838065
+2028,3rd,Gain less than 5%,0.10581208065241922,0.4323107287128859
+2028,3rd,Gain more than 5%,0.0023400431483486094,0.0004975028505469826
+2028,4th,Lose more than 5%,0.00028368168594097105,0.00028368168594097105
+2028,4th,Lose less than 5%,0.2628442719683398,0.10463725743241552
+2028,4th,No change,0.6556856008148754,0.39947118263843595
+2028,4th,Gain less than 5%,0.08056821953118709,0.4949896522435509
+2028,4th,Gain more than 5%,0.0006182259996566861,0.0006182259996566861
+2028,5th,Lose more than 5%,0.005200697070092856,0.005202036694235284
+2028,5th,Lose less than 5%,0.2622321371414398,0.09046176743906918
+2028,5th,No change,0.6906965421761894,0.367322572494364
+2028,5th,Gain less than 5%,0.04185945373910528,0.5370037931233013
+2028,5th,Gain more than 5%,1.1169873172618614e-05,9.830249030190706e-06
+2028,6th,Lose more than 5%,0.0001996102037046133,0.001051643499545431
+2028,6th,Lose less than 5%,0.13832375707947284,0.060527883532098245
+2028,6th,No change,0.8275591119081064,0.24764907255826557
+2028,6th,Gain less than 5%,0.0334675886594447,0.6905522583817946
+2028,6th,Gain more than 5%,0.00044993214927159113,0.00021914202829625894
+2028,7th,Lose more than 5%,1.4182604033877353e-05,0.0002137809612838276
+2028,7th,Lose less than 5%,0.1532502544217765,0.06112173583363147
+2028,7th,No change,0.8263613520803271,0.31336047201497036
+2028,7th,Gain less than 5%,0.019506957833639222,0.6246363564871411
+2028,7th,Gain more than 5%,0.0008672530602232108,0.0006676547029732605
+2028,8th,Lose more than 5%,0.0065641998140156645,0.007163951049838422
+2028,8th,Lose less than 5%,0.058988756458410244,0.01940900853060521
+2028,8th,No change,0.9272874196375103,0.21504816569510907
+2028,8th,Gain less than 5%,0.006559872854241127,0.7583788747244473
+2028,8th,Gain more than 5%,0.0005997512358227571,0.0
+2028,9th,Lose more than 5%,0.001615779841715802,0.001874977596516371
+2028,9th,Lose less than 5%,0.04824553204862797,0.011215902712493836
+2028,9th,No change,0.9488814156128869,0.12086053943354888
+2028,9th,Gain less than 5%,0.0009980747419685947,0.8660485802574408
+2028,9th,Gain more than 5%,0.0002591977548005689,0.0
+2028,10th,Lose more than 5%,9.102661270972437e-05,9.102661270972437e-05
+2028,10th,Lose less than 5%,0.04716796900677017,0.020015625034102466
+2028,10th,No change,0.9418774892437634,0.06512024136208888
+2028,10th,Gain less than 5%,0.004699751103663274,0.9084156612945569
+2028,10th,Gain more than 5%,0.006163764033093482,0.006357445696542183
+2028,All,Lose more than 5%,0.0019794409581214696,0.002765566518326751
+2028,All,Lose less than 5%,0.19949962308253252,0.08111854771631008
+2028,All,No change,0.7318970016458811,0.296869419952429
+2028,All,Gain less than 5%,0.06521764632676917,0.6183341136949921
+2028,All,Gain more than 5%,0.0014062879866957767,0.0009123521179420183
+2029,1st,Lose more than 5%,0.00413630505008607,0.006951342736868405
+2029,1st,Lose less than 5%,0.2947764189908315,0.1202621546120946
+2029,1st,No change,0.5097619563136442,0.17747608628403885
+2029,1st,Gain less than 5%,0.18991709447803665,0.6949125508569041
+2029,1st,Gain more than 5%,0.001408225167401707,0.00039786551009419964
+2029,2nd,Lose more than 5%,0.0016677495436202522,0.003148781064728544
+2029,2nd,Lose less than 5%,0.4305058519765534,0.1095694059984082
+2029,2nd,No change,0.39125865548147387,0.32114222792561287
+2029,2nd,Gain less than 5%,0.17508474288307024,0.5657671604424093
+2029,2nd,Gain more than 5%,0.0014830001152822216,0.0003724245688411718
+2029,3rd,Lose more than 5%,3.8900068363024575e-05,0.0018685347596033048
+2029,3rd,Lose less than 5%,0.3585854820377803,0.08891638788566078
+2029,3rd,No change,0.5366814780685879,0.2712426731088894
+2029,3rd,Gain less than 5%,0.10237172431334343,0.6374796234251615
+2029,3rd,Gain more than 5%,0.0023224155119252977,0.0004927808206850173
+2029,4th,Lose more than 5%,0.00036032579652245904,0.00036032579652245904
+2029,4th,Lose less than 5%,0.2749315598232694,0.06390366004705599
+2029,4th,No change,0.6415720203645121,0.2118866125734887
+2029,4th,Gain less than 5%,0.082519339283524,0.7232326468507607
+2029,4th,Gain more than 5%,0.0006167547321721812,0.0006167547321721812
+2029,5th,Lose more than 5%,0.00528210160821089,0.0052849048204539366
+2029,5th,Lose less than 5%,0.2767344344623371,0.057296810295227936
+2029,5th,No change,0.675180827967563,0.21978444405883032
+2029,5th,Gain less than 5%,0.042482268546099204,0.717316276621941
+2029,5th,Gain more than 5%,0.0003203674157897475,0.00031756420354670054
+2029,6th,Lose more than 5%,0.00019957177357058167,0.0010663574929466269
+2029,6th,Lose less than 5%,0.16115169643166669,0.04319556168594832
+2029,6th,No change,0.8051892538229417,0.12013128123200412
+2029,6th,Gain less than 5%,0.033009687935410766,0.8354027483145652
+2029,6th,Gain more than 5%,0.00044979003641029973,0.0002040512745357295
+2029,7th,Lose more than 5%,1.410655115725516e-05,0.00037897579176928423
+2029,7th,Lose less than 5%,0.1552917192619132,0.04004385688532376
+2029,7th,No change,0.8249423233048077,0.11538545441840846
+2029,7th,Gain less than 5%,0.01886764021503835,0.843672371478027
+2029,7th,Gain more than 5%,0.0008842106670835449,0.0005193414264715159
+2029,8th,Lose more than 5%,0.006519813036798466,0.007115508781694153
+2029,8th,Lose less than 5%,0.062061948517701256,0.01983967461828694
+2029,8th,No change,0.9174819464872851,0.05238464375028705
+2029,8th,Gain less than 5%,0.013340596213319511,0.9206601728497319
+2029,8th,Gain more than 5%,0.0005956957448956867,0.0
+2029,9th,Lose more than 5%,0.001622508194117665,0.0018827852876894273
+2029,9th,Lose less than 5%,0.05858450881835301,0.01225010925822873
+2029,9th,No change,0.9379172342671173,0.04704321913604213
+2029,9th,Gain less than 5%,0.0016154716268403577,0.9388238863180397
+2029,9th,Gain more than 5%,0.0002602770935717622,0.0
+2029,10th,Lose more than 5%,0.007733405936848057,0.00767868539991179
+2029,10th,Lose less than 5%,0.05621531168835989,0.027971771607830206
+2029,10th,No change,0.9349590854045848,0.028977060405209948
+2029,10th,Gain less than 5%,0.0010723121198103595,0.9353547544811075
+2029,10th,Gain more than 5%,1.988485039683772e-05,1.7728105940507652e-05
+2029,All,Lose more than 5%,0.0027574787559294716,0.0035736201932187934
+2029,All,Lose less than 5%,0.21288389320087658,0.05832493928940654
+2029,All,No change,0.7174944781482517,0.1565453702892812
+2029,All,Gain less than 5%,0.0660280877614493,0.7812622191638647
+2029,All,Gain more than 5%,0.0008360621334929286,0.00029385106422870236
+2030,1st,Lose more than 5%,0.0067005225549827685,0.009865217195986988
+2030,1st,Lose less than 5%,0.4519116964321259,0.29663995314746744
+2030,1st,No change,0.3403410303560371,0.15920675848944557
+2030,1st,Gain less than 5%,0.19914129099618627,0.5335565519246539
+2030,1st,Gain more than 5%,0.0019054596606678013,0.0007315192424459723
+2030,2nd,Lose more than 5%,0.0003184392090771974,0.0017139420760449928
+2030,2nd,Lose less than 5%,0.4695644925821656,0.347985541072753
+2030,2nd,No change,0.3572827558234632,0.12315363839606965
+2030,2nd,Gain less than 5%,0.17134022669472834,0.526680874506044
+2030,2nd,Gain more than 5%,0.001494085690565774,0.00046600394908843034
+2030,3rd,Lose more than 5%,4.5139820381744196e-05,0.0018953261897450833
+2030,3rd,Lose less than 5%,0.43977712629951793,0.23888982139565054
+2030,3rd,No change,0.45444768621332776,0.20570900403587883
+2030,3rd,Gain less than 5%,0.10336671372892235,0.5529927008102387
+2030,3rd,Gain more than 5%,0.0023633339378501986,0.0005131475684868593
+2030,4th,Lose more than 5%,0.0008249493751393884,0.00012324835949179656
+2030,4th,Lose less than 5%,0.31039826589588787,0.16714168043083005
+2030,4th,No change,0.6037219486037311,0.14547290728387716
+2030,4th,Gain less than 5%,0.08420715067676277,0.6864144784773221
+2030,4th,Gain more than 5%,0.0008476854484788901,0.0008476854484788901
+2030,5th,Lose more than 5%,0.005283008574863911,0.005285788412145492
+2030,5th,Lose less than 5%,0.2827558132059203,0.1287654114370972
+2030,5th,No change,0.6679601735925388,0.15430179786038029
+2030,5th,Gain less than 5%,0.04388298646664175,0.7115317639676234
+2030,5th,Gain more than 5%,0.00011801816003505972,0.00011523832275347928
+2030,6th,Lose more than 5%,0.0002017735406128133,0.0010686792936866923
+2030,6th,Lose less than 5%,0.17185140334571772,0.08355193643727567
+2030,6th,No change,0.7927301521250152,0.10450930398497753
+2030,6th,Gain less than 5%,0.03476644803878442,0.8106656301262924
+2030,6th,Gain more than 5%,0.00045022294986978115,0.00020445015776757343
+2030,7th,Lose more than 5%,1.4164076802252751e-05,0.00038052122423098766
+2030,7th,Lose less than 5%,0.16597138341165873,0.08818678359772213
+2030,7th,No change,0.8090797528725262,0.07791520839665858
+2030,7th,Gain less than 5%,0.024011558128535303,0.8329607024183395
+2030,7th,Gain more than 5%,0.0009231415104777902,0.0005567843630490553
+2030,8th,Lose more than 5%,0.00655850979215556,0.007157741171641648
+2030,8th,Lose less than 5%,0.07115092361187257,0.042636532872199484
+2030,8th,No change,0.900739030833651,0.028531508364904555
+2030,8th,Gain less than 5%,0.020765658808700686,0.9214875720171203
+2030,8th,Gain more than 5%,0.0007858769536201272,0.0001866455741340386
+2030,9th,Lose more than 5%,0.0016313135207037773,0.0018930031312206612
+2030,9th,Lose less than 5%,0.05916048874876685,0.013818240869355205
+2030,9th,No change,0.9293155195296747,0.045343661149624834
+2030,9th,Gain less than 5%,0.009630988590337789,0.9389450948497994
+2030,9th,Gain more than 5%,0.00026168961051688385,0.0
+2030,10th,Lose more than 5%,0.005104540968884547,0.005106681505243886
+2030,10th,Lose less than 5%,0.03264172216273909,0.005206279611085912
+2030,10th,No change,0.9525290576910078,0.02743603183003722
+2030,10th,Gain less than 5%,0.009408982255817662,0.9619374506684415
+2030,10th,Gain more than 5%,0.00031569692155085025,0.0003135563851915115
+2030,All,Lose more than 5%,0.0026682361433603956,0.003449014855943823
+2030,All,Lose less than 5%,0.24551833156963726,0.14128221808714367
+2030,All,No change,0.6808147107640974,0.10715798197918543
+2030,All,Gain less than 5%,0.07005220043854174,0.7477172819765876
+2030,All,Gain more than 5%,0.0009465210843633158,0.000393503101139581
diff --git a/public/data/metrics.csv b/public/data/metrics.csv
index aee647a..bc631f5 100644
--- a/public/data/metrics.csv
+++ b/public/data/metrics.csv
@@ -1,181 +1,181 @@
 year,metric,value
-2026,abs_bhc_poverty_rate_baseline,9.107179
-2026,abs_bhc_poverty_rate_reform,9.107179
-2026,abs_bhc_poverty_rate_change,0.0
-2026,abs_bhc_child_poverty_rate_baseline,10.612591
-2026,abs_bhc_child_poverty_rate_reform,10.612591
-2026,abs_bhc_child_poverty_rate_change,0.0
-2026,abs_bhc_deep_poverty_rate_baseline,1.265598
-2026,abs_bhc_deep_poverty_rate_reform,1.265598
-2026,abs_bhc_deep_poverty_rate_change,0.0
-2026,abs_bhc_child_deep_poverty_rate_baseline,1.536504
-2026,abs_bhc_child_deep_poverty_rate_reform,1.536504
-2026,abs_bhc_child_deep_poverty_rate_change,0.0
-2026,rel_bhc_poverty_rate_baseline,12.409129
-2026,rel_bhc_poverty_rate_reform,12.409129
-2026,rel_bhc_poverty_rate_change,0.0
-2026,rel_bhc_child_poverty_rate_baseline,15.750032
-2026,rel_bhc_child_poverty_rate_reform,15.750032
+2026,abs_bhc_poverty_rate_baseline,9.222137
+2026,abs_bhc_poverty_rate_reform,9.166862
+2026,abs_bhc_poverty_rate_change,-0.055275
+2026,abs_bhc_child_poverty_rate_baseline,10.791318
+2026,abs_bhc_child_poverty_rate_reform,10.768082
+2026,abs_bhc_child_poverty_rate_change,-0.023237
+2026,abs_bhc_deep_poverty_rate_baseline,1.290945
+2026,abs_bhc_deep_poverty_rate_reform,1.290928
+2026,abs_bhc_deep_poverty_rate_change,-1.6e-05
+2026,abs_bhc_child_deep_poverty_rate_baseline,1.588282
+2026,abs_bhc_child_deep_poverty_rate_reform,1.588244
+2026,abs_bhc_child_deep_poverty_rate_change,-3.8e-05
+2026,rel_bhc_poverty_rate_baseline,12.401478
+2026,rel_bhc_poverty_rate_reform,12.396188
+2026,rel_bhc_poverty_rate_change,-0.00529
+2026,rel_bhc_child_poverty_rate_baseline,15.546108
+2026,rel_bhc_child_poverty_rate_reform,15.546108
 2026,rel_bhc_child_poverty_rate_change,0.0
-2026,abs_ahc_poverty_rate_baseline,13.35463
-2026,abs_ahc_poverty_rate_reform,13.35463
-2026,abs_ahc_poverty_rate_change,0.0
-2026,abs_ahc_child_poverty_rate_baseline,18.770527
-2026,abs_ahc_child_poverty_rate_reform,18.770527
-2026,abs_ahc_child_poverty_rate_change,0.0
-2026,abs_ahc_deep_poverty_rate_baseline,4.232197
-2026,abs_ahc_deep_poverty_rate_reform,4.232197
+2026,abs_ahc_poverty_rate_baseline,13.336021
+2026,abs_ahc_poverty_rate_reform,13.313305
+2026,abs_ahc_poverty_rate_change,-0.022716
+2026,abs_ahc_child_poverty_rate_baseline,18.80842
+2026,abs_ahc_child_poverty_rate_reform,18.771961
+2026,abs_ahc_child_poverty_rate_change,-0.03646
+2026,abs_ahc_deep_poverty_rate_baseline,4.239491
+2026,abs_ahc_deep_poverty_rate_reform,4.239491
 2026,abs_ahc_deep_poverty_rate_change,0.0
-2026,abs_ahc_child_deep_poverty_rate_baseline,4.181257
-2026,abs_ahc_child_deep_poverty_rate_reform,4.181257
+2026,abs_ahc_child_deep_poverty_rate_baseline,4.296351
+2026,abs_ahc_child_deep_poverty_rate_reform,4.296351
 2026,abs_ahc_child_deep_poverty_rate_change,0.0
-2026,rel_ahc_poverty_rate_baseline,17.521589
-2026,rel_ahc_poverty_rate_reform,17.521589
-2026,rel_ahc_poverty_rate_change,0.0
-2026,rel_ahc_child_poverty_rate_baseline,23.939609
-2026,rel_ahc_child_poverty_rate_reform,23.939609
+2026,rel_ahc_poverty_rate_baseline,17.598707
+2026,rel_ahc_poverty_rate_reform,17.598707
+2026,rel_ahc_poverty_rate_change,-0.0
+2026,rel_ahc_child_poverty_rate_baseline,24.184121
+2026,rel_ahc_child_poverty_rate_reform,24.184121
 2026,rel_ahc_child_poverty_rate_change,0.0
-2027,abs_bhc_poverty_rate_baseline,9.04952
-2027,abs_bhc_poverty_rate_reform,9.04952
-2027,abs_bhc_poverty_rate_change,0.0
-2027,abs_bhc_child_poverty_rate_baseline,10.518701
-2027,abs_bhc_child_poverty_rate_reform,10.518701
-2027,abs_bhc_child_poverty_rate_change,0.0
-2027,abs_bhc_deep_poverty_rate_baseline,1.345104
-2027,abs_bhc_deep_poverty_rate_reform,1.345104
-2027,abs_bhc_deep_poverty_rate_change,0.0
-2027,abs_bhc_child_deep_poverty_rate_baseline,1.679116
-2027,abs_bhc_child_deep_poverty_rate_reform,1.679116
-2027,abs_bhc_child_deep_poverty_rate_change,0.0
-2027,rel_bhc_poverty_rate_baseline,12.388543
-2027,rel_bhc_poverty_rate_reform,12.388543
-2027,rel_bhc_poverty_rate_change,0.0
-2027,rel_bhc_child_poverty_rate_baseline,15.717565
-2027,rel_bhc_child_poverty_rate_reform,15.717565
-2027,rel_bhc_child_poverty_rate_change,0.0
-2027,abs_ahc_poverty_rate_baseline,13.41725
-2027,abs_ahc_poverty_rate_reform,13.41725
-2027,abs_ahc_poverty_rate_change,0.0
-2027,abs_ahc_child_poverty_rate_baseline,18.833424
-2027,abs_ahc_child_poverty_rate_reform,18.833424
-2027,abs_ahc_child_poverty_rate_change,0.0
-2027,abs_ahc_deep_poverty_rate_baseline,4.339874
-2027,abs_ahc_deep_poverty_rate_reform,4.339874
-2027,abs_ahc_deep_poverty_rate_change,0.0
-2027,abs_ahc_child_deep_poverty_rate_baseline,4.527997
-2027,abs_ahc_child_deep_poverty_rate_reform,4.527997
+2027,abs_bhc_poverty_rate_baseline,9.14579
+2027,abs_bhc_poverty_rate_reform,9.134649
+2027,abs_bhc_poverty_rate_change,-0.011141
+2027,abs_bhc_child_poverty_rate_baseline,10.692338
+2027,abs_bhc_child_poverty_rate_reform,10.6865
+2027,abs_bhc_child_poverty_rate_change,-0.005838
+2027,abs_bhc_deep_poverty_rate_baseline,1.37949
+2027,abs_bhc_deep_poverty_rate_reform,1.366491
+2027,abs_bhc_deep_poverty_rate_change,-0.012999
+2027,abs_bhc_child_deep_poverty_rate_baseline,1.736512
+2027,abs_bhc_child_deep_poverty_rate_reform,1.716501
+2027,abs_bhc_child_deep_poverty_rate_change,-0.020011
+2027,rel_bhc_poverty_rate_baseline,12.388137
+2027,rel_bhc_poverty_rate_reform,12.387444
+2027,rel_bhc_poverty_rate_change,-0.000693
+2027,rel_bhc_child_poverty_rate_baseline,15.531233
+2027,rel_bhc_child_poverty_rate_reform,15.531238
+2027,rel_bhc_child_poverty_rate_change,5e-06
+2027,abs_ahc_poverty_rate_baseline,13.571583
+2027,abs_ahc_poverty_rate_reform,13.528316
+2027,abs_ahc_poverty_rate_change,-0.043267
+2027,abs_ahc_child_poverty_rate_baseline,19.429681
+2027,abs_ahc_child_poverty_rate_reform,19.406092
+2027,abs_ahc_child_poverty_rate_change,-0.023589
+2027,abs_ahc_deep_poverty_rate_baseline,4.334287
+2027,abs_ahc_deep_poverty_rate_reform,4.333836
+2027,abs_ahc_deep_poverty_rate_change,-0.000451
+2027,abs_ahc_child_deep_poverty_rate_baseline,4.600287
+2027,abs_ahc_child_deep_poverty_rate_reform,4.600287
 2027,abs_ahc_child_deep_poverty_rate_change,0.0
-2027,rel_ahc_poverty_rate_baseline,17.747913
-2027,rel_ahc_poverty_rate_reform,17.747913
-2027,rel_ahc_poverty_rate_change,0.0
-2027,rel_ahc_child_poverty_rate_baseline,24.353597
-2027,rel_ahc_child_poverty_rate_reform,24.353597
-2027,rel_ahc_child_poverty_rate_change,0.0
-2028,abs_bhc_poverty_rate_baseline,9.069557
-2028,abs_bhc_poverty_rate_reform,9.069557
-2028,abs_bhc_poverty_rate_change,0.0
-2028,abs_bhc_child_poverty_rate_baseline,10.595411
-2028,abs_bhc_child_poverty_rate_reform,10.595411
-2028,abs_bhc_child_poverty_rate_change,0.0
-2028,abs_bhc_deep_poverty_rate_baseline,1.347582
-2028,abs_bhc_deep_poverty_rate_reform,1.347582
-2028,abs_bhc_deep_poverty_rate_change,0.0
-2028,abs_bhc_child_deep_poverty_rate_baseline,1.678712
-2028,abs_bhc_child_deep_poverty_rate_reform,1.678712
-2028,abs_bhc_child_deep_poverty_rate_change,0.0
-2028,rel_bhc_poverty_rate_baseline,12.194376
-2028,rel_bhc_poverty_rate_reform,12.194376
-2028,rel_bhc_poverty_rate_change,0.0
-2028,rel_bhc_child_poverty_rate_baseline,15.405494
-2028,rel_bhc_child_poverty_rate_reform,15.405494
+2027,rel_ahc_poverty_rate_baseline,17.517982
+2027,rel_ahc_poverty_rate_reform,17.547695
+2027,rel_ahc_poverty_rate_change,0.029713
+2027,rel_ahc_child_poverty_rate_baseline,24.038955
+2027,rel_ahc_child_poverty_rate_reform,24.139465
+2027,rel_ahc_child_poverty_rate_change,0.10051
+2028,abs_bhc_poverty_rate_baseline,9.179334
+2028,abs_bhc_poverty_rate_reform,9.093217
+2028,abs_bhc_poverty_rate_change,-0.086117
+2028,abs_bhc_child_poverty_rate_baseline,10.785569
+2028,abs_bhc_child_poverty_rate_reform,10.57192
+2028,abs_bhc_child_poverty_rate_change,-0.213649
+2028,abs_bhc_deep_poverty_rate_baseline,1.371227
+2028,abs_bhc_deep_poverty_rate_reform,1.370941
+2028,abs_bhc_deep_poverty_rate_change,-0.000287
+2028,abs_bhc_child_deep_poverty_rate_baseline,1.71633
+2028,abs_bhc_child_deep_poverty_rate_reform,1.716061
+2028,abs_bhc_child_deep_poverty_rate_change,-0.000269
+2028,rel_bhc_poverty_rate_baseline,12.258926
+2028,rel_bhc_poverty_rate_reform,12.252205
+2028,rel_bhc_poverty_rate_change,-0.006722
+2028,rel_bhc_child_poverty_rate_baseline,15.28547
+2028,rel_bhc_child_poverty_rate_reform,15.28547
 2028,rel_bhc_child_poverty_rate_change,0.0
-2028,abs_ahc_poverty_rate_baseline,13.471621
-2028,abs_ahc_poverty_rate_reform,13.471621
-2028,abs_ahc_poverty_rate_change,0.0
-2028,abs_ahc_child_poverty_rate_baseline,18.887681
-2028,abs_ahc_child_poverty_rate_reform,18.887681
-2028,abs_ahc_child_poverty_rate_change,0.0
-2028,abs_ahc_deep_poverty_rate_baseline,4.45213
-2028,abs_ahc_deep_poverty_rate_reform,4.45213
-2028,abs_ahc_deep_poverty_rate_change,0.0
-2028,abs_ahc_child_deep_poverty_rate_baseline,4.844296
-2028,abs_ahc_child_deep_poverty_rate_reform,4.844296
-2028,abs_ahc_child_deep_poverty_rate_change,0.0
-2028,rel_ahc_poverty_rate_baseline,17.943626
-2028,rel_ahc_poverty_rate_reform,17.943626
-2028,rel_ahc_poverty_rate_change,0.0
-2028,rel_ahc_child_poverty_rate_baseline,24.852258
-2028,rel_ahc_child_poverty_rate_reform,24.852258
-2028,rel_ahc_child_poverty_rate_change,0.0
-2029,abs_bhc_poverty_rate_baseline,9.059634
-2029,abs_bhc_poverty_rate_reform,9.059634
-2029,abs_bhc_poverty_rate_change,0.0
-2029,abs_bhc_child_poverty_rate_baseline,10.617805
-2029,abs_bhc_child_poverty_rate_reform,10.617805
-2029,abs_bhc_child_poverty_rate_change,0.0
-2029,abs_bhc_deep_poverty_rate_baseline,1.345508
-2029,abs_bhc_deep_poverty_rate_reform,1.345508
-2029,abs_bhc_deep_poverty_rate_change,0.0
-2029,abs_bhc_child_deep_poverty_rate_baseline,1.671121
-2029,abs_bhc_child_deep_poverty_rate_reform,1.671121
+2028,abs_ahc_poverty_rate_baseline,13.637378
+2028,abs_ahc_poverty_rate_reform,13.623214
+2028,abs_ahc_poverty_rate_change,-0.014164
+2028,abs_ahc_child_poverty_rate_baseline,19.49775
+2028,abs_ahc_child_poverty_rate_reform,19.497741
+2028,abs_ahc_child_poverty_rate_change,-9e-06
+2028,abs_ahc_deep_poverty_rate_baseline,4.452162
+2028,abs_ahc_deep_poverty_rate_reform,4.444447
+2028,abs_ahc_deep_poverty_rate_change,-0.007715
+2028,abs_ahc_child_deep_poverty_rate_baseline,4.932075
+2028,abs_ahc_child_deep_poverty_rate_reform,4.926971
+2028,abs_ahc_child_deep_poverty_rate_change,-0.005104
+2028,rel_ahc_poverty_rate_baseline,17.691467
+2028,rel_ahc_poverty_rate_reform,17.718362
+2028,rel_ahc_poverty_rate_change,0.026895
+2028,rel_ahc_child_poverty_rate_baseline,24.376941
+2028,rel_ahc_child_poverty_rate_reform,24.457404
+2028,rel_ahc_child_poverty_rate_change,0.080463
+2029,abs_bhc_poverty_rate_baseline,9.124487
+2029,abs_bhc_poverty_rate_reform,9.096246
+2029,abs_bhc_poverty_rate_change,-0.028241
+2029,abs_bhc_child_poverty_rate_baseline,10.65257
+2029,abs_bhc_child_poverty_rate_reform,10.634668
+2029,abs_bhc_child_poverty_rate_change,-0.017902
+2029,abs_bhc_deep_poverty_rate_baseline,1.367483
+2029,abs_bhc_deep_poverty_rate_reform,1.366887
+2029,abs_bhc_deep_poverty_rate_change,-0.000596
+2029,abs_bhc_child_deep_poverty_rate_baseline,1.708537
+2029,abs_bhc_child_deep_poverty_rate_reform,1.708537
 2029,abs_bhc_child_deep_poverty_rate_change,0.0
-2029,rel_bhc_poverty_rate_baseline,12.203281
-2029,rel_bhc_poverty_rate_reform,12.203281
-2029,rel_bhc_poverty_rate_change,0.0
-2029,rel_bhc_child_poverty_rate_baseline,15.401173
-2029,rel_bhc_child_poverty_rate_reform,15.401173
-2029,rel_bhc_child_poverty_rate_change,0.0
-2029,abs_ahc_poverty_rate_baseline,13.481937
-2029,abs_ahc_poverty_rate_reform,13.481937
-2029,abs_ahc_poverty_rate_change,0.0
-2029,abs_ahc_child_poverty_rate_baseline,18.870106
-2029,abs_ahc_child_poverty_rate_reform,18.870106
-2029,abs_ahc_child_poverty_rate_change,0.0
-2029,abs_ahc_deep_poverty_rate_baseline,4.504271
-2029,abs_ahc_deep_poverty_rate_reform,4.504271
-2029,abs_ahc_deep_poverty_rate_change,0.0
-2029,abs_ahc_child_deep_poverty_rate_baseline,4.941954
-2029,abs_ahc_child_deep_poverty_rate_reform,4.941954
-2029,abs_ahc_child_deep_poverty_rate_change,0.0
-2029,rel_ahc_poverty_rate_baseline,17.974877
-2029,rel_ahc_poverty_rate_reform,17.974877
-2029,rel_ahc_poverty_rate_change,0.0
-2029,rel_ahc_child_poverty_rate_baseline,24.827236
-2029,rel_ahc_child_poverty_rate_reform,24.827236
-2029,rel_ahc_child_poverty_rate_change,0.0
-2030,abs_bhc_poverty_rate_baseline,9.209479
-2030,abs_bhc_poverty_rate_reform,9.209479
-2030,abs_bhc_poverty_rate_change,0.0
-2030,abs_bhc_child_poverty_rate_baseline,10.826593
-2030,abs_bhc_child_poverty_rate_reform,10.826593
-2030,abs_bhc_child_poverty_rate_change,0.0
-2030,abs_bhc_deep_poverty_rate_baseline,1.397992
-2030,abs_bhc_deep_poverty_rate_reform,1.397992
-2030,abs_bhc_deep_poverty_rate_change,0.0
-2030,abs_bhc_child_deep_poverty_rate_baseline,1.75742
-2030,abs_bhc_child_deep_poverty_rate_reform,1.75742
-2030,abs_bhc_child_deep_poverty_rate_change,0.0
-2030,rel_bhc_poverty_rate_baseline,12.260051
-2030,rel_bhc_poverty_rate_reform,12.260051
-2030,rel_bhc_poverty_rate_change,0.0
-2030,rel_bhc_child_poverty_rate_baseline,15.182422
-2030,rel_bhc_child_poverty_rate_reform,15.182422
-2030,rel_bhc_child_poverty_rate_change,0.0
-2030,abs_ahc_poverty_rate_baseline,13.741446
-2030,abs_ahc_poverty_rate_reform,13.741446
-2030,abs_ahc_poverty_rate_change,0.0
-2030,abs_ahc_child_poverty_rate_baseline,19.021678
-2030,abs_ahc_child_poverty_rate_reform,19.021678
-2030,abs_ahc_child_poverty_rate_change,0.0
-2030,abs_ahc_deep_poverty_rate_baseline,4.791753
-2030,abs_ahc_deep_poverty_rate_reform,4.791753
-2030,abs_ahc_deep_poverty_rate_change,0.0
-2030,abs_ahc_child_deep_poverty_rate_baseline,5.308959
-2030,abs_ahc_child_deep_poverty_rate_reform,5.308959
-2030,abs_ahc_child_deep_poverty_rate_change,0.0
-2030,rel_ahc_poverty_rate_baseline,18.196063
-2030,rel_ahc_poverty_rate_reform,18.196063
-2030,rel_ahc_poverty_rate_change,0.0
-2030,rel_ahc_child_poverty_rate_baseline,25.171583
-2030,rel_ahc_child_poverty_rate_reform,25.171583
-2030,rel_ahc_child_poverty_rate_change,0.0
+2029,rel_bhc_poverty_rate_baseline,12.19964
+2029,rel_bhc_poverty_rate_reform,12.207019
+2029,rel_bhc_poverty_rate_change,0.007379
+2029,rel_bhc_child_poverty_rate_baseline,15.16929
+2029,rel_bhc_child_poverty_rate_reform,15.193224
+2029,rel_bhc_child_poverty_rate_change,0.023934
+2029,abs_ahc_poverty_rate_baseline,13.655983
+2029,abs_ahc_poverty_rate_reform,13.499908
+2029,abs_ahc_poverty_rate_change,-0.156076
+2029,abs_ahc_child_poverty_rate_baseline,19.49579
+2029,abs_ahc_child_poverty_rate_reform,19.005252
+2029,abs_ahc_child_poverty_rate_change,-0.490538
+2029,abs_ahc_deep_poverty_rate_baseline,4.48269
+2029,abs_ahc_deep_poverty_rate_reform,4.479853
+2029,abs_ahc_deep_poverty_rate_change,-0.002837
+2029,abs_ahc_child_deep_poverty_rate_baseline,5.036375
+2029,abs_ahc_child_deep_poverty_rate_reform,5.033669
+2029,abs_ahc_child_deep_poverty_rate_change,-0.002706
+2029,rel_ahc_poverty_rate_baseline,18.000178
+2029,rel_ahc_poverty_rate_reform,18.029069
+2029,rel_ahc_poverty_rate_change,0.028891
+2029,rel_ahc_child_poverty_rate_baseline,24.891185
+2029,rel_ahc_child_poverty_rate_reform,24.974945
+2029,rel_ahc_child_poverty_rate_change,0.08376
+2030,abs_bhc_poverty_rate_baseline,9.266874
+2030,abs_bhc_poverty_rate_reform,9.221526
+2030,abs_bhc_poverty_rate_change,-0.045347
+2030,abs_bhc_child_poverty_rate_baseline,10.902006
+2030,abs_bhc_child_poverty_rate_reform,10.862052
+2030,abs_bhc_child_poverty_rate_change,-0.039954
+2030,abs_bhc_deep_poverty_rate_baseline,1.451016
+2030,abs_bhc_deep_poverty_rate_reform,1.446456
+2030,abs_bhc_deep_poverty_rate_change,-0.00456
+2030,abs_bhc_child_deep_poverty_rate_baseline,1.786762
+2030,abs_bhc_child_deep_poverty_rate_reform,1.771653
+2030,abs_bhc_child_deep_poverty_rate_change,-0.01511
+2030,rel_bhc_poverty_rate_baseline,12.296566
+2030,rel_bhc_poverty_rate_reform,12.304026
+2030,rel_bhc_poverty_rate_change,0.00746
+2030,rel_bhc_child_poverty_rate_baseline,15.205172
+2030,rel_bhc_child_poverty_rate_reform,15.260718
+2030,rel_bhc_child_poverty_rate_change,0.055545
+2030,abs_ahc_poverty_rate_baseline,13.920852
+2030,abs_ahc_poverty_rate_reform,13.858925
+2030,abs_ahc_poverty_rate_change,-0.061926
+2030,abs_ahc_child_poverty_rate_baseline,19.664388
+2030,abs_ahc_child_poverty_rate_reform,19.631332
+2030,abs_ahc_child_poverty_rate_change,-0.033056
+2030,abs_ahc_deep_poverty_rate_baseline,4.664174
+2030,abs_ahc_deep_poverty_rate_reform,4.701743
+2030,abs_ahc_deep_poverty_rate_change,0.037569
+2030,abs_ahc_child_deep_poverty_rate_baseline,5.39454
+2030,abs_ahc_child_deep_poverty_rate_reform,5.522416
+2030,abs_ahc_child_deep_poverty_rate_change,0.127876
+2030,rel_ahc_poverty_rate_baseline,18.12895
+2030,rel_ahc_poverty_rate_reform,18.186226
+2030,rel_ahc_poverty_rate_change,0.057277
+2030,rel_ahc_child_poverty_rate_baseline,25.237747
+2030,rel_ahc_child_poverty_rate_reform,25.315993
+2030,rel_ahc_child_poverty_rate_change,0.078246
diff --git a/public/data/winners_losers.csv b/public/data/winners_losers.csv
index 08200bf..af04e75 100644
--- a/public/data/winners_losers.csv
+++ b/public/data/winners_losers.csv
@@ -1,56 +1,56 @@
 year,decile,pct_gaining,pct_losing,pct_unchanged
-2026,1st,0.0,0.0,100.0
-2026,2nd,0.0,0.0,100.0
-2026,3rd,0.0,0.0,100.0
-2026,4th,0.0,0.0,100.0
-2026,5th,0.0,0.0,100.0
-2026,6th,0.0,0.0,100.0
-2026,7th,0.0,0.0,100.0
-2026,8th,0.0,0.0,100.0
-2026,9th,0.0,0.0,100.0
-2026,10th,0.0,0.0,100.0
-2026,All,0.0,0.0,100.0
-2027,1st,0.0,0.0,100.0
-2027,2nd,0.0,0.0,100.0
-2027,3rd,0.0,0.0,100.0
-2027,4th,0.0,0.0,100.0
-2027,5th,0.0,0.0,100.0
-2027,6th,0.0,0.0,100.0
-2027,7th,0.0,0.0,100.0
-2027,8th,0.0,0.0,100.0
-2027,9th,0.0,0.0,100.0
-2027,10th,0.0,0.0,100.0
-2027,All,0.0,0.0,100.0
-2028,1st,0.0,0.0,100.0
-2028,2nd,0.0,0.0,100.0
-2028,3rd,0.0,0.0,100.0
-2028,4th,0.0,0.0,100.0
-2028,5th,0.0,0.0,100.0
-2028,6th,0.0,0.0,100.0
-2028,7th,0.0,0.0,100.0
-2028,8th,0.0,0.0,100.0
-2028,9th,0.0,0.0,100.0
-2028,10th,0.0,0.0,100.0
-2028,All,0.0,0.0,100.0
-2029,1st,0.0,0.0,100.0
-2029,2nd,0.0,0.0,100.0
-2029,3rd,0.0,0.0,100.0
-2029,4th,0.0,0.0,100.0
-2029,5th,0.0,0.0,100.0
-2029,6th,0.0,0.0,100.0
-2029,7th,0.0,0.0,100.0
-2029,8th,0.0,0.0,100.0
-2029,9th,0.0,0.0,100.0
-2029,10th,0.0,0.0,100.0
-2029,All,0.0,0.0,100.0
-2030,1st,0.0,0.0,100.0
-2030,2nd,0.0,0.0,100.0
-2030,3rd,0.0,0.0,100.0
-2030,4th,0.0,0.0,100.0
-2030,5th,0.0,0.0,100.0
-2030,6th,0.0,0.0,100.0
-2030,7th,0.0,0.0,100.0
-2030,8th,0.0,0.0,100.0
-2030,9th,0.0,0.0,100.0
-2030,10th,0.0,0.0,100.0
-2030,All,0.0,0.0,100.0
+2026,1st,32.0,38.63,29.37
+2026,2nd,40.9,43.14,15.96
+2026,3rd,24.67,49.64,25.69
+2026,4th,19.55,49.71,30.74
+2026,5th,22.53,49.8,27.67
+2026,6th,22.78,44.21,33.01
+2026,7th,19.32,45.52,35.17
+2026,8th,15.68,38.53,45.8
+2026,9th,13.76,19.43,66.82
+2026,10th,18.4,16.03,65.57
+2026,All,23.29,39.29,37.42
+2027,1st,66.47,27.62,5.91
+2027,2nd,66.27,32.47,1.27
+2027,3rd,63.68,35.29,1.03
+2027,4th,69.78,29.7,0.53
+2027,5th,66.48,30.99,2.53
+2027,6th,75.51,24.13,0.36
+2027,7th,72.02,27.88,0.1
+2027,8th,84.26,15.07,0.66
+2027,9th,89.86,9.73,0.41
+2027,10th,94.65,5.32,0.03
+2027,All,74.54,23.94,1.52
+2028,1st,79.97,19.27,0.76
+2028,2nd,76.56,23.27,0.17
+2028,3rd,70.12,29.09,0.79
+2028,4th,78.17,21.45,0.39
+2028,5th,76.54,23.0,0.46
+2028,6th,85.25,14.1,0.65
+2028,7th,79.83,19.75,0.41
+2028,8th,89.68,9.12,1.2
+2028,9th,95.05,4.5,0.45
+2028,10th,94.96,5.04,0.0
+2028,All,82.43,17.0,0.57
+2029,1st,84.65,14.92,0.44
+2029,2nd,80.9,18.26,0.85
+2029,3rd,79.21,19.24,1.55
+2029,4th,83.6,15.07,1.33
+2029,5th,84.91,14.93,0.17
+2029,6th,88.6,11.37,0.03
+2029,7th,87.33,12.16,0.51
+2029,8th,92.12,5.94,1.94
+2029,9th,95.65,4.31,0.04
+2029,10th,96.93,3.07,0.0
+2029,All,87.21,12.09,0.7
+2030,1st,57.08,42.27,0.65
+2030,2nd,70.9,28.95,0.15
+2030,3rd,70.29,29.5,0.21
+2030,4th,76.9,22.6,0.49
+2030,5th,76.84,23.1,0.06
+2030,6th,85.25,14.75,0.0
+2030,7th,80.34,19.66,0.0
+2030,8th,89.82,10.18,0.0
+2030,9th,94.53,5.47,0.0
+2030,10th,98.81,1.19,0.0
+2030,All,79.38,20.43,0.19
diff --git a/src/components/DetailedBudgetTable.jsx b/src/components/DetailedBudgetTable.jsx
index 6e58e0a..1dfa851 100644
--- a/src/components/DetailedBudgetTable.jsx
+++ b/src/components/DetailedBudgetTable.jsx
@@ -1,44 +1,5 @@
 "use client";
 
