A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers

A choice experiment model for beef: What US consumer responses tell us about relative preferences for food safety, country-of-origin labeling and traceability

Who benefits from agricultural subsidies is an open question. Economic theory predicts that the entire subsidy incidence should be on the farmland owners. Using a complementary set of policy quasi experiments, I find that farmers who rent the land they cultivate capture 75 percent of the subsidy, leaving just 25 percent for landowners. This finding contradicts the prediction from neoclassical models. The standard prediction may not hold because of less than perfect competition in the farmland rental market; the share captured by landowners increases with local measures of competitiveness in the farmland rental market.

/pdf/the-incidence-of-u-s-agricultural-subsidies-on-farmland-27zzoh9q5u.pdf

The Incidence of U.S. Agricultural Subsidies on Farmland Rental Rates

This paper surveys 130 studies that apply frontier efficiency analysis to financial institutions in 21 countries. The primary goals are to summarize and critically review empirical estimates of financial institution efficiency and to attempt to arrive at a consensus view. We find that the various efficiency methods do not necessarily yield consistent results and suggest some ways that these methods might be improved to bring about findings that are more consistent, accurate, and useful. Secondary goals are to address the implications of efficiency results for financial institutions in the areas of government policy, research, and managerial performance. Areas needing additional research are also outlined.

Efficiency of financial institutions: international survey and directions for future research

Factors affecting the adoption of crop insurance, forward contracting, and spreading sales are analyzed using multivariate and multinomial probit approaches that account for simultaneous adoption and/or correlation among the three risk management adoption decisions. Our empirical results suggest that the decision to adopt crop insurance, forward contracting, and/or spreading sales are correlated. Richer insights can be drawn from our multivariate and multinomial probit analysis than from separate, single-equation probit estimation that assumes independence of adoption decisions. Some factors significantly affecting the adoption of the risk management tools analyzed are proportion of owned acres, off-farm income, education, age, and level of business risks.

/pdf/factors-affecting-farmers-utilization-of-agricultural-risk-1h7a924n9b.pdf

Factors Affecting Farmers' Utilization of Agricultural Risk Management Tools: The Case of Crop Insurance, Forward Contracting, and Spreading Sales

Farm financial performance measures are evaluated for producers across five age groups. The debt-to-asset ratio is highest for farmers in the lessthan-30 age group, 45.5 percent, and decreases across age groups. Repayment capacity is strongest for farmers in the less-than-30 age group, 2.1:1, and weakest for farmers in the 50-59 age group, 1.3:1. Operating profit margins tend to increase as farmers become more experienced. A key element in the financial evaluation of farmers through the life cycle is differing degrees of land ownership.

Financial Stages of a Farmer's Life: Effects on Credit Analysis Measures

In an efficient financial market, interest rates on comparable loans will be the same across banks. Therefore, disparities in loan rates should reflect differences in credit risk, loan characteristics (e.g., maturity and size), funding and servicing costs, and other characteristics of the lender-borrower relationship. Any resulting rate differences on comparable loans reflect imperfections in the financial market, barriers to the efficient flow of funds, and less than perfectly competitive lending conditions. However, Federal Reserve data indicate that interest rates on non-real estate farm loans made by commercial banks during 1989 and 1990 ranged from 9.0 percent to 14.9 percent and from 6.0 percent to 13.9 percent in 1995 (Walraven and Ricci; Walraven, Ott, and Rosine). Causes of these wide differences at specific points in time are not well understood, although substantial variation among regions in commercial banks' market shares of farm debt (Wilson and Barkley) suggest differences in lending competition and pricing arrangements. This study focuses on understanding the observed differences in average annual interest rates on non-real estate farm loans from commercial banks. The analysis uses concepts and approaches followed by similar studies in the finance literature that examined differences in

An Analysis of Interest Rate Differences on Non-Real Estate Farm Loans

An analysis of the scale economies and cost efficiencies in the farm credit system

Young Farmer Lending: A Farm-Level Evaluation of FmHA’s Beginning Farmer Program

This paper provides a comparison between grain farms in Ontario and Illinois. The intent is to focus on how the two regions compare, economically, and how government programs in each region differ and affect asset valuations. In general the results indicate that the two economies move in tandem when it comes to revenue, income, and cash flow generation