1 — U.S. BUREAU OF LABOR STATISTICS • bl s.gov

Metadata

Taylor J. Wilson

Economist

Division of Consumer Expenditure Surveys

American Society of Hispanic Economists Training Seminar

11 August 2016
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ASHE Seminar: Metadata Roadmap

 What is metadata?

 Why is metadata important?

 BLS and metadata

 International standards

 Pushing the frontier
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What is metadata?

 “...structured information that 

describes, explains, locates, or 

otherwise makes it easier to 

retrieve, use, or manage an 

information resource.” –

National Information 

Standards Organization (NISO)

 Data used to describe 

something

 Metadata is a role for data
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Types of Metadata

 Descriptive Metadata

Resource for the purpose of identification and 

discovery.

 Structural Metadata

How compound objects in a system fit together.

 Administrative Metadata

Provides information to manage a resource within 

a system.
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Descriptive Structural

Administrative

Variable Name

Variable Description

Keywords

String v. Numeric

Skip Patterns

Dependencies

Ordering

Grouping Schemes

Permissions

File Location

Time of Creation

Date Modified

Paradata
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ASHE Seminar: Metadata Roadmap

 What is metadata?

 Why is metadata important?

 BLS and metadata

 International standards

 Pushing the frontier
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Why is metadata important?

 Archivists, Scientists, 

Economists, Librarians, 

and others all work with 

data in some form. 

 All professions are 

interested in efficient 

resource management

 Understanding your 

data makes your data 

easier to navigate and 

use. 

Source: National Archives, Photo of War Department Records before being 

transferred to the National Archives in 1935
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Variable Organization

 Good data management improves efficiency

 Self describing variables

Consumer Expenditure 

Survey (CE) Income 

Variables

Survey of Consumer 

Finances (SCF) Income 

Variables

FINCBTAX INCOME

INTRDVX NORMINC

ROYESTX WAGEINC

OTHREGX BUSSEFARMINC

WELFAREX INTDIVINC

RETSURVX KGINC

NETRENTX SSRETINC

OTHRINCX TRANSFOTHINC
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Tracking Systematic Change

Input 1

Input 2

Input 3

Variable Adjustment Output 2016 Variable

Input 1

Input 2

Input 3

Variable Adjustment Output 2017 Variable 2

Example: One name changes and a processing input move
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Accountability

 BLS produces many datasets which 

have users ranging from academics 

to policy makers to the American 

public.

 Questions involving data use, 

structures, and changes are 

frequent. 

 Good metadata allows operational 

questions to be answered quickly 

and efficiently. 

65536
11 — U.S. BUREAU OF LABOR STATISTICS TATISTICS • bl s.gov bl s.gov

ASHE Seminar: Metadata Roadmap

 What is metadata?

 Why is metadata important?

 BLS and metadata

 International standards

 Pushing the frontier
12 — U.S. BUREAU OF LABOR STATISTICS TATISTICS • bl s.gov bl s.gov

BLS and Metadata

 Institutional silos are not conducive to inter-program 

communication.

 No general standard on metadata construction or 

storage.

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BLS and Metadata cont.

 Each program office is largely self-governing 

with a hierarchy of management. 

 It is currently very difficult for inter-program 

office communication to occur.

 It is even more difficult for a consensus 

regarding the organization of metadata to be 

achieved. 
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What do we have in common?

 Social Science Surveys

Establishment Surveys

Household Surveys

 Economics oriented

BLS produces seven principal federal economic 

indicators (PFEI)

 Interested in serving the public good with high 

quality data.
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Descriptive Structural

Administrative

Variable Name

Variable Description

Keywords

String v. Numeric

Skip Patterns

Dependencies

Ordering

Grouping Schemes

Permissions

File Location

Time of Creation

Date Modified

Computer Scientists

Economists Survey Methodologists
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Administrative

Permissions

File Location

Time of Creation

Date Modified

Computer Scientists

 Easiest metadata to 

capture

 Auto-generated by 

computers

 Challenge: Capture and 

storage

Where we stand
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Where we stand

 Survey specific

 Naming conventions: low 

agreement, enforcement, 

and coverage

 Challenge: Inter-office 

communication and 

sharing variable creation 

methodology.

Descriptive

Variable Name

Variable Description

Keywords

String v. Numeric

Economists
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Where we stand

 Survey specific

 Arguably the most 

difficult of the three to 

harmonize

 Challenge: Inter-office 

communication and 

harmonization of 

methodology.

Structural

Skip Patterns

Dependencies

Ordering

Grouping Schemes

Survey Methodologists
19 — U.S. BUREAU OF LABOR STATISTICS TATISTICS • bl s.gov bl s.gov

ASHE Seminar: Metadata Roadmap

 What is metadata?

