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By Jonah Abrams

Jonah Abrams received the first place award in the HUEA x Harvard Economics Review international high school economics essay competition.

(Cover photo from Mark Rowland with Your Marketing Rules)

Patrick Henry asked for liberty or death. A group of economists and psychologists have proven that too much liberty is, if not death, a different kind of sub-optimal. It turns out that often when we have more choices, we paradoxically are worse off. We make poor decisions, and we feel subjectively bad about them. In the market we see the practical response to this concept in the limited number of offerings in a Bonobos store, the spare interface of an iPhone, and the musings of tidying-guru Marie Kondo.

This now popular idea that “less is more” sounds wise and can be comforting, but it is just as wrong as perfect liberty. Instead, we gain from additional options when the benefits of having the additional options outweigh the costs of processing the choice. The costs rise with the choice difficulty and complexity. The benefit of additional options is valuable when the stakes are high so that the effort subjects are willing to expend is high as well. Even in this case,sometimes the benefit of an extra option is offset by two behavioral biases - hedonic adaptation and regret. Based on economists’ research over the last several years, we now have a structure to help us determine when we should limit our choices.

The idea of choice overload has a long history beginning with Aristotle who described the difficulty people faced when presented with two equally good choices. This idea more recently was popularized in 2000 with what has been somewhat breathlessly called “one of the most memorable economic studies of the last half century.” It used a simple product, jam. Thirty percent of supermarket consumers that saw a 6 jam display bought a product. For those that saw a 24 jam display, just 3% bought a jam. This “analysis paralysis” for jam ended up being just one of many examples of the the impact of choice overload on many decisions we make. It was found in other consumer discretionary products like chocolate selection and consumer electronics , but also in areas as varied as pensions, medical choices, and dating. When 401(k) plans offer more funds, participation rates fall precipitously - for every 10 extra funds a 401(k) plan offers, participation rates fall by 1.5% to 2%. Similarly, when doctors are offered two medicine choices to prescribe instead of one, they paradoxically double their referrals to specialists because they are unable to make a decision. When online daters are offered a large choice of partners and can reverse their decision, they are less satisfied with their partner selection than those offered a small set of partners with no chance of reversing their decision.

The economics of choice has not been without controversy. There have been many studies that found no paradox of choice - that more choice is in fact better. For instance, Daniel Mochon has written about single option aversion. When Williams Sonoma added a second $429 breadmaker to their previous single offering at $279, the sales of the $279 version doubled. The same results hold for consumers choosing nightclubs and savings accounts. Consumers, at least to a point, prefer larger assortments when presented with them. These types of results seem to directly contradict the general thrust of the choice overload hypothesis.

Until recently, it was not known whether the paradox of choice was in fact a paradox. However, two meta-analytic reviews, one by Chernev et al. in 2015 and a second by McShane et al. in 2018 , focused on the context in which choice overload might apply, rather than assuming that its effect was universal. These studies provide a new, more nuanced view of choice. It turns out that choice overload is very contingent on the structure of the choice.

These studies found that the paradox of choice is in fact a paradox but that more choice is not always worse. More importantly, they provided a taxonomy to help us understand when choice overload might apply. There are four main factors that moderate the effect. The first is choice difficulty. This is defined as how many attributes describe each choice or how well ordered the presentation of the choices is. A second factor is choice complexity. Complexity is reduced when there is a dominant option. It is increased when attributes of each option are not alignable such as choosing between one car with an alarm system and another with a sunroof. A third factor is the degree of preference uncertainty - do you know what an ideal choice would look like before you are exposed to the choices. Finally, there is effort; in particular, how much effort you are willing to expend. This is generally proportional to the stakes of the choice.

The results of these studies are largely intuitive. Both Chernev and McShane found choice overload when common sense tells us that the moderators make it more likely: when the task is difficult, when it is complex, when the subject has poorly articulated preferences or when the effort the subject is willing to expend is low. In the opposite conditions, more choices in fact lead to better outcomes. These particular findings are true (with some currently unexplained exceptions ) across a broad range of outcome researchers measured: option selection “goodness,” choice satisfaction, and switching post choice.

