economic policy research paper

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• E60 - General
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• E62 - Fiscal Policy
• E63 - Comparative or Joint Analysis of Fiscal and Monetary Policy; Stabilization; Treasury Policy
• E64 - Incomes Policy; Price Policy
• E65 - Studies of Particular Policy Episodes
• E66 - General Outlook and Conditions
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• F02 - International Economic Order and Integration
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• F10 - General
• F11 - Neoclassical Models of Trade
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• F32 - Current Account Adjustment; Short-Term Capital Movements
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• F34 - International Lending and Debt Problems
• F35 - Foreign Aid
• F36 - Financial Aspects of Economic Integration
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• F40 - General
• F41 - Open Economy Macroeconomics
• F42 - International Policy Coordination and Transmission
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• G33 - Bankruptcy; Liquidation
• G34 - Mergers; Acquisitions; Restructuring; Corporate Governance
• G35 - Payout Policy
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• G41 - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets
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• G50 - General
• G51 - Household Saving, Borrowing, Debt, and Wealth
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• H25 - Business Taxes and Subsidies
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• J01 - Labor Economics: General
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• J12 - Marriage; Marital Dissolution; Family Structure; Domestic Abuse
• J13 - Fertility; Family Planning; Child Care; Children; Youth
• J14 - Economics of the Elderly; Economics of the Handicapped; Non-Labor Market Discrimination
• J15 - Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination
• J16 - Economics of Gender; Non-labor Discrimination
• J17 - Value of Life; Forgone Income
• J18 - Public Policy
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• J21 - Labor Force and Employment, Size, and Structure
• J22 - Time Allocation and Labor Supply
• J23 - Labor Demand
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• J31 - Wage Level and Structure; Wage Differentials
• J33 - Compensation Packages; Payment Methods
• J38 - Public Policy
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• J41 - Labor Contracts
• J44 - Professional Labor Markets; Occupational Licensing
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• J58 - Public Policy
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• J60 - General
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• J62 - Job, Occupational, and Intergenerational Mobility
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• J64 - Unemployment: Models, Duration, Incidence, and Job Search
• J65 - Unemployment Insurance; Severance Pay; Plant Closings
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• O15 - Human Resources; Human Development; Income Distribution; Migration
• O16 - Financial Markets; Saving and Capital Investment; Corporate Finance and Governance
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• Q01 - Sustainable Development
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• Q10 - General
• Q11 - Aggregate Supply and Demand Analysis; Prices
• Q12 - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
• Q13 - Agricultural Markets and Marketing; Cooperatives; Agribusiness
• Q14 - Agricultural Finance
• Q15 - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment
• Q17 - Agriculture in International Trade
• Q18 - Agricultural Policy; Food Policy
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• Q20 - General
• Q21 - Demand and Supply; Prices
• Q23 - Forestry
• Q25 - Water
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• Q30 - General
• Q32 - Exhaustible Resources and Economic Development
• Q33 - Resource Booms
• Q34 - Natural Resources and Domestic and International Conflicts
• Q35 - Hydrocarbon Resources
• Q38 - Government Policy
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• Q40 - General
• Q41 - Demand and Supply; Prices
• Q42 - Alternative Energy Sources
• Q43 - Energy and the Macroeconomy
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• Q50 - General
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• Q52 - Pollution Control Adoption Costs; Distributional Effects; Employment Effects
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• Q54 - Climate; Natural Disasters; Global Warming
• Q55 - Technological Innovation
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• R48 - Government Pricing and Policy
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• 15 April 2024

Revealed: the ten research papers that policy documents cite most

• Dalmeet Singh Chawla 0

Dalmeet Singh Chawla is a freelance science journalist based in London.

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Policymakers often work behind closed doors — but the documents they produce offer clues about the research that influences them. Credit: Stefan Rousseau/Getty

When David Autor co-wrote a paper on how computerization affects job skill demands more than 20 years ago, a journal took 18 months to consider it — only to reject it after review. He went on to submit it to The Quarterly Journal of Economics , which eventually published the work 1 in November 2003.

Autor’s paper is now the third most cited in policy documents worldwide, according to an analysis of data provided exclusively to Nature . It has accumulated around 1,100 citations in policy documents, show figures from the London-based firm Overton (see ‘The most-cited papers in policy’), which maintains a database of more than 12 million policy documents, think-tank papers, white papers and guidelines.

“I thought it was destined to be quite an obscure paper,” recalls Autor, a public-policy scholar and economist at the Massachusetts Institute of Technology in Cambridge. “I’m excited that a lot of people are citing it.”

The most-cited papers in policy

Economics papers dominate the top ten papers that policy documents reference most.

Data from Sage Policy Profiles as of 15 April 2024

The top ten most cited papers in policy documents are dominated by economics research. When economics studies are excluded, a 1997 Nature paper 2 about Earth’s ecosystem services and natural capital is second on the list, with more than 900 policy citations. The paper has also garnered more than 32,000 references from other studies, according to Google Scholar. Other highly cited non-economics studies include works on planetary boundaries, sustainable foods and the future of employment (see ‘Most-cited papers — excluding economics research’).

These lists provide insight into the types of research that politicians pay attention to, but policy citations don’t necessarily imply impact or influence, and Overton’s database has a bias towards documents published in English.

Interdisciplinary impact

Overton usually charges a licence fee to access its citation data. But last year, the firm worked with the London-based publisher Sage to release a free web-based tool that allows any researcher to find out how many times policy documents have cited their papers or mention their names. Overton and Sage said they created the tool, called Sage Policy Profiles, to help researchers to demonstrate the impact or influence their work might be having on policy. This can be useful for researchers during promotion or tenure interviews and in grant applications.

Autor thinks his study stands out because his paper was different from what other economists were writing at the time. It suggested that ‘middle-skill’ work, typically done in offices or factories by people who haven’t attended university, was going to be largely automated, leaving workers with either highly skilled jobs or manual work. “It has stood the test of time,” he says, “and it got people to focus on what I think is the right problem.” That topic is just as relevant today, Autor says, especially with the rise of artificial intelligence.

Most-cited papers — excluding economics research

When economics studies are excluded, the research papers that policy documents most commonly reference cover topics including climate change and nutrition.

Walter Willett, an epidemiologist and food scientist at the Harvard T.H. Chan School of Public Health in Boston, Massachusetts, thinks that interdisciplinary teams are most likely to gain a lot of policy citations. He co-authored a paper on the list of most cited non-economics studies: a 2019 work 3 that was part of a Lancet commission to investigate how to feed the global population a healthy and environmentally sustainable diet by 2050 and has accumulated more than 600 policy citations.

“I think it had an impact because it was clearly a multidisciplinary effort,” says Willett. The work was co-authored by 37 scientists from 17 countries. The team included researchers from disciplines including food science, health metrics, climate change, ecology and evolution and bioethics. “None of us could have done this on our own. It really did require working with people outside our fields.”

