Researchers using panel data to estimate causal effects routinely choose among three approaches to using past outcomes: difference-in-differences (DID), conditioning on lagged outcomes (matching, M), and a hybrid that does both (DIDM). The corresponding identifying assumptions are non-nested, leaving little guidance on which to report. We give conditions under which the corresponding estimands are ordered, with DIDM bracketed between matching and DID. This makes DIDM the minimax-regret choice among the three under a broad class of loss functions. We recommend reporting DIDM as the headline estimate, with matching and DID as bounds. We illustrate in applications.

Scholarly debates on China's agricultural growth between 1949 and 1986 continue to differ over the extent of the price scissors, the effect of heavy industrial investment, the role of the 1978 reforms, and the impact of decollectivization on irrigation. Using a single dataset and complementary econometric methods, this paper addresses each of these controversies. The results show that 1952--1957 was the only net extraction period across all three channels, after which the state channelled a net inflow of about 168.6 billion yuan into agriculture via fiscal and credit instruments. Heavy industrial investment exerted a significant positive lagged effect on agriculture, while the contemporaneous negative correlation stemmed from the zero-sum nature of the investment share indicator. The input-output elasticity shifted abruptly in 1970, and collective agricultural loans broke in 1971, both pointing to the rectification effects of the North China Agricultural Conference. Disaster prevention capacity fell from 0.70 under the collective era to 0.53 after household contracting, mainly because the collective maintenance system collapsed rather than because state investment declined. After 1979 the price elasticity of agricultural supply approached zero, suggesting that the 1979 procurement price increase acted more like a one-off recalibration than a sustained marginal incentive.

This study constructs novel biodiversity related media risk indicators for France, Germany, Italy, and Spain over 2015-2025, capturing media attention to biodiversity threats using the GDELT Global Knowledge Graph. Using panel Granger causality tests and an augmented inverse probability weighting (AIPW) event-study design, we find highly significant evidence that biodiversity risk reduces stock prices, with effects peaking between 3 and 10 months after a shock. Moreover, we uncover a marked asymmetry whereby the positive effects of low biodiversity risk episodes outweigh the negative effects of high-risk episodes. Results are robust across quantiles of the return distribution and hold when controlling for European equity market volatility and economic policy uncertainty. Our findings provide the first evidence that biodiversity media narratives drive stock market valuations in Europe.

Two-sample IV is a popular estimation method when the outcome and treatment variables are available in different samples, whereas instruments are available in both samples. The standard estimator is two-sample two-stage least squares estimator, which is efficient under homoskedasticity and homogeneity of the samples. We develop a robust two-step procedure for efficient estimation under general heteroskedasticity and heterogeneity of the samples, and propose a related two-sample Hansen overidentification test. A key feature of our approach is that only summary statistics from the linear regressions of the reduced form and first-stage in the two samples are needed. These are the six objects of the estimated coefficient vectors, and the homoskedastic and heteroskedasticity robust estimated variance matrices. We further show that the first-stage F-statistic in the treatment sample can be used as a test for weak instruments in the standard way under homoskedasticity and homogeneity, with the relative bias here a proportional bias. We propose an extension of the effective F-statistic of Montiel-Olea and Pflueger (2013) for the heteroskedastic case, following the generalization in Windmeijer (2025). We illustrate the estimators and tests in an application studying the effect of education on voting behavior from Marshall (2019), with cluster robust inference.

AI augmentation breaks the accounting link between labor time and productive contribution, yet firms continue to evaluate talent through time-based overhead bundles. This paper develops a forecasting framework for the transition from time-based talent accounting to output-based talent ROI in the human-AI era. The framework centres on Theorem 3 (ROI Inversion at τ*) as the empirical spine, with four mechanism theorems: overhead non-additivity, augmentation-saved-time pathways, innovation-premium amplification, and human-AI dyad attribution uncertainty. Korea's staged 52-hour workweek mandate provides an empirical early-warning case. In a DART panel of 365 listed firms (2,281 firm-year observations), the SG&A-to-revenue ratio rose from 18.26 percent in 2018 to 20.06 percent in 2020, corrected mildly in 2021-2022, and peaked at 20.10 percent in 2024. Under the revenue-percentile cohort proxy, two-way fixed effects (+1.56 pp, p = 0.049), pooled event-study estimates (+4.21 pp at t = +3, p = 0.001), and Callaway-Sant'Anna doubly-robust staggered DiD estimates (+4.51 pp at t = +4) converge on a positive overhead-pressure signature. A 2015-2017 backward extension (224 firms, 601 observations) supplies pre-treatment data, providing evidence against pre-existing upward-trend confounds. We read the Korean evidence not as a direct τ* estimate or a point causal magnitude, but as, to our knowledge, the first empirically documented signature of the pre-τ overhead-pressure regime, where time-based accounting still dominates while AI augmentation and labor-time compression jointly raise overhead. Output-based firms are forecast to outperform time-based peers by 1.5-2.0 percentage points in firm-level TFP growth by 2032. The contribution is a forecasting model and managerial planning tool for the shift to AI-augmented talent ROI accounting.

