Applied Economics 2022/23 – Formative Assignment 2
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Applied Economics 2022/23 – Formative Assignment 2
Dear Applied Economics students,
This is the second formative assignment. It does not count towards your final grade. But it is an excellent way to prepare for the summative assignment at the end of this term and a great opportunity to receive feedback on whether you understand the main concepts covered so far. You should therefore take it seriously.
We will offer a Q&A session on Tuesday at 9am (November 29), where you can ask questions related to this assignment. We will not provide solutions during this session. But we might give you a few more hints or clarify some points. This is a pure Q&A session. We will only respond to questions you ask and not present any new material. Please click on the link below to join the session:
https://bristol-ac-uk.zoom.us/j/3268059713?pwd=ZG5pNHNzdzJiQ3d6K2tHM3pmSytNUT09
If you want to, you can work in groups. In this case, only one group member should submit the assignment. But make sure to list the student numbers of all group members at the top of the first page.
Please follow the submission guidelines below. Read them carefully.
Best of success!
Toman & Hans
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SUBMISSION GUIDELINES Submission deadline: Friday, December 2, 10:59 a.m. (UK time) Submit the assignment via Blackboard: Go to the Blackboard site of this unit and click on "Assessment, submission and feedback" and then on "Applied Economics Formative Assignment 2". Submit a PDF document. You can save a Word document as PDF. For equations, you can use Word’s equation editor. Make sure that all equations and symbols are correctly displayed before you submit. Submit only one file that contains the answers to all 3 questions. Include your student number at the top of the first page. If you work in groups, only one group member should submit the assignment. Make sure to list the student numbers of all group members at the top of the first page. Use Arial or Calibri font, 12-point font size, 1.5 line spacing throughout (only potential exception: Tables, figures, and equations). Use titles for your sections that clearly specify which question you are answering in each section. |
Summer schools and test scores
Parents, teachers, and policy makers are very interested in ways to help struggling pupils in schools.
Summer schools are an initiative that has gained a lot of popularity in many schools. It allows children to catch-up during the summer break and hopefully be at the same level as their peers when they all return after the summer break. However, there is little evidence that these summer schools work (and maybe it would be better to also give the struggling children a break).
In the R tutorial we created a fictitious setting to illustrate how we could study whether summer schools improve learning measured in terms of test scores . In this setting the summer schools are optional and free, but families have to sign-up for the summer schools and drive their children there themselves. The summer school takes place in the summer between year 5 and year 6.
Question 1: Explain why comparing test scores in year 6 for children who attended the summer school, to test scores in year 6 for those who did not attend the summer school is unlikely to inform us about the causal effect of summer schools. You can use results fromyour Online R tutorial to support your arguments (200 words).
Question 2: A group of researchers sent out reminder letters encouraging families to sign-up for the summer schools, to a randomly selected subset of the families. The researchers plan to use these letters as an instrumental variable to study the effect of the summer school. Discuss whether receiving the letter is likely to be a valid instrument for identifying the causal effect of attending a summer school on test scores. You can use results fromyour Online R tutorial to support your arguments (250 words).
Question 3: Another group of researchers realised that there is data on test scores before and after the treatment. They therefore suggest using difference-in-differences approach to identify the causal effect of attending summer schools on test scores. Explain how the difference-in-differences approach would work in this case and the key identifying assumption. You can use results fromyour Online R tutorial to support your arguments (250 words).
2022-12-15