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It seems worthwhile to pause occasionally and take stock of the balance of trade between economics and health economics. Martin Feldstein (1974) did this fourteen years ago at the ASSA meetings, and sufficient time has lapsed to bring up the issue again. Here, however, I undertake only a fragment of the task, confining the discussion to some remarks about econometrics and health economics. If one were writing about economic theory and health economics, one might focus on the importance for health economics of both uncertainty and the physician's dual role as supplier and patient agent. In discussing econometrics and health economics, however, other features of health economics are relevant:
Toward a Formal Science of Economics provides a unifying way to look at the concept of economic science. Toward a Formal Science of Economics provides a unifying way to look at the concept of economic science. It lays a foundation for the axiomatic method, focusing on applications in economics and econometrics, and including discussions in logic, epistemology, and probability theory. Each chapter deals with a topic of fundamental importance to a rigorous science of economics while illustrating an aspect of the axiomatic method. Stigum describes an introductory course in mathematical logic, developing a symbolic language for mathematics and discussing the strengths and weaknesses of the axiomatic method. He presents the standard theory of consumer choice, illustrating different aspects of the use of the axiomatic method and evaluating economic theories of individual behavior. He takes up problems in the foundations of econometrics and choice under uncertainty and offers an introduction to nonstandard analysis that leads to discussion of exchange and probability in hyperspace. A section on epistemology completes Stigum's construction of a formal unitary methodological basis for theoretical and empirical science. The last three parts of the book apply these methodological tools to various topics in economics and econometrics including empirical analyses of the permanent income hypothesis and consumer choice among risky and nonrisky assets; discussion of determinism, uncertainty, and the utility hypothesis; and study of topics of importance to the analysis of economic time series.
INTRODUCTION In order to understand and formulate economic theories, we tend to classify the types of movements which characterize economic time series as trend, cyclical, seasonal, and irregular. The idea that each component has separate and different causal forces is implicit in many of the discussions on the decomposition. Among the four components, two were considered to be of prime interest to economists. Whereas theories of economic growth suggest models which explain the secular or trend component of economic aggregates, the bulk of macroeconomics focuses on models explaining the stylized facts of the reoccurring cyclical component. The other two components, namely the seasonal and the irregular, were mostly viewed as a nuisance and of no major interest to us economists for the simple reason that we have almost no theoretical developments on economic models of seasonality. Consequently, without any interest in seasonality and considering that it was common until recently to separate growth models from business cycle models, the large majority of empirical macroeconomics has adopted a strategy of seasonally adjusting and detrending each series separately prior to any inference about the business cycle. Lately economic theorists studying stochastic growth theory have suggested models integrating the growth and cyclical components of economic time series, viewing expansions and contractions simply as the acceleration and the slowing down of the overall economic growth process. Time series econometricians, on the other hand, focused their attention on the econometric estimation and testing of parametric models with trending processes. Nowadays empirical macroeconomists tend to be more careful about trends and pay more attention to issues such as common trends and the interaction of cyclical and secular fluctuations.
An extensive synthesis is provided of the concepts, measures and techniques of Information Theory (IT). After an axiomatic description of the basic definitions of “information functions”, “entropy” or uncertainty and the maximum entropy principle, the paper demonstrates the power of IT as both an interpretive and techinically productive tool. It is argued that this power and universality is promarily due to the common need for (i) measures of distance and discrimination and, (ii) appropriate partitioning- aggregation properties. IT offers a very suggestive unification for a bewildering and arbitrary set of approaches that have evolved in different disciplines. Applications are discussed or indicated. These applications have relevance to economics, finance, industrial organization, marketing, statistical ingerence and model selection, political science and communication. A main focus of the discussion is the generative power of IT measures in statistical examinations of unknown distributions and random phe...
