Corruption Uniquely Alters How Institutions Promote Economic Growth
Welcome to the world of my research, where I explore this intricate relationship between institutional quality and economic growth. My work dives into how institutions shape economic dynamics, focusing on issues like corruption, the impact of the euro, and the role of institutional frameworks in economic convergence. Discovering Corruption’s Effect on Economic Growth When we think about the factors that drive or hinder economic growth, corruption often surfaces as a prime suspect. But how exactly does corruption influence the economic trajectory of a nation? Our recent study (Beyaert, García-Solanes and Lopez-Gomez, 2023) takes a fresh approach to answering this question, using innovative regression tree analysis and advanced econometric techniques to explore the nuanced effects of corruption on growth across 103 countries over the period from 1996 to 2017. Traditional economic growth models, like those developed by Barro and Sala-i-Martin (1992) and Mankiw et al. (1992), have typically treated corruption as a direct drag on growth. However, this study challenges that assumption by suggesting that corruption's impact is more indirect—and complex—than previously thought. Rather than applying a one-size-fits-all model, the researchers utilized regression tree methodology, which allowed them to segment countries into different subgroups based on specific characteristics (or "split variables") and then estimate distinct growth models for each group. The regression tree algorithm was allowed to determine endogenously whether the group divisions should be based on corruption, “Rule of Law”, or a combination of both. In particular, the analysis showed that separating countries into just two primary groups was best: those with high levels of corruption and those with low levels of corruption. This segmentation is crucial as it reveals how the impact of traditional growth factors—like capital accumulation and population growth—varies significantly across these groups. In nations with higher levels of corruption, these traditional growth determinants play a more significant role compared to countries with lower corruption levels. Essentially, corruption seems to exacerbate the influence of these factors, making them more critical in determining economic outcomes. Data for the study were sourced from authoritative references such as the International Country Risk Guide (ICRG) and the World Bank's Worldwide Governance Indicators. These sources provided key variables, including levels of corruption and the quality of the Rule of Law, which are essential for understanding the institutional landscape of each country. To enhance accuracy, we employed the GUIDE (Generalized Unbiased Interaction Detection and Estimation) methodology, which addresses the selection bias issues common in other analytical methods like the CART algorithm. The GUIDE method facilitated a more precise identification of split variables, leading to more reliable and nuanced results. It resulted in two groups, as detailed in Table 1. Table 1. Countries Grouped by Corruption Levels Detected by Regression Tree High Corruption Group Low Corruption Group Algeria, Angola, Argentina, Albania, Armenia, Belarus, Bangladesh, Bolivia, Burkina Faso, Cameroon, China, Rep. Congo, Cote d’Ivoire, Dominican Republic, Egypt, Gabon, Ghana, Guatemala, Guinea-Bissau, Honduras, Indonesia, India, Jamaica, Kenya, Latvia, Mali, Malawi, Mexico, Mozambique, Niger, Nigeria, Pakistan, Papua New Guinea, Panama, Paraguay, Philippines, Russia, Sierra Leone, Togo, Thailand, Tunisia, Turkey, Uganda, Ukrania, Zimbabwe Australia, Austria, Bahrain, Belgium, Botswana, Brazil, Bulgaria, Canada, Chile, Colombia, Costa Rica, Croatia, Czech Republic, Denmark, Estonia, Ecuador, El Salvador, Finland, France, Gambia, Germany, Greece, Hong Kong, Hungary, Iceland, Ireland, Israel, Italy, Japan, Jordan, Korea, Rep., Kuwait, Lithuania, Luxembourg, Malaysia, Madagascar, Morocco, Namibia, Netherlands, Nicaragua, New Zealand, Norway, Peru, Poland, Portugal, Romania, Singapore, Senegal, Slovak Republic, Slovenia, Spain, Sri Lanka, Sweden, Switzerland, United Kingdom, United States, Uruguay, Zambia Source: https://doi.org/10.1108/AEA-11-2021-0297. Copyright © 2022, Arielle Beyaert, José García-Solanes and Laura Lopez-Gomez. Published in Applied Economic Analysis. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) license. Subsequently, after dividing the sample into these two subgroups, a traditional cross-sectional Solow model was estimated, incorporating standard drivers such as investment, initial GDP per capita, and population growth. To ensure the robustness of these findings, the researchers utilized instrumental variable estimations, drawing on the methodology of Gründler and Potrafke (2019) to refine and enhance the instruments employed. This advanced econometric technique addresses the issue of endogeneity—a common challenge in growth studies—by ensuring that the identified relationships are truly causal rather than merely correlational. By improving the instruments and applying them within each identified subgroup, the study provides a more reliable estimation of the growth models. One of the study's key revelations is that corruption does not have a statistically significant direct effect on economic growth in either the most corrupt or least corrupt countries. Instead, its influence is more subtle, indirectly altering how traditional growth determinants operate; the algorithm revealed growth parameter differences between countries, showing that those with higher levels of corruption were farther away from reaching an economic steady state. These findings resonate with the work of Durlauf et al. (2001), which identifies parameter heterogeneity in the Solow model, suggesting that corruption contributes significantly to this variability. The evidence points to a clear pattern: the more corrupt a country, the more pronounced the effect of growth determinants. This means that corrupt countries are generally further from their economic steady state, and thus, experience a stronger impact from factors like investment. Interestingly, the study also highlights a developmental divide: more developed nations typically exhibit lower corruption levels, while less developed countries struggle with higher corruption. This finding emphasizes the dual challenge facing developing nations—not only must they fight corruption, but they must also focus on enhancing investment and managing population growth effectively. Geographically, the analysis identifies three major regions: Europe and North America, with low corruption; Africa and Latin America, characterized by higher corruption levels often linked to their colonial histories; and Asia, marked by significant corruption variability. Notably, China and Russia, despite both being part of the high corruption group, exhibit divergent growth trajectories. Russia’s corruption has been a drag on its growth, while China has managed to sustain rapid growth rates despite similar corruption levels. The difference is attributed to the more organized nature of corruption in China compared to Russia. In conclusion, this study urges policymakers and economists to reconsider how they model and address the effects of corruption on economic growth. It’s important for policymakers to recognize that improving institutional quality can help lower corruption. By recognizing that corruption's impact is largely indirect and varies significantly across different contexts, and by employing more sophisticated econometric techniques like improved instrumental variables, more tailored and effective strategies can be developed to foster economic development, especially in countries where corruption remains a significant hurdle. Regional Case Study: How the Eurozone Navigates Corruption and Institutional Quality While global discussions often focus on the broad impacts of economic integration, it’s essential to zoom in on specific regions to truly understand these effects. Shifting the lens to the Eurozone, my research delves into the influence of the euro on corruption control within this unique economic area. Using a counterfactual analysis based on the methodology of Abadie, Diamond, and Hainmüller (2015) and conducted by Beyaert, García-Solanes, and Lopez-Gomez (2023), we explored the hypothetical scenario of a Eurozone without the euro. Surprisingly, the findings suggest that the introduction of the euro has not worsened corruption control in southern Eurozone countries. Instead, economic integration and standardized practices seem to have a stabilizing effect on corruption levels. Still, the Eurozone has not achieved institutional convergence, as highlighted by Beyaert, García-Solanes, and Lopez-Gomez (2019) and García-Solanes, Beyaert, and Lopez-Gomez (2021). There are notable asymmetries in institutional quality among member states, creating macroeconomic imbalances, economic frictions, and inefficiencies, which potentially reduce the Eurozone's overall GDP (García-Solanes et al., 2023). This underscores the need for more uniform institutional standards to address these disparities. My research delves further into this economic convergence within the Eurozone. Using Phillips and Sul (2007, 2009) tests and a logit model, my findings suggest that successful economic convergence heavily depends on strong normative and judicial frameworks (Lopez-Gomez, forthcoming 2024). Countries with robust governance and legal systems tend to converge more effectively, while those with weaker institutions face persistent economic gaps. Finally, an analysis of income per capita and corruption control using panel Granger causality and cointegration tests (Lopez-Gomez, 2023) reveals no bidirectional causal relationship between economic growth and improved corruption control. The Great Recession further weakened this relationship, especially post-2008, with notable exceptions in Eastern Eurozone countries. Taken as a whole, the results of my research indicate that anti-corruption measures need to be tailored to the specific institutional contexts of each country, as a one-size-fits-all approach is unlikely to be effective. The Broader Implications Looking at the broader picture, my research consistently highlights one key theme: institutions are more than just background structures, they are pivotal in shaping economic behavior and outcomes. It’s a complex but crucial relationship, and unraveling how institutions interact with broader economic dynamics is essential for crafting effective solutions. Understanding these interconnections is key to developing policies that not only address immediate issues but also foster long-term stability and growth. So, where does this leave us? The future of economic growth and development hinges on our ability to enhance institutional quality. This means not just fighting corruption or reforming legal systems, but fostering environments where effective governance can flourish. The journey ahead is filled with challenges, but also immense opportunities. As we continue to piece together the puzzle of economic growth, one thing is clear: the role of institutions cannot be overstated. They are the hidden gears that drive economic performance. Thanks for joining me on this exploration of how institutions and economic growth intersect. It’s a journey through models, data, and analysis, driven by a passion for understanding and improving our economic systems. Here’s to the ongoing quest to piecing together the puzzle of economic growth! References Abadie, A., Diamond, A., and Hainmueller, J. (2015). Comparative politics and the synthetic control method. American Journal of Political Science, 59(2), 495-510. Beyaert A., García-Solanes J., Lopez-Gomez L. (2019). Do institutions of the euro area converge?, Economic Systems, 43(3-4), 100720. Beyaert A., García-Solanes J., Lopez-Gomez L. (2023). Corruption, quality of institutions and growth. Applied Economic Analysis. https://doi.org/10.1108/AEA-11-2021-0297 Beyaert A., García-Solanes J., Lopez-Gomez L. (2023). Did the euro really increase corruption in the eurozone?, Hacienda Pública Española-Review of Publics Economics, 3-36. García-Solanes. J, Beyaert, A and Lopez-Gomez, L. (2021). Clubes de convergencia en la zona del Euro, Papeles de Economía Española. García-Solanes. J, Beyaert, A and Lopez-Gomez, L. (2023).Convergence in Formal and Informal Institutions and Long-Run Economic Performance in the Euro Area. The Routledge Handbook of Comparative Economics Systems, 256. Lopez-Gomez, L. (2023). On the relationship between income and control of corruption in the Eurozone. The European Journal of Comparative Economics, 20 (1), 3-37. http://dx.doi.org/10.25428/1824-2979/020 Lopez-Gomez, L. (2024). The role of political institutions on the economic convergence process. Journal of institutional economics. Forthcoming. Phillips, P. C., and Sul, D. (2007). Transition modeling and econometric convergence tests. Econometrica, 75(6), 1771-1855. Phillips, P. C., and Sul, D. (2009). Economic transition and growth. Journal of applied econometrics, 24(7), 1153-1185. Header image credit: Aideon via Pixabay (Free for use under the Pixabay Content License)
Imagine this: you're trying to solve a complex puzzle where every piece represents a different facet of economic performance. Now, imagine that the shape of some of these pieces are influenced by the quality of the institutions in place—things like governance, legal frameworks, and anti-corruption measures. As it turns out, the fit of these pieces can make or break the overall picture of economic growth. Institutions, in this sense, are not just background players; they are fundamental to how the economic puzzle comes together.
