Archivio istituzionale della Ricerca - Bocconi
Not a member yet
30225 research outputs found
Sort by
The hidden battle for IP protection in alliances
No full textTo prevail in the complex battle for intellectual property protection in alliances, managers must recognize the intrusive practices of predatory partners and implement effective protective practices that their partners cannot easily outsmart. Our research and interviews with executives offer insights into six protective practices that counter the intrusive practices off alliance partners. We identify a paradox whereby a company that may rely on protection for its proprietary knowledge may in fact train its partner how to better protect their own knowledge in subsequent alliances
Social contact patterns in rural and urban settings, Mozambique, 2021-2022
Get PDFFew sources have reported empirical social contact data from resource-poor settings. To address this shortfall, we recruited 1,363 participants from rural and urban areas of Mozambique during the COVID-19 pandemic, determining age, sex, and relation to the contact for each person. Participants reported a mean of 8.3 (95% CI 8.0-8.6) contacts per person. The mean contact rates were higher in the rural site compared with the urban site (9.8 vs 6.8; p<0.01). Using mathematical models, we noted higher vaccine effects in the rural site when comparing empirical (32%) with synthetic (29%) contact matrices and lower corresponding vaccine effects in the urban site (32% vs 35%). Those effects were prominent in younger (0-9 years) and older (≥60 years) persons. Our work highlights the importance of empirical data, showing differences in contact rates and patterns between rural and urban sites in Mozambique and their nonnegligible effects in modeling potential effects of vaccine interventions
Family firms in entrepreneurial finance: the case of corporate venture capital
Get PDFWe show that families are an engine of venturing activities: almost 30 percent of corporate venture capital (CVC) deals in the US from 2000 to 2017 originated from family firms. Family firms, primarily those led by family CEOs, orchestrate CVC activities differently than non-family firms: they syndicate more often and with more reputable investors, join larger syndicates, and make more proximate deals (geography- and industry-wise). This approach to corporate venturing maps into performance results: family CVC-backed ventures exhibit a higher likelihood of successful exit. Collectively, our results shed light on the important, and largely unexplored, role of family firms in CVC
Il diritto di voto preso sul serio. La partecipazione dei cittadini residenti all'estero alle elezioni politiche
No full textKey audit matters as insights into auditors’ professional judgement: evidence from the European Union
No full textThe European Union (EU) Regulation 537/2014 introduced the mandatory disclosure of Key
Audit Matters (KAMs) within an auditor’s report, with the aim of increasing the informational
value of these reports. Extant research, however, shows contrasting results as to whether KAM
disclosure is providing relevant information to stakeholders. Moreover, concerns have been raised
about unintended consequences from KAM disclosure, with respect to the process that leads to the
issuance of the audit report. Using a sample of 6,164 firm-year observations for the period
2017–2021, related to 1,660 unique firms listed in all EU Member States, we find that the number
of KAMs is positively associated with audit fees, audit report lags and the probability that an
opinion different from the standard unqualified opinion is issued. Moreover, we document that
both KAMs related to entity-level and account-level risks are positively associated with audit fees,
whereas only entity-level KAMs drive the positive association with audit report lags and the
issuance of a modified opinion. Our research speaks directly to EU legislators, the International
Auditing and Assurance Standards Board, the US Public Company Accounting Oversight Board,
and any other regulators around the globe that have mandated the disclosure of KAMs within
audit reports
Toward multiscalar measures of inequality in archaeology
Get PDFThe Gini coefficient is a statistical measure commonly used to characterize distributions of socioeconomic quantities. Archaeologists and social scientists have recently adopted this method to analyze ancient inequality by targeting specific proxy variables (e.g., residential unit size, burial data, etc.). Variations in the Gini are then examined in relation to key factors such as time, geography, and subsistence. Yet, Gini coefficients could be obtained across different scales of aggregation, from small neighborhoods within a larger settlement to an ensemble of multiple settlements that are part of the same polity. These different scales of aggregation represent considerable methodological and theoretical challenges, as larger scales might, for example, imply greater social and economic variation within groups and thus affect the Gini coefficients. Furthermore, these issues can also be exacerbated by the idiosyncrasies and limitations of historical and archaeological datasets. This paper discusses the potential and challenges of measuring Gini coefficients at and above the scale of individual archaeological sites, contrasting different approaches and discussing how each can reveal insights into different patterns of past wealth inequality, addressing methodological, empirical, and theoretical implications arising from the multiscalar nature of human interactions
Essays on Heterogeneity in Macroeconomics and Labor Markets
Get PDFThis thesis is composed of three independent chapters.
The first project studies how wealth can determine employment risk over the business cycle through labor market sorting. I find that workers with low liquid wealth face higher employment risk and propose a theory of wealth-sorting into risky jobs to rationalize it. I then integrate wealth-sorting in a quantitative model with aggregate risk to study its implications for wages and job transitions over the business cycle, as well as its consequence for long-term inequality. Key findings are that recessions depresses labor market outcomes relatively more for the poor; and that the interaction between employment risk and wealth accumulation generates a “poverty trap”.
The second project empirically uncovers a novel transmission mechanism of monetary policy to individual labor income, working through the employers. Using matched employer- employee data from Germany, we show that firms differ in the degree of income insurance they provide to their workers. Our main finding is that wages of workers in large firms are relatively more sensitive to monetary policy shocks. This is particularly true for above- median workers and cannot be fully explained by a worker component or sorting. Moreover, monetary policy shocks exacerbate labor income inequality. The effect is relatively stronger in easings, driven by a large increase in wages for top earners.
