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Occupational injury risk wage premium
Safety Science 118 (2019) 337–344
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Occupational injury risk wage premium a,⁎
Paweł Strawiński , Dorota Celińska-Kopczyńska a b
T
b
Dr. habil., Faculty of Economic Sciences, University of Warsaw, Dluga 44/50, 00-241 Warsaw, Poland PhD, University of Warsaw, Poland
ABSTRACT
This study examines the occupational injury wage premium and its influence on the gender wage gap. To this end, we quantify the part of the wage premium that may be the result of occupational risk, and then estimate the share of the gender wage gap attributed to occupational risk. Existing studies on occupational risk and its influence on the economy are limited by the availability of data. As a result, the findings related to the injury risk wage premium are inconsistent. We use a detailed, disaggregated data set on accidents at work and calculate the accident risks for groups of occupations. Then, we estimate an extended wage equation and perform the Oaxaca–Blinder wage decomposition. Wage compensation for non-serious accident risk is negative, but is positive for serious accident risk. However, the results for fatal accident risks are inconclusive. Furthermore, the accident risk accounts for 15%–30% of the explained wage gap between men and women. Our findings resolve the inconsistent results in the literature on the relation between occupational injury risk and wages. Our results suggest that personal perceptions of occupational risk are inaccurate, perhaps because workers are usually fully informed about work-related risks only after wage negotiations are concluded and a contract is signed.
1. Introduction Occupational injuries are common and have a strong impact on the economy. According to the International Labour Organisation (ILO), almost 1 million work-related accidents occur every day worldwide; that is, every 15 s, 153 workers have a work-related accident and one worker dies from a work-related accident. The cost of such accidents is estimated to be 4% of the global Gross Domestic Product (ILO, 2016). The standard treatment of occupational risk in the labour market is conducted in terms of the theory of compensating wage differentials. According to this theory, workers must be compensated with higher wages for accepting unpleasant work conditions (Olson, 1981). Therefore, market forces ensure that a wage premium is paid to employees who perform job tasks associated with a higher risk of occupational injury, implying there is a positive relationship between wages and job risk in the market. In other words, wages increase with job risk, all other factors being equal. An occupational risk premium reflects the probability that workers employed in a particular occupation will suffer a work-related injury. However, empirical evidence on the relation between the risk of occupational injury and wages is mixed. For instance, Leeth and Ruser (2003) showed a positive relation between the two, whereas Kuhn and Ruf (2009) found a negative relation. Furthermore, estimating an occupational injury risk premium is not straightforward because male workers are overrepresented in the most dangerous occupations. Many studies have confirmed worker sorting across occupations (e.g. see Johnson and Stafford, 1998). The
⁎
implication of sorting based on risk aversion is that if men and women vary in terms of their willingness to accept greater risk in exchange for a higher wage, they will work in different occupations. Hence, it is difficult to separate the injury risk premium from the wage effects of sorting. This study has two main aims. First, we assess the size of the wage premium with regard to occupational risk. Second, we investigate the extent to which the difference between the occupational injury risk of the jobs that men and women hold and the corresponding compensation can help to explain the observed gender wage differentials. To achieve these aims, we use standard economic tools: an extended Mincer-type wage equation and the Oaxaca–Blinder decomposition. In summary, we assess whether the occupational risk premium is predicted by the theory of compensated wage differentials. The novelty of our approach lies in the detailed level of our analysis. Previous studies are typically based on aggregated sample survey data. In contrast, we take advantage of the unique Polish legal system that requires that firms report each accident at work. To estimate the occupational injury risk, we use official register data taken from statistical cards on accidents at work in Poland for the period 2011–2014. Next, we merge the data on accidents with data taken from the Structure of Wages and Salaries by Occupations 2014 for Poland. This empirical strategy allows us to resolve the contradictory evidence currently available on the relation between occupational injury risk and wages. Furthermore, we offer a possible explanation as to why the results in studies differ so significantly. In this regard, our findings show that the measure of injury risk and the data aggregation both affect the results.
Corresponding author. E-mail address: [email protected] (P. Strawiński).
https://doi.org/10.1016/j.ssci.2019.04.041 Received 17 September 2018; Received in revised form 5 April 2019; Accepted 27 April 2019 Available online 28 May 2019 0925-7535/ © 2019 Elsevier Ltd. All rights reserved.
Safety Science 118 (2019) 337–344
P. Strawiński and D. Celińska-Kopczyńska
The remainder of this article is organised as follows. Section 2 reviews the literature on the occupational injury risk wage premium. Section 3 discusses the properties of the data on accidents and wages. In Section 4, we present our methodology and estimation results. Section 5 discusses the results, as well as possible limitations of the study, and lastly, Section 6 concludes the paper.
injury risk measure in the wage equation increases the explained part of the gender wage gap by 1–3 percentage points (or up to 12% of the explained part of the gap). Hersh (1998) analysed gender-specific estimates for injury and illness incidence rates by both industry and occupation for the US economy. The study showed that although women are less likely than men to be injured on the job, their injuries are not negligible, accounting for about a third of all injuries and illnesses that result in days away from work. After adjusting this number for gender differences in employment, the results show that women face a job risk of 70% of that of men. However, men and women receive similar wage compensation for occupational injury risk. Leeth and Ruser (2003) also analysed data on the US economy, showing that non-fatal injury risk increases the wages for both men and women, but that the gain is much higher for women. The non-fatal injury risk for men is associated with wage premiums of 0.9%–1.4%; for women it is associated with premiums of 2.8%–4.5%. The wage premiums for fatal injury risk are significantly lower (0.5%–0.9% for men). In the case of women, the wage premiums are positive, but statistically insignificant. The second strand of literature discusses methodological approaches. Several examples of analyses at the disaggregated level of occupations are available (e.g. DeLeire and Levy, 2004; Grazier and Sloane, 2006). The authors typically calculate risk rates by industry or occupation and then match these rates to another data set. For instance, Berkhout and Damen (2016) studied the accident frequency for distinct occupational groups within the human capital theory framework. They showed that the male incidence rate is, on average, four times higher than the female rate. In addition, Wang et al. (2016) showed that there is a 10% negative risk premium related to jobs that are perceived as being risky. DeLeire and Levy (2004) assigned fatal and non-fatal injury risks to occupations using data from the Bureau of Labor Statistics Survey of Occupational Injuries and Illness and the Census of Fatal Occupational Injuries. These data provide the number of injuries and fatalities for three-digit occupation levels from 1992 to 1999. There is also information on the severity of non-fatal injuries, including the median number of days missed from work per injury for each occupation. However, their analysis was restricted to full-time workers, because part-time workers allocate some time to household production, for which risk data are not available. In addition, their sample was limited to workers aged 25 to 34 because current risk measures are inaccurate for older workers, who have accumulated occupation-specific knowledge. Conversely, Grazier and Sloane (2006) included part-time workers in their analysis because they expected family status to be an important determinant of whether an individual works full-time or part-time. They pointed out that excluding part-timer workers would result in a disproportionate elimination of parents from the analysis. A similar empirical exercise was conducted by Giergiczny (2008) for Poland. The aim of the study was to estimate the value of statistical life. Fatal injury risk premium estimates based on a three-digit occupational risk measure vary from 1.5% to 4.6%, depending on the level of aggregation for economic sector dummies (NACE levels). Sandy et al. (2001) showed that using industry average risk rates fails to capture sufficiently precise differences between the levels of risk exposure for workers in different occupations in the same industry. Ideally, one would want to estimate the risk for each occupation-byindustry cell, but most occupations record zero injuries or deaths, making this idea impractical. Lalive (2003) argued that using aggregate risk as a proxy for a work-related risk is a valid approach if job tasks are similar within occupations or industries. However, this approach may fail if there is substantial heterogeneity in the workplace risk within an industry or an occupation. Thus, the dominant approach is to assign the same risk level to all workers. However, in the case of heterogeneity, the estimates of the compensating wage differentials will be biased. It is also worth referring to Grazier and Sloane (2006), who conducted their analysis at the two-digit level of the International Standard
2. Literature review Studies on the influence of occupational injury risk on wages and wage differentiation are limited owing to data and methodological problems. There exist two strands of literature in this regard. The first identifies the determinants of sorting workers between safe and risky jobs, and the second calculates the implicit value of life or the implicit value per statistical injury (i.e. the wage reduction associated with reducing the expected risk by one percentage point). In both settings, the dominant approach relies on some published measure of the risk level by occupation, at various degrees of aggregation, and then matches this risk variable with worker survey data (Viscusi, 1993). Men tend to take up employment in riskier occupations more often than women do. The authors explain this effect using psychosocial factors, such as mothers being more devoted to children and being more involved in household duties and childcare. Numerous studies, and particularly those based on the United States (see Viscusi and Aldy, 2003 for a comprehensive review of this subject), have confirmed this observation. For instance, the results of Crosetto and Filippin’s (2017) experiments showed that the availability of a safe option causally induces women to behave in a less risk-tolerant manner in comparison with men. This observation explains why women tend to choose occupations that require safer job tasks and, hence, work in environments with a lower risk of injury. DeLeire and Levy (2004) and Grazer and Sloane (2006) tested the worker sorting hypothesis for the US and the UK labour markets, respectively. Both studies used family structure as a proxy for the degree of risk aversion when testing the hypothesis that risk-averse individuals will choose safer occupations. They found that women are more riskaverse than men, and that people who are married or have children are more risk averse than those not engaged in relation without children. Moreover, differences in the risk of death on the job among occupations is an important reason why men and women choose different occupations. The estimated sizes of these effects suggest that differences in physical risk across occupations explain about one-quarter of the occupational gender segregation in the United States, whereas the effect of gender segregation is significantly smaller in the United Kingdom. As suggested by King (1992), differences in occupational injury risk can partly explain the existence of a gender wage gap. That is, individuals in occupations with a high injury risk are compensated for that risk by means of bonus payments. At the same time, male workers are overrepresented in the most dangerous occupations, such as scaffolders or miners, whereas women typically work in relatively safer occupations. Thus, if compensatory wage differentials for a high risk of injury exist for both genders, and if the distributions of occupational risks differ between male and female workers, then part of the gender pay gap can be explained by the differences in injury risk men and women experience. Therefore, it is remarkable that most studies on the gender wage gap have disregarded such differences as a potential explanatory variable (Viscusi and Aldy, 2003). By merging data on occupational injury risk with German and US panel data on individual workers, Kluve and Schaffner (2007) analysed gender wage differentials while taking into account the risk of a fatal occupational injury. Furthermore, they used the Oaxaca–Blinder method for Tobit models to decompose the gender wage gap, both with and without considering the risk of a fatal injury. The results indicate that compensating wage differentials for risky jobs are reflected in the gender wage gap, which is caused by an unequal distribution of occupational injury risk between men and women. The inclusion of an 338
Safety Science 118 (2019) 337–344
P. Strawiński and D. Celińska-Kopczyńska
Classification for Occupations (ISCO), and to Leeth and Ruser (2003), who calculated the frequency of fatal and non-fatal workplace injuries for three-digit census occupations for both men and women. To compensate for rare events, they used data from 1996 to 1998. Their results are more precise than those obtained at the one-digit ISCO level. This suggests that the level of data disaggregation increases the precision of the results. Empirical evidence on the relationship between occupational injury risk and wages is mixed. Some studies show that workers who choose riskier professions earn a wage bonus, whereas others show a negative risk premium. For instance, Kuhn and Ruf (2009) examined the monetary compensation for the risk of non-fatal accidents in Switzerland. They used the number of accidents within cells defined over industry and skill-level of the job, and capitalise on the partial panel structure of their data to empirically isolate the wage component specific to an employer. The main advantage of their data is that the measurement errors in the risk data and the industry affiliations of workers are, arguably, of minor significance. The Swiss Wage Structure Survey (SWSS; Lohnstrukturerhebung (LSE)) obtains data from employers rather than from employees. The results show that using the accident risk at lower levels of aggregation, narrower samples of workers, and the wage component specific to a firm yields higher estimates of risk compensation. Nevertheless, Kuhn and Ruf (2009) suggested there is negative or zero compensation for a non-fatal accident risk at the workplace. Wang, Cheng and Smyth (2016) conducted an analysis within the human capital framework and identified a negative 10% premium for working in hazardous occupations.
