The continuous struggle for evidence-based policy-making can have some important and potentially negative implications in terms of the capacity of prompt identification of problems. Policy-makers have to balance the need for prompt reaction with the need for justified action, by distinguishing signal from noise. Delayed actions are often ineffective; at the same time, short-term evidence can lead to opposite effects. In any case, government have scarce resources and need to prioritize interventions on the most important problems.
For instance the significant underestimation of the risks of the housing bubble in the late 2000s, and the systemic reaction that it would lead to, led to delayed reactions. The detection of the ozone hole was delayed because satellite detection instruments were calibrated to consider as "errors" measurements outside a certain boundary; it turned out that correct low measurement of ozone were assessed as false negative.
Systemic changes do not happen gradually, but become visible only when it's too late to intervene or the cost of intervening is too high. For example, ICT is today recognized as a key driver of productivity and growth, but evidence to prove this became available at a distance of years from the initial investment. In fact the initial lack of correlation between ICT investment and productivity growth was mostly due to incorrect measurement of ICT capital prices and quality. Subsequent methodologies found that computer hardware played an increasing role as a source of economic growth (see inter al. Colecchia and Schreyer 2002, Jorgenson and Stiroh 2000, Oliner and Sichel 2000).
The problem is in this case is therefore twofold: to collect data more rapidly; and to analyze them with a wider variety of models that account for systemic, long term effects and that are able to detect and anticipate weak signals or unexpected wild cards.
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