diff --git a/backend/api/metadata_registry.py b/backend/api/metadata_registry.py
index 6fe49f3..bde78dd 100644
--- a/backend/api/metadata_registry.py
+++ b/backend/api/metadata_registry.py
@@ -61,6 +61,16 @@
             "Income values are in nominal dollars (not inflation-adjusted across years)."
         ],
     },
+    "unemployment_rate": {
+        "source": (
+            "U.S. Census Bureau, American Community Survey 5-Year Estimates "
+            "(Table B23025)"
+        ),
+        "lastUpdated": "2023",
+        "caveats": [
+            "Estimates for small geographies may have high margins of error."
+        ],
+    },
     "qcew_employment": {
         "source": (
             "U.S. Bureau of Labor Statistics, "
diff --git a/backend/api/routes/post_routes/post_acs5_db.py b/backend/api/routes/post_routes/post_acs5_db.py
index 3a4bf1d..2507a8b 100644
--- a/backend/api/routes/post_routes/post_acs5_db.py
+++ b/backend/api/routes/post_routes/post_acs5_db.py
@@ -1,5 +1,6 @@
 import logging
 
+import pandas as pd
 from fastapi import APIRouter
 
 from api.metadata_registry import get_metadata
@@ -11,83 +12,336 @@
 router = APIRouter()
 
 
+def _aggregate_to_state(df: pd.DataFrame, average=False) -> pd.DataFrame:
+    """Aggregate county-level data to state level by summing Value and averaging Percent."""
+    if df.empty:
+        return df
+    # Sum the Value column (population counts) and average the Percent column
+    agg_dict = {}
+    for col in df.columns:
+        if col in ['year', 'Section', 'Variable']:
+            agg_dict[col] = 'first'
+        elif average and col == 'Value':
+            agg_dict[col] = 'mean'
+        elif col == 'Value':
+            agg_dict[col] = 'sum'
+        elif col == 'Percent':
+            agg_dict[col] = 'mean'
+
+    result = df.groupby(['year', 'Section', 'Variable'],
+                        as_index=False).agg(agg_dict)
+    # Round percent to 1 decimal
+    if 'Percent' in result.columns:
+        result['Percent'] = result['Percent'].round(1)
+    return result
+
+
+# Demographics
 @router.post("/load/acs5-db/tidy/demographics")
 async def tidy_demographics(request: FilterRequest):
-    rows = DB.execute(
-        """
-        SELECT year, Section, Variable, Value, Percent
-        FROM b10_census
-        WHERE NAME = ?
-          AND CAST(year AS INTEGER) BETWEEN ? AND ?
-        ORDER BY year, Section, Variable
-        """,
-        [request.name, request.year_min, request.year_max],
-    ).df()
+    # If requesting Vermont (state-level), aggregate all counties
+    if request.name.lower() == "vermont":
+        rows = DB.execute(
+            """
+            SELECT year, Section, Variable, Value, Percent
+            FROM b10_census
+            WHERE geo_type = 'county'
+              AND CAST(year AS INTEGER) BETWEEN ? AND ?
+            ORDER BY year, Section, Variable
+            """,
+            [request.year_min, request.year_max],
+        ).df()
+        rows = _aggregate_to_state(rows)
+    else:
+        rows = DB.execute(
+            """
+            SELECT year, Section, Variable, Value, Percent
+            FROM b10_census
+            WHERE NAME = ?
+              AND CAST(year AS INTEGER) BETWEEN ? AND ?
+            ORDER BY year, Section, Variable
+            """,
+            [request.name, request.year_min, request.year_max],
+        ).df()
     return make_response(data=rows, metadata=get_metadata("demographics"))
 