.....

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Clarissa Wern Ting Wong is one of our 2020 winners for the HIEEC.

The economist Keynes once posited that when the quantitative calculation of expected utility can hardly help us make a decision, it is our “animal spirits” – or emotional states – that kick in and urge us into action (Keynes, 1936). This surmise proved prescient. From unbridled optimism that fuels an economic bubble, to stubbornness that undermines policies meant to raise social welfare, many suboptimal decision patterns that repeat themselves throughout history can be attributed to people’s “irrational” sides getting the better of them (Akerlof et al., 2009).

How We Make Suboptimal Decisions

A rational economic agent acting in accordance with expected utility theory is expected to evaluate a set of perfect information using mathematical logic: He calculates the utility he could gain from each option. Then, he chooses the option that maximises his utility within his budget constraints. However, real-life decisions are not informed purely by such mathematical calculations, but also by heuristics, biases and misconceptions which make us imperfect decision-makers.

Heuristics are mental shortcuts that people use to save time and mental effort in decision-making. Using these heuristics sometimes leads to statistically systematic errors in one’s information processing, also known as cognitive biases. For example, people tend to form an impression based on only a few salient examples that come to mind. This leads to the availability bias, whereby a minority of salient information disproportionately influences one’s judgement on the probability of something happening (Thaler, 2009). Resultant distortions in people’s probability judgements can lead them to make suboptimal decisions. For example, influenced by the preceding bull run in Internet stocks and the palpable optimism of fellow investors, investors in the 1990s came to display “irrational exuberance” in their expectations (Greenspan, 1996) and grossly overvalued Internet stocks. Energy consumers, on the other hand, tend to overconsume energy when they are not provided salient information like their level of energy usage (Thaler, 2009). People make suboptimal decisions when they over- or under-estimate an action’s utility: In one instance, important but non-salient information, like energy usage, is ignored; In another instance, salient but unrepresentative information, like overoptimistic expectations, is used to form the big picture.

Other cognitive biases can similarly distort one’s ability to make rational decisions. People tend to feel losses (shown as the red arrow in Figure 1) about twice as hard as gains (shown as the blue arrow) for the same change in wealth from a reference point (Tversky et. al, 2000). This leads them to exhibit loss aversion bias.

Loss aversion bias has been used to explain the endowment effect, where people tend to value goods they own higher than an identical good they do not own (Thaler, 2015). This is because they likely overvalue their loss in utility should they give up what they own. Consequences can be serious: If policymakers exhibit sufficient loss aversion, they may overprotect loss-making sectors, or develop anti-trade biases (Tovar, 2009).

Besides cognitive biases, misconceptions can also influence suboptimal decision making. As shown in Figure 2 below, World Bank development staff were generally shown to have believed that the poor were more suspicious of vaccines that they actually were. If such biases form assumptions in models which are used to predict an audience’s vaccine receptivity, this could lead to suboptimal allocation of resources for health outreach initiatives.

Separately, public health beneficiaries in developing countries can also hold misconceptions that impede policymakers’ efforts. In a South Asian nation, 35-50% of poor, lesser-educated women wrongly perceived the appropriate treatment for diarrhoea to be a reduction of water intake (World Bank, 2015). In fact, rehydration of the body is essential to treat diarrhoea. This caused the beneficiaries to undervalue the utility of the Oral Rehydration Therapy (ORT) programme, and thus under-consume it. Committing to misconceptions that are prevalent in a certain society can cause people to make suboptimal judgements.

Hitherto, we have discussed how biases, heuristics and misconceptions affect both governor and governed, both buyer and seller. If these factors result in suboptimal decision making, one forgoes the opportunity to choose an alternative option that would have yielded greater utility in the long run – avoiding over-buying inflated stocks, saving energy, bettering trade policies or receiving healthcare treatment.

