The incentives of political agents to enforce tax collection are key determinants of the levels of compliance. We study the electoral response to the Ghost Buildings program, a nationwide anti tax evasion policy in Italy that used innovative monitoring technologies to target buildings hidden from tax authorities. The program induced monetary and non-monetary benefits for non-evaders and an increase in local government expenditures. A one standard deviation increase in town-level program intensity leads to a 4.8% increase in local incumbent reelection rates. In addition, these political returns are higher in areas with lower tax evasion tolerance and with higher efficiency of public good provision, implying complementarity among enforcement policies, the underlying tax culture, and the quality of the government.
Die Regierung von Matteo Renzi gerät durch die Bankenkrise sowie die Oppositionspartei des Komikers Beppe Grillo unter Druck. Ein Wahlsieg Grillos hätte fatale Konsequenzen.
Wie Eltern die schulische Erziehung ihrer Kinder steuern, beeinflusst das spätere Humankapital. Das wiederum ist entscheidend für das Wirtschaftswachstum und für künftige Ungleichheit.
Gamma and beta oscillations are routinely observed in motor-related brain circuits during movement preparation and execution. Entrainment of gamma or beta oscillations via transcranial alternating current stimulation (tACS) over primary motor cortex (M1) has opposite effects on motor performance, suggesting a causal role of these brain rhythms for motor control. However, it is largely unknown which brain mechanisms characterize these changes in motor performance brought about by tACS. In particular, it is unclear whether these effects result from brain activity changes only in the targeted areas or within functionally connected brain circuits. Here we investigated this issue by applying gamma-band and beta-band tACS over M1 in healthy humans during a visuomotor task and concurrent functional magnetic resonance imaging (fMRI). Gamma tACS indeed improved both the velocity and acceleration of visually triggered movements, compared with both beta tACS and sham stimulation. Beta tACS induced a numerical decrease in velocity compared with sham stimulation, but this was not statistically significant. Crucially, gamma tACS induced motor performance enhancements correlated with changed BOLD activity in the stimulated M1. Moreover, we found frequency- and task-specific neural compensatory activity modulations in the dorsomedial prefrontal cortex (dmPFC), suggesting a key regulatory role of this region in motor performance. Connectivity analyses revealed that the dmPFC interacted functionally with M1 and with regions within the executive motor system. These results suggest a role of the dmPFC for motor control and show that tACS-induced behavioral changes not only result from activity modulations underneath the stimulation electrode but also reflect compensatory modulation within connected and functionally related brain networks. More generally, our results illustrate how combined tACS-fMRI can be used to resolve the causal link between cortical rhythms, brain systems, and behavior.
Neurobiological models of self-control predominantly focus on the role of prefrontal brain mechanisms involved in emotion regulation and impulse control. We provide evidence for an entirely different neural mechanism that promotes self-control by overcoming bias for the present self, a mechanism previously thought to be mainly important for interpersonal decision-making. In two separate studies, we show that disruptive transcranial magnetic stimulation (TMS) of the temporo-parietal junction—a brain region involved in overcoming one’s self-centered perspective—increases the discounting of delayed and prosocial rewards. This effect of TMS on temporal and social discounting is accompanied by deficits in perspective-taking and does not reflect altered spatial reorienting and number recognition. Our findings substantiate a fundamental commonality between the domains of self-control and social decision-making and highlight a novel aspect of the neurocognitive processes involved in self-control.
Given that the range of rewarding and punishing outcomes of actions is large but neural coding capacity is limited, efficient processing of outcomes by the brain is necessary. One mechanism to increase efficiency is to rescale neural output to the range of outcomes expected in the current context, and process only experienced deviations from this expectation. However, this mechanism comes at the cost of not being able to discriminate between unexpectedly low losses when times are bad versus unexpectedly high gains when times are good. Thus, too much adaptation would result in disregarding information about the nature and absolute magnitude of outcomes, preventing learning about the longer-term value structure of the environment. Here we investigate the degree of adaptation in outcome coding brain regions in humans, for directly experienced outcomes and observed outcomes. We scanned participants while they performed a social learning task in gain and loss blocks. Multivariate pattern analysis showed two distinct networks of brain regions adapt to the most likely outcomes within a block. Frontostriatal areas adapted to directly experienced outcomes, whereas lateral frontal and temporoparietal regions adapted to observed social outcomes. Critically, in both cases, adaptation was incomplete and information about whether the outcomes arose in a gain block or a loss block was retained. Univariate analysis confirmed incomplete adaptive coding in these regions but also detected nonadapting outcome signals. Thus, although neural areas rescale their responses to outcomes for efficient coding, they adapt incompletely and keep track of the longer-term incentives available in the environment.
The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008–2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0.
We study targeted information in a duopoly model with differentiated products, allowing for consumers with limited attention. The presence of inattentive consumers incentivizes firms to behave as if they were mass-advertisers, despite their ability to direct their mes- sages precisely towards consumers with the strongest preferences. We show that the scope for targeting as an efficient marketing instrument can be severely reduced, for both firms and consumers, if the standard assumption of unbounded attention capacities is dropped. A central insight of our model is that limited attention may explain the recent evidence on increased ad-blocking, which has become a key concern to the entire advertising in- dustry. Our main findings are robust to several variations, including price and salience competition as well as varying quality of the available marketing data.
We study the differential impact of large exchange rate devaluations on the cost of living at different points on the income distribution. Across product categories, the poor have relatively high expenditure shares in tradeable products. Within tradeable product categories, the poor consume lower-priced varieties. Changes in the relative price of tradeables and the relative prices of lower-priced varieties following a devaluation will affect the cost of the consumption basket of the low-income households relative that of the high-income households. We quantify these effects following the 1994 Mexican peso devaluation and show that their distributional consequences can be large. In the two years that follow the devaluation, the cost of the consumption basket of those in the bottom decile of the income distribution rose between 1.46 and 1.6 times more than the cost of the consumption basket for the top income decile.