Sverker Sörlin, an environmental historian at the KTH Royal Institute of Technology in Stockholm, agrees that papers with a diverse set of authors often attract more policy citations. “It’s the combined effect that is often the key to getting more influence,” he says.

Has your research influenced policy? Use this free tool to check

Sörlin co-authored two papers in the list of top ten non-economics papers. One of those is a 2015 Science paper 4 on planetary boundaries — a concept defining the environmental limits in which humanity can develop and thrive — which has attracted more than 750 policy citations. Sörlin thinks one reason it has been popular is that it’s a sequel to a 2009 Nature paper 5 he co-authored on the same topic, which has been cited by policy documents 575 times.

Although policy citations don’t necessarily imply influence, Willett has seen evidence that his paper is prompting changes in policy. He points to Denmark as an example, noting that the nation is reformatting its dietary guidelines in line with the study’s recommendations. “I certainly can’t say that this document is the only thing that’s changing their guidelines,” he says. But “this gave it the support and credibility that allowed them to go forward”.

Broad brush

Peter Gluckman, who was the chief science adviser to the prime minister of New Zealand between 2009 and 2018, is not surprised by the lists. He expects policymakers to refer to broad-brush papers rather than those reporting on incremental advances in a field.

Gluckman, a paediatrician and biomedical scientist at the University of Auckland in New Zealand, notes that it’s important to consider the context in which papers are being cited, because studies reporting controversial findings sometimes attract many citations. He also warns that the list is probably not comprehensive: many policy papers are not easily accessible to tools such as Overton, which uses text mining to compile data, and so will not be included in the database.

The top 100 papers

“The thing that worries me most is the age of the papers that are involved,” Gluckman says. “Does that tell us something about just the way the analysis is done or that relatively few papers get heavily used in policymaking?”

Gluckman says it’s strange that some recent work on climate change, food security, social cohesion and similar areas hasn’t made it to the non-economics list. “Maybe it’s just because they’re not being referred to,” he says, or perhaps that work is cited, in turn, in the broad-scope papers that are most heavily referenced in policy documents.

As for Sage Policy Profiles, Gluckman says it’s always useful to get an idea of which studies are attracting attention from policymakers, but he notes that studies often take years to influence policy. “Yet the average academic is trying to make a claim here and now that their current work is having an impact,” he adds. “So there’s a disconnect there.”

Willett thinks policy citations are probably more important than scholarly citations in other papers. “In the end, we don’t want this to just sit on an academic shelf.”

doi: https://doi.org/10.1038/d41586-024-00660-1

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How Quickly Do Prices Respond to Monetary Policy?

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FRBSF Economic Letter 2024-10 | April 8, 2024

With inflation still above the Federal Reserve’s 2% objective, there is renewed interest in understanding how quickly federal funds rate hikes typically affect inflation. Beyond monetary policy’s well-known lagged effect on the economy overall, new analysis highlights that not all prices respond with the same strength or speed. Results suggest that inflation for the most responsive categories of goods and services has come down substantially from recent highs, likely due in part to more restrictive monetary policy. As a result, the contributions of these categories to overall inflation have fallen.

Monetary policy affects inflation with a lag. This means that, although interest rates react quickly when the Federal Reserve raises the federal funds rate, the effects on inflation are slower and indirect. Higher interest rates increase borrowing costs, slowing investment and overall demand, which ultimately eases the pressure on prices. Understanding the timing and strength of this mechanism is key for policymakers.

Many researchers have estimated the speed and strength of the economy’s response to monetary policy, notably Romer and Romer (2004). The focus is typically a broader measure of inflation, such as headline or core, which reflects an average across many goods and services. However, not all prices of the component goods and services react to monetary policy in the same way. For example, food and energy prices, which are excluded from core but included in headline inflation, often move more in response to global market fluctuations, such as changes in international oil prices, rather than to changes in domestic monetary policy.

In this Economic Letter , we estimate how prices of different goods and services respond to changes in the federal funds rate and use those estimates to build a monetary policy-responsive inflation index. We find substantial variation in how prices react to monetary policy, which suggests that understanding the makeup of overall inflation can provide insights into the transmission of monetary policy to inflation. The extent to which categories that are more responsive to the federal funds rate contribute to inflation affects how much slowing in economic activity is needed to reduce overall inflation. Our analysis indicates that recent ups and downs of inflation have been focused in categories that are most sensitive to monetary policy. Inflation rates for the most sensitive categories—and their contributions to headline inflation—rose from the first half of 2020 through mid-2022, reaching a higher peak than headline inflation, and then began to decline. The inflation rate for this most responsive group of goods and services categories is now close to its pre-2020 rate. Our findings suggest that the Fed’s rate hikes that began in March 2022 are exerting downward pressure on prices and will continue to do so in the near term. Our estimated lags are consistent with the view that the full effects of past policy tightening are still working their way through the economy.

Measuring how prices react to monetary policy

To understand which goods and services are most responsive to monetary policy, we need to determine how their prices react to changes in the federal funds rate, the Federal Reserve’s main policy rate. Because the Federal Reserve adjusts the federal funds rate target in response to macroeconomic developments, including inflation, we use a transformation of the federal funds rate in our estimation. This transformed series, developed by Romer and Romer (2004) and updated by Wieland and Yang (2020), captures the differences between Federal Reserve staff forecasts and the chosen target rate, leaving only policy shocks, or movements in the federal funds rate that are not driven by actual or anticipated changes in economic conditions. We use this series as a so-called instrument for the federal funds rate, such that our results can account for how the federal funds rate itself, rather than its transformation, affects inflation.

We use an approach developed by Jordà (2005) that compares two forecasts—with and without rate shocks—to estimate how the federal funds rate affects price movements over time. Specifically, we estimate the relationship between the federal funds rate and the cumulative percent change in prices, controlling for recent trends in the federal funds rate, inflation, and economic activity. Repeating this estimation over multiple horizons produces a forecast comparison, or impulse response function, that gives us an estimate of the expected percent change in prices following a rate increase. For example, applying this method to the headline personal consumption expenditures (PCE) price index indicates that four years after a 1 percentage point increase in the federal funds rate, overall prices are typically about 2.5% below what they would have been without the rate increase.

Creating a policy-responsive inflation index

We estimate impulse response functions separately for the 136 goods and services categories that collectively make up headline PCE inflation. Figure 1 shows examples of the largest cumulative percent price declines over a four-year period in response to a 1 percentage point increase in the federal funds rate. The goods and services categories selected as examples account for large shares of total expenditures in headline PCE inflation. We also include one example of the few categories where prices do not decline, higher education, shown as a small positive value.