Sustainability May Be Bolstered By Income: Household Cooking Fuel Choice in a Developing Country
The analysis revealed that an increase in durable asset accumulation heightens the likelihood of households adopting cleaner cooking fuels. Therefore, I find that the Ghanaian government could incentivize the adoption of clean fuels, consequently mitigating environmental pollution, by implementing policies aimed at augmenting household income. More broadly, this means that economic development can have positive effects on sustainability, something that policymakers in other regions should take note of. Introduction The seventh Sustainable Development Goal aims to ensure access to clean and affordable energy by 2030. This goal is crucial because reliance on unclean energy sources like firewood, charcoal, and kerosene cause significant environmental and health issues, including air pollution, deforestation, soil degradation, and harmful emissions that contribute to climate change. These sources emit harmful gasses such as CO2, methane, and nitrogen oxides, which adversely impact human health. Studies by Younger et al. (2022) and Amegah et al. (2012) reveal detrimental health effects from exposure to these gasses, including reduced birth weight and increased neonatal mortality in low-income countries like Ghana). In Ghana, the use of unclean energy stems from “energy poverty”, where households lack access to modern, clean and affordable energy sources. This forces them to resort to dirty and inefficient fuel sources. Factors contributing to this lack of access include low income levels, high prices of modern energy sources, and geographic location. Shockingly, energy poverty in Ghana is reported to be at a staggering 82.5% (Adusah-Poku and Takeuchi, 2019). This study aims to ascertain the impact of durable asset accumulation on the selection of household cooking fuels in Ghana. It posits that durable assets afford households greater financial capacity and purchasing power, enabling them to meet various needs, including access to cleaner fuels. The analysis distinguishes between rural-urban households and households headed by males versus females. The study acknowledges that the main obstacle hindering households from adopting clean fuels is their relatively higher cost. Consequently, households with elevated wages are more inclined to utilize clean fuels owing to their affordability. Moreover, the energy ladder hypothesis posits that as household wages rise, there is a corresponding transition towards cleaner fuel usage. In essence, lower-wage households tend to resort to dirty fuels, whereas those with higher incomes opt for clean fuels (Alem et al. 2016; Bofah, Appiah-Konadu, and Ngwu 2022). Thus, an upsurge in household wages is anticipated to increase the probability of households opting for clean fuels. Methodology Data The investigation relies on microdata extracted from the Ghana Living Standards Survey (GLSS) conducted in 2013 and 2017 by the Ghana Statistical Service (GSS). The sample size comprises 25,569 observations. The study categorizes various fuel types into two groups: dirty and clean fuels. The primary metric for gauging asset accumulation adopts the approach advocated by Filmer and Pritchett (2001), akin to the methodology employed in formulating the wealth index within the Demographic and Health Surveys (DHS). Empirical Model The study regresses the fuel choice of households on the durable asset accumulation index, controlling for household characteristics such as wage, size, location, and poverty status. The logistic regression technique is employed, with average marginal estimates. An instrumental variable estimation (IV) addresses the potential endogeneity problem, using the two-year lag of theft incidents as an instrument. To streamline the analysis, various fuel types were categorized into two groups: dirty and clean fuels. The dirty fuel category encompasses fossil fuel-derived sources such as firewood, charcoal, kerosene, sawdust, and crop and animal residue, while the clean fuel category comprises LPG and electricity. The study aims to discern how durable asset accumulation influences the probability of households opting for clean fuels, with a focus on facilitating the transition towards cleaner energy sources. An asset accumulation index was constructed by amalgamating various forms of durable assets with potential monetary value. These encompass items such as televisions, radios, sewing machines, refrigerators, fans, computers, cars, plots of land, and more outlined in Sections 12.4 and 12.5 of the GLSS. To ensure the index's consistency and avoid negative values, standardization was applied, rendering the index to range between 0 and 1. A model explaining the fuel choice of a household against factors that might influence this choice is given by: E(Fuel choices | Asset accumulation, Age of household’s head, Gender of household’s head, Household size, Household wage income, Geographical location, Father’s education, Household poverty status, Marital status, Expenditure on electricity) which leads to the following functional form: 𝑦 = 𝛽0 + 𝛽1asset_accumulation + 𝛽2age_head + 𝛿3female_head + ⋯ +𝜉i where 𝑦 represents fuel choice, and all other factors mentioned are represented in the ellipsis. Since the dependent variable is a dichotomous variable that lies between 1 and 0, that is, whether a household uses clean fuel or not, I use a logistic regression (Rodriguez, 2007). Supposing a linear model where the probability of 𝑦I is a linear function of its predictors, I estimate the log odds of energy fuel choice given by the following: logit(𝑦i) = 𝛽pXi+ 𝜉I As a result, the model assumes that the linear probability model is a function of natural log odds. To address the potential endogeneity problem associated with asset accumulation, a two-year lag of theft incidents is used. I argue that suffering caused by a theft case two years ago is associated with the current value of total household assets, but not directly on fuel choice in the present. The two-year lag of theft incidents will influence fuel choice only through its influence on the current value of assets. After fitting the model, the Pearson or Hosmer-Lemeshow goodness-of-fit test (gof) suggested that there is no significant difference between the observed and the expected values, indicating a good fit for the model. Further, the Link test returns a p-value of 0.000 for the variable of prediction, indicating that the model is properly specified. The same test also returns a p-value of 0.486 for the variable of squared prediction, suggesting no evidence of model misspecification since it is insignificant. These results suggest that the model does not suffer from specification errors. Results and Discussion The analysis revealed that a one-unit increase in the asset accumulation index increases the household’s likelihood of adopting clean fuels by approximately 7.32 percentage points. Higher income levels enable households to afford cleaner and more efficient cooking fuels like LPG or electricity. This finding is statistically significant at the 5% level. To ensure robustness and accuracy of this result, a bootstrapping procedure with 1000 replications was conducted. The bootstrapped standard errors and confidence intervals further validated the consistency of the estimated coefficient, confirming the reliability of the conclusion. Therefore, policies aimed at increasing household income and assets can incentivize the adoption of clean fuels, thereby mitigating environmental pollution and improving public health. A good strategy for achieving clean energy usage in low-income countries should therefore include economic development aspects. These findings provide valuable insights for policymakers and stakeholders working towards achieving sustainable energy goals; sustainability can be more easily achieved when the households making energy decisions have a sufficient level of income. The author is conducting more research into a related topic to deepen our understanding of the link between sustainability and other factors like education and inequality. First, with the upcoming paper “Female educational inequality and household cooking fuel choice: Evidence from the Ghana Demographic and Health Survey (2022)”, the author hopes to uncover whether better educational outcomes for women correlates with a higher likelihood of adopting cleaner cooking fuels. It is hypothesized that greater inequality in education would likely result in a continued reliance on traditional, unclean cooking fuels due to financial constraints. The author is also working on another upcoming paper, “Educational inequality and Child Stunting: Evidence from the Multiple Indicator Cluster Survey (MICS, 2018) in Ghana”. The study will investigate how education equips parents with better knowledge and resources to provide adequate nutrition and healthcare for their children, potentially reducing the incidence of child stunting. By focusing on these dynamics, the study aims to highlight the critical role of education in improving child health and development outcomes. References Adusah-Poku, F., & Takeuchi, K. (2019). Energy poverty in Ghana: any progress so far? Renewable and Sustainable Energy Reviews, 112, 853-864. Alem, Y., Beyene, A. D., Köhlin, G., & Mekonnen, A. (2016). Modeling household cooking fuel choice: A panel multinomial logit approach. Energy Economics, 59, 129-137. Amegah, A. K., Jaakkola, J. J., Quansah, R., Norgbe, G. K., & Dzodzomenyo, M. (2012). Cooking fuel choices and garbage burning practices as determinants of birth weight: a cross-sectional study in Accra, Ghana. Environmental Health, 11, 1-10. Ansong, E. (2015). The association between household consumer durable assets and maternal health-seeking behavior in Ghana. Women & Health, 55(5), 485-504. Bofah, R. O., Appiah-Konadu, P., & Ngwu, F. N. (2022). Transition to cleaner cooking energy in Ghana. Clean Energy, 6(1), 193-201. Filmer, D., & Pritchett, L. (2001). The effect of household wealth on educational attainment: evidence from 35 countries. Population and development review, 25(1), 85-120. Kafle, K. R., & Dean, J. (2015). Effects of asset ownership on child health indicators and educational performance in Tanzania. Rodriguez, G. (2007). Logit Models for Binary Data (p. Chapter 3). Retrieved from http://data.princeton.edu/wws509/notes/ Younger, A., Alkon, A., Harknett, K., Louis, R. J., & Thompson, L. M. (2022). Adverse birth outcomes associated with household air pollution from unclean cooking fuels in low-and middle-income countries: a systematic review. Environmental research, 204, 112274. Header image credit: Pexels from Pixabay (Free for use under the Pixabay Content License)
The Sustainable Development Goals advocate ensuring universal access to clean and affordable energy by 2030, prompting a global push toward transitioning from unclean energy sources to cleaner alternatives. In response, Ghana has subsidized the cost of clean energy; however, the financial burden of this transition remains substantial for households. The role of household asset accumulation in facilitating this transition has been underexplored in existing literature; examples include Kafle and Dean (2015) and Ansong (2015). Utilizing logistic and instrumental variables (IV) regression methods along with data from the Ghana Living Standards Survey (round 6 & 7), I investigated how durable asset accumulation influences households' choice of cooking fuels in the research paper "The Nexus Between Household Durable Asset Accumulation And Household Cooking Fuel Choice in a Developing Country".
Economic Development, Innovation and Growth (JEL O)
Agricultural and Environmental Economics (JEL Q)
Assumptions Mainstream economic theory is built upon a foundation of assumptions that are becoming increasingly outdated. For example, the way that businesses operate has changed drastically over the lifetime of the discipline, though many of the core assumptions of economics have remained unchanged. The shift towards the ‘knowledge economy’ and the idea of ‘economies of scale’ are proof of this business evolution. Both of these concepts have great appeal for capitalists – if companies get bigger, they benefit from cost savings. Bulk buying, advertising discounts, lower borrowing costs, and the ability to reduce fixed costs proportionally can all help bigger organisations reduce their average spend. Image credit: University of Bath. However, should companies get too large, ‘diseconomies of scale’ can set in. Perhaps production processes for different components can’t scale up at the same speed, communications get lost in translation, or workers start to feel less motivated. The theory implies that as businesses grow, they eventually hit limitations. This should mean that no company can grow large enough to corner the market. At least, that’s what we used to think. Grow – Continuously. While some companies still have scope for diseconomies of scale, in other cases, these forces can be offset by ‘increasing returns to scale’ which allow growing companies to reinforce their gains. Often described as ‘being ahead to get further ahead’, if a company grows by clever strategy – or even by chance – increasing returns can magnify this advantage. Therefore, the product, company, or technology can go on to lock in the market. In the new intangible economy, companies can grow – continuously. There are different sources of increasing returns, too. For example, the idea of ‘learning by doing’ (the more experience you have in doing something, the better you become) is one mechanism by which companies can experience increasing returns. Take the production of lithium-ion batteries for electric vehicles (EVs). According to Morgan Stanley researcher Michael Mauboussin, “cumulative output has moved Tesla down the cost curve faster than its competitors” for batteries and EV production. Morgan Stanley analysis finds that Tesla has benefited from lower battery costs and operates the most productive automobile plant in the US. Another example is ‘network effects’, when the value of a good or service increases as more people use it. Platform businesses are a good example of these. If a ride-share company such as Uber attracts more riders, they will find it easier to recruit more drivers, customers will be able to find rides faster, these effects will attract more customers, and so on. Image credit: University of Bath. The AI Catalyst: Accentuating Superstar Status The advent of artificial intelligence (AI) is poised to further entrench the position of larger companies, amplifying their growth in sales, employment, and market valuations. By leveraging the power of AI, these "superstar" companies could solidify their dominance and exacerbate existing power imbalances within the global economy. Counterbalancing Forces: Regulation and Complacency Despite their formidable advantages, the rise of knowledge companies is not unstoppable. Regulations and the threat of complacency could serve as counterbalancing forces, curbing their unchecked growth and dominance. Regulatory Oversight Regulatory bodies can intervene to prevent monopolistic practices and promote fair competition, as exemplified by the recent US antitrust lawsuit against Apple alleging "exclusionary, anticompetitive conduct." While tech giants often defend such actions as necessary for innovation, regulatory oversight remains a crucial check on their power. The Complacency Trap Success can also breed complacency, leading companies to become overly reliant on their "golden goose" and neglect innovation. As warned by experts, even technologies with powerful network effects, like the fax machine, can be superseded by better alternatives if companies fail to adapt and innovate. Embracing the Next Wave As the renowned economist W. Brian Arthur cautioned, "the ability to profit under increasing returns is only as good as the ability to see what's coming in the next cycle and to position oneself for it." While the current wave of technological advancements has enriched some companies to stardom and reshaped the global economy, their fortunes are not guaranteed. Complacency and a failure to anticipate the next disruptive innovation could render even the mightiest companies obsolete. As Arthur sagely advised, "Those who have made a killing on this cycle should not become complacent," for "technology comes in successive waves," and those who position themselves for the next wave may emerge victorious. Image credit: University of Bath. Fuel Your Mind. Transform Your Career. Packed with engaging, challenging, real-world content, you can further your understanding of the world of economics with the University of Bath’s Applied Economics (Banking and Financial Markets) online MSc. Find out how you can make strategic decisions that could impact the next wave of economic growth. Join the next student intake. INOMICS would like to thank the University of Bath for their support in the production of this article. Header image credit: University of Bath.
