diff --git a/changelog_entry.yaml b/changelog_entry.yaml
index af5448c..8b63c21 100644
--- a/changelog_entry.yaml
+++ b/changelog_entry.yaml
@@ -1,5 +1,4 @@
 - bump: minor
   changes:
     added:
-    - 5% dropout in calibration.
-    - Code quality improvements.
+    - Add employer pension contributions.
diff --git a/docs/_toc.yml b/docs/_toc.yml
index 4b8640a..2b6fe13 100644
--- a/docs/_toc.yml
+++ b/docs/_toc.yml
@@ -3,3 +3,4 @@ root: intro
 chapters:
 - file: methodology.ipynb
 - file: validation.ipynb
+- file: pension_contributions.ipynb
diff --git a/docs/pension_contributions.ipynb b/docs/pension_contributions.ipynb
new file mode 100644
index 0000000..e41b9d3
--- /dev/null
+++ b/docs/pension_contributions.ipynb
@@ -0,0 +1,25 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pension contributions\n",
+    "\n",
+    "We take pension contributions from the FRS data where available. This includes:\n",
+    "\n",
+    "* Employee contributions (~£30 billion in FY2022)\n",
+    "* Personal contributions (~£5 billion in FY2022)\n",
+    "\n",
+    "We do not have data on employer contributions, but from aggregate data this is around [250% of employee contributions](https://www.gov.uk/government/statistics/workplace-pension-participation-and-savings-trends-2009-to-2022/workplace-pension-participation-and-savings-trends-of-eligible-employees-2009-to-2022). We apply this as a multiplier to estimate employer contributions for each worker."
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/policyengine_uk_data/datasets/frs/enhanced_frs.py b/policyengine_uk_data/datasets/frs/enhanced_frs.py
index f0f5727..92844d9 100644
--- a/policyengine_uk_data/datasets/frs/enhanced_frs.py
+++ b/policyengine_uk_data/datasets/frs/enhanced_frs.py
@@ -56,7 +56,7 @@ class EnhancedFRS_2022_23(EnhancedFRS):
     input_frs = ExtendedFRS_2022_23
     time_period = 2022
     end_year = 2028
-    url = "release://PolicyEngine/ukda/1.3.0/enhanced_frs_2022_23.h5"
+    url = "release://PolicyEngine/ukda/1.4.0/enhanced_frs_2022_23.h5"
 
 
 def reweight(
diff --git a/policyengine_uk_data/datasets/frs/frs.py b/policyengine_uk_data/datasets/frs/frs.py
index 2862df3..203ffc4 100644
--- a/policyengine_uk_data/datasets/frs/frs.py
+++ b/policyengine_uk_data/datasets/frs/frs.py
@@ -115,7 +115,7 @@ class FRS_2022_23(FRS):
     label = "FRS (2022-23)"
     file_path = STORAGE_FOLDER / "frs_2022_23.h5"
     time_period = 2022
-    url = "release://PolicyEngine/ukda/1.3.0/frs_2022_23.h5"
+    url = "release://PolicyEngine/ukda/1.4.0/frs_2022_23.h5"
 
 
 def add_id_variables(frs: h5py.File, person: DataFrame, household: DataFrame):
@@ -745,7 +745,7 @@ def add_expenses(
         * 52
     )
 
-    frs["private_pension_contributions"] = max_(
+    frs["personal_pension_contributions"] = max_(
         0,
         sum_to_entity(
             pen_prov.PENAMT[pen_prov.STEMPPEN.isin((5, 6))],
@@ -754,10 +754,13 @@ def add_expenses(
         ).clip(0, pen_prov.PENAMT.quantile(0.95))
         * 52,
     )
-    frs["occupational_pension_contributions"] = max_(
+    frs["employee_pension_contributions"] = max_(
         0,
         sum_to_entity(job.DEDUC1.fillna(0), job.person_id, person.index) * 52,
     )
+    frs["employer_pension_contributions"] = (
+        frs["employee_pension_contributions"] * 2.5
+    )  # Rough estimate based on aggregates.
 
     frs["housing_service_charges"] = (
         pd.DataFrame(
diff --git a/policyengine_uk_data/utils/imputations/income.py b/policyengine_uk_data/utils/imputations/income.py
index 509e334..9110a5b 100644
--- a/policyengine_uk_data/utils/imputations/income.py
+++ b/policyengine_uk_data/utils/imputations/income.py
@@ -19,7 +19,6 @@
     charitable_investment_gifts="GIFTINV",
     employment_expenses="EPB",
     other_deductions="MOTHDED",
-    pension_contributions="PENSRLF",
     person_weight="FACT",
     benunit_weight="FACT",
     household_weight="FACT",
@@ -79,7 +78,6 @@ def generate_spi_table(spi: pd.DataFrame):
     "employment_expenses",
     "property_income",
     "gift_aid",
-    "pension_contributions",
 ]