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.
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Stefanie Stantcheva named Clark Medalist 2025
Stantcheva is a Professor of Political Economy at Harvard University, and an NBER Research Fellow. Stantcheva was awarded the John Bates Clark Medal for her work on how tax policy influences innovation, uncovering that tax policies significantly impact innovators' behaviors. Her studies, such as "Taxation and Innovation in the 20th Century," conducted with co-authors, reveal high elasticities in innovation in response to changes in tax policies. Another key contribution is her work on optimal tax policy, as demonstrated in "Optimal Taxation and Human Capital Policies over the Life Cycle," where she proposes income-contingent education loans as a means to incentivize human capital investment. She has utilized a variety of tools to explore the interplay between government taxation and private behavior, including mechanism design to analyze optimal tax policy in dynamic scenarios, empirical studies on the impact of taxes and subsidies on innovators' decisions, and surveys and experiments to examine public perceptions of redistribution and tax policies. Moreover, Stantcheva has pioneered the use of surveys and experiments to gauge public understanding and preferences on various policy issues, including tax policy, redistribution, and climate change. Through her innovative methodologies, she has shed light on the factors influencing support for redistribution and views on tax policies. Stantcheva's comprehensive body of work has provided valuable insights for informing policy debates on tax system design, offering fresh perspectives on traditional public finance questions. Her groundbreaking research has not only advanced the understanding of optimal tax policies but has also deepened our knowledge of how individuals perceive key policy issues. The John Bates Clark Medal is awarded annually by the American Economic Association to an economist under the age of forty, employed at a US institution, who is judged to have made the most significant contribution to economic thought and knowledge. Stantcheva joins a prestigious group of previous winners, including many economists who have subsequently won the Nobel Prize such as Esther Duflo (Clark Medalist in 2010), David Card (1995), Paul Krugman (1991), Joseph Stiglitz (1979), and Milton Friedmann (1951). Professor Stantcheva was featured in a January 2025 article on INOMICS about influential voices and rising stars among female economists. Image Credit: American Economic Association
Stefanie Stantcheva, an accomplished economist, has been named the Clark Medalist for 2025 for her significant contributions to the field of public economics.
John N. Friedman appointed Inaugural Dean of Brown University's Watson School of International and Public Affairs
The school, set to launch on July 1, 2025, will focus on critical issues such as social mobility, education, policymaking, and other pressing matters. The new school will incorporate various research centers and initiatives, to approach global issues from diverse perspectives. Supported by a generous gift from Mary and Jerome Vascellaro, the Vascellaro Family Dean of International and Public Affairs position will ensure the school's prominence in preparing future leaders in global policymaking. Friedman, a distinguished economist and public affairs scholar, has been a faculty member at Brown since 2015. He earned his Bachelor's degree and PhD from Harvard, and has worked as special advisor to the President on the National Economic Council from 2013 to 2014 and was an assistant professor of public policy at Harvard University’s Kennedy School from 2009 to 2014. He is also a co-editor of the American Economic Review. With his background in economic research and public service, Friedman's leadership will be expected to enhance the school's profile and impact at a time when nuanced scholarship is crucial. The establishment of the Watson School follows years of planning to enhance Brown's expertise in international affairs, providing opportunities for undergraduate and graduate students to engage in policy-related activities. Friedman's appointment aims to foster a collaborative community of scholars and practitioners, advancing the mission to address the complex challenges of today's world. Image Credit: Azurae Cruz / Brown University
John N. Friedman has been named as the inaugural dean of Brown University's Thomas J. Watson Jr. School of International and Public Affairs.
Maxi Günnewig wins the Philipp Sandner Award in Digital Finance
Günnewig was awarded 10,000 euros for his work on smart banks, which explores the integration of banks with blockchain-based smart contracts in future financial systems. His research aims to prevent bank runs and costly government interventions by leveraging technology to eliminate financial fragility and prevent future crises. The award was presented at the Crypto Assets Conference on 26 March 2025. The international award honors young researchers focusing on blockchain, digital assets, machine learning, artificial intelligence, payment systems, and financial platforms. Günnewig expressed his gratitude and pride in receiving the award, which is named after Professor Philipp Sandner, a former professor at the Frankfurt School and founding director of the Frankfurt School Blockchain Centre (FSBC) who passed away in January 2024. The selection process includes a review by a scientific committee and final selection by a jury comprising professionals from business, science, and public administration. Image Credit: Frankfurt School of Finance and Management
Maxi Günnewig, a postdoctoral researcher at the University of Bonn, has won the Philipp Sandner Award in Digital Finance Research from the Frankfurt School of Finance & Management and WM Gruppe.
Frankfurt School of Finance & Management
The Logic Underpinning the Basic Solow-Swan Model The logic underlying the model is straightforward. In the Solow-Swan economy, individuals have savings rates that yield an economy-wide average level of saving, s. Money not saved in each period is consumed. Meanwhile, savings are used to fuel investment I that grows the capital stock. There is a natural depreciation of the capital stock, denoted by ẟ, over time. For the purposes of the model, there is no foreign economy to trade with, so the Solow-Swan economy is a closed economy (or “in autarky”). With the model, it’s possible to assume the population and level of technology are growing, and possible to assume they are not. These facts come together to determine economic growth, Y. The exact specifics of this relationship depend on the functional form of the specific production function chosen. Generally, the more savings an economy has, the more capital stock and therefore economic output it can support. However, with more capital stock comes more depreciation. In per capita terms, there is a limit to how much growth the economy can sustain given its savings rate. The model predicts that the economy will trend towards a “steady-state” where the savings rate and the rate of depreciation offset one another such that the per capita amount of capital remains unchanged. In other words, while the overall economy can be growing in size, the population, depreciation, and (per capita) capital stock grow alongside it such that all individuals have a consistent amount of income in perpetuity. Figure 1 shows the basic Solow-Swan model graphically. Figure 1: the Solow-Swan model This graph depicts the general train of logic discussed above. The economy produces goods (here, capital k) according to a production function. This production grants individuals their income. Some part of people’s income is saved, and overall savings are depicted by the green curve sf(k). Depreciation and the “law of motion” (defined below) are hinted at by the “minimum I(t)” line. This line shows how much investment the economy needs in order to prevent the total capital stock from diminishing due to depreciation. Since investment is fueled by savings, the economy must choose an amount of savings that is above or equal to the minimum investment line. Otherwise, the economy shrinks in the next period as depreciation outpaces production.