key: cord-0060836-nthhofv7
authors: Petrakis, Panagiotis E.
title: Uncertainty
date: 2020-08-18
journal: Theoretical Approaches to Economic Growth and Development
DOI: 10.1007/978-3-030-50068-9_6
sha: 1bc49663685203f998e5c718fa75eade1899d01a
doc_id: 60836
cord_uid: nthhofv7

This chapter aims to clarify the concept of uncertainty in economic thought. It distinguishes the concepts of risk and uncertainty and their sources within the economy. Finally, the analysis emphasizes the effects of uncertainty on the real economy, which are visible in the shrinking of production and the rising unemployment, to the detriment of the investment climate.

1. Macroeconomic uncertainty: refers to macroeconomic variables, such as GDP growth and unemployment rate. 2. Microeconomic uncertainty: refers to the evolution of business results. 3. Higher order uncertainty: the uncertainty of individuals in relation to the beliefs of others.

Trying to measure risk and uncertainty is critical processes for businesses to be able make strategic decisions about future situations. The rejection of traditional methods of measuring uncertainty led to the creation of modern ways of measuring it, which fall into two main categories:

• Quantitative indexes: they result from the creation of quantitative models that are used to make strategic decisions under conditions of uncertainty over the short term, while their predictability may sometimes extend up to the medium-term. Other quantitative indicators of uncertainty focus on deviation from the trend of a variable or on volatility. Volatility Index (VIX) reflects the expectations of volatility and represents the uncertainty of the actors about future prices. • Qualitative indexes: they result from scenario planning 2 and are used for strategic decision-making under uncertainty over a long time span, from five up to fifty years.

These two basic categories of uncertainty measurement (quantitative vs. qualitative variables) may have different starting points (use of quantitative vs. qualitative models), differentiated time span (short-term vs. long-term planning), and differentiated target (strategic planning for decision-making under low uncertainty vs. high uncertainty). When policymakers face an uncertain situation, they have three strategic options available:

1. Waiting: A waiting strategy enables policymakers to see how things will turn out and, once the negative effects are mitigated, take action. 2. Focusing: Focusing as a strategic option, implies that all resources can be directed to specific activities. 3. Flexibility: If the flexibility strategy is followed, policymakers have the ability to adapt easily and directly to ongoing external changes, in the belief that they can adequately assess future situations.

However, the existence of uncertainty in a highly competitive, globalized environment, reduces the likelihood of adequately assessing future situations through quantitative indexes of uncertainty. Therefore, scenario planning is the only option for managing future uncertainty situations. The more policymakers avoid risk, the stronger the incentive they have to wait, postponing action for a later time. If policymakers act early, while there is still uncertainty, they must decide on how to manage their resources. They must choose whether to put all resources in a single scenario, or allocate them into different scenarios, thereby gaining in flexibility. This presupposes that they have previously created the appropriate scenarios, which include all the variables (economic, political, cultural, and geographical) that may influence future developments.

By extension, scenario can significantly contribute to the attempt to approach the "cloudy and uncertain future," as Aristotle also, characteristically mentions.

The nature of uncertainty is mainly due to two factors:

• incomplete knowledge and information (incomplete knowledge or subjective uncertainty); • the existence of continuous and volatile events (system/process volatility or objective uncertainty).

Limited knowledge as a factor of uncertainty comes directly from the following sources (Van Asselt & Rotmans, 2002; Walker et al., 2003 ):

1. Measurement inexactness: This is the uncertainty caused by the inadequate ability to measure the parameters and to correctly assess the information received, leading to uncertain situations, or by errors in the method of calculating uncertainty. 2. Lack of observations of measurements: This is important data and information very useful in calculating uncertainty, but which cannot be collected and used so as to limit it. 3. Practically immeasurable: These are cases where it is possible to calculate uncertainty on the basis of existing technical and technological capabilities, but this is not feasible due to other reasons, independent of such capabilities, e.g. lack of resources and high costs. 4. Conflicting evidence: Through the use of databases and recording of available data, through divergent estimates of qualitative aggregates, and due to the incorrect use of these, it is difficult to determine the nature of uncertainty and to calculate it. 5. Reducible ignorance: This is the case where uncertainty comes from the inability of businesses to understand what they actually know and what has escaped their attention. 6. Indeterminacy: This refers to a satisfactory level of knowledge of the data, the situations, and the institutional framework of market functioning, which, nevertheless, leads to the perception that the results are completely unpredictable.

