Uncertainty about business prospects is a reality for any business. When deciding to hire new workers or invest in new technology, companies don’t know if it will result in increased sales and profits, due to factors beyond their control. Instead, they forecast future revenue (and other performance metrics) and factor in the uncertainty around those forecasts. They think about situations where things can turn out worse than expected, leaving them with too many workers and unused investments, or the opposite when things turn out better. Only after evaluating these scenarios can companies decide whether to hire these workers or invest in this technology.
When faced with high uncertainty, companies typically have the ability to wait and see to avoid making mistakes. This option is more attractive when the business environment is highly unpredictable and the decision is costly to reverse, such as when it is expensive to lay off workers or resell machinery and equipment. But it is also costly in itself: waiting means delaying or canceling certain projects that would have been profitable. In theory, such delays can have significant economic consequences. They could reduce a country’s productivity if many businesses end up operating at suboptimal scale or with suboptimal technology. This problem is potentially more severe in developing and emerging economies, where inadequate business investment and technology adoption often impede productivity and economic growth.
In practice, however, economists struggle to understand how uncertainty affects business and the macroeconomy. This is partly because standard measures of uncertainty such as stock market volatility and forecaster disagreement do not capture uncertainty at the level of individual firms; i.e. the uncertainty of business leaders perceive around their forecasts of future sales and performance. Only recently have researchers made substantial progress in directly measuring this firm-level subjective uncertainty. The state-of-the-art methodology uses surveys of business leaders that elicit a series of scenarios about the future results of one’s own business and a probability for each scenario. This combination of scenarios and probabilities allows researchers to construct measures of forecasts and business uncertainty as perceived by each individual manager.
So far, most efforts to measure subjective activity forecasts and uncertainty have been restricted to a handful of high-income countries like the US and the UK. But new data collected by the World Bank shows that a simplified version of this cutting-edge methodology also works well in developing and emerging economies. This is an important development because many researchers felt that it would be difficult to conduct this type of survey in developing countries, where companies and their managers may be less sophisticated. New data from the World Bank refutes these concerns and reveals systematic differences in how business leaders perceive uncertainty between countries that have different income levels.
The data in question comes from the World Bank’s Business Pulse and Enterprise Surveys, which were created to track the impact of the coronavirus pandemic on the private sector. Both surveys include a module that generates a central, optimistic and pessimistic scenario for own company’s future sales, as well as probabilities for each scenario. Over 23,000 businesses in 41 countries across Eastern Europe, Asia, Africa and Latin America participated between April 2020 and March 2022. The countries covered cover a wide range of income levels, Madagascar at the bottom of the scale to Poland at the top of the scale.
It turns out that the measures of business sales forecasts and uncertainty constructed from this World Bank data capture a lot of information about the business outlook that managers are aware of, as the following stylized facts show.
First, future sales forecasts predict actual future sales as reported in the follow-up survey interviews (Figure 1). Second, managers who express greater uncertainty at the time of forecasting tend to make larger forecasting errors (Figure 2). This second fact indicates that the survey-based measure of business uncertainty captures the degree of unpredictability or volatility in business sales and mirrors similar survey results in advanced economies.
Figure 1. Sales forecasts predict actual sales
Notes: Clustered scatterplot of sales made during follow-up interview versus sales forecast (forecast) for the next six months on the horizontal axis. Both realized and expected sales are expressed relative to 2019 levels.
Figure 2. Companies reporting higher uncertainty have larger forecast errorsNotes: Pooled scatterplot of absolute error between sales forecast (i.e. six-month forecast) and actual sales at follow-up interview, versus subjective uncertainty regarding six-month sales. Both realized and expected sales are expressed relative to 2019 levels.
Second, there are systematic differences in business uncertainty between countries at different levels of development.— a new stylized fact. Firms in the poorest countries, ie those with the lowest GDP per capita, tend to have higher levels of uncertainty on average (Chart 3). Previous research had shown that employment, sales and investment data were more erratic in low-income countries. But now it’s clear that it’s not due to poor quality or noisy data. Instead, business leaders actually perceives the uncertainty of being three to six times higher in these low- and middle-income countries than in the United States or the United Kingdom. Thus, high levels of trade uncertainty are likely to distort investment and employment patterns in low-income countries. This finding brings researchers one step closer to demonstrating that indeed, some countries might not develop and grow because their unpredictable business environment encourages companies to wait and see too much, rather than invest and improve productivity.
Third, the negative relationship between uncertainty and GDP per capita is not easily explained. It does not appear to stem from differences in the composition of the business sector across countries. Nor is it systematically linked to the volatility of exchange rates or economic cycles, which are often higher in developing and emerging countries. Instead, there appears to be a strong relationship between economic development and the degree of risk and unpredictability (i.e. uncertainty) that businesses perceive in their economic environment.
Figure 3. Employment-weighted business uncertainty decreases with GDP per capita.
Notes: This graph plots employment-weighted subjective uncertainty in each country, averaging the waves of the World Bank’s Business Pulse and Enterprise Surveys against the country’s 2019 GDP per capita on the horizontal axis. We weigh companies by employment in each country. UK and US values taken as averages for April 2020 – December 2021 and April 2020 – March 2022 respectively.
Evidence from these World Bank investigations has at least two policy implications. First, central banks and governments in low- and middle-income countries can collect forecast and uncertainty data as part of their routine business surveys, and thus obtain timely information on business prospects. Such data could be a boon for policymakers and researchers interested in macroeconomic fluctuations and business dynamics in these countries. In addition, country-specific surveys could also collect forecasts and data on price, employment or investment uncertainties that could be useful for the conduct of monetary, fiscal and business development policy.
Second, addressing and reducing the degree of uncertainty that businesses perceive through specific policy interventions could play an important role in supporting investment and business growth in developing countries, generating positive effects for macroeconomics. And the economic gains resulting from the increased political priority of business uncertainty could also bring greater stability to political and social spheres, which in turn impacts the business environment.