<o:p></o:p> The next sections dive into the mathematical formulation of the Solow-Swan model, which is critically important – and frequently tested in economics courses. It will describe in math symbols what the above discussed in words, then dive deeper into the model’s behavior. The Math Behind Solow-Swan To start building the model mathematically, some core assumptions must be made: Time is discrete (e.g., = 1, 2,..., but t ≠ 1.5) and continues infinitely. The economy is composed of households that are identical. Importantly, this means all households have the same savings rate s. We assume this is a constant.<o:p></o:p> Assume that every person in the economy is a worker and makes up a part of the total labor force L.<o:p></o:p> The economy is also composed of individual firms that compete in competitive markets and take all prices as given. Because of this, prices can be normalized to 1.<o:p></o:p> The economy produces only one good, which is the capital used in production. This is sufficient for the economy to function normally.<o:p></o:p> There are constant returns to scale.<o:p></o:p> There are positive and diminishing marginal returns to the factors of production.<o:p></o:p> There is no growth in either the population or the level of technology.<o:p></o:p> Many of these assumptions can be removed, and advanced courses will explore the implications of removing them. First we need to represent competitive, optimized firms, which are actors in the economy (together with individuals). In both the Solow-Swan model, as in real life, firms can solve an optimization problem.. depending on the functional form of the production function. This is: max.L≥0,K≥0 Y = (1)F(K,L,A) - wL - rK<o:p></o:p> where Y is output, F(K,L,A) is the production function F (for example, this could be Cobb-Douglas) with factors K, L, and A (representing capital, labor, and technology), w is the wage rate paid to labor L, and r is the interest rate of capital K (this can also be thought of as a rental rate, as if firms “rent” the capital from households). The term “(1)” represents the price of the final output, which we assume firms can’t choose (since they’re perfectly competitive) and which is therefore normalized to 1.<o:p></o:p> Note that the term “maxL≥0,K≥0”shows this is a maximization problem where choices of both labor and capital cannot be negative. Firms solve this optimization problem by taking the partial derivative with respect to L and K, the two factors they must utilize. This shows that firms employ labor and capital up to the point where the marginal productivity of each is equal to their cost: 𝝏FL(K,L,A) = w 𝝏FK(K,L,A) = r As a side note, keep in mind that since a functional form for the production function was not defined, we cannot find the exact form of the partial derivatives above. Often, economics classes won’t choose a production function (because it isn’t necessary when discussing theory), instead referring to some unknown production function F(K,L,A). If we decided to use a basic Cobb-Douglas function, these first-order conditions would become: 𝝏FL(K,L,A) = 𝛼AL𝛼-1K𝛽= w 𝝏FK(K,L,A) = 𝛽AL𝛼K𝛽-1= r Since firms employ labor and capital up to the point where the cost of utilizing them is equal to their marginal productivity, and since prices are set by the competitive market and not by firms, this means that firms are making zero profits in equilibrium – in other words, this economy exhibits perfect competition. Therefore, when studying the Solow-Swan model, we can ignore the question of who owns which firms. Mathematically Reaching the Steady-State Equilibrium Above, we defined how individual firms behave in this economy. Now, it’s time to find out how firms’ output causes the economy to grow over time in the Solow-Swan model. This requires defining several equations that state many of the same ideas we reached logically before. In the following, (t) denotes time. First, Y(t) = C(t) + I(t), since income (Y) is either spent (C, which stands for consumption) or saved (which is equivalent to investment I by the savings-investment identity). Since the economy is closed, there is no foreign trade (net exports NX = 0), and we assume there is no government spending (G = 0). Combining this fact with the equation we just defined yields: I(t) = S(t) = Y(t) - C(t) Where S(t) is savings across the economy. We assumed that individual households save a constant fraction s of their income Y, which means that the above can be transformed again and then rearranged to yield: S(t) = sY(t) = Y(t) - C(t); rearranging, C(t) = Y(t) - sY(t) or, equivalently C(t) = (1 - s)Y(t) This shows that consumption is a function of income minus savings. Next, we examine the growth of the capital stock. Logically, the amount of capital K in the next period is equal to the amount of capital the economy has now, minus capital that depreciates during this time period, plus new capital made in this period (as a result of investment I(t)). Putting this logic into math terms, we get: K(t+1) = K(t) - ẟK(t) + I(t), which simplifies to K(t+1) = (1 - ẟ)K(t) + I(t) where ẟ represents depreciation (capital that is worn out and ruined from being used). We can replace I(t) in this equation to show how the Solow model achieves its steady-state equilibrium. Our next step is to recognize that I(t) = S(t) = sY(t) as above, and replace it in this equation: K(t+1) = (1 - ẟ)K(t) + I(t) = (1 - ẟ)K(t) + sY(t) Rearranging, and replacing Y(t) with the (unknown) production function: K(t+1) = sF(K,L,A) + (1 - ẟ)K(t) This last equation describes the Solow-Swan model’s equilibrium state! It shows that the evolution of capital stock in the economy is a function of the savings rate plus the amount of capital left over from each previous period. This is known as the “law of motion” in the Solow-Swan model. Interpreting The Steady-State Equilibrium This isn’t just an ordinary static equilibrium. Since the model defines growth over time, the law of motion describes the economy’s equilibrium growth trajectory over the entire course of the future. As we stated before, in the long run the economy grows such that each successive generation of people has the same income in per capita terms as the previous generations, even though the economy is growing in absolute terms. To examine the model further, we transform these equations into their per capita equivalents. To do this, we divide each variable by the population, which in our case is labor L (because we assumed that all households are composed of workers). Economists denote these per capita variables with a lowercase letter. For example, we divide the evolution of capital stock K(t+1) by L to yield (K(t+1))/L, and redefine this as simply k(t+1). Economists will also divide the general form of the production function by labor to yield F(K/L,L/L,A/L) = f(k,1,A/L). When we apply this method to the entire law of motion equation, it yields: k(t+1) = sf(k,1,A/L) + (1 - ẟ)k(t) This may seem silly or redundant since we’ve just changed some letters to lowercase and divided everything by the same factor. But, this step helps economists to state clearly in mathematical terms what’s happening – we’ve shifted our analysis from looking at economy-wide statistics to simply looking at each person’s slice of the total economic pie. This helps to reveal an important truth about the model, because in the Solow-Swan model, the growth of the total economic pie can be very different than growth in individuals’ well-being. Now that the model is in per capita terms, we can define the steady-state level of capital stock per capita that solves the law of motion equation as k*. This is the point where each subsequent year’s per capita capital stock is the same as the year before it – the amount of capital stock (and therefore income) dedicated to each individual is unchanging. Or, in mathematical terms, k* solves the following equation: k* = sf(k*,1,A/L) + (1 - ẟ)k* At the steady state, while the overall amounts of capital, labor and output can be changing, the per capita amounts are not! Inserting a production function and solving for k* yields: k* = s / ẟ This relation shows that the steady-state level of capital is equal to the ratio of savings to depreciation. In other words, it shows that the relative size of the savings rate compared to the depreciation rate determines how much k the economy can support in perpetuity. If we allow for population and technology growth in the model, the law of motion becomes: k(t+1) = sf(k,1,A/L) - (n + g + ẟ)k(t) And the solution in terms of k* becomes: k* = s / (n + g + ẟ) where n is the population growth rate and g is the technology growth rate. In essence, this equation has the same meaning. It additionally accounts for the fact that output must grow along with the population, and must keep pace with technology growth (so that individuals aren’t using outdated equipment). Graphing