Table 1 Number of accidents at work by the severity of the outcome and the gender of the subject (2011–2014). Gender
Severity
2011
2012
2013
2014
Women
Non-serious Serious Fatal Total
31,322 70 16 31,408
30,292 72 22 30,386
32,021 61 15 32,097
32,635 60 13 32,708
Men
Non-serious Serious Fatal Total
64,793 633 388 65,814
59,730 555 328 60,613
55,431 477 262 56,170
55,213 470 250 55,933
97,222
91,000
88,267
88,641
Total
Source: Own calculations, based on the database of statistical cards on accidents at work.
characteristics. Employers are legally obliged to report each accident. Moreover, there are two reports for each accident. The first is submitted immediately after the accident and the second is submitted six months later. The second report is used to update the severity of the accident. These unique legal requirements make the resulting data set highly reliable. Accidents are rare, unexpected events. To obtain a non-zero number of accidents for a particular group of workers, following previous studies (e.g. Leeth and Ruser, 2003), we aggregate data from four consecutive years (2011–2014). The time span is limited by the frequent ISCO adjustments. For example, changes occur as a result of occupational groups being merged, or occupations are moved from one occupational group to another. Thus, providing a unique key for merging the classifications is not possible. The database of statistical cards on accidents at work include 365,128 reported accidents that occurred during the period 2011–2014. Of these, 126,599 involved women, and 238,529 involved men. In Table 1, we present the annual number of the accidents that occurred from 2011 to 2014, categorized by the severity of the outcomes. The number of accidents varies across the years, with the highest in 2012 and the lowest in 2013 (see Table 1). It is worth emphasising the gender difference in the number of accidents, where nearly two-thirds of accidents happened to men. The structure of the accidents in terms of the severity of the outcomes was stable over time: non-serious accidents at work accounted for the majority of accidents (about 99.0%), while the numbers of serious and fatal accidents were of the same order of magnitude. Similar results on the stability of risk and the 1% rate of serious occupational accidents were also found for the Netherlands by Berkhout and Damen (2016). Accidents involving men outnumber those involving women: the overall number of accidents for women is about half that of men, and women are less frequently the subjects of serious and fatal accidents, even by order of magnitude. However, the structure of the severity of the outcomes of accidents is similar between men and women. Accidents classified as being serious or fatal account for 1%–2% of the overall number of accidents. We merge the accident data with data taken from the Survey of Wages and Salaries by Occupations (SES) database;1 therefore, the scope of the analysis is limited to those working in firms that employ 10 or more workers. Half of the working population work in such firms, and accidents in these firms constitute over 94.2% of work-related accidents during the period 2011–2014 (compare Tables 1 and 2). Interestingly, accidents in firms that employ 10 or more workers account for 94.3% of non-serious accidents, 85.2% of serious accidents, and 78.8% of fatal accidents. It seems that small firms underreport non-fatal
3. Data To estimate occupational injury risk, we use official register data. As noted by Berkhout and Damen (2016), sample surveys are usually too small in relation to the probability of observing sufficient accidents to precisely estimate occupational injury risk. As such, we rely on register data from the Polish statistical cards on accident at work. 3.1. Data on accidents Occupational injury risk is regarded as the probability of a worker suffering a particular work-related injury. A good proxy for this risk can be obtained by examining the number of accidents at work, which are defined as sudden events caused by an external factor that lead to injury or death and that happened in connection with work (CSO, 2015). Accidents at work can be categorised in at least two ways (CSO, 2015). The first depends on the severity of the outcome. A fatal accident at work is one in which the injured person dies at the site of the accident or within six months of the date of the accident. On the other hand, a serious accident at work is one that results in serious bodily harm (i.e. loss of sight, hearing, speech, or fertility, or the disruption of other primary bodily functions); incurable and life-threatening diseases, permanent mental illness, or a permanent, total or significant inability to work; or permanent, significant disfigurement or distortion of the body. A non-serious accident at work is neither serious nor fatal. The second categorisation is based on the number of people involved. In an individual accident at work, only one person is injured, whereas in a collective accident, more than one person is injured. We use official register data collected from statistical cards on accident at work in Poland to estimate occupational injury risk. We use databases on the Polish economy because of the country’s unique legal stance and the resulting high reliability of the data. Statistical cards of accidents at work are part of a programme of public statistical surveys and cover all accidents at work, as well as accidents considered equivalent to accidents at work. Each person’s accident is counted as a single accident at work, regardless of whether the accident was individual or collective. A such, the database contains information on accidents, their effects, and several personal and employer
1 The survey is described in detail in Section 3.2. Polish Survey of Wages and Salaries by Occupation is a part of EU Structure of Earnings Survey (SES).
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We take the Cartesian product of the NACE section and the threedigit ISCO code for occupations to simultaneously control for economic section and occupation, because some occupations are specific to economic sections (e.g. miners), while others are not (e.g. transport and storage labourers). Finally, we obtain 2633 distinct occupational groups belonging to a NACE section for both genders in which at least one accident of any severity was reported. This solves the problem of overrepresentation of certain groups of occupations in a given section (e.g. miners typically work in mining and quarrying, while electricians may be employed by firms in any economic section). To prevent a downward bias in the estimation results possibly arising from too many empty (zero-valued) accident cells and high-risk ratios for cells with few observations, we construct an alternative measure of the accident ratio. We calculate the accident ratio per 1000 workers for each gender and section group, and for each gender and three-digit ISCO occupation group. Then, as an accident measure, we use the product of these two numbers, divided by the average value of the first total accident ratio measure. The average value of the alternative measure is slightly higher than the first measure, which means that accident risk is overestimated. The estimated variance of the alternative occupational risk measure is significantly lower.
Table 2 Number of accidents at work by the severity of the outcome and the gender of the subject of the accident in firms employing 10 or more workers (2011–2014). Gender
Severity
2011
2012
2013
2014
Women
Non-serious Serious Fatal Total
29,925 64 14 30,003
28,752 65 21 28,838
30,441 54 14 30,509
30,937 56 10 31,003
Men
Non-serious Serious Fatal Total
60,981 544 305 61,830
55,981 465 257 56,703
51,988 399 203 52,590
51,916 398 196 52,510
91,833
85,541
83,099
83,513
Total
Source: Own calculations, based on the database of statistical cards on accidents at work.
accidents. There is a consensus among researchers that small firms are more likely to underreport or to not report work-related accidents (Pransky et al., 1999; Daniels and Marlow, 2005). Next, we calculate accident ratios as the number of accidents per 1000 employed workers in firms employing 10 or more workers. For analysis, we use wage data about workers in firms employing 10 or more workers. We limit the accident data to guarantee data coherency. There are 85,997 accidents per year in period 2011–2014 and 7,123,198 full-time employed (FTE) workers. This gives total accident rate of 12.07 per 1000 FTE workers. This includes 11.965 non-serious accidents per 1000 FTE workers, 0.072 serious accidents per 1000 FTE workers and 0.036 fatal accidents per 1000 FTE workers. For statistical reasons, we have excluded occupational groups with no more than four observations from the sample. We present the sample values of risk measures in Table 3. The obtained accident ratios are slightly larger than those published by the Central Statistical Office of Poland. Table 4 presents the five sections of the economy characterised by the highest accident ratios. The accident ratios presented in Table 4 are 30%–50% higher than the official estimates for the entire economy. The source of the difference is that almost the same number of accidents is divided by a smaller number of workers, that is, only those working in firms employing 10 or more workers. The economic sections characterised by the highest accident ratios for the entire working population and those for men are almost the same. The jobs with the highest accident ratios are in mining and quarrying, construction and agriculture, and forestry and fishing. For women, the riskiest occupations are in human health and social work, personal services (other services), and agriculture. This reflects the gender segregation in the workplace. There are 130 occupational groups at the three-digit ISCO classification level for which at least one accident of any severity is reported between 2011 and 2014. Previously, we found significant differences in the accident ratios among economic sections. Now, we concentrate on the three-digit ISCO code groups. Analysing the data in Table 5, we conclude that the occupational groups characterised by the highest occupational risk differ between genders. The most dangerous occupations for men are paramedical practitioners, ship deck crews and related workers, and animal producers. For women, the most dangerous occupations are animal producers; car, van, and motorcycle drivers; and heavy truck and bus drivers.
3.2. Data on wages We use data on wages and individual characteristics of employees taken from the SES database 2014, provided by the Central Statistical Office in Poland. The survey is carried out every two years and covers entities of the national economy with more than nine employees. The database includes both full- and part-time employees who worked for the entire month of October (CSO, 2014). The advantage of the SES survey is the high reliability of its wage data. The wages are reported by the accounting departments, so they are almost as reliable as administrative data. Another advantage of the SES data is the size of the database. As of 2014, the SES survey covered 12.8% of all enterprises with more than nine employees. The sample comprises approximately 730,500 observations. The main disadvantage of the database is that it is representative only of entities employing more than nine employees.2 3.3. Data merging process To create a database containing information on occupational injury risk and wages, we merge information from the database of statistical cards on accidents at work for 2011–2014 with data taken from the SES survey for 2014. Here, we use exact matching on economic sections (NACE section), three-digit level ISCO classifications, and gender. For each combination of these variables in the SES database, we link the computed average risk ratios. We matched information for 4134 of 4175 distinct groups (over 99%). However, we were unable to match all injury ratios for specific groups of workers because the SES only includes firms employing 10 or more workers. We also perform an analysis at the four-digit ISCO classification level. However, for nearly 39% of the combinations of economic section, gender, and four-digit ISCO code in the accident database, we were unable to match observations from the SES data on wages. We are aware that accident data should precede wage data. As of
Table 3 Risk of injuries by severity type (per 1000 workers). Severity
mean
Non-serious Serious Fatal
11.113 0.068 0.034
minimum
median
maximum
Std. dev.