 
+# Education
 @router.post("/load/acs5-db/tidy/education")
 async def tidy_education(request: FilterRequest):
-    rows = DB.execute(
-        """
-        SELECT year, Section, Variable, Value, Percent
-        FROM b15003_education
-        WHERE NAME = ?
-          AND CAST(year AS INTEGER) BETWEEN ? AND ?
-        ORDER BY year, Variable
-        """,
-        [request.name, request.year_min, request.year_max],
-    ).df()
+    if request.name.lower() == "vermont":
+        rows = DB.execute(
+            """
+            SELECT year, Section, Variable, Value, Percent
+            FROM b15003_education
+            WHERE geo_type = 'county'
+              AND CAST(year AS INTEGER) BETWEEN ? AND ?
+            ORDER BY year, Variable
+            """,
+            [request.year_min, request.year_max],
+        ).df()
+        rows = _aggregate_to_state(rows)
+    else:
+        rows = DB.execute(
+            """
+            SELECT year, Section, Variable, Value, Percent
+            FROM b15003_education
+            WHERE NAME = ?
+              AND CAST(year AS INTEGER) BETWEEN ? AND ?
+            ORDER BY year, Variable
+            """,
+            [request.name, request.year_min, request.year_max],
+        ).df()
     return make_response(data=rows, metadata=get_metadata("education"))
 
 
+# Housing (TODO: Fix statewide aggregation for housing variables that are not counts, e.g. median rent)
 @router.post("/load/acs5-db/tidy/housing")
 async def tidy_housing(request: FilterRequest):
-    rows = DB.execute(
-        """
-        SELECT year, Section, Variable, Value, Percent
-        FROM b_housing
-        WHERE NAME = ?
-          AND CAST(year AS INTEGER) BETWEEN ? AND ?
-        ORDER BY year, Variable
-        """,
-        [request.name, request.year_min, request.year_max],
-    ).df()
+    if request.name.lower() == "vermont":
+        rows = DB.execute(
+            """
+            SELECT year, Section, Variable, Value, Percent
+            FROM b_housing
+            WHERE geo_type = 'county'
+              AND CAST(year AS INTEGER) BETWEEN ? AND ?
+            ORDER BY year, Variable
+            """,
+            [request.year_min, request.year_max],
+        ).df()
+        rows = _aggregate_to_state(rows, )
+    else:
+        rows = DB.execute(
+            """
+            SELECT year, Section, Variable, Value, Percent
+            FROM b_housing
+            WHERE NAME = ?
+              AND CAST(year AS INTEGER) BETWEEN ? AND ?
+            ORDER BY year, Variable
+            """,
+            [request.name, request.year_min, request.year_max],
+        ).df()
     return make_response(data=rows, metadata=get_metadata("housing"))
 
 
+# Labor Force
 @router.post("/load/acs5-db/tidy/labor-force")
 async def tidy_labor_force(request: FilterRequest):
-    rows = DB.execute(
-        """
-        SELECT year, Section, Variable, Value, Percent
-        FROM b_economic
-        WHERE NAME = ?
-          AND Section = 'Labor Force'
-          AND CAST(year AS INTEGER) BETWEEN ? AND ?
-        ORDER BY year, Variable
-        """,
-        [request.name, request.year_min, request.year_max],
-    ).df()
+    if request.name.lower() == "vermont":
+        rows = DB.execute(
+            """
+            SELECT year, Section, Variable, Value, Percent
+            FROM b_economic
+            WHERE geo_type = 'county'
+              AND Section = 'Labor Force'
+              AND CAST(year AS INTEGER) BETWEEN ? AND ?
+            ORDER BY year, Variable
+            """,
+            [request.year_min, request.year_max],
+        ).df()
+        rows = _aggregate_to_state(rows)
+    else:
+        rows = DB.execute(
+            """
+            SELECT year, Section, Variable, Value, Percent
+            FROM b_economic
+            WHERE NAME = ?
+              AND Section = 'Labor Force'
+              AND CAST(year AS INTEGER) BETWEEN ? AND ?
+            ORDER BY year, Variable
+            """,
+            [request.name, request.year_min, request.year_max],
+        ).df()
     return make_response(data=rows, metadata=get_metadata("labor_force"))
 