and Convergence Figure 2: the Solow-Swan Model in per capita terms Figure 2 shows the Solow-Swan model graphically in per capita terms. The following discussion is in per capita terms as well. In the graph, sf(k) represents the savings rate, f(k) shows overall productivity (and therefore income), and ẟk shows depreciation of the capital stock. The Solow-Swan model’s law of motion means that the economy trends toward k* over time. The graph helps visualize this truth. Notice how k* appears at the point where the curves for capital production per capita (determined by savings times output, sf(k)), and depreciation ẟk meet. Given s, if the economy were producing at a point k1 > k*, the graph clearly shows that depreciation would outpace the production of new capital – because where k1 > k*, the line ẟk is greater than the curve sf(k). This means that each subsequent period would have a slight decrease in the amount of capital per capita, until depreciation and the production of new capital balance each other out. This happens precisely where the two lines intersect – at k*. If the economy produces capital at a point k2 < k*, meanwhile, the economy’s production of capital stock per capita outpaces depreciation – shown where the line ẟk is below the curve sf(k). In this range, the amount of depreciation increases as the amount of capital per capita rises, until the two forces balance each other at k*. This convergence occurs (mathematically speaking) due to the fact that depreciation increases linearly while production increases with diminishing returns. But, k* in Figure 2 looks a little small. How can we tell when the economy has reached the best possible steady-state k*? Savings and the Golden Rule That question brings us to one last aspect of the Solow-Swan model: how the savings rate s affects the economy, and how to find the best savings rate. Take a look at how s affects the law of motion: K(t+1) = (1 - ẟ)K(t) + sY(t) Clearly, the stock of capital – and therefore economic growth – increases as s increases. Increasing s would increase sY(t) and thus K(t+1) (and the per capita level of savings sf(k)), leading to a higher k*. But, if individuals save too much, they won’t have enough money left over for consumption! Recall the consumption equation we defined earlier: C(t) = Y(t) - sY(t) or, equivalently, C(t) = (1 - s)Y(t) Logically, consumption C(t) decreases as s increases. Therefore, economic agents face the important choice of how much to save so that they can consume enough while still contributing to economic growth for future generations. Graphically, it’s easy to see that if s increases, the per capita level of capital k* that exists in the steady state will increase, because the function sf(k) will increase. This is shown in Figure 3, where savings increased from s0 to s1: Figure 3: Solow-Swan model after an increase in s Still, if s increases too much, current generations will suffer as their consumption plummets. The Golden Rule, then, is the rate of savings that maximizes the steady-state level of consumption per capita k* for all generations. It’s called the “Golden Rule” because each generation does to others what they would have wanted done to themselves – they save enough to make sure everyone else can consume the optimal amount in perpetuity. Mathematically, this can be found by maximizing the per capita consumption equation with respect to k: maxkc(t) = y(t) - sy(t) Because we want to solve in terms of k, we manipulate this into an equivalent expression. We know that sy(t) = ẟk in the steady state, and per capita output y(t) is equal to f(k), so we can transform the equation as follows: maxk c(t) = f(k) - ẟk Taking the derivative with respect to k, setting equal to 0, and solving then yields: f’(k) = ẟ The amount of k that solves this first order condition is referred to as k*gold. Keep in mind that the specific form of the derivative f’(k) depends on the chosen production function. Astute readers will notice that f’(k) is the marginal productivity of capital, so this finding is equivalent to stating that MPK = ẟ. It’s easy to “eyeball” this point on the graph. Consumption is equal to the vertical difference between f(k) and ẟk in the graph (because savings is the area under the curve sf(k), and consumption is what’s left of income f(k) after subtracting savings). k*gold is the k* that maximizes consumption – or in other words, it’s where this vertical difference is largest. The Solow-Swan Model and South Korea’s “Miracle” Growth The Solow-Swan model can be used to describe growth in many cases in history. For example, it’s well-known that South Korea was a poor nation before the 1960s, and later experienced rapid growth and development that made it one of the world’s most developed nations. This is often referred to as a “growth miracle”. One of the main reasons for the transformation is that South Korea’s government began to focus on “export-driven industrialization” in the early 1960s. At this time, the government encouraged investment into export industries to increase trade with foreign nations, and also invested heavily into education to develop a skilled workforce. This raised per capita income, which rapidly led to further innovation and industrialization that fueled growth. Today, South Korea has one of the largest GDPs in the world, in both nominal and PPP-adjusted terms. We can explain this growth using the Solow-Swan model. Before the 1960s when per capita income was near subsistence level, there was very little saving – perhaps putting the South Korean economy on a savings and growth path just like s0f(k) in Figure 3. Once government policies spurred industrialization and invested in education, both per capita productivity and savings grew. This would have increased South Korea’s k* as savings increased (just like the shift from s0f(k) to s1f(k) in Figure 3). But there’s more – it would also have caused f(k) to increase, which would cause the optimal balanced growth path k* that the economy could sustain to be greater than before. Whew! The Solow-Swan model contains an incredible depth of nuance – and advanced courses will dive much deeper. Still, if you’ve made it this far, congratulate yourself with a well-earned break! Good to Know Versions of the Solow-Swan model were independently developed by Robert Solow and Trevor Swan, building off of an earlier Harrod-Domar model. The Solow-Swan model was credited to both economists and came to fruition in 1956. One of its benefits is that it connects to microeconomics via the behavior of individual households and the production function, which itself can be thought of as a summation of the productivity of individual firms.
The Solow-Swan model is an economic growth model that uses a production function (often Cobb-Douglas) to determine the long-term growth path of an economy. It further shows how the economy trends towards a steady state level of capital per capita, depending on the savings rate, and allows us to determine what the best possible savings rate would be. This cornerstone growth model is often taught in introductory macroeconomics courses, re-taught in-depth for advanced courses, and used as the basis of many other models of the economy, so it helps to get familiar with it.
Cagé and Yanagizawa-Drott awarded 2025 Yrjö Jahnsson Award
Cagé's and Yanagizawa-Drott's work, spanning France, the USA, and Africa, emphasizes the powerful influence of media, including social media, on shaping political beliefs and actions. They warn against the risks associated with controlled media and stress the importance of media independence for a robust democracy. The prestigious award, established in 1993 by the Finnish Yrjö Jahnsson Foundation, recognizes European economists under 45 who have made significant contributions to theoretical and applied research essential to the economics field in Europe. The Award Committee, chaired by Kalina Manova and including members Esther Duflo, Nicola Fuchs-Schündeln, Ari Hyytinen, and Marco Ottaviani, selected Cagé and Yanagizawa-Drott for their groundbreaking work in the political and media spheres. Their research highlights the critical need for a free and independent media landscape to uphold democratic principles and safeguard against the potentially detrimental effects of media manipulation. Image Credits: Science Po / David Yanagizawa-Drott
Julia Cagé (Professor of Economics, Science Po) and David Yanagizawa-Drott (Professor of Economics, University of Zurich) have been honored with the 2025 Yrjö Jahnsson Award for their impactful research on political economy and media.
