No abstract

Any opinions expressed in this paper are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but IZA takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity. The IZA Institute of Labor Economics is an independent economic research institute that conducts research in labor economics and offers evidence-based policy advice on labor market issues. Supported by the Deutsche Post Foundation, IZA runs the world's largest network of economists, whose research aims to provide answers to the global labor market challenges of our time. Our key objective is to build bridges between academic research, policymakers and society. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author.

The amount of biomedical data continues to grow rapidly. However, the ability to collect data from multiple sites for joint analysis remains challenging due to security, privacy, and regulatory concerns.We present a Secure Federated Learning architecture, MetisFL, which enables distributed training of neural networks over multiple data sources without sharing data. Each site trains the neural network over its private data for some time, then shares the neural network parameters (i.e., weights, gradients) with a Federation Controller, which in turn aggregates the local models, sends the resulting community model back to each site, and the process repeats.Our architecture provides strong security and privacy. First, sample data never leaves a site. Second, neural parameters are encrypted before transmission and the community model is computed under fully-homomorphic encryption. Finally, we use information-theoretic methods to limit information leakage from the neural model to prevent a "curious" site from performing membership attacks.We demonstrate this architecture in neuroimaging. Specifically, we investigate training neural models to classify Alzheimer's disease, and estimate Brain Age, from magnetic resonance imaging datasets distributed across multiple sites, including heterogeneous environments where sites have different amounts of data, statistical distributions, and computational capabilities.

Purpose This paper aims to analyse the relevance of management and productivity in the behaviour of firms in international trade. Design/methodology/approach Using a survey of Spanish manufacturing firms, the authors use a management quality index to serve as a proxy for the good management practice of the firm. Findings The results demonstrate that exporter and multinationals firms are more productive and better managed than domestic firms. Furthermore, in the periods in which switcher firms decide to export or to invest abroad, they are better managed but are not more productive than in the rest of the periods. Finally, results indicate that regardless of its positive relationship with productivity, management also has a direct impact on the firm’s probability of exporting and involving in foreign direct investment. Originality/value This paper aims to reconcile the recent international trade literature, which focusses on the role of productivity heterogeneity in international trade, with the international business literature, concentrated on depicting the key management practices that impact internationalization.

The main aim of this work is to explain the Chilean gender wage gap using a dynamic monopsony model to estimate labor supply elasticities at the …rm level. To the best of our knowledge, our study is the …rst to measure monopsony power at the …rm level using voluntary separations and the …rst to apply this methodology to estimate such elasticities for a middle-income country. Our results suggest that elasticities of labor supply to …rms are rather small, which implies that …rms have market power. We also found that Chilean men earn approximately 19% more than women as a result of the di¤erence in labor supply elasticities by gender, ceteris paribus. Furthermore, we found that the magnitude of between-…rm di¤erences in elasticities are more than twice the magnitude of within-…rm di¤erences, suggesting that the gender wage gap is driven more by structural factors that generate gender sorting to …rms. Finally, we found that elasticities for a high-income country (United States) are 63% and 100% higher than those obtained for a middle-income country for men and women, respectively, suggesting higher labor market frictions in middle-income countries for men and even higher for women.