 
+# Income
 @router.post("/load/acs5-db/tidy/income")
 async def tidy_income(request: FilterRequest):
-    rows = DB.execute(
-        """
-        SELECT year, Section, Variable, Value, Percent
-        FROM b_economic
-        WHERE NAME = ?
-          AND Section = 'Income'
-          AND CAST(year AS INTEGER) BETWEEN ? AND ?
-        ORDER BY year, Variable
-        """,
-        [request.name, request.year_min, request.year_max],
-    ).df()
+    if request.name.lower() == "vermont":
+        rows = DB.execute(
+            """
+            SELECT year, Section, Variable, Value, Percent
+            FROM b_economic
+            WHERE geo_type = 'county'
+              AND Section = 'Income'
+              AND CAST(year AS INTEGER) BETWEEN ? AND ?
+            ORDER BY year, Variable
+            """,
+            [request.year_min, request.year_max],
+        ).df()
+        rows = _aggregate_to_state(rows, average=True)
+    else:
+        rows = DB.execute(
+            """
+            SELECT year, Section, Variable, Value, Percent
+            FROM b_economic
+            WHERE NAME = ?
+              AND Section = 'Income'
+              AND CAST(year AS INTEGER) BETWEEN ? AND ?
+            ORDER BY year, Variable
+            """,
+            [request.name, request.year_min, request.year_max],
+        ).df()
     return make_response(data=rows, metadata=get_metadata("income"))
 
