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We develop a tractable model of endogenous production networks. Each one of a number of products can be produced by combining labor and an endogenous subset of the other products as inputs. Different combinations of inputs generate (prespecified) levels of productivity and various distortions may affect costs and prices. We establish the existence and uniqueness of an equilibrium and provide comparative static results on how prices and endogenous technology/input choices (and thus the production network) respond to changes in parameters. These results show that improvements in technology (or reductions in distortions) spread throughout the economy via input–output linkages and reduce all prices, and under reasonable restrictions on the menu of production technologies, also lead to a denser production network. Using a dynamic version of the model, we establish that the endogenous evolution of the production network could be a powerful force towards sustained economic growth. At the root of this result is the fact that the arrival of a few new products expands the set of technological possibilities of all existing industries by a large amount—that is, if there are n products, the arrival of one more new product increases the combinations of inputs that each existing product can use from 2n-1 to 2ⁿ, thus enabling significantly more pronounced cost reductions from choice of input combinations. These cost reductions then spread to other industries via lower input prices and incentivize them to also adopt additional inputs.

Purpose Any kind of production flow is obtained not from individual production organizations but from a more or less widespread Production Network of interconnected production modules located in different places and times. All of these modules are, consciously or not, necessarily connected, interacting and cooperating in a coordinated way to combine and arrange, step by step, the factors, materials, components, manpower, machines and equipment to obtain flows of products’ final goods, in particular’ and to sell these where there is a demand for them. The purpose of this paper is to determine, in logical and formal terms, the minimum conditions that bring about the formation of production networks and to discover the laws that explain their dynamics over time. Design/methodology/approach At the global level, the continuous and accelerated economic progress of mankind is witnessed. There is an increase in the quantity and quality of satisfied and yet to be satisfied needs, of attained and yet to be attained aspirations. The increase in productivity and in quality has become unstoppable and appears to guide the other variables in the system. It is natural to ask who produces and governs these phenomena. It does indeed seem there is a Ghost in the “Production” machine whose invisible hand produces growing levels of productivity and quality, increases the quality and quantity of satisfied needs and aspirations and reduces the burden of work, thus producing increasingly higher levels of progress in the entire economic system. This conceptual framework gives a simple answer: there is nothing metaphysical about this evolution towards unstoppable and irreversible progress, and it is produced by the spontaneous genesis and activity of selfish nodes and governed by the rules and laws of the production networks. Findings The author has identified ten “rules of selfish behavior” on the part of the nodes, whose application necessarily and inevitably produces three evolutionary dynamic processes “which refer to the network as an entity” which the author has called the “rules of the production networks” to emphasize their cogency: continual expansion, elasticity-resiliency and continual improvement in performance. The cognitive and creative processes that characterize the nodes do not allow us to predict the actual evolution of production networks; nevertheless, if it is assumed that nodes “consciously or not” follow the 10 “rules of selfish behavior”, then several typical trends, or behavioural schema, can be deduced which the author has called as the “laws of networks”, to highlight their apparent inevitability and cogency. Research limitations/implications More than any other structure, Production Networks display Holland’s features and Arthur’s properties as their modules, viewed as autonomous entities with cognitive functions, represent a collectivity of agents that interact and exchange information with their environment to maintain over time their internal processes through adaptation, self-preservation, evolution and cognition, making individual and collective decisions as part of a network of micro behaviours. Social implications This new conception of production through production networks, which takes into account the “rules” and “laws” regulating their behaviour, also sheds new light on the development of networks and their natural tendency to become globalized. Originality/value Although the concept of a network is becoming more popular in economic and business studies, it is yet to see an interpretation of production as deriving exclusively from the actions of increasingly larger networks. This paper presents an integrated view of production that does not discard the notion that production is carried out by organizations and companies but introduces the broader concept of the integration among organizations, which must be interpreted as nodes of a broader network that produces the flows of all the components needed to obtain the flow of a specific product. This represents an innovative view that will help us in understanding the difficulties policymakers encounter in governing production and controlling the basic variables that characterize it, specifically productivity, quality, quantity, prices and value. This perspective also allows to derive rules and laws for the behaviour of production networks that appear to be cogent and unvarying over time.

Objective: The objective of the article is to identify and systemize the governance dynamics and related socio- economic consequences of the fintech transformation in banking, while acknowledging spatial contexts. Research Design Methods: The research framework comprised Global Production Networks (GPN), Global Value Chain (GVC), and co-evolutionary approaches to guide a systematic literature review in the Scopus, Web of Science, and Taylor Francis databases for 2016-2021. The final sample comprised 76 sources that became the basis for selective coding and the synthesis of the results.Findings: Fintech impacted banking governance by creating a dual and interrelated system of global financial networks and a ‘mosaic’ of territorial financial ecologies and ecosystems, where incumbent banks held an im- portant but not exclusive position. The fintech-enhanced governance transformations had both positive socio- economic effects (improved efficiency, expanded range of services, and inclusion of unbanked or under-served customers) and negative effects (over-indebtedness, surveillance, and exclusion of some customers). Wider so- cio-economic consequences refered to sustainable development and changes in economic and social behaviour. Implications Recommendations: A research framework and agenda for future studies related to the dy- namics of fintech-driven governance in banking have been elaborated. The article derives the immediate and wider economic and social consequences of fintech-driven transformations. The results can also be applied in public policies oriented towards sustainable socio-economic development.Contribution Value Added: The study provides theoretical and policy-relevant contributions. Firstly, it broadens the research on the transformation of banking governance in the spatial context. Secondly, it contributes theoretically by proposing a research framework of GVC and GPN governance augmented by a co-evolutionary perspective. Thirdly, the article informs policy that seeks financial inclusion for cohesive and sustainable development.

As middle-income countries have grown their manufacturing sectors considerably over recent decades, attention has turned to countries that fall at the bottom of the industrial ladder. In such countries, challenges related to corruption combined with intensely competitive pressures from China and other countries appear to preclude possibilities for state leaders and bureaucracies to engineer industrial growth. Drawing upon an original survey of 229 apparel shop owners in Kyrgyzstan and dozens of interviews, this article analyzes the creation of a regional Eurasian production network in a country where we would least expect to see manufacturing dynamism. I adopt an everyday international political economy approach to the study of production networks in this country – considered to have a challenging business environment with high corruption – focusing on the ways in which shop owners and other intermediaries understand and relate to state bureaucrats at different nodes of the network. Doing so expands our understanding of the varieties of pathways to contemporary export-oriented production, including ones that emerge at the margins of the law.

During strategic network design, not only strategic but also operational decisions must be made long before a production network is put into operation. These include determining the location and size of inventories within the production network and setting operational parameters for production lines, such as the shift model. However, the large solution space comprising a high number of highly uncertain design parameters makes these decisions challenging without decision support. Therefore, data farming offers a potential solution, as synthetic data can be generated via the execution of multiple simulation experiments spanning the solution space and then analyzed using data mining techniques to provide data-based decision support. However, data farming has not yet been applied to strategic network design due to the lack of adequate solution space management. To address this shortcoming, this paper presents a structured solution space management approach that integrates production network-specific requirements and Design of Experiment (DoE) methods. The approach enables converting the solution space in strategic network design into individual input data sets for simulation experiments, generating a comprehensive database that can be mined for data-based decision support. The applicability and validity of the comprehensive approach are ensured via a case study in the automotive industry.

Global production networks (GPN) are organizational platforms through which actors in different regional and national economies compete and cooperate for a greater share of value creation, transformation, and capture through geographically dispersed economic activity. Existing conceptual frameworks on global value chains (GVC) and what we term GPN 1.0 tend to under-theorize the origins and dynamics of these organizational platforms and to overemphasize their governance typologies (e.g., modular, relational, and captive modes in GVC theory) or analytical categories (e.g., power and embeddedness in GPN 1.0). Building on this expanding literature, our article aims to contribute toward the reframing of existing GPN-GVC debates and the development of a more dynamic theory of global production networks that can better explain the emergence of different firm-specific activities, strategic network effects, and territorial outcomes in the global economy. It is part of a wider initiative-GPN 2.0 in short-that seeks to offer novel theoretical insights into why and how the organization and coordination of global production networks varies significantly within and across different industries, sectors, and economies. Taking an actor-centered focus toward theory development, we tackle a significant gap in existing work by systematically conceptualizing the causal drivers of global production networks in terms of their competitive dynamics (optimizing cost-capability ratios, market imperatives, and financial discipline) and risk environments. These capitalist dynamics are theorized as critical independent variables that shape the four main strategies adopted by economic actors in (re)configuring their global production networks and, ultimately, the developmental outcomes in different industries, regions, and countries.

We examine how many and what kind of firms ultimately rely on foreign inputs, sell to foreign markets, and are affected by trade shocks. To capture that firms can trade indirectly by buying from or selling to domestic firms that import or export, we use Belgian data with information on both domestic firm-to-firm sales and foreign trade transactions. We find that most firms use a lot of foreign inputs, but only a small number of firms show that dependence through direct imports. While direct exporters are rare, a majority of firms are indirectly exporting. In most firms, however, indirect export is quantitatively modest, and sales at home are the key source of revenue. We show that what matters for the transmission of foreign demand shocks to a firm’s revenue is how much the firm ultimately sells to foreign markets, not whether these sales are from direct or indirect export.

This paper analyzes a general equilibrium economy featuring input-output connections, imperfect competition, and external economies of scale owing to entry and exit. The interaction of input-output networks with industry-level market structure affects the amplification of shocks and the pattern of diffusion in the model, generating cascades of firm entry and exit across the economy. In this model, sales provide a poor measure of the systemic importance of industries. Unlike the relevant notions of centrality in competitive constant-returns-to-scale models, systemic importance depends on the industry's role as both a supplier and a consumer of inputs, as well as the market structure of industries. A basic calibration of the model suggests that aggregate output is three times more volatile in response to labor productivity shocks when compared to a perfectly competitive model.

In many production environments, a fixed network of capacity is shared flexibly between multiple products with random demands. What is the best way to configure the capacity of the production network and to allocate the available capacity to meet predetermined fill rate requirements? We develop a new approach for network capacity configuration and allocation and characterize the relationship between the capacity of the network and the attainable fill rate levels for the products, taking into account the flexibility structure of the network. This builds on a new randomized allocation mechanism to deliver the desired services. We use this theory to investigate the connection between the flexibility structure and capacity configuration. We provide a new perspective to the well-known phenomenon that “long chain is almost as good as the fully flexible network”: for given target fill rates, the required capacity level in a long-chain network is close to that in a fully flexible network and is much lower than a dedicated system. We apply these insights and techniques on problems arising in the design of last-mile delivery operations and in semiconductor production planning, using real data from two companies. This paper was accepted by Terry Taylor, operations management.

In this framing paper for the special issue, we map significant research on global production networks during the past decade in economic geography and adjacent fields. In line with the core aim of the special issue to push for new conceptual advances, the paper focuses on the central elements of GPN theory to showcase recent rethinking related to the delimiting of global production networks, underlying political-economic drivers, actor-specific strategies and regional/national development outcomes. We suggest that the analytical purchase of this recent work is greater in research that has continued to keep a tight focus on the causal links between the organizational configurations of global production networks and uneven development. Concomitantly, considerable effort in the literature has gone into expanding the remit of GPN research in different directions, and we thus engage with five domains or ‘constituent outsides’ that relate to the state, finance, labour, environment and development. We believe such cross-domain fertilisation can help realize GPN 2.0’s potential for explaining uneven development in an interconnected world economy.