The prevalence of sub-optimal decision-making implies that the application of traditional economic theory is limited in the real world. General Laws like the Law of Supply and Demand logically optimise resource allocation, but only if one makes rational choices. Influenced by a multitude of biases and heuristics and egged on by the lightning-speed pace of information spread, people inevitably make irrational choices. In the past, news of the Titanic’s sinking took hours to reach news outlets. Today, online tweets and posts make information, both real and fake, available instantaneously. Traders across the globe may be triggered to make knee-jerk, heuristic-influenced trading decisions. This increases the chance that market prices may overshoot their true value.

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Jihoo Lim is one of our 2020 winners for the HIEEC.

Machines, which are naturally mild, and easily kept in order, may be said now to devour men, and unpeople in times of COVID-19, as did sheep in the 15th century. The pandemic is expediting the arrival of automation which may help explain the recent K-shaped recovery. Artificial intelligence (“AI” hereafter) and robots that have been put into simple and repetitive production processes at a certain industry in the past are rapidly replacing manpower even for the tasks that were thought to be impossible only without human power. Eventually, we are threatened by concerns that AI might displace the workers permanently.

People in the past also feared that machines would steal their jobs. Ned Rudd, a young apprentice, who was believed to have led the Luddite movement, shouted to the crowd: “As more machines become available, they displace the workers, threatening our survival. Now, let's destroy those machines!” With the introduction of spinning machines into the textile manufacturing industry, which used to be made by hand, spinning machines produced much more fabric in a shorter time than the workers. As capitalists began lending spinning machines to craftsmen, workers lost their jobs, which eventually caused riots. With oppression by legal and military force, eventually the movement was suppressed. Coercive measures were not, however, the only response to the movement. For instance, concerned about the unemployment problem, the British government enacted a legislation requiring the employment of three people—a driver, an engine worker, and a rider—to operate a car. However, this legislation was eventually abolished due to its failure to keep up with the trend of advancement of technology, and the job of coachman has become extinct.

Rud and his followers have predicted that the emergence of spinning machines will permanently displace jobs. Contrary to this specter, it turns out that new jobs in many industries have been created hundreds of times as many as disappeared. We have learned from the experience of the Industrial Revolution in the 19th century that although the displacement effect may take control in the short run, the productivity effect ends up dominating, having a positive impact on employment in the longer run.

Then, will this story turn out to be the case now even 200 years after the Luddites? The scenario that rapid automation will permanently take all of workers' jobs has yet to be observed. Rather, many economists still argue that AI and robotics complement regular workers, instead of replacing them. Even job polarization emerging in recent years suggests that machines could hardly displace both high-skilled and low-skilled jobs since the former requires highly sophisticated intelligence while the latter is too economically costly to automate.

These arguments seem to assume that there are certain tasks that cannot be processed by any other entities but humans. Though this was true in the past, can such patterns thus far be the case even in the future? This time may be different from the past in that rapid progress of AI shows a possibility of a variety of high-skilled tasks displaced, including translation, academic research, medical treatment, entertainment, and other tasks requiring empathy, which have been previously considered safe from automation.

In the past, the field in which machines replaced human labor was limited to relatively mid- and low-skilled workers. By contrast, in recent years, as seen in the case of AlphaGo, technological innovation has evolved to the stage where robots can automatically learn and improve from experience without being explicitly programmed. They can, therefore, be extended to fields requiring creative or artistic sense, from doctors, lawyers, and economists to novelists and composers.

As such, it would be no exaggeration to say that we may witness that AI and robots will kill all the jobs in the present generation, opposed to what the technological innovations have done to our job thus far. Let's take self-driving automobiles for example. The invention of automobiles in the past, while displacing a coachman from history permanently, has created a large number of new jobs, including workers in automobile factories, car salesmen, car insurance salesmen, taxi or truck drivers, and even road makers, traffic policemen and junkyard employees.

