Free riding occurs when individuals lack proper incentives to contribute, or cannot be stopped from benefiting from others’ actions. If you maintain a garden and you live close to a bee farm, you will very likely benefit from extra pollination in your garden as a result of the farm’s bee stock. But, you won’t have to pay for this service, you probably won’t stop the bees from pollinating your garden, and you’re very unlikely to offer to pay for the free pollination you receive. In this scenario, you would be free riding. This is because you’d be enjoying the benefits of a positive externality (pollination) without contributing to the market for beekeeping services. The garden example is fairly innocuous. However, free riding can be an issue in certain situations, particularly with public property or common resources. In other words, contributing to a public good can often leave incentives to free ride. For example, consider a shared apartment with multiple roommates and a shared kitchen. The kitchen is used by everyone, but nobody “owns” the kitchen; everyone can use it freely. <11an>limit:3;categories:Bachelor's Programs;disciplines:Economics</11an> As many have experienced, over time the kitchen is likely to degrade significantly. In the absence of any interventions, it will become overused, dirty, and under-cleaned. Dishes will probably pile up in the sink. This is another example of free riding. The roommates are all incentivized to provide as little cleaning service as possible, and hope that another roommate will do the work for them, saving them time and effort. In this case, cleaning provides a positive externality, and is being under-provided due to free riding behavior. There are methods to solve the roommate’s kitchen sink problem, and others like it (such as slacking off during a group project, or littering). These include negotiating to create some sort of contract, or building and enforcing social norms. For example, if the roommates agree to a cleaning schedule, social norms are put into place. A roommate who shirks from then on is likely to receive the ire of their roommates, and will suffer socially for their lack of contributions. Free riding can occur on a larger scale too. An important characteristic of public goods like roads, public transit, national defense, and similar goods and services is that they are non-excludable. This means individuals can’t be easily prevented from using them. These goods and services provide positive externalities but, in a private market, they would be under-provided because individuals would hope that others would pay for it and they could free ride. Just as with other markets that feature externalities, government intervention is one solution to the problem. Through taxes, governments can pay for public infrastructure, provide national defense, etc. However, it’s not easy for the government to know the optimal amount of each good to be provided, and taxes introduce deadweight loss, so this solution may not be perfect in practice. Good to Know The provision of public goods with positive externalities often creates free riding incentives. This scenario can be represented by the Prisoner’s Dilemma. Imagine a scenario where the dilemma being fought over is not freedom from prison, but cleaning the kitchen, or contributing to a group project. In both cases, an individual’s utility is maximized when the service is provided without them exerting any effort. Thus, everyone’s preferred outcome is one where they defect. Likewise, free riding when overuse creates a negative externality can be seen in the Tragedy of the Commons scenario. Consider pollution. A factory that pollutes the air or the water in a community creates a negative externality. Without a government to regulate, it’s likely that cleanup operations will be left to the community members or a non-profit volunteer organization. In this case, the polluting corporation free rides on the cleanup efforts of the community.
The free rider problem in economics is a situation where certain individuals benefit from others’ actions for free, and so “free ride” by taking advantage of the situation. This is (usually) fully rational behavior from an economic standpoint. Often, when free riding occurs in a market, it signifies the presence of externalities and means that the good or service is being under-provided or overused. Free riding in economic theory can thus be a symptom of market failure.
Working in the USA: How to apply for a work visa after graduation
First of all, congratulations! You’re on your way to becoming a full-fledged economist. But, if you want to transition from student to economist in the “land of the free and the home of the brave”, you’ll need to prepare to apply for your US work visa. We’re here to help you begin tackling the process. Read on for information about getting your United States work visa, and our tips for making the entire affair go smoothly. From student to job seeker: the H-1B visa program If you’re not from the US but completed your economics studies in the US, you probably had to get a Type F student visa. The process for attaining this visa is explained in the linked article. The US student visa does not authorize working in the country long-term. Once you complete your studies, you have a 60-day grace period where you’re allowed to remain in the US, and then you must leave the country. But fear not: if you successfully received a Type F visa and studied in the US, you’ll be eligible to apply for a change of status from non-working student to an employed worker. There are two ways to do this: either apply for a non-immigrant H-1B visa that allows working in the US, which is the most common and straightforward method, or apply for an immigrant visa. This latter visa type means you intend to live in the US long-term. However, if you plan to settle in the US long term, it’s entirely possible to apply for the H-1B visa and then later apply for permanent residence. In fact, this is one of the most common methods used by international students who plan to stay in the US after graduating. This method is typically recommended even if you want to live in the US permanently; directly applying for a permanent stay visa is more difficult than receiving an H-1B first. To apply for an H-1B visa, you must find a job in the US. Then, the employer can sponsor your application for the H-1B, which is necessary for your visa application to be processed. Because of the need for an employer sponsor, it pays to take full advantage of the career services, job fairs, and other employment events at your US university campus. This can help you network and find a full-time job that suits you quickly after you graduate. Be sure to check with university officials for more information on these events! Working before you find your employer sponsor: OPT and CPT But if you don’t have access to career services, or can’t find a job before you graduate, fret not – you don’t need to have a job lined up immediately to stay in the US. You can apply for temporary work authorization first, and work for up to one year before you find a job with an employer who can sponsor your H-1B. If you work in a STEM field, you may have up to three years. This work authorization is known as Optional Practical Training (OPT). The OPT period lasts for up to a year after graduation. Again, if you’re working in a STEM field, you can extend the period for another 17 months on top of the initial year. To apply, you must be a student on an F-1 student visa studying in the US and planning to work in the US after you graduate, in the same field you’re studying. You must apply using Form I-20, which must be signed by the designated university official (DSO) who handles immigration and foreign student affairs. If successful, you’ll then receive your Employment Authorization Document (EAD). Each university may have a varying process for assisting their students with this application. If you plan to pursue an OPT, find your DSO beforehand and be sure to communicate with them before getting started. Remember, you do need an employer sponsor in order to eventually apply for an H-1B – which you’ll need to do during your OPT period if you plan to stay in the US. So, during your OPT period, make sure you’re still looking for a job that will sponsor your H-1B application after your work authorization period. How the H-1B visa system works Whether you apply for an OPT period or not, the formal requirements to qualify for an H-1B visa are as follows: A valid job offer from a U.S. employer for a role that “requires specialty knowledge”; this requirement typically means you need at least a university degree to reasonably work the job Proof of a bachelor’s degree or equivalent experience in the field you’ll be employed in, i.e., economics A $10 application fee (accurate as of this writing, but the fees are subject to increase) Your employer must show that there is a lack of qualified U.S. applicants for the role If your application is selected for further processing, your employer will need to pay the government a fee and submit a Form I-129 The US typically has many applicants apply for immigration each year, but the government only allows new visas up to a certain quota. Thus, valid H-1B visas are awarded via a lottery system. Extra lottery slots are reserved for applicants who have a Master’s degree or higher. Further, the visa cap is ignored for applicants who are sponsored by a university, a university-affiliated non-profit, or a government research organization. Thus, if your employer matches one of these categories, you have better-than-usual chances of receiving an H-1B. Unfortunately, the fact that H-1B visas are awarded via lottery means that even if you do everything right, you might be unlucky and not be drawn from the lottery. On the bright side, though, this means that your school performance doesn’t matter when you’re considered for the visa. You still need a company to sponsor you, but once you have a valid and complete H-1B visa application, your name will be entered in the lottery and your chances will be the same as everyone else. Thus, it’s wise to prepare a backup plan in case you don’t win the lottery the first time around. Will you have enough time remaining on your existing visa to stay and try again next year (such as if you have a three-year work authorization in a STEM field), or should you make contingency plans? Remember that you can always re-enter the lottery later, provided you still have an employer sponsoring you. Failing to receive an H-1B from the lottery is not the same as your visa application being rejected; it simply means the immigration quota was full and you’ll need to try again next year. A quick tip about switching jobs on the H-1B Because the H-1B is tied to employment, it will be more difficult than usual to switch jobs or employers while you’re in the US on this visa. It is possible through a process known as the H-1B transfer; this is done by first accepting a job offer from the new employer, and then applying for a transfer. However, this is a cumbersome process and could put you at a disadvantage on the job market. Thus, if you have options, you should consider carefully the first job that you decide to take on when applying for the visa. Staying longer: applying for permanent status after your H-1B visa Your H-1B visa can be extended and allow you to stay in the US for up to six years. If you intend to settle in the US permanently, this gives you plenty of time to begin applying for a more permanent stay visa if you so wish. After receiving your H-1B, you’ll be able to apply for a change of status to US permanent resident and receive the “green card” if you’re successful. This will grant you the ability to stay in the US permanently and change jobs as much as you want, although it is not considered citizenship. For more on the green card and citizenship, be sure to check out the US Department of State’s and the US Citizenship and Immigration Service’s websites. Hopefully, this article will help you get started on launching your career in the US. Remember that many people have completed the process before you; don’t get discouraged if it takes some time. Best of luck! References https://www.uscis.gov/working-in-the-united-states https://hbr.org/2022/08/a-guide-to-u-s-work-visas-for-international-students https://internationaloffice.berkeley.edu/students/employment/opt Header image credit: Image by YOONHEECHO from Pixabay
So, you’re studying for your economics degree in the United States of America. It's going well, and you’re enjoying life in the USA, so much that you’re thinking about staying longer and looking for a job in the US.
Studying in the USA: How to apply for a student visa
Applying for the US student visa might seem to be a daunting process, but we’re here to help make sense of it. The process is relatively straightforward, although it has many pieces. Read on to learn about how to apply for the student visa for the USA and our tips for making the application process easier for you. Note that this information is accurate as of the publish date above. Some of these requirements could be changed by the US authorities over time, however. Be sure to double check with the appropriate US government authority. Note that this article only covers studying in the United States; it does not cover work visa requirements for the US. But fear not – we have another article for just that! First step: Entering the US To enter the US, many travelers will need a Type B tourist visa. This is a nonimmigrant visa that allows someone to stay in the US temporarily, but does not allow long-term stay. If a prospective student wishes to travel in the US – perhaps to explore university campuses – before they acquire their student visa and start university, this is the visa to apply for. Details on applying for the Type B visa can be found at the US Department of State’s website. Then, a prospective student with a Type B visa can apply for a Type F visa to allow long-term stay for studies with a proper application supplemented by Form I-539. However, this strategy is not guaranteed and so is not recommended. Rather, it’s more secure to apply for a Type F visa from the get-go – but this requires acceptance to a university program in the US. Some travelers will be able to enter the US without an official visa at all. However, these travelers must still fill out the Electronic System for Travel Authorization form (available here) as part of the US’s Visa Waiver program. As of this writing, the list of eligible nationalities includes 40 countries that can be found here, including most of Europe, Japan, South Korea, New Zealand, Australia, Singapore, and Taiwan. These travelers can stay for up to 90 days without needing a visa, but must be careful not to overstay. Requirements for a student “Type F” visa To study in an accredited university program in the US – whether for undergraduate or graduate studies – you’ll need a Type F visa. One important fact to note before getting started is that you cannot apply for a visa without proof of acceptance to a US university, and you can’t apply more than 365 days in advance of your program’s start date. Besides this, the exact requirements for this visa are subject to vary depending on the specific embassy or consulate you apply in, as well as the country you come from. Nevertheless, we’ve compiled a general overview of the requirements so you can begin to prepare. Visa application form After being accepted to a US university, you’ll be eligible to apply for the Type F visa. One of the first steps is to fill out Form DS-160, print it out, and take it to the visa interview with you. This form requires including a picture. According to the Department of State’s website, among other requirements this picture must be: Sized such that the head is large and visible and the full face is visible without any shadow being cast on it Taken within the last 6 months (ensuring it accurately reflects your current appearance), in front of a plain white or off-white background, taken in full-face view directly facing the camera, with a neutral facial expression and both eyes open Taken in non-uniform clothing that you normally wear on a daily basis (normal wear includes religious clothing that is worn daily) Lacking any obscuring headwear (unless worn daily for a religious purpose), but including a hearing device or similar articles if you normally wear them. Further and more specific requirements can be found at the US Department of State’s website. In practice, reputable photo shops and studios should be aware of the requirements for visa application photos for all major countries, and will be able to take your photo with the appropriate specifications. Schedule a visa interview Find the US embassy or consulate nearest to you, and apply for an interview. Applying early is recommended; depending on the city, the earliest available interview slot may not be available for thirty days or more. Note that you’ll be required to pay a non-refundable fee to apply for the interview. Documents typically required for a US student visa A number of documents are typically required for the visa application and interview, though again, this may vary by the specific embassy or consulate that you apply to. Be sure to check with your local embassy before applying. Nonetheless, the documents that are typically required include: A valid passport that remains valid for at least six months after the planned end of your stay in the US The aforementioned DS-160 visa application form At least two copies of your photo as detailed above A filled out form I-20 (provided by your school after you are accepted) Consular officers may request additional documents during your visa process, including: Proof of academic qualifications such as transcripts or standardized test scores Proof of sufficient funds to live on during your stay Once you have successfully received your visa, you’ll be ready to plan your trip into the US. Restrictions on entering the US with a Type F student visa To enter the US after receiving your student visa, you’ll need to bring your copies of forms I-20 and DS-160 as well as your valid passport to the port-of-entry where you intend to enter the country. Then, it’s just a matter of settling into your new home and beginning your studies. Officially, you are not required to show proof of accommodation to receive your visa. However, when entering the US, you may be required to show proof of accommodation or proof of financial means. If you can’t provide any proof that you have a place to stay, you may not be allowed to enter even if you have the proper visa type. Further, universities themselves may request such information when processing your application, and in order to receive a Type F visa you’ll need proof of acceptance to a US university. Therefore, you should start finding a place to stay or gathering financial records early on in the process to make the process easier for yourself. Working while studying in the US Students on a Type F visa are allowed to work on-campus in their first year of studies, but not elsewhere. After the first year, they are allowed to work in the same or a related field as their course of study, subject to approval by school and immigration officials. Questions about this should be directed to the Designated School Official, the employee of the university who manages Student and Exchange Visitor Information System (SEVIS) requests for their institution. Studying in the US, as with studying abroad in general, can be a rewarding and valuable experience. We at INOMICS encourage anyone interested to give it a shot – hopefully, this article has helped you to realize that the student visa process is entirely doable. We wish you the best of luck as you get started on your educational journey in the “land of the free and home of the brave”! References https://travel.state.gov/content/travel/en/us-visas/study/student-visa.html Header image credit: Hyein Nam from Pixabay (Free for use under the Pixabay Content License)
So you want to study abroad in the United States of America, the largest economy in the world. In keeping with its “melting pot” reputation, the US has been an education destination for many over the years, and is filled with many high-quality, internationally competitive universities. Of course, if you’re an international student, you’ll probably need to get a visa before you can go.
