Causal Inference for Microeconometrics Assessment 2
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Causal Inference for Microeconometrics
Assessment 2: article review and research idea
Due date: 10th of October at 2pm
General instructions
Format This assignment should be no more than 3 pages (not including the title page and references). Your report must be word processed using 11 point font or larger with single spacing. I suggest that you use 2 pages for part I and 1 page for part II but you can organise this differently as long as it is 3 pages overall.
Marking A marking guide is provided for each question. Please answer all questions and sub-questions to get full marks. Writing style matters in economics and will be taken into account in the marking for each question: be accurate, precise and structure your arguments logically. Misuse of causal language will be penalised. An example of an excellent past assessment is provided in ”An excellent example (90 out of 100)” - keep in mind though that this is only an example.
Submission You should upload your assignment via the Turnitin submission link (in the Assessment 2 folder under Assessment). You are only able to submit once so make sure you finalise everything before submitting. You are strongly encouraged to submit your work reasonably earlier than the deadline, to avoid potential technical issues. Please submit the assignment on time: I am not allowed to accept late submissions (unless there are exceptional circumstances), so try not to put me in the very uncomfortable position of not accepting a late submission. Penalties will apply as described in the ECP (unless an extension has been granted prior to the due date). Do not hand in a hard copy.
Artificial intelligence (AI) usage This assessment has been designed to be challenging, authentic and complex. Whilst students may use AI technologies, successful completion of this assessment will require students to critically engage in the task for which artificial intelligence will provide only limited support and guidance. A failure to reference AI use may constitute student misconduct under the Student Code of Conduct. If you use generative AI or AI-assisted technology, please include the following statement directly before the references at the end of your manuscript: ”During the preparation of this work, I used [NAME TOOL / SERVICE] in order to [REASON]. After using this tool/service, the author reviewed and edited the content as needed and takes full responsibility for the content of the publication.”
Group work You are allowed to discuss this assignment with peers. However, this is not a group assignment, which means that you must write up the assignment in your own words and submit it separately. The marking system will check the similarity, and UQ’s student integrity and misconduct policies on plagiarism apply.
Aim This is an exercise to develop your independence and critical thinking in writing a report and drafting a research proposal. Economists are engaged in research in a wide variety of areas today. Undergraduate and graduate students, academic economists, policy-makers working in the civil service and central banks, professional economists working in private sector banks or industry may all need to write summaries and proposals that involve economic data. Depending on the topic and intended audience, the length and technicality of these reports can vary widely but tend to follow a similar structure (as seen in class). This structure should guide your future empirical work, including this assessment.
Only the main elements are outlined below: please refer to lecture 8 for more details. Most importantly: take it as fun, because this is what research is about!
Part I: article review (60 points)
Instructions The aim of this part of the assignment is to select one paper (and only one!), read it very carefully, understand it well and write a solid and easy-to-read report. Please try to enjoy this assignment: chose a paper you like and spend some time on it. Try to understand it and use your own words to summarise the paper following the structure provided below. You can refer to the lecture slides for a more detailed discussion of what goes in each of the sections in an empirical paper. Please do not copy and paste whole parts from the paper. To avoid any confusion, you have to briefly summarise the paper you chose, you don’t have to critically assess the chosen paper.
(0) Select one of the papers from the list below and posted on Blackboard. This is a list of some well-published papers including recent publications at the frontier of knowledge from various fields (health, labour, education, history, immigration etc) to cover various interests. However if there was another paper that you like better, please talk to me about it (to ensure that it fits the brief for the assessment).
(1 - 10 points) The introduction to a paper is the most important part. The introduction will include a lot of information about the paper: (a) the research question (and why do we care?); (b) the gap in the literature it is trying to address; (c) the contribution(s); (d) the data and identification strategy (which can be part of (c)); (e) results; (f) structure of the paper.
Q) In your report, please identify (a), (b) and (c) for your chosen paper and summarise these points in a way that is well understood to a more general audience (avoid technical jargon!).
(2 - 15 points) The data section contains a detailed description of the data and its sources.
Q) In your report, describe the dataset for your chosen paper: where is it from (sources, dates, country...)? Why is this dataset well suited to answering the research question? How was it sampled and what is the sample size? Identify the most important variables: how are they defined? what is their mean (this information is usually in a table or figure but also check the text)?
(3 - 15 points) In the empirical strategy section, a paper identifies the econometric techniques used and the equations estimated to address the research question and any identification problem (e.g. OVB).
