Creating Better Support: Economics Students’ Recommendations for Success
Research shows that unclear or fragmented support structures in higher education increase cognitive load and can disproportionately affect students from underrepresented backgrounds. This has implications for progression, attainment and continuation (de Henau, 2024; Sweller, 2024; Strayhorn, 2021; Thomas, 2012). In this context, effective support provision depends not only on availability, but on clarity, accessibility, and how easily students can navigate systems in practice. To investigate this, I carried out focus group discussions with students towards the end of the first-year of their degree. Students shared their views on the current support offered and how this could be better tailored to meet their diverse needs. The recommendations presented below were constructed using inductive thematic analysis and are grounded in students’ views and perspectives, illustrated through direct quotes. While student insights form the foundation of the analysis, the recommendations do not simply represent direct student proposals. Instead, they reflect analytically developed responses to recurring barriers identified in the data, informed by evidence-based practice in student support and inclusive curriculum design (de Henau, 2024; Biggs and Tang, 2015; Thomas, 2012). The recommendations aim to improve the visibility, accessibility and effectiveness of student support. The findings are particularly relevant within the context of increasing sector-wide attention on student belonging, continuation, attainment and inclusive educational practices. Top five actionable suggestions from focus group discussions These findings build on complementary work examining barriers to student engagement with economics support schemes, which provides additional context on why students do not always make use of available support (Williams Higgins, 2026). 1. Use a centralised dashboard: a single hub outlining all schemes, their purpose and access details. Many first-year students are unsure of the purpose for each type of support they are offered. This is complicated by the fact that these are usually introduced during the induction/welcome week when students are presented with a large volume of information. Even at the end of the academic year, some students were still unclear or held misconceptions on the student support available. Students suggested that posting session timings on platforms that students regularly use – such as Blackboard or other virtual learning environments (VLEs) – would reinforce visibility and awareness. Email fatigue meant that they missed out on some resources. Students also reported that a mid-term/later reintroduction of support schemes was helpful. Reintroduction means that once students are more settled in the university the information becomes more relevant and its purpose more apparent. “Maybe having the timings of the different support sessions posted on Blackboard or the check-in app, somewhere everyone can see often, so you're constantly reminded. Mainly I get the info from emails, but we already get so many, so it's not a priority. I just tend to forget I can access this kind of resource.” “Yeah, I think it did take a bit of time to absorb all the different resources that we have to offer, like deciding which one to actually go to and which one use, especially for like the [peer-led] sessions. I didn't really realise it was a thing until a few months into university.” Using a support map is one low-cost and practical way to address this issue (see Figure 1). Support maps are a quick visual introduction to the options for student support and can be used by all university staff when assisting students. What’s more, these do not require all staff to have detailed knowledge of all the support available. This approach is grounded in evidence that shows how embedded, clearly navigable support structures reduce cognitive load and strengthen belonging and progression, particularly for students from under‑represented backgrounds (de Henau, 2024; Sweller, 2024; Strayhorn, 2021). Figure 1: Example of a student support map categorising key support options Source: Author-generated using Napkin AI It is also important to include the relevant first point of contact for university support in communication with students. That is, where students should go if they are confused. Usually, students talk to the first person they see or the person who they are most comfortable with, often their lecturer or a tutor. Using a support map is one way to ensure that all student facing staff have enough information to signpost and that a consistent message is being shared. Building on these findings, I recommend implementing a centralised dashboard, supported by a visual support map, to improve clarity, navigation and consistency in how support is communicated and accessed. 2. Incorporate flexible scheduling/hybrid formats: rotate times; offer online and in-person options; mix drop-in/bookable. When providing services, flexibility is key. Students suggested rotating times for some optional services. Office hours, while seen by institutions as a key resource are usually underutilised and research highlights a mismatch between institutional intention for office hours and student perceptions of them as the last resort in an academic crisis (Smith et al., 2017). Students also highlighted that office hours can clash with core units and at times, tutors’ office hours coincided with lectures for the same unit. As office hours can be seen as daunting, having such additional barriers can increase the likelihood of them being unused. “I don't really use the other ones, mainly because they're not convenient and they clash with other things that I'm doing...” “I think sometimes some of the support comes at the same time as I'm having another lesson… if they were maybe set at different times, I might go more.” Where possible, I recommend offering online and in-person options. For some students attending in-person requires significant time commitments and may be costly depending on where they live. 3. Provide prep guidance to reduce intimidation: help students to prepare for office hours and other sessions. Where there are many different types of support, students may be unsure where to seek help for specific issues or how to approach the support sessions available. As a result, they may not attend sessions to avoid looking foolish. Students highlighted this uncertainty, noting: “...maybe at the start of the [year] if you were told exactly like what the intended use are for each of the sessions, it would be a better way of knowing when you have a specific problem which one to use.” “I thought you only go [to office hours] if you have really big questions, but I realised, oh, even if it's like something small, at least I can still just go and ask because it's better to ask, like, rather than like, you know, you just sit in the darkness and you just think that you kind of know it. So I think it was also only because in [the spring term], my lecturer pushed us to use more office hours and just ask more questions. A lot of it, I think, was like a lot of people were just quite intimidated. Yeah, me included.” This is backed up by research that shows that students often state they have no idea what office hours are for (Smith et al., 2017). Given this context, I recommend that departments and schools implement clear guidance on how to approach the sessions as a part of standard support provision. This guidance should include the goal or intended use of the session and examples of appropriate questions. It should be delivered using a variety of media to ensure accessibility and consistency across the student cohort. 4. Enhance communication strategies: repeat key messages; promote via teaching staff; concise digests over multiple emails. Students are presented with a lot of information, as well has having coursework and deadlines. To avoid important details being lost along the way, students have indicated that when it comes to support schemes, staggered communication is effective. When they are re-introduced to a support scheme, once they are more settled in their course, they are more likely to remember, see the value of the scheme, and may even engage with it. “[The peer scheme] and [drop-in] café, I think they did do introductions as well in September… the fact they came in maybe one month later, to explain what they were for, that definitely helped a lot more. Because I think in the first few weeks of starting school, you're just learning new content…that's why I feel like I didn't really know about these sessions that much until they came back later.” One way to increase student engagement with support schemes is to embed reminders into their lectures and seminars/small group classes. Students highlighted that their use of office hours and discussion boards increased when their use was promoted and encouraged by their teaching team. Students called for a reduced reliance on emails, due to email fatigue (see point 1). Some would prefer having all support information published in one place: “I think just maybe like a singular blackboard post with each singular method of the kind of extra support you can get, because obviously it's all on blackboard, but it's all kind of in different places.. But I think that would maybe make it clearer than kind of just in welcome week. Each thing being advertised in things, because obviously with Blackboard there's the permanence about it, like it's always there on a singular post.” Concise weekly digests from programme teams and student support services could reduce fatigue and improve recall. This could be implemented through existing communications channels without requiring additional resource investment. 5. Streamline overlapping schemes: consolidate similar initiatives to reduce confusion and choice overload. Reducing and streamlining support options may be more effective to reduce duplication and overload. Where there are multiple options, it is important to be clear about their purpose (as discussed in point 1). “I feel like… it’s not clear how [the peer session] is different to office hours, [drop-in] café, or Q&A sessions… they seem to overlap in their functionality… I wouldn’t know what questions are better suited to one rather than the other… they’re kind of an undifferentiated block… if you were told exactly what the intended use is for each, it would be easier to know which one to use.” Nevertheless, having some variety is important to meet the needs of students with different learning styles, as well as the diversity of the student body in general. Students have suggested that instead of relying on office hours for example, instructors could consider adding an additional lecture or review session for complex modules or to provide assessment support. Such an approach can provide more even access to support and reduce the large number of students seeking support at peak times, which can result in increased staffing pressures and reduced access for students due to timing and capacity constraints. Discussion Effective student support requires more than just services. The focus groups highlight several important dynamics. First, there is a tension between the availability of multiple support options and students’ ability to navigate them effectively. Second, confidence and perception barriers influence engagement, particularly with resources such as office hours. Third, students rely on relational and contextual cues, such as encouragement from teaching staff, in deciding whether to access support. Finally, there is a mismatch between institutional assumptions and students’ lived experiences of navigating support systems. Taken together, the recommendations operate at different levels of implementation. Low-cost quick wins include support maps, clearer signposting, repeated communication, and guidance on how to use support services. Medium-term structural improvements include better timetable coordination, greater use of hybrid provision, and review of overlapping support schemes. At a strategic level, the findings suggest the importance of designing support systems that are not only comprehensive but also simple to navigate and inclusive by design. These approaches are particularly important from an inclusion perspective, as students from different educational backgrounds may interpret and use support differently, reinforcing the need for clarity, explicit guidance and accessible structures (Thomas, 2012; Lea and Street, 2006). For example, commuting students may benefit from hybrid and flexible provision, first-generation students may require clearer guidance about available support and how to access it, and students entering higher education through different educational pathways may have different expectations of when and how support should be used. In summary, effective support schemes are visible, well-timed, strengthen student confidence and incorporate the student voice (Figure 2). Figure 2: Key dimensions of effective student support: visibility, timing, confidence, and student voice Source: Author-generated using Napkin AI It is very important to note that providing a range of resources will mean that not all students will use all of the resources. As a result, student engagement should be considered across the range of available resources, rather than through the uptake of any single support scheme, to more accurately assess overall engagement patterns. This highlights the importance of evaluating support systems holistically, recognising that effective provision depends not only on the quantity of support available but also on how easily students can identify, access and use the support most relevant to their needs. Ethics Approval This study received ethical approval from the University of Bristol School of Economics Research Ethics Committee. All participants provided informed consent prior to taking part in the research, including specific consent for anonymised quotations to be used in publications and other research outputs. Procedures were implemented to ensure anonymity, confidentiality and the right to withdraw. Data were stored securely in accordance with University of Bristol data protection policies. References Biggs, J. and Tang, C., 2015. Constructive alignment: An outcomes-based approach to teaching anatomy. In Teaching anatomy: A practical guide (pp. 31-38). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-08930-0_4 de Henau, J., 2024. The impact of academic skills development on student outcomes in higher education–Literature review. Academic Skills Development Project. University of Oxford. Literature Review| Centre for Teaching and Learning. Available at: https://www.ctl.ox.ac.uk/sites/default/files/ctl/documents/media/academic_skills_literature_review.pdf Lea, M.R. and Street, B.V., 2006. The" academic literacies" model: Theory and applications. Theory into practice, 45(4), pp.368-377. Available at: https://www.tandfonline.com/doi/abs/10.1207/s15430421tip4504_11 Smith, M., Chen, Y., Berndtson, R., Burson, K.M. and Griffin, W., 2017. " Office Hours Are Kind of Weird": Reclaiming a Resource to Foster Student-Faculty Interaction. InSight: A Journal of Scholarly Teaching, 12, pp.14-29. Available at: https://eric.ed.gov/?id=EJ1152098 Strayhorn, T.L., 2021. Exploring ethnic minority first-year college students’ well-being and sense of belonging: A qualitative investigation of a brief intervention. American Journal of Qualitative Research, 6(1), pp.42-58. Available at: https://doi.org/10.29333/ajqr/11422 Sweller, J., 2024. Cognitive load theory and individual differences. Learning and Individual Differences, 110, p.102423. Available at: https://doi.org/10.1016/j.lindif.2024.102423 Thomas, L., 2012. Building student engagement and belonging in Higher Education at a time of change: a summary of findings and recommendations from the What Works? Student Retention & Success programme. Higher Education Academy. Available at: https://www.advance-he.ac.uk/knowledge-hub/building-student-engagement-and-belonging-higher-education-time-change-summary Williams Higgins, M., 2026. Barriers to Student Engagement with Economics Support Schemes: Insights from First Year Students. Ideas Bank, the Economics Network. https://doi.org/10.53593/n4501a Image Credits: Melisa Williams Higgins / Economics Network This article was produced in cooperation with the Economics Network, the largest and longest-established academic organisation devoted to improving the teaching and learning of economics in higher education. Learn more about the Economics Network here.
In many UK universities, students’ first-year grades do not contribute to their final degree classification. The first year provides a critical period of transition, shaping students’ confidence, help-seeking behaviours, sense of belonging and engagement with their subject and university life. Student support is an important pillar of this foundation, and students’ voices are important in creating the right type of support.
The Math Classes to Take if You’re Serious About Economics (or Data Science)
However, as our mathematical prowess and computing capabilities grew, economics naturally grew more quantitative as well. Nowadays, even Bachelor’s courses in economics are relatively math-heavy, requiring basic knowledge of calculus from early on. That’s not to say that economics was ever a purely qualitative field — it’s always been in a comfortable relationship with mathematics. Adam Smith, one of the earliest economists, was an accomplished mathematician himself. In fact, many economists were originally mathematicians who became interested in economics at some point in their careers. And it’s noteworthy that Joseph Schumpeter — the economist who coined the term “creative destruction” — was a strong advocate of marrying math and economics, despite seldom using math himself (because, as other economists have retold it, he allegedly regretted that he wasn’t very good at it despite recognizing its value). While it is possible to pursue a career in economics without strong quantitative skills, refusing to partake in them at all is certainly an uphill battle. And so, we’re providing the following list of quantitative subjects that are increasingly integral (ha, ha) to becoming an economist in today’s world. In fact, they’re also arguably necessary if you want to become a data scientist or natural scientist as well! Calculus This subject should be thought of as a prerequisite for serious study in economics, computer science, and data science. Almost every single course in economics will feature several of the following concepts: derivatives, limits, integrals, and/or algebraic manipulation of complicated expressions including logarithms and exponentials. For a brief explanation as to why, consider that optimizing a function — or in other words, finding a maximum or a minimum — requires taking derivatives. When we consider how consumers maximize their utility or how firms minimize their factor costs, we must use derivatives and other mathematical tools. Thus, optimization is a core building block of both macro- and microeconomics, which deal with the maximizing behavior of consumers and firms. Calculus is perhaps the first challenging math subject that students take after learning algebra in their early years, and it can often leave a sour aftertaste when first encountered. Still, it’s one of the fundamental building blocks of mathematical literacy, and it remains an indispensable tool in any scientist’s toolkit. Linear Algebra Linear algebra is a branch of math that deals with systems of linear equations, which can be written in matrix form. Matrices can thus be thought of as big systems of equations (or a group of interrelated vectors). When these equations are all linear, they can be solved just like we learned in algebra classes. But when matrices grow to become massive — think of when an economist gathers thousands of rows of data about (say) tax revenue, which are all related to each other and thus form one massive system of equations — finding a solution becomes difficult. A system of two equations is simple enough to solve, but two thousand? (Nevermind the fact that often, systems of equations based on real-world data don’t have an algebraic solution at all!) Enter linear algebra, which fortunately allows us to manipulate these matrices and solve the systems they represent. And, yes, it’s extremely important to economics; most economics courses default to using matrix notation for lecture notes, model descriptions, and more. That’s because economics data (and most datasets in general) are matrices! Anytime an economist or data scientist collects quantitative (and many times qualitative) data about the economy, it gets stored in a matrix. This is a natural result of working with data, not an arbitrary decision by math-obsessed economists or data scientists. Large amounts of data inevitably get stored in some sort of “list”, and a matrix is essentially a large list. Vectors and matrices are also the language of econometrics, which is a core tool for any modern economist. Econometricians and macroeconomists especially must become very comfortable with linear algebra, though it’s a necessary skill for essentially all economists. Differential Equations If algebra problems take a bit of unraveling, differential equations are like the thick sailor’s knots that hold cruise ships to their moorings. Differential equations are probably the single most important quantitative subject for natural scientists to know, as they govern an incredible amount of real-world relationships in the sciences. They’re also, unfortunately, frequently impossible to solve with a pencil and paper. A basic differential equation defines a relationship between an equation and any number of its own derivatives. The solution to a differential equation is not a number or variable, but an equation that solves the system. A simple example is the size of a population. The current population of human beings is based on the number of humans alive last year, which in turn is based on the number of humans alive the year before it, and so on. It also depends on the rate at which people reproduce. So, in order to determine (or predict) the size of a population at a point in time, we must know the size of the population at some point beforehand, the rate at which the population grows (which is defined by the first derivative), and the rate at which that growth changes (second derivative). This can be modeled by a differential equation. In economics, differential equations come up most often in macroeconomics, since most real-world macroeconomic data depend on their past values and growth rates of myriad interrelated factors. However, they’re still useful for all economists — and for many other data scientists and natural scientists — to know. Real Analysis Real analysis can be considered the first genuine course in mathematics that students ever take. Everything else on this list can be considered subjects that merely teach how to do calculations. In contrast, real analysis uses logic to show how mathematical calculations can be true in the first place, and prove how they work. It is the basic language of professional mathematicians. A typical real analysis course uses logic and set theory to prove how real numbers work and how we know the math of calculus is true. Rather than calculate answers, typical real analysis problems ask students to prove mathematical statements. As such, learning how to write arguments in mathematical logic is a core skill that this course teaches — and it’s often the first time students ever encounter something like it. This might sound arcane and unnecessary for a student of economics. Yet, since real analysis teaches students how to reason in math terms, and since nearly all economic theory is either based upon or translated into math, real analysis is one of the best ways to become highly proficient at PhD-level economics. In my personal experience, Master’s-level courses in economics taught calculations, and were difficult and interesting in their own right. But graduate/PhD-level economics goes further, forcing students to understand why things work and what quantitative facts about economic behavior mean (and imply about other economic phenomena). Graduate microeconomics courses in particular could be described as real analysis applications masquerading as economics. And PhD-level macroeconomics courses frequently refer to and depend on theorems, proofs, and concepts that real analysis courses prove. Take it from my personal experience: in my class of 12 PhD students, I was one of 4 or 5 who had never studied real analysis before. As a result, I had to work harder than most of my peers to understand certain topics, so much so that I actually decided to cram in an entire online course of real analysis in between my last week of classes and my first exam. It was a painful but incredibly enlightening experience. Learn from my struggles and take note: the time you spend learning (even just basic) real analysis will pay itself off in spades. It is highly recommended, most especially for aspiring microeconomists. Statistical Inference Statistics is deeply related to math, but can be considered its own field. Still, we’d be remiss not to include it here, as understanding statistics is essential for economists. Basic statistics courses often introduce students to fundamentals like the normal distribution and basic methods like constructing confidence intervals. Every applied economist absolutely must learn how to wield statistical methods effectively. That’s because real-world data is messy and contains noise. When collecting data and attempting to disentangle economic truths from them, likely by running a regression, it’s the tools and methods of statistics that allow an economist to make statements and draw conclusions. Research paper conclusions are built off of proper statistical analysis! Statistical inference classes are therefore very important for an aspiring economist, and any data-minded scientist. Econometrics courses often teach statistical techniques and analyses side-by-side with linear algebra concepts, but seldom have time to explain the underpinnings of statistics (or linear algebra, for that matter). Fortunately, many economics programs do include statistical inference classes as part of the curriculum, and it is highly recommended that students take these courses if given the chance. You owe it to yourself “Painful but enlightening” could probably describe most courses in mathematics. Still, if you’re serious about becoming an economist, a data scientist, or even a natural scientist, these subjects will come up in your field. As such, you owe it to yourself to give them a shot as soon as possible. And don’t be discouraged when things are difficult. Often, learning a new subject — especially a quantitative one — can be challenging. It can take a long time for things to “click”, but repetition and genuine effort will reward you in the long run. And, it’s much easier to attempt to learn them beforehand, rather than try to learn foundational concepts alongside the economic applications that are built off of them! Image Credit: JComp/Freepik
Love it or hate it, economics is a discipline that becomes increasingly mathematical the deeper you get into it. It wasn’t always this way; many contributions to the field have been made through logical arguments and even philosophy.
