Profitability and occupational injuries in U.S. underground coal mines☆
Highlights
► We examine the association between profitability and underground coal mine injuries. ► A negative binomial random effects model was used. ► We find an inverse relation between profitability and each injury indicator used. ► Financially stressed mines cannot afford to forgo investing in worker safety.
Introduction
Coal plays a crucial role in the U.S. economy. In 2009, 1.1 billion tons of mined coal produced more than half of all the electricity used in the country and generated more than $4 billion in export revenue. During the same year, approximately 90,000 workers were employed in coal production, more than half of whom worked underground (United Mine Workers of America, n.d., PBS, n.d., EIA, 2010).
Underground coal mining has been and remains one of the most dangerous occupations in the country (Zimmerman, 1981, Bennett and Passmore, 1984, Reardon, 1993, Toscano and Windau, 1993, Kowalski-Trakofler et al., 2005, Esterhuizen and Gürtunca, 2006). In recent years, the fatal occupational injury rate in underground coal mining has been six times higher than that in all private industry (CDC, 2001, Groves et al., 2007, BLS, 2010). Studies have also shown that the costs associated with occupational fatal and nonfatal injuries in coal mines have been increasing (BLS, 2007, NIOSH, 2008, Margolis, 2010, Moore et al., 2010).
Several explanations for the high number of injuries occurring in some mines have been proposed in the literature, including geological factors such as low seam height (Boden, 1985, Fotta and Mallett, 1997), room-and-pillar mining method (Pfleider and Krug, 1973, Boden, 1985, Pappas et al., 2003), small mine size (The President's Commission on Coal, 1980, National Research Council, 1982, Fotta and Mallett, 1997, Grayson, 2001), nonunionized workforce (National Research Council, 1982, Appleton and Baker, 1984, Morantz, 2011), less experienced and younger miners (Hull et al., 1996, Margolis, 2010), inadequate miner training (Dames and Moore, 1977, FlorJancic, 1981, Zimmerman, 1981), incomplete understanding of the return on safety investments (Brody et al., 1990), inadequate safety regulations (The President's Commission on Coal, 1980, Mendeloff, 1980, FlorJancic, 1981, Neumann and Nelson, 1981), and no prior experience with disaster (Madsen, 2009). Some of these factors, such as geological conditions, mining method, and mine size, might reflect how “easy to mine” a particular mine might be. In addition, differences in the level of investment in occupational injury prevention might explain some of the variation in the incidence rate and severity of injuries among underground coal mines.
The link between the financial strength of mines and the incidence of occupational injuries has been explored through the correlation between productivity and safety in at least two major studies by the National Research Council (1982) and Grayson (2001). While these studies supported the industry belief that “a productive mine is a safe mine,” their findings were not very robust after controlling for other variables. One possible explanation may be that productivity, measured in tons of coal produced per hour, is an imperfect measure of a mine's financial strength. For example, in 2009, the average price of underground coal was $32.32/ton in Utah, while it was $78.75/ton in Virginia (EIA, 2010). Therefore, a mine in Virginia that is less productive than a mine in Utah might actually be more profitable than the mine in Utah.
Financially strong mines can reduce the incidence of occupational injuries by investing more in worker safety. For example, they can more easily improve the overall mining system, hire experienced workers, and provide comprehensive safety training to their workers than mines that are struggling to survive. There is evidence that investments in safety can boost the profitability of mines by lowering several categories of employer costs, such as insurance and wage premiums, workers’ compensation benefit payments, and frequent production disruptions associated with injuries (Brody et al., 1990, Cutler and James, 1996, Yakovlev and Sobel, 2010, Moore et al., 2010). Similar results have also been reported in other industries such as nuclear power plants (Waddock and Graves, 1997).
If a mine is not financially strong, however, employers might not believe they can afford to invest in occupational injury prevention, especially if the injuries targeted by the investment have a relatively low expected probability of occurring in the absence of prevention (Hopkins, 1999). This means that less profitable mines might not shift scarce financial resources from producing coal to investing in occupational injury prevention because the short-term benefits might not seem to exceed the costs of prevention. As a result, less profitable mines might be less likely to invest as much in safety as more profitable mines would.
In this study, we examined whether the profitability of underground coal mines was associated with the incidence rate of occupational injuries. We hypothesized that, after controlling for mine age, workforce union status, mining method, and geographic region, the incidence rates of all reported injures, reported injuries with lost workdays, and the most serious injuries reported would be higher in less profitable mines.
Section snippets
Methods
When using discrete and non-negative data, such as number of injuries, it is common to use count data models, such as Poisson or negative binomial. To determine which model to use, we examined the fitness of each distribution to our dependent variables. Fig. 1 presents the fitness of the Poisson and the negative binomial models using the number of all injures as an example. Details about the data used are provided in Section 3.
The Poisson model often does not fit actual data well due to its
Data and measurement of variables
Two primary data sources were used in this study. The first includes the employment and accident/injury databases of the Mine Safety and Health Administration (MSHA) for the period 1992 through 2008 (see www.msha.gov/stats/statinfo.htm). NIOSH has converted some of these data into SPSS and dBase IV file formats (see www.cdc.gov/niosh/mining/data/). The MSHA employment data file contains basic information for each coal mine such as type (e.g. surface or underground), total number of hours worked
Descriptive statistics
Table 1 provides summary descriptive statistics for all the variables used in the analyses. For the 17 years in the period 1992–2008, our panel data set included 1407 mines resulting in 5669 records, each representing one underground mine-year with complete information.
We used data on 87,080 (317 fatal and 86,763 nonfatal) injuries, 58,791 nonfatal injuries with lost workdays, 3423 ‘most serious’ injuries (including the 317 fatalities), and 1.45 billion h worked. Dividing the number of injuries
Results of the negative binomial random effects model
Table 2, Table 3, Table 4 present the results of the negative binomial random effects model for each dependent variable. Note again that the exposure variable was the number of hours worked in each mine during each year. The z-statistics tested the null hypothesis that the IRR = 1, i.e. that there was no relationship between the main explanatory or each control variable and the incidence rate of injuries. We also reported the 95% confidence intervals of the estimated IRR. The χ2 likelihood ratio
Discussion
The results of the negative binomial random effects model supported our hypothesis that profitability, our main explanatory variable, and the incidence rate of injuries were inversely related in U.S. underground coal mines. As shown in Table 2, Table 3, Table 4, the coefficient of the profitability variable (logarithm of the real total revenue per hour worked) took the hypothesized negative sign (IRR < 1) and was statistically significant across all three injury indicators. A 10% increase in
Acknowledgments
We thank the U.S. Energy Information Administration (EIA) for allowing us to use non-publically available information through an Information Access Agreement between EIA and NIOSH. We would also like to thank Gregory Wagner (MSHA), Stephen Hudock, Susan Moore, Frank Hearl, and John Piacentino (all from NIOSH) for their helpful comments and suggestions on the earlier version of the paper.
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Disclaimer: The findings and conclusions in this paper are those of the authors and do not necessarily represent the views of the National Institute for Occupational Safety and Health or the Mine Safety and Health Administration.