US energy prices and inflation in September 2026 compared to Bid… — Data analysis

US energy prices and inflation in September 2026 compared to Biden administration 2021-2025

Model: Kimi K2.6 Checked on: 21/09/2026

US energy prices and inflation in September 2026 compared to the Biden administration (2021‑2025)

OECD consumer price data for the United States are currently available only through August 2026; September 2026 figures have not yet been published¹.

Headline inflation

The latest year‑over‑year all‑items CPI inflation rate stood at 3.40 % in August 2026².
During the Biden administration (averaging the full calendar years 2021‑2024 that Biden was in office), headline inflation averaged 4.95 %².
Consequently, the most recent reading is roughly 1.5 percentage points below the Biden‑era average.
In level terms, the all‑items price index rose 20.7 % during the Biden years (January 2021 to December 2024) and has climbed a further 6.1 % since then, for a total increase of 28.1 % from January 2021 to August 2026².

Energy inflation

Energy is tracked through the combined OECD “Energy” category (housing fuels plus transport fuels)³.

Energy prices were rising far faster than the overall basket: 16.28 % year‑over‑year in August 2026².
During 2021‑2024, energy inflation averaged 10.27 %².
The August 2026 energy inflation rate is therefore about 6 percentage points higher than the average under the Biden administration.
Cumulatively, the energy price index surged 30.8 % during the Biden years and has jumped an additional 22.9 % since December 2024, bringing the total increase from January 2021 to August 2026 to 60.7 %².

Sub‑components (available through December 2024 only)

Housing fuels (electricity, gas and other fuels) averaged 7.46 % annual inflation during 2021‑2024, slowing to 2.74 % by December 2024².
Transport fuels (gasoline and motor oil) averaged 13.12 % annual inflation during 2021‑2024, but had cooled to just 1.66 % by December 2024².
The sharp re‑acceleration of the combined energy index to 16.3 % in August 2026 therefore reflects a strong post‑2024 resurgence in energy costs rather than a continuation of the late‑Biden trend².

A visual summary of headline versus energy price levels, year‑over‑year rates, and the cumulative change since January 2021 is shown in the chart generated during the analysis².

Code Run 1

Dataset: DSD_PRICES@DF_PRICES_ALL

Dataset filters for `DSD_PRICES@DF_PRICES_ALL`

Filter	Value(s)
REF_AREA	USA
FREQ	M
EXPENDITURE	_T, CP045_0722, CP045, CP0722

Program output

Max time period: 2026-08
_T: max = 2026-08
CP045_0722: max = 2026-08
CP045: max = 2024-12
CP0722: max = 2024-12

Index levels latest:
_T: latest = 2026-08, value = 141.332
  Jan 2021 = 110.364
  Aug 2026 = 141.332
CP045_0722: latest = 2026-08, value = 162.655
  Jan 2021 = 101.210
  Aug 2026 = 162.655
CP045: latest = 2024-12, value = 135.827
CP0722: latest = 2024-12, value = 135.269

YoY growth latest:
_T: latest = 2026-08, value = 3.397%
CP045_0722: latest = 2026-08, value = 16.277%
CP045: latest = 2024-12, value = 2.740%
CP0722: latest = 2024-12, value = 1.655%

Code

import pandas as pd

file_path = DATASET_FILE
df = pd.read_csv(file_path)

# Get all data for USA without time restrictions
us_df = df[df['REF_AREA'] == 'USA'].copy()

print("Max time period:", us_df['TIME_PERIOD'].max())

# Check if any expenditure has 2026-09
for exp in ['_T', 'CP045_0722', 'CP045', 'CP0722']:
    sub = us_df[us_df['EXPENDITURE'] == exp]
    print(f"{exp}: max = {sub['TIME_PERIOD'].max()}")

# Let's focus on what we need
# Extract N/_Z index levels and N/GY YoY growth
idx_df = us_df[(us_df['ADJUSTMENT'] == 'N') & (us_df['TRANSFORMATION'] == '_Z') & (us_df['METHODOLOGY'] == 'N')]
idx_df = idx_df[['EXPENDITURE', 'TIME_PERIOD', 'value']].rename(columns={'value': 'index_level'})

yoy_df = us_df[(us_df['ADJUSTMENT'] == 'N') & (us_df['TRANSFORMATION'] == 'GY') & (us_df['METHODOLOGY'] == 'N')]
yoy_df = yoy_df[['EXPENDITURE', 'TIME_PERIOD', 'value']].rename(columns={'value': 'yoy_growth'})