In today’s world, the discipline of economics finds itself at an inflection point. The mainstream orthodoxy, often criticised for being made up of half-truths, is starting to unravel. As a result, a transformative shift is happening.
From Gambling to Economic Growth: how a Greek goddess inspired a new model for Italy’s local development
In the author’s own words, “Strange, perhaps, for a book that intends to deal with economic issues to bring Greek mythology into question.” He continues: “Certainly, it was not my intention to disturb the Gods of Olympus except that, absentmindedly reopening a booklet on Greek mythology, I once again came across the myth of Athena after about thirty years…The rereading of this myth and this very original Goddess made me reflect on the qualities and characteristics that every healthy society should possess to guarantee balanced and constant progress over time.” According to Giliberto, for some years now there has been debate in Italy about the need to undertake new models of territorial development. To that end, the book hypothesizes a desirable “cultural conversion”. By encouraging local citizens to invest even small sums of their money into local development rather than gambling with it, economic growth could be stimulated. Further, citizens would become more aware of the businesses and economic details of their region, increasing civic involvement. The Athena Model: regional development via local stock exchanges Giliberto notes that research has shown that at a certain point, increasing wealth no longer increases happiness. But that doesn’t mean that development isn’t a worthy goal, particularly in areas where the local economy has declined. And, Giliberto asserts that this model of development could serve two purposes: not only economic development, but also fostering cultural growth by better serving under-developed areas. He writes: “On the one hand, new theoretical approaches to well-being tend to demonstrate that it is necessary to review traditional models of development. In the case of my book, the intention is to reverse a cultural trend...Often, parts of the local population are abandoned to intellectual isolation, which also leads to forms of addiction [such as] gambling, alcohol and drugs...Perhaps the time has come to involve citizens in participating in the development and progress of local communities…not only from an economic point of view.” To facilitate such a transformation, the author proposes creating “a circuit of small local investment exchanges governed as ‘game firm’ systems…managed online”. This would involve the creation of small stock exchanges that local residents could invest in. But these wouldn’t be typical stock exchanges, nor would they be tied into existing larger-scale exchanges. Instead, these exchanges would be set up by regional government bodies and open to participation by local companies and residents. Local firms would release details of specific investment projects they’d like to undertake, rather than simply releasing ownership shares of their company. Local investors would then be choosing not only a company, but also betting on whichever investment projects seemed most promising. Image credit: DC Studio on Freepik. The author envisions providing key details about businesses’ operations, including classic financial report cards, to help local citizens place their “bets” into specific investment project details. The author also mentions creating simple information sheets with details about the businesses, their products created, the amount of workers they employ and their average turnover, and details on the planned investments that the companies would actually use the money for. “This information should be able to guide citizens in their virtuous bets…In fact, every local company that intends to make its own investment puts it up for auction for a share it deems appropriate, and on the basis of the information prepared on it by a local online rating, every citizen can decide to invest their money...The repayment plans would be on a quarterly, half-yearly or annual basis, depending on the size of the investment…[this] could generate a widespread system of territorial investments and an increase in business support infrastructure. Part of the shares invested in incentives will, in fact, flow into a dedicated fund for territorial infrastructure.” There’s some consumer behavior logic in this proposal; investing in stocks is quite similar to gambling in some respects. The investor puts in money to back a certain individual or event that they expect will perform well, accepting some risk. However, in this case, both the “gambler” and the business being “gambled” upon stand to directly benefit. That is, rather than a winner-take-all approach as with traditional gambling, investing in local firms instead can generate income that benefits both parties. Perhaps, then, there’s ample room to turn a penchant for traditional gambling into a healthier form, putting money towards productive uses. Giliberto dubs this the “Athena Model”, inspired by his reconnection with the goddess of wisdom’s philosophies. The author suggests that taxes would likely be needed to help set up and run the infrastructure, which don’t have to be large but nevertheless may impose some deadweight loss. Giliberto suggests, however, that citizens who partake in the system could even be incentivized through tax credits – which may help get around that particular issue: “Local administrations would also take care of increasing this local financial circuit by dedicating small percentages of local taxes to the financing of functional infrastructure works, which in turn may serve local businesses. This also guarantees small amounts of return from investments made through tax microcredits to citizens.” If such a system were to succeed in helping regions develop economically, the initial tax burden could even pay for itself in the medium-to-long run. As the region develops, and businesses thrive, more income would be generated – leading to higher future tax revenues for the local and national government. Challenges to success Of course, there are a number of challenges that could hinder such a system from paying off. Incentives and the information provided to local residents must be carefully chosen such that productivity is incentivized and rewarded, while “cooking the books” is heavily discouraged. Businesses must be held accountable; investment projects they receive funding for must be attempted, for example. Giliberto’s model also presumes that there is enough income (and businesses with ambition) in a given region to support this type of system in the first place. And, local residents must be willing to invest more time into this “quasi-gambling” than they normally would. After all, many gamblers simply rely on the knowledge they’ve gained through personal experience to place their bets. In a theoretical online stock exchange system, these same residents likely would at least need to learn about the other investment options before betting on their favorite local winery. And, if the primary draw of gambling for individuals is entertainment, not the chance to make money, the local exchanges may fail to draw enough interest in the first place – the harsh reality is that betting on a game of football is probably more entertaining to gamblers than betting on a cafe’s future success. However, even ignoring the possible side effect of shifting gambling tendencies from “wasteful” private operations into productive public investments, this Athena Model has potential. Overall, the effect of the citizen's investment behavior would support local businesses, hopefully increasing economic development and eventually creating new job opportunities and generating an increase in income and consumer spending. Giliberto concludes: “Is all this utopia?...the word utopia…literally means "no-place", a place that does not exist...it is necessary for our country to draw lessons from the past, establishing, as other European countries have done, mission task forces to ensure that the [Recovery] funds are used promptly and fully.” Such a model, well-constructed, could very well prove to be up to the task. What do you think? Feel free to discourse with us in the comments! References 1: https://www.statista.com/topics/4032/gambling-industry-in-italy/
Is it possible to reinvigorate under-developed regions by turning the public’s penchant for gambling into a force for economic growth? That is one of the questions asked by labor policy economist Luigino Giliberto in his debut book, Athena’s Reasons (currently offered in Italian). The book details a model of regional economic growth that aims to reinvigorate Italy’s less developed areas, inspired by the ancient Greek goddess Athena.
Mesoeconomics: Missing Link, or Needless Pedantry?
Microeconomics is generally concerned with the optimal decision-making of individual economic actors, while macroeconomics considers the behavior of economies and economic data from a bird’s-eye view. But what if these two labels aren’t sufficient to encompass all of economics? Are economists missing something by failing to think outside of these “big or small” labels? That’s the question asked and answered by the term “mesoeconomics”, which purports to be the crucially important in-between that the field of economics has been missing. This term has been around for some time (it was first coined in 1986 by economist Yew-Kwang Ng) but has yet to become very popular among mainstream economists. That may be because there hasn’t been enough attention on the term to produce a significant body of research for it yet. Or, it could be because it’s a distinction that is simply not necessary to have within the field of economics. This article will dive into both sides of the argument, helping you make your own informed opinion. Will mesoeconomics become the next exciting new sub-field, like game theory, behavioral economics, and others before them – or is it overly pedantic? Before discussing the potential pros and cons of popularizing this new label, let’s first define it. Defining mesoeconomics Mesoeconomics is often defined as “the intermediary between micro- and macro-economics”, but this definition is quite lacking, as it does not explain what, exactly, is in between. Where “micro” is concerned with an individual economic agent’s decision-making, and “macro” is concerned with larger-scale economic behavior, mesoeconomics studies how society’s institutions – made up of individuals – interact based on the incentives that they face. As such, two existing sub-fields of economics can already be considered mesoeconomic; regional economics (often considered macroeconomics) and industrial organization (often considered microeconomics). To learn more about mesoeconomics, let’s take a deeper look at some arguments for re-labeling these topics. Supporters of the mesoeconomics label would argue that the focus of regional economics is not the same as true macroeconomics. That’s partly because regional economics studies the regional policies and systems that shape a region’s economic performance. Meanwhile, macroeconomic studies typically study economic performance at large, and may not always consider the variations in regional policies making up the overall economic performance. “Mesoeconomists” would argue that regional economics tackles an “in-between” area, where systems are larger-scope than with individual decision-making (micro) but still more detailed than with studying larger economic phenomena which may not need to take into account the nuance of specific individual policies (macro). Industrial organization extends the “theory of the firm”, studying how firms interact and form markets. This is generally considered a microeconomics endeavor, because the basic unit of analysis is the individual firm. Mesoeconomists, however, would claim that industrial organization stretches into the study of an ecosystem of firms that create industries and form markets, and can treat markets or industries as their own entity within the context of such study. This takes it a level above microeconomics – but not quite at the massive, national or global economy-wide scale of macroeconomics. Besides these examples, one of the major focuses of mesoeconomics is economic policy. There are myriad policies – and nuances to how they’re implemented – that can cause large shifts in expected behavior in industries and markets. Therefore, understanding how industries operate and how they’re intertwined is crucial to designing effective policies! Mesoeconomics aims to draw attention to this (arguably) neglected area, raising the bar for economics as a whole. If this sounds like an excellent idea to you, you’re in good company. It’s time to discuss the pros of popularizing the mesoeconomics label. Pros of mesoeconomics Were mesoeconomics to become more “mainstream”, such that economics students began to take “Mesoeconomics 101” alongside their micro and macro courses, discourse about economics would undoubtedly shift. Arguably, this would be for the better. First, more explicit attention to the practical side of how economists’ recommendations actually play out in the real world is certainly a good thing. This increased attention to translatability could earn economists some credit in the post-financial crisis and post-COVID world, where the failures of professional macroeconomists to predict economic ails have been noted by the public. At best, this mesoeconomic focus could help economists study and identify future man-made crises in the first place (like the 2008 financial crisis, which precious few saw coming). And, it could drive more economics research towards practical applications of policy, perhaps leading to even better policy recommendations that get taken more seriously by politicians. Finally, if “meso” was seen as a third sibling to micro- and macroeconomics, universities and researchers would experience a rapid increase in attention being given to these practical concerns. This could serve to further legitimize the field of economics in the eyes of the public. And, it would likely lead economists further away from the “white room” theorizing problem that has been a consistent critique of the discipline. In writing, “white room syndrome” refers to a scene that lacks enough description to help readers picture the “reality” – an example is a string of dialogue without any references to the surroundings. Similarly, economic ideas are often criticized for resting on unrealistic assumptions that lack real-world grounding, many times even by the economist who came up with the idea! For instance, Alfred Marshall formalized many classic microeconomics concepts, but he recognized their limitations and encouraged his students to surpass his work. Maybe introducing mesoeconomics would make Marshall proud of his intellectual descendants – or maybe, he would simply argue that it doesn’t represent a new innovation so much as needless jargon. Cons of mesoeconomics One of the greatest arguments to make against the need for mesoeconomics is that it doesn’t actually do anything new. Attempting to further sub-divide the field of economics into more labels arguably does not produce new ideas, and may be overly pedantic. Further, this third label may simply confuse laypeople, policymakers, and new economics students without generating meaningfully new discussions. Even though mesoeconomics purports to study industries and the economy from a middle-level view, one might argue that economists already do this routinely throughout their work. For instance, policy recommendations are very often made after studying the local, regional, or national situation. And, middle-level institutions (like state governments) are perfectly capable of hiring economists to help them. Whether we call it mesoeconomics or not, the work is already being done. Further, even from a theoretical standpoint, one could argue that an increased focus on the “middle” between micro- and macroeconomics is both poorly defined and not necessary. At what point does something cease to be a microeconomics analysis, but not yet become a macroeconomics one? Micro- and macro-analyses aren’t neatly defined by the geographical area studied; rather, they use quite different mathematical and statistical tools to analyze data and answer specific questions. For example, microeconomic techniques can easily be used to study the behavior of international trade, if each country is viewed as an individual economic agent that must optimize its trade balance. Similarly, macroeconomic techniques can be applied to small areas to test macroeconomic theories. The geographical scale doesn’t define the discipline; the theories and analyses utilized do. So, to carve a well-defined space for itself, mesoeconomics must have a well-defined set of theories and techniques that are unique enough to be recognizably different. Finally, the label “mesoeconomics” was coined in 1986, nearly forty years ago at the time of this writing. But the term is still very niche – many economics students never hear the word during their entire Master’s or PhD programs, the news rarely if ever mentions it, and research papers seldom use the term. A quick search of the term “mesoeconomics” on RePEc’s EconPapers database returns only 241 results total, and only 10 that have been updated or published within the last year. This heavily suggests that economists have not needed to adopt the term, simply because it isn’t very useful. What do you think? Will mesoeconomics take off and become its own respected area of economics, or remain doomed to niche arguments in small corners of the field? Feel free to share your take in the comments below.
