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Home > Research proposals - 38th cycle

Research proposals - 38th cycle

Within the PhD Programme in Economics and Management, particular emphasis will be given to research proposals on the following subjects:

Behavioral approach to individual managerial attitudes towards exploration and exploitation

Proponent: Luigi Mittone The importance of the impact that managerial decisions have on organizations is well known. Recently a new stream of literature inspired by Behavioral Economics and based on the use of laboratory as well as field experiments has emerged and is becoming highly competitive within the organization science debate. Among the topics that better fit this approach the one regarding exploration and exploitation is among the most promising. At the Cognitive and Experimental Economics Laboratory (CEEL) of the University of Trento a research team has started two years ago to investigate on this topic and offers excellent conditions to join this line of research to young scientists interested in this area and methodologies.

Esg scores: methods and applications

Proponents: Eleonora Broccardo, Ericka Costa and Sandra Paterlini Environmental, social, and governance (ESG) scores measure companies’ activities concerning sustainability and are organized on three pillars: Environmental (E-), Social (S-), and Governance (G-). Different approaches have been proposed to compute ESG scores for companies, which rely on the aggregation of many sources of information. So far, most approaches and ESG vendors focus on large companies, while research is much needed for small and medium enterprises. Based on previous literature, it is evident that three major issues are still to investigate: It is urgent to focus on using state-of-art statistical and machine learning methods to define new ESG scores also for small-medium companies in order to better capture the risk and performance profile and characterize companies’ sustainability impact on different dimensions. It is important to complement the quantitative analysis of the project with a more qualitative perspective that can measure the SMEs’ sustainability impact on different dimensions, through qualitative methods. Finally, both the qualitative and the quantitative approaches should be integrated to evaluate how ESG sustainability measurement could affect the credit access of the organizations. The PhD project could focus on these dimensions to better advance the knowledge regarding the applicability to ESG to SMEs and their impact to credit access.

Financial Networks: estimation and reconstruction approaches to prevent systemic crises

Proponents: Lucio Gobbi, Sandra Paterlini, Roberto Tamborini After the recent crises, there is an increasing awareness of practitioners, regulators and academics that the "too big to fail" and the level of interconnectedness of financial institutions can pose dramatic threats to the entire economic system, with potential dramatic consequences on society. Therefore, numerous studies now focus on trying to capture the complexity of the system by using network analysis approaches, which allow to model multi-lateral relationships between institutions with different degrees of intensity, providing often a quite realistic, but sometimes hard to interpret, picture of the real-world status quo. Network analysis provides essential tools to analyse risk in financial institutions, both to the individual institution and to the system as a whole. By observing the degrees of interconnectedness, contagion and spillover effects of banks, firms and the entire economic systems and by looking at a financial network structure in its complexity, policy makers can see the critical points during stress periods and set-up adequate and viable solutions, both in preparing for or in repairing the effects of a crisis. Still, access to data on the full status quo network is often limited, despite marginal distribution are increasingly available. Network reconstruction tools can then allow not only to estimate networks but more importantly help to detect factors that drive crises, their implications and ideally develop early warning measures to avoid potential catastrophic effects. Further, developing these factors should lead to a better understanding of behavioral models, which drive network connections, and also lead to counter factual analysis, to guide effective policy. In this project, starting from the data and from a simple economic model based on fixed cost, we aim to analyze how the actual network evolves in time with respect to alternative optimal network configurations and which are the socially optimal network structures. These optimal networks will be derived using different indicators, such as loan diversification, riskiness, interconnectedness, and liquidity and information contagion. Moreover, by studying the evolution in time of the network architecture with respect to optimal reference models, we plan to build early-warning indicators that forecast future crises and avoid critical situations for the system as a whole. To detect robust signals we will combine methods from network analysis with regularization approaches to handle noisy and high dimensional data. These empirical approaches are designed to compute and detect relevant interactions with respect to very large and heterogeneous data sets, involving a large number of unknown parameters. Our research aims to have an impact on helping regulators and politicians setting up new rules in order to better control the interconnectedness of the financial system. Ideally the research promotes an understanding of the type of connectivity that is useful for risk diversification and sharing. At the same time the researcher should advocate the level of complexity that should instead be avoided to prevent future financial crises and to minimize their costs on society. Moreover, the analysis of different network configurations, determined as optimal structures, by considering different economic and financial aspects, such as diversification or tail-risk, should allow shedding lights on the interaction of such crucial factors. This in turn determines factors which play a key role in increasing systemic risk and should therefore be better monitored (or even regulated) to prevent future crises and avoid their burden on society. We believe that our research will be of interests also for investors and financial institutions by pointing out the systems’ weaknesses and hopefully helping to strengthen the best practices in risk management, as well as to provide new tools to capture connectivity and dependence within, and between institutions.

