The Efficiency and Evolution of R&D Networks

This work introduces a new model to investigate the efficiency and evolution of networks of firms exchanging knowledge in R&D partnerships. We first examine the efficiency of a given network structure in terms of the maximization of total profits in the industry. We show that the efficient network structure depends on the marginal cost of collaboration. When the marginal cost is low, the complete graph is efficient. However, a high marginal cost implies that the efficient network is sparser and has a core-periphery structure. Next, we examine the evolution of the network struc- ture when the decision on collaborating partners is decentralized. We show the existence of mul- tiple equilibrium structures which are in general inefficient. This is due to (i) the path dependent character of the partner selection process, (ii) the presence of knowledge externalities and (iii) the presence of severance costs involved in link deletion. Finally, we study the properties of the emerg- ing equilibrium networks and we show that they are coherent with the stylized facts of R&D net- works.R&D networks, technology spillovers, network efficiency, network formation

The impact of social pressure on tax compliance: A field experiment

We study the effect of social pressure on tax compliance, focusing on the compliance of shop sellers to the legal obligation of releasing tax receipts for each sale. We carry out a field experiment on bakeries in Italy, where a strong gap exists between the legal obligation and the actual behavior of sellers. Social pressure is manipulated by means of an explicit request for a receipt when not released. We employ an innovative approach to the identification of the treatment effect. We find that a single request for a receipt causes a 17 per cent rise in the probability of a receipt being released for a sale occurring shortly thereafter, causing on average more than two receipts to be released. We also find strong evidence of persistence in compliance decisions. (C) 2016 Elsevier Inc. All rights reserved

Financial fragility and distress propagation in a network of regions

We investigate how the financial fragility in the real economy is affected by the average level of interdependence among agents across different regions of the economy. To this end, we develop a parsimonious agent-based model of firms and banks organized in geographic regions. The model is built on the framework of an existing class of models for business fluctuations. The goal of our exercise is to clarify the effect on systemic failures of the interplay between network interconnectedness and financial acceleration. In particular, we investigate the probability of individual and systemic failures with varying levels of interconnectedness. We find that, in the absence of financial acceleration, connectivity makes the system more resilient. In contrast, in the presence of financial acceleration, the probability of both individual and systemic failures are minimized at intermediate level of diversification

The Network of Global Corporate Control

The structure of the control network of transnational corporations affects global market competition and financial stability. So far, only small national samples were studied and there was no appropriate methodology to assess control globally. We present the first investigation of the architecture of the international ownership network, along with the computation of the control held by each global player. We find that transnational corporations form a giant bow-tie structure and that a large portion of control flows to a small tightly-knit core of financial institutions. This core can be seen as an economic \u201csuper-entity\u201d that raises new important issues both for researchers and policy makers

Credit Default Swaps Drawup Networks: Too Tied To Be Stable?

We analyse time series of CDS spreads for a set of major US and European institutions on a pe- riod overlapping the recent financial crisis. We extend the existing methodology of {\epsilon}-drawdowns to the one of joint {\epsilon}-drawups, in order to estimate the conditional probabilities of abrupt co-movements among spreads. We correct for randomness and for finite size effects and we find significant prob- ability of joint drawups for certain pairs of CDS. We also find significant probability of trend rein- forcement, i.e. drawups in a given CDS followed by drawups in the same CDS. Finally, we take the matrix of probability of joint drawups as an estimate of the network of financial dependencies among institutions. We then carry out a network analysis that provides insights into the role of systemically important financial institutions.Comment: 15 pages, 5 figures, Supplementary informatio

Systemic risk in a unifying framework for cascading processes on networks

We introduce a general framework for models of cascade and contagion processes on networks, to identify their commonalities and differences. In particular, models of social and financial cascades, as well as the fiber bundle model, the voter model, and models of epidemic spreading are recovered as special cases. To unify their description, we define the net fragility of a node, which is the difference between its fragility and the threshold that determines its failure. Nodes fail if their net fragility grows above zero and their failure increases the fragility of neighbouring nodes, thus possibly triggering a cascade. In this framework, we identify three classes depending on the way the fragility of a node is increased by the failure of a neighbour. At the microscopic level, we illustrate with specific examples how the failure spreading pattern varies with the node triggering the cascade, depending on its position in the network and its degree. At the macroscopic level, systemic risk is measured as the final fraction of failed nodes, X*, and for each of the three classes we derive a recursive equation to compute its value. The phase diagram of X* as a function of the initial conditions, thus allows for a prediction of the systemic risk as well as a comparison of the three different model classes. We could identify which model class leads to a first-order phase transition in systemic risk, i.e. situations where small changes in the initial conditions determine a global failure. Eventually, we generalize our framework to encompass stochastic contagion models. This indicates the potential for further generalization

