Peer rejection and social information-processing factors in the development of aggressive behavior problems in children.

The relation between social rejection and growth in antisocial behavior was investigated. In Study 1,259 boys and girls (34% African American) were followed from Grades 1 to 3 (ages 6-8 years) to Grades 5 to 7 (ages 10-12 years). Early peer rejection predicted growth in aggression. In Study 2,585 boys and girls (16% African American) were followed from kindergarten to Grade 3 (ages 5-8 years), and findings were replicated. Furthermore, early aggression moderated the effect of rejection, such that rejection exacerbated antisocial development only among children initially disposed toward aggression. In Study 3, social information-processing patterns measured in Study 1 were found to mediate partially the effect of early rejection on later aggression. In Study 4, processing patterns measured in Study 2 replicated the mediation effect. Findings are integrated into a recursive model of antisocial development.

Information programs for technology adoption: The case of energy-efficiency audits

We analyze technology adoption decisions of manufacturing plants in response to government-sponsored energy audits. Overall, plants adopt about half of the recommended energy-efficiency projects. Using fixed effects logit estimation, we find that adoption rates are higher for projects with shorter paybacks, lower costs, greater annual savings, higher energy prices, and greater energy conservation. Plants are 40% more responsive to initial costs than annual savings, suggesting that subsidies may be more effective at promoting energy-efficient technologies than energy price increases. Adoption decisions imply hurdle rates of 50-100%, which is consistent with the investment criteria small and medium-size firms state they use. © 2003 Elsevier B.V. All rights reserved.

Efficient Prices at Any Cost: Does Algorithmic Trading Deter Information Acquisition?

I demonstrate a powerful tension between acquiring information and incorporating it into asset prices, the two core elements of price discovery. As a salient case, I focus on the transformative rise of algorithmic trading (AT) typically associated with improved price efficiency. Using a measure of the relative information content of prices and a comprehensive panel of 37,325 stock-quarters of SEC market data, I establish instead that algorithmic trading strongly decreases the net amount of information in prices. The increase in price distortions associated with the AT “information gap” is roughly $42.6 billion/year for U.S. common stocks around earnings announcement events alone. Information losses are concentrated among stocks with high shares of algorithmic liquidity takers relative to algorithmic liquidity makers, suggesting that aggressive AT powerfully deters fundamental information acquisition despite its importance for translating available information into prices.