帮写Qroduce a very brief high-level policy memo detailing your findings代写R编程

Guidelines

For project, you will produce a very brief high-level policy memo detailing your findings, and provide betw een 2-5 additional items (at least one figure and one table +3 optional items) to present your findings in more detail. Please use the data provided for thattopic (data files are available ).

Please read these guidelines carefully and in their entirety, as it is your responsibility to understand all of the project requirements.

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, you may only use the provided versions of the data. You are not required to do any outside research, but you may.

1. Topic Summaries

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?

2. Guidelines

You are responsible for two deliverable :

• MEMO: A 1500-word maximum, single-spaced policy memo presenting your research and conclu-sions. You must concisely describe how you used data to answer the question at hand, your main fi ndings, and the fundamental limitations to your analysis in clear language for a reader that may not h ave a comprehensive understanding of regression analysis. (Any references you include will not coun t 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?

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?

• 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.

• 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?

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

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

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.

• 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.

• 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.

Do File

• Does your .R file run without error?

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