Graphical model estimation for economic and financial analysis

Proponents: Emanuele Taufer e Sandra Paterlini Globalization and recent crises, from the financial to the pandemic one, have shown the importance of using modelling tools capable of considering firms that are not only the “too big to fail” but also the “too interconnected to fail”. Moreover, dependence modelling allows to model the propagation of shocks through the network and understand better the potential spillover effects to the entire economic and financial systems. Therefore, graphical models have found recently large applications in economics and finance, as they allow to model dependence among variables, such as for example firms or banks, and provide an estimate of the relationships among them. In fact, graphical models are useful for representing a set of random variables and their conditional dependence structure. A graphical model is made by two elements: a graph G(N, E) and a joint distribution f. The set of nodes N = {1, 2, ..., p} of the graph represents random variables over which the joint distribution is defined. Instead, the edge set E represents the pairs of variables which are conditionally dependent given the remaining variables. Two main research streams on using graphical models for financial and economic data are the need to move away from a joint Gaussian distribution as well the need to estimate networks with a large set of nodes (i.e. firms, banks, etc). First, we propose to extend the framework both by extending the target set of distributions, considering for example t-Student or Normal Inverse Gaussian distributions and also by introducing approximations, such as the Gram-Charlier one. Second, there is a need to extend model selection and estimation methods based on penalization, such as LASSO or elastic-net, to deal with the large dimension of the problems at hand and the specific characteristics of economic and financial applications. Applications to real-world data will include the estimation of interbank networks, supply-chain networks as well as other relevant economic and financial networks. References Bernardini, D. Paterlini, S. and Taufer, E. (2021) https://arxiv.org/pdf/2102.01053.pdf Lauritzen, S. (1996) Graphical Models. Oxford University Press. Meinshausen, N. and Buehlmann, P. (2006) High-dimensional graphs and variable selection with the lasso. The Annals of Statistics, 34(3):1436–1462

Machine learning for high frequency data analysis

Proponents: Flavio Bazzana and Sandra Paterlini High frequency data (HFD) contains a lot of information about the market microstructure, being a valuable source of information for financial research. Due to the large amount of data to be processed as well as some characteristics (i.e. zero inflation) standard statistical tools often fail to deal with such type of data. However, hardware development joint to the establishment of machine learning (ML) methods have nowadays opened new frontiers for research. In fact, booth supervised and supervised methods from the ML field have started to find an increasing amount of applications in HFD analysis. Some examples are the development of reinforcement learning methods for high frequency trading, modeling order book dynamics, price prediction and risk controls with high frequency trading and the use of ML tools for optimal market making. Relying on a comprehensive HF database and research supervision, the candidate will have the possibility to explore and propose innovative solutions related to the use of machine learning methods for HFD analysis. In particular, at the Department of Economics and Management at UNITN, we have the availability of a large database of HFD for different markets as well preferred access and queuing to the university cluster computer. Some References https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3744765 https://arxiv.org/abs/2101.07107 https://arxiv.org/abs/2007.07319 https://www.cis.upenn.edu/~mkearns/papers/KearnsNevmyvakaHFTRiskBooks.pdf https://arxiv.org/abs/1912.10343 https://www.tandfonline.com/doi/abs/10.1080/14697688.2022.2028888 https://www.sciencedirect.com/science/article/pii/S1062940820301376

