Contributions published at Department of Economics (Florian Scheuer)

The seemingly rampant economic selfishness suggested by many recent corporate scandals is at odds with empirical results from behavioural economics, which demonstrate high levels of prosocial behaviour in bilateral interactions and low levels of dishonest behaviour. We design an experimental setting, the ‘Big Robber’ game, where a ‘robber’ can obtain a large personal gain by appropriating the earnings of a large group of ‘victims’. In a large laboratory experiment (N = 640), more than half of all robbers took as much as possible and almost nobody declined to rob. However, the same participants simultaneously displayed standard, predominantly prosocial behaviour in Dictator, Ultimatum and Trust games. Thus, we provide direct empirical evidence showing that individual selfishness in high-impact decisions affecting a large group is compatible with prosociality in bilateral low-stakes interactions. That is, human beings can simultaneously be generous with others and selfish with large groups.

Every decision we take is accompanied by a characteristic pattern of response delay, gaze position, pupil dilation, and neural activity. Nevertheless, many models of social decision making neglect the corresponding process tracing data and focus exclusively on the final choice outcome. Here, we argue that this is a mistake, as the use of process data can help to build better models of human behavior, create better experiments, and improve policy interventions. Specifically, such data allow us to unlock the “black box” of the decision process and evaluate the mechanisms underlying our social choices. Using these data, we can directly validate latent model variables, arbitrate between competing personal motives, and capture information processing strategies. These benefits are especially valuable in social science, where models must predict multi‐faceted decisions that are taken in varying contexts and are based on many different types of information.

Multivariate GARCH models do not perform well in large dimensions due to the so-called curse of dimensionality. The recent DCC-NL model of Engle et al. (2019) is able to overcome this curse via nonlinear shrinkage estimation of the unconditional correlation matrix. In this paper, we show how performance can be increased further by using open/high/low/close (OHLC) price data instead of simply using daily returns. A key innovation, for the improved modeling of not only dynamic variances but also of dynamic correlations, is the concept of a regularized return, obtained from a volatility proxy in conjunction with a smoothed sign of the observed return.

The share of low-income countries in global exports nearly tripled between 1990 and 2015, driven largely by the rapid emergence of China as an exporting powerhouse. While research in economics had long acknowledged that trade with lower-income countries could raise income inequality in Europe and the US, empirical estimates indicated only a modest contribution of trade to growing national skill premia. However, if workers are not highly mobile across firms, industries and locations, then the unequal impacts of trade can manifest along different margins. Recent evidence from countries across Europe and the US shows that growing import competition from China differentially reduced earnings and employment rates for workers in more trade-exposed industries, and for the residents of more trade-exposed geographic regions. These adverse impacts were often largest for lower-skilled individuals. We show that domestic manufacturing employment declined much more in countries that saw a large growth of net imports from China (such as the UK and the US), than in countries that maintained relatively balanced trade with China (such as Germany and Switzerland). Drawing on a new analysis for the UK, we further show that trade with China contributed to job loss in manufacturing, but also to substantial declines in consumer prices. However, while the adverse labour market impacts were concentrated on specific groups of workers and regions, the consumer benefits from trade were widely dispersed in the population, and appear similarly large for high-income and low-income households. Globalisation has thus created pockets of losers, and recent evidence indicates that in addition to financial losses, residents of regions with greater exposure to import competition also suffer from higher crime rates, a deterioration of health outcomes, and a dissolution of traditional family structures. We argue that new import tariffs such as those imposed by the US in 2018 and 2019 are unlikely to help the losers from globalisation. Instead, displaced workers may be better supported by a combination of transfers to avert financial hardship, skills training that facilitate reintegration into the labour market, and place-based policies that stimulate job creation in depressed locations

In a canonical model of military conflict, victory and defeat depend stochastically on the difference of resources deployed by the conflict parties. The present paper offers a comprehensive analysis of that model. The unique Nash equilibrium reflects either (i) peace, (ii) submission, (iii) insurgency, or (iv) war. Intuitive predictions regarding possible transitions between these types of equilibria are obtained. The analysis identifies advances in weaponry as an important driver of conflict and, less often so, of its resolution. The formal derivation exploits the variation-diminishing property of higher-order Pólya frequency functions.