Everyone’s heard of micro- and macroeconomics. The two terms are so deeply ingrained in the world of economics that even lay people generally know what they refer to. Almost everything economists analyze can be arguably given one of these labels.
The INOMICS Questionnaire: Fratzscher vs Blanchard
Olivier Blanchard is the C. Fred Bergsten Senior Fellow at the Peterson Institute for International Economics and the Robert M. Solow Professor of Economics emeritus at the Massachusetts Institute of Technology (MIT). After obtaining his PhD in economics from MIT in 1977, he taught at Harvard University before returning to MIT in 1982. He was chair of the economics department from 1998 to 2003. In 2008, he took a leave of absence to serve as economic counselor and director of the research department at the International Monetary Fund, where he stayed until 2015. He then joined the Peterson Institute. Blanchard has worked on a wide set of macroeconomic issues, including the role of monetary and fiscal policy, speculative bubbles, the labor market and determinants of unemployment, economic transition in former communist countries, the nature of the Global Financial Crisis, and the recent burst of inflation. He is the author of many books and articles, including two textbooks on macroeconomics (one at the graduate level with Stanley Fischer and the other at the undergraduate level). He is a past editor of the Quarterly Journal of Economics and the NBER Macroeconomics Annual; he is also a founding editor of the American Economic Journal: Macroeconomics. He is a fellow and former Council member of the Econometric Society, a past president of the American Economic Association, and a member of the American Academy of Arts and Sciences. Marcel Fratscher: What is your favorite place on earth? Olivier Blanchard: The French island where I have spent most of my summers, and, with time, a longer and longer period of time each year. Zen, sea, and fortunately and unfortunately, zoom. MF: Outside of economics, what occupation would you choose if you could be absolutely anything? OB: Concert pianist (although there is zero evidence that I have the slightest gift in that particular direction). But music moves me more than nearly anything else. MF: What is your favorite piece of music? OB: Gymnopediés by Eric Satie. MF: What is the virtue you appreciate the most? OB: Straightforwardness, honesty. MF: Your all-time favourite figure in economics? OB: I shall choose close to home: my advisor, Bob Solow. I admire him for the importance and the beauty of his work, but also his human qualities. I admire, but am not sure I would have loved him, Keynes. MF: Your #1 economics blog? OB: I do not read economic blogs. Nothing personal. (I am more a short Twitter person). On general blogs, Ezra Klein. MF: Your ideal student? OB: Somebody from whom I learn. I have written papers with many of my students, learned a lot and always enormously enjoyed it. MF: What should be done to address a gender bias in research in economics? OB: I am proud of what we have done at the American Economic Association. I think the right institutions and rules are in place. (Some) male behavior unfortunately will take more time to adjust.* *Editor’s note: Aside from making policy changes to improve the environment for women in economics, the AEA launched the Committee on the Status of Women in the Economics Profession. An update on the Committee and the AEA’s work can be found in the 2023 CSWEP News Issue IV on the AEA’s website. MF: What is the most misguided research agenda in economics? OB: Let me avoid labeling it misguided, but Real Business Cycle theory and methodology is the quintessential example of a disrupting technology. 50 years after its inception, I am still not sure whether the benefits are offsetting the costs. It is definitely becoming more useful by incorporating more distortions and more heterogeneity, but it suffers from nearly genetic problems, such as the black box aspect. This is actually made worse by the increased complexity of modern DSGE versions. MF: What is the most promising current research field or issue in economics? OB: Quite clearly, all the fields that benefit from the increasing availability of large data sets. But, in general, looking for examples at the NBER working paper list each week or so, I am amazed at the richness and the scope of current research. They include forays in behavioral issues, use of experiments, forays at the border of sociology and psychology, and work on institutions. MF: Where does economic research have the most influence on policy-making? OB: In a lot of places, antitrust, macro, etc. But, and this is a plaidoyer pro domo, there have to be people with one foot in research, and one foot in the real world, to be able to translate the conclusions of research to specific policy recommendations. MF: On what issues should policy listen more to economists? OB: All the time…but especially when economists derive conclusions at odds with what would seem to be common sense. The paradox of saving, the incidence of taxes, the market for lemons, etc. MF: What is your career advice to a young economics researcher? OB: You need to demonstrate two things. Your analytical abilities, i.e. your ability to cleanly formalize an issue and derive implications. And more importantly, your creativity, i.e. thinking in a new way about an essential issue. The challenge: Balancing the two in your thesis. Start with a question to which you do not know, but would like to know, the answer (and one where you think the answer will be interesting to others too). Preferably not from an extension of another paper. References: The Peterson Institute for International Economics’s bio page for Dr. Olivier Blanchard was referenced when writing the introduction to this article. It can be found at the following address: https://www.piie.com/experts/senior-research-staff/olivier-blanchard Header Image: Composite. Photos kindly provided by Marcel Fratzscher and Olivier Blanchard.
The following article first appeared in the INOMICS Handbook 2024. Download the INOMICS Handbook 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 2024 edition of the INOMICS Handbook Questionnaire. This time, he poses the questions to Dr. Olivier Blanchard, a prominent macroeconomist, who graciously took to the spotlight in this latest round. Thus, the back-and-forth has been dubbed Fratzscher vs. Blanchard, as is tradition. Dr. Blanchard offers us some interesting food for thought: the interview touches on the joy of conducting research with former students, the sometimes counterintuitive things we learn from economics, an analysis of Real Business Cycle Theory, and more.
Indeed, Alfred Marshall is considered one of the founding thinkers of neoclassical economics. He was a mathematician and philosopher who later became a microeconomist. His work formalized foundational economic concepts and theories such as supply and demand, price elasticity of demand, consumer and producer surplus, marginal utility, and production costs to produce some of the earliest cohesive economic models. He was even instrumental in making economics its own separate subject for the very first time at Cambridge, where he taught for much of his career. The entire field of economics was greatly influenced by both Marshall and several of his pupils, including famous economists Arthur Pigou and John Maynard Keynes. Alfred Marshall’s life Marshall was born in 1842 in London, England. He was at one time considering a career in the clergy, but abandoned the idea after he demonstrated significant talent for mathematics. Thereafter, Marshall attended St. John’s College in Cambridge, earning accolades in math (such as “Second Wrangler” in the Cambridge Mathematical Tripos) and securing his future as an academic. At first, Marshall wasn’t an economist – few in his time were! Rather, he studied subjects like math, physics, philosophy, and ethics, like many other thinkers of his time. It was allegedly Marshall’s love for ethics that led him to eventually study economic decision-making, as he saw economics as a means of improving the human condition for the common man. This was an ideal he shared with Adam Smith, another (earlier) important British thinker in economics. Thus, Marshall lent his considerable intellect to economic pursuits. In 1865, he was elected to a fellowship at St. John’s College at Cambridge. He married in 1877, left St. John’s, and taught at the University of Bristol and then at Oxford for a period afterward. In 1885, he became the Professor of Political Economy at the University of Cambridge, where he remained until his retirement in 1908. During this time, Marshall turned the study of economics into its own, self-contained discipline, creating the Faculty of Economics and Politics and helping to launch the first formal economics degree program at Cambridge in 1903. Famously, Marshall taught economic giants Arthur C. Pigou and John Maynard Keynes, who each went on to make their own grand contributions to the field, and often had great things to say about Marshall’s influence and teaching. The book Principles of Economics was Alfred Marshall’s magnum opus. He worked on it from 1881 to 1890. It was rapidly recognized as a success and a great achievement in economics upon its publishing. Yet Marshall was unsatisfied with the limiting assumptions of classical economics (some of which he helped to popularize!), so his work shouldn’t be viewed as a definitive snapshot of his economic beliefs. Instead, Marshall was keen to have his students surpass him and replace his models with more accurate ones, such that he was known to gift them copies of his Principles of Economics with signed messages such as ‘To [name], in the hope that in due course [they] will render this treatise obsolete’. Alfred Marshall died in Cambridge in 1924, at the age of 81. Alfred Marshall’s contributions to economics Marshall’s contributions to economics are numerous. As a former mathematician, he formulated many concepts in the field with equations. This approach helped him formally define many of the concepts mentioned at the beginning of this article, which other economists of his time had not been able to do. Nevertheless, he also attempted to distill economic insights into language that was understandable by non-mathematicians, a crucial skill for successful economists even in today’s world. This is evidenced by his placing math equations and graphs only in the footnotes and appendices of his Principles of Economics, not the main text. Besides Principles of Economics, Marshall’s other economics works include the 1879 text The Economics of Industry, which he co-wrote with his wife Mary Paley (a former student of his, and one of the first-ever female economics students. She was denied a degree on the basis of being a woman, a discriminatory policy that her husband Alfred Marshall supported; but nevertheless she was a brilliant economist in her own right). Alfred Marshall was also a crucial figure in popularizing the idea of “thinking on the margin”. Marginal analysis today is a core and foundational concept in economics, where someone’s decision is based on the benefit that the next unit provides, not the sum total of all units. The so-called “Marginal Revolution” began with economists William Jevons, Carl Menger, and Léon Walras in the 1870s. Marshall is one of a “second wave” of economists who, seeing the brilliance of these marginal ideas, set about integrating them into economic models. John Keynes, his former student, said this of his work: “[William] Jevons saw the kettle boil and called out with the delighted voice of a child; Marshall too had seen the kettle boil and sat down silently to build an engine.” According to Keynes, Marshall’s writing also put an end to a debate over whether cost or demand determined the value of items in a market, positing convincingly that it was actually both. As such, Marshall is credited with developing the idea that the price and output supplied of a good are determined by the intersection of supply and demand. This approach is so foundational as to seem obvious to economists today, which speaks to the success of his idea! Marshall also introduced early ideas of the different “time horizons” that economists frequently reference. He pointed out that in the short run, changes in demand tend to have more effects on markets than costs, while in the long run supply-side facts like costs can significantly shape markets. According to Keynes, Marshall was also the first to provide an actual formula for price elasticity of demand – which earlier authors had mentioned, but not formalized. He did the same with consumer and producer surplus as well, which French engineer and later self-taught economist Jules Dupuit had originally described. Marshall’s works were broad, covering even more topics than these. His students Keynes and Pigou, and later economists like Joseph Schumpeter, have several recorded quotes about the thoroughness of Marshall’s Principles that are readily available online. These include the fact that Marshall’s models and formulae hidden in the footnotes of the Principles often provided solutions to problems that were only thought of later on. So, next time you’re studying economics – particularly microeconomics topics – don’t be surprised to hear Alfred Marshall being given the credit! References 1 https://www.econlib.org/library/Enc/bios/Marshall.html 2 https://carleton.ca/keirarmstrong/learning-resources/selected-biographies/marshall-alfred-1842-1924/ 3 https://hope.econ.duke.edu/sites/hope.econ.duke.edu/files/documents/caldwell_2004_ALFRED%20MARSHALL%20and%20the%20CAMBRIDGE%20SCHOOL_0.pdf Header image credit: wikimedia / public domain.
