# 3.3. Business costs of infrastructure disruptions: Utilization losses

Frequent disruptions of electricity, water or transport infrastructure often mean that firms are unable to utilize their full production capacity. Capacity utilization is a common measure of the effectiveness with which a firm converts its resources into output – and it is reported in the Enterprise Surveys. A firm that is frequently forced to halt production – for instance due to power outages, or due to input shortages caused by transport disruptions or upstream production stops – will be operating below its full capacity, i.e. have relatively lower levels of capacity utilization. This in turn will affect firm-level factor productivity and sales.

This section focusses on the capacity utilization of firms and distinguishes and assesses direct and indirect infrastructure disruptions impacts by extending the analysis to power, water and transport disruptions.

Unlike in the case of power outages, the Enterprise Surveys do not directly ask firms about the sales losses associated with water outages and transport disruptions. However, considering utilization reductions due to infrastructure disruptions in addition to self-reported sales losses makes it possible to estimate the firm-level cost of water and transport disruptions – in addition to power outages. Moreover, this estimation strategy presents two advantages. First, we are able to verify the sales loss estimates due to power outages obtained in the previous section. If the direct effect of electricity disruptions on utilization rates are estimated to be in the same order of magnitude as on sales losses, this would support the relevance and validity of the approach. It would also address the potential issue of measurements errors of self-reported sales losses due to power outages. Second, it is important to recognize that the indirect effects of infrastructure disruptions can potentially significantly exceed direct effects. For instance, even if a firm is not directly affected by infrastructure breakdowns (e.g. by experiencing a power outage onsite), it may still be forced to stop production as interruptions along the supply chain stifle input supply or output demand. Such indirect effects are unlikely to be taken into account by firms in their self-reported sale losses estimates due to electricity outages but will be included in the infrastructure utilization rates.

Unfortunately, the ES do not provide information about supply chain linkages. Instead, our econometric strategy proxies supply chain disturbances using regional disruption levels. Specifically, we average blackout and water supply interruption durations for all firms in any given region and introduce this region-level variable as a separate regressor into the regression model. We also interact it with the firmspecific disruption level to distill the pure direct and indirect impacts on utilization rates.

To assess this issue systematically we analyze a model that simultaneously estimates the impact of water, electricity, and transport disruptions on firms’ utilization rates. The model is fit to a pooled dataset of 29,675 firms from 95 predominantly middle-income countries, sampled between 2007 and 2014. Estimation is conducted by weighted ordinary least squares using normalized sample design stratification weights that sum to unity for each survey wave. Standard errors clustered at the regional level to account for regressor correlation with region fixed effects and uncertainty associated with region-specific marginal effects.

As in Section 4.1, the model is based on a harmonized global panel dataset constructed from the World Bank’s Enterprise Surveys. As part of these surveys, firms report on the quality of water and electricity infrastructure, including the average monthly frequency and duration of service disruptions. Firms also report transport disruptions using a subjective ordinal scale. These firm-specific subjective measures allow us to compute regional average disruption levels for blackouts, water shortages and transport disruptions. Those region-level variables can then be used to estimate how disruptions outside any firm’s boundaries affect individual firms – that is, indirect effects. Results for this approach are presented in Table 5.

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