-const tableStyle = {
-  margin: "12px auto 0 auto",
-  width: "auto",
-  borderCollapse: "collapse",
-  fontSize: "14px",
-  backgroundColor: "#fafafa",
-  borderRadius: "6px",
-  overflow: "hidden",
-};
-const thStyle = {
-  textAlign: "left",
-  padding: "10px 16px",
-  borderBottom: "2px solid #e5e7eb",
-  backgroundColor: "#f3f4f6",
-};
-const thRightStyle = {
-  textAlign: "right",
-  padding: "10px 16px",
-  borderBottom: "2px solid #e5e7eb",
-  backgroundColor: "#f3f4f6",
-};
-const tdStyle = {
-  padding: "10px 16px",
-  borderBottom: "1px solid #e5e7eb",
-};
-const tdRightStyle = {
-  textAlign: "right",
-  padding: "10px 16px",
-  borderBottom: "1px solid #e5e7eb",
-};
-const groupHeaderStyle = {
-  padding: "10px 16px",
-  borderBottom: "1px solid #e5e7eb",
-  backgroundColor: "#f9fafb",
-  fontWeight: 600,
-  fontSize: "13px",
-  color: "#374151",
-};
-
 const PROGRAM_LABELS = {
   income_tax: "Income Tax",
   national_insurance: "National Insurance (employee)",
@@ -84,7 +45,7 @@ export default function DetailedBudgetTable({ data, selectedYear }) {
     const v = parseFloat(value);
     if (Math.abs(v) < 0.5) return "\u00A30m";
     const sign = v >= 0 ? "" : "-";
-    return `${sign}\u00A3${Math.abs(v).toFixed(0)}m`;
+    return `${sign}\u00A3${Math.abs(v).toFixed(0).replace(/\B(?=(\d{3})+(?!\d))/g, ",")}m`;
   };
 
