We propose a dynamic general equilibrium model that yields testable implications about the fiscal policy run by governments of different political color. Successive generations of voters choose taxation, expenditure, and government debt through repeated elections. Voters are heterogeneous by age and by the intensity of their preferences for public good provision. The political equilibrium switches stochastically between left- (pro-public goods) and right-leaning (pro-private consumption) governments. A shift to the left (right) is associated with a fall (increase) in government debt, an increase (fall) in taxation, and an increase (fall) in government expenditures. However, left-leaning governments engage in more debt accumulation during recessions. These predictions are shown to be consistent with the time-series evidence for the United States in the postwar period, and also with the evidence for a panel of OECD countries.
This paper studies the effect of disclosing conflicts of interest on strategic communication when the sender has lying costs. I present a simple economic mechanism under which such disclosure often leads to more informative, but at the same time also to more biased messages. This benefits rational receivers but exerts a negative externality from them on naive or delegating receivers; disclosure is thus not a Pareto-improvement among receivers. I identify general conditions of the information structure under which this effect manifests and show that whenever it does, full disclosure is socially inefficient. These results hold independently of the degree of receivers' risk-aversion and for an arbitrary precision of the disclosure statement.
Time is an extremely valuable resource but little is known about the efficiency of time allocation in decision-making. Empirical evidence suggests that in many ecologically relevant situations, decision difficulty and the relative
reward from making a correct choice, compared to an incorrect one, are inversely linked, implying that it is optimal to use relatively less time for difficult choice problems. This applies, in particular, to value-based choices, in which the relative reward from choosing the higher valued item shrinks as the values of the other options get closer to the best option and are thus more
difficult to discriminate. Here, we experimentally show that people behave sub-optimally in such contexts. They do not respond to incentives that favour the allocation of time to choice problems in which the relative reward for choosing the best option is high; instead they spend too much time on problems in which the reward difference between the options is low. We demonstrate this by showing that it is possible to improve subjects’ time allocation with a simple intervention that cuts them off when their
decisions take too long. Thus, we provide a novel form of evidence that organisms systematically spend their valuable time in an inefficient way, and simultaneously offer a potential solution to the problem.
We study the effects of the adoption of new agricultural technologies on structural transformation. To guide empirical work, we present a simple model where the effect of agricultural productivity on industrial development depends on the factor bias of technical change. We test the predictions of the model by studying the introduction of genetically engineered soybean seeds in Brazil, which had heterogeneous effects on agricultural productivity across areas with different soil and weather characteristics. We find that technical change in soy production was strongly labor saving and led to industrial growth, as predicted by the model.
Deficits in empathy enhance conflicts and human suffering. Thus, it is crucial to understand how empathy can be learned and how learning experiences shape empathy-related processes in the human brain. As a model of empathy deficits, we used the well-established suppression of empathy-related brain responses for the suffering of out-groups and tested whether and how out-group empathy is boosted by a learning intervention. During this intervention, participants received costly help equally often from an out-group member (experimental group) or an in-group member (control group). We show that receiving help from an out-group member elicits a classical learning signal (prediction error) in the anterior insular cortex. This signal in turn predicts a subsequent increase of empathy for a different out-group member (generalization). The enhancement of empathy-related insula responses by the neural prediction error signal was mediated by an establishment of positive emotions toward the out-group member. Finally, we show that surprisingly few positive learning experiences are sufficient to increase empathy. Our results specify the neural and psychological mechanisms through which learning interacts with empathy, and thus provide a neurobiological account for the plasticity of empathic reactions.
Goal-directed choices should be guided by the expected value of the available options. However, people are often influenced by past costs in their decisions, thus succumbing to a bias known as the “sunk-cost effect.” Recent functional magnetic resonance imaging data show that the sunk-cost effect is associated with increased activity in dorsolateral prefrontal cortex (dlPFC) and altered crosstalk of the dlPFC with other prefrontal areas. Are these correlated neural processes causally involved in the sunk-cost effect? Here, we employed transcranial direct current stimulation (tDCS) to examine the role of the dlPFC for biasing choices in line with the cost of past expenses. Specifically, we applied different types of tDCS over the right dlPFC while participants performed an investment task designed to assess the impact of past investments on current choices. Our results show a pronounced sunk-cost effect that was significantly increased by anodal tDCS, but left unaltered by cathodal or sham stimulation. Importantly, choices were not affected by stimulation when no prior investments had been made, underlining the specificity of the obtained effect. Our findings suggest a critical role of the dlPFC in the sunk-cost effect and thus elucidate neural mechanisms by which past investments may influence current decision-making.
We study the effects of the adoption of new agricultural technologies on structural transformation. To guide empirical work, we present a simple model where the effect of agricultural productivity on industrial development depends on the factor bias of technical change. We test the predictions of the model by studying the introduction of genetically engineered soybean seeds in Brazil, which had heterogeneous effects on agricultural productivity across areas with different soil and weather characteristics. We find that technical change in soy production was strongly labor saving and led to industrial growth, as predicted by the model.
This study analyzes the effect of all-day (AD) primary school programs on maternal labor supply. To account for AD school selectivity and selection into AD primary school programs I estimate bivariate probit models. To identify these models I exploit variation in the allocation of investments to AD primary schools across time and counties. This variation results from the public investment program "Future Education and Care" (IZBB) which was introduced by the German federal government in 2003. My results indicate for mothers with primary school-aged children in Germany (excluding Bavaria) a significantly positive effect of AD primary school programs on labor supply at the extensive margin. On average, mothers who make use of AD primary school programs are 26 ppts more likely to be employed than mothers who do not make use of these programs. This large effect is robust to alternative specifications and estimation methods and mainly concentrated in states with AD primary school student shares of up to 20%. On the contrary, there is no evidence for an impact of these programs on maternal labor supply at the intensive margin (full-time vs. part-time).
This study investigates how well weekly Google search volumes track and predict bank failures in the United States between 2007 and 2012, contributing to the expanding literature that exploits internet data for the prediction of events. Different duration models with time-varying covariates are estimated. Higher Google search volumes go hand in hand with higher failure rates, and the coefficients for the Google volume growth index are highly significant. However, Google’s predictive power quickly dissipates for future failure rates.