# Also get _T with S/_Z (seasonally adjusted index)
sa_idx = us_df[(us_df['EXPENDITURE'] == '_T') & (us_df['ADJUSTMENT'] == 'S') & (us_df['TRANSFORMATION'] == '_Z')]
sa_idx = sa_idx[['EXPENDITURE', 'TIME_PERIOD', 'value']].rename(columns={'value': 'sa_index_level'})

print("\nIndex levels latest:")
for exp in ['_T', 'CP045_0722', 'CP045', 'CP0722']:
    sub = idx_df[idx_df['EXPENDITURE'] == exp]
    if not sub.empty:
        print(f"{exp}: latest = {sub['TIME_PERIOD'].max()}, value = {sub[sub['TIME_PERIOD'] == sub['TIME_PERIOD'].max()]['index_level'].values[0]:.3f}")
        if exp == 'CP045_0722':
            print(f"  Jan 2021 = {sub[sub['TIME_PERIOD'] == '2021-01']['index_level'].values[0]:.3f}")
            print(f"  Aug 2026 = {sub[sub['TIME_PERIOD'] == '2026-08']['index_level'].values[0]:.3f}")
        if exp == '_T':
            print(f"  Jan 2021 = {sub[sub['TIME_PERIOD'] == '2021-01']['index_level'].values[0]:.3f}")
            print(f"  Aug 2026 = {sub[sub['TIME_PERIOD'] == '2026-08']['index_level'].values[0]:.3f}")

print("\nYoY growth latest:")
for exp in ['_T', 'CP045_0722', 'CP045', 'CP0722']:
    sub = yoy_df[yoy_df['EXPENDITURE'] == exp]
    if not sub.empty:
        latest = sub['TIME_PERIOD'].max()
        print(f"{exp}: latest = {latest}, value = {sub[sub['TIME_PERIOD'] == latest]['yoy_growth'].values[0]:.3f}%")

Code Run 2

Dataset: DSD_PRICES@DF_PRICES_ALL

Images

us_inflation_energy_analysis.svg

Dataset filters for `DSD_PRICES@DF_PRICES_ALL`

Filter	Value(s)
REF_AREA	USA
FREQ	M
EXPENDITURE	_T, CP045_0722, CP045, CP0722

Program output


Headline CPI (_T):
  Jan 2021 = 110.364
  Dec 2024 = 133.157  (+20.7% during Biden)
  Aug 2026 = 141.332  (+6.1% after Biden)
  Total Jan 2021 to Aug 2026 = +28.1%

Energy (CP045_0722):
  Jan 2021 = 101.210
  Dec 2024 = 132.338  (+30.8% during Biden)
  Aug 2026 = 162.655  (+22.9% after Biden)
  Total Jan 2021 to Aug 2026 = +60.7%


=== Average YoY Inflation Rates ===

Headline CPI (_T):
  Average YoY 2021-2024 (Biden): 4.95%
  Latest YoY (2026-08): 3.40%

Energy (CP045_0722):
  Average YoY 2021-2024 (Biden): 10.27%
  Latest YoY (2026-08): 16.28%
  Latest vs Biden avg: +6.01 percentage points

Housing fuels (CP045):
  Average YoY 2021-2024 (Biden): 7.46%
  Latest YoY (2024-12): 2.74%

Transport fuels (CP0722):
  Average YoY 2021-2024 (Biden): 13.12%
  Latest YoY (2024-12): 1.66%


Chart saved to us_inflation_energy_analysis.svg

Code

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

file_path = DATASET_FILE
df = pd.read_csv(file_path)

# Filter relevant data
us_df = df[(df['REF_AREA'] == 'USA') & (df['METHODOLOGY'] == 'N')].copy()

# Index levels (N/_Z)
idx = us_df[(us_df['ADJUSTMENT'] == 'N') & (us_df['TRANSFORMATION'] == '_Z')][['EXPENDITURE', 'TIME_PERIOD', 'value']].copy()
idx.rename(columns={'value': 'index'}, inplace=True)
idx['TIME_PERIOD'] = pd.to_datetime(idx['TIME_PERIOD'])
idx = idx.sort_values(['EXPENDITURE', 'TIME_PERIOD'])