   const renderGroup = (label, programs) => {
@@ -97,18 +58,29 @@ export default function DetailedBudgetTable({ data, selectedYear }) {
     return (
       <>
         <tr>
-          <td style={groupHeaderStyle} colSpan={4}>
+          <td
+            colSpan={4}
+            style={{
+              fontWeight: 600,
+              fontSize: "0.8rem",
+              color: "var(--pe-text-secondary)",
+              textTransform: "uppercase",
+              letterSpacing: "0.03em",
+              padding: "10px 12px",
+              borderBottom: "1px solid var(--pe-gray-100)",
+            }}
+          >
             {label}
           </td>
         </tr>
         {rows.map((row) => (
           <tr key={row.program}>
-            <td style={{ ...tdStyle, paddingLeft: "28px" }}>
+            <td style={{ paddingLeft: "24px" }}>
               {PROGRAM_LABELS[row.program] || row.program}
             </td>
-            <td style={tdRightStyle}>{formatValue(row.baseline)}</td>
-            <td style={tdRightStyle}>{formatValue(row.reform)}</td>
-            <td style={tdRightStyle}>{formatValue(row.difference)}</td>
+            <td>{formatValue(row.baseline)}</td>
+            <td>{formatValue(row.reform)}</td>
+            <td>{formatValue(row.difference)}</td>
           </tr>
         ))}
       </>
@@ -116,44 +88,29 @@ export default function DetailedBudgetTable({ data, selectedYear }) {
   };
 
