Pradeep Kumar Panda
Uncertainty around the current state of the economy as well as its future outlook plays an important role in determining the evolution of macroeconomic outcomes.
Economic agents find it difficult to muster confidence and take decisions when they are unsure about the likely trajectory of the economy. This prompts people to change their decisions – it may force consumers to delay consumption of goods and services or it may influence firms’ decision to invest in capital or hire labour. Until recently, only negative macroeconomic and financial outcomes, such as fall in GDP growth, employment, stock markets, corporate earnings etc., were considered to be sources of uncertainty. However, uncertainty can also arise due to political economy factors which eventually percolate into economic policies. Statements, actions and decisions taken by policymakers with respect to fiscal, monetary, structural and regulatory policies can also affect the wider economy and its future outcomes. Hence, it is important to understand the nature of uncertainty that may arise from different sources and analyse its likely impact on domestic economic activity and financial markets.
An issue related to understanding the economic effects of uncertainty is the measurement of uncertainty. In general, periods of lesser uncertainty are characterized by stable economic conditions which provides a conducive environment for the economy to grow at its potential. On the contrary, higher uncertainty hurts economic activity making the economy perform below its potential. However, since uncertainty is not directly observable, alternate ways of measuring uncertainty becomes an important task. The lack of a clear definition of uncertainty makes the task of measurement even more difficult.
Various methods have been suggested in the literature to measure uncertainty through observable economic and financial outcomes. Some of the most popular methods are those on financial market volatility and observed divergence in the forecasts of professional forecasters. Assuming that financial markets take into account all types of risks and factors affecting the economy – which is then reflected in prices – uncertainty can be proxied using realised or implied market volatility such as CBOE Volatility Index (VIX). Similarly, the approach based on forecasts by professional forecasters assumes that such forecasts consider all possible information available at that time. If forecasters disagree with each other on the current state of the economy and its outlook, there will be divergence in their forecasts. This divergence can be used to measure uncertainty.
Lately, newer data sources like newspapers, economic research reports and internet search intensity have been leveraged to construct uncertainty measures. Since uncertainty ultimately affects the decisions of the economic agents – consumers, workers, investors and so on – this novel approach measures uncertainty from the perspective of economic agents. Newspapers carry analyses of financial markets, political outlook, expert opinions on the economy and thus reflect the state of the economy allowing people to form their decisions. Uncertainty is then proxied from these newspapers using various text mining techniques. Likewise, people also make use of the internet to search for information, especially when unanticipated events, such as market crashes or electoral outcomes hit the economy. The availability of internet search data in public domain has now made it possible to compute internet search-based uncertainty index.
For India, an uncertainty index has only been made available by Baker, also called as BBD-EPU index. Most of the literature on assessing the economic impact of uncertainty in India has used BBD-EPU index. In fact, the recent Economic Survey 2018-19 also uses BBD-EPU index to analyse the impact of uncertainty on investment. Thus, so far, not only there have been a limited number of studies in this area, most of these studies have relied on a single uncertainty indicator. Further, no attempts have been made to construct any alternate measures of uncertainty for India.
The economic concept of uncertainty was first defined by Knight in the year 1921. While he recognized that risk and uncertainty are related, he deemed risk to follow a known probability distribution over a set of events. On the other hand, Knight defined uncertainty as “peoples’ inability to forecast the likelihood of events happening” i.e., it is a situation in which economic agents cannot predict the likely state of the economy in the future. A heightened level of uncertainty about the future inhibits the ability of economic agents to take decisions. The Global Financial Crisis 2008 is one such recent example of a period of heightened uncertainty. In fact, an increase in uncertainty during the crisis is considered as one of the main factors behind the deep recession and the prolonged recovery that followed. Since then, and not surprisingly, policymakers and economists have taken a renewed interest in understanding the channels through which uncertainty manifests and impacts the economy.
From a theoretical point of view, the potential channels of transmission of uncertainty to the real economy are manifold. The first of such channels works through the firms by affecting their investment decisions. This is often termed as the “real options” channel which causes firms to postpone investments and hiring of labour. Similarly, the “cost of financing” channel also plays a role in reducing investment by raising the risk premium and increasing the cost of borrowing. The “precautionary savings” channel working at the household-level, causes people to often delay consumption expenditure on durable goods – such as houses and cars – when they encounter high uncertainty. While the transmission channels mentioned above impede consumption, investment and hiring, uncertainty can also have a “positive” effect on economic activity under certain conditions. The Oi-Hartman-Abel effect postulates that if agents can flexibly expand to benefit from good outcomes, and, quickly contract during bad outcomes, they may benefit from increased uncertainty. Such an effect, however, is believed to be strong only in the medium to long run.
