Search Results (8)

This study investigates how an increase in the quality of business ventures, measured as their success probability, affects trust and return on investment (ROI) in situations where the investor–entrepreneur interaction is affected by moral hazard and asymmetric information. We model a repeated trust problem between investors and entrepreneurs, featuring moral hazard and adverse selection. Hidden Markov techniques and computer simulations are used to derive the main results. We find that trust and ROI may decline as quality improves. Although lenders tend to reduce the requirements for granting initial credit, they nevertheless become less tolerant of current borrowers who fail to pay back. Additionally, we demonstrate a novel substitution effect, where lenders prefer new borrowers over existing borrowers that experienced early failures. The main conclusions of our study are that while impressing early on is effective in gaining first access to credit, it may nevertheless hurt the cause of getting credit in subsequent periods, following an early failure. In business environments plagued with ex post moral hazard, entrepreneurs might do better by gaining trust first and impressing later. Furthermore, our results imply that in a thriving economy, not only are bad loans made, but good loans are lost as well. Full article

Purpose. The purpose of this research is to propose a tool for designing a microcredit risk pricing strategy for borrowers of microfinance institutions (MFIs). Design/methodology/approach. Considering the specific characteristics of microcredit borrowers, we first estimate and measure microcredit risk through the default probability, applying a parametric technique such as logistic regression and a non-parametric technique based on an artificial neural network, looking for the model with the highest predictive power. Secondly, based on the Basel III internal ratings-based (IRB) approach, we use the credit risk measurement for each borrower to design a pricing model that sets microcredit interest rates according to default risk. Findings. The paper demonstrates that the probability of default for each borrower is more accurately adjusted using the artificial neural network. Furthermore, our results suggest that, given a profitability target for the MFI, the microcredit interest rate for clients with a lower level of credit risk should be lower than a standard, fixed rate to achieve the profitability target. Practical implications. This tool allows us, on the one hand, to measure and assess credit risk and minimize default losses in MFIs and, secondly, to promote their competitiveness by reducing interest rates, capital requirements, and credit losses, favoring the financial self-sustainability of these institutions. Social implications. Our findings have the potential to make microfinance institutions fairer and more equitable in their lending practices by providing microcredit with risk-adjusted pricing. Furthermore, our findings can contribute to the design of government policies aimed at promoting the financial and social inclusion of vulnerable people. Originality. The personal characteristics of microcredit clients, mainly reputation and moral solvency, are crucial to the default behavior of microfinance borrowers. These factors should have an impact on the pricing of microcredit. Full article

Agricultural insurance is instrumental in consolidating the gains of poverty alleviation and advancing rural revitalization. It significantly aids in the efficient allocation of agricultural production factors, which in turn enhances agricultural output and bolsters the evolution of modern agriculture. Therefore, utilizing data from 583 household surveys and employing endogenous transformation and intermediary effect models, this paper analyzes the production factor allocation effect and specific mechanism of agricultural insurance. It focuses on small-scale farmers and new agricultural operators, exploring how insurance contributes to the advancement of modern agricultural practices. The results show the following: (1) Agriculture insurance can significantly affect the agricultural scale input behavior of farmers such as land input scale and input scale, agricultural machinery application behavior such as the degree of mechanization and water conservancy application, agricultural technology adoption behavior, and planting structure selection behavior, thereby helping to modernize agriculture. (2) There is heterogeneity in the impact of agriculture insurance on the allocation of production factors for small farmers and new agricultural operators. For small farmers, agriculture insurance has a significant promoting effect on their agricultural machinery application behavior, agricultural technology adoption behavior, and planting structure selection behavior. For new agricultural operators, agriculture insurance significantly promotes their agricultural scale input behavior, agricultural machinery application behavior, and agricultural technology adoption behavior. (3) In terms of the mechanism of action, agriculture insurance mainly promotes agricultural scale input behavior through land transfer, facilitates agricultural machinery application behavior by purchasing agricultural machinery equipment and services, encourages agricultural technology adoption behavior by strengthening agricultural technology training, and enhances professional production levels by increasing the scale of insured planting, thereby contributing to the development of modern agriculture. Based on this, several policy suggestions have been proposed. These include enhancing the directionality of agriculture insurance policies, improving the collaborative interaction mechanism between agriculture insurance and agricultural credit financing, and adopting certain reward and punishment measures to curb moral hazard. Full article

