Enabling Economic Statistics 

Modernization

Jessica Wellwood, U.S. Census Bureau

October 24, 2023

2023 Research and Policy Conference

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Note: Any opinions and conclusions expressed herein are those of the author and do not reflect the 

views of the U.S. Census Bureau. The Census Bureau has reviewed this data product to ensure 

appropriate access, use, and disclosure avoidance protection of the confidential source data 

(Disclosure Review Board (DRB) approval number: CBDRB-FY23-ESMD002-031).
Transformation at the Census Bureau

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PROBLEM

Declining 

Response Rates

Shrinking

Budgets

Increased 

demand for 

data
3

Leverage 

existing data

Promote 

Innovation

Simplify 

Processes

Transformation at the Census Bureau

SOLUTION

DATA CENTRIC
4

Linkable in 

nature

Agile in 

Structure

Accessible for 

research or 

production

Transformation at the Census Bureau

VISION 

To create Enterprise-wide frames that are... 
GEOSPATIAL JOB

BUSINESS

List of 

Addresses and 

their attributes 

(i.e., address 

has a non- residential use)

List of 

Businesses and 

their attributes 

(i.e., business 

has employees)

List of People 

and their 

attributes (i.e., 

person is a 

worker)

ENTERPRISE FRAMES

Linkage 

Infrastructure

Creating an Infrastructure to Modernize the Census Bureau’s Statistical Foundation

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Frames Program
The Business Frame is NOT the Business Register!

Business Register

Master list of businesses with associated core 

attributes 

• Full Coverage of business population

• Linkages between BR statistical units 

(e.g. Establishments, Enterprises)

• Updated from administrative data (IRS, 

SSA, BLS) and select census programs

• Data are owned by Business Register Staff

Business FRAME

Collection of auxiliary data, harmonized business 

data across multiple sources, linked to the BR, and 

stored in a central location

• In-depth data specific to a defined subpopulation of Business Entities* 

• Linkages across data sources and time (LBD)

• Data owned by designated data provider

• All data sources welcome!

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*Examples of Business Entities are governments, publicly traded companies, records in a specific NAICS

Business REGISTER
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Admin

Survey

Third 

party

Public 

Data

Business Register

Survey Data

Third Party Data

Admin Data

Public Data

And More!

Collection of Data 

across multiple 

sources

Harmonized

and linked

to the BR

Stored in a central location

What is the Business Frame?
Collection of rich, harmonized business data 

across multiple sources, linked to the BR, and 

stored in a central location

What?

TO FACILITATE THE DEVELOPMENT OF 

NEW AND IMPROVED DATA PRODUCTS Why?

Create a relational database that links data 

together, utilizing probabilistic matching How?

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Business Frame
Scope the Work – Phase 1: Prototype

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Goal: Leverage existing data more effectively

Objective: Demonstrate link-ability and utility

Can we link data to the Business Register in a reliable and useful way?

Acceptance Criteria: We have database, with all data loaded and 

connected in a meaningful way.
Select the 

Data

Develop & 

Apply 

Methodology

Design the 

Architecture

Construct 

the database

Internal Use Only 10

How are we going to do this?
Select the Data Sources

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Governments Master Address File (GMAF)

What: List of state & local governments, and 

core attributes 

Why: Known overlap with BR, share/validate

coverage, classification, financial data 

and contact information

Longitudinal Business Database (LBD)

What: Links BR establishments over time, 

calculates firm age and size

Why: Longitudinal dimension for use in 

sampling parameter, measuring 

business dynamism

Point-of-sale data provided by Third Party*

What: Monthly credit card transactions 

aggregated to the product level for select 

retailers

Why: Timeliness of data (more current than BR), 

new level of granularity

Non-Employer Statistics by Demographics (NES-D)

What: Administrative data product assigning 

business level demographic characteristics 

to non-employer companies

Why: Demographic dimension to business data,

Linking mechanism to Demographic Frame

*Data purchased from Circana, a private 

sector market research company
Develop Methodology

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Common Identifier

• Match records based on a shared 

identifier in both data sets

• High Accuracy, Minimal Resources

Probabilistic Matching

• Utilizes machine learning to conduct 

pair-wise matching

• Varied Accuracy, High Learning Curve

Analyst Review

• Analyst matches records between 

data sources

• High Accuracy, Resource Intensive

123=123

ABC=ABC

5XY=5XY
Apply Methodology

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Governments Master Address File (GMAF)

What: List of state & local governments, and 

core attributes 

Why: Known overlap with BR, share/validate

coverage, classification, financial data 

and contact information

Longitudinal Business Database (LBD)

What: Links BR establishments over time, 

calculates firm age and size

Why: Longitudinal dimension for use in 

sampling parameter, measuring 

business dynamism

Point-of-sale data provided by Third Party*

What: monthly credit card transactions 

aggregated to the product level for select 

retailers

Why: Timeliness of data (more current than BR), 

new level of granularity

Non-Employer Statistics by Demographics (NES-D)

What: Administrative data product assigning 

business level demographic characteristics 

to non-employer companies

Why: Demographic dimension to business data,

Linking mechanism to Demographic Frame

*Data purchased from Cirana, a private 

sector market research company
Design the Data Architecture

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Expandable Adaptable Sustainable

What does every data source have in common?