Models of Manager and Firm Performance

Supervision by: Enrico Zaninotto, Maria Laura Frigotto Building on the literature on human capital (Becker, 1964), this project focuses on the models of manager intended as the managers’ different patterns of development of managerial competencies, in terms of education, training and mobility across different countries (e.g.; DeFillippi & Arthur, 1996; Hamori & Koyuncu, 2011; Salvato, Minichilli, & Piccarreta, 2012; Schmid & Mitterreiter, 2021). This project mainly aims to understand how models of manager are associated with various productivity outcomes as it has been shown for management practices on firm productivity (Bloom & van Reenen 2007, 2010). The aims of this project are: 1. to systematize the evidence on different models of management, highlighting the role of institutions and corporate governance on the selection of managers; 2. to relate models of managers with management practices; 3. to relate models of managers with organizational performance outcomes: productivity, profitability, innovation, and corporate social responsibility. In this regard, special attention will be paid on disentangling pure shareholder view, enlightened shareholder view and stakeholder view (Jensen, 2001; Bebchuk & Tallarita, 2020). A further area of inquiry concerns women (e.g. Bonet, Cappelli, & Hamori, 2020), whether they belong to a different model of manager, and what specificity they display in relation to the variables above. References Bebchuk, L. A., & Tallarita, R. (2020). The illusory promise of stakeholder governance. Cornell L. Rev., 106, 91. Bonet, R., Cappelli, P., & Hamori, M. (2020). Gender differences in speed of advancement: an empirical examination of top executives in the fortune 100 firms. Strategic Management Journal, 41(4), 708-737. DeFillippi, R. J., & Arthur, M. B. (1994). The boundaryless career: A competency-based perspective. Journal of Organizational Behavior, 15, 307-324. Hamori, M., & Koyuncu, B. (2011). Career advancement in large organizations in Europe and the United States: Do international assignments add value? International Journal of Human Resource Management, 22(4), 843-862. Salvato, C., Minichilli, A., & Piccarreta, R. (2012). Faster route to the CEO suite: Nepotism or managerial proficiency? Family Business Review, 25(2), 206-224. Schmid, S., & Mitterreiter, S. (2021). Top managers' career variety and time to the top. European Management Review. 18( 4), 476– 499.

Performance management system and social impact measurement in hybrid organizations

