Because it is the most complex situation, we focus our analysis on the specific case where both (on an individual basis) who is affected from a shock and the value of the loss are unknown (or known without precision) by policy makers. The analysis focuses on critical design choices and their relation to the distributional characteristics of the shock that is affecting households, which will ultimately define the impact of the response. Rather than providing a formal, theoretical characterization between design choices and impacts, we use microsimulations based on household survey data for Albania, Moldova, and North Macedonia to illustrate important relations and trade-offs. Simulations also present the advantage to allow assessing the effectiveness of actual instruments available to policy makers (such as increasing the benefit paid through existing social programs or expanding coverage to different populations, as selected through varying targeting methods), something that would not be feasible with a theoretical model. In this case, we focus on different design options associated with one social protection instrument: cash transfers. We do not take into account behavioral responses or general equilibrium effects, so that we consider first-order effects only.

The policy relevance of bringing forward practical solutions for the design of social protection responses to shocks is high. Analyses of the social protection responses to the COVID-19 pandemic suggest that in many cases policy makers attempted to cover the informal sector using a variety of design “tweaks” to existing programs and their delivery systems. For instance, some countries topped-up benefits to existing beneficiaries (Kosovo), others expedited/automatized enrollment processes into existing programs (Moldova) or provided a one-off transfer to former beneficiaries or people in the program beneficiary registry (not current beneficiaries; Albania), while others provided a flat, universal transfer to ensure that informal sector workers are covered (Serbia).<sup>12</sup> Alas, the variety of designs is not the result of optimizing responses to a given type of shock (these were all responses to the same shock, hitting countries in similar ways), but rather of implementation challenges and political economy considerations faced by policy makers.

To illustrate the impact of these design choices while keeping the analysis tractable, we do not classify these covariate shocks by their origin (i.e., health, flood, drought, etc.) but rather by the distributional impacts they have on households. In particular, we distinguish between shocks that affect people’s income or wealth proportionally, leaving the income distribution and income/wealth rankings unchanged, and those that affect some people substantially more than others and change the shape of the income distribution and its rankings.

The distinction is subtle, but important. If shocks affect incomes uniformly, then existing programs and their targeting mechanisms, such as those that assess welfare through means tests or proxy means tests, can relatively easily identify a significant share of people who fell into poverty because of the shock, as many of them would have been near the poverty line even before the shock hit – hence an increase in benefits with possibly some program expansion to cover the “new” poor may be close to an optimal response. On the other hand, if a shock affects some people substantially more than others, the “new” poor may have a different profile than the “old” poor, and effective responses may require an approach that differs from increases in benefits or expanding the program using the existing welfare metric

> 12 See Gentilini et al. (2020a) for a more detailed account.

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