Dr. Dirk Schumacher takes over role of Chief Economist at KfW Group (Germany)
KfW is one of the world’s leading promotional banks. On behalf of the Federal Republic of Germany and its constituent states, it finances projects and investments that support economic, social and environmental living conditions across the globe. KfW CEO Stefan Wintels praised Schumacher's appointment, noting his experience in the context of the German and wider European economy. “Having worked for a wide range of different banks as well as the European Central Bank, Dr Schumacher will bring many years of broad experience of economic analysis” Schumacher's career includes roles at Commerzbank, Goldman Sachs, and most recently, Natixis, where he led the European Macro Research Team. He will take over the Economics Department at KfW Research, succeeding Dr Fritzi Köhler-Geib, who left the bank in November 2024. Photo credit: KfW-Bildarchiv / Alexander Kempf
Dr Dirk Schumacher today takes over the role of Chief Economist at KfW Group.
Princeton Economics Appoints Four New Faculty Members: Jäger, Carry, Dano and Sandomirskiy
Professor Simon Jäger joins from MIT to focus on labor markets and inequality. Formerly head of the IZA Institute of Labor Economics in Germany, and advisor to the German government, his recent research delves into workers' beliefs about wages impacting job search and negotiation. Jäger expressed his enthusiasm for joining Princeton, stating, "The Department and the Section have an extraordinary legacy in advancing labor economics and industrial relations research, and it is both humbling and inspiring to join this remarkable community and shape it going forward." Pauline Carry joins from the University of Chicago, having previously earned her PhD at Institut Polytechnique de Paris. Now she has been appointed as an Assistant Professor at Princeton Economics. She focuses on labor economics and macroeconomics, most recently examining the effects of working time regulations on employment allocation. Expressing her excitement about her new role, Carry articulated, "Princeton is such a special place for economics; I am very excited to be a part of this group. I look forward to learning from colleagues and students, and contributing to the vibrant scholarly community here." Kevin Dano was most recently a postdoctoral research associate at Princeton, and has now been appointed as an Assistant Professor. He specializes in econometrics, panel data, and networks, introducing new methods in dynamic discrete choice models and network settings. Reflecting on his new position, Dano expressed his gratitude, saying, "I am thrilled to officially join Princeton Economics. Looking ahead, I'm excited to continue growing as a scholar in this fantastic community." Lastly, Fedor Sandomirskiy has been promoted to Assistant Professor, having previously worked in the department as a research scholar and lecturer. He is a microeconomic theorist who studies information economics and economic design, blending microeconomic insights with algorithmic game theory. His research explores information acquisition and mechanisms to improve outcomes in strategic interactions. Clearly looking forward to his new role at Princeton, Sandomirskiy was quoted as saying: "Princeton has long existed in my mind as something between legend and textbook—a place that shaped modern research in so many fields and gave birth to game theory. I never dreamed I could become part of it." Each new faculty member brings a unique expertise and research focus to Princeton's Economics Department, taking up their duties with the Spring Term underway from March 2025. Image Credits: Princeton University
Princeton University's Economics Department has been busy hiring in recent months. Now four prominent new faculty members join for the Spring 2025 semester: Simon Jäger, Pauline Carry, Kevin Dano and Fedor Sandomirskiy.
(Oxford) Diploma : Macroeconomics/Behavioural Economics
Key details: 48 hours of lectures on macroeconomics Several workbooks on macroeconomics Hundreds of discussions on macroeconomics Regular updates via Educational Announcements Behavioural Economics The Instructor has macroeconomics textbooks on Amazon Five reasons to TAKE THIS COURSE right now: 1. You cannot lose - 30 day refund if you really really don't like the course BUT if you DO like it: a. updated lectures b. case studies based on very very current news items c. detailed responses to messages d. I constantly attempt to have a 'class atmosphere' e.g. throwing questions open to all e. regular Announcements that are not disguised sales ploys f. regular FREE access to numerous other courses offered by me 2. I am a lecturer/teacher in Economics by trade i.e. I teach full-time for a living. I have been Principal, Director of Studies and Head of Economics. (Oxford, UK) I have lectured in University, adult evening classes and also run over 50 seminars in the UK 3. I am/have been an Examiner for FIVE different examination boards 4. I have 100+ books on Amazon - many of them about business/economics 5. The success of my students has been featured on the BBC, Daily Telegraph, The Times and I have been featured on TV in 2 countries. MOTIVATION I make courses on Udemy primarily because I enjoy the process of causing learning. Many of my courses are to improve lives. One of the Economics courses is to raise money for charity. (100% of revenue goes to the charity) Fundamentally this course here, is about helping you. About the course Using ONLY aggregate demand and aggregate supply, this course explains the basics of macro economic policy. The lectures are fully supported by notes - often repeated to ensure learning - that may be copied and used for other studies. If you wish to understand more as to why governments cut/raise taxes, cut/raise government expenditure, how this impacts on inflation, unemployment and growth - then this is the course for you. Topics: Gross Domestic Product (GDP): Measures the total value of all goods and services produced over a specific time period within a nation's borders. Unemployment Rate: Indicates the percentage of the labour force that is jobless and actively seeking employment. Inflation: Describes the rate at which the general level of prices for goods and services is rising, and subsequently, purchasing power is falling. Monetary Policy: Involves the management of a nation's money supply and interest rates by its central bank to control inflation and stabilize currency. Fiscal Policy: Refers to the use of government spending and taxation to influence the economy. Interest Rates: The cost of borrowing money or the return for investing, which are critical in monetary policy and personal finance. Balance of Payments: A record of all economic transactions between residents of a country and the rest of the world in a given period. Exchange Rates: The value of one currency for the purpose of conversion to another, impacting international trade and investments. Economic Growth: The increase in the inflation-adjusted market value of the goods and services produced by an economy over time. Business Cycles: The upward and downward movements of GDP and other economic indicators over time, encompassing periods of expansion and contraction. Supply and Demand: The fundamental economic model of price determination in a market, showing how prices vary based on a balance between product availability and consumer desire. Aggregate Demand and Supply: The total demand for goods and services and total supply of goods and services within an economy at a given overall price level and in a given time period. Budget Deficits and Surplus: A budget deficit occurs when expenses exceed revenue, and a surplus occurs when revenue is higher than expenses. Public Debt: The total amount of money owed by the government to creditors, which can influence a country's fiscal policy and economic health. Trade Policies: Regulations and tariffs that a country applies to its imports and exports, affecting its balance of trade. Labour Markets: The supply and demand for labour, in which employees provide the supply and employers the demand. Capital Markets: Where savings and investments are channelled between suppliers who have capital and those who are in need of capital. Economic Indicators: Statistics that provide information about the overall health of the economy, such as GDP, unemployment rate, and inflation rate. Recessions: A period of temporary economic decline during which trade and industrial activity are reduced, generally identified by a fall in GDP in two successive quarters. Stagflation: A situation in which the inflation rate is high, the economic growth rate slows, and unemployment remains steadily high, presenting a dilemma for economic policy. The course is pitched at a beginner level and thus is IDEAL for anyone just starting an economics course or simply wants to understand macro-economic policy. The initial lectures are short - egg-timer size! - and, if you look at other egg-timer courses, you will know that if you have any questions then they will be answered very quickly. PLUS the course also includes Behavioural Economics. Behavioral economics melds psychology with economics, exploring why people sometimes make irrational decisions, and how their behaviour does not always follow the predictions of economic models. It challenges traditional assumptions of rational choice, investigating heuristics, biases, and framing effects that influence consumption, investment, and other economic activities. Who this course is for: Everyone interested in the world economies Anyone commencing a course in macro-economics 1st year Degree students Anyone who has an interest in understanding the basics of economic policy Non-specialists in Economics Beginners in Economics
Mastering Markets and Minds: An Interactive Journey Through Macroeconomics and Behavioural Economics. By Chris Sivewright / Oxford School of Learning.
