Mini Project Submissions

mini project/ideas/idea1.md

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+# Idea #1
+### Economics Discipline - Malena Malka Goldman
+
+**Research Question: How does the changes of inflation trends affect the price of the canasta basica nacional in Argentina**
+The Canasta Básica Nacional (CBN) in Argentina refers to a set of essential goods and services used to measure poverty and indigence levels. It consists of two key components:
+
+_Canasta Básica Alimentaria (CBA) – Basic Food Basket_
+* Represents the minimum amount of food required to meet basic nutritional needs.
+* A household whose income is below the CBA is considered indigent (indigencia), meaning they cannot afford even essential food items.
+
+_Canasta Básica Total (CBT) – Total Basic Basket_
+* Includes the CBA plus other essential expenses like housing, transportation, education, and healthcare.
+* A household whose income is below the CBT is considered poor (pobreza), meaning they can afford food but not other basic necessities.
+
+**Tractable data**
+For this project, I will be utilizing official information provided by both local and international statistical entities. On the one hand, I will be using a dataset from the National Institute of Statistics and Censuses from the Argentine Republic (INDEC) that contains information regarding the price of both the CBA and CBT from April of 2016 to December of 2024. This data is collected by the national office and is official and approved by the state. I will cross-examine this data with Argentina's monthly inflation rates.
+
+**Data retrieval**
+Although it is difficult to access the data sets provided by the Federal Reserve or the International Monetary Fund, I will be accessing the Inflation rates data through the trading economics website, which replicates multiple economic indexes. The link to access the data is the following:
+https://tradingeconomics.com/argentina/inflation-cpi
+And the link to access the CBA data is the following:
+https://www.indec.gob.ar/indec/web/Nivel4-Tema-4-43-149
+My goal would be to create a unique data frame combining both pieces of information and run my analysis.
+
+**Correct specification of the model**
+I'm not entirely sure what type of analysis would be most beneficial regarding this information, I guess I would need to look at the data first before making any decisions. Initially, I would like to run a linear regression model to be able to predict what prices would both the CBA and CBT have depending on the Inflation rate that month. However, I recognize that the price of the basket keeps rising, so maybe being able to cluster specific Interest rates with the percentage change in price of the CBA or CBT could also be interesting. I am open to suggestions :)
+
+**Implications for stakeholders**
+The findings of this study have significant implications for multiple stakeholders, including policymakers, economists and social organizations. For government entities and policymakers, understanding the relationship between inflation and the Canasta Básica Alimentaria can inform targeted economic policies, such as social assistance programs and wage adjustments, to mitigate the impact of rising costs on vulnerable populations. In addition, businesses and labor unions can use these insights to advocate for fair wage policies that align with inflation trends. Additionally, financial institutions and investors can leverage this analysis to assess economic stability and forecast consumer purchasing power. 
+
+**Ethical, legal, societal implications**
+This research can enhance public awareness of inflation’s impact on basic living costs, potentially influencing social movements and public discourse. However, the responsibility extends to ensuring that the findings do not contribute to fear-mongering but instead provide constructive insights that contribute to economic stability and improved social welfare policies. From an ethical standpoint, ensuring transparency and accuracy in data handling is crucial, as misleading conclusions could influence public perception and policy decisions. Given the socio-economic sensitivity of this issue, researchers must be cautious about potential biases in data sources and analysis, ensuring that findings are presented responsibly to avoid misinterpretations that could exacerbate economic anxieties.

mini project/ideas/idea2.md

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+# Idea #2
+### Study of International Student OPT - Malena Malka Goldman
+
+**Research Question: What are the most common majors among international students in undergraduate education, and how do these majors influence their likelihood of participating in Optional Practical Training (OPT)?**
+
+Optional Practical Training (OPT) is a temporary work authorization program that allows international students on F-1 visas to gain practical work experience in their field of study in the U.S. after graduation. OPT is typically available for up to 12 months, but students in STEM (Science, Technology, Engineering, and Mathematics) fields may apply for a 24-month extension, allowing them to work for a total of 36 months.
+
+**Tractable data**
+For this project, I will be utilizing official data from reputable sources that track international student trends in the United States. Specifically, I will analyze datasets from Open Doors and SEVIS (Student and Exchange Visitor Information System).
+The Open Doors Academic Level and Places of Origin dataset provides information on the distribution of international students by academic level (undergraduate, graduate, non-degree) and country of origin. Additionally, the Field of Study by Place of Origin dataset offers insights into the most common majors pursued by students from different countries. These datasets, published by the Institute of International Education (IIE), offer comprehensive and state-approved statistics on international student enrollment trends.
+To deepen the analysis, I will cross-examine this data with SEVIS statistics, which provide real-time tracking of international students in the U.S., including participation in Optional Practical Training (OPT). This comparison will help identify correlations between students’ academic backgrounds, country of origin, and their likelihood of engaging in OPT after graduation.
+
+**Data retrieval**
+As mentioned before, I will combine and transform data from Open Doors and SEVIS to analyze international student trends in the U.S. By merging academic level, field of study, and country of origin data with OPT participation statistics, I will create a unified framework for cross-examination. This transformation will allow for a deeper understanding of how academic and demographic factors influence students’ post-graduation choices. The links to access the data are the following:
+_Academic Level and Places of Origin Data_
+https://opendoorsdata.org/data/international-students/academic-level-and-places-of-origin/
+_Field of Study by Place of Origin_
+https://opendoorsdata.org/data/international-students/fields-of-study-by-place-of-origin/
+_SEVIS OPT data_
+https://studyinthestates.dhs.gov/sevis-data-mapping-tool/january-2024-sevis-data-mapping-tool-data
+
+**Correct specification of the model**
+To analyze international student trends and OPT participation, I will use a clustering analysis approach to group students based on academic level, field of study, and country of origin. The two main clustering methods I will consider are k-means clustering, which is efficient for large datasets, and hierarchical clustering, which can capture more complex relationships between groups.
+
+**Implications for stakeholders**
+This research project and its findings could offer significant benefits for students, universities, and potential employers. Prospective college students could use the insights to inform their decisions on which major to declare, considering how OPT participation may impact their ability to stay and gain work experience in the U.S. Universities, in turn, could tailor their academic and career support services to better align with the trends in OPT participation, enhancing their international student experience. Additionally, employers seeking to hire international talent could use the findings to refine their recruitment strategies, focusing on fields with higher OPT retention rates and ensuring they are tapping into the most promising student pipelines.
+
+**Ethical, legal, societal implications**
+One of my main concerns with this research is its political and social sensitivity, especially given the shifting administration and heightened debates around immigration. Public perceptions of international students are particularly vulnerable to policy changes, making it crucial to approach this study with care. The findings could influence discussions on OPT policies, student retention, and immigration, potentially shaping public opinion and institutional decisions. While the insights may help students make informed choices about their academic and career paths, there is also a risk of unintentionally reinforcing stereotypes based on nationality, major, or career prospects. To ensure fairness and equity, the research must be conducted with a neutral, data-driven approach, avoiding unintended biases. Additionally, maintaining strict data privacy and anonymization will be essential to protect student information and uphold ethical standards.