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The growing number of flood events in urban areas is causing far‐reaching economic losses and social disruptions. To implement suitable flood risk management strategies and measures for flood prevention and mitigation, it is paramount to adequately assess economy‐wide losses associated with potential flood events. The assessment of potential flood damage in the USA relies significantly on the Federal Emergency Management Agency (FEMA) Hazus Flood Model. A primary requirement to reduce uncertainty is to empirically gather user input data. Consequently, there is a need to integrate publicly accessible business data for accurate evaluation of losses in business activities. We present a novel approach for estimating both the direct losses in business activities during a flood and the total economy‐wide impact that arise from these disruptions. First, we integrate the FEMA Hazus Flood Model with Reference Solutions business‐level data to estimate the reduction in business activities (i.e., direct losses) following a 100‐year flood event in New Hanover County, North Carolina. Second, we estimate the broader economy‐wide impact these business activity losses have on total industry output, employment, value‐added and household income using a regional computational general equilibrium model. The advantage of our approach is the easy replicability of the business loss estimation process for US cities and regions of interest, which, when used in a follow‐up economic impact study, leads to estimating the total economy‐wide losses in economic activities. Policy makers relying solely on direct physical and economic damages would, by ignoring indirect losses, underestimate the benefits of investing in flood mitigation and protection. Plain Language Summary: Floods have increased globally over the last decades causing massive socioeconomic losses. Development of better resilience strategies for coastal and river cities requires methodologies that properly estimate the total, that is, the direct and the indirect, regional costs of large flood events. The first objective of this research is to estimate potential business interruptions due to flooding of firms and businesses. We demonstrate the value of using publicly available business‐level information in combination with the HAZUS flood model to estimate these direct business losses. Our second objective is to assess the economy‐wide impacts of estimated business losses within a computational general equilibrium model, a powerful regional economic impact model, since flood‐related losses of business activities within flood zones (direct effects) trigger additional losses (indirect effects) throughout the entire regional economy. Key Points: Estimate direct losses of business activities using publicly available Reference Solution data and Federal Emergency Management Agency US Hazus flood‐depth functionsAnalyze economy‐wide socioeconomic impacts of flood events using a regional computation general equilibrium modelData and methods can be applied anywhere in the USA where flood hazard information is available to extend impact assessments to include economy‐wide impacts of flooding [ABSTRACT FROM AUTHOR]

We extend the literature on Bayesian model comparison for ordinary least-squares regression models to include spatial autoregressive and spatial error models. Our focus is on comparing models that consist of [...]

This paper estimates the effects of knowledge spillovers on patent growth rates across 335 European regions over the 1989u1999 period. We propose a dynamic model based on an innovation production function. A Bayesian approach is used to take into account areaspecific innovation and spatial spillovers. The estimation of the model proceeds via Markov Chain Monte Carlo simulation. The results show significant positive and negative spatial effects on innovative activity. The model allows for a rich spatial specification, which we illustrate by incorporating transport proximity measured by transportation time between regions to augment the typical spatial proximity measure of connectivity between regions. Doing this produces more pronounced spatial spillovers that exhibit a more polarized spatial pattern than a model relying on spatial proximity alone.

In the presented paper, we estimate the impacts of three distinct housing policy interventions to deal with distressed and abandoned residential properties: leaving vacant buildings in their current state, remodeling/rebuilding residential structures, and razing vacant structures. We show that vacant structures negatively impact nearby housing up to 4.1%, but when demolition is followed by complete reconstruction, neighboring housing can be appreciated by as much as 14.1%. In addition, negative spillover effects from foreclosures and vacant buildings are primarily observed in low‐income and middle‐income Census tracts, whereas positive spillover effects generated by remodeling are seen in high‐income Census tracts. [ABSTRACT FROM AUTHOR]