Morris Davis appointed to Council of Economic Advisors as Chief Housing Economist
In his new role, Davis will provide guidance to president Trump on policies aimed at reducing housing costs and expanding housing availability. His expertise will be crucial in advising on strategies to increase housing supply and alleviate regulations that drive up housing costs. Davis expressed his honor in serving the country and his dedication to making housing affordable for all, adding: "My goal is to help the president make housing affordable for everyone." New Chairman Steve Miran told Rutgers Business School, where Davis has been employed since 2014, that “Morris will play a pivotal role as we work to build more homes and make housing affordable again for American families, helping to usher in President Trump’s economic Golden Age.” The Dean of Rutgers Business School, Lei Lei, in turn praised Davis and his role so far in influencing public policy: "Morris is a proven leader with innovative ideas that can positively affect real estate productivity and public policy today." Davis received his PhD in Economics from the University of Pennsylvania. He is noted for a much cited 2012 paper, which argued against subsidies for major US mortgage entities Fannie Mae and Freddie Mac and for a cut in mortgage interest deductions. The latter is an approach that has also been proposed by president Trump. Davis further advocates, for example in a 2019 paper, for consideration of the current market when making policy changes that affect the cost of borrowing. The Council of Economic Advisers, consisting of approximately 45 members, has been funded by Congress since 1946 to offer research supporting the president in formulating economic policies. Davis's appointment will last until December 2025. Image Credit: Rutgers Business School
Morris Davis, a professor at Rutgers Business School specializing in real estate, has been appointed to the White House Council of Economic Advisers as the chief housing economist. The news follows the high profile appointment last week of Stephen Miran as head of the CEA.
Roberto Serrano awarded the 2024 King of Spain Economics Prize
The award, established in 1986 by the José Celma Prieto Foundation, aims to honor individuals who contribute to economic knowledge and societal well-being. The ceremony, held at the Bank of Spain, was attended by economic and academic figures. Serrano, a Spanish-born economist currently teaching at Brown University in the U.S., received recognition for his academic and research achievements. King Felipe highlighted Serrano as a role model and emphasized the importance of rigorous research and knowledge dissemination in society. Serrano, who has published extensively on game theory and economics, has overcome challenges due to a visual impairment, becoming a respected figure in the academic world. His dedication to research and teaching has earned him accolades such as being named among "The Best 300 Professors" by Princeton Review. Image Credit: Casa de S.M. el Rey
King Felipe VI of Spain has presented the "King of Spain Economics Award 2024" to economist Roberto Serrano for his outstanding scientific and professional work in the field of economics.
Winta Beyene receives Marie Skłodowska-Curie Fellowship for work on Household Finance and Financial Resilience
Her project, titled The Credit Safety Net and the Costs of Credit Supply Shocks, delves into how households navigate unexpected income shocks, particularly those related to health, under varying levels of credit availability. Beyene's research sheds light on the impact of credit constraints on financial strategies like selling assets or adjusting homeownership and employment, aiming to propose policy solutions that can bolster social safety nets and shield financially vulnerable households. The European Commission, through its Horizon Europe program, sponsors the Marie Skłodowska-Curie Fellowship, providing funding, international networking opportunities, and professional growth for exceptional postdoctoral researchers. With this recognition, Beyene is set to advance her investigations in financial stability and the well-being of households. The Leibniz Institute for Financial Research SAFE ("Sustainable Architecture for Finance in Europe"), based in Frankfurt am Main, Germany, is dedicated to interdisciplinary research on financial markets and their participants in Europe as well as to research-based, independent policy advice. The institute focuses on the cooperation of researchers from the fields of finance, economics, law, and political science. Dr. Winta Beyene received her PhD from the SFI Swiss Finance Institute and the University of Zürich, having previously studied at the University of St. Gallen. Image Credit: SAFE Frankfurt
Winta Beyene, a Postdoctoral Researcher specializing in banking and finance at SAFE (Germany), has been granted a prestigious Marie Skłodowska-Curie Fellowship for her work on household finance and financial resilience.
Bank of Canada announces Reka Juhasz as recipient of the 2025 Governor’s Award
The Governor's Award provides financial support to academics at Canadian universities excelling in areas crucial to the Bank of Canada's functions, offering an annual research grant of $30,000 for up to two year. Juhasz's research primarily focuses on the impact of industrial policy on economic growth and how governments can enhance the effectiveness of such policies. This work is essential as global trade dynamics evolve and affect costs and inflation, with implications for monetary policy, a core function of the Bank. Dr. Juhasz is also the co-founder of The Industrial Policy Group and was the recipient of the Alexander Gerschenkron Prize in 2016. Additionally, Dr. Juhasz's research covers areas like international trade, economic history, growth, and development. She has been recognized for her contributions in top economic journals and as a mentor in her academic department. Image Credit: University of British Columbia
Dr. Reka Juhasz, an Assistant Professor of Economics at the Vancouver School of Economics, University of British Columbia, has been named the recipient of the Bank of Canada's 2025 Governor's Award.
Economist Antonio Aranibar Appointed as UN Resident Coordinator in Equatorial Guinea
With over 20 years of experience in sustainable development, governance, and peacebuilding, Aranibar previously served in various roles. Most recently he was the UN Special Advisor in Venezuela (until 2024) and between 2016 and 2018 he was Head of Office for the UN Verification Mission in Colombia, where he was involved in the peace process between the Colombian government and the Revolutionary Armed Forces of Colombia (FARC). He has also worked as a researcher and director for projects focusing on conflict prevention, dialogue promotion, and institutional reform in Latin America and the Caribbean. Aranibar holds a master's degree in econometrics from the Autonomous University of Madrid and a bachelor's degree in economic development from the University of Paris IX-Dauphine. The Resident Coordinator is the highest-ranking UN representative at the country level, reporting directly to the UN Secretary General. Resident Coordinators lead UN Country Teams and coordinate UN support to countries in implementing the 2030 Agenda. They represent the UN to engage with governments, civil society, and other partners to address the country's needs and priorities to achieve the Sustainable Development Goals, while also promoting UN values, coordinating operational activities, and managing the local UN team. Image Credit: United Nations Sustainable Development Group
Antonio Aranibar of Bolivia has been appointed by United Nations Secretary-General António Guterres as the United Nations Resident Coordinator in Equatorial Guinea, effective March 16, 2025, with the host Government’s approval. Aranibar takes over from Anna M. Pont, who, from 2022 to 2024, led the UN's program to implement the UN's Sustainable Development Goals in Equatorial Guinea.