0 0 0
8.690 0.018 0.002
200.000 9.615 16.129
10.943 0.128 0.089
2 The survey does not include the following: apprentices, persons engaged in outwork (home-workers), students maintaining vacation or diploma practices, members of workers’ groups organised by other units and appointed to work in the reporting units (e.g. soldiers, labour corps members, convicts), persons on maternity or childcare leave, persons enrolled in schools or PhD studies (and similar), persons employed in intervention and public works, and persons on sick leave. For more information about the sample selection scheme, see the Structure of Wages and Salaries by Occupations in October 2014, CSO, available from www.stat.gov.pl.
Source: Own computations, based on the database of statistical cards on accidents at work, 2011–2014. 340
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Table 4 Sections of the economy with the highest accident ratios. Working population
Men
Mining and quarrying Construction Agriculture, forestry and fishing Water supply and waste management Human health and social work Administrative and support services Manufacturing
18.28 17.63 17.37 16.82 15.69 15.13 15.07
Women
Mining and quarrying Human health and social work Water supply and waste management Construction Agriculture, forestry and fishing Administrative and support services Manufacturing
20.20 19.97 19.67 19.54 18.72 17.53 17.51
Human health and social work Other social activities Agriculture, forestry and fishing Administrative and support services Manufacturing Transporting and storage Wholesale and retail trade
14.83 13.73 13.17 12.32 10.03 9.30 7.68
Source: Own computations, based on the database of statistical cards on accidents at work, 2011–2014.
NACE section; ACCi are accident ratios; and εi is an error term.
Table 5 Occupational groups at the three-digit ISCO level with the highest accident rates.
The personal characteristics of a worker, such as gender, education, and work experience, describe accumulated human capital and are used as proxies for worker productivity. We also expect workplace characteristics to act as proxies for productivity and to affect wages. The main coefficients of interest are those of the accident ratio variables. The accident ratios represent the numbers of injured persons per 1000 employed persons in firms that employ 10 or more workers. We discriminate between accident ratios for all, fatal, and serious accidents. Individuals working in the same occupation are exposed to different risk factors, depending on the section of the economy. Accident ratios are obtained for cells created by industry and occupational group at the three-digit ISCO classification level and represent the influence of occupational injury risk on wages. The natural interpretation of these coefficients is the implicit price of risk (Black and Kniesner, 2003) and their role is to capture the size of wage premiums related to occupational risks. The risk variables can be interacted with worker age or experience (for a discussion on this topic, see Viscusi (1993)), but in our case, the quantitative impact of such interaction on the results is negligible. To ensure the robustness of the results, we estimate the wage Eq. (1) for the full sample of workers (model 1) and for several subsamples. The specifications of model 2 are the same as those of model 1, but the sample is limited to workers in the high productivity age group (25–55). Similarly, model 3 is based on the subsample of full-time workers, because these workers are exposed to risk factors for a longer period. Then, model 4 is based on the subsample of full-time employees between the ages of 25 and 55. Model 5 is similar to model 3, except the accident ratios are limited to three times the average value of the population accident rate. Model 6 is similar to model 4, except the accident ratio is limited to three times the average value of the population accident rate. We estimate the latter two models to verify whether eliminating observations with the highest accident rates significantly influences the results. Finally, the last two model specifications include three distinct accident ratio measures in the wage equation for different types of accident severity. Model 7 is based on Eq. (1), estimated on a subsample of full-time employees. Model 8 is similar to model 7 but excludes observations with an accident rate that is three times higher than the average value. Models 1–6 are estimated using the two accident measures described in Section 3.1. In Table 6, we present the results for the accident rate calculated for each cell by gender, occupation, and economic section. Table 7 shows the results for the alternative measure of the accident ratio, that is, the accident rate by section multiplied by the accident rate by occupational group. Owing to space limitations, we do not report the coefficients of the control variables in Eq. (1).3 The estimates for all models in Tables 6 and 7 are consistent with economic theory. The wage premium for work experience is positive,
Working population 224 Paramedical practitioners 835 Ship deck crew and related workers 612 Animal producers 342 Sports and fitness workers 511 Travel attendants, conductors and guides 933 Transport and storage labourers Men 224 Paramedical practitioners 835 Ships' deck crew and related workers 612 Animal producers 342 Sports and fitness workers 532 Personal care workers in health services 511 Travel attendants, conductors, and guides Women 612 Animal producers 832 Car, van, and motorcycle drivers 833 Heavy truck and bus drivers 511 Travel attendants, conductors and guides 751 Food processing and related trade workers 812 Metal processing and finishing plant operators
297.62 65.83 50.87 42.15 34.67 30.60 312.90 65.05 55.70 51.98 45.94 37.26 41.67 39.11 38.27 30.92 26.61 25.38
Note: Names of occupations are taken from ILO (2008). Source: Own computations, based on the database of statistical cards on accidents at work, 2011–2014.
October 2014 (for which we possess wage data), information on accidents at work was only available up to 2013, because the data on accidents are published at the end of the year. Furthermore, we are not able to use 2016 wage data because of changes in the classification of occupations in 2015. However, any bias should be negligible. 4. Empirical results 4.1. Wage equation The database contains information on individuals’ wages and personal characteristics, such as gender, age, level of education, work tenure, and occupational group. It also includes employer characteristics, such as ownership sector, enterprise size, location, and NACE section. All variables are used as controls in the Mincer equation below. We start with a simple hedonic wage regression, called a Mincertype equation, of the following form:
ln(wi) =
0
+
i PERi
+
j JOB j
+
k ACCk
+ i,
(1)
where: ln(wi) is the natural logarithm of gross monthly salary (PLN); PERi denotes the following characteristics of individuals: highest level of formal education, number of years of work experience (and its square), and gender of an employee (1 for female, 0 for male); JOBi denotes job characteristics: size of the firm (small, medium, big, very big), ownership sector (1 for public, 0 for private), and
3
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Detailed results are available on request.
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Table 6 Selected parameters of wage models (Eq. (1)). Variable/Model
1
2
3
4
5
6
Experience Experience squared Female Accident rate Female × accident N R2
0.0260 −0.0004 −0.2014 −0.0025 −0.0007 730,483 0.43
0.0298 −0.0005 −0.2160 −0.0028 −0.0008 597,201 0.42
0.0255 −0.0004 −0.2177 −0.0027 −0.0004 674,613 0.43
0.0291 −0.0005 −0.2329 −0.0030 −0.0003 553,422 0.42
0.0255 −0.0004 −0.2245 −0.0078 −0.0018 662,978 0.44
0.0291 −0.0005 −0.2385 −0.0081 −0.0018 544,530 0.43
Note: Numbers in bold indicate significance at the 1% level. Source: Own computations, based on merged databases.
but with a decreasing return. The negative coefficients for women reflect that they receive lower wages, on average. The coefficients of the accident rates are negative and statistically significant, suggesting that workers are not properly compensated in terms of wages for working under hazardous conditions. There are several explanations for this phenomenon. The first is statistical in nature: there is a measurement problem because accidents are rare events. Thus, information collected over a limited period may obscure what happens in reality. The second explanation relates to human behaviour. People seem to underestimate the probability of rare events and undervalue the negative consequences of potential unfavourable events. Therefore, work-related risks may be underestimated by employees. A third explanation is that firms have more bargaining power during wage negotiations and, therefore, wages do not fully compensate workers for accident risks. The estimates for the model with the disaggregated measures of accident risk in Table 8 are more intuitive than those in Table 7. According to our preferred empirical model specifications from Table 8, wage compensations can be found in Eq. (7): −0.31% for non-serious accidents for men and −0.34% for women (additional component for women is not significant) which translates to −11.1 PLN. The reference point is average earnings of full-time employees which was 3725.10 PLN in 2014 according to the Structure of Earnings Survey. However, the wage compensation for serious accident risk is positive, statistically significant, and estimated at 5% to 15%, which amounts to 187.75 PLN and to 555.80PLN, accordingly. Employees working in occupations characterised by a high likelihood of several days of absence as a consequence of work-related accidents are compensated for that risk with higher wages. Compensation for fatal accident risk is positive and statistically insignificant, but smaller than that for serious accidents and amounts for 30.90 PLN for men and −37.25 PLN for women. Again, this may be an issue of imprecise measurement. Dickens (1984) emphasises that fatal accidents in particular are, fortunately, rare events. Thus, the precision of the risk measures is low in comparison with the other variables in the wage equation. Unfortunately, the estimated amounts of wage compensation are not directly comparable among studies due to methodological differences. Estimations for different countries are based on data of different quality. Similarly to Berkhout and Damen (2016), we use population data, whereas, e.g. Leeth and Ruser (2003) or Grazier and Sloane (2006) use survey data. The studies
Table 8 Wage models for different severity risk measures. Variable/Model
7
8
Experience Experience squared Female Non-serious accident rate Female × Non-serious accident rate Serious accident rate Female × Serious accident rate Fatal accident rate Female × Fatal accident rate N R2
0.0255 −0.0004 −0.2174 −0.0031 −0.0003 0.0504 0.0988 0.0083 −0.0183 674,613 0.43
0.0255 −0.0004 −0.2250 −0.0091 −0.0010 0.1572 0.0371 0.0020 0.0184 662,978 0.44
Note: Numbers in bold indicate significance at the 1% level. Source: Own computations, based on merged databases.