Now, would the rise of self-driving cars, in turn, kill or create jobs? If AI replaces human intelligence and creativity in design, production, sales, driving, and accident handling of automobiles, all workers in automotive industries may lose jobs. Some still argue that driverless cars could create brand-new industries while endangering a great number of current jobs. We however doubt that newly created jobs are safe from attack by AI, since AI in near future are expected to perform creative or sophisticated tasks, thereby overcoming the limitations posed by those designed in the 18th century. For that reason, in 2017, Nitin Gadkari, the minister of Transportation of India, declared that it would ban the introduction of autonomous vehicles to protect jobs in the country.

What kind of future will unfold if this possibility becomes a reality? Indeed, technological progress and the limitless potential of AI can possibly boost productivity and efficiency to an immeasurable extent. Nevertheless, the wealth inequality will widen substantially provided that the fruits are distributed to capitalists who own the technology and capital. Given diminishing marginal propensity to consume, rising wealth disparity lowers the effective demand, thereby making it impossible to find someone to consume goods and services produced on an enormous scale at a lower price than in any other era, which disables economic growth from being sustainable.

Now, we note that the biggest challenges facing us going forward would be how to provide for displaced workers, and how to keep our economy sustainable in a highly unequal society. In other words, in a world where AI and robots replace human workers, the most significant task we need to address would not be how to efficiently produce, but how to fairly distribute the outputs produced by AI. Among various resolutions, it is necessary to seriously consider “universal basic income (UBI)” where the government provides every citizen with a set amount of money on a regular basis, non-proportional to the level of wealth or employment status. The intention behind this idea is to ensure that all citizens can live a minimum of human life, thereby providing them with financial security. This differs from the general social security system in that this is paid to individuals, not households regardless of whether or not they have other incomes.

However, there are a few concerns over a UBI: (1) that it could reduce incentives to work or innovate, (2) that it would not be truly effective to mitigate inequality, and (3) what could meet human desire for social cohesion and participation without jobs.

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Prajwal Pandey is one of our 2020 winners for the HIEEC.

The coronavirus pandemic has changed our society forever, particularly in terms of the global economy and markets. This essay explores the economic effects of the ongoing pandemic and the necessary policy work needed to counter these effects.

Impact on Markets and Economies

Conventional wisdom conveys that this pandemic has had negative effects on markets and economic growth. This is due to lower disposable incomes and propensities to consume on the demand-side, as well as supply chain shocks and draconian lockdown measures forcing businesses to shut on the supply-side. This has culminated in a $9 trillion short-term global loss in cumulative output. However, long-term effects may differ. To visualize the long-term impact of the pandemic on growth rates, consider a standard Solow-Swan neoclassical growth model.

Key growth factors in this model have suffered over the coronavirus pandemic. For example, population growth rates (n) have decreased internationally, as is exemplified in the US, where population growth is expected to be at its lowest in over 100 years. Additionally, technological innovation (g) has suffered. This is because FDI inflows, that help finance innovation, have reduced by 49% globally, and the openness to trade ratios of economies have been damaged by recently implemented protectionist policies, thereby disabling the efficient cooperation of economies for innovation. Nevertheless, the effects of this on the steady-state output equilibrium is nullified by increases in savings rates (s). Households have been saving a higher proportion of their disposable income for the long-term, due to the increased economic uncertainty created by the pandemic. This is exemplified in the UK, where the savings ratio of Q2 2020 is 20.6% higher than that of Q2 2019, culminating in higher levels of investment being financed, thereby growing total new capital stocks. Assuming that depreciation rates remain constant, this will ensure that output levels remain relatively unscathed.

Many may argue against this inference using Keynes’ Paradox of Thrift; if autonomous saving rates increase then there will be a decrease in consumption and aggregate demand, thereby decreasing economic growth and total savings. However, this argument ignores Say’s law; supply creates its own demand, meaning that as savings, capital stock and supply increase, decreases in aggregate demand will be offset. Additionally, as banks have more funds for lending through increased savings rates, lending by commercial banks will increase, leading to consumer disposable income increasing. Hence, aggregate demand will be sustained. This is demonstrated in the US, where consumer credit increased by 4.4% and 2.1% in September and October respectively, corresponding to increases in consumer spending by 1.3% and 0.3% respectively. Thus, as this savings-driven effect continues in the long-run, the negative effects of other growth factors will be retracted.