We propose a model-grounded RAG-based AI economist with an agentic framework for economic scenario analysis using large language models (LLMs) and knowledge graphs. While LLMs can generate fluent economic narratives, economists are often required to make economic claims grounded by economic theory and real-world data. Based on this motivation, this study proposes an RAG-based AI economist, which utilizes knowledge graphs including economic data and theory and LLM-based agents to plan the analysis, retrieve relevant evidence, select appropriate models, and generate reports. In our framework, we do not produce quantitative claims directly with the language model alone; instead, we generate narratives grounded in explicit model-based computations and linked to the retrieved evidence via AI agents. We refer to our framework as an AI economist agent. We evaluate the AI economist agent in two applications: economist report generation for U.S. inflation persistence and Federal Reserve policy, and bank stress-test narrative generation for U.S. commercial real estate refinancing stress. The results illustrate how grounding the generated reports improves their economic coherence and traceability.

Why does massive AI investment fail to generate commensurate productivity gains? We argue the paradox is theoretically generated: prevailing production function frameworks encounter a structural boundary by treating AI as a separable factor of production without modeling the cognitive mediation through which AI generates productive value. This directs investment toward deployment when productivity requires prior development of what we term convergence capacity (C). We propose the Intellectually Converged Human (ICH) framework, a fifth-stage framework for production function theory: H-hat = H[1 + phi(A,C)], where effective productive capacity equals human capital (H) scaled by an augmentation factor [1 + phi], with phi jointly determined by AI utilization intensity (A) and convergence capacity (C), a four-dimensional cognitive construct encompassing embodied understanding, metacognition, temporal integration, and integrative thinking. The production function Y = F(K, H-hat) provides a human-centered mechanism for Solow's TFP residual: A_Solow = [1 + phi(A,C)]^(1-alpha). The framework predicts three augmentation regimes with distinct policy implications. Descriptive cross-national analysis of 20 OECD economies shows the AIxC interaction is associated with 86% of TFP variance versus 31% for AI alone, a pattern-consistent finding in the small-n theoretical tradition. South Korea exemplifies national-scale under-augmentation: high H, substantial A, low C produce phi = 0. We distinguish convergence capacity from adjacent constructs, absorptive capacity, dynamic capability, and human capital, and demonstrate that C constitutes the specific cognitive mediator that prior frameworks have left implicit. We derive C-first policy prescriptions and offer three empirically testable propositions with a falsifiable 10-year forecast.

We study ramping procurement co-optimized with economic dispatch under net-demand uncertainty. We examine two flexible ramp product designs implemented by grid operators: single-interval and multi-interval co-optimization. Both rely on rolling-window stochastic optimization with binding and advisory interval decisions. We develop analytical frameworks to evaluate generator profits, consumer payments, bid cost recovery (BCR), and operational efficiency. In particular, net-demand uncertainty may lead to generator under-compensation, requiring discriminatory BCR. While operational efficiency is invariant to energy and ramp prices, producer profits and consumer payments depend critically on pricing. We examine locational marginal pricing (LMP) and two uniform pricing: maximum dispatch cost pricing (MDCP) and maximum temporal locational marginal pricing (MTLMP). With out-of-market BCR, LMP yields discriminatory energy prices, whereas MDCP eliminates BCR and MTLMP does so in most cases. This property enables us to establish truthful bidding incentives for price-taking generators under MDCP. Our analysis highlights trade-offs between single- and multi-interval co-optimization and pricing designs: single-interval energy-ramp co-optimization is advantageous under high forecast uncertainty and moderate ramping requirements, whereas multi-interval co-optimization is superior when net-demand forecasts are relatively accurate and ramp needs are challenging. Empirical results on CAISO and ERCOT data show that MDCP and MTLMP increase producer profits with negligible BCR, albeit at the expense of higher consumer payments relative to LMP.

We estimate that data centers caused average retail electricity rates to fall modestly in the United States from 2015 to 2024 using an instrumental variables approach. Despite prevailing sentiment, the finding is consistent with economic reasoning: existing large power system fixed costs, economies of scale in transmission and distribution, and declining unit costs for generation imply that durable demand growth lowers average prices. We find patterns of economies of scale for transmission, distribution, and generation costs as well as within and across retail customer classes. We caution that future supply constraints could reverse the effect.

Organizations and researchers show increasing interest in using large language models (LLMs) in place of human participants in A/B tests, in the hope of experimenting faster and at lower cost. We study when a treatment effect estimated on LLM outcomes can recover the effect that would have been measured on the human population of interest. Distributional equivalence between LLM and human outcomes would make any standard estimator valid but is unrealistic. We therefore develop a statistical framework that adapts surrogate endpoint theory to LLMs, showing that calibrating LLM outcomes to human outcomes identifies the average treatment effect under surrogacy and comparability conditions that are jointly weaker than distributional equivalence. We present a falsification test for surrogacy and a bound on the worst-case bias from limited overlap between the LLM and human samples. We further show that the stochasticity inherent to LLMs can weaken surrogacy for identification while also introducing bias and variance during estimation, but that using an average over multiple LLM draws per unit as the surrogate mitigates these issues. Simulations validate the results, and an empirical application to A/B tests on Upworthy headlines shows that raw LLM predictions recover only 39\% of the human treatment effect while nonparametric calibration closes the gap. A central takeaway is that A/B testing on LLMs yields correct results only by assumption, whereas A/B testing on humans is correct by design, and that the required assumptions are hardest to justify precisely where A/B testing on LLMs promises the greatest benefit. We discuss the role of LLM choice, prompting, and temperature as design variables, the compounded challenge posed by long-term outcomes, and how to size human pilot studies for validation.