7. Irreducible ignorance: This refers to the inability to compensate for uncertainty due to the fact that markets cannot evaluate it because of complete ignorance. Uncertainty due to system volatility is caused by (Petrakis & Konstantakopoulou, 2015 ):

1. Inherent randomness of nature: This is the existence of the uncertain and completely unpredictable function of societies and economies worldwide, which has a significant impact on business activity. 2. Value diversity: This refers to the uncertainty caused by different perspectives, subjectivity of views, experiences, cultural background, and rules under which societies and markets function. 3. Human behavior: This includes irrational behavior and continuous variations in the behavior of individuals which directly affect strategic business planning and impede the decision-making process. 4. Societal variability: This refers to social, economic, and cultural variables and dynamics and the ways in which they determine the nature of uncertainty. More specifically, it relates to the different way social processes and negotiations are performed with different value systems, to the exertion of social pressure, to the way economic institutions operate and to cultural specificity. 5. Technological surprises: This includes the uncertainty caused by the dynamics of technology and the prospects of new technological achievements and innovations, and the penetration of technology into a market, as well as the receptivity to these of the society and economy in which the business operates ( Fig. 6 .1).

The creation of filters reducing the uncertainty that threatens businesses with failure in their investment policy and activity plays a crucial role in strategic decision-making. These filters lead to the classification of uncertainty into levels (Courtney, 2001; Courtney, Kirkland & Viguerie, 1997) . At the base of the pyramid are placed the most common situations of uncertainty, while as we go up the pyramid, we come across situations of complete uncertainty and zero predictability. The situations described below have been studied mainly at the level of business and executives making decisions at the microeconomic level, but this scheme also applies System/process vola lity to the macroeconomic level, when there are no countervailing forces to eliminate them ( Fig. 6 .2).

The first level of uncertainty refers to those cases where the future can be estimated with a satisfactory degree of predictability. At this level, we can satisfactorily know the realization of an action as well as the time to achieve it. In the clear enough future, economic actors can make more confident decisions by limiting the degree of failure, as they are able to identify trends that will prevail in the short and long term through proper study of market parameters. Statistics, data, and market research (e.g., demographics, cultural variables, study of the competition and the industry, financial statements) may point to a direction on how demand may potentially be formed. Another characteristic of this level is the existence of parameters that, while seemingly unknown, can become known and identifiable through appropriate processing. A typical example of 1-level uncertainty in businesses is the possibility of a new competitor entering the market and providing similar services.

The second level of uncertainty refers to more problematic situations than the first, as several new parameters emerge. At this particular level of uncertainty, the outcome is only partially known. Time, however, plays a very important role in trying to predict when that outcome will be achieved. At the second level of uncertainty, the perspective is known in advance, but the parameters are more unclear that relate to the timing of achieving the outcome, as well as the particular circumstances under which it will take place, even though the expected outcome will be identifiable with some clarity. At the business level, the strategy each player will follow in the market depends more on the strategy of their competitors and less on what kind of strategy the player himself will choose. However, even competitors' strategies at this level are relatively unclear, as, pure business activities aside, the macroeconomic environment and the intentions of the institutions, play a key role.