 
+# Median Age
+@router.post("/load/acs5-db/tidy/demographics/median-age")
+async def tidy_median_age(request: FilterRequest):
+    # If requesting Vermont (state-level), aggregate all counties
+    if request.name.lower() == "vermont":
+        rows = DB.execute(
+            """
+            SELECT year, Section, Variable, Value
+            FROM b10_census
+            WHERE geo_type = 'county' AND Variable = 'Median Age'
+              AND CAST(year AS INTEGER) BETWEEN ? AND ?
+            ORDER BY year, Section, Variable
+            """,
+            [request.year_min, request.year_max],
+        ).df()
+        rows = _aggregate_to_state(rows, average=True)
+    else:
+        rows = DB.execute(
+            """
+            SELECT year, Section, Variable, Value, Percent
+            FROM b10_census
+            WHERE Variable = 'Median Age' AND NAME = ?
+              AND CAST(year AS INTEGER) BETWEEN ? AND ?
+            ORDER BY year, Section, Variable
+            """,
+            [request.name, request.year_min, request.year_max],
+        ).df()
+    return make_response(data=rows, metadata=get_metadata("demographics"))
+
+
+# Unemployment Rate
+@router.post("/load/acs5-db/tidy/unemployment-rate")
+async def tidy_unemployment_rate(request: FilterRequest):
+    if request.name.lower() == "vermont":
+        rows = DB.execute(
+            """
+            SELECT year, Unemployment_Rate AS Value, Unemployment_Rate AS Percent
+            FROM unemployment_rate
+            WHERE NAME LIKE '%County, Vermont'
+              AND CAST(year AS INTEGER) BETWEEN ? AND ?
+            ORDER BY year
+            """,
+            [request.year_min, request.year_max],
+        ).df()
+        # Average unemployment rate across counties for state level
+        if not rows.empty:
+            rows = rows.groupby(['year'], as_index=False).agg(
+                {'Value': 'mean', 'Percent': 'mean'})
+            rows['NAME'] = 'Vermont'
+    elif request.name.lower().endswith(" county, vermont") and request.name.count(',') == 1:
+        # County-level: aggregate town-level data for the specified county
+        rows = DB.execute(
+            """
+            SELECT year, Unemployment_Rate AS Value, Unemployment_Rate AS Percent
+            FROM unemployment_rate
+            WHERE NAME LIKE ?
+              AND CAST(year AS INTEGER) BETWEEN ? AND ?
+            ORDER BY year
+            """,
+            [f"%{request.name}%", request.year_min, request.year_max],
+        ).df()
+        if not rows.empty:
+            rows = rows.groupby(['year'], as_index=False).agg(
+                {'Value': 'mean', 'Percent': 'mean'})
+            rows['NAME'] = request.name
+    elif request.name.count(',') >= 2:
+        # Town-level: names in unemployment_rate include suffixes like "city" or "town"
+        town_name, rest = request.name.split(',', 1)
+        rows = DB.execute(
+            """
+            SELECT year, NAME, Unemployment_Rate AS Value, Unemployment_Rate AS Percent
+            FROM unemployment_rate
+            WHERE NAME LIKE ?
+              AND CAST(year AS INTEGER) BETWEEN ? AND ?
+            ORDER BY year
+            """,
+            [f"{town_name.strip()}%{rest.strip()}",
+             request.year_min, request.year_max],
+        ).df()
+    else:
+        rows = DB.execute(
+            """
+            SELECT year, NAME, Unemployment_Rate AS Value, Unemployment_Rate AS Percent
+            FROM unemployment_rate
+            WHERE NAME = ?
+              AND CAST(year AS INTEGER) BETWEEN ? AND ?
+            ORDER BY year
+            """,
+            [request.name, request.year_min, request.year_max],
+        ).df()
+    return make_response(data=rows, metadata=get_metadata("unemployment_rate"))
+
+
+# Median Earnings
+@router.post("/load/acs5-db/tidy/median-earnings")
+async def tidy_median_earnings(request: FilterRequest):
+    if request.name.lower() == "vermont":
+        rows = DB.execute(
+            """
+            SELECT year, estimate AS Value, variable AS Variable
+            FROM median_earnings
+            WHERE NAME LIKE '%County, Vermont'
+              AND CAST(year AS INTEGER) BETWEEN ? AND ?
+            ORDER BY year
+            """,
+            [request.year_min, request.year_max],
+        ).df()
+        # Average median earnings across counties for state level
+        if not rows.empty:
+            rows = rows.groupby(['year', 'Variable'], as_index=False).agg(
+                {'Value': 'mean'})
+            rows['NAME'] = 'Vermont'
+    elif request.name.lower().endswith(" county, vermont") and request.name.count(',') == 1:
+        # County-level: aggregate town-level data for the specified county
+        rows = DB.execute(
+            """
+            SELECT year, estimate AS Value, variable AS Variable
+            FROM median_earnings
+            WHERE NAME LIKE ?
+              AND CAST(year AS INTEGER) BETWEEN ? AND ?
+            ORDER BY year
+            """,
+            [f"%{request.name}%", request.year_min, request.year_max],
+        ).df()
+        if not rows.empty:
+            rows = rows.groupby(['year', 'Variable'], as_index=False).agg(
+                {'Value': 'mean'})
+            rows['NAME'] = request.name
+    elif request.name.count(',') >= 2:
+        town_name, rest = request.name.split(',', 1)
+        rows = DB.execute(
+            """
+            SELECT year, NAME, estimate AS Value, variable AS Variable
+            FROM median_earnings
+            WHERE NAME LIKE ?
+              AND CAST(year AS INTEGER) BETWEEN ? AND ?
+            ORDER BY year
+            """,
+            [f"{town_name.strip()}%{rest.strip()}",
+             request.year_min, request.year_max],
+        ).df()
+    else:
+        rows = DB.execute(
+            """
+            SELECT year, NAME, estimate AS Value, variable AS Variable
+            FROM median_earnings
+            WHERE NAME = ?
+              AND CAST(year AS INTEGER) BETWEEN ? AND ?
+            ORDER BY year
+            """,
+            [request.name, request.year_min, request.year_max],
+        ).df()
+
+    return make_response(data=rows, metadata=get_metadata("median_earnings"))
+
+
 # ---------------------------------------------------------------------------
 # DP-series combined explorer (DP02 / DP03 / DP04 / DP05)
 # ---------------------------------------------------------------------------
diff --git a/backend/api/routes/post_routes/post_qcew.py b/backend/api/routes/post_routes/post_qcew.py
index 48fd704..94de3be 100644
--- a/backend/api/routes/post_routes/post_qcew.py
+++ b/backend/api/routes/post_routes/post_qcew.py
@@ -22,25 +22,40 @@
 