UNITS & DATA
Design the Data Architecture

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UNIT TABLE

DATA TABLES

DATA TABLES

DATA TABLES UNIT TABLE

DATA TABLES DATA TABLES

DATA TABLES INTEGRATION 

POINT
Design the Data Architecture

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Governments Master Address File 

(GMAF)

Point Longitudinal Business Database (LBD) -of-sale data provided by Third Party*

Non-Employer Statistics by Demographics 

(NES-D)

*Data purchased from Cirana, a private 

sector market research company

BR UNITS

LBD DATA Point of Sale 

Data

GMAF 

UNIT GMAF DATA

THIRD PARTY 

UNIT

LBD 

UNIT

NESD DATA NES-D

UNIT
Business Frame 

Conceptual Data Architecture

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Employer Unit

(establishment, enterprise)

Administrative 

Units

(EIN, PIK)

Non employer Unit Common Identifier

EIN or PIK

Common Identifier

EIN

LBD Data 

firm age & size

NES-D Data

Demographic 

characteristics of businesses

BR REVENUE

BR Payroll

Government Unit

Common Identifier

EIN

Probablistic Matching

Name, Address

Common Identifer

GMAF Unit

Common Identifer

Alpha + Store Number + Name

Common Identifier

EMPUNIT

BR Data 

(derived for statistical unit)

revenue, payroll

Circana Data 

Revenue, product data

Government_Data 

Payroll, employment, 

expenditures

KEY

Data

Unit

Integration Point
Data Architecture Design Decisions

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Business Rules

Normalized Entities Extensible Data Model

 Linked records provided by source 

systems deconstructed

 Duplication reconciled and referential 

integrity built into model

 Optimal storage partitioning enabled for 

query performance

Technology Agnostic

 Foreign key links to other enterprise frames 

(Jobs, Demographic, Geospatial)

 Core characteristics assigned to UNIT entities 

to readily accommodate new data/measures

 Enterprise integration points established and 

agreed upon by all business data owners

 Cardinality* provided visually intuitive 

business rule representation for users

 Consistency of data relationships confirmed 

with source system data owners

 Source system relationships preserved for 

enterprise cohesiveness

* The numerical relationship between records across entities in a data 

model is called “cardinality”

 ANSI SQL database constructs (tables, 

columns, data types, keys, constraints, 

indexes) 

 Scripted, repeatable data population into 

prototype
Construct the Database

• 5 Data 

 Sources

• 43 Tables

• 1141

 Columns

EIN UNITS

PK

....

Employer Units

PK

....

Employer Unit Address

PK

....

EINUNIT to NONEMPLOYER

PK,FK1

PK,FK2

-

- -

...

REFERENCE_PERIODS

PK

...

AUDIT_LOG

BR_GMAF_CROSSWALK

PK,FK1

PK,FK2

FIRM DEMOGRAPHICS (NES -D)

PK

PK,FK1

EIN ADDRESS

PK,FK1

PK,FK2

PK

PK

EIN to EIN

PK,FK2

PK,FK1

PK

PK

Employer ADDRESS

PK,FK1

PK

PK

Employer Unit Annual Data

PK,FK1

PK,FK2

...

Employer unit Contact Info

PK,FK1

...

Employer Unit – Multi Unit Data

PK,FK2

PK,FK1

...

EMP_EIN

PK,FK1

PK,FK3

PK

PK

Employer to Employer

PK,FK2

PK,FK1

PK

PK

LBD2020

PK,FK1

PK

FK1

FK2

EIN UNIT_Sample Info

PK,FK1

PK,FK2

...

EEIN Unit IRS Income Data

PK,FK1

PK,FK2

...

EIN UNIT IRS Data

PK,FK1

PK,FK2

...

EIN UNIT IRS PAYROLL

PK,FK1

PK,FK2

...

SSN UNITS

PK

PK,FK1

PK

...

SSN ADDRESS

PK,FK2

PK,FK1

PK,FK1

PK,FK1

PK

PK

SSNUNITS_NONEMPLOYER

PK,FK1

PK,FK1

PK,FK1

...

CIRCANA

PK,FK1

PK,FK1

PK

PK

CIRCANA TOTALS

PK,FK2

PK,FK1

PK,FK1

PK,FK1

PK,FK1

KEY

Data

Unit

Integration Point
Assessing the Challenges

Tangible 

• Differences between data sources 

• Physical differences between files

• Conceptual differences in definitions

• Laws and regulations vary across 

data sources

• Managing data access

Non-Tangible

• Maintaining Data Integrity 

• Ensuring data is used responsibility

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Questions

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Jessica.L.Wellwood@census.gov

301-763-7211
 2023 FCSM Research & Policy Conference

 October 24th – 26th , 2023

 College Park Marriott Hotel & Conference 

Center, Hyattsville, MD

Enabling Economic Statistics Modernization

Jessica Wellwood, U.S. Census Bureau

Erica Marquette, U.S. Census Bureau

Ali Obaidi, MITRE 

Adrienne Chen-Young, MITRE 

The U.S. Census Bureau is prototyping a vision of an integrated infrastructure containing a comprehensive list of government and nongovernment businesses, with their core attributes, in a Business Frame that will enable linkages to respondent data, administrative data, 

and other enterprise demographic, geospatial, and job data. The Business Frame will assess challenges with merging records from multiple 

sources using identity matching algorithms to disambiguate data and capitalize on the similarities in data currently distributed across the 

enterprise, to create a robust centralized repository defining the complex population and relationships among businesses. Existing 

statistical programs can leverage this integrated business environment as they look to meet the demand for cross-domain data and

increased granularity of attributes needed for economic data products. This presentation will describe the motivation behind the vision to 

strengthen research capabilities and promote innovation via the Business Frame. Further it will discuss the approach and findings in 

designing the data architecture for this modernized environment that will expand the capacity to answer critical questions about the 

nation’s economy.