equilibrium models of the economy, with linkages across sectors and firms, are needed to gauge more complex medium-term effects.

Despite these shortcomings, our simple framework proved useful to inform policymakers in our partner countries in real time, at the onset of the lockdowns. Researchers in high-income countries have recently used a wealth of digitally-generated high-frequency information on economic activity to study the impact of COVID-19 (e.g. Chetty et al. 2020). Many developing countries nowadays generate similarly detailed digital information (e.g. transaction-level records from e-billing systems and credit/debit card reporting), but still need to build the infrastructure to use these data for timely economic analysis. This project illustrates the benefits of using harmonized administrative tax data for just-in-time policy analysis, and aims to create a bridge for researchers and policy makers to work with such data. Administrative tax data remain selected, since they only cover the formal sector, and should thus be complemented with survey-based studies of the informal sector (Alfaro et al. 2020). Yet, formal firms are the relevant group to study from a public finance perspective, as they remit the lion’s share of taxes and because they can be reached directly with tax policy measures and liquidity provision.

This paper is organized as follows. In section 2 we present the data and the modeling assumptions. In section 3 we estimate the direct impact of the pandemic on firms’ profitability, payroll and exit rates. In section 4, we examine the implications for public finances and potential policy responses through wage subsidies. Section 5 concludes.

# **2 Data and Modeling Assumptions**

## **2.1 Data**

Our study uses firm-level corporate tax records from 10 countries – a diverse group of countries spanning three continents.<sup>4</sup> Table 1 shows the descriptive statistics by country, ranked in increasing order of GDP per capita. Countries’ GDP ranges from 643 USD per capita in Uganda to 12,027 USD in Costa Rica. Population size also varies substantially, from 1.5 million inhabitants in Eswatini to 110 million in Ethiopia. Similarly, the number of firms in the data ranges from 3,791 in Eswatini to over 80,000 in Costa Rica. The number of firms by inhabitants is generally higher in richer countries.

Our analysis relies on a few key variables: a firm’s sector of activity, its revenue, profits and total costs, and the breakdown of costs into fixed costs (machinery, buildings, etc.), material costs (inventory, inputs into production) and labor costs (the wage bill). The profit rate is defined as profits (the tax base) divided by turnover. We use a firm’s sector to determine its revenue loss from the lockdown<sup>5</sup> : we assign firms to the

> 4These data are not publicly available, except the data for Ecuador which are published here. The Ecuadorian data contains the tax declarations of firms required to file balance sheets (hence excluding very small firms).

> 5As mentioned above, we use the term lockdown here and thereafter to refer to the reduced form of the shock. The 100, 50 and 20% shocks can correspond to a period of total or partial inactivity for 3 or 5 months or more, with imperfect compliance of firms to lockdown restrictions, or an extended period of reduced activity.

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