Education for the future: an empirical approach to measuring the effect of automation on university outcomes
By Tony Martini, Economics ::: Advisor: Michael Jones ::: Presentation ID: AM_D39 ::: Abstract: This study suggests new methods of measuring the effectiveness of universities in protecting students from the impact of automation on the labor market. Using US government data on occupational activities and traditional measures of university outcomes, we produce a ranking of college majors by their susceptibility to automation and determine correlations between predicted traditional and automation-based outcomes. These are combined into a complete metric for the "automation resistance" of a university. Using an approach centered on distinctly human skills, occupational activities can be ranked by their automation resistance. Institutions which develop student skills with a focus on automation resistance should likewise demonstrate greater long-term viability. We demonstrate this methodology through a case study using data from the University of Cincinnati.
It has been argued that foundation ::: funding has distorted methods in American sociology in the direction of quantification. ::: This argument rests on a number of assumptions, of which the key one is that in the ::: absence of such funding, method would have developed differently. Data on the ::: methods of funded and unfunded research articles are analysed, and show that the trend ::: to increasing levels of quantification is almost equally present in unfunded work, which ::: suggests that funding should not be held responsible for the trend.