U.S. business and household records back to 1997

What is Data Axle?

Data Axle is one of the largest U.S. business and consumer data providers, with roots going back to the company that built the original American business directory. Data Axle compiles billions of business and household records each year from public filings, directories, surveys, and partner sources, then standardizes and verifies them into structured datasets. On Dewey, the focus is the historical archive: decades of snapshots of U.S. firms, households, and neighborhoods built for longitudinal research.

‍

What academic researchers should know about Data Axle data

Two distinct historical archives are available on Dewey. The Historical Consumer Data file covers household level records from 2006 to present with fields for household income, home value, years in residence, and location, and assigns every household a persistent Family ID so researchers can track how the same household changes across years. The Historical Business and Residential Datasets extend further back, from 1997 through the prior year, and split into two collections: a business file with company name, employee size, industry, address, geocodes, years in business, SIC and NAICS codes, sales volume, and census tract and block, and a residential file with household addresses, dwelling unit information, household and location indicators, demographic and neighborhood characteristics, and geocodes. Records carry persistent IDs, which makes year over year comparisons reliable without manual data reconstruction. Data is compiled and verified from multiple sources, with the provider processing over 29 billion records each year.

‍

Why academic researchers choose Data Axle on Dewey

The big draw for researchers is longitudinal continuity. Most business and consumer data products are snapshots, useful for one moment in time but painful when you need to follow firms or households across decades. Data Axle's persistent IDs at both the household and establishment level let researchers run year-over-year and decade-over-decade analyses without writing fragile record linkage code, which is why the data has supported peer-reviewed work on inequality, neighborhood effects, and financial behavior. The combination of business records (with sales volume, employment, and SIC and NAICS codes) and residential records (with demographic and neighborhood attributes) on the same provider also means you can study firms and households together, instead of stitching together separate datasets with different vintages and definitions. Pairing Data Axle with ATTOM property records, BrightQuery firm financials, ClimateCheck climate risk ratings, or SafeGraph foot traffic supports multidisciplinary work on local economic change, housing markets, and small business dynamics.

‍

Data Axle academic research ideas and use cases

Neighborhood change. Data Axle's longitudinal household and business records make it possible to define and date neighborhood change with much more precision than decennial Census waves alone. Researchers can use household income, home value, and years in residence to trace which neighborhoods are gaining and losing wealth, then layer in business openings and closures to study which kinds of retail and service activity follow or precede those shifts.

Small business dynamics and entrepreneurship. The business file's coverage of company name, employment, sales volume, industry codes, and years in business is well suited to research on firm entry and exit, immigrant entrepreneurship, and the spatial dynamics of small business. Researchers can study the role of local conditions in business formation, how restaurants and retail respond to demographic shifts, or how shocks like recessions and natural disasters reshape local industry mix over time.

Financial behavior, credit access, and household outcomes. Linking household income, home value, and years in residence to ZIP code, census tract, and census block geographies opens up research on financial inclusion, credit access, and the long-term effects of housing stability and instability. Pairing Data Axle with ATTOM property records or BrightQuery firm data is useful for studies that need to connect households, employers, and the local financial environment.

Migration, mobility, and population change. With households tracked across years through persistent Family IDs, researchers can study where people move, how long they stay, and what happens to the neighborhoods they leave and arrive in. Combined with ClimateCheck climate risk ratings, this design is also useful for emerging research on climate driven migration and the demographic reshuffling that follows wildfires, floods, and heat exposure.

Local economic forecasting and natural experiments. Decades of consistently structured business and household records support natural experiment designs around plant openings and closures, infrastructure investments, federal programs, and local policy changes. Researchers studying place based interventions, opportunity zones, or the local effects of federal funding can use Data Axle as a continuous baseline against which to measure outcomes for both residents and firms.

‍

Dive deeper with Dewey documentation

Detailed information on onboarding with Dewey, data partner details, and technical documentation on data access.

View documentation

‍