"Vibe coding" and "vibe analytics" have been framed as a democratization of technical capability. This paper argues that AI-assisted methodology more broadly, or what I call "vibe methodology," also democratizes the failure modes specific to each domain. When AI assists with methods whose validity depends on assumptions that cannot be verified from the output alone (a class I call "vibe inference"), the failure surface is structurally different: the output does not reliably signal invalidity, and when it does, recognizing the signal requires the expertise the workflow bypasses. I focus on "vibe econometrics," the subset of AI-assisted causal analysis where identification can be named faster than it can be audited. The claim of this paper is not that AI invents inferential failures that did not previously exist, but that it changes their incidence, observability, and persuasive force enough to create a practically distinct governance problem. This results in three failure modes: method-data mismatch, where AI bypasses expertise at execution; confidence laundering, where AI amplifies the credibility of formatted output; and invisible forking, which spans both. What is new is not the
Financial and economic history is strewn with bubbles and crashes, booms and busts, crises and upheavals of all sorts. Understanding the origin of these events is arguably one of the most important problems in economic theory: are economies intrinsically unstable, and can one ``stabilize unstable economies''? In this review I argue, from a physicist's vantage point, that the concept of equilibrium -- so central to mainstream economic thinking -- is likely to be the exception rather than the rule in large, complex, interacting systems. Drawing on a series of stylized ``toy'' models, I show how excess volatility, endogenous crises and crashes, inflation swells and persistent inequalities can all emerge naturally from genuinely out-of-equilibrium dynamics, without invoking large exogenous shocks. Three generic mechanisms recur throughout: trapping in a multiplicity of history-dependent equilibria; the impossibility of dynamically reaching equilibrium, leading to oscillations and chaos; and the spontaneous evolution towards fragile, marginally stable states -- the self-organized criticality paradigm. I stress that these are phenomenological scenarios rather than calibrated theories: th
Examining the economic impact of noise pollution from a lens of household is a burgeoning field in the study of environmental sustainability. Economics studies cover the source, measure, consequence of noise pollution, as well as the econometric methods used to identify the causal impact of noise pollution on socioeconomic welfare. There are broadly four major noise origins along with the industrial growth and urban development, which are airport, railway, urban traffic, and neighborhood. Four general kinds of measures or data sources are used in economics studies to capture the noise variations, namely, proximity to noise origins, real-time noise monitor records, household surveys, and administrative records on noise complaints. The socioeconomic consequences of noise pollution span from physical or mental health to happiness, violence and suicide, housing market capitalization, and inequality. In economics studies, generally three types of econometric methods are used to identify causal impact of noise pollution on the household's welfare, which are instrumental variable estimation, difference-in-difference estimation, randomized and quasi-natural experiments. The causal impact o
Can AI effectively perform complex econometric analysis traditionally requiring human expertise? This paper evaluates AI agents' capability to master econometrics, focusing on empirical analysis performance. We develop ``MetricsAI'', an Econometrics AI Agent built on the open-source MetaGPT framework. This agent exhibits outstanding performance in: (1) planning econometric tasks strategically, (2) generating and executing code, (3) employing error-based reflection for improved robustness, and (4) allowing iterative refinement through multi-round conversations. We construct two datasets from academic coursework materials and published research papers to evaluate performance against real-world challenges. Comparative testing shows our domain-specialized AI agent significantly outperforms both benchmark large language models (LLMs) and general-purpose AI agents. This work establishes a testbed for exploring AI's impact on social science research and enables cost-effective integration of domain expertise, making advanced econometric methods accessible to users with minimal coding skills. Furthermore, our AI agent enhances research reproducibility and offers promising pedagogical applic