Stephen Miran appointed as Chairman of the Council of Economic Advisers under President Trump
Dr. Miran worked as a senior advisor in the Treasury Department in the first Trump Administration. He holds a PhD in economics from Harvard, and until his appointment at the CEA was a fellow at the Manhattan Institute in New York, and a senior strategist at Hudson Bay Capital Management, where he recently (November 2024) published a "User's Guide to Restructuring the Global Trading System". In this, he investigates and recommends how tariffs and other tools could be used by the US government. Miran highlights the effectiveness of tariffs on China from 2018-2019, emphasizing the negligible macroeconomic consequences and increased U.S. Treasury revenue. He suggests managing the strong dollar's impact on American manufacturing through policies aiming to improve burden sharing with trading partners. Miran also proposes negotiation tactics, including gradual tariff increases, forward guidance on policy, and tax cuts to counteract price competition for U.S. exporters. He further warns against retaliatory tariffs, stating the U.S. can withstand escalation better due to its strong consumer demand and robust markets. Trump's emphasis on American production and low taxes has been reiterated and confirmed through statements and policies in the first quarter of 2025. Miran's confirmation and imminent appointment is timely as these policies, including tariffs, come into effect in April 2025 on multiple imports from Mexico, Canada, China and the European Union. Image Credit: Manhattan Institute
Stephen Miran is the new Chairman of the Council of Economic Advisers (CEA). He was confirmed by unanimous vote on March 12 2025 by the Senate Committee on Banking, Housing and Urban Affairs.
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?
BBVA Foundation announces Frontiers of Knowledge Award Winners in Economics, Finance, and Management 2025
The trio were recognized for their establishment of rigorous foundations for studying business cycle fluctuations and their development of the New Keynesian paradigm. Their model integrates monopolistic competition and nominal and real rigidities into dynamic general equilibrium models with rational expectations, analyzing the effects of monetary and fiscal policy on economic activity, unemployment, and inflation. The laureates' work has significantly influenced central banks' monetary policy decisions globally and has become a key component of economic teaching. The multidisciplinary panel of evaluators praised their groundbreaking research, which emphasizes the role of expectations in economic decision-making and has guided central banks in designing effective monetary policies, especially during times of crisis. The awards, established in 2008, aim to recognize contributions that expand knowledge within a discipline or bridge interdisciplinary areas, acknowledging the crucial role of knowledge in addressing contemporary challenges. Download the full press release here. Image Credit: BBVA Foundation
Economists Olivier Blanchard (MIT), Jordi Galí (CREI/CEPR/NBER), and Michael Woodford (NBER/CEPR/CESifo) have been awarded the prestigious BBVA Foundation Frontiers of Knowledge Awards in Economics, Finance, and Management for their contributions to modern macroeconomic analysis.
The Price of Love - A Valentine’s Day Look at the Cost of Romance
Valentine's Day spending reflects the economic climate, with key factors affecting consumer confidence, inflation and jobs affecting people's spending2. When the economy is doing well, unsurprisingly, people spend more, buying bigger gifts and planning grand celebrations. So let's get into the economics of Valentine's Day a bit more. The Rising Costs of Valentine’s Day Valentine’s Day has evolved into a multi-billion-dollar industry, with spending trends rising each year. Many would argue that it has become a commercialised celebration driven by consumerism, where businesses capitalise on romantic expectations to boost sales. Social media has magnified these romantic expectations, where individuals often showcase their gifts and experiences, creating a culture of comparison and competition. The desire to "outperform" others in demonstrating love and affection further fuels consumer spending. The costs associated with celebrating love include: Gifts and Flowers: The price of roses often surges in February due to increased demand, demonstrating classic supply and demand principles. Florists and retailers anticipate this spike, adjusting prices to maximise profits during this peak season. Dining Out: Restaurants capitalize on the holiday by offering special (and often pricier) Valentine’s menus. Many establishments introduce fixed-price dinners with curated courses, which are marketed as exclusive experiences, thereby justifying higher prices. Jewellery and Luxury Items: Retailers view Valentine’s Day as a peak sales period, aggressively marketing high-end gifts as ultimate expressions of love. Campaigns often emphasize that nothing says "I love you" like a sparkling diamond or luxury watch, reinforcing the association between material value and emotional significance. Spending statistics reveal the staggering scale of Valentine’s Day as an economic event. The US tops the chart by a significant margin for average spending per person, with an astonishing $192.50. According to Statista, it is predicted that in the U.S. alone, consumers will spend approximately $6.5 billion on jewellery, $5.4 billion on a night out, $5.4 billion on flowers and candy, and “only” $1.4 billion on cards3. These figures highlight how the holiday drives substantial revenue across multiple industries, from florists to fine dining to luxury retailers. In comparison, the United Kingdom ranks second, with average spending per person significantly lower at a predicted $52-$70 for 2025, less than half the amount spent by the average American 4. This stark contrast underscores the cultural and economic differences in how Valentine’s Day is celebrated across countries. Behavioural Economics and Romantic Spending Behavioural economics and psychology offer profound insights into why individuals often spend more than they intend on Valentine’s Day. This can be attributed to a complex interplay of cognitive biases, emotional drivers, and social influences that shape consumer behaviour in subtle yet powerful ways. One of the most significant factors is the influence of social norms and peer pressure. Valentine’s Day is culturally framed as a time to express love and affection through material gifts and special experiences. Societal expectations often create an implicit standard of what