LEAP supports the delivery of economics programmes, ensuring that teaching is research-led, and exposes students to new research at all levels of study.
A key feature of our programmes is that modules are research-led, meaning they engage students with the research being done by the people teaching them. Research-led teaching happens at all levels, from first year undergraduate to Doctoral training
On this page, you can find case studies of research-led teaching. The cases show the breadth and depth of research at Leeds and our practical, policy-relevant, interdisciplinary and pluralist approach to economics.
Annina Kaltenbrunner is an expert on international macroeconomics, and researches on currency hierarchies, work that she includes in her teaching. Here she discusses how she does so:
“I have worked and published extensively for some time in the broad field of international macroeconomics, with research projects on exchange rates, currency hierarchies, financialisation and international financial subordination.
I co-teach (with Gary Dymski and Bianca Orsi) a final-year module on international economics: integration and governance. The module runs all year and typically has 60-70 students on it. It covers the four broad components of international economics: trade, productive capital (including foreign direct investment and global value chains), money and exchange rates, and short-term financial flows like portfolio and bank flows. The module is always changing because the world changes: it is therefore a challenging but stimulating module to teach.
A specific piece of research I have introduced into my teaching is my work, published in the Journal of Post Keynesian Economics and the Cambridge Journal of Economics, on currency hierarchies. Traditional theory treats exchange rates as relative prices which equilibrate markets (either the goods market or financial markets). In Post Keynesian theory, however, exchange rates are relations between international monies, which highlights the role of money in the (international) economy. Crucially, determinants of exchange rates differ according to which money one looks at. Those currencies at the top of the hierarchy (for instance the US Dollar) change differently to those from, say, developing economies. Thus, developing economies’ exchange rates may suffer from effects not experienced by the US Dollar, meaning those countries may be, for instance, more constrained in what fiscal policy they can use.
I teach my research because it is one of the main Post Keynesian theories on the topic, one which I think matters, having real-world implications. I also think it is good to show students actual ongoing research. I don’t consider that I am trying to establish credibility with the students, but I do think that teaching topics I am researching adds to the authenticity of the learning experience. It is partly for that reason that I often focus on Brazilian data when exemplifying some of the theories or international phenomena discussed in the Module; however, also, it is good to challenge any lingering Eurocentrism in the students by repeatedly looking at global data and focusing on a global Southern country. Frankly, as well, my expertise and strengths lie in these topics, whereas others teaching on the module can deliver more micro-focused or political economy material.
The challenges of teaching the material are several; first, balancing the depth and detail with the topic against the need to provide an overview is a continuous calculation. Second, there is, inescapably, a large volume of material to cover. Third, as mentioned, students typically have less understanding or focus on developing countries than they do with the US Dollar or even the Chinese Renminbi. Fourth, the topics covered are necessarily interdisciplinary, which adds to the challenge of the volume of resources being large; unsurprisingly, perhaps, the module attracts many joint honours students.
We try to address these challenges in various ways. The module has a consistent structure throughout: on each topic, we examine current data, then consider neo-classical (and its modern variants) treatments and a leading alternative approach. Having considered data and theory, for each area we then explore the institutional arrangements that govern them. We use different types of sources, including many newspaper articles. We also align the teaching and learning with the assessment: students present jointly a project paper, and then write it up individually. They then choose an essay on one of the topics discussed in the seminars, allowing us to offer continuous feedback to them.”
Suman Seth and Gaston Yalonetzky research together on distributional analysis, work that they include in their teaching. Here they discuss how they do so:
“We have worked and published extensively for some time in the fields of development economics, well-being, poverty and inequality, with a particular focus on measuring these phenomena and their meaningful applications. We co-lead two modules at Leeds, a final year undergraduate module on development economics and a Masters module on distributional analysis for economic development. The undergraduate module is quite broad and captures diverse aspects of development, but the Masters module that we designed in the 2016-17 academic year is built on our research expertise. We discuss here the Masters module, which runs over one semester and is open to students beyond those on the Economics Masters course. Thus, the student body is diverse in terms of subject background, nationality and experience as some students have worked for, or are sponsored by, their governments. Our teaching in this module tackles the challenges of bridging the gap between the technical materials and their applications, and show the policy usefulness and implications of the theoretical and empirical tools that we teach. This module exemplifies the unique Leeds approach, bringing multi-faceted, interdisciplinary, policy-relevant research into our teaching.
We draw on our research outputs throughout the module. Our research has recently focused on developing methodologies and their applications involving non-monetary indicators that are characteristically different from their monetary counterparts (e.g., income). The early part of the module utilises two books co-authored by Suman Seth with colleagues at the World Bank and with colleagues at Oxford Poverty and Human Development Initiative. We also teach from our recently published research papers. For example, in the first part of the module we teach from our co-authored research on multidimensional poverty in slums exploring how a monetary approach and a multidimensional approach could identify very different group of poor people (published in Social Indicators Research) and also exploring how these tools could be used for impact evaluation of anti-poverty programs (Published in Review of Income and Wealth), research that recently won the Business School’s Dean’s Award the best journal article with high impact potential. Similarly, in the second half of the module, we teach a methodology on measuring poverty using ordinal variables (primarily non-monetary) that we have recently proposed and published in the World Bank Economic Review. The essential purpose of our contribution is to develop a methodology that allows prioritising improvements among the poorest in the society.
The research underlying our work is conceptually complex and technical statistically, so we consider carefully how to deliver it. Delivering the material is an act of translation, and how we do that is an exercise in cost-effectiveness: we know the material is interesting and important, and we want the students to engage with it, inputting some of their own effort; however, if the cost of engaging with the material is too high for the student, they may opt not to do so. Thus, we need to incur some of the cost instead. That means making some difficult choices.
So, when teaching the material, we consider what is its essence and focus on delivering that effectively. Some of the nuance of the material is inevitably lost, but in the context of the module, we want students to grasp the gist of the work, rather than getting into the detail. The students are at an advanced level, so we can assume that foundational theoretical and statistical concepts such as the nature of different types of data are known and understood; however, as with presenting the material at a generalist conference, we need to motivate it, to convince the students it is intellectually worth an investment from them.
Whilst we can assume some prior knowledge, we also recognize that the material is new to the students, and that they need to be able to relate it to their previous understanding. One way we do this is via use of judicious examples, of different degrees of sophistication. For instance, to introduce the concepts we use simple examples, such as comparing mean values of self-reported health indicators using alternative (arbitrary) scales and noting how the comparisons are not robust to the alternative choices of scale. These allow the student to gain confidence and maintain engagement and are useful to foreground more difficult examples using real world data. We reinforce this learning by asking students to submit as assessed project report analysing real-life microdata, for example from slums of Indian cities.
We chose to include this material in our Masters module because we consider the issue it addresses important. On reflection, we recognize too that our students value that their teachers are working and publishing in this area, as it gives our comments greater authenticity. We had not considered that we are seeking to establish our authority in the area, but it is possible that we feel some pressure to demonstrate that. More than that, we enjoy the material and think it’s genuinely useful.”
All Leeds economics undergraduate students complete a final year research project on a topic of their choice and are supported through direct supervision. They are also taught a research-focused programme at all three levels of their degree, which includes significant exposure to maths and statistics training.