Theory suggests that new market entrants play a special role for the creation of new technological pathways
required for the development and diffusion of more sustainable forms of production, consumption, mobility
and housing. Unconstrained by past technological investments, entrants can introduce more radical environmental
innovations than incumbent firms whose past R&D decisions make them locked into path-dependent
trajectories of outdated technologies. Yet, little research exists which provides empirical evidence
on new ventures’ role in the technological transition towards decarbonization and dematerialization.
This is mainly because patenting is rare among start-ups and also no historical track record about
their R&D investments exists, both data sources commonly used to determine a company’s technological
footprint. To enable the identification of clean technology-oriented market entrants and to better
understand their role as adopters and innovators for sustainable market solutions, this paper presents
a framework that systematically maps new ventures’ business models to a set of well-defined clean technologies.
For this purpose, the framework leverages textual descriptions of new entrants’ business summaries
that are typically available upon business registration and allow for a good indication of their technological
orientation. Furthermore, the framework uses textual information from patenting activities of established
innovators to model semantic representations of technologies. Mapping company and technology descriptions
into a common vector space enables the derivation of a fine-granular measure of entrants’ technological
orientation. Applying the framework to a survey of German start-up firms suggests that clean technology-oriented
market entrants act as accelerators of technical change: both by virtue of their existing products
and services and through a high propensity to introduce additional environmental innovations.
PLoS One
An integrated data framework for policy guidance during the coronavirus pandemic: Towards real-time decision
support for economic policymakers
Julian Oliver Dörr, Jan Kinne, David Lenz, and
2 more authors
Usually, official and survey-based statistics guide policymakers in their choice of response instruments
to economic crises. However, in an early phase, after a sudden and unforeseen shock has caused unexpected
and fast-changing dynamics, data from traditional statistics are only available with non-negligible
time delays. This leaves policymakers uncertain about how to most effectively manage their economic
countermeasures to support businesses, especially when they need to respond quickly, as in the COVID-19
pandemic. Given this information deficit, we propose a framework that guided policymakers throughout
all stages of this unforeseen economic shock by providing timely and reliable sources of firm-level
data as a basis to make informed policy decisions. We do so by combining early stage ‘ad hoc’ web analyses,
‘follow-up’ business surveys, and ‘retrospective’ analyses of firm outcomes. A particular focus of
our framework is on assessing the early effects of the pandemic, using highly dynamic and large-scale
data from corporate websites. Most notably, we show that textual references to the coronavirus pandemic
published on a large sample of company websites and state-of-the-art text analysis methods allowed
to capture the heterogeneity of the pandemic’s effects at a very early stage and entailed a leading
indication on later movements in firm credit ratings. While the proposed framework is specific to the
COVID-19 pandemic, the integration of results obtained from real-time online sources in the design
of subsequent surveys and their value in forecasting firm-level outcomes typically targeted by policy
measures, is a first step towards a more timely and holistic approach for policy guidance in times
of economic shocks.
@article{Dörr2023,ind={Peer-reviewed journals},author={{Dörr}, Julian Oliver and Kinne, Jan and Lenz, David and Licht, Georg and Winker, Peter},doi={10.1371/journal.pone.0263898},journal={PLoS ONE},number={2},pages={e0263898},volume={17},year={2022}}
2021
SBEJ
Small firms and the COVID-19 insolvency gap
Julian Oliver Dörr, Georg Licht, and Simona Murmann
COVID-19 placed a special role on fiscal policy in rescuing companies short of liquidity from insolvency.
In the first months of the crisis, SMEs as the backbone of Germany’s economy benefited from large and
mainly indiscriminate aid measures. Avoiding business failures in a whatever-it-takes fashion contrasts,
however, with the cleansing mechanism of economic crises: a mechanism which forces unviable firms out
of the market, thereby reallocating resources efficiently. By focusing on firms’ pre-crisis financial
standing, we estimate the extent to which the policy response induced an insolvency gap and analyze
whether the gap is characterized by firms which were already struggling before the pandemic. With the
policy measures being focused on smaller firms, we also examine whether this insolvency gap differs
with respect to firm size. Our results show that the COVID-19 policy response in Germany has triggered
a backlog of insolvencies that is particularly pronounced among financially weak, small firms, having
potential long-term implications on entrepreneurship and economic recovery.
@article{Dörr2021b,ind={Peer-reviewed journals},author={{Dörr}, Julian Oliver and Licht, Georg and Murmann, Simona},doi={10.1007/s11187-021-00514-4},issn={0921-898X},journal={Small Business Economics},pages={887--917},title={Small firms and the COVID-19 insolvency gap},volume={58},year={2021}}
Theses
2022
Ph.D.
Essays on the Application of Statistical Learning in Empirical Economic Research