The timing of intergenerational transfers, tax policy, and aggregate savings

An analysis of the interest rate and savings effects of fiscal policy in an overlapping generations framework, discussing the circumstances under which capital's steady-state marginal product varies.Saving and investment ; Interest rates ; Consumption (Economics)

Types of RNFE activities and their returns: framework and findings (NRI report no. 2754)

In this study the rural non-farm economy (RNFE) is defined as being all those income-generating activities (including income in-kind) that are not agricultural but located in rural areas. This paper looks at the issues that arise with classifications of the RNFE. It then summarises the information contained in 55 studies concerned with the rural non-farm economy using a framework developed by the authors, which reflects the proposed typology, and finally compares the returns in the rural non-farm economy to those in agriculture in developing countries

\u27Texas Maroon’ Bluebonnet

The Texas state flower, the bluebonnet, encompasses all six of the Lupinus species native to Texas. The most widespread and popular bluebonnet, Lupinus texensis Hook., is a winter annual that produces violet-blue [violet-blue group 96A, Royal Horticultural Society (RHS), 1982] racemes in early to midspring and is predominately self-pollinating. The Texas Dept. of Transportation uses this species widely for floral displays along roadsides throughout much of the state (Andrews, 1986). Rare white and even rarer pink variants exist in native populations, and a breeding project was initiated in 1985 to develop bluebonnets with novel flower colors for use as bedding plants. ‘Abbott Pink’ was the first seed-propagated cultivar to be developed from this program (Parsons and Davis, 1993). The second cultivar, ‘Barbara Bush’ with novel lavender shade flowers, was developed more recently (Parsons et al., 1994). As with the cultivars previously developed, we used recurrent phenotypic selection to develop ‘Texas Maroon’. This cultivar is intended for use as a bedding plant for maroon flower color

Non- and Minimally-Invasive Methods to Investigate Megalithic Landscapes in the Brú na Bóinne World Heritage Site (Ireland) and Rousay, Orkney Islands in North-Western Europe

The paper summarizes results of an on-going project in the Boyne Valley in Ireland and in Orkney in the north of Scotland. The research of the Romano-Germanic Commission and our partners aimed to investigate the interaction of social, economic, cultural and environmental phenomena in different types of landscapes in a diachronic perspective. Our exploration of the landscapes was based on geophysical prospection, remote sensing and sedimentological analysis, and we adopted a systematic approach that integrated the various approaches in a GIS. In the Boyne Valley large areas were investigated on the periphery of the monuments of Newgrange, Knowth and Dowth. The field work on the Orkney Islands is focussing on tracing settlement patterns connected to chambered tombs, on the Island of Rousay. The use of a similar research design in both regions produces compound databases, something that is crucial for comparing trajectories of change in Neolithic land use, and in understanding those changes

Fertilizer Value of Cattle Dunghills in a Pasture Field

There were nearly 2.5 million cattle and calves in Kentucky in 1988. Most of these were maintained under pastureland conditions. Nutrients taken up by pasture plants, consumed by cattle, and re-cycled back onto fields by fecal and urine excretions can be a major source of nutrients for maintaining pastureland productivity. In order to estimate the value of this under grazing conditions, observations were made on a pasture field in Casey County, Kentucky, following stocking of the field with cattle

DATA DRIVEN APPLICATION STORE LISTING OPTIMIZATION

A computing system (e.g., a cloud server that hosts an application store) may predict the effect of modifying marketing assets (e.g., text, an image, a screenshot, a description, a video, etc.) of an application (hereinafter referred to as an “app”) on acquisitions (e.g., installations) for the app. The computing system may generate these predictions based on historical performance data (e.g., data relating to modifications to one or more marketing assets of the app and to acquisitions for the app) of marketing assets for a variety of types (e.g., lifestyle apps, social media apps, utility apps, productivity apps, entertainment apps including games, etc.) of apps on the app store. In some examples, the historical performance data may include the origin country, language, device type, purchase history, acquisition history, and/or the like of potential customers (e.g., customers of the app) such that the predictions generated by the computing system may be based on one or more of those factors. The computing system may then provide these predictions to a user (e.g., an app developer) of the application store to help (e.g., by providing benchmarks and/or recommendations) the user modify the marketing assets of the user’s app in a manner predicted to increase acquisitions for the user’s app in the app store. For example, the computing system may cause the user’s computing device (e.g., a smartphone, a tablet, a laptop) to display a statistical model (e.g., a cat and whisker plot, a bar graph, etc.) indicating the relationship between modifying one or more marketing assets and acquisitions for any type of app. The computing system may also provide statistics such as the average acquisition, the range of acquisition, the distribution of acquisition, the standard deviation of acquisition, and/or the like. Such statistics may represent acquisition benchmarks for guiding the user in modifying the user’s marketing assets

DATA DRIVEN APPLICATION STORE LISTING OPTIMIZATION

A computing system (e.g., a cloud server that hosts an application store) may predict the effect of modifying marketing assets (e.g., text, an image, a screenshot, a description, a video, etc.) of an application (hereinafter referred to as an “app”) on acquisitions (e.g., installations) for the app. The computing system may generate these predictions based on historical performance data (e.g., data relating to modifications to one or more marketing assets of the app and to acquisitions for the app) of marketing assets for a variety of types (e.g., lifestyle apps, social media apps, utility apps, productivity apps, entertainment apps including games, etc.) of apps on the app store. In some examples, the historical performance data may include the origin country, language, device type, purchase history, acquisition history, and/or the like of potential customers (e.g., customers of the app) such that the predictions generated by the computing system may be based on one or more of those factors. The computing system may then provide these predictions to a user (e.g., an app developer) of the application store to help (e.g., by providing benchmarks and/or recommendations) the user modify the marketing assets of the user’s app in a manner predicted to increase acquisitions for the user’s app in the app store. For example, the computing system may cause the user’s computing device (e.g., a smartphone, a tablet, a laptop) to display a statistical model (e.g., a cat and whisker plot, a bar graph, etc.) indicating the relationship between modifying one or more marketing assets and acquisitions for any type of app. The computing system may also provide statistics such as the average acquisition, the range of acquisition, the distribution of acquisition, the standard deviation of acquisition, and/or the like. Such statistics may represent acquisition benchmarks for guiding the user in modifying the user’s marketing assets