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Bolívar Muñoz, J., Daponte Codina, A., López Cruz, L., & Mateo Rodríguez, I. (2009). Influencia de las características individuales y de las condiciones laborales en la gravedad de las lesiones por accidente de trabajo registradas en Andalucía en 2003. Revista Española de Salud Pública 83 (6), 847-861.

Fabiano, B., Currò, F., & Pastorino, R. (2004). A study of relationship between occupational injuries and firm size and type in the Italian industry. Safety Science 42 (7), 587-600.

Kelsh, M. A., & Sahl, J. D. (1996). Sex differences in Work-related Injury Rates among Electric Utility Workers. American Journal of Epidemiology, 143 (10), 1050-1058.

Salminen, S. (2004). Have young workers more injuries than older ones? An international literature review. Journal of Safety Research, 35 (5), 513-521.

Smith, P. M., & Mustard, C. A. (2004). Examining the associations between physical work demands and work injury rates between men and women in Ontario, 1990–2000. Occupational Enviromental Medicine 61 (9), 750-756.

Sorensen, O. H., Hasle, P., & Bach, E. (2007). Working in small enterprises - Is there a special risk? Safety Science 45 (10), 1044Sorock, Gary S.& Courtney, Theodore K. (1997). Advancing analytical epidemiologic studies of occupational injuries. Safety Science, 25 (1-3), 29-43.

Taiwo, O. A., Cantley, L. F., Slade, M. D., Pollack, K. M., Vegso, S., Fiellin, M. G., y otros. (2008). Sex Differences in Injury Patterns Among Workers in Heavy Manufacturing. American Journal of Epidemiology, 169 (2), 161-166.

Villanueva, V., & García, A. M. (2011). Individual and occupational factors related to fatal occupational injuries: A case-control study.

Accident, Analysis and Prevention 43 (1), 123-127.

Occupational Safety and HygieneInternational Symposium on

Which companies have more accidents? Analysis of the manufacturing sector in Andalusia from 2003 to 2008 Carrillo, Jesusa, Pérez, Venturab, Onieva, Luisc a Junta de Andalucía, Avenida Hytasa 14 -1, 41006 Sevilla email: jacarcas@gmail.com ; b Universidad de Sevilla, Camino de los Descubrimientos s/n, Sevilla e-mail: ventura@gie.esi.us.es ; c Universidad de Sevilla, Camino de los Descubrimientos s/n, Sevilla e-mail: onieva@us.es

1. INTRODUCTION Injury rate is an indicator of the labour risk prevention performance. Injury rate is a lagging indicator that only has safety interpretation when we can control exposure levels whereas other indicators can be more helpful for safety purposes as those from safety climate assessment or safety audits. Moreover, occupational accidents have a highly skewed distribution. In spite of these limitations, it is the most used indicator and therefore there is an interest in which factors can explain differences in company’s injury rate.

Causes of occupational accidents can be classified as micro-level, meso-level and macro-level (Cedergren & Petersen, 2011). Most causes are attributed to microlevel (worker and job conditions) but other causes (at macro and meso levels) can influence accident occurrence. At macrolevel (regional or national level), some studies have suggested relationship between the injury rate and some macroeconomic variables (Davis, Jones & Nuñez, 2009).

Our interest is to identify possible explanatory variables of injury rates at mesolevel. In this study we focus on company injury rates. We have analysed injury rates of manufacturing companies in the south of Spain (Andalusia) collected from accident reports registered from 2003 to 2008. This study offers a perspective on the relationship between injury rates of a company and some explanatory variables. Typical factors at company level would be economic and financial, organizational, safety management and profile of staff. There is strong evidence that safety management practices are explanatory (Geldart, Smith, Shannon, & Lohfeld, 2010). Some experts have also pointed out that economic performance at company level could be related to safety management effectiveness (Fernández-Muñiz, Montes-Peón & VázquezOrdás, 2009) (Arocena, Nuñez & Villanueva, 2008). Moreover, some studies have tried to demonstrate the economic advantages of safety management (Verbeek, Pulliainen & Kankaanpää 2009) whereas investment in safety has no effect in the company’s economic performance (Kankaanpää, Suhonene & Valtonen, 2008).Very few analyses have been done at enterprise level (Filer, & Golve, 2003) that include economic factors. Company size (Leigh, 1989) (Fabiano, Currò & Pastorino, 2004) has been found significant to explain the injury rate at company level.