Public goods in economics are goods that are both non-excludable and non-rivalrous. These characteristics of public goods mean that if one person uses the good, it does not reduce others’ potential enjoyment of the good, and further that people cannot be prevented from accessing the good. It’s easiest to illustrate the concept by using an example. A lighthouse provides a service that’s considered a public good. Lighthouses help guide ships by providing a signal that land is nearby, and warn against potential dangers in the water. The signal sent by lighthouses cannot be given to some ships but prevented from reaching others – it simply gets emitted. This makes the service non-excludable. Similarly, the signal put out by a lighthouse doesn’t diminish in usefulness as more ships receive it. Thus, a lighthouse’s service is also non-rival. Another example of a public good is the air we breathe. People can’t be stopped from using air, nor does breathing limit someone else’s ability to breathe. Yet more examples of public goods are disease prevention and national defense. Both of these services benefit everyone, and when one person benefits from them, others aren’t prevented from benefitting either. Because they are both non-excludable and non-rival, public goods are often subject to the free rider problem in economics. This problem arises when people can freely access a good or service, and as such are not incentivized to help pay for it or maintain it – in other words, they “free ride”. Free riders benefit from the work of others at no cost to themselves, and they refuse to pay if they have a choice. Because of this behavior, public goods that aren’t naturally occurring (like breathable air) are often provided by a government via taxation, as the private market will almost always under-provide them. In fact, public goods usually have large positive externalities, for instance: national defense makes everyone safer, parks improve neighborhoods (note these can also be classified as a common resource; more on that in the Good to Know section), disease prevention improves public health, etc. Goods with positive externalities are usually under-provided by the market, which is one form of market failure. The government usually taxes the citizenry to provide these services themselves and mitigate that market failure, improving society’s overall welfare. Therefore, public goods are typically either provided by a government, or are naturally occurring. The non-rival and non-excludable nature of public goods make them virtually impossible to sell for a profit. However, radio programs are one example of a public good that have been provided privately by companies operating in a market – although even this required some regulation to allow the market to operate. Radio stations buy the right to use specific radio frequencies from the relevant authority (usually a governmental body) and are then allowed to broadcast their signal via radio waves. Radio is a public good. Radio waves are freely available for any citizen with a radio to tune in to, and listening to the radio program does not prevent others from doing so. Further, anyone with a radio can’t be stopped from listening to any radio station. So, how do radio stations make money? Advertising; advertisers seeking new audiences support the radio station by purchasing time on the station’s program. Of course, this means that the cost for radio listeners isn’t entirely free after all: they must pay the cost of their time listening to ads instead of their favorite program when they use their radio. Good to Know Certain public goods, due to their non-excludable nature, are subject to the Tragedy of the Commons problem. Although air is non-excludable and non-rival, it can be degraded by pollution. Indeed, this degradation – in other words, air becoming less enjoyable as others pollute it – is characteristic of a common resource. Economists will often classify air as both a public good and a common resource, depending on the context. If air is plentiful, and the analysis is limited to a specific area where pollution is not a concern, then fresh air exhibits qualities of a public good. Otherwise, especially if pollution is a concern, fresh air is better classified as a common resource. This example shows how the line between public goods and common resources can be blurred sometimes. Examples of pure public goods that are always fully non-excludable and fully non-rival are hard to come by, but the categorization is still quite useful for economic analysis.
There are many different ways that economists classify the different types of goods and services sold in the economy. The characteristics of goods – for instance, whether they are rivalrous or excludable – help economists classify goods into one of four categories. These are club goods, private goods, public goods, and common resources.
2023 Nobel Prize in Economics - The Winner
Receiving this prize makes Claudia Goldin only the third woman to win the Nobel Prize in Economic Sciences (following Elinor Ostrom in 2009 and Esther Duflo in 2019), and makes her the first to do so alone, i.e. without sharing the prize with another economist. The research that awarded the prize The research that won Dr. Goldin the Nobel Prize examines the gender pay gap between men and women through the centuries. To start, Dr. Goldin gathered U.S. data across the past 200 years. She analyzed this data to look for trends in employment and earnings for both men and women to study how the gender pay gap evolved – and to explain why it still exists today. Part of the reason for her Prize is that this information can help society to fight the gender pay gap, ensuring that equally qualified men and women are paid the same amount. According to the Royal Swedish Academy’s press release1, Chair of the Committee for the Prize in Economic Sciences Jakob Svensson had this to say about Dr. Goldin’s research: “Understanding women’s role in the labour market is important for society. Thanks to Claudia Goldin’s groundbreaking research we now know much more about the underlying factors and which barriers may need to be addressed in the future.” A few of Dr. Goldin’s findings stand out. First, the participation of women in the workforce has evolved over time in a roughly U-shaped pattern (see Figure 1, which was illustrated by Johan Jarnestad for the Nobel Prize organization). A few centuries ago, before the Industrial Revolution – when society was more agrarian – women participated in the labor force at a rate of almost 60%. During the decades from the late eighteenth century through the start of the twentieth, women’s workforce participation steadily declined as society industrialized. Then, in the early twentieth century, it started to rise again. Dr. Goldin postulates that several factors gave rise to this resurging increase in women’s workforce participation. First, educational attainment for women began to climb in the twentieth century. That trend in particular has continued; in the present day, the majority of college degrees are now earned by women rather than men in most western nations, including the U.S3. Changing social expectations aided the increase in workforce participation, too. Around the 1960s, cultural expectations about a woman’s role in the family became less strictly about motherhood and child-rearing5. Dr. Goldin theorizes that younger women began to expect less and less that they needed to devote their lives to managing a household and raising a family first, as their mothers did. This enabled women to tackle their career without as much stigma (although that’s not to say these expectations have ceased to be an issue for women in the workforce in many countries today). Another factor that enabled women’s participation in the post-industrial workforce to rise again was better access to contraceptives, particularly “the pill”. This gave women more power to choose when and if to become mothers, which can potentially put their careers on hold, traditionally significantly more so than for men. Detailing the gender pay gap Yet, despite increasing educational attainment and workforce participation, men still earn more than women on average, even when they are of the same skill and experience level as their female counterparts. Dr. Goldin finds that although the pay gap for men and women can be explained to a large extent (at least historically) by educational attainment and career choice, the modern gap is more complicated. Even between men and women in the same career field, the pay gap widens significantly once a woman has her first child. From that point on, the earnings gap between men and women remains throughout the rest of their careers. The Pew Research Center additionally finds that the gender pay gap in the US has remained mostly unchanged for the past twenty years. The data shows that in the US, women earn roughly 82% of what an experience-equivalent man would earn on average. Last year, INOMICS Salary Survey data showed that on average in economics careers, male economists are paid 23.1% more than female economists. This figure remained remarkably stable at roughly 20% for men and women with a Master’s degree, and also men and women with a PhD. The pay gap varies significantly by employer type within economics, however; the worst offenders according to the 2022 data are private employers, with an average pay gap of 35%. Universities and governments exhibited a gender pay gap of again roughly 20%. Dr. Claudia Goldin: Bio Labor economist and economic historian Dr. Claudia Goldin was born in 1946 in New York, NY, USA. She received her BA in economics from Cornell University, after originally intending to study microbiology. She received her PhD in economics from the University of Chicago in 1972. After receiving her PhD, Dr. Goldin began to teach; she taught at various universities including Princeton before being offered tenure at Harvard in 1990. Another first: she was the first woman to be offered tenure in Harvard’s economics department. She became an editor for the Journal of Economic History from 1984 until 1988. She served as the president of the Economic History Association and the American Economic Association in 1999 to 2000 and 2013 to 2014, respectively. She is a member of the National Academy of Sciences and the American Philosophical Society, and was elected a fellow of the American Academy of Political and Social Science, the Society of Labor Economists, the Cliometric Society, the Econometric Society, and the American Academy of Arts and Sciences. Dr. Goldin is also the author of many successful books, including Understanding the Gender Gap: An Economic History of American Women, a book that details some of her research on the topic that won her the Nobel Prize. She has supplemented this book with further research on topics like the impact of the contraceptive pill on women’s career decisions, the usage of woman’s surnames after marriage, and the fact that women have become the majority of undergraduate degree earners. Previous to her Nobel Prize award, Dr. Goldin has been the recipient of many awards and accolades, including (but not limited to): the Richard A. Lester Award for the Outstanding Book in Industrial Relations and Labor Economics in 1990, 2008, and 2021 the American Economic Association’s Carolyn Shaw Bell Award in 2005 the 2009 Mincer Prize for her labor economics contributions the IZA Prize in Labor Economics in 2016 the 2019 BBVA Frontiers in Knowledge award in economics the 2020 Nemmers award in economics You can read the full Nobel Prize in Economic Sciences press release at the Royal Swedish Academy of Sciences’ webpage. References 1: https://www.nobelprize.org/prizes/economic-sciences/2023/press-release/ 2: https://scholar.harvard.edu/goldin/biocv 3: https://www.kva.se/en/news/the-prize-in-economic-sciences-2023/ 4: https://www.richmondfed.org/publications/research/econ_focus/2014/q4/interview 5: https://www.usnews.com/news/articles/2010/03/12/the-1960s-a-decade-of-change-for-women Header image credit: fill via Pixabay.
The famous 2023 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel, colloquially known as the Nobel Prize in Economics, has been awarded to a Harvard University economist this year: Dr. Claudia Goldin. The prize – worth 11 million Swedish crowns, or about $1 million or roughly €949.000 – was awarded to her by the Royal Swedish Academy of Sciences “for having advanced our understanding of women’s labour market outcomes…Her research reveals the causes of change, as well as the main sources of the remaining gender gap.”1
Common resources in economics are goods that are rival, but non-excludable. This means that if one person uses the good, it reduces others’ ability to enjoy the good. But, people cannot be prevented from accessing the good. This unique blend of characteristics can cause problems with the usage of common resources. For example, a public park is considered a common resource. Public parks are freely available to anyone who wishes to go to them. But at the same time, if one person goes to the park, it means there’s less space left for others to enjoy. Park area is not infinite; if everyone tried to go at the same time, it would be far too crowded and noisy to be enjoyable. Moreover, because a public park is free and non-excludable, individuals’ incentives to keep the park clean and tidy are very low. It’s quite easy for people to use the park, litter, and go home without cleaning up after themselves, which is often privately optimal (at least from a purely rational standpoint, economically speaking). Over time, this degrades the space. In economics, this situation is known as the free-rider problem. In order to keep the park clean and usable, a government authority or the charity of kind citizens is needed. Some natural resources are also considered common resources. An example is the amount of fish in the ocean. The ocean is a vast resource that every coastal nation in the world can utilize. It holds an abundant (and regenerating), but at any one time finite supply of fish. Crucially, nobody owns the ocean, and while it is very difficult for one nation to prevent another one from fishing (non-excludable), fishing the ocean leaves fewer fish for others to find (rival). Each (coastal) nation tends to maximize their own private utility functions from ocean fishing; they choose to fish at a privately optimal level. On its own, this amount will not deplete the oceans, because the ocean is so large compared to just one country. But, if every nation chooses to fish its own privately optimal amount of fish, the supply of fish in the ocean will be reduced below the replacement level (the point at which the supply in the ocean will naturally replenish itself). This leaves fewer and fewer fish left in the ocean each year, degrading the space, making everyone worse off over time. Incidentally, pollution in the ocean is another way – similar to the public park example discussed above – that this common resource can be degraded. Without cooperating to produce and enforce international law that protects the ocean, each nation is incentivized to over-pollute. These problems might sound familiar; they are the Tragedy of the Commons. Common resources can exhibit both free-rider and Tragedy of the Commons problems because the incentives in place often encourage participants to act in their own private interest, which in most cases is not aligned with society’s best interests overall. Rivalrous goods inherently demand that each selfish individual take as much as possible as fast as possible, while non-excludability means everyone can rush to do so. Overcoming this issue and maintaining the common resource can be very difficult, as it requires independent economic agents to negotiate and cooperate. Good to Know Common resources are related to externalities as well, as they often allow negative externalities to occur. Being non-excludable but rival, these goods incentivize over-use, which is itself a negative externality. Another way to conceptualize this: with common resources, “market” participants do not consider the cost of their actions on others in the “price” of their “purchase” decision. For example, when one nation overfishes, they’re fishing a privately optimal amount without considering the negative impacts on other countries from their overfishing. This constitutes a negative externality and contributes to the Tragedy of the Commons problem.