 @router.post("/load/qcew/employment")
 async def employment_by_sector(request: FilterRequest):
-    county = (request.filters or {}).get("County", [None])[0]
+    county = (request.filters or {}).get("County", [None])[
+        0] if request.filters else None
 
-    query = """
-        SELECT year, quarter, quarter_label, sector, employment_4qma
-        FROM qcew
-        WHERE sector != 'Total'
-        {county_filter}
-        ORDER BY year, quarter, sector
-    """
-    if county:
-        rows: pd.DataFrame = DB.execute(
-            query.format(county_filter="AND County = ?"), [county]
-        ).df()
+    # For state-level (no county specified), aggregate all counties
+    if not county and (not request.name or request.name.lower() == "vermont"):
+        query = """
+            SELECT year, quarter, quarter_label, sector, employment_4qma
+            FROM qcew
+            WHERE sector != 'Total'
+            ORDER BY year, quarter, sector
+        """
+        rows: pd.DataFrame = DB.execute(query).df()
+    elif county:
+        query = """
+            SELECT year, quarter, quarter_label, sector, employment_4qma
+            FROM qcew
+            WHERE sector != 'Total'
+            AND County = ?
+            ORDER BY year, quarter, sector
+        """
+        rows: pd.DataFrame = DB.execute(query, [county]).df()
     else:
-        rows = DB.execute(query.format(county_filter="")).df()
+        # No county and not Vermont state-level - return empty
+        return make_response(data=[], metadata=get_metadata("qcew_employment"))
 
     if rows.empty:
         return make_response(data=[], metadata=get_metadata("qcew_employment"))
 
+    # For state-level, aggregate by summing employment across counties
+    if not county and (not request.name or request.name.lower() == "vermont"):
+        rows = rows.groupby(['year', 'quarter', 'quarter_label', 'sector'], as_index=False).agg({
+            'employment_4qma': 'sum'
+        })
+
     # Pivot to wide format: one row per quarter_label, one column per sector
     wide = rows.pivot_table(
         index=["year", "quarter", "quarter_label"],
diff --git a/design/plots.md b/design/plots.md
index 0f0361b..57cb361 100644
--- a/design/plots.md
+++ b/design/plots.md
@@ -40,6 +40,18 @@ These only render when a table-primary `ChartItem` is toggled to Chart view. The
 **Variables:** `Total Housing Units` (left axis, count), `Renter-Occupied Units` (left axis, count), `Median Home Value` (right axis, dollars).
 **Plot:** three-line `LineChart` with dual Y-axes. Left axis formatted with `toLocaleString()`; right axis in `$Xk`.
 
+### `UnemploymentTrendChart`
+**Variables:** `Unemployment Rate` (left axis, Percentage)
+**Plot:** one-line `LineChart` with one Y-axis. Dual-line comparisons are available. 
+
+### `EarningsTrendChart`
+**Variables:** `Median Earnings` (left axis, $ Amount)
+**Plot:** Three-line `LineChart` (Male vs Female vs All Workers) with one Y-axis. Comparisons are available. 
+
+### `MedianAgeTrendChart`
+**Variables:** `Median Age` (left axis, Age in years)
+**Plot:** One-line `LineChart` with one Y-axis. Dual-line comparisons are available. 
+
 ---
 
 ## Employment Area Chart
diff --git a/frontend/src/components/Charts/TrendCharts.tsx b/frontend/src/components/Charts/TrendCharts.tsx
index db46f86..a40e044 100644
--- a/frontend/src/components/Charts/TrendCharts.tsx
+++ b/frontend/src/components/Charts/TrendCharts.tsx
@@ -121,6 +121,82 @@ export const DemographicsTrendChart = <TData,>({
   );
 };
 