also differ in the level of aggregation of accident data, e.g. DeLeire and Levy (2004) used 2-digit occupational codes and Kuhn and Ruf (2009) utilized data aggregated at the industry level. Also, different estimation methods were used, e.g. panel data methods (Kuhn and Ruf 2009) or conditional logit model (DeLeire and Levy 2004). 4.2. Wage decomposition To examine the extent to which injury risk wage premiums contribute to gender wage inequalities, we use the hedonic wage Eq. (1) and perform the Oaxaca–Blinder decomposition. Our dependent variable is the difference between male and female workers’ logarithms of wages. This difference is decomposed into the so-called explained part of the wage gap and the unexplained part of the wage decomposition. To analyse the contribution of the accident ratio to the gender wage gap, we examine the detailed decomposition (i.e. the contributions of each component) (see Table 9). Similar to the estimation of the wage equation, to provide robustness to our results, we provide different sample setups for wage Eq. (1) and then perform the decomposition. In model 1, we estimate the wage equation on a subsample of full-time workers. In model 2, the sample is also limited to full-time workers, but accident measure is categorized by
Table 7 Wage models for an alternative risk measure. Variable/Model
1
2
3
4
5
6
Experience Experience squared Female Accident rate Female × accident N R2
0.0260 −0.0004 −0.2046 −0.0041 −0.0046 730,483 0.41
0.0299 −0.0005 −0.2155 −0.0041 −0.0049 597,201 0.41
0.0255 −0.0004 −0.2237 −0.0042 −0.0037 674,613 0.40
0.0291 −0.0005 −0.2348 −0.0042 −0.0040 553,422 0.41
0.0257 −0.0004 −0.2509 −0.0071 −0.0022 629,490 0.41
0.0294 −0.0005 −0.2629 −0.0072 −0.0024 517,484 0.41
Note: Numbers in bold indicate significance at the 1% level. Source: Own computations, based on merged databases. 342
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behaviour. In addition, persons with dependants do not expose themselves to unnecessary risks. This effect is found to be especially strong for women. Second, the wage data are taken from a sample, albeit a large sample, of about 12.8% of workers in firms that employ 10 or more workers. This prevents us from analysing each occupation separately. The sample selection argument often raised in labour market analyses is not applicable here, because accident risk premiums refer only to those who work. In addition, we are aware that accident data should precede wage data. However, the bias from considering accident data from the same year as the wage data should be negligible. Unfortunately, there is no straightforward solution to the abovementioned data problems. Alternatives include conducting our own large-scale survey, with an overrepresentation of workers in occupations with few accidents, or pursuing a general census on accidents at work and wages. We use accident ratios as proxies for occupational risk. A competitive approach could be to consider the risks of occupational diseases (e.g. ratios of disease cases per year to the number of employees). The strength of the latter approach relies on the intuitive reasoning that employees are more concerned about the probability of suffering from an occupation-related disease and the subsequent decrease in quality of life than they are about the probability of a fatal accident. Occupationrelated diseases also seem to be more common, mostly because of a biased perception of their frequency and consequences. While an accident is a single event, the effects of which may be temporary, the effect of an occupation-related disease is usually permanent. Moreover, similarly to the difference between the formal and common understanding of accidents at work, not every disease intuitively linked with an occupation is regarded as an occupational disease. In addition, to the best of our knowledge, a detailed database on occupational diseases is not available.
Table 9 Wage decomposition. Variable/model
1
2
3
4
5
6
Difference Explained Accident rate Accident rate (%) Unexplained
0.123 −0.098 −0.018 0.184 0.221
0.123 −0.095 −0.013 0.142 0.218
0.129 −0.111 −0.046 0.413 0.240
0.129 −0.102 −0.034 0.330 0.232
0.123 −0.127 −0.061 0.480 0.251
0.143 −0.124 −0.063 0.506 0.267
Note: Numbers in bold indicate significance at the 1% level. Source: Own computations, based on merged databases.
accident severity, which is our preferred specification. In model 3, we limit the sample to full-time employees between the ages of 25 and 55, with an accident ratio not exceeding three times the average. Model 4 is similar to model 3, but with an accident measure for each accident severity. Finally, models 5 and 6 use the alternative measure for accident risk. We estimate model 5 on the subsample of full-time employees, and model 6 on the subsample of full-time employees aged 25–55 and with an accident ratio not exceeding three times the average value. The results of the wage decomposition suggest that accident risks contribute significantly to the explanation of the existing wage gap. However, the estimated size of the impact of the accident risk on the explained part of the wage gap depends on the model specification: in the models based on full-time workers, the accident risk explains 15%–20% of the wage gap, whereas, for workers aged 25 to 55, the accident risk explains 30%–40% of the wage gap. In the models that use the alternative risk measure, the estimated impact is even higher. Note that in models 2 and 4, the coefficients of the fatal accident rate are non-significant. Moreover, in those models, the estimated impact of accident risks on the wage gap is lower than in a similar model with a single risk measure. Hence, pooling the different risk levels may overestimate the impact of the accident rate.
6. Conclusions
5. Discussion, Limitations, and future research directions
In this study, we assessed the sizes of wage premiums related to occupational risk. We also investigated the extent to which differences in the risk of occupational injury for men and women (and the corresponding wage compensation) help to explain the observed gender wage differentials. To this end, we linked occupational injury register data to a large survey on wages and salaries. Then, we estimated several specifications of the wage equation and performed an Oaxaca–Blinder decomposition for the wage gaps. The results for the models with aggregate accident indicators suggest that workers are not properly compensated in terms of wages for working under hazardous conditions. This can be explained in several ways. As Daniels and Marlow (2005) suggest, the employees may fail to report the accidents because of, e.g. fear of reprisal or loss of pay. In turn, there is a measurement problem; the statistics on the casualties are flawed which results in the underestimation of the work-related risk. Therefore, it is not properly reflected in wages. Another explanation comes from Kahneman and Tversky (1979). While estimating the probability of tail events, individuals succumb to their biases of judgement. They may overestimate it (Kahneman and Tversky, 1979) or even underestimate it (Krawczyk and Rachubik, 2019). Moreover, accidents are rare events, and we employ data for a four-year period only. Therefore, if an accident occurred during that period, we may have overestimated the accident risk for that group of workers. Similarly, if an accident did not occur, this may have resulted in an underestimation of the risk. Better and more intuitive results are obtained from models with disaggregated accident measures. These measures treat accidents of different severity levels separately. Compensation for a non-serious accident risk is found to be statistically significant and negative. Conversely, compensation for a serious accident risk is positive and is estimated to be between 5% and 15%. The results for a fatal accident
Our results have strengths and limitations. One of the main strengths is data reliability. The data source on accidents is an official register and the wage data are taken from a large sample survey (about 12.8% of the population), conducted according to established international standards. In addition, the large sample makes our analysis more credible. For instance, we were able to merge over 99% of the observations from two sources. The methodology we use is similar to the one used by Kluve and Schaffner (2007) with some differences. We analyse different severity level injuries while Kluve and Schaffner (2007) concentrated on fatal injury risk only. Moreover, we use more detailed risk data and do not upper-censored wage data which allows us to use standard OaxacaBlinder wage decomposition. Another strength of the study is the use of well-established economic models in the literature. Specifically, we use a Mincer-type wage regression and the Oaxaca–Blinder decomposition. Both are widely used and are popular economic tools used to analyse wages and their determinants. A further advantage of our study is our analysis at different levels of data aggregation. That is, we divided the sample into more than 4000 subgroups and estimated the coefficients for different levels of accident severity. Although we aggregated the data based on the three-digit level of ISCO coding, we could deepen our analysis by examining four-digit level groups (i.e. each occupation, separately). However, in that case, we could not match the accident data to the worker sample data for nearly 40% of the groups. The first limitation of our study is the lack of information on workers’ social backgrounds. Empirical evidence suggests that formal and informal personal relationships affect workers’ risk-taking 343
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risk are statistically insignificant owing to the rarity of their occurrence. Our results explain the mixed evidence in the literature, where the use of different accident measures causes contradictory evidence on the relationship between wages and accident risk. As in several other studies (e.g. Grazier and Sloane, 2006; Kuhn and Ruf, 2009), in the case of an aggregated risk indicator, we found a negative relationship between accident risk and wages. The results of our detailed analysis with disaggregated risk indicators are in line with those of Kuhn and Ruf (2009) for non-fatal accidents and with those of DeLeire and Levy (2004) for fatal accidents. Finally, we decompose the wage gap into explained and unexplained parts and calculate the contribution of the difference in accident risk between men and women to the explained part. The estimated wage gap is 0.13, with −0.09 to −0.13 for the explained part and 0.21 to 0.26 for the unexplained part. The negative sign of the explained part means that, despite women having better characteristics, on average (e.g. they are better educated), they earn less than men do, on average. The accident risk explains 15% to 30% of the explained part of the wage gap, which is more than, for instance, the part explained by work experience. The part of the wage gap explained by risk measures is higher than in the case of Germany or the United States, simply because we use a wider and more detailed definition of injury risk.
Black, Dan A., Kniesner, Thomas J., 2003. On the measurement of job risk in hedonic wage models. J. Risks Uncertainty 27, 205–220. https://doi.org/10.1023/ A:1025889125822. Paolo, Crosetto, Antonio, Filippin, 2017. Safe options induce gender differences in risk attitudes. IZA Discussion Paper 10793. CSO, 2015. Accidents at work 2014, Central Statistical Office of Poland. CSO, 2014. The structure of Wages and Salaries by Occupations in 2014, Central Statistical Office of Poland. Christine, Daniels, Peter, Marlow, 2005. Literature Review on the Reporting of Workplace Injury Trends. Health and Safety Laboratory, Buxton, The United Kingdom. Thomas, DeLeire, Helen, Levy, 2004. Worker sorting and the risk of death on the job. J. Labor Econ. 22, 925–953. Dickens, William T., 1984. Differences between risk premiums in union and non-union wages and the case for occupational safety regulations. Am. Econ. Rev. 74 (2), 320–323. Marek, Giergiczny, 2008. Value of a statistical life – the case of Poland. Environ. Resour. Econ. 41, 209–221. https://doi.org/10.1007/s10640-007-9188-2. Suzanne, Grazier, Sloane, Peter J., 2006. Accident risk, gender, family status and occupational choice in the UK. IZA Discussion Paper 2302. Joni, Hersh, 1998. Compensating differentials for gender-specific job injury risks. Am. Econ. Rev. 88, 598–607. International Labour Organisation, 2008. International Standard Classification of Occupations. Available at http://www.ilo.org/public/english/bureau/stat/isco/ isco08/ (accessed: 16.10.2017). International Labour Organisation (2016). Safety and health at work. Available at http:// www.ilo.org/global/topics/safety-and-health-at-work (accessed: 16.10.2017). Johnson George, E., Stafford Frank, P., 1998. Alternative approaches to occupational exclusion. In: Christina Jonung, Inga Persson (Eds.) Women’s Work and Wages, Routledge, New York. Kahneman, D., Tversky, A., 1979. Prospect Theory: An Analysis of Decision under Risk. Econometrica 68, 263–291. King, Mary C., 1992. Occupational segregation by race and sex, 1940–1988. Monthly Labor Rev. 115 (4), 30–36. Jochen, Kluve, Sandra, Schaffner, 2007. Gender wage differentials and the occupational injury risk. Evidence from Germany and the US. Ruhr Economic Papers, 28. Michał, Krawczyk, Joanna, Rachubik, 2019. The representativeness heuristic and the choice of lottery tickets: A field experiment. Judgement and Decision Making 14 (1). Andreas Kuhn, Oliver, Ruf, 2009. The value of a statistical injury. New evidence from the Swiss labor market. IZA Discussion Paper, 4409. Rafael, Lalive, 2003. Did we overestimate the value of health? J. Risk Uncertainty 27, 171–193. https://doi.org/10.1023/A:1025685024913. Leeth, John D., John, Ruser, 2003. Compensating wage differentials for fatal and nonfatal injury risk by gender and race. J. Risks Uncertainty 27, 257–277. https://doi.org/10. 1023/A:1025845310801. Olson, Craig A., 1981. An analysis of wage differentials received by workers on dangerous jobs. The J. Hum. Resour. 16, 167–185. Glenn, Pransky, Terry, Snyder, Allard, Dembe, Jay, Himmelstein, 1999. Under-reporting of work-related disorders in the workplace: a case study and review of the literature. Ergonomics 42 (1), 171–182. https://doi.org/10.1080/001401399185874. Robert, Sandy, Elliott, Robert F., Stanley, Siebert W., Xiandong, Wei, 2001. Measurement error and the effects of union on the compensating differentials for fatal workplace risks. J. Risks Uncertainty 23, 33–56. https://doi.org/10.1023/A:1011112631522. Kip, Viscusi W., 1993. The value of risks to life and health. J. Econ. Literature 31, 1912–1946. Kip, Viscusi W., Aldy, Joseph A., 2003. The value of a statistical life: A critical review of market estimates throughout the world. J. Risk Uncertainty 27, 5–76. https://doi. org/10.1023/A:1025598106257. Haining, Wang, Zhiming, Cheng, Russell, Smyth, 2016. Are Chinese workers compensated for occupational risk? J. Indus. Relations 58, 111–130. https://doi.org/10.1177/ 0022185615598192.