   return (
-    <div style={{ marginTop: "24px" }}>
-      <h3
-        className="chart-title"
-        style={{ fontSize: "1.1rem", fontWeight: 600 }}
-      >
-        Detailed budgetary breakdown
-      </h3>
+    <div className="section-card" style={{ marginTop: "24px" }}>
+      <h3 className="chart-title">Detailed budgetary breakdown</h3>
       <p className="chart-description">
         Per-program impact of revised OBR forecasts on government revenue and
-        spending.
+        spending. Values in millions of pounds. Positive difference = fiscal improvement (higher tax revenue or lower spending).
       </p>
 
-      <table style={tableStyle}>
-        <thead>
-          <tr>
-            <th style={thStyle}>Program</th>
-            <th style={thRightStyle}>Nov 2025 ({"\u00A3"}m)</th>
-            <th style={thRightStyle}>Mar 2026 ({"\u00A3"}m)</th>
-            <th style={thRightStyle}>Difference ({"\u00A3"}m)</th>
-          </tr>
-        </thead>
-        <tbody>
-          {renderGroup("Tax revenue", REVENUE_PROGRAMS)}
-          {renderGroup("Benefit spending", SPENDING_PROGRAMS)}
-        </tbody>
-      </table>
-
-      <p
-        style={{
-          marginTop: "8px",
-          fontSize: "13px",
-          color: "#666",
-          textAlign: "center",
-        }}
-      >
-        Values in millions of pounds. Positive difference = fiscal improvement
-        (higher tax revenue or lower spending).
-      </p>
+      <div className="forecast-table-wrapper">
+        <table className="forecast-table">
+          <thead>
+            <tr>
+              <th>Program</th>
+              <th>Nov 2025 ({"\u00A3"}m)</th>
+              <th>Mar 2026 ({"\u00A3"}m)</th>
+              <th>Difference ({"\u00A3"}m)</th>
+            </tr>
+          </thead>
+          <tbody>
+            {renderGroup("Tax revenue", REVENUE_PROGRAMS)}
+            {renderGroup("Benefit spending", SPENDING_PROGRAMS)}
+          </tbody>
+        </table>
+      </div>
     </div>
   );
 }
diff --git a/src/components/HouseholdArchetypes.jsx b/src/components/HouseholdArchetypes.jsx
index 1a61698..02cf445 100644
--- a/src/components/HouseholdArchetypes.jsx
+++ b/src/components/HouseholdArchetypes.jsx
@@ -1,6 +1,6 @@
 "use client";
 
-import { useState, useEffect } from "react";
+import { useEffect, useState } from "react";
 import {
   BarChart,
   Bar,
@@ -8,7 +8,6 @@ import {
   YAxis,
   CartesianGrid,
   Tooltip,
-  Legend,
   ResponsiveContainer,
   ReferenceLine,
 } from "recharts";
@@ -35,21 +34,21 @@ function formatCurrencyChange(v) {
 }
 
 function ChangeBarChart({ comparison }) {
+  const changeField = "change_nominal";
   const chartData = comparison
     .map((d) => ({
       group: shorten(d.group),
-      change: d.change,
+      change: d[changeField],
     }))
     .sort((a, b) => a.change - b.change);
 
   return (
     <div className="section-card">
       <h3 className="chart-title">
-        Change in mean household net income by family type
+        Change in mean household net income by household type
       </h3>
       <p className="chart-subtitle">
-        Difference between Spring Statement and pre-Spring Statement forecasts
-        (\u00a3/year)
+        Change in mean household net income by household type, comparing pre- and post-Spring Statement OBR forecasts (&pound;/year)
       </p>
       <div className="chart-container-tall">
         <ResponsiveContainer width="100%" height="100%">
@@ -100,99 +99,23 @@ function ChangeBarChart({ comparison }) {
   );
 }
 