constitutes an appropriate display of affection, leading individuals to feel obligated to spend money to meet these norms. The fear of social judgment or being perceived as less committed can drive people to purchase extravagant gifts, not necessarily out of personal desire but to conform to the expectations set by their social circles and media portrayals. Loss aversion, a key concept in behavioural economics, further amplifies this tendency. According to this principle, people experience the pain of loss more intensely than the pleasure of an equivalent gain. In the context of Valentine’s Day, the potential “loss” is emotional—the fear of disappointing a partner, causing relational strain or appearing neglectful5. This fear can motivate individuals to overspend, as they aim to avoid the negative emotional consequences associated with perceived inadequacy or failure to meet their partner's expectations. The halo effect6 also plays a pivotal role in Valentine’s Day spending. This cognitive bias causes people to associate the quality of a gift with the depth of the giver’s affection. Expensive gifts are often perceived as indicators of greater love and commitment, leading consumers to equate higher spending with stronger emotional expression. This misconception can pressure individuals to choose costly items, believing that their monetary value will enhance the perceived sincerity of their sentiments. Another influential bias is anchoring7 where initial exposure to a price sets a mental benchmark that influences subsequent purchasing decisions. High-end advertisements and luxury brand promotions prevalent during the Valentine’s season establish these anchors, making moderately priced items seem more reasonable or affordable by comparison. This effect can subtly nudge consumers towards spending more than they initially planned, as their perception of value has been skewed by earlier price exposures. The scarcity principle8 adds yet another layer to the dynamics of Valentine’s Day spending. Limited-time offers, exclusive deals, and seasonal products create a sense of urgency and exclusivity. This scarcity effect taps into the fear of missing out (FOMO), compelling consumers to make impulsive purchases to secure what they perceive as rare or fleeting opportunities. Retailers strategically exploit this bias through marketing tactics that highlight the limited availability of certain products, thereby driving up demand and, consequently, consumer spending. Cultural Variations in Valentine's Day Celebrations Valentine's Day is celebrated differently around the world, reflecting diverse cultural norms and traditions. In Japan, for example, women typically give chocolates to men on February 14, with men reciprocating a month later on White Day9. In South Korea, the celebration extends even further, with couples marking the 14th of every month as a special occasion, such as Kiss Day and Rose Day10. In Finland and Estonia, Valentine's Day is more about celebrating friendship rather than romantic love, known as Friend's Day11. In contrast, countries like France and Italy emphasise romantic gestures steeped in historical traditions, such as exchanging love letters and symbolic gifts. These cultural variations influence how people spend money on the holiday, with local customs shaping the types of gifts, experiences, and economic behaviours associated with Valentine's Day. Conclusion: Balancing Love and Economics While love is priceless, the way we celebrate it comes with financial implications. Recognising the behavioural and economic factors behind romantic spending can help individuals navigate Valentine’s Day with both their hearts and wallets in mind. Perhaps the best investment is not in expensive gifts but in meaningful, lasting experiences that strengthen relationships without breaking the bank. Works Cited “The untold truth about Valentine's Day: From violence to modern-day romance.” NBC Washington, 2025, https://www.nbcwashington.com/entertainment/holidays/history-behind-valentines-day/3824661/. “The Cost of Valentine's Day in the US vs Other Countries.” Remitly, 2025, https://blog.remitly.com/lifestyle-culture/cost-of-valentines-day/. “Valentine's Day total expected spending on selected gifts in the United States in 2025, by category.” Statista, 2025, https://www.statista.com/statistics/285034/us-valentine-s-day-sales-by-category/. Barber, Sophie. “Valentine’s Day statistics 2025: What is the average spend? Source: https://www.finder.com/uk/credit-cards/best-credit-cards/valentines-day-statistics Analysis conducted by finder.com.” Finder, 2025, https://www.finder.com/uk/credit-cards/best-credit-cards/valentines-day-statistics. Tsipursky, Gleb. “Valentine’s Day Gift Giving with a Purpose: How to Make a Difference Together.” Intentional Insights, 2024, https://intentionalinsights.org/valentines-day-gift-giving-with-a-purpose-how-to-make-a-difference-together/. “Why do positive impressions produced in one area positively influence our opinions in another area?” The Decision Lab, https://thedecisionlab.com/biases/halo-effect. “Anchoring (heuristic).” Behavioural Economics .com, https://www.behavioraleconomics.com/resources/mini-encyclopedia-of-be/anchoring-heuristic/. Chen, James. “Scarcity Principle: Definition, Importance, and Example.” Investopedia, 2024, https://www.investopedia.com/terms/s/scarcity-principle.asp. “White Day.” About Japan: A Teachers Resource, 2025, https://aboutjapan.japansociety.org/white_day#sthash.tsPqtrVT.dpbs. “Why the 14th day of each month in Korea is special.” Korea.net, 2021, https://www.korea.net/NewsFocus/HonoraryReporters/view?articleId=200941. Godin, Melissa. “Why Valentine’s Day in These European Countries Is All About Friendship.” The Times, 2020, https://time.com/5778265/valentines-day-finland-estonia/.
With Valentine’s Day approaching, it's intriguing to explore the economics of love—how much we spend, why we do it, and whether it’s worth the investment. Romance, like any market, operates on supply, demand and consumer behaviour with prices fluctuating based on cultural expectations and economic conditions. Valentine’s Day traces its origins to ancient Rome, where the festival of Lupercalia on Feb 13-15 celebrated fertility and matchmaking. Later, it was linked to Saint Valentine, a priest who secretly performed marriages against Emperor Claudius II’s orders and was executed on February 14, 269 AD. By the 14th century, poets like Geoffrey Chaucer romanticised the day, associating it with courtship. By the 19th century, mass-produced Valentine’s cards became popular, evolving into today’s global celebration of love, marked by gifts, flowers, and heartfelt gestures1.
