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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

  95% CI  
    Temp Injured Temp Non-injured Non-temp Injured Non-temp Non-Injured IRR Min Max Not Significant
California Total 51,227 203,383 2,007,337 12,551,306 1.46 1.45 1.47  
Florida Total 6,233 105,267 267,486 6,919,928 1.50 1.47 1.54  
  Construction 772 7,008 3,832 239,608 6.30 5.85 6.79  
  Production 312 22,718 2,536 252,904 1.36 1.21 1.53  
  Transportation/Logistics 657 27,383 6,568 389,222 1.41 1.30 1.53  
  Office 150 37,500 2,966 1,283,704 1.73 1.47 2.03  
Massachusetts Total 3,128 44,644 150,883 2,993,880 1.36 1.32 1.41  
Minnesota Total 3,188 43,210 102,393 2,470,801 1.72 1.67 1.79  
Oregon Total 3,545 26,275 115,787 1,505,527 1.66 1.61 1.72  
  Construction 69 1,501 1,378 54,212 1.77 1.40 2.25  
  Production 176 8,684 2,001 93,049 0.94 0.81 1.10 *
  Transportation/Logistics 184 4,066 2,862 111,288 1.73 1.49 2.00  
  Office 25 6,725 831 249,489 1.12 0.75 1.66 *

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

  95% CI
Occupational Danger Category Injuries per 10,000 workers Temporary Non-Temporary Incidence Rate Ratio Lower Upper
1 x <18.44 406,950 22,388,800 0.7323047 0.730205 0.7344104
2 18.44 <= x <42.40 505,170 18,432,850 1.104146 1.101347 1.106953
3 42.40 <= x <87.50 202,680 27,665,830 0.2951541 0.293914 0.2963993
4 87.50 <= x <157.38 658,190 21,055,050 1.259437 1.256724 1.262157
5 157.38 <= x 1,107,560 26,510,640 1.683173 1.680652 1.685697

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
  B S.E. Wald df Sig. Exp(B)
Step 1* Temp 1.398 .021 4629.873 1 0.000 4.047
  Age 16-24 -.508 .013 1515.357 1 0.000 .602
  Age 25-34 -.162 .010 282.089 1 .000 .850
  Age 45-54 .162 .008 366.060 1 .000 1.176
  Over 55 .091 .009 101.277 1 .000 1.095
  Male -.070 .007 101.530 1 .000 .933
  Dangerous job 1.344 .007 39348.975 1 0.000 3.834
  Constant -4.839 .008 385307.699 1 0.000 .008

* 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
BBootstrap*
BiasStd. ErrorSig. (2-tailed)95% Confidence Interval
LowerUpper
1.398-.010.024.0011.3341.432
-.508.000.013.001-.534-.482
-.162-.001.010.001-.183-.145
.162.000.008.001.146.179
.091.000.009.001.073.109
-.070.000.007.001-.083-.057
1.344-.001.006.0011.3311.356
-4.839.001.008.001-4.854-4.822

* 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

            95% CI
Amputations Temp Total Temps Non-Temp Total Non Temps Risk Ratio lower upper
Florida 48.00 111,500.00 983.00 7,187,414.00 3.15 2.36 4.21
California 108.00 254,610.00 1,999.00 14,558,643.00 3.09 2.55 3.75
Oregon 40.00 29,820.00 700.00 1,621,314.00 3.11 2.26 4.27
Massachusetts 23.00 47,772.00 519.00 3,144,763.00 2.92 1.92 4.43
Caught In              
Florida 365.00 111,500.00 9,628.00 7,187,414.00 2.44 2.20 2.71
California 2,454.00 254,610.00 57,895.00 14,558,643.00 2.42 2.33 2.52
Oregon 275.00 29,820.00 4,116.00 1,621,314.00 3.63 3.22 4.10
Struck by              
Florida 950.00 111,500.00 30,952.00 7,187,414.00 1.98 1.86 2.11
California 7,424.00 254,610.00 259,614.00 14,558,643.00 1.64 1.60 1.67
Oregon 690.00 29,820.00 15,598.00 1,621,314.00 2.41 2.23 2.59
Heat Related              
Florida 8.00 111,500.00 183.00 7,187,414.00 2.82 1.39 5.72
California 66.00 254,610.00 1,796.00 14,558,643.00 2.10 1.64 2.69

Appendix B: Count of Florida Workers by Characteristics

 Age   
  16 to 2425 to 3435 to 4445 to 44Over 55Dangerous JobSexNon Adjusted Count
InjuredNot Temp000010F8,163
000010M3,813
000011F2,504
000011M9,358
100000F2,199
100000M1,816
100001F435
100001M3,064
000100F8,818
000100M4,181
000101F3,956
000101M14,163
001000F5,748
001000M3,675
001001F2,800
001001M12,867
010000F4,116
010000M3,268
010001F1,575
010001M9,404
Temp000010F62
000010M31
000011F44
000011M221
100000F44
100000M28
100001F29
100001M156
000100F82
000100M63
000101F77
000101M525
001000F70
001000M47
001001F80
001001M483
010000F54
010000M62
010001F50
010001M398
Not InjuredNot Temp000010F713,566
000010M553,805
000011F89,292
000011M325,671
100000F464,904
100000M310,034
100001F35,282
100001M196,531
000100F830,571
000100M567,128
000101F116,001
000101M428,358
001000F770,164
001000M560,539
001001F103,524
001001M413,341
010000F709,647
010000M497,074
010001F70,092
010001M364,232
Temp000010F6,571
000010M2,278
000011F411
000011M970
100000F2,344
100000M638
100001F179
100001M1,687
000100F7,642
000100M2,599
000101F649
000101M2,395
001000F6,635
001000M3,919
001001F755
001001M2,026
010000F5,885
010000M2,818
010001F395
010001M1,607

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

Occupation Z-score Dangerous Job
23 Legal occupations -1.044254 0
15 Computer and mathematical science occupations -1.0144028 0
13 Business and financial operations occupations -0.9670347 0
17 Architecture and engineering occupations -0.9642812 0
11 Management occupations -0.9254674 0
19 Life, physical, and social science occupations -0.8743228 0
27 Arts, design, entertainment, sports, and media occupations -0.6809308 0
43 Office and administrative support occupations -0.6062071 0
25 Education, training, and library occupations -0.5976757 0
39 Personal care and service occupations -0.5373449 0
29 Healthcare practitioner and technical occupations -0.4347778 0
21 Community and social service occupation -0.4303198 0
41 Sales and related occupations -0.4185365 0
35 Food preparation and serving related occupations 0.1736597 0
31 Healthcare support occupations 0.2963881 0
37 Building and grounds cleaning and maintenance occupations 0.6167752 1
51 Production occupations 0.8409043 1
47 Construction and extraction occupations 0.8749958 1
33 Protective service occupations 1.5029233 1
49 Installation, maintenance, and repair occupations 1.5560121 1
45 Farming, fishing, and forestry occupations 1.577786 1
53 Transportation and material moving occupations2.05611111
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