Q) In your report, describe the econometric technique(s) and present the main equation(s) estimated in your chosen paper. Explain identification issues with OLS that are specific to your paper (e.g. sample selection, omitted variables, simultaneity, measurement errors...). Discuss how the chosen technique help address these potential identification issues.
(4 - 12 points) The result section typically describes the main empirical findings which are reported in tables and/or figures.
Q) In your report, describe and interpret the main coefficients from the main regression (pay particular attention to whether these can be causally interpreted). Then, summarise the main empirical findings of the paper by relating back to the research question and the economic issue(s) under investigation.
(5 - 8 points) The conclusions provide the main points of the paper around the contribution and main findings. Sometimes it extends to providing policy implications.
Q) In your report, describe the paper’s main insights: over and beyond the main results what have we learned that may be useful (e.g. with respect to the literature, to an economic theory, to policy implications).
HINT: If you think that one or two figures are so important that you want to include them in your report, please do so. Please do not copy and paste whole tables.
Part II: research idea (40 points)
Instructions Now it’s your turn! In this part, you will write an empirical research proposal. The aim of this part of the assignment is to pick a topic you are passionate about, identify a research question and address some of the practicalities (data sources, identification strategy...). You might use this research proposal for further research or for a future thesis, so choose a topic that you will enjoy thinking about for some time. The aim is to convince the reader that this is a significant question and a feasible project. Please try to enjoy this assignment: enthusiasm is a big part of convincing the reader. The structure provided below follows the same structure as the summary in Part I and should be used as a guide. It will be important to exercise your judgement and adjust to the context of your own project.
(1 - 8 points) The first thing to present is the research question. Finding an interesting economic question seems difficult but to get some inspiration, you can think of the discussions we had in class, the academic papers that we discussed or that you summarised in part I, newspaper articles you have read recently, and even your own personal experiences. This subsection is about showing that the question you propose to investigate is significant enough to warrant the investigation, i.e. that the results are likely to prove useful (for a practitioner, a policy maker, an academic in a different field...). A way to tackle the significance question is to think about why you would like to work on this topic, or why anyone would want to do so. Ask yourself: ”Why is it important? What is interesting about this? Suppose I find an answer, what use would it be? To who?”. You then need to think about the scope of the question: it is important to pose a very specific question that, in principle, can be answered in one piece of research rather than a 10-year agenda.
Q) In your proposal, please identify the research question and its significance: why do we care? Is it addressing a costly social problem? How might a policy maker/practitioner/someone else use these results? Describe these points in a way that is well understood to a more general audience (avoid technical jargon!) and be specific.
(2 - 8 points) The second important aspect of a research proposal is to demonstrate that the project will make an original contribution. With the research question in mind, go and read more academic literature about it to see what has already been done and find out what aspects have been exhausted, what is neglected, what are the main ideas, issues and controversies in the area. You can do this with a google search (google or google scholar) for ”the research topic” and then read papers from major academic journals. You can also take a look at the papers from their literature review to get a good sense of what has been done and what debates may be unsolved. The gaps and debates are sometimes clearly articulated in the academic literature but the contribution can also come from new knowledge that would be useful in the real world but have not yet been articulated in the academic literature. Try to think about what potential results you would expect to obtain, who would benefit from this new knowledge and the potential policy implications. This will help you identify the niche of your project, i.e. where it fits in the literature. Note that it is not because something has not been done, that it is necessarily a nice contribution: it could be that it has not been done because it is not that important or it is not feasible!
Q) Briefly summarise the state of the literature (referencing a few papers that are most closely related to your research question). Then explain the gap or debate in the literature that your research question is trying to address. Finally, describe your contribution.
(3 - 12 points) The next stage of the proposal tackles the feasibility aspect. It is really important to have a plan for data access or collection at the proposal stage. Otherwise you may spend weeks reading papers only to find out that the reason what you propose has never been done is because there is no data! Read closely the data sections of papers in your reference list (from section 2) and investigate other possible datasets. Some well-known Australian datasets you may want to consider are: the Census (from the Australian Bureau of Statistics); the longitudinal panel of households (HILDA); the longitudinal panel of children (LSAC); the longitudinal business data (BLADE). Identify the data suited to your research question: check that it contains the variables you need, has the required sample size and identify the access protocols. Alternatively if you are planning to collect or generate your own data, set up a plan for data collection: think about ethics applications, costs that may be associated and potential technical skills needed.