Proponent: Ericka Costa The nonprofit sector has grown rapidly in many developed countries over the last decade, playing a central role in providing public services (Grossi et al., 2017, Haigh et al., 2015). Within this sector, there has been emerging growth in so-called “hybrid organisations”, organisations seeking to achieve social missions through the use of market mechanisms (Ebrahim et al., 2014; Mair and Marti, 2006; Costa and Pesci, 2016). In essence, they are able to combine an orientation towards social values and a desire to gain profits (as a means to an end; Dees, 1998). Despite growth in the number of hybrid organisations and their increasing academic recognition, there remains much to be understood in terms of their institutional mechanisms, governance rules and accountability functioning (Ebrahim et al., 2014; Grossi et al., 2017; Costa and Pesci, 2016; Costa et al., 2014). Indeed, traditional accounting theories focus on the investor-owned enterprise perspective according to which organisations pursue the production of goods and services in order to maximise economic value for shareholders (Palmer and Vinten, 1998); such a perspective encounters limitations when dealing with hybrid organisations (Hofmann and McSwain, 2013). Two primary shortcomings exist: first, the bottom line for hybrid organisations is not based on the maximisation of shareholders’ economic value for shareholders; rather, it is more broad and complex because it encompasses the creation of “social value” for the community as a whole; second, hybrid organisations are characterised by a different stakeholder profile because their governance structure includes multiple stakeholders, and their activities benefit a broad range of stakeholders. It thus becomes difficult to obtain a meaningful picture of social enterprise performance compared to pure public or private entities (Grossi et al., 2017). Focusing only on economic and financial indicators fails to offer a comprehensive evaluation of nonprofit organisational performance. For-profit organisations summarise their economic and financial performances in financial statements because shareholders consider profit to be the company’s mission; in contrast, hybrid organisations recognise no direct correlation between increments of achievement in the organisation’s mission and its financial performance (Moore, 2000). To reflect the dual nature of hybrid organisations with both financial and social value, these organisations have begun to experiment with certain accounting practices measuring not only economic performance but also social results (Bagnoli and Megali, 2011; Manetti, 2014; Nicholls, 2009). Difficulties aligning these measurement systems sometimes arise because these two types of value creation are intrinsically connected rather than in direct opposition in a zero sum equation (Emerson, 2003). This difference has created a need for a more complex, multi-directional and multi-stakeholder performance measurement system (PMS, hereafter) (Grossi et al., 2017; Christensen and Ebrahim, 2006; Najam, 1996). In defining the hybrid organisations’ PMS, managers have no common or standardised approach because they conduct multiple and diverse activities for which there are few common benchmarks or standards. What should a PMS for a social enterprise look like? As Costa and Pesci (2016) outline, a universal reply to this question does not exist. Social enterprises differ in size, degree of formality, form, sector, geographic scope, rationale for operation, stakeholders and other circumstances; therefore, it is difficult to normatively support a standardised and universal metric for measuring social enterprise performance (see also Palmer and Vinten, 1998). Recently the debate on PMS and social enterprise accountability has been propelled by the “theory-driven evaluation” method (Rogers, 2007), according to which organisations observe how different programmes and initiatives cause intended or observed outcomes and impacts (Ebrahim and Rangan, 2010; Epstein and McFarlan, 2011; Ebrahim et al., 2014). This method follows the impact value chain (Clark et al., 2004), which is a “logic chain of results” in which organisational inputs (e.g., money, staff time, capital assets) are used to support activities and services (e.g., health services, schooling, job training, etc.). These activities ultimately result in the delivery of outputs to a target beneficiary population (i.e., results that a social enterprise and a nonprofit organisation can measure or assess directly). The identified output can lead to different effects and changes in beneficiaries’ attitudes, behaviours, knowledge, skills, and/or status, i.e., the outcome of the social enterprise’s activity. Short-term benefits and changes then can foster a societal impact on the broader society in the long term (Ebrahim and Rangan, 2010; Epstein and McFarlan, 2011; Clark et al., 2004; Ebrahim et al., 2014; Costa and Pesci, 2016). Many scholars have contributed to the theoretical domain of the impact value chain by highlighting that within hybrid organizations’ complex PMS, no universal measures can be defined without engaging different stakeholders(Costa and Pesci, 2016; Ebrahim and Rangan, 2014; Ebrahim et al., 2014; Mair and Marti, 2006). However, there is a need for more empirical exploration in this direction (Grossi et al., 2017; Haigh et al., 2015; Ebrahim et al., 2014; Mair and Marti, 2006) in order to increase our knowledge regarding accountability and PMS for hybrid and nonprofit organisations (Gray et al., 2001). In more detail, what many authors suggest is that it is now urgent to investigate the “participatory dimension” of PMS and social impact measurement in hybrid organizations (Costa et al., 2018; Arvidson et al., 2013; Gibbon and Dey, 2011) which allows stakeholders to be engaged in the designing of the social impact metrics. In this regard, it seems that the assessment of a key stakeholder role for designing PMS and social impact measures may become a critical element, rather than a starting point for the development of a better impact measurement.

Productivity dynamics and firm’s performance: the role of technological change and international activity

Abstract: he project aims at studying empirically the performance and the productivity dynamics of firms and their determinants. In particular, the focus will be to analyse the role of international activity, innovation, digital technologies, cooperation behaviour, and managerial capabilities in shaping performance. Furthermore, the empirical evidence suggests that diversity related to the sector of activity, age, and the firm’s size are critical factors in understanding firms' different performances. In this respect, the investigation should deepen the knowledge of heterogeneity factors to link each determinant to performance and clarify their joint role. Finally, the study should put forward proper theoretical explanations. The empirical investigation will be based on a longitudinal micro-data and use counterfactual econometric models to disentangle the causal effects from confounding factors. References: Gkypali A., Love J. H., Roper S. (2021). “Export status and SME productivity: Learning-to-export versus learning-to-exporting”, Journal of Business Research 128 486 – 498. Golovko E., Valentini G. (July 2014). “Selective Learning-by-Exporting: Firm Size and Product Versus Process Innovation”, Global Strategy Journal. İpek, İ. (2019). Organizational learning in exporting: A bibliometric analysis and critical review of the empirical research. International Business Review, 28(3), 544–559. Manez, J. A., Rochina‐Barrachina, M. E., & Sanchis‐Llopis, J. A. (2015). The dynamic linkages among exports, R&D and productivity. The World Economy, 38(4), 583–612. https://doi.org/10.1111/twec.12160 Van Beers, C., & Zand, F. (2014). R&D cooperation, partner diversity, and innovation performance: An empirical analysis. The Journal of Product Innovation Management, 31(2), 292–312. Proponent: Roberto Gabriele, EMIL

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