Economic Notes最新文献

This paper investigates the way in which the financial market defines and evaluates different business models, using a sample of listed European banking groups from 2006 to 2015. The main findings suggest that the financial market seems to associate a better risk-return trade-off to non-banking fees than to banking ones and that the performance of different business models varies depending on context conditions. In particular, in the current economic context, characterised by the combination of slow economic growth with historically low levels of interest rates, the market-oriented business model tends to over-perform. These findings have strategic implications for bank managers, regulators, and supervisors, due to the impact of the crises on banking business, profitability and risk and the new challenges they entail.

This paper investigates the effects on riskiness and performance of gender diversity in Italian bank boards. Italy deserves special attention because European comparisons for the 2000s show that it was among the European Union countries where women were least represented in bank boardrooms, and this gap remains even in a quota law regime. Our main econometric results suggest that gender diversity may have a positive impact on the credit portfolio riskiness and on profitability. Both results may be driven by women's higher risk aversion and their higher propensity to monitor activities, suggesting that women are “gold dust” for Italian banks. These findings, obtained by using a unique data set over the period 1995–2010 and by taking into account possible sources of endogeneity, support the effectiveness on bank performance of greater female presence on boards. Moreover, our econometric set-up could offer a benchmark for further analysis on gender diversity effects for many countries, where a gender quota law is still not in place.

Financial inclusion, especially for a developing nation like India, plays a vital role in framing policies to ensure the development of the underprivileged. Applying the maximum–minimum technique for normalization and inverse of the Euclidean distance for consolidation, the present study attempts to construct an index of financial inclusion across all the states and union territories of India for 2003–2018. It further explores the effect of regional classifications and productivity on the achievement of financial inclusion. Analysis suggests Chandigarh be placed at the top in terms of mean financial inclusion scores for the study period. In addition, comparison among the regional classifications advocates for significant differences. Whereas the difference between the groups based on productivity remains nonsignificant. Theoretical propositions expect uniform achievement in financial inclusion among the states of a country. However, certain macroeconomic and other factors seem to reduce the esteemed equity.

We propose a tool to predict risks to economic growth and international business cycles spillovers: the gross domestic product (GDP)-Network conditional value at risk (CoVaR). Our methodology to assess Growth-at-Risk is composed of two building blocks. In the first step, we apply a machine learning methodology, namely the network-based NETS by Barigozzi and Brownlees, to identify significant linkages between pair of countries. In the second step, applying the CoVaR methodology by Adrian and Brunnermeier, and exploiting international statistics on trade flows and GDPs, we derive the entire distribution of Economic Growth spillover exposures at the bilateral, country and global level for different quantiles of tail events on economic growth. We find that Economic Growth Spillover probability distribution is time-varying, left-skewed and in some cases bi- or even multi-modal. Second, we prove that our two-step approach outperforms alternative one-step quantile regression models in predicting risks to economic growth. Finally, we show that Global exposure to economic growth tail events is decreasing over time.

The paper makes two contributions. First, it analyzes net foreign assets (NFA) and liabilities in selected Arab countries. Second, the paper examines the effects of policy variables that affect the accumulation of NFA and its components, analyzing how the existence of a sovereign wealth fund (SWF), the country's exchange rate regime, and the development of its financial system affect its NFA. The main findings show that the presence of a SWF is positively and significantly associated with foreign direct investment in Arab countries. While financial development matters, intermediate exchange rate regimes are associated to more uncertainty compared to fixed or flexible ones. Our results remain robust even we control for the endogeneity between SWF and NFA components.

Given the importance of the U.S. in global commodity markets, the goal is to explore whether U.S. economic policy uncertainty impacts the price performance of certain commodities. The analysis uses the Granger causality in quantiles method that allows us to test whether there are different effects under different market conditions. The results document that economic uncertainty impacts the returns on the commodities considered, with the effects clustering around the tail of their conditional distribution. Robust evidence was obtained under alternative definitions of uncertainty.

In the aftermath of the Great Recession, the number of bank branches declined in most developed countries. In this paper, we investigate how banks have downsized their branch networks in Italy, by comparing the pre- and post-crisis spatial distribution of branches. By using a detailed data set that includes a wide set of controls for the characteristics of each bank branch, we estimate the probability of a branch being closed as a function of its distance from both proprietary and competitors' branches. We find that banks are more prone to close branches in those areas where other proprietary branches are closer and where competitors' branches are closer. This indicates that, since the start of the crisis, banks have closed branches especially in those areas where their proprietary network was relatively more populated and the competition was fiercer.

This paper studies the determinants of technological catch-up considering spatial and sectoral aggregation of industries. We investigate how geographical and technological proximity to the technology leader impact regional employment growth. We model technological progress by means of a hierarchical process of catch-up to the technology leader. We also incorporate measures for knowledge spillover effects to test the roles of competition, specialisation, and diversity at the industry level. Empirical results using data at the county level for different economic sectors (2-dig NAICS) for the United States indicate that human capital plays a crucial role in promoting sectoral employment growth. The association between technological/geographical distance to the technology leader and employment growth varies across sectors.