Q) In your proposal, describe the dataset that you would like to use: (i) source contact (e.g. is it available to anyone? Or will it be collected as part of an RCT/experiment? Or do you need to apply for access and if so with who?); (ii) time-frame of collection (e.g. number of weeks/months until data is available); (iii) time-frame for data cleaning (e.g. 5 hrs per week over 2 months). Then explain why this dataset is the best suited to answer your research question (think about the structure of the data, the sample, the variables).
(4 - 12 points) In the empirical strategy section, the aim is to identify the econometric technique and regressions to estimate to address the feasibility from the technical point of view: do you have all that is needed in you dataset? What are the potential threats to identification and how does the strategy you chose address these?
Q) Please identify the econometric technique and the equations to be estimated. Please discuss potential econometric issues arising from an OLS estimation (e.g. sample selection, omitted variables, simultane- ity, measurement errors) and explain how the chosen strategy addresses these issues. Finally, provide some evidence that assumptions are likely to be met (e.g. you have an IV that is credibly exogenous).
DO NOT FORGET TO INCLUDE A REFERENCE LIST!
List of papers for Part I
Abouk, R., Adams, S. J., Feng, B., Maclean, J. C., & Pesko, M. F. (2019). The effect of e-cigarette taxes on pre-pregnancy and prenatal smoking, and birth outcomes. National Bureau of Economic Research. Almond, D., Doyle Jr, J. J., Kowalski, A. E., & Williams, H. (2010). Estimating marginal returns to medical care: Evidence from at-risk newborns. The quarterly journal of economics, 125(2), 591-634.
Bagues, M., Sylos-Labini, M., & Zinovyeva, N. (2017). Does the gender composition of scientific com- mittees matter?. American Economic Review, 107(4), 1207-1238.
Baranov, V., De Haas, R., & Grosjean, P. (2023). Men. Male-biased sex ratios and masculinity norms: evidence from Australia’s colonial past. Journal of Economic Growth, 1-58.
Becker, S. O., Grosfeld, I., Grosjean, P., Voigtländer, N., & Zhuravskaya, E. (2020). Forced migra- tion and human capital: Evidence from post-WWII population transfers. American Economic Review, 110(5), 1430-1463.
Bettinger, E. P., Long, B. T., Oreopoulos, P., & Sanbonmatsu, L. (2012). The role of application as- sistance and information in college decisions: Results from the H&R Block FAFSA experiment. The Quarterly Journal of Economics, 127(3), 1205-1242.
Chan, M. K., Herault, N., Vu, H., & Wilkins, R. (2023). The Effect of Job Search Requirements on Family Welfare Receipt. Journal of Labor Economics, forthcoming.
Ebenstein, A., Lavy, V., & Roth, S. (2016). The long-run economic consequences of high-stakes examina- tions: Evidence from transitory variation in pollution. American Economic Journal: Applied Economics, 8(4), 36-65.
Kroft, K., Lange, F., & Notowidigdo, M. J. (2013). Duration dependence and labor market conditions: Evidence from a field experiment. The Quarterly Journal of Economics, 128(3), 1123-1167.
Lee, D. S., Moretti, E., & Butler, M. J. (2004). Do voters affect or elect policies? Evidence from the US House. The Quarterly Journal of Economics, 119(3), 807-859.
Lemieux, T., & Milligan, K. (2008). Incentive effects of social assistance: A regression discontinuity approach. Journal of Econometrics, 142(2), 807-828.
Masera, F., & Rosenberg, M. (2021). Slavocracy: Economic Elite and the Support for Slavery. Available at SSRN 4009954.
Mastrobuoni, G., & Pinotti, P. (2011). Legal status of immigrants and criminal behavior: evidence from a natural experiment. Bank of Italy Temi di Discussione (Working Paper) No, 813.
Parey, M., & Waldinger, F. (2011). Studying abroad and the effect on international labour market mobility: Evidence from the introduction of ERASMUS. The economic journal, 121(551), 194-222.
Shayo, M., & Zussman, A. (2011). Judicial ingroup bias in the shadow of terrorism. The Quarterly journal of economics, 126(3), 1447-1484.
Waldinger, F. (2010). Quality matters: The expulsion of professors and the consequences for PhD student outcomes in Nazi Germany. Journal of political economy, 118(4), 787-831.
2025-10-09