The Geographic and Social Distance in Finance.
详细信息
文摘
Our research interests lie in studying the economic behavior, choices, and actions of individuals given their geographical and social proximity to others, and analyze the consequences of such decisions to the financial health and survival of households, firms, and the macro economy. Network analysis and spatial econometrics take account of information spill-overs and constraints of behaviors as consequence of the geographical and social distance between and among individuals. In this research, we apply those techniques to analyze aspects of corporate governance and explanations for the recent housing crisis. The literature on principle-agent problems has devoted most of its attention to aligning the CEOs incentives with public shareholders. The problems resulting from directors excessive loyalty to CEOs are largely ignored. In the first essay of the dissertation, we apply social network analysis to study the effectiveness of independent directors in directing the firm. We define powerful independent directors to be those with high social network centrality, and thus high social influence over their peers. We show that boards dominated by powerful independent directors are less likely to demonstrate excessive loyalty to CEOs. They carry out the duties of monitoring and advising more effectively, resulting in superior financial performance and higher firm value. In the second and third essays of the dissertation, we study how financial risks are spread by proximity and contagion. Given advancing technology and globalization, financial markets are ever-more linked. We examine Rajans 2010) &ldquo；credit for income&rdquo； hypothesis as a root cause of U.S. mortgage defaults during the financial crisis of 2008. Over the past several decades, as U.S. household income became more unequal, those with stagnant incomes took on high leverage to boost their consumption to keep up with their wealthier neighbors. We provide empirical support for the credit for income hypothesis using household-level data showing that default is highest for middle-income, low-educated borrowers&mdash；precisely the ones with stagnating income. We also apply spatial models to identify the &ldquo；hot spots&rdquo； of defaults. We use Spatial Statistics Anselin, 1988, 1990) to analyze data of residential foreclosures at county level to account for the unusual concentration of foreclosures observed in the south Pacific, east North Central, and south Atlantic regions. We find that spatial correlation plays an important role in explaining the large number of mortgage foreclosures that are clustered strongly in those regions. Moreover, default contagions are more severe at counties with younger households and largely usage of variable loans.