increase government involvement in the financing of the operation of the SOE, not transfers to fund investments.<sup>2</sup> Using these new measures of fiscal injections, we show that the fiscal costs of supporting infrastructure SOEs across countries is usually below 1 percent of GDP<sup>3</sup> but that support—and the performance of SOEs—is volatile and needed more often than Bova et al. (2019) acknowledge. We identify 44 country-year events of fiscal injections to infrastructure SOEs that are above 0.2 percent of GDP (see table A.2 in the appendix for a comprehensive list). We document a slow dripping of support in the average country during the average year that, over a handful of years, turns into points of GDP. This evidence indicates that fiscal risk is not only about “tail risk” but also about the risks associated with the daily operation of infrastructure SOEs.

We also show the variation in fiscal costs by sector and the variety of instruments used to support infrastructure SOEs in different countries. Average annual fiscal costs range from 0.04 percent of GDP for airlines and airports to 0.12 percent for railways and 0.24 percent for power and roads. There is also wide heterogeneity in how governments support SOEs.

We use conventional tools from the corporate finance literature on defaults to assess the effectiveness of forward-looking measures of fiscal risk to predict the need of fiscal injections in SOEs and provide a simple tool that can help prevent fiscal disasters stemming from the operation of SOEs. We rely on the Altman Z-score, a traditional measure of default risk, as applied to firms that are not publicly traded, a measure known as the Z” score. We calculate the Altman Z” score using the Infrastructure SOE Database and show that it can be used to forecast the need for fiscal injections in SOEs. Our estimates reinforce the idea that ex ante monitoring of SOE financials can significantly reduce the need for fiscal injections and thus reduce overall fiscal risk in a country.

The paper is organized as following. Section 2 defines a typology of fiscal risks. Section 3 describes the data. Section 4 defines the fiscal injection variables and presents the descriptive analysis of fiscal injections. Section 5 presents the methodology and findings on predicting fiscal risks. Section 6 summarizes the paper’s main conclusions.

> 2 Using gross figures to analyze fiscal risk may overestimate the actual fiscal cost of a bailout (from a recapitalization and/or substitution of liabilities, for example) and confuse bailouts/fiscal risk with transfers to provide productive assets to an SOE, a common practice around the world. Therefore, in contrast to existing approaches to measure fiscal risk, we examine fiscal injections net of changes in assets. See the definitions in section 4.

> 3 Our measures of fiscal injection capture the explicit fiscal costs of SOEs. They differ from the measure of quasifiscal deficit used by Huenteler et al. (2020), who measure the implicit or potential fiscal cost as “the difference between the cash collected by the existing utility and the revenues that would be collected without bill collection losses by a utility applying cost-recovery tariffs . . . and achieving commercial and operational efficiency.”

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