Figure 1 Reaction to a policy rate increase: Selected PCE categories

The takeaway from Figure 1 is that headline PCE inflation is made up of categories that differ in their responsiveness to increases in the federal funds rate. Some respond more strongly, such as those with larger typical cumulative price declines, while others respond less strongly, such as those with smaller typical price declines. Focusing on the most responsive categories can shed light on how monetary policy has influenced the path of inflation over the post-pandemic period. We use our results to divide the categories into two groups of goods and services. The most responsive group (blue bars) contains goods and services whose largest cumulative percent price decline over a four-year window is in the top 50% of all such declines. The least responsive group (red bars) contains goods and services in the bottom 50%.

Following the methods in Shapiro (2022), we use these two groups, along with the share of total expenditures for each good or service, to create two new aggregate PCE inflation measures. Figure 2 shows their 12-month percent changes over time. The blue shading marks the period from mid-2019 until early 2020 when the Federal Reserve lowered the federal funds rate. The vertical yellow line marks the start of the most recent tightening cycle in March 2022. Inflation in the most responsive categories (blue line) is more volatile than overall headline PCE inflation (green line) from the Bureau of Economic Analysis (BEA), and inflation in the least responsive categories is less volatile (red line).

Figure 2 Most and least responsive inflation rates

After the start of the 2020 recession, inflation rates for both categories rose but have since come down from their recent peaks. This pattern is particularly pronounced for the most responsive inflation group, for which inflation peaked at 10.5% in mid-2022 and has fallen to 0.9% as of January 2024; this is just under its average of 1% from 2012, when the Federal Reserve officially adopted a numerical inflation objective, to 2019. Inflation in the least responsive group peaked later, in early 2023, and has fallen only slightly to 3.8% as of January 2024; it remains well above its 2012–2019 average of 1.8%.

How does policy-responsive inflation react to rate increases?

The inflation rates of categories in the most and least responsive groups can move for reasons beyond changes in the federal funds rate, such as global or national macroeconomic developments. To assess the specific role of policy rate increases, we use the methodology described earlier to estimate how the most and least responsive inflation groups tend to react to rate hikes.

The results in Figure 3 suggest that an increase in the federal funds rate typically starts exerting downward pressure on the most responsive prices after about 18 months, when the line showing the impulse response function falls below zero. Month-to-month price changes start falling after a little over a year, depicted when the slope drops below zero and stays negative. This is quicker than the response of overall headline prices from the BEA (not shown), which becomes negative after a little over 24 months and shows month-to-month declines after about 18 months.

Figure 3 Reaction of most and least responsive prices to rate hikes

Because we grouped inflation categories based on the size of their response, there is not necessarily a tie-in to the speed of each categories’ change. However, our results suggest that looking at the most responsive goods and services may also be a useful way of assessing how quickly monetary policy affects inflation.

Applying the typical impact timing of the most responsive group of goods and services to the most recent tightening cycle, shown by the federal funds rate line in Figure 4, leads to several conclusions. First, rate cuts from 2019 to early 2020 could have contributed upward price pressures starting in mid- to late 2020 and thus could explain some of the rise in inflation over this period. Second, the tightening cycle that began in March 2022 likely started putting downward pressure on prices in mid-2023 and will continue to do so in the near term. This is consistent with the view that the full effects of monetary policy tightening have yet to be felt. Finally, though inflation for the most responsive categories has been falling since mid-2022, the early part of this decline was likely to have been driven more by changes in prevailing economic conditions than by policy tightening, given estimated policy lags. Some research has considered whether policy lags have shortened (see, for example, Doh and Foerster 2021); however, because inflation began falling mere months after the first rate hike, the drop in inflation may have been too soon to be caused by policy action.

Figure 4 Headline inflation contributions and the federal funds rate

Our findings in this Letter are useful for broadening our understanding of how monetary policy affects inflation. For example, if inflation and the contributions to overall headline inflation are high in a set of categories that are more responsive to monetary policy, as was the case in early 2022, then rate hikes during the most recent tightening cycle are likely to continue to reduce inflation due to policy lags. On the other hand, though inflation in the least responsive categories may come down because of other economic forces, less inflation is currently coming from categories that are most responsive to monetary policy, perhaps limiting policy impacts going forward.

Doh, Taeyoung, and Andrew T. Foerster. 2022. “ Have Lags in Monetary Policy Transmission Shortened? ” FRB Kansas City Economic Bulletin (December 21).

Jordà, Òscar. 2005. “Estimation and Inference of Impulse Responses by Local Projections.” American Economic Review 95(1), pp. 161–182.

Romer, Christina, and David Romer. 2004. “A New Measure of Monetary Shocks: Derivation and Implications.” American Economic Review 94(4), pp. 1,055–1,084.

Shapiro, Adam. 2022. “ A Simple Framework to Monitor Inflation .” FRB San Francisco Working Paper 2020-29.

Wieland, Johannes, and Mu‐Jeung Yang. 2020. “Financial Dampening.” Journal of Money, Credit and Banking 52(1), pp. 79–113.

Opinions expressed in FRBSF Economic Letter do not necessarily reflect the views of the management of the Federal Reserve Bank of San Francisco or of the Board of Governors of the Federal Reserve System. This publication is edited by Anita Todd and Karen Barnes. Permission to reprint portions of articles or whole articles must be obtained in writing. Please send editorial comments and requests for reprint permission to [email protected]

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The pandemic and economic policy uncertainty

Saud asaad al‐thaqeb.

1 College of Business Administration, Kuwait University, Kuwait City Kuwait

Barrak Ghanim Algharabali

Khaled tareq alabdulghafour.

2 Independent Scholar, Kuwait City Kuwait

The events that occurred after the worldwide diffusion of COVID‐19 provide a real‐life example of how uncertainty can severely affect the global economy. This paper reviews literature on the negative impacts of the economic policy uncertainty index (EPU) on individuals, businesses, governments, and economies at the local and international levels. This reveals that a high EPU is associated with adverse effects on households, corporations, and governments, which tend to delay many financial decisions under high uncertainty, which leads to lower consumption, fewer issuances of debt, fewer investments, and higher unemployment. The effects of political and regulatory uncertainty also extend to the commodity markets, such as the adverse effects on both oil and gasoline markets, and can potentially create adverse impacts on the crypto‐currency market and its potential growth. We demonstrate that governmental uncertainty also affects financial, housing, and equity markets; debt issuances; and the entire economy. This underscores the importance of considering EPU as a risk factor. The association with several components of the global economy reflects not only the EPU index's critical influence, but also the importance of risk management. Our results lead us to consider the gravity of economic policy uncertainty and call for innovation across different sectors to mitigate its adverse effects.

1. INTRODUCTION

As 2019 came to an end, the highly infectious novel coronavirus (hereafter, “COVID‐19”) spread rapidly from its origins in Wuhan, China. It impacted not only public health, but also the worldwide economy and its stability, ending an 11‐year bull market. The world had not seen anything like this disease since the 1918 influenza pandemic, the effects of which were extrapolated by its uncertainty. The COVID‐19 pandemic increased the uncertainty in daily life (Caggiano, Castelnuovo, & Kima,  2020 ) due to several factors, including the uncertainty of the pandemic's duration, how it will consequently change the world, and whether another pandemic would impact the global economy. These unanswered issues increase uncertainty for both legislators and corporations. Further, this pandemic highlighted uncertainty's severe economic effects. Economic policies have been increasingly uncertain in previous decades due to several other factors, such as anti‐globalization, and populist movements have increased worldwide since the preceding decade's global financial crises. The COVID‐19 pandemic is one example of how uncertain economic policies distorted the vision for the economy, affect all market participants, and illustrate the global economy's interconnections.