A typical example of 2-level uncertainty is the case when the state wishes to change the laws regarding how certain sectors operate and the "opening up" of professions. Before the state changes such laws, there is a time of intense uncertainty, including uncertainty about when such a situation will take effect. Moreover, until the final agreement is reached, there will be restlessness, consultations, and behind-the-scenes discussions between the social partners and the businessmen, as well as adoption and ratification of laws by the legislative powers. All strategies to prevent 2-level uncertainty must come from the use of distinct scenarios that consider all possible alternatives.

The third level of uncertainty is associated with the likelihood of even higher restrictions on the ability to predict the outcome. Based on the third level of uncertainty, the potential final outcomes are numerous and very difficult to predict, even if we resort to the logic of creating many alternative scenarios. The bounds between which possible outcomes fluctuate are very wide, making it difficult to develop strategies and make effective and profitable decisions. Consequently, at this level of uncertainty, there are no physically discrete scenarios associated with the outcome.

A typical example of 3-level uncertainty are businesses that want to operate and invest in new and emerging sectors of the economy or markets unknown to them in terms of geography-mainly in developed countries-in order to promote their products and services. Businesses that are active in or want to enter new and unknown sectors face problems related to the unclear institutional and legal environment in which they wish to operate and the demand that is expected to be formed, as the impact is thoroughly unknown of the products and services of the business on consumers. This obviously hampers the business strategy, as both the cost of production and the pricing policy to be followed are often hidden from view.

The fourth level of uncertainty relates to the existence of uncertainty about possible outcomes. This is a level of uncertainty in quite particular circumstances, where economic actors are unable to understand or even approximate the outcome, regardless of the intensity and range of scenarios used. Unlike the third level of uncertainty, the range itself of possible outcomes is very fuzzy and impossible to determine. At the fourth level of uncertainty volatile macroeconomic variables emerge which economic actors cannot define or even barely distinguish and which call for oracular powers rather than scientific methods.

A typical example of 4-level uncertainty is the investment effort of businesses to expand by investing in either emerging markets or markets with severe macroeconomic problems and in a state of complete volatility, as in many countries in Africa, Latin America, and Asia. In such economies, there is a great uncertainty about the perspectives of the economy, regarding key macroeconomic variables inextricably linked to demand, such as inflation evolution, unemployment, and foreign exchange rates. The problem is even more aggravated in countries under totalitarian regimes, with a high rate of corruption, as well as in countries where war conflicts are raging. Other problems that businesses face in cases of 4level uncertainty are the lack of proper legal framework and the constant changes of laws, regulations, and property rights, related to either the investment regime or the tax regime.

Level 5: An unknown future

The fifth and last level of uncertainty is identified with the unknown in all its manifestations. The complete ignorance that results from the fifth level of uncertainty, can only be accounted by extreme concepts such as "totally unpredictable" or "enormous surprise."

The levels of uncertainty have been graded in such a way as to show the investment potential of the economic actors, and at the same time, enable them to understand what strategies to develop and what decisions to make in order to limit the uncertain situations they face and manage to increase the chances of successful business. It should be made clear that the level of risk a business faces, regardless of the industry or the market in which it operates, does not necessarily remain constant throughout its business course. A business, depending on the strategies it adopts and their results, may shift from one level of uncertainty to another one, either higher or lower. There are specific strategies that a company can follow to limit its exposure to risk for each level of uncertainty (uncertainty management).

At least half of the strategic problems that businesses have to face belong to the second and third levels of uncertainty, while most of the rest belong to the first level. However, most executives, in their attempt to develop strategies dealing with uncertainty, act as if developing strategies to deal with uncertainty of the first or fourth level. Of course, this doesn't seem appropriate, as a different set of strategies needs to be followed for each level of uncertainty.