Econometrics, Statistics and Quantitative Methods (JEL C)
Discovering Graduate Economics Through a Summer School
In 2024 and 2025, two undergraduates, Ellen Lac (University of Edinburgh) and Jem Grimley (University of Warwick), attended the Fundamentals of Graduate Economics & Finance summer school run by the University of Oxford’s Department of Economics. Here is what they found most valuable. At a glance • What it is: a one-week intensive introduction to core graduate foundations • What you study: mathematical analysis and econometrics, supported by seminars and problem-solving • Who it suits: undergraduates preparing for postgraduate economics, and early-stage graduates who want to strengthen quantitative foundations • What you gain: a clearer sense of graduate expectations, plus a cohort network and insight sessions on pathways in economics Before the course: preparation that pays off <img src="https://d35w6hwqhdq0in.cloudfront.net/transform/scale,h=227,w=303,fillArea=True:webp/c00b05794cdc3d9628289588bce8f29d.jpg" data-align="right" data-entity-uuid="43aa85a1-9c9a-4bf6-990d-df80a3844fc1" data-entity-type="file" alt="A tour of Oxford during an economics and finance summer school at the University of Oxford." width="227" height="303" data-caption="A tour of Oxford during the Fundamentals of Graduate Economics and Finance Summer School at the University of Oxford"> Both Ellen and Jem emphasised the value of the preparatory reading provided in advance. With a structured reading list, students can bridge gaps in knowledge and arrive ready to engage with more advanced concepts. • Jem found the materials especially helpful for consolidating what he already knew, and for identifying areas to revise before teaching began. • Ellen felt the preparation helped bring students from different academic backgrounds onto a shared footing. If you are considering the course, the message from both students is simple: do the preparation. It makes the first few days far more manageable. The academic core: building the toolkit The week is designed to introduce the foundations many students encounter early in postgraduate economics. Teaching focuses on both conceptual understanding and how to apply tools under time pressure. Mathematical analysis The first half of the course focuses on mathematical analysis, taught by Professor Ines Moreno de Barreda. Jem described this part as demanding at first, and said it pushed him to build “resilience” as the pace accelerated. Ellen, whose degree had not included dedicated maths modules for economics, found that the teaching helped her get comfortable with abstract ideas more quickly than she expected. Both students highlighted the same takeaway: this material is not just theoretical. It is the foundation you rely on when graduate economics becomes more technical. Econometrics The second half of the course moves into econometrics, taught by Professor James Duffy. Jem appreciated how the teaching used practical examples to ground the techniques in real-world questions. Ellen noted that econometrics is central to postgraduate study, particularly when it comes to reading research critically and producing dissertation work that stands up to scrutiny. Both also pointed to the value of the seminar structure. Having space to ask questions, test understanding, and work through problems interactively helped them build confidence quickly. Insight sessions: pathways, applications and confidence Alongside the academic programme, Ellen and Jem both highlighted the “insight sessions”, where participants meet current and former postgraduate students and professionals working in economics-related roles. Ellen found these sessions especially useful because they made the range of possible pathways feel concrete. Hearing directly from people at different stages helped her picture what she might want next, and what preparation would be most valuable. The cohort experience: learning with people who take it seriously Both students spoke warmly about the social side of the week, not as a separate extra, but as part of what makes the experience work. A one-week intensive course is easier when you have a group around you who are tackling the same material and comparing notes. From informal meet-ups early in the week to college dinners later on, the schedule creates space to connect. Ellen also highlighted punting as a memorable moment to slow down and reflect on how much they had covered in a short time. The broader point is simple: if you are considering postgraduate economics, building a peer network can matter almost as much as the content. What they took away By the end of the week, both Ellen and Jem described leaving with clearer direction and more confidence. • Jem felt more prepared to tackle econometrics, and more realistic about what it would take to succeed at postgraduate level. • Ellen described a renewed determination to push her academic boundaries, and a clearer sense of what graduate study might involve. Both would recommend the summer school to students who want an honest preview of graduate economics, and who are ready for an intensive week that rewards effort. In closing, Jem and Ellen invite others to seize this opportunity, urging, “If you can see yourself enjoying this or wanting to challenge yourself academically, you should apply. You’ll leave with not just knowledge but an inspiration that lasts a lifetime.” About the students Ellen Lac is an undergraduate economics student at the University of Edinburgh and former Vice-President of the University of Edinburgh Economics Society. She took part in the Fundamentals of Graduate Economics & Finance summer school in 2024. Read Ellen’s full Student Diary entry. Jem Grimley is an undergraduate economics student at the University of Warwick. He took part in the Fundamentals of Graduate Economics & Finance summer school in 2025. Read Jem’s full Participant Reflection. Sponsored Content: This article was written by the INOMICS team, in partnership with the University of Oxford’s Department of Economics, based on Student Diary posts. The article has been reviewed by the INOMICS editorial team, and meets our publishing standards. INOMICS only publishes articles that we consider to be of value to INOMICS users. Image Credits: Oxford University Economics Summer Schools
If you are an undergraduate student of economics, the prospect of a Master’s, MRes or PhD can feel hard to picture. You might be wondering what the pace is like, how mathematical the first term really is, and what “graduate-level” expectations look like day to day. One practical way to find out is to take part in a summer school designed specifically as a bridge into postgraduate study. These courses introduce more advanced methods than most undergraduate programmes cover in depth, and they help you identify what to strengthen before you apply.
How AI is Re-shaping Economics Postgraduate Education - An Insider View from a Current PhD Student
Below I present three main reasons why it is becoming increasingly difficult to imagine postgraduate education without the use of AI. Whether you’re a student or a lecturer, knowing how to use AI effectively — or just being aware of how students are already making use of it — will help you succeed. 1) AI offers better searches for niche information Many of the common AI models can be used as a search tool, making them the first truly new innovation enabling Internet searches since the emergence of sites like Yahoo! and Ask.com - and of course google - in the second half of the 1990s. This search feature makes it a great study and research partner, beyond what a typical user can accomplish with a search engine. One of the weaknesses of traditional search engines is that it can be hard to find the specific information you need, especially if you’re searching for something niche, or if the language used for that topic is generic. For example, consider a scenario where your favorite bakery is a small mom-and-pop shop named “Croissant”. Or, for a more economics-relevant example, consider searching for content about the inverse elasticity of substitution in (specifically) the neoclassical growth model. In both cases, it can be extremely difficult to find the right information you need. In the first case it’s hard to craft a good search with such limited language, since “croissant” will return far too many irrelevant results. And in the latter case, the need to be extremely specific (thus using so many words) means that a traditional search will end up getting lots of irrelevant content as well. Even using advanced search functions might be ineffective, or at least time-consuming. But AI has the ability to sidestep a lot of these problems. That’s partly because if an LLM answers with something you weren’t looking for, you can just tell it that, and it will pivot to your intended meaning instead. This makes LLMs ideal tools to begin a search for esoteric information — although the ever-present chance of hallucination must be kept in mind (more on that later!). 2) AI models are great as study tools AI models can go far beyond being search engines — they can explain their output and interact with the reader. This is where one of their core use cases lies: as a study tool, or even a supplementary TA. AI models are frequently used by economics postgraduate students, with some using them on a daily basis to aid studies. AI can parse even the most complicated equations and break down complicated or long-winded paragraphs into more digestible bits. This helps students learn new concepts faster than if they’d spent time puzzling over arcane lecture notes, reading a textbook, finding a helpful YouTube video, or waiting to attend office hours. If someone is puzzled over how to get started with a difficult problem, asking an AI model can help them know where to begin. Still, there are limitations. There’s no shortcut to true understanding, and sometimes AI output isn’t sufficient. And of course, LLMs suffer from the hallucination problem, so AI output must be treated with suspicion and tested rigorously to ensure it’s accurate. In the context of an economics course, though, ensuring output is accurate can be a great way to learn! In my personal experience as a PhD student, fellow economics postgrads have agreed that AI is best used to either help with starting a difficult problem or help understand the steps taken in a sparsely-explained proof, problem, or model setup. Often, research papers, textbooks and lecture notes skip several important steps at a time when deriving or showing a concept, much to the ire of students. AI can help bridge the gap by explaining the omitted steps. This is something that AI models have grown quite adept at — given a specific enough prompt. In this way, AI takes on the role of a miniature TA, available at any time a student may be studying. Further, AI can even be used to generate practice problems and mock exam questions, making them good tools to ensure understanding. But these aren’t the only things AI can be helpful for. They can also help economics students parse long stretches of code… 3) AI is an ideal partner for coding Economics is an increasingly broad and cross-disciplinary field. As such, knowledge of mathematics and social sciences are important, but increasingly, data science competence and coding skills are becoming invaluable for economists. This is where LLMs are particularly useful, especially for postgraduate students who don’t have as much practical experience yet. One of the first things that a professor recommended to me during my economics PhD was to use AI to help me write code. Economists are increasingly required to use statistical software programs to do their jobs and their research, and even limited coding skills go a very long way. Even so, the vast majority of economics courses don’t teach software skills — often, there’s only time for an introductory course into one program, and students end up having to learn the rest on their own. This is a gap in the economists’ education that, for better or worse, degree programs often don’t have enough time to cover. Nobody should rely fully upon AI to code everything for them, and my professor wasn’t telling us to do so. Rather, as coding is an important but secondary skill for an economist, my professor recommended that we use AI to help us learn while doing. And, as he pointed out, using AI in this manner makes managing the hallucination problem quite easy: simply test the code the AI gave you. If it works, great! If not, clearly the AI made a mistake. Of course, this process works best for small pieces of code — asking the AI to change the color of a graph, or create a formula to update a matrix, for example. Asking it to write an entire research paper, or compile code to clean a whole database, is a recipe for disaster (and, as I argued before in an article justifying why I did not use AI for my PhD application, is only robbing yourself of the ability to learn). Conclusion: AI can be great, but remember: it’s only a tool There are, of course, pitfalls to avoid with AI. Relying on its output too much is a mistake, and students who over-use it may fail to learn as much as they thought. This is important because economics exams are typically held in person, without the aid of computational devices (outside of perhaps a small calculator). Thus, when grades are on the line, AI often won’t be available. For this reason alone, it’s imperative that students learn course content and how to think about and solve economics problems without relying on AI. In fact, the advent of AI makes it even more likely that, at least in the short run, economics degree programs will continue to rely on in-person exams to assess students. Yet aspiring economists shouldn’t fear this. Any economist worth their salt will know their field, and won’t need to rely on AI to do their job. Yet AI tools can — as with so many other industries — greatly increase the productivity of an already capable economist. Likewise, economics instructors shouldn’t be afraid of AI tools. Embracing them and training students to use them properly can actually increase engagement and learning. Even if you as a lecturer don’t feel confident teaching how to use AI, it’s imperative that you are aware of AI’s capabilities. This applies when designing any assessments, projects, or homework that isn’t done in person, as students could make use of AI while doing them. How has AI been used in your economics degree program? Have you experienced professors or students recommending it, or actively incorporating it into the syllabus? Share your experiences in the comments below! Header Image Credits: halfpoint, via Canva Pro License / Daniil Komov from Pexels, via Canva Pro License / metamorworks from Getty Images, via Canva Pro License
We all know that AI models are becoming increasingly commonplace, with hundreds of software companies now offering AI-powered tools, and dozens of iterations of ChatGPT and similar LLMs popping up every year. While some bemoan AI tools, they’ve also been lauded as powerful and helpful tools that we ought to be using every day. Regardless of where you stand on the AI-usage spectrum, one thing is certain: AI models are reshaping education, even at the highest levels. Economics is no exception. Even for PhD students like me, AI tools are becoming a feature of student life — and when used smartly, can be a great asset to learning.
What Studying Business Taught Me About Economics
Before diving deeper, let me state clearly: I don’t believe this is the best strategy that an aspiring economist could take for their studies. If you love economics, it’s much more efficient to simply study economics. Still, my undergraduate degree — and my subsequent experiences working in industry — have given me some valuable perspective about business and economic theory. In particular, this article shares some largely intangible factors that I think about often when reading (relevant) economics research papers, or thinking about my own future research. I hope that it offers food for thought to current business students who may be thinking about a switch to economics within their current program or for a postgraduate degree. Without further ado, let’s dive in! The economically ideal firm: a rarity in reality When firms are discussed in economic theory, they’re usually depicted as lean, efficient, profit-maximizing entities that adjust rapidly to market conditions. In a similar vein as the ever-present rational economic man, this is good enough for the purposes of learning, and it greatly simplifies the math that economists must perform (without losing important nuance). Still, from my time in both studying businesses and working in them, I’ve learned and experienced many things that show this isn’t always the case. In fact, I think it’s almost never the case that a firm lives up to the economic ideal of a profit-maximizing machine (a hot take for the Neoclassicals in the room, but perhaps not surprising for the rest of you!). There are many reasons for this, some of which surprised me when I started working for the first time. Soft skills are hard to measure, but intensely important in the real world In the world of business, it’s no secret that soft skills are important. Soft skills include the ability to interact well with other coworkers, and importantly to present oneself and one’s work as useful and impressive. An employee who does great work but isn’t very good at making it known, or presenting it well, will appear to be a less effective employee than one with excellent public speaking and self-promotion skills. And employees who are fairly social, work well with others in teams, and handle their own emotions and conflicts professionally will be much more effective than those who do not. Many of the following points in this article deal with soft skills in some capacity, and as such we’ll move on to those points more specifically. But before getting into the nitty-gritty of the firm itself, it’s worth pointing out that one of the most critically important soft skills can begin before a job even starts: networking. Business has reinforced how network effects are powerful and must be accounted for In my experience — and likely very many other people’s — networking is the single most effective way to find a job. I’ve managed to land several jobs through cold applications, but (in most cases) only after sending hundreds of resumes out to potential employers, the majority of which received no response. For many, networking seems to be the only ray of hope in a sea of automatically rejected applications. But make no mistake: firms aren’t solely at fault for a difficult job market. Firms usually receive hundreds or even thousands of applications for a single position, a large amount of which may be clearly unqualified. This forces hiring managers to streamline the process of going through applications as much as possible. So, when a current employee recommends an applicant, a hiring manager knows that person is likely to be a quality candidate (as long as the employee is in good standing!) and their application immediately stands apart from the pile. Networking has several further benefits for both prospective employees and prospective employers. For instance, if a current employee recommends an applicant, it suggests that that prospective employee would likely be a good fit for the firm’s culture as well, which is very difficult to determine beforehand if an applicant is a total stranger. This grants that recommended applicant another advantage over the rest. Within economics, we have a well-researched concept called “network effects”, which reflect just these kinds of forces — and more. In labor economics, network effects might refer to broader situations, such as immigrant communities helping other immigrants move to and survive in a far-off land (think of, for example, the colonization of the Americas — network effects among European settlers in the first successful colonies made it easier for future settlers to arrive and survive). Generally, network effects lower the transaction cost for individuals and/or firms to find a suitable match in the labor market. My experiences in the labor market — not to mention casual discussions with a professor of economic history — have specifically emphasized to me how important these network effects can be! Networking doesn’t merely end once someone is hired, however. Relationships within a firm are often critical indicators of an employee’s long-term success, as well — and this gets to the heart of those critically important, intangible factors present in the business world. Incentives within firms are often messy and far from the economic ideal We must recognize that firms are made up of individuals. This simple statement has a large consequence, because individuals face their own set of incentives — and these are almost never perfectly aligned with the firm’s. Still, even though many firms can end up becoming a messy tangle of incentives, these incentive issues are (admittedly) usually insignificant. Employees must perform well enough to retain their paycheck, after all. Nevertheless, occasionally firm-employee incentive misalignment can lead to costly consequences. It’s easy to find stories in the news about employees who, after being fired, cost the firm large amounts of money — especially in tech (see for example this Infosecurity article). Often these acts of revenge are illegal, and former employees are prosecuted, but this does not reverse the damage the firm suffers. Still, this type of incident isn’t limited to ex-employees. Consider an employee working for a firm who questions their job security. In such a case, they’d be heavily incentivized to make themselves irreplaceable by any means possible, or simply to aggrandize themselves to make it appear that they were irreplaceable. Again using a tech example, one way an employee can do this is by writing critical code in an unintuitive manner and without using comments (or providing other documentation) to help other developers read it. This artificially and inefficiently increases the cost for a firm to fire this specific employee, and thus artificially increases the employee’s job security at the cost of the firm’s performance. In extreme cases, employees may even be incentivized to sabotage initiatives of the firm in order to maintain their own importance in the firm’s hierarchy. Inflated CEO/executive pay is another example of misaligned incentives. An individual CEO is heavily incentivized to give themselves as much credit for the firm’s success as possible, and to capture as much of the firm’s profit as they can for themselves (usually in the form of bonuses). This can be mitigated by a firm’s board of directors, who are typically the “boss” of the CEO in a public company, but if the board and the CEO are friendly this check may fail. Rather than pay the CEO millions of dollars in bonuses each year, it’s likely in the best interest of the company to keep most of that money and reinvest it into the firm’s activities, or even spread it among employees to increase morale or loyalty. There are many, many more examples of incentive issues that could be discussed here. But the existence of these point to another aspect of human behavior that influences business performance: interpersonal relationships. Managers and office politics greatly influence productivity Humans are primates with varied and intricate social dynamics. This can be wonderful…but it also creates a lot of problems for firms. Office politics are rarely discussed in economics courses (and seldom discussed in business courses, either!), yet they are an omnipresent force that shapes a workplace immensely. Work that would otherwise be a dream job for someone may end up being a nightmare purely because of interpersonal relationships; conversely, a job that someone strongly dislikes might be tolerable thanks to the presence of great coworkers, managers and leadership. When I first began working, I was shocked at how important “office politics” were. I had thought that simply doing great work would get me noticed. I also expected that managers would be good at seeing through the “surface level” of performance and identifying when employees were doing value-added work, when they were simply looking busy, and when a doomed project made an employee look less productive than they actually were (or vice versa). Yet, this was not the case in my (admittedly subjective!) experience. Often, employees who stayed later in the office were seen as more productive compared to employees who routinely left at 5PM on the dot. Yet, simple logic would suggest that this is precisely the opposite! If someone must continually stay late to finish their work, it’s likely that they’re less efficient than other coworkers who leave “on time”, all else equal. Of course, it’s possible that some employees simply have more work to do than others — but staffing is the manager’s job, and thus they should be aware of relative workloads and balance them accordingly. In several roles, I witnessed that being favored by managers meant that employees’ work was valued and lauded almost without fail. Meanwhile, some disfavored but very hard-working employees went unrecognized. Related to this, managers in multiple roles I held frequently seemed unable to disentangle the performance of employees from the performance of the specific projects they were tasked with. While it’s true that an employee can make or break a project, it’s also true that some products or projects are simply doomed to failure, while others are “slam-dunks” (a simple proof of this: the failure of Google Glass, which despite parent company Alphabet’s best efforts, simply did not have a viable market. Even the best marketing team in the world likely could not have made it work). In summary, office politics and evaluations of employees can be closely tied with how managers perceive the work that employees do, rather than the actual quality of the work done. These forces culminated in a founding member of one of my former work departments quitting over unfair treatment, and quickly getting snapped up by a competing firm. It also led to a new employee being promoted twice in the span of 6 months, putting them in a more senior role than other employees with more tenure than them — simply because the project they were on was overperforming (in my opinion, largely due to the sales efforts of that new employee’s very experienced manager). Both situations worsened morale significantly for other employees. These are admittedly subjective experiences, yet most people who have worked before can likely relate to them. And, people who haven’t worked much before probably have friends or family who have griped about similar situations. Outside of managerial “politics”, there are many interpersonal relationships that form the work environment which can influence productivity. Some people may not like each other, and therefore may not be effective teammates. Conversely, some people might get along too well, and might experience reductions in productivity because they’re having fun discussing non-work-related items for long periods of time. Sometimes employees even date each other, and later may break up, which can obviously introduce a significant tension into a team. These inefficiencies can be mitigated by firms in various ways, but are still issues that can take time and resources to deal with. Unfortunately, it’s very difficult to have good information about the true state of interpersonal relationships, for both managers and employees. Of course, these inefficiencies and internal politics in general are very, very difficult to measure empirically and study. Employees often have imperfect information about their own firm The consulting industry is massive, and it’s dominated by the “Big 4”: EY, Deloitte, PwC, and KPMG. These acronym-loving firms offer management consulting services, in which client companies hire them to do a deep dive into specific business problem and pose possible solutions. Statista shows that the worldwide market size for management consulting services was over $1 trillion in 2023. The fact that this market exists — nevermind that it is so large— shows that firms do not have perfect information. The reason why is simple: if firms had perfect information, they wouldn’t need to hire management consultants! What’s more, employees — including managers and executives — often have relatively little information about their own firm and its activities. It’s difficult to know what all of your immediate coworkers are doing, nevermind what every other area of the firm is responsible for. In larger firms, employees may be routinely surprised by developments made by other parts of the firm. Some departments may even have goals that are opposed to or incongruent with one another! From an efficiency standpoint, this may not be as big of an issue as it seems; after all, the job of executives is to guide and direct the firm, and it’s expected that they would have better-than-average insight into the firm’s activities. If a department was underperforming or misaligned, they could shrink, cut, or adapt it. Still, it’s easy to see how the economic ideal of a single profit-maximizing entity might not be realistic due to these factors. Many economic models have adapted to include situations of imperfect information, as it’s a major factor that economists have recognized for a long time. In fact, models continue to evolve — and if economists deem it relevant and helpful, I expect that many of the topics discussed in this article will continue to be incorporated into research and increasingly intricate and realistic economic models. Economic models tend to be simplified, but that’s often enough Despite sometimes having unrealistic assumptions, our general economic theories still work very well most of the time. We’ve previously discussed on the INOMICS blog that economic models, especially when considering their intended purpose, usually do not need extremely realistic assumptions. That argument need not be repeated here. Still, most of the topics discussed in this article are ripe for future research, and some economic models may benefit from a more comprehensive integration of several of these concepts. Just as labor economists have modeled the frictions in the labor market before, models that take into account the internal frictions of incentives and relationships — and research that aims to estimate how much they cost — will continue to deepen our understanding, and perhaps even help to influence policy. Behavioral economics, labor economics, firm theory, and game theory are all areas that have touched upon some of these important intangible factors before, and increasingly in recent years. For examples, see this 2023 paper about misaligned incentives between contractors and utility companies; another 2023 paper that details how misaligned incentives can possibly benefit leaders; and yet another 2023 paper discussing how organizational politics affects employees’ engagement. Yet, these areas remain ripe for future exploration. I suspect that many of the brilliant economists of tomorrow will find ways to deepen our understanding of these areas with their research. Perhaps, if you find a suitable Master’s or PhD in economics, you might even be one of them! Image Credits: AI Generated Image using Canva AI (Prompts: Abstract Image Business Economics)
When I was an undergraduate student and took my first economics class, I was hooked. But, rather than major in economics, I chose to major in business. That’s because I wanted to understand how the economy worked at its “smallest” unit, which I surmised was the individual business. Economics can of course study individuals and their decision-making too, but at least on the supply side, I thought that a single business could be considered the smallest unit of the economy. Economic theory can tell us how firms behave in a perfectly theoretical world…but how do they actually function? This was a question that captured my attention, and I decided to major in business (with a concentration in management consulting) to understand the working world as much as I could.
