Welfare costs from output and price fluctuations with heterogeneous agents and utility recovery - Ong, Natalie Jia Min (2019)
In this globalized world, sound macroeconomic policies play an imperative role in economic success. This paper seeks to aid policy-makers in their decision-making processes by quantifying the welfare costs of business cycle fluctuations for them to formulate sound economic policies to improve the welfare of the economy. However, due to a lack of crucial information about agents' utility and risk preferences in an economy with heterogeneous agents, conventional methodologies resort to the modelling of closed-form utility functions and the baseless selection of aggregate risk aversion parameter. These introduce large variability in cost of risks estimates and pose as obstacles in obtaining the true or close to the true cost of risks which policy-makers aim to achieve. In this paper, the derivation of agents' risk preferences from aggregate data is made possible through a theoretical framework constructed around how heterogeneous agents in an endowment economy optimizes their utility amid risks, and subsequently analysing a small risk scenario at equilibrium. The result is a first-order approximation of the cost of risks that is directly proportional to the covariance between price and output fluctuations. In order to substantiate this newly introduced methodology, estimates are obtained from empirical output and price data and analysed alongside conventional estimates for reliability. The estimation process involved various contextual data manipulation to sieve out from datasets the desired fluctuations. With the theoretical framework, this paper arrives at the cost of risks estimates between 0.0009 - 0.0015% of consumption goods. Some of these values were found to be comparable in terms of order of magnitudes while all of them are smaller than the benchmark estimate. The discrepancy arguably stems from added information about agents' risk preferences. The empirically derived aggregate risk aversion parameter values suggest that conventional ones are largely overestimated.