| Abstract: |
This dissertation thesis consists of three essays on asymmetric information problem in small and medium sized enterprises (SMEs) finance and Microfinance. The aim of the thesis is to address the key problem in the credit rationing in the SME finance and microfinance and strive to improve the credit analyzing model with the help of soft information. The first essay investigates the factors that hinder the growth of SMEs using a World Bank dataset, and access to finance is found to be their biggest constrain to growth. Asymmetric information between small business owners and banks generates high interest rates, complex application procedures and high collateral requirements, which are found to be the biggest obstacles business owners face when they seek external financing. Small business owners who cannot get loans from banks will turn to microfinance as an alternative source of funds. In the second essay, a new dataset from disintermediated Peer to Peer (P2P) lending market is used to investigate credit rationing efficiency when there is no financial intermediary. The results show the existence of adverse selection where investors are predisposed to making inaccurate diagnoses of signals and gravitate to borrowers with low creditworthiness, while inadvertently screening out those with high creditworthiness. This implies that although disintermediation can decrease transaction costs, it increases credit risk because the peer lenders lack professional credit rationing experience. We also find that this misdiagnosis is particularly evident with finance oriented (hard) signals, while lenders can distinguish better the social and psychological related (soft) signals. Given that developing countries commonly lack a solid financial credit bureau and that financial information is hard to verify, in the third essay we examined whether the soft social and psychological information can be used to improve the credit analyzing model. The results show that soft social and psychological related information can improve the predictive power of the credit model and serve as a substitution when hard financial information is difficult to verify and under weak credit bureau conditions. |
