How We Calculated Injury Rates for Temp and Non-Temp Workers

Related Story: Temporary Work, Lasting Harm

Summary of Findings

An analysis of data from worker’s compensation claims in
California, Florida, Massachusetts, Minnesota and Oregon over a five-year
period found that the incidence of temporary worker workplace injuries was
between 36 percent and 72 percent higher than that for non-temporary workers.

When workers were grouped by occupation, this gap widened
significantly for workers in certain blue-collar, more-dangerous occupations
and narrowed for workers in less dangerous occupations.

Temporary workers also are disproportionately clustered
in high-risk occupations, our research found. Temporary workers were 68 percent
more likely than non-temporary workers to be working in the 20 percent of
occupations with the highest injury rate as measured by the U.S. Bureau of Labor
Statistics.

Introduction

The safety of temporary workers on the job has become an
issue of growing concern in the public health community. Such workers, recruited
by temp agencies for jobs in factories, warehouses, offices and other worksites
on a daily basis, make up a larger share of the American labor market than ever
before, according to the most recent government jobs report in November 2013. There
are now 2.78 million workers in the temporary help services industry. The
American Staffing Association, the industry’s trade group, says that some 13
million people, nearly 1 in 10 workers, found a job through a staffing agency
in 2012.

The director of the federal Occupational Safety and
Health Administration has said that he is alarmed by the number of temp workers
being killed on the first day on the job. Earlier this year, OSHA launched an
initiative to raise awareness about the dangers temp workers face, as well as
the responsibilities of temp agencies and the companies that use temp workers.
But attempts to improve policies protecting temp workers have been limited by a
lack of basic data, such as whether temp workers get injured more than regular
workers, what types of injuries they suffer and whether certain occupations are
of special concern.

The main resource for workplace safety data is the Bureau
of Labor Statistics’ (BLS) Survey of Occupational Injuries and Illnesses.
Worksites are required to keep a log of injuries. Every year, BLS economists
collect data from those logs from 200,000 establishments to estimate workplace
injuries and illnesses nationwide.

It’s impossible to compare the injury rates of temp
worker to those of regular workers using this survey for two reasons. First, companies
compiling the records are not required to indicate whether a temp or regular employee was harmed by an accident.  Second, BLS surveys have found that many
worksite employers are not aware they are supposed to include injuries suffered
by temps in their logs, meaning that a large number of temp worker injuries likely
go uncounted.

Recently, public health researchers have begun to discuss
whether state workers’ compensation data could be used to monitor injuries
among temp and other contingent workers left out of the BLS survey. A 2010
study of Washington state workers’ compensation claims found that temp agency
workers had higher rates of injuries. The rates were twice as high in the
construction and manufacturing sectors. Washington State’s workers’ comp system
is somewhat unique in that (1) the state fund is the only player in the insurance
market, (2) it uses a unique coding system that identifies temp workers in
specific classifications, such as “temporary staffing services – warehousing
operations,” and (3) the state collects data on the number of hours people
work, making it possible to calculate injury rates. Most states collect payroll
data but not number of employees or hours worked, meaning that to calculate
injury rates, one would need to use an outside source to obtain the employment
data necessary to calculate injury rates.

In 2001, University of Minnesota researchers did such a
study. The analysis compared workers’ comp costs and claims frequency among
regular full-time workers, part-time workers and temporary and leased workers.
To calculate claims frequency rates, researchers used outside data from the
Census Bureau’s Current Population Survey. The researchers found that both cost
and claims frequency were many times higher for temp and leased workers.

Methodology

How we got the data

ProPublica set out to compare the rate of workers’ compensation claims of temp
workers and regular workers in as many states as possible. Reporters contacted
workers’ comp system administrators and ratings bureaus in 25 states.

Using workers’ comp
records to track temp worker injuries nationwide was not possible. In many
states, such as New Jersey, claims are considered confidential. In many others,
such as New York, there is no way to distinguish temp workers from regular
workers because the state does not collect industry information or it is rarely
reported on claims. Other states were problematic because of the way claims are
reported to the state. In Texas, for example, employers aren’t required to
carry workers’ comp insurance. Those employers are still required to report
injuries to the state, but auditors have found that many fail to do so. In
Illinois, about half of the claims are filed on paper and never entered into a
computer system.