This pandemic has also affected the world's supply and demand on both macro and microlevels. Forced closures of business and government‐imposed quarantines, curfews, and travel bans have placed the world in a “Great Lockdown” that has impacted every sector. This also drastically reduced the labour market (Coibion, Gorodnichenko, & Weber,  2020 ) and the output of goods and services as a consequence. The uncertainty of this pandemic's duration has halted production worldwide, which has led oil markets to fall to an all time low, with crude futures traded at negative prices. Workers have also been laid off due to these indefinite closures and travel bans, and the unemployment rate in the United States reached 14.7% in April (Bureau of Labour Statistics,  2020 ), the highest since 1940. After a decade of expansions, the International Monetary Fund (IMF) is projecting considerably decreased global growth in 2020.

Having a useful measure that reflects the economy's levels of uncertainty is vital given that uncertainty can severely impact the entire economy; hence, this paper primarily aims to reflect the uniqueness of the economic policy uncertainty (EPU) index and the ongoing studies and findings from EPU literature that could provide more avenues for research. First, Baker, Bloom, and Davis ( 2016 ) created a distinct EPU index that builds on and accounts for several factors, including prior measures of uncertainty. Collectively, this index attempts to reflect all sources of uncertainty within the economy, which made the index attractive to and widely cited by scholars from different fields. The EPU index has become prominent in the economic field in general as one of the best measures for uncertainty. Their index has also continuously evolved, as its founders are gradually adding new countries and sub‐indices that account for specific types or sources of uncertainty.

This work also aims to illustrate the importance of the EPU as coined by Baker et al. ( 2016 ) to individuals, policymakers, corporations, and all market participants. This is especially vital after witnessing how the uncertainty from COVID‐19 has affected both local and global economies. This paper first reviews literature on economic policy uncertainty's impacts on all market participants to commend the EPU's use in determining risk factors in decision‐making processes. After underscoring its effects, we discuss how governments, policymakers, and corporations should act during high EPU to address such a challenge.

A thorough investigation of literature reveals that high EPU is associated with adverse effects on all components of the economic system. High uncertainty tends to stall many financial decisions at both the individual and corporate levels (Bloom,  2009 , 2014 ). In other words, uncertainty pushes individuals and corporations to act more conservatively, which could lead to lower overall economic consumption and growth, fewer debt issuances, and higher unemployment (Bloom,  2009 ; Caggiano, Castelnuovo, & Figueres,  2017 ; Kahle & Stulz,  2013 ). This highlights why is it vital for governments to find avenues that would mitigate EPU's adverse consequences. High EPU also affects inflation rates (Jones & Olson,  2013 ) and the currency exchange markets (Balcilar, Gupta, Kyei, & Wohar,  2016 ); the housing market also experiences adverse effects (Alola & Uzuner,  2020 ). Further, the effects of governmental uncertainty extend to the commodity markets, as studies demonstrate that it adversely affects the oil (You, Guo, Zhu, & Tang,  2017 ), gasoline (Olanipekun, Olasehinde‐Williams, & Saint Akadiri,  2019 ), and futures markets (Fang, Chen, Yu, & Xiong,  2018 ). Moreover, high EPU has potentially adverse effects on the crypto‐currency market (Matkovskyy, Jalan, & Dowling,  2020 ).

These findings have led current scholars to agree that EPU negatively affects the economy. This consensus is based on numerous studies of different countries that employ different econometric techniques. This also reflects the importance of risk management and presents a call for innovation across different sectors to mitigate the adverse effects of EPU and uncertainty in general.

The remainder of this paper is organized as follows. Section  2 reviews the background on economic policy uncertainty in terms of its definitions and measurements, including a sub‐section on COVID‐19 and uncertainty. Section 3 is divided into two subsections as follows: Section  3.1 discusses why governments must consider EPU as a risk factor by reviewing the literature addressing its impact on macro‐ and micro‐economies through the financial and commodity markets. Section  3.2 addresses the need for corporations to consider EPU by demonstrating its effects on stock market returns, corporate capital investments and spending, corporate finance, and risk management. Section  4 concludes.

2. BACKGROUND

2.1. what is economic policy uncertainty.

Economic policy uncertainty can be defined in various ways, but is broadly considered to denote the unanticipated changes that affect the economic system that could lead to changes in governmental policies. In other words, it reflects the economy's fluctuations due to the unpredictability of fiscal, political, regulatory, and monetary policies. The unpredictability of economic and financial decisions under higher EPU could lead to the postponing of several decisions. For instance, high uncertainty from the failed coup in Turkey has pushed Moody's Investors Services to downgrade Turkey's credit rating (Davis,  2016 ). It is also clear how several corporations and countries are currently taking longer to make economic decisions due to the unpredictability of COVID‐19.

Studies support this assertion by indicating that higher uncertainty leads to higher economic effects in recessions and downturns because of the delays in financial and consumption decisions (Baker et al.,  2016 ). Many variables can affect the level of uncertainty, with short‐ or long‐term effects; however, when EPU increases, its effects are expected to have long‐term effects on investments and economic growth (Sahinoz & Erdogan Cosar,  2018 ). It is essential to develop measures to reflect a country's levels of uncertainty given that uncertainty severely impacts the entire economy.

2.2. COVID‐19 and uncertainty

The COVID‐19 pandemic significantly increased levels of uncertainty in various aspects of daily life (Caggiano et al.,  2020 ), and many aspects of the virus remain unknown to medical experts and epidemiologists (Fauci, Lane, & Redfield,  2020 ). No one knows when the world will return to normal, and it is unclear as to when a vaccine will be ready; this has pushed the authors to highlight the importance of global cooperation and its public, private, and non‐profit sectors to produce a vaccine (Corey, Mascola, Fauci, & Collins,  2020 ; Gates,  2020 ). Many countries implemented different types of lockdowns and quarantine measures, which increased stress and panic (Qiu et al.,  2020 ). Scarce medical supplies, including masks and ventilators, have led to a competition between different nations to accumulate them, which has pushed hospitals and health institutions to ration their inventories. These factors have triggered a spike in uncertainty worldwide.

Baker, Bloom, Davis, and Terry ( 2020 ) provide evidence that current uncertainty levels are much higher than those during the 2008–2009 Great Recession, and are closer to the level of the Great Depression in the United States. They also claim that most of the current economic slowdown is a product of the extremely high uncertainty due to the COVID‐19 outbreak. Sharif, Aloui, and Yarovaya ( 2020 ) also confirm that COVID‐19 has significantly impacted political and regulatory uncertainty. Albulescu ( 2020 ) also observes that daily announcements regarding the number of infected and deaths positively affect the levels of EPU.