Sustainable investing and climate transition risk: A portfolio rebalancing approach

The authors studied how greenness can be combined with other investment criteria to construct sets of corporate bond portfolios with decreasing exposure to climate transition risk. They apply the methodology to the European Central Bank’s asset purchase program. They define a weaker market neutrality principle as investing proportionally to the bond amount outstanding within Climate Policy Relevant Sectors. The portfolio rebalancing leads to a 10% reduction of exposure to climate transition risk. Then, the authors studied the relationship between bonds’ rebalancing and issuers’ environmental, social, and governance (ESG) characteristics and greenhouse gas (GHG) emissions. Bonds issued by firms with low (high) ESG risk and GHG emissions are more likely to be bought (sold) in the rebalancing. Finally, they analyzed the implications of portfolio rebalancing on financial markets, finding that changes in yields would be limited to less than 80 basis points on individual bonds. The approach can contribute to inform climate-aware portfolio rebalancing and sustainable investment strategies

Credit default swaps networks and systemic risk

Credit Default Swaps (CDS) spreads should reflect default risk of the underlying corporate debt. Actually, it has been recognized that CDS spread time series did not anticipate but only followed the increasing risk of default before the financial crisis. In principle, the network of correlations among CDS spread time series could at least display some form of structural change to be used as an early warning of systemic risk. Here we study a set of 176 CDS time series of financial institutions from 2002 to 2011. Networks are constructed in various ways, some of which display structural change at the onset of the credit crisis of 2008, but never before. By taking these networks as a proxy of interdependencies among financial institutions, we run stress-test based on Group DebtRank. Systemic risk before 2008 increases only when incorporating a macroeconomic indicator reflecting the potential losses of financial assets associated with house prices in the US. This approach indicates a promising way to detect systemic instabilities

Backbone of complex networks of corporations: The flow of control

We present a methodology to extract the backbone of complex networks based on the weight and direction of links, as well as on nontopological properties of nodes. We show how the methodology can be applied in general to networks in which mass or energy is flowing along the links. In particular, the procedure enables us to address important questions in economics, namely, how control and wealth are structured and concentrated across national markets. We report on the first cross-country investigation of ownership networks, focusing on the stock markets of 48 countries around the world. On the one hand, our analysis confirms results expected on the basis of the literature on corporate control, namely, that in Anglo-Saxon countries control tends to be dispersed among numerous shareholders. On the other hand, it also reveals that in the same countries, control is found to be highly concentrated at the global level, namely, lying in the hands of very few important shareholders. Interestingly, the exact opposite is observed for European countries. These results have previously not been reported as they are not observable without the kind of network analysis developed here.Comment: 24 pages, 12 figures, 2nd version (text made more concise and readable, results unchanged

Complex Derivatives

The intrinsic complexity of the financial derivatives market has emerged as both an incentive to engage in it, and a key source of its inherent instability. Regulators now faced with the challenge of taming this beast may find inspiration in the budding science of complex systems. When financial derivatives were cast in 2002 as latent 'weapons of mass destruction', one might have expected the world at large to sit up and listen — particularly in the wake of subsequent events that led to the financial crisis of 2008. Instead, the derivatives market continues to grow in size and complexity (Fig. 1), spawning a new generation of financial innovations, and raising concerns about its potential impact on the economy as a whole. A derivative instrument is a financial contract between two parties, in which the value of the payoff is derived from the value of another financial instrument or asset, called the underlying entity. In some cases, this contract acts as a kind of insurance: in a credit default swap, for example, a lender might buy protection from a third party to insure against the default of the borrower. However, unlike conventional insurance, in which a person necessarily owns the house she wants to insure, derivatives can be negotiated on any underlying entity — meaning anyone could take out insurance on the house in question. Speculation therefore emerges as another reason to trade in derivatives. By engaging in a speculative derivatives market, players can potentially amplify their gains, which is arguably the most plausible explanation for the proliferation of derivatives in recent years. Needless to say, losses are also amplified. Unlike bets on, say, dice — where the chances of the outcome are not affected by the bet itself — the more market players bet on the default of a country, the more likely the default becomes. Eventually the game becomes a self-fulfilling prophecy, as in a bank run, where if each party believes that others will withdraw their money from the bank, it pays each to do so. More perversely, in some cases parties have incentives (and opportunities) to precipitate these events, by spreading rumours or by manipulating the prices on which the derivatives are contingent — a situation seen most recently in the London Interbank Offered Rate (LIBOR) affair. Proponents of derivatives have long argued that these instruments help to stabilize markets by distributing risk, but it has been shown recently that in many situations risk sharing can also lead to instabilities