Ultimately,
ProPublica obtained claims databases from California (2008-12), Florida (2008-12),
Massachusetts (2008-12) and Oregon (2007-12) and aggregate claims data for
Minnesota (2007-11). The data comes from the first report of injury (FROI) and
subsequent report of injury (SROI) forms that employers and claims administrators
are required to file with the state administrative office. It is important to
note that data is not comparable between states because every state has
different rules for what is considered a reportable claim. In California, the
standard is any injury resulting in more than a full day of time off or
requiring medical treatment beyond first aid. In Florida, the threshold is
seven days off. In Massachusetts, lost-time claims are defined as more than
five days of lost wages. In Minnesota and Oregon, the standard is more than
three days away from work. There are also differences in the labor markets,
especially the market for temporary workers, in each state.

Identifying Temp Workers

Temporary workers were identified using the employers’
North American Industrial Classification System (NAICS) codes. Under this
system, which is used by most federal agencies, “temporary help services” is a
separate industry, identified with the code 561320. California also allows
claims to be filed with the older Standard Industrial Classification (SIC)
codes. For the temp help industry, ProPublica was able to convert these codes
to the NAICS system.

Unlike other states, Massachusetts’ database did not
contain industry codes but did contain employer name. Because Massachusetts
requires all temp agencies to register with the state, ProPublica was able to
match several years of the agency registry to the claims database to identify
temp agencies.  In addition,
ProPublica searched the workers’ comp database for keywords, such as
“staffing,” “personnel,” and “labor,” and then researched the companies to identify
temp agencies that were missing from the registry.

Information for explaining the analysis

ProPublica analyzed the workers’ comp data in three ways.

1. ProPublica calculated total claims for temp agency workers and non-temp workers
and calculated a claims rate using employment data from the Quarterly Census of
Employment and Wages (QCEW). This BLS census counts all employees in the United
States by industry and geography. Every quarter, every business is required to
report for unemployment insurance taxes purposes to their state how many
employees they had on the payroll. QCEW is considered to be the most reliable
source of employment data for this analysis because, similar to workers’ comp data, it is required to be reported by companies to the
state for insurance rating.

Combining the QCEW counts with counts of worker injuries from
the workers’ compensation data obtained in California, Florida, Massachusetts,
Minnesota, and Oregon, we were able to construct incidence rate ratios, also
called risk ratios, by dividing the rate of injuries for temporary workers by
that of non-temporary workers. We assessed the statistical significance of the
risk ratios by calculating 95% confidence intervals.

In Florida and Oregon, the workers’ compensation data was
rich enough that we were also able to identify the occupations of injured
workers, and thus also construct incidence rate ratios for temporary versus
non-temporary workers in various occupations.

Figure 1: Workplace Injury Incidence Rates and Risk Ratios by State and Occupation

The workers’ compensation data also classifies injuries by
type, for example ‘struck by or against object’ and ‘amputation.’ It was also
possible to calculate risk ratios for just workers with the same type of
injury. (See Appendix A.)

2. It is important to consider occupation when analyzing
temp worker injuries because the composition of the temp industry is very
different from the labor market as a whole. For example, temp workers are
over-represented in manufacturing, warehouse and office occupations and
under-represented in sales and restaurant jobs.

To assess the greater occupational risk faced by
temporary workers, we classified each of the 618 BLS Broad Occupational
Categories into quintiles – five ranked groups — by their injury
incidence rate from the BLS Survey of
Occupational Injuries and Illnesses. 
We then grouped all temporary and non-temporary workers into each of
these occupational danger categories and calculated the relative incidence of the
two types of worker in each danger category. This allowed us to essentially
compare the number of temps in each  category with the number of temps
we would expect to see in each category, if temp workers were distributed
across occupational risk levels as non-temp workers. We found that temps and
non-temps were relatively equally distributed in the two least-dangerous
occupational categories, but non-temp workers were much more concentrated in
the middle, while temp workers were disproportionately represented in the two categories
representing the most dangerous occupations.

Figure 2: Rate of temporary and non-temporary workers in
occupations ranked by injury rate

While several states had occupation data, only one state,
Oregon, had detailed Standard Occupational Classification (SOC) codes that
could be matched to BLS employment data. In Florida, ProPublica coded the text
occupation fields, first, using the NIOSH Industry & Occupation
Computerized Coding System (NIOCCS), an automated coding program which was
released by NIOSH (the National Institute for Occupational Safety and Health) in
December 2012. ProPublica then coded the remainder of the fields manually,
using the Census 2010 Occupation Index, the BLS SOC index, and the National
Council on Compensation Insurance (NCCI) Scopes Manual. Any occupation that was
unfamiliar was coded using job duties most commonly listed for them in online
job postings.

Employment data for the QCEW program, which was used in
the overall analysis, does not include data on occupation. So ProPublica used
2012 research estimates published in May 2013 by the BLS Occupational
Employment Statistics (OES) program. This is an annual survey of nonfarm
establishments. The estimates include data from six semi-annual survey panels
over a three-year period, covering 1.2 million establishments.

