Data Linkage in the Survey of Occupational

Injuries and Illnesses

Ellen Galantucci

Research Mathematical Statistician

Office of Compensation and Working Conditions

Bureau of Labor Statistics

November 3, 2021

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Outline

◮ Description of the Survey of Occupational Injuries and

Illnesses (SOII)

◮ SOII linkage with Occupational Safety and Health

Administration (OSHA)

◮ Probabilistic

◮ OSHA Injury Tracking Application (ITA) Identification

Number

◮ Employer Identification Number

◮ Uses of linked data

◮ Burden reduction

◮ Imputation

◮ Non-response bias analysis

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Survey of Occupational Injuries and Illnesses

◮ The Survey of Occupational Injuries and Illnesses (SOII)

program has collected injury and illness information from

business establishments each year since 1972

◮ Currently, the survey uses an annual sample of approximately

230,000 establishments and has a response rate of about 89%

◮ The survey covers wage and salary earners and some

volunteers across all 50 states, D.C., and three U.S. territories

in most industries

◮ While some types of establishments are excluded, such as

agricultural establishments with fewer than 10 employees

(NAICS 111 and 112) and the self-employed, the survey

covers the vast majority of the United States economy

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Survey of Occupational Injuries and Illnesses

◮ Collects summary information, such as total number of cases

resulting in days away from work or days of job transfer and

work restrictions

◮ Also collects detailed case and demographic information about

some injury or illness cases

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Occupational Safety and Health Administration (OSHA)

◮ In 2016, OSHA issued a rule that would require certain

establishments to submit summary case information on an

annual basis

◮ Data would be collected through their Injury Tracking

Application (ITA)

◮ Applies to all business establishments with 250 or more

employees in industries required to maintain OSHA logs and

all establishments with 20-249 employees in industries that

have historically high rates of occupational injuries and

illnesses

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Office of Management and Budget (OMB)

◮ Two agencies within the Department of Labor were now

collecting the same information

◮ This was an increased burden on establishments that had to

submit the same information to both agencies

◮ The overlap in reporting requirements was about 40% of the

SOII sample

◮ OMB tasked us with finding a way to reduce burden

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Probabilistic Linkage

◮ The two datasets lack good quality, unique identifiers

◮ Variables such as establishment name, address, and

employment totals are noisy

◮ The BLS hired contractors to attempt to link establishments

in the two datasets

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Probabilistic Linkage

◮ The contractors created a probabilistic linkage methodology

based on Fellegi-Sunter (1969)

◮ Establishment name, address, industry, employment total,

phone number, and email addresses domain used for matching

◮ Had to standardize variables using methods such as geocoding

◮ Linked the OSHA ITA data to the Quarterly Census of

Employment and Wages (QCEW) rather than the SOII data

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Some Concerns with Probabilistic Linkage Methodology

◮ Level of blocking (zip code) reduced size of potential matches

but eliminated some accurate pairs

◮ Challenges accounting for addresses that housed multiple

establishments

◮ Geocoding difficulties

◮ Weighting based on how often matches occurred within the

datasets, which meant variables like employment not seen as

important

◮ QCEW and OSHA both have multiple fields for establishment

names

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Addition of Collection of OSHA ITA ID and EIN

◮ SOII began collecting OSHA ITA IDs at the end of 2018 data

collection

◮ OSHA began collecting EINs during 2019 data collection

◮ These two variables would provide better identifiers that could

be matched but would need to be validated

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Collection of OSHA ITA ID

◮ 6,388 establishments provided a numerical, non-zero OSHA

ITA ID during 2018 data collection

◮ The number of establishments that provide this increased

substantially in 2019 data collection

◮ 221 establishments submitted their industry classification

(NAICS code)

◮ 292 submitted their Internet Data Collection Facility (IDCF)

identification number which is used for logging in to the SOII

data collection instrument

◮ 399 additional establishments submitted a number that had no

match in the OSHA database

◮ 5,476 remaining submitted numbers were potential matches

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Variables Considered for Validating OSHA ITA ID

◮ Employment totals within 30%

◮ Required for match

◮ About 90% met this criterion

◮ NAICS code and zip code

◮ About 50% of those that matched on employment matched on

NAICS code and zip code

◮ About 25% of those that matched on employment matched on

zip code but not NAICS code

◮ About 15% of those that matched on employment matched on

NAICS but not zip code

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Validating Accuracy of Matches

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Validating Accuracy of Matches

◮ If Days Away from Work and Days of Job Transfer and Work

Restriction counts both matched, it was considered a match

◮ If the total case count matched, it was considered a match

◮ Everything else was manually validated using a combination of

establishment name, address, and name of the person who

submitted the data

◮ For every unit that matched on NAICS and/or zip code, the

company matched though the specific establishment could not

always be confirmed as correct

◮ For remaining units, 17/56 appear to be the same company

◮ The combination of employment and either NAICS code or

zip code appears to be sufficient to validate the accuracy of

the OSHA ITA ID matches

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Matching by Employer Identification Numbers (EIN)

◮ EINs are not unique to establishments - they are unique to

companies

◮ Despite that, using other variables such as address,

employment totals, and establishment names can help to

match establishments between SOII and OSHA

◮ In some cases, there are multiple establishments with the

same EIN in close proximity, which are nearly impossible to

differentiate between

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Uses for linked data

◮ Reduce burden in SOII

◮ BLS now has an API to collect data from OSHA

◮ Any units that have already reported to OSHA and can be

matched do not have to report summary information to SOII

◮ Imputation or sample supplementation in SOII

◮ We are brainstorming ways to use the data from OSHA to fill

in gaps that exist within the SOII data

◮ The challenge is that the data have not had the same data

quality screening as normal SOII data has

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Uses for linked data

◮ Non-response bias analysis

◮ Using EIN in combination with other variables, I impute the

data of SOII non-respondents

◮ I use those imputations to predicte injury and illness counts by

industry

◮ This analysis has shown that SOII data do not suffer from

non-response bias

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CONTACT INFORMATION

Ellen Galantucci, Ph.D.

Research Mathematical Statistician

Statistical Methods Group

Office of Compensation and Working Conditions

Bureau of Labor Statistics

galantucci.ellen@bls.gov

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