 Why is metadata important?

 BLS and metadata

 International standards

 Pushing the frontier
20 — U.S. BUREAU OF LABOR STATISTICS TATISTICS • bl s.gov bl s.gov

International Standards

 Dublin Core Metadata Initiative (DCMI)

 Machine-Readable Cataloging (MARC)

 Metadata Object Description Schema (MODS)

 Text Encoding Initiative (TEI)

 Federal Geographic Data Committee (FGDC)

 Data Documentation Initiative (DDI)
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Dublin Core Metadata Initiative

 Developed for digital libraries

 Broad general principals which can be targeted by 

any metadata scheme

 Interoperability – A system working with other 

systems without special effort by the customer

Level 1: Shared vocabularies defined in natural language

Level 2: Shared vocabularies based on formal schematics

Level 3: Shared formal vocabularies in exchangeable records

Level 4: Shared formal vocabularies and constraints in records

Source: Dublin Core Metadata Initiative Website, accessed July 2016, http://dublincore.org/metadata-basics/
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International Standards

 Dublin Core Metadata Initiative (DCMI)

 Machine-Readable Cataloging (MARC)

 Metadata Object Description Schema (MODS)

 Text Encoding Initiative (TEI)

 Federal Geographic Data Committee (FGDC)

 Data Documentation Initiative (DDI)
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Data Documentation Initiative (DDI)

 International metadata standard for describing social 

science datasets.

 Applies Dublin Core Elements in a way that makes 

sense for social science datasets and surveys.

 Tool box framework allows for an ad hoc solution to 

the interoperability problem between survey 

programs. 

“Enter once, use often.”

Effective Data Sharing

Standardized Terminology
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Who uses DDI?

 Center for Comparative European Survey Data

 Consortium of European Social Science Data Archives

 Centralising and Integrating Metadata from European Statistics

 Inter-university Consortium for Political and Social Research

 International Household Survey Network

 Documenting the Survey Cycle using DDI

 The Data Liberation Initiative Metadata Collection

 Canadian Research Data Centres Program
25 — U.S. BUREAU OF LABOR STATISTICS TATISTICS • bl s.gov bl s.gov

ASHE Seminar: Metadata Roadmap

 What is metadata?

 Why is metadata important?

 BLS and metadata

 International standards

 Pushing the frontier
26 — U.S. BUREAU OF LABOR STATISTICS TATISTICS • bl s.gov bl s.gov

 The Consumer Expenditure Survey (CE) plans 

to pilot a system implementing the DDI 

Standards. 

 CE is an old and complex survey with a large 

volume of metadata that is valuable to a user.

Pushing the Frontier

CE Begins DOL Created

1888 1913 1979 2004 20132016

Annual Survey Income 

Imputation TAXSIM

Not to scale; not all changes are marked or labeled

CE Timeline: Major Milestones
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One Survey, Two Dimensions

 Most end users are 

interested in changes 

across time. 

 Changes to a variable 

through the 

processing phases 

helps users 

understand how the 

data is generated.

2013

2014

2015

2016

Phase II, III, IV

Phase II, III, IV

Phase II, III, IV

Phase II, III, IV
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Dear Sir/Madam,

I have accessed a derived dataset with information on the 

educational attainment of the household’s reference person 

before the year 1996. This information was codified on a scale of 

1 to 7, but I don’t know the meaning of the codes. 

Could you tell me their meaning?

Thank you!
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Answering Inquiries

 Many copies of the same information is not 

conducive to:

Building time series

Efficiently locating information

Efficiently utilizing government resources
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Example DDI Solution

Education

EDUC_REF 1 EDUC_REF 2 EDUC_REF 3

 Variable 

representation 

upon creation

 Year: 1984

 Coded: 1-7

 Variable 

representation 

upon change

 Year: 1996

 Coded: 00, 11-17

 Variable 

representation 

upon change

 Year: 2013

 Coded: 00, 11-16
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Benefits of Metadata Management

 Minimizes lag time between question and answer.

 Improves processing efficiency

 Users will be able to answer their own questions 

without ambiguity. 

 Reduces analytical errors from 

data anomalies and 

inconsistencies.

 Creates better, more user-friendly 

survey data.
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Pushing the Frontier

 CE is motivated to provide users with effective 

metadata documentation. 

 Focusing on reuse allows us to create more 

user-friendly documentation for the CE 

Survey.

 Adhering to the international standards will 

encourage moves toward interoperability. 
Contact Information

33 — U.S. BUREAU OF LABOR STATISTICS • bl s.gov

Taylor J. Wilson

Economist

Division of Consumer Expenditure Surveys

www.bls.gov/cex/

202-691-6550

wilson.taylor@bls.gov