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Broad, long-term financial and economic datasets are a scarce resource, in particular in the European context. In this paper, we present an approach for an extensible, i.e. adaptable to future changes in technologies and sources, data model that may constitute a basis for digitized and structured long- term, historical datasets. The data model covers 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 company and stock market data, we discuss a relational implementation of this approach.
Why does the schooling gap close while the wage gap persists across country income comparisons?
(2023)
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.
In this study, we introduce a novel entity matching (EM) framework. It com-bines state-of-the-art EM approaches based on Artificial Neural Networks (ANN) with a new similarity encoding derived from matching techniques that are preva-lent in finance and economics. Our framework is on-par or outperforms alternative end-to-end frameworks in standard benchmark cases. Because similarity encod-ing 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.