Top Tips for Applying to a UK or European PhD Program
So, without further ado, here are my top tips when applying to a UK or European-style PhD program. 1. Give yourself plenty of time Applying for a PhD isn’t something you can accomplish in a single night, or even a single week. In fact, applying might well take you a few months — especially if you need to contact a potential supervisor before applying, which a fair portion of schools in the UK and Europe require. In fact, even the process of identifying the institutions you wish to apply to, and learning how their application portals and processes work, will take some time. Many application portals are hosted on outdated-looking website infrastructure that can be difficult to navigate, and some will even create a unique applicant inbox for you that will house your communications. In this case, the university won’t email you directly, and you’ll have to check each portal for updates (this is admittedly rare, but one of the universities I applied to used this system!). Moreover, every university may have slightly different quirks with regard to the documents they require. It really pays to be aware of these things in advance, and make sure you have every document you need for each institution you wish to apply to. 2. Understand the type of program you’re applying to There are actually a few different types of economics PhD programs you can apply to (check out the linked article for more on this). US-style programs are normally 5-year commitments that include 2 years of classes before you begin your dissertation research, and the applications are treated very similarly to other university program applications. In these programs, there are usually qualifying exams that you must pass in order to continue with your degree, but typically during this entire period of study you are considered a PhD student. European-style PhD programs, however, often only last for 3 years and don’t include the coursework portion. Instead, you seek a supervisor (by reaching out to professors at specific economics departments) and conduct your research immediately. However, many European universities do include programs designed to prepare you for this 3-year PhD portion! Sometimes, this involves a year or two of coursework before you’re expected to begin your research. Often, these types of programs may be referred to as “2+3” or “1+4” programs on university websites to differentiate them from the more traditional 3-year model. If you are applying to PhDs at European institutions, take a careful look at the program outline on their website so you know which type of program you’re applying to. Another aspect where these programs differ is in the visa process. Most of the time, visas for these USA- or European-style programs cover the entire expected length of study. However, this may not be the case for UK-style programs. Some UK programs are 3-year programs and some are 4- or 5-year programs. Typically, what would be considered the coursework portion of a PhD in the USA is actually a separate degree in the UK, often called a “Master’s of Research” (or MRes; similar names such as “Master of Letters” or “Master of Philosophy” may be used). An MRes is not the same degree as a PhD! Even so, they’re designed to prepare you for a PhD, and some institutions explicitly state that passing their MRes program first is how most students are expected to pursue a PhD at their institution. In the UK, this distinction matters because the type of student visa you have depends on if you are in the MRes or PhD portion of your degree. And, when you progress from the MRes to the PhD portion of your program, you’ll have to get a second student visa (because the initial visa will only cover your Master’s of Research studies). Yet another way that PhD programs can differ is in where you apply. In some countries – particularly northern European countries such as Norway and Denmark – universities may require you to apply to PhD programs through a job portal, not a typical university-hosted application portal. That’s because some PhDs are funded by government programs or research grants, and as such are treated more like job applications in certain countries. This means that, if you want to apply to some PhDs, you’ll have to wait until a PhD job is posted in the portal before you can apply to it. Finally, economics PhD programs can also vary by how much education they expect you to have before applying. In some programs, particularly those that include a coursework component, you can apply with just a Bachelor’s degree — though having a Master’s can give you a leg up over other applicants. Some MRes programs in the UK, even though they are considered Master’s programs, expect you to have earned a relevant Master’s degree already, while some accept applicants with a Bachelor’s. It’s necessary to check the programs you’re applying to and see whether or not your current education qualifies you to apply. Further, you can explore the program’s website and see if you feel that your current education is sufficient for the program and your goals. For example, I sought programs with a coursework component. I wanted to spend additional time learning research methods and mastering advanced econometric models that I didn’t have much exposure to during my Master’s studies (such as gravity models). I believe this will help me to become a better researcher from the start. However, you may feel fully prepared to dive into a 3-year PhD, in which case extra coursework might feel like a burden. Regardless of the differences, the end goal of all these different types of programs is almost always the same: to turn you into a successful economics researcher. Clearly, though, there are many different ways this can happen. Indeed, while not the focus of this article (because I ruled them out fairly early on in my search) there are even research assistantships that let you begin a PhD after working as an RA for a researcher! Since there are so many different types of programs and PhD paths out there, you must set aside time to carefully research the options you have, and ensure you know the structure of each program you plan to apply to. It’s very likely that your particular history lends itself to some of these program types more than others! Set yourself up for success by doing this work in advance. 3. Look for Scholarships and Studentships Once you’ve identified institutions that you want to apply to, you should begin to look for scholarships that could help you at that institution. Some universities helpfully list relevant scholarships on their website; you ought to take note of any that you’d qualify for. I learned the hard way that, with some UK institutions, the exact program I wanted to apply for was actually listed as a scholarship! Some institutions consider their MRes and PhD applications separately, even though they may state that the MRes is an expected first step before applying for their PhD. But, in at least two cases, I found (or was told about) an MRes+PhD scholarship option, where being awarded the scholarship guarantees you entry into the PhD (after successfully passing the MRes qualifying exams, of course). This is a great choice if you — like me — want to do additional high-level coursework, and fully plan to continue with your PhD research immediately after. Further, many European countries have scholarship programs for foreign students. You ought to spend time researching what is on offer in the countries of the institutions you’re applying to; government scholarships are rarely listed on university websites, even though they can be a great way to fund your studies. You should also search for “studentships” and “pre-doctoral fellowships”, not just “scholarships”. Studentships are very similar to scholarships, but typically include a work component where you must teach for a certain number of TA hours (this can be a fantastic way to see if you’d enjoy being a professor!). And pre-doctoral fellowships — or just fellowships — is very similar to a studentship, but typically requires you to work as a research assistant rather than a TA. 4. Ask for letters of recommendation at least 1 month before applying Often, the number of letters of recommendation that an institution requires varies. Almost every economics PhD application requires at least two, and many stipulate that both of these must come from academics. Some schools may require three — in fact, almost every economics PhD program in the USA at top institutions (as of 2025) require three letters of recommendation. This is likely not a surprise, but these letters take time to acquire. Once you identify the type of program you want to apply to, you should check how many letters they require, and make a plan to ask your (former) professors — which you should do at least one month in advance of your application deadline. As a note, some universities do accept letters from non-academic sources, particularly if you’ve been out of academia for a few years. But, academic letters are typically given more weight, and some schools explicitly state that they will only accept academic ones. If you’re unsure, err on the side of academic letters. 5. Get ready to research: Research proposals are likely necessary for UK applications, and possibly needed for others If you’re applying to UK programs, you should expect to write a research proposal. This is essentially an outline and preliminary research review of your intended PhD research project, and as such, it needs to be thorough and high-quality if you hope to get admitted. We’ve already written about how you should write your research proposal in our Advice column. Some other European schools require research proposals as well, but they are especially popular for UK-based program applications. Conversely, programs based in the USA almost never ask for one of these. Still, keep in mind that even if a university doesn’t ask for a research proposal, they may still require you to contact potential supervisors with a project idea, in which case you’ll still want to have a well-defined and thought-out research question at minimum. 6. Be prepared to make time for admissions interviews At first, I did not realize that some programs do live interviews with candidates before making their admissions decisions. But this is something you’ll need to be prepared for. A smaller, but still significant, portion of universities will ask for these before making an admissions decision. This is another reason why reading your field’s literature can be very helpful. Even if you don’t have to write a research proposal, you’ll be asked about your own research interests in these interviews! I’d recommend reading at least some papers published within the past several years on the sub-topics you’re interested in. And, reading some of the papers published by that university’s faculty can never hurt your chances… 7. Get familiar with the faculty’s research interests As part of the application process, you should learn the interests and research specialities of the faculty at the schools you apply to. You don’t need an intimate understanding of each professor’s greatest work, but you will need to know generally the topics that the school’s faculty specializes in researching. This will help you identify a suitable topic to write your research proposal on (if you have to write one), will help you confirm during an admissions interview that you understand the program you’re applying to (and show them you’re a good fit for their faculty), can help you write a more targeted personal statement, and — importantly — can help you determine if a certain program is the right one for you. After all, if you have your heart set on researching macro-level trends, but a specific university’s faculty is heavily weighted towards behavioral economics and microeconometrics research, you’d most likely be happier and more successful at a different institution! Make sure you don’t spend too much time pursuing an application at a university that doesn’t even have research in the sub-field you’re interested in. 8. Prepare to take the GRE (or another exam) Although it’s becoming less common, many universities will still require you to submit a graduate aptitude test score of some sort. The GRE is a very commonly required one, and it could take you a week or so to sign up for a test and take it. But, even though the test has been streamlined in recent years, it’s a good idea to study GRE materials for at least a week or two before taking the exam. After all, you’ll be competing against test-takers who are actively studying the test content, and your percentile score is going to matter most for economics admissions committees — especially for the quantitative section. Helpfully, a few universities post the average quantitative GRE scores for their incoming economics PhD classes: from my own personal research, I had identified 165 (out of 170) as the minimum score that many economics PhD programs will accept for the quantitative section. Top institutions sometimes require a near-perfect score, closer to 168. I’ve also seen one program website state outright that if your quantitative GRE score is below 160, you should not apply at all. With the GRE, as with other application materials you’ll need, it really pays to take your time and prepare. For schools that accept the exam, your (quantitative) score is a critically important way to show that you have what it takes to be an economist. 9. Take Notes If this all sounds like a lot to keep track of, that’s because it is! Especially if you plan to apply for multiple schools, you’ll quickly start to forget which institution needs what, and what stage of the application process you’re at with each of them. So, unless you have a photographic memory, taking notes is a fantastic idea. I highly recommend you create a spreadsheet where you can list out: a) The universities you’re applying to b) Your application portal login for each university c) Which specific documents each application requires (personal statement, original Bachelor’s certificate, GRE scores, how many letters of recommendation, etc.) d) Which universities you have submitted which of these documents to already e) The names of potential supervisors you’ve identified at each of the institutions f) Research specialities of the faculty g) Research proposal topics that would fit each university (where necessary) h) The next step you need to take for that university’s application i) Anything else that will help you stay organized! Keep in mind that actually pursuing your PhD will be quite a worthy challenge on its own; you ought to make the process of applying as straightforward as you can. Taking meticulous notes is a great way to make it easier on yourself. 10. Take breaks, and remember your personal goals Applying for a PhD is hard work. This is one of the key reasons why giving yourself ample time for the process is important. Set weekly goals, and take breaks. After all, you likely have work or current studies to balance out while you’re applying for PhDs, too. It can be incredibly helpful to have a personal goal in mind throughout this process — and frankly, your PhD studies as well. What mark do you want to have on your sub-field of economics? Or, what do you hope to accomplish that an economics PhD is required for? These goals will be important motivators to help you keep going when you’re knee-deep in some administrative work, stressing about your letters of recommendation, or trying to find a research question that feels doable and original. And don’t forget that INOMICS is here to help. You can browse our PhD programs and scholarship listings, and check out our Advice column for more helpful information. Sean McClung is, after a well structured search and successful application process, an MRes/PhD Student at Queen Mary University of London, School of Economics and Finance. Image Credit: Theo Felton / Pexels Free Licence (via Canva Premium Licence)
Recently, I applied to PhD programs in the UK and Europe. I’m from the USA originally, but I got my Master’s in Economics and Management Science in Berlin, Germany. So, when it came time to apply for economics PhDs, I cast a wide net across Europe. I’ve proven that I can handle applying to, moving to, and studying in a foreign country, after all. But it turns out that I still had a lot to learn about applying to PhDs! That’s partly because US-style PhD programs are different from UK- or European-style PhD programs, and partly because applying for a PhD is just different than applying for a Master’s degree.
1. Understand what type of PhD you want Perhaps surprisingly, there are different ways to acquire a PhD, and some of them are very likely to appeal to you more than others. There are American-style PhD programs and European-style PhD programs, each of which can suit different applicants better than others; further, there are research assistant positions that can lead into PhDs, Master’s programs that can lead to PhDs, and even Master’s programs that are considered part of a larger PhD program. For more detail about these different avenues, be sure to check out our article on the Master of Research (MRes) and our article on American vs. European-style PhD programs. Once you’ve got a good idea of which type of program interests you, next comes the difficult task of identifying the specific institutions you’re interested in. But the first step to doing this is often looking inward, first. 2. Think about your research interests and location preferences You are unique and have your own interests and skills, which unfortunately means that there’s no easy and quick way to discover which economics departments are the most interesting or best fit for you. While there are many places where you can begin your search, it really helps to have an understanding of what you want first. Your research interests are probably the single most important factor determining which economics departments will be a good fit for you. Even if you don’t have any strong research interests (yet!), you still very likely have general areas of economics that interest you more than others. If macroeconomics courses always excited you, you should naturally look for universities with a strong focus on macroeconomic research, and avoid programs with more of a microeconomics focus. If identifying your own interests seems daunting or overly prescriptive, here’s a great way to start narrowing it down: think about the research papers you’ve been exposed to before. If you’ve read any research papers that intrigued you, look up their authors and the institutions where those authors were (or are) employed. Chances are, those universities have a good economics department that conducts research in areas you find interesting, with several potential supervisors for your future work. Many programs will require you to write a research proposal, or at least a personal essay about research topic(s) that you would like to pursue. If you’re still unsure where exactly your interests lie, preparing these materials will help you solidify your feelings about what areas of research you would be most inclined towards. It’s worth noting that many PhD programs understand and accept that you may change your mind about your exact research topic once you start your PhD, and that’s okay. A notable exception to this are some European-style PhD programs that may require you to begin your research immediately; these are typically about 3 years long and you generally need to have a supervisor before being admitted to the program. In any case, in order to acquire a PhD, you will have to conduct research on a specific topic. Figuring out what interests you now is an excellent first step to help you succeed, so that you can ensure you’re studying in a department that can support you and your interests. 3. Find economics PhD programs and make a list of suitable ones After you’ve identified your research interests, you need to make a good list of economics PhD programs that you want to apply to. You ought to apply to four or five institutions minimum (but probably not more than ten). Start building your list with the programs you may have already found or heard about, but cast your net wider than this. Several resources can help you here. Organizations like RePEc and Times Higher Ed maintain ranked lists of economics departments, which can help you identify programs that you ought to look into. Don’t place too much emphasis on ranking alone, however. There are very many economics departments with excellent staff that will prepare you very well for your career as an economist. All else equal, aim for institutions that have consistently high rankings, but it’s more important to ensure that your interests align with those of the department, and that the institution is in a location you would enjoy (or at least not dislike) living in for several years. If you’d be willing to relocate, consider economics departments in other regions or even on other continents that would be suited to your interests. It may well be that a university far from you has the perfect program, and you’ll never know if you don’t look! INOMICS PhD Program Listings can help you get started here, as our program listings include universities around the world. This search process can be as thorough as you need. If you come into the process with a good idea of what institutions you are interested in, consider looking for similar institutions in other regions as a backup plan. If you have no idea where to start, you can avoid boiling the ocean by asking yourself which criteria are most important to you, and start by looking at institutions with those criteria — for example, economics departments that have at least one professor conducting research into microfinance, if that is one of your core interests. Or, perhaps you can filter by region, and begin your search in a specific country or region that you want to study in. 4. Narrowing your list At first, it might turn out to be easy to find dozens of programs at quality institutions that you might enjoy, but then it may be hard to narrow them down. If you’re struggling to differentiate the programs, you may need to do more research. We suggest trying to answer the following questions: What are the research interests of that economics department and its faculty? Are those topics exciting, and can you think of research you’d like to conduct that could build off of those areas? What papers in the last five years have the department’s faculty published? Do any of these interest you? Are there TA or RA opportunities that you could pursue at that university? What do the job placements of recent graduates from that program look like? Are new PhDs from that institution getting jobs that you’d want to have? Does the economics department have any specialized research groups? Does the economics department collaborate with other institutions? How competitive is your application compared to others for that institution? This may not be easy to answer, but sometimes economics departments post the average test scores and/or GPAs of their incoming class, which you can use to compare. Where is the institution located, and do you think you’d enjoy living there? A PhD is a lengthy process, and if you dislike big cities but are only applying to the Paris School of Economics, the London School of Economics, and Columbia University in New York City, perhaps you ought to de-emphasize these and look for campus-based universities, or options in smaller towns or cities. And, all else equal, take both the overall university and the economics department’s rank into consideration. 5. Dive into specific application requirements Once you have a list of places to apply, the most important thing you can do to succeed is stay organized. Keep a spreadsheet or an easy-to-navigate word document where you can keep your notes. These notes should include things like your impressions of an institution, what stage of the application process you’re in, and why you’re interested in that program. Of course, some institutions may have application requirements that are difficult or onerous for you to complete — for instance, most economics departments in the US require at least three academic letters of recommendation, which can be difficult to acquire. Some institutions — particularly some located in northern European countries like Denmark and Sweden — may require you to apply to specific job openings rather than a more general PhD program entry. Many programs require test scores (usually the GRE), while some are waiving those entirely. In cases where the application requirements are difficult or even impossible for you to complete, it pays to know in advance so that you can spend your search time looking at other institutions. Further, there are often minor variations in the type or format of application materials that various institutions want you to submit. Sometimes, universities require that you contact a potential supervisor before applying; you must know this and attempt to do so well in advance of the application deadline. And, whether or not you need to write a personal statement or answer more specific essay questions can vary by institution. These are all examples of things you should keep track of in your notes. Keep it Structured and Start Applying! All of these together will lead you into compiling a pretty comprehensive overview of the institutions you want to apply to. This overview will help you recall the details you found during your search, and also make it easier to extend your search if you need to. Once you’ve begun the application process, use your notes to keep track of the latest correspondence you’ve had with each institution, and any lingering to-dos you may have with them. This will help reduce the stress you feel throughout the process, and when you’re accepted to a program you’re excited about, you can celebrate. We wish you all the best of luck with your search and your applications! Image Credit: James Wheeler / Pexels Free Licence (via Canva Premium)
Economists often need PhDs to reach their career goals. According to INOMICS’ Salary Report data — and as Figure 3 in our PhD pay premium article shows — the vast majority of professional economists earn a PhD at some point in their careers. Academic economists are expected to acquire one in order to do research, while a PhD can often be a prerequisite for high-level industry economist jobs. But no matter how experienced, all PhD economists, at one point, were mere students searching for a good place to study. While important, how to search for the right place to study is seldom discussed…and there are thousands of universities with great economics departments out there. Feeling overwhelmed, and curious for some tips for your own search? Read on!