High uncertainty can complicate firms' activities, in that it can compel firms to postpone their investment decisions (Chu & Fang,  2020 ) and assume less debt (Dong, Liu, & Chang,  2019 ), which could create a more severe economic crisis; consequently, less cash is injected into the economy. Alternatively, Baker et al. ( 2020 ) reveal that no disease in history has had such forceful impacts on the stock market as COVID‐19.

These findings strengthen the hypothesis that the uncertainty caused by COVID‐19 has caused lower economic growth, above‐average bankruptcy rates, and high unemployment rates. The disease has not caused these effects, but rather, the uncertainty that accompanies it by deterring government officials, corporate executives, and even individuals to make any decisions due to the magnitude of uncertainty from this pandemic. This has significantly complicated the decision‐making process for executives in all sectors, whether private, public, or non‐profit.

2.3. Methodology of measuring economic policy uncertainty

The EPU index was built by considering previous efforts and indices that attempted to measure economic uncertainty, and is based on three different aspects to measure uncertainty: newspaper coverage, stock market volatility, and expectations as gathered from business surveys. As EPU is a comprehensive concept that focuses on unexpected shifts in the economic system, Baker et al. ( 2016 ) decided to measure more than one component in an attempt to cover all possible sources of unforeseen changes in the financial and economic system.

The first part of the index is based on newspaper coverage of topics related to governmental uncertainty in leading newspapers within each country covered by the index; this includes political and regulatory uncertainty coverage in the media. The second part of the index focuses on the documents published by the United States' Congressional Budget Office, and primarily those that cover new tax information. Specifically, changes in taxes—such as the expiration of some taxes in the subsequent 10 years—can affect the levels of EPU. The third part of the EPU index is based on the economic and financial forecasts from the Federal Reserve Bank, and focuses on the future expectations of macro‐level variables, such as the consumer price index and governmental expenditures.

The index is available for many countries, although currently on a daily basis for only the United States and the United Kingdom, and on a monthly basis for 26 countries: Australia, Belgium, Brazil, Canada, Chile, China, Colombia, Croatia, France, Germany, Greece, Hong Kong, India, Ireland, Italy, Japan, South Korea, Mexico, the Netherlands, Pakistan, Russia, Singapore, Spain, Sweden, the United Kingdom, and the United States. Each of these indices is based on each country's different indexing and sources of policies, news, and taxes. The index's creators are still working on expanding the number of countries and constructing an index based on immigration concerns and other policy uncertainty variables. Davis ( 2016 ) builds on the work of Baker et al. ( 2016 ) to construct a unified global EPU index based on the weighted average of data of the most economically influential nations, with the argument that such nations cover most of the world's output.

As uncertainty significantly affects economic decisions at the individual, business, and government levels, such as investments and consumption, scholars are interested in a useful measure that reflects the level of EPU. Thus, they have used several methods to measure the approximate levels of uncertainty in the economy. One is the Chicago Board Options Exchange's implied volatility index (VIX), which reflects uncertainty in the equity market. Julio and Yook ( 2012 ) posited that a dummy variable should be used for election years given the new policies that are proposed at that time. Da, Engelberg, and Gao ( 2015 ) developed a “FEARS” index based on the views and fears of investors. Jurado, Ludvigson, and Ng ( 2015 ) used aggregate economic and financial variables to construct a macro‐economic uncertainty index; another popular avenue in constructing an economic uncertainty proxy is based on the information included in the Federal Reserve Bank's surveys and forecasts. Further, Manela and Moreira ( 2017 ) proposed the idea of using an index that is based on the text in newspapers. Hassan, Hollander, van Lent, and Tahoun ( 2019 ) proposed another proxy for economic uncertainty based on a text analysis of quarterly earnings conference calls. Scotti ( 2016 ) also proposed a real‐time activity index based on two components: unexpected events, and the government and economy.

One factor that makes the EPU index unique and attractive for scholars from different fields is its public availability, as this ensures everyone can use it. The data available in the index begins in 1985, and originally reflected the levels of economic policy uncertainty in the United States. It was then expanded by gradually adding data for different countries. The index reflects large‐scale spikes during economic downturns, such as the second Gulf War, the dot‐com crisis, and the 2008–2009 Great Recession. Simultaneously, the index is highly associated with many of the previous measures used as proxies to measure uncertainty. These factors have led researchers in the field to use the index and cite it more than 4,000 times.

The EPU index is also distinguished by its continuous evolution, as its creators are continually adding to the index; for example, one new addition involves the introduction of categorical EPU data based exclusively on data gathered from news media, which has been organized into several sub‐indices. Categorical EPU data are currently available for only the United States, Greece, and Japan. Its various categorical sub‐indices for the United States include monetary policies, taxes, fiscal policies, government spending, healthcare, national security entitlement programs, regulations, financial regulations, trade policies, and sovereign debt. This can help scholars who are interested in data on these specific sub‐indices.

Another addition to the EPU index includes Twitter‐based uncertainty indices, which are based on all English tweets since January 1, 2010, and include any keywords related to uncertainty, the economy, and the equity markets. Other additions to the index include the Infectious Disease Equity Market Volatility index, which is also newspaper‐based and tracks infectious disease news' effects on the equity markets. Moreover, one new index assesses how recently increased migration rates have contributed to increasing uncertainty, mostly in Europe. They also provide indexes related to trade policies, world uncertainty, financial stress, firm‐level political risk, geopolitical risk, and firm‐level uncertainty.

The EPU index's founders collectively built on all preceding efforts and measures to construct the EPU index. Hence, this paper aims to highlight empirical EPU research. The accessibility of the publicly available EPU index has popularized EPU as a research topic, which has compelled us to focus on previous efforts in this field to help scholars to identify any uninvestigated avenues and questions.

3. LITERATURE REVIEW

While reviewing the EPU index's economic impacts, we focused on both governments and corporations. The following two sections will examine why governments and corporations should consider EPU as a risk factor by reviewing the literature addressing its impact on macro‐ and micro‐economies through the financial and commodity markets. We then determine how the EPU affects financial and commodity markets in addition to corporate finance and other corporate decisions by reviewing its effects on stock market returns; corporate capital investments, spending, and finance; and risk management.

3.1. Why should governments consider EPU as a risk factor?

The current COVID‐19 outbreak highlights the importance of governments discovering avenues to mitigate their national uncertainty. Modern EPU literature provides ample evidence of how levels of uncertainty can adversely affect different aspects of the economy, such as the commodities, financial, and housing markets, and debt, among others. This highlights the importance of a severe approach to decreasing uncertainty.