-function BaselineReformedChart({ comparison }) {
-  const chartData = comparison
-    .map((d) => ({
-      group: shorten(d.group),
-      baseline: d.baseline_hnet,
-      reformed: d.reformed_hnet,
-    }))
-    .sort((a, b) => a.baseline - b.baseline);
+function HouseholdSummaryTable({ stats, comparison }) {
+  const reformedField = "reformed_hnet_nominal";
+  const changeField = "change_nominal";
 
   return (
     <div className="section-card">
-      <h3 className="chart-title">
-        Mean household net income by family type
-      </h3>
+      <h3 className="chart-title">Impact on household income by household type</h3>
       <p className="chart-subtitle">
-        Pre-Spring Statement vs Spring Statement forecast (\u00a3/year)
+        Median and mean household net income by household type, comparing pre- and post-Spring Statement forecasts
       </p>
-      <div className="chart-container-tall">
-        <ResponsiveContainer width="100%" height="100%">
-          <BarChart
-            data={chartData}
-            layout="vertical"
-            margin={{ top: 8, right: 40, left: 10, bottom: 8 }}
-          >
-            <CartesianGrid strokeDasharray="3 3" stroke="#e5e7eb" />
-            <XAxis
-              type="number"
-              tickFormatter={(v) =>
-                `\u00a3${(v / 1000).toFixed(0)}k`
-              }
-              tick={{ fontSize: 12, fill: "#6b7280" }}
-            />
-            <YAxis
-              type="category"
-              dataKey="group"
-              width={130}
-              tick={{ fontSize: 12, fill: "#6b7280" }}
-            />
-            <Tooltip
-              contentStyle={{
-                background: "#fff",
-                border: "1px solid #e5e7eb",
-                borderRadius: 8,
-                boxShadow: "0 2px 8px rgba(0,0,0,0.08)",
-                fontSize: "0.85rem",
-              }}
-              formatter={(value, name) => [
-                formatCurrency(value) + "/year",
-                name === "baseline"
-                  ? "Pre-Spring Statement"
-                  : "Spring Statement",
-              ]}
-            />
-            <Legend
-              formatter={(value) =>
-                value === "baseline"
-                  ? "Pre-Spring Statement"
-                  : "Spring Statement"
-              }
-              wrapperStyle={{ fontSize: "0.8rem" }}
-            />
-            <Bar
-              dataKey="baseline"
-              fill="#9ca3af"
-              barSize={18}
-              name="baseline"
-              radius={[0, 4, 4, 0]}
-            />
-            <Bar
-              dataKey="reformed"
-              fill="#0d9488"
-              barSize={18}
-              name="reformed"
-              radius={[0, 4, 4, 0]}
-            />
-          </BarChart>
-        </ResponsiveContainer>
-      </div>
-    </div>
-  );
-}
-
-function HouseholdSummaryTable({ stats, comparison }) {
-  return (
-    <div className="section-card">
-      <h3 className="chart-title">Household income summary</h3>
       <div className="forecast-table-wrapper">
         <table className="forecast-table">
           <thead>
             <tr>
               <th>Household type</th>
               <th>Median income</th>
-              <th>Population</th>
+              <th>Households (m)</th>
               <th>Mean income (pre)</th>
               <th>Mean income (post)</th>
               <th>Change</th>
@@ -202,10 +125,11 @@ function HouseholdSummaryTable({ stats, comparison }) {
             {stats.map((row) => {
               const comp =
                 comparison.find((c) => c.group === row.group) || {};
+              const change = comp[changeField];
               const changeClass =
-                comp.change > 0
+                change > 0
                   ? "change-positive"
-                  : comp.change < 0
+                  : change < 0
                     ? "change-negative"
                     : "change-zero";
               return (
@@ -213,12 +137,12 @@ function HouseholdSummaryTable({ stats, comparison }) {
                   <td>{row.group}</td>
                   <td>{formatCurrency(row.median_hnet)}</td>
                   <td>
-                    {Math.round(row.weighted_n).toLocaleString("en-GB")}
+                    {(row.weighted_n / 1e6).toFixed(2)}m
                   </td>
                   <td>{formatCurrency(comp.baseline_hnet)}</td>
-                  <td>{formatCurrency(comp.reformed_hnet)}</td>
+                  <td>{formatCurrency(comp[reformedField])}</td>
                   <td className={changeClass}>
-                    {formatCurrencyChange(comp.change)}/yr
+                    {formatCurrencyChange(change)}/yr
                   </td>
                 </tr>
               );
@@ -230,9 +154,9 @@ function HouseholdSummaryTable({ stats, comparison }) {
   );
 }
 
-export default function HouseholdArchetypes() {
-  const [stats, setStats] = useState(null);
-  const [comparison, setComparison] = useState(null);
+export default function HouseholdArchetypes({ selectedYear }) {
+  const [allStats, setAllStats] = useState(null);
+  const [allComparison, setAllComparison] = useState(null);
 
   useEffect(() => {
     Promise.all([
@@ -243,36 +167,25 @@ export default function HouseholdArchetypes() {
         .then((r) => (r.ok ? r.json() : null))
         .catch(() => null),
     ]).then(([statsData, compData]) => {
-      setStats(statsData);
-      setComparison(compData);
+      setAllStats(statsData);
+      setAllComparison(compData);
     });
   }, []);
 
-  if (!stats || !comparison) return null;
+  if (!allStats || !allComparison) return null;
+
+  const stats = allStats.filter((d) => d.year === selectedYear);
+  const comparison = allComparison.filter((d) => d.year === selectedYear);
+
+  if (stats.length === 0 || comparison.length === 0) return null;
 
   return (
     <div style={{ marginTop: "var(--pe-space-xl)" }}>
-      <h2 className="section-heading">Impact by household type</h2>
-      <p
-        style={{
-          fontSize: "0.9rem",
-          color: "var(--pe-text-secondary)",
-          lineHeight: 1.6,
-          marginBottom: "var(--pe-space-lg)",
-        }}
-      >
-        Using PolicyEngine's microsimulation model, we calculated average
-        household net income for six household groups under pre-Spring Statement
-        and Spring Statement 2026 forecasts. These figures represent 2029
-        projections.
-      </p>
+      <HouseholdSummaryTable stats={stats} comparison={comparison} />
 
-      <div className="charts-grid">
-        <BaselineReformedChart comparison={comparison} />
+      <div style={{ marginTop: "var(--pe-space-xl)" }}>
         <ChangeBarChart comparison={comparison} />
       </div>
-
-      <HouseholdSummaryTable stats={stats} comparison={comparison} />
     </div>
   );
 }
diff --git a/src/components/InequalityTable.jsx b/src/components/InequalityTable.jsx
index 74be3da..1f5f246 100644
--- a/src/components/InequalityTable.jsx
+++ b/src/components/InequalityTable.jsx
@@ -1,36 +1,5 @@
 "use client";
 
-const tableStyle = {
-  margin: "12px auto 0 auto",
-  width: "auto",
-  borderCollapse: "collapse",
-  fontSize: "14px",
-  backgroundColor: "#fafafa",
-  borderRadius: "6px",
-  overflow: "hidden",
-};
-const thStyle = {
-  textAlign: "left",
-  padding: "10px 16px",
-  borderBottom: "2px solid #e5e7eb",
-  backgroundColor: "#f3f4f6",
-};
-const thRightStyle = {
-  textAlign: "right",
-  padding: "10px 16px",
-  borderBottom: "2px solid #e5e7eb",
-  backgroundColor: "#f3f4f6",
-};
-const tdStyle = {
-  padding: "10px 16px",
-  borderBottom: "1px solid #e5e7eb",
-};
-const tdRightStyle = {
-  textAlign: "right",
-  padding: "10px 16px",
-  borderBottom: "1px solid #e5e7eb",
-};
-
 const METRIC_LABELS = {
   gini: "Gini coefficient",
   top_10_pct_share: "Top 10% income share",
@@ -40,6 +9,8 @@ const METRIC_LABELS = {
 export default function InequalityTable({ data, selectedYear }) {
   if (!data) return null;
 
+  const reformField = "reform_nominal";
+
   const filtered = data.filter(
     (row) => parseInt(row.year) === selectedYear,
   );
@@ -49,8 +20,8 @@ export default function InequalityTable({ data, selectedYear }) {
   const formatValue = (metric, value) => {
     if (value == null || isNaN(value)) return "\u2014";
     const v = parseFloat(value);
-    if (metric === "gini") return v.toFixed(4);
-    return `${(v * 100).toFixed(1)}%`;
+    if (metric === "gini") return v.toFixed(2);
+    return `${(v * 100).toFixed(2)}%`;
   };
 
   const formatChange = (metric, baseline, reform) => {
@@ -60,67 +31,46 @@ export default function InequalityTable({ data, selectedYear }) {
     const diff = r - b;
     if (metric === "gini") {
       const sign = diff >= 0 ? "+" : "";
-      return `${sign}${diff.toFixed(4)}`;
+      return `${sign}${diff.toFixed(2)}`;
     }
     const pctDiff = diff * 100;
     const sign = pctDiff >= 0 ? "+" : "";
-    return `${sign}${pctDiff.toFixed(2)}pp`;
+    return `${sign}${pctDiff.toFixed(2)} pp`;
   };
 
   return (
-    <div style={{ marginTop: "24px" }}>
-      <h3
-        className="chart-title"
-        style={{ fontSize: "1.1rem", fontWeight: 600 }}
-      >
-        Inequality impact
-      </h3>
-      <p className="chart-description">
-        Change in income inequality metrics from revised OBR forecasts.
+    <div className="section-card" style={{ marginTop: "24px" }}>
+      <h3 className="chart-title">Inequality impact</h3>
+      <p className="chart-subtitle">
+        Change in Gini coefficient and top income shares under revised OBR forecasts, based on equivalised household net income
       </p>
 
-      <table style={tableStyle}>
-        <thead>
-          <tr>
-            <th style={thStyle}>Metric</th>
-            <th style={thRightStyle}>Nov 2025</th>
-            <th style={thRightStyle}>Mar 2026</th>
-            <th style={thRightStyle}>Change</th>
-          </tr>
-        </thead>
-        <tbody>
-          {["gini", "top_10_pct_share", "top_1_pct_share"].map((metric) => {
-            const row = filtered.find((r) => r.metric === metric);
-            if (!row) return null;
-            return (
-              <tr key={metric}>
-                <td style={tdStyle}>{METRIC_LABELS[metric]}</td>
-                <td style={tdRightStyle}>
-                  {formatValue(metric, row.baseline)}
-                </td>
-                <td style={tdRightStyle}>
-                  {formatValue(metric, row.reform)}
-                </td>
-                <td style={tdRightStyle}>
-                  {formatChange(metric, row.baseline, row.reform)}
-                </td>
-              </tr>
-            );
-          })}
-        </tbody>
-      </table>
-
-      <p
-        style={{
-          marginTop: "8px",
-          fontSize: "13px",
-          color: "#666",
-          textAlign: "center",
-        }}
-      >
-        Based on equivalised household net income. The Gini coefficient ranges
-        from 0 (perfect equality) to 1 (perfect inequality).
-      </p>
+      <div className="forecast-table-wrapper">
+        <table className="forecast-table">
+          <thead>
+            <tr>
+              <th>Metric</th>
+              <th>Pre-Spring Statement</th>
+              <th>Spring Statement</th>
+              <th>Change</th>
+            </tr>
+          </thead>
+          <tbody>
+            {["gini", "top_10_pct_share", "top_1_pct_share"].map((metric) => {
+              const row = filtered.find((r) => r.metric === metric);
+              if (!row) return null;
+              return (
+                <tr key={metric}>
+                  <td>{METRIC_LABELS[metric]}</td>
+                  <td>{formatValue(metric, row.baseline)}</td>
+                  <td>{formatValue(metric, row[reformField])}</td>
+                  <td>{formatChange(metric, row.baseline, row[reformField])}</td>
+                </tr>
+              );
+            })}
+          </tbody>
+        </table>
+      </div>
     </div>
   );
 }
diff --git a/src/components/IntraDecileChart.jsx b/src/components/IntraDecileChart.jsx
index a77d717..25b2172 100644
--- a/src/components/IntraDecileChart.jsx
+++ b/src/components/IntraDecileChart.jsx
@@ -28,9 +28,11 @@ const OUTCOME_ORDER = [
   "Gain more than 5%",
 ];
 
-export default function IntraDecileChart({ data, selectedYear }) {
+export default function IntraDecileChart({ data, selectedYear, termsMode = "nominal" }) {
   if (!data) return null;
 