Economic and financial ratios are usually used to identify company economic performance. Ratios usually used for these purposes are sales, operating margin, debt ratio and profitability. If we find that there is a relationship between injury rate and such ratios, they can be included as explanatory variables in regression models. These regression models are interesting in terms of public policies and program evaluation, because these models can be used to detect which companies have worse safety performance and would need public assessment. Besides, these models can be used to evaluate the success of public programs and also of private preventive initiatives.


2.1 Accident reports.

In Spain, it is mandatory that all accidents that result in an absence from work of one or more days must be notified. Data sets are collected from official Workplace Incident Notification Forms. This accident report is reported by electronic means (Delt@). For each report we have information about worker age, months experience in company, occupation, seniority and some technical circumstantial information both from workers and companies. Relapses, accidents incurred when travelling to and from work and others that did not cause any absence from work, are excluded from the study.

Accident severity is classified as non-severe, severe (serious accident with long recovery decided by physicians) and fatal (deadly).

We have calculated some variables associated to company’s profile. Although injured workers are a bias selection, we used those characteristics to estimate indicators about those possible explanatory variables about company’s staff like percentage of women (Taiwo, Cantley et al, 2008), age (Salminen, 2004), tenure or contract type (Benavides, Benach, Muntaner, Delclos, Catot & Amable, 2006) (Saloniemi & Salminen, 2010). Another possible explanatory variable obtained from accident data was percentage of accidents in weekends for each company (Folkard & Tucker 2003) (Vegso, Cantley, Slade, Taiwo, Sircar, Rabinowitz et al 2007).

2.2 Cohort of companies with economic and financial data.

Companies are identified with there CIF (fiscal identity number). Only companies with the main activity in manufacturing sector are included (NACE rev.1 from 15 to 37 section approved by Council Regulation EEC nº 3037/1990 as the statistical classification of economic activities in the European Community). As accidents and injury outcomes are very rare in small companies, and if there is not any accident we lack of information about the company, we have selected only companies with at least one accident in every year of the period 2003-2008. The financial accounting data has been gathered from “Andalusian Companies Economic and Financial Analysis” published by “The Andalusian Financial Enterprise Analysis Institute”, and it is data collected from official accounting reports. In this study we use accounting data for 2007 year (Central de Balances, 2009). We have only considered companies with accidents in every year of the period 2003 to 2008. This is to assure in longitudinal studies that we have information about the company. We have chosen only industrial manufacturing companies with accounting data gathered available. According to these criteria, a set of 1,183 companies were selected. We only include in this study activities with at least 50 cases.

Finally, 1,003 companies from 8 activities were included in the study.

2.3 Descriptive Statistics.

There are significant differences for injury rates in terms of both activity (see Table 1) and company size (see Table 2). Most authors consider as a rule of thumb that for regression analysis needs a minimum of ten cases per explanatory variable.

–  –  –

In terms of company size there are also significant differences. However, micro and small companies mean injury rates are calculated with companies with accidents in every year, so many small companies are not included. This can explain that their injury rates are smaller.

–  –  –


In Andalusian manufacturing companies we have found a relationship between their turnover and profits per employee and their risk of being included in public assessment (PAEMSA).

Turnover and profits per employee are both significant in univariate and multivariate analysis. Companies with a bigger turnover per employee have usually higher relative injury rates. This can be explained both in terms of work stress and working pace and in terms of intensity in the use of production assets.

Besides, companies with a high profitability per employee show higher relative injury rates. Tradeoffs between safety and profitability are a common concern among safety experts. Although most authors consider that a proper safety management should be interesting from the economic point of view (this known as safety business case), some companies may be able to have economic success even with theoretical worse safety behaviour. Public enforcement of European Frame Work of Health and Safety at Work should discourage such temptation.

