Research on farmers’ households credit behavior and social capital acquisition

Abstract

Farmer’s credit is significant to growing the farmer’s revenue and the progression of the rural economy. Several scholars have focused on the impact of social capital on the farmers’ credit availability, but the research deduction is still not combined. In addition, credit behavior is an important element that researchers pay attention to. However, the study mainly focuses on the effects of social capital on the credit behavior of farmer households and its large positive effect, a scientific approach for measuring farmer households’ social capital was presented. The strategy attempted to develop an empirical model of farmer households’ loan behavior based on business performance and the role of social capital. Through regression analysis of the influence of social capital on the channels of farmer households’ access to credit, it was determined that the error correlation coefficients of the credit by the formal channel and the informal channel were 0.153, 0.158, and 0.152, respectively, for the three models. The significance levels of the correlation coefficients were all greater than 1 percent, showing that the regression results were relatively accurate and passed the significance test. However, these results indicated that financial channels pay attention to farmers’ credit and social capital acquisition to reduce the risk of advancing. Moreover, informal money lenders such as friends and relatives focused on the social capital of the farmer. The study is helpful for policymakers to make the strategies for the farmer to get credit behavior from financial institutions. Moreover, this study is beneficial in highlighting some limitations and giving future directions to researchers.

Digital Literacy Level and Formal Credit Constraints: Probit Analysis of Farm Households’ Borrowing Behavior in China

“…The regression findings above indicate that Chinese farmers' present digital literacy level primarily manifests in their foundational use of digital tools, with less conspicuous proficiency demonstrated in the advanced utilization of such technologies [76]. The regression results from models (1) to (4) reveal several significant findings: Firstly, at the personal characteristics level, age demonstrates a marked adverse relationship at the 1% significance level, indicating that senior farmers are less disposed towards utilizing formal channels for borrowing, possibly due to their reduced risk tolerance and preference for economic stability [77]. Conversely, health and marital status display significant positive correlations, indicating that healthier and married farmers are more likely to opt for formal loans.…”