Literature offers evidence of the different avenues through which EPU can affect the economy. For instance, Davis ( 2016 ) observes that designing complicated governmental systems and programs can increase uncertainty; this highlights the importance of simplifying regulatory systems to create predictability, which will decrease uncertainty. One example in the United States involves simplifying the tax code, as it is one of the more complicated tax systems worldwide. Similarly, literature proves a positive association exists between levels of uncertainty and corruption (Goel & Ram,  2013 ; Goel & Saunoris,  2017 ).

High uncertainty can also lead to higher unemployment rates, and can affect personal investment and consumption decisions, as consumers typically focus on necessities during periods of high uncertainty and recessions (Bernanke,  1983 ). Thus, significantly less spending and investments occur, and are less attractive under high uncertainty. This translates into the decreased production of goods, economic output, and total wealth. Bloom ( 2009 ) also indicates that high uncertainty can negatively affect economic growth.

It is difficult for individuals, households, and corporations to make appropriate economic decisions without proper, transparent economic policies. Bekaert, Hoerova, and Lo Duca ( 2013 ) note that EPU has impacts on monetary policy; further, EPU is responsible for significant volatility in unemployment (Caggiano et al.,  2017 ). Consequently, some scholars have argued that uncertainty shocks have more predictive power than monetary policy in predicting and understanding unemployment rates, and primarily during economic downturns (Caggiano, Castelnuovo, & Groshenny,  2014 ). Monetary policy uncertainty also impacts the financial markets and exchange rates (Mueller, Tahbaz‐Salehi, & Vedolin,  2017 ). Uncertainty about fiscal policies also impacts overall economic activity (Fernández‐Villaverde, Guerrón‐Quintana, Kuester, & Rubio‐Ramírez,  2015 ). The 2008–2009 Great Recession led to high economic and financial uncertainty that translated into volatility in corporate production and a decline in labour (Arellano, Bai, & Kehoe,  2019 ).

The association between EPU and both production and inflation is harmful to both variables (Jones & Olson,  2013 ). Further, Leduc and Liu ( 2016 ) also empirically support the finding that times of high EPU tend to produce low inflation and high unemployment. Similarly, Pierce and Schott ( 2016 ) demonstrate that uncertainty's effects on employment are conditional given the extent to which the industry is exposed to international trade, as a higher exposure to international trade leads to increased effects.

Dai, Zhang, Yu, and Li ( 2017 ) also note that EPU influences exchange rates, as higher uncertainty is associated with more fluctuations and currency risk. Balcilar, Gupta, Kyei, and Wohar ( 2016 ) also confirm that uncertainty can increase instability in the currency exchange market, which compels them to claim that uncertainty levels have some predictive power when forecasting the returns on investments in currency exchange rates. Mueller et al. ( 2017 ) also demonstrate that monetary policy uncertainty is associated with adverse effects in the financial and currency exchange markets.

Generally, EPU creates both micro‐ and macro‐level economic effects; specifically, increasing the EPU deters households, governments, and corporations from investments and spending. These effects highlight that governments are critical in designing straightforward policy systems that decrease economic uncertainty, as these systems can be vital in combating surges in uncertainty that affect local, regional, and global economies.

3.1.1. Financial markets and political risk

On the one hand, increasing EPU leads to higher volatility in the overall economy and market; Brogaard and Detzel ( 2015 ) argue that the EPU index could serve as a proxy to help forecast financial market returns. On the other hand, some scholars argue that increased uncertainty should be treated as a risk factor (Hoque & Zaidi,  2019 ) given EPU's negative impacts on the overall market and economy (Pástor & Veronesi,  2012 ). Several studies show that periods of high uncertainty are associated with lower returns (Hoque & Zaidi,  2019 ; Pástor & Veronesi,  2012 ), lower stock prices (Pástor & Veronesi,  2013 ), and lower valuations of banks. Lui et al. (2018) demonstrated that higher EPU reduces the debt issued to China's private firms, but such an association does not exist among state‐owned firms. Alola and Uzuner ( 2020 ) discover evidence that uncertainty could also affect pricing in the housing market.

Nagar, Schoenfeld, and Wellman ( 2019 ) indicate that uncertainty levels affect trading activities, the information available to investors, and disclosures. They find that higher levels of uncertainty lead to more voluntary disclosures—however, information asymmetry also increases during periods of uncertainty. Nevertheless, Christou, Cunado, Gupta, and Hassapis ( 2017 ) determine that EPU's impact differs based on the economy's durability and the stock market's size. Carrière‐Swallow and Céspedes ( 2013 ) observe that this impact is much higher in emerging markets. Simultaneously, the risks of uncertainty can spill over among various countries or regions (Choudhry, Hassan, & Shabi,  2020 ).

One particular stream of research focuses on political uncertainty's macro‐level effects on financial markets to reveal that EPU peaks during presidential election periods (Baker & Bloom,  2013 ; Julio & Yook,  2012 ). Simultaneously, the levels of investments, debt, and equity issuances among firms decrease before the election, but increase later (Jens,  2017 ). These findings correlate with Julio and Yook's ( 2012 ) results. Along this same stream of research, Boutchkova, Doshi, Durnev, and Molchanov's ( 2012 ) findings highlight how uncertainty regarding political outcomes can challenge various corporations. These authors demonstrate that government instability and global political risk could lead to fewer freedoms and less efficiency and flexibility in the business environment. Moreover, corporations that function in environments with high political risk decrease their hiring and investment activities. Alternatively, businesses may attempt to reduce the risks and effects of uncertainties by increasing their spending on lobbying (Hassan et al.,  2019 ); this could allow them to at least partially manage their uncertainty by supporting individuals that aim for outcomes closer to their desires. These findings all highlight uncertainty as a severe risk factor.

3.1.2. Commodity markets

The effects of EPU extend to several other components of the economy. Matkovskyy et al. ( 2020 ) provide evidence that high EPU leads to more volatility in the bitcoin market. On the other hand, Wu, Tong, Yang, and Derbali ( 2019 ) indicate that neither bitcoin nor gold are sufficient safe havens during high policy uncertainty, although they do observe that the bitcoin market is more responsive to changes in the EPU index. For example, gold has been relatively more stable during the COVID‐19 pandemic as a safe haven than bitcoin, even under high EPU.

In contrast, Wang, Xie, Wen, and Zhao ( 2019 ) provide evidence that the effects of EPU do not spill over to the bitcoin market, which compelled these authors to argue that bitcoin can be a haven during high uncertainty. Demir, Gozgor, Lau, and Vigne ( 2018 ) also argue that bitcoin could serve as a haven during high EPU. This discrepancy in the results reflects an open avenue for further research in the relationship between EPU and the cryptocurrency market.

Rehman ( 2018 ) reveals evidence that oil can be another variable to predict some shocks in EPU; further, evidence also exists that high EPU can negatively affect the oil market (You et al.,  2017 ). Moreover, Fang et al. ( 2018 ) also note that high uncertainty can also affect long‐term oil stocks, and these authors argue that the oil futures market is not a suitable investment option under high EPU. This argument correlates with recent events in the US futures market when oil prices became negative.