IS-LM Model (Investment-Savings / Liquidity preference - Money supply)
The two major components of the IS-LM model are the “Investment-Savings” curve (or IS curve), and the “Liquidity preference-Money supply” curve (or LM curve). Where they intersect, the real interest rate and real economic output are balanced, such that the “product market” and the “money market” are in equilibrium; in other words, there is just enough money in circulation to facilitate all of the transactions that economic agents want to conduct. The model was originally developed in the 1930s by John Hicks, who was expanding on the ideas of John Maynard Keynes. At the time, controlling the money supply was seen as a more important lever for policymakers than it typically is today. Since the IS-LM model can help explain how the money supply influences economic growth (for instance, as measured by GDP), it became a key tool of macroeconomic policy until later that century. In the 1970s and 1980s, when inflation became more of a major economic concern, the IS-LM model fell out of favor and policymakers started to focus on inflation and interest rates more than the money supply. Despite the model’s limitations, however, economists usually agree that it’s a good foundation for students, and thus it’s still widely taught in introductory macroeconomics courses. Figure 1 shows the basic IS-LM model in equilibrium. The IS (Investment Savings) Curve The graph of the IS Curve shows every point in the economy where overall income and the real interest rate are “in equilibrium”. Thus, the real interest rate r is on the vertical axis and income (or output) Y on the horizontal axis. Yet, these quantities don’t directly affect each other; the curve is effectively hiding equilibrium in two different markets that determine its shape. These two markets are the “expenditures market” or “Keynesian cross diagram” (the top graph in Figure 2), which shows where income and expenditures in the economy are equal, and the “goods market” (the middle graph in Figure 2), where the real interest rate holds desired savings and desired investment in equilibrium. In a way, the IS curve is built by taking one axis from each of these two diagrams and smashing them together. This is best illustrated with Figure 2 below. The top graph in Figure 2 depicts an economic expansion where an increase in demand led to higher economic output. Due to the higher output, the amount of savings (equivalently, investment) in the economy increases, represented by a movement along the I(r) curve in the middle graph. Because there is a higher demand for savings, the interest rate falls, since it represents the return on savings that banks pay out. These two realities – increased output and a decreased interest rate – are represented by a single movement along the IS curve in the bottom graph. The IS curve is downward-sloping because as the interest rate falls, more investments are worth funding (remember that interest rates represent the cost of borrowing, too: in a falling interest-rate scenario, borrowing money is cheaper!). The increased amount of investments increases potential economic growth. Conversely, at a higher interest rate, fewer economic agents wish to borrow to fund productive projects; this decreases potential growth. Taken from another perspective, the IS curve in economics also shows combinations of the real interest rate and output where the economy is in equilibrium, which in turn determines, or can be used to help anticipate, the level of aggregate demand. The LM (Liquidity-Money) Curve The LM curve shows where the money market is in equilibrium. At every point on the LM curve, liquidity preferences, income, and the real interest rate are such that the amount of money demanded is equal to the money supply. Recall that the interest rate represents the return to savings, and therefore the “cost” of holding onto cash. As the interest rate increases, the demand for money decreases. Conversely, as income increases, money demand also increases. To see why, suppose someone is holding $1000 in cash. This would feel like a lot of money for a college student with a limited income – but to a wealthy capitalist, $1000 is worth much less compared to the money they have available. It probably feels more like pocket change. Generally, when incomes (and by extension wealth) rise, money demand also rises. The “liquidity preferences” part of the LM curve’s name reflects the amount of cash people are willing to hold in general, regardless of their income or interest rates. When people prefer to hold more cash, relatively speaking, the LM curve will be flatter (because it will take a larger increase in the interest rate to convince them to put their cash into savings), and vice versa. Figure 3 shows the derivation of the LM curve from the money market equilibrium. In Figure 3, the money demand curve slopes downward in the first graph because as the interest rate falls, there is less incentive to save money and less cost of holding cash. A shift in money demand (which can happen due to, for example, consumer preferences) shifts the interest rate in response. Note that since the money supply is exogenously determined by the central bank (and commercial banks), Figure 3 determines the interest rate that will result from the central bank’s monetary policy and the amount of money demand. When the economy expands and GDP increases, money demand will shift upwards, increasing the interest rate at the fixed money supply. This is due to classic supply and demand dynamics; with a higher demand for money but the same supply, the “price” of holding money (the interest rate!) must increase. The LM curve is upward-sloping in Figure 3, which is a facsimile of the curve in Figure 1. Note that on the left graph of Figure 3, the horizontal axis measures money demand/supply, but on the right side of Figure 3 (and in Figure 1) it measures income. Thus, the LM curve slopes upwards in Figure 1 because as income rises, money demand (and the interest rate) increases. This difference merely reflects a change in perspective, and should not be confused. The IS-LM Equation To consider the dynamics of the IS-LM model, it helps to look at the algebra behind both curves. The IS Curve is defined by the equation: Y = C(Y – T(Y)) + I(r) + G + NX(Y) where Y represents income (or output), C represents consumption, T shows taxes (as a function of income), I stands for investment, G is government spending, and NX is net exports. Students of macroeconomics will likely recognize this as very similar to the equations for aggregate demand and the expenditure approach of calculating GDP. But, there are a few key differences to discuss. Often in mathematics, parentheses are used next to a variable to denote what factors determine the level of that variable. Here, then, the terms I(r) and NX(Y) show that here investment is a function of the real interest rate r, and net exports are a function of income Y. This makes sense; as income increases, people buy more goods, including goods from abroad (imports). And, clearly, the level of investment responds to changes in the real interest rate. The C(Y – T(Y)) term shows that consumption in the IS curve is a function of both income and taxes (which itself is a function of income). This makes sense, too. The more income people have, the more they can spend and the more they’re taxed; and, taxes reduce discretionary income. The equation for the LM Curve follows: M / P = L(i,Y) where M is the money supply, P is the price level (such that M / P shows the “real” money supply) and liquidity preferences L are a function of the nominal interest rate i and income Y. A specific functional form for the liquidity preferences equation isn’t given, but we know that it’s negatively related to the nominal interest rate and positively related to income (since people hold more cash as the interest rate declines and as income increases). IS-LM Dynamics Keeping these equations in mind shows clearly what economic phenomena will shift either the IS curve or the LM curve. Changes in one of the IS curve’s components – consumption, income, taxes, government spending, investment, or net exports – will cause a shift in the IS curve and therefore change the IS-LM equilibrium. For example, consider a case where the government increases spending. While this must be funded by increases in taxes, which reduce consumption (in part due to crowding out), suppose that the multiplier effect is large and the increase in government spending ends up promoting growth. This increases income Y, and leads to an upward shift in the IS curve. The increase in income is represented by the upward shift of the IS Curve, leading to a higher real interest rate at the new intersection with the LM Curve. The economy’s output grows as income rises, and the equilibrium interest rate is higher than before. Suppose that, simultaneously, the central bank increases the money supply M such that M / P increases. This shifts the LM curve to the right, increasing output/income while reducing the interest rate. Figure 4 shows a possible result of these changes. In Figure 4, after both curves shifted, the economy ended up with a higher level of output and an unchanged interest rate. Of course, it’s possible for the interest rate to be higher (if the IS curve shifts more than the LM curve) or lower (if the LM curve shifts more than the IS curve). These simple examples represent two important levers for policymakers. The shift in the IS curve resulted from an act of fiscal policy, and the shift in the LM curve from an act of monetary policy. The usefulness of depicting these changes and predicting their outcomes led to the model’s former popularity. Good to Know However, the IS-LM model has fallen out of favor in recent years for several reasons. First, the model’s assumption that central banks focus on changing the money supply to affect the economy can be seen as outdated. Nowadays, central banks tend to focus on the interest rate itself (though there are notable exceptions to this, including “quantitative easing” policies in western economies prompted by the 2008 financial crisis) rather than the money supply, such that many economic models directly assume that the interest rate is set by the central bank. Second, and very importantly, the IS-LM model assumes that the price level is constant. Notice how the price level P appears in the denominator of the LM curve equation, and is not considered a function of any particular variable (it is exogenous). Further, the real money supply is depicted as a vertical line in the money market. It may seem strange now, since inflation is considered a critically important indicator of economic health, but until the 1970s (when stagflation reared its ugly head and stumped economists), it was much less of a concern for most policymakers. Obviously, this feature of the IS-LM model makes it unsuitable for studying inflation, and thus the model is unable to propose any policy solutions to combat inflation issues. This severely limits its usefulness, and macroeconomists have largely moved away from it. Yet despite these fatal flaws, the IS-LM model is still widely taught in macroeconomics courses (though it tends to disappear in advanced macroeconomics). It’s a fundamental model that brings together many important concepts, and that undergraduates will certainly be tested on.
The IS-LM Model is a foundational macroeconomic model of describing the relationship between the interest rate and economic output. Using two curves, it depicts an array of equilibria for the economy and can be used to assess the potential impacts of fiscal and monetary policies.
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>