Insights into Central Banking Careers with Dr. Gómez-Pineda
This includes advice about which skills aspiring economists should learn, the differences between research and policy economists, topics that prospective central bankers should focus on in their studies, and more. Any serious student of economics – and indeed, many curious economists – will benefit from hearing Dr. Gómez-Pineda’s advice. Javier G. Gómez Pineda is Senior Economist (Investigador Principal) at Banco de la República (the central bank of Colombia). He also teaches Monetary Economics at Universidad del Rosario in Bogotá. His research interests are in Monetary Economics and International Economics; he is author of the textbook Dinero, banca y mercados emergentes, los países emergentes en la economía global, 2010, Ed. Alfaomega; and he holds a PhD in Economics from the University of Chicago. Are you an economist already working in the field? You, like Dr. Gómez-Pineda, have a chance to assist INOMICS’ economist job market research. Simply fill out our anonymous, 3-5 minute Salary Survey and help us research the state of the economics job market in 2023. Without further ado, read on for the interview! Sean McClung: How did you become an economist – was it always your plan? Javier G. Gómez-Pineda: Choosing a career can be challenging. In the beginning I was not very sure about my career choice, I considered at least two alternatives, but in the end I decided to study economics. I think what ultimately led me to this choice was perhaps the idea that what is important in choosing a career is not how you earn an income, but how you will spend the rest of your life in a way that contributes and serves others. Later, you discover that in order to serve others, you need to know how to be useful, to have a good command of your field, your techniques and your work. At the end of my undergraduate studies, I worked as a research assistant (RA) for Professor Lauchlin Currie. It was he who encouraged me: “if you want to be a good economist, you have to study abroad.” At that time, there were no graduate programs in Colombia, but there were scholarships for graduate studies abroad, provided that the studies were made at a top university. These Central Bank scholarships were named after Professor Lauchlin Currie. My main challenge was the English language; not so much the reading, because I had studied English reading in my undergraduate studies; but more the listening and writing. So, I did some English work before I applied to graduate school. I studied American English at an institute and learned some grammar. At the same time, I studied British English at another institute and learned some conversation skills. I also traveled to the United States for a short time to study English and to expand my vocabulary a little. In the end, I passed the English exam and got a place on a graduate program. However, at the beginning of my first year of graduate studies, I struggled with listening comprehension. By the middle of the first year, it was not the language that was the problem, but the math. I overcame the “math scare” by putting more emphasis on economic intuition. I think in the process I managed to learn some English, get over the math scare, and study abroad, but I still strive to become a better economist and write more in English. SM: What part of your educational background would you say is most valuable to your current work? JGP: I think a valuable part was my thesis work during my graduate studies; this was due to my empirical thesis. It was this work that got me into running regressions. Since then, I have done more empirical work than theoretical work. I think another part of the training that I found valuable was the macro problem sets. In one or two of them, I had to learn how to run macro models on the computer as well as get familiar with some of the software packages. Of course, the most valuable part of the educational background were the courses themselves, especially in macroeconomics, international economics and econometrics. However, education does not stop when you graduate. Theory evolves, better techniques become available, new software packages are developed. As research economists, our job is to contribute to these developments, and so we must continue to educate ourselves by at least reading the most relevant authors in our field and attending conferences. As policy economists, our job is to incorporate these developments into our analysis so that our work is based on the most appropriate methods. SM: How has your day-to-day as a central banker been different than you expected (if at all)? JGP: I expected that my work would involve running regressions, as I had done in my thesis work, and that turned out to be true. In contrast, I did not expect that my work would involve DSGE models, Kalman filtering, and Bayesian estimation, which I had learned in the university, but that turned out to be part of my work. SM: What is/are the most rewarding part(s) of the job for you? JGP: The contribution of an economist is sometimes less tangible than that of other professions. Unlike economists, a doctor can have a big impact on a person’s life in a very concrete way, and the medical profession can contribute to the health of the people in a country or even in the world. I think the work of a macroeconomist should have a small impact on the country while the economics profession should have an impact on the economic well-being of the people in the country. For me, the most rewarding part of the job has been to see that the inflation model is used by the Central Bank as an input in the interest rate policy decision, to know that our monetary policy report is valued by the interest-rate-setting committee, and to see that our policy working papers are used as a reference point for understanding monetary and macroprudential policy in Colombia. At the end of the day the expectation is that this work will hopefully have some impact in the country. It is also rewarding to be able to share this knowledge with students at the university. SM: As a central banker, do you get to research? What is your favorite part of that research? How is it different from academic research? JGP: Central banks typically have two types of economists: research economists and policy economists. Research economists write papers that are published as central bank working papers and in academic journals. Policy economists work on the periodic inflation forecasts, the Inflation Report (IR); and the periodic financial stability assessments, the Financial Stability Report (FSR). The IR supports interest rate policy decisions and the FSR supports macroprudential policy decisions. I work about 80 percent of the time as a research economist and 20 percent as a policy economist. Since I work on monetary economics in both roles, my work as a research economist and as a policy economist overlap. In Colombia, the central bank has a committee of research economists that prepares a quarterly monetary policy note to the central bank’s interest-rate-setting committee. I am a member of this three-person research economists committee, and this work represents about 20 percent of my work as a policy economist. The time devoted to research may vary across economists. For instance, in Colombia the central bank has research economists that devote 100 percent of their time to research. Conversely, some policy economists do not have time left for research and some might work about 20 percent of the time as research economists. They publish this research as technical notes in the IR and FSR, as working papers and sometimes in journals. Academic research covers a wide range of topics, while central bank research tends to focus on monetary and financial stability. Academic research tends to produce sophisticated analytical and algebraic results; however, at times the quantitative dimension of academic research is not as sophisticated as that sought for by central bank economists. Academic research tends to emphasize the sign and significance of estimated coefficients, while central bank research tends to emphasize the quantitative performance of the models, issues such as the impact of interest rate decisions on inflation over time, in exact percentage points, also quantifying the amount of uncertainty involved in the transmission. While there are differences between academic and central bank research, there are also similarities. Both academic and central bank research share the current paradigm of the New Neoclassical Synthesis, see Goodfriend and King (1997) and Woodford (2009). Some academic economists sometimes hold policy positions in central banks, and some central bank economists also teach at the university. SM: In your experience, what would you say is the best type of experience for prospective central bank hires to have? JGP: For me, a sufficient condition is a strong interest and vocation in monetary economics or macroeconomics. A thesis in monetary economics or in international economics is a clear sign of this interest. Beyond the thesis, a working paper or article in monetary economics is also a good signal. Other skills are very welcome, but not strictly necessary, as they can be acquired on the job. These include familiarity with software packages such as R, Python and Matlab and a good command of the English language. SM: What other tips, advice, or insights do you have for younger economists interested in monetary policy or central banking? JGP: My advice would be: read the literature and get into the habit of reading the papers. In addition, keep in mind that what you sow is what you shall reap, so read the literature and also get familiar with the software packages used to build your estimations and models; get those skills. Finally, for those who are not native English speakers, it is important to learn how to write papers. There are several books and articles available on this subject. One starting point is McClosky (2000) and also Banco Central de Chile (2017). It is a good idea to go through this literature and try to learn how to write papers. If possible, get help from an English editor at your institution or on your own. SM: What else (if anything) do you think should be shared about your career path or current role? JGP: Central banks face issues that are common to the central banking community as a whole. So try to make your research relevant not only to your central bank, but if possible to the central banking community; if possible, work with a global approach. And finally, group research is better than individual research. So, it is important to work in groups and write papers with others. References Banco Central de Chile, “Guía de Estilo en Inglés” (Accessed Sep. 19, 2023), 2017. Goodfriend, Marvin, and King Robert. “The new neoclassical synthesis and the role of monetary policy.” NBER Macroeconomics Annual 12 (1997): 231-283. McClosly, Deidre, Economical Writing, 2nd Ed. Waveland Press Inc, 2000. Woodford, Michael. “Convergence in macroeconomics: Elements of the new synthesis.” American Economic Journal: Macroeconomics 1, nº 1 (2009): 267-279. Header image credit: Dr. Javier G. Gómez-Pineda.
Dr. Javier G. Gómez-Pineda, our interview guest for the 2023 INOMICS Handbook, graciously agreed to return for a second interview. This time, the INOMICS team prepared some career-related questions for the central banker. With his answers, Dr. Gómez-Pineda shares his wisdom about central banking and economist careers in general with you, our readers.
Public Economics & Economic Policy (JEL H)
Macroeconomics and Monetary Economics (JEL E)
A gentle introduction to LaTeX for economists
Enter LaTeX, a typesetting system that’s been developed for researchers of all stripes to write and neatly print properly formatted mathematical formulae. If you’re able to learn and use it, you’ll be able to write important documents such as your economics thesis or a research paper in no time. While LaTeX isn’t the only option economists have to handle this work, it is a commonly used and freely available option. The most common alternatives include Markdown and plug-ins for Adobe InDesign. That’s right – anyone can get started practicing LaTeX for free, using a popular site called Overleaf. Because the basic version of Overleaf is a free and easy to use LaTeX editor (though paid plans are available for more features and integrations), the rest of this article will assume that the reader is using it. This is a skill that’s great to have for your economics coursework and future career, and not terribly difficult to learn, so let’s dive into some LaTeX basics. Before you start: understanding mathematical symbols The first step to learning LaTeX and writing professional-looking economic formulae is understanding what those formulae are and how they are typically written. We’ve already shared an article that describes many of the commonly used symbols in economics. If you find yourself struggling to read some of the strange symbols presented in economic formulae during lectures or in textbooks, the linked Math Symbols article is a great place to start. Once you’re familiar with the symbols typically used in economic formulae, continue reading! Getting started in LaTeX: the preamble The “preamble” in LaTeX is a section found at the beginning of the document. In Overleaf, this will be at the top of the left-hand side Code Editor section. The preamble is written by the author, and contains important code for setting up the document. This is where you’ll install packages and other functions that you’ll need, as well as set document-wide defaults like margin spacing and font size. The preamble – and thus the document – always starts with the “\documentclass{}” command. This defines what type of document is being produced. Inside the curly braces can be written a number of words; “article” is probably the most commonly used, and will serve most purposes. Thus, the first line of code is typically “\documentclass{article}”. Research papers ought to use this command, and it’s also suitable for a casual learner to start using LaTeX. Other document classes tend to be more specialized, such as “beamer” for presentations, “minimal” for debugging and “slides” for a slideshow. An important fact to know is that you can set certain parameters for your document, and any equation or function that you type, using brackets such as [] right after a command. For example, you can set the font size equal to 12 for your entire document using “\documentclass{article}[12pt]”. We’ll return to the usefulness of these brackets later when we discuss graphing. Other lines of code that will be entered in the preamble include packages that you’ll need to install, similar to software programs like R that econometricians use. There are many, many packages containing specific and niche tools, and LaTeX does not automatically have access to all of them. So, by inserting lines of code like “\usepackage{pgfplots}”, specific functions will be loaded so that you’re able to access them when you need them. Some common packages that you may need to install as an economist include, but are not limited to: pgfplots: contains useful functions for graphing. tikz: another package that contains useful functions for graphing. amsmath: developed by the American Mathematical Society (AMS); contains useful functions and improvements for writing equations in LaTeX. amssymb: again developed by AMS, contains useful math symbols that you can insert into your document. graphicx: allows you to insert images into your document from an external source, like your hard drive. tabularx: contains further options when making a table. multirow: if you’re using a lot of tables, this package allows entries to cover more than one row, which can be useful. mathpazo: sets a standard font used when printing mathematical characters. nicefrac: an option for typesetting in-line fractions neatly. subfig: allows you to more easily create a figure that’s broken into multiple sub-figures. footnote: further options for formatting your footnotes. natbib: contains helpful support for handling in-text citations and the bibliography. If you need more specific packages for your economics coursework, your instructor is likely to provide them for you. If you’re working on your PhD thesis or other research, your supervisor(s) or colleague(s) might be able to help you find specific packages you may need on top of these basic suggestions. After installing packages, you ought to include lines for the title, author, and date the document was written. The commands for this are “\title”, “\author”, and “\date”; fill these in with braces as with other commands. For instance, if this article was written in LaTeX, it would have these three lines of code: “\author{Sean McClung}” “\title{A gentle introduction to LaTeX for economists}” “\date{October 2 2023}” At the end of the preamble, you’ll need to write “\begin{document}”. This tells LaTeX that the code below this line is used to actually generate your document. With the preamble out of the way, you – and this article – are now ready to move on to the meat and potatoes. Writing your first equation in LaTeX There are a few commonly used commands that you can use to write an equation in LaTeX. These include (but are not limited to): Equation: this will print your equation with a line number, which can be useful in some contexts. Use “equation*” instead if you don’t want line numbers. Align: vertically aligns multiple equations (this makes them look neat in your document when printed and avoids awkward spacing). Can also be used to break up multiple equations into columns, should you wish to present them that way. Multiline: allows long equations to be split up on multiple lines so that they display properly. Gather: Another option to simply print a number of centered equations. The differences in these options tend to be minor, and in many cases which one you choose won’t affect your output much. But, using the right one for the right niche circumstance can still make the difference between a professional-looking paper and a haphazard one, so it pays to learn the difference. After you’ve decided on one of the above options (and picked a formula