of disruptions.<sup>6</sup> Being a country-level variable, it well reflects the systemic nature of an economy’s transport system and is not an ordinal perception index. This means it allows level comparison across firms and meaningful interpretation of marginal effects in the model. However, as a country-level variable, the LPI does not allow us to distinguish firm-level and regional variation (which is necessary for distinguishing direct and indirect effects). The results for this section are presented in Annex A.4.

Transport reliability is shown to have a positive and highly significant impact on firms’ utilization rates. Increasing the Logistics Performance Index on _Timeliness_ by 1 point – which, for instance, corresponds to the difference between Tanzania and South Korea – raises firms’ utilization rates by 5.8 percentage points on average (Annex A.4, model II). Introducing other country-level controls barely moves the point estimate, however, it inflates the standard error due to collinearity with the country-level LPI variable which also changes slowly across years. Among infrastructure disruption variables, transport is most potent to explain dependent’s variation.

Finally, governance quality as measured by the World Governance Indicators strongly improves firms’ utilization rates. While Table 5 shows results for the sub-indicator “Governance Effectiveness”, any other World Governance Indicator – such as “Control of Corruption” – yields very similar estimation results (not shown). Specifically, a one-step increase – for example the difference between Japanese and South African government effectiveness – would raise average utilization by roughly 7 percentage points (Table 5, model III). Not only is the marginal effect large, but also the variance explained, as measured by standardized beta coefficients (not shown). For further details refer to Kornejew, Rentschler, and Hallegatte (2019).

Note that using a fractional response regression model with a logit or probit link function – which in principle better suits the bounded dependent variable – has virtually no impact on these conclusions. We therefore present standard OLS coefficients for easier interpretation.

3.4. The cost of unreliable infrastructure to firms in developing countries This section estimates the global losses caused by infrastructure disruptions that reduce firms’ utilization rates. For 118 mostly low- and middle-income countries, unreliable infrastructure leads to utilization losses worth 151 billion real 2018 USD, amounting to 0.59% of sample GDP.

This section uses a slightly modified version of the model in the previous section to compute these costs. First, firm-level utilization rate reductions due to unreliable power, water, and transport infrastructure are estimated in a regression controlling for the same variables as described above. In contrast to the model in Table 5, only direct effects of the infrastructure variables are included, dropping regional terms as well as interactions. While the regional variables are useful to disentangle direct and indirect effects, this distinction is not necessary for the estimation of the overall cost of disruptions. We apply weights that are modified to include employment as a proxy of firm’s contribution to national value-added. Using these weights, we aggregate within each country to obtain a national estimate of the utilization losses due to unreliable infrastructure. Country-level costs are computed by applying the weighted utilization losses to nation GDP. Where several survey rounds are available for one country, only the newest one is considered.

> 6 The LPI is a benchmarking tool designed to help countries identify the challenges and opportunities in trade logistics. It benchmarks countries in six categories related to trade logistics, including the efficiency of customs, infrastructure and timeliness, and computes a score between 1 (low logistics performance) to 5 (high).

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