In popular culture, the aphorism “God created economists to make weather forecasters look good” is an amusing nod to the challenges of successfully predicting economic situations and outcomes. Weather forecasters, after all, are notoriously unreliable! Economists comprise some of the most respected minds in intellectual society, drawing conclusions from mathematics, government, politics, psychology, and many other well-respected fields. One might reasonably expect their predictions to have a higher success rate than the weatherman gesticulating at a swirling rainbow map on your television, foreseeing bright sunshine in your area instead of the impending Category 5 hurricane.
Yet this may not always be the case. In fact, economists are known to make similar blunders, sometimes with much more drastic consequences than an inaccurate weather report. The question is: what does this entail for not only economics, but also humanity at large?
The Nature of Economics
Economics and weather forecasting may seem as different as black and white on the surface, but this isn’t truly the case. Economics and meteorology are actually very similar: both involve predicting changes in incredibly complex systems that can be completely upended by miniscule developments. [1]
Economics is a fickle science, extremely nuanced and constantly influenced by the ups and downs of human behavior, market dynamics, and government decisions. At its core, the saying weighs the unpredictability of seemingly more reliable economists with the inaccuracies of weather forecasters. The true claim of the aphorism is that economists are actually even less successful with their predictions; thus, this juxtaposition casts meteorologists in a positive light since they are correct more frequently in comparison. The saying therefore calls for a deeper inquiry into the methodologies, historical accuracies, and inherent uncertainties within economics to examine the extent to which it is true.
Methodology and Predictive Accuracy
Economists employ various methods to make predictions, including mathematical models, statistical analysis, and qualitative assessments of economic indicators. Additional methods can depend on the particular subcategory of economics in question. For instance, behavioral economists are likely to consider psychological factors as well. Over time, these methodologies have evolved, but the accuracy of economic forecasts remains contentious. Unlike the natural sciences, where empirical data typically leads to precise predictions, economics grapples with the complexities of human behavior, market dynamics, and unforeseen externalities.
John Maynard Keynes first proposed a framework that emphasized the role of aggregate demand in shaping economic outcomes. [2] Keynesian economics challenges the classical view of self-regulating markets and advocates for government intervention to stabilize economies during periods of recession. While Keynesian principles have influenced economicpolicy for decades, the complexities of real-world economic systems often defy the simplistic models and predictions set forth in theory.
Challenges and Human Behavior
Empirical studies demonstrate the challenges of economic prediction. Economic forecasts carry inherent uncertainty. Despite the expertise of economists, their predictions often fare no better than random chance, illustrating the unpredictable nature of economic phenomena. [3] This unpredictability stems from various factors, including the complexity of economic systems, the interplay of multiple variables, and the influence of exogenous shocks.
Furthermore, the dynamic nature of human behavior and market psychology introduces additional layers of uncertainty, making it difficult to anticipate and accurately forecast economic trends. Richard Thaler introduced insights that challenged the traditional economic assumptions regarding human rationality that Keynesian theory is founded on. His work highlighted the rolepsychological biases and cognitive limitations play in economic decision-making. Thaler’s research demonstrated that individuals often deviate from rational behavior, leading to systematic errors in economic forecasts. [4] Behavioral economics sheds light on why economic predictions may fall short, as traditional models often fail to account for irrational yet predictable human behaviors. Kahneman and Tversky, for example, discussed the concept of “loss aversion,” whereby individuals place greater weight on avoiding losses than acquiring equivalent gains, which can lead to suboptimal decision-making in economic contexts. [5] Similarly, Thaler brought up the “endowment effect”, in which individuals ascribe higher value to items they own compared to equivalent items they do not own, leading to inefficiencies in markets. [6] Successfully predicting the future actions of the most complex species on the planet is a daunting, if not impossible, task whose difficulty may provide an explanation for the inconsistencies between economic predictions and results.