Economic Development, Innovation and Growth (JEL O)
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What my Finance Internship Taught me about Economics
1. Finance is a rich area of research…and the industry can be artificially complex Finance is not primarily concerned with the stock market, although that’s what most people immediately think of. TIAA-CREF stands for “Teachers Insurance and Annuity Association of America-College Retirement Equities Fund”. It’s a massive firm that began when US entrepreneur Andrew Carnegie decided to create a not-for-profit pension service for academic professors in 1918. He later created a board of governors to allow educators themselves to help run the non-profit. Even within this one single firm, I was blown away by the sheer amount of different things that existed. Products and services across this huge firm were varied and sometimes had little to do with each other. TIAA has dozens upon dozens of financial products: annuities, IRAs, HSAs, guaranteed and variable lifetime income vehicles, and more. I could scarcely wrap my head around them all…but that’s, I suspect, a natural result of how the finance industry has evolved. For example, if finance was easy to navigate, anyone would be able to plan and execute a sufficient retirement plan for themselves. Choosing the right investment vehicles or the right mix of assets would be straightforward. There are other benefits that firms like TIAA could end up serving — for instance, the ability to pool money with other investors to create larger funds — but overall, from a microeconomics perspective, the finance industry is heavily disincentivized from making finance easy to understand to laypeople. That may be partly because it would cost the finance industry, or society in general, money to broadly educate the public, and partly because some firms’ services benefit from general lack of knowledge about finance. Financial advisors are a great example of this: they’re a useful and thriving service in part because many people don’t have the knowledge (or time to acquire said knowledge) that would let them invest or pursue their financial goals effectively on their own. This isn’t necessarily malicious. After all, medical doctors are hardly incentivized to educate patients about injuries or diseases that they’re not currently facing, just as financial advisors are likely to provide advice about a specific financial need to specific clients, and not attempt to fill in all of the gaps in their understanding. (Keep in mind that many financial advisors exist to help wealthy clients manage their fortunes, too, which is a useful service that falls outside the bounds of this analogy). Still, the dark side of this complexity can be seen in the fallout of the 2008-2009 market crisis, which was made worse by banks packaging subprime mortgages (and other assets of questionable value) into new, nearly-inscrutable investments and selling them off. Overvaluation of these assets — which didn’t properly take into account the risks of default, or how rising interest rates and falling home prices could lead to it — led to a devastating butterfly effect that crashed the global economy. And this is just one example of one type of financial asset, mainly sold by commercial banks involved in real estate. There are many, many different banks, insurance companies, brokerage firms, accounting and tax firms, credit card and payment processing companies, real estate companies, and more, along with plenty of sub-categories for each of these. Each of these organizations might package and sell dozens of products with difficult-to-parse names: CDs, HSAs, ETFs, annuities, commodities, etc. In short, the finance industry is massive. This means that the movement of money throughout the economy can be very complex, too. The interplay between all of these different firms and the markets they participate in, not to mention foreign markets (and the foreign exchange market itself), determine how much money is available in M1 and M2, affect interest rates, can signal the onset of a recession or recovery, and much more. By working alongside the TIAA executives during my internship, I got a very interesting glimpse into how keeping all of this complexity straight was difficult — even for them! In fact, the entire premise of their strategy retreat suggested that… 2. Human capital is critical, even in “quantitative” industries It might be easy to picture a financial firm as a cut-throat, number-crunching entity that doesn’t need any help allocating its resources. Simply get the most money possible from every interaction, and mercilessly cut off any projects that fall below some (at times arbitrary) level of ROI. Yet the entire point of my internship was to help executives strategize about their portfolios, and also give them tools — via a management consulting skills framework — to think strategically in the future as well. It’s not easy to determine what the best course of action is, even for finance executives! And finance is one of the most quantitative industries that exists; finding the optimal course of action is likely even harder in industries that don’t have the ability to measure performance as “easily” or “objectively”. How should an art gallery, nail salon, museum, or fast fashion company decide what product mix to offer, or when a new initiative isn’t worth continuing? The answer usually boils down to the following: people with a lot of experience in the industry make an informed, but not all-knowing, decision. In other words, human capital is extremely important for organizations, something that firms have known for a long time. Salaries are higher for more experienced employees, after all, reflecting this (among other factors) — and it’s much easier to get a job in an industry where you already have experience compared to one you’ve never worked in before. Human capital is the key ingredient that allows companies to parse through uncertain data and find the best course of action. It’s also where innovation comes from — which is the driving force behind creative destruction, and arguably economic growth itself. My finance internship showed me that even in a deeply quantitative organization, human capital was still a key component of success, and was seen as worth investing in. It also challenged many core assumptions of the more basic economic models I’d learned in class — clearly, perfect information was not something the firm had. Of course, this doesn’t necessarily invalidate those models — many of them have specific uses that make them still perfectly valid to teach and utilize. 3. Within firms, it’s not all about the money TIAA is a not-for-profit and was built around the idea of helping professors invest for retirement. As such, it doesn’t have quite the same environment as a for-profit finance entity might. However, my internship still showed me — quite clearly — the value of what economists call a “warm glow”, which is a feeling that comes when you feel good about the work that you’re doing. The TIAA “retreat” I interned with began with a clear reminder to the executives that their company was not for profit, and aimed to serve people and help them reach their financial goals. Framing the business in this way clearly resonated with the executives, and gave them that “warm glow” feeling. The company’s appeal to their core values and the fact that the executives’ work ultimately helps people reinforced the idea that their work was important and meaningful, and helped the executives to feel proud of their role and excited to strategize. I’ve witnessed plenty of for-profit companies using similar tactics as well, such as healthcare companies sharing stories of heroic hospital workers or patients who received care within their facilities. These appeals can increase employee productivity, and in many cases probably firm loyalty as well. In recent decades, there’s been an increasing focus on “corporate social responsibility” (CSR). Firms are increasingly expected to use some of their profits to help improve society around them, and are simultaneously expected to reduce their negative impacts on the environment as much as possible. Certifications have popped up in the past several decades to help consumers find particularly responsible companies, including the “B Corp” designation. This “corporate responsibility” is another important aspect for firms to consider that can be hard to quantify, but can absolutely affect the bottom line. Some for-profit firms are even founded with an explicit mission to make the world a better place in some measurable way — like Patagonia, which aims to have a positive environmental impact (“Protect our home planet” being one of the core values listed on their website). In some countries there are even novel legal corporate forms, for example since 2005 “Community Interest Companies” (CICs) in the UK, or “gemeinnutzliche” (literally “publically beneficial”) limited companies ( gGmbH) since 2013 in Germany. Beyond the firm’s perspective, workers themselves are often increasingly searching for jobs that can give them a sense of purpose, or a “warm glow” from the course of their regular job. Deloitte’s “2025 Gen Z and Millennial Survey” found that “Roughly nine in 10 Gen Zs (89%) and millennials (92%) consider a sense of purpose to be important to their job satisfaction”. When searching for a new job, many potential employees nowadays need a bit more motivation than just earning a salary. This conflicts with the general premise of a cold, hard, economic look at an industry — firms dedicated only to optimizing profits, and employees dedicated only to optimizing their output and thus paychecks — though it doesn’t necessarily disprove it. It’s clear to see how firms could enjoy a boost from a good reputation garnered through CSR initiatives, for one. And, applying an economics perspective, a student might consider how utility-maximizing workers might flock to companies with a strong “warm glow”-giving purpose, because that sense of purpose may increase their personal utility more than a few extra thousand dollars of salary would. Whether or not your internship is within the finance industry or in another sector, the experience is highly likely to teach you valuable things about the real world that you can take into your studies, and consider again through an economic lens. After all, economics is an amazing field with plenty of theory about the economy and how it works — but it can help to get some real, on-the-ground experience too. If you’re considering an internship, be sure to check out INOMICS’ internship listings; we wish you the best of luck in your search. 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Towards the end of my undergraduate degree, I had the chance to work with upper-level managers on a strategy retreat as part of a summer finance internship. The internship was at TIAA-CREF (now known as just TIAA), and the retreat intended to help the firm strategize about the direction they should take in several aspects of the company. As a Bachelor’s student specializing in consulting, it was my responsibility to help them make use of a management consulting framework and mindset while doing it. This experience was my first foray into the finance industry. As I’ve progressed in my own studies, it’s given me plenty to reflect on as I’ve encountered various economic theories of the firm. Read on for the top three things I learned about economics from my finance internship!
Having recently completed my PhD, in this piece I share everything I learned about doing a doctorate, drawn from my own experience. I like to call it the bad kind of advice. You will not find it in official PhD manuals, and your supervisors probably will not spell it out for you either. But you will hear it from those who have cooked in this kitchen: former PhD students like me, who came out the other side with the degree in hand. Marry your research topic A PhD is the kind of commitment you make in a long-term relationship. Over four years (or more), you will likely stay within the same field, so choose wisely. Think of it as keeping the spark alive: you will go to conferences where people mostly discuss your topic, read endless papers about it, and produce your own knowledge within it. All of this, day after day (weekends off are not guaranteed). Of course, you will have some flexibility to branch out within your field, but the core focus tends to remain the same, so genuine curiosity and passion for your topic help immensely. When I began my PhD journey, I felt pretty solid in my choice. I had already written my Master’s thesis on the same topic and worked as a research assistant during my undergrad years on a related project. That early start helped me familiarise myself with the field, the key literature, and the main gaps. Because the topic felt so familiar, I was genuinely excited to keep reading those papers. That kept me going without ever feeling bored. Grow comfortable with asking questions Your first big question will be your research question. But rest assured, many more follow: technical questions like how to cluster standard errors for a tricky model; practical ones like how to book accommodation for a conference using graduate school funds. Later on, you’ll face a bigger one: I am nearly done — what do I do next, and how exactly? I was never shy about asking questions when I felt uncertain, and it helped me learn how things work fast. That efficiency will serve you well in the long run. Ask your supervisors, your grad school support team, your fellow PhDs, and alumni from your faculty. Ask people whose paths you admire about their experience during and after the doctorate (trust me, they’ll be glad to share). A well-asked question can be worth a lot, because so is the answer. Accept a marginal contribution Being ambitious is very important in a PhD, and so is staying true to reality. Many PhD candidates draw pride and drive from aiming for something meaningful: a strong publication or a spot at a top conference. But stay clear-eyed: your core goal is to learn deeply about your topic, develop your toolkit, and finish that manuscript. I found it freeing to accept that my thesis would probably make a marginal contribution (I know, we all massage that phrase in our papers to sound more impressive). There is no shame in not revolutionising your field just now. What matters is that you become a professional, know your domain well, and claim your spot in the community. Strive for technical correctness Your thesis must be technically correct and, if possible, innovative. There is a lot of talk about pushing PhD candidates to the cutting edge of new ideas. And yes, research should be about creativity and discovery. But here is the reality: you are expected to finish a manuscript in four years. After that, funding ends, and so might your contract or visa. This is a daring situation. The risks of betting everything on a high-wire, groundbreaking idea can be huge, with no guarantee of payoff. Nobody wants you stuck mid-air. Your graduate school funds you for a reason: to produce a finished thesis. You want a decent job market paper and a few side projects, all done before your funding runs dry. Keep in mind that your thesis must be methodologically sound, well-written and should explore something that is still under-explored. If it meets those criteria, send it off. Meet people and make connections Remember: after four years, you will need a job. Do not expect the job market to roll out a red carpet for you just because you have “Doctor” title in front of your name. It helps to prepare early. If networking is not your strength, treat it as a skill you can practice. Talk to people at conferences, workshops, and seminars. Ask what they do, share what you do, and show up as a professional-in-the-making (preferably in the field you ‘married’). This builds a clear image of you that the people you meet will remember. With good connections, you can reach out later for advice, collaborations, or references. Do not underestimate this channel: it can open unexpected doors, especially if you are unsure what comes next after the PhD. Take on a side hustle (but only if you can handle it) This one might raise eyebrows, but hear me out. A PhD demands a deep, long focus on one big thing. But sometimes it helps to do something else, too. In economics, it can be quite straightforward: a side gig might be contributing to policy reports or applied research that draws on your academic base but focuses on time-sensitive questions policymakers need answers to now. (On this topic, check out my other INOMICS blog on the difference between an academic and policy economist). During my PhD, I contributed to several policy papers through my earlier connections. It kept my other skills sharp and helped position me for my post-PhD plans. This was to signal: look, I did not just write a thesis; I co-authored reports and stayed connected to the field I wanted to enter next. But be mindful: this can backfire. If you overstretch yourself, your main project may suffer, and you risk delays. Only do it if you know you can manage the load. The best PhD is a done PhD This one kept me sane in the final stretch. I knew how much I could realistically do within the timeframe, and I wanted to finish strong without stalling my post-PhD plans. Research is always some kind of ‘work in progress’. There is always more to find, and more to refine, and that is the beauty of this business. But a PhD has an endpoint. One day, you must put that final dot on the page, also known as ‘submit’. Your supervisors will guide you here: when your projects feel 90% complete, ask them what it takes to wrap them up. Use that moment to turn years of hard work into a finished product and let yourself move on. Good luck — and may my bad advice serve you well. Hanna Sakhno is a policy economist with a PhD from the University of Groningen in the Netherlands. Originally from Ukraine, Hanna has also worked at the European Central Bank and at a leading economic think tank in Kyiv, where she contributed to labour market and education policy reforms. Image Credit: Yaroslav Shuraev from Pexels, via Canva
You are about to start your journey as a PhD student this fall. Congratulations! It must feel like you now have all the time in the world to turn that research proposal you submitted with your application into reality. So, how do you make the most of that experience?
The purpose of a model is to (more) easily study real life Models in economics are imaginary constructs that approximate how the real-world economy functions, so that economists can predict how economic concepts and policies (would) work. Even though models are imaginary, they’re useful because each of them is composed of a set of rules and assumptions that attempt to reflect real-world behavior. Many of these rules and assumptions are defined by math symbols. Once a model is fully constructed, economists most often use math to determine what the model ends up saying, or predicting, about the real world. These conclusions inspire most of the discussion and debate about economic models. Economists will often debate if the assumptions make sense, if a given model is accurate, and whether or not the model can help us learn about real life. Because models are abstract, it’s very easy for economists to adjust them. This can be done by changing assumptions, or adding new ones. Often, new and useful models are built by adding onto older ones in this way. Entire schools of thought in economics are often defined by this very activity. For example, when Keynesians decried the unrealistic assumptions of classical economic models (particularly the idea that all markets will self-correct and reach a socially optimal equilibrium, rendering government intervention virtually useless), classicists adjusted their existing models to account for more realistic behavior. This led to the “Neoclassical” school of thought. It may seem like we’ve had plenty of time to build all of the economic models we need, but that’s far from the case. Theoretical economics papers are still routinely published, and economists are diving ever deeper into both theory and data to create models that can better approximate real life. For example, macroeconomists continue to build macroeconomic models that directly flow out of microeconomic behavior, and microeconomists continue to build increasingly complicated models using learnings from fields like behavioral economics. Models’ modular complexity makes them excellent for learning Economic models are also highly useful for another important area: teaching. Economics students are often introduced to models like the aforementioned Solow-Swan and OLG models early on in their economics coursework. But, since models can include varying levels of complexity, students often revisit earlier models with added assumptions and more complicated constructions. Revisiting models in this way allows a student to focus on the new complexities and implications, since they already understand how the model works generally. This makes models great tools for professors to use to demonstrate new theoretical concepts, and introduce more math and proofs to students. Here’s a fun exercise: if you’ve studied an economic model in an economics class, search RePEc or Google Scholar for economics papers that discuss that model. Open one, and read the author(s)’ discussion of the model. You may be shocked to find that the model looks far, far more complicated than you remember from class! Rather than confusing yourself, learning to interpret the more advanced versions of models (and compare them to what you already know) can give you a great glimpse into the frontiers of economic thought. And, you’ll find that many of the models’ assumptions remain either unchanged or are only lightly modified to allow for more realistic behavior. In fact, many economic models include some key assumptions that you should become familiar with. Let’s dive into the most common ones and discuss their usual implications. Common model assumptions to know Perfect Competition One common and important assumption that many models include is that markets are perfectly competitive. It’s important to master this introductory concept, because its implications are wide-ranging, and relaxing or changing this basic assumption often greatly changes model outcomes. For example, perfect competition implies: - Markets always clear. - Goods are homogenous. - Firms are “price takers”: they cannot set prices. - Firms make zero profits. - Firms utilize inputs to the point where their cost equals their marginal productivity; i.e., firms utilize labor to the point where marginal productivity of labor equals the wage rate. Simplified goods Very often when using a model, economists aren’t concerned with the exact goods that people buy. That’s because the specific products that people purchase usually don’t matter for the model’s outcomes. Instead, high-level concepts like overall income and utility – and how they relate to policies or macroeconomic phenomena like recessions — are more important. To see this, imagine that you’re studying a model where individuals spend all their income to maximize their utility (a very common analysis!). In this construct, what matters is the total amount of utility the individual can purchase with their limited resources (typically represented by their income). We usually don’t care which exact goods make up that utility, though — whether it’s two dozen eggs, a steak, or a restaurant meal doesn’t really matter, because it doesn’t change our economic takeaways (unless, of course, we are specifically studying people’s food preferences — but in that case, we’d probably need real data, not a theoretical model!). Because the specific mix of goods doesn’t normally matter, many models include some form of a simplified goods assumption. This means that we assume the economy produces only one or two goods, period. Further, this assumption usually states that these goods are homogenous (all the same), and fully sufficient for the economy and society to function normally. This assumption is useful mainly because it simplifies the math, and lets economists focus on the meaningful high-level takeaways. This is easy to see by considering a Lagrangian Optimization problem that an economic agent might face. Economists solve these types of problems for many models, to determine how individuals will behave in that model, which in turn determines overall economy-wide outcomes. Fundamentally, the budget constraint portion of these optimization problems must account for every price of every distinct item that the agent could purchase. In a model with one or two goods, the problem is easier and less unwieldy than if hundreds or thousands of goods were included. Moreover, abstracting away the myriad products available to us prevents nebulous concepts like preferences, brand loyalty, or cultural tastes from affecting the model’s outcomes. Rational agents The “rational economic man” is a straightforward fellow who lives in the world of economic theory. This rational agent is someone who always carefully considers all options they have, and chooses the optimal utility-maximizing decision every time. Rational agents therefore behave very predictably, and are quite helpful for economists attempting to describe how the world works. Assuming that all agents are rational (or at least that, on average, the typical agent is rational and thus economy-wide behavior will average out to rational behavior) allows economists to predict how — for example — certain fiscal policies might affect consumer behavior. This is also helpful partly because assuming that people aren’t rational can be more complicated than it’s worth. It’s true that in real life people can make mistakes, and sometimes make irrational decisions. But, this is incredibly difficult to model in a theoretical economy. How people make mistakes, and when people behave irrationally, are most likely quasi-random events and would be much more complicated to model than people who always choose the best decision. Further, from an economic theory standpoint, it’s usually not very helpful to allow for irrational behavior in models. When given a choice, almost every real person would prefer to make the best choice and avoid mistakes. If a bad decision ends up wasting money or time, that person’s utility suffers — but this doesn’t represent an interesting decision about allocating scarce resources. It was just a mistake that the person would, again, almost certainly choose to undo. Therefore, modeling mistakes or realistically irrational behavior likely wouldn’t change the model’s outcomes, and thus often isn’t necessary when constructing a model. Representative agents The term “representative agents” is often used when describing a model. In plain language, this assumption means that the model assumes everyone is the same. In combination with the rational agents assumption, most models therefore assume that every agent in the economy seeks to maximize their utility in a rational and identical way. Keep in mind that an “agent” in economics discussions can refer to individual consumers, producers, or households. Different models tend to focus on one or more of these entities and refer to them as agents. The representative agents assumption is clearly unrealistic, but removing it wouldn’t change much about most models’ takeaways – just like the simplified goods assumption. In this case, assuming that everyone is the same means that economists can study how just one individual would rationally behave in the model, and then simply extrapolate or multiply that behavior onto an economy-wide scale. If this seems ill-thought or lazy, consider that each representative agent can be thought of as the average of all other agents in the economy. This simply lets economists ignore the idiosyncrasies of every theoretical individual, and get to the pertinent takeaways. Model assumptions (and omissions!) determine specific use cases Many of the assumptions above seem very unrealistic or limiting. And, the justification for using them is often because they simplify the math and let economists get to a relevant takeaway without spending too long on minutiae. While this may seem frustrating or obtuse, this fact hints at a very important concept: models have specific use cases where they are relevant and useful. Outside of these relevant use cases, a specific model probably should not be considered — or at least its findings should be used very carefully, if at all, and with a good portion of context and caveat. In other words, specific models are made for a reason and used for specific purposes. If we know what those are, we can intelligently decide when and how to use the model, and when it’s best to set it aside. It’s easiest to illustrate this with an example. Suppose that an economist wants to examine how, in theory, changing a pension plan might affect economic outcomes. Pensions are awarded to older employees once they reach the age of retirement. First consider the Solow-Swan model, which features identical agents that live forever. In this model economy, each agent maximizes a utility function and chooses to purchase and consume a homogenous good; they also choose a portion of their income to save. This saving behavior could be useful when studying pensions, but agents that live forever don’t mesh very well with the idea of retirement: when would an immortal agent retire? That’s a strange — and not very useful — question to consider. Next, consider the OLG model. It goes against the grain of many simpler models by including two different types of representative agents rather than one. It also states that agents live for precisely two time periods, when they are “young” and when they are “old”. These are very simple changes, but they create many layers of added complexity. This allows us to view how those two types of agents interact with each other, how they behave differently from each other and from themselves across time, and how having a life cycle affects their behavior. Clearly, for the economist studying pension plans, the OLG model is the better choice. The specific assumptions it includes are far more helpful when discussing how pensions might affect economic behavior. In the OLG model, agents can work in the first period and retire in the second; but in the Solow-Swan model, they live forever and there’s no clear retirement age. This illustrates another important facet of economic models: the assumptions and rules that they don’t include can tell us what to use them for, and when to set them aside. It’s clear from this example that the Solow-Swan model isn’t the best choice when examining pensions. It doesn’t include any assumptions about age or retirement, which should tell us that it was not intended to examine these concepts. The OLG model is much better suited to these examples, because it includes additional assumptions about aging over time. That’s not to say the Solow-Swan model isn’t useful. It’s a very popular model for studying economic growth and savings more generally than the OLG model, because it doesn’t include additional complexity in the behavior of specific agents. This makes it a better choice for economists studying how fiscal policy might impact long-run economic growth, for instance, where the decision of when an individual should retire or how they might react to pension plans isn’t especially relevant. Criticizing and debating models — the right way In this way, each model tends to serve a set of general purposes, but they may not be very helpful when applied for study outside of those purposes. Economists understand this; many would quickly agree that a certain model does not realistically portray all of human activity. But, the vast majority of models don’t have to. They only need to realistically portray outcomes related to the specific use case they’re intended to help us study. And, the modular nature of models means that economists can — and very often do — repurpose existing models to study their specific research question. Many economics papers include a section that identifies the basic model the authors intend to use, then introduces additional assumptions based on their research question. Once a model has been defined, it’s very worthwhile to debate whether or not it makes sense for the task at hand, and if it models economic activity realistically enough to be worth using. Debates over model shortcomings are most productive when we consider what they’re used for. But, beware of discussions about how unrealistic a model is when that discussion doesn’t acknowledge the intended use case. And keep in mind that even glaringly unrealistic assumptions – such as the Solow-Swan model’s infinitely-lived agents — don’t disqualify a model from use. They simply point to the proper use cases. One last note: models in econometrics The word “model” takes on a slightly different meaning in econometrics contexts. While the best econometric models are informed by economic theory, in regression analysis the word “model” often refers to the specific form of a regression model used. There are many, many different types of regression models – gravity models, probit and logit models, Poisson models, ARIMA models, the famous OLS model…the list continues. These models all include and imply a set of assumptions and rules, just like more general theoretical ones do. But, in these cases, the assumptions and rules are more immediately math- and statistics-minded. These models very frequently take a step beyond the theoretical world and utilize real-world data to take a specific form, then help the economist study whether or not an economic theory is supported by the real-world evidence. Still, there are parallels between these two types of models. It’s important to learn about the assumptions of each type of regression model to learn when they can be used, and when their assumptions are too unrealistic for the task at hand – just like theoretical models. This requires a thorough understanding of econometrics, which can be a harrowing — but rewarding — journey! In summary: models are abstract constructs that economists love to use. They’re helpful tools with proper use cases, but it’s important to consider what a model’s assumptions imply. And, each model has limitations that imply times when they should probably be disregarded. Interested in learning more economic models? Check out our Economics Terms A-Z, or take the plunge and look for a course of study yourself! Image Credit: Andrey_Popov on Shutterstock (licensed image, copyright)
The word "model" is used a lot in discussions about economics. However, economics courses often don’t spend a lot of time defining the word or what exactly it means. Rather, students usually end up piecing together their idea of what models are after learning several specific examples (such as the Solow-Swan or OLG models). This article fills in the gap by discussing the fundamentals of models and how they’re used in economics. There are many similarities between the myriad models utilized in the field, including common use cases and common assumptions. Understanding these commonalities will help you use and interpret economic models effectively, avoid misconceptions, and identify when these models are not necessarily relevant or useful.
