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We investigate the hypothesis that macroeconomic fluctuations are primitively the results of many microeconomic shocks. We define fundamental volatility as the volatility that would arise from an economy made entirely of idiosyncratic sectoral or firm-level shocks. Fundamental volatility accounts for the swings in macroeconomic volatility in the major world economies in the past half-century. It accounts for the \"great moderation\" and its undoing. The initial great moderation is due to a decreasing share of manufacturing between 1975 and 1985. The recent rise of macroeconomic volatility is chiefly due to the growth of the financial sector.

Wind turbines have shown significant advancements in efficiency and power output in the last 20 years. However, during the same time period, parallel advances in the manufacturing of wind blades have not happened due to the reluctance of wind blade manufacturers to make significant capital investments into unproven automation technologies. This reluctance is due in part to the lack of access to a robust techno‐economic model that can give feedback on if and how these investments will impact the overall cost to make a blade. In the current research, existing costing methods are reviewed and a new techno‐economic model for estimating the number of man‐hours required to manufacture a wind turbine blade is proposed. A set of standardized input parameters for the proposed model is developed from time studies completed at three wind blade manufacturing facilities. The proposed model uses 77 discrete processing steps. The credibility of the proposed model is validated by its ability the predict the required man‐hours for the manufacture of previously produced blades by the blade manufacturers. Additionally, validation is done against models of publicly available designs. The proposed model delivers more accurate and realistic estimates over prior models reflecting the application of current production techniques and detailed design information.

Socio‐economic development pathways and their implications for the environment are highly uncertain, and energy transitions will involve complex interactions among sectors. Here, traditional Monte Carlo analysis is paired with scenario discovery techniques to provide a richer portrait of these complexities. Modeled uncertain input variables include costs of advanced energy technologies, energy efficiency trends, fossil fuel resource availability, elasticities of substitution for labor, capital, and energy across economic sectors, population growth, and labor and capital productivity. The sampled values are simulated through a multi‐sector, multi‐region, recursively dynamic model of the world economy to explore a range of possible future outcomes. We find that many patterns of energy and technology development are possible for various long‐term environmental pathways and that sectoral output for most sectors is little affected through 2050 by the long‐term temperature target, but with tight constraints on emissions, emission intensities must fall much more rapidly. Scenario discovery techniques are applied to the large uncertainty ensembles to explore if there are prevailing storylines behind outcomes of interest. An illustrative investigation focused on different levels of economic growth shows many combinations of pathways and no single storyline emerging for a given economic outcome. This method can be extended to other outcomes of interest, exploring the nature of scenarios with both tail and median outcomes. Sampling from a Monte Carlo generated ensemble provides a rich set of scenarios to investigate, and potentially aids in avoiding heuristic biases in less structured scenario approaches. Plain Language Summary Simulation models are often used to explore future development pathways and their impacts on energy, emissions, economies and the environment. This requires making assumptions about various socio‐economic conditions, such as how fast populations and economies will grow, the cost of technology options, or the amount of fossil fuels available. Different assumptions have significant impacts on model results, yet analyses typically only test a few alternatives. Here, we develop and use probability distributions to capture this uncertainty. We draw samples from these distributions, run an energy‐economic model hundreds of times, and quantify the resulting uncertainty in model outcomes, providing insight into their likelihood. We focus on results related to emissions and output from different economic sectors, as well as energy and electricity technologies. We also apply approaches to find scenarios of interest from within the database of scenarios. We find that many patterns of energy and technology development are possible under a given long‐term environmental pathway (such as a 2C scenario) or a given economic outcome (such as high or low GDP). This approach can help identify biases in perceptions of what “needs” to happen to achieve certain outcomes, and shows that there are many pathways to a successful energy transition. Key Points Traditional uncertainty quantification approaches can be combined with scenario discovery techniques to provide new insights Many patterns of energy and technology development are possible under a given long‐term environmental pathway Long‐term temperature targets have a much larger impact on sectoral emissions intensities than on sectoral output through 2050

We show that a theory of earnings and wealth inequality, based on the optimal choices of ex ante identical households that face uninsured idiosyncratic shocks to their endowments of efficiency labor units, accounts for the U.S. earnings and wealth inequality almost exactly.