Because of the small size of the survey sample, the OES
data does not go down to the specificity of the 5-digit industry level for
temporary help services (56132); so ProPublica had to use the broader 4-digit
level for the employment services industry group (5613). That group also
includes two other industries: employment placement agencies, i.e. recruiting
firms, and professional employer organizations (PEOs), which are human
resources outsourcing firms which assume an employer’s responsibilities for tax
and insurance purposes and then lease the employees back to the company that
supervises them.

3. ProPublica also wanted to consider whether differences
between temporary and non-temporary workers might be causing the injury gap to
be overstated.  For example, are younger workers more likely to be temporary and also more likely to be injured?

To assess this possibility, ProPublica conducted a logistic
regression analysis of 117,274 2010 and 2011 workers’ compensation claims from
Florida, and demographic information about workers in Florida from the American
Community Survey. We obtained microdata from IPUMS, which enabled us to
construct cell counts for each of the combinations of variables in our model.
To obtain counts of uninjured workers in each cell, we subtracted the
corresponding counts of injured workers. (For a table of the data used in this
model, see Appendix B.)

The estimates of the number of workers with various
combinations of characteristics in the American Community Survey differ
somewhat from the Quarterly Census of Employment and Wages data used above. So
we first ran a regression to determine the increased odds of temporary worker
injury without controlling for worker characteristics. This regression found
that temporary workers had 3.8-fold higher odds of being injured.

Then, including age, sex and occupation information, we
found that the odds increased to over 4-fold higher, suggesting that comparing
more similar groups of workers actually increases the gap in odds between
temporary workers and non-temporary workers. Thus, concerns that worker
characteristics would negate the increased odds of injury for temporary-workers
appear unfounded. To the contrary, controlling for worker characteristics
actually increased the ‘temp effect.’

Figure 3: Logistic Regression Model

Variables in the Equation

* Variable(s) entered on step 1: Temp, Age 16-24, Age 25-34, Age 45-54, Over 55, Male, Dangerous job.

Figure 4: Logistic Regression Model Coefficients – Bootstrap Confidence Intervals

Bootstrap for Variables in the Equation

* Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples

Because worker characteristics did not significantly
affect the regression model, we simplified our analysis by calculating a
stratified risk ratio for workers in blue-collar jobs. When we calculated that
ratio for temps and non-temps, in Florida, we found that temps were six times
more likely to be injured.

Strengths and Limitations

While other studies have examined workers’ compensation
claims in a single state to assess increased workplace injury risk for
temporary workers, this study has found a consistently large and significant
result across a diverse array of states, including two of the largest.

The main limitations are the result of the use of
workers’ compensation data for this analysis. In many states the data is not
publicly available at all. Where data is available, there is considerable
variation in collection and reporting methods between states.

Workers’ compensation claims, particularly the first reports
of injury (FROIs), are an imperfect record of injuries. Some workers file false
claims. Some employers suppress legitimate claims. As with any data set where records are filed by multiple people, some claims
administrators provide more accurate and complete information than others. To
limit these imperfections, we tried to use only accepted claims wherever
possible. Such is the limitation of public administrative claims data collected
by state governments. Future researchers could seek more complete and detailed
claims data from private insurance companies.

In each of the states where we were able to obtain data,
there were significant difficulties in using it for this type of analysis. In
particular, it is important to have a reasonably accurate way of identifying
temporary workers, but this is made difficult by confidentiality rules that
prohibit release of employer names, and a lack of standardization of
occupational coding and text descriptions.

While we were able to control for age, sex and occupation
in Florida, it would also be interesting to control for other variables like
race and job tenure, which could impact the results. Unfortunately, job tenure
and race are rarely included in the states’ workers’ compensation records.

Temporary workers also appear to face barriers to filing
workers comp claims.  In general,
temporary workers are less educated, far less likely to be represented by a
union and far more likely to have limited English proficiency. In addition,
temp workers may be disproportionately drawn from men and women who lack
immigration status. While we can’t estimate this effect precisely, it could be
contributing to a significant undercount of temp worker injuries in this data.

Given the promise of this and other analyses, we hope
that they will serve as impetus for regulators or others to start collecting
standardized and comprehensive data on this important issue affecting an
increasing number of workers, many of whom labor under limited protection.

Appendix A: Temporary Worker versus Non Temporary Worker Risk Ratios by Type of Injury

Appendix B: Count of Florida Workers by Characteristics

Appendix C: Occupations With Injury Rate Z-Score and Dangerous Job Flag