Working Papers

Abstract: Design styles are essential for categorizing products and product ensembles in many design-intensive domains, such as interior and fashion design. Consumers use design styles as a heuristic in product searches and purchasing decisions. However, the literature lacks consistent and comprehensive design style characterizations, leading to a plethora of non-comparable research insights and, in turn, to inefficiencies in the development and marketing of products and product ensembles. These issues are addressed by standardizing the style profiles of eight highly influential interior design styles. First, each design style's most characteristic aesthetic attributes and symbolic associations are identified. Based on these characteristics, human coders were trained to manually classify 1647 design ensembles. Leveraging the resulting novel ground truth data, state-of-the-art computer vision image classification algorithms are used, and artificial neural networks (ANN) are trained to identify prominent design styles in any interior ensemble. Both the sources and the models are released via Marquetry, an online interior ensemble image classification application, enabling future researchers to use the standardized style characterization, enhancing the comparability of future research results, and empowering practitioners and managers with the opportunity to make more informed strategic decisions in their pictorial communication with consumers. Keywords: Artificial neural networks, Computer vision, Design styles, Image classification, Interior ensembles, Product aesthetics, Style characteristics
Abstract: In this study, we introduce a novel entity matching (EM) framework. It combines state-of-the-art EM approaches based on Artificial Neural Networks (ANN) with a new similarity encoding derived from matching techniques that are prevalent in finance and economics. Our framework is on-par or outperforms alternative end-to-end frameworks in standard benchmark cases. Because similarity encoding is constructed using (edit) distances instead of semantic similarities, it avoids out-of-vocabulary problems when matching dirty data. We highlight this property by applying an EM application to dirty financial firm-level data extracted from historical archives. Keywords: Entity matching, Entity resolution, Database linking, Machine learning, Record resolution, Similarity encoding
Abstract: Traditional market research relies primarily on self-reports to investigate consumer preferences. As this potentially introduces subjectivity and biases, researchers and managers started exploring innovative but intrusive and costly neuromarketing methods. Addressing the challenges of existing approaches, the authors propose using automatic facial expression analysis to enhance preference elicitation, remaining cost-effective and noninvasive. Using Machine Learning methods, this approach demonstrates that combining traditional survey data with facial expressions significantly improves choice and preference prediction, increasing accuracy by up to 7%, surpassing self-reported emotions and survey data. When directly compared, micro-expressions exhibit up to 8% greater predictive power than self-reported emotions, indicating their superiority for accurate insights compared to traditional survey-based emotion measurements. Additionally, the outlined approach can be applied practically, employing Artificial Neural Network models predicting preferences using both, survey and facial expression data. The results support that companies can enhance market analysis by incorporating automatic facial expression analysis. Keywords: Choice Modelling, Face Reading Technology, Micro Expressions, Machine learning, Preference Prediction
Abstract: Shortages and surpluses appear in many markets both under exceptional and typical circumstances. This article proposes an assessment of the appropriateness of market-clearing in econometric modeling. The methodology allows the comparison of equilibrium and disequilibrium models with known likelihoods. Its performance is examined in a controlled environment using large-scale simulations of five market models. An application of the methodology using US retail and scanner deodorant data shows that, during times of distress, exogenous shocks can improve the effectiveness of the price mechanism. The results of this article may serve as empirical justifications of deviations from market-clearing. Keywords: Markov switching, Disequilibrium, Marginal effects, market-clearing, Maximum likelihood, model selection
Abstract: This study investigates how financing conditions were affected during the 2009 financial crisis using German firms financial statement data gathered for regulatory purposes. Policies addressing financing difficulties often presume a higher vulnerability of small firms based on various proxies of financial constraints that use size as one key constituent. In contrast, our analysis is based on structural estimations of financial constraints that disentangle it from size, which allows us to study the relativistic effects between small and large firms. We show that the worsening of financing conditions during the crisis did not depend on a firm's size. Instead, it was the unavailability of financing alternatives, the limited capacity in providing collateral, and the operational risk that intensified financial constraints. Our results suggest that policies that address these underlying causes of vulnerability during financial contractions instead of in general enhancing access to funding for SMEs are potentially more efficient. Keywords: SME borrowing, Disequilibrium, financial constraints, firm-level data