to write!), you’ll be ready to start typing. Simply type out your desired equation using the proper symbols, and LaTeX will be ready to produce professional-looking output of it. A note – if you need to use fractions, you must do so using the “\frac{}{}” command. This can be confusing at first. However, it’s quite simple to use. The expression in the numerator belongs in the first set of braces, while the denominator goes in the second set of braces. Thus, to type 1/2 as a fraction, you’d enter “\frac{1}{2}”. Very quickly, you’ll probably realize that you need to insert a specific symbol that isn’t found on a keyboard – like \(\partial\). This is done by first typing a “\” symbol, which tells LaTeX that you’re about to use a special character (or type a new command). When you do this, a list of symbol and function names will appear next to your cursor. You can scroll this list to choose the option you want, or keep typing to narrow the list. Simply press “Enter” or click on the option you want. You can even type out the entire name and ignore the list, if you wish. In the coding window, this won’t appear to do much. But, when you compile your work, you’ll see LaTeX print out “\(\partial\)” instead of “\partial” on the document compilation output. Read on for how to compile and check out your work done so far. The “Recompile” button You now know how to write formulae in LaTeX. But, since the Code Editor doesn’t automatically show you the exact output, it can be hard to know precisely what you’ve written. Further, at first you’ll probably run into many simple error codes as you get used to the new language of LaTeX. How can you check this to review your progress and make corrections? At the top-right of your Overleaf screen, you’ll see the “Recompile” button. Clicking this button will execute your LaTeX code. This will cause your entire document to appear as if it was being printed out on the document screen to the right. Then, you can check visually if your formulae turned out as expected. If there are any errors, the compile will fail, and the document screen will display an error message instead of your work. Helpfully, the offending line(s) on the left-hand side Code Editor will be highlighted with a red symbol. Hovering your mouse over the symbol will show a message describing what went wrong on that line. Check your code to ensure you’re using the proper syntax, and start small so that it will be easy to identify errors. Search engines like Google are great friends to have during these steps. Now that you can write equations – and compile the document – here’s an example. Try copying this code in your own Overleaf project, and see if you can manipulate the equation yourself. The following code produces the Cross Elasticity of Demand equation shown below: \begin{equation*} \epsilon_{AB} = \frac{\Delta\mathit{q}_{A}}{\Delta\mathit{p}_{B}} = \frac{\frac{{dq}_{A}}{\mathit{q}_{A}}}{\frac{{dp}_{B}}{p_B}} \end{equation*} \begin{equation*} \epsilon_{AB} = \frac{\Delta\mathit{q}_{A}}{\Delta\mathit{p}_{B}} = \frac{\frac{{dq}_{A}}{\mathit{q}_{A}}}{\frac{{dp}_{B}}{p_B}} \end{equation*} Making graphs in LaTeX Making graphs in LaTeX is intimidating at first, because even a simple graph requires using many different lines of code. However, it’s quite intuitive to create graphs once you get the hang of it. As with writing equations, it’s best to start small. Start with a simple graph as an end goal, and introduce one or two elements at a time until you’ve finished the graph. It will be easier to identify errors and learn that way. To start drawing a graph using LaTeX, you’ll need to use one of a few commands just like when writing an equation. The most commonly used commands are: begin{tikzpicture}: used after installing the tikz package. This line of code tells LaTeX that the following lines are being used to create a graphic of some sort. Then, the line “begin{axis}” should follow for most economics applications. begin{axis}: this command will create an x-y axis. You can set its dimensions and scale; it defaults to a maximum x value of 10 and likewise for y values. The aforementioned brackets are also very useful when creating your axis, allowing you to set the axis parameters. For instance, “\begin{axis}[xmax = 15, ymax = 15]” will create a larger axis than the default, showing elements up to 15 units away from the origin instead of the default 10. Then, LaTeX expects you to introduce elements via equations or plotting them on the graph, somewhat similarly to how a graphing calculator works. You’ll typically use the “\addplot{}” command to graph equations. For instance, to add a simple line, you can use a linear equation such as: "\addplot [domain = 1:10, restrict y to domain = 0:10, color = blue, very thick] {x+1}". This will graph the simple line y = x + 1 as a blue line on your graph. A few other useful tricks have been shown in the brackets: you can set the domain as shown, and even restrict the domain of the function if you only want to show part of the line. For example, you can adjust the above command to read “restrict y to domain = 3:5” if you only want to show the parts of y = x + 1 that cross through the points (2,3) and (4,5). You can also produce a dashed line by typing “dashed” instead of “very thick”. Try it! Of course, lines need labels in economics. You can add a label to your line with the “\node” command, like so: “\node at (4, 1) {$D$}” prints a nice D, for demand curve, at the point (4,1). Finally, the “draw” command is a useful tool to know about when graphing. It allows you to create more free-form shapes and lines without needing to specify an actual equation to draw them. This can be a great option when you want to draw a graph that involves undefined points, sharp angles, or odd shapes. For example, you can use the command “\draw[dashed,gray] (5, 2) to (5,0)” to draw a dashed, gray line from the point (5, 2) to the point (5,0). As an example, the following code (with a proper preamble already written) produces the simple graph shown below: \begin{tikzpicture} \begin{axis}[ xmin = 0, xmax = 10, ymin = 0, ymax = 10, axis lines* = left, xtick = \empty, ytick = \empty, clip = false, scale = 1.5, ] % Curves \addplot [domain = 0:10, restrict x to domain = 1:9, color = green, very thick] {-x+10}; \addplot [domain = 0:10, restrict x to domain = 1:9, color = blue, very thick] {x}; \node [] at (9, 0.8) {$D$}; \node [] at (9, 9.2) {$S$}; \node [left] at (0, 5) {$p^*$}; \node [] at (5, -0.3) {$q^*$}; \node [left] at (0, 9.8) {price}; \node [left] at (9.8, -0.3) {quantity}; \draw[dashed] (5,0) -- (5,5); \draw[dashed] (0,5) -- (5,5); \end{axis} \end{tikzpicture} See if you can tell which commands produced which elements on the graph. Try copying this code into your own Overleaf project, and see if you can manipulate the elements. This will help you learn quite swiftly! Once you’ve started making graphs, and got the hang of it, we have a further practice suggestion for you. All of the graphs in our Economics Terms A-Z were made using LaTeX. Why not pick a graph from one of these articles – such as Demand Curve or Market Equilibrium – and see if you can recreate them in LaTeX? If you’re feeling confident, try making a complicated one like Deadweight Loss or Edgeworth Box. The “end” command One final and important note; at the end of each equation, graph, and document, the “end{}” command must be typed with the \ symbol just before it. This is the counterpart to the “begin{}” command that starts a graph, equation, or document. Without a matching “end{}”, you’ll receive error messages. Fortunately, when you type “begin{}”, this is automatically produced for you in Overleaf. But it still pays to be aware of its importance. If you end up deleting an “end{}” on accident, you’ll receive error messages. The tip of the iceberg This article has only shared the very basics of using LaTeX for economics. If you’re just looking to get started with learning LaTeX for practical application purposes, you should now be all set to start writing equations, making graphs, and even drafting your economics thesis or research paper. But as with most tools and applications, a vast sea of advanced knowledge awaits. If you want to learn more, head over to Overleaf’s “learn” section for more in-depth explanations and tutorials. There are also numerous online resources that will be able to help you find specific code if you’re stuck. Best of luck! Header image credit: Elchinator on Pixabay (free for use under the Pixabay content license).
Economists often run into situations where they need to type out mathematical formulae or draw up a graph, especially for research papers. But, anyone who’s attempted to do so knows that math symbols and complicated graphs are very difficult to produce using typical word processor options. So, what’s a humble econometrician to do?
Purpose of the job: To manage all commercial aspects of projects, from assisting the tendering process to the settlement of accounts. Seek to minimise costs and enhance value, while still achieving the required standards and quality. Key Objectives Maintain contract documentation e.g. applications for payment, quotations, variations, notifications and ensure such is provided in accordance with the terms and conditions of contracts Monitor and control income and expenditure on contracts e.g. interim/final applications for payment/final accounts/payments for late monies etc. liaising where necessary with the relevant people e.g. clients, Project Managers, senior management etc. Produce relevant monthly reports for the Commercial Manager e.g. cost and value reports, cashflow forecasts, cost to completes, turnover forecasts, WIP reports, risk and opportunity registers, earned value analysis etc. ensuring information is complete and accurate Maintain compliance of contracts in accordance with corporate procedures quietened Main Responsibilities Undertake programme monitoring to ensure identification of delays and entitlement to time Administer effective potential and actual contract change control ensuring timely submission of correct notices and prompt agreement/implementation with client and subcontractors Build positive relationships with subcontractors and ensure subcontracts are properly let. Ensure financial and contractual control of subcontractors accounts in accordance with legislation and corporate procedures Develop proactive and appropriate relationships with both internal and external stakeholders; to provide the appropriate guidance to make good business decisions Work with Project Managers to support the efficient and effective execution of projects ensuring contractual arrangements for cost, quality and delivery are achieved • Ensure all commercial activates are in line with the Commercial Strategy and meet all operational, financial, commercial, legislative standards and targets To attend contract handover meetings, start up meetings, progress meetings and contract review meetings as necessary Provide support to Sales and Estimating for all commercial activities To ensure that Contracts are set up according to Project and Company requirements including the use of WBS codes were relevant and that all associated paperwork and accurate records are collated and filed appropriately Ensure that any potential issues are raised with the relevant personnel at the earliest opportunity Responsible for open and adequate communication with peers, line management, senior management, project delivery teams and with clients as necessary To undertake the administration of the sub-contracts to ensure that the Sales contracts are appropriately supported Contributing positively to the effectiveness of project team commercial performance to include priming project managers and their teams on key risks/opportunities in customer contracts, effectively participating as necessary in validation of projects won and interrogating and reporting periodic reviews of lessons learnt Preparation and submission of Interim application for payment, analysing payment notices and monitoring cash receipts. Progress recovery of outstanding debts and resolution of financial exceptions Any other ad hoc duties as may be required and as are commensurate with the grade of the post. Qualifications, Knowledge, Experience and Skills Essential Candidates to have a minimum of 5 years’ experience from a main contracting or project delivery background HND/degree (or equivalent) in Quantity Surveying Previous experience of commercial management and procurement of subcontractors, including chairing regular meetings, measurement and control cycle, certificates and accruals calculations Good contractual and commercial understanding of JCT/NEC/ICHEME forms of contract and knowledge and understanding of Construction Law Ability to demonstrate a good knowledge of monthly reporting and earned value analysis Have knowledge and experience in estimation and cost analysis Excellent communication, numeric and computer literacy skills, proficient in Microsoft Office Excellent organisation and time management skills, with the ability to work under pressure and to tight deadlines Ability to work effectively in a team and delegate, but also able to work on own initiative and build relationships with clients’ surveyor Wide exposure to applications, claims process, placing sub-contracts, negotiation of contracts and agreeing final accounts. Be a problem solver/critical thinker with good attention to detail and good negotiation skills Takes ownership and accountability and demonstrates a professional approach to self and others Desirable Building productive business relationships at all stakeholder levels Results driven self-starter Member of RICS or other relevant professional body, or working towards CSCS card Measures • Performance based KPI’s • Routine appraisals • Feedback from peers Salary: £35K - £45K Dependant on experience Job Type: Permanent. Salary: £35000 - £45000/annum.
Econometrics, Statistics and Quantitative Methods (JEL C)
Why So Serious? 10 Hilarious Economics Jokes to Lighten the Mood
1. An Economist's Guessing Game Did you hear about the trained professionals who get paid to guess wrong? They're called economists! With their expertise in analyzing complex systems, economists often find themselves making educated guesses about the future. And let's face it, sometimes their predictions can be quite off the mark. But hey, at least they're trying! As Laurence J. Peter once said, “An economist is an expert who will know tomorrow why the things he predicted yesterday didn't happen today.” 2. The Light Bulb Conundrum How many economists does it take to change a light bulb? None. If the light bulb needed changing, the market would have already done it! After all, many economists prefer a hands-off approach to the economy, believing that market forces will naturally lead to optimal outcomes. In this case, they're taking that approach quite literally! 3. Handling the Gross Domestic Product What do an economist, plumber, and garbage disposal worker have in common? They all handle the gross domestic product! It turns out that the economic concept of GDP has a lot in common with the everyday work of different professions. It reminds us that even the most mundane tasks can have an economic impact. 4. Avoiding Rollercoaster Rides Why did the economist refuse to go on a roller coaster? Because he didn't want to experience any "ups" or "downs"! The economist's preference for stability and avoiding unpredictable or volatile situations is well-known. Perhaps this extends even to a recreational context like going on a roller coaster. 5. Noah, the Economist Which biblical character would have made a great economist? Noah! After all, while everyone's stocks were in liquidation, his were afloat. It shows that even in difficult times, there's always room for a bit of humor. Just ask the biblical Noah, or your friendly neighborhood economist. 6. An Economist’s Career in Fortune Telling Why did the economist take a job as a fortune teller? Because he wanted to specialize in "forecasts" and "predictions"! Well then, this seems to be something economists and fortune tellers have in common. 7. The Seasonally Adjusted Graduate Student Did you hear about the economics graduate student who got injured by diving into a pool in winter? He forgot to seasonally adjust! Seasonally adjusting data is a common practice in economics, but even economists can forget to account for seasonal variations in an important analysis. 8. The Rockstar Economist Why did the economist start a band? Because he wanted to study the economies of “scale”! A witty way of combining economics and music: use musical scales to sing the praises of cost efficiencies through scaling up operations, aka, economies of scale. 9. Guaranteed Method to Confuse an Economist How do you confuse an economist? Ask them to explain the "real world"! While studying, remember economists' tendency to operate in the theoretical realm – there’s always a potential gap between economic models and the complexities of real-life economic dynamics. 10. Trickle Down Economics Finally, I have a joke about trickle-down economics, but most people won’t get it. Get it? While economics may be a serious and complex field, it's important to remember that laughter can lighten the mood. These jokes serve as a reminder that even the most educated individuals in the world of economics can appreciate a good sense of humor. So, next time you find yourself overwhelmed by economic theories and concepts, take a moment to enjoy a good laugh. A little humor can go a long way in making the subject more accessible and enjoyable. So, why not share these jokes with your fellow econ enthusiasts and spread a little laughter? After all, a smile is always a valuable commodity, no matter the economic climate. Header image credit: Tumiso on Pixabay. (Free for use under the Pixabay Content License)
Are you feeling overwhelmed by the complexities of economics? Do you need a break from the serious and often confusing world of resource distribution, scarcity, and production? Look no further! We have collected 10 hilarious economics jokes that are sure to bring a smile to your face. So sit back, relax, and get ready to laugh!