Payments to compensate landowners for carrying out costly land-use measures that benefit endangered biodiversity have become an important policy instrument. When designing such payments, it is important to take into account that spatially connected habitats are more valuable for many species than isolated ones. One way to incentivize provision of connected habitats is to offer landowners an agglomeration bonus, that is, a bonus on top of payments they are receiving to conserve land if the land is spatially connected. Researchers have compared the cost-effectiveness of the agglomeration bonus with 2 alternatives: an all-or-nothing, agglomeration payment, where landowners receive a payment only if the conserved land parcels have a certain level of spatial connectivity, and a spatially homogeneous payment, where landowners receive a payment for conserved land parcels irrespective of their location. Their results show the agglomeration bonus is rarely the most cost-effective option, and when it is, it is only slightly better than one of the alternatives. This suggests that the agglomeration bonus should not be given priority as a policy design option. However, this finding is based on consideration of only 1 species. We examined whether the same applied to 2 species, one for which the homogeneous payment is best and the other for which the agglomeration payment is most cost-effective. We modified a published conceptual model so that we were able to assess the cost-effectiveness of payment schemes for 2 species and applied it to a grassland bird and a grassland butterfly in Germany that require the same habitat but have different spatial-connectivity needs. When conserving both species, the agglomeration bonus was more cost-effective than the agglomeration and the homogeneous payment; thus, we showed that as a policy the agglomeration bonus is a useful conservation-payment option. Los pagos para compensar a los terratenientes por llevar a cabo medidas costosas de uso de suelo que benefician a la biodiversidad en peligro se han convertido en un instrumento político importante. Cuando se diseñan dichos pagos, es importante considerar que los hábitats conectados espacialmente son más valiosos para muchas especies que los hábitats aislados. Una forma de incentivar la provisión de hábitats conectados es ofreciéndole a los terratenientes un bono por aglomeración, que es un bono adicional a los pagos que reciben por conservar el suelo si este está espacialmente conectado. Los investigadores han comparado la rentabilidad del bono por aglomeración con dos alternativas: un pago por aglomeración todo o nada, en el que los terratenientes reciben un pago sólo si las parcelas de suelo conservado tienen cierto nivel de conectividad espacial; y un pago homogéneo espacialmente, en el que los terratenientes reciben un pago por parcelas de suelo conservado independientemente de su ubicación. Sus resultados muestran que el bono por aglomeración es rara vez la opción más rentable y cuando lo es, es mejor que una de las alternativas por muy poco. Esto sugiero que al bono por aglomeración no se le debería dar prioridad como opción de diseño de políticas. Sin embargo, este hallazgo está basado en la consideración de una sola especie. Examinamos si lo mismo aplicaba para dos especies, una para la que el pago homogéneo es mejor y otra para la que el pago por aglomeración es el más rentable. Modificamos un modelo conceptual publicado, de tal manera que pudimos evaluar la rentabilidad de los esquemas de pago para dos especies y lo aplicamos a un ave de pastizal y a una mariposa de pastizal de Alemania que requieren el mismo hábitat pero tienen necesidades espaciales y de conectividad diferentes. Cuando se conservaron ambas especies, el bono por aglomeración fue más rentable que el pago por aglomeración o el pago homogéneo; por esto, mostramos que el bono por aglomeración es una opción útil de pago por conservación.

