Hugo Storm

Direct Payments, Spatial Competition, and Farm Survival in Norway

We argue that farm survival is influenced by neighboring farmers’ characteristics and, in particular, by the direct payments neighboring farmers receive. The article shows empirically that these interdependencies are crucial for an assessment of the effects of direct payments on farm survival. Using spatially explicit farm-level data for nearly all Norwegian farms, a spatial probit model is estimated to explain farm survival from 1999 to 2009 controlling for spatial farm interdependence. We show that ignoring spatial interdependencies between farms leads to a substantial overestimation of the effects of direct payments on farm survival. To our knowledge, this article is the first attempt to empirically analyze the importance of neighboring interdependencies for the effects of direct payments on farm survival.

Modelling farm structural change: A feasibility study for ex-post modelling utilizing FADN and FSS data in Germany and developing an ex-ante forecast module for the CAPRI farm type layer baseline

The present study aims to develop a prototype analytical tool to assess structural changes at the farm level in EU-27 using the Farm Accountancy Data Network (FADN) combined with the Farm Structure Survey (FSS). For the purpose of this study, farm structural change is related to the change in production systems, therefore a change in farm size and farm entry/exit into one sector/farm typology. In the ex-post analysis of structural change two methodologies are presented, one in which structural change is analysed from a discrete perspective using a Markov approach, whereas the second uses the continuous perspective to evaluate the type of farming over time using MCI (Multiplicative Competitive Interaction) models. The methodolgies are applied in selected German regions and the goodness of fit in the out of sample prediction is compared. In the ex-ante methodology, the existing farm module of CAPRI (Common Agricultural Policy Regionalised Impact System) is expanded by considering the findings of the statistical ex-post-analysis when projecting farm-type structural change in the baseline trends. Results show that the Markov prediction may outperform naive prediction methods but that the quality of the prediction is critically dependent on the model specification. A higher in-sample fit does not necessarily lead to better out-of-sample prediction, which potentially indicates that the effects of specific explanatory variables may change over time. In addition, introducing structural change into the CAPRI farm type baseline improve policy impact assessment and hence a more reliable and consistent farm grid for simulations is constructed.

Reducing omitted-variable bias in spatial-interaction models by considering multiple neighbourhoods

ABSTRACT A major challenge in the analysis of micro-level spatial interaction is to distinguish actual interactions from the effects of spatially correlated omitted variables. We propose extending the simple spatially lagged explanatory (SLX) model to include two spatial weighting matrices at different spatial scales to reduce omitted-variable bias. The approach is suitable when actual interaction takes place on a smaller local level, while the omitted variables are spatially correlated at a larger regional level and correlated with the included characteristics. We provide an empirical motivation and use Monte Carlo simulation to illustrate the bias-reduction effects in certain settings.