Olanipekun et al. ( 2019 ) observe that EPU can directly affect gasoline prices, with other evidence in literature that higher uncertainty could lead to lower returns from oil and gas corporations (Kang, de Gracia, & Ratti,  2017 ). Moreover, Balcilar, Bekiros, and Gupta ( 2017 ) indicate that EPU can help in predicting oil markets' returns, while Yang ( 2019 ) claims EPU conveys information to influence oil prices, which could directly affect oil prices.

These findings are essential and highlight the importance of governments realizing how uncertainty can adversely affect some commodity markets. Several governments worldwide are currently dependent on oil as a primary revenue source, such as those on the Gulf Cooperation Council). The previous examples reveal that uncertainty could significantly affect petroleum markets, highlighting the importance of such oil‐dependent governments' pursuit of avenues to decrease their levels of uncertainty and diversify their sources of income.

3.2. Why should corporations consider EPU as a risk factor?

Related literature and the previous sections in this paper have demonstrated how EPU can severely impact entire economies in general, and corporations in particular as one component of that economy. Balcilar, Gupta, and Segnon ( 2016 ) observe that high EPU occurs during recessions, while low EPU occurs during economic booms. The decreased economic growth due to EPU decreases the overall wealth in the economy and lowers corporate profitability. As cash flow uncertainties decrease, firms' profitability is also expected to decrease (Kahle & Stulz,  2013 ; Mian & Sufi,  2010 ).

Kelly, Pástor, and Veronesi ( 2016 ) also indicate that uncertainty is an essential component of risk management, in that corporations bear the high costs of political uncertainty, while these are adequately priced in the equity options market. These authors note that political uncertainty has more severe effects on weaker economies, and that these could spill over from one country to another. Thus, the severity of uncertainty's effects on corporations and the economy depends on the levels of uncertainty and the country's size.

High uncertainty is also associated with the postponement of investment and other major decisions, as well as the ceasing or slowdown of corporate hiring (Bloom,  2014 ). The poor overall economic prospects associated with EPU can increase risk premiums, which can lead to increased borrowing costs and decreased corporate productivity (Brunnermeier,  2009 ). Simultaneously, EPU could also affect bank credit growth. Bordo et al. (2016) revealed a negative association between banks' credit growth and EPU, indicating that various levels of uncertainty can affect banking and financing.

3.2.1. Corporate investments, debt issuances, and spending

High uncertainty can also affect corporations and the banking sector; namely, policy uncertainty can negatively affect foreign investments. The levels of spending and borrowing among corporations decline sharply during economic downturns and at times of high uncertainty (Kahle & Stulz,  2013 ). Moreover, Gulen and Ion ( 2016 ) demonstrate that corporate capital investments decreased by approximately 32% during the 2008–2009 Great Recession, which confirms that EPU affects corporate investments. The same study observed that these effects are more severe for corporations that depend more on governmental contracts. This correlates with Rodrik's ( 1991 ) findings, in that firms in emerging economies typically decrease their investments due to uncertainty. Alternatively, another study shows that an increase in policy uncertainty could lead to a long‐term increase in capital costs, which could decrease both production and investments (Jeong,  2002 ); thus, EPU can even affect corporations' production levels in many countries worldwide.

Colombo ( 2013 ) notes that the impacts of EPU can spill over from one country to another, in that an EPU shock in the United States can lead to lower production levels and prices in Europe. This confirms the argument that corporations act conservatively under high uncertainty (Kim & Kung,  2016 ), which could compel companies to postpone or even cancel some of their future projects. Simultaneously, other studies indicate that the effects of EPU depend on several factors, including the firm (Yu, Fang, Du, & Yan,  2017 ) and the country in which the company functions (Boutchkova et al.,  2012 ). Further, Carrière‐Swallow and Céspedes ( 2013 ) demonstrate that emerging markets witness more severe effects from EPU. Kang, Lee, and Ratti ( 2014 ) posit that the firm's size is also significant, as uncertainty has less severe effects on large corporations.

Literature reveals some avenues that could help firms to mitigate the effects of EPU. For example, Wang, Chen, and Huang ( 2014 ) note that Chinese firms use internal financing to decrease the effects of uncertainty. Simultaneously, companies that depend less on the government witness fewer effects from EPU; thus, companies can consider these options if they plan to reduce the risks of uncertainty.

While multiple avenues exist in which EPU can affect corporations, literature reveals that EPU's adverse effects on corporations hold in different countries as well as in countries with different developed or developing economies. Higher EPU can decrease corporate investments, corporate borrowing, and spending from individuals and households, which could decrease companies' profitability for companies.

3.2.2. Corporate finance and risk management

Uncertainty also has different effects on corporate and financial management actions in various parts of firms, and two effects on corporate policies in particular. Internally, increasing the EPU can lead lower‐level managers to take less risk. Therefore, management overall will act more conservatively and became more risk‐averse when government policies are unclear, regardless of the country. As a result, firms implement and engage with less risky projects during times of high uncertainty given insider decisions. Conversely, outsiders pressure firms in multiple ways, such as changes in taxes policies and regulations or the introduction of new laws. Uncertainty about governmental policies decreases the economy's supply of capital and increases friction in financial markets. These effects have both occurred during the spread of the COVID‐19 pandemic. For example, corporations under high EPU during COVID‐19 have chosen to be more conservative, or have been forced to become so due to market conditions (Bloom,  2009 , 2014 ).

Under high EPU, the costs of financing, mergers and acquisitions, and IPOs are all negatively affected, and financing costs in particular (Colak, Durnev, & Qian,  2017 ; Jens,  2017 ; Kelly et al.,  2016 ; Pástor & Veronesi,  2012 , 2013 ), which is vital as corporations hold increasing amounts of debt. Mergers and acquisitions are also negatively affected during times of high uncertainty, with two primary changes that emerge: the quantity of such transactions decrease, while the amount of time required to complete these processes increases (Bonaime, Gulen, & Ion,  2018 ; Nguyen & Phan,  2017 ). Uncertainty also affects valuations by affecting the size and type of associated payments. Deals tend to be made with stock and smaller premiums are paid (Nguyen & Phan,  2017 ). This is especially the case as it becomes more challenging to value firms in such times of uncertainty. Further, higher levels of political uncertainty lead to fewer IPOs and lower prices (Colak et al.,  2017 ). This effect is amplified when state governments have more influence on the local economy and a company's larger geographical exposure.

Im, Park, and Zhao ( 2017 ) demonstrate that uncertainty significantly impacts a firm's cash holdings and dividends; under high uncertainty, firms hold more cash (Li,  2019 ). Cash is more valued during these times as a precautionary measure for both firms and investors. This was also witnessed during the COVID‐19 pandemic, whether through firms' voluntary actions or involuntarily mandated by regulators. Mollagholamali, Javadi, and Al‐Thaqeb ( 2015 ) support the same finding in US markets, but use a larger sample of 19 international markets to support the twin‐agency theory, in which firms hold less cash and pay more dividends during times of uncertainty to avoid agency risks and reduce government expropriation (Xu, Chen, Xu, & Chan,  2016 ).