Further research should be done, and better indicators of safety management should be included to decide which economic and financial ratios are explanatory of safety at work. This preliminary result shows that there is a risk for management if they misunderstand the importance of occupational safety. The key issue is to identify the mechanisms that lead to a good economic performance with lack of occupational safety in order to provide new regulations that help to avoid that unfaithful competence.

This paper shows that there is an important research field to be explored. The language used in this topic would be easy to understand by managers and can provide safety advisors with new tools. At the end of the day, top management need to be convinced that safety matters but also that safety is good business.

5. REFERENCES Agresti, Alan (2002). Categorical Data Analysis. New Jersey: Wiley Series in Probability and Statistics.

Arocena, P., Nuñez, I., & Villanueva, M. (2008). The impact of prevention measures and organizational factors on occupational injuries. Safety Science, 46 (1), 1369-1384.

Bailer, A. J., Reed, L. D., & Stayner, L. T. (1997). Modelling fatal injury rates using Poisson regression: a case study of workers in agriculture, forestry and fishing. Journal of Safety Research, 28 (3), 177-186.

Benavides, F. G., Benach, J., Muntaner, C., Delclos, G. L., Catot, N., & Amable, M. (2006). Associations between temporary employment and occupational injury: what are the mechanisms? Occupational Enviromental Medicine 63 (6), 416-421.

Cedergren, A., & Petersen, K. (2011). Prerequisites for learning from accident investigations – A cross-country comparison of national accident investigation boards. Safety Science, 49 (8-9), 1238-1245.

Central de Balances. Análisis Económico-Financiero de la Empresa Andaluza 2009. Retrieved November 14th, 2011 from http://www.centraldebalancesdeandalucia.es/ Davis, R., Jones, P., & Nuñez, I. (2009). The impact of the business cycle on occupational injuries in the UK. Social Science & Medicine 69 (2), 178-182.

Fabiano, B., Currò, F., & Pastorino, R. (2004). A study of relationship between occupational injuries and firm size and type in the Italian industry. Safety Science 42 (7), 587-600.

Filer, R. K., & Golve, D. L. (2003). Debt, Operating Margin and Investment in Workplace Safety. Journal of Industrial Economic,s (3), 359-381.

Fernández-Muñiz, B., Montes-Peón, J. M., & Vázquez-Ordás, C. J. (2009). Relation between occupational safety management and firm performance. Safety Science, 47 (7), 980-991.

Folkard, S., & Tucker, P. (2003). Shift work, safety and productivity. Occupational Medicine, 53 (2), 95-101.

Geldart, S., Smith, C. A., Shannon, H. S., & Lohfeld, L. (2010). Organizational practices and workplace health and safety: A crosssectional study. Safety Science, 48 (5), 562-569 Kankaanpää, E., Suhonen, A., & Valtonen, H. (2008). Promoting prevention with economic arguments – The case of Finnish occupational health services. BMC Public Health, 8 (130), 1-8 Leigh, J. (1989). Firm size and occupational injury and illness incidence rates in manufacturing industries. Journal of Community Health, 14 (1), 44-52.

Quintana, R., & Pawlowitz, I. (1999). A Poisson model for work-related musculoskeletal disorder cost estimation. Safety Science, 32 (1), 19-31.

Salminen, S. (2004). Have young workers more injuries than older ones? An international literature review. Journal of Safety Research, 35 (5), 513-521.

Saloniemi, A., & Salminen, S. (2010). Do fixed-term workers have a higher injury rate? Safety Science, 48 (6), 693-697.

Taiwo, O. A., Cantley, L. F., Slade, M. D., Pollack, K. M., Vegso, S., Fiellin, M. G., y otros. (2008). Sex Differences in Injury Patterns Among Workers in Heavy Manufacturing. American Journal of Epidemiology, 169 (2), 161-166.

Vegso, S., Cantley, L., Slade, M., Taiwo, O., Sircar, K., Rabinowitz, P., y otros. (2007). Extended Work Hours and Risk of Acute Occupational Injury: A Case-Crossover Study of Workers in Manufacturing. American Journal of Industrial Medicine, 50 (8), 597Verbeek, J., Pulliainen, M., & Kankaanpää, E. (2009). A systematic review of occupational safety and health business cases.

Scandinavian Journal of Work environmental Health, 35 (6), 403-412.

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