Manpower Constraints and Corporate Policies

Manpower constraints are the pervasive lack of specialized high- and low-skill workers, irrespective of the wage firms might offer

Unconventional Fiscal Policy, Inflation Expectations, and Consumption Expenditure

Unconventional fiscal policies incentivize households to accelerate consumption by generating future consumer price ination, and offer an alternative to unconventional monetary policy (Correia et al. (2013)). We use a natural experiment to study the causal effect of unconventional fiscal policies on consumption expenditure via the inflation-expectations channel. The German administration unexpectedly announced in November 2005 a three-percentage-point increase in value-added tax (VAT) effective in 2007. This shock increased German households’ inflation expectations during 2006, as well as actual inflation in 2007. Matched households in other European countries serve as counterfactuals in a difference-in-differences identification design. German households’ willingness to purchase durable goods increased by 34% after the shock, compared to matched foreign households. Income or wealth effects do not appear to drive these results, and we do not find evidence of intratemporal substitution from non-durable to durable consumption

Telecracy: Testing for Channels of Persuasion

We consider the long-lived slant towards Berlusconi in political information on Italian television (TV ). We exploit a shock to the slanted exposure of viewers: idiosyncratic deadlines to switch to digital TV from 2008 to 2012, which increased the number of freeview channels tenfold. The switch caused a drop in the vote share of Berlusconi’s coalition by between 5.5 and 7.5 percentage points. The effect was stronger in towns with older and less educated voters. At least 20 percent of digital users changed their voting behavior after the introduction of digital TV. Our evidence is consistent with the existence of persuasion- biased viewers

Historical antisemitism, ethnic specialization, and financial development

Historically, European Jews have specialized in financial services while being the victims of antisemitism. We find that the present-day demand for finance is lower in German counties where historical antisemitism was higher, compared to otherwise similar counties. Households in counties with high historical antisemitism have similar saving rates but invest less in stocks, hold lower saving deposits, and are less likely to get a mortgage to finance homeownership after controlling for wealth and a rich set of current and historical covariates. Present-day antisemitism and supply-side forces do not fully explain the results. Households in counties where historical antisemitism was higher distrust the financial sector more—a potential cultural externality of historical antisemitism that reduces wealth accumulation in the long run

Extracting the Multiscale Causal Backbone of Brain Dynamics

The bulk of the research effort on brain connectivity revolves around statistical associations among brain regions, which do not directly relate to the causal mechanisms governing brain dynamics. Here we propose the multiscale causal backbone (MCB) of brain dynamics, shared by a set of individuals across multiple temporal scales, and devise a principled methodology to extract it. Our approach leverages recent advances in multiscale causal structure learning and optimizes the trade-off between the model fit and its complexity. Empirical assessment on synthetic data shows the superiority of our methodology over a baseline based on canonical functional connectivity networks. When applied to resting-state fMRI data, we find sparse MCBs for both the left and right brain hemispheres. Thanks to its multiscale nature, our approach shows that at low-frequency bands, causal dynamics are driven by brain regions associated with high-level cognitive functions; at higher frequencies instead, nodes related to sensory processing play a crucial role. Finally, our analysis of individual multiscale causal structures confirms the existence of a causal fingerprint of brain connectivity, thus supporting the existing extensive research in brain connectivity fingerprinting from a causal perspective

The evolving causal structure of equity risk factors

In recent years, multi-factor strategies have gained increasing popularity in the financial industry, as they allow investors to have a better understanding of the risk drivers underlying their portfolios. Moreover, such strategies promise to promote diversification and thus limit losses in times of financial turmoil. However, recent studies have reported a significant level of redundancy between these factors, which might enhance risk contagion among multi-factor portfolios during financial crises. Therefore, it is of fundamental importance to better understand the relationships among factors. Empowered by recent advances in causal structure learning methods, this paper presents a study of the causal structure of financial risk factors and its evolution over time. In particular, the data we analyze covers 11 risk factors concerning the US equity market, spanning a period of 29 years at daily frequency. Our results show a statistically significant sparsifying trend of the underlying causal structure. However, this trend breaks down during periods of financial stress, in which we can observe a densification of the causal network driven by a growth of the out-degree of the market factor node. Finally, we present a comparison with the analysis of factors cross-correlations, which further confirms the importance of causal analysis for gaining deeper insights in the dynamics of the factor system, particularly during economic downturns. Our findings are especially significant from a risk-management perspective. They link the evolution of the causal structure of equity risk factors with market volatility and a worsening macroeconomic environment, and show that, in times of financial crisis, exposure to different factors boils down to exposure to the market risk factor