Acknowledgements The authors are grateful to two anonymous referees whose comments improved quality of the text and to the participants of the seminars at University of Warsaw and of the WIEM2017 and Macromodels 2017 conferences for their helpful comments and suggestions and Aleksandra Majchrowska and Paulina Broniatowska for general comments and proofreading. Funding This study was supported financially by the National Science Centre, Poland [research grant 2015/19/B/HS4/03231]. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ssci.2019.04.041. References Berkhout Peter, H.G., Martin, Damen, 2016. Estimating individual occupational risk using registration data. Saf. Sci. 82, 95–102. https://doi.org/10.1016/j.ssci.2015.08.013.
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Safety Science journal homepage: www.elsevier.com/locate/safety
Occupational injury risk wage premium a,⁎
Paweł Strawiński , Dorota Celińska-Kopczyńska a b
T
b
Dr. habil., Faculty of Economic Sciences, University of Warsaw, Dluga 44/50, 00-241 Warsaw, Poland PhD, University of Warsaw, Poland
ABSTRACT
This study examines the occupational injury wage premium and its influence on the gender wage gap. To this end, we quantify the part of the wage premium that may be the result of occupational risk, and then estimate the share of the gender wage gap attributed to occupational risk. Existing studies on occupational risk and its influence on the economy are limited by the availability of data. As a result, the findings related to the injury risk wage premium are inconsistent. We use a detailed, disaggregated data set on accidents at work and calculate the accident risks for groups of occupations. Then, we estimate an extended wage equation and perform the Oaxaca–Blinder wage decomposition. Wage compensation for non-serious accident risk is negative, but is positive for serious accident risk. However, the results for fatal accident risks are inconclusive. Furthermore, the accident risk accounts for 15%–30% of the explained wage gap between men and women. Our findings resolve the inconsistent results in the literature on the relation between occupational injury risk and wages. Our results suggest that personal perceptions of occupational risk are inaccurate, perhaps because workers are usually fully informed about work-related risks only after wage negotiations are concluded and a contract is signed.
1. Introduction Occupational injuries are common and have a strong impact on the economy. According to the International Labour Organisation (ILO), almost 1 million work-related accidents occur every day worldwide; that is, every 15 s, 153 workers have a work-related accident and one worker dies from a work-related accident. The cost of such accidents is estimated to be 4% of the global Gross Domestic Product (ILO, 2016). The standard treatment of occupational risk in the labour market is conducted in terms of the theory of compensating wage differentials. According to this theory, workers must be compensated with higher wages for accepting unpleasant work conditions (Olson, 1981). Therefore, market forces ensure that a wage premium is paid to employees who perform job tasks associated with a higher risk of occupational injury, implying there is a positive relationship between wages and job risk in the market. In other words, wages increase with job risk, all other factors being equal. An occupational risk premium reflects the probability that workers employed in a particular occupation will suffer a work-related injury. However, empirical evidence on the relation between the risk of occupational injury and wages is mixed. For instance, Leeth and Ruser (2003) showed a positive relation between the two, whereas Kuhn and Ruf (2009) found a negative relation. Furthermore, estimating an occupational injury risk premium is not straightforward because male workers are overrepresented in the most dangerous occupations. Many studies have confirmed worker sorting across occupations (e.g. see Johnson and Stafford, 1998). The
⁎
implication of sorting based on risk aversion is that if men and women vary in terms of their willingness to accept greater risk in exchange for a higher wage, they will work in different occupations. Hence, it is difficult to separate the injury risk premium from the wage effects of sorting. This study has two main aims. First, we assess the size of the wage premium with regard to occupational risk. Second, we investigate the extent to which the difference between the occupational injury risk of the jobs that men and women hold and the corresponding compensation can help to explain the observed gender wage differentials. To achieve these aims, we use standard economic tools: an extended Mincer-type wage equation and the Oaxaca–Blinder decomposition. In summary, we assess whether the occupational risk premium is predicted by the theory of compensated wage differentials. The novelty of our approach lies in the detailed level of our analysis. Previous studies are typically based on aggregated sample survey data. In contrast, we take advantage of the unique Polish legal system that requires that firms report each accident at work. To estimate the occupational injury risk, we use official register data taken from statistical cards on accidents at work in Poland for the period 2011–2014. Next, we merge the data on accidents with data taken from the Structure of Wages and Salaries by Occupations 2014 for Poland. This empirical strategy allows us to resolve the contradictory evidence currently available on the relation between occupational injury risk and wages. Furthermore, we offer a possible explanation as to why the results in studies differ so significantly. In this regard, our findings show that the measure of injury risk and the data aggregation both affect the results.
Corresponding author. E-mail address: [email protected] (P. Strawiński).
https://doi.org/10.1016/j.ssci.2019.04.041 Received 17 September 2018; Received in revised form 5 April 2019; Accepted 27 April 2019 Available online 28 May 2019 0925-7535/ © 2019 Elsevier Ltd. All rights reserved.
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The remainder of this article is organised as follows. Section 2 reviews the literature on the occupational injury risk wage premium. Section 3 discusses the properties of the data on accidents and wages. In Section 4, we present our methodology and estimation results. Section 5 discusses the results, as well as possible limitations of the study, and lastly, Section 6 concludes the paper.
injury risk measure in the wage equation increases the explained part of the gender wage gap by 1–3 percentage points (or up to 12% of the explained part of the gap). Hersh (1998) analysed gender-specific estimates for injury and illness incidence rates by both industry and occupation for the US economy. The study showed that although women are less likely than men to be injured on the job, their injuries are not negligible, accounting for about a third of all injuries and illnesses that result in days away from work. After adjusting this number for gender differences in employment, the results show that women face a job risk of 70% of that of men. However, men and women receive similar wage compensation for occupational injury risk. Leeth and Ruser (2003) also analysed data on the US economy, showing that non-fatal injury risk increases the wages for both men and women, but that the gain is much higher for women. The non-fatal injury risk for men is associated with wage premiums of 0.9%–1.4%; for women it is associated with premiums of 2.8%–4.5%. The wage premiums for fatal injury risk are significantly lower (0.5%–0.9% for men). In the case of women, the wage premiums are positive, but statistically insignificant. The second strand of literature discusses methodological approaches. Several examples of analyses at the disaggregated level of occupations are available (e.g. DeLeire and Levy, 2004; Grazier and Sloane, 2006). The authors typically calculate risk rates by industry or occupation and then match these rates to another data set. For instance, Berkhout and Damen (2016) studied the accident frequency for distinct occupational groups within the human capital theory framework. They showed that the male incidence rate is, on average, four times higher than the female rate. In addition, Wang et al. (2016) showed that there is a 10% negative risk premium related to jobs that are perceived as being risky. DeLeire and Levy (2004) assigned fatal and non-fatal injury risks to occupations using data from the Bureau of Labor Statistics Survey of Occupational Injuries and Illness and the Census of Fatal Occupational Injuries. These data provide the number of injuries and fatalities for three-digit occupation levels from 1992 to 1999. There is also information on the severity of non-fatal injuries, including the median number of days missed from work per injury for each occupation. However, their analysis was restricted to full-time workers, because part-time workers allocate some time to household production, for which risk data are not available. In addition, their sample was limited to workers aged 25 to 34 because current risk measures are inaccurate for older workers, who have accumulated occupation-specific knowledge. Conversely, Grazier and Sloane (2006) included part-time workers in their analysis because they expected family status to be an important determinant of whether an individual works full-time or part-time. They pointed out that excluding part-timer workers would result in a disproportionate elimination of parents from the analysis. A similar empirical exercise was conducted by Giergiczny (2008) for Poland. The aim of the study was to estimate the value of statistical life. Fatal injury risk premium estimates based on a three-digit occupational risk measure vary from 1.5% to 4.6%, depending on the level of aggregation for economic sector dummies (NACE levels). Sandy et al. (2001) showed that using industry average risk rates fails to capture sufficiently precise differences between the levels of risk exposure for workers in different occupations in the same industry. Ideally, one would want to estimate the risk for each occupation-byindustry cell, but most occupations record zero injuries or deaths, making this idea impractical. Lalive (2003) argued that using aggregate risk as a proxy for a work-related risk is a valid approach if job tasks are similar within occupations or industries. However, this approach may fail if there is substantial heterogeneity in the workplace risk within an industry or an occupation. Thus, the dominant approach is to assign the same risk level to all workers. However, in the case of heterogeneity, the estimates of the compensating wage differentials will be biased. It is also worth referring to Grazier and Sloane (2006), who conducted their analysis at the two-digit level of the International Standard
2. Literature review Studies on the influence of occupational injury risk on wages and wage differentiation are limited owing to data and methodological problems. There exist two strands of literature in this regard. The first identifies the determinants of sorting workers between safe and risky jobs, and the second calculates the implicit value of life or the implicit value per statistical injury (i.e. the wage reduction associated with reducing the expected risk by one percentage point). In both settings, the dominant approach relies on some published measure of the risk level by occupation, at various degrees of aggregation, and then matches this risk variable with worker survey data (Viscusi, 1993). Men tend to take up employment in riskier occupations more often than women do. The authors explain this effect using psychosocial factors, such as mothers being more devoted to children and being more involved in household duties and childcare. Numerous studies, and particularly those based on the United States (see Viscusi and Aldy, 2003 for a comprehensive review of this subject), have confirmed this observation. For instance, the results of Crosetto and Filippin’s (2017) experiments showed that the availability of a safe option causally induces women to behave in a less risk-tolerant manner in comparison with men. This observation explains why women tend to choose occupations that require safer job tasks and, hence, work in environments with a lower risk of injury. DeLeire and Levy (2004) and Grazer and Sloane (2006) tested the worker sorting hypothesis for the US and the UK labour markets, respectively. Both studies used family structure as a proxy for the degree of risk aversion when testing the hypothesis that risk-averse individuals will choose safer occupations. They found that women are more riskaverse than men, and that people who are married or have children are more risk averse than those not engaged in relation without children. Moreover, differences in the risk of death on the job among occupations is an important reason why men and women choose different occupations. The estimated sizes of these effects suggest that differences in physical risk across occupations explain about one-quarter of the occupational gender segregation in the United States, whereas the effect of gender segregation is significantly smaller in the United Kingdom. As suggested by King (1992), differences in occupational injury risk can partly explain the existence of a gender wage gap. That is, individuals in occupations with a high injury risk are compensated for that risk by means of bonus payments. At the same time, male workers are overrepresented in the most dangerous occupations, such as scaffolders or miners, whereas women typically work in relatively safer occupations. Thus, if compensatory wage differentials for a high risk of injury exist for both genders, and if the distributions of occupational risks differ between male and female workers, then part of the gender pay gap can be explained by the differences in injury risk men and women experience. Therefore, it is remarkable that most studies on the gender wage gap have disregarded such differences as a potential explanatory variable (Viscusi and Aldy, 2003). By merging data on occupational injury risk with German and US panel data on individual workers, Kluve and Schaffner (2007) analysed gender wage differentials while taking into account the risk of a fatal occupational injury. Furthermore, they used the Oaxaca–Blinder method for Tobit models to decompose the gender wage gap, both with and without considering the risk of a fatal injury. The results indicate that compensating wage differentials for risky jobs are reflected in the gender wage gap, which is caused by an unequal distribution of occupational injury risk between men and women. The inclusion of an 338