A typical example of 5-level uncertainty is the financial crisis that started with the collapse of the US mortgage market in 2007 and, of course, the devastating consequences that followed in the wake of this crisis, through its spread to European countries. This whole situation made particularly clear the disadvantages of how the markets of the developed Western world had been operating up to that point and helped to change the balance-economic and social-globally. Another typical example of 5-level uncertainty is the large-scale damages related to natural phenomena that are almost impossible to predict, e.g., the massive earthquake in Japan in 2011 which had nuclear repercussions and greatly affected the economy of a particularly robust country and, naturally, the businesses operating in it.

As can be seen from the above analysis, the possibilities for developing flexible risk hedging strategies at the fifth level of uncertainty are very limited and transnational measures and agreements are required to be taken. But even these possibilities require highly flexible, diplomatic, and complementary policies, as each country is primarily interested in defending its own particular interests.

The Great Recession of 2008 extended the climate of uncertainty already present in the global economy during the last decades of the twentieth century, into the first decades of the twenty-first century as well. In an attempt to quantify uncertainty, Baker, Bloom, and Davis (2015) invented the Economic Policy Uncertainty-EPU Index for the USA 3 and subsequently, for a number of major economies (Australia, Canada, Europe, France, Italy). Index is the weighted average of the national EPU indices, based on the GDP of 16 countries: Australia, Brazil, Canada, China, France, Germany, India, Ireland, Italy, Japan, Russia, South Korea, Spain, United Kingdom, and the United States of America. The peaks in the graph reflect the intensification of the climate of uncertainty due to significant developments after the 1980s. Figure 6 .3 shows the Global EPU Index. The most important negative events that have taken place are reflected in the strong peaks of the index, such as the Great Recession of 2008, the Brexit, and the COVID-19 pandemic of 2020. Figure 6 .4 presents the risk levels in the largest economies based on the Oxford Economics Economic Risk Index. The evolution of the index follows a stabilization course at higher levels than in the pre-Great Recession of 2008 period, until the beginning of 2020. 4 Focusing on the latest major source of uncertainty, the COVID-19 pandemic, one can observe a projection of the evolution of the world and how rare events can shape the global outlook on risk and uncertainty in the future.

Covid-19 has spread almost all over the world. This development has led to the imposition of quarantine measures and social distancing, while the fear of transmitting the virus and the loss of income have increased uncertainty around the world. Ahir, Bloom, and Furceri (2018) in an effort to quantify the uncertainty associated with Covid-19 pandemic, construct the World Pandemic Uncertainty Index (WPUI) which is a measure of comparison with the uncertainty caused by previous pandemics/epidemics. As Fig. 6.5 presents, the level of uncertainty associated with the Covid-19 pandemic was unprecedented. On March 31, 2020 the uncertainty was three times higher than the uncertainty that prevailed during the SARS epidemic in the period 2002-2003 and twenty times the uncertainty during the Ebola epidemic. The level of uncertainty surrounding the coronavirus is expected to remain high as active cases of Covid-19 infected continue to exist (at the time the book was written) and it was not yet clear when the crisis would end.

High uncertainty historically coincides with periods of low growth and tighter financial conditions. The level of uncertainty associated with the coronavirus crisis was no exception, as the economic impact was visible in the countries affected by the pandemic.

The Covid-19 crisis has shown in the most categorical way that rare events at any time can take place and change the prevailing social and economic conditions. The big question is whether we are prepared to deal with future rare events.

The answer to the above question is not easy as generally people tend to be well prepared for high-probability events following the right actions, and consequently the losses of a negative event are quite small. In contrast, in low-probability events, individuals are initially unprepared for these events, and then take bad actions and as a result the expected losses are great. Crisis management is the second major issue that arises. Maćkowiak and Wiederholt (2020) suggest a guide for future similar situations:

1. When a rare event occurs, individuals must recognize that they are not prepared to act in this situation and that they must think carefully before making decisions. 2. Second, policymakers must recognize that individuals do not behave in an optimal manner in a rare event. 3. Policymakers will need to set up a central information processing system to take advantage of economies of scale in information processing to help them make their choices. 4. Approving legislation that subsidizes or requires preparation for future rare events can improve the well-being of society.