The Job Outlook for Economists in the US
Are economists in demand? Is it a good idea to pursue a Master’s degree or Ph.D. in economics right now? If you’re pondering questions like these, good news. In today’s world, the answers appear to be “yes”, and “yes”, respectively. This article will examine growth in economics jobs in the US using Bureau of Labor Statistics (BLS) data together with our own data from the 2022 INOMICS Salary Report. If you would like to anonymously help INOMICS continue to learn about the economics job market, and empower economists to make informed career decisions, we implore you to take 3-5 minutes to answer our anonymous Salary Survey. Job growth for economist roles in the US The US BLS publishes job outlooks for various careers, showing how demand for those jobs is expected to change in the US over time. This data gives a sense of the future growth of economist roles, and can help economics students get an idea of the job opportunities they can expect. Although this article dives into US data, these findings can be thought of as an indicator for other regions with comparable socioeconomic profiles as the US, too. Last updated on September 6, 20231, the BLS reports that the number of positions for economists from 2022 through 2032 is expected to grow at a rate of 6% over the decade. This is roughly twice as fast as the economy-wide average; the BLS states that “The average growth rate for all occupations is 3 percent”. Economist job growth also beats out job growth in other social sciences, which are projected to grow at 5%. The data shows that there are currently about 17,600 economists employed in non-academic roles in the US. 6% job growth means that by 2032, the BLS expects there to be about 18,650 non-academic economists employed in the US (though they also project 1,100 new positions, which would represent a 6.5% increase). Similarly, the BLS estimates that there are 11,640 economics faculty positions in the US as of May 2022. A job growth estimate for economics professors is not given. However, utilizing past BLS data can help us examine the trend and learn more. Table 1 below records the number of economics postsecondary teaching positions in the US according to past BLS data. Table 1: Employment numbers for US economics faculty BLS estimate as of May… Economics Postsecondary Faculty Positions 2022 11,640 2021 11,790 2020 13,080 2019 13,270 2018 12,750 2017 12,770 It’s difficult to judge how economics faculty positions will evolve in the US over time based on Table 1 alone. These positions appear to be on a slight downward trend over time. It’s highly likely that the pandemic reduced job opportunities at universities, the largest employer of economists (see Table 4). Further, Inside Higher Ed reported in July 2022 that faculty were leaving their positions at high rates during the so-called “Great Resignation”, which may have impacted the industry. But, this slight downward trend in economics faculty mirrors a slight downward trend in postsecondary faculty positions in the US across all disciplines since the pandemic. And, it’s likely that increasing demand for industry economists will increase demand for both economics research and economics degrees. Both of these factors increase demand for economics professors, who conduct research and train new economists. Overall, the number of industry economist jobs is increasing and is expected to continue increasing faster than average. Meanwhile, economist jobs in academia have declined somewhat since the pandemic. Employment and Wages for economists in the US A note – the BLS uses the terms “Economists” and “Economics Teachers, Postsecondary” to describe economists employed outside of academia and within it, respectively. In the following tables, I term these “Industry Economists” and “Academic Economists” to align with other articles on INOMICS, and our own salary reports. Table 2 below summarizes the BLS data on the number of industry and academic economist positions, as well as the mean annual wages for both1,2. Table 2: Employment and wages for US economists in 2022 Employment Mean Annual Wage Industry Economists 16,370 $128,180 Academic Economists 11,640 $122,750 Table 2 shows that the mean annual wages are comparable (and quite high) for both industry and academic economists. As a comparison, 2022 INOMICS Salary Report data found average pay for US economists at $136,650. However, we know that these simple averages mask some nuance. INOMICS Salary report data show that industry economists tend to out-earn academic economists, except for when academic economists are promoted to full professor. This promotion grants them a salary comparable to top executives in industry. For a more detailed look at the breakdown of average economist salaries around the world, see the 2022 INOMICS Salary Report. And, be on the lookout for the 2023 Salary Report, which you can still support by answering our anonymous Salary Survey. Table 3 examines wages by percentile as well, for a more holistic analysis1,2,3: Table 3: Percentile wage estimates for Economists and Economics Professors in 2022 (BLS data): Percentile 10% 25% Median 75% 90% Industry Economists $62,480 $81,810 $113,940 $156,940 $207,230 Academic Economists $51,480 $76,580 $103,930 $144,980 $213,680 Table 3 shows that median economist salaries are lower than average salaries in the US. This means that high-earning economists bring up the average, so that the distribution of economist salaries in the US is right-skewed. It seems that highly experienced economists, whether in industry or academia, command very high salaries in the US – another sign that highly skilled economists are in demand. The BLS data also supports the INOMICS Salary Survey findings that industry economists tend to out-earn their academic peers through the middle of their careers, until academic economists earn the rank of full professor, where they earn salaries comparable to top executives in industry. High demand for economists will make it easier for economics graduates to find work, and will likely push up the wages that experienced economists can enjoy. Thus, economics remains a practical and eminently employable field of study. But, who exactly will be hiring these economists? Industry breakdown of economists employed in the US compared to the world Economist jobs are further broken down by category in the BLS data. Table 4 reproduces some of this data taken from the BLS2,3: Table 4: Economist Employment Estimates (BLS data): Employer Type Employment Annual mean wage % of economists employed Colleges, Universities, and Professional Schools 10,250 $127,410 41.05% Federal Executive Branch (OEWS Designation) 4,440 $133,220 17.78% Management, Scientific, and Technical Consulting Services 3,410 $152,330 13.66% Scientific Research and Development Services 1,740 $124,060 6.97% State Government, excluding schools and hospitals (OEWS Designation) 1,620 $80,850 6.49% Junior Colleges 1,350 $88,190 5.41% Local Government, excluding schools and hospitals (OEWS Designation) 1,190 $91,330 4.77% Business, Professional, Labor, Political, and Similar Organizations 560 $130,080 2.24% Central Bank 260 $220,460 1.04% Other Businesses* 150 $193,092 0.60% *This category consolidates the BLS’s “Nondepository Credit Intermediation”, “Legal Services”, and “Accounting, Tax Preparation, Bookkeeping, and Payroll Services” categories. The BLS data shows that universities are the largest employers of economists in the US by a significant margin. The federal government is another major employer, followed by management and technical consulting firms. Other employers of economists include junior colleges (schools that grant associate’s degrees or other certifications that are not Bachelor’s degrees), research institutes, local and state governments, and other businesses. Worldwide INOMICS data can be compared with US-only BLS data to get a sense of how representative the US data may be of economics careers worldwide. Although the employer categories are different, it appears that both sets of data contain similar proportions of economists in government, academic, and industry roles. 55% of the worldwide 2022 INOMICS Salary Report respondents were employed at a university, compared to the BLS’ 48.3% (found by summing the Universities and Junior Colleges categories). Academia is the largest employer of economists around the world, though it appears to be less so in the United States. 13% of Salary Survey respondents were employed in private businesses, while 16.5% of economists in the US were employed in private businesses (found by summing “Other Businesses”, “Business, Professional, Labor, Political, and Similar Organizations”, and “Management, Scientific, and Technical Consulting Services”). There are some differences as well. 5% of worldwide Salary Survey respondents were employed in a Central Bank, while just over 1% of economists in the US are employed by the US central banking system. In contrast, while 25.25% of economists in the US were employed in federal or state governments, only 11% of Salary Survey respondents worked for a government. The comparison shows that US employment numbers may understate the availability of central bank and academia roles and overstate the availability of government roles worldwide. Nevertheless, the proportions of economists in different roles in the US appears fairly representative. For regional breakdowns of economist employment within the US, see the BLS’s website2,3. Support more research into economist careers If this information is useful to you, please take a couple of minutes to complete the (anonymous) INOMICS Salary Survey. We use these responses to study the job market for economists and help current and future economists make informed career decisions. Filling out the survey takes only a few minutes, and you’ll receive a copy as soon as the Salary Report is published. References 1: https://www.bls.gov/ooh/life-physical-and-social-science/economists.htm 2: https://www.bls.gov/oes/current/oes193011.htm 3: https://www.bls.gov/oes/current/oes251063.htm#nat Header image credit: rawpixel.com via magnific (free license)
Being (or becoming!) an economist probably seems like a good idea. After all, you’re reading the INOMICS Blog! Beyond reading relevant advice and interesting articles, it can be helpful to have real data to answer questions about the job market for economists.
Scientists rejoice: the UK rejoins the Horizon Europe programme
This general maxim can be applied to the growth of knowledge as well as the trade of physical goods. When researchers are allowed to specialize in their own topic areas and then collaborate with the other researchers around the world, knowledge – and therefore human capital – grows more than it would otherwise. Research and innovation can therefore be thought of similarly to the trade of physical goods; the more that is shared, the merrier. That’s part of why news about an agreement between the UK government and a prominent EU research program have given European and UK-based researchers – including many economists – renewed hope in the future of science. The Horizon Europe program On September 7, 2023, it was announced that the UK is set to rejoin the Horizon Europe research programme, after an agreement between the UK’s Prime Minister Rishi Sunak and European Commission president Ursula von der Leyen. It is expected that the UK will rejoin the programme effective January 1, 2024. The Horizon Europe programme is a framework for collaborating research across borders in the EU (and around the world) that includes researchers from top research institutes, technology companies and universities. The programme presents a framework of societal goals, some shared by member nations, and some (such as the UN’s Sustainable Development Goals) that are not just limited to relevance in the EU. The programme benefits researchers by streamlining complex administrative requirements and allowing access to a wide pool of potential funding; together, this makes it easier for a diverse body of researchers to work together, which lowers the barriers to advancing science. The programme notably features an open science mandate as well, helping to break down artificial barriers to sharing knowledge. According to a fact sheet released by the European Commission, as of 2021 the programme accounted for 17% of global R&D expenses and was at least partially responsible for 25% of the world’s top scientific publications. It has a budget of €95.5 billion across the years 2021-2027, which it grants to research projects across six different topic “clusters”. Funding in the programme is not just limited to EU countries. While a small proportion of each project’s team is usually required to be EU-based, researchers around the world are very much allowed to be part of research teams that receive funding from the programme. <11an>limit:3;categories:Jobs</11an> History with the UK Originally, the UK remained part of the Horizon programme even after the “no deal” Brexit situation came to pass. However, the UK left the programme after negotiations over the Northern Ireland Protocol caused relations to grow tense. This had several effects detrimental to the progress of science, particularly in the UK. It became more difficult for researchers in the UK to collaborate with colleagues in other parts of the EU through Horizon-funded research, and it cut off UK funding to Horizon Europe research projects. On top of the difficulties introduced by Brexit, this made it more difficult for scientists to conduct research in the UK, and less attractive for prospective scientists to settle in the UK or seek partnerships with UK-based institutions or researchers. Organizations based in Europe, uncertain about the future ability of their UK colleagues to cooperate in a post-Brexit landscape, were incentivized to find non-UK options for funding and research collaborations. As a result, the UK lost a significant number of researchers and received significantly less money from EU sources. What does this mean for economists in Europe? By rejoining Horizon, the UK is unlocking unparalleled research opportunities. This has the potential to alleviate much of the pain caused in the past few years. Economists in Europe, particularly those in the UK, should find that this change is positive or neutral for their work, with little downside. It will be easier for economists to collaborate with British or European colleagues, and funding might be more accessible for some projects. Moreover, science as a whole will benefit as researchers face fewer barriers to international collaboration and increased access to funding. To learn more about Horizon Europe, consider downloading a fact sheet about the programme, available at the European Commission’s website. Have you or your department been affected by the changes in the UK’s status in Horizon Europe, or have you been affected as an academic post-Brexit? Please share your experience in the comments below. References https://www.independent.co.uk/news/uk/politics/rishi-sunak-european-union-government-horizon-brexit-b2406606.html https://research-and-innovation.ec.europa.eu/funding/funding-opportunities/funding-programmes-and-open-calls/horizon-europe_en Header image credit: vwalakte on Freepik/Magnific (free license)
International cooperation is important for economic growth, because it allows societies to grow more than they could on their own. When countries specialize in the economic areas they are relatively more productive in (utilizing their comparative advantages) and trade with other nations, everyone is made better off.
John Maynard Keynes was a philosopher and economist who made a massive impact on the field. He is often placed next to luminaries such as Adam Smith and Karl Marx as one of the great founding thinkers in economic theory, and for good reason. Keynes’ ideas formed the basis of Keynesianism, a school of economic thought that was the first to seriously challenge the classical way of thinking. Keynesianism is one of the major disciplines or philosophies of economics, as opposed to the classical, Neoclassical, or monetarist (and etc.) schools of thought. Until Keynes’ new theories, economists had been stuck with classical models of economics that were a great starting point for economic theory, but were ultimately too inflexible and unrealistic. (Eventually, economists who saw value in the classical models gave rise to the Neoclassical school of thought, which has updated and improved classical economic thinking to remedy some of the criticisms thrown at it). Economics was forever changed as a result of this new perspective, giving rise to many new models and theories and cementing Keynes’ place in the history books. <11an>limit:3;categories:Bachelor Programs, Master Programs;disciplines:Economics</11an> The life of Keynes Keynes was born in Cambridge, England, and remained there to study at King’s College in Cambridge. He received his degree in mathematics in 1905. There, he was influenced by the eminent economists Alfred Marshall and Arthur Cecil Pigou, whom he studied under. Keynes bounced around between academic and government employment during his adult life. In 1908, he became a lecturer at King’s College. He left to work for the British government in the India Office, which was tasked with overseeing British rule in India. Here he wrote his first major book, Indian Currency and Finance, in 1913. Later, he returned to the university to teach again until 1915. Keynes left university employment again to work for the British Treasury, where he was the chief representative during the peace talks that led to the Treaty of Versailles, which was signed in 1919. However, Keynes left the Treasury soon after, as he thought the Treaty was unnecessarily burdensome to the Germans. In fact, Keynes wrote his work The Economic Consequences of the Peace to state this, correctly predicting that the punitive measures levied by the Treaty would keep Germany poor and politically unstable. Famously, that political instability allowed the Nazis to rise to power less than twenty years later. Keynes returned to Cambridge as a professor after his career with the Treasury. Later, he contributed to both the Bretton Woods conference that helped to devise post-World War II international trade regulations, and the formation of the International Monetary Fund (IMF) at the same conference. Keynes passed away in 1946, just a few years after the Bretton Woods agreement. Keynes’ contributions to economics When the Great Depression hit in the 1930s, then-contemporary economic theory could not explain why the Depression was so severe. Classical economic models tend to explain how the economy self-corrects over time, and classical economists believed the market would self-correct and government intervention would do nothing at best. Yet, with the Depression, the economy continued to stagnate with massive unemployment and a lack of self-correcting market behavior that these models could not explain. Keynes’ initial theories were an attempt to explain the times. Beyond the issue of describing the Great Depression, he did not see a way for classical economic theory to explain how economies can come out of recessions. So, he reassessed and started to develop theories of economics that could account for the Depression, and that showed how governments could help their economies to recover. This new reasoning was readily accepted by political figures at the time, who embraced government spending as a policy tool. U.S. President Franklin D. Roosevelt’s “New Deal” policies, which involved much government spending as a way to spur the economy out of the Great Depression, were urged on by Keynes himself in a letter he wrote to the American president in 1933. In this line of reasoning, and with his works Tract on Monetary Reform and Treatise on Money, Keynes contributed to the theory of monetary policy. He believed that a stable economy required stable interest rates, so advocated for the central bank to adjust the interest rate based on the price level. If the price level rose, Keynes thought the central bank should lower the interest rate, and vice-versa. The basic idea behind this thinking is that as the interest rate rises, borrowing becomes more expensive, so individuals and businesses will consume less; this can counter inflation. But, it was Keynes’ work The General Theory of Employment, Interest and Money that revolutionized economics more than any of his other works. This book introduced many foundational concepts that have become important theories and given rise to much more economic thinking since. For example, the concept of aggregate demand – the principle that the overall demand in an economy is the sum of consumer demand, government spending, and investment – was created and popularized by this book. He also showed that unemployment was probably impossible to prevent without sufficient government spending, which later gave rise to the idea of a natural rate of unemployment. This was in direct opposition to classical economic models, which again assumed that the market would achieve full employment on its own via self-correcting behavior. Keynes’ The General Theory also advocated for government spending to stimulate the economy during economic downturns, to lessen the impact of the recession. Notably, Keynes argued that it would even be a good thing for governments to take on debt to do so, in an era where balancing the government budget was seen as crucially important. Despite this, Keynes was a proponent of a free, perfectly competitive market, believing in Adam Smith’s idea of the invisible hand. Keynes’ legacy Of course, Keynes’ ideas were eventually built upon and critiqued, for example that his models cannot account for sticky wages or prices, and that they would overstate the usefulness of government spending as that encourages crowding out. As a result, Keynesian economics has changed greatly since the titular economist introduced the ideas. In particular, Keynesian ideas have been expanded upon by economists such as John Hicks, Robert Solow, Paul Samuelson, and others - producing much of modern economic thought in the process. Through their work and that of still more economists, the New Keynesian field has emerged with models that can account for some of the criticisms levied at Keynes’ original work. Yet the revolution that Keynes started has retained his name, and rightfully so. References https://www.econlib.org/library/Enc/bios/Keynes.html https://www.britannica.com/biography/John-Maynard-Keynes Header image credit: IMF, Public domain, via Wikimedia Commons.
Students often learn about important figures in economics only briefly and in passing, yet the content taught in economics courses often comes from brilliant economists such as these.
So, how do you find the exchange program that’s the right fit for you? It can be a tough decision and one you should deliberate properly, so read on for INOMICS' best tips to help you find the ideal exchange program tailor-made for you. <11an>limit:3;categories:Bachelor's Programs;disciplines:Economics</11an> Location, Location, Location One of the biggest issues to consider when thinking about an exchange program is – obviously – where you would like to stay. You should consider not only the country that you would like to visit, but also the specific city or region (and even different regions of your home country). Are you a fan of big, bustling cities? Or would you prefer a small town or somewhere in the countryside? Would you like to be in a cosmopolitan place, surrounded by people from all over the world, or would you prefer to be somewhere more homogeneous with a strong shared culture? The area around your university of choice will greatly affect your experience studying there, so be sure you research the region to know what you’re getting yourself into. Naturally, you need to think about language as well. You probably won’t have time to learn a whole new foreign language before your planned semester exchange – unless, of course, you plan it far enough in advance to take language courses one or two years before. This could be a fantastic way for you to solidify those skills and help you get the most out of your degree. But keep in mind that foreign universities may not only offer classes in the local language. For instance, you might be able to study entirely in English during a semester exchange while abroad in France, Brazil, or many other countries. Be sure to explore the options that you have with your current language skills even in other countries – you might be pleasantly surprised with the options. Naturally, if language remains a concern for you, it’s worth considering an exchange program that takes place in a country with the same mother tongue, or even within your home country. The National Student Exchange program in North America, for example, offers study away programs within the United States and Canada, potentially allowing you the benefits of a different perspective on your studies but without the cultural and linguistic challenges of living in another country. When and how long to go? The next big issue to consider is the length of time that you would like to spend on exchange. Short-term programs tend to run for a few weeks to three months, often over the summer break. They allow you to have the experiences of studying abroad (or just farther away from home), without interrupting or changing the schedule of your regular learning. These programs rarely include a full course-load compared to a normal semester, instead focusing intensively on a smaller handful of classes. However, don't assume that such a short-term program will be easy, or not require any work! Often short-term programs are the most study intensive, as all the materials must be compressed into a smaller number of hours. Although it may be less intimidating given the shorter time you will spend away, a short term trip can also have its drawbacks. Inevitably, you have less time to settle in, and often just as you feel like you’ve gotten used to the new setting, the trip is done and you are sent back off home. And, a short-term trip is less likely to be as much of a cornerstone of your life experience as a longer trip could be. A long-term exchange, on the other hand, typically lasts for six months to a year or more, and may include teaching from a local university which forms a part of your degree back at your home institution. So, with sufficient planning you won’t increase your overall semester count. This means that you get the true local's experience of attending university in your host country, and gain a different perspective on your subject. Such a longer term exchange can help you more fully experience a new part of your country or a foreign culture. It can allow you to develop new language skills, study your field under the guidance of experts with a different perspective, or with different strengths than the faculty at your home institution, and help you to network with colleagues from around the world. And, a longer term exchange abroad allows you to immerse yourself fully in a new culture and experience what studying abroad is like, while getting support on topics like visas and finding housing from your home institution. But longer exchanges come with their own drawbacks as well. A year can be a long time for an undergraduate student in particular to miss out socially at their home university – possibly some of the most formative years of your adult life. Not to mention the family, friends and traditions that might be left behind for a time. Plus, studying on exchange for a long time can put strain on your path to graduation if you aren’t careful, even if it’s at a university that partners with your home university, for example if you need to catch up on exams or coursework when you return. Degree Planning Exchange programs are usually not a core part of the degree experience you originally signed up for. They are typically not considered when university officials design curricula and schedule classes. Thus, it pays to be careful academically when considering an exchange program. When choosing a program, it might be exciting to jet off to the fanciest destination available. But, you must be aware of how the credits will transfer and what it will mean for the remainder of your degree. Exchange programs probably offer a limited selection of courses that your home university will accept as part of your degree program, and they might have prerequisites. Ensure you qualify for the courses you intend to take while on exchange. The last thing you want is for your exchange program to prevent you from graduating on time! Further, particularly when studying abroad, the course content in classes with the exact same name likely differs. To ensure you don’t miss any foundational topics, you should study the syllabi from your home institution’s course and your options on the exchange program to compare. Are there any major topics covered at one university, but not the other? Are there any topics missing from the exchange program? Checking the syllabi can ensure that you don’t accidentally put yourself behind your colleagues at home. There’s a chance that you could be the first person from your home university to take a specific class at your host institution while on exchange. Or, if you might even be the first person to go on exchange to that particular university in general. In both of these cases, you’ll need to work closely with administrative officials – and possibly class professors – from each institution. Be prepared to proactively ask questions about course content, fill out required forms, and argue the case for the new course credits you’d like to have transferred. Talk to other students who have done it before One important resource that’s easy to forget is those who have come before you: former exchange students! Universities will sometimes maintain a list of students who have studied abroad or done exchange programs at specific universities before, or organize an open-day or presentation where you can hear from study-abroad graduates. Alternatively, students who complete such a program can opt-in to such a list, and might be willing to talk to you about their experience. Don’t be afraid to ask around about students who have done the program before you – their advice will be invaluable. Funding Finally, it's important to consider how you will pay for the expenses of your exchange program. You will need to cover your travel expenses, insurance, accommodation costs, living costs, and in some cases university fees too. Generally, for short-term programs you will be expected to cover these costs yourself. Long-term programs, however, often have scholarships available which will cover these expenses. Be sure to check out the details of your program for specifics, as those programs which include scholarship funding often have lengthy application processes. Find out about them early to give yourself the best possible chance of securing one. An exchange program can be an important, unique, and awesome part of your university studies. We hope this article has helped you be inspired to discover which program is right for you. Header Image Credit: Hircus on Shutterstock (licensed image - copyright)
Exchange programs allow students to study at a different institution as part of their degree. Typically, students will use this opportunity to study abroad at a foreign partner of their home institution, though exchange programs can sometimes take place in the same country. Regardless, these programs are a fantastic way for students to broaden their horizons, take specific courses at another institution, and study abroad without committing to living multiple years in another country.
The INOMICS Questionnaire: Fratzscher vs Wursten
Jesse Wursten is a postdoctoral researcher at the Faculty of Economics and Business at KU Leuven in Belgium, and is funded by the Flemish Science Foundation (FWO); he is additionally affiliated with CWED-IRLE and UC Berkeley. Dr. Wursten’s main research focus is on applied labor economics. Other research interests of his include innovation economics and monetary policy. MF: What is your favorite place on earth? Jesse Wursten: Home. MF: Outside of economics, what occupation would you choose if you could be absolutely anything? JW: IT Entrepreneur. There's something exciting about owning your own business that is hard to match in a classic employer-employee relation. In a way, it is the ultimate sign that what you do is relevant to other people, otherwise you wouldn't have any customers. IT has the advantage of having relatively modest entry barriers, as you generally don't need expensive equipment or large teams to get to a first product (you could make a bad mobile app in a day so to speak, which is unheard of for physical products). I also feel like it’s an industry where I have the right skills to succeed: once you've identified a gap in the market, you need to figure out a technical solution (programming), but also interface that solution with the customer, especially if the solution is to show data of some sort. This is exactly what you do during an empirical economics PhD: you gather data and develop code to address an issue and then need to explain your solution as clearly as possible in a paper. So many apps fail at the customer interface aspect. For example, the app of an extremely popular brand of sports electronics produces post-workout graphs that are absolutely useless because a single outlier completely squashes all other data, or because the scales are not multiples of 5 (try interpolating between 13 and 20 on a smartphone screen). MF: What is the virtue you appreciate the most? JW: Honesty. MF: Your all-time favorite figure in economics? JW: Keynes. MF: Your ideal student? JW: Interested in the material, takes time to think things through beyond the obvious. MF: What should be done to address gender bias in research in economics? JW: I think there are two elements that make life especially hard for female researchers. One person (the supervisor) holds tremendous power over doctoral students. As long as this remains true, sexual harassment will remain problematic in the profession as victims are extremely disincentivized to speak up as it could literally end their career (need those recommendation letters). One option would be to require each student to have at least one co-supervisor, who is not necessarily actively involved in the research project, but is always available for meetings and questions and can become a second source of recommendation letters, funding, ... if issues arise with the main supervisor. The profession is horrible for anyone looking to start a family and have reasonable working hours. This also affects men of course, but they tend to realize that later in life (based on purely anecdotal evidence). Women are also still looked at first when it comes to taking care of children (although it seems to be changing, especially in higher educated circles). As long as making a career in econ requires moving thousands of miles and thus completely upending any local networks you may have, the profession is a nightmare for family oriented people. Men can sometimes choose to have kids later in life (= when they are tenured), women do not have that freedom. Likewise, combining research, teaching and admin with a healthy family life is almost impossible in most places. As long as the community as a whole considers 40+ hour/week workers as motivated rather than toxic, this will remain a problem. I say toxic because careers in econ are close to a zero sum game. If too many people raise the (working hours) bar, this puts pressure on all others. MF: What is the most promising current research field or issue in economics? JW: I'm biased as a labor economist, but I think one of the biggest questions of the coming decades is how we should structure our labor markets to maximize both efficiency and social welfare. The pandemic has shown that the demise of low-education jobs is not inevitable and the retirement boom catastrophe is still approaching without any sign of delay. What can be done to ensure that those who want to work, can do so, regardless of background and capabilities? How much should they earn for that work? Etc. MF: Where does economic research have the most influence on policy-making? JW: Monetary policy is extremely research-based, probably because it is the domain of technocrats rather than politics. I also believe policy is mostly affected by "simple" high-level theory, often so simple we take it for granted. Empirical evidence can then inform the details, especially for theoretically ambiguous situations (e.g. how high should minimum wages be?). I am not convinced that complicated models or overconfident empirical analyses contribute much to how we organize the world, though the former might still serve intermediate purposes within the academic community. MF: On what issues should policy listen more to economists? JW: Anything that involves potential externalities or second order effects. Economics offer simple yet potent frameworks to think about those situations, but these rarely seem to reach policymakers. The response to the gas crisis in Europe is a clear example. There are options there that bolster the purchasing power of the poor, reduce carbon emissions and prevent subsidies flowing into Russian coffers. Yet most countries went for simple but misguided policies that at best achieve the first and fail horribly at the latter two. MF: What is your career advice to a young economics researcher? JW: Keep an open mind about career opportunities. A recent statistic showed that less than 10%* of PhD students stay in academia, yet everyone considers this the norm. Instead, use the PhD to identify your skills and interests and do not be afraid to pivot towards alternative careers if research and teaching turn out not to be your comparative advantage. *The statistic came from our research support office, showing the share of FWO (the #1 science funder in Flanders) grantees that are still in academia a few years later. Images provided, with kind permission.
Professor Marcel Fratzscher, esteemed macroeconomist and president of the DIW Berlin, once again asks questions about economics and life in general to a peer in the field. This time, he has the pleasure of being joined by postdoctoral researcher Dr. Jesse Wursten, who generously agreed to take the hot seat in this exchange. Thus, the back-and-forth has been dubbed Fratzscher vs. Wursten, as is tradition. What follows is a conversation that offers insight into some of the challenges of balancing family with an economics career, the usefulness of economists when it comes to analyzing policy externalities, and more.
Club goods in economics are a type of good that is excludable, but non-rivalrous. This means that although using a club good doesn’t prevent others from enjoying it, and doesn’t reduce the amount available for others, it is possible to stop people from using the good in the first place. For this reason, club goods might occasionally be referred to as non-rival private goods. A classic example of a club good is membership in an exclusive organization such as a golf club or a gym. In this case, purchasing a membership doesn’t reduce the amount of memberships available for others, nor does using your membership reduce others’ ability to use their memberships. Astute students will notice that more specific parts of the club – for example, the golf course or specific gym equipment – might suffer if too many members try to use it at the same time, since the amount of space is limited. This problem is sometimes referred to as a congestion problem, since realistically there cannot be infinite users of the membership space. However, there could theoretically be infinite members, as the membership itself is not made more scarce when people sign up. So, economists would still classify a gym or golf club membership (and similar types of goods) as club goods. Another example of a club good that doesn’t suffer a congestion problem is a streaming service subscription. When you subscribe to a streaming service, you pay for access to a number of movies, TV shows, etc. Before you can access these goods, you must pay for the service, making the subscription excludable. Utilizing the service does not cause anyone else to enjoy the movies or shows on it less, nor does it cause fewer movies to be available to others, so the streaming service is also non-rival. Finally, using the service doesn’t crowd other people out of enjoying the movie as with a piece of gym equipment, so in this case this club good does not suffer a congestion problem. Good to Know Club goods that are deemed necessary for society are often provided via regulated natural monopolies. For example, electric utilities provide a very important service since power is necessary for many of the modern luxuries in our economies. But, high setup costs and relatively low costs of undercutting potential new entrants means it’s likely that a natural monopoly will form in the market. For example, setting up a power generator and electrical infrastructure to move that power into businesses and homes takes a lot of investment. And, once such a system is in place, it’s quite difficult for a new entrant to drum up enough initial investment to compete with the incumbent firm. This creates a natural monopoly situation where the incumbent firm can charge high prices, crowding out some end users of electricity. But, adding new users onto an existing electrical grid is relatively easy and cheap compared to making new electrical infrastructure, so the company can easily grow. These market characteristics – high start-up costs before any product or service can be sold, but relatively low cost of adding new users – tend to allow club goods to thrive. Unfortunately, monopolies (and in this case, even a perfectly competitive market) would under-provide electricity. In this type of situation, usually the government will step in to ensure incentives are properly aligned so that electricity is provided to everyone – typically involving the creation of a subsidy.