Why I Didn’t Use AI for my PhD Application
However, as a member of INOMICS’ editorial board, I found that AI tools back then lacked the ability to write a thorough, in-depth, and engaging article about economics topics such as how corruption can affect economic growth or the pros and cons of focusing on “mesoeconomics”. We decided to not use AI tools in our article-writing or editing process. Similarly, I decided not to use AI tools at all when I was applying for PhD programs. That’s not to say that I think these models can’t ever be helpful – I’ve found that AI LLM models are very helpful for one thing in particular: generating ideas. However, I’m serious about becoming a PhD economist, and was determined to submit the very best of my own writing for my applications. Further, there’s always a concern that using AI tools would be considered a form of plagiarism. I think not using AI tools at all for my application was the right choice, and I think it’ll be the right choice for you, too. What’s more, even if AI models become incredibly capable “economists” on their own, I think eschewing them will still be the right choice for your application process. Without further ado, let’s explore the reasons why. First: hallucination adds extra work, and relying on AI delays you from building your own expertise AI models are known to hallucinate. That means, when using them for an important purpose, checking the AI’s output for accuracy is critically important – but that adds work that I didn’t feel was worth it for my application. I felt that the effort I’d have needed to spend studying each aspect of the AI model’s output (to ensure it was accurate) would have been inefficient. Instead, I spent time reading the economics literature myself, seeing what interested me, and reading about how other economists in the space structured their research. This was very helpful, especially as I began to outline my own phd research proposal. Think of it this way: if you want to be a successful PhD student, you’ll need to understand a large portion of the literature in your subfield anyway. You may as well get started during the application process! This will greatly help you with any research proposal you must write, and with any conversations (or admissions interviews!) you have with potential supervisors. Rather than take an AI model’s potentially erroneous output and start from there, I searched Google Scholar and RePEc myself. I read the literature to see if there was a gap that my ideas could fill. I put together a methodology that I knew was sound, using econometric methods that I was confident I understood. I also found public datasets that I knew were accessible and relevant, to ensure that realistically usable data was available – and to show that on my application. Finally, I wrote my own ideas for future research, and studied the research interests of the faculty at the institutions I was applying to. Sometimes, doing something the “hard way” really is the best in the long run. After all, there are no free lunches. Second: personal essays require a personal touch Some schools require specific essay questions or have topics they want you to broach in your personal statement. For this reason, it’s highly advisable to double-check each institution’s expectations for your personal statement (if they do, this is usually found or referenced in the school’s online portal where you upload the statement). I had to write several different personal statements, as each school had slightly different needs. Clearly, these statements require a personal touch. If you outsource the work to a machine, you’re preventing the admissions committee from hearing your voice, and from reading your story as you would tell it. This clashes with the entire point of a personal statement: in your application, you must show the admissions committee who you are and why you wish to study for an economics PhD. This may sound like a “fluffy” or silly concern, but the truth is that admissions committees get many applications from very qualified students. Sometimes, deciding who to give an offer to can come down to less measurable factors, such as how well the committee can picture you succeeding at their institution or fitting into their faculty’s research interests. A good personal statement will help you convince them that you’re a good candidate and pursuing a PhD for the right reasons. Apart from this, many admissions officers can, or at least claim that they can, identify work written by an AI. Whether it is an overuse of stock phrases, an inconsistent register, or a “genericness” in the style or argumentation, ultimately if an admissions officer or potential supervisor suspects that a submission is AI written, at best they will treat your application with scepticism, or at worst discard it. Overall, you ought to give the admissions team as much of a sense of who you are as you can. Express your personality, show them your enthusiasm. AI simply can’t do that as well as you. Third: AI output is not your work, and this limits your growth Different people may feel differently about whether or not, or to what extent, using AI tools constitutes plagiarism. Whichever camp you personally fall into, though, the following statement is true: an AI model produces output that you did not write yourself. And this is a problem if you aim to be the best economist you can be. This is related to the first point about the hallucination problem, but goes deeper. AI tools offer a cognitive shortcut. However, if you’re always taking a shortcut, even if only to kick off ideas and begin your work, you’re robbing yourself of the ability to learn how to start a research process well, which can make future research tasks harder for you. I know this would likely be true for me, because I’d noticed this tendency in myself long before the advent of popular AI models. Allow me to explain. When learning math in grad school, I often felt very confident that I understood the teacher’s example as they worked it in class. Yet, as soon as they stepped away from the chalkboard and asked me to solve a similar problem on my own, my mind would often blank. Suddenly, recalling the right steps to take – and understanding exactly what they meant – was much harder. I think this is a relatable experience for many, and I believe that over-relying on AI tools is just the same as relying on a teacher to begin solving a math problem for you. It seems innocuous when you still have the aid of the teacher. A limit or integral — or the beginning of an econometric theorem — all seem very straightforward when your teacher sets them up for you. But when the aid is removed, suddenly, our brains may panic because they haven’t actually learned how to properly get started. Remember, your brain is an expert in finding shortcuts, and that sometimes means you don’t learn as well as you think you did! Aside from this, research has shown that if you over-rely on AI tools you may – even subconsciously – become worse at detecting hallucinations, and stop thinking as much about serious issues like plagiarism. As Zhai, Wibowo, and Li put it in a 2023 paper1: “Despite the undeniable advantages of AI dialogue systems in streamlining research processes and enhancing academic efficiency, our analysis reveals a concerning trend: the potential erosion of critical cognitive skills due to ethical challenges such as misinformation, algorithmic biases, plagiarism, privacy breaches, and transparency issues.” Thus, I’m confident that over-relying on AI tools to begin – say – your research proposal literature review also limits your growth as an economist, even if you don’t realize it. If you don’t take the time to actually read and process the papers in economics yourself, you are missing out on a great deal of knowledge. Even simple meta-knowledge about what exists in the body of economics literature can be important for your career. It’s potentially embarrassing to not know of a famous or seminal paper in economics, and it certainly wouldn’t look good if you aren’t aware of the literature in your own niche subfield. Fourth: AI can’t output work up to the same quality as you or I can Truthfully, the thought of using AI on my PhD application never seriously crossed my mind. That’s because, through my time writing and editing for INOMICS, I’ve continually tested new AI models to see how good they are at writing thoughtful economics articles. I have yet to find an AI model that can write a helpful, informative, and analytic article the way that Hanna Sakhno, Dr. Sahar Milani, Dr. Tom McKenzie, Dr. Gómez-Pineda, or I can. AI models have yet to demonstrate the ability to go beyond presenting common discourse about a topic and teach the reader something new, or present things in a new light. And I know that if AI models aren’t up to my standards for what we post on the INOMICS website, it will not be up to an admissions committee’s standards for a PhD application essay or research proposal, either. I’m confident that someone seriously interested in pursuing an economics PhD is much brighter than even our most advanced LLMs or neural networks today. Perhaps that will change one day in the future – but even if it does, doing the work yourself will develop you into a much better economist than relying on AI output ever would. Conclusion I learned a lot throughout the process of applying for my economics PhD. I gained a solid preliminary understanding of my topic area that will help me immensely when I begin to write my first publishable paper. Further, after combing through the literature myself, I’m confident that the research I want to pursue would be advancing the field in a way that hasn’t been done before, which is exciting. I don’t think AI had a place in that process. And, at the end of the day, my hard work landed me a very generous offer of admission at an excellent university that I’m excited about. So, my fellow economist hopefuls, take it from me: don’t rely on AI models to write your applications for you. You owe it to yourself to do the hard work. After all, there are no free lunches in economics or in life. References: 1: Zhai, C., Wibowo, S. & Li, L.D. The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review. Smart Learn. Environ. 11, 28 (2024). https://doi.org/10.1186/s40561-024-00316-7 A note on AI usage at INOMICS Here at INOMICS, we’re not against using new technology. On the contrary: on our website, you’ll find helpful AI tools – such as a feature that generates questions (and answers) for our Economics Terms A-Z articles, or an AI Summary button for our article pages. But, when it comes to producing articles and resources, quality and accuracy are our highest priority. So while we sometimes use AI tools to help us brainstorm ideas, suggest phrasing, translate existing text, or generate interesting header images for articles (like the one on this page!), when we have experimented using AI to produce longer articles, we have always found the results to be lacking, not to mention sharing many of the concerns expressed in this article. As such we do not (yet) publish Advice articles, Blog posts or economics terms/lessons that were partially or wholly written using AI. Image Credit: INOMICS, using Canva Magic Studio AI Image Generator
AI tools have become widespread since news of ChatGPT-3.5 prompted me, in early 2023, to make an OpenAI account. I remember sitting in a coffee shop, sipping on a latte, and holding a half-dozen “conversations” with the newly released model. I asked it a series of math questions. I asked it about politics and about poetry. I asked it to write me a short story. I even asked it questions about niche topics like Settlers of Catan to see if it “understood” strategy in the context of a game. And, of course, I asked it about economics.
Take a deep breath, read this article, and start brainstorming! Step 0: Understand What a Research Proposal is For The main purpose of your research proposal is to give the admissions committee an idea of two things: your ability to conduct quality research in economics, and your suitability for the program itself. Because of its importance, it’s also highly advisable to start this process as early as possible – ideally several months before application deadlines. Your proposal should indicate to the admissions committee that you understand the field and are capable of (with the right training and supervision) producing quality economics research. The topic you choose, the initial literature review you conduct, your intended methodology, and more: all of these will signal to the admissions committee that you either understand what it takes to conduct serious research, or that you do not. But even if the committee believes your research proposal is excellent, you may not be admitted. That’s because the second purpose of the proposal is to show that your research interests are a good fit for that specific economics department’s work. Remember, to earn your PhD, there must be a faculty member at that school who’s willing to supervise your research! You could have an absolutely amazing research proposal on microfinance, yet be rejected by the program on the grounds that none of their economics faculty are interested in microfinance. That’s why the next two steps in the process are critically important. Step 1: Identify your Research Interests Before you start writing your proposal, you obviously need a topic. It’s important to identify what you’re most interested in, especially since – if you’re accepted – research in this area will become your life for the next several years! The field of economics is vast, and there are a wide variety of papers being published. If you’re unsure where to start, first consider the economics classes you’ve enjoyed most in the past. Do you enjoy microeconomic theory, or empirical macroeconomics? Perhaps behavioral economics is more your style, or maybe environmental economics is calling to you. Once you’ve chosen a general subfield (or two), reading recently published papers in those subfields is a great way to start digging for a topic. Ask yourself: which of these papers are most interesting? What research methods could you alter, or employ for a different but related question, to produce original work? What further research identified by the authors sounds interesting to you? Is there any topic or question that you think is missing from current research output? If you’re still struggling to settle on a topic after exploring, check out our article about how to choose a topic for your economics research for more advice. Step 2: Investigate the School and Faculty Research Interests You’ve chosen a research topic; excellent work! Now, recall that an important part of the research proposal’s job is to show the admissions committee that your interests align with those of the economics department. It’s time for you to start a very important investigation. This process will look different depending on the type of program you apply to. For instance, many European-style PhD programs will require you to reach out to potential supervisors directly, and receive their approval before you submit your application. In this case, you must research the school’s faculty, check their research output, and message the individuals that you think would be both interested in your research idea and make an effective supervisor for you – preferably far in advance of the deadline. American-style PhD programs, or PhD programs that begin with a taught component like some MPhil/PhD options, may not ask you to identify a supervisor when you apply. However, even these programs will be judging your proposal based on how well it fits within the research interests of the economics department – so you’ll still need to do some digging. Overall, you should expect to spend a decent amount of time looking up professors and their research interests, as well as any specialized research groups within the economics department and the department’s research output. Resources like RePEc may help you out here. As you investigate, ask yourself – honestly – whether you could see these faculty members being interested in your topic and choosing to supervise you. If your answer isn’t a clear “yes”, keep in mind that it will be significantly harder for that admissions committee to justify giving you an offer. Depending on how many schools you intend to apply to, you may even consider writing multiple research proposals on different topics. While this may sound like a lot of work, it’s a far better strategy than writing just one proposal and hoping it fits everywhere. Step 3: Drafting: What to Include in Your Proposal After you’ve chosen a research topic and confirmed that there are professors who would most likely want to supervise your idea, it’s time to start drafting. You can think of a research proposal as a mini-research paper, where you walk through the typical steps of doing research but stop short of actually conducting data collection and testing hypotheses. Instead, you simply describe what you would do. A research proposal typically includes the following sections: - Introduction/research question - Preliminary literature review - Proposed methodology & proposed data - Possible future research - References & Bibliography You may also choose to include small sections on the potential supervisors you’ve identified at the program in question, why your research is a good fit for the department, and your funding situation. Introduction & research question This brief section should be thought of like an abstract, except that you don’t have conclusions to share. Instead, state clearly what you aim to research, and what your main research questions/hypotheses are. Preliminary literature review This is an important section. Here, you ought to write a literature review as if you’re actually going to conduct the research. What is the current state of the economics literature surrounding this question? What insights have other papers offered? Where’s the gap in the literature? What papers will you be mainly relying on or building off of? Answering these questions as you write your review will help show the admissions committee that you understand how to read the literature and conduct quality research. Don’t skimp on this section, or the reading you must do in order to build it properly. The more you understand about the literature surrounding your topic, the easier it will be for you to write about your methodology, and the more well-prepared you’ll be for potential interviews. A side note here about using AI – while AI tools can help you identify sources that are relevant to your literature review, do not rely on them completely. Further, do not use AI to write any portion of your research proposal. If you use AI to help find sources, take the time to then read the sources carefully and critically. Are they really as relevant as they seem? Do the findings truly match any claims you have made in your proposal? Don’t get caught out by trusting that an AI suggestion is a good suggestion. As of 2025, AI tools are far from infallible when it comes to postgraduate level research, and in the worst case might even create statistics out of thin air to support your argument (this is a common AI problem known as “hallucination”). Use AI tools with extreme caution, if at all! Methodology & Data In this section, you should aim to be as clear and objective as possible. Restate your hypothesis, then describe the methods you intend to employ to test it. List specific data sources as much as you can. Use econometric language, identify specific models you’ll likely employ, or describe the type of experiment you’ll design. This section should show clearly that you’ve put serious thought into answering your research questions and that you know how to test your hypotheses effectively. Further, listing actual data sources will make it clear that your research idea is feasible, not just hypothetical. Along with the literature review, this section should be the largest and most comprehensive in your proposal. Don’t hold anything back – aim to impress your readers here! Future research Including a small section about future research that could stem from your work is a nice touch. The faculty and admissions committee understand that a research proposal doesn’t always end up being exactly what a PhD student will work on. But, if you list future or related research that could stem from this proposal, it’ll be easy for the committee to see how you could continue to become an expert on this particular sub-topic, and how you could contribute to the literature for years to come. It could also be a good way for you to pivot, if you later decide that your original proposal wouldn’t be the best dissertation topic. References & Bibliography Citing your sources is crucially important. That’s partly because plagiarism is a serious offense, of course. But in addition, having a quality bibliography and reference section is another great signal to the committee that you’re a good candidate and should be seriously considered. Step 4: Reaching Out to Potential Supervisors Drafting your research proposal is no walk in the park, so once you’ve finished, pat yourself on the back! Depending on the types of programs you’re applying to, identifying a willing supervisor may or may not be part of the application process. If it is, you should start reaching out to faculty members who might be willing to supervise you as soon as you have an initial proposal drafted. Include the draft in your message to them, along with your CV. Typically, professors are fairly responsive. Even so, your mileage may vary with this process. If your proposal seems overly amateurish or underbaked, you’re unlikely to get a positive response, even if the topic aligns with the individuals’ research interests. Further, some professors may not be accepting new students. Sometimes university websites indicate which professors are open to receiving new PhD students, but it’s entirely possible that your first choice of supervisor isn’t available. Even if you do receive a positive response, you may be asked to make updates or edits before a professor is willing to say “yes”. If you’ve reached this point, don’t despair – it’s actually a very good sign that you’re on the right track, and that you’ve done well in finding a potential supervisor with similar research interests to you. Step 5: Don’t Give Up! Writing a quality research proposal is hard work. So is studying for your PhD! But it’s important to keep in mind that there is a reason for all of this work. Finding and getting admitted to the right economics program will make your journey towards earning your PhD much more enjoyable. Moreover, it’ll help immensely when the trials and difficulties of PhD research threaten to burn you out. Crafting your research proposal should get you excited to narrow down into a specific topic, and imagine getting your own research published. What question have other economists not answered, but you think is important or interesting? How can you uniquely contribute to the literature? Your research proposal should give you a clear idea of the answer! Image Credit: Fizkes / Shutterstock (Licensed Image - Copyright) <11an>limit3;categories:PhD Programs</11an>
If you’re applying for a European-style economics PhD or Master’s of Research program, it’s highly likely that you’ll be asked to submit a research proposal. This is a document where you’ll describe the original research project you intend to pursue if you’re accepted into the economics program. This is going to be a fairly intense process. While not legally binding, this document is a major part of your application, and it’s extremely important to write a convincing and high-quality proposal. Giving yourself as much time as possible to work on it can only help you. However, if you’re applying to PhDs for the first time, this is also most likely your first time writing an economics research proposal. So, what to do?