The aim of this study was to evaluate the reproducibility of voxel-wise temporal signal-to-noise ratio (tSNR) on repeated scans across runs, sessions, and days. A group of 21 participants was scanned 16 times (4 runs per session, 2 sessions per day, 2 separate days) in a functional magnetic resonance imaging (fMRI) study on a 3T Philips Achieva scanner. For each run, we calculated t-value and tSNR maps. To ascertain that the results were not specific to the scanner, one volunteer was scanned with four fMRI runs in a single session on the above 3T Philips scanner as well as a 3T Siemens Prisma scanner. The coefficient of variation of voxel-wise tSNR across the 16 repeats was up to 25%, while the range relative to the mean of all observations was up to 80%. The voxel-wise variability of tSNR on the two different scanners was similar, indicating a general issue. Despite its use in evaluating the quality of fMRI data, we found only a weak relationship between tSNR and t-values. There is very high variability in voxel-wise tSNR, which should be considered while planning future studies that aim to identify small and focal fMRI effects or the benefits of incremental improvement in methods.

This paper establishes existence of subgame perfect equilibrium in pure strategies for a general class of sequential multi-lateral bargaining games, without assuming a stationary setting. The only required hypothesis is that utility functions are continuous on the space of economic outcomes. In particular, no assumption on the space of feasible payoffs is needed. The result covers arbitrary and even time-varying bargaining protocols (acceptance rules), externalities, and other-regarding preferences. As a side result, we clarify the meaning of assumptions on “continuity at infinity.”

There is substantial evidence that women tend to support different policies and political candidates than men. Many studies also document gender differences in a variety of important preference dimensions, such as risk-taking, competition and pro-sociality. However, the degree to which differential voting by men and women is related to these gaps in more basic preferences requires an improved understanding. We conduct an experiment in which individuals in small laboratory “societies” repeatedly vote for redistribution policies and engage in production. We find that women vote for more egalitarian redistribution and that this difference persists with experience and in environments with varying degrees of risk. This gender voting gap is accounted for partly by both gender gaps in preferences and by expectations regarding economic circumstances. However, including both these controls in a regression analysis indicates that the latter is the primary driving force. We also observe policy differences between male- and female-controlled groups, though these are substantially smaller than the mean individual differences - a natural consequence of the aggregation of individual. preferences into collective outcomes.

We study how payoff valence affects voting behavior on the distribution of monetary outcomes framed as gains or losses in a group when using standard plurality voting (PV) procedures and when using approval voting (AV). The latter method allows the subjects to approve of as many alternatives as they wish and has been shown to eliminate the incentives to vote strategically. For both methods, we observe that voters express higher support for egalitarian allocations (and lower support for selfish options) when sharing gains than when sharing losses. Moreover, the average number of approved alternatives per ballot is higher when distributions are framed in terms of gains than when they are framed in terms of losses. We also discuss under which circumstances the shift in voting behavior is more likely to produce changes in the electoral outcome. The results suggest that framing manipulations (payoff valence) can significantly impact voting behavior.

Many economies in Western Europe have experienced a sizeable increase in income inequality since the 1980s, and inequality has grown even more rapidly in the United States. Whereas educated workers in skilled occupations benefited from rising salaries, wages have stagnated for many less educated workers in unskilled occupations. The rising inequality in advanced economies coincided with a period of globalisation that was characterised by rapid growth in international merchandise trade. Basic economic models predict that trade could contribute to greater inequality in skill-abundant advanced economies, as globalisation leads such countries to specialise in skill-intensive industrial sectors, which raises labour demand for skilled workers but reduces demand for unskilled ones. Yet despite this theoretical link between trade and inequality, empirical analyses long concluded that increased trade was not a major cause of increasing inequality in advanced economies. However, this perspective on trade and inequality has evolved during the decade of the 2010s, as a growing body of empirical research found sizeable impacts of trade shocks on labour markets and inequality. During the same period, international trade has become a more contentious subject in political debate, and a many-decades-old trend towards greater trade liberalisation has been broken by new tariffs that resulted in a ‘trade war’ between the US and China.

