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One of the main lessons of the recent financial crisis is that the network structure of the banking system has to be taken into account to assess systemic risk.

In this chapter, we analyze the topological properties of the network of the Euro Area banking sector with the primary aim of assessing the importance of a bank in the financial system with respect to ownership and control of other credit institutions.

The network displays power law distributions in both binary and weighted degree metrics indicating a robust yet fragile structure and a direct nexus between an increase of control diversification and a rise in the market power. Therefore, while in good time the network is seemingly robust, in bad times many banks can go into distress simultaneously. This behavior opens a narrow for Central bank’s actions. In particular, we investigate whether the Single Supervisory Mechanism introduced by the European Central Banks and based on banks’ total asset is a good proxy to quantify their systemic importance. Results indicate that not all the financial institutions with high value of total asset are systemically important but only few of them.

Financial systemic risk is often assessed by the interconnectedness of financial institutes (FI) in terms of cross-ownership, overlapping investment portfolios, interbank credit exposures, etc. Less is known about the interconnectedness between FIs through the lens of consumer credits. Using detailed consumer credit data in Canada, this chapter constructs a novel banking network to measure FIs’ interconnectedness in the consumer credit markets. Results show that FIs on average are more connected to each other over the sample period, with the interconnectedness measure increases by 19% from 2013 Q4 to 2019 Q4. FIs with more diversified portfolios are more connected in the network. Among various types of FIs, secondary FIs have the notable increase in interconnectedness. Domestic Systemically Important Banks and secondary FIs offering a broad range of loan products are more connected to large FIs, while those specialized in single loan types are more connected to their industry peers. FI connectedness is also significantly related to their participation in the mortgage markets.

This paper is a keynote address prepared for a conference on “Entangled Political Economy” sponsored by the Wirth Institute. In keeping with the conventions of such an address, I look both backward and forward, while placing more emphasis on looking forward. In looking backward, I compare and contrast two orientations toward political economy: additive and entangled. In looking forward, I explore some of the analytical challenges that confront efforts to pursue a vision of entangled political economy. While these challenges are substantive in character, those efforts necessarily rest on methodological presumptions. Accordingly, the paper opens by reviewing some of those methodological presumptions before turning to the substantive articulations and challenges.

The ability to develop and maintain an effective network is an entrepreneurial competency. There are differences in female social networks' features as well as in the way of expanding it comparing to men's. But, there is little attention to female entrepreneurs' networks. This chapter focuses on the dynamics of female entrepreneurs' networks at different stages of the business life cycle. A qualitative study was conducted on a sample of Iranian female entrepreneurs in the trade sector situated in Tehran. The results of data analysis of 10 semi-structured interviews show a considerable change in networking activity over time. Although the most important role of networking for women entrepreneurs in both early-stage and established businesses is to achieve opportunities, information, and resources. Also, the combination of female entrepreneurs' networks is most male-dominated. And the main challenge of networking in the early-stage is being female, while it becomes rather an advantage for established ones. Finally, Iranian female entrepreneurs become more aware of networking benefits over time.

Misinformation can influence decision-making by fueling individual's belief, prejudices, and stereotypes. In the context of international trade, misinformation refers to the spread of false or misleading information and facts mostly with the malicious intent of maligning reputation of products, services, trade policies of a country and thus negatively influencing liberal trade policies toward that country. Stereotypes and prejudices fueled by misinformation coupled with economic nationalism and populism cast a dark shadow over the liberal international order. Exaggerated claims about unfair trade practices laced with stereotypes, prejudices, and misinformation can fuel tensions and may eventually lead to trade dispute and retaliatory action such as the imposition of tariffs or breakdown of trade blocs. Fake News, as a term, came into prominence recently during the 2016 US elections. The spread of fake news during the election generated remarkable interest among researchers. While most research focused on the effect of misinformation, a few studies have shown the influence of misinformation in changing trade preferences. The intricate connection among trading partners can propagate misinformation. Misinformation can lead policymakers to undertake protectionist policies. However, policies driven by misinformation, taken by major economies, can have strong rippling effects on other trading partners because of their strong network connectedness. Therefore, it motivates us to understand and evaluate international trade in terms of network statistics. This chapter provides an in-depth analysis of the network effects of some major and emerging economic powers involved in bilateral or multilateral trade agreements.