With the piquing interest in uncertainty as a real macroeconomic phenomenon, the literature has also analysed the economy-wide impact of uncertainty. The empirical literature on uncertainty seems to have taken three main approaches to identify the causal impact of uncertainty on firms and consumers. The first of these approaches relies on estimating the movement of output, investment and employment following increases in uncertainty. This approach works well in the case of unanticipated shocks to uncertainty but not when such shocks are correlated with other unobserved factors or are predicted in advance.
The second approach uses structural models to quantify the impact of uncertainty shocks. In a general equilibrium model with heterogeneous firms, labour and capital adjustment costs, and countercyclical uncertainty, study found that average increase in uncertainty during recessions reduces output by 3% followed by a rapid recovery, in the first and second year, respectively. On the other hand, some studies have found only a marginal impact of uncertainty on growth. Such mixed results are regarded to be symptomatic of sensitive modelling assumptions and linear nature of standard business cycle models. Nevertheless, accounting for the nonlinear impact of uncertainty on economic activity has been found to produce much amplified effects.
Uncertainty shocks have also been found to have high impact in the presence of frictions in the labour and financial markets. Moving from business cycle effects of uncertainty to its potential impact in the long run study found that uncertainty shocks negatively affects economic activity in the long run. Lastly, the third approach is premised on exploiting natural experiments to estimate the uncertainty impact. Baker and Bloom use natural disasters, terrorist attacks and political shocks as instruments to capture the impact of uncertainty. Earlier, Stein and Stone use a similar approach to analyse US firms and find that firms exposed to greater uncertainty have lower investment, hiring and advertisement.
Closely connected with the empirical assessment of uncertainty is the issue of its measurement. The conventional approach relies on finance or forecast-based measures to proxy uncertainty. While the finance-based approach utilizes stock market volatility indicators as a measure of uncertainty, the forecast-based approach models the disagreement between forecasts of professional forecasters and the actual economic outcome. Departing from this conventional approach, some recent efforts have leveraged data on news articles and internet search intensity to quantify uncertainty. In their seminal work, Baker use textual data from newspapers to create an index of economic policy uncertainty using the frequency of usage of certain keywords in the news articles.
Ghirelli in a similar spirit, build on this approach by increasing both the number of keywords and newspapers, in the case of Spain. Recognising that this approach may be subject to measurement error due to human intervention, Azqueta-Gavaldon, Saltzman and Yung, and Tobback use Natural Language Processing (NLP) and Machine Learning (ML) methods to create uncertainty indices using the same data source. Similarly, data on internet-based search intensity available through Google Trends has also been used to create uncertainty indices in various country-specific studies. The literature generally suggests that uncertainty negatively impacts the macroeconomy and financial markets across developed and developing countries.
The importance of uncertainty in the evolution of financial markets and macroeconomic conditions of a country has been highlighted in various studies. To capture overall uncertainty in the economy, a single index was constructed using a principal components approach. This index captures views of news media as well as economic agents and can capture both domestic and international events.
The validity of uncertainty index for India is assessed in terms of its impact on financial markets as well as the real economy. As the theory suggests, uncertainty is positively correlated with risk measures while it shows a negative relationship with measures of economic activity in India. Based on granger causality test, financial markets appear to factor-in uncertainty in advance, while uncertainty seems to ‘lead’ the changes in investments and consumption. Using a robust local projections-based econometric framework, we assess the impact of uncertainty shocks on financial markets and the real economy. Results suggest that financial markets, private investment, inflation and overall economic activity are negatively impacted by heightened uncertainty. The findings are in line with results obtained in other country- specific studies. From a policy perspective, our results suggest that policymakers can use the information on uncertainty for devising policy framework and institutional arrangements that foster sound and predictable policies.
The creation of a novel dataset and automated algorithms to compute uncertainty indices undertaken for this study should pave way for further research on uncertainty within and outside the Reserve Bank. For instance, it is now possible to create specific indicators on uncertainty related to Trade Policy, Fiscal Policy, Monetary Policy, Regulatory Policy etc., using our dataset and algorithms. It may also be possible to compute state-level indicators of uncertainty, which can be used to study the impact of uncertainty on investment flows and medium-term economic activity at the state-level in India. Lastly, such uncertainty indices can also help strengthen policy simulation exercises to study the impact of low/high uncertainty scenarios and improve near-term projection of macroeconomic variables which exhibit high degree of sensitivity to uncertainty.
(The author is an economist based in New Delhi. Views are personal.)
