PATSTAT and links between innovation and ETS

Patent data are a crucial source of information on innovation. But despite enormous efforts by the European Patent Office the PATSTAT database requires substantial processing to allow sensible research. Expert of WP4 have implemented probabilistic machine-learning algorithms that allow to improve the quality and the information content of the PATSTAT database. In the framework of the SIMPATIC project this unique dataset has been delivered under milestone Nr. 30.

The following papers describe the methodology:

1. A flexible, scaleable approach to the international patent “name game” 

by Mark Huberty, Amma Serwaah, and Georg Zachmann

The inventors in PATSTAT are often duplicates: the same person or company may be split into multiple entries in PATSTAT, each associated to different patents. In this paper, we address this problem with an algorithm that efficiently de-duplicates the data. It needs minimal manual input and works well even on consumer-grade computers. Comparisons between entries are not limited to their names, and thus this algorithm is an improvement over earlier ones that required extensive manual work or overly cautious clean-up of the names.

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2. A scaleable approach to emissions-innovation record linkage

by Mark Huberty, Amma Serwaah, and Georg Zachmann

PATSTAT has patent applications as its focus. This means it lacks information on the applicants and/or the inventors. In order to have more information on the applicants, we link PATSTAT to the CITL database. This way the patenting behavior can be linked to climate policy. Because of the structure of the data, we can adapt the de-duplication algorithm to use it as a matching tool, retaining all of its advantages.

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3. Remerge: regression-based record linkage with an application to PATSTAT

by Michele Peruzzi, Georg Zachmann, Reinhilde Veugelers

We further extend the information content in PATSTAT by linking it to Amadeus, a large database of companies that includes financial information. Patent microdata is now linked to financial performance data. This algorithm compares records in PATSTAT with records in Amadeus using multiple variables, and learns their relative weights by asking the user to find the correct links in a small subset of the data. Since it is not limited to comparisons among names, it is an improvement over earlier efforts and is not overly dependent on the name-cleaning procedure in use. It is also relatively easy to adapt the algorithm to other databases, since it uses the familiar concept of regression analysis.

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Simulation tests on GEM-E3

General equilibrium models are commonly used to quantify the implications of alternative economic policies (e.g. change in tax rates, imposition of import tariffs etc.) on economic growth, competitiveness and employment. In recent years, they are increasingly employed to study the macro-economic and sectoral impacts of climate change mitigation policies and measures, as climate change constitutes one of the most important global policy challenges [1]. General equilibrium integrated assessment models (IAMs) are complex modeling tools that are able to measure the cost of alternative energy and climate policies, while integrating other policy priorities, such as innovation through investments in R&D, economic development, enhancement of industrial competitiveness in global markets, uptake of low carbon technologies and security of energy and food supply. The results of IAMs are of great interest to international climate policy makers for evaluating long‐term climate policy targets and their relation to short‐term action.

Within the SIMPATIC project, E3M-Lab focused on studying endogenous growth and innovation arising from policies and activities related to GHG emission abatement. Towards this end, the GEM-E3-RD model has been significantly expanded in order to explicitly identify technologies and sectors that are expected to play a key role in meeting ambitious GHG reduction targets [3]. The main modeling improvements concern:

  • The bottom-up representation of the energy system and GHG emissions reduction options, especially with regard to power generation technologies, energy efficiency investments, detailed representation of the transport sector (including mobility electrification and deployment of biofuels) and energy demand for households
  • The separate representation of the most important clean energy producing industrial sectors and their global trade using bottom-up data derived from various sources
  • Endogenous representation of R&D expenditures realised by each production sector
  • Endogenous learning by doing: “experience” curves have been explicitly introduced in GEME3-RD, in which productivity improvements of clean energy producing sectors depend on their cumulative production
  • Endogenous learning by research mechanism, which links productivity improvements with the accumulated knowledge R&D stock
  • Incorporation of knowledge depreciation rates and inter-regional spillovers for low and zero carbon technologies

The bottom-up representation of the energy system, the distinct modeling of the clean energy producing sectors and the incorporation of endogenous learning mechanisms[1] in the GEM-E3-RD model represent a considerable challenge because they must be embedded within the rigorous specification of the general equilibrium context. The development of a comprehensive integrated assessment modelling framework that encompasses the multi market equilibrium of CGE models with a consistent bottom-up representation of energy demand and supply, power generation structure and technological dynamics constitutes a long-standing challenge for the modelling community.

