Bancarization and violent attacks from guerrilla and other illegal groups in Colombia

Income shocks affect violence through the opportunity cost and rapacity effects. Existing studies focus on the impact of transitory shocks, especially commodity price innovations. This paper builds on this literature and studies the causal effect of permanent income shocks on armed conflict in Colombia. Using a rich dataset reporting all guerrilla and other armed groups' attacks by municipality between 2009 and 2014 and information on the provision of banking services, it shows that increasing bancarization leads to reductions in violence. These results have important implications for public policy in countries with a long history of violence. They suggest that promoting financial inclusion is useful for reducing conflict.     

How can investors build a better portfolio in small open economies? Evidence from Asia’s Four Little Dragons

This study investigates the role of hedging and portfolio design among stocks, exchange rates, and gold in small open economies (SOEs) from 4 January 2000 to 31 March 2020. We adopt the trivariate dynamic conditional correlation-fractionally integrated asymmetric power ARCH model and unconditional quantile regression model, and our findings show that the hedging role of the U.S. dollar (USD) and gold against stocks differs under regular and extreme market conditions. The USD can act as a powerful hedge asset for stocks in regular market periods. Moreover, during the global financial crisis and COVID-19 outbreak, the safe-haven effect of gold becomes stronger for almost all stocks, whereas the USD can serve as a strong safe haven against stock markets of Korea, Taiwan, and Singapore when stock returns are extremely low. In terms of portfolio designing, we find that adding the USD and gold to portfolios improves their hedging effectiveness, and the optimally weighted stock-USD-gold portfolio is the best portfolio strategy, irrespective of referring to return or risk.     

The time‐varying performance of UK analyst recommendation revisions: Do market conditions matter?

This study examines the time‐varying performance of investment strategies following analyst recommendation revisions in the UK stock market, with specific emphasis on the impact of changing market conditions. We find a negative relationship between the recommendation performance and market conditions as measured in terms of past market return and market volatility. In particular, the upgrade (downgrade) portfolio generates significantly positive (negative) net abnormal returns in bad market conditions (e.g., the dot‐com bubble burst in 2000 and the credit crisis in 2007), but not in other periods of time. Moreover, our non‐temporal threshold regression analysis shows that the reported negative relationship disappears when market conditions become better, i.e., when the past market return (market volatility) is higher (lower) than a certain level, indicating the importance of taking non‐linearity into account in the long sample period as examined in this study. Our time‐series bootstrap simulations further confirm that the superior recommendation performance in bad market conditions is not due to random chance; analysts have certain skills in making valuable up/downward revisions in bad markets.     

Geography of Italian student mobility: A network analysis approach

Understanding the phenomenon of intra- and international student mobility has become increasingly relevant to the organization of tertiary education systems. Using microdata information provided by the Italian National Student Archive on the cohorts of students enrolled at university in the academic years 2011–12 and 2014–15, we consider a network analysis approach to investigate the incoming and outgoing student flows between territories and universities. More specifically, the paper aims to shed light on the dynamics of Italian student mobility networks at both the bachelor's and master's degree levels by considering attractiveness indicators combined with network centrality measures, clustering techniques for network data and explanatory models. We analyze the partition of the global network structure by means of blockmodeling analysis and we explain the determinants of the differences among universities in attracting students adopting a quantile regression analysis.     

Why cryptocurrency markets are inefficient: The impact of liquidity and volatility

In this research, we study the multifractality, long-memory process, and efficiency hypothesis of six major cryptocurrencies (Bitcoin, Ethereum, Monero, Dash, Litecoin, and Ripple) using the time-rolling MF-DFA approach. For an in-depth analysis, this study uses the quantile regression approach to examine the determinants of efficient markets. The results show that all markets present evidence of long-memory property and multifractality. Furthermore, the inefficiency of cryptocurrency markets is time-varying, and Dash is the least inefficient market while Litecoin is the most inefficient. Finally, we find that higher liquidity improves but higher volatility weakens the efficiency of cryptocurrencies, depending on the quantiles. Therefore, we conclude that high liquidity with low volatility helps active traders to arbitrage away opportunities, resulting in market efficiency.     

Does bank capitalization matter for bank stock returns?

We examine US bank capitalization and its association with bank stock returns, and find that the book- and market-based capital ratios show different patterns. Fama-MacBeth regressions and portfolio analyses suggest that banks’ market-based capital ratios are negatively associated with banks’ stock returns during the (tranquil) 1994–2007 period while book-based capital ratios are positively associated with banks’ stock returns during the (turbulent) 2008–2014 period. These results suggest that the effect of bank capitalization on bank stock returns depends on the capital measure used and the period considered.     

