Mr. Ashok Vir Bhatia, Ms. Srobona Mitra, Anke Weber, Mr. Shekhar Aiyar, Luiza Antoun de Almeida, Cristina Cuervo, Mr. Andre O Santos, and Tryggvi Gudmundsson
This note weighs the merits of a capital market union (CMU) for Europe, identifies major obstacles in its path, and recommends a set of carefully targeted policy actions.
European capital markets are relatively small, resulting in strong bank-dependence, and are split sharply along national lines. Results include an uneven playing field in terms of corporate funding costs, the rationing out of collateral-constrained firms, and limited shock absorption. The benefits of integration center on expanding financial choice, ultimately to support capital formation and resilience. Capital market development and integration would support a healthy diversity in European finance. Proceeding methodically, the note identifies three key barriers to greater capital market integration in Europe: transparency, regulatory quality, and insolvency practices. Based on these findings, the note urges three policy priorities, focused on the three barriers. There is no roadblock—such steps should prove feasible without a new grand bargain.
Cornelia Hammer, Ms. Diane C Kostroch, and Mr. Gabriel Quiros-Romero
Big data are part of a paradigm shift that is significantly transforming statistical agencies, processes, and data analysis. While administrative and satellite data are already well established, the statistical community is now experimenting with structured and unstructured human-sourced, process-mediated, and machine-generated big data. The proposed SDN sets out a typology of big data for statistics and highlights that opportunities to exploit big data for official statistics will vary across countries and statistical domains. To illustrate the former, examples from a diverse set of countries are presented. To provide a balanced assessment on big data, the proposed SDN also discusses the key challenges that come with proprietary data from the private sector with regard to accessibility, representativeness, and sustainability. It concludes by discussing the implications for the statistical community going forward.