At the same time, the COVID-19 pandemic made it clear that uncertainty may be due to policy responses to the virus and not just the pandemic itself. While the rise in uncertainty stemming from international politics is largely due to populist phenomena (Brexit, Trump authorities), there is no shortage of cases where policymakers have exacerbated the climate of uncertainty. A typical example is during the Great Recession of 2008 where the central banks contributed to policy uncertainty, alongside increased international uncertainty (Müller, 2020) . The global outlook on risks and uncertainty expected to affect the economy and investment climate in the long term in the early twenty-first century is delineated (World Economic Forum, 2016, 2017) by:

1. Extreme weather events (e.g., floods, storms, etc.): Significant damages to property, infrastructure, and the environment, as well as human casualties due to extreme weather events. 2. Large-scale migration: Unintentional migration caused by conflicts, disasters, environmental, or economic reasons. 3. Major natural disasters (e.g., earthquakes, tsunamis, volcanic eruptions, geomagnetic storms): Damages to property, infrastructure, and the environment, as well as human casualties due to geophysical disasters.

4. Large-Scale Terrorist Attacks: Individuals or non-state groups with political or religious objectives that successfully cause large-scale human casualties or material damages. 5. Incidents of massive data theft: Illegal exploitation of private or other official data on an unprecedented scale. 6. Failure to mitigate the effects of climate change and to adapt to it:

Governments and businesses failing to implement or adopt effective measures to mitigate climate change to protect individuals and help businesses affected by climate change. 7. Intrastate conflicts with local/regional consequences: Bilateral or multilateral differences within states, escalating to economic (e.g., trade/monetary war, nationalization of resources), military, social, or other types of conflicts. 8. Illegal trade (e.g., illegal financial flows, tax evasion, human trafficking, organized crime, etc.): Large-scale activities outside the legal framework, such as illegal financial flows, tax evasion, human trafficking, counterfeiting, and organized crime, which undermine social interactions, regional or international partnerships and global development. 9. High structural unemployment or underemployment: A prolonged high level of unemployment or insufficient utilization of the productive potential of the working population, which prevents economies from achieving high employment rates. 10. Crisis over the quantity of available water: Significant reduction of the quality and quantity of clean water available, resulting in harmful effects on human health or/and economic activity. 11. Failure of national governments (e.g. failure of the rule of law, corruption, political stalemate, etc.): Inability to govern nations of geopolitical importance due to the weak rule of law, corruption, and political stagnation.

Beyond the above, however, global risk factors are also expected to be affected by the emerging technologies of the fourth industrial revolution. How will human beings be able to handle the challenges of new technologies is a complicated question. Too fast and large-scale reform may slow progress, but a lack of governance can exacerbate risks as well as create uncertainty, not useful to potential investors and innovators.

The high levels of uncertainty regarding macroeconomic figures indicate that the overall effective functioning of economies is deteriorating. The impact of uncertainty on the economy varies as it shapes the behaviors and actions of economic actors, leading to deviations from the optimal pattern of resource allocation. The effects of uncertainty and risk on the real economy become evident by the contraction of production, increasing unemployment, and a decline in investment activity. Production, employment, productivity, and investment are being reduced in response to an unexpected increase in uncertainty. The role of uncertainty in investment decision-making has been pointed out in the literature by Marschak (1949) and Arrow (1968) . The evolution of uncertainty is intertwined with the evolution of the economic cycle and, particularly, with the recession phase, as we observe businesses either delaying or postponing their investment activity. As a result, the effects of the recession are widening and the negative effects of the crisis become even more apparent. During growth periods, levels of uncertainty are lower than during periods of recession.