# YoY growth (N/GY)
yoy = us_df[(us_df['ADJUSTMENT'] == 'N') & (us_df['TRANSFORMATION'] == 'GY')][['EXPENDITURE', 'TIME_PERIOD', 'value']].copy()
yoy.rename(columns={'value': 'yoy_pct'}, inplace=True)
yoy['TIME_PERIOD'] = pd.to_datetime(yoy['TIME_PERIOD'])
yoy = yoy.sort_values(['EXPENDITURE', 'TIME_PERIOD'])

# Seasonally adjusted all-items index (S/_Z)
sa_idx = us_df[(us_df['EXPENDITURE'] == '_T') & (us_df['ADJUSTMENT'] == 'S') & (us_df['TRANSFORMATION'] == '_Z')][['TIME_PERIOD', 'value']].copy()
sa_idx.rename(columns={'value': 'sa_index'}, inplace=True)
sa_idx['TIME_PERIOD'] = pd.to_datetime(sa_idx['TIME_PERIOD'])
sa_idx = sa_idx.sort_values('TIME_PERIOD')

# Helper function to get index value at a specific date
def get_index(exp, date_str):
    sub = idx[(idx['EXPENDITURE'] == exp) & (idx['TIME_PERIOD'] == date_str)]
    if sub.empty:
        return None
    return sub['index'].values[0]

# Define dates
jan_2021 = pd.Timestamp('2021-01-01')
dec_2024 = pd.Timestamp('2024-12-01')
aug_2026 = pd.Timestamp('2026-08-01')

# Calculate cumulative changes
for exp, label in [('_T', 'Headline CPI'), ('CP045_0722', 'Energy')]:
    start = get_index(exp, jan_2021)
    mid = get_index(exp, dec_2024)
    end = get_index(exp, aug_2026)
    
    if start and end:
        total_change = (end / start - 1) * 100
        print(f"\n{label} ({exp}):")
        print(f"  Jan 2021 = {start:.3f}")
        if mid:
            biden_change = (mid / start - 1) * 100
            post_change = (end / mid - 1) * 100
            print(f"  Dec 2024 = {mid:.3f}  (+{biden_change:.1f}% during Biden)")
            print(f"  Aug 2026 = {end:.3f}  (+{post_change:.1f}% after Biden)")
        else:
            print(f"  Aug 2026 = {end:.3f}")
        print(f"  Total Jan 2021 to Aug 2026 = +{total_change:.1f}%")

# Average YoY during Biden years (2021-2025, but we'll use 2021-2024 full years since Dec 2024 is last full year)
# Actually let's compute Jan 2021 - Dec 2024 average, and also Jan 2021 - Jan 2025 if possible
print("\n\n=== Average YoY Inflation Rates ===")
for exp, label in [('_T', 'Headline CPI'), ('CP045_0722', 'Energy'), ('CP045', 'Housing fuels'), ('CP0722', 'Transport fuels')]:
    sub = yoy[yoy['EXPENDITURE'] == exp].copy()
    if sub.empty:
        continue
    sub['year'] = sub['TIME_PERIOD'].dt.year
    
    # Biden period: 2021-2024 (full years in office)
    biden_avg = sub[(sub['year'] >= 2021) & (sub['year'] <= 2024)]['yoy_pct'].mean()
    # Latest available month
    latest = sub.iloc[-1]
    
    print(f"\n{label} ({exp}):")
    if biden_avg is not np.nan:
        print(f"  Average YoY 2021-2024 (Biden): {biden_avg:.2f}%")
    print(f"  Latest YoY ({latest['TIME_PERIOD'].strftime('%Y-%m')}): {latest['yoy_pct']:.2f}%")
    
    # If energy and headline, show ratio
    if exp == 'CP045_0722' and biden_avg is not np.nan:
        print(f"  Latest vs Biden avg: {latest['yoy_pct'] - biden_avg:+.2f} percentage points")

# Create visualizations
fig, axes = plt.subplots(2, 2, figsize=(14, 10))

# Plot 1: Index levels for Headline CPI and Energy
ax = axes[0, 0]
for exp, label, color in [('_T', 'Headline CPI', 'blue'), ('CP045_0722', 'Energy', 'red')]:
    sub = idx[idx['EXPENDITURE'] == exp].copy()
    ax.plot(sub['TIME_PERIOD'], sub['index'], label=label, color=color, linewidth=2)

ax.axvline(pd.Timestamp('2021-01-01'), color='gray', linestyle='--', alpha=0.7, label='Biden term start')
ax.axvline(pd.Timestamp('2025-01-01'), color='gray', linestyle='-.', alpha=0.7, label='Biden term end')
ax.set_title('US Consumer Price Index Levels (Jan 2020 = ~100 base)')
ax.set_ylabel('Index (2015=100)')
ax.legend()
ax.grid(True, alpha=0.3)