+  const shareField = `share_${termsMode}`;
+
   const filtered = data.filter(
     (row) =>
       parseInt(row.year) === selectedYear && row.decile !== "All",
@@ -49,7 +51,7 @@ export default function IntraDecileChart({ data, selectedYear }) {
       const row = filtered.find(
         (r) => r.decile === decile && r.outcome === outcome,
       );
-      point[outcome] = row ? parseFloat(row.share) * 100 : 0;
+      point[outcome] = row ? parseFloat(row[shareField]) * 100 : 0;
     });
     return point;
   });
diff --git a/src/components/PersonalTab.jsx b/src/components/PersonalTab.jsx
index ecbaaab..7a500d5 100644
--- a/src/components/PersonalTab.jsx
+++ b/src/components/PersonalTab.jsx
@@ -56,8 +56,8 @@ const TENURE_TYPES = [
 ];
 
 const COLORS = {
-  positive: "#059669",
-  negative: "#dc2626",
+  positive: "#22C55E",
+  negative: "#EF4444",
   teal: "#319795",
   tealDark: "#2c7a7b",
   text: "#1e293b",
diff --git a/src/components/PopulationTab.jsx b/src/components/PopulationTab.jsx
index 427ee6b..79caf17 100644
--- a/src/components/PopulationTab.jsx
+++ b/src/components/PopulationTab.jsx
@@ -2,8 +2,9 @@
 
 import { useState, useMemo, useEffect } from "react";
 import {
-  BarChart,
+  ComposedChart,
   Bar,
+  Line,
   XAxis,
   YAxis,
   CartesianGrid,
@@ -11,9 +12,7 @@ import {
   ResponsiveContainer,
   ReferenceLine,
 } from "recharts";
-import IntraDecileChart from "./IntraDecileChart";
 import InequalityTable from "./InequalityTable";
-import DetailedBudgetTable from "./DetailedBudgetTable";
 import HouseholdArchetypes from "./HouseholdArchetypes";
 import parseCSV from "../../lib/parseCSV";
 
@@ -32,9 +31,10 @@ function MetricsBar({ metrics, winnersLosers, distributional, year }) {
       (d) => d.year === year && d.decile === "All",
     );
     if (allRow) {
+      const absChange = allRow.absolute_change_nominal;
       cards.push({
         label: "Average household impact",
-        value: `${allRow.absolute_change >= 0 ? "+" : ""}\u00a3${allRow.absolute_change.toFixed(0)}/year`,
+        value: `${absChange >= 0 ? "+" : ""}\u00a3${absChange.toFixed(0)}/year`,
       });
     }
   }
@@ -62,7 +62,7 @@ function MetricsBar({ metrics, winnersLosers, distributional, year }) {
     );
     if (povertyChange) {
       cards.push({
-        label: "Poverty rate change",
+        label: "Absolute poverty rate (BHC) change",
         value: `${povertyChange.value >= 0 ? "+" : ""}${povertyChange.value.toFixed(2)} pp`,
       });
     }
@@ -82,111 +82,6 @@ function MetricsBar({ metrics, winnersLosers, distributional, year }) {
   );
 }
 
-function DistributionalChart({ data, year }) {
-  const chartData = useMemo(() => {
-    if (!data || data.length === 0) return [];
-    return data
-      .filter(
-        (d) => d.year === year && d.decile !== "All" && d.decile !== "Overall",
-      )
-      .map((d) => ({
-        decile: d.decile,
-        impact: d.absolute_change,
-      }))
-      .sort(
-        (a, b) =>
-          DECILE_ORDER.indexOf(a.decile) - DECILE_ORDER.indexOf(b.decile),
-      );
-  }, [data, year]);
-
-  if (chartData.length === 0) return null;
-
-  return (
-    <div className="section-card">
-      <h3 className="chart-title">Average annual impact by income decile</h3>
-      <p className="chart-subtitle">
-        Change in household net income ({"\u00a3"}/year), {year}-
-        {(year + 1).toString().slice(-2)}
-      </p>
-      <div className="chart-container-tall">
-        <ResponsiveContainer width="100%" height="100%">
-          <BarChart
-            data={chartData}
-            margin={{ top: 8, right: 24, left: 16, bottom: 32 }}
-          >
-            <CartesianGrid strokeDasharray="3 3" stroke="#e5e7eb" />
-            <XAxis
-              dataKey="decile"
-              tick={{ fontSize: 12, fill: "#6b7280" }}
-              tickLine={false}
-              label={{
-                value: "Income decile",
-                position: "insideBottom",
-                offset: -16,
-                style: { fill: "#374151", fontSize: 12, fontWeight: 500 },
-              }}
-            />
-            <YAxis
-              tick={{ fontSize: 12, fill: "#6b7280" }}
-              tickFormatter={(v) =>
-                `${v >= 0 ? "" : "-"}\u00a3${Math.abs(v).toFixed(0)}`
-              }
-              tickLine={false}
-              axisLine={false}
-              width={70}
-              allowDecimals={false}
-              tickCount={6}
-              label={{
-                value: "Change in net income (\u00a3/year)",
-                angle: -90,
-                position: "insideLeft",
-                dx: -8,
-                style: {
-                  textAnchor: "middle",
-                  fill: "#374151",
-                  fontSize: 12,
-                  fontWeight: 500,
-                },
-              }}
-            />
-            <ReferenceLine y={0} stroke="#6b7280" strokeWidth={1} />
-            <Tooltip
-              contentStyle={{
-                background: "#fff",
-                border: "1px solid #e5e7eb",
-                borderRadius: 8,
-                boxShadow: "0 2px 8px rgba(0,0,0,0.08)",
-                fontSize: "0.85rem",
-              }}
-              formatter={(value) => [
-                `${value >= 0 ? "+" : ""}\u00a3${value.toFixed(2)}/year`,
-                "Impact",
-              ]}
-              labelFormatter={(label) => `${label} decile`}
-            />
-            <Bar
-              dataKey="impact"
-              fill="#0d9488"
-              radius={[4, 4, 0, 0]}
-              name="Impact"
-            />
-          </BarChart>
-        </ResponsiveContainer>
-      </div>
-      <p
-        style={{
-          fontSize: "0.75rem",
-          color: "#9ca3af",
-          marginTop: "12px",
-        }}
-      >
-        Decile 1 = lowest income households, Decile 10 = highest income
-        households. Values show average annual change in household net income.
-      </p>
-    </div>
-  );
-}
-
 function WinnersLosersChart({ data, year }) {
   const chartData = useMemo(() => {
     if (!data || data.length === 0) return [];
@@ -194,12 +89,16 @@ function WinnersLosersChart({ data, year }) {
       .filter(
         (d) => d.year === year && d.decile !== "All" && d.decile !== "Overall",
       )
-      .map((d) => ({
-        decile: d.decile,
-        gaining: d.pct_gaining,
-        losing: d.pct_losing ? -Math.abs(d.pct_losing) : 0,
-        unchanged: d.pct_unchanged || 0,
-      }))
+      .map((d) => {
+        const gaining = d.pct_gaining;
+        const losing = d.pct_losing ? -Math.abs(d.pct_losing) : 0;
+        return {
+          decile: d.decile,
+          gaining,
+          losing,
+          net: gaining + losing,
+        };
+      })
       .sort(
         (a, b) =>
           DECILE_ORDER.indexOf(a.decile) - DECILE_ORDER.indexOf(b.decile),
@@ -212,12 +111,12 @@ function WinnersLosersChart({ data, year }) {
     <div className="section-card">
       <h3 className="chart-title">Winners and losers by income decile</h3>
       <p className="chart-subtitle">
-        Percentage of households gaining vs losing, {year}-
+        Share of households seeing a net gain or loss in income under revised OBR forecasts, by income decile, {year}-
         {(year + 1).toString().slice(-2)}
       </p>
       <div className="chart-container-tall">
         <ResponsiveContainer width="100%" height="100%">
-          <BarChart
+          <ComposedChart
             data={chartData}
             margin={{ top: 8, right: 24, left: 16, bottom: 32 }}
             stackOffset="sign"
@@ -241,7 +140,8 @@ function WinnersLosersChart({ data, year }) {
               axisLine={false}
               width={48}
               allowDecimals={false}
-              tickCount={6}
+              domain={[-80, 80]}
+              ticks={[-80, -60, -40, -20, 0, 20, 40, 60, 80]}
             />
             <ReferenceLine y={0} stroke="#6b7280" strokeWidth={1} />
             <Tooltip
@@ -252,27 +152,39 @@ function WinnersLosersChart({ data, year }) {
                 boxShadow: "0 2px 8px rgba(0,0,0,0.08)",
                 fontSize: "0.85rem",
               }}
-              formatter={(value, name) => [
-                `${Math.abs(value).toFixed(1)}%`,
-                name === "gaining" ? "Gaining" : "Losing",
-              ]}
+              formatter={(value, name) => {
+                if (name === "net") return [`${value >= 0 ? "+" : ""}${value.toFixed(1)}%`, "Net"];
+                return [
+                  `${Math.abs(value).toFixed(1)}%`,
+                  name === "gaining" ? "Gaining" : "Losing",
+                ];
+              }}
               labelFormatter={(label) => `${label} decile`}
             />
             <Bar
               dataKey="gaining"
-              fill="#059669"
+              fill="#2d6396"
               stackId="stack"
               name="gaining"
               radius={[4, 4, 0, 0]}
             />
             <Bar
               dataKey="losing"
-              fill="#dc2626"
+              fill="#0d9488"
               stackId="stack"
               name="losing"
-              radius={[0, 0, 4, 4]}
+              radius={[4, 4, 0, 0]}
+            />
+            <Line
+              dataKey="net"
+              type="monotone"
+              stroke="#1f2937"
+              strokeWidth={2}
+              dot={{ r: 4, fill: "#1f2937", stroke: "#1f2937" }}
+              name="net"
+              isAnimationActive={false}
             />
-          </BarChart>
+          </ComposedChart>
         </ResponsiveContainer>
       </div>
     </div>
@@ -281,39 +193,21 @@ function WinnersLosersChart({ data, year }) {
 
 export default function PopulationTab({ data }) {
   const [selectedYear, setSelectedYear] = useState(2026);
-  const [extraData, setExtraData] = useState({
-    inequality: null,
-    intraDecile: null,
-    detailedBudget: null,
-  });
+  const [inequalityData, setInequalityData] = useState(null);
 
-  // Load extra CSV files for new components
   useEffect(() => {
-    Promise.all([
-      fetch("/data/inequality.csv")
-        .then((r) => (r.ok ? r.text() : null))
-        .catch(() => null),
-      fetch("/data/intra_decile.csv")
-        .then((r) => (r.ok ? r.text() : null))
-        .catch(() => null),
-      fetch("/data/detailed_budgetary_impact.csv")
-        .then((r) => (r.ok ? r.text() : null))
-        .catch(() => null),
-    ]).then(([inequality, intraDecile, detailedBudget]) => {
-      setExtraData({
-        inequality: inequality ? parseCSV(inequality) : null,
-        intraDecile: intraDecile ? parseCSV(intraDecile) : null,
-        detailedBudget: detailedBudget ? parseCSV(detailedBudget) : null,
-      });
-    });
+    fetch("/data/inequality.csv")
+      .then((r) => (r.ok ? r.text() : null))
+      .then((text) => {
+        if (text) setInequalityData(parseCSV(text));
+      })
+      .catch(() => null);
   }, []);
 
-  const hasDistributional =
-    data?.distributional && data.distributional.length > 0;
   const hasWinnersLosers =
     data?.winnersLosers && data.winnersLosers.length > 0;
 
-  if (!hasDistributional && !hasWinnersLosers) {
+  if (!hasWinnersLosers) {
     return (
       <div className="data-message">
         <p>
@@ -327,7 +221,7 @@ export default function PopulationTab({ data }) {
 
   return (
     <div style={{ animation: "fadeIn 0.3s ease-out" }}>
-      <div className="year-selector">
+      <div className="year-selector" style={{ marginBottom: "var(--pe-space-lg)" }}>
         {YEARS.map((year) => (
           <button
             key={year}
@@ -346,13 +240,9 @@ export default function PopulationTab({ data }) {
         year={selectedYear}
       />
 
-      <div className="charts-grid">
-        {hasDistributional && (
-          <DistributionalChart
-            data={data.distributional}
-            year={selectedYear}
-          />
-        )}
+      <HouseholdArchetypes selectedYear={selectedYear} />
+
+      <div style={{ marginTop: "var(--pe-space-xl)" }}>
         {hasWinnersLosers && (
           <WinnersLosersChart
             data={data.winnersLosers}
@@ -361,22 +251,10 @@ export default function PopulationTab({ data }) {
         )}
       </div>
 
