US Corporate Debt-to-GDP — Daily CSV Download (Corporate Leverage)

Corporate debt relative to GDP measures the leverage embedded in the US business sector. When this ratio rises sharply — as it did before 2001, 2008, and 2020 — it signals that corporate balance sheets are stretched and vulnerable to tightening financial conditions. The ratio has structural uptrend since the 1980s, reflecting financialization and the leveraged buyback cycle. FRED series BCNSDODNS divided by GDP.

Dataset: US Corporate Debt-to-GDP Ratio (1950–2026) · Updated —


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Source: FRED series BCNSDODNS · Federal Reserve — Financial Accounts (Z.1) via FRED

Macro Takeaway

This indicator is a key component of the macro-financial monitoring framework. Its current level relative to its historical distribution — captured in the percentile and z-score above — provides immediate context for whether conditions are historically normal, stretched, or compressed.

Cross-referencing with the high yield credit spreads and the bank lending standards helps situate this indicator within the broader macro regime.

Dataset Overview

IndicatorUS Corporate Debt-to-GDP Ratio (1950–2026)
GeographyUnited States
FrequencyQuarterly
Period1950–2026
Variablesdate, corporate_debt_gdp_pct
FormatCSV, Excel (XLSX)
SourcesFederal Reserve — Financial Accounts (Z.1) via FRED
Last updated—

Dataset Variables

The CSV and Excel files contain the following columns.

ColumnTypeDescription
dateDate (YYYY-MM-DD)Observation date (quarterly)
corporate_debt_gdp_pctFloatNonfinancial corporate debt as % of GDP

Column names match the CSV headers exactly.

Download the Complete Dataset

The full dataset is available in CSV and Excel formats.

FRED Direct CSV Access

The underlying data is available from FRED under series code BCNSDODNS:

https://fred.stlouisfed.org/graph/fredgraph.csv?id=BCNSDODNS

Direct CSV Access — Eco3min Structured Dataset

https://eco3min.fr/dataset/us-corporate-debt-gdp.csv

This URL returns the complete dataset in CSV format. It can be used directly in pandas, R, curl, or any data tool.

Using the Dataset in Python

import pandas as pd

url = "https://eco3min.fr/dataset/us-corporate-debt-gdp.csv"
df = pd.read_csv(url, parse_dates=["date"])

print(df.head())
print(df["corporate_debt_gdp_pct"].describe())

Using the Dataset in R

library(readr)

url <- "https://eco3min.fr/dataset/us-corporate-debt-gdp.csv"
df <- read_csv(url)

head(df)
summary(df$corporate_debt_gdp_pct)

Both examples load the dataset directly from the URL — no download or API key required.

Methodology

Nonfinancial corporate business debt (FRED: BCNSDODNS) divided by nominal GDP (FRED: GDP). Both series are quarterly. The ratio captures total credit market instruments — bonds, loans, commercial paper — relative to output.

This dataset is updated quarterly via automated pull from the FRED API.

Historical Regimes

Historical regime analysis for this dataset will be added in a future update. The key stats block above provides immediate context for the current reading relative to the full historical distribution.

Related Macroeconomic Datasets

Corporate debt-to-GDP provides the structural context for credit risk analysis. High leverage ratios mean the corporate sector is more sensitive to rate hikes, spread widening, and earnings downturns — the conditions that precede credit events.

Related Research

Corporate leverage is the slow-moving variable behind fast-moving credit spreads. When debt-to-GDP is elevated, the credit system is fragile — and spread widening episodes tend to be more severe.

Macroeconomic Dataset Hub

This dataset is part of the Eco3min macro-financial data repository.

Explore the Eco3min Dataset Hub

Sources

  • Federal Reserve — Financial Accounts (Z.1) via FRED

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