Therefore, we can illustrate this dynamic on the below Solow-Swan growth diagram. Adjusting the relevant variable functions from baseline, the long-run output equilibrium decreases only fractionally. Therefore, despite initial deterioration in markets and economic growth in the short-term, global output levels and markets will remain relatively unaffected after the pandemic is resolved.

Contrary to initial assumptions that the universality of the pandemic will lead to more egalitarian societies, this pandemic has worsened labor market inequality. Low-income workers have found their hours and wages cut to a greater proportion than their high-income counterparts (as much as times as much in the UK for example). This is due to low-wage workers typically having lower marginal revenue products than their higher-paid and skilled colleagues. Hence, firms make these low-ability workers redundant before higher-skilled workers to maintain productivity. To approximate the macro effects of this on income inequality, we can observe the effects of previous pandemics on the Gini coefficient of countries. Modeling the income distributional effect of pandemics by estimating the impulse response function (the dynamic reaction of the Gini coefficient) directly from local projections is the log of the distribution variables (the Gini coefficient) for country i in year t, and is responsive to variables such as time fixed and country fixed effects. Empirical estimates, utilizing said model, have conveyed increases in both market and net Gini coefficients of 0.75% to 1.25% 5 years after pandemics on average. When adjusting for heterogeneity in the effects of pandemic events on economic activity, estimates for the current pandemic’s effect on the global Gini coefficient increase to as much as 2% due to the severe economic contractions over several episodes of the pandemic. This estimation is consistent with that of the IMF, which has estimated the Gini coefficients of emerging market and developing economies to potentially rise to 42.7% (around the same level as 2008). Thus, it can be seen that inaction of governments on this matter will see the reversal of all the improvements in labor market equality all across the world since the 2008 recession.

Furthermore, wealth inequality has intensified due to the coronavirus pandemic. Utilizing Piketty’s famous r>g hypothesis*, we can analyze the mechanism underlying this increase. Drastic increases in capital gains (r) for wealthy business owners and stakeholders has been signaled by the S&P 500 index closing at a record high of $3,389.78 6 months after the genesis of the coronavirus plunge in markets. Contrastingly, global GDP per capita has decreased by around $690 from 2019 to 2020, indicating severe decreases in economic growth (g). Hence, trends of deepening wealth inequality, accelerated by the coronavirus pandemic, are evident.

Policy Proposals

Evidently, sound policy work is needed to counter the economic stress of the coronavirus. It is vital that fiscal policy facilitates the ensuing market recovery. Furthermore, it is critical that existing redistributive mechanisms, specifically taxation and welfare benefit spending, are enhanced to counter the negative effects of the pandemic on labor market inequality.

Thus, governments should increase taxes for greater budgetary revenue accumulation for redistribution, whilst still minimizing potential distortions to the ensuing economic recovery. Considering that income taxation is the primary source of revenue for most governments, we can look to optimal income tax theory to inform policy recommendations. Working from an elasticity based model, economist Emmanuel Saez has controversially called for a top marginal tax rate (τ) of 71% for maximizing revenue accumulation without compromising economic efficiency or aggregate productivity.

However, Saez relies on compensated and uncompensated labor supply elasticity being controversially as low as 0.2 for the 71% estimate. Economists have argued that this greatly underestimates the total deadweight loss of such a high tax, due to behavioral effects like shifting income into non-taxable forms and tax evasion being unaccounted for.

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By Jonah Abrams

Jonah Abrams received the first place award in the HUEA x Harvard Economics Review international high school economics essay competition.

(Cover photo from Mark Rowland with Your Marketing Rules)

Patrick Henry asked for liberty or death. A group of economists and psychologists have proven that too much liberty is, if not death, a different kind of sub-optimal. It turns out that often when we have more choices, we paradoxically are worse off. We make poor decisions, and we feel subjectively bad about them. In the market we see the practical response to this concept in the limited number of offerings in a Bonobos store, the spare interface of an iPhone, and the musings of tidying-guru Marie Kondo.

