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Independent Analysis Project Guidelines

(Due March 14, 2024 11:59pm)

For your independent analysis project, you will answer a question using data and regression tools learned this quarter. You will produce a very brief high-level policy memo detailing your findings, and provide between 2-5 additional items (at least one figure and one table +3 optional items) to present your findings in more detail. Please choose your topic from the three categories listed below, and use the data provided for that topic (data files are available on Canvas).

Please read these guidelines carefully and in their entirety, as it is your responsibility to understand all of the project requirements. The last section of these guidelines is a page outlining all of the assignment rules. Submission of this assignment indicates that you understand and have adhered to all of the assignment rules, and have worked entirely independently.

In all cases, you must do cross-sectional analysis (i.e., no time series or panel). Part of your job will be to figure out how to best address your topic using a cross-sectional framework. Across topics, you may only use the provided versions of the data. You are not required to do any outside research, but you may. You must cite any sources that you use in accordance with normal academic integrity standards.

1. Topic Summaries

(A) Environmental Policy:

Deforestation, Epidemiological, and Socio-economic Data in the Amazon

Deforestation is a major concern when considering the long run impacts of climate change. For de-veloping countries, like Brazil, limiting excessive deforestation while also promoting economic devel-opment remains a challenge. The interplay between these concerns is of particular importance. This dataset broadly contains municipality-level measures of deforestation, forest degradation, economic activity, development indicators, a multidimensional poverty measure and population data. Potential questions that you could explore include:

– What is the effect of deforestation on educational contributions to poverty?

– How do mining operations alter the extent of forest degradation?

– Do temperature anomalies affect the share of municipalities that are considered pastures?

(B) Economic Development:

US County-Level Development Indicators Data

The U.S. Counties Development Indicators Dataset compiles information from various government and university sources. You can treat this dataset as a cross-section of all U.S. counties (including Puerto Rico’s) with the most recent information for each group of variables. The “demographics” sec-tion gathers information from the 2020 census. Other sections include economic indicators (real GDP, income, employment, and poverty in 2021); education indicators between 2017 and 2021; and healthindicators (mortality rate and life expectancy in 2019). In some sections, the data is disaggregated by racial/ethnic identities. The dataset has 3221 observations (counties) and 60 variables.

Use the dataset provided to answer one of the following questions or come up with your own research questions:

– How does education influence unemployment rates?

– What could explain the geographical variation in poverty across U.S. Counties?

– How do any of these indicators affect GDP?

– Are there racial/ethnic gaps in any of these relations?

– Are there gender gaps in any of these relations?

(C) Political Behavior and Opinion:

Tracking Europeans’ Opinions on Brexit and the EU

Brexit marked a turning point in EU history. For the first time an EU member state left the EU, leading to concerns about the stability of the EU as a whole. Indeed, Brexit carried significant spillover effects in the other EU member states, both in terms of the loss of cooperation gains that disintegration entails, and for the risks of political contagion. This event has received a lot of media coverage and opinions regarding the utility or futility of “Brexit” varied widely, both in the UK as well as across Europe. But what determines public opinion towards Brexit?

The survey data includes information on public opinion across 28 EU Member States including ques- tions probing respondents’ attitudes towards the EU, perceptions of the Brexit process, as well as general political attitudes and demographic characteristics. The identities of all participants are anonymized. The data covers four survey waves between 2018 and 2019. Here are some sample questions you could explore (but do not be limited by these suggestions):

– What determines a person’s opinion regarding Brexit?

– What explains variation in the national level public opinion regarding Brexit?

– What influences people’s beliefs about the EU and the right course of action?

– How does someone’s opinion about Brexit affect public opinion on their own country’s role in the EU or on the EU more generally?

2. Guidelines

You are responsible for two deliverables (all of which will be submitted electronically via Canvas/Turnitin):

• MEMO: A 1500-word maximum, single-spaced policy memo presenting your research and conclu-sions. You will find it hard to summarize everything you have done in this amount of space. But you must concisely describe how you used data to answer the question at hand, your main findings, and the fundamental limitations to your analysis in clear language for a reader that may not have a compre-hensive understanding of regression analysis. (Any references you include will not count against your word total.)

– SUPPORTING INFORMATION: A combination of 2 to 5 additional items—Figures or Tables—that help present your research. You must have at least one table that presents your main analysis results and one figure; you can add up to three more items (graphs or tables). These figures and tables should include descriptive captions that stand alone (i.e., that allow readers to understand what they are reporting by only looking at the table/figure and their caption). These items should be clearly labeled, included in a separate section following your memo, and should be referenced from within your memo.

• SCRIPT: An .R file that replicates all of the analysis for your memo and supporting information. Please comment your .R file so that we can easily navigate your code (e.g., /* Generate Figure 1: Interaction Effects */).

Your analysis (all three components considered together) should have the following general structure:

1. Motivation and Theoretical Underpinnings: You should begin by presenting the motivating ques-tion, or set of questions, and a clear explanation of the theory guiding your analysis. WHY are you doing what you are doing? Are there intellectual schools of thought that guide your intuition? What hypothesis (or hypotheses) are you testing and what do you expect to find?