+
+// ---------------------------------------------------------------------------
+// Demographics: Median Age Chart
+// ---------------------------------------------------------------------------
+export const MedianAgeTrendChart = <TData,>({
+  chart,
+}: {
+  chart: ChartItem<TData>;
+}) => {
+  const data = chart.data as any[];
+  const compareData = (chart.compareData ?? []) as any[];
+  if (!data || data.length === 0) return null;
+
+  const years = Array.from(new Set(data.map((r) => r.year))).sort();
+  const labels = chart.chartParams?.legendLabels as
+    | [string, string]
+    | undefined;
+  const cmpName = labels?.[1] ?? 'Comparison';
+
+  const buildPoint = (rows: any[], year: number) => {
+    const find = (label: string) =>
+      rows.find((r) => r.year === year && r.Variable === label)?.Value ?? null;
+    return {'Median Age': find('Median Age')};
+  };
+
+  const plotData = years.map((year) => ({
+    year,
+    ...buildPoint(data, year),
+    ...(compareData.length > 0
+      ? {
+          'Median Age (cmp)': buildPoint(compareData, year)['Median Age'],
+        }
+      : {}),
+  }));
+
+  return (
+    <>
+      {compareData.length > 0 && <CompareNote name={cmpName} />}
+      <ResponsiveContainer width="100%" height="100%">
+        <LineChart
+          data={plotData}
+          margin={{ top: 10, right: 20, left: 0, bottom: 5 }}
+        >
+          <CartesianGrid strokeDasharray="3 3" stroke="#e0d8cc" />
+          <XAxis dataKey="year" tick={{ fontSize: 12 }} />
+          <YAxis tick={{ fontSize: 12 }} domain={['auto', 'auto']}
+            tickFormatter={(value) => Number(value).toFixed(0)} />
+          <Tooltip formatter={(val: any) => val != null ? `${Number(val).toFixed(1)} years` : '—'}/>
+          <Legend />
+          <Line
+            type="monotone"
+            dataKey="Median Age"
+            stroke="#154734"
+            strokeWidth={2}
+            dot={false}
+          />
+          {compareData.length > 0 && (
+            <>
+              <Line
+                type="monotone"
+                dataKey="Median Age (cmp)"
+                name="Median Age"
+                stroke="#154734"
+                strokeWidth={1.5}
+                strokeDasharray="6 4"
+                dot={false}
+                legendType="none"
+              />
+            </>
+          )}
+        </LineChart>
+      </ResponsiveContainer>
+    </>
+  );
+};
+
 // ---------------------------------------------------------------------------
 // Education: all attainment levels except "Some College, No Degree"
 // ---------------------------------------------------------------------------
@@ -322,6 +398,202 @@ export const HousingTrendChart = <TData,>({
   );
 };
 
