Against the backdrop of the recently introduced Anti-Waste and Circular Economy Law (2020), in two days, we will examine the design and potential impact of the repairability index by going through all major stages of a discrete choice experiment (DCE).
In Day 1, we will start with an introductory lecture on DCE. We will discuss the potential of DCE by comparing it with other traditional impact evaluation techniques such as randomized control trials. After that we will design a DCE together, going through the experimental design, questionnaire design and online setup. Our target is to come up with a ready-to-go survey so that you and your friends can fill in the survey and provide data for the activities in Day 2.
We kick start Day 2 with a mini lecture on random utility theory. The knowledge will prove to be essential in understanding how the data set should be structured and analyzed. By this time, you will understand better why a rather simple looking logistic regression model can inform us how people valuate things. We will then spend time to prepare the data for analysis. This is the time to apply your Stata skills to clean and re-structure the data to make them fit for further data analysis. We will then analyze the data and conclude by discussing our findings related to the value and/or design of the repairability index and reflecting on the original survey design.
After completing this part of the course, students will be able to design a discrete choice experiment, conduct it in an online environment, analyze survey data, and provide recommendations to firms or policymakers based on the findings. Students will also have ample opportunities to apply and sharpen their data management skills using Stata. At a more general level, students should be able to reflect on the possibilities and limitations of the method.
What are discrete choice experiments (DCE)? How may we assess the impact of the repairability index on green transitions? What kind of questions can and cannot be answered by DCE?
We will come up with a question answerable with DCE.
We will identify relevant attributes and levels, and then decide what to keep in the survey. We will also discuss issues related to label and visual display.
We add additional questions to the survey. Other practical issues such as the number and sequence of questions will also be covered.
It is time to set up randomization on Qualtrics. We will also prepare a consent statement and conduct a pilot study. We will also talk about sampling strategy and how to distribute online surveys.
It is not so straightforward to see why a simple logit model can tell us how people valuate certain things. Some knowledge about the theory is going to help us understanding how to analyze the data. The data downloaded from Qualtrics are not yet ready for analysis. We need to reshape the data in a way that fits the command that everyone knows. An understanding of the theory will shed us some light on how the data set should look like.
Once we know how the data should look like, we are ready to make our hands dirty. The seemingly simple task could be daunting. You think you are good at Stata? This is the time to test it.
Time to run the model now. We will estimate the willingness to pay and use the estimation results for some scenario comparison. Note that they may not make perfect sense given our research objective and experimental design. But at least you know the steps and can apply them when they are appropriate.
We will also discuss our findings related to the value and/or design of the repairability index. To what extent can information induce behavioural changes? Finally, we will have had relatively little time to design our experiment. Things may go wrong in the design process that makes the design not measure up to our objectives. It is time to reflect on the original survey design and propose ways to improve it.
Please complete the readings before our first-day meeting. Readings 1 and 2 are webpage, which you can finish in 10 minutes. Reading 3 introduces what discrete choice experiments are. Reading 4 (section 4 only) will become handy in Session 2 (see the Schedule below) when we finalize the choice sets. Reading 5 is particularly useful in Sessions 2, 5 and 7, when we set up an achievable objective and analyze our data. An understanding of the random utility theory will help you makes sense of the estimation.
As the starting point, we often assume that economic agents are rational, and that people will react to information from a label or an index positively. But what are the missing links which would imply that a simple repairability index may not do the magic? Reading 6 will give us some additional food for thoughts. It is optional as we may not have time to cover that during session 8.
For this part of the course, there are two grading components: Participation (30%) and one Written Report (70%). The sessions are highly interactive and require constant inputs from students. The participation grade is based on whether students actively engage in class activities and their contributions to the discussion during the two days.
The report is individual based. Students are asked to, based on the empirical findings of the DCE, discuss one further action which they can take to advance the position of a (hypothetical) organization of your choice. It can be a private firm in a specific sector (e.g., electronic appliance manufacturing or retail, plastic materials, clothing, you name it), an (International) NGO, a social enterprise, or a government body, in a selected country of your choice.
The report should have the following structure:
Give your readers further information about the organisation. It can be a real enterprise (e.g., Carrefour, Schneider Electric), but please do not assume that your readers know the organisations by their names. What is the nature of the business or (public) services the organisation provides? What is the country of origin of the organisation? It could be a local firm, a multinational corporation, or a government body, for example.
In which way the repairability index is related to the selected organisation? Highlight a relevant finding from the DCE. (This could be based on some further analysis; but this is not strictly required. Shall you choose to do so, please provide some further details about the extension in an appendix). Discuss the implications of the findings to the organisation.
Note: Image you are trying to make a new proposal to your job supervisor. You need to briefly explain what you find and the implications of it to your organisation, your department and/or your team. Be concise and to the point. Avoid jargons and technical details. They are not interested in the details of DCE or how you find them. They just want to know the importance of the findings. What do they need to know? Why do they need to know that? These two guiding questions will help you determine what (not) to put in this section of the report.
You got your supervisors’ attention. They saw the relevance and an opportunity. But what should be done next? Pitch your idea here. New follow-up studies? Revision of the index? Redesign of a product? The recommendation needs to be specific and relevant to the findings. Some details are required so that your readers can appreciate your brilliant idea, but excessive details are discouraged. So long as the purpose and the idea are sufficiently clear, such that your friends or partner can understand and see the point, that’s enough (i.e., to the point that they stop asking you “what does it mean” and “so what?”).
The report should have 1000 to 1500 words, excluding appendix and references, if there are any. Please include your name, your ID and word count in your submission.