Journal Articles

Abstract: The schooling gap diminishes because the services sector becomes more pronounced for high-income countries, and the paid hours gap closes. Although gender wage inequality persists across country income groups, differences in schooling years between females and males diminish. We assemble a novel dataset, calibrate a general equilibrium, multi-sector, -gender, and -production technology model, and show that gender-specific sectoral comparative advantages explain the paid hours and schooling gap decline from low- to high-income economies even when the wage gap persists. Additionally, our counterfactual analyses indicate that consumption subsistence and production share heterogeneity across both income groups and genders are essential to explain the co-decline of the schooling and paid hours gaps. Our results highlight effective mechanisms for policies aiming to reduce gender inequality in schooling and suggest that the schooling gap decline and the de-invisibilization of female paid work observed in high-income countries are linked by structural sector movements instead of wage inequality reductions. Keywords: Development, Education, Gender gaps, Labor, Structural change
Abstract: Market models constitute a significant cornerstone of empirical applications in business, industrial organization, and policymaking macroeconomics. The econometric literature proposes various estimation methods for markets in equilibrium, which entail a market-clearing structural condition, and disequilibrium, which are described based on a structural short-side rule. Nonetheless, maximum likelihood estimations of such models are computationally demanding, and software providing simple, out-of-the-box methods for estimating them is scarce. Therefore, applications rely on project-specific implementations for estimating these models, which hinders research reproducibility and result comparability. This article presents the R package markets, which provides a common interface with generic functionality simplifying the estimation of models for markets in equilibrium and disequilibrium. The package specializes in estimating demanded, supplied, and aggregated market quantities and absolute, normalized, and relative market shortages. Its functionality is exemplified via an empirical application using a classic dataset of United States credit for housing starts. Moreover, the article details the scope and design of the implementation and provides statistical measurements of the computational performance of its estimation functionality gathered via large-scale benchmarking simulations. The markets package is free software distributed under the Expat license as part of the R software ecosystem. It comprises a set of estimation and analysis tools that are not directly available from either alternative R packages or other statistical software projects. Keywords: Disequilibrium, Marginal effects, Market clearing, Maximum likelihood, Short side rule, Shortages
Abstract: Broad, long-term financial, and economic datasets are scarce resources, particularly in the European context. In this article, we present an approach for an extensible data model that is adaptable to future changes in technologies and sources. This model may constitute a basis for digitized and structured long-term historical datasets for different jurisdictions and periods. The data model covers the specific peculiarities of historical financial and economic data and is flexible enough to reach out for data of different types (quantitative as well as qualitative) from different historical sources, hence, achieving extensibility. Furthermore, we outline a relational implementation of this approach based on historical German firm and stock market data from 1920 to 1932. Keywords: Cliometrics, Databases, Economic history, Financial data, Germany

Book Chapters

Abstract: This chapter analyzes a central part of an EU-funded, seven-nations development project for the comprehensive interdisciplinary design of a European system to collect and collate historical financial and firm data (named EurHisFirm)—the responsibility of the authors was the design of a Common Data Model (CDM). Against the background that successful information systems are of the type “sociotechnical systems” between human applicants and information technology—mutually driving each other but likewise also depending on the input of the respective opposite side—we have strong indications that in complex decision situations human cooperation deficiencies substantially outweigh expectable exponential advancements of the information technology. The reason is presumably that amongst diverse and self-confident nations—actually persons—(likewise in important sub-national groups of responsibility, e.g., communal authorities or firms) reaching an agreement on data and other standards is an overly lengthy process that often ends with foul compromises. We understand our contribution to bundle substantial indications toward a possible enhancement of the state-of-the-art—however, fellow researchers should thoroughly investigate the approach.

