Abstract
This book investigates the role of relationship lending in Japanese local competitive markets. The authors used data from a 2008 survey by the Organization for Small and Medium Enterprises and Regional Innovation, Japan (SME Support, Japan) to extract soft information factors from questionnaire answers relative to the use of soft information in making lending decisions. Ultimately, the authors analyzed the influence of using soft information on lender performance. Therefore, this chapter first provides an overview of Japanese banking institutions as well as the characteristics of the small and medium-sized/regional financial institutions that comprise the analysis sample: regional banks , second-tier regional banks , and shinkin banks . Second, this chapter describes the survey questionnaire’s objectives and methods, and presents regional institutions’ current status regarding their use of soft information. Third, the chapter explains the factor analysis and the procedures to determine the number of underlying factors and retain the items that correlate to them. This chapter then incorporates the univariate analysis to further report the preliminary results of soft information’s influence on lender performance.
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Notes
- 1.
The post-war period for Japan’s financial system existed from the 1950s to the late 1970s. One characteristic of Japan’s post-war financial system was its rigid segmentation by regulation. The businesses in which each financial institution could engage was limited by laws, orders, and administrative guidance. The separation between banking and trust businesses, and between long-term and short-term banking, was a salient feature of the Japanese financial system (Osugi 1990; Mabuchi 1993). This helped mobilize financial resources into prioritized industries in Japan’s high-growth era.
- 2.
Each item’s relationship to the underlying factor is expressed by its factor loading.
- 3.
Uchida (2011) applied a factor analysis to data on loan screening for SMEs in Japan, and discovered that banks emphasize three factors when deciding whether to grant loans: the relationship, financial statement, and collateral/guarantee factors. This book applies the same statistical procedure to the questionnaire data.
- 4.
The loan officer counsels the borrower, collects all pertinent information from the borrower and business environment surrounding its industry, and analyzes and verifies all information on the borrower’s loan application.
- 5.
Appendix 3.2 describes the benefits of the combination of these rules.
- 6.
The regression model can accept biased estimations of the regression coefficients for the sake of decreasing variability. Thus, this model estimates biased factor scores (Kosfeld and Lauridsen 2008). This book also estimates factor scores using Bartlett’s method, which produces factor scores with a mean of zero and a variance equal to the squared multiple correlation between the estimated and true factor values. The scores may correlate even when the factors are orthogonal. The result of estimated factor scores conforming to the Bartlett model is essentially the same as what conforms to the regression model. This consequently ensures the robustness of this book’s results.
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Appendices
Appendix 3.1: Survey Questionnaire Items (Conducted by SME Support, Japan)
The survey was conducted in 2008 by the “Working Group on Intellectual Asset-Based Finance for Small and Medium-Sized Enterprises” under the “Study Group on Intellectual Asset-Based Management for Small and Medium-Sized Enterprises” at the Organization for Small and Medium Enterprises and Regional Innovation, Japan (SME Support, Japan). The questionnaire comprised 13 financial items and 54 non-financial items.
The 54 non-financial items were categorized into 7 groups, as follows: (1) human capital (top management); (2) external business environments ; (3) business content ; (4) customers and suppliers; (5) human capital (employees); (6) foundations of management, such as the management’s philosophy, business model, and employee evaluation system; and (7) risk management and the corporate governance structure.
The authors labeled 38 characteristics as soft information, which has much in common with non-financial information, but by definition it focuses on unquantifiable, unverifiable, and sometimes subjective information (see Sect. 1.3) (Table 3.5).
Appendix 3.2: The Underlying Factors in the Factor Analysis
Although several heuristic rules can be used, this book extracts three underlying factors in conducting its factor analysis , in line with a combination of Kaiser-Guttman’s rule and Cattell’s screening criterion: (1) organizational systems, (2) business and management leadership, and (3) networks or alliances/partnerships . Combining these rules has the following benefits: practitioners can clearly select underlying factors with eigenvalues of ≥ 1; a factor with an eigenvalue of 1 accounts for as much variance as a single variable, and the factors that explain at least the same amount of variance as a single variable are worth retaining in the analysis.
Figure 3.4 provides a scree plot that illustrates eigenvalues on the y-axis and the number of factors on the x-axis. The point where the slope of the curve clearly levels off indicates the number of factors that the analysis should generate.
The scree plot in the factor analysis
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Yosano, T., Nakaoka, T. (2019). Survey Data from Japanese Regional Banks and Using Soft Information in Lending Decisions. In: Utilization of Soft Information on Bank Performance. SpringerBriefs in Economics(). Springer, Singapore. https://doi.org/10.1007/978-981-13-8472-1_3
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DOI: https://doi.org/10.1007/978-981-13-8472-1_3
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