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The application of economics principles to the analysis of behavior has yielded novel insights on value and choice across contexts ranging from laboratory animal research to clinical populations to national trends of global impact. Recent innovations in demand curve methods provide a credible means of quantitatively comparing qualitatively different reinforcers as well as quantifying the choice relations between concurrently available reinforcers. The potential of the behavioral economic approach to inform public policy is illustrated with examples from basic research, pre‐clinical behavioral pharmacology, and clinical drug abuse research as well as emerging applications to public transportation and social behavior. Behavioral Economics can serve as a broadly applicable conceptual, methodological, and analytical framework for the development and evaluation of empirical public policy.

Rationale Behavioral economic drug purchase tasks quantify the reinforcing value of a drug (i.e., demand). Although widely used to assess demand, drug expectancies are rarely accounted for and may introduce variability across participants given diverse drug experiences. Objectives Three experiments validated and extended previous hypothetical purchase tasks by using blinded drug dose as a reinforcing stimulus, and determined hypothetical demand for experienced effects while controlling for drug expectancies. Methods Across three double-blind, placebo-controlled, within-subject experiments, cocaine (0, 125, 250 mg/70 kg; n =12), methamphetamine (0, 20, 40 mg; n =19), and alcohol (0, 1 g/kg alcohol; n =25) were administered and demand was assessed using the Blinded-Dose Purchase Task. Participants answered questions regarding simulated purchasing of the blinded drug dose across increasing prices. Demand metrics, subjective effects, and self-reported real-world monetary spending on drugs were evaluated. Results Data were well modeled by the demand curve function, with significantly higher intensity (purchasing at low prices) for active drug doses compared to placebo for all experiments. Unit-price analyses revealed more persistent consumption across prices (lower α ) in the higher compared to lower active dose condition for methamphetamine (a similar non-significant finding emerged for cocaine). Significant associations between demand metrics, peak subjective effects, and real-world spending on drugs also emerged across all experiments. Conclusions Orderly demand curve data revealed differences across drug and placebo conditions, and relations to real-world measures of drug spending, and subjective effects. Unit-price analyses enabled parsimonious comparisons across doses. Results lend credence to the validity of the Blinded-Dose Purchase Task, which allows for control of drug expectancies.

Operating reserve demand curves (ORDCs) have become part of the electricity market design in several power systems. They improve the security of supply through enhanced peak prices that occur already when the system is running low on operating reserves, before an actual shortfall occurs. Previous research, however, suggests that the ORDC’s impact on resource adequacy would be thwarted by the merit order effect. Hence, we propose a methodology to model the investment in markets with ORDC, which specifically captures the interaction with renewable deployment. A stylized power system setting is used to determine the market equilibrium at different stages of decarbonization, and compared to a conventional energy-only market. Classical ORDCs consistently increase reliability by attracting additional investments. This effect can be amplified by “shifting” the ORDC, increasing the willingness to pay for balancing reserves. Our results suggest that perfect reliability can be achieved with only moderate cost increases.

This paper examines, through a modified aggregate demand curve, the reduction in equilibrium income caused by the presence of precautionary demand in the money demand function. Specifically, this paper rigorously analyses the transformation of the well-known fiscal and monetary policy multipliers, β and γ, commonly found in macroeconomic theory textbooks. Ceteris paribus, an increase (decrease) in precautionary money demand reduces (increases) equilibrium income, as can be seen through the modified multipliers β and γ. Multiple contingencies that emerged suddenly between 2008 and 2023 may have altered agents’ perceptions regarding precautionary money demand. This work contributes to the adaptation of some well-established tools of macroeconomic theory to address events of this nature.

Scarcity pricing is a mechanism for improving the valuation of reserve capacity in real-time electricity markets. The goal of scarcity pricing is to mitigate the missing money problem and enhance investment in flexible resources. The implementation of scarcity pricing is underway in a number of U.S. markets, including Texas and PJM. The implementation is also currently under consideration in Belgium. As the mechanism was originally conceived in the context of a U.S.-style two-settlement system, its implementation in a European setting poses a number of interesting market design dilemmas which can affect the back-propagation of scarcity prices to forward day-ahead markets for energy and reserve capacity. We propose a modeling framework for analyzing these market design choices based on stochastic equilibrium, and use this modeling framework in order to represent and analyze a wide range of market design proposals. We report results on a case study of the Belgian electricity market.