+
+// ---------------------------------------------------------------------------
+// Economics: Unemployment Rate
+// ---------------------------------------------------------------------------
+
+export const UnemploymentTrendChart = <TData,>({
+  chart,
+}: {
+  chart: ChartItem<TData>;
+}) => {
+  const data = chart.data as any[];
+  const compareData = (chart.compareData ?? []) as any[];
+  if (!data || data.length === 0) return null;
+
+  const years = Array.from(new Set(data.map((r) => r.year))).sort();
+  const labels = chart.chartParams?.legendLabels as
+    | [string, string]
+    | undefined;
+  const cmpName = labels?.[1] ?? 'Comparison';
+
+  const buildPoint = (rows: any[], year: number) => {
+    const row = rows.find((r) => r.year === year);
+    return {
+      'Unemployment Rate': row?.Value ?? null,
+    };
+  };
+
+  const plotData = years.map((year) => ({
+    year,
+    ...buildPoint(data, year),
+    ...(compareData.length > 0
+      ? {
+          'Unemployment Rate (cmp)': buildPoint(compareData, year)['Unemployment Rate'],
+        }
+      : {}),
+  }));
+
+  return (
+    <>
+      {compareData.length > 0 && <CompareNote name={cmpName} />}
+      <ResponsiveContainer width="100%" height="100%">
+        <LineChart
+          data={plotData}
+          margin={{ top: 10, right: 20, left: 0, bottom: 5 }}
+        >
+          <CartesianGrid strokeDasharray="3 3" stroke="#e0d8cc" />
+          <XAxis dataKey="year" tick={{ fontSize: 12 }} />
+          <YAxis unit="%" tick={{ fontSize: 12 }} domain={['auto', 'auto']} />
+          <Tooltip formatter={(val: any) => (val != null ? `${val}%` : '—')} />
+          <Legend />
+          <Line
+            type="monotone"
+            dataKey="Unemployment Rate"
+            stroke="#154734"
+            strokeWidth={2}
+            dot={false}
+          />
+          {compareData.length > 0 && (
+            <>
+              <Line
+                type="monotone"
+                dataKey="Unemployment Rate (cmp)"
+                name="Unemployment Rate"
+                stroke="#e07b39"
+                strokeWidth={1.5}
+                strokeDasharray="6 4"
+                dot={false}
+                legendType="none"
+              />
+            </>
+          )}
+        </LineChart>
+      </ResponsiveContainer>
+    </>
+  );
+};
+
+// ---------------------------------------------------------------------------
+// Economics: Median Earnings (Male vs Female vs All Workers)
+// ---------------------------------------------------------------------------
+
+export const EarningsTrendChart = <TData,>({
+  chart,
+}: {
+  chart: ChartItem<TData>;
+}) => {
+  const data = chart.data as any[];
+  const compareData = (chart.compareData ?? []) as any[];
+  if (!data || data.length === 0) return null;
+
+  const years = Array.from(new Set(data.map((r) => r.year))).sort();
+  const labels = chart.chartParams?.legendLabels as
+    | [string, string]
+    | undefined;
+  const cmpName = labels?.[1] ?? 'Comparison';
+
+  const buildPoint = (rows: any[], year: number) => {
+    const find = (label: string) =>
+  rows.find(
+    (r) => String(r.year) === String(year) &&
+           r.Variable === label
+  )?.Value ?? null;
+    return {
+      'Male Full-Time Workers': find('DP03_0093'),
+      'Female Full-Time Workers': find('DP03_0094'),
+      'All Workers': find('DP03_0092'),
+    };
+  };
+
+  const plotData = years.map((year) => ({
+    year,
+    ...buildPoint(data, year),
+    ...(compareData.length > 0
+      ? {
+          'Male Full-Time Workers (cmp)': buildPoint(compareData, year)['Male Full-Time Workers'],
+          'Female Full-Time Workers (cmp)': buildPoint(compareData, year)['Female Full-Time Workers'],
+          'All Workers (cmp)': buildPoint(compareData, year)['All Workers'],
+        }
+      : {}),
+  }));
+  return (
+    <>
+      {compareData.length > 0 && <CompareNote name={cmpName} />}
+      <ResponsiveContainer width="100%" height="100%">
+        <LineChart
+          data={plotData}
+          margin={{ top: 10, right: 20, left: 0, bottom: 5 }}
+        >
+          <CartesianGrid strokeDasharray="3 3" stroke="#e0d8cc" />
+          <XAxis dataKey="year" tick={{ fontSize: 12 }} />
+          <YAxis tick={{ fontSize: 12 }} domain={['auto', 'auto']} tickFormatter={(v) => 
+            `$${(v / 1000).toFixed(0)}k`} />
+          <Tooltip formatter={(value: any) => 
+            value != null? `$${Number(value).toLocaleString('en-US', {maximumFractionDigits: 0,})}`: '—'} />
