This summer, I had the chance to be involved in research for the first time under professors Dr. Judd Kessler and Dr. Clayton Featherstone. Our ongoing project’s goal is to propose a new teacher and job matching mechanism to Enseña Chile (eCh), A Chilean organization tasked with assigning new teachers to schools based on their preferences, similar to the US’s Teach for America. We believe that eCh is not making optimal matches for teachers – it only takes limited ordinal preferences into account (i.e. ranking your preferences from 1 to 10) and strictly provides preference in a first-come-first-serve basis. Thus, eCh is unable to maximize the utility of their teacher applicants. With the use of simple computer programs, our project proposes that the total utility of teacher applicants could be maximized.
My task in this project was to analyze the data the professors had already collected and write some codes to output neater data and compare it with the actual eCh outcomes. I used Stata, a program unlike Python or Java, which I – a non-STEM person who never thought I would be writing code – had to learn from scratch. With existing work from the professors and information on the teachers’ preferences and available positions, I created datasets of our proposed outcome (the counterfactual), and compared them with eCh’s actual assignments in this statistical program. As Stata is not as widely used as other programming software (with the added fact that I had no experience to programming prior to this), there were many times when I could not find a solution to my task anywhere. Only after leaving my computer and thinking about it for some time or when I discuss with my professors about the problem would I find the solution. During these encounters, I learned how to input Pareto dominance through comparing cardinal and ordinal preferences, a useful tool for economics.
Different from academics, however, this was the first time I was dedicated in one task for a long period of time – I would be given a week’s worth of tasks, and discuss my results with the professors at the end of every week. This was even more challenging because all my work was done at home, and eventually, when I came back to Korea for the summer, even our meetings became online – it became hard to keep track of my work, and when I could finish it. However, after pulling through the work, and gaining the opportunity to work with the professors until the end of this project, I am confident to say that those struggles were all worth it, and I have gained a new sense of professional responsibility. Additionally, the project sparked an interest in computer programming inside me, which I will definitely pursue throughout my college career.
This summer, I had the chance to be involved in research for the first time under professors Dr. Judd Kessler and Dr. Clayton Featherstone. Our ongoing project’s goal is to propose a new teacher and job matching mechanism to Enseña Chile (eCh), A Chilean organization tasked with assigning new teachers to schools based on their preferences, similar to the US’s Teach for America. We believe that eCh is not making optimal matches for teachers – it only takes limited ordinal preferences into account (i.e. ranking your preferences from 1 to 10) and strictly provides preference in a first-come-first-serve basis. Thus, eCh is unable to maximize the utility of their teacher applicants. With the use of simple computer programs, our project proposes that the total utility of teacher applicants could be maximized.
My task in this project was to analyze the data the professors had already collected and write some codes to output neater data and compare it with the actual eCh outcomes. I used Stata, a program unlike Python or Java, which I – a non-STEM person who never thought I would be writing code – had to learn from scratch. With existing work from the professors and information on the teachers’ preferences and available positions, I created datasets of our proposed outcome (the counterfactual), and compared them with eCh’s actual assignments in this statistical program. As Stata is not as widely used as other programming software (with the added fact that I had no experience to programming prior to this), there were many times when I could not find a solution to my task anywhere. Only after leaving my computer and thinking about it for some time or when I discuss with my professors about the problem would I find the solution. During these encounters, I learned how to input Pareto dominance through comparing cardinal and ordinal preferences, a useful tool for economics.
Different from academics, however, this was the first time I was dedicated in one task for a long period of time – I would be given a week’s worth of tasks, and discuss my results with the professors at the end of every week. This was even more challenging because all my work was done at home, and eventually, when I came back to Korea for the summer, even our meetings became online – it became hard to keep track of my work, and when I could finish it. However, after pulling through the work, and gaining the opportunity to work with the professors until the end of this project, I am confident to say that those struggles were all worth it, and I have gained a new sense of professional responsibility. Additionally, the project sparked an interest in computer programming inside me, which I will definitely pursue throughout my college career.