General Economics Papers

ALT We examine the joint PV and storage operational and investment decisions of a prosumer under net energy metering (NEM) and peak/off-peak tariffs. Legacy NEM compensates net exports at the retail rate over a netting period, typically under time-invariant pricing. To better reflect system costs, tariff features are being reconsidered, with many U.S. states adopting time-varying prices and export compensation below the retail price. We formulate the prosumer decision problem as a cost minimization problem with charge/discharge, PV investment, and storage investment decisions. We analytically derive the optimal decisions based on the tariff's membership in one of six regions that partition the tariff space. Using these solutions, we examine the relationship between PV and storage, identifying when they are complements or substitutes. Furthermore, we identify the relationship between private DER incentives and the distribution utility's (DU's) net cost of serving the prosumer. For both stor

ALT This paper proves a rigidity theorem characterizing the Gini-type Lorenz-area ordering under general decision-theoretic constraints together with weak Lorenz dominance, thereby resolving the characterization problem pursued in this line of research on egalitarianism. This does not, of course, bring the broader inquiry to an end. In a certain sense, it may instead mark a step beyond the infancy of our attempt to connect social choice theory, welfare economics, and formal moral philosophy through a common mathematical language. The main proof strategy was originally obtained by the author's own hand, though 5.6sol and Astra could effortlessly reproduce it without any additional hint.

ALT For binary-action games on bipartite networks, strategic complements and strategic substitutes are one and the same: each can be transformed into the other by relabelling the actions of every player on exactly one side. In this note, I show that bipartiteness is necessary and sufficient for such a relabelling; as a consequence, bipartite networks are precisely those on which every binary-action game with strategic substitutes must have a pure-strategy Nash equilibrium.

ALT The energy crisis of 2021 to 2023 caused a sharp increase in Nordic electricity prices, with substantial differences across bidding zones. We use this variation to examine whether issuers exposed to higher local electricity prices were more likely to choose green bonds over conventional bonds. We construct a dataset of bonds listed on Oslo Børs between 2019 and 2025 by combining ESMA FIRDS reference data, including matured bonds, with Euronext green bond classifications and issuer registry information. We then match issuers to their electricity bidding zones and examine the relationship between local power prices and green bond choice using issuer and quarter fixed effects. The share of green bonds among new listings increased from 15% in 2019 to 48% in 2024, but we find no statistically significant evidence that higher local electricity prices increased the likelihood of green bond issuance. This finding is consistent across price exposure windows of three to twelve months and for bot

ALT This paper investigates the role of work in providing meaning, purpose, community, and other non-monetary benefits. Using an original cross-national survey of 8,234 working-age adults across the UK, US, Denmark, and India, I study the factors that motivate people in their work, with an eye toward the future AI economic transformation. I find that 81% of respondents report being satisfied with their job, and a majority across all four countries are strongly motivated by the non-monetary components of their employment. Using an original "Work Values" Index, I show that one third of working respondents are motivated intrinsically by their job–valuing their role for its own sake. Nearly one quarter are driven by social and community-related aspects of their employment, 17% by social status, and just over one quarter by the transactional financial benefits of their job. Although a majority of people would leave their role if offered their current salary as a guaranteed income, one fifth of

ALT Many investors hold leveraged exchange-traded funds (LETFs) for more than one day. Because these products reset leverage daily, their longer-horizon returns need not equal the stated leverage multiple times the underlying return. We explain this gap using a return identity that separates what we call the convexity bonus, volatility drag, and product costs. We also discuss several common ways to measure LETF performance and highlight how these measures can produce misleading assessments of how real-world LETFs perform. We propose evaluating ex-post performance using realized terminal wealth, which measures how much invested wealth grew. For ex-ante analysis, we propose using expected log returns to quantify how fast capital compounds over time. We show that estimating expected cumulative return by compounding the historical arithmetic mean can rank investments backwards, report gains for funds that lose money, suggest that infinite leverage is optimal when expected returns exceed borrow

ALT We argued in an earlier paper (Getty and Tkachenko 2025b), from data reported for net saving and market-value capital by 94 nations over the period 1980-2021, that net saving has had little or no effect on capital growth at the global scale. Here we extend the study backward to the earliest year, in each country, for which those data have been reported or reconstructed. We find a still smaller margin of possibility (1.6% versus 6.4%) of contribution of net saving to capital growth at the collective scale. We also propose and defend an alternative explanation of capital growth, and suggest a larger role for the cash flow concept in economic analysis.

