This research explores the factors that influence the adoption of barrel-aging techniques by US-based craft brewers from 2008 to 2014. Particular focus is placed on the importance of influence from geographically close peer breweries as a way to understand the effects of local influence or knowledge spillovers from agglomeration. Combining data on brewery-level production and estimates of the timing of the release of barrel-aged (BA) beers, I find evidence that nearby releases of BA beers increase the likelihood of a brewery introducing its first BA beer. However, national trends appear to be a stronger influence. These effects are robust to estimating on subsamples of brewery and metro sizes and controlling for a local demand proxy.

Viticulture is essential for reducing pesticide use and associated risks. Often the adoption of individual pesticide reduction measures is investigated in isolation, and little is known on broader patterns and the joint adoption of measures. We address this gap by analyzing adoption choices of Swiss grape growers across a large number of pesticide reduction measures, using a contingency analysis and a k-means clustering algorithm. We focus on how measure, farm, and farmer characteristics correlate with this adoption. The analysis uses survey data collected among 436 Swiss grapevine producers. Results indicate that farmers in our sample appear to exploit complementary effects between measures. Moreover, the cluster analysis reveals that Swiss producers can be split into two groups of roughly equal size, with one adopting a greater variety of pesticide reduction measures, and the other relying more on pesticides alone. We further identify significant differences in farm and farmer characteristics that could explain this variation in measure adoption. Our analysis has important implications for research and policy. Firstly, they underline the importance of fostering the adoption of efficient and effective measure bundles. Secondly, they highlight the need for targeted policies to mobilize farmers relying mostly on pesticides to diversify their plant protection practices and thus contribute to overall pesticide reduction.

Technological similarity enables wine operators to share best practices, benchmark against industry standards, and identify new areas of innovation. Despite this, measuring similarity is notoriously challenging. In this paper, I use sentence embeddings on wine patent data to show how similarity compares across different models. I validate the results both internally and externally, showing large discrepancies in annual trends. The results underscore the importance of selecting suitable models for market assessment, providing a valuable primer for both wine operators and technologists.