Hasil untuk "Economics"

L. Walras

N. Kaldor

M. Kochen

R. Coase

A. Kahn

G. O'driscoll, M. Rizzo, Roger W. Garrison

V. Smith

R. Cooter, T. Ulen

S. Folland, A. Goodman, M. Stano

Chapter 1: Introduction Chapter 2: Microeconomic Tools for Health Economics Chapter 3: Statistical Tools for Health Economics Chapter 4: Economic Efficiency and Cost Benefit Analysis Chapter 5: Production of Health Chapter 6: The Production, Cost, and Technology of Health Care Chapter 7: Demand for Health Capital Chapter 8: Demand and Supply for Health Insurance Chapter 9: Consumer Choice and Demand Chapter 10: Asymmetric Information and Agency Chapter 11: The Organization of Health Insurance Markets Chapter 12: Managed Care Chapter 13: Nonprofit Firms Nonprofit Firms Chapter 14: Hospitals and Long-term Care Chapter 15: The Physician's Practice Chapter 16: Health Care Labor Markets and Professional Training Chapter 17: The Pharmaceutical Industry Chapter 18: Equity, Efficiency and Need Chapter 19: Government Intervention in Health Care Markets Chapter 20: Government Regulation: Principal Regulatory Mechanisms Chapter 21: Social Insurance Chapter 22: Comparative Health Care Systems and Health System Reform Chapter 23: Health System Reform Chapter 24: The Health Economics of Bads Chapter 25: Epidemiology and Economics: HIV/AIDS in Africa

Rok Spruk

This paper studies the long-run economic and institutional consequences of Iran's confrontation with the West, treating the 2006-2007 strategic shift as the onset of a sustained confrontation regime rather than a discrete sanctions episode. Using synthetic control and generalized synthetic control methods, I construct transparent counterfactuals for Iran's post-confrontation trajectory from a donor pool of countries with continuously normalized relations with the West. I find large, persistent losses in real GDP and GDP per capita, accompanied by sharp declines in foreign direct investment, trade integration, and non-oil exports. These economic effects coincide with substantial and durable deterioration in political stability, rule of law, and control of corruption. Magnitude calculations imply cumulative output losses comparable to civil-war settings, despite the absence of internal armed conflict. The results highlight confrontation as a deep and persistent economic and institutional shock, extending the literature beyond short-run sanctions effects to sustained geopolitical isolation.

Joel M Thomas, Abhijit Chakraborty

This study investigates the economic complexity of Indian states by constructing a state-industry bipartite network using firm-level data on registered companies and their paid-up capital. We compute the Economic Complexity Index and apply the fitness-complexity algorithm to quantify the diversity and sophistication of productive capabilities across the Indian states and two union territories. The results reveal substantial heterogeneity in regional capability structures, with states such as Maharashtra, Karnataka, and Delhi exhibiting consistently high complexity, while others remain concentrated in ubiquitous, low-value industries. The analysis also shows a strong positive relationship between complexity metrics and per-capita Gross State Domestic Product, underscoring the role of capability accumulation in shaping economic performance. Additionally, the number of active firms in India demonstrates a persistent exponential growth at an annual rate of 11.2%, reflecting ongoing formalization and industrial expansion. The ordered binary matrix displays the characteristic triangular structure observed in complexity studies, validating the applicability of complexity frameworks at the sub-national level. This work highlights the usefulness of firm-based data for assessing regional productive structures and emphasizes the importance of capability-oriented strategies for fostering balanced and sustainable development across Indian states. By demonstrating the usefulness of firm registry data in data constrained environments, this study advances the empirical application of economic complexity methods and provides a quantitative foundation for capability-oriented industrial and regional policy in India.

Ian Crawford, Carl-Emil Pless

We study the associations between everyday economic decision-making quality and people's emotional states. Using high-frequency, highly disaggregated consumer "scanner" data, we show that the cost of poor decision-making is substantial, on average equal to around half of day-to-day consumption budgets. While material circumstances help explain decision-making quality, how people feel about those circumstances is equally important. Contrary to evidence that stress and worry impair performance in settings where distraction is costly, we find these same feelings are associated with improved decision-making for frequently made consumption choices. This is consistent with worry increasing attentiveness to decisions within households' locus of control.

Jonathan D. Day, J. C. Wendler

Ruxin Chen, Zeqiang Zhang

The application of Reinforcement Learning (RL) to economic modeling reveals a fundamental conflict between the assumptions of equilibrium theory and the emergent behavior of learning agents. While canonical economic models assume atomistic agents act as `takers' of aggregate market conditions, a naive single-agent RL simulation incentivizes the agent to become a `manipulator' of its environment. This paper first demonstrates this discrepancy within a search-and-matching model with concave production, showing that a standard RL agent learns a non-equilibrium, monopsonistic policy. Additionally, we identify a parametric bias arising from the mismatch between economic discounting and RL's treatment of intertemporal costs. To address both issues, we propose a calibrated Mean-Field Reinforcement Learning framework that embeds a representative agent in a fixed macroeconomic field and adjusts the cost function to reflect economic opportunity costs. Our iterative algorithm converges to a self-consistent fixed point where the agent's policy aligns with the competitive equilibrium. This approach provides a tractable and theoretically sound methodology for modeling learning agents in economic systems within the broader domain of computational social science.

