Capital accumulation in a model of growth and creative destruction

Capital accumulation and creative destruction is modeled together with risk-averse households. The novel aspect - risk-averse households - allows to use well-known models not only for analyzing long-run growth as in the literature but also short-run fluctuations. The model remains analytically tractable due to a very convenient property of the householdÕs investment decision in this stochastic continuous-time setup. Classification-E32, O41, O31creative destruction, risk averse households, capital accumulation, endogenous fluctuations and growth

OPTIMAL RISK MANAGEMENT, RISK AVERSION, AND PRODUCTION FUNCTION PROPERTIES

For production risk with identified physical causes, the nature of risk, production characteristics, risk preference, and prices determine optimal input use. Here, a two-way classification for pairs of inputs – each input as being risk increasing or decreasing and pairs as being risk substitutes or complements – provides sufficient conditions to determine how risk aversion should affect input use. Unlike the Sandmo price risk averse firm may produce more expected output and use more inputs than a risk neutral firm. Sufficient conditions to determine types for pairs of inputs are also related to properties of the production function.Production Economics, Risk and Uncertainty,

Should central banks really be flexible?

In this paper I show that central bank flexibility may not be desirable when it encourages trade unions to behave more aggressively. The argument is based on a model where risk averse trade unions interact with a central bank. A flexible central bank stabilizes economic shocks and reduces output volatility. This enables trade unions to realize higher real wages without risking the unemployment of some insider workers. Risk averse insiders demand higher real wages, generate more inflation and more unemployment. The overall e ect on welfare may be negative. A conservative central bank instead increases output and employment on average but raises output volatility. The argument also sheds new light on the issue of optimum currency areas. Wage claims are lower and employment is higher in a currency union if national trade unions expect the central bank to do less to secure employment of insider workers in their country. JEL Classification: E52, E58central bank credibility, central bank flexibility, Optimum Currency Area

Price pressures

We study price pressures in stock prices—price deviations from fundamental value due to a risk-averse intermediary supplying liquidity to asynchronously arriving investors. Empirically, twelve years of daily New York Stock Exchange intermediary data reveal economically large price pressures. A $100,000 inventory shock causes an average price pressure of 0.28% with a half-life of 0.92 days. Price pressure causes average transitory volatility in daily stock returns of 0.49%. Price pressure effects are substantially larger with longer durations in smaller stocks. Theoretically, in a simple dynamic inventory model the ‘representative’ intermediary uses price pressure to control risk through inventory mean reversion. She trades off the revenue loss due to price pressure against the price risk associated with remaining in a nonzero inventory state. The model’s closed-form solution identifies the intermediary’s relative risk aversion and the distribution of investors’ private values for trading from the observed time series patterns. These allow us to estimate the social costs—deviations from constrained Pareto efficiency—due to price pressure which average 0.35 basis points of the value traded. JEL Classification: G12, G14, D53, D6

Measuring Risk Aversion Model-Independently

We propose a new method to elicit individuals' risk preferences. Similar to Holt and Laury (2002), we use a simple multiple price-list format. However, our method is based on a general notion of increasing risk, which allows classifying individuals as more or less risk-averse without assuming a specic utility framework. In a laboratory experiment we compare both methods. Each classies individuals almost identically as risk-averse, -neutral, or -seeking. However, classications of individuals as more or less risk-averse dier substantially. Moreover, our approach yields higher measures of risk aversion, and only with our method these measures are robust toward increasing stakes

Quantifying uncertainty in pest risk maps and assessments : adopting a risk-averse decision maker’s perspective

