Employee Involvement, Technology and Evolution in Job Skills: A Task-Based Analysis

Abstract

The author investigates the evolution of job skill distribution using task data derived from the U.K. Skills Surveys of 1997Surveys of , 2001Surveys of , and 2006Surveys of , and the 1992 Employment Survey in Britain. He determines the extent to which employee involvement in the workplace and computer technologies promote the use of higher order cognitive and interactive skills. He finds that literacy, other communication tasks, and self-planning skills have grown especially fast. Numerical and problem-solving skills have also become more important, but repetitive physical skills have largely remained unchanged. He finds that employee involvement and computer technologies privilege the use of greater generic skills but substitute for repetitive physical tasks. However, the classification of all tasks as either routine or non-routine is found to be problematic. Finally, the author finds a strong connection between the rising use of more academic skills and the education level required for entry into the labor market.

Explaining job polarisation in Spain from a task perspective

“…Autor and Handel (2013), who use a similar type of survey to derive individual task measures (the Princeton Data Improvement Initiative survey, PDII), argue that their data have a greater explanatory power for occupations and wages than those derived from the O*Net. 7 The EWCS is administered by the European Foundation for the Improvement of Living and Working Conditions (Eurofound) and has become an established source 7 Previous papers that have used workers-reported information to build task measures include Spitz-Oener (2006) for Germany, Green (Green 2012) for the UK, and Autor and Handel (2013) for the US.…”

Against the Grain? Assessing Graduate Labour Market Trends in Germany Through a Task-Based Indicator of Graduate Jobs

“…Compared with other high-income countries, the German economy deploys relatively little labour in 'graduate jobs', which could limit the range of adequate employment opportunities for new graduates (Henseke and Green 2017). Technological change, globalisation, as well as changing management practices are thought to complement graduate skills and thus drive up demand (e.g., Akerman et al 2015;van Reenen 2011;Blinder and Krueger 2013;Foster-McGregor et al 2013;Green 2012), whilst substituting for codifiable and offshorable-'routine'-work tasks (Autor et al 2003(Autor et al , 2008. The hypothesis of 'routinization' provides a strong narrative for why the growing supply of graduate labour in many countries has been met with stable or even increasing pecuniary returns, when the middle of the job skill distribution has been hollowing out (e.g., Goos et al 2014;Dustmann et al 2009).…”

Lousy pay with lousy conditions: the role of occupational desegregation in explaining the UK gender pay and work intensity gaps

“…The skill use variables are derived from a whole range of questions asking respondents how important various tasks are in their job. These are combined using factor analysis in Green (2012) to provide a number of skill-use variables, but only Numeracy, Literacy, Problem Solving and Professional Communication are used in this paper since these capture gender differences in the non-routine tasks thought to be complementary to technical change, see Lindley (2012). 9 Again following Green (2012) computer use complexity is also included to look for differences in technological skills.…”

“…These are combined using factor analysis in Green (2012) to provide a number of skill-use variables, but only Numeracy, Literacy, Problem Solving and Professional Communication are used in this paper since these capture gender differences in the non-routine tasks thought to be complementary to technical change, see Lindley (2012). 9 Again following Green (2012) computer use complexity is also included to look for differences in technological skills. 10 Overall Table 2 shows that the education levels of men and women have increased over time to the extent that the gender gap in the proportion of graduates has completely closed.…”

“…The potential answers are 1 "Not at all important" 2 "Not very important" 3 "Fairly important" 4 "Very important" 5 "Essential". Green (2012) uses 32 job tasks to generate 8 specific measures of tasks by averaging the scores of the component tasks. Table A2 in the Appendix provides detailed descriptions of these task measures used in this paper.…”