How efficient airways act as role models and in what dimensions? A superefficiency DEA model enhanced by social network analysis

In this empirical study, a five-stage methodology is used to examine the efficiency of 45 worldwide known airline companies from the financial, operation and marketing perspectives. Initially, the superefficient data envelopment model is run with inputs and outputs that are selected based on the literature review. However, because 21 out of 45 airline companies are found to be efficient based on this analysis, a stepwise regression-based mechanism is applied to four reduced models – one for each output variable – for better discrimination. The outputs are, namely, net profit margin (financial output), passengers carried, on-time departure performance (operational outputs), and customer satisfaction (marketing output). In this way, the significant input variables are found for each reduced model. In the third stage, in order to provide even more discrimination, social network-based eigenvector centrality values are used as the weights of the superefficiency scores, and the strengths and weaknesses of efficient airlines for each output are specified in terms of their related significant inputs. The results show that, when net profit margin is taken as an output, Vietnam Airlines has the top weighted superefficiency value and excels in terms of available seat kilometers and liquidity, but it should improve its debt level. Although Norwegian Airlines has the highest efficiency with respect to debt level, it is not the best role model because its eigenvector centrality value is relatively low. However, Norwegian airlines also has the highest weighted superefficiency and acts as a role model in terms of on-time departures with respect to this output. Its main strength is liquidity, and it has no significant weaknesses. On the other hand, in terms of overall satisfaction and passengers carried, Vietnam Airlines and Thai Airways are the leaders, respectively. Vietnam Airlines is the only superefficient company with respect to overall satisfaction, while the basic strengths of Thai Airways in terms of passengers carried are its employee and fleet, and it has no significant weakness. A final aggregation of the results is made by making pairwise comparisons of the relative importance of four outputs for 7 experts selected from different departments of airline companies. According to the results, Net Profit Margin has the highest priority, followed by On-time Departure and Overall Customer Satisfaction, while passengers carried has the lowest importance. Based on these relative priorities, it can be said that Vietnam Airlines can be accepted as the top performing airline company, followed by Norwegian Airlines.     

Comparison of public perception and risk management decisions of aircraft noise near Taoyuan and Kaohsiung International Airports

This study uses multiple linear regression to identify factors contributing to perceived risk among residents near Taoyuan and Kaohsiung International Airports, the effect of perceived risk on their willingness to reduce risk, and consumption preferences that can reduce risk. Results indicated that residents' risk perception near Taoyuan Airport is lower than that near Kaohsiung Airport. Noise pollution experience, perceived probability of environmental contamination and negative effects, and perceived severity of catastrophic consequences significantly increase residents' perceived risks. Residents are willing to recognize and participate in mitigating the risks of aircraft noise pollution. The more risk residents perceive, the more willing they are to participate in disaster reduction and investigate means of improving the risk environment.     

Monetary policy and food inflation in South Africa: A quantile regression analysis

Although optimal monetary policy stabilizes food inflation theoretically, empirical studies remain limited not only in the context of volumes and the estimation approaches, but are focused on selected advanced and emerging countries to the neglect of Africa where poverty and dominance of food in the consumption basket are more pronounced. We provide empirical evidence in the context of South Africa using quantile regressions. Rising food prices are destabilized even further by restrictive monetary policy; a finding that has ramifications for inflation targeting, especially given that a quarter of the country’s population is food poor.     

山东省耕地后备资源变化及驱动因素分析

[目的]揭示耕地后备资源变化规律及其驱动因素是进行耕地后备资源合理保护、开发利用的重要基础,但目前对于耕地后备资源变化及驱动因素的研究较为匮乏。[方法]文章以山东省为例,基于2003年和2015年两期耕地后备资源数据库,在分析耕地后备资源时空变化的基础上,采用空间回归模型分析了其变化的驱动因素。[结果]2003~2015年期间,山东省集中连片耕地后备资源减少了30.81万hm~2,其中可开垦土地减少24.97万hm~2,可复垦土地减少5.84万hm~2。[结论]总人口、文盲率、农民人均纯收入、建设用地扩张速度、第一产业产值占总产值比重等是山东省耕地后备资源变化的显著性驱动因素,并且总人口和建设用地扩张的驱动作用最大,受教育程度和产业结构调整的作用次之,农民人均纯收入变化对耕地后备资源变化的影响最小。"宏观统筹协调,异地代补"是解决"耕地后备资源空间分布不均衡、供需不匹配",实现耕地占补平衡、高效合理的开发利用耕地后备资源的必经之路。但异地代补的数量比例和质量应严格控制,并注重对补充耕地地区的经济补偿和政策倾斜,逐步缩小与发达地区之间的差距。