Learning Multiscale Non-stationary Causal Structures

This paper addresses a gap in the current state of the art by providing a solution for modeling causal relationships that evolve over time and occur at different time scales. Specifically, we introduce the multiscale non-stationary directed acyclic graph (MN-DAG), a framework for modeling multivariate time series data. Our contribution is twofold. Firstly, we expose a probabilistic generative model by leveraging results from spectral and causality theories. Our model allows sampling an MN-DAG according to user-specified priors on the time-dependence and multiscale properties of the causal graph. Secondly, we devise a Bayesian method named Multiscale Non-stationary Causal Structure Learner (MN-CASTLE) that uses stochastic variational inference to estimate MN-DAGs. The method also exploits information from the local partial correlation between time series over different time resolutions. The data generated from an MN-DAG reproduces well-known features of time series in different domains, such as volatility clustering and serial correlation. Additionally, we show the superior performance of MN-CASTLE on synthetic data with different multiscale and non-stationary properties compared to baseline models. Finally, we apply MN-CASTLE to identify the drivers of the natural gas prices in the US market. Causal relationships have strengthened during the COVID-19 outbreak and the Russian invasion of Ukraine, a fact that baseline methods fail to capture. MN-CASTLE identifies the causal impact of critical economic drivers on natural gas prices, such as seasonal factors, economic uncertainty, oil prices, and gas storage deviations

A chemical screen identifies the chemotherapeutic drug topotecan as a specific inhibitor of the B-MYB/MYCN axis in neuroblastoma

The transcription factor MycN is the prototypical neuroblastoma oncogene and a potential therapeutic target. However, its strong expression caused by gene amplification in about 30% of neuroblastoma patients is a considerable obstacle to the development of therapeutic approaches aiming at eliminating its tumourigenic activity. We have previously reported that B-Myb is essentially required for transcription of the MYCN amplicon and have also shown that B-MYB and MYCN are engaged in a feed forward loop promoting the survival/proliferation of neuroblastoma cells. We postulated that pharmacological strategies breaking the B-MYB/MYCN axis should result in clinically desirable effects. Thus, we implemented a high throughput chemical screen, using a curated library of ~1500 compounds from the National Cancer Institute, whose endpoint was the identification of small molecules that inhibited B-Myb. At the end of the screening, we found that the compounds pinafide, ellipticine and camptothecin inhibited B-Myb transcriptional activity in luciferase assays. One of the compounds, the topoisomerase-1 inhibitor camptothecin, is of considerable clinical interest since its derivatives topotecan and irinotecan are currently used as first and second line treatment agents for various types of cancer, including neuroblastoma. We found that neuroblastoma cells with amplification of MYCN are more sensitive than MYCN negative cells to camptothecin and topotecan killing. Campothecin and topotecan caused selective down-regulation of B-Myb and MycN expression in neuroblastoma cells. Notably, forced overexpression of B-Myb could antagonize the killing effect of topotecan and camptothecin, demonstrating that the transcription factor is a key target of the drugs. These results suggest that camptothecin and its analogues should be more effective in patients whose tumours feature amplification of MYCN and/or overexpression of B-MYB

Historical Antisemitism, Ethnic Specialization, and Financial Development

For centuries, Jews in Europe have specialized in financial services. At the same time, they have been the victims of historical antisemitism on the part of the Christian majority. We find that present-day financial development is lower in German counties where historical antisemitism was higher, compared to otherwise similar counties. Households in counties with high historical antisemitism have similar savings rates but invest less in stocks, hold lower bank deposits, and are less likely to get a mortgage-but not to own a house-after controlling for wealth and a rich set of current and historical covariates. Present-day antisemitism and supply-side forces do not appear to fully explain the results. Present-day households in counties where historical antisemitism was higher express lower trust in finance, but have levels of generalized trust similar to other households

Learning Multi-Frequency Partial Correlation Graphs

Despite the large research effort devoted to learning dependencies between time series, the state of the art still faces a major limitation: existing methods learn partial correlations but fail to discriminate across distinct frequency bands. Motivated by many applications in which this differentiation is pivotal, we overcome this limitation by learning a block-sparse, frequency-dependent, partial correlation graph, in which layers correspond to different frequency bands, and partial correlations can occur over just a few layers. To this aim, we formulate and solve two nonconvex learning problems: the first has a closed-form solution and is suitable when there is prior knowledge about the number of partial correlations; the second hinges on an iterative solution based on successive convex approximation, and is effective for the general case where no prior knowledge is available. Numerical results on synthetic data show that the proposed methods outperform the current state of the art. Finally, the analysis of financial time series confirms that partial correlations exist only within a few frequency bands, underscoring how our methods enable the gaining of valuable insights that would be undetected without discriminating along the frequency domain