Corporate decisions and policies tend to be more risk‐averse under uncertainty, a tendency based on senior management's risk aversion that positively relates to the levels of uncertainty. In addition to such aversion, uncertainty regarding future cash flows can decrease profitability. Further, some firms tend to have conservative payout policies during high uncertainty (Kahle & Stulz,  2013 ).

However, EPU's adverse effects are not limited to corporate policies, but apply to a firm's innovation. Bhattacharya, Hsu, Tian, and Xu ( 2017 ) empirically show that innovation activities significantly decreased during high uncertainty. Their work argues that political and policy‐related uncertainty reduces innovation, which is evident in the decreased number of patents and citations near election years and periods of higher EPU.

Overall, literature demonstrates that firms are more conservative and risk‐averse in their decisions during high EPU. Prior research also reveals that a high EPU index also negatively influences capital expenditures, mergers and acquisition activities, payout policies and payouts, cash holdings, and innovation. The COVID‐19 pandemic clearly illustrates these effects worldwide. Thus, policymakers and legislators should carefully analyze the phenomenon to create new rules structured to help businesses and individuals avoid uncertainty's negative influence on corporate actions. As EPU affects the prices of commodities, currencies, and securities, firms must monitor uncertainty and include it in their risk profiles. This field includes a real need for innovation given that firms and individuals do not hedge the risks of political uncertainty, despite the minimal cost of doing so. Moreover, corporations and lawmakers worldwide must find tools and strategies to mitigate and manage the risks of EPU to limit or offset the losses caused by such fluctuations.

4. CONCLUSION

This study surveys literature regarding the seminal EPU index developed by Baker et al. ( 2016 ). The current global events after the COVID‐19 outbreak—or specifically, how these increased uncertainty—help highlight how uncertainty can severely affect all components of the global economy. However, uncertainty is a product of pandemics as well as economic crises, such as the 2008 Great Recession and the dot‐com bubble; wars are also associated with high uncertainty. This study provides a thorough background on how the EPU index was developed based on most prior uncertainty measures and proxies used in literature to provide a reliable measure of uncertainty. The study also covers the current evolutions and advancements of the EPU index.

This thorough analysis of literature reveals that the EPU index should be taken seriously as an indicator of risk by governments and corporations. Governments must consider uncertainty as a risk factor because severe issues accompany higher EPU levels that can cause distress on both macro‐ and micro‐levels, such as higher unemployment, slower economic growth, and decreased investments in any given economy. Uncertain monetary policies can also impact currency exchange rates. The ripple effect from high EPU can also reach the commodities market, which is an essential pillar in many economies. Devastated commodity prices can affect an entire country's budget, and particularly countries that highly depend on those commodities. Thus, governments should design clearer, simpler policies to decrease uncertainty and minimize the previously mentioned consequences.

However, high EPU also affects corporations: as uncertainty increases, corporate investments, debt issuances, and spending all sharply decrease. Corporations tend to be more risk‐averse in these situations and more cautious in decision‐making—all negative attributes associated with high EPU. Literature also reveals that high EPU can lead to fewer mergers and acquisitions, major projects can be placed on hold, and many contracts could be terminated. Innovation may even stagnate during these times. Consequently, corporations must consider EPU in their risk departments and include it in their risk profiles. These firms should also consider hedging against the risks that accompany high levels of EPU.

As literature is still evolving, several available avenues can still provide more answers on how EPU could affect the economy. For instance, a discrepancy exists in the findings on how EPU affects the crypto‐currency market that could serve as an open avenue for further research. Simultaneously, several credit‐ratings agencies have continued to review and change credit ratings of several corporations and governments worldwide. This illustrates another available research avenue regarding whether any association exists between EPU levels and credit ratings at the corporate or government levels. Further, is there any innovative method to decrease the uncertainty surrounding sovereign credit ratings?

The EPU index's uniqueness and strength motivated us to write this study and present the findings from EPU literature. The information in this study clearly highlights that policymakers and decision‐makers in the private and public sectors should focus more on how uncertainty can affect their organizations. This work also provides more avenues for future research and risk management innovation as it relates to uncertainty.

Al‐Thaqeb SA, Algharabali BG, Alabdulghafour KT. The pandemic and economic policy uncertainty . Int J Fin Econ . 2022; 27 :2784–2794. 10.1002/ijfe.2298 [ CrossRef ] [ Google Scholar ]

[Correction added on 09 December 2020 after first online publication. The citation ‘Baker et al 2016’, and associated page numbers, which appeared in the abstract in the original publication has been removed.]

Contributor Information

Saud Asaad Al‐Thaqeb, Email: wk.ude.abc@beqahtlA .

Barrak Ghanim Algharabali, Email: wk.ude.abc@ilabarahglaB .

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NU students finish in the top three at Fiscal Challenge

We are pleased to announce that the Northeastern University Fiscal Challenge team finished third nationally in the finals. The Northeastern team presented their research in Washington, DC on Friday, April 12th. Congratulations to:

• Alex Shilov
• Meggie Jensen
• Ivan Blinov
• Vedant Mundhra 
• Olivia Parsons
• Eddie Lowney
• Ethan McGeever
• George Burkov
• Dan Pollianikov
• Bobby Squires

In 2023 , the NU Fiscal Challenge Team competed in the final round of the 2023 Fiscal Challenge and finished in 3rd place.

In 2022 , the Northeastern University Fiscal Challenge team was selected for the final round and presented their research in Washington, DC where they won the competition.

Each year, the Fiscal Challenge competition, with support from the Peter G. Peterson Foundation, tasks colleges around the country with creating a 20 minute presentation showcasing various fiscal policies to reach a target US Debt-to-GDP ratio over a 30 year period. From Social Security tax policy to Immigration reform, the team creates a comprehensive overview of the fiscal state of the US and how their policies would help maintain fiscal health over the long term. The competition, then selects six finalist teams to travel to Washington, DC, to present and defend their policies to professors and leading economists.

You can join:

If you are a Northeastern undergraduate and you would like to participate, join the mailing list at Fiscal Challenge Team – Northeastern University Economics Society or  reach out to Bobby Squires at [email protected] .

https://www.linkedin.com/company/fiscal- challenge/

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Political typology quiz.

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Where do you fit in the political typology?

Are you a faith and flag conservative progressive left or somewhere in between.

Take our quiz to find out which one of our nine political typology groups is your best match, compared with a nationally representative survey of more than 10,000 U.S. adults by Pew Research Center. You may find some of these questions are difficult to answer. That’s OK. In those cases, pick the answer that comes closest to your view, even if it isn’t exactly right.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .