Tunneling behavior, which is defined as the transfer of assets and profits out of a firm for the benefit of the firm's controlling shareholders, has become the focus of increasing attention in the theoretical and empirical literature. There are some corporate governance procedures, however, that help to protect investors against tunneling. This paper applies agency theory to study how the two basic mechanisms - legal protection on investor rights and ownership concentration - work together to constrain tunneling in a system of corporate governance. Analytical results in this paper show that tunneling is negatively related to the effectiveness of investor protection, while the relation between tunneling and ownership concentration is non-monotonic because both outcomes are determined by fundamentals including the effectiveness of investor protection, firm return and volatility of return, firm size, controllers' attitude towards risk, etc.After describing the theoretical framework in detail, the rest of the dissertation is taken up in assembling and assessing various pieces of evidence to see whether or not the predictions from the model are consistent with empirical evidence. I discuss several well-known cases of tunneling in the U.S. and Western European countries to show how tunneling happens in developed countries with good law enforcement and how tunneling is treated differently by different legal systems.The model makes several predictions about the determinants of corporate ownership concentration that are examined empirically. I study both cross-country and within-country variations in corporate ownership concentration with two newly constructed data sets. The first dataset contains 3875 public companies across states in the U.S. over a 10-year period (1992~2002) and the second dataset covers 1070 stock companies across 45 countries (regions) in a 10-year period (1992~2002). I find that corporate ownership concentration varies systematically with the effectiveness of investor protection and with firm-specific fundamentals such as firm size, firm return, and volatility of firm return in ways that are consistent with the model's predictions.JEL classification: G34; G32; D23; K49; L25; O5