Alfred Marshall is a famous economist who made great contributions to the field when it was still relatively new towards the end of the 19th century. Yet, students often learn about important figures in economics only briefly and in passing, although the content taught in economics courses often comes from brilliant economists such as Marshall.
Austrian Economics: Historical Contributions and Modern Warnings
Although these “schools” are separately defined, many modern economists probably consider themselves to fall into an ideological mix of several, and theories from a school can be useful even if an economist considers themselves a “member” of another. This article focuses on the latter of these schools of thought, which happens to be set apart from the others in a unique way. Austrian economics is considered “heterodox”, or not mainstream. The Austrian school of economics may not even be considered legitimate economics by some modern economists. This article provides a brief history of the school, its contributions to economic theory and the reasons it’s generally no longer considered orthodox. Finally, there is a gentle warning about how the label may be misused in modern discourse. Defining Austrian economics To an Austrian economist, economics is about individual people making choices based on their preferences – in other words, human behavior (or “praxeology”, as Austrian school thinkers like to reference). Austrian economists are often also concerned with the unintended consequences of these individual choices, which have lasting effects on economic activity. Due to this concept of unintended consequences, Austrian economists tend to purport the idea that economic institutions form in unintentional and inefficient ways, as the result of individual agents’ own decisions and actions. In this view, it’s no one person who designs the economic systems we put in place – rather, they result as a by-product of the self-interested actions that individuals take. Then, because these systems are not purposefully designed, and because it’s impossible to know the preferences and utilities of every individual, Austrian economists tend to believe that government activity is usually harmful. In this way, they agree with classical economists who believe that government intervention is neutral at best. However, Austrian school economists disagree with the classical economists on other key ideas; the classicals believe that money is neutral, while the Austrians assert that money is not neutral at all (similar to the monetarists). In the present day, there are two major camps of Austrian thinkers, with two historical figures acting as their guide. One camp functions as stated already, being staunchly against government intervention and rejecting much of neoclassical economic theory. They follow the philosophies of Ludwig von Mises and Murray Rothbard. The other camp is more lax about these concepts and may not always be against intervention, though they certainly don’t promote it as (neo-)Keynesians would. These Austrians accept more of neoclassical economics than the others, and follow the thinking of Friedrich Hayek, who won the Nobel Prize in 1974 for his contributions to “the theory of money and economic fluctuations” (alongside Gunnar Myrdal). Austrian economists believe that economic truths can be learned by conducting “thought experiments” that don’t have to rely on data. This involves using logic and reasoning to postulate their theories. Austrian economists therefore tend to downplay (or altogether reject) the role of econometrics and data in economics – as many of them believe that human behavior cannot be modeled effectively in mathematics as conveniently as many orthodox economic theories attempt to. In the modern age, this stands in stark contrast to mainstream, orthodox economics, where empirical evidence and quantitative skills are considered critically important to support or discredit economic theories and models. Modern academic research prizes falsifiable hypotheses and quantitative analysis to support theories, so these skills remain crucial for economists hoping to publish in top journals. This is, of course, a relatively recent development; economists began to favor econometric methods more heavily in the mid-twentieth century. Until then, most economists’ methods were much more similar to those of Austrian economists. Yet, more recently Austrian economists have tended to reject some economic theories and concepts that are widely accepted by those in the mainstream, further separating them ideologically. For example, many Austrian thinkers would reject the idea that the free market can be inefficient, while the majority of mainstream economists would agree that externalities, asymmetric information, and more can make markets inefficient (though of course, they may disagree about the best interventions to solve the problem, if any!). The vast majority of professional economists today consider themselves non-Austrian, and would consider Austrian economics at its best to be misguided. Economist Leland Yeager wrote that he opposed Austrian economists’ “...ultra-subjectivism in value theory and particularly in interest-rate theory, their insistence on unidirectional causality rather than general interdependence, and their fondness for methodological brooding, pointless profundities, and verbal gymnastics"1. Similarly, economist Paul A. Samuelson wrote that “...the exaggerated claims that used to be made in economics for the power of deduction and a priori reasoning…Fortunately, we have left that behind us.”2 However, this ideological divide was not always so stark. The beginnings of Austrian economics Austrian economics is widely considered to have begun upon the completion of Carl Menger’s Principles of Economics, which was published in 1871. This work put forth the idea that the logic of individual choices, even when those choices are uniquely human and not “economically rational”, should form the basis of economic analysis. Nevertheless, although he is considered the “father” of the Austrian school, Menger himself wasn’t necessarily opposed to or opposed by mainstream economists of his time. He dedicated his seminal work to a prominent German economist (William Roscher), who at the time belonged to a philosophical camp that touted historical evidence as key to learning economic truths and proving theories. This camp became a major force opposing the emergence of Austrian economics, which argued that historical facts were merely illustrative of human behavior, but that theory alone should be relied upon. In fact, it was these German “historical school” economists who labeled Menger’s ideas as “Austrian economics” in the first place. The term was meant to be derogatory, but it was later adopted by Austrian economists, who began using it to label themselves. Contributions of Austrian economists Economists used to be quite limited in their ability to conduct quantitative analysis, when calculators and computers weren’t widely available. Conducting procedures like regression analysis by hand can be extraordinarily tedious, and being forced to research economic data by finding historical records in libraries was much more time-consuming than downloading an economic dataset from the Internet. It should be no surprise, then, that Austrian theories and methods – that don’t always rely on empirical evidence – were quite relevant and more accepted by economists in the past, as they reflected mainstream approaches of the time. In fact, Austrian economists have contributed to theories and ideas that support what is very much considered orthodox today. For example, Austrian ideas are credited with influencing the “Marginal Revolution” in economics, which occurred in the late 1800s and introduced the now-mainstream idea of “thinking on the margin” or “marginal analysis” to economics. For example, the “father of Austrian economics” himself, Carl Menger, is partially credited – alongside famous economists such as Léon Walras – with developing the idea of marginal analysis. The “Marginal Revolution” (or marginalist revolution) introduced concepts like the theory of marginal utility for the first time. These ideas showed that economic agents make decisions based on their consumption of the “next unit”, as they weigh the costs and benefits of each action they take. Core economics concepts such as diminishing marginal returns sprang from this period, and continue to be widely acknowledged and taught in mainstream economics today. Opportunity cost is another example of an economic theory first introduced by an Austrian economist, as it’s attributed to Friedrich von Wieser, an Austrian economist who formulated the idea around the same time, in the late 19th century. Therefore, it’s undeniable that Austrian economists in the past have contributed to the development of the field of economics today. And, Friedrich Hayek winning the Nobel Prize in Economic Sciences in 1974 gave a brief resurgence of legitimacy to the Austrian school. But even by that point, Austrian economics was widely considered heterodox by most professional economists. As time went on, and as math, statistics and econometrics became increasingly important in economics, the majority of economists came to believe that quantitative analysis of empirical data is a key tool to furthering our understanding of the economy. As a result, Austrian economics is now considered heterodox in large part due to what other economists would call a lack of empirical rigor (of course, some Austrian economists may actually use empirical methods and so argue this point, but the school overall remains less committed to empirical evidence than many modern economists are comfortable with). The accusations run in both directions; Austrian economists have levied critiques against mainstream economists, as well. According to some Austrian economists, orthodox schools of thought fail to account for the unintended consequences of economic policy or individual choice. Others would claim that an over-dependence on quantitative rigor has led modern economics astray. While it’s not correct to “throw the baby out with the bath water” with regards to quantitative methods in economics, there may be some merit to this latter claim. Occasionally, mainstream economics is criticized by other actors as well for failing to see the “big picture” from among the strings of equations and data. One example of this is the 2008 financial crisis that precipitated the Great Recession. Very few economists correctly predicted the crisis or warned about the weaknesses in the global financial system. Arguably, a lack of “big picture” thinking partly led to the failure of economics to prevent catastrophe. This illustrates how economists should be careful not to get caught fully up in data or empiricism so they can still see the whole picture – though of course, data and empirical analysis are key tools when used properly. Finally, other factors (like corporate greed and lax legislation) were arguably much more to blame for the crisis than economists' failure to raise the alarm about the economy, although that task certainly does belong in the economics wheelhouse. The intertwining of the far right and Austrian economics Regardless of ideological differences, readers ought to consider the economic theories and evidence critically when encountering a self-described Austrian economist in the present day. Unfortunately, Austrian economics’ fondness for thought experiments, de-emphasis of quantitative rigor, and the label “economics” itself has caused the school of thought to be an attractive ideological tool for disingenuous actors in contemporary times. Because these facets of the school lower the barrier to entry for individuals who haven’t studied economics (at least to feel that they are competent economists), they have allowed unsavory groups, including far-right ideologues, to flock to “Austrian economics” as a means of disguising hateful ideologies as “economic theories”. Austrian economic theories heavily favor unregulated free markets, and are against almost every form of government intervention. These facts, too, tend to attract political actors who fall into libertarian, anarchist, or right-wing camps, who then tout the theories of Austrian economics as a priori fact and use them to push their own policy agendas without much factual backing. Further, this lower barrier to entry required by Austrian views encourages the development of “armchair economists”. This is in line with the general decline in trust of experts that has plagued society in the 21st century. It’s easier for an individual with a moderate view to read the writings of extreme “Austrian economist” conservatives touting these a priori theories as fact, than it is for them to work through a detailed economic paper using quantitative methods that provide a sound empirical basis, let alone study a degree in economics and learn about the vast depth of nuance that characterizes modern economic research on topics that might seem obvious at first – like the effects of a minimum wage. These actors may then very well consider themselves on par with professional economists, and even write “economics” opinion pieces without any sort of formal training, and repeat ideas that far-right actors have pushed as economic truth. For example, racist individuals can point to Austrian school style non-interventionism to push the idea that affirmative action, or any sort of policy that benefits minority interests, are destructive and should never be implemented for any reason. Similarly, any government redistribution programs – such as those to alleviate poverty, support retired veterans, etc. – are considered wasteful and a net loss for society. These views easily lend themselves to the types of far right-wing ideas that quickly spiral into anti-immigrant, racist, or other ultra-conservative positions. For example, far-right ideologues can take the anti-affirmative action or anti-redistribution stance, and go a direct step of (bad) logic further to state that these policies are really just discrimination against white people, decry them as morally wrong, and stoke the flames of racist anger – while hiding behind the guise of analyzing society as an unbiased “economist”. Sometimes, these actors may use Austrian positions to denounce the work of professional economists, central banks, and governments. And, a proportion of “armchair” Austrian economists may completely disregard the findings of economics professionals. Be wary of such discourse, and be sure to look to the