This now popular idea that “less is more” sounds wise and can be comforting, but it is just as wrong as perfect liberty. Instead, we gain from additional options when the benefits of having the additional options outweigh the costs of processing the choice. The costs rise with the choice difficulty and complexity. The benefit of additional options is valuable when the stakes are high so that the effort subjects are willing to expend is high as well. Even in this case,sometimes the benefit of an extra option is offset by two behavioral biases - hedonic adaptation and regret. Based on economists’ research over the last several years, we now have a structure to help us determine when we should limit our choices.

The idea of choice overload has a long history beginning with Aristotle who described the difficulty people faced when presented with two equally good choices. This idea more recently was popularized in 2000 with what has been somewhat breathlessly called “one of the most memorable economic studies of the last half century.” It used a simple product, jam. Thirty percent of supermarket consumers that saw a 6 jam display bought a product. For those that saw a 24 jam display, just 3% bought a jam. This “analysis paralysis” for jam ended up being just one of many examples of the the impact of choice overload on many decisions we make. It was found in other consumer discretionary products like chocolate selection and consumer electronics , but also in areas as varied as pensions, medical choices, and dating. When 401(k) plans offer more funds, participation rates fall precipitously - for every 10 extra funds a 401(k) plan offers, participation rates fall by 1.5% to 2%. Similarly, when doctors are offered two medicine choices to prescribe instead of one, they paradoxically double their referrals to specialists because they are unable to make a decision. When online daters are offered a large choice of partners and can reverse their decision, they are less satisfied with their partner selection than those offered a small set of partners with no chance of reversing their decision.

The economics of choice has not been without controversy. There have been many studies that found no paradox of choice - that more choice is in fact better. For instance, Daniel Mochon has written about single option aversion. When Williams Sonoma added a second $429 breadmaker to their previous single offering at $279, the sales of the $279 version doubled. The same results hold for consumers choosing nightclubs and savings accounts. Consumers, at least to a point, prefer larger assortments when presented with them. These types of results seem to directly contradict the general thrust of the choice overload hypothesis.

Until recently, it was not known whether the paradox of choice was in fact a paradox. However, two meta-analytic reviews, one by Chernev et al. in 2015 and a second by McShane et al. in 2018 , focused on the context in which choice overload might apply, rather than assuming that its effect was universal. These studies provide a new, more nuanced view of choice. It turns out that choice overload is very contingent on the structure of the choice.

These studies found that the paradox of choice is in fact a paradox but that more choice is not always worse. More importantly, they provided a taxonomy to help us understand when choice overload might apply. There are four main factors that moderate the effect. The first is choice difficulty. This is defined as how many attributes describe each choice or how well ordered the presentation of the choices is. A second factor is choice complexity. Complexity is reduced when there is a dominant option. It is increased when attributes of each option are not alignable such as choosing between one car with an alarm system and another with a sunroof. A third factor is the degree of preference uncertainty - do you know what an ideal choice would look like before you are exposed to the choices. Finally, there is effort; in particular, how much effort you are willing to expend. This is generally proportional to the stakes of the choice.

The results of these studies are largely intuitive. Both Chernev and McShane found choice overload when common sense tells us that the moderators make it more likely: when the task is difficult, when it is complex, when the subject has poorly articulated preferences or when the effort the subject is willing to expend is low. In the opposite conditions, more choices in fact lead to better outcomes. These particular findings are true (with some currently unexplained exceptions ) across a broad range of outcome researchers measured: option selection “goodness,” choice satisfaction, and switching post choice.

.....

篇幅有限，扫码即可免费获取完整版论文pdf

篇幅有限，扫码即可免费获取完整版论文pdf

篇幅有限，扫码即可免费获取完整版论文pdf

The duty of a central bank is to pursue monetary stability — customarily defined by low inflation and steady output growth. Recent inflation levels have run larger than ever in the last half-decade (Desilver, 2022). In response, central banks across the world are synchronously hiking interest rates without
consulting each other (Moschella et al., 2022). This raises the question: what is the most effective way for Western central banks to tame inflation while limiting recessionary forces globally?