Pest risk maps are important decision support tools when devising strategies to minimize introductions of invasive organisms and mitigate their impacts. When possible management responses to an invader include costly or socially sensitive activities, decision-makers tend to follow a more certain (i.e., risk-averse) course of action. We presented a new mapping technique that assesses pest invasion risk from the perspective of a risk-averse decision maker. We demonstrated the method by evaluating the likelihood that an invasive forest pest will be transported to one of the U.S. states or Canadian provinces in infested firewood by visitors to U.S. federal campgrounds. We tested the impact of the risk aversion assumption using distributions of plausible pest arrival scenarios generated with a geographically explicit model developed from data documenting camper travel across the study area. Next, we prioritized regions of high and low pest arrival risk via application of two stochastic ordering techniques that employed, respectively, first- and second-degree stochastic dominance rules, the latter of which incorporated the notion of risk aversion. We then identified regions in the study area where the pest risk value changed considerably after incorporating risk aversion. While both methods identified similar areas of highest and lowest risk, they differed in how they demarcated moderate-risk areas. In general, the second-order stochastic dominance method assigned lower risk rankings to moderate-risk areas. Overall, this new method offers a better strategy to deal with the uncertainty typically associated with risk assessments and provides a tractable way to incorporate decisionmaking preferences into final risk estimates, and thus helps to better align these estimates with particular decision-making scenarios about a pest organism of concern. Incorporation of risk aversion also helps prioritize the set of locations to target for inspections and outreach activities, which can be costly. Our results are especially important and useful given the huge number of camping trips that occur each year in the United States and Canada

Quantifying uncertainty in pest risk maps and assessments : adopting a risk-averse decision maker’s perspective

Pest risk maps are important decision support tools when devising strategies to minimize introductions of invasive organisms and mitigate their impacts. When possible management responses to an invader include costly or socially sensitive activities, decision-makers tend to follow a more certain (i.e., risk-averse) course of action. We presented a new mapping technique that assesses pest invasion risk from the perspective of a risk-averse decision maker. We demonstrated the method by evaluating the likelihood that an invasive forest pest will be transported to one of the U.S. states or Canadian provinces in infested firewood by visitors to U.S. federal campgrounds. We tested the impact of the risk aversion assumption using distributions of plausible pest arrival scenarios generated with a geographically explicit model developed from data documenting camper travel across the study area. Next, we prioritized regions of high and low pest arrival risk via application of two stochastic ordering techniques that employed, respectively, first- and second-degree stochastic dominance rules, the latter of which incorporated the notion of risk aversion. We then identified regions in the study area where the pest risk value changed considerably after incorporating risk aversion. While both methods identified similar areas of highest and lowest risk, they differed in how they demarcated moderate-risk areas. In general, the second-order stochastic dominance method assigned lower risk rankings to moderate-risk areas. Overall, this new method offers a better strategy to deal with the uncertainty typically associated with risk assessments and provides a tractable way to incorporate decisionmaking preferences into final risk estimates, and thus helps to better align these estimates with particular decision-making scenarios about a pest organism of concern. Incorporation of risk aversion also helps prioritize the set of locations to target for inspections and outreach activities, which can be costly. Our results are especially important and useful given the huge number of camping trips that occur each year in the United States and Canada

Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making

We draw attention to an important, yet largely overlooked aspect of evaluating fairness for automated decision making systems---namely risk and welfare considerations. Our proposed family of measures corresponds to the long-established formulations of cardinal social welfare in economics, and is justified by the Rawlsian conception of fairness behind a veil of ignorance. The convex formulation of our welfare-based measures of fairness allows us to integrate them as a constraint into any convex loss minimization pipeline. Our empirical analysis reveals interesting trade-offs between our proposal and (a) prediction accuracy, (b) group discrimination, and (c) Dwork et al.'s notion of individual fairness. Furthermore and perhaps most importantly, our work provides both heuristic justification and empirical evidence suggesting that a lower-bound on our measures often leads to bounded inequality in algorithmic outcomes; hence presenting the first computationally feasible mechanism for bounding individual-level inequality.Comment: Conference: Thirty-second Conference on Neural Information Processing Systems (NIPS 2018

The Relationship Between Risk Attitudes and Heuristics in Search Tasks: A Laboratory Experiment

Experimental studies of search behavior suggest that individuals stop searching earlier than predicted by the optimal, risk-neutral stopping rule. Such behavior could be generated by two different classes of decision rules: rules that are optimal conditional on utility functions departing from risk neutrality, or heuristics derived from limited cognitive processing capacities and satisfycing. To discriminate among these two possibilities, we conduct an experiment that consists of a standard search task as well as a lottery task designed to elicit utility functions. We find that search heuristics are not related to measures of risk aversion, but to measures of loss aversion