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Classification for Occupations (ISCO), and to Leeth and Ruser (2003), who calculated the frequency of fatal and non-fatal workplace injuries for three-digit census occupations for both men and women. To compensate for rare events, they used data from 1996 to 1998. Their results are more precise than those obtained at the one-digit ISCO level. This suggests that the level of data disaggregation increases the precision of the results. Empirical evidence on the relationship between occupational injury risk and wages is mixed. Some studies show that workers who choose riskier professions earn a wage bonus, whereas others show a negative risk premium. For instance, Kuhn and Ruf (2009) examined the monetary compensation for the risk of non-fatal accidents in Switzerland. They used the number of accidents within cells defined over industry and skill-level of the job, and capitalise on the partial panel structure of their data to empirically isolate the wage component specific to an employer. The main advantage of their data is that the measurement errors in the risk data and the industry affiliations of workers are, arguably, of minor significance. The Swiss Wage Structure Survey (SWSS; Lohnstrukturerhebung (LSE)) obtains data from employers rather than from employees. The results show that using the accident risk at lower levels of aggregation, narrower samples of workers, and the wage component specific to a firm yields higher estimates of risk compensation. Nevertheless, Kuhn and Ruf (2009) suggested there is negative or zero compensation for a non-fatal accident risk at the workplace. Wang, Cheng and Smyth (2016) conducted an analysis within the human capital framework and identified a negative 10% premium for working in hazardous occupations.
Table 1 Number of accidents at work by the severity of the outcome and the gender of the subject (2011–2014). Gender
Severity
2011
2012
2013
2014
Women
Non-serious Serious Fatal Total
31,322 70 16 31,408
30,292 72 22 30,386
32,021 61 15 32,097
32,635 60 13 32,708
Men
Non-serious Serious Fatal Total
64,793 633 388 65,814
59,730 555 328 60,613
55,431 477 262 56,170
55,213 470 250 55,933
97,222
91,000
88,267
88,641
Total
Source: Own calculations, based on the database of statistical cards on accidents at work.
characteristics. Employers are legally obliged to report each accident. Moreover, there are two reports for each accident. The first is submitted immediately after the accident and the second is submitted six months later. The second report is used to update the severity of the accident. These unique legal requirements make the resulting data set highly reliable. Accidents are rare, unexpected events. To obtain a non-zero number of accidents for a particular group of workers, following previous studies (e.g. Leeth and Ruser, 2003), we aggregate data from four consecutive years (2011–2014). The time span is limited by the frequent ISCO adjustments. For example, changes occur as a result of occupational groups being merged, or occupations are moved from one occupational group to another. Thus, providing a unique key for merging the classifications is not possible. The database of statistical cards on accidents at work include 365,128 reported accidents that occurred during the period 2011–2014. Of these, 126,599 involved women, and 238,529 involved men. In Table 1, we present the annual number of the accidents that occurred from 2011 to 2014, categorized by the severity of the outcomes. The number of accidents varies across the years, with the highest in 2012 and the lowest in 2013 (see Table 1). It is worth emphasising the gender difference in the number of accidents, where nearly two-thirds of accidents happened to men. The structure of the accidents in terms of the severity of the outcomes was stable over time: non-serious accidents at work accounted for the majority of accidents (about 99.0%), while the numbers of serious and fatal accidents were of the same order of magnitude. Similar results on the stability of risk and the 1% rate of serious occupational accidents were also found for the Netherlands by Berkhout and Damen (2016). Accidents involving men outnumber those involving women: the overall number of accidents for women is about half that of men, and women are less frequently the subjects of serious and fatal accidents, even by order of magnitude. However, the structure of the severity of the outcomes of accidents is similar between men and women. Accidents classified as being serious or fatal account for 1%–2% of the overall number of accidents. We merge the accident data with data taken from the Survey of Wages and Salaries by Occupations (SES) database;1 therefore, the scope of the analysis is limited to those working in firms that employ 10 or more workers. Half of the working population work in such firms, and accidents in these firms constitute over 94.2% of work-related accidents during the period 2011–2014 (compare Tables 1 and 2). Interestingly, accidents in firms that employ 10 or more workers account for 94.3% of non-serious accidents, 85.2% of serious accidents, and 78.8% of fatal accidents. It seems that small firms underreport non-fatal
3. Data To estimate occupational injury risk, we use official register data. As noted by Berkhout and Damen (2016), sample surveys are usually too small in relation to the probability of observing sufficient accidents to precisely estimate occupational injury risk. As such, we rely on register data from the Polish statistical cards on accident at work. 3.1. Data on accidents Occupational injury risk is regarded as the probability of a worker suffering a particular work-related injury. A good proxy for this risk can be obtained by examining the number of accidents at work, which are defined as sudden events caused by an external factor that lead to injury or death and that happened in connection with work (CSO, 2015). Accidents at work can be categorised in at least two ways (CSO, 2015). The first depends on the severity of the outcome. A fatal accident at work is one in which the injured person dies at the site of the accident or within six months of the date of the accident. On the other hand, a serious accident at work is one that results in serious bodily harm (i.e. loss of sight, hearing, speech, or fertility, or the disruption of other primary bodily functions); incurable and life-threatening diseases, permanent mental illness, or a permanent, total or significant inability to work; or permanent, significant disfigurement or distortion of the body. A non-serious accident at work is neither serious nor fatal. The second categorisation is based on the number of people involved. In an individual accident at work, only one person is injured, whereas in a collective accident, more than one person is injured. We use official register data collected from statistical cards on accident at work in Poland to estimate occupational injury risk. We use databases on the Polish economy because of the country’s unique legal stance and the resulting high reliability of the data. Statistical cards of accidents at work are part of a programme of public statistical surveys and cover all accidents at work, as well as accidents considered equivalent to accidents at work. Each person’s accident is counted as a single accident at work, regardless of whether the accident was individual or collective. A such, the database contains information on accidents, their effects, and several personal and employer
1 The survey is described in detail in Section 3.2. Polish Survey of Wages and Salaries by Occupation is a part of EU Structure of Earnings Survey (SES).
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We take the Cartesian product of the NACE section and the threedigit ISCO code for occupations to simultaneously control for economic section and occupation, because some occupations are specific to economic sections (e.g. miners), while others are not (e.g. transport and storage labourers). Finally, we obtain 2633 distinct occupational groups belonging to a NACE section for both genders in which at least one accident of any severity was reported. This solves the problem of overrepresentation of certain groups of occupations in a given section (e.g. miners typically work in mining and quarrying, while electricians may be employed by firms in any economic section). To prevent a downward bias in the estimation results possibly arising from too many empty (zero-valued) accident cells and high-risk ratios for cells with few observations, we construct an alternative measure of the accident ratio. We calculate the accident ratio per 1000 workers for each gender and section group, and for each gender and three-digit ISCO occupation group. Then, as an accident measure, we use the product of these two numbers, divided by the average value of the first total accident ratio measure. The average value of the alternative measure is slightly higher than the first measure, which means that accident risk is overestimated. The estimated variance of the alternative occupational risk measure is significantly lower.
Table 2 Number of accidents at work by the severity of the outcome and the gender of the subject of the accident in firms employing 10 or more workers (2011–2014). Gender
Severity
2011
2012
2013
2014
Women
Non-serious Serious Fatal Total
29,925 64 14 30,003
28,752 65 21 28,838
30,441 54 14 30,509
30,937 56 10 31,003
Men
Non-serious Serious Fatal Total
60,981 544 305 61,830
55,981 465 257 56,703
51,988 399 203 52,590
51,916 398 196 52,510
91,833
85,541
83,099
83,513
Total
Source: Own calculations, based on the database of statistical cards on accidents at work.
accidents. There is a consensus among researchers that small firms are more likely to underreport or to not report work-related accidents (Pransky et al., 1999; Daniels and Marlow, 2005). Next, we calculate accident ratios as the number of accidents per 1000 employed workers in firms employing 10 or more workers. For analysis, we use wage data about workers in firms employing 10 or more workers. We limit the accident data to guarantee data coherency. There are 85,997 accidents per year in period 2011–2014 and 7,123,198 full-time employed (FTE) workers. This gives total accident rate of 12.07 per 1000 FTE workers. This includes 11.965 non-serious accidents per 1000 FTE workers, 0.072 serious accidents per 1000 FTE workers and 0.036 fatal accidents per 1000 FTE workers. For statistical reasons, we have excluded occupational groups with no more than four observations from the sample. We present the sample values of risk measures in Table 3. The obtained accident ratios are slightly larger than those published by the Central Statistical Office of Poland. Table 4 presents the five sections of the economy characterised by the highest accident ratios. The accident ratios presented in Table 4 are 30%–50% higher than the official estimates for the entire economy. The source of the difference is that almost the same number of accidents is divided by a smaller number of workers, that is, only those working in firms employing 10 or more workers. The economic sections characterised by the highest accident ratios for the entire working population and those for men are almost the same. The jobs with the highest accident ratios are in mining and quarrying, construction and agriculture, and forestry and fishing. For women, the riskiest occupations are in human health and social work, personal services (other services), and agriculture. This reflects the gender segregation in the workplace. There are 130 occupational groups at the three-digit ISCO classification level for which at least one accident of any severity is reported between 2011 and 2014. Previously, we found significant differences in the accident ratios among economic sections. Now, we concentrate on the three-digit ISCO code groups. Analysing the data in Table 5, we conclude that the occupational groups characterised by the highest occupational risk differ between genders. The most dangerous occupations for men are paramedical practitioners, ship deck crews and related workers, and animal producers. For women, the most dangerous occupations are animal producers; car, van, and motorcycle drivers; and heavy truck and bus drivers.