Keynesian theory places uncertainty at the center of the debate on the evolution of the economy, starting with the Great Depression of 1929. The sources of uncertainty and its impact on economic performance vary. However, they have the same effect, as they spread throughout the economy as apparent long-term effects. The impact of uncertainty on the economy can be determined at macro-economic and microeconomic levels. At the macroeconomic level, uncertainty has an impact on fiscal and monetary policy. At the microeconomic level, increased uncertainty affects the consumption and investment decisions of businesses and households. As a result, the potential output of the economy is curtailed, causing a deepening of the recession.

Many studies investigate the impact of uncertainty on economic activity, as uncertainty plays an important role in economic policymaking. Complex relationships that are created in an environment dominated by uncertainty about the future state of the economy, do not favor the making of long-term policy decisions. Decisions are limited to a shorter-term with a lack of long-term planning. So, the existence of uncertainty affects the investment decision-making process both in the short and long term. Suspension of investment and employment, due to high uncertainty, may lead to economic recession. Moreover, the existence of uncertainty may have a significant effect on the cost of consumption or lead workers to seek higher wages which, if implemented, will hurt employment and hence, investments and GDP. The impact of uncertainty also affects other areas, such as increased financing costs, high-risk aversion, and intensification of the principal-agent problem. In conditions of high uncertainty, economic actors demand a higher risk premium resulting in higher borrowing costs. In such a case, higher borrowing costs create a constraint on the economy as businesses have no access to financing.

A typical characteristic of countries with high uncertainty over time is reflected in the prevalence of the traditional SME productivity model, leading to absence of innovative business activity, the prevalence of smallscale business activity and, from time to time, the incidence of "bank liquidity panic."

While it is widely accepted that volatility (or uncertainty) has an impact on the real economy, the problem of endogeneity poses problems concerning the direction of causality. While we realize that uncertainty slows economic growth, it is not easy to empirically explore the inverse relationship, namely, whether bad economic conditions increase the levels of uncertainty in the economy.

The existing literature identifies another situation through which uncertainty affects economic performance (Bloom, 2014) . This is associated with the emergence of the "wait and see" rule, as economic actors postpone decision-making for the future. But this way, businesses do not invest while, at the same time, consumers limit their purchases only to durable goods.

Moreover, there are theoretical approaches that support the opposite view, namely, that uncertainty can contribute to improved economic performance (Bar-Ban & Strange, 1996) . Sandmo (1970) and Black (1987) believe that increased uncertainty can accelerate the average growth rate as investors seek higher average returns as a result of higher risk. Finally, there are also studies showing that the effect of uncertainty on economic activity is not significant (Bachmann & Bayer, 2011; Bachmann, Elstener, & Sims 2010; Knotek & Khan, 2011) .

Adam Smith, and Carl Menger, considers uncertainty as the fundamental source of economic phenomena, since the economy is not a fully defined system and therefore, the problem of knowledge is insurmountable. The second approach, influenced by marginal economics, interprets uncertainty as a subjective probability problem. Indeed, William Stanley Jevons (1835-1882), based on the idea of regularity and average, assumes that future certainty could be achieved on the basis of sufficient historical evidence. The neoclassical approach, in tackling the problem of uncertainty, assumes full information and rational action by individuals. 2. Company executives used Scenario Planning try to predict the future.

Scenario planning was developed by the military and used during World War II as a key preparation tool to counter enemy strategies. In an environment of rapid technological changes, businesses use scenario planning to identify potential market opportunities. Scenario planning is usually used for long-term decision-making actions that will be made by the company at present and whose results will remain visible for a long time (5-50 years) (Kennedy and Avila, 2013) . 3. US EPU index calculates policy-related economic uncertainty, which consists of three types of underlying data: The first component quantifies the coverage by the policy-related type of economic uncertainty. The second component reflects the number of federal provisions of the tax code that will apply in the coming years. The third component uses disagreement among financial analysts as a means of estimating uncertainty. 4. However the course of the index is expected to change in 2020 with the outbreak of COVID-19 crisis, as at the time the book was written (May 2020) there were no available data.

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