Recent studies have suggested close functional links between overt visual attention and decision making. This suggests that the corresponding mechanisms may interface in brain regions known to be crucial for guiding visual attention – such as the frontal eye field (FEF). Here, we combined brain stimulation, eye tracking, and computational approaches to explore this possibility. We show that inhibitory transcranial magnetic stimulation (TMS) over the right FEF has a causal impact on decision making, reducing the effect of gaze dwell time on choice while also increasing reaction times. We computationally characterize this putative mechanism by using the attentional drift diffusion model (aDDM), which reveals that FEF inhibition reduces the relative discounting of the non-fixated option in the comparison process. Our findings establish an important causal role of the right FEF in choice, elucidate the underlying mechanism, and provide support for one of the key causal hypotheses associated with the aDDM.

Theoretical accounts distinguish between motivational ('wanting') and hedonic ('liking') dimensions of rewards. Previous animal and human research linked wanting and liking to anatomically and neurochemically distinct brain mechanisms, but it remains unknown how the different brain regions and neurotransmitter systems interact in processing distinct reward dimensions. Here, we assessed how pharmacological manipulations of opioid and dopamine receptor activation modulate the neural processing of wanting and liking in humans in a randomized, placebo-controlled, double-blind clinical trial. Reducing opioid receptor activation with naltrexone selectively reduced wanting of rewards, which on a neural level was reflected by stronger coupling between dorsolateral prefrontal cortex and the striatum under naltrexone compared with placebo. In contrast, reducing dopaminergic neurotransmission with amisulpride revealed no robust effects on behavior or neural activity. Our findings thus provide insights into how opioid receptors mediate neural connectivity related to specifically motivational, not hedonic, aspects of rewards.

Under rotation-equivariant decision theory, sample covariance matrix eigenvalues can be optimally shrunk by recombining sample eigenvectors with a (potentially nonlinear) function of the unobservable population covariance matrix. The optimal shape of this function reflects the loss/risk that is to be minimized. We solve the problem of optimal covariance matrix estimation under a variety of loss functions motivated by statistical precedent, probability theory, and differential geometry. A key ingredient of our nonlinear shrinkage methodology is a new estimator of the angle between sample and population eigenvectors, without making strong assumptions on the population eigenvalues. We also introduce a broad family of covariance matrix estimators that can handle all regular functional transformations of the population covariance matrix under large-dimensional asymptotics. In addition, we compare via Monte Carlo simulations our methodology to two simpler ones from the literature, linear shrinkage and shrinkage based on the spiked covariance model.

We propose a new sufficient statistic to measure the ex-post welfare gains from trade in CES models featuring any productivity distribution or pattern of selection into production and exporting. Our statistic is based on a single data moment, the change in the market share of continuing domestic producers, and a single structural parameter, the elasticity of substitution between products. We apply our statistic to measure Canada's gains from the Canada-US Free Trade Agreement using data on observed firm selection and simulated firm selection in a calibrated model with a flexible extensive margin. We find that welfare gains are substantially smaller than implied by welfare formulas that assume that the extensive margin behaves according to a standard Melitz-Pareto model with iso-elastic import demand.

In this paper we study how a student's ordinal rank in a peer group affects performance and specialisation choices in university. By exploiting data with repeated random assignment of students to teaching sections, we find that a higher rank increases performance and the probability of choosing related follow-up courses and majors. We document two types of dynamic effect. First, earlier ranks are less important than later ranks. Second, responses to rank changes are asymmetric: improvements in rank raise performance, while decreases in rank have no effect. Rank effects partially operate through students’ expectations about future grades.