A sustainable financing strategy for SMEs should aim to enhance a low-cost collateral-free supply of loans to SMEs with good track records of repayments to banks. In this paper, we suggest two alternative financing models for SMEs that address certain borrowing constraints of SMEs. First, the model incorporates institutional mechanisms involving the government, banks, and SMEs. The strategy employs a two-pronged approach: (i) the government enhances the supply of loanable funds to banks, and (ii) identifies good SME borrowers through skills development programs and introduces them to banks. This model will reduce default risk and allow banks to offer lower-interest and collateral-free credit to SMEs, thereby improving their access to finance and performance. Second, the model could be extended to accommodate digital finance using a data-driven credit risk score of the borrowers to reduce banks’ default risks and transaction costs with or without government funds. The proposed model could resolve the moral hazard and selection bias problems. Our proposed models are based on a public-private partnership approach and therefore could solve certain borrowing constraints of SMEs. Our empirical results support the model outcomes and therefore are consistent with the predictions of our theoretical models. Full article

Moral observer-licensing happens when observers condone actors’ morally questionable conduct due to the actors’ history of moral behaviors. In four studies (N = 808), we investigated this phenomenon in the context of cyberspace and its contributing factors and boundary conditions. The pilot study determined what participants perceived as typically moral and immoral behaviors in cyberspace. Then, in Study 1, participants condemned a story character’s online immoral behavior less often when they were informed of the character’s prior online moral behavior than when they were not, which indicates moral observer-licensing in cyberspace. Study 2 confirmed the presence of moral observer-licensing in cyberspace and further demonstrated that a character’s prior moral or immoral behavior online respectively reduces or intensifies the perceived negativity of the character’s subsequent immoral behavior. Finally, Study 3 showed that participants who identified with the victim in a hypothetical scenario showed less forgiveness and more condemnation of a character’s immoral behavior than those who identified with the perpetrator or the bystander. These findings are of theoretical and practical significance for our understanding of cyber ethics. Full article

Significant differences in loan terms between demographically distinct groups of borrowers in the United States are often interpreted as evidence of systematic ethnic, racial or gender discrimination by lenders. The appearance and interpretation of such discrimination has long been a controversial issue in public policy and has significant implications for both the economic efficiency and equity of credit markets. Arising from concern for borrowers disadvantaged by such discrimination, the design and implementation of regulations preventing the disparate treatment of demographically distinct groups by lenders are generally considered to have enhanced the equality of access to credit. Unfortunately, existing research has not examined whether this gain in social equity comes at a cost in efficiency borne by all market participants. The reliance on adverse selection or moral hazard in current models of limited lending and credit rationing poses difficulties in empirical testing for the presence and magnitude of such costs. This paper offers a novel theoretical framework in which lending discrimination can endogenously arise in the presence of value-maximizing lenders competing in an economy with complete markets, common knowledge and arbitrage-free pricing. By avoiding the reliance of current models on the exogenous presence of adverse selection or moral hazard, this framework allows potential efficiency costs to beexamined in a market environment without an ex ante assumption of informational market failure. Owing to the presence of common knowledge among participants, we first show how equilibrium loan terms to borrowers in different demographic classes can diverge in such an efficient environment. We then apply the properties exhibited in market equilibria to measure the potential costs of misallocating credit risk owing to the type of regulations observed in actual credit markets. Full article

We address the moral hazard problem of securitization using a principal-agent model where the investor is the principal and the lender is the agent. Our model considers structured asset-backed securitization with a credit enhancement (tranching) procedure. We assume that the originator can affect the default probability and the conditional loss distribution. We show that the optimal form of retention must be proportional to the pool default loss even in the absence of systemic risk when the originator can affect the conditional loss given default rate, yet the current regulations propose a constant retention rate. Full article

Various types of government credit guarantee programs exist for small- and medium-sized enterprises (SMEs). The SMEs guaranteed by these programs can resolve their financial difficulties by obtaining loans from banks or being included in a pool for the issuance of primary collateralized bond obligations. However, the loan default rate for these supported firms is high owing to their moral hazard, which can be associated with unethical behavior in the accounting process. Since the stakeholders of credit guarantee programs initiated by the government include not only lenders and borrowers, but also taxpayers, the default risk of moral hazard must be minimized. Thus, an additional evaluation step is required to deal with accounting ethics, which has not thus far been considered in the literature. In this study, we propose an accounting ethics-based credit scoring model as a complementary approach, which can be used to select suitable borrowers. The proposed model is expected to reduce the default rate resulting from the moral hazard associated with unethical accounting behaviors in the supported firms. Full article