3.2. Data on wages We use data on wages and individual characteristics of employees taken from the SES database 2014, provided by the Central Statistical Office in Poland. The survey is carried out every two years and covers entities of the national economy with more than nine employees. The database includes both full- and part-time employees who worked for the entire month of October (CSO, 2014). The advantage of the SES survey is the high reliability of its wage data. The wages are reported by the accounting departments, so they are almost as reliable as administrative data. Another advantage of the SES data is the size of the database. As of 2014, the SES survey covered 12.8% of all enterprises with more than nine employees. The sample comprises approximately 730,500 observations. The main disadvantage of the database is that it is representative only of entities employing more than nine employees.2 3.3. Data merging process To create a database containing information on occupational injury risk and wages, we merge information from the database of statistical cards on accidents at work for 2011–2014 with data taken from the SES survey for 2014. Here, we use exact matching on economic sections (NACE section), three-digit level ISCO classifications, and gender. For each combination of these variables in the SES database, we link the computed average risk ratios. We matched information for 4134 of 4175 distinct groups (over 99%). However, we were unable to match all injury ratios for specific groups of workers because the SES only includes firms employing 10 or more workers. We also perform an analysis at the four-digit ISCO classification level. However, for nearly 39% of the combinations of economic section, gender, and four-digit ISCO code in the accident database, we were unable to match observations from the SES data on wages. We are aware that accident data should precede wage data. As of
Table 3 Risk of injuries by severity type (per 1000 workers). Severity
mean
Non-serious Serious Fatal
11.113 0.068 0.034
minimum
median
maximum
Std. dev.
0 0 0
8.690 0.018 0.002
200.000 9.615 16.129
10.943 0.128 0.089
2 The survey does not include the following: apprentices, persons engaged in outwork (home-workers), students maintaining vacation or diploma practices, members of workers’ groups organised by other units and appointed to work in the reporting units (e.g. soldiers, labour corps members, convicts), persons on maternity or childcare leave, persons enrolled in schools or PhD studies (and similar), persons employed in intervention and public works, and persons on sick leave. For more information about the sample selection scheme, see the Structure of Wages and Salaries by Occupations in October 2014, CSO, available from www.stat.gov.pl.
Source: Own computations, based on the database of statistical cards on accidents at work, 2011–2014. 340
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Table 4 Sections of the economy with the highest accident ratios. Working population
Men
Mining and quarrying Construction Agriculture, forestry and fishing Water supply and waste management Human health and social work Administrative and support services Manufacturing
18.28 17.63 17.37 16.82 15.69 15.13 15.07
Women
Mining and quarrying Human health and social work Water supply and waste management Construction Agriculture, forestry and fishing Administrative and support services Manufacturing
20.20 19.97 19.67 19.54 18.72 17.53 17.51
Human health and social work Other social activities Agriculture, forestry and fishing Administrative and support services Manufacturing Transporting and storage Wholesale and retail trade
14.83 13.73 13.17 12.32 10.03 9.30 7.68
Source: Own computations, based on the database of statistical cards on accidents at work, 2011–2014.
NACE section; ACCi are accident ratios; and εi is an error term.
Table 5 Occupational groups at the three-digit ISCO level with the highest accident rates.
The personal characteristics of a worker, such as gender, education, and work experience, describe accumulated human capital and are used as proxies for worker productivity. We also expect workplace characteristics to act as proxies for productivity and to affect wages. The main coefficients of interest are those of the accident ratio variables. The accident ratios represent the numbers of injured persons per 1000 employed persons in firms that employ 10 or more workers. We discriminate between accident ratios for all, fatal, and serious accidents. Individuals working in the same occupation are exposed to different risk factors, depending on the section of the economy. Accident ratios are obtained for cells created by industry and occupational group at the three-digit ISCO classification level and represent the influence of occupational injury risk on wages. The natural interpretation of these coefficients is the implicit price of risk (Black and Kniesner, 2003) and their role is to capture the size of wage premiums related to occupational risks. The risk variables can be interacted with worker age or experience (for a discussion on this topic, see Viscusi (1993)), but in our case, the quantitative impact of such interaction on the results is negligible. To ensure the robustness of the results, we estimate the wage Eq. (1) for the full sample of workers (model 1) and for several subsamples. The specifications of model 2 are the same as those of model 1, but the sample is limited to workers in the high productivity age group (25–55). Similarly, model 3 is based on the subsample of full-time workers, because these workers are exposed to risk factors for a longer period. Then, model 4 is based on the subsample of full-time employees between the ages of 25 and 55. Model 5 is similar to model 3, except the accident ratios are limited to three times the average value of the population accident rate. Model 6 is similar to model 4, except the accident ratio is limited to three times the average value of the population accident rate. We estimate the latter two models to verify whether eliminating observations with the highest accident rates significantly influences the results. Finally, the last two model specifications include three distinct accident ratio measures in the wage equation for different types of accident severity. Model 7 is based on Eq. (1), estimated on a subsample of full-time employees. Model 8 is similar to model 7 but excludes observations with an accident rate that is three times higher than the average value. Models 1–6 are estimated using the two accident measures described in Section 3.1. In Table 6, we present the results for the accident rate calculated for each cell by gender, occupation, and economic section. Table 7 shows the results for the alternative measure of the accident ratio, that is, the accident rate by section multiplied by the accident rate by occupational group. Owing to space limitations, we do not report the coefficients of the control variables in Eq. (1).3 The estimates for all models in Tables 6 and 7 are consistent with economic theory. The wage premium for work experience is positive,
Working population 224 Paramedical practitioners 835 Ship deck crew and related workers 612 Animal producers 342 Sports and fitness workers 511 Travel attendants, conductors and guides 933 Transport and storage labourers Men 224 Paramedical practitioners 835 Ships' deck crew and related workers 612 Animal producers 342 Sports and fitness workers 532 Personal care workers in health services 511 Travel attendants, conductors, and guides Women 612 Animal producers 832 Car, van, and motorcycle drivers 833 Heavy truck and bus drivers 511 Travel attendants, conductors and guides 751 Food processing and related trade workers 812 Metal processing and finishing plant operators
297.62 65.83 50.87 42.15 34.67 30.60 312.90 65.05 55.70 51.98 45.94 37.26 41.67 39.11 38.27 30.92 26.61 25.38
Note: Names of occupations are taken from ILO (2008). Source: Own computations, based on the database of statistical cards on accidents at work, 2011–2014.
October 2014 (for which we possess wage data), information on accidents at work was only available up to 2013, because the data on accidents are published at the end of the year. Furthermore, we are not able to use 2016 wage data because of changes in the classification of occupations in 2015. However, any bias should be negligible. 4. Empirical results 4.1. Wage equation The database contains information on individuals’ wages and personal characteristics, such as gender, age, level of education, work tenure, and occupational group. It also includes employer characteristics, such as ownership sector, enterprise size, location, and NACE section. All variables are used as controls in the Mincer equation below. We start with a simple hedonic wage regression, called a Mincertype equation, of the following form:
ln(wi) =
0
+
i PERi
+
j JOB j
+
k ACCk
+ i,
(1)
where: ln(wi) is the natural logarithm of gross monthly salary (PLN); PERi denotes the following characteristics of individuals: highest level of formal education, number of years of work experience (and its square), and gender of an employee (1 for female, 0 for male); JOBi denotes job characteristics: size of the firm (small, medium, big, very big), ownership sector (1 for public, 0 for private), and
3
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Detailed results are available on request.
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Table 6 Selected parameters of wage models (Eq. (1)). Variable/Model
1
2
3
4
5
6
Experience Experience squared Female Accident rate Female × accident N R2
0.0260 −0.0004 −0.2014 −0.0025 −0.0007 730,483 0.43
0.0298 −0.0005 −0.2160 −0.0028 −0.0008 597,201 0.42
0.0255 −0.0004 −0.2177 −0.0027 −0.0004 674,613 0.43
0.0291 −0.0005 −0.2329 −0.0030 −0.0003 553,422 0.42
0.0255 −0.0004 −0.2245 −0.0078 −0.0018 662,978 0.44
0.0291 −0.0005 −0.2385 −0.0081 −0.0018 544,530 0.43
Note: Numbers in bold indicate significance at the 1% level. Source: Own computations, based on merged databases.
but with a decreasing return. The negative coefficients for women reflect that they receive lower wages, on average. The coefficients of the accident rates are negative and statistically significant, suggesting that workers are not properly compensated in terms of wages for working under hazardous conditions. There are several explanations for this phenomenon. The first is statistical in nature: there is a measurement problem because accidents are rare events. Thus, information collected over a limited period may obscure what happens in reality. The second explanation relates to human behaviour. People seem to underestimate the probability of rare events and undervalue the negative consequences of potential unfavourable events. Therefore, work-related risks may be underestimated by employees. A third explanation is that firms have more bargaining power during wage negotiations and, therefore, wages do not fully compensate workers for accident risks. The estimates for the model with the disaggregated measures of accident risk in Table 8 are more intuitive than those in Table 7. According to our preferred empirical model specifications from Table 8, wage compensations can be found in Eq. (7): −0.31% for non-serious accidents for men and −0.34% for women (additional component for women is not significant) which translates to −11.1 PLN. The reference point is average earnings of full-time employees which was 3725.10 PLN in 2014 according to the Structure of Earnings Survey. However, the wage compensation for serious accident risk is positive, statistically significant, and estimated at 5% to 15%, which amounts to 187.75 PLN and to 555.80PLN, accordingly. Employees working in occupations characterised by a high likelihood of several days of absence as a consequence of work-related accidents are compensated for that risk with higher wages. Compensation for fatal accident risk is positive and statistically insignificant, but smaller than that for serious accidents and amounts for 30.90 PLN for men and −37.25 PLN for women. Again, this may be an issue of imprecise measurement. Dickens (1984) emphasises that fatal accidents in particular are, fortunately, rare events. Thus, the precision of the risk measures is low in comparison with the other variables in the wage equation. Unfortunately, the estimated amounts of wage compensation are not directly comparable among studies due to methodological differences. Estimations for different countries are based on data of different quality. Similarly to Berkhout and Damen (2016), we use population data, whereas, e.g. Leeth and Ruser (2003) or Grazier and Sloane (2006) use survey data. The studies
Table 8 Wage models for different severity risk measures. Variable/Model
7
8
Experience Experience squared Female Non-serious accident rate Female × Non-serious accident rate Serious accident rate Female × Serious accident rate Fatal accident rate Female × Fatal accident rate N R2
0.0255 −0.0004 −0.2174 −0.0031 −0.0003 0.0504 0.0988 0.0083 −0.0183 674,613 0.43
0.0255 −0.0004 −0.2250 −0.0091 −0.0010 0.1572 0.0371 0.0020 0.0184 662,978 0.44
Note: Numbers in bold indicate significance at the 1% level. Source: Own computations, based on merged databases.
