Simin Xie

Competition and Relationship Lending in Small Business Credit (with Yaming Gong and Samuel Rosen)

Previously circulated as “The Impact of Lender Competition on Small Business Loan Pricing: Evidence from the SBA 7(a) Program”

Informational frictions in small business credit markets can weaken or even reverse the usual link between lender competition and loan prices. Using the full population of SBA 7(a) loans from 2009 to 2022, we find that relationship-intensive segments show a flatter — and at times negative — relationship between market concentration and interest rates, while transactional segments behave the way standard market-power models predict. A merger-based design isolating changes in competition within the SBA market confirms that relationship-lending incentives govern how market structure passes through to small business loan pricing.

Public Guarantees, Private Discretion: Evidence from Small Business Lending

Government loan guarantees can expand credit, but they may also create moral hazard by insulating private lenders from the losses associated with their lending decisions. I study this trade-off in the SBA’s Preferred Lender Program, which delegates loan approval to selected lenders without removing the federal guarantee. Using the universe of SBA 7(a) loans, I study lender portfolios around entry into the PLP and compare delegated versus non-delegated loans within the same lender. Despite this concern, delegation is not associated with deterioration in loan performance. Yet this portfolio-level stability masks systematic sorting within lender portfolios: relative to reviewed loans from the same lender, delegated long-maturity loans charge off more, while delegated short-maturity loans charge off less. These patterns suggest that lenders use delegated authority selectively rather than simply shifting risk onto the government guarantee.

Financial Management (FIN 3101)

Instructor, Temple University, Summer 2026

Student Feedback Form ratings (5-point scale): organized and prepared for class, 4.7; effective in helping me learn the material, 4.6; learned a great deal from this course, 4.6.

• “The lectures were very in depth.”
• “The take home problems and examples in Excel allowed me to further understand certain topics.”
• “The instructor was extremely nice and encouraging of questions, making sure students understood what was being taught.”

Based on 19 of 41 enrolled students (46% response rate, against a 24% university-wide average).