assignment extremely detailed and very long instructions
Project Instructions For this project, you will be required to use data from one of the following articles: Angrist, J. and V. Lavy (2009). The effects of high stakes high school achievement awards: Evidence from a randomized trial. American Economic Review 99(4), 1384 – 1414. [Data] Banerjee, A., E. Duflo, R. Glennerster, and C. Kinnan (2015). The miracle of microfinance? Evidence from a randomized evaluation. American Economic Journal: Applied Economics 7(1), 22 – 53. [Data] Banerji, R. J. Berry, and M. Shotland (2017). The impact of maternal literacy and participation programs: Evidence from a randomized evaluation in India. American Economic Journal: Applied Economics 9(4), 303 – 337. [Data] Bertrand, M., and S. Mullainathan (2004). Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. American Economic Review 94(4), 991 – 1013. [Data] Chong, A. I. Cohen, E. Field, E. Nakasone, and M. Torero (2016). Iron deficiency and schooling attainment in Peru. American Economic Journal: Applied Economics 8(4), 222 – 255. [Data] Gneezy, U., J. List, J. Livingston, X. Qin, S. Sadoff, and Y. Xu (2019). Measuring success in education: The role of effort on the test itself. American Economic Review: Insights 1(3), 291 – 308. [Data] Muralidharan, K. and V. Sundararaman (2011). Teacher performance pay: Experimental evidence from India. Journal of Political Economy 119(1), 39 – 77. [Data] You must let me know (via email) which of these articles you are interested in no later than 8am on January 25; if I do not hear from you by then, I will make the choice for you. As noted on the syllabus, you are not being asked to replicate the article that you are getting your data from. Instead, once you let me know which of these articles you are interested in, I will suggest a slight variation on it for you to do (e.g., if the original article analyzed performance for all students, I might suggest that you focus only on the performance of boys). Ultimately, your aim in this project is to answer a causal (not “casual”) question such as the following: Does being placed into a small class cause students to perform better academically? To do so, you will be required to use a regression model of the following form: Outcomei = α + βTreatmenti + Xiγ + Ui , where Outcomei is the outcome (e.g., a test score) for the ith individual, Treatmenti is equal to 1 if the ith individual receives the treatment (e.g., being placed into a small class) and 0 otherwise, Xi is a vector of control variables (e.g., age, gender, etc.) for the ith individual, and Ui is an idiosyncratic error term. The main parameter of interest is β, which is known as the “average treatment effect” or ATE (no one really cares what α or γ are). Thus, the null hypothesis you will want to test is H0 : β = 0. If any of this is unclear to you, please make sure to spend some time watching my videos that review the background material you are expected to be familiar with from your previous courses in statistics/econometrics. Submission Instructions The project will be completed through 4 “instalments” each worth 20% of your final grade (the other 20% is the test). You should think of these instalments not as 4 separate pieces of work, but rather 4 versions of the same piece of work, each one being “better” than the one that came before it. That is, each instalment should not only add new features, but also improve the existing features (this means fixing any technical errors you had previously, making your writing more clear, etc.). 1 Instalment submissions must be made via a private Google Drive folder that I will share with you once you let me know which article you are interested in. Specifically, each instalment will require you to upload files named paper-x.pdf and code-x.txt, where x is the instalment number (e.g., your first instalment will require files named paper-1.pdf and code-1.txt). The file named paper-x.pdf is to be a PDF file containing the latest version of the write-up for your project. The file named code-x.txt is to be a plain text file containing the latest version of your R code. For the first instalment, you will also need to upload your data file (do not modify this file in any way, i.e., do not rename it or convert it to a different format). All of these files must be contained entirely within the Google Drive folder I share with you; please do not create any subfolders within this folder! If you have done everything correctly, you will have uploaded exactly 9 files in this folder by the end of the course (2 for each instalment plus 1 containing your data). Please note that failure to precisely follow the above submission guidelines will result in a mark of zero. For example, if you were to upload your write-up as a Word file rather than a PDF file, or your R code as a rich text file rather than a plain text file, you would get a zero.1 Your R Code The single most important thing to keep in mind about this project is that I need to be able to replicate all of your results. To run your code, I will set my working directory to the Google Drive folder I have shared with you and enter the following command: source("code-x.txt") (where x is the instalment number). Your code needs to be written so that the above produces every single number that appears in your write-up. If this doesn’t work for any reason, you will get a mark of zero. Thus, I recommend that you work in exactly the same fashion yourself rather than “interactively” (i.e., typing commands directly into the R console). Indeed, before submitting any instalment, you should re-start the R console and run the above command to make sure you get the results you are expecting. For the purpose of this project, you are not permitted to use any R packages except for haven (for reading data saved in Stata format) and sandwich (for computing HC standard errors). Below are some general guidelines for your R code. If you fail to follow of any of these guidelines, you will get a mark of zero. • Do use the following as your very first line to ensure R’s memory is cleared: rm(list=ls()) • Do not include any line beginning with > (i.e., lines that you copied from the R console). • Do not include any calls to the setwd() function. • Do not include any “path” references when reading in your data. That is, you should have something like read.table("data.txt") rather than read.table("/Users/JaneDoe/ECN723/data.txt"), and just manually set the working directory in R to the location where you’ve saved your data file (remember: when I run your code, I will set my working directory to the Google Drive folder containing I have shared with you, i.e., the folder containing your data file). • Do not include any calls to the install.packages() function or the remove.packages() function. However, do make sure to include a call to the library() function for any package(s) you use. • Do not include any calls to functions that open a graphical interface such as the View() function (you can use this yourself if you would like, but it will just create an error for me). • Do not create separate data frames for your treated and non-treated groups. You should have a single data frame containing all of your observations, and within this data frame, there should be a treatment variable equal to 1 for observations in the treated group and 0 for observations in the non-treated group. • Use the attach() function exactly once (and make sure to do so only after you have “cleaned” you
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