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EMET8002 Case Studies in Applied Economic
Analysis and Econometrics
Semester 2 2024
Computer Lab in Week 4
This week we use the 2015 Programme for International Student Assessment (PISA) dataset for our analysis. PISA assesses skills of 15-year-old students across many countries and involves data on student performance (reading, mathematics and science knowledge), as well as parent, school and teacher questionnaires. The data is publicly available and can be downloaded from the PISA website under the section “SPSS (TM) Data Files (compressed)” (https://www.oecd.org/pisa/data/2015database/).
PISA codebooks and questionnaires are available through the same link. However, these are large files and take sometime to download and convert to Stata format. Therefore, we have prepared a smaller merged dataset of the Student and School questionnaire data files which now only includes the relevant variables. You can download this data (“Week4_PISA_data.dta”) from Wattle.
Question 1: Categorical Dependent Variables
We are interested in what determines whether a student has to repeat a grade at school at least once. We call our dependent variable “repeated” and this is a dummy variable that is equal to 1 if a student has repeated a grade at least once, and equal to 0 when the student has never repeated a grade.
(a) Drop missing observations for the “repeated” variable (hint: missing values are sometimes coded as “.” or “.a”; you may consider “keep” or “drop” commands).
(b) There are several dummy variables that we will include as independent variables.
These are usually coded as “1” or “0”, but in the PISA dataset these are given as “2” or “1” . For example, “male” is equal to 2 if the student is male and equal to 1 if the student is female. Recode these dummy variables such that they are equal to 1 if the variable description applies to the student, and 0 otherwise (e.g., the “male” dummy is equal to 1 if the student is male, and equal to 0 if the student is female). Do this for the ”male”, “international_lan”, “quiet study” and “training” dummy variables. (hint: you can use the “replace” command)
(c) Explain why we only need to include one dummy variable (e.g., “male”) in the regression to describe a binary variable with two possible values (e.g., male or female).
(d) Run alogit model with “repeated” as the dependent variable and the following independent variables: “international_lan”, “male”, “mother_edu”, “father_edu”, “quiet_study”, “school_size”, “class_size”, “training”, “computer” . How do you interpret the estimated coefficients?
(e) Exclude missing values for all independent variables. Repeat the logit model from part d but estimate it without missing values. Are there any differences? Which model do you prefer?
(f) Repeat your preferred logit model from part e but estimate it using clustered standard errors (by school). When do we use clustered standard errors and why?
(g) What are the effects of mother’s education (“mother_edu”) on whether a student has to repeat a grade? Include joint tests in your answer. (hint: this variable is based on the survey question: "What is the <highest level of schooling> completed by your mother?" Possible answers: ISCED level 3A (coded as 1); ISCED level 3B, 3C (coded as 2); ISCED level 2 (coded as 3); ISCED level 1 (coded as 4); She did not complete ISCED level 1 (coded as 5). For definitions of ISCED education levels see,for example, https://stats.oecd.org/glossary/detail.asp?ID=1436)
(h) Calculate and interpret odds ratios for the logit regression in part d.
(i) Calculate and interpret marginal effects for father’s education (“father_edu”) based on your logit regression part d.
(j) Run both logit and probit models using the same variables as in part d and export your results to an excel file. How do your results differ across the two models? (hint: commonly used commands to export data from Stata are the “outreg2” and “estout” commands)
(k) Compare the predicted probability values for father’s education (“father_edu”) for both logit and probit models from part j and export them to an excel file. How do your results differ across the two models?
Question 2: Preparation for Research Proposal [not required for problem set]
The Research Proposal is due on 1 September 2023. First of all, you are expected to provide a summary of the main findings, data and methods of the paper that you have chosen for your research project as well as a brief literature review. Next, you need to propose a minor extension to the original paper. Your idea for the extension needs to be backed up with prior literature and/or theory. You need to explain why your research question is interesting, what you expect to find, and how you are able to test it (with specific statistical hypotheses, data and methods). There is no need to do any analysis at this stage.
(a) Identify a couple of references from the academic literature that are relevant to your project. How are they similar or different from your paper? (methods, data, sample period, country). Are their findings consistent or contradictory to your paper?
(b) Discuss what involves developing an effective research question. You may refer to section 3.1.2 of the following document:https://www.economicsnetwork.ac.uk/handbook/ugresearch/31
(c) Come up with a few ideas for your extension. Why do you think that they are interesting? Based on theory and prior literature, what do you expect to find? Formulate your idea in the form of a research question and a formal statistical hypothesis. Make sure that your extension is feasible given the data and time constraints that you have.