The third project studies how monetary policy shapes the aggregate and distributional effects of an imported energy price shock. Based on the observed heterogeneity in consumption exposures to energy and household wealth, we build a quantitative small open-economy HANK model that matches salient features of the Euro Area data. We find that energy price shocks always reduce aggregate consumption, and households with little wealth are more adversely affected. Policy responses inducing strong increases in interest rates in response to inflation amplify the negative aggregate outcomes in the short-term, but lead to a faster recovery. However, low-wealth households are further adversely affected
Search for a new home: refugee stock and Google search
Get PDFFollowing the assumption that trends of online queries may indicate intentions and help to predict human behavior, this study addresses the general issue of analyzing, nowcasting, and predicting migrant decisions through an analysis of Google search patterns in the case of Syrians in Turkey. Aiming to contribute to the literature on predicting migration patterns, we examine the relationship between Google search queries for province names in Turkey and the number of Syrians under temporary protection across provinces from January 2016 to December 2019 and demonstrate a positive and significant association. Then, we explore the predictive power of Google searches in predicting the stock of Syrians under temporary protection in Turkey across provinces. We exploit the alphabetical difference between Turkish and Arabic as the method of differentiation between host and migrant populations. Our findings indicate that Google searches can be good predictors for estimating refugee stocks, especially when traditional data are not available. They can also be helpful in forecasting the changing pattern of migrant stocks at frequent intervals, to which conventional socioeconomic indicators are less sensitive due to their less frequent reporting periods
Advances in Bayesian cross-validation and tensor modelling
Get PDFThe first chapter presents a work I undertook with my supervisor Giacomo Zanella. We propose a novel estimator for the log pointwise predictive density(lppd) of a Bayesian model. The naive method to calculate this quantity would require running n markov chain Monte Carlo (MCMC) chains, resulting in an unfeasible computational cost. A classical approach to overcome such a problem is to leverage importance sampling, which although solves the computational hurdle results in a estimator that can be potentially very unstable. We propose to generate the samples from a particular mixture of the leave-one-out posteriors within the typical importance sampling framework. We provide theoretical proofs of the stability of our estimator for a broad family of models also in the challenging asymptotic regime of infinite dimensionality of the data, as well as both synthetic and real data applications displaying the superior performance of our estimator compared to competitors in the literature.
The second chapter presents a project I undertook under the supervision of Giacomo Zanella and Omiros Papaspiliopoulos. In this work, we focus on studying how the local convergence rate of alternating least squares(ALS) algorithms behaves as we increase the size of the problem in the context of matrix completion. We first show that, under full design, classical ALS is non-scalable. Studying the geometric properties of the optimization landscape, we propose a modification to the classical ALS algorithm, which we term ALS with Gram Calibration, and we show that such an algorithm is scalable under full design. We then provide empirical evidence that such a behavior is maintained in various sparsity scenarios.
The third and fourth chapters present the works I conducted during the visiting student period at Duke University under the supervision of David Dunson and Peter Hoff, respectively. Both works concentrate on Bayesian formulations of the Candecomp/Parafac (CP) decomposition. In the third chapter, which focuses on modelling dynamically evolving binary networks, we leverage the CP decomposition as a building block to propose a novel non-parametric tensor decomposition. We prove that such a decomposition is flexible enough to represent any underlying tensor, and we also show that our prior has full support. We then provide empirical evidence of our model capabilities, both for a synthetic design and a real dataset from ecology. In the fourth chapter, we develop a Bayesian hierarchical CP model with multiplicative error and apply it to a dataset of excitation-emission matrices (EEMs) from different sources of the Neuse River. Compared to classical optimization-only procedures, our proposed model allows our proposed model allows the borrowing of information across sources and the incorporation of available knowledge through the prior. Moreover, the multiplicative error term explicitly models the positivity of the data. We show some very promising initial results, with future research looking to extend those and leverage the generative capabilities of our model in other tasks.
The fifth chapter presents a I carried out with Christoph Feinauer, Barthelemy Meynard-Piganeau and Carlo Lucibello. It focuses on protein inverse folding, in which the task is to generate a sequence of amino acids that will fold into a desired three-dimensional functioning protein. Such a problem is highly complex, also because such a mapping is notoriously many-to-one, meaning that many sequences fold into the same three-dimensional structure in nature. Typical deep-learning approaches, though, focus solely on mapping the native sequence to the structure, failing to model this diversity. We hence propose a novel deep learning architecture, which we term InvMSAFold , that explicitly models this diversity. We show the benefits of this modelling choice with various experiments, demonstrating how our work could be helpful in many protein-engineering pipelines
Circular economy, corporate sustainability reporting and equity risk in European markets: first findings
No full textAlongside the surge in corporate non-financial disclosure (NFD), investors and regulators are acknowledging the growing relevance of the circular economy (CE) in sustainability, leading to a rise in the market for circular assets. This study investigates the relationship between a company’s degree of circularity, its NFD, and market-based equity risk. Using a sample of 644 enti-ties listed in EU-15 markets plus Switzerland, we find that circularity, when isolated from its NFD component, is negatively associated with both total and systematic equity risk, thereby confirming that the CE acts as a de-risking factor independently of NFD. However, additional analyses on the role of NFD on CE show that it is not a standalone means whereby equity risk may be mitigated, but a signalling tool instead: to achieve de-risking, NFD about CE is necessary but, in isolation, not sufficient. These findings suggest that managers should engage more in circular business models, investors should increase circular assets in their portfolios, and policymakers should focus on increasing the NFD’s scope and content as an effective means for steering capital toward circular assets