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We shed light on a money-for-privacy trade-off in the market for smartphone applications (“apps”). Developers offer their apps at lower prices in return for greater access to personal information, and consumers choose between low prices and more privacy. We provide evidence for this pattern using data from 300,000 apps obtained from the Google Play Store (formerly Android Market) in 2012 and 2014. Our findings show that the market’s supply and demand sides both consider an app’s ability to collect private information, measured by the apps’s use of privacy-sensitive permissions: (1) cheaper apps use more privacy-sensitive permissions; (2) given price and functionality, demand is lower for apps with sensitive permissions; and (3) the strength of this relationship depends on contextual factors, such as the targeted user group, the app’s previous success, and its category. Our results are robust and consistent across several robustness checks, including the use of panel data, a difference-in-differences analysis, “twin” pairs of apps, and various measures of privacy-sensitivity and app demand. This paper was accepted by Anandhi Bharadwaj, information systems.

Are there positive or negative externalities in knowledge production? We analyze whether current contributions to knowledge production increase or decrease the future growth of knowledge. To assess this, we use a randomized field experiment that added content to some pages in Wikipedia while leaving similar pages unchanged. We compare subsequent content growth over the next 4 years between the treatment and control groups. Our estimates allow us to rule out effects on 4-year growth of content length larger than twelve percent. We can also rule out effects on 4-year growth of content quality larger than four points, which is less than one-fifth of the size of the treatment itself. The treatment increased editing activity in the first 2 years, but most of these edits only modified the text added by the treatment. Our results have implications for information seeding and incentivizing contributions. They imply that additional content may inspire future contributions in the short- and medium-term but do not generate large externalities in the long term.

Missing values are a major problem in all econometric applications based on survey data. A standard approach assumes data are missing at random and uses imputation methods or even listwise deletion. This approach is justified if item nonresponse does not depend on the potentially missing variables’ realization. However, assuming missingness at random may introduce bias if nonresponse is, in fact, selective. Relevant applications range from financial or strategic firm-level data to individual-level data on income or privacy-sensitive behaviors. In this paper, we propose a novel approach to deal with selective item nonresponse in the model’s dependent variable. Our approach is based on instrumental variables that affect selection only through a partially observed outcome variable. In addition, we allow for endogenous regressors. We establish identification of the structural parameter and propose a simple twostep estimation procedure for it. Our estimator is consistent and robust against biases that would prevail when assuming missingness at random. We implement the estimation procedure using firm-level survey data and a binary instrumental variable to estimate the effect of outsourcing on productivity.

\"A tax case is a de novo proceeding and the thoughts, procedures, conclusions, reasoning, or factual findings of Internal Revenue Service employees about a taxpayer's liability are irrelevant.\"Any attorney who has handled a tax case on a taxpayer's behalf has likely heard this argument, or a variation of it, during the discovery process, during motion practice, or even in the courtroom when attempting to introduce evidence. Many attorneys from the Internal Revenue Service (\"Service\") or Department of Justice who defend or bring tax cases on the government's behalf have likely asserted some version of the \"de novo doctrine.\" Indeed, the Service and Department of Justice commonly raise the de novo doctrine in an attempt to defeat taxpayers' requests to discover or introduce into evidence information about the Service's audit or facts and information gathered by the Service and contained in its audit file.2 The asserted ground for withholding such information from taxpayers is that none of it is relevant to the court's inquiry, which is to determine the merits of taxpayers' liabilities.

A large body of evidence from in vitro studies suggests that GABA is depolarizing during early postnatal development. However, the mode of GABA action in the intact developing brain is unknown. Here we examine the in vivo effects of GABA in cells of the upper cortical plate using a combination of electrophysiological and Ca 2+ -imaging techniques. We report that at postnatal days (P) 3–4, GABA depolarizes the majority of immature neurons in the occipital cortex of anaesthetized mice. At the same time, GABA does not efficiently activate voltage-gated Ca 2+ channels and fails to induce action potential firing. Blocking GABA A receptors disinhibits spontaneous network activity, whereas allosteric activation of GABA A receptors has the opposite effect. In summary, our data provide evidence that in vivo GABA acts as a depolarizing neurotransmitter imposing an inhibitory control on network activity in the neonatal (P3–4) neocortex. GABA depolarizes immature neurons in the central nervous system, yet the mode of GABA action in the developing brain is unknown. Here the authors demonstrate that in vivo GABA acts as a depolarizing neurotransmitter imposing inhibitory control on network activity in the mouse postnatal day 3–4 neocortex.