Applying for a PhD: Should you do a Master of Research?
Introducing the Master of Research<o:p></o:p> The Master of Research (or MRes) is an advanced post-graduate degree that is considered a stepping-stone towards PhD study. Often, and perhaps confusingly, this type of degree requires applicants to have a Master’s degree in the relevant subject area already. Importantly, the two types of Master’s are often differentiated: a Master of Science in Economics is often referred to as a “taught” Master’s, while an MRes is referred to as a “research” Master’s. However, an MRes is still a Master’s degree and not a PhD-level certification.<o:p></o:p> These degrees can go by several different names, and are most commonly available in the UK, Australia, Ireland, Singapore, and Hong Kong. These programs may also be listed as MPhil/PhD programs (standing for Master of Philosophy/PhD), or “Master of Letters”, abbreviated M.Litt programs (typically in Ireland). Although this type of degree can be very helpful as a stepping-stone to a PhD, they can be little-known outside of the countries that offer them.<o:p></o:p> MRes programs aim to prepare their students for serious academic research work, and expect that students come already equipped with significant subject matter knowledge – hence why many outright require applicants to already hold a taught Master’s degree. These programs typically last one to two years and are rarely longer. Most programs also require students to write a Master’s thesis (even if you’ve already written one before!). After completing an MRes in economics, students will have training not only in economics subject matter, but in conducting economic research as well, something that Master of Science programs often don’t have much time for. Many MRes programs – as evidenced by the MPhil/PhD moniker – often expect their students to continue into a PhD program immediately afterward.<o:p></o:p> Is an MRes right for you?<o:p></o:p> Pursuing an Economics MRes (or equivalently, an MPhil/PhD or MLitt program) could be an excellent choice for your path to a PhD. However, it’s far from the only option. How to decide? One reason to pursue an MRes over immediately applying for PhD programs is that an MRes will help your academic profile become stronger. The additional training in research specifically shows that you’re serious about pursuing academic work, and a high mark on the MRes thesis could be a significant indicator of your suitability for an economics PhD. Thus, this could be a great option for former Master’s students with good, but not great, grades. Grades are very important when applying for PhD programs. High marks in an MRes could cover nicely for more average grades in a Master of Science, making you a more competitive PhD applicant.<o:p></o:p> More importantly – also for those with top grades in their previous economics degree – an MRes program is a gentler way to explore the rigor and lifestyle of conducting academic research than simply jumping straight into a PhD program. This makes it an excellent choice for students who are still “on the fence” with regard to a PhD, as you’ll get a strong sense of what PhD research would entail. If the MRes goes well, and the work is enjoyable, it’s a great sign, both for you and for potential PhD supervisors, that you’re ready for the rigor and discipline required to get your PhD. If not – perhaps a career in industry is a better choice for you. Finally, completing an MRes gives you more time to meet students and faculty and explore the field of economics. If you’re unsure of what specifically you’d like to research, this can be a great way to explore sub-fields on the frontier of economics research and the methods you must master in order to succeed. It can also help you build relationships with faculty, who can guide and advise you, and who may even become your future PhD supervisor if you choose to continue. It’s important to note that MRes students are typically expected to continue on to a PhD, but it’s also possible to leave after completing the MRes and seek a job in industry. Some research-intensive professions may appreciate the additional research experience without requiring a PhD. However, this situation corresponds to a minority of graduates. If you plan to work in industry after earning your MRes, it’s highly advisable to have specific positions in mind that would justify the MRes qualification, but not require a PhD.<o:p></o:p> RA vs MRes: similar benefits, different experiences Applying directly to a PhD is very possible, particularly for strong students with excellent grades who have had opportunities to participate in academic research during their studies. But, for those students who wish to spend some time gaining more research experience before diving into the full commitment of a PhD, it helps to understand the differences between doing an MRes or working as an RA. It’s worth noting that many of the benefits of earning an MRes have some overlap with the benefits you’d enjoy from working as a Research Assistant. If you want research experience before diving into a PhD, both are options you should strongly consider. Research Assistants help professors conduct actual academic work that will be published. It’s a great way to get your name out there and begin to add research credentials to your academic CV. However, being an RA limits your exposure to the project(s) that you’re given. If you don’t end up liking the sub-field of that specific research, it can be awkward to pivot to a new one. And, you will likely be very beholden to the needs of the project(s) and professor(s) leading them, with little room to explore your own research interests. An MRes offers less of a chance to have your name appear in published research, but is a more structured way to gain additional research experience. And, you’ll be able to write your Master thesis in a field that interests you the most, while building a potential foundation for your PhD thesis. Finally, the added benefit of earning high grades in an MRes shouldn’t be overlooked if you wish to improve your academic profile. Both RAs and MRes programs may be connected to a PhD program. In both cases, you may be able to start your PhD in parallel. Usually, for an RAship, the PhD is expected to be related to the project you are working with, and your supervisor is usually expected to be one of the professors you’ve been working for. For an MRes program, your eventual PhD topic may be more flexible – though it’s certainly advisable to do your MRes thesis on a topic you expect to explore via your future dissertation. Finally, one major difference between these two paths is funding. Research assistants are paid, while MRes students must often pay tuition (though of course, scholarships may be available). Financial considerations are of course very important, and you must make the right decision for your current situation and goals.<o:p></o:p> Concluding Thoughts<o:p></o:p> Overall, applying for a Master of Research program is a perhaps lesser-known but excellent option when preparing for a PhD. Whether or not it’s the right path for you depends on your own goals and personal situation. We hope this article was helpful. Here at INOMICS, we wish you all the best in your studies and future endeavors. Good luck! Image Credit: Atstock Productions / Shutterstock (licensed image, copyright) <11an>limit3;categories:PhD Candidate;Research Assistant / Technician;PhD Programs</11an><o:p></o:p>
There are many paths to preparing for an economics PhD. Most students apply for PhD programs soon after finishing their Master’s degree. Some even apply directly after their Bachelor’s degree. Others work first, for example as a research assistant before applying. There are benefits to all of these approaches, and depending on your academic accomplishments and personal goals, some paths may suit you better than others. In this article we look in depth at the merits of a Masters of Research (MRes) as an option for PhD hopefuls. We also compare the MRes path with a direct application for a PhD after your studies, or with a year or two of work experience before applying for a PhD.<o:p></o:p>
Extracurricular activities to help your economics career while at university
Being involved in extracurriculars at university can make your time there more enjoyable by enabling you to meet friends, develop intellectually, and stay fit. You may not have considered it, but relevant hobbies for economists can even be advantages in your career development, too! This article explores some suitable economics hobby ideas that you might find beneficial. Although lots of these suggestions will help you whatever career you decide to pursue, some may be a little more useful specifically for a career in economics. Browse our program listings for economics opportunities 1. Debate team Joining the debate team will give you the chance to practice your oral presentation, argumentation, and critical thinking skills – all of which will be a big asset to you in any economics job search. It's also a great way to practice your public speaking, which can be a helpful advantage in job interviews. The ability to think through a position and support it with facts and logic is incredibly relevant to any research you do, too. On a fundamental level, research is posing a question, and either supporting or detracting from that question using facts and logic – just like debate! In economics research, the facts may need to be taken from a literature review or generated by your own econometric analysis, but the fundamentals are remarkably similar. Debate will also help your communication skills, which are one of the most important soft skills to have, useful in all aspects of life. Being able to communicate your ideas and research effectively, or back up your economics findings in your career, is essential to being successful. 2. Student governance Almost every university will have some kind of students' union or student council which you could join. Doing so shows future employers that you are responsible and engaged with political and practical issues. It also gives you the chance to make real changes to the way that your university is run. Being a part of the students' union shows proactivity and leadership – that you have (productive) opinions and aren't afraid to voice them and fight for them. It implies you can speak your mind well in public, but also that you have a level of emotional empathy that enables you to listen to and appreciate – or at least respect – other points of view, even when they stand in direct contradiction with your own. Being involved in politics is also very useful for a job in economics, particularly policy-adjacent roles. 3. IT skills Students may have the opportunity to assist university staff and/or students with technical problems. Often, universities employ student workers in IT help roles as a first line of defense against tech issues, helping other students on campus resolve computer problems – and escalating to a more seasoned professional for non-routine fixes. These types of roles may also give you limited experience with coding, especially if your job includes performing functions for university webpages. Software proficiency is becoming increasingly important for economists, so demonstrating technical skill in this area is a bonus for most employers. Economists often need to use statistical software like R, SAS, Stata, and more for their work, and other tools like LaTeX will be useful in their careers. Showing employers that you have the skills needed to master new software tools quickly can be a big plus for your future job prospects. 4. Charity fundraising Volunteering can be a rewarding experience and can help you grow a lot as a person. It can also be good for your career prospects, even beyond just showing your strength of character. If you want to work in a non-profit after university, or conduct research, then the ability to bring in funding (either by typical fundraisers or by securing grants) is a big requirement of many jobs. Showing that you've already been successful at fundraising while you were at university can put you at the head of the pack. Moreover, charity fundraising can often give you the opportunity to travel. Plenty of charities work around the country and in other nations around the world. Travelling is one of the most rewarding things you can do, and employers like to see that their potential employees are well-rounded and have some life experience, which includes getting to know other cultures. Aside from this, charity work is simply a noble thing to do and can show that you're emotionally intelligent and tolerant, two essential skills to have in an ever more diverse workplace. And, you might even benefit from that “warm glow” that stodgy economists talk about when describing altruistic behavior. 5. Sports teams Sports teams are often a big part of university life, and most universities provide such a variety of sports clubs that almost everyone can find something that matches their interests and talents. These might offer the opportunity to travel around the country and occasionally the world, especially if you join something a little bit more niche like scuba diving. And of course, sports are a great way to meet new people who could become lifelong friends, improve your teamwork and social skills, and stay fit while doing it. Best of all, from a career point of view, if you are head of a team and organizing matches, you have already shown your ability to lead a group which is a great skill to demonstrate. 6. Student newspaper Student newspapers are great places to develop writing and editing skills, and to get used to a fast pace of work with tight deadlines. There are usually multiple areas of the newspaper you can work on, including design, editorial, and of course, writing and journalism. Many newspapers have local news, political reports, and comment sections that deal with more sensitive issues. Even if you just write a few times rather than having a set role at the paper, it means you'll slowly build your portfolio of written work (which can be useful if you’re asked for non-academic writing samples), have some interesting things to put on your CV – and of course, seeing your name in print is always a wonderful feeling. Some university papers may even pay for your work. Learning how to write well can be an essential skill if you're going to be communicating complicated economics ideas to non-economist audiences for whichever company or institute you're working for. Clear communication skills are an area that employers have already identified as important for economics graduates. Academic language is often impenetrable, so being able to show that you can present your ideas clearly and concisely outside of academic contexts is an invaluable skill. 7. Learning a language Learning a language is one of the most productive things you can do in life. With language skills, you can work in an international company and be an extremely valuable asset to them, or you could even move into specialized economist roles that require multiple languages such as international politics, working as Chief of Staff for an international development project, or conducting research with economists in other regions. It also means if you ever decide you want to live abroad, you have a head start; not only could you live, for example, in Germany, but many people report learning a third language easier once they have already learned a second. Reports have also come out that suggest learning a new language is one of the best favors you can do for your brain, with some studies suggesting it can even help prevent Alzheimer's. Many universities' language courses are of a high standard – as one would expect – and as they are generally diverse, multicultural environments, with plenty of opportunities to practice with other students. If you want to learn a language, adding a few courses to your schedule during your studies is an excellent way to get started. 8. Entrepreneurship For the really ambitious student, why not start your own business? It may not feel like it, but at university you'll be in an ideal environment with plenty of time and resources to develop your own ideas. That’s because adult life outside of uni is often much busier. Often, universities have entrepreneur challenges where students pit their ideas against one another in order to win a certain pool of money that might even help them get the idea off the ground. They also often offer advice and support for students who have business ideas, host entrepreneurship courses, and of course, you might have the opportunity to befriend a business professor to tutor you. Whether or not your business is successful, you'll learn a huge amount from trying to start a business before your peers have even got a foot into the workplace. Plus, it's a clear demonstration of your independence and self-motivation which employers will appreciate, especially if you plan to specialize in financial economics or wish to work in a policy analysis role related to business. 9. Chess Club or Go Club The age-old games of chess and Go both have massive followings, chess traditionally in the West and Russia, and Go more traditionally in East Asia – although both are now played worldwide. Aptitude in either game can be seen as a sign of strategic thinking, dedication, and intelligence. So, if you already enjoy chess or Go, joining a university club for the hobby or doing well in tournaments can be a perk on your future economics resume. Both chess and Go can develop your critical thinking and ability to improvise and adapt successfully, too. This idea extends to other strategic hobbies as well. Other strategy games likely help develop similar skills and show your acumen just like chess or Go can, but employers will likely only be impressed if they actually know what the game is. Header image credit: Lukas Prudil on Pixabay (published under pixabay content license)
Attending university can be a ton of fun, but it’s also supposed to prepare you for the next stage in your career. Normally, this involves a lot of studying. But breaks and hobbies are important to have, too.