Chaofeng Wu

We propose a framework that recasts scientific novelty not as a single attribute of a paper, but as a reflection of its position within the evolving intellectual landscape. We decompose this position into two orthogonal dimensions: \textit{spatial novelty}, which measures a paper's intellectual distinctiveness from its neighbors, and \textit{temporal novelty}, which captures its engagement with a dynamic research frontier. To operationalize these concepts, we leverage Large Language Models to develop semantic isolation metrics that quantify a paper's location relative to the full-text literature. Applying this framework to a large corpus of economics articles, we uncover a fundamental trade-off: these two dimensions predict systematically different outcomes. Temporal novelty primarily predicts citation counts, whereas spatial novelty predicts disruptive impact. This distinction allows us to construct a typology of semantic neighborhoods, identifying four archetypes associated with distinct and predictable impact profiles. Our findings demonstrate that novelty can be understood as a multidimensional construct whose different forms, reflecting a paper's strategic location, have measurable and fundamentally distinct consequences for scientific progress.

Alberto Baccini, Lucio Barabesi, Carlo Debernardi

This paper investigates the impact of the global financial crisis on the shape of economics as a discipline by analyzing EconLit-indexed journals from 2006 to 2020 using a multilayer network approach. We consider two types of social relationships among journals, based on shared editors (interlocking editorship) and shared authors (interlocking authorship), as well as two forms of intellectual proximity, derived from bibliographic coupling and textual similarity. These four dimensions are integrated using Similarity Network Fusion to produce a unified similarity network from which journal communities are identified. Comparing the field in 2006, 2012, and 2019 reveals a high degree of structural continuity. Our findings suggest that, despite changes in research topics after the crisis, fundamental social and intellectual relationships among journals have remained remarkably stable. Editorial networks, in particular, continue to shape hierarchies and legitimize knowledge production.

Dan Anderberg, Rachel Cassidy, Anaya Dam et al.

One in three women globally experiences intimate partner violence (IPV), yet little is known about how such trauma affects economic decision-making. We provide causal evidence that IPV influences women's time preferences - a key parameter in models of savings, investment, and labor supply. We combine two empirical strategies using four distinct datasets. First, in two randomized recall experiments in Ethiopia, we randomly assigned women to recall specific acts of abuse before eliciting their intertemporal choices. Women with IPV experiences prompted to recall IPV display significantly greater impatience than otherwise similar women who are not prompted. Second, we exploit exogenous reductions in IPV generated by two randomized interventions - one involving cash transfers, the other psychotherapy - and use treatment assignment as an instrument for IPV exposure. Women who experience reduced IPV as a result of treatment exhibit more patient time preferences. Together, these results provide consistent, novel causal evidence that exposure to IPV induces individuals to discount the future more heavily. This evidence suggests a psychological channel through which violence can perpetuate economic disadvantage and constrain women's ability to take actions - such as saving, investing, or exiting abusive relationships - that require planning over time.

Heyang Ma, Qirui Mi, Qipeng Yang et al.

Economic decision-making depends not only on structured signals such as prices and taxes, but also on unstructured language, including peer dialogue and media narratives. While multi-agent reinforcement learning (MARL) has shown promise in optimizing economic decisions, it struggles with the semantic ambiguity and contextual richness of language. We propose LAMP (Language-Augmented Multi-Agent Policy), a framework that integrates language into economic decision-making and narrows the gap to real-world settings. LAMP follows a Think-Speak-Decide pipeline: (1) Think interprets numerical observations to extract short-term shocks and long-term trends, caching high-value reasoning trajectories; (2) Speak crafts and exchanges strategic messages based on reasoning, updating beliefs by parsing peer communications; and (3) Decide fuses numerical data, reasoning, and reflections into a MARL policy to optimize language-augmented decision-making. Experiments in economic simulation show that LAMP outperforms both MARL and LLM-only baselines in cumulative return (+63.5%, +34.0%), robustness (+18.8%, +59.4%), and interpretability. These results demonstrate the potential of language-augmented policies to deliver more effective and robust economic strategies.

Marcus Buckmann, Quynh Anh Nguyen, Edward Hill

We investigate whether the hidden states of large language models (LLMs) can be used to estimate and impute economic and financial statistics. Focusing on county-level (e.g. unemployment) and firm-level (e.g. total assets) variables, we show that a simple linear model trained on the hidden states of open-source LLMs outperforms the models' text outputs. This suggests that hidden states capture richer economic information than the responses of the LLMs reveal directly. A learning curve analysis indicates that only a few dozen labelled examples are sufficient for training. We also propose a transfer learning method that improves estimation accuracy without requiring any labelled data for the target variable. Finally, we demonstrate the practical utility of hidden-state representations in super-resolution and data imputation tasks.

Aleksandar Keseljevic, Stefan Nikolic, Rok Spruk

We investigate the long-term impact of civil war on subnational economic growth across 78 regions in five former Yugoslav republics from 1950 to 2015. Leveraging the outbreak of ethnic tensions and the onset of conflict, we construct counterfactual growth trajectories using a robust region-level donor pool from 28 conflict-free countries. Applying a hybrid synthetic control and difference-in-differences approach, we find that the war in former Yugoslavia inflicted unprecedented regional per capita GDP losses estimated at 38 percent, with substantial regional heterogeneity. The most war-affected regions suffered prolonged and permanent economic declines, while capital cities experienced more transitory effects. Our results are robust to extensive variety of specification tests, placebo analyses, and falsification exercises. Notably, ethnic tensions between Serbs and Croats explain up to 40 percent of the observed variation in economic losses, underscoring the deep and lasting influence of ethnic divisions on economic impacts of the armed conflicts.