evidence, research, or teachings backing any self-described Austrian positions, to determine what lines of reasoning are based on legitimate economic theorizing, and which are not. Be aware of bad-faith critiques of professional economists Whether connected to political agendas or not, there are modern-day Austrian economists who disparage the field of economics at large. The label “economics” has given a sense of legitimacy to these actors, emboldened by the writings of so-called Austrian economists who may have an agenda. Some of these writings come from self-described Austrian think tanks or other ideologically-focused organizations. One clear example is Jonathan Newman’s writing for the Mises Institute. In a March 2024 article, “Economics Needs a New Methodenstreit Based on Austrian Methodology”, Newman sets about criticizing mainstream economics (“Even a cursory look at what passes for economic scholarship in mainstream journals today shows that the methodological errors that were exploded by Menger and Mises persist”). One of the underlying assumptions in Newman’s writing is that state intervention is always undesirable – such that it is a foregone conclusion that the American Economic Review articles he shares must be biased because they conclude that state intervention or government programs led to positive results in some cases. His article also notably demonstrates the disdain for econometric methods common in modern Austrian economics discourse. Newman’s third and fourth examples of poorly-done economics research are simply an abstract about econometric methodology and a screenshot showing several equations. These are apparently evidence of those papers’ lack of usefulness. As another, more in-depth example, Henry Hazlitt’s 1946 book Economics in One Lesson3 is a popular and acclaimed read among contemporary Austrian economists. Unfortunately, the book contains a large amount of disingenuous language that seeks to discredit professional economists, and proposes the Austrian model of thinking as the obviously superior alternative. The following quotes offer examples taken from the book. “…the whole of economics can be reduced to a single lesson, and that lesson can be reduced to a single sentence. The art of economics consists in looking not merely at the immediate but at the longer effects of any act or policy; it consists in tracing the consequences of that policy not merely for one group but for all groups…Nine-tenths of the economic fallacies that are working such dreadful harm in the world today are the result of ignoring this lesson.” (page 5) Here, Hazlitt attempts to reduce the entirety of economics to one sentence, and in the same breath claims implicitly that economists routinely fail to consider longer-term effects of their policy suggestions. “But comparatively few people today make this error; and those few consist mainly of professional economists. The most frequent fallacy by far today, the fallacy that emerges again and again in nearly every conversation that touches on economic affairs…the central sophism of the “new” economics, is to concentrate on the short-run effects of policies on special groups and to ignore or belittle the long-run effects on the community as a whole” (page 5) This quote more blatantly targets “professional economists” as people who fail to consider long-run effects of their policy recommendations, a fallacy that other people would not make. “An elementary fallacy. Anybody, one would think, would be able to avoid it after a few moments’ thought. Yet the broken-window fallacy, under a hundred disguises, is the most persistent in the history of economics…It is solemnly reaffirmed every day by great captains of industry…by learned statisticians using the most refined techniques, by professors of economics in our best universities. In their various ways they all dilate upon the advantages of destruction…they see almost endless benefits in enormous acts of destruction. They tell us how much better off economically we all are in war than in peace.” (page 13) This quote from Hazlitt stands out as a neat summary of a chapter that disingenuously presents economists as professionals that celebrate destruction. Hazlitt correctly identifies that destruction is not good, and in fact that it’s wasteful when the money that would have gone towards other things must go towards rebuilding instead. However, he asserts that professional economists do not make this connection. “The analysis of our illustrations has taught us another incidental lesson. This is that, when we study the effects of various proposals…the conclusions we arrive at usually correspond with those of unsophisticated common sense.” (page 177) This quote from Hazlitt’s book asserts the idea that correct economic conclusions “usually correspond with those of unsophisticated common sense”. This is, clearly, a minimization and denunciation of the field of economics at large. The above direct quotes are filled with bold claims about the ineptitude of professional economists. This type of charged language is a red flag signifying that the written work itself has not been written with a full understanding of economics, its aims, and role in decision making. Nor, clearly, has it been written in good faith, or without bias. It should be clear that these types of emotionally-charged critiques of mainstream economics often contain dishonest assertions and sweeping generalizations about how economists think, advise, and research. To believe that the scores of professional economists working around the globe are unwilling or unable to consider the consequences of the policies they research, support, or criticize, is to think that all economists are short-sighted at the very best – and is a blanket insult to the profession. Yet they come from a self-described Austrian economist who did not study economics. The author of the book, Henry Hazlitt, was a journalist who wrote for the business and finance sections of several publications. However, his writing is often cited by present-day “Austrian economists” and touted as fact. In summary: the legacy and pitfalls of Austrian economics In summary, then, readers should take care when encountering self-described Austrian economists. In the past, such economists did contribute to economic theory and helped to advance the field. Today, however, Austrian economics has remained rooted in the past such that it is no longer considered mainstream, preferring to remain with thought experiments and eschew the empirical research that economics at large has embraced. Further, the label “Austrian economics” is often applied to harmful or disingenuous political positions, to grant them an air of legitimacy that the average member of the public may not be informed enough to think twice about. The best legacy of Austrian economics is the serious theorizing that gave rise to concepts like marginal analysis and opportunity cost, and Friedrich Hayek’s Nobel Prize-winning study of money and economic fluctuations. But unfortunately, readers must be wary of ultra-conservative, anti-expert pundits and self-described economists who take the label to further their own, sometimes problematic and politically-charged ideas without substantial evidence to back them. References 1: Yeager, Leland B. (1997). "Austrian Economics, Neoclassicism, and the Market Test". Journal of Economic Perspectives. 11 (4): 153–165. 2: Samuelson, Paul A. (September 1964). "Theory and Realism: A Reply". The American Economic Review. American Economic Association: 736–739. 3: Hazlitt, H. (1946). Economics in One Lesson. Harper & Brothers Publishers. First edition reprint accessed at https://www.liberalstudies.ca/wp-content/uploads/2014/11/Economics-in-One-Lesson_2.pdf. Header image credit: Mises Institute - Fair use image of Friedrich Hayek retrieved from Wikipedia at https://en.wikipedia.org/wiki/File:Friedrich_Hayek_portrait.jpg.
There are many schools of thought in economics. Each rallies around one or more different theories about how the world works, and builds models to support those theories and explain the economy. These schools include the (neo)classical, (neo)Keynesian, monetarist, and Chicago schools. Readers may have heard about the Austrian school of thought as well.
Cultural goods Heavy metal is an example of a cultural good. Cultural goods, like books, art, and music, can be studied using the standard tools of economics, such as supply and demand. Yet, cultural goods have unique characteristics that require further examination. Cultural goods embody aspects of both experience goods and information goods. Assessing their quality without experiencing them is challenging, as their value lies in the information they contain rather than in tangible commodities that are more easily compared. As a consequence, heavy metal music relies on word-of-mouth advertising within underground social networks to build a following. Quality signals, like fans wearing band T-shirts and posting on social media platforms, contribute to the genre’s growth, even amidst fluctuations in commercial popularity. Cultural goods like heavy metal music also generate positive externalities, meaning they produce value beyond private consumption. Heavy metal music helps develop social cohesion among its dedicated fanbase, and it inspires future musicians. These types of positive externalities are a common feature of cultural goods; part of their value is in helping groups of like-minded people form communities. On the other hand, cultural goods can also produce negative externalities, as not everyone will appreciate the noise pollution of their neighbor blaring death metal around the clock. Superstar theory Another way to consider the distinct attributes of cultural goods is to apply Sherwin Rosen’s superstar theory. Rosen (1981) demonstrated why a small number of top performers appropriate the majority of the earnings in certain industries like music, art and professional sports. We can apply this theory to heavy metal by considering two American thrash metal bands that became popular in the 1980s: Anthrax and Metallica. Both bands have a similar style and sound, but Metallica has a slight edge over Anthrax in terms of catchy, memorable melodies. In other words, they are imperfect substitutes. As the superstar theory suggests, this small difference in quality results in a large difference in earnings thanks to recording and amplification technology that allows for economies of scale. Packed stadiums and global album distribution allowed Metallica to become a household name with an estimated $1 billion dollar net worth in 2023. Meanwhile Anthrax, still considered one of the “Big Four” thrash metal bands (together with Megadeth and and Slayer), has a net worth of only $39 million. This “winner-takes-all” dynamic helps explain why it is challenging for the majority of metal musicians to make a living from their craft. Globalization and economic growth Heavy metal originated from the industrial landscape of post-war Birmingham, England, with pioneering bands Black Sabbath and Judas Priest in the late 1960s. Their influence has since crossed borders, reaching nearly every country in the world. However, the number of heavy metal bands is distributed unevenly across countries. These days, most metal music is produced in high income nations with the best access to healthcare and education. In particular, Finland had the highest ratio of heavy metal bands per 100,000 residents in 2021 and 2022. Since heavy metal is a highly technical and virtuosic style of music, it requires significant human capital investment. And, because the likelihood of success is lower compared to other industries, pursuing a career in heavy metal is more feasible in countries with strong social safety nets and opportunities for advanced musical training. Thus, it is not surprising that most metal bands now originate from countries with higher standards of living. Nonetheless, this change in economic circumstances shaped the evolution of metal music. While it once was primarily a reflection of working class struggles, it has become more diverse and intellectual, with over 25 distinct sub-genres including progressive metal, symphonic metal, and folk metal. Technological change Technology is an important driver of economic growth, but it also changes how we produce and consume cultural goods.The music industry radically changed in the shift from physical media like vinyl records, tapes, and CDs to digital formats accessed via the internet. Heavy metal was at the epicenter of this transformation in 1999 with the landmark case Metallica vs. Napster Inc. Napster was a peer-to-peer file sharing program developed to facilitate the transfer of high quality digital music files, called MP3s. Metallica drummer Lars Ulrich became an outspoken opponent of the service after an unfinished version of the song “I Disappear,” recorded for the Mission: Impossible II soundtrack, was leaked to Napster. Ulrich argued that Napster made Metallica’s entire catalog of music available for free without asking for permission, prompting Metallica to sue for over $10 million in copyright infringement damages. The lawsuit raised new concerns about the role of technology and the scope of intellectual property laws for recorded music, and set a precedent for future legal proceedings and industry practices. Napster settled with Metallica and ceased operations in 2001, though they were quickly replaced by other similar services. Because Metallica had originally gained notoriety, in part, through underground tape trading, their actions were seen by metal fans as hypocritical. Ultimately, improvements in internet speed made streaming more convenient, essentially lowering the price of music relative to the costs of piracy. Looking back, file sharing was likely beneficial for underground music like heavy metal, because it allowed the music to propagate in countries where it was otherwise inaccessible due to religious laws and authoritarian control of cultural goods. Conclusion The example of heavy metal economics is a fun application that can help spark student interest in economics. Importantly, it serves as a reminder that art and culture are inextricably linked with economic circumstances, highlighting the impact that market forces can have on creative expression. References Krueger, A. B. (2019). Rockonomics: A backstage tour of what the music industry can teach us about economics and life. Penguin Random House. Rosen, S. (1981). The economics of superstars. The American Economic Review, 71(5), 845-858. Header image credit: Mainzer on Pixabay.
