Matthias Dorner

A novel technology-industry concordance table based on linked inventor-establishment data

Mapping technologies into industries is frequently required in empirical innovation studies, but many concordances only provide coarse mappings. We develop novel concordance tables between industries and technologies making use of linked inventor-employee patent data for Germany. The data comprise 235,933 patents filed between 1999 and 2011 at the European Patent Office. Data on inventors are matched and disambiguated with social security records available at the Institute for Employment Research. Employment data recorded in this database include detailed industry codes describing the industrial activities of the inventors’ establishments. The linked inventor-establishment microdata allow us to identify the precise industry of origin of inventions, combine them with technology classifications from the inventors’ patents and to generate novel concordance tables. We evaluate our approach by comparing the concordance tables with existing work, and we discuss the validity of patent statistics by industries as indicators for innovation.

Is There a Life Cycle in All Industries? First Evidence from Industry Size Dynamics in West Germany

ABSTRACT We propose a novel nonlinear regression approach to test whether the size of industries develops over time along the stylized pattern assumed by the industry life cycle theory. We apply our model on data covering the full spectrum of 205 NACE industries including services in West Germany between 1976 and 2009 and four indicators describing industry size (employment, establishments, entries and exits). The results of our large scale analysis show that in most industries indeed size develops along a cyclical path, albeit this development is not universal. Furthermore, we provide first empirical evidence on service industries where we show that the number of establishments and employees frequently develop in line with what is found for most (product) industries.

Generating useful test data for complex linked employer-employee datasets

When data access for external researchers is difficult or time consuming it can be beneficial if test datasets that mimic the structure of the original data are disseminated in advance. With these test data researchers can develop their analysis code or can decide whether the data are suitable for their planned research before they go through the lengthly process of getting access at the research data center. The aim of these data is not to provide any meaningful results. Instead it is important to maintain the structure of the data as closely as possible including skip patterns, logical constraints between the variables, and longitudinal relationships so that any code that is developed using these test data will also run on the original data without further modifications. Achieving this goal can be challenging for complex datasets such as linked employer-employee datasets (LEED) where the links between the establishments and the employees also need to be maintained. Using the LEED of the Institute for Employment Research we illustrate how useful test data can be developed for such complex datasets. Our approach mainly relies on traditional statistical disclosure control (SDC) techniques such as data swapping and noise addition for data protection. Since statistical inferences need not be preserved, high swapping rates can be applied to sufficiently protect the data. At the same time it is straightforward to maintain the structure of the data by adding some constraints on the swapping procedure.