No Arabic abstract
Literature about the scholarly impact of scientific research offers very few contributions on private sector research, and the comparison with public sector. In this work, we try to fill this gap examining the citation-based impact of Italian 2010-2017 publications distinguishing authorship by the private sector from the public sector. In particular, we investigate the relation between different forms of collaboration and impact: how intra-sector private publications compare to public, and how private-public joint publications compare to intra-sector extramural collaborations. Finally, we assess the different effect of international collaboration on private and public research impact, and whether there occur differences across research fields.
This paper quantifies the significance and magnitude of the effect of measurement error in satellite weather data in the analysis of smallholder agricultural productivity. The cross-country analysis leverages multiple rounds of georeferenced, nationally-representative, panel household survey data that have been collected over the last decade. These data are spatially-linked with a range of geospatial weather data sources and related metrics. The goal is to provide systematic evidence on obfuscation methods, satellite data source, and weather metrics in order to determine which of these elements have strong predictive power over a large set of crops and countries and which are only useful in highly specific settings.
The mission statement (MS) is the most used organizational strategic planning tool worldwide. The relationship between an MS and an organizations financial performance has been shown to be significantly positive, albeit small. However, an MSs relationship to the macroeconomic environment and to organizational innovation has not been investigated. We implemented a Structural Equation Modeling using the SCImago Institutional Ranking (SIR) as a global baseline sample and assessment of organizational research and innovation (RandI), an automated MS content analysis, and the Economic Complexity Index (ECI) as a comprehensive macroeconomic environment measure. We found that the median performance of organizations that do not report an MS is significantly higher than that of reporting organizations, and that a path-dependence driven by the States long-term view and investment is a better explanatory variable for organizational RandI performance than the MS construct or the intermediate-term macroeconomic environment.
In this paper we study the impact of errors in wind and solar power forecasts on intraday electricity prices. We develop a novel econometric model which is based on day-ahead wholesale auction curves data and errors in wind and solar power forecasts. The model shifts day-ahead supply curves to calculate intraday prices. We apply our model to the German EPEX SPOT SE data. Our model outperforms both linear and non-linear benchmarks. Our study allows us to conclude that errors in renewable energy forecasts exert a non-linear impact on intraday prices. We demonstrate that additional wind and solar power capacities induce non-linear changes in the intraday price volatility. Finally, we comment on economical and policy implications of our findings.
Agricultural research has fostered productivity growth, but the historical influence of anthropogenic climate change on that growth has not been quantified. We develop a robust econometric model of weather effects on global agricultural total factor productivity (TFP) and combine this model with counterfactual climate scenarios to evaluate impacts of past climate trends on TFP. Our baseline model indicates that anthropogenic climate change has reduced global agricultural TFP by about 21% since 1961, a slowdown that is equivalent to losing the last 9 years of productivity growth. The effect is substantially more severe (a reduction of ~30-33%) in warmer regions such as Africa and Latin America and the Caribbean. We also find that global agriculture has grown more vulnerable to ongoing climate change.
This paper proposes a public-private insurance scheme for earthquakes and floods in Italy in which property-owners, the insurer and the government co-operate in risk financing. Our model departs from the existing literature by describing a public-private insurance intended to relieve the financial burden that natural events place on governments, while at the same time assisting individuals and protecting the insurance business. Hence, the business is aiming at maximizing social welfare rather than profits. Given the limited amount of data available on natural risks, expected losses per individual have been estimated through risk-modeling. In order to evaluate the insurers loss profile, spatial correlation among insured assets has been evaluated by means of the Hoeffding bound for r-dependent random variables. Though earthquakes generate expected losses that are almost six times greater than floods, we found that the amount of public funds needed to manage the two perils is almost the same. We argue that this result is determined by a combination of the risk aversion of individuals and the shape of the loss distribution. Lastly, since earthquakes and floods are uncorrelated, we tested whether jointly managing the two perils can counteract the negative impact of spatial correlation. Some benefit from risk diversification emerged, though the probability of the government having to inject further capital might be considerable. Our findings suggest that, when not supported by the government, private insurance might either financially over-expose the insurer or set premiums so high that individuals would fail to purchase policies.