Job Tasks, Time Allocation, and Wages

Abstract

While a burgeoning literature has extolled the conceptual virtues of directly measuring the underlying job tasks that define work activities, in practice task-based approaches have been hampered by well-known data limitations. We study wage determination using data collected specifically to address these limitations. Most fundamentally, we construct the first longitudinal dataset containing job-level task information for individual workers. New quantitative task measures detail the amount of time spent performing People, Information, and Objects tasks at different skill levels. These measures have clear interpretations, suggest natural proxies for on-the-job human capital accumulation, and provide methodological guidance for future data collection initiatives. A model of comparative advantage highlights the benefits of our data and guides specification and interpretation of empirical models. We provide several new findings about the effect of current and past tasks on wages. First, current job tasks are quantitatively important, with high skilled tasks being paid double the rate of low skilled tasks. Second, there is no evidence of learning-by-doing (i.e., effects of past tasks) for low skilled tasks, but strong evidence for high skilled tasks. Current and past high skilled information tasks are particularly valuable, although high skilled interpersonal tasks also play a significant role. Shifting 10 percent of work time from low skilled people tasks to high skilled information tasks increases a worker’s yearly wage by 22% after ten years. Additional human capital accumulation accounts for 70% of this increase, and the direct effect of performing different tasks accounts for the remainder.

Keywords: Human Capital, Job Tasks, Learning-By-Doing, Wage Growth

JEL Classification: J22, J24, J31