Rapid increases in food supplies have reduced global hunger, while rising burdens of diet-related disease have made poor diet quality the leading cause of death and disability around the world. Today's "double burden" of undernourishment in utero and early childhood then undesired weight gain and obesity later in life is accompanied by a third less visible burden of micronutrient imbalances. The triple burden of undernutrition, obesity, and unbalanced micronutrients that underlies many diet-related diseases such as diabetes, hypertension and other cardiometabolic disorders often coexist in the same person, household and community. All kinds of deprivation are closely linked to food insecurity and poverty, but income growth does not always improve diet quality in part because consumers cannot directly or immediately observe the health consequences of their food options, especially for newly introduced or reformulated items. Even after direct experience and epidemiological evidence reveals relative risks of dietary patterns and nutritional exposures, many consumers may not consume a healthy diet because food choice is driven by other factors. This chapter reviews the evidence on diet
This paper establishes the theoretical and practical foundations for using Large Language Models (LLMs) as measurement instruments for latent economic variables -- specifically variables that describe the cognitive content of occupational tasks at a level of granularity not achievable with existing survey instruments. I formalize four conditions under which LLM-generated scores constitute valid instruments: semantic exogeneity, construct relevance, monotonicity, and model invariance. I then apply this framework to the Augmented Human Capital Index (AHC_o), constructed from 18,796 O*NET task statements scored by Claude Haiku 4.5, and validated against six existing AI exposure indices. The index shows strong convergent validity (r = 0.85 with Eloundou GPT-gamma, r = 0.79 with Felten AIOE) and discriminant validity. Principal component analysis confirms that AI-related occupational measures span two distinct dimensions -- augmentation and substitution. Inter-rater reliability across two LLM models (n = 3,666 paired scores) yields Pearson r = 0.76 and Krippendorff's alpha = 0.71. Prompt sensitivity analysis across four alternative framings shows that task-level rankings are robust. Obv
With the reversal of Roe v. Wade in 2022, many U.S. employers announced they would reimburse employees for abortion-related travel expenses. This action complements increasingly common employer policies subsidizing employee access to assisted reproductive technologies such as in-vitro fertilization and egg freezing. This article reflects on why employers offer these benefits and whether they enhance or undermine reproductive justice. From the employer's perspective, abortion and assisted reproductive technologies help women to plan childbearing around the demands of their jobs. Both are associated with delayed childbirth and reduced fertility, which lower the costs of motherhood to employers. However, firm subsidization of these services does not further reproductive justice because it reifies structures which incentivize women to delay childbirth and reduce fertility, and it reinforces economic and reproductive inequalities. We conclude by questioning whether reproductive justice is possible without transforming the economy so that it prioritizes care over profits.
This paper investigates Large Language Models (LLMs) ability to assess the economic soundness and theoretical consistency of empirical findings in spatial econometrics. We created original and deliberately altered "counterfactual" summaries from 28 published papers (2005-2024), which were evaluated by a diverse set of LLMs. The LLMs provided qualitative assessments and structured binary classifications on variable choice, coefficient plausibility, and publication suitability. The results indicate that while LLMs can expertly assess the coherence of variable choices (with top models like GPT-4o achieving an overall F1 score of 0.87), their performance varies significantly when evaluating deeper aspects such as coefficient plausibility and overall publication suitability. The results further revealed that the choice of LLM, the specific characteristics of the paper and the interaction between these two factors significantly influence the accuracy of the assessment, particularly for nuanced judgments. These findings highlight LLMs' current strengths in assisting with initial, more surface-level checks and their limitations in performing comprehensive, deep economic reasoning, suggesti
The goal of this paper is to investigate the importance of providing visual "big pictures" in the teaching of economics. The plurality and variety of concepts, variables, diagrams, and models involved in economics can be a source of confusion for many economics students. However, reviewing the existing literature on the importance of providing visual "big pictures" in the process of learning suggests that furnishing students with a visual "big picture" that illustrates the ways through which those numerous, diverse concepts are connected to each other could be an effective solution to clear up the mentioned mental chaos. As a practical example, this paper introduces a "big picture" that can be used as a good resource in intermediate macroeconomics classes. This figure presents twenty-seven commonly-discussed macroeconomic diagrams in the intermediate macroeconomics course, and gives little detail on some of these diagrams, aiming at helping students to get the whole picture at once on a single piece of paper. This macroeconomics big picture mostly focuses on the routes through which common diagrams in macroeconomics are connected to each other, and finally introduces the general ma