To answer, this essay makes three observations:

1. Owing to increased integration, trade flows, and global value chains, inflation operates on an international scale.
2. Given Observation 1, most integrated economies face similar inflationary threats, and all have incentive to individually tighten their monetary policies.
3. When Observation 2 occurs, and central banks amplify each other’s policies without cautious cooperation, negative spillovers are created while a resulting mutually damaging cycle feeds both inflationary and recessionary forces.

Taking these three observations together, this essay concludes that it is uneconomical for monetary policies to differ but dangerous for monetary policies to blindly match one another. Monetary policies should resemble each other only to the extent that they are carefully calibrated and driven by central bank cooperation.

Globalized Inflation

The Global Slack Hypothesis postulates that “domestic inflation rates have now become more a function of global, rather than domestic economic conditions'' (Milani, 2009). Using the Phillips Curve, analysts find that global slack — unused economic resources — is as important as domestic slack in forecasting short-term inflation dynamics (Wynne, 2009; Borio et al., 2007). This is also considered an openeconomy extension of the traditional closed-economy Phillips Curve (Garcia, 2012). When exchange rates are included in an open-economy model, the Philips Curve flattens, indicating that individual central banks hold less policy control over inflation behavior (see Figure 1, IS vs. RX curve).

In recent years, a scholarly consensus has agreed that globalization has a wide impact on nearly all economic activities (Frankel, 2000). In the United States, imports as a share of GDP increased from 4% in 1950, to more than 18 percent today (Wynne). In the E.U., imports as a share of GDP have grown from 20% in 1970 to over 46% in 2021 (World Bank). This means that the final consumption basket of an average citizen consists of both foreign and domestic goods, making global inflation a factor of domestic inflation.

Specific domestic causes of inflation certainly exist. However, this internal inflation can easily be imported to other nations via globalization. Global value chains (GVCs) exist when “different stages of the production process are located across different countries” (OECD). By virtue of a GVC, price inflation of an input produced in one country can translate to inflation in another country that imports this inflated input. For example, if prices increase for U.S. aerospace parts and the U.K. imports these inflated aerospace parts to build planes, the U.K. will also experience plane price inflation. Broadly, this trend assumes the massive effect of “importing” inflation from one country to another (Auer).

Given the globalized nature of inflation, tightening monetary policy cannot be one-dimensional. Pricelevel dynamics now respond to global forces, complicating the impact of domestic-focused monetary policy. Auer deduces that central banks must coordinate with each other to target specific causes of inflation (2017). Some factors causing inflation “are beyond the control of individual central banks” (Auer).

Effects of Uncoordinated Monetary Policies

Applying their Open-Economy Macroeconomic Model, Obstfeld and Rogoff find risk in central banks conducting monetary policy centered only on a national, but not global perspective (2002). With inflation globalized (see Figure 2) and central banks all raising interest rates without any careful communication or
coordination (see Figure 3), unintended negative consequences are in the wind. Central banks should adopt similar monetary policies, increasing interest rates to cool inflation, but they require a cautionary cooperation regime. This section identifies three effects of this absence of cooperation: a) overestimation,
b) competitive appreciation cycle, c) spillovers into developing nations.

Absent careful calibration, central banks could very well overestimate the monetary contraction needed to tame inflation. By aggressively pushing interest rates in the same direction, central banks amplify each other’s policies without accounting for the feedback loop (Obstfeld, 2022). The World Bank recently
warned that if monetary policies so sightlessly match each other, “they could be mutually compounding…and steepen the global growth slowdown” (Morris, 2022). Central banks must collaborate to assess their collective impact on global demand and lower the global recession risk. Monetary policies are misguided
without cooperation as they cannot target the root cause of inflation, especially if it is imported through global value chains (Auer). Only through communicated policy calibration can individual central banks minimize avoidable economic slowdowns.

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