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Governments throughout the world are requiring greater use of economic analysis as a way of informing policy decisions. This paper provides a comprehensive analysis of the use of impact assessment in the European Union, using US assessments as a benchmark. We find that recent EU impact assessments include more economic information than they did in the past, although important items are still missing. We also provide evidence that the quality of EU impact assessment increases with the expected cost of a proposal. Furthermore, we find that the quality of EU assessments that report high total costs is similar to that of US assessments.

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We present a statistical model that can be employed to monitor the time evolution of the COVID‐19 contagion curve and the associated reproduction rate. The model is a Poisson autoregression of the daily new observed cases and dynamically adapt its estimates to explain the evolution of contagion in terms of a short‐term and long‐term dependence of case counts, allowing for a comparative evaluation of health policy measures. We have applied the model to 2020 data from the countries most hit by the virus. Our empirical findings show that the proposed model describes the evolution of contagion dynamics and determines whether contagion growth can be affected by health policies. Based on our findings, we can draw two health policy conclusions that can be useful for all countries in the world. First, policy measures aimed at reducing contagion are very useful when contagion is at its peak to reduce the reproduction rate. Second, the contagion curve should be accurately monitored over time to apply policy measures that are cost‐effective.