+          <Legend />
+          <Line
+            type="monotone"
+            dataKey="Male Full-Time Workers"
+            stroke="#1432ab"
+            strokeWidth={2}
+            dot={false}
+          />
+          <Line
+            type="monotone"
+            dataKey="Female Full-Time Workers"
+            stroke="#e03fd0"
+            strokeWidth={2}
+            dot={false}
+          />
+          <Line
+            type="monotone"
+            dataKey="All Workers"
+            stroke="#494b4d60"
+            strokeWidth={2}
+            dot={false}
+          />
+          {compareData.length > 0 && (
+            <>
+              <Line
+                type="monotone"
+                dataKey="Male Full-Time Workers (cmp)"
+                name="Male Full-Time Workers (cmp)"
+                stroke="#1432ab"
+                strokeWidth={1.5}
+                strokeDasharray="6 4"
+                dot={false}
+                legendType="none"
+              />
+              <Line
+                type="monotone"
+                dataKey="Female Full-Time Workers (cmp)"
+                name="Female Full-Time Workers (cmp)"
+                stroke="#e03fd0"
+                strokeWidth={1.5}
+                strokeDasharray="6 4"
+                dot={false}
+                legendType="none"
+              />
+              <Line
+                type="monotone"
+                dataKey="All Workers (cmp)"
+                name="All Workers (cmp)"
+                stroke="#494b4d60"
+                strokeWidth={1.5}
+                strokeDasharray="6 4"
+                dot={false}
+                legendType="none"
+              />
+            </>
+          )}
+        </LineChart>
+      </ResponsiveContainer>
+    </>
+  );
+};
+
 // ---------------------------------------------------------------------------
 // Generic two-location trend chart for the DP-combined explorer
 // data:        [{year, Value}] for side A
diff --git a/frontend/src/components/Charts/configs/ChartDefs.tsx b/frontend/src/components/Charts/configs/ChartDefs.tsx
index 46a69c5..c51d005 100644
--- a/frontend/src/components/Charts/configs/ChartDefs.tsx
+++ b/frontend/src/components/Charts/configs/ChartDefs.tsx
@@ -90,6 +90,21 @@ export const chartDefs: ChartDef[] = [
       extraParams: { year_min: 2010, year_max: 2023 },
     },
   },
+  {
+    id: 'median_age',
+    title: 'Median Age',
+    url: `${BASE_API_URL}/load/acs5-db/tidy/demographics/median-age`,
+    xField: '',
+    yField: '',
+    subtype: 'renderTableEstimates',
+    trendChart: 'MedianAgeTrendChart',
+    categories: ['Demographics'],
+    filterKey: '',
+    dataKey: '',
+    tableConfig: {
+      extraParams: { year_min: 2010, year_max: 2023 },
+    },
+  },
   {
     id: 'education',
     title: 'Educational Attainment — Percent',
@@ -179,6 +194,38 @@ export const chartDefs: ChartDef[] = [
       extraParams: { year_min: 2010, year_max: 2023 },
     },
   },
+  // Unemployment Rate
+  {
+    id: 'unemployment_rate',
+    title: 'Unemployment Rate — Percent',
+    url: `${BASE_API_URL}/load/acs5-db/tidy/unemployment-rate`,
+    xField: '',
+    yField: '',
+    subtype: 'renderTable',
+    trendChart: 'UnemploymentTrendChart',
+    categories: ['Labor & Economy'],
+    filterKey: '',
+    dataKey: '',
+    tableConfig: {
+      extraParams: { year_min: 2010, year_max: 2023 },
+    },
+  },
+  // Median Earnings
+  {
+    id: 'earnings',
+    title: 'Median Earnings - Value',
+    url: `${BASE_API_URL}/load/acs5-db/tidy/median-earnings`,
+    xField: '',
+    yField: '',
+    subtype: 'renderTableEstimates',
+    trendChart: 'EarningsTrendChart',
+    categories: ['Labor & Economy'],
+    filterKey: '',
+    dataKey: '',
+    tableConfig: {
+      extraParams: { year_min: 2010, year_max: 2023 },
+    },
+  },
   // Employment (QCEW quarterly, stacked by sector)
   {
     id: 'employment',
diff --git a/frontend/src/components/Charts/index.tsx b/frontend/src/components/Charts/index.tsx
index 3e49ebd..20c7e68 100644
--- a/frontend/src/components/Charts/index.tsx
+++ b/frontend/src/components/Charts/index.tsx
@@ -13,8 +13,11 @@ export {
 } from './DemographicsTable';
 export {
   DemographicsTrendChart,
+  MedianAgeTrendChart,
   EducationTrendChart,
   HousingTrendChart,
+  UnemploymentTrendChart,
+  EarningsTrendChart,
   DPTrendChart,
 } from './TrendCharts';
 export { EmploymentAreaChart } from './EmploymentAreaChart';