Economics can be used to study almost anything, including popular music. For example, the late Alan Krueger analyzed the music industry, examining live concert revenue, copyright protection, and streaming services in the book Rockonomics. Similarly, economics can offer insights about heavy metal specifically, a type of rock music characterized by loud distorted guitars, intense rhythms, and powerful vocal styles.
The Author Irving Fisher is a revered mathematical economist and is considered by some to be "the greatest economist the United States has ever produced" (Schumpeter, 1997), despite his record of promoting harmful ideas (Fisher was known to be a proponent of eugenics, and endorsed racist ideas). Nevertheless, his books and reports in economics have been praised for their clarity and ease of understanding. Known in economics for his inveterate outlook, Fisher is credited with championing a range of reforms to the monetary system, including the compensated dollar, price level stability rule, 100% Money, Stamped Money Prohibition, and government-funded medical care, among others. Fisher’s inspiration for writing the book: losing in stocks In the 1920s, Fisher computed a stock market index that was regularly reported and published in reputed papers. On October 15, 1929, the New York Times quoted Fisher's statement at a meeting of the Purchasing Agent Association. The Times believed that Fisher's attitude "fell into almost unqualified optimism". A few days later on October 24, also known as Black Thursday, the stock market crash of 1929 (Bordo & Rockoff, 2011) proved the Times right and Fisher very wrong. The Great Depression had begun. Fisher's wrong prediction has become legendary. But rather than let it keep him down, the debacle influenced Fisher to reflect on his predictions and to write the book "Booms and Depressions". It went on to become the holy grail for research into business cycle fluctuations, debt cycles, and economic downturns. Book Overview In the book "Booms and Depressions", Fisher compiles nine main factors that caused the depression and accelerated its adverse effects, with over-indebtedness as the first main factor. The other factors include volume of currency, price level, net worth, profits, production, trade and employment, and optimism and pessimism. Over-indebtedness is when outstanding debts (whether they are individual, corporate, or national) are sizable relative to other economic factors such as assets, income, gold, and liquidities. In his book, Fisher affirms that all other factors play an auxiliary role compared with two dominant factors: "over-indebtedness to start with and deflation following soon after". Though other factors may be more evident in specific episodes, their role is often subservient to over-indebtedness and deflation, acting as effects or symptoms of debt vulnerability. Debt also amplifies the deleterious effects of overinvestment, overconfidence, and over-speculation. Fisher also states that the money market and debt market are closely tied. The volume of currency is measured not only in the deposits sense, but also as credit currency created by “the pen and ink of the banker”. This currency originates and terminates in the banking entries, which is balanced out in regular times by an alternate entry, but gets wiped out in recessions as people and firms struggle to repay their debts. Due to this tendency, the adage "one man's debt is another asset," as advocated by (Krugman, 2011) does not always hold up. Moreover, Fisher provides a general understanding of the relation between real debt and the price level which is crucial to gauge the economy's direction. In simple words, if the price level doubles, the real value of debt halves and if the price level halves, the real value of the debt doubles. Inflation and deflation thus have a contrasting effect on the real debt and its subsequent impact on the real economy. While one may argue that deflation will increase the value of money, it also increases the value of debt. This makes paying off debt difficult, and decreases spending and investment at the same time. A similar indication is given by (Minsky, 1992) who argues that “distress selling” affects the economy by reducing asset prices. In the absence of refinancing opportunities and impaired cash flow from operations during a recession, firms are forced to sell assets (sometimes at large discounts) to pay their obligations. Distress selling and assets falling in value recursively feed on each other, exasperating the initial shock of deflation. Fisher deduced the following chain of consequences in how the nine factors interact. It starts with shock to the system affecting either the interest rates or the productive or earning capacity. The after-effect of the shock forces a company into debt liquidation and distress selling, which results in contraction of deposit currency as bank loans get paid off. This consequently slows down the velocity of circulation. What follows is a fall in the level of prices, which results in a fall in the net worth of business. This hastens bankruptcies and results in falling profits, reduced output, and declines in trade and employment. All these factors usher in a pessimistic sentiment and loss of confidence, which leads individuals and firms to favor hoarding money. This results in a further reduction in the velocity of circulation. The market forces attempt to rebalance the money demand with the supply by shifting the interest rate. In major instances, we notice a fall in the nominal, or money, rates and a rise in the real, or commodity, interest rates (Fisher, 1933). The book’s reception among economists Fisher’s observations in the book have been supported by the likes of many well-known economists over the years. These include Ben Bernanke, John Kenneth Galbraith, Charles Kindleberger, Milton Friedman, Hyman Minsky, Paul Samuelson and Christina Romer, all of whom have commented on the work in their books, papers or speeches. The admirable quality of the book lies not only in its vivid explanation of the Great Depression but in providing a framework for analysis of future crises. This wisdom of this book is invoked time and again as new financial and economic crises emerge. Since its publishing the book has been cited by highly influential papers, reports and other books on investment management and the financial crisis. In that spirit, we can compare the recent economic downturn to the Great Depression to show that many economic factors identified by Fisher are moving in the same manner as before and after the Great Depression. The first significant factor is the debt. Reports from the Institute of International Finance show that the global level of debt hit an all-time high record in 2023, with roughly 80% of that debt buildup coming from mature markets such as the US and France. This is echoed by reports from the World Bank, showing that the world has been in a “fourth wave” of increasing debt, warning that it could end in crisis. While the role of deflation is debatable (it prevailed in the starting months of 2020 only to be taken over by inflationary pressures), this appears to be a time where deflation would have devastating consequences and likely lead the world into another debt crisis. Among the other factors, the interest rate seems to be the defining variable for future recovery. A rise in the interest rate will shift the dynamics of the price levels. Economists are divided on whether the stimulus and accommodative monetary policy that was used to deter the COVID crisis will result in inflationary or deflationary trends in the long term. The presence of inflation and low-interest rates has stabilized the financial environment, but the trend may soon reverse as central banks raise interest rates. Other factors such as the volume of currency, corporate profits, and velocity of circulation have also moved in the same direction as Fisher predicted, though with less intensity. That’s due to the unprecedented government and central bank support which has prevented debt liquidation and distress selling thus far. The vital question to ask here is, “for how long?”. Despite the apparent recovery, evidence from past recessionary episodes provides a sense that the prosperity may be short-lived. Fisher’s chain of events may have been postponed, but its possibility not annulled. Book Details Booms and Depressions: Some First Principles Publisher: Adelphi Company, New York ISBN 1453697640, 9781453697641 Buy on Alibris (US) Buy on Amazon (international) References Fisher, I. (1933). Debt-Deflation Theory of Great Depressions. Econometrica, 337–357. https://doi.org/10.2307/1907327 Krugman, P. (2011). Debt Is (Mostly) Money We Owe to Ourselves. The New York Times. https://krugman.blogs.nytimes.com/2011/12/28/debt-is-mostly-money-we-owe-to-ourselves/ Minsky, H. P. (1992). The Financial Instability Hypothesis. The Financial Instability Hypothesis, 74, 1–10. https://doi.org/10.1117/12.774256 Schumpeter, J. A. (1997). Ten Great Economists. Ten Great Economists. https://doi.org/10.4324/9780203202371 Header image credit: Sweetaholic on Pixabay.
The 1932 book "Booms and Depressions" by Irving Fisher, along with his 1933 Econometrica paper "The Debt-Deflation theory of Great Depression" earmarked the start of a new era for modern macroeconomics and financial literature. This article reviews the book in light of the current economic and financial scenario.
Macroeconomics and Monetary Economics (JEL E)
Adam Smith is arguably the most famous economist. His name is almost inevitably thrown around within the first few minutes of every introduction to economics course, and of course his famous book An Inquiry into the Nature and Causes of the Wealth of Nations is oft-discussed. But neither Adam Smith nor his contemporaries would have described him as an economist for most of his life. Instead, Smith was a philosopher, professor of logic, and sociologist. Today, he is often credited with launching the field of economics. Some even call him the “father of capitalism”, although Smith himself never used that word. So who was Adam Smith really, and why did he become so famous in economics? The life of Adam Smith Smith was born in Scotland in 1723 and lived until 1790. He was a philosopher as well as economist – indeed, economics as a field didn’t really exist until after Adam’s transformational The Wealth of Nations had been published. In 1737, Adam Smith matriculated at the University of Glasgow, graduating in 1740 at the age of 17. He then undertook graduate studies at Oxford, leaving there in 1746. In 1751, Smith was appointed professor of logic at the University of Glasgow, and a year later he was appointed the Chair of Moral Philosophy at the same institution. He was a fully participating member of university society, giving lectures daily and being elected the dean of the faculty in 1758. Adam Smith published his first book, The Theory of Moral Sentiments, in 1759. In it, he theorized on human nature, which laid a foundation for his later assertions about economic behavior. The work is primarily concerned with the moral character of humans and how reason interacts with selfish passion. It’s worth noting that even in this book Smith had stated that some “invisible hand” compelled the self-seeking rich to improve society, leading to benefits for everyone, although it would not be until Smith’s later writings that the phrase gained more recognition. Smith was offered a lucrative job as tutor for a duke’s son, so he left his professorship in 1763 and traveled to France. From there, he had the opportunity to meet other luminaries such as Voltaire, a major figure in the French Enlightenment. Scholars debate how much his conversations with the French Enlightenment figures influenced his work. Adam Smith later moved to London and was inducted into the Royal Society in 1767. Ten years later, the work that has made him so famous in economics – An Inquiry into the Nature and Causes of the Wealth of Nations, more commonly known as The Wealth of Nations, was published in 1776. Adam Smith was no stranger to success. He garnered a reputation for himself during his life; according to the Encyclopedia Britannica, Smith “enjoyed a high measure of contemporary fame”, and his later works were lauded by contemporaries and recognized as important by “British governing circles”. This latter part was no doubt due to how The Wealth of Nations gave rise to classical economic theory, which provided defensible, reasonable alternatives to the prevailing mercantilist ideas of the time. How Wealth of Nations advanced economic thought The Wealth of Nations has five main sections. First, it describes self-interest and the division of human laborers. Next, it discusses the roles of stock and capital in a “commercial society”. The third section describes the formation of then-modern society, and of commercial markets, throughout history. It examines the origins of commerce from hunting and gathering up through agriculture, feudalism and finally an unconstrained market economy. Then, the book turns to how economic growth, as a force that is driven by human nature, results from the interaction of different parts of the economy. And finally, it examines the role of the government, particularly examining the limits of government power to positively influence markets. Smith posits that economic growth would result as long as governments didn’t stifle competition in markets. And of course, within his most famous book is Adam Smith’s famous hypothesis: that the self-interested actions of individual people, summed together, actually create economic activity that is in the benefit of society overall. Following from Smith’s earlier work The Theory of Moral Sentiments, he posits that those self-interested actions arise from the emotional and rational states of man. In The Wealth of Nations, Smith famously writes: “It is not from the benevolence of the butcher, the brewer, or the baker, that we expect our dinner, but from their regard to their own self-interest.” Following from this line of thinking, Smith argued that – led by an invisible hand – markets are self-correcting. In this view, the ubiquitous “invisible hand” represents the unseen market forces that compel selfish individuals to act in a way that happens to be in society’s best interest. And a crucial component of these market forces is competition, which The Wealth of Nations describes fastidiously. By pitting one person’s desire for self-betterment against another’s, competition produces (economically) productive outcomes. This idea greatly influenced economic theories of the time (and continue to do so today); the classical economists believed that any intervention in the markets was doomed to make things worse, as all markets would self-correct eventually. As he was an ardent supporter of free market competition, Smith was critical of monopolies and of then-popular government policies that supported them. In fact, rather than acting as one might expect of a non-interventionist, pro-capitalist thinker, Adam Smith wrote at great length about morality, and saw great value in supporting all members of a “commercial society”. Smith believed that the natural growth of competitive, unregulated markets should be used to better everyone. In The Wealth of Nations he writes, “But what improves the circumstances of the greater part can never be regarded as an inconveniency to the whole. No society can surely be flourishing and happy, of which the far greater part of the members are poor and miserable.” Smith’s legacy in economics Adam Smith’s ideas were lauded and embraced within his lifetime. They went on to form the basis of the classical school of economics, and indeed kicked off economics as a serious field of study. Of course, earlier philosophers and political theorists had contributed to discussions that led to the rise of ideas like Smith’s, yet his famous The Wealth of Nations is often considered the beginning of economics as its own, separate field of thought and study. Classical economists began to influence economic policy greatly, shifting much of the world away from mercantilist practices (hoarding gold and silver, increasing exports) towards laissez-faire economic policies that served to promote market competition. This included de-emphasizing having a trade imbalance in favor of the home country, and promoting free trade (the value of free trade was further supported by David Ricardo’s economic theories, which came after Smith’s work). Part of the prevailing mercantilist ideal of the time was that acquiring more assets and keeping them within a country would lead to growth and wealth for that nation. Mercantilists supported trade restrictions partly for this reason. Smith countered this idea with the theory that it was the product of labor (in other words, productivity) that determined a nation’s wealth, irrespective of their balance of trade. Labor that produced goods resulted in economic output, and increasing that would increase a nation’s wealth, allowing them to purchase more goods from home or abroad. Whether or not exports were larger than imports becomes a relatively superfluous question in this case, which was news to the mercantilists. In fact, Smith argued that free trade can increase a nation’s wealth by opening up more opportunities for labor to work. The larger the market, the more opportunity for productive labor to supply the markets, and the more economic growth. David Ricardo later expanded on this line of thought with the theory of comparative advantage. Adam Smith’s ideas and the classical school of economic thought it launched became the prevailing advice for governments managing their economies. These views weren’t seriously challenged until the Great Depression in the late 1920s, which saw a period of several years of high unemployment and low output, in many parts of the world, that classical economic models couldn’t explain. This left room for John Maynard Keynes to expand economic theory, giving rise to the golden age of Keynesianism. That’s not to say that Adam Smith’s work has become obsolete. Classical economists have continued to build off of Smith’s original ideas while responding to criticism of their theories, eventually giving rise to the neoclassical school of thought. And regardless of their particular ideological leanings, economists today all owe a debt to Adam Smith and his famous book, which is considered the start of the modern field of economics. References https://www.britannica.com/biography/Adam-Smith/Legacy https://iep.utm.edu/smith/#H3 https://www.econlib.org/library/Enc/bios/Smith.html Header Image Credit: scotlandstudycentre via Pixabay
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.