also differ in the level of aggregation of accident data, e.g. DeLeire and Levy (2004) used 2-digit occupational codes and Kuhn and Ruf (2009) utilized data aggregated at the industry level. Also, different estimation methods were used, e.g. panel data methods (Kuhn and Ruf 2009) or conditional logit model (DeLeire and Levy 2004). 4.2. Wage decomposition To examine the extent to which injury risk wage premiums contribute to gender wage inequalities, we use the hedonic wage Eq. (1) and perform the Oaxaca–Blinder decomposition. Our dependent variable is the difference between male and female workers’ logarithms of wages. This difference is decomposed into the so-called explained part of the wage gap and the unexplained part of the wage decomposition. To analyse the contribution of the accident ratio to the gender wage gap, we examine the detailed decomposition (i.e. the contributions of each component) (see Table 9). Similar to the estimation of the wage equation, to provide robustness to our results, we provide different sample setups for wage Eq. (1) and then perform the decomposition. In model 1, we estimate the wage equation on a subsample of full-time workers. In model 2, the sample is also limited to full-time workers, but accident measure is categorized by
Table 7 Wage models for an alternative risk measure. Variable/Model
1
2
3
4
5
6
Experience Experience squared Female Accident rate Female × accident N R2
0.0260 −0.0004 −0.2046 −0.0041 −0.0046 730,483 0.41
0.0299 −0.0005 −0.2155 −0.0041 −0.0049 597,201 0.41
0.0255 −0.0004 −0.2237 −0.0042 −0.0037 674,613 0.40
0.0291 −0.0005 −0.2348 −0.0042 −0.0040 553,422 0.41
0.0257 −0.0004 −0.2509 −0.0071 −0.0022 629,490 0.41
0.0294 −0.0005 −0.2629 −0.0072 −0.0024 517,484 0.41
Note: Numbers in bold indicate significance at the 1% level. Source: Own computations, based on merged databases. 342
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behaviour. In addition, persons with dependants do not expose themselves to unnecessary risks. This effect is found to be especially strong for women. Second, the wage data are taken from a sample, albeit a large sample, of about 12.8% of workers in firms that employ 10 or more workers. This prevents us from analysing each occupation separately. The sample selection argument often raised in labour market analyses is not applicable here, because accident risk premiums refer only to those who work. In addition, we are aware that accident data should precede wage data. However, the bias from considering accident data from the same year as the wage data should be negligible. Unfortunately, there is no straightforward solution to the abovementioned data problems. Alternatives include conducting our own large-scale survey, with an overrepresentation of workers in occupations with few accidents, or pursuing a general census on accidents at work and wages. We use accident ratios as proxies for occupational risk. A competitive approach could be to consider the risks of occupational diseases (e.g. ratios of disease cases per year to the number of employees). The strength of the latter approach relies on the intuitive reasoning that employees are more concerned about the probability of suffering from an occupation-related disease and the subsequent decrease in quality of life than they are about the probability of a fatal accident. Occupationrelated diseases also seem to be more common, mostly because of a biased perception of their frequency and consequences. While an accident is a single event, the effects of which may be temporary, the effect of an occupation-related disease is usually permanent. Moreover, similarly to the difference between the formal and common understanding of accidents at work, not every disease intuitively linked with an occupation is regarded as an occupational disease. In addition, to the best of our knowledge, a detailed database on occupational diseases is not available.
Table 9 Wage decomposition. Variable/model
1
2
3
4
5
6
Difference Explained Accident rate Accident rate (%) Unexplained
0.123 −0.098 −0.018 0.184 0.221
0.123 −0.095 −0.013 0.142 0.218
0.129 −0.111 −0.046 0.413 0.240
0.129 −0.102 −0.034 0.330 0.232
0.123 −0.127 −0.061 0.480 0.251
0.143 −0.124 −0.063 0.506 0.267
Note: Numbers in bold indicate significance at the 1% level. Source: Own computations, based on merged databases.
accident severity, which is our preferred specification. In model 3, we limit the sample to full-time employees between the ages of 25 and 55, with an accident ratio not exceeding three times the average. Model 4 is similar to model 3, but with an accident measure for each accident severity. Finally, models 5 and 6 use the alternative measure for accident risk. We estimate model 5 on the subsample of full-time employees, and model 6 on the subsample of full-time employees aged 25–55 and with an accident ratio not exceeding three times the average value. The results of the wage decomposition suggest that accident risks contribute significantly to the explanation of the existing wage gap. However, the estimated size of the impact of the accident risk on the explained part of the wage gap depends on the model specification: in the models based on full-time workers, the accident risk explains 15%–20% of the wage gap, whereas, for workers aged 25 to 55, the accident risk explains 30%–40% of the wage gap. In the models that use the alternative risk measure, the estimated impact is even higher. Note that in models 2 and 4, the coefficients of the fatal accident rate are non-significant. Moreover, in those models, the estimated impact of accident risks on the wage gap is lower than in a similar model with a single risk measure. Hence, pooling the different risk levels may overestimate the impact of the accident rate.
6. Conclusions
5. Discussion, Limitations, and future research directions
In this study, we assessed the sizes of wage premiums related to occupational risk. We also investigated the extent to which differences in the risk of occupational injury for men and women (and the corresponding wage compensation) help to explain the observed gender wage differentials. To this end, we linked occupational injury register data to a large survey on wages and salaries. Then, we estimated several specifications of the wage equation and performed an Oaxaca–Blinder decomposition for the wage gaps. The results for the models with aggregate accident indicators suggest that workers are not properly compensated in terms of wages for working under hazardous conditions. This can be explained in several ways. As Daniels and Marlow (2005) suggest, the employees may fail to report the accidents because of, e.g. fear of reprisal or loss of pay. In turn, there is a measurement problem; the statistics on the casualties are flawed which results in the underestimation of the work-related risk. Therefore, it is not properly reflected in wages. Another explanation comes from Kahneman and Tversky (1979). While estimating the probability of tail events, individuals succumb to their biases of judgement. They may overestimate it (Kahneman and Tversky, 1979) or even underestimate it (Krawczyk and Rachubik, 2019). Moreover, accidents are rare events, and we employ data for a four-year period only. Therefore, if an accident occurred during that period, we may have overestimated the accident risk for that group of workers. Similarly, if an accident did not occur, this may have resulted in an underestimation of the risk. Better and more intuitive results are obtained from models with disaggregated accident measures. These measures treat accidents of different severity levels separately. Compensation for a non-serious accident risk is found to be statistically significant and negative. Conversely, compensation for a serious accident risk is positive and is estimated to be between 5% and 15%. The results for a fatal accident
Our results have strengths and limitations. One of the main strengths is data reliability. The data source on accidents is an official register and the wage data are taken from a large sample survey (about 12.8% of the population), conducted according to established international standards. In addition, the large sample makes our analysis more credible. For instance, we were able to merge over 99% of the observations from two sources. The methodology we use is similar to the one used by Kluve and Schaffner (2007) with some differences. We analyse different severity level injuries while Kluve and Schaffner (2007) concentrated on fatal injury risk only. Moreover, we use more detailed risk data and do not upper-censored wage data which allows us to use standard OaxacaBlinder wage decomposition. Another strength of the study is the use of well-established economic models in the literature. Specifically, we use a Mincer-type wage regression and the Oaxaca–Blinder decomposition. Both are widely used and are popular economic tools used to analyse wages and their determinants. A further advantage of our study is our analysis at different levels of data aggregation. That is, we divided the sample into more than 4000 subgroups and estimated the coefficients for different levels of accident severity. Although we aggregated the data based on the three-digit level of ISCO coding, we could deepen our analysis by examining four-digit level groups (i.e. each occupation, separately). However, in that case, we could not match the accident data to the worker sample data for nearly 40% of the groups. The first limitation of our study is the lack of information on workers’ social backgrounds. Empirical evidence suggests that formal and informal personal relationships affect workers’ risk-taking 343
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risk are statistically insignificant owing to the rarity of their occurrence. Our results explain the mixed evidence in the literature, where the use of different accident measures causes contradictory evidence on the relationship between wages and accident risk. As in several other studies (e.g. Grazier and Sloane, 2006; Kuhn and Ruf, 2009), in the case of an aggregated risk indicator, we found a negative relationship between accident risk and wages. The results of our detailed analysis with disaggregated risk indicators are in line with those of Kuhn and Ruf (2009) for non-fatal accidents and with those of DeLeire and Levy (2004) for fatal accidents. Finally, we decompose the wage gap into explained and unexplained parts and calculate the contribution of the difference in accident risk between men and women to the explained part. The estimated wage gap is 0.13, with −0.09 to −0.13 for the explained part and 0.21 to 0.26 for the unexplained part. The negative sign of the explained part means that, despite women having better characteristics, on average (e.g. they are better educated), they earn less than men do, on average. The accident risk explains 15% to 30% of the explained part of the wage gap, which is more than, for instance, the part explained by work experience. The part of the wage gap explained by risk measures is higher than in the case of Germany or the United States, simply because we use a wider and more detailed definition of injury risk.
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Acknowledgements The authors are grateful to two anonymous referees whose comments improved quality of the text and to the participants of the seminars at University of Warsaw and of the WIEM2017 and Macromodels 2017 conferences for their helpful comments and suggestions and Aleksandra Majchrowska and Paulina Broniatowska for general comments and proofreading. Funding This study was supported financially by the National Science Centre, Poland [research grant 2015/19/B/HS4/03231]. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ssci.2019.04.041. References Berkhout Peter, H.G., Martin, Damen, 2016. Estimating individual occupational risk using registration data. Saf. Sci. 82, 95–102. https://doi.org/10.1016/j.ssci.2015.08.013.
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