Purpose: Green computing is a way of using the computer resource in an eco-friendly while maintaining and decreasing the harmful environmental impact. Minimising toxic materials and reducing energy usage can also be used to recycle the product.

Need for the Study: The motivation of the study is to use green computing resources to decrease carbon emissions and their adverse effect on the environment.

Methodology: The study uses a qualitative method of collecting resources and data to address the opportunities, challenges, and future trends in green computing for Sustainable Future Technologies. The study focusses on multiple kinds of cloud computing services collected and executed into single remote servers. The service demand processor offers these services to the client per their needs. The simultaneous requests to access the cloud services, processing and expertly managing these requests by the processors are discussed and analysed.

Findings: The findings suggest that green computing is an upcoming and most promising area. The number of resources employed for green computing can be beneficial for lowering E-waste so that computing can be environmentally friendly and self-sustainable.

Practical Implications: Green computing applies across all industries and service sectors like healthcare, entertainment, tourism, and education. The convergence of technologies like Cloud Computing, AI, and Internet of Things (IoT) is greatly impacting Green Supply Chain Management (GSCM) market.

Traditional Austrian cycle theory starts from general equilibrium and explains how an expansion of bank credit unmatched by an expansion of saving can create a cycle of boom-and-bust, and with the bust followed by restoration of normality. In contrast, this paper offers a non-equilibrium reformulation of those earlier Austrian insights, which expands and refocuses the analytical agenda of macro theory. Our key analytical feature is the conceptualization of a macro economy as constituted through an open-ended ecology of plans. Within this framework, macro variables are not primitives but are derivative from micro-level interaction. In turn, the computation of optimizing actions is beset with undecidability. The theory of entrepreneurial choice that is suitable for this analytical framework is based on rule-following or algorithmic choice and not on computational maximization. What results is a macro ecology, the internal operation of which entails natural volatility. What are called policy actions, moreover, operate inside and not outside the ecology, and can create induce volatility within the ecology.

The recurring implementation and continuous maintenance of price controls implies a deep incongruence between public policy and economic common sense. Yet, economists do not tire of concluding their papers with policy recommendations as if oblivious to the ineffectiveness of their efforts. By assuming that policy is an object of choice, economists have no alternative but to naively hope for a decision-maker sensitive to economic logic. An alternative approach is to think of policy, not as an object of choice but as an outcome of a competitive process. From this perspective, the often-lamented disregard for economic principles is not a characteristic of a deficient policymaker, but a systemic quality of institutional arrangements. I illustrate my argument with the analysis of the implementation of rigid prices for reimbursed pharmaceuticals in Poland.

This paper tests the feasibility and empirical implications of a spatial econometric model with a full set of interaction effects and weight matrix defined as an equally weighted group interaction matrix applied to research productivity of individuals. We also elaborate two extensions of this model, namely with group fixed effects and with heteroskedasticity. In our setting, the model with a full set of interaction effects is overparameterised: only the SDM and SDEM specifications produce acceptable results. They imply comparable spillover effects, but by applying a Bayesian approach taken from LeSage (2014), we are able to show that the SDEM specification is more appropriate and thus that colleague interaction effects work through observed and unobserved exogenous characteristics common to researchers within a group.

This chapter is concerned with the estimation of spillover effects when outcomes arise as a consequence of bilateral interactions instead of from individual actions. In this type of environments, outcomes are generated on links instead of on nodes of a network, like bilateral prices in over-the-counter markets. The author proposes a link-based spatial autoregressive (SAR) model and discusses identification conditions and a two step least square estimation procedure. The author shows analytically that using a standard node-based SAR, which models nodes instead of links’ outcomes, produces misleading results when the data generating process is link-based. The methodology is illustrated using Monte Carlo experiments and real data from an interbank network.