infonet-economy

Firms frequently attempt to increase profits by replacing some existing workers with new lower-wage workers. However, this strategy may be ineffective in an incomplete-contract environment because the new workers may provide lower effort in response to their lower wages and hiring new lower-wage workers may damage the remaining original workers' reciprocal relationship with the firm. We conduct an experiment to examine this issue and find that when new lower-wage workers become available, firms hire them to replace original higher-wage workers and pay the new workers lower wages. However, these lower wages do not improve firm profit because the decision to hire new lower-wage workers causes both the new and remaining workers to provide lower effort. Moreover, hiring lower-wage workers reduces new workers' payoffs and thus decreases social welfare. These unintended consequences suggest that firms should consider both the wage savings and the potential costs when deciding whether to replace some workers with new lower-wage workers. We discuss the implications of our findings for contract design, hiring practices, and managerial accountants.

The mispricing of marketing performance indicators (e.g., brand equity, churn, and customer satisfaction) is an important element of arguments in favor of the financial value of marketing investments. Evidence for mispricing can be assessed by examining whether or not portfolios composed of firms that load highly on marketing performance indicators deliver excess returns. Unfortunately, extant portfolio formation methods that require the use of a risk model are open to the criticism of time-varying risk factor loadings due to the changing composition of the portfolio over time. This is a serious critique as the direction of the induced bias is unknown. As an alternative, we propose a new method and construct portfolios that are neutral with respect to the desired risk factors a priori. Consequently, no risk model is needed when analyzing the observed returns of our portfolios. We apply our method to a frequently studied marketing performance indicator, customer satisfaction, and using various ways of measuring customer satisfaction, we do not find any convincing evidence that portfolios that load on high customer satisfaction lead to abnormal returns.

Recognizing intentions of strangers from facial cues is crucial in everyday social interactions. Recent studies demonstrated enhanced event-related potential (ERP) responses to untrustworthy compared to trustworthy faces. The aim of the present study was to investigate the electrophysiological correlates of automatic processing of trustworthiness cues in a visual oddball paradigm in two consecutive experimental blocks. In one block, frequent trustworthy (p = 0.9) and rare untrustworthy face stimuli (p = 0.1) were briefly presented on a computer screen with each stimulus consisting of four peripherally positioned faces. In the other block stimuli were presented with reversed probabilities enabling the comparison of ERPs evoked by physically identical deviant and standard stimuli. To avoid attentional effects participants engaged in a central detection task. Analyses of deviant minus standard difference waveforms revealed that deviant untrustworthy but not trustworthy faces elicited the visual mismatch negativity (vMMN) component. The present results indicate that adaptation occurred to repeated unattended trustworthy (but not untrustworthy) faces, i.e., an automatic expectation was elicited towards trustworthiness signals, which was violated by deviant untrustworthy faces. As an evolutionary adaptive mechanism, the observed fast detection of trustworthiness-related social facial cues may serve as the basis of conscious recognition of reliable partners.

The diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) is based on subjective measures despite evidence for multisystemic structural and functional deficits. ADHD patients have consistent neurofunctional deficits in motor response inhibition. The aim of this study was to apply pattern classification to task-based functional magnetic resonance imaging (fMRI) of inhibition, to accurately predict the diagnostic status of ADHD. Thirty adolescent ADHD and thirty age-matched healthy boys underwent fMRI while performing a Stop task. fMRI data were analyzed with Gaussian process classifiers (GPC), a machine learning approach, to predict individual ADHD diagnosis based on task-based activation patterns. Traditional univariate case-control analyses were also performed to replicate previous findings in a relatively large dataset. The pattern of brain activation correctly classified up to 90% of patients and 63% of controls, achieving an overall classification accuracy of 77%. The regions of the discriminative network most predictive of controls included later developing lateral prefrontal, striatal, and temporo-parietal areas that mediate inhibition, while regions most predictive of ADHD were in earlier developing ventromedial fronto-limbic regions, which furthermore correlated with symptom severity. Univariate analysis showed reduced activation in ADHD in bilateral ventrolateral prefrontal, striatal, and temporo-parietal regions that overlapped with areas predictive of controls, suggesting the latter are dysfunctional areas in ADHD. We show that significant individual classification of ADHD patients of 77% can be achieved using whole brain pattern analysis of task-based fMRI inhibition data, suggesting that multivariate pattern recognition analyses of inhibition networks can provide objective diagnostic neuroimaging biomarkers of ADHD.

Incidental negative emotions unrelated to the current task, such as background anxiety, can strongly influence decisions. This is most evident in psychiatric disorders associated with generalized emotional disturbances. However, the neural mechanisms by which incidental emotions may affect choices remain poorly understood. Here we study the effects of incidental anxiety on human risky decision making, focusing on both behavioral preferences and their underlying neural processes. Although observable choices remained stable across affective contexts with high and low incidental anxiety, we found a clear change in neural valuation signals: during high incidental anxiety, activity in ventromedial prefrontal cortex and ventral striatum showed a marked reduction in (1) neural coding of the expected subjective value (ESV) of risky options, (2) prediction of observed choices, (3) functional coupling with other areas of the valuation system, and (4) baseline activity. At the same time, activity in the anterior insula showed an increase in coding the negative ESV of risky lotteries, and this neural activity predicted whether the risky lotteries would be rejected. This pattern of results suggests that incidental anxiety can shift the focus of neural valuation from possible positive consequences to anticipated negative consequences of choice options. Moreover, our findings show that these changes in neural value coding can occur in the absence of changes in overt behavior. This suggest a possible pathway by which background anxiety may lead to the development of chronic reward desensitization and a maladaptive focus on negative cognitions, as prevalent in affective and anxiety disorders.