This quote from the famous economist Keynes raises a number of areas that help us begin to answer the question of how to succeed as an economist. Reading the quote, an economics student may ask: in the modern age, what are the necessary skills one must possess in order to become a “successful” economist? The answer is, of course, hugely subjective. It may also fluctuate as one’s own career goals change. Here, we will identify some of the age-old must-haves – as well as some of the new skills economists ought to acquire in the age of climate change, globalization, and AI. Before we begin: the need for economists The present age is a remarkable time, probably among the most exciting and challenging times ever to enter the field. Many economic models and presumptions of the past are continually showing themselves to be outdated and in need of theoretical improvements. This has much to do with the new challenges facing our generation. Whether it’s in confronting climate deterioration, demographic change, the rise of AI and automation, wealth inequality, or even incorporating basic realism into textbooks, there is room for increasingly advanced economic models that can more accurately account for the complexities of real life. Beyond the need for improved economic models, though, is a societal and political need. In handling many of these emerging challenges – some of which were wrought by an inability to regulate our own activities – the ailing neoliberal orthodoxy has found itself ill equipped. To borrow the words of economist Howard Reed: “until we ditch the old textbook, we’ll never face up to the challenges of the modern world”. So far, there has been little sign of this happening any time soon; as Prospect editor-in-chief Tom Clark has observed of most Western governments, “out-dated frameworks still inform policy prescriptions”. In fact, searching for alternative solutions may partly explain why there’s been a resurgence of nationalist and even fascist sentiment across democracies in recent times – but those systems have been tried before, and surely do not hold the answers society needs. The responsibility, then, lies with the next generation of economists to combat this deficit of ideas. Optimistic, open-minded, and determined to make the world more livable for all, it is they that must provide the insight, innovation, and policy stewardship needed to guide our future path. Think you have what it takes to answer the call? Read on. What’s needed: the (quantitative) basics It should be no surprise at this point that a familiarity with mathematics and statistics is vital to any aspiring economist. Competence in these areas will allow one to analyze data using econometric techniques such as regression analysis, a necessary skill for nearly any career in economics. Beyond the bread-and-butter quantitative skills, familiarity with data science will help economists navigate popular economic software such as Matlab, R, Python, and STATA. Variants of these software programs will, through necessity, become your future friends; it’s probably best to make their acquaintance as soon as possible. In fact, the entire discipline of data science increasingly intersects with economics. This will only become more true over time as AI tools continue to percolate through the economy. As such, a familiarity with computer science, data science, and programming can only help you. Some recent economics graduates may not yet be comfortable with handling large numerical datasets, using statistical software programs, or building bulletproof economic models to interpret data. It’s easy to take coursework only in one’s specialty or interests during an economics Master’s degree, for example, and to have forgotten the more numerically-focused introductory courses. If you find yourself in this situation, fear not. Consider signing up for professional skills trainings and/or summer schools to fill in the gaps in your skillset, which will not only improve your career prospects, but help you to become an even more capable and well-rounded economist. It’s never a bad idea to explore options like these to keep you on top of your game. Regardless, in order to analyze economic data, learn, and deliver powerful insights that can change the world, competence with the quantitative elements of the field is key. Interdisciplinary knowledge Beyond mathematical and data proficiency, economists ought to be curious, independent thinkers able to reason soundly, and possess the humility to admit when the data has proven one’s own theory wrong. These are excellent qualities to have for an economist, allowing them to glean new truths with minimal bias. Great strides in economics have come from dispassionately examining why things are the way they are and using the resulting models to help prescribe how they can be made better. Economists also tend to benefit greatly from “thinking outside the box”, sometimes going beyond the strict parameters of economics for tools and answers. This is where familiarity with other social sciences becomes key. Learning about other disciplines, particularly those that relate to economics in some way, can help an economist to make breakthroughs. Clearly then, modern economists (and economic theory itself) can benefit from taking up an interdisciplinary approach to research. Dr. Olivier Blanchard, our 2024 Handbook interviewee, named this kind of creativity as an important quality that young economists must demonstrate in order to excel. Communication Skills Another crucial skill is communication, in a clear and concise form, both verbal and written. The complexities of economics can easily be lost on important stakeholders like clients, politicians, constituents, and community members who – we should remind ourselves – don’t have advanced degrees in economics. This is important; the fact that economic processes underpin so much of governmental policy means it’s imperative that decision makers and ultimately the public are up to speed on economic issues, prospective policies and the like. But not everyone can study for a degree in economics. Thus, a great economist will be able to convey complicated, data-backed economic theories through understandable, accessible language. This is quite the skill, and its absence from public discourse can be potentially very damaging. Few would argue, for instance, that the entire British electorate had all of the relevant facts at hand when deliberating withdrawal from the European Union. Further, politicians and the news media around the world often routinely misunderstand the nuances of economics. As Lord Thomas Macauley once stated, as an example of this: “Free trade, one of the greatest blessings which a government can confer on a people, is in almost every country unpopular”. The benefits of comparative advantage have been well documented, yet many modern voters around the world rally to the call of protectionist policies, not realizing that those policies will ultimately increase the costs of the goods they buy every day. This is one example to demonstrate the importance of clear economic communication. But of course, it isn’t just public-facing economists who should master communication skills. Business clients and other stakeholders alike should be presented economic facts and recommendations in understandable terms so that they can make properly informed decisions, too. Know this: history fuels continuous improvement in economics One of the many reasons economists were lambasted for their failure to predict the economic crash of 2008 was that it exposed some unrealistic assumptions that economists had grown accustomed to leaning on. This includes analyzing the decision-making of individuals as if everyone was perfectly rational at all times. Pre-2008 economic orthodoxy often assumed that people followed a very rational thought process when faced with a decision. First, list all possible outcomes of all possible options, attach a value to each, and assess potential risk. Next, perform a comprehensive analysis based on the expected returns of each option, and choose the optimal decision. Is this process rational? Certainly. Realistic? Well… Evidently, such purely rational thinking was absent in the lead up to 2008, where banks famously began to package subprime loans and sell them as financial securities that were overvalued. The systematic understatement of risk, fueled by moral hazard, represented a bet that cost the world dearly. Partly in response to such tragedies, economics since its inception has accepted criticisms of its models and failures, and used them to create more robust and realistic models of the economy. One major example of this is the advent of behavioral economics, which increasingly asks how real people behave towards decision-making and risks in ways that aren’t necessarily optimal. It incorporates psychological, cultural, social, and emotional factors into its theories, and is a prime example of how economics can benefit from other social sciences. Calling all future economists In summary, and though there is no strict formula, great economists typically have the statistics and math skills to work with data and derive meaningful insights. They often have a specialization in one or two areas, but benefit greatly from an understanding of others. They are able to emerge from the depths of analysis and communicate the high-level meaning to other important stakeholders who don’t have the same training. And, the absolute best of them possess a drive and motivation to do the right thing, to advance theory, and to make the world a better place. Now more than ever, economists are uniquely suited to help the world understand and navigate the potentially tumultuous future being presented by challenges like climate change and the explosive growth of AI tools. Perhaps you can take up the call and become an economist who will guide us all towards prosperity. The above article includes small sections that were originally written by William Pearse. Header image credit: Freepik.com / Magnific (Free license)
The following article first appeared in the INOMICS Handbook 2024. Download the INOMICS Handbook “The master-economist must possess a rare combination of gifts. He must reach a high standard in several different directions and must combine talents not often found together. He must be mathematician, historian, statesman, philosopher…He must study the present in the light of the past for the purposes of the future. No part of man's nature or his institutions must lie entirely outside his regard. He must be purposeful and disinterested in a simultaneous mood; as aloof and incorruptible as an artist, yet sometimes as near the earth as a politician.” – John Maynard Keynes
Economists Have a Sense of Humor Too: Economics Memes That Will Make You “LOL”
Now, let's dive into some of the funniest economics memes from the past. These memes have achieved viral status and have been shared and enjoyed by economists and non-economists alike. From witty one-liners to clever visual puns, these memes are guaranteed to make you burst out laughing, or at least chuckle quietly to yourself. So, sit back, relax, and get ready to laugh at the lighter side of economics. 1. For all Adam Smith critics, this one’s for you! The critics and supporters of Adam Smith’s theories have been debating each other for years. However, by using only one meme, critics of the invisible hand have managed to make quite the comeback! Image credit: https://www.instagram.com/economist_memes/ & Image copyright: @the_economy_stupid 2. The importance of keeping up with the latest data For an economist, anything concerning data is no laughing matter. Our data is the air we breathe, it is all we need, and yes, we would even come back to life for the sake of our data. Image credit: https://www.instagram.com/economist_memes/ 3. Turns out the 2008 Financial crisis was not the only major Economic downturn this generation has seen. We all had high hopes of economic recovery after COVID-19, but it seems that Simon had a different opinion… Image credit: Sara Taha 4. Flashbacks of the 2008 Financial Crisis and the good old days. After a first-hand experience of the impacts of the COVID 19 pandemic, Russia-Ukraine war, and world-record global warming…it seems easy to look back at 2008 and laugh. Image credit: https://www.facebook.com/elasticeconmemes 5. A traumatic experience for business majors Business students all around the world today are still trying to recover from the damage done by economics courses. Not only did these students suffer personally, but their GPAs took a bad hit too. Image credit: https://www.instagram.com/economist_memes/ 6. Economists’ predictions and forecasts Yes, as economists, we are usually very proud when we make predictions that are on point. And yes, we told you so. Image credit: Sara Taha 7. Econometrics: quite a shocking experience for many We all remember our first time being introduced to the beast they call econometrics. Our lives were never the same again. Image credit: https://www.instagram.com/thepotatoeconomist/ 8. Trickle down economics Well…we’re not so sure if everyone’s going to get this one. Image credit: https://imgflip.com/m/political_jokes/tag/economics 9. An alternate way to test your econ knowledge Image credit: https://twitter.com/econ_memess/status/1275002305113460736 At the end of the day, economist jokes bring a much-needed dose of humor to the world of economics. They remind us that even the most serious subjects can be approached with a light-hearted attitude. Whether you're an economist looking for a good laugh or someone curious about economics, these memes offer a fun and accessible way to engage with the field. So, the next time you come across an economics meme, don't hesitate to share it and spread the laughter! In fact, why not share your own favorite economics joke in the comments below? Header image credit: benzoix on Freepik.de
Economists are often immersed in the serious and complex world of numbers, charts, and theories. But contrary to popular belief, economists do have a sense of humor too! In fact, there is a whole subculture of economics memes that bring a lighthearted and comical perspective to the field.
A gentle introduction to LaTeX for economists
Enter LaTeX, a typesetting system that’s been developed for researchers of all stripes to write and neatly print properly formatted mathematical formulae. If you’re able to learn and use it, you’ll be able to write important documents such as your economics thesis or a research paper in no time. While LaTeX isn’t the only option economists have to handle this work, it is a commonly used and freely available option. The most common alternatives include Markdown and plug-ins for Adobe InDesign. That’s right – anyone can get started practicing LaTeX for free, using a popular site called Overleaf. Because the basic version of Overleaf is a free and easy to use LaTeX editor (though paid plans are available for more features and integrations), the rest of this article will assume that the reader is using it. This is a skill that’s great to have for your economics coursework and future career, and not terribly difficult to learn, so let’s dive into some LaTeX basics. Before you start: understanding mathematical symbols The first step to learning LaTeX and writing professional-looking economic formulae is understanding what those formulae are and how they are typically written. We’ve already shared an article that describes many of the commonly used symbols in economics. If you find yourself struggling to read some of the strange symbols presented in economic formulae during lectures or in textbooks, the linked Math Symbols article is a great place to start. Once you’re familiar with the symbols typically used in economic formulae, continue reading! Getting started in LaTeX: the preamble The “preamble” in LaTeX is a section found at the beginning of the document. In Overleaf, this will be at the top of the left-hand side Code Editor section. The preamble is written by the author, and contains important code for setting up the document. This is where you’ll install packages and other functions that you’ll need, as well as set document-wide defaults like margin spacing and font size. The preamble – and thus the document – always starts with the “\documentclass{}” command. This defines what type of document is being produced. Inside the curly braces can be written a number of words; “article” is probably the most commonly used, and will serve most purposes. Thus, the first line of code is typically “\documentclass{article}”. Research papers ought to use this command, and it’s also suitable for a casual learner to start using LaTeX. Other document classes tend to be more specialized, such as “beamer” for presentations, “minimal” for debugging and “slides” for a slideshow. An important fact to know is that you can set certain parameters for your document, and any equation or function that you type, using brackets such as [] right after a command. For example, you can set the font size equal to 12 for your entire document using “\documentclass{article}[12pt]”. We’ll return to the usefulness of these brackets later when we discuss graphing. Other lines of code that will be entered in the preamble include packages that you’ll need to install, similar to software programs like R that econometricians use. There are many, many packages containing specific and niche tools, and LaTeX does not automatically have access to all of them. So, by inserting lines of code like “\usepackage{pgfplots}”, specific functions will be loaded so that you’re able to access them when you need them. Some common packages that you may need to install as an economist include, but are not limited to: pgfplots: contains useful functions for graphing. tikz: another package that contains useful functions for graphing. amsmath: developed by the American Mathematical Society (AMS); contains useful functions and improvements for writing equations in LaTeX. amssymb: again developed by AMS, contains useful math symbols that you can insert into your document. graphicx: allows you to insert images into your document from an external source, like your hard drive. tabularx: contains further options when making a table. multirow: if you’re using a lot of tables, this package allows entries to cover more than one row, which can be useful. mathpazo: sets a standard font used when printing mathematical characters. nicefrac: an option for typesetting in-line fractions neatly. subfig: allows you to more easily create a figure that’s broken into multiple sub-figures. footnote: further options for formatting your footnotes. natbib: contains helpful support for handling in-text citations and the bibliography. If you need more specific packages for your economics coursework, your instructor is likely to provide them for you. If you’re working on your PhD thesis or other research, your supervisor(s) or colleague(s) might be able to help you find specific packages you may need on top of these basic suggestions. After installing packages, you ought to include lines for the title, author, and date the document was written. The commands for this are “\title”, “\author”, and “\date”; fill these in with braces as with other commands. For instance, if this article was written in LaTeX, it would have these three lines of code: “\author{Sean McClung}” “\title{A gentle introduction to LaTeX for economists}” “\date{October 2 2023}” At the end of the preamble, you’ll need to write “\begin{document}”. This tells LaTeX that the code below this line is used to actually generate your document. With the preamble out of the way, you – and this article – are now ready to move on to the meat and potatoes. Writing your first equation in LaTeX There are a few commonly used commands that you can use to write an equation in LaTeX. These include (but are not limited to): Equation: this will print your equation with a line number, which can be useful in some contexts. Use “equation*” instead if you don’t want line numbers. Align: vertically aligns multiple equations (this makes them look neat in your document when printed and avoids awkward spacing). Can also be used to break up multiple equations into columns, should you wish to present them that way. Multiline: allows long equations to be split up on multiple lines so that they display properly. Gather: Another option to simply print a number of centered equations. The differences in these options tend to be minor, and in many cases which one you choose won’t affect your output much. But, using the right one for the right niche circumstance can still make the difference between a professional-looking paper and a haphazard one, so it pays to learn the difference. After you’ve decided on one of the above options (and picked a formula to write!), you’ll be ready to start typing. Simply type out your desired equation using the proper symbols, and LaTeX will be ready to produce professional-looking output of it. A note – if you need to use fractions, you must do so using the “\frac{}{}” command. This can be confusing at first. However, it’s quite simple to use. The expression in the numerator belongs in the first set of braces, while the denominator goes in the second set of braces. Thus, to type 1/2 as a fraction, you’d enter “\frac{1}{2}”. Very quickly, you’ll probably realize that you need to insert a specific symbol that isn’t found on a keyboard – like \(\partial\). This is done by first typing a “\” symbol, which tells LaTeX that you’re about to use a special character (or type a new command). When you do this, a list of symbol and function names will appear next to your cursor. You can scroll this list to choose the option you want, or keep typing to narrow the list. Simply press “Enter” or click on the option you want. You can even type out the entire name and ignore the list, if you wish. In the coding window, this won’t appear to do much. But, when you compile your work, you’ll see LaTeX print out “\(\partial\)” instead of “\partial” on the document compilation output. Read on for how to compile and check out your work done so far. The “Recompile” button You now know how to write formulae in LaTeX. But, since the Code Editor doesn’t automatically show you the exact output, it can be hard to know precisely what you’ve written. Further, at first you’ll probably run into many simple error codes as you get used to the new language of LaTeX. How can you check this to review your progress and make corrections? At the top-right of your Overleaf screen, you’ll see the “Recompile” button. Clicking this button will execute your LaTeX code. This will cause your entire document to appear as if it was being printed out on the document screen to the right. Then, you can check visually if your formulae turned out as expected. If there are any errors, the compile will fail, and the document screen will display an error message instead of your work. Helpfully, the offending line(s) on the left-hand side Code Editor will be highlighted with a red symbol. Hovering your mouse over the symbol will show a message describing what went wrong on that line. Check your code to ensure you’re using the proper syntax, and start small so that it will be easy to identify errors. Search engines like Google are great friends to have during these steps. Now that you can write equations – and compile the document – here’s an example. Try copying this code in your own Overleaf project, and see if you can manipulate the equation yourself. The following code produces the Cross Elasticity of Demand equation shown below: \begin{equation*} \epsilon_{AB} = \frac{\Delta\mathit{q}_{A}}{\Delta\mathit{p}_{B}} = \frac{\frac{{dq}_{A}}{\mathit{q}_{A}}}{\frac{{dp}_{B}}{p_B}} \end{equation*} \begin{equation*} \epsilon_{AB} = \frac{\Delta\mathit{q}_{A}}{\Delta\mathit{p}_{B}} = \frac{\frac{{dq}_{A}}{\mathit{q}_{A}}}{\frac{{dp}_{B}}{p_B}} \end{equation*} Making graphs in LaTeX Making graphs in LaTeX is intimidating at first, because even a simple graph requires using many different lines of code. However, it’s quite intuitive to create graphs once you get the hang of it. As with writing equations, it’s best to start small. Start with a simple graph as an end goal, and introduce one or two elements at a time until you’ve finished the graph. It will be easier to identify errors and learn that way. To start drawing a graph using LaTeX, you’ll need to use one of a few commands just like when writing an equation. The most commonly used commands are: begin{tikzpicture}: used after installing the tikz package. This line of code tells LaTeX that the following lines are being used to create a graphic of some sort. Then, the line “begin{axis}” should follow for most economics applications. begin{axis}: this command will create an x-y axis. You can set its dimensions and scale; it defaults to a maximum x value of 10 and likewise for y values. The aforementioned brackets are also very useful when creating your axis, allowing you to set the axis parameters. For instance, “\begin{axis}[xmax = 15, ymax = 15]” will create a larger axis than the default, showing elements up to 15 units away from the origin instead of the default 10. Then, LaTeX expects you to introduce elements via equations or plotting them on the graph, somewhat similarly to how a graphing calculator works. You’ll typically use the “\addplot{}” command to graph equations. For instance, to add a simple line, you can use a linear equation such as: "\addplot [domain = 1:10, restrict y to domain = 0:10, color = blue, very thick] {x+1}". This will graph the simple line y = x + 1 as a blue line on your graph. A few other useful tricks have been shown in the brackets: you can set the domain as shown, and even restrict the domain of the function if you only want to show part of the line. For example, you can adjust the above command to read “restrict y to domain = 3:5” if you only want to show the parts of y = x + 1 that cross through the points (2,3) and (4,5). You can also produce a dashed line by typing “dashed” instead of “very thick”. Try it! Of course, lines need labels in economics. You can add a label to your line with the “\node” command, like so: “\node at (4, 1) {$D$}” prints a nice D, for demand curve, at the point (4,1). Finally, the “draw” command is a useful tool to know about when graphing. It allows you to create more free-form shapes and lines without needing to specify an actual equation to draw them. This can be a great option when you want to draw a graph that involves undefined points, sharp angles, or odd shapes. For example, you can use the command “\draw[dashed,gray] (5, 2) to (5,0)” to draw a dashed, gray line from the point (5, 2) to the point (5,0). As an example, the following code (with a proper preamble already written) produces the simple graph shown below: \begin{tikzpicture} \begin{axis}[ xmin = 0, xmax = 10, ymin = 0, ymax = 10, axis lines* = left, xtick = \empty, ytick = \empty, clip = false, scale = 1.5, ] % Curves \addplot [domain = 0:10, restrict x to domain = 1:9, color = green, very thick] {-x+10}; \addplot [domain = 0:10, restrict x to domain = 1:9, color = blue, very thick] {x}; \node [] at (9, 0.8) {$D$}; \node [] at (9, 9.2) {$S$}; \node [left] at (0, 5) {$p^*$}; \node [] at (5, -0.3) {$q^*$}; \node [left] at (0, 9.8) {price}; \node [left] at (9.8, -0.3) {quantity}; \draw[dashed] (5,0) -- (5,5); \draw[dashed] (0,5) -- (5,5); \end{axis} \end{tikzpicture} See if you can tell which commands produced which elements on the graph. Try copying this code into your own Overleaf project, and see if you can manipulate the elements. This will help you learn quite swiftly! Once you’ve started making graphs, and got the hang of it, we have a further practice suggestion for you. All of the graphs in our Economics Terms A-Z were made using LaTeX. Why not pick a graph from one of these articles – such as Demand Curve or Market Equilibrium – and see if you can recreate them in LaTeX? If you’re feeling confident, try making a complicated one like Deadweight Loss or Edgeworth Box. The “end” command One final and important note; at the end of each equation, graph, and document, the “end{}” command must be typed with the \ symbol just before it. This is the counterpart to the “begin{}” command that starts a graph, equation, or document. Without a matching “end{}”, you’ll receive error messages. Fortunately, when you type “begin{}”, this is automatically produced for you in Overleaf. But it still pays to be aware of its importance. If you end up deleting an “end{}” on accident, you’ll receive error messages. The tip of the iceberg This article has only shared the very basics of using LaTeX for economics. If you’re just looking to get started with learning LaTeX for practical application purposes, you should now be all set to start writing equations, making graphs, and even drafting your economics thesis or research paper. But as with most tools and applications, a vast sea of advanced knowledge awaits. If you want to learn more, head over to Overleaf’s “learn” section for more in-depth explanations and tutorials. There are also numerous online resources that will be able to help you find specific code if you’re stuck. Best of luck! Header image credit: Elchinator on Pixabay (free for use under the Pixabay content license).
Economists often run into situations where they need to type out mathematical formulae or draw up a graph, especially for research papers. But, anyone who’s attempted to do so knows that math symbols and complicated graphs are very difficult to produce using typical word processor options. So, what’s a humble econometrician to do?
The GRE is changing: ETS to streamline the test in September 2023
As such, many of the most educated people in the world have spent time studying the GRE and taking the test. There’s a plethora of resources to help students study the test’s structure and the materials likely to be on it while they prepare to get the highest score possible. Because of the GRE’s importance in graduate admissions, it’s beneficial for educators, university staff, and prospective graduate students to stay up-to-date with the exam. On that note, a new announcement from ETS – the company that owns and administers the GRE – came out on May 31, 2023 with details on new changes that are coming to the test. GRE being shortened to quicken scoring time and reduce test-taking fatigue ETS is making significant changes to reduce the length of the GRE. Specifically, their press release states that the GRE (which currently takes about 4 hours to complete) “will take less than 2 hours to complete — roughly half the time of the current test”. Notably, this will make it one of the shortest major tests available in university admissions, particularly at the graduate level. This is being done for a few reasons. First, ETS is aiming to “offer candidates a better experience that values their time and reduces anxiety and fatigue”. With stress and anxiety prevalent in our society, this is a welcome change. The second major reason for introducing a shorter test is to provide a faster turnaround on scoring. The new test will reduce scoring time from 10-15 days to 8-10 days. This change will additionally help students complete and submit their graduate applications more quickly. Adjustments to GRE structure to allow for these changes You might be wondering how this new goal will impact the test structure; after all, cutting the time to take the test in half must involve some sort of sacrifice. To that end, ETS has described the actual test structure changes on their website. It appears that they have aimed to “trim the fat” by reducing the size of certain portions and eliminating non-scored sections, without reducing the GRE’s usefulness as a gauge of a student’s academic preparedness. First, the number of questions in both the Quantitative and Verbal Reasoning sections are being reduced by 46 questions (specifics are not given, but this is 46 among both sections; for example, perhaps 23 questions from each section will be cut). Further, the Analyze an Argument task is being cut from the writing section, there will be only one essay instead of two, and the unscored section is being removed from the test entirely. Despite featuring fewer questions, the test still aims to provide graduate admissions offices with the results and tools they need to assess applicant’s cognitive skills. The test will continue to measure the same things it always has; the question types are not being changed, and neither are the test sections. They’re only being streamlined. A full, more detailed breakdown of the new test’s exact structure can be also found on the ETS website. Note for students planning to take the GRE The fact that the questions themselves are not changing – only the test structure – means that GRE prep being done now is still valid for the shorter test. So, if you’re a student preparing for the GRE, keep doing what you have been already and you’ll be in good shape. Registration for the new GRE is already open. To sign up, simply register for a test date after the change takes effect on September 22, 2023. More details can be found in their new FAQ document available for download. — We would like to thank ETS Global for their support in the production of this INOMICS article.