# Plot 2: YoY growth rates
ax = axes[0, 1]
for exp, label, color in [('_T', 'Headline CPI', 'blue'), ('CP045_0722', 'Energy', 'red')]:
    sub = yoy[yoy['EXPENDITURE'] == exp].copy()
    ax.plot(sub['TIME_PERIOD'], sub['yoy_pct'], label=label, color=color, linewidth=2)
    # Add average line for 2021-2024
    biden_avg = sub[(sub['TIME_PERIOD'].dt.year >= 2021) & (sub['TIME_PERIOD'].dt.year <= 2024)]['yoy_pct'].mean()
    ax.axhline(biden_avg, color=color, linestyle=':', alpha=0.7, label=f'{label} avg 2021-24 ({biden_avg:.1f}%)')

ax.axvline(pd.Timestamp('2021-01-01'), color='gray', linestyle='--', alpha=0.7)
ax.axvline(pd.Timestamp('2025-01-01'), color='gray', linestyle='-.', alpha=0.7)
ax.set_title('US YoY Inflation Rates (%)')
ax.set_ylabel('YoY % Change')
ax.legend(fontsize=8)
ax.grid(True, alpha=0.3)

# Plot 3: Cumulative change since Jan 2021
ax = axes[1, 0]
for exp, label, color in [('_T', 'Headline CPI', 'blue'), ('CP045_0722', 'Energy', 'red')]:
    sub = idx[idx['EXPENDITURE'] == exp].copy()
    sub = sub[sub['TIME_PERIOD'] >= '2020-01-01']
    base = sub[sub['TIME_PERIOD'] == '2021-01-01']['index'].values[0]
    sub['cum_change'] = (sub['index'] / base - 1) * 100
    ax.plot(sub['TIME_PERIOD'], sub['cum_change'], label=label, color=color, linewidth=2)

ax.axvline(pd.Timestamp('2021-01-01'), color='gray', linestyle='--', alpha=0.7)
ax.axvline(pd.Timestamp('2025-01-01'), color='gray', linestyle='-.', alpha=0.7)
ax.set_title('Cumulative Price Change Since Jan 2021 (%)')
ax.set_ylabel('% Change from Jan 2021')
ax.legend()
ax.grid(True, alpha=0.3)

# Plot 4: Energy vs Headline inflation gap
ax = axes[1, 1]
merged = pd.merge(
    yoy[yoy['EXPENDITURE'] == '_T'][['TIME_PERIOD', 'yoy_pct']].rename(columns={'yoy_pct': 'headline'}),
    yoy[yoy['EXPENDITURE'] == 'CP045_0722'][['TIME_PERIOD', 'yoy_pct']].rename(columns={'yoy_pct': 'energy'}),
    on='TIME_PERIOD'
)
merged['gap'] = merged['energy'] - merged['headline']
ax.fill_between(merged['TIME_PERIOD'], merged['gap'], alpha=0.3, color='purple')
ax.plot(merged['TIME_PERIOD'], merged['gap'], color='purple', linewidth=2)
ax.axhline(0, color='black', linestyle='-', alpha=0.3)
ax.axvline(pd.Timestamp('2021-01-01'), color='gray', linestyle='--', alpha=0.7)
ax.axvline(pd.Timestamp('2025-01-01'), color='gray', linestyle='-.', alpha=0.7)
ax.set_title('Energy Inflation Minus Headline Inflation (pp)')
ax.set_ylabel('Percentage Points')
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('us_inflation_energy_analysis.svg')
plt.close()

print("\n\nChart saved to us_inflation_energy_analysis.svg")

OECD dataset: Consumer price indices (CPIs, HICPs), COICOP 1999

US energy prices and inflation in September 2026 compared to Biden administration 2021-2025

Headline inflation

Energy inflation

Sub‑components (available through December 2024 only)

Dataset filters for DSD_PRICES@DF_PRICES_ALL

Program output

Code

Images

Dataset filters for DSD_PRICES@DF_PRICES_ALL

Program output

Code

Sub‑components (available through December 2024 only)

Dataset filters for `DSD_PRICES@DF_PRICES_ALL`

Dataset filters for `DSD_PRICES@DF_PRICES_ALL`