Uncertainty about business prospects is a fact of life for any business. When deciding whether to hire new workers or invest in new technology, companies don’t know if doing so will lead to higher sales and profits due to factors beyond their control. Instead, they forecast future sales revenue (and other performance metrics) and take into account the uncertainty surrounding those forecasts. They consider situations where things may turn out to be worse than predicted, leaving them with too many workers and wasted investment—or vice versa when things turn out better. Only after weighing these scenarios can businesses decide whether to hire these workers or invest in this technology.
When faced with high uncertainty, firms also typically have the option to wait and see to avoid mistakes. This option is most attractive when the business environment is highly unpredictable and the decision is expensive to reverse, such as when it is expensive to lay off workers or resell machinery and equipment. But it is also expensive in itself: waiting means delaying or canceling some projects that would be profitable. In theorysuch delays can have major economic consequences. They can reduce a country’s productivity if many firms end up operating at a suboptimal scale or with suboptimal technology. This problem is potentially more serious in developing and emerging economies, where inadequate business investment and technology adoption often reduce productivity and economic growth.
Uncertainty of measurement
In practiceHowever, economists struggle to understand how uncertainty affects business and the macro economy. Part of the reason is that standard measures of uncertainty, such as stock market volatility and forecast disagreement, do not capture uncertainty at the level of individual firms; this is the uncertainty of business managers I perceive around their projections for future sales and performance. Only recently have researchers made significant progress in measuring this directly subjective uncertainty at the firm level. The state-of-the-art methodology uses surveys of business managers who derive a series of scenarios for their own firm’s future performance and the probability of each scenario. This combination of scenarios and probabilities allows researchers to construct business measures predictions and business uncertainty as perceived by each individual manager.
Until now, most efforts to measure subjective business predictions and uncertainty are limited to a handful of high-income countries such as the US and the UK. But new data collected by the World Bank shows that a simplified version of this state-of-the-art methodology also works well in developing and emerging economies. This is an important development because many researchers believe that it would be difficult to conduct this kind of study in developing countries, where businesses and their managers may be less sophisticated. New data from the World Bank refutes these concerns and reveals systematic differences in the way business managers perceive uncertainty in countries with different income levels.
The data in question comes from the World Bank’s Business Pulse and Enterprise Surveys, which are designed to track the impact of the coronavirus pandemic on the private sector. Both studies include a module that derives a central, optimistic, and pessimistic scenario for future own-firm sales along with probabilities for each scenario. Over 23,000 businesses in 41 countries in Eastern Europe, Asia, Africa and Latin America participated between April 2020 and March 2022. Countries covered cover a wide range of income levels, from Madagascar at the low end to Poland at the high end .
As it turns out, the measures of business sales forecasts and uncertainty constructed from these 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 follow-up survey interviews (Figure 1). Second, managers who express greater uncertainty during the forecast tend to make larger forecast errors (Figure 2). This second fact says that the survey-based measure of business uncertainty captures the degree of unpredictability or variability of firms’ sales and reflects similar results from survey efforts in advanced economies.
Figure 1. Sales forecasts predict actual sales
Notes: Scatter plot of follow-up interview sales versus sales expectations (forecast) for the next six months on the horizontal axis. Realized and expected sales are expressed relative to 2019 levels.
Figure 2. Firms reporting higher uncertainty make larger forecasting errorsNotes: Scatterplot of the absolute error between sales expectations (ie, six-month-ahead forecasts) and actual sales at the follow-up interview versus subjective uncertainty about six-month-ahead sales. 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. Enterprises in poorer countries, i.e. those with lower levels of GDP per capita tend to have higher levels of uncertainty on average (Figure 3). Previous research has shown that employment, sales and investment data are more volatile in lower-income countries. But it is now clear that this is not due to low quality data or noise. Instead, business managers actually perceive uncertainty to be three to six times higher in these low- and middle-income countries than in the US or the UK. Thus, high levels of business uncertainty are likely to distort investment and employment patterns in lower-income countries. This finding brings the researchers one step closer to showing that indeed some countries may fail to develop and grow because their unpredictable business environment encourages firms to wait and see too much instead of investing and improving their productivity.
Third, the negative relationship between uncertainty and GDP per capita is not easily explained. It does not appear to come from differences in the composition of the business sector across countries. It is also not systematically related to the volatility of exchange rates or business cycles, which are often higher in developing and emerging countries. Instead, there appears to be a robust relationship between economic development and the amount of risk and unpredictability (ie, uncertainty) that firms perceive in their economic environment.
Figure 3. Employment-weighted business uncertainty decreases with GDP per capita.
Notes: This figure plots employment-weighted subjective uncertainty in each country averaged over waves from the World Bank Business Pulse and Enterprise Surveys against the country’s 2019 GDP per capita on the horizontal axis. We weight companies by employment in each country. UK and US values taken as averages for April 2020 – December 2021 and April 2020 – March 2022 respectively.
The evidence from these World Bank studies has at least two policy implications. First, central banks and governments in low- and middle-income countries can realistically collect forecast and uncertainty data as part of their routine business surveys and thereby obtain timely information on business prospects. Such data could be useful to policymakers and researchers interested in macroeconomic fluctuations and firm dynamics in these countries. In addition, country-specific surveys can also collect forecasts and data on uncertainty about prices, employment or investment, which can be useful for the conduct of monetary, fiscal and business development policy.
Second, addressing and reducing the amount of uncertainty that firms perceive through specific policy interventions can play an important role in supporting investment and firm growth in developing countries, generating positive macroeconomic effects. The economic benefits of making business uncertainty a higher political priority could also bring greater political and social stability, which in turn has implications for the business environment.