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.
Seit Beginn des Bundes-Investitionsprogramms „Zukunft Bildung und Betreuung“ (IZBB) im Jahr 2003 hat sich der Anteil der Grundschulkinder, die ganztägig eine Schule besuchen, mehr als vervierfacht. Vor diesem Hintergrund untersucht der vorliegende Beitrag zum einen, welche demografischen und sozioökonomischen Merkmale Kinder aufweisen, die ganztägige Schulangebote nutzen. Zum anderen wird der Frage nachgegangen, wie sich die Zusammensetzung dieser Grundschüler im Vergleich zu Grundschülern, die keine ganztägigen Schulangebote nutzen, mit dem Ausbau der Ganztagsschule verändert hat. Ist über die Zeit eine Konvergenz oder eine Divergenz in den gruppenspezifischen Nutzermerkmalen zu beobachten? Für diese Untersuchungen werden Daten des Sozio-oekonomischen Panels (SOEP) und der Zusatzstudie „Familien in Deutschland“ (FiD) verwendet, die Vergleiche der gruppenspezifischen Teilnahme vor und nach dem Ausbau der Ganztagsschule zulassen. Die Ergebnisse zeigen, dass sich in Westdeutschland Nichtteilnehmer und Teilnehmer an ganztägigen Schulangeboten im Hinblick auf sozioökonomische Merkmale, wie das Einkommen, annähern, es also diesbezüglich zu einer Konvergenz kommt. In Ostdeutschland findet hingegen eine Konvergenz bei Merkmalen der Haushaltsstruktur statt. Generell gibt es wenig Evidenz für eine Divergenz.
Since the launch of the public investment program ‘Future Education and Care’ (IZBB) in 2003 the share of primary school aged children participating in all-day schooling in Germany has more than quadrupled. Against this backdrop, this study analyzes, the demographic and socioeconomic characteristics which are possibly related to a higher probability of attendance. Moreover, it analyzes changes in the composition of students participating in all-day schooling. Has there been rather a convergence or a divergence in the characteristics of participants and non-participants over time? Using data from the German Socio-Economic Panel (SOEP) and the additional survey “Families in Germany” (FiD), our results show that in West Germany all-day school participants have become more similar according to their socioeconomic characteristics (in particular income), hence supporting the convergence hypothesis. In East Germany our findings also provide evidence for convergence. In contrast to West Germany, however, convergence applies to characteristics related to the household structure. Overall, there is no indication for divergence over time.
The so-called "Rise of the Machines" has fundamentally transformed the organization of work during the last four decades. While enthusiasts are captivated by the new technologies, many worry that these machines will eventually lead to mass unemployment, as robots and computers substitute for human labor. Are computers just about to take over from humans? You will receive the answer to this and related questions from Prof. David Dorn, a specialist in international trade and labor markets.
Economists have typically assumed that the only way for leaders to get people to do things is to use carrots (e.g., pay raises) or sticks (e.g., threats of firing) to incentivize a desired behavior. One of the important contributions of recent research on leadership is to test the extent to which this really is true. Can leaders also motivate and inspire workers by their statements and speeches? Or, is the best way to get followers to do something by creating hard incentives for them? You will receive the answers to these and related questions from Prof. Roberto Weber, a leading specialist in behavioral and experimental economics.
We extend the equivalence between Bayesian and dominant strategy implementation established by Gershkov et al. (Econometrica, 2013) to environments with non-linear utilities satisfying the average single-crossing property and the convex-valued assumption. The new equivalence result produces novel implications to the literature on the principal-agent problem with allocative externalities, environmental mechanism design, and public good provision.