Inferring on others' (potentially time-varying) intentions is a fundamental problem during many social transactions. To investigate the underlying mechanisms, we applied computational modeling to behavioral data from an economic game in which 16 pairs of volunteers (randomly assigned to “player” or “adviser” roles) interacted. The player performed a probabilistic reinforcement learning task, receiving information about a binary lottery from a visual pie chart. The adviser, who received more predictive information, issued an additional recommendation. Critically, the game was structured such that the adviser's incentives to provide helpful or misleading information varied in time. Using a meta-Bayesian modeling framework, we found that the players' behavior was best explained by the deployment of hierarchical learning: they inferred upon the volatility of the advisers' intentions in order to optimize their predictions about the validity of their advice. Beyond learning, volatility estimates also affected the trial-by-trial variability of decisions: participants were more likely to rely on their estimates of advice accuracy for making choices when they believed that the adviser's intentions were presently stable. Finally, our model of the players' inference predicted the players' interpersonal reactivity index (IRI) scores, explicit ratings of the advisers' helpfulness and the advisers' self-reports on their chosen strategy. Overall, our results suggest that humans (i) employ hierarchical generative models to infer on the changing intentions of others, (ii) use volatility estimates to inform decision-making in social interactions, and (iii) integrate estimates of advice accuracy with non-social sources of information. The Bayesian framework presented here can quantify individual differences in these mechanisms from simple behavioral readouts and may prove useful in future clinical studies of maladaptive social cognition.

We explore empirically how capital inflows into the US and financial deregulation within the United States interacted in driving the run-up (and subsequent decline) in US housing prices over the period 1990-2010. To obtain an ex ante measure of financial liberalization, we focus on the history of interstate-banking deregulation during the 1980s, i.e. prior to the large net capital inflows into the US from China and other emerging economies. Our re- sults suggest a long shadow of deregulation: in states that opened their banking markets to out-of-state banks earlier, house prices were more sensitive to capital inflows. We provide evidence that global imbalances were a major positive funding shock for US wide banks: different from local banks, these banks held a geographically diversified portfolio of mort- gages which allowed them to tap the global demand for safe assets by issuing private-label safe assets backed by the country-wide US housing market. This, in turn, allowed them to expand mortgage lending and lower interest rates, driving up housing prices.

Authors' names are deliberately alphabetical. The authors are grateful to Michele Bernasconi, Monika Bütler, Alain Cohn, Simon Evenett, Ernst Fehr, Simon Gächter, Jens Grosser, Sally Gschwend, John Hey, Martin Leroch, Hervè Moulin, Ryan McKay, Clemens Puppe, Rupert Sausgruber, Robert Sugden, Christian Thöni, Jean-Robert Tyran, Roberto Weber, as well as the audiences at the NYU Experimental Political Science Conference 2010, ASSET Meeting 2009, ESA European Regional Meeting 2008/2009, APET Conference 2009, EPCS Conference 2008, Bocconi University, Florida State University, IMT - Lucca, University of Messina, Universitat Jaume I of Castellòn, and University of Zürich for very helpful discussions and comments. The data used in this study are stored on the AJPS Data Archive on Dataverse.

Despite that a wealth of evidence links striatal dopamine to individualś reward learning performance in non-social environments, the neurochemical underpinnings of such learning during social interaction are unknown. Here, we show that the administration of 300 mg of the dopamine precursor L-DOPA to 200 healthy male subjects influences learning about a partners’ prosocial preferences in a novel social interaction task, which is akin to a repeated trust game. We found learning to be modulated by a well-established genetic marker of striatal dopamine levels, the 40-bp variable number tandem repeats polymorphism of the dopamine transporter (DAT1 polymorphism). In particular, we found that L-DOPA improves learning in 10/10R genoype subjects, who are assumed to have lower endogenous striatal dopamine levels and impairs learning in 9/10R genotype subjects, who are assumed to have higher endogenous dopamine levels. These findings provide first evidence for a critical role of dopamine in learning whether an interaction partner has a prosocial or a selfish personality. The applied pharmacogenetic approach may open doors to new ways of studying psychiatric disorders such as psychosis, which is characterized by distorted perceptions of others’ prosocial attitudes.

We juxtapose the effects of trade and technology on employment in U.S. local labor markets between 1990 and 2007. Labor markets whose initial industry composition exposes them to rising Chinese import competition experience significant falls in employment, particularly in manufacturing and among non-college workers. Labor markets susceptible to computerization due to specialization in routine task-intensive activities experience significant occupational polarization within manufacturing and nonmanufacturing but no net employment decline. Trade impacts rise in the 2000s as imports accelerate, while the effect of technology appears to shift from automation of production activities in manufacturing towards computerization of information-processing tasks in non manufacturing.