Aggregate data

Summary

Aggregate data is high-level data which is acquired by combining individual-level data. For instance, the output of an industry is an aggregate of the firms’ individual outputs within that industry. Aggregate data are applied in statistics, data warehouses, and in economics. There is a distinction between aggregate data and individual data. Aggregate data refers to individual data that are averaged by geographic area, by year, by service agency, or by other means. Individual data are disaggregated individual results and are used to conduct analyses for estimation of subgroup differences. Aggregate data are mainly used by researchers and analysts, policymakers, banks and administrators for multiple reasons. They are used to evaluate policies, recognise trends and patterns of processes, gain relevant insights, and assess current measures for strategic planning. Aggregate data collected from various sources are used in different areas of studies such as comparative p

Official source

https://en.wikipedia.org/wiki/Aggregate_data

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What makes companies pursue an Open Science strategy?

Julio Diego Salvador Raffo, Markus Simeth

Whereas recent scholarly research has provided many insights about universities engaging in commercial activities, there is still little empirical evidence regarding the opposite phenomenon of companies disseminating scientific knowledge. Our paper aims to fill this gap and explores the motivations of firms that disclose research outcomes in a scientific format. Besides considering a dimension internal to the firm, we focus particularly on knowledge sourcing from academic institutions and the appropriability regime. We conduct an econometric analysis with firm-level data from the fourth edition of the French innovation survey (CIS) and matched scientific publications for a sample of 2512 R&D performing firms from all manufacturing sectors. This analysis provides evidence that firms are more likely to adopt academic principles if they need to access scientific knowledge that is considered important for their innovation development, whereas the mere existence of collaborative links with academic institutions is not a strong determinant. Furthermore, the results suggest that the inclination of firms to publish is sensitive to the level of knowledge spillovers in a sector and the effectiveness of legal appropriation instruments. (C) 2013 Elsevier B.V. All rights reserved.

Elsevier Science Bv2013

Environmental innovation, policy and economic performance

Claudia Pellegrin

The objective of this thesis is to investigate three economic issues related to environmental innovation: evaluating how private investments in environmental innovation react to technological policy; testing whether environmental policy harms economic performance; analyzing whether and how energy-saving process innovation impacts economic performance. This thesis provides novel empirical evidence on these issues by means of econometric analysis on firm-level data. The results from this thesis show that firms' environmental investments react positively to the highest amounts of public subsidy. Environmental policy, contrarily to the expectations, enhances economic performance of firms. Lastly, energy-saving process innovation does not play a significant role in reducing production costs of firms. These results suggest that environmental and economic goals are not in trade-off at the current state and that energy saving is not an incentive per se. Thus, strengthening both environmental policy and technological policy for environmental innovation is recommended.

EPFL2017

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A macroscopic loading model applicable to time-dependent and congested pedestrian flows in public walking areas is proposed. Building on the continuum theory of pedestrian flows and the cell transmission model for car traffic, an isotropic framework is developed that can describe the simultaneous and potentially conflicting propagation of multiple pedestrian groups. The model is formulated at the aggregate level and thus computationally cheap, which is advantageous for studying large-scale problems. A detailed analysis of several basic flow patterns including counter- and cross flows, as well as two generic scenarios involving a corner- and a bottleneck flow is carried out. Various behavioral patterns ranging from disciplined queueing to impatient jostling can be realistically reproduced. Following a systematic model calibration, two case studies involving a Swiss railway station and a Dutch bottleneck flow experiment are presented. A comparison with the social force model and pedestrian tracking data shows a good performance of the proposed model with respect to predictions of travel time and density.

Pergamon-Elsevier Science Ltd2014