2. Data Selection: You must explain how you use the data to test your hypothesis and answer the question at hand. What are the data you are using and why can they help you answer the question of interest? What is the dependent (outcome) variable? What is (are) the independent variable(s) of interest? This should include a discussion of case selection in light of your theory: explain why you are using the subset of data you are using (both in terms of observations and variables). You should also include a concise description of any data manipulations/variables you have generated (how and why). Anyone who reads your paper and looks at your do file should be able to easily replicate your analysis.

3. Methodology / Explanation of Model(s): You should present your model(s) with clear justifications for your variable selection and the functional form of your variables, including any interaction terms. What are you controlling for, and why?

4. Regression Analysis and Results: Your main analysis should be a series of regressions testing the effect of your independent variable(s) of interest on your outcome variable. All models should be reported in a clearly-labeled regression table on your supporting information. Explain the progression of your analysis clearly (e.g., adding other independent variables; testing interactions, etc.). Use graphics and simulations where appropriate. Discuss which model(s) have the strongest statistical and practical significance. Interpret the meaning of your coefficients in a useful manner and discuss the goodness of fit of your model.

5. Threats to Validity, Regression Diagnostics: Your analysis should include discussion of potential vi-olations of the Gauss-Markov Assumptions. If you exclude variables because of high multicollinearity, please explain why, and present the appropriate diagnostics. You should discuss potential problems with the Zero Conditional Mean and Homoskedasticity assumptions. If such problems exist, discuss the implications for your analysis. Deal with these problems as you are able; if you are unable to address them sufficiently in your analysis, discuss the impact on your ability to estimate regression parameters and conduct hypothesis testing.

6. Discussion and Conclusion: You should conclude with a thoughtful summary of your results, and a clear set of policy-relevant conclusions. You should also discuss the limits of your analysis, including problems with the data (e.g., selection bias and measurement error). How would you improve this research design? What would be the next steps in your research?

A final note: You will do much more analysis than you can present in the memo and graphics. A huge part of the work here will be in compressing what you have done into your findings. You will need to spend time on the writing and presentation, so make sure to leave yourself time to do that. We recommend spending Week 8 getting to know your data, reading, and planning your analysis; Week 9 should be devoted to running all of your analysis; and Week 10 should be devoted to writing, editing, and putting together your final deliverables.

3. Grading Rubric

The project is out of 100 points total:

45pts: Memo

• Have you clearly articulated the motivation for the analysis, your theory, and how you used the data to test your hypotheses? What outcomes are you examining, and what is/are the main independent variables of interest? Are there other observable implications of your theory/hypothesis? How did you examine those? (10pts)

• What are alternative explanations for what you find and how did you account for them? What kinds of controls did you use and why? How did you balance the various goals of model building (thorough-ness v. simplicity, etc.)? Have you interpreted your models clearly and correctly? Does your analysis progression make sense? You should be telling a story here. (13pts)

• Have you addressed potential issues with data (outliers, measurement error) and violations of the Gauss-Markov assumptions, and dealt with them as you are able? (10pts)

• Have you summarized your findings with appropriate policy-relevant conclusions and discussed limi-tations to your analysis? (7pts)

• Presentation: Is your memo clearly-written, easy to follow, and compelling? Have you eliminated spelling and grammatical errors? (5pts)

45pts: Supporting Information

• Motivating figure - you should have one figure that motivates your analysis clearly by providing a visual display of the question. Remember that the caption of your figure should explain what is meaningful, so that readers can understand even if they do not read the memo. (13pts)

• It is said that a picture is worth a thousand words. Have you used additional graphics/figures to tell your story (motivation, illuminating results, diagnosing problems, etc.)? Remember, you can have up to 5 items including your regression table. (10pts)

• Have you included a clearly-labeled regression table with all of your results? Have you summarized the table clearly with a few sentences-long caption, so that a casual reader can understand what the table shows without having to read the memo? Remember that variable names are often meaningless to a reader, and you can often summarize grouping variables and controls for clarity. (22pts)

Remember that presentation matters for each of these items: Are your graphics presented professionally; is your table easy to read? Would we put it on the wall here at GPS?

10pts: Do File

• Does your .R file run without error? (5pts)

• Does your .R file run and replicate everything in your report? Have you commented it so we can navigate it easily? (5pts)

4. Rules

Submission of your project indicates that you understand and attest to the following:

1. I have worked independently on this project and have not discussed or shared information about this assignment with anyone else.

2. I understand that my final report will be screened for plagiarism and unfair collaboration via tur- nitin.com and manual inspection.

3. I have not used anyone else’s materials as guidelines, including past and present students at GPS.

4. I understand that I am not allowed to ask the TAs/professor for any help on this project beyond clari- fication of data set structure and/or language translation questions.

5. I have not asked other faculty for guidance on this analysis.

6. I have fully and correctly cited all external sources that have contributed to my thoughts, development, and presentation of this analysis.

7. I understand that failure to abide by any of these rules will result in automatic failure of both the project and the course.