Recently, there is a growing interest in evaluation/ validation and diagnosing the behaviour of integrated assessment energy-economy models. Kriegler et al [12] propose a diagnostic scheme that can be applied to a wide range of models to identify model behaviour patterns in a series of exogenously determined carbon price scenarios. It is important to note that the objective of diagnostic analysis conducted in [12] is not to capture policy implications in any detail but rather to try to characterize the model response to single policy signals (such as carbon price). Schwanitz [13] sets up a general framework for model evaluation and compares model behaviour with stylized patterns observed in historical data (e.g. Kaldor facts).

The objective of the current report is to present the simulation runs performed in order to illustrate the properties of the enhanced version of the GEME3-RD model. Modelling developments implemented in GEME3-RD are tested with analytical shocks on a series of exogenous assumptions/policy instruments (e.g. carbon price of the EU-28 region, change in learning by doing and learning by research rate assumptions, different assumptions for Armington elasticities). Model runs are also performed by deactivating the new endogenous R&D mechanisms incorporated in GEME3-RD in order to examine the impact of the adopted specification on model results. In contrast to policy experiments usually conducted by Integrated Assessment Models [11, 15], diagnostic simulations do not aim to capture the policy dimension in detail but rather try to characterize the GEME3-RD model response to single policy signals in a way that illustrates the underlying cause‐effect chain. The objective of running stylized simulation tests is to establish confidence in the main properties and behavior of the GEME3-RD model, to test the robustness of model results in response to single policy signals and to inform modellers and policy makers about the model behaviour in sophisticated policy applications.

The remainder of the report is structured as follows: Section two introduces briefly the main characteristics and improvements of the GEME3-RD model performed in the context of the SIMPATIC project. Section three, the report includes a brief specification of the reference scenario constructed using the new enhanced version of the GEME3-RD model. Section four provides the description of the set of shocks considered in the study. Sections 5-8 analyse the macro-economic, climate and technological implications of the alternative shocks examined. Section nine concludes.

Modification of GEM-E3 technological innovation module

Climate change is one of the greatest global policy challenges, as it is widely recognised that unabated climate change can have large impacts on human societies and economic development. Despite the global nature of climate change impacts, the outcome of the recent Conferences of the Parties (COP) to the UNFCCC1 held in Copenhagen (2009), Cancun (2010), Durban (2011), Doha (2012) and Warsaw (2013) suggest that the ideal of coordinated and stringent global climate policy action is highly unlikely to become a nearterm reality. As a result, emphasis has shifted from global cooperative action to regional climate policies and to the integration of other policy priorities, such as energy security, uptake of low carbon technologies, avoidance of external costs (air pollution, human health, etc.), innovation through R&D, industrial competitiveness and economic development policies. (more…)

National or international public funding? Subsidies or loans? Evaluating the innovation impact of R&D support programmes

This working paper is co-authored by Lourdes Moreno.

The objective of this study is to compare the effect of different types of public support for R&D projects on firms’ technological capabilities. We distinguish between low-interest loans and subsidies and between national and European support. Using data on 2,319 Spanish firms during the period 2002-2005, we estimate a multivariate probit to analyse the determinants of firms’ participation in public R&D programmes and, later, the impact of this participation on firms’ technological capabilities using different indicators. The results provide evidence of the effectiveness of all treatments for improving firms’ innovative performance. (more…)