Business degrees are popular and lead to high earnings. Female business graduates, however, earn less than their male counterparts. These gender differences can be traced back to university, where women shy away from majors like finance that lead to high earnings. In this paper, we investigate how the gender composition of peers in business school affects women’s and men’s major choices and labor market outcomes. We find that women who are randomly assigned to teaching sections with more female peers become less likely to choose male-dominated majors like finance and more likely to choose female-dominated majors like marketing. After graduation, these women end up in jobs where their earnings grow more slowly. Men, on the other hand, become more likely to choose male-dominated majors and less likely to choose female-dominated majors when they had more female peers in business school. However, men’s labor market outcomes are not significantly affected. Taken together, our results show that studying with more female peers in business school increases gender segregation in educational choice and affects labor market outcomes.

Climate change already has a negative impact on the environment and our societies, and this impact will get worse over the course of this century. How much worse? This will depend on our ability to reduce greenhouse gas emissions. Achieving the necessary reduction in emissions, while maintaining (and improving) worldwide living standards can only be achieved through innovation. Fortunately, innovation is not manna from heaven; it is conducted by scientists and firms and it reacts to market and policy incentives. It is therefore up to governments to steer it toward clean technologies. In this public paper, I will review recent economic research on the role of innovation in the design of climate policy. After a quick introduction to the challenges posed by climate change, I will show that current technological trends – though promising – are unlikely to be sufficient to limit warming to 2°C. Can policy then effectively boost green innovation? Recent evidence shows that this is definitely the case. How should global climate policy be designed to leverage this innovation response? What about unilateral policies? Some innovations are “grey”: they permit the replacement of particularly dirty technologies with less dirty but still polluting ones. The shale gas revolution is an example. Can these “grey” innovations backfire?

Selective serotonin reuptake inhibitors (SSRIs) are widely used to treat psychiatric disorders with affective biases such as depression and anxiety. How SSRIs exert a beneficial action on emotions associated with life events is still unknown. Here we ask whether and how the effectiveness of the SSRI fluoxetine is underpinned by neural mechanisms in the ventral striatum. To address these issues, we studied the spiking activity of neurons in the ventral striatum of monkeys during an approach-avoidance task in which the valence assigned to sensory stimuli was manipulated. Neural responses to positive and negative events were measured before and during a 4-week treatment with fluoxetine. We conducted PET scans to confirm that fluoxetine binds within the ventral striatum at a therapeutic dose. In our monkeys, fluoxetine facilitated approach of rewards and avoidance of punishments. These beneficial effects were associated with changes in tonic and phasic activities of striatal neurons. Fluoxetine increased the spontaneous firing rate of striatal neurons and amplified the number of cells responding to rewards versus punishments, reflecting a drug-induced positive shift in the processing of emotionally valenced information. These findings reveal how SSRI treatment affects ventral striatum neurons encoding positive and negative valence and striatal signaling of emotional information. In addition to a key role in appetitive processing, our results shed light on the involvement of the ventral striatum in aversive processing. Together, the ventral striatum appears to play a central role in the action of SSRIs on emotion processing biases commonly observed in psychiatric disorders.

Decisions are based on the subjective values of choice options. However, subjective value is a theoretical construct and not directly observable. Strikingly, distinct theoretical models competing to explain how subjective values are assigned to choice options often make very similar behavioral predictions, which poses a major difficulty for establishing a mechanistic, biologically plausible explanation of decision-making based on behavior alone. Here, we demonstrate that model comparison at the neural level provides insights into model implementation during subjective value computation even though the distinct models parametrically identify common brain regions as computing subjective value. We show that frontal cortical regions implement a model based on the statistical distributions of available rewards, whereas intraparietal cortex and striatum compute subjective value signals according to a model based on distortions in the representations of probabilities. Thus, better mechanistic understanding of how cognitive processes are implemented arises from model comparisons at the neural level, over and above the traditional approach of comparing models at the behavioral level alone.