An Inquiry into the Nature and Causes of the Wealth of
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
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.
Public Economics & Economic Policy (JEL H)
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.
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 Challenges Faced by Small Island Economies
Although visiting an island might not inspire most people to think about economics (except, of course, for our inquisitive readers!), after reading this overview you will likely notice these challenges – and the ways that different islands have mitigated them – when visiting one. We suggest you find yourself a good, cozy spot on the beach as you read on. Prelude: extending these challenges to other areas This article is focused on smaller islands – for instance, in the Pacific or Caribbean. Larger islands (such as Japan, the UK or New Zealand) or those that are part of a major land-based nation typically do not face as many of these challenges. Nevertheless, many of the challenges discussed in this article are also applicable to small countries on the mainland, too. In 1984 Benito Legarda wrote the article “Small island economies” for the IMF, still relevant today. It gives an overview of some of the challenges that apply to both islands and other small economic areas. Interested readers can refer to Legarda’s article for more context on not just small islands, but other small economic areas too. Without further ado, read on for the economic challenges faced by small island economies. Natural resources are scarce on most islands When people think of small island economies, they often think of two things – beaches and tourists. While it’s true that some natural resources (such as fish and beautiful natural sights) abound for many islands, the reality is that other important natural resources are generally hard to come by. Most islands simply do not have much land area. Thus, the bounty of natural resources available tends to be severely limited compared to countries on the major continents. Many of the resources that allow societies to thrive – such as mineral deposits, nearby forests, abundant wild animals, freshwater sources, etc. – are much less likely to form on small landmasses. Indeed, somewhat ironically, freshwater availability can be a major problem for many islands. Of course, there are small land-locked countries as well – such as Liechtenstein – that may face a similar challenge. But, two factors make this likely less severe than for an island of similar size. First, small landlocked countries can still trade with their neighbors far easier than an island typically can since land routes are available. Second, the fact that a small landlocked nation is located on a major continent means that there will likely be relatively more natural abundance, as the landmass it’s situated on is likely to have substantial natural resources. Meanwhile, a small island might have never had enough area to sustain (for example) a major forest. This lack of land also severely limits the inhabitants’ ability to raise livestock and grow crops, which in turn limits the size of the population that can be independently supported on an island. Cultivation is sometimes difficult on smaller islands, too, due to rocky soil or lack of suitable climate conditions. Of course, some volcanic islands do have nutrient-rich soil for plants to thrive, but land availability is still a concern. As an example, even Hawaii – a relatively large and well-established island in the United States – faces concerns about optimizing land use to increase food security. To this end, Hawaii’s Department of Business Economic Development & Tourism produced a report titled “Increased Food Security and Food Self-Sufficiency Strategy” in 2012 outlining a plan to reduce reliance on food imports, among other things. Clearly then, even well-connected islands aren’t immune to geographically-related economic growth challenges. The local economy is usually not diversified Because of their small size and the lack of abundant natural resources, many islands are forced to build their economies off of tourism and one or two primary export products, typically some type of tropical produce, fish, or a particular natural resource that may be in relatively high supply. This lack of diversity can cause the local economy to be very sensitive to the market for one or two goods. If most of an island’s trade involves just two major exports, any negative shock to the market for one of those goods can cause a serious downturn in economic activity on the island. A severe enough shock could lead to a state of recession. This is especially difficult given that islands typically rely on imports; a reduction in exports means that the island must reduce imports or take on debt. The effects of the initial economic shock can therefore affect the local economy more than it would have otherwise, reducing overall economic welfare. Meanwhile, a dependency on tourism – while it can bring significant revenue during certain seasons – has its own drawbacks. Overcrowding or overtourism can stress the natural resources present on an island, particularly infrastructure. Excess tourism can degrade local infrastructure since tourists use it but don’t contribute to its upkeep or maintenance. Tourists’ lack of knowledge or care about proper disposal behavior on an island can create pollution, particularly during peak seasons. Tourists can also lower the welfare of local communities if they obstruct the lives of the permanent residents. Crowded towns, restaurants, beaches or other amenities can be a headache for the locals during peak seasons. Animosity between the two groups might form and influence social interactions in a negative way. Finally, a dependency on tourism can compound economic woes during events like COVID-19, making a recession even worse than it would have been otherwise. Transaction costs are often high for island economies The aforementioned lack of land and abundant natural resources means that islands must often import many of the goods that are necessary for daily life – such as food. A large portion of the goods sold on an island tend to be manufactured outside of the island and shipped there. But importing goods typically raises their price, even without factoring in the trade barriers (like tariffs) that could artificially increase the cost. This is because shipping goods, especially over longer distances, increases the costs producers must bear to bring their goods to the market. This in turn increases the price of the final good for consumers. Transportation methods also contribute to import costs. Most islands are small and relatively isolated from the major economies of the world, often even those that are part of a larger country. And most islands are reachable only by air or by ship, which severely limits the movement of people and goods into and out of them. Train and road travel is often not possible. This lack of options can raise transaction costs, even though shipping by sea is a relatively cheap way to trade. In the case of a small island with limited population, it may not be worth it for a large container ship to visit such a small market, compounding the issue. Local market size is limited, constraining growth of local businesses On a small island with scarce resources, the size of the population is bound to be small as well. Only with consistently supplied outside resources can the population continue to grow past a certain point. This means that island businesses face a long run growth challenge. The local market is easily saturated due to size constraints, at which point further growth can only be accomplished by developing a market presence elsewhere. But the high costs of shipping and lack of physical locations in other countries can make this very difficult. Due to this limited size, it’s unlikely that local businesses will grow beyond a certain point, and some sectors of the economy are unlikely to develop at all. To fill these gaps, foreign companies can ship goods into the island to be sold. But when locals buy these foreign products, the money leaves the island rather than staying in the local economy, as it would if there were local businesses providing the same goods or services. Infrastructure challenges Being located on an island comes with its own set of infrastructure challenges, too. Island infrastructure is a challenge to set up and maintain for several reasons. First, there is often a lack of immediately available heavy equipment or construction materials, which usually must be imported. Second, island infrastructure tends to be exposed to additional weathering elements than many continental locations. Intense sunlight, tropical storms, ocean tides, sea level rise, salty water and air, etc. all conspire to corrode and weather infrastructure more quickly than in most landlocked locations. Further, islands’ vulnerability to natural disasters like storms doesn’t help this fact. Storm surges and major hurricanes can be devastating to an island when they pass through. But physical infrastructure isn’t the only type of infrastructure that is costly to install and maintain on islands. Internet and payment infrastructure can be limited on islands, particularly those with smaller populations. Again, this is at least partially due to the naturally small size of island markets. However, recent years have seen an improvement in Internet connection speeds in many islands as undersea cables have been built to provide better service to remote regions, so this particular infrastructure challenge is increasingly being met. The importance of free trade agreements Due to their unique geography, free trade agreements can be very helpful for these island economies. These agreements typically include promises from members that tariffs and other trade barriers will be severely reduced or eliminated. For example, a 2016 IMF working paper, “The Impact of Trade Agreements: New Approach, New Insights”, found that free trade agreements could “generate substantial gains, on average an increase of exports by 80 percentage points over ten years”. Freer trade helps each member nation to leverage its comparative advantage with its trading partners as much as possible. In this way, the members in the trade agreement can boost their economies further collectively than each could do on their own. There are already a number of free trade agreements in effect among various island regions. For example, PICTA is a free trade agreement shared between 14 small island nations in the Pacific region that has been in effect since 2007. The much older CARIFTA agreement (which began in 1965) is a free trade agreement originally shared by 4 Caribbean islands, and has been gradually expanded to include most of the region – now 20 islands are a part of the agreement (some of which are independent nations, while others are overseas territories of larger nations). The agreement is now known as CARICOM. Next time you visit an island, keep an eye out for signs of these economic challenges, and the ways in which local society has adapted to them. You might discover a new research question, have an innovative idea of your own, or simply gain something to ponder while sipping from your freshly-cut coconut. Header image credit: Pixabay (free for use under the Pixabay Content License)
Islands bring to mind a particular vision of beaches, coconuts, and relaxation that’s hard to beat. Many people think of them as the ultimate vacation destination, and indeed tourism is important for many islands. Yet due to their unique geography, most islands in the world face a set of economic challenges that many mainland nations don’t experience.