In the traditional ultimatum game experiment, two players are randomly and anonymously matched. One person, the proposer, receives a fixed amount of money and must offer a suggested split of this money with another person, the responder. The responder must accept or reject the offer as it is presented. They are told they will play the game exactly one time. For example, assume Player 1, the proposer, receives $20 to split with Player 2, the responder. Figure 1 below illustrates the choices available to each player and payoff amounts associated with each choice. Player 1 may choose an even split, giving $10 to each person, or an uneven split, such as keeping $18 and offering $2. Player 2 may either accept or reject this offer. If Player 1 offers an even split, and Player 2 accepts it, both receive $10. If Player 1 offers an uneven split that Player 2 accepts, then Player 1 gets $18 and Player 2 gets $2. If Player 2 rejects the offer, both players receive $0. Figure 1: Illustration of payoffs in an Ultimatum Game Traditional economic theory suggests people are rational, self-interested, and seek to maximize their utility. In theory, proposers would offer the least amount of money they believe a responder would accept, while responders would accept any amount of money offered because a few dollars is better than nothing. The results from ultimatum game experiments used in a variety of real-life settings, however, suggest the potential for responders to reject an “unfair” offer encourages proposers to propose more even splits of the money. The degree of familiarity, or social distance, between two people may also affect their choices. When players come from a shared social group (e.g., a village or a class in school), offers tend to be more “fair” or evenly split. Additionally, responders often reject offers of 20% or less even though they experience a financial cost from this choice (Camerer, 2003; Oosterbeek, Sloof, and Van de Kuilen, 2003). These results challenge the expectation that traditional economic theory would give us, as discussed above. The results do this by suggesting that receivers gain some value in signaling that unfair offers are unacceptable, and proposers likely account for the probability that an unfair offer is rejected when making their proposal. Further Reading Hoffman et al. (1996) examined ultimatum game behavior when participant roles were determined by a contest. Before participating in the experiment, subjects took a current events quiz, and their scores were ranked. Higher scoring individuals became proposers who were matched with a lower-scoring responder. Results revealed proposers offered substantially less when they felt their roles were justified and legitimate compared to when roles were assigned randomly. Additionally, responders tended to accept lower offers when they were splitting $10. When the amount of money to split was $100, however, rejection rates for lower offers increased significantly. The authors concluded responders were willing to accept the proposer’s role as justifiable when the stakes are low, but they became less willing to accept the proposers earned their roles legitimately as the stakes increased. Good to Know A modified version of the ultimatum game is the Dictator Game. In this experimental setting, one person (the dictator) receives an endowment and must decide how much of it to offer to an anonymous, random recipient. Unlike in the ultimatum game, however, the recipient must accept the amount offered and has no ability to punish the dictator if the offer is unacceptable. Traditional economic theory suggests players acting as dictators would make offers of $0 since there are no consequences. But, real-life experimental results suggest when the players are unknown to each other, dictators’ offers are lower. As with the findings from ultimatum game experiments, when there is less social distance between participants, dictators often allocate some money to the recipients, reducing the amount for themselves. This tendency to offer a non-zero share of the endowment indicates people may consider not only self-interest, but also ideas of fairness and altruism in their decision-making, especially in organizations. Less social distance between organizational members may encourage more prosocial behavior, improving the welfare of the organization and organizational outcomes. Research on dictator game experiments suggests information about the recipient may affect how much a dictator offers, too. Brañas-Garza (2006) examined dictator behavior in three different information conditions. First, when dictators received no information about recipients, 71% of dictators donated nothing. The average donation was 10% of the endowment. Second, when dictators were informed recipients were poor and from underdeveloped countries where this money could be useful, 46% of dictators donated the full endowment and 22% donated nothing. Third, when dictators were told the recipients were poor and that donations would be in medicines rather than money, 72% of dictators donated the full amount, and the average donation was 65% of the endowment. The authors concluded that knowing recipients were poor encouraged more altruistic behavior from the dictators.
The ultimatum game is a variation of game theory that has become popular in the field of experimental economics. The game involves players making sequential moves in a simple take-it-or-leave-it bargaining environment.
Assessing Cryptocurrency's Potential to Overthrow the U.S. Dollar's Dominance in the Financial System
While it’s true that the United States wields considerable influence over the global financial system because it controls the dollar, it is not the sole determinant of the system’s direction and functioning. Other major economies, such as China, the European Union, and Japan, also play significant roles in shaping the global economic landscape. Additionally, international organizations like the International Monetary Fund (IMF) and the World Bank exert influence through their policies and lending practices. Central banks, such as the Federal Reserve in the United States and the European Central Bank in the Eurozone, have a pivotal role in the global financial system as well. They are responsible for managing monetary policy, regulating financial institutions, and maintaining financial stability. Central banks exert influence over the global economy indirectly through their control of domestic interest rates, the money supply, and exchange rates. Their decisions have far-reaching implications for inflation, employment, and economic growth. On a national scale, banks are also a key source of credit for households, businesses, and governments, and their lending activities have a crucial impact on the health of the economy. In fact, investment banking and investment firms alone make up a significant portion of the current financial system. These institutions not only provide access to capital, but they also play a key role in managing risk, influencing corporate governance, and contributing to market liquidity. Challenges and Limitations of the Current Financial System While the current financial system has served the global economy in roughly the same form since the 1950s, it is not without its challenges and limitations. One of the main criticisms is its vulnerability to financial crises and economic downturns. The global financial crisis of 2008, for example, exposed many of the weaknesses and risks inherent in the system. The interconnectedness of financial institutions, the lack of transparency, and the excessive risk-taking by banks (see moral hazard) were all factors that contributed to the crisis. Such interconnectedness means that shocks in one part of the world can quickly spread and impact the entire system. Additionally, the current system is often criticized for its lack of inclusivity and its unequal distribution of wealth. Many argue that the benefits of economic growth are not shared equitably, leading to social and economic disparities. The concentration of power in a few dominant currencies, like the U.S. dollar, can create imbalances and inequalities in the global economy. This creates a situation where countries and businesses that do not use those currencies (or peg their own currencies to the dollar) are at a disadvantage. First, if a significant portion of global trade and investment is conducted in a few dominant currencies, it can be challenging for smaller currencies to compete. This can make it harder for businesses and countries that use less ubiquitous currencies to access global markets, leading to economic disparities – especially if it’s difficult for these businesses and countries to buy the dominant currencies at affordable rates. Second, dominant currencies can create an uneven playing field in terms of access to credit and financing. Countries and businesses that have better access to the dominant currencies may be able to borrow money at lower interest rates, while those that do not may face higher borrowing costs. This can make it harder for smaller and less developed economies to access financing, exacerbating economic inequalities. Third, dominant currencies can influence global economic policies and standards. Countries that use the dominant currencies may have more influence over global institutions and policy decisions, while those that do not may have less of a voice. This can lead to asymmetric power dynamics and policy decisions that favor the dominant currencies and those that use them. The Use of Cryptocurrency as an Alternative to Traditional Currencies In recent years, cryptocurrencies have emerged as a potential alternative, providing a way to trade without using traditional currencies. Built on decentralized blockchain technology, cryptocurrencies offer the promise of secure, transparent, and efficient transactions without the need for financial intermediaries. Bitcoin, the first and most well-known cryptocurrency, paved the way for a wave of digital currencies that have gained traction among investors and consumers alike. Other commonly known cryptocurrencies include Ethereum, Tether, BNB, and XRP. New cryptocurrencies appear often, though few become popular enough to become household names. Cryptocurrencies are not controlled by any central authority, making them resistant to government interference and manipulation. They have been touted by many as a potential threat to the dominance of the US dollar in the global economy. One of the main ways that cryptocurrencies could challenge the dominance of the dollar is by reducing the reliance of other countries on the US financial system. Because the US dollar is currently the dominant global currency, many countries hold significant amounts of dollars in their foreign exchange reserves and conduct the majority of their international trade in that currency. This makes them vulnerable to fluctuations in the value of the dollar and to political pressures from the US government. Cryptocurrencies provide an alternative to the dollar – and to foreign exchange in general – that is not subject to the same political and regulatory pressures. This is because cryptocurrencies are decentralized, meaning that they are less vulnerable to political interference, such as government manipulation of interest rates or currency controls. By using cryptocurrencies for international trade and investment, countries can reduce their dependence on the US financial system and potentially increase their economic independence. Another way that cryptocurrencies could challenge the dominance of the US dollar is by providing a more efficient and cost-effective means of conducting cross-border transactions. Traditional cross-border transactions can be slow and expensive, with fees and exchange rates adding significant costs to the transaction. Cryptocurrencies, on the other hand, can facilitate instant, low-cost cross-border transactions, potentially making them a more attractive option for international trade and investment. Other advantages that make them appealing for global trade include: Global Accessibility: Cryptocurrencies can be accessed by anyone with an internet connection, regardless of their location. This enables cross-border transactions without the need for traditional banking infrastructure, making it easier for businesses in different countries to engage in trade. Security: Cryptocurrencies use advanced cryptographic techniques to secure transactions and protect against fraud. The use of blockchain technology ensures transparency and immutability, making it difficult to tamper with transaction records. This enhances trust and reduces the risk of fraud or counterfeiting. Faster Transactions: Traditional cross-border transactions can be time-consuming, involving multiple intermediaries and requiring several days for settlement. Cryptocurrency transactions, on the other hand, can be completed within minutes or even seconds, enabling faster trade and reducing delays. Lower Transaction Costs: Traditional financial institutions often charge high fees for cross-border transactions, which can be a barrier to global trade for small businesses. Cryptocurrencies eliminate the need for intermediaries, resulting in significantly lower transaction costs. Reduced impact of politically-motivated trade barriers: Cryptocurrencies can reduce the impact of political tensions between countries on trade and investment. For example, if two countries are facing a trade dispute but their populations use a common cryptocurrency, it could potentially make trade and investment between those countries less vulnerable to political tensions and costly trade barriers. Financial Inclusion: Cryptocurrencies have the potential to provide financial services to the unbanked population, which is estimated to be around 1.7 billion people globally. By bypassing traditional banking systems, cryptocurrencies can enable individuals and businesses in underserved regions to participate in global trade. This has the additional benefit of providing a means for people to participate in the financial system, even in times of political upheaval. Smart Contracts: Some cryptocurrencies, like Ethereum, support smart contracts. These self-executing contracts automatically enforce the terms and conditions of an agreement, eliminating the need for intermediaries and reducing the risk of fraud. Smart contracts have the potential to streamline complex international trade processes, such as supply chain management and customs clearance. Potential refuge from inflation: Cryptocurrencies can provide a hedge against inflation and currency devaluation. Some cryptocurrencies, such as Bitcoin, have a fixed supply, which means that they cannot be devalued through inflationary monetary policies. If the crypto market’s volatility can be reduced, this can provide a level of stability and predictability to the economy that is not always possible with traditional currencies. Challenges facing the adoption of cryptocurrencies Overall, cryptocurrencies offer a range of advantages that can revolutionize global trade by increasing efficiency, reducing costs, and improving accessibility. However, they still face significant hurdles before they can seriously augment or even replace traditional currencies. One of the main challenges is the volatility of cryptocurrencies. The value of cryptocurrencies can fluctuate wildly, which makes them a risky investment and can make it difficult to use them as a stable store of value. There are also concerns about the potential for fraud, money laundering, and illicit activities to be facilitated by the anonymous transactions that cryptocurrency allows for. Regulatory authorities around the world are grappling with how to strike the right balance between fostering innovation and protecting consumers and investors. Another of the main challenges is the current lack of widespread adoption and acceptance. While some businesses and individuals have embraced cryptocurrencies, many others remain skeptical and prefer to stick with traditional forms of payment. Unless cryptocurrencies are accepted as a form of payment in more day-to-day transactions, this problem is likely to remain. Another risk is that the lack of a regulating authority leaves the cryptocurrency market open to more market manipulation than would otherwise be possible. This is also a contributing factor to the market volatility of cryptocurrency. This includes familiar stock market schemes such as the “pump and dump”. Other schemes include “spoofing” which involves a market manipulator placing a large order to influence other traders while intending to cancel the order. Wash trading is another tactic where small groups of investors trade crypto back and forth to simulate a high trading volume, although “real” trades are not taking place. Policy Responses and Recommendations The key to the success of utilizing cryptocurrencies as a more widespread means of exchange lies in striking the right balance between innovation and regulation. As technology advances and cryptocurrencies become more mainstream, their impact on the financial system will become increasingly significant. To best take advantage of the potential upsides of cryptocurrencies, policymakers should consider implementing the following practices: Regulatory Frameworks: Establish comprehensive regulatory frameworks to govern the use and trading of cryptocurrencies. These frameworks should address risks such as market manipulation, fraud, and money laundering while providing clarity and protection for investors and users. It’s worth noting that these frameworks would exist to provide safety and stability to crypto users – they would not give politicians control over crypto itself in the same way that governments and central banks manage most currencies today. International Cooperation: Foster international cooperation among financial governing bodies to harmonize regulations and ensure consistent approaches to cryptocurrency oversight. Collaboration can help mitigate cross-border risks and promote global financial stability. Risk Assessment: Conduct thorough risk assessments of cryptocurrencies to identify potential systemic risks and develop appropriate mitigation strategies. These assessments should consider factors such as price volatility, market manipulation, and the potential for financial contagion (the spread of financial distress from one institution, market, region, to others, often resulting in a systemic crisis). Education and Awareness: Promote education and awareness programs to enhance public understanding of cryptocurrencies. This can help minimize misinformation and ensure informed decision-making among users and investors. Conclusion: Assessing the Potential Impact of Cryptocurrency on the Global Financial System If cryptocurrencies were to gain widespread adoption, it would have profound implications for the global banking system. Banks, as intermediaries in the current financial system, would need to adapt to the rise of digital currencies. They would have to develop new business models and explore ways to integrate cryptocurrencies into their existing infrastructure. Additionally, central banks would need to redefine their role in a world where traditional currencies are no longer the primary means of exchange if digital currencies overtook traditional ones in popularity. As the global financial system continues to evolve, the role of cryptocurrency is likely to become more prominent. And this rise presents both opportunities and challenges for the global financial system. While it is unlikely that cryptocurrencies will completely overthrow the U.S. dollar's dominance in the near future, they have the potential to complement traditional currencies and offer an alternative means of exchange. The key in utilizing them lies in addressing the concerns and limitations associated with cryptocurrencies while harnessing their potential benefits. Header image credit: fabrikasimf on Freepik / Magnific (free license)
For decades, the U.S. dollar has reigned supreme as the world's primary reserve currency. Its dominance can be attributed to several factors, including the size and stability of the U.S. economy, the political influence of the United States, and the widespread acceptance of the dollar in international trade and finance. The dollar's status as the de facto global currency has significant implications for the functioning of the global financial system.