As the proportions of variable renewable sources (VRSs) such as solar and wind energy increase rapidly in the power system, their uncertainties inevitably undermine power supply reliability and increase the amount of operating reserve resources required to manage the system. However, because operating reserves have the characteristics of a public good and their value is related to the social cost of blackouts, it is difficult to determine their market price efficiently, which leads to inefficiencies in procuring operating reserves. This study estimates the operating reserve demand curve (ORDC) of the Korean power system to provide an effective basis for measuring the proper value and quantity of operating reserves needed to meet the reliability standard. A stochastic dynamic optimization model is applied to incorporate the probabilistic characteristics of VRS and the inter-hour constraint, which is necessary for analyzing load-following reserves. An econometric model and the Monte Carlo simulation method are used to generate the forecast profiles of solar and wind generation. The results indicate that the proper amount of hourly operating reserves needed in 2034 is approximately 4.4 times higher than that in 2020 at the current reserve offer price. The ORDC of 2020 has a price-inelastic shape, whereas the ORDC of 2034 has a price-elastic shape because the reserve requirement varies considerably with its offer price level in the high-VRS penetration case. This variability is due to alternatives, such as VRS curtailment or load shedding, which can replace the reserve requirement. This study also showed that VRS curtailment is an effective balancing resource as an alternative to reserves.

The optimal management of scarce transboundary water resources among competitive users is expected to be challenged by the effects of climate change on water availability. The multiple economic and social implications, including conflicts between neighbouring countries, as well as competitive sectors within each country are difficult to estimate and predict, to inform policy-making. In this paper, this problem is approached as a stochastic multistage dynamic game: we develop and apply a novel framework for assessing and evaluating different international strategies regarding transboundary water resources use, under conditions of hydrological uncertainty. The Omo-Turkana transboundary basin in Africa is used as a case study application, since it increasingly faces the above challenges, including the international tension between Kenya and Ethiopia and each individual country’s multi-sectoral competition for water use. The mathematical framework combines a hydro-economic model (water balance, water costs and benefits), and an econometric model (production functions and water demand curves) which are tested under cooperative and non-cooperative conditions (Stackelberg “leader–follower” game). The results show the cross-country and cross-sectoral water use—economic trade-offs, the future water availability for every game case, the sector-specific production function estimations (including residential, agriculture, energy, mining, tourism sectors), with nonparametric treatment, allowing for technical inefficiency in production and autocorrelated Total Factor Productivity, providing thus a more realistic simulation. Cooperation between the two countries is the most beneficial case for future water availability and economic growth. The study presents a replicable, sophisticated modelling framework, for holistic transboundary water management.

The energy sector is moving towards a low-carbon, decentralised, and smarter network. The increased uptake of distributed renewable energy and cheaper storage devices provide opportunities for new local energy markets. These local energy markets will require probabilistic price forecasting models to better describe the future price uncertainty. This article considers the application of probabilistic electricity price forecasting models to the wholesale market of Great Britain (GB) and compares them to better understand their capabilities and limits. One of the models that this paper considers is a recent novel X-model that predicts the full supply and demand curves from the bid-stack. The advantage of this model is that it better captures price spikes in the data. In this paper, we provide an adjustment to the model to handle data from GB. In addition to this, we then consider and compare two time-series approaches and a simple benchmark. We compare both point forecasts and probabilistic forecasts on real wholesale price data from GB and consider both point and probabilistic measures.

Wildlife consumption can be viewed as an ecosystem provisioning service (the production of a material good through ecological functioning) because of wildlife's ability to persist under sustainable levels of harvest. We used the case of wildlife harvest and consumption in northeastern Madagascar to identify the distribution of these services to local households and communities to further our understanding of local reliance on natural resources. We inferred these benefits from demand curves built with data on wildlife sales transactions. On average, the value of wildlife provisioning represented 57% of annual household cash income in local communities from the Makira Natural Park and Masoala National Park, and harvested areas produced an economic return of U.S.$0.42 ha⁻¹· year⁻¹. Variability in value of harvested wildlife was high among communities and households with an approximate 2 orders of magnitude difference in the proportional value of wildlife to household income. The imputed price of harvested wildlife and its consumption were strongly associated (p< 0.001), and increases in price led to reduced harvest for consumption. Heightened monitoring and enforcement of hunting could increase the costs of harvesting and thus elevate the price and reduce consumption of wildlife. Increased enforcement would therefore be beneficial to biodiversity conservation but could limit local people's food supply. Specifically, our results provide an estimate of the cost of offsetting economic losses to local populations from the enforcement of conservation policies. By explicitly estimating the welfare effects of consumed wildlife, our results may inform targeted interventions by public health and development specialists as they allocate sparse funds to support regions, households, or individuals most vulnerable to changes in access to wildlife. Valoración Económica de la Caza de Subsistencia de Vida Silvestre en Madagascar

In recent years, the Chinese government has gradually introduced the multi-child policy to control the aging phenomenon. The aging of the Chinese population has aroused international attention. How the aging of the Chinese population will affect housing prices makes this study focus on the question. This study fitted the housing demand curve using the CGSS database and found that the housing demand will continue to rise as residents enter middle age. After residents enter old age, housing demand shows a slow decline. Then, this study establishes the spatial Dubin model to analyze the influence mechanism of population aging on housing prices in China, and finds that aging has a positive impact on housing prices. However, with the intensification of the aging phenomenon in China, the benefits brought by the welfare housing distribution system to the elderly will be gradually consumed, and the insufficient supply of social labor force and the stagnation of total output, its positive impact on housing prices will gradually weaken or even become negative.