The findings from these surveys have provided new insights into consumer behavior, such as the impact of subjective earnings uncertainty on precautionary saving.1 The Bank of Italy was also a pioneer in using probabilistic questioning to elicit expectations from businesses in its 1993 Italian Survey of Investment in Manufacturing. Data from this survey have been used to analyze the impact of uncertainty about future product demand on the investment decisions of Italian manufacturing firms.2 As always, the views expressed are my own and do not necessarily reflect those of the Federal Reserve Bank of New York or of the Federal Reserve System. Why are expectations data useful? The importance of compiling high-quality data on the expectations held by economic agents has been increasingly recognized in both academic research and policymaking. Most economic decisions involve uncertainty, and are therefore determined not only by preferences but also by expectations for future outcomes. Typically, neither preferences nor expectations are directly observed – a condition that poses a significant challenge to understanding economic behavior. Although different combinations of preferences and expectations may be consistent with the same observed behavior, they could result in different responses to shocks or to alternative policies and could therefore have different implications for public policy.3 The standard way to address this problem within economic models has been to assume rational expectations – that is, expectations that are consistent with equilibrium behavior in the model.
We need to ensure that intermediaries build, over time, stronger capital and liquidity cushions, especially for trading activities. The Basel committee has been very active and currently is devising proposals. But our main priority is to reduce procyclicality in our financial systems. Stronger capital and liquidity cushions must be built in good times and drawn down in bad times. The essential contribution however will come from a change in our provisioning rules. The objective is to build automatic and “through the cycle” stabilizers in a transparent way and according to a rule based system. I believe there is a lot to learn from the Spanish dynamic provisioning approach. Finally, nothing will work if we do not address the issue of incentives. In a market based economy, incentives determine the behaviours of agents. Regulators must aim at creating an environment that is conducive to both efficiency and stability and that can avoid excessive risk taking. This is why compensation practices are so important. They should be structured so as to induce appropriate risk behaviour and encourage "true" financial innovation. Competition between financial institutions in hiring staff creates a coordination problem and leads to many excesses, acknowledged by the institutions themselves. Compensation policies should therefore be guided by clear and internationally agreed rules covering: (i) the involvement of senior management in traders’ compensation policies, (ii) detailed disclosure requirements, (iii) clear and direct links between the payment of bonuses and the longer term profitability of institutions, i.e.