-      <IntraDecileChart
-        data={extraData.intraDecile}
-        selectedYear={selectedYear}
-      />
-
       <InequalityTable
-        data={extraData.inequality}
+        data={inequalityData}
         selectedYear={selectedYear}
       />
-
-      <DetailedBudgetTable
-        data={extraData.detailedBudget}
-        selectedYear={selectedYear}
-      />
-
-      <HouseholdArchetypes />
     </div>
   );
 }
diff --git a/src/spring_statement_data/calculators.py b/src/spring_statement_data/calculators.py
index af93110..6b6734a 100644
--- a/src/spring_statement_data/calculators.py
+++ b/src/spring_statement_data/calculators.py
@@ -5,6 +5,9 @@
 
 Uses native MicroSeries from PolicyEngine — sim.calculate() returns
 MicroSeries with weights.
+
+Real-terms values use PolicyEngine's built-in ``real_household_net_income``
+(deflated to 2026 prices via ``inflation_adjustment``).
 """
 
 import microdf as mdf
@@ -61,18 +64,26 @@ def calculate(self, year: int) -> list[dict]:
         baseline, reformed = _create_simulations()
 
         baseline_income = baseline.calculate("household_net_income", year)
-        reform_income_raw = reformed.calculate("household_net_income", year)
+        reform_income_nominal_raw = reformed.calculate("household_net_income", year)
+        baseline_income_real = baseline.calculate("real_household_net_income", year)
+        reform_income_real_raw = reformed.calculate("real_household_net_income", year)
         income_decile = baseline.calculate("household_income_decile", year)
 
-        # API v2: reform MicroSeries uses baseline weights
-        reform_income = mdf.MicroSeries(
-            reform_income_raw.values, weights=baseline_income.weights
+        # Align reform weights to baseline
+        reform_income_nominal = mdf.MicroSeries(
+            reform_income_nominal_raw.values, weights=baseline_income.weights
+        )
+        reform_income_real = mdf.MicroSeries(
+            reform_income_real_raw.values, weights=baseline_income_real.weights
         )
 
         valid = np.array(income_decile) >= 0
         baseline_income = baseline_income[valid]
-        reform_income = reform_income[valid]
-        income_change = reform_income - baseline_income
+        baseline_income_real = baseline_income_real[valid]
+        reform_income_nominal = reform_income_nominal[valid]
+        reform_income_real = reform_income_real[valid]
+        income_change_nominal = reform_income_nominal - baseline_income
+        income_change_real = reform_income_real - baseline_income_real
         decile_values = income_decile[valid]
 
         results = []
@@ -82,28 +93,42 @@ def calculate(self, year: int) -> list[dict]:
             if not mask.any():
                 continue
 
-            change_sum = income_change[mask].sum()
             baseline_sum = baseline_income[mask].sum()
-            relative = (change_sum / baseline_sum) * 100 if baseline_sum > 0 else 0
-            avg_change = income_change[mask].mean()
+            baseline_sum_real = baseline_income_real[mask].sum()
+
+            change_sum_nom = income_change_nominal[mask].sum()
+            change_sum_real = income_change_real[mask].sum()
+            relative_nom = (change_sum_nom / baseline_sum) * 100 if baseline_sum > 0 else 0
+            relative_real = (change_sum_real / baseline_sum_real) * 100 if baseline_sum_real > 0 else 0
+            avg_change_nom = income_change_nominal[mask].mean()
+            avg_change_real = income_change_real[mask].mean()
 
             results.append({
                 "year": year,
                 "decile": DECILE_LABELS[decile - 1],
-                "absolute_change": round(float(avg_change), 2),
-                "relative_change": round(float(relative), 4),
+                "absolute_change_nominal": round(float(avg_change_nom), 2),
+                "absolute_change_real": round(float(avg_change_real), 2),
+                "relative_change_nominal": round(float(relative_nom), 4),
+                "relative_change_real": round(float(relative_real), 4),
             })
 
-        total_change = income_change.sum()
+        total_change_nom = income_change_nominal.sum()
+        total_change_real = income_change_real.sum()
         total_baseline = baseline_income.sum()
-        overall_relative = (
-            (total_change / total_baseline) * 100 if total_baseline > 0 else 0
+        total_baseline_real = baseline_income_real.sum()
+        overall_relative_nom = (
+            (total_change_nom / total_baseline) * 100 if total_baseline > 0 else 0
+        )
+        overall_relative_real = (
+            (total_change_real / total_baseline_real) * 100 if total_baseline_real > 0 else 0
         )
         results.append({
             "year": year,
             "decile": "All",
-            "absolute_change": round(float(income_change.mean()), 2),
-            "relative_change": round(float(overall_relative), 4),
+            "absolute_change_nominal": round(float(income_change_nominal.mean()), 2),
+            "absolute_change_real": round(float(income_change_real.mean()), 2),
+            "relative_change_nominal": round(float(overall_relative_nom), 4),
+            "relative_change_real": round(float(overall_relative_real), 4),
         })
 
         return results
@@ -113,6 +138,7 @@ class MetricsCalculator:
     """Calculate summary metrics including poverty rates.
 
     Ensures reform MicroSeries uses baseline weights (API v2 parity).
+    Poverty rates are binary variables — no nominal/real distinction needed.
     """
 
     def calculate(self, year: int) -> list[dict]:
@@ -177,49 +203,70 @@ def calculate(self, year: int) -> list[dict]:
         baseline, reformed = _create_simulations()
         results = []
 
-        for sim, label in [(baseline, "baseline"), (reformed, "reform")]:
-            income = sim.calculate("equiv_household_net_income", year)
-            income_vals = income.values.copy()
-            income_vals[income_vals < 0] = 0
-
-            hh_count = sim.calculate("household_count_people", year)
-            original_weights = income.weights.copy()
-
-            person_weights = original_weights * np.array(hh_count)
-            income_ms = mdf.MicroSeries(income_vals, weights=person_weights)
-
-            gini = income_ms.gini()
-            in_top_10 = income_ms.decile_rank() == 10
-            in_top_1 = income_ms.percentile_rank() == 100
-
-            income_hh = mdf.MicroSeries(income_vals, weights=original_weights)
-            total = income_hh.sum()
-            top_10_share = income_hh[in_top_10].sum() / total if total > 0 else 0
-            top_1_share = income_hh[in_top_1].sum() / total if total > 0 else 0
-
-            for metric, value in [
-                ("gini", gini),
-                ("top_10_pct_share", top_10_share),
-                ("top_1_pct_share", top_1_share),
-            ]:
-                results.append({
-                    "year": year,
-                    "metric": metric,
-                    label: float(value),
-                })
+        # Compute baseline once (nominal: equiv_household_net_income)
+        b_income = baseline.calculate("equiv_household_net_income", year)
+        b_vals = b_income.values.copy()
+        b_vals[b_vals < 0] = 0
+        b_hh_count = baseline.calculate("household_count_people", year)
+        b_original_weights = b_income.weights.copy()
+        b_person_weights = b_original_weights * np.array(b_hh_count)
+
+        b_income_ms = mdf.MicroSeries(b_vals, weights=b_person_weights)
+        b_gini = b_income_ms.gini()
+        b_in_top_10 = b_income_ms.decile_rank() == 10
+        b_in_top_1 = b_income_ms.percentile_rank() == 100
+        b_income_hh = mdf.MicroSeries(b_vals, weights=b_original_weights)
+        b_total = b_income_hh.sum()
+        b_top_10_share = b_income_hh[b_in_top_10].sum() / b_total if b_total > 0 else 0
+        b_top_1_share = b_income_hh[b_in_top_1].sum() / b_total if b_total > 0 else 0
+
+        # Reform nominal: equiv_household_net_income
+        r_income_nom = reformed.calculate("equiv_household_net_income", year)
+        r_hh_count = reformed.calculate("household_count_people", year)
+        r_original_weights = r_income_nom.weights.copy()
+        r_person_weights = r_original_weights * np.array(r_hh_count)
+
+        # Reform real: real_household_net_income / household_equivalisation_bhc
+        r_real_hni = reformed.calculate("real_household_net_income", year)
+        r_equiv_bhc = reformed.calculate("household_equivalisation_bhc", year)
+        r_real_equiv_vals = np.where(
+            np.array(r_equiv_bhc) > 0,
+            np.array(r_real_hni) / np.array(r_equiv_bhc),
+            0.0,
+        )
 
-        # Merge baseline and reform into single rows
-        merged = {}
-        for row in results:
-            key = row["metric"]
-            if key not in merged:
-                merged[key] = {"year": year, "metric": key}
-            if "baseline" in row:
-                merged[key]["baseline"] = row["baseline"]
-            if "reform" in row:
-                merged[key]["reform"] = row["reform"]
+        reform_results = {}
+        for mode, r_vals_raw in [("nominal", r_income_nom.values.copy()), ("real", r_real_equiv_vals.copy())]:
+            r_vals = r_vals_raw.copy()
+            r_vals[r_vals < 0] = 0
+
+            r_income_ms = mdf.MicroSeries(r_vals, weights=r_person_weights)
+            r_gini = r_income_ms.gini()
+            r_in_top_10 = r_income_ms.decile_rank() == 10
+            r_in_top_1 = r_income_ms.percentile_rank() == 100
+            r_income_hh = mdf.MicroSeries(r_vals, weights=r_original_weights)
+            r_total = r_income_hh.sum()
+            r_top_10_share = r_income_hh[r_in_top_10].sum() / r_total if r_total > 0 else 0
+            r_top_1_share = r_income_hh[r_in_top_1].sum() / r_total if r_total > 0 else 0
+
+            reform_results[mode] = {
+                "gini": r_gini,
+                "top_10_pct_share": r_top_10_share,
+                "top_1_pct_share": r_top_1_share,
+            }
+
+        merged = []
+        for metric in ["gini", "top_10_pct_share", "top_1_pct_share"]:
+            baseline_val = {"gini": b_gini, "top_10_pct_share": b_top_10_share, "top_1_pct_share": b_top_1_share}[metric]
+            merged.append({
+                "year": year,
+                "metric": metric,
+                "baseline": float(baseline_val),
+                "reform_nominal": float(reform_results["nominal"][metric]),
+                "reform_real": float(reform_results["real"][metric]),
+            })
 
-        return list(merged.values())
+        return merged
 
 
 class IntraDecileCalculator:
@@ -246,58 +293,78 @@ def calculate(self, year: int) -> list[dict]:
         baseline, reformed = _create_simulations()
 
         baseline_income = baseline.calculate("household_net_income", year)
-        reform_income_raw = reformed.calculate("household_net_income", year)
+        reform_income_nominal_raw = reformed.calculate("household_net_income", year)
+        baseline_income_real = baseline.calculate("real_household_net_income", year)
+        reform_income_real_raw = reformed.calculate("real_household_net_income", year)
         income_decile = baseline.calculate("household_income_decile", year)
         hh_count_people = baseline.calculate("household_count_people", year)
 
-        reform_income = mdf.MicroSeries(
-            reform_income_raw.values, weights=baseline_income.weights
-        )
-
-        # API v2 percentage change
-        absolute_change = (reform_income - baseline_income).values
-        capped_baseline = np.maximum(baseline_income.values, 1)
-        capped_reform = np.maximum(reform_income.values, 1) + absolute_change
-        pct_change = (capped_reform - capped_baseline) / capped_baseline
-
         people = mdf.MicroSeries(
             np.array(hh_count_people), weights=baseline_income.weights
         )
 
         results = []
-        all_decile_shares = []
 
-        for decile in range(1, 11):
-            in_decile = np.array(income_decile) == decile
-            decile_total = people[in_decile].sum()
+        mode_data = [
+            ("nominal", baseline_income, reform_income_nominal_raw),
+            ("real", baseline_income_real, reform_income_real_raw),
+        ]
 
-            decile_shares = {}
-            for i, label in enumerate(self.LABELS):
-                lower, upper = self.BOUNDS[i], self.BOUNDS[i + 1]
-                in_bin = (pct_change > lower) & (pct_change <= upper)
-                share = people[in_decile & in_bin].sum() / decile_total if decile_total > 0 else 0
-                decile_shares[label] = share
+        for mode, base_inc, reform_inc_raw in mode_data:
+            reform_income = mdf.MicroSeries(
+                reform_inc_raw.values, weights=base_inc.weights
+            )
 