Conference Papers and Proceedings

Abstract: This paper reports results from the design phase of EurHisFirm. Its goal is to integrate isolated and badly accessible financial data sets on 19 th and 20 th century European companies so that users can query the data as if they reside in one large database. In addition, it wants to stimulate database construction by providing not only methodology and tools to connect to and collaborate with existing ones, but also a collaborative platform, based on machine learning and artificial intelligence, that allows harvesting data in a semi-automatic way. We present the proof-of-concept results of this platform in addition to the performance of matching algorithms, which are necessary to connect and collate the different constituent databases as well as to connect them to contemporary commercial databases. Keywords: Eurhisfirm, historical cross-country company data, matching
Abstract: Broad, long-term financial, and economic datasets are a scarce resource, particularly in the European context. In this paper, we present an approach for an extensible data model that is adaptable to future changes in technologies and sources. This model may constitute a basis for digitised and structured long-term historical datasets. The data model covers the specific peculiarities of historical financial and economic data and is flexible enough to reach out for data of different types (quantitative as well as qualitative) from different historical sources, hence, achieving extensibility. Furthermore, based on historical German firm and stock market data, we discuss a relational implementation of this approach. Keywords: Eurhisfirm, M5.1, data models extensibility, historical cross-country company data, preservation principle, mendeley-tags =M5.1

Technical Reports

Abstract: The fourth report of Work Package 5 provides the latest revisions of the Common Data Model standard specifications. The different foundational elements of the Common Data Model are presented and explained. The report also summarisesthe results of stakeholder feedback and describes their implications on the Common Data Model. Finally, we give an outlook on the further development of the Common Data Model and its components. Keywords: D5.4, Eurhisfirm, common data model, historical cross-country company data, mendeley-tags =D5.4
Abstract: The second report of Work Package 5 completes the discussion of the preliminary back-end design concepts of the common data model. The approach of the report is characterized by the principle of least intrusiveness. The proposed solutions respect national idiosyncrasies and allow national centers to advance in a collaborative but independent manner. The report starts by reviewing the data formats of the countries of the consortium. It draws from identification theory and proposes appropriate principles and requirements for the common model's identification design. It examines the functional and informational requirements for identifying various data items and for linking historical data from within the consortium to external databases with contemporary data. It outlines that the common model's implementation mostly benefits from employing both relational and non-relational technologies to address different issues. It highlights appropriate, subsequent steps for cross-country harmonization, firm-linking, data transformation processes, and data governance. Keywords: D5.2, Eurhisfirm, common data model, historical cross-country company data, mendeley-tags =D5.2
Abstract: The report reviews a selection of existing micro-level data-model implementations both from within as well as outside the consortium's countries and identifies best design practices. It proposes preliminary model concepts for EURHISFIRM's metadata scheme and evaluation criteria for assessing the effectiveness of historical, cross-country, company-level data models. Since there is no precedence in designing such models, the report methodologically introduces a conceptual 2-dimensional separation on the information space that EURHISFIRM's model aims to cover and reviews representative implementations from each subpart. The first dimension concerns the time domain. In this dimension, the reviewed models are classified either as contemporary or as historical. The second dimension concerns the cross-country domain. Models here are classified either as national or as international. The analysis constitutes one fundamental block upon which the process of synthesizing national models into a unified European common model builds. Keywords: D5.1, Eurhisfirm, historical cross-country company data, national data models, mendeley-tags =D5.1

Ph.D. Thesis

Abstract: My thesis discusses topics that range from the behavioral characteristics of dynamic decisionmaking to the efficiency of the collective forces in free markets. The purpose of the thesis is not to provide a universal link of these topics, and the contributions of each chapter focus on issues that are of particular interest on their own, the empirical example of the second chapter demonstrates the importance of combining the economic thinking of the introspective and the detached. I hope that the reader will appreciate both the comprehensive approach of the thesis and the detailed-oriented analysis of the topics of each chapter.