ALT I use LLMs to forecast treatment effects of research studies pre-registered on the AEA RCT Registry and the Open Science Framework. I focus on studies whose results were first publicly released in 2026 and elicit forecasts from models whose training or knowledge cut-off dates were before the relevant set of papers was released. First, LLM models extract forecasting questions from the pre-registrations or pre-analysis plans. These questions elicit forecasted treatment effects; not all resolve in the subsequent papers, but those that do can be used to consider forecast accuracy. Forecasts were elicited for 889 treatment effects from 39 papers with results disclosed in January 2026 or later, or 673 from 33 papers after screening. Forecasts were elicited in raw units but I focus on those that could be standardized and therefore compared across papers. Currently, the median absolute forecast error is 0.054 SD for the best model, the average absolute error is 0.093 SD, and the MSE is 0.0218.

ALT Using ward-level panel data for 1927 to 1935 and fixed-effects models, this study examines how pawnshops were related to property-related crime in interwar Tokyo. Accordingly, public pawnshops targeting low-income borrowers are compared with profit-oriented private pawnshops. The results reveal that public lending is negatively associated with theft and positively associated with fraud and extortion, whereas private lending is negatively associated with fraud and extortion as well as embezzlement. A simple monetization exercise shows that offense counts and victim losses can yield different assessments. Overall, the relationship between credit and crime depended on both institutional design and crime type.

ALT How should a dependent economy respond when a polycrisis turns economic integration from a source of efficiency into a surface of vulnerability? This paper develops the exposure-capability framework, which maps a country's strategic options along two dimensions - the concentration of its external dependence and the depth of its productive capability - and yields four ideal strategies. It applies the framework to Hungary's large-scale bet on electric-vehicle battery manufacturing, asking what the concentrated exposure purchased: movement toward a higher position in the battery value chain, as a catch-up gamble, or the reproduction of the country's existing position under a new product, as dependent entrenchment. Using a firm-level panel of Hungary's largest companies for 2010-2024, it tests four hypotheses and examines the conditions attached to the support that financed the investments. The sector has expanded enormously, but its value capture neither rises beyond the recovery from con

ALT We test various hypotheses for the invention and spread of agriculture: higher temperatures, increased seasonality, carbon dioxide fertilization, sea level rise, and technological progress on the extensive margin. We successfully replicate Matranga (QJE 2024). His seasonality hypothesis survives alternative data, estimators, and competing hypotheses. Population density played a role in the invention of agriculture, while spatial diffusion was important for its spread. Sea level rise and climate change had a minor effect on the population density of the areas that first adopted agriculture. Extrapolating a logit model for the invention of agriculture, some now-submerged areas in the Persian Gulf, Mediterranean, and Yellow Sea were suitable for early agriculture. Over time, technological progress was the main factor in extending agriculture's extensive margin.

ALT After decades of trade liberalization, the 2020s mark a profound shift in the globalization narrative, with tariffs once again becoming a central instrument of trade policy. This reversal also challenges how the effects of trade policy are evaluated. Much of the empirical literature has been developed to assess bilateral tariff reductions or elimination and the associated erosion of preferential advantages. Rising multilateral tariffs pose a different question: they alter not only the trade costs faced by directly affected exporters but also the relative market access of all competing suppliers, potentially reinforcing existing preferences. This paper develops a practical framework for translating tariff changes into trade effects in this new policy environment. We argue that liberalization and protectionism should not be treated as symmetric policy experiments: their effects operate through different changes in relative market access and therefore require careful consideration of how

ALT Generative AI changes the scarcity of different forms of human capital. It makes codified knowledge cheaply available at the point of production, while judgment acquired through practice remains tied more closely to the worker. We ask what this does to two familiar prices in the wage structure: the return to schooling and the return to experience. In a short-run framework, machine-supplied codified knowledge substitutes directly for the codified component of worker human capital, lowering the schooling gradient. If codified and practice-based knowledge are complements, the return to experience rises where learning through practice is fastest; technology-specific experience can also become obsolete, rotating the experience profile rather than shifting it uniformly. Using US Current Population Survey earnings data from 2018 through June 2026, we compare occupations in the top and bottom quartiles of the pre-ChatGPT Felten-Raj-Seamans exposure index. After the public release of ChatGPT, t

ALT Most-favored-nation (MFN) tariff increases do more than raise trade costs. By changing the relative tariff treatment of competing exporters, they increase the value of existing trade preferences, reshaping relative market access and trade allocation. While the trade-reducing effects of higher tariffs are well established, much less is known about this mechanism. We investigate whether higher MFN tariffs amplify the competitive advantage of preferential partners, generating trade diversion. Using domestic and international trade data, we estimate a structural gravity model with CES-consistent bilateral tariff margins and examine recent changes in US trade policy as an empirical application. We find that higher MFN protection simultaneously reduces imports and redirects trade toward preferential exporters. These effects are highly heterogeneous across sectors and regions, with the largest losses concentrated in intermediate and capital goods, highlighting the role of MFN tariffs in resha