Neuronal network activity in the developing brain is generated in a discontinuous manner. In the visual cortex during the period of physiological blindness of immaturity, this activity mainly comprises retinally triggered spindle bursts or Ca2+ clusters thought to contribute to the activity-dependent construction of cortical circuits. In spite of potentially important developmental functions, the spatial structure of these activity patterns remains largely unclear. In order to address this issue, we here used three-dimensional two-photon Ca2+ imaging in the visual cortex of neonatal mice at postnatal days (P) 3–4 in vivo. Large-scale voxel imaging covering a cortical depth of 200μm revealed that Ca2+ clusters, identified as spindle bursts in simultaneous extracellular recordings, recruit cortical glutamatergic neurons of the upper cortical plate (CP) in a column-like manner. Specifically, the majority of Ca2+ clusters exhibit prominent horizontal confinement and high intra-cluster density of activation involving the entire depth of the upper CP. Moreover, using simultaneous Ca2+ imaging from hundreds of neurons at single-cellular resolution, we demonstrate that the degree of neuronal co-activation within Ca2+ clusters displays substantial heterogeneity. We further provide evidence that co-activated cells within Ca2+ clusters are spatially distributed in a non-stochastic manner. In summary, our data support the conclusion that dense coding in the form of column-like Ca2+ clusters is a characteristic property of network activity in the developing visual neocortex. Such knowledge is expected to be relevant for a refined understanding of how specific spatiotemporal characteristics of early network activity instruct the development of cortical circuits. •3D Ca2+ imaging was used to record activity from the neonatal mouse visual cortex.•3D Ca2+ clusters were revealed in the upper cortical plate.•Ca2+ clusters recruit glutamatergic cells in a column-like manner.•Ca2+ clusters and spindle bursts are homologous events.•The spatial distribution of co-activated cells is non-stochastic.

Neuronal network activity in the developing brain is generated in a discontinuous manner. In the visual cortex during the period of physiological blindness of immaturity, this activity mainly comprises retinally triggered spindle bursts or Ca(2+) clusters thought to contribute to the activity-dependent construction of cortical circuits. In spite of potentially important developmental functions, the spatial structure of these activity patterns remains largely unclear. In order to address this issue, we here used three-dimensional two-photon Ca(2+) imaging in the visual cortex of neonatal mice at postnatal days (P) 3-4 in vivo. Large-scale voxel imaging covering a cortical depth of 200μm revealed that Ca(2+) clusters, identified as spindle bursts in simultaneous extracellular recordings, recruit cortical glutamatergic neurons of the upper cortical plate (CP) in a column-like manner. Specifically, the majority of Ca(2+) clusters exhibit prominent horizontal confinement and high intra-cluster density of activation involving the entire depth of the upper CP. Moreover, using simultaneous Ca(2+) imaging from hundreds of neurons at single-cellular resolution, we demonstrate that the degree of neuronal co-activation within Ca(2+) clusters displays substantial heterogeneity. We further provide evidence that co-activated cells within Ca(2+) clusters are spatially distributed in a non-stochastic manner. In summary, our data support the conclusion that dense coding in the form of column-like Ca(2+) clusters is a characteristic property of network activity in the developing visual neocortex. Such knowledge is expected to be relevant for a refined understanding of how specific spatiotemporal characteristics of early network activity instruct the development of cortical circuits.

Using data on 4.1 million apps at the Google Play Store from 2016 to 2019, we document that GDPR induced the exit of about a third of available apps; and in the quarters following implementation, entry of new apps fell by half. We estimate a structural model of demand and entry in the app market. Comparing long-run equilibria with and without GDPR, we find that GDPR reduces consumer surplus and aggregate app usage by about a third. Whatever the privacy benefits of GDPR, they come at substantial costs in foregone innovation.