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Physical Work Intensity and the Split Workday: Theory and Evidence from Spain

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Abstract

This study uses a job-design model and the 2002–2003 Spanish Time Use Survey to explore the existence of a previously overlooked relationship between physical work intensity and the split workday. The theoretical model developed predicts that the incidence of working split shifts may increase with physical work intensity if and only if the degree of recovery allowed by the mid-workday break is directly proportional to the physical load of the work done. Occupation-specific estimates of energy expenditure are constructed for Spain which permit investigating empirically the relationship between physical work intensity and the split workday.

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Notes

  1. This literature is part of a larger body of research concerned with the consequences of non-standard work hours. See, for example, Presser (1988, 1994), Kostiuk (1990), Bryson and Forth (2007), Rapoport and Le Bourdais (2008), Williams (2008), and Brachet et al. (2012).

  2. The accumulation of fatigue can also be viewed as impairing workers’ performance, which is indeed the perspective adopted by Dragone (2009) and Brachet et al. (2012).

  3. h, l, and κ are given to the worker. Marchetti and Nucci (2014) consider a related case with variable effort and hours.

  4. An alternative measure of objective physical job requirements can be derived from the O*NET 4.0 database (http://onetcenter.org) (see Zavodny 2015). O*NET 4.0 data on physical job requirements are superior to METs in that they combine time spent on seven different body positions, although, on the other hand, they do not contain information about intensity or pace and are not available for almost a quarter of occupations. Tudor-Locke et al. (2009) also assigned MET values to non-work activities reported in the American Time Use Survey (ATUS), which have been used to investigate the linkage between gasoline prices and physical activity (Sen 2012), energy expenditure in women (Archer et al. 2013), and the impact of the business cycle on physical activity (Colman and Dave 2013).

  5. In the more recent 2009–2010 STUS, the occupation was recorded with a lower level of detail. This is the main reason why this analysis focuses on 2002–2003 survey.

  6. The CNO-94 lay within the ISCO-88 framework, whereas the Census 2002 OCS was based on the 2000 Standard Occupational Classification (SOC) system. While both schemes classified occupations based on work performed and on required skills, the latter applied additionally the notion of “job families”, whereby people who work together are classified in the same group regardless of their skill level (Scopp 2003). The complete structure of the CNO-94 was accessed at www.ine.es/clasifi/cno94.xls on May 3, 2015. INE (1994) provided examples of occupations which do (and do not) fall under each Primary Group, but to which no specific codes were assigned. The 2000 SOC arranged occupations into 23 major groups and 821 detailed categories, which are defined at www.bls.gov/soc/2000/socguide.htm. The U.S. Census then aggregated the detailed SOC categories into 509 detailed census categories within the same 23-major group framework. The crosswalk for comparing data from 2002 OCS to 2000 SOC was accessed at www.census.gov/people/io/files/occ2000t.pdf on May 5, 2015.

  7. The variance matrix estimator (15) relies on asymptotics that are in the number of occupations (a total of 197 in this study), which is the grouping dimension with the fewest number of clusters (Cameron and Miller 2015). Hansen (2007) has considered the behavior of this estimator finding satisfying asymptotic properties in panels of much smaller cross-sectional size.

  8. The aggregate industry and occupation controls are at the level of broad groups of the 1993 Spanish National Classification of Economic Activities and the CNO-94, respectively. Groups comprising less than 1% of the sample were merged with related groups.

  9. The longest break’s length was also analyzed using a linear model estimated by OLS, as measurement error in the explaining variable is of less concern when the estimating model is linear (see e.g. Stewart 2013). The two-way clustering by occupation and household was implemented with the Stata user-written command cgmreg (Cameron and Miller 2015). Effects derived from the linear model were similar to those yielded by the ERM.

  10. An OLS regression of time spent on short breaks on x2 yielded a small though statistically different from zero effect of the MET value: In a regular working day, a 1 MET increase is predicted to extend time spent on short breaks by approximately 1 min.

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Acknowledgements

My thanks to an anonymous referee, Associate Editor Shin-Yi Chou, Carme Molinero, Phil Tucker, and seminar participants at the 18th INFER Annual Conference for helpful comments and suggestions. Financial support from research project CREVALOR, funded by the Diputación General de Aragón and the European Social Fund, is gratefully acknowledged.

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  1. Centro Universitario de la Defensa de Zaragoza, Academia General Militar, Ctra. de Huesca s/n, 50090, Zaragoza, Spain

    Jorge González Chapela

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González Chapela, J. Physical Work Intensity and the Split Workday: Theory and Evidence from Spain. J Labor Res 39, 329–353 (2018). https://doi.org/10.1007/s12122-018-9269-8

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