A primer on how popular AI models work and their current limitations
As AI continues to feature prominently in the news cycle, there’s bound to be speculation about the future. Indeed, AI’s potential to disrupt the labor market has been perhaps the biggest subject of discussion. But often, these conversations are had by people who haven’t studied the actual workings of these new AI tools. In many news articles or public discussions, the actual capabilities of current AI tools can be poorly defined or understood, particularly the capabilities of large language models (LLMs) as ChatGPT has nearly become a household name. Therefore, it’s worth spending time to learn how current LLMs work, so that we will all have a better understanding of how LLMs might realistically shape the economy as these conversations continually arise. This article will therefore focus on LLMs, though there are other types of AI still being researched and developed. <11an>limit:3;categories:Conferences</11an> How do these LLMs work? ChatGPT, and many models like it – such as Google’s Bard (or LaMDA), Microsoft’s Bing AI, and a host of others – is a large language model (LLM). LLMs are algorithms that generate text based on training data that is given to the model during development. These models are usually made up of a neural network – a software structure modeled after the way human neurons function. Specifically, these models make use of transformer architecture, a specific type of neural network developed by data scientists interested in deep learning. These models can respond to questions that humans ask them, and generate (usually) accurate responses. They “learn” when people provide feedback, helping the model to re-weight certain responses if they were incorrect. The core process at work for all these activities is an algorithm that aims to accurately predict the most likely word that comes next. When queried, the model builds a response by using probabilities to determine those most likely next words. It will do this repeatedly until the response is finished. Once a response is finished, certain parameters can be changed by the professionals training the model to make it respond differently (i.e. more accurately) to the same queries. Over time, this training process results in the development of a complete and powerful model like ChatGPT-4 that has been trained with massive amounts of data (specifically 45 gigabytes, whereas GPT-3 was trained with "just" 17 gigabytes of data), and can answer many prompts in a useful way. Training data partially defines the usefulness and limitations of AI models Naturally then, training data is incredibly important for the development of AI models based on LLMs. That’s because the specific training data used is influential in determining what responses the model will produce. This has a few key implications for the limitations of these tools. First, human knowledge and expertise is required in order to build a useful LLM. People can speculate if AI tools will eventually be able to replace human workers – like replacing doctors’ diagnoses, for instance. But in order to create such an expert LLM, a large amount of medical information and knowledge (in this case) is needed. At least for now, this information must be compiled by humans. And, it pays to have subject-matter experts (such as medical doctors) test the output of any model to ensure it’s accurate. This suggests that there may be net-new jobs for subject matter experts to assist with the development of subject-specific AI tools. Another consequence of the importance of training data is that biases and prejudices can become internalized by AI models if not dealt with. LLMs don’t exhibit critical thinking skills in the way humans do (at least for now). This means that a new LLM will “learn” any bias, prejudice, or myth as fact if those biases are present in their training data. Clearly, this can produce incorrect or unfavorable results as the models are deployed, if the biases are not detected beforehand. This is another reason why carefully cultivated training data is valuable. The fact that useful training data is required to build a good LLM tool means that the intentional selection and maintenance of high-quality, relevant training data – and the ability to minimize any biases the tool might learn from that data – will become a critical need in the economy. This can create an important new role for human experts in various fields. And, certain companies might specialize in providing subject-matter specific training data for AI models that others can purchase or subscribe to. Will we ever develop an AI that’s as smart as we are? One common mistake that people make about AI models, in particular LLMs, at the moment is attributing too much independent intelligence to the tools. It’s easy to think that an AI model is a thinking being or individual without understanding how the model works, especially given people’s penchant for personifying things. However, no type of AI has yet reached the point where it can be considered an independently intelligent, thinking entity. AI experts have coined the term “artificial general intelligence” for such a theoretical entity, as opposed to “narrow” intelligence that is good at a specific task, like controlling a car, giving media recommendations based on a user profile, or responding to questions in a human-like way. If and when a more “general intelligence” AI does exist, it would theoretically be able to grow, evolve, and adapt to new functions beyond its basic structure. It would be able to “think” and learn, self-critically improve its own functioning, reason similar to how humans do, and tackle any problem a human can. Expert opinions vary on the likelihood of such a model being possible, and when it would be developed if so. Interested readers can check out this article on the Conversation to read a few experts’ thoughts on the subject. Other limitations: hallucinations and calculators Especially given that AI models have not reached a human level of intelligence (yet), they will continue to have clear limitations in practice. LLMs in particular occasionally struggle to differentiate between fact and fiction. It’s quite possible to convince an LLM of something patently false, which will potentially derail its future text generation. Sometimes, LLMs can even invent fake sources or facts; this is known as “hallucination”. Hallucination can be difficult to spot if the user doesn’t have expertise in that field of knowledge. And, this can cause serious problems; imagine if an LLM-based AI tool gave a medical diagnosis that was not only incorrect, but completely fabricated! New users often attempt to use LLMs like ChatGPT as a kind of search engine, as well. It may appear that these models can instantly search the Internet to provide an answer to any question. But, the hallucination problem, and understanding how the underlying algorithm works, quickly reveals that “search” results in an LLM cannot be trusted. This is because the models don’t actually search the Internet (at least, most of them at the time this article was written; in the future, it’s likely that many models will have full up-to-date Internet access). They simply generate words repeatedly using probabilities based on the training data that was used to build them; it’s entirely possible, even likely, that a search question posed to an LLM will be incorrect or entirely hallucinated. Moreover, the models were trained on a now-out-of-date block of text taken from the Internet, so even if the model does produce a correct answer, any time-sensitive information will quickly become obsolete without continual updates. A related issue is that LLMs are (again, at least for now) bad at math, and cannot be used as calculators. This is often surprising to new users, but it makes perfect sense when considering the actual algorithm that composes models like ChatGPT. These models, again, predict the next most likely word. They don’t recognize math symbols as being uniquely different than any other word in our everyday language. As such, these models don’t actually compute any math; rather, they create strings of responses that have a high likelihood of being “correct”. When that comes to regular language, this is often fine, allowing LLMs to generate large blocks of coherent text. But when it comes to the very precise, rules-based language of mathematics, this is a major limitation for LLMs. However, other AI models have been developed (and will be increasingly refined) that are optimized for math (and coding, though LLMs are better at coding than math at this moment). It’s possible that LLMs become more reliable in these capacities in the future, too, but at the moment their responses shouldn’t be blindly trusted. Update: 16 December 2023: Researchers have just successfully solved an "unsolvable" math problem using AI. The article "Mathematical discoveries from program search with large language models", early access to which was published in Nature on 14th December 2023 (the final paper is not published as of this writing) details how a team of researchers using Google's DeepMind used two AI tools in tandem to find the solution. It's noteworthy that this process still presented some of the above limitations of large language models. In short, the LLM tool used by the research team proposed millions of suggestions, and upon evaluation of those answers, only the best responses were kept. Then the problem was started anew. Over the course of several days, this eventually led the algorithm and the research team to a correct answer to the unsolvable problem. This demonstrates two things: first, the hallucination problem is still present, as millions of responses had to be thrown out until the right one was reached. This also demonstrates the emergence property that transformer models sometimes display; in other words, a functionality that was not explicitly coded (solving an unsolvable math problem) was nevertheless a function the model could do. Clearly, AI tools will continue to evolve well beyond the original scope of this article. Their usage in everyday organizations have the potential to greatly increase economic efficiency, though naturally they pose a creative destruction risk to many industries as well. So, what can AI do right now? As of this writing, LLM AI models are very good at rote tasks and producing messages. It can be quite useful to have an LLM generate an email or similar message quickly. It’s relatively straightforward to get them to write in a specific style, or emphasize a certain tone, as well. But these tasks aren’t the only thing LLMs can do. They can be used to automate customer service, write software, contribute to research and development, perform sales operations, summarize large datasets quickly, “read” and quickly digest any given block of text, and more. This has the potential to shake up many industries, including software development, call centers and customer support, and more. AI tools have also notoriously been used to generate images and art with large degrees of success. These models usually aren’t just LLMs; they use LLMs to help with natural language processing, then other algorithms to help generate images from text. It’s true that the limitations discussed above have prevented LLMs from completing more impressive tasks on their own, like writing a best-selling novel (though many AI-generated “nonsense” books have flooded digital book lists lately), or publishing research. However, these tools have already greatly diminished the work required for such tasks in many cases. AI writing tool assistants have already become fairly advanced, although they aren’t quite producing works independently yet. For example, several research papers published this year have noted that ChatGPT was used to help write them (although prominent journals like Science have declared that ChatGPT cannot be an author). These tools can also help someone write a (non-“nonsense”) book in mere days, by generating the foundation of a story that generally just needs editing. This could drastically change the writing and publishing industries, or cause writers and editors to start competing with one another as LLM tools perform much of the actual writing. Regardless, it may only be a matter of time before the next Game of Thrones is written by an LLM. Emergence and the future of AI Regardless of the current deficiencies or limitations, it’s clear that AI tools are becoming a permanent fixture of the economy. They have a wide array of applications and use cases as aforementioned. And, if history is any indication, AI tools will only get better from here. The idea of an artificial general intelligence was already discussed, and while current AI models do not exhibit this type of ability, they do embody another phenomenon like it. Emergence in computer science can be defined as a model demonstrating new abilities that it was not explicitly trained to do. For example, in this article one researcher writes about how complex LLMs were able to correctly identify movies based on only a set of emojis. Often, we don’t quite understand how the underlying mechanism – in this case, the neural network’s probability-based word selection – produces the emergent abilities. If the concept of emergence seems odd, consider it from a biological perspective. Humans are made up of trillions of cells, each of which has a defined structure and a specific set of tasks within the body. Biologists and chemists have described with great detail how proteins, carbohydrates, lipids, and nucleic acids (the four basic building blocks of biology) operate within our bodies. We understand how substances can move across cell membranes, how blood carries oxygen, etc. But, it’s extremely difficult to describe how consciousness develops from the basic biological and chemical processes occurring in our bodies. In this way, consciousness can be thought of as an emergent property derived from the physical biochemistry of a human being. In a similar way, LLMs have demonstrated some capabilities that are hard to trace back to the underlying neural network. These emergent capabilities tend to appear in large models, but not smaller ones. Often, AI researchers describe a tipping point where a large enough model can suddenly perform new tasks that weren’t possible before. Emergence – and the fact that widely used, publicly-available LLMs are relatively recent – suggests that AI models will only continue to improve in the future. It’s likely that the current limitations of LLMs and other types of AI models will be minimized over time as we get better and better at training them, and as we discover increasingly efficient means of building AI models. Like much technological progress in the economy, time will likely be kind to the growth of AI. But, it still pays to be informed about how these models actually work, in order to have meaningful conversations about their impact on the future economy. Other references https://www.mlq.ai/what-is-a-large-language-model-llm/ Header image credit: rawpixel.com on Freepik / Magnific (free license).
Artificial intelligence tools are becoming more and more ubiquitous with each passing week. The development of ChatGPT-3.5 in particular launched conversations about the capabilities of AI into the mainstream. Since then, news stories and speculation about AI supplanting (and sometimes replacing) human workers continue to be released. The economics of AI is, as a result, becoming an important topic in our field.
The GRE is changing: ETS to streamline the test in September 2023
As such, many of the most educated people in the world have spent time studying the GRE and taking the test. There’s a plethora of resources to help students study the test’s structure and the materials likely to be on it while they prepare to get the highest score possible. Because of the GRE’s importance in graduate admissions, it’s beneficial for educators, university staff, and prospective graduate students to stay up-to-date with the exam. On that note, a new announcement from ETS – the company that owns and administers the GRE – came out on May 31, 2023 with details on new changes that are coming to the test. GRE being shortened to quicken scoring time and reduce test-taking fatigue ETS is making significant changes to reduce the length of the GRE. Specifically, their press release states that the GRE (which currently takes about 4 hours to complete) “will take less than 2 hours to complete — roughly half the time of the current test”. Notably, this will make it one of the shortest major tests available in university admissions, particularly at the graduate level. This is being done for a few reasons. First, ETS is aiming to “offer candidates a better experience that values their time and reduces anxiety and fatigue”. With stress and anxiety prevalent in our society, this is a welcome change. The second major reason for introducing a shorter test is to provide a faster turnaround on scoring. The new test will reduce scoring time from 10-15 days to 8-10 days. This change will additionally help students complete and submit their graduate applications more quickly. Adjustments to GRE structure to allow for these changes You might be wondering how this new goal will impact the test structure; after all, cutting the time to take the test in half must involve some sort of sacrifice. To that end, ETS has described the actual test structure changes on their website. It appears that they have aimed to “trim the fat” by reducing the size of certain portions and eliminating non-scored sections, without reducing the GRE’s usefulness as a gauge of a student’s academic preparedness. First, the number of questions in both the Quantitative and Verbal Reasoning sections are being reduced by 46 questions (specifics are not given, but this is 46 among both sections; for example, perhaps 23 questions from each section will be cut). Further, the Analyze an Argument task is being cut from the writing section, there will be only one essay instead of two, and the unscored section is being removed from the test entirely. Despite featuring fewer questions, the test still aims to provide graduate admissions offices with the results and tools they need to assess applicant’s cognitive skills. The test will continue to measure the same things it always has; the question types are not being changed, and neither are the test sections. They’re only being streamlined. A full, more detailed breakdown of the new test’s exact structure can be also found on the ETS website. Note for students planning to take the GRE The fact that the questions themselves are not changing – only the test structure – means that GRE prep being done now is still valid for the shorter test. So, if you’re a student preparing for the GRE, keep doing what you have been already and you’ll be in good shape. Registration for the new GRE is already open. To sign up, simply register for a test date after the change takes effect on September 22, 2023. More details can be found in their new FAQ document available for download. — We would like to thank ETS Global for their support in the production of this INOMICS article.
The Graduate Record Examinations, more commonly known as the GRE, is one of the most important standardized tests in the world for admission to post-Baccalaureate programs. It’s very often required by universities when considering admission into their Master’s or even PhD programs.