Predicting the Work Task Replacement Effects of the Adoption of Machine Learning Technology
by Shreya Hurli
Abstract
This paper develops a methodology to attempt to predict which tasks in the workforce will be resistant to the replacement of labor by machine learning technology in the near future given current technology and technology adoption trends. Tasks are individual activities completed as parts of a job. Prior research in the field suggests that characteristics of tasks (non-roteness, creativity, analysis/cognitive work) that make them harder for machine learning technology to complete are good predictors of whether those tasks will be resistant to replacement in the workforce. This study utilizes O*NET (Occupational Information Network) task description and education data from October 2015 to August 2020 and Bureau of Labor Statistics salary data to use task characteristics to predict tasks’ resistance to replacement. Normalized scores, average salaries, and average worker education levels are calculated to quantify the relative presence or absence of non-roteness, creativity, and cognitive work in a task. This paper then uses the calculated scores, salary, and education data, as well as a number of interaction terms as inputs to a support vector machine (SVM) model to predict which tasks will be resistant to decline in their shares of workplace tasks weighted by the jobs under which the tasks fall. Using task characteristics, the SVM predicts that just approximately 39% of tasks are likely to be resistant to replacement. These tasks tend to be highly non-deterministic (very non-rote, analytical/cognitive, and/or creative) in nature.
Professor David Berger, Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
JEL Codes: J23, J24, O33
Variations in Turkey’s Female Labor Market: The Puzzling Role of Education
By Rachel Anderson
Although Turkey ranks among the world’s 20 largest economies, female labor force participation in Turkey is surprisingly low. Relative to other developed countries, however, the proportion of Turkish women in senior management is high. One explanation for these contrasting pictures of Turkey’s female labor force is education. To better understand how women’s education and household characteristics explain variations in Turkey’s female labor market, I use annual Turkish Household Labour Force Survey data from 2004–2012 to estimate five probabilities: the likelihood that a woman (1) participates in the labor force, or is employed in an (2) agricultural, (3) blue collar, (4) lower white collar, or (5) upper white collar job. I find that labor force participation is relatively high among female primary school graduates, who are most likely to work in agricultural and blue collar jobs. Highly educated married women are the most likely group to participate in upper white collar jobs, and families favor sending single daughters over wives to work during periods of reduced household income.
Advisor: Kent Kimbrough, Timur Kuran | JEL Codes: C51, J21, J23 | Tagged: Employment, Labor-force Participation, Occupation Women
Maternal Labor Decisions and the Effects on Adolescent Risky Behavior
by Stephen M. LaFata
Abstract
This paper examines the effects of maternal employment on the decisions of
adolescents to engage in risky behavior. I attempt to control for possible endogeneity of
maternal employment by implementing instrumental variables. Ultimately, except for low
SES families, maternal labor is found to have no statistically significant effects on adolescent
risky behavior. Though low SES adolescents are found to benefit from a working mother,
this may be a result of endogeneity; possible endogeneity controls through instrumental
variables are ineffective, opening the door to future research with better endogeneity
controls.
Professor Marjorie B. McElroy, Faculty Advisor
JEL Codes: J1, J23,
