be shared across the distribution of income or consumption, which sectors, areas and regions are likely to be impacted, and what are the characteristics of those who would become poor as a result of the crisis? In order to be useful to policymakers in countries, these questions would have to be analyzed _ex ante,_ with available data that in most cases pre‐date the crisis, rather than be delayed till the time post (or during) crisis data become available. Even in cases where some real‐time data is available from crisis‐ affected sectors or countries, an _ex ante_ approach can be useful to simulate future impacts for hypothetical scenarios that are not available from real‐time data, especially in countries where the situation on the ground is changing rapidly.

Current approaches to analyze _ex ante_ the impact of a macroeconomic shock, with the limited data available in most countries, are somewhat inadequate in addressing the kind of questions posed above. To improve upon existing approaches given the typical data constraints seen in most developing countries, we develop in this paper a micro‐simulation model to evaluate _ex‐ante_ the distributional impacts of the crisis. Section 2 outlines a rationale for building such a model, including the value added from this approach compared to existing methods. Section 3 presents the model, starting with an overview of the approach and subsequently outlining each step in detail, and ending with the key limitations and caveats to the model. In Section 4, we discuss a country case study with an application of our model (the case of Bangladesh). Section 5 concludes with implications of the results for Bangladesh and possible areas for extension in the course of future work.

# **2. Rationale for our approach**

Currently, there are two approaches commonly used within the World Bank to assess the welfare (primarily consumption or income poverty) impacts of the crisis: the output elasticity of poverty method, and PovStat (World Bank PovertyNet). The elasticity method involves using historical trends of output and poverty to determine the responsiveness of poverty rates to growth in output (and consumption), which is then combined with macroeconomic projections to estimate the impacts of future reduced growth on poverty. Although this method is easy to implement and serves as a convenient benchmark, it is limited in its predictive capability since it yields only aggregate poverty impacts, with no account of the broader distributional effects. It may also prove deficient in predicting poverty impacts during a crisis that affects output growth in a way not entirely consistent with the recent growth experience in a country. This crisis, for example, is likely to impact some sectors more than others and have a disproportionate impact on inflows like external remittances that directly impact household income.

PovStat is an EXCEL‐based World Bank simulation package, which uses household survey data and macroeconomic projections as inputs and estimates changes in poverty and inequality indicators. Although it allows for the impacts to occur through multiple channels, it offers no easy way to account for changes in non‐labor income (such as remittances), which has important implications in the context of this crisis in some countries. By focusing exclusively on household heads (and ignoring the employment status of other household members), PovStat also does not allow for a full accounting of labor market impacts. Perhaps the most important limitation of PovStat is that it generates estimates for poverty and inequality (aggregate or disaggregated by regions/groups) but not the kind of distributional 3