The estimation of agricultural policy effects on soil erosion—An application for the bio-economic model MODAM

As agricultural policies affect land use, they have effects on the amount of soil erosion in agricultural regions through changes of the economic conditions of agricultural production. Prices of inputs and outputs, regulations and incentives can change, forcing or encouraging farmers to adopt new crop rotations. This paper shows how a bio-economic model can be used to describe and estimate the effects of policies on agricultural production and the risk of soil erosion at the example of a region in North-Eastern Germany. The model uses both an assessment tool that is based on a fuzzy-logic approach for the estimation of soil erosion risk of cropping practices, and a linear programming model, that simulates farmers’ economic behaviour under the assumption of gross margin maximisation being the main goal of farmers’ actions.The analysed policy options were both a targeted and an untargeted incentive programme for reduced tillage, and a restriction option where high erosive crops are not allowed on high erodible field types. The results show that policy changes can have an impact on soil erosion. Furthermore, soil conservation policies are shown to have different levels of efficiency in terms of reduced soil erosion related to the costs of the policy. In the case of this study, a restriction option was more efficient than the incentive options. The results of such simulations can serve as a decision support for the development of soil conservation policies and help to foresee the effects of general changes of agricultural policies.     

On the use of flexible mixing distributions in WTP space: an induced value choice experiment

In this study, we use data from an induced value choice experiment to compare estimates from mixed logit models in willingness to pay (WTP) space using different parameter distributional assumptions. Specifically, we test differences in WTP estimates when using flexible parameter mixing distributions (i.e. Legendre polynomials, step functions and splines) and conventional parameter distributions (normal and lognormal). Similar WTP estimates are obtained. However, we observe that WTP estimates are statistically different from the induced value when conventional distributions are assumed, but they are not when more flexible distributions are assumed. This suggests that flexible distributions can provide more reliable WTP estimates.     

Import penetration and returns to tasks: recent evidence from the Peruvian labour market

This paper provides original evidence on the impact of import penetration on wages of individuals performing manual/cognitive task-intensive jobs in the Pe     

Metal prices made in China? A network analysis of industrial metal futures

In addition to being the world's greatest consumer and producer of industrial metals, China now also features the most actively traded industrial metal futures contracts worldwide. To examine China's role in the global price formation process of industrial metal futures markets, we use a sample of 29 futures contracts traded on exchanges in the United States, the United Kingdom, India, and China. We estimate vector autoregressive models and conduct variance decompositions, which are then visualized in the form of networks. The results indicate that China, despite its role as key actor in both real and financial industrial metal markets, is a price taker.     

Robust Small Area Estimation under Spatial Non‐stationarity

The effective use of spatial information in a regression‐based approach to small area estimation is an important practical issue. One approach to account for geographic information is by extending the linear mixed model to allow for spatially correlated random area effects. An alternative is to include the spatial information by a non‐parametric mixed models. Another option is geographic weighted regression where the model coefficients vary spatially across the geography of interest. Although these approaches are useful for estimating small area means efficiently under strict parametric assumptions, they can be sensitive to outliers. In this paper, we propose robust extensions of the geographically weighted empirical best linear unbiased predictor. In particular, we introduce robust projective and predictive estimators under spatial non‐stationarity. Mean squared error estimation is performed by two analytic approaches that account for the spatial structure in the data. Model‐based simulations show that the methodology proposed often leads to more efficient estimators. Furthermore, the analytic mean squared error estimators introduced have appealing properties in terms of stability and bias. Finally, we demonstrate in the application that the new methodology is a good choice for producing estimates for average rent prices of apartments in urban planning areas in Berlin.     

Economists tackle the challenges of a pandemic

Business Economics - The COVID-19 pandemic radically and rapidly changed the world, including the world of business economists. Eight NABE members employed in a wide variety of fields discuss how...