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by Shreya Hurli
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.
Advisors: Professor David Berger, Professor Michelle Connolly | JEL Codes: J23, J24, O33
By Jacob Chasan
A new kernel1 is in town. The current industry-standard for resource allocation on computers does not take the user’s preferences into account, rather programs are given access to resources based on the time that each requested to be run. Although this system can lead to solutions that minimize the time it takes for a program to receive an allocation, it often leads to an incentive misalignment between the programs and the user. This misalignment is exacerbated as the current queue based systems have no inherent mechanism to prevent a tragedy of the commons issue, whereby programs take more resources from the system than the value they provide to the user. By shifting to a market-based approach, where computing resources are allocated to programs based on how much utility the user receives from each program, the incentives of the programs and the users align. With inherent market mechanisms to keep the incentives aligned, this new paradigm leads to at least superior levels of utility for a user.
1As described in subsequent parts of this paper, the kernel is the core program within an operating system which is given the authority to allocate the hardware resources amongst the programs on the computer.
Advisors: Professor Benjamin C. Lee, Professor Atila Abdulkadiroglu, Professor Michelle Connolly | JEL Codes: C8, C80
Immigrant Workers in a Changing Labor Environment: A study on how technology is reshaping immigrant earnings
By Grace Peterson
This research determines how automation affects immigrant wages in the US and how closely this impact follows the skills-biased technical change (SBTC) hypothesis. The present study addresses this question using American Community Survey (ACS) data from 2012 to 2016 and a job automation probability index to explain technological change. This research leverages OLS regressions to evaluate real wage drivers, grouping data by year, immigration status, and education level. According to the SBTC hypothesis, high skill immigrant wages should be less negatively affected by technological change than low skill immigrant wages. Univariate analysis suggests that the SBTC hypothesis is even stronger for US = immigrants than native-borns, as high skill immigrants have a lower average probability than low skill immigrants of having their jobs automated, and the difference in effect on high versus low skilled workers is larger for immigrant than native-borns. However, multivariate analysis asserts that technological change affects low skill immigrants’ wages less than high skilled individuals’ wages, which counters the SBTC hypothesis.
Advisors: Professor Grace Kim | JEL Codes: J15, J24, J31, J61, E24
By Ricardo Martínez-Cid and Gonzalo Pernas
This paper investigates the supply-side and demand-side factors that explain the success of mobile money markets. Namely, we argue that there exists a set of Goldilocks conditions that best supports mobile money services. A population must have exposure to financial services to understand mobile money and have a high enough level of income to have a use for these services. However, the population must also not have access to highly developed banking architecture, such that their banking needs are already satisfied. By comparing El Salvador and Kenya, countries in different stages of development, we find empirical support for our hypothesis. Our evidence suggests that low income regions and households with some exposure to financial services are more likely to use mobile money than fully banked people who enjoy a higher income.
Advisor: Erica Field | JEL Codes: E40, E42, G21, G23, O12, O16, O17
By Hong Zhu
M-PESA, the hugely popular mobile money system in Kenya, has been celebrated for its potential to “bank the unbanked” and increase access to financial services. This paper provides evidence to support this idea and explores mechanisms through which this might be the case. It specifically looks at the savings products held by individuals and how this changes in relation to M-PESA use. It then constructs an index for measuring the extent to which individuals are integrated into the formal financial sector. This paper argues that M-PESA’s effect on financial inclusion is a growing phenomenon, which suggests that keeping pace with the rapid evolutions of this mobile money system should be a high priority for researchers. As this paper elucidates, M-PESA has become notably more integrated with the formal financial sector in 2013 as compared to 2009, which holds implications for user behavior.
Advisor: Michelle Connolly, Xiao Yu Wang | JEL Codes: D14, E42, G21, G23, O1, O17, O16, O33 | Tagged: Financial Inclusion, Mobile Money, Savings,Technology