ALT Studies on price-optimized EV charging consistently find that the common price signal synchronizes charging of the entire fleet of cars in the cheapest hours and creates new "rebound" load peaks in low-voltage grids. Almost all of these studies treat wholesale prices as exogenous, i.e., unaffected by the shifting EV load. This paper argues that this assumption overstates the problem: charging in low-price hours raises the wholesale price in those hours, which spreads charging across hours in equilibrium. I simulate price-optimized charging under real-time prices calibrated to German 2025 data. Even when adjusting for the increase in electricity demand, the coincident import peak of the local grid rises by 260%. After adding a model of equilibrium wholesale prices that takes the price-quantity feedback into account, the increase is reduced to a mere 5%. Allowing for vehicle-to-grid, the export peak rises by 115% and falls by 6%, respectively. The result holds across vehicle and charging

ALT This study develops a country-level measure of the economic-industrial capacity of European NATO Allies to mobilise, replenish, maintain, and sustain military capability under strategic stress. The measure comprises four components: macroeconomic capacity, sectoral resilience, defence-industrial capability, and logistics and sustainment. The analysis covers 28 European Allies and evaluates a balanced baseline against five geoeconomic stress scenarios. The results distinguish baseline capacity from stress vulnerability and provide an assessment framework for identifying the economic-industrial bottlenecks that can constrain sustained military readiness. Baseline scores vary materially across countries, with a sample mean of 0.610 and a range from 0.471 to 0.759. The calibrated geoeconomic shocks reduce effective capacity in every scenario, though the effects are highly heterogeneous. The combined-stress scenario has the largest average effect, lowering the sample mean to 0.491, a declin

ALT AI progress can feed back into AI research in two ways. Capability improvements allow AIs to perform a larger share of AI research tasks, while efficiency improvements raise AI research output per unit of compute on tasks already automated. In a task automation model of AI research, I show that a software-driven intelligence explosion - a self-sustaining acceleration in AI progress with fixed compute and human labor - requires both margins to improve sufficiently fast, and neither margin can substitute for the other. To see whether these conditions hold, I calibrate both margins with data on recent AI progress. I estimate that capability progress is likely fast enough to support an SIE, while efficiency progress is on the knife-edge. In Monte Carlo simulations, an SIE occurs in 49% of draws, indicating that an SIE is plausible but uncertain based on historical trends in AI progress. The single largest source of uncertainty is the rate of efficiency progress, whose uncertainty is respon

ALT Most analyses of AI and employment ask which jobs are exposed. This paper asks what happens to the pipeline that produces experts. It extends the model of Caosun and Aral (2026) from a single worker to a firm with two cohorts, juniors and seniors, and studies how expertise formation changes when codified work is automated. Because junior work is the most codified, demand for juniors falls first; the stock of seniors falls later but further, as the pipeline stops replacing the experts who retire. In the baseline calibration, junior demand bottoms out about a fifth below its 2022 level before recovering most of that ground, while the senior stock falls by a quarter and later halves. Because expertise forms through unassisted practice on work that AI cannot do, the time needed to develop an expert from within rises from about five years to nine. The costs fall on a different party from the one that decides on adoption: a manager with a three-year horizon sets AI use to maximise near-term

ALT Population aging and fertility decline are occurring simultaneously worldwide, straining labor supply and pension systems. China began a gradual delayed-retirement (GDR) reform in 2025, raising statutory retirement ages on different schedules for men, female cadres, and female workers across birth cohorts. To evaluate its effects on older individuals and within the family, we develop a model of older adults' retirement, intergenerational transfers, and adult children's fertility decisions, with parameters estimated via fuzzy regression discontinuity at the pre-2025 statutory retirement ages. Counterfactual simulations show that, for mature reform cohorts, the GDR reform increases older employment and monetary transfers to adult children but reduces grandparental childcare. Because the reduction in childcare outweighs the increase in monetary transfers, fertility and older adults' welfare decline, with particularly large losses among lower-income women. Fully flexible retirement, mandat

ALT We study a model in which artificial intelligence lowers the cost of writing faster than the cost of reading. Authors differ in reputation, and time-constrained readers, such as referees, choose between ignoring, verifying, and trusting papers. As writing costs fall, the band of verified papers shrinks: outsiders are no longer read, and established authors are no longer checked. In the “costless writing limit,” the share of verified papers vanishes, and a paper's impact depends on its author's reputation alone. Readers can escape the flood by asking AI to summarize the literature. These summaries aggregate papers reliably only if their errors are sufficiently independent. Once the share of papers written with the same AI exceeds a threshold, summaries inherit the AI's errors, and AI review filters out the original research that could correct them.