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 explanator
This study investigates the relationship between the market volatility of the iShares Asia 50 ETF (AIA) and economic and market sentiment indicators from the United States, China, and globally during periods of economic uncertainty. Specifically, it examines the association between AIA volatility and key indicators such as the US Economic Uncertainty Index (ECU), the US Economic Policy Uncertainty Index (EPU), China's Economic Policy Uncertainty Index (EPUCH), the Global Economic Policy Uncertainty Index (GEPU), and the Chicago Board Options Exchange's Volatility Index (VIX), spanning the years 2007 to 2023. Employing methodologies such as the two-covariate GARCH-MIDAS model, regime-switching Markov Chain (MSR), and quantile regressions (QR), the study explores the regime-dependent dynamics between AIA volatility and economic/market sentiment, taking into account investors' sensitivity to market uncertainties across different regimes. The findings reveal that the relationship between realized volatility and sentiment varies significantly between high- and low-volatility regimes, reflecting differences in investors' responses to market uncertainties under these conditions. Additiona
A fundamental challenge for modern economics is to understand what happens when actors in an economy are replaced with algorithms. Like rationality has enabled understanding of outcomes of classical economic actors, no-regret can enable the understanding of outcomes of algorithmic actors. This review article covers the classical computer science literature on no-regret algorithms to provide a foundation for an overview of the latest economics research on no-regret algorithms, focusing on the emerging topics of manipulation, statistical inference, and algorithmic collusion.
Recent studies in psychology and neuroscience offer systematic evidence that fictional works exert a surprisingly strong influence on readers and have the power to shape their opinions and worldviews. Building on these findings, we study what we term Potterian economics, the economic ideas, insights, and structure, found in Harry Potter books, to assess how the books might affect economic literacy. A conservative estimate suggests that more than 7.3 percent of the world population has read the Harry Potter books, and millions more have seen their movie adaptations. These extraordinary figures underscore the importance of the messages the books convey. We explore the Potterian economic model and compare it to professional economic models to assess the consistency of the Potterian economic principles with the existing economic models. We find that some of the principles of Potterian economics are consistent with economists models. Many other principles, however, are distorted and contain numerous inaccuracies, contradicting professional economists views and insights. We conclude that Potterian economics can teach us about the formation and dissemination of folk economics, the intuiti
As the amount of economic and other data generated worldwide increases vastly, a challenge for future generations of econometricians will be to master efficient algorithms for inference in empirical models with large information sets. This Chapter provides a review of popular estimation algorithms for Bayesian inference in econometrics and surveys alternative algorithms developed in machine learning and computing science that allow for efficient computation in high-dimensional settings. The focus is on scalability and parallelizability of each algorithm, as well as their ability to be adopted in various empirical settings in economics and finance.
This paper investigates the economic feasibility of replacing human labor with robotics and automation in Qatar's manufacturing and service sectors. By analyzing labor costs, productivity gains, and implementation expenses, the study assesses the potential financial impact and return on investment of robotic integration. Results indicate the sectors where automation is economically viable and identify challenges related to workforce adaptation, policy, and infrastructure. These insights provide guidance for policymakers and industry stakeholders considering automation strategies in Qatar.
We analyse 'stop-and-go' containment policies that produce infection cycles as periods of tight lockdowns are followed by periods of falling infection rates. The subsequent relaxation of containment measures allows cases to increase again until another lockdown is imposed and the cycle repeats. The policies followed by several European countries during the Covid-19 pandemic seem to fit this pattern. We show that 'stop-and-go' should lead to lower medical costs than keeping infections at the midpoint between the highs and lows produced by 'stop-and-go'. Increasing the upper and reducing the lower limits of a stop-and-go policy by the same amount would lower the average medical load. But increasing the upper and lowering the lower limit while keeping the geometric average constant would have the opposite effect. We also show that with economic costs proportional to containment, any path that brings infections back to the original level (technically a closed cycle) has the same overall economic cost.