We do not have a good measure of the effects of fiscal policy in a recession because the methods that we use to estimate the effects of fiscal policy—both those using the observed outcomes following different policies in aggregate data and those studying counterfactuals in fitted model economies—almost entirely ignore the state of the economy and estimate \"the\" government multiplier, which is presumably a weighted average of the one we care about—the multiplier in a recession—and one we care less about—the multiplier in an expansion. Notable exceptions to this general claim suggest this difference is potentially large. Our lack of knowledge stems significantly from the focus on linear dynamics: vector autoregressions and linearized (or close-to-linear) dynamic stochastic general equilibrium (DSGE) models. Our lack of knowledge also reflects a lack of data: deep recessions are few and nonlinearities hard to measure. The lack of statistical power in the estimation of nonlinear models using aggregate data can be addressed by exploiting estimates of partial-equilibrium responses in disaggregated data. Microeconomic estimates of the partial-equilibrium causal effects of a policy can discipline the causal channels inherent in any DSGE model of the general equilibrium effects of policy. Microeconomic studies can also provide measures of the dependence of the effects of a policy on the states of different agents, which is a key component of the dependence of the general-equilibrium effects of fiscal policy on the state of the economy.

A central difficulty in economics is to create a model with both good business cycle properties and asset pricing properties. I show how to solve this difficulty by a simple portable modeling device: the “disasterization” of models. Take an economy with good business cycle properties and create a new, “disasterized” economy, which is essentially identical to the original one except that disasters can destroy part of the capital stock and productivity. In such a disasterized economy, asset prices exhibit high and volatile risk premia, but macro variables remain unchanged. Perturbations of this benchmark allow for feedback from finance to macro.

Macroeconomists overwhelmingly believe that macroeconomics requires microfoundations, typically understood as a strong eliminativist reductionism. Microfoundations aims to recover intentionality. In the face of technical and data constraints macroeconomists typically employ a representative-agent model, in which a single agent solves the microeconomic optimization problem for the whole economy, and take it to be microfoundationally adequate. The characteristic argument for the representative-agent model holds that the possibility of the sequential elaboration of the model to cover any number of individual agents justifies treating the policy conclusions of the single-agent model as practically relevant. This eschatological justification is examined and rejected.

Recent literature on systematic conservation planning has focused strongly on economics. It is a necessary component of efficient conservation planning because the question is about effective resource allocation. Nevertheless, there is an increasing tendency toward economic factors overriding biological considerations.Focusing too narrowly on economic cost may lead us back toward solutions resembling those obtained by opportunistic choice of areas, the avoidance of which was the motivation for development of systematic approaches. Moreover, there are many overlooked difficulties in incorporating economic considerations reliably into conservation planning because available economic data and the free market are complex.For instance, economies based on free markets tend to be shortsighted, whereas biodiversity conservation aims far into the future. Although economic data are necessary, they should not be relied on too heavily or considered separately from other sociopolitical factors. We suggest focusing on development of more-comprehensive ecological-economic modeling, while not forgetting the importance of purely biological analyses that are needed as a point of reference for evaluating conservation outcomes. La literatura reciente sobre la planificación de la conservación sistemática se concentra fuertemente en la economía. Es un componente necesario de la planificación eficiente de la conservación porque el asunto es sobre la asignación efectiva de recursos. Sin embargo, hay una tendencia creciente hacia el dominio de los factores económicos sobre las consideraciones biológicas. Concentrarse demasiado en el costo económico puede llevarnos en retroceso hacia soluciones semejantes a las obtenidas por la selección oportunista de áreas, cuyo rechazo fue la motivación para el desarrollo de aproximaciones sistemáticas. Más aun, se pasan por alto muchas dificultades en la incorporación confiable de consideraciones económicas en la planificación de la conservación porque los datos económicos disponibles y el mercado libre son complejos.Por ejemplo, las economías basadas en mercados libres tienden a ser cortas de vista, mientras que la conservación de la biodiversidad apunta lejos hacia el futuro. Aunque los datos económicos son necesarios, no se debe confiar demasiado en ellos ni considerarlos separadamente de otros factores sociopolíticos. Sugerimos concentrarse en el desarrollo de un modelo económico-ecológico más integral, sin olvidar la importancia de los análisis puramente biológicos que se requieren como punto de referencia para la evaluación de los resultados de conservación.