+            # API v2 percentage change
+            absolute_change = (reform_income - base_inc).values
+            capped_baseline = np.maximum(base_inc.values, 1)
+            capped_reform = np.maximum(reform_income.values, 1) + absolute_change
+            pct_change = (capped_reform - capped_baseline) / capped_baseline
+
+            all_decile_shares = []
+
+            for decile in range(1, 11):
+                in_decile = np.array(income_decile) == decile
+                decile_total = people[in_decile].sum()
+
+                decile_shares = {}
+                for i, label in enumerate(self.LABELS):
+                    lower, upper = self.BOUNDS[i], self.BOUNDS[i + 1]
+                    in_bin = (pct_change > lower) & (pct_change <= upper)
+                    share = people[in_decile & in_bin].sum() / decile_total if decile_total > 0 else 0
+                    decile_shares[label] = share
+
+                    results.append({
+                        "year": year,
+                        "decile": DECILE_LABELS[decile - 1],
+                        "outcome": label,
+                        f"share_{mode}": float(share),
+                    })
+
+                all_decile_shares.append(decile_shares)
+
+            # "All" = mean of 10 decile proportions (API v2)
+            for label in self.LABELS:
+                mean_share = np.mean([d[label] for d in all_decile_shares])
                 results.append({
                     "year": year,
-                    "decile": DECILE_LABELS[decile - 1],
+                    "decile": "All",
                     "outcome": label,
-                    "share": float(share),
+                    f"share_{mode}": float(mean_share),
                 })
 
-            all_decile_shares.append(decile_shares)
-
-        # "All" = mean of 10 decile proportions (API v2)
-        for label in self.LABELS:
-            mean_share = np.mean([d[label] for d in all_decile_shares])
-            results.append({
-                "year": year,
-                "decile": "All",
-                "outcome": label,
-                "share": float(mean_share),
-            })
+        # Merge nominal and real rows by (year, decile, outcome)
+        merged = {}
+        for row in results:
+            key = (row["year"], row["decile"], row["outcome"])
+            if key not in merged:
+                merged[key] = {"year": row["year"], "decile": row["decile"], "outcome": row["outcome"]}
+            if "share_nominal" in row:
+                merged[key]["share_nominal"] = row["share_nominal"]
+            if "share_real" in row:
+                merged[key]["share_real"] = row["share_real"]
 
-        return results
+        return list(merged.values())
 
 
 class DetailedBudgetaryImpactCalculator:
@@ -318,14 +385,13 @@ def calculate(self, year: int) -> list[dict]:
                 continue
 
             sign = 1 if is_tax else -1
-            difference = sign * (r_val - b_val) / 1e6
 
             results.append({
                 "year": year,
                 "program": program_name,
                 "baseline": float(b_val / 1e6),
                 "reform": float(r_val / 1e6),
-                "difference": float(difference),
+                "difference": float(sign * (r_val - b_val) / 1e6),
             })
 
         return results
@@ -339,15 +405,18 @@ class WinnersLosersCalculator:
     def calculate(self, year: int) -> list[dict]:
         baseline, reformed = _create_simulations()
 
-        baseline_income = baseline.calculate("household_net_income", year)
-        reformed_income = reformed.calculate("household_net_income", year)
+        baseline_income_real = baseline.calculate("real_household_net_income", year)
+        reformed_income_real = reformed.calculate("real_household_net_income", year)
         income_decile = baseline.calculate("household_income_decile", year)
 
-        change = reformed_income - baseline_income
-        weights = np.array(baseline_income.weights)
-        change_arr = np.array(change)
+        weights = np.array(baseline_income_real.weights)
         decile_arr = np.array(income_decile)
 
+        change = mdf.MicroSeries(
+            reformed_income_real.values, weights=baseline_income_real.weights
+        ) - baseline_income_real
+        change_arr = np.array(change)
+
         results = []
 
         for decile in range(1, 11):
@@ -402,16 +471,24 @@ def calculate(self, year: int) -> pd.DataFrame:
         reformed_income = np.array(
             reformed.calculate("household_net_income", year)
         )
+        baseline_income_real = np.array(
+            baseline.calculate("real_household_net_income", year)
+        )
+        reformed_income_real = np.array(
+            reformed.calculate("real_household_net_income", year)
+        )
         income_decile = np.array(
             baseline.calculate("household_income_decile", year)
         )
 
-        change = reformed_income - baseline_income
+        change_nominal = reformed_income - baseline_income
+        change_real = reformed_income_real - baseline_income_real
         mask = baseline_income <= self.MAX_INCOME
 
         df = pd.DataFrame({
             "baseline_income": baseline_income[mask],
-            "net_impact": change[mask],
+            "net_impact_nominal": change_nominal[mask],
+            "net_impact_real": change_real[mask],
             "decile": income_decile[mask],
         })
 
@@ -437,11 +514,11 @@ def calculate(
     ) -> list[dict]:
         baseline, reformed = _create_simulations()
 
-        baseline_income = np.array(
-            baseline.calculate("household_net_income", year)
+        baseline_income_real = np.array(
+            baseline.calculate("real_household_net_income", year)
         )
-        reformed_income = np.array(
-            reformed.calculate("household_net_income", year)
+        reformed_income_real = np.array(
+            reformed.calculate("real_household_net_income", year)
         )
 
         results = []
@@ -454,11 +531,11 @@ def calculate(
             name = row.get("name", row.get("constituency_name", ""))
             w = weights[idx, :]
 
-            baseline_ms = mdf.MicroSeries(baseline_income, weights=w)
-            reform_ms = mdf.MicroSeries(reformed_income, weights=w)
+            baseline_ms = mdf.MicroSeries(baseline_income_real, weights=w)
+            reform_ms_real = mdf.MicroSeries(reformed_income_real, weights=w)
 
             baseline_sum = baseline_ms.sum()
-            reform_sum = reform_ms.sum()
+            reform_sum = reform_ms_real.sum()
             weighted_count = baseline_ms.count()
 
             avg_gain = (
diff --git a/src/spring_statement_data/pipeline.py b/src/spring_statement_data/pipeline.py
index a1cc933..de03a3e 100644
--- a/src/spring_statement_data/pipeline.py
+++ b/src/spring_statement_data/pipeline.py
@@ -24,6 +24,7 @@
 from .reforms import (
     DEFAULT_YEARS,
     generate_economic_forecast_json,
+    get_pre_statement_scenario,
     save_economic_forecast_json,
 )
 
@@ -150,9 +151,122 @@ def generate_all_data(
         if len(df) > 0:
             _save_csv(df, output_dir / f"{name}.csv")
 
+    # Household archetypes (stats + comparison JSONs)
+    _generate_household_archetypes(output_dir, years=years)
+
     print(f"\nAll data saved to {output_dir}/")
     return results
 
 
+# Household type classification
+_HH_GROUPS = [
+    "Single adult, no children",
+    "Couple, no children",
+    "Single parent",
+    "Couple with children",
+    "Single pensioner",
+    "Pensioner couple",
+]
+
+
+def _classify_household(sim, year):
+    """Return an array classifying each household into an archetype group.
+
+    All variables mapped to household level for consistent shapes.
+    """
+    # num_adults/num_children are benunit-level; map to household
+    n_adults = np.array(sim.calculate("num_adults", year, map_to="household"))
+    n_children = np.array(sim.calculate("num_children", year, map_to="household"))
+    # is_SP_age is person-level; map_to="household" gives max across members
+    is_sp_age = np.array(
+        sim.calculate("is_SP_age", year, map_to="household")
+    ).astype(bool)
+
+    groups = np.full(len(n_adults), "", dtype=object)
+    groups[(n_adults == 1) & (n_children == 0) & (~is_sp_age)] = "Single adult, no children"
+    groups[(n_adults >= 2) & (n_children == 0) & (~is_sp_age)] = "Couple, no children"
+    groups[(n_adults == 1) & (n_children > 0)] = "Single parent"
+    groups[(n_adults >= 2) & (n_children > 0)] = "Couple with children"
+    groups[(n_adults == 1) & (n_children == 0) & is_sp_age] = "Single pensioner"
+    groups[(n_adults >= 2) & (n_children == 0) & is_sp_age] = "Pensioner couple"
+    return groups
+
+
+def _generate_household_archetypes(output_dir: Path, years: list[int] = None):
+    """Generate household_stats.json and household_comparison.json for all years."""
+    from policyengine_uk import Microsimulation
+    import microdf as mdf
+
+    if years is None:
+        years = DEFAULT_YEARS
+
+    scenario = get_pre_statement_scenario()
+    baseline = Microsimulation(scenario=scenario)
+    reformed = Microsimulation()
+
+    all_stats = []
+    all_comparison = []
+
+    for year in years:
+        print(f"  Household archetypes: {year}...")
+        groups = _classify_household(baseline, year)
+        baseline_hnet = baseline.calculate("household_net_income", year)
+        reform_hnet_raw = reformed.calculate("household_net_income", year)
+        baseline_hnet_real = baseline.calculate("real_household_net_income", year)
+        reform_hnet_real_raw = reformed.calculate("real_household_net_income", year)
+
+        # Align reform weights to baseline
+        reform_hnet_nominal = mdf.MicroSeries(
+            reform_hnet_raw.values, weights=baseline_hnet.weights
+        )
+        reform_hnet_real = mdf.MicroSeries(
+            reform_hnet_real_raw.values, weights=baseline_hnet_real.weights
+        )
+        weights = np.array(baseline_hnet.weights)
+
+        for group in _HH_GROUPS:
+            mask = groups == group
+            if not mask.any():
+                continue
+
+            b_inc = baseline_hnet[mask]
+            r_inc_nom = reform_hnet_nominal[mask]
+            r_inc_real = reform_hnet_real[mask]
+            b_inc_real = baseline_hnet_real[mask]
+            w = weights[mask]
+
+            mean_b = float(b_inc.mean())
+            mean_r_nom = float(r_inc_nom.mean())
+            mean_r_real = float(r_inc_real.mean())
+            mean_b_real = float(b_inc_real.mean())
+            median_b = float(b_inc.median())
+            weighted_n = float(w.sum())
+
+            all_stats.append({
+                "year": year,
+                "group": group,
+                "mean_hnet": round(mean_b),
+                "median_hnet": round(median_b),
+                "weighted_n": round(weighted_n),
+            })
+            all_comparison.append({
+                "year": year,
+                "group": group,
+                "baseline_hnet": round(mean_b),
+                "reformed_hnet_nominal": round(mean_r_nom),
+                "reformed_hnet_real": round(mean_r_real),
+                "change_nominal": round(mean_r_nom - mean_b),
+                "change_real": round(mean_r_real - mean_b_real),
+            })
+
+    output_dir.mkdir(parents=True, exist_ok=True)
+    with open(output_dir / "household_stats.json", "w") as f:
+        json.dump(all_stats, f, indent=2)
+    print(f"Saved: {output_dir / 'household_stats.json'}")
+    with open(output_dir / "household_comparison.json", "w") as f:
+        json.dump(all_comparison, f, indent=2)
+    print(f"Saved: {output_dir / 'household_comparison.json'}")
+
+
 if __name__ == "__main__":
     generate_all_data()
diff --git a/src/spring_statement_data/reforms.py b/src/spring_statement_data/reforms.py
index 288ff1c..6d2d6ca 100644
--- a/src/spring_statement_data/reforms.py
+++ b/src/spring_statement_data/reforms.py
@@ -135,6 +135,35 @@ def get_pre_statement_scenario() -> Scenario:
 get_reform_scenario = get_pre_statement_scenario
 
 
+# =============================================================================
+# Real-terms deflator: convert reform (March 2026) nominal £ to baseline
+# (November 2025) price levels.
+#
+# For year Y the cumulative price index from 2025 base is:
+#   P(Y) = product of (1 + cpi[t]) for t in 2025..Y
+# The deflator to express reform income in baseline prices is:
+#   deflator(Y) = P_baseline(Y) / P_reform(Y)
+# =============================================================================
+
+_CPI_PATH = "gov.economic_assumptions.yoy_growth.obr.consumer_price_index"
+_NOV_CPI = {int(k[:4]): v for k, v in _NOVEMBER_2025[_CPI_PATH].items()}
+_MAR_CPI = {int(k[:4]): v for k, v in _MARCH_2026[_CPI_PATH].items()}
+
+
+def get_real_deflator(year: int) -> float:
+    """Return the deflator to convert reform nominal £ to baseline prices.
+
+    Multiplying reform income by this deflator expresses it in the same
+    price level as the baseline, enabling a real-terms comparison.
+    """
+    baseline_cumulative = 1.0
+    reform_cumulative = 1.0
+    for y in range(2025, year + 1):
+        baseline_cumulative *= 1 + _NOV_CPI.get(y, 0)
+        reform_cumulative *= 1 + _MAR_CPI.get(y, 0)
+    return baseline_cumulative / reform_cumulative
+
+
 def generate_economic_forecast_json() -> dict:
     """Generate the JSON data structure for the economic forecast tab.