Case Studies
History is littered with the records of economists who made bold proclamations for future economic developments, only to find themselves bitterly wrong. Consider one of the most notorious instances of this phenomenon, the 2007-2009 global financial crisis, which stands as a glaring example of economists’ forecasting failures. Renowned economists and financial institutions failed to anticipate the magnitude of the disaster, revealing the limitations of traditional economic models in capturing systemic risks. Reinhart and Rogoff explain that the failure of these entities, along with the Federal Reserve and other central banks, to predict the bseverity and duration of the Great Recession highlights the limitations of macroeconomic models in capturing complex interactions within financial systems. [7]
The failure to anticipate the severity and contagion of the financial disaster exposed the shortcomings of prevailing forecasting methodologies. Traditional economic models, which rely on historical data and linear assumptions, struggled to capture the complex interactions and interdependencies within financial markets. Moreover, the reliance on mathematical models and quantitative analyses led many economists to overlook qualitative factors and systemic risks that ultimately precipitated the crisis. This spurred a reevaluation of forecasting methodologies, highlighting the need for more robust predictive frameworks. [7] The incident caught many economists off guard. There had previously been a prevailing belief in the stability of financial markets, fueled by a prolonged period of economic growth and seemingly low levels of volatility. This optimism proved to be misplaced, as the collapse of the subprime mortgage market in the United States triggered a domino effect that reverberated throughout the global financial system.
Conversely, some economists ordain events that never materialize. L.P. de la Horra explains how economist Ravi Batra gained national attention for his prediction of a second Great Depression in 1990. [8] The American stock market actually grew by 18% that year! The case of Batra illustrates the potential consequences of inaccurate economic forecasts. Batra’s prediction of a second Great Depression garnered widespread attention and influenced public sentiment and policy decisions. When the predicted catastrophe did not occur, it led to skepticism about the reliability of economic forecasts and undermined confidence in the discipline of economics itself.
Meanwhile, other events are entirely unpredictable in nature. The COVID-19 pandemic exposed the vulnerability of economic forecasts to unforeseen shocks. The sudden onset of the pandemic disrupted global supply chains, shuttered businesses, and triggered economic recessions worldwide. Surico and Galeotti explained that the unprecedented scale of the crisis underscored the need for more adaptive forecasting methodologies. [9] Similarly, Baldwin and Wyplosz noted that the unexpected outcomes of political events, such as Brexit or the election of populist leaders in other countries, can have profound economic repercussions that elude conventional forecasting models. [10] Such events are usually impossible to predict using economics alone and would require the supplement of disciplines such as history and politics.
Verdict
The aphorism does indeed carry a grain of truth regarding the challenges of economic prediction. While weather forecasts benefit from established scientific principles and technological advancements, economists must contend with the complexities of human behavior and changes in the market.
While economists continue to play a vital role in society, their ability to predict future outcomes remains imperfect. The one thing economists and weather forecasters alike can always be certain about is this: nothing is certain. Economics often examines what will happen to a variable in a given situation if all other factors are held constant. We cannot operate on the assumption “ceteris paribus” – all else equal – that only one variable changes. The inherent uncertainties in economic systems, coupled with the unpredictability of human behavior, will always pose formidable challenges to accurate predictions.
Sources
[1] Mackintosh, J. (2017). Three Economists Walk Into a Bar. Wall Street Journal. [online] 9 Jan.
[2] Keynes, J. M. (1936). The General Theory of Employment, Interest, and Money. Palgrave Macmillan.
[3] Tetlock, P. E., & Gardner, D. (2015). Superforecasting: The Art and Science of Prediction. Crown Publishers.
[4] Thaler, R. H. (2015). Misbehaving: The Making of Behavioral Economics.
[5] Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-292.
[6] Thaler, R. H. (1980). Toward a Positive Theory of Consumer Choice. Journal of Economic Behavior & Organization, 1(1), 39-60.
[7] Reinhart, C. M., & Rogoff, K. S. (2009). This Time is Different: Eight Centuries of Financial Folly. Princeton University Press.
[8] de la Horra, L.P. (2018). 5 of the Worst Economic Predictions in History | Luis Pablo de la Horra.[online] fee.org. Available at: https://fee.org/articles/5-of-the-worst-economic-predic tions-in-history.
[9] Surico, P., and Galeotti, A. (2020). The Economics of a Pandemic: The Case of COVID-19. Wheeler Institute for Business and Development.
[10] Baldwin, R., & Wyplosz, C. (2015). The Economics of European Integration (5th ed