The Graduate Record Examinations, more commonly known as the GRE, is one of the most important standardized tests in the world for admission to post-Baccalaureate programs. It’s very often required by universities when considering admission into their Master’s or even PhD programs.
Which Concentration Should You Choose For Your Master's Degree in Economics?
A degree concentration in economics is an area of specialist study within the field of economics which you give particular attention to. Specializing in a particular sub-field can boost your already high employability as an economist even further, and pursuing a concentration is a first step in that direction. To help you decide whether a concentration is right for you – and which one – this article will provide a list of common economics degree concentrations and a brief overview of each. Hopefully, you find one that inspires you! Bear in mind that in certain economics departments, some concentrations may have specific entrance requirements above and beyond the entrance requirements for the economics Master's. Be sure to check the websites of the universities that you’re applying to for more details. Applied Economics Applied Economics, unlike other concentrations on this list, is not truly its own sub-field. Rather, applied economics concentrations teach students to develop and interpret econometric models that can be used to make decisions about specific subject areas – like national and foreign policy, energy strategy, environmental or health economics, etc. As opposed to a more theoretical approach, applied economics is all about the practical application of economic concepts in the real world. Certain institutions might have more of a focus on some of these areas, but not others, depending on their strengths. If you’re very interested in health economics (for example), be sure to search for institutions that offer coursework in health economics specifically. An “Applied Economics” concentration typically requires a solid knowledge of mathematics and the ability to reason about economics, and will appeal to those who want to make concretely useful policy suggestions which are based on economic knowledge. This concentration is a great idea for students who wish to become an industry economist. However, this might not be the best approach for students who are more interested in research, as the concentration typically focuses more on applied methods and real-world applications than theory, as its name suggests. Econometrics / Quantitative Methods An Econometrics or Quantitative Methods concentration focuses heavily on the mathematical and statistical side of economics. While every economist is going to need some familiarity with econometrics in order to do their jobs, this concentration dives deep. Students are highly encouraged to come with at least some background in statistics or calculus in order to succeed in this concentration. Students who complete an econometrics concentration may develop less specific “real world” subject matter expertise than others, so this may not be a great choice for students who have a clearly defined research interest (unless that interest is in econometrics, of course!). However, this concentration provides an extremely solid foundation for future research or PhD studies, since often the hardest part about conducting economic research is doing an appropriate quantitative analysis. Econometricians who do not specialize in a particular real-world application nevertheless lay the groundwork for other economists’ research. They are instrumental to the development of economics as a serious research discipline – just look to the accomplishments of Dr. Wooldridge. And, it’s relatively easier to pivot from a quantitative methods focus into any other economic subfield than to do the same pivot from other concentrations. That’s because econometric methods are the foundation of solid, repeatable, trustworthy economic research, and form the backbone of knowledge generation for economists. Economic History For students who want to combine the study of history with economics, good news! Economic history is an established subfield that is often offered as a concentration. Study in economic history involves using modern economic methods to examine historical situations and institutions. While this deepens our understanding of historical events, another main purpose of this field is to learn about how economic relationships work in the real world and test economic theory, so that contemporary economists can apply those findings to modern situations. This is important and useful work, because it’s usually difficult to test economic theories directly. Economists often cannot create experiments as easily as other scientists, due to moral and logistical concerns. Thus, using historical events as sorts of case studies allows economists to test economic theory in a real-world situation. There are plenty of academic opportunities for economic historians, though students interested in an industry economist job might be better served focusing on a concentration like applied economics instead. Health Economics Health Economics is a concentration that looks at how the principles of economics are applied to health in our society, including but certainly not limited to the functioning of the healthcare industry. Students in this concentration willI examine topics such as the tradeoffs between different types of healthcare systems, the effects of health problems or healthcare availability on economic outcomes, the behavioral modeling of healthcare use, and more. This concentration is particularly well-suited to students interested in healthcare, of course. But it’s also a useful concentration to consider for students interested in developmental economics and gender economics. Research in this field is alive and well, and there are plenty of opportunities for economists with this concentration to work in either academia or industry. International Economics For those economics students with an interest in international relations, foreign policy, trade and foreign direct investment, and/or globalization, the concentration of International Economics should be considered. Although every institution has its own unique take on teaching these subjects, this concentration commonly includes subjects such as international trade, foreign investment, and international political economy. Check the specific course offerings of the institution you’re considering to see which elements of international economics are emphasized. This concentration should be strongly considered by any students who wish to work in a role that involves government policy or policy analysis, international trade regulation, market integration across regions, or possibly even international development. Labor Economics Labor Economics is a branch that focuses on the functioning of the labor market. This is perhaps one of the oldest and most traditional fields of study for economists, though that doesn’t mean it’s outdated; study of systems of labor is alive and well. This sub-field is all about the study of how employers and employees react to changes in the work environment – including wages, prices, regulations, etc. This typically includes study on subjects such as minimum wages, labor regulations and their economic consequences, unionization, unemployment, worker migrations, and possibly even unpaid work like household labor (after all, one valid response to lowered wages is to stay home instead!). Labor economists can find themselves working for a wide variety of employers, including universities, think tanks, consulting firms, governments, and more. This is a very flexible sub-field that can even be combined with others – for example, the more specialized sub-field of international labor economics studies the movements of workers between countries. Finance / Financial Management Those who want to work in financial regulation, in public finance roles or in the finance industry, – for example in a bank or an investment group – should consider taking a concentration in finance or in financial management. The jobs which this can lead to are typically very well compensated, and a finance specialization offers much real-world applicability. Many economics departments include monetary economics coursework options within their financial economics concentrations. Some offer monetary economics concentrations that typically deviate from the core financial economics coursework somewhat, but still share many classes with the standard financial economics degree. So ,students who are interested in central banking ought to look for financial economics concentrations that include monetary economics coursework. It’s worth noting that many MBA programs offer finance concentrations as well. The advantage you’ll have coming from an economics background is that you’ll understand markets and financial decisions from an economic standpoint, and you’ll likely have a better quantitative foundation. Your analysis would arguably be more in-depth compared to the average MBA. Meanwhile, MBAs will give you a greater understanding about the business world and related functions, like corporate finance and marketing; these degrees are typically broader, while an economics-based finance degree is typically deeper. For economics students who love finances, financial markets, and how money works in the economy, look no further than a concentration in financial economics. There are many industry opportunities for economists with such training. But, there are also academic research opportunities for the more research-inclined – research on emerging areas of finance and new instruments being created are always in demand. Developmental Economics / International Development Students who wish to follow in the footsteps of economists like Dr. Esther Duflo, and use their knowledge to promote social good, may be interested in a concentration in Developmental Economics. Also sometimes referred to as international development, this concentration studies poverty and its causes, and how development interventions can improve quality of life for people around the world. It includes topics like policy planning and advocacy, the study of foreign aid interventions, and health, education, and finance-related programs that can assist the world’s poor. Other topics that might be included under this concentration include more general studies of how economic growth occurs and how policy contributes to growth. Economics students who want to work in an NGO or at a major aid organization like the IMF or the World Bank should strongly consider this type of concentration. Research opportunities in this field abound, as well; randomized controlled trials have become popularized by the 2019 Nobel Prize winners in economics, and are extensively used in this sub-field to research how economic theory works out in low-income and developing economy settings. Environmental Economics Environmental economics has become an increasingly popular topic as the impacts of global warming have been felt over the years. This sub-field studies the effects of climate and climate change on our societies and the economic systems that run in them. It also deals with sustainability concerns; expect to study topics like the appropriate interventions that can be made to account for externalities across global supply chains to reduce negative environmental impacts. Environmental economics is ripe for research, and industry positions are becoming more in demand. However, this field is much younger than the others on this list. This means that there are fewer established career paths in the field, which is exciting and challenging for aspiring environmental economists. Additionally, environmental economics programs are mostly more recent additions, so it might be necessary to double check if the university you’re interested in has one. If you’re looking to make your mark on a relatively newer field of economics, are interested in climate change, and care for sustainability, look no further than a concentration in environmental economics. Behavioral Economics This sub-field is the intersection between economics and psychology. It studies decision-making behavior and dares to ask the question: what if our assumptions about rational economic man are wrong? Behavioral economists often conduct experiments that are very similar to those used in psychology studies, though usually with distinctly economics themes (i.e., people’s lying or truth-telling behavior when anonymously sending or receiving money from others). Academic research in this field is quite vibrant, and insights from this sub-field routinely improve the economic models used by micro- and even macroeconomists. As such, expect a behavioral economics concentration to include a large amount of psychology. These courses also typically include deep study of foundational microeconomics concepts, like preference relations. While behavioral insights are increasingly changing macroeconomic models, this is a distinctly microeconomics sub-field. Academic research in this field is vibrant, but there are still many industry economist opportunities for behavioral economists. Non-academic career options for a behavioral economist include consulting, market research and advertising, and even jobs in behavioral finance. Urban Economics Do you love city planning, resource management, policy analysis, and economics? Urban economics is a sub-field that studies our societies from the perspective of cities, towns and smaller communities. Urban economists focus on studies of crime, public education, municipal resource management and distribution, regulation at the city level, housing, public transportation, agglomeration economies, and more. Because urban economists specialize in cities, towns and similar societal structures, they often touch many different areas of economics, though these are usually microeconomics focused. So, students who are interested in a broad swath of economics topics might find an interesting home in this sub-field. For example, an urban economist might need finance experience to help analyze municipal budgets or monetary policies. They might benefit from some environmental economics knowledge to study urban sustainability. Or, they might need experience with developmental economics or health economics to help analyze the availability of and improvements for public services. Research and academic positions (like an economics professor position) are of course available for urban economists. Non-academic positions most often include positions in local government or policy analysis roles. <11an>limit:5;categories:Courses|Programs;disciplines:Econometrics, Statistics and Quantitative Methods (JEL C)|Economic History (JEL N)| Agricultural and Environmental Economics (JEL Q)| Urban, Rural and Transportation Economics (JEL R)</11an> Conclusions These concentrations are some of those most commonly offered, though of course this is not an exhaustive list. Part of the appeal of higher education is the opportunity to learn from leading economists and researchers about their interests and field of expertise, and these intellectuals routinely help to shape and design their institutions’ course catalog. Use these descriptions as a guideline, but you should still do your own research into the right economics program for you. Happy studying! Header Image Credit: by Arek Socha from Pixabay (free for use under the Pixabay content license)
If you're planning to study economics at a Master's level, then you may have the opportunity to pursue a concentration, sometimes also referred to as a major. While rarer, some PhD programs may also offer concentrations, though many are already specialized degrees (or require you to have your own research agenda).
A quick guide to math symbols in economics
As such, economists need to have some quantitative skills. The more comfortable they are with math and econometrics, the better for their research and career opportunities. But, economists are not mathematicians. So, when students of economics take Bachelor’s or Master’s courses while studying for their economics degree, they might be presented with symbol-heavy lecture notes. Without understanding these symbols, students will probably struggle to read them, leaving their meaning unclear. This article aims to fill that gap. What are the most commonly used math symbols in economics, and what do they mean? Symbols and definitions Math symbols \(\pm\) - this symbol means “plus or minus”. It’s most often used when describing an interval of some sort, such as a confidence interval. \(\preceq\) - this symbol, and others like it, are used often in economics to describe preference relations. Preferences are the building blocks of the demand curve. This symbol is seldom used outside of microeconomics courses that specifically focus on preferences. \(\propto\) - this symbol means that the variable on the left side is proportional to the variable on the right side. \(\sim\) - this symbol means “similar to”. \(\approx\) - this symbol means “approximately”, which means something is almost, but not quite, equal to something else. \(\parallel\) - parallel. In economics, this is used most often in econometrics when describing how things relate to each other geometrically. \(\perp\) - this means perpendicular or “orthogonal”. Perpendicular means two things intersect at a 90 degree angle in geometry. In econometrics, orthogonal means that one variable has zero power to explain another variable, because they have a covariance equal to zero. \(\in\) - this means “in”, that a variable on the left side is contained within a variable, group, or concept on the right side. It can be reversed to mean the opposite. For example, often in economics, a theory will describe which values of x are possible. If x can only take on real number values, the description might start with x \(\in\) \(\mathbb{R}\). This means that x can only be a real number, because it is “in” the set of \(\mathbb{R}\). \(\otimes\) - this symbol is used to describe the Kronecker product, which is a matrix algebra concept that comes up occasionally in econometrics. \(\partial\) means “partial”, and is used to denote a partial derivative. IN is often used to denote the identity matrix with dimension N; it’s used frequently in statistics and econometrics. \(\cdot\) this symbol is placed above a variable to signify the derivative with respect to time of that variable. This symbol is commonly used in macroeconomics, but seldom outside of that context. Greek letters These are often used to denote variables, but some carry special meaning because of how often they’ve been used in certain contexts. \(\alpha\) - the letter alpha, often used with Cobb-Douglas functions to denote the exponents. Also used when discussing p-values in hypothesis testing; the “alpha” level sets the number that you compare the hypothesis test’s result to, in order to determine if the result is statistically significant (such as 0.05 for 5% significance, which is often the default). \(\beta\) - the letter beta, also used as an exponent in Cobb-Douglas functions. But, it’s most famously used when describing regression formulae. In that context, this letter usually has a subscript and is the shorthand way of saying “the regression coefficient on the variable it’s being multiplied with”. \(\epsilon\) - the letter epsilon. This letter is also commonly used in regression analysis, and usually denotes the error term of the regression. Many introductory econometrics classes, and any other course that showcases regression analysis, will make frequent use of this symbol. Many of the assumptions that allow economists to justify using a regression model involve statements made about the error term. \(\delta\) - the letter delta. This symbol is used often to denote the change in a variable, or depreciation in macroeconomics. It’s often confused with the symbol for partial derivatives, which looks very similar! \(\lambda\) - the letter lambda. It is used most often when solving constrained optimization problems using the Lagrangian method; in this case it stands for the shadow price of the budget constraint. This letter will be seen very often by microeconomics students. \(\mu\) - the letter mu. This letter is used often in statistics and usually denotes the mean, or average, of something. \(\rho\) - the letter rho. Often used to indicate the correlation coefficient between two variables. \(\Sigma\) - the capital letter sigma. Used frequently to denote the sum of things. \(\sigma\) - the lower case sigma. Used often in regression analysis and statistics to denote standard deviation; when squared, it is the variance of the subject in question. \(\Phi\) - the capital letter phi. Usually used to denote a probability distribution in statistics, most commonly the cumulative distribution function (cdf) of the normal distribution. \(\phi\) - the lower case phi. Often used to denote the probability distribution function (pdf) of the normal distribution in statistics. But, this letter is frequently used outside of this context too, unlike the capital version described above. \(\chi\) - the letter chi. Used in statistics to denote the chi-squared distribution, which is a probability distribution that is used a lot in regression analysis. \(\Omega\) - the capital letter omega. Used often in econometrics to be the symbol for a matrix. The following letters usually don’t carry special meaning on their own, but are often used in economics regardless. They are commonly used as placeholders for a variable when discussing a theory or equation. \(\gamma\) - the letter gamma. \(\eta\) - the letter eta. \(\kappa\) - the letter kappa. \(\nu\) - the letter nu. \(\tau\) - the letter tau. \(\upsilon\) - the letter upsilon. \(\Psi\) - the capital letter psi. \(\psi\) - the lower case letter psi. \(\omega\) - the lower case letter omega. Other statistics symbols The following symbols are often used in economic statistics contexts. Note that the Greek letters above contain many symbols used in these contexts as well. | means “given that” in a statistics sense, i.e., X | Y = 2 means “X given the fact that Y equals 2”. \(\mathbb{E}\) means “the expectation of”. Typically there is a defined variable, matrix, etc. placed in brackets next to it (for example, X); then, this means the expectation, or expected value, of X. This symbol is used extremely often in statistics. \(\mathbb{V}\) means “the variance of”. It’s used in the same way as the expectation symbol above, so when something is placed in brackets next to this, it means the variance of that variable, matrix, etc. Not to be confused with \(\sigma\)2, which stands for a specific number. \(\sigma\)2 is the symbol for the variance of a variable, matrix, etc. This stands for the actual number that is the variance of something. \(\overline{X}\): the bar above a variable denotes the average. This symbol is used very often in economic formulae, for example when describing the sum of least squares. Set theory symbols Set theory is used to describe how groups of things relate to one another. They’re similar to a Venn diagram, but in the language of math. \(\subset\) - this symbol shows that the variable or group on the left side of it is a subset of (included in) the group on the right side of it. With a slash through it, it means the opposite. \(\subseteq\) - the same as the above symbol, except that the subset it describes can also be equal to the variable or group on the right side. It does not have to be completely contained within the second group. \(\supset\) - this symbol denotes a superset, which is the opposite of a subset. It means that the group on the left side of this symbol contains the group on the right side. \(\cap\) - this is the symbol for the intersection of two sets, or the things they both contain that are the same. \(\cup\) - this is the symbol for the union of two sets, or everything that is contained in at least one of the sets. \(\emptyset\) - this is the “empty set” symbol, which is simply a set that contains nothing. Sets of numbers The following symbols are still technically sets as they describe groups of numbers, like “rational numbers” or “natural numbers”. These are used very often in economic formulae even when other set symbols might not appear. \(\mathbb{N}\) is the symbol for natural numbers. \(\mathbb{Z}\) is the symbol for the set including all integers. \(\mathbb{Q}\) is the symbol for the set of all rational numbers. \(\mathbb{R}\) is the symbol for the set of all real numbers. Other useful symbols \(\exists\) means “there exists at least one”. It’s commonly seen in proofs, which tend to be used in econometrics- or statistics-heavy courses. \(\exists!\) is a variation on the symbol above that means “there exists one and only one”. \(\forall\) means “for all”, usually in the context of stating some truth in an econometric context, i.e., stating that a particular equation is true for all i in some set of observed values N. \(\implies\) means “implies”; the function, formula, or variable on the left side implies some relationship written on the right side. \(\iff\) means “if and only if”. Reading economic formulae Now that the commonly used symbols have been introduced, we can use them to read and interpret an economic formula. The below example is the Goldsmith equation, used in macroeconomics to describe the growth of capital stock over time: \begin{equation*} \dot{K_t} = \mathit{I_t}-\delta\mathit{K_t} = \mathit{s}F(\mathit{K_t},\mathit{A_t},\mathit{L_t}) - \delta\mathit{K_t} \end{equation*} The first variable in this formula stands for the derivative of capital with respect to time, signified by the dot above the Kt. Recall from the list of math symbols above that the dot means the derivative of something with respect to time. This is equivalent to the investment (which here is defined as It) at a point in time minus the depreciation times the capital stock at a point in time. Recall that the symbol \(\delta\) from above is often used to denote depreciation. Then, on the far right-hand side, it is broken down further. It’s multiplied by s, which stands for the savings rate. This is multiplied by a function of capital, labor, and technological growth. The function’s exact form is not defined for us yet, so it’s simply written as F(Kt,At,Lt), which is common practice in math. \begin{equation*} \mathit{YED}_{A} = \frac{\Delta\mathit{q}_{A}}{\Delta\mathit{Y}} = \frac{\frac{\partial{q}_{A}}{\mathit{q}_{A}}}{\frac{\partial{Y}}{Y}} \end{equation*} This equation, meanwhile, describes the income elasticity of demand with respect to good A. It tells us exactly what this elasticity is and how to calculate it. The income elasticity of demand YEDA is equal to the change in the quantity demanded divided by the change in income. \begin{equation*} \mathbb{E}[X] = \mathbb{E}[\mathbb{E}[X|Y]] \end{equation*} This formula is a foundational theorem in econometrics, known as the Law of Iterated Expectations. Recall that the \(\mathbb{E}\) symbol means “expectation”. This formula states the following in mathematical shorthand. Our expectation of X in general is equal to our expectation of the value of X that is expected given some value that the variable Y could take on. In other words, we have some general idea of what X should be over the whole range of possible Y values (which have not been defined yet). This expectation is the same as what we generally expect X to be. In more precise statistical language, this formula means that the expected value of X is equal to the expectation of the conditional expectation of X given Y. It took many words to describe what a simple formula could convey with only a few symbols. Clearly, using these symbols can help economists communicate ideas much more effectively than by just using words! Header Image Credit: geralt on Pixabay (free for use under the Pixabay Content License)
Studying economic theories and methods requires an understanding of mathematical symbols. These symbols are used as shorthand to describe theoretical concepts in economics textbooks, theories, and research papers.