Most analysts of corporations and industries adopt the focal perspective of a single prototypical organization. Many analysts also study corporations primarily in terms of their internal organizational structures or as complex systems of financial contracts. Glenn Carroll and Michael Hannan bring fresh insight to our understanding of corporations and the industries they comprise by looking beyond prototypical structures to focus on the range and diversity of organizations in their social and economic setting. The result is a rich rendering of analysis that portrays whole populations and communities of corporations."The Demography of Corporations and Industries" is the first book to present the demographic approach to organizational studies in its entirety. It examines the theory, models, methods, and data used in corporate demographic research. Carroll and Hannan explore the processes by which corporate populations change over time, including organizational founding, growth, decline, structural transformation, and mortality. They review and synthesize the major theoretical mechanisms of corporate demography, ranging from aging and size dependence to population segregation and density dependence. The book also explores some selected implications of corporate demography for public policy, including employment and regulation.In this path-breaking book, Carroll and Hannan demonstrate why demographic research on corporations is important; describe how to conduct demographic research; specify fruitful areas of future research; and suggest how the demographic perspective can enrich the public discussion of issues surrounding the corporation in our constantly evolving industrial society. All researchers and analysts with an interest in this topic will find The Demography of Corporations and Industries an invaluable resource.
Network analysis of inter-industry payment flows reveals structural economic relationships invisible to traditional bilateral measurement approaches, with significant implications for real-time economic monitoring. Analysing 532,346 UK payment records (2017--2024) across 89 industry sectors, we demonstrate that graph-theoretic features which include centrality measures and clustering coefficients improve payment flow forecasting by 8.8 percentage points beyond traditional time-series methods. Critically, network features prove most valuable during economic disruptions: during the COVID-19 pandemic, when traditional forecasting accuracy collapsed (R2} falling from 0.38 to 0.19), network-enhanced models maintained substantially better performance, with network contributions reaching +13.8 percentage points. The analysis identifies Financial Services, Wholesale Trade, and Professional Services as structurally central industries whose network positions indicate systemic importance beyond their transaction volumes. Network density increased 12.5\% over the sample period, with visible disruption during 2020 followed by recovery exceeding pre-pandemic integration levels. These findings suggest payment network monitoring could enhance official statistics production by providing leading indicators of structural economic change and improving nowcasting accuracy during periods when traditional temporal patterns prove unreliable.
The article studies the concept of circular economy as an alternative of traditional linear model oriented to efficient use of natural resources, cutting wastes and development of closed material cycles. The basis of circular economy is formed by the principle of maximum use of resources through recycling and repeated use of materials. Transition to circular economy can become an important strategic element for Russia, especially in conditions of such challenges as dependence on import and sanctions. The article provides analysis of data concerning recycling and import of plastics in Russia, where considerable volumes of plastic wastes cannot be used due to insufficient development of recycling infrastructure. The proposed economic model shows that transition to recycling of 95% of imported plastics can both create a serious economic potential for import substitution and result in extra economic benefits, including new sources of revenues, jobs and transition to economy of closed cycle without necessity of raw material purchase with foreign currency. In conclusion the authors underlined the necessity of complex approach to effective recycling of plastics and cutting plastic wastes within the frames of goals of sustainable development in the Russian Federation.
Primasari Fitria, Ayu Geby Gisela Syaputri, Muhammad Bahrul Ulum
This study aims to examine the extent to which World Oil Prices and Infrastructure Spending influence Indonesia's Budget Balance in 2015-2024. The data used in this study are secondary data from 2015-2024. The analysis method used is multiple linear regression analysis. The analysis results indicate that crude oil prices and infrastructure expenditure simultaneously have a significant effect on the Budget Balance, with an F-value of 5.720 and a significance level of 0.013 < 0.015. Partially, infrastructure expenditure has a significant impact on the state budget deficit, with a coefficient of β = 0.456 and p < 0.05, whereas crude oil prices do not have a significant effect (β = -0.106, p = 0.28). This is due to fluctuations in crude oil prices that do not always align with the continuously increasing trend of the state budget deficit each year. Infrastructure expenditure plays a crucial role in supporting economic growth, prompting the government to increase capital spending for development.
RESUMEN: Este trabajo tiene por objetivo principal analizar, a la luz de las fluctuaciones de los precios de los commodities, la evolución de la integración de países latinoamericanos seleccionados (Argentina, Brasil, Colombia, Chile, México y Perú) en cadenas globales de valor (CGV) durante el periodo 1998-2018. A partir de información proveniente de las matrices insumo-producto globales de la OCDE, se calculan los encadenamientos backward y forward, así como la participación y posicionamiento en CGV, de los casos seleccionados, utilizando una descomposición matemática de las exportaciones en términos de valor agregado. La representación de trayectorias de posicionamiento en CGV, para cada país en el tiempo, arroja evidencia de comportamientos inerciales y dependientes de las fluctuaciones de los precios de los commodities. Se concluye que la integración de países latinoamericanos en CGV fue acotada debido principalmente a la reprimarización de las economías y a la ausencia o fracaso de políticas de industrialización.
Martin Åhlén, Suzanna Törnroth, Åsa Wikberg-Nilsson
This article introduces the Design Sensibility Approach—a sensorial and embodied process for making sense of possible futures. The approach is applied through a case study on speculative energy design in the home, conducted and adapted within a participatory workshop held at a regional art hall in Northern Sweden. It unfolds in four phases—Imagine, Make, Explore, and Reflect—across a broader timeline comprising pre-workshop, active workshop, and post-workshop stages. During the workshop, participants were invited to engage with their senses through a series of activities designed to prompt reflection on their own future energy imaginaries, which they materialized using a MakeTools kit. The results reveal three themes: emotional responses elicited from embodied experiences with energy; energy as a lifestyle; and critique of the political landscape surrounding resource extractivism in Northern Sweden. These findings inform the research question: How might the human senses be leveraged to create stronger emotional connections with future domestic energy products and systems? The article concludes by proposing concrete applications of the Design Sensibility Approach at individual, community, and governance levels, highlighting its ethical and inclusive dimensions as areas for future development.
Oleg V. Pavlov, Natalia V. Smirnova, Elena V. Smirnova
A literacy-targeted approach to economic instruction draws on insights from cognitive science. It highlights that students process complex economic information by constructing and modifying schemas that represent economic material. Following this approach, we developed a set of instructional activities centered around causal diagrams that promote a deeper understanding of economic topics beyond the traditional lecture-based methods. Our results show that structural debriefing activities can be used effectively to introduce students to the causal diagrams that explain key economic relationships in the national income model, government-purchases multiplier and tax multiplier.
The massive digital footprints generated by bike-sharing systems in megacities like Shanghai offer a novel perspective on the urban socio-economic fabric. This study investigates whether these daily mobility patterns can quantitatively map the city's underlying social stratification. To overcome the persistent challenge of acquiring fine-grained socio-economic data, we constructed a multi-layered analytical dataset. We annotated 2,000 raw bike trips with local economic attributes, derived from a novel data enrichment methodology that employs a Large Language Model (LLM), and integrated contextual features of the built environment. A Random Forest model was then utilized as an interpretable framework to determine the key factors governing the relationship between mobility behavior and local economic status. The analysis reveals a compelling and unambiguous finding: a neighborhood's economic level, proxied by housing prices, is the single most dominant predictor of its bike-sharing patterns, substantially outweighing other geographic or temporal factors. This economic determinism manifests in three distinct ways: (1) a spatial clustering of resources, a phenomenon we term the \textit{club effect}, which concentrates mobility infrastructure and usage in affluent areas; (2) a functional dichotomy between necessity-driven, utilitarian usage in lower-income zones and flexible, recreational usage in wealthier ones; and (3) a nuanced inverted U-shaped adoption curve that identifies the urban middle class as the system's primary user base.
This paper presents a novel quantitative approach for comparative economic studies, addressing limitations in current classification methods. Conventional approaches in comparative economics often rely on ad hoc and categorical classifications, leading to subjective judgments and disregarding the continuous nature of the spectrum of economic systems. These can result in subjectivity and significant information loss, particularly for countries with systems near categorical borders. To overcome these shortcomings, the present paper proposes distance-based indices for objective categorization, considering economic foundations and using hard data. Accordingly, the paper introduces institutional similarity indices--Capitalism Similarity Index (CapSI), Communism Similarity Index (ComSI), and Socialism Similarity Index (SocSI)-which reflect countries' positions along the economic system continuum. These indices adhere to mathematical rigor and are grounded in the mathematical fields of real analysis, metric spaces, and distance functions. By classifying 135 countries and creating GIS maps, the practical applicability of the proposed approach is demonstrated. Results show a high explanatory power of the introduced indices, suggesting their beneficial usage in comparative economic studies. The paper advocates for their adoption due to their objectivity and ability to capture structural and institutional nuances without subjective judgments while also considering the continuous nature of the spectrum of economic systems.
Evolutionary demography is a community of researchers in a range of different disciplines who agree that “nothing in evolution makes sense except in the light of demography” (Carey and Vaupel 2005). My focus here is a subset of this research (henceforth ‘evolutionary demography’ or ‘evolutionary anthropology’) that originated in anthropology in the late 1970s and which typically examines micro-level phenomena concerning reproductive decision-making and the evolutionary processes generating observed patterns in reproductive variation. Scholars in this area tend to be more involved in long-term anthropological fieldwork than any other area of the evolutionary sciences. But card-carrying anthropologists are declining among their number as researchers increasingly come from other backgrounds in the biological and social sciences, with an associated decline in the contribution of ethnographic work. Most practitioners identify with the sub-field of human behavioral ecology – the application of sociobiological principles to human behavior – and distinguish themselves from the sister fields of evolutionary psychology and cultural evolution. Human behavioral ecology has been criticized for abstracting away the details of both culture and psychology in its focus on adaptive explanations of reproductive behavior, and for its commitment to ultimate over proximate causation. This chapter explores these critiques. Inspired by EA Hammel’s seminal paper “A theory of culture for demography” (Hammel 1990), I examine how the culture concept is used in evolutionary research. Like Hammel, I argue that a theory of culture for evolutionary demography requires engaging more seriously with (and in) ethnographic work. I highlight some challenging examples to motivate discussion about adaptive reproduction and natural fertility. Going further, I advocate for cultural evolution as an integrative framework for bringing both culture and psychology into the core of evolutionary demography research. This will involve expanding our theoretical and conceptual toolkits: (1) building and testing proximate mechanistic models, (2) delineating and evaluating causal claims at multiple levels of analysis and time scales, and (3) exploring co-evolution or feedback between demography and culture.
The new industrial era has brought new opportunities and chances for the entire business development. Smart machines, artificial intelligence, cloud computing, the Internet of Things, big data are taking over many jobs and roles, thus leaving room for the development of new skills and abilities. The rapid technological development in terms of automation and digitization has made machines replace human work. In this sense, it is a matter of time when technology will replace traditional accountants. (Management) accountants who want to adapt and survive in the digital world have to improve their offer and change the focus from data calculation to interpretation of results and business management. Thus, by applying new digital information technology tools, management accounting can provide quality information for determining the costs of products and services, performance measurement, planning and control, strategic and operational decision-making and the like. The general objective of this paper is to review the potential impact of digital information technologies on the usefulness of cost accounting systems and organizational performance in healthcare institutions in the Republic of Serbia with the help of statistical analysis of the relationship between the selected variables. The results of the analysis show that digital technologies have a great impact on the usefulness of the cost accounting system. Also, the largest number of respondents pointed out that improved IT systems have a positive effect on increasing organizational performance.
We present ABIDES-Economist, an agent-based simulator for economic systems that includes heterogeneous households, firms, a central bank, and a government. Agent behavior can be defined using domain-specific behavioral rules or learned through reinforcement learning by specifying their objectives. We integrate reinforcement learning capabilities for all agents using the OpenAI Gym environment framework for the multi-agent system. To enhance the realism of our model, we base agent parameters and action spaces on economic literature and real U.S. economic data. To tackle the challenges of calibrating heterogeneous agent-based economic models, we conduct a comprehensive survey of stylized facts related to both microeconomic and macroeconomic time series data. We then validate ABIDES-Economist by demonstrating its ability to generate simulated data that aligns with the relevant stylized facts for the economic scenario under consideration, following the learning of all agent behaviors via reinforcement learning. Specifically, we train our economic agents' policies under two broad configurations. The first configuration demonstrates that the learned economic agents produce system data consistent with macroeconomic and microeconomic stylized facts. The second configuration illustrates the utility of the validated simulation platform in designing regulatory policies for the central bank and government. These policies outperform standard rule-based approaches from the literature, which often overlook agent heterogeneity, shocks, and agent adaptability.
We develop an analytical synthesis that bridges data-driven Distributionally Robust Optimization (DRO) and Economic Decision Theory under Ambiguity (DTA). By reinterpreting standard regularization and DRO techniques as data-driven counterparts of ambiguity-averse decision models, we provide a unified framework that clarifies their intrinsic connections. Building on this synthesis, we propose a novel DRO approach that leverages a popular DTA model of smooth ambiguity-averse preferences together with tools from Bayesian nonparametric statistics. Our baseline framework employs Dirichlet Process (DP) posteriors, which naturally extend to heterogeneous data sources via Hierarchical Dirichlet Processes (HDPs), and can be further refined to induce outlier robustness through a procedure that selectively filters poorly-fitting observations during training. Theoretical performance guarantees and convergence results, together with extensive simulations and real-data experiments, illustrate the method's favorable performance in terms of prediction accuracy and stability.
The urban transition is generally imagined as a large-scale permanent migration of people from villages to cities. The formation of new cities is also theorized as occurring through the migration of people. However, recent scholarship implies that parts of India may be witnessing an urbanization process that depends on natural population growth rather than in-migration. This claim carries significant implications for urban theory, but it has never been tested empirically. This article addresses that gap by examining migration patterns in India alongside urbanization—measured in terms of densification of population and built-up area and an economic transition away from agriculture. I find that certain parts of the country, notably the eastern Indo-Gangetic Plain, are exhibiting all the trends constitutive of urbanization even as they experience negative net migration—a phenomenon I term “urbanization from within.” My analysis also highlights that these same regions see high rates of temporary out-migration—suggesting that human mobilities may play a role in the in situ urbanization of rural settlements, but not in the ways that foundational urban and development theories would predict. I discuss the inequalities of India's economic transition and its spatial regime of social welfare as possible causal underpinnings of the trends I observe. The article's findings suggest that urban social scientists should reevaluate long-held assumptions about the relationship between urbanization and migration in the context of 21st-century urban transitions.
This paper fundamentally reformulates economic and financial theory to include electronic currencies. The valuation of the electronic currencies will be based on macroeconomic theory and the fundamental equation of monetary policy, not the microeconomic theory of discounted cash flows. The view of electronic currency as a transactional equity associated with tangible assets of a sub-economy will be developed, in contrast to the view of stock as an equity associated mostly with intangible assets of a sub-economy. The view will be developed of the electronic currency management firm as an entity responsible for coordinated monetary (electronic currency supply and value stabilization) and fiscal (investment and operational) policies of a substantial (for liquidity of the electronic currency) sub-economy. The risk model used in the valuations and the decision-making will not be the ubiquitous, yet inappropriate, exponential risk model that leads to discount rates, but will be multi time scale models that capture the true risk. The decision-making will be approached from the perspective of true systems control based on a system response function given by the multi scale risk model and system controllers that utilize the Deep Reinforcement Learning, Generative Pretrained Transformers, and other methods of Generative Artificial Intelligence (genAI). Finally, the sub-economy will be viewed as a nonlinear complex physical system with both stable equilibriums that are associated with short-term exploitation, and unstable equilibriums that need to be stabilized with active nonlinear control based on the multi scale system response functions and genAI.
As the United States is witnessing elevated racial differences pertaining to economic disparities, we have found a unique example contrary to the traditional narrative. Idaho is the only US state where Blacks earn more than Whites and all other races. In this paper, we examine how Idaho Blacks might have achieved economic success and, more importantly, what factors might have led to this achievement in reducing racial and economic disparities. Preliminary research suggests that fewer barriers to land ownership, smaller populations, well-knit communities, men's involvement in the family, and a relatively less hostile environment have played a significant role. Further research by historians can help the nation uncover the underlying factors to see if some factors are transportable to other parts of the country.
Economic Resilience is a dynamic condition of the nation's economic life that contains tenacity and resilience that contains the ability to develop national strength in facing and overcoming all threats, obstacles, disturbances, obstacles, and challenges originating from abroad and from within the country. Asymmetric war is a war that involves two or more actors through a broad battlefield that includes Trigatra (geography, demography, and natural resources) and Pancagatra (ideology, political economy, socio-culture, and defense and security). The purpose of the research is to provide government input on the importance of economic resilience in the face of asymmetric war. This study uses the Strategy Theory written by Clausewitz which views several things related to the elements of war, namely Means, Way, and End. Research with qualitative methods is aimed at understanding social phenomena from the participant's perspective. The result of this study is economic resilience needs strengthening in the digital economy, digital bureaucracy and banking strengthening
The digital advertising development features and prospects in the world and in the Russian Federation in particular have been considered. An approach to defining the advertising concept essence in the digital field has been presented. A summary of the scientific research results has made it possible to identify the industry’s growth constraints and its main development drivers, as well as define the importance and distinguishing features of modern advertising campaigns in the digital space. Based on analysis of current data from relevant organisations for the digital advertising market, an increase in growth rates has been identified overall by region and country, as well as for individual advertising segments.
The goal of this paper is to evaluate the informational content of sentiment extracted from news articles about the state of the economy. We propose a fine-grained aspect-based sentiment analysis that has two main characteristics: 1) we consider only the text in the article that is semantically dependent on a term of interest (aspect-based) and, 2) assign a sentiment score to each word based on a dictionary that we develop for applications in economics and finance (fine-grained). Our data set includes six large US newspapers, for a total of over 6.6 million articles and 4.2 billion words. Our findings suggest that several measures of economic sentiment track closely business cycle fluctuations and that they are relevant predictors for four major macroeconomic variables. We find that there are significant improvements in forecasting when sentiment is considered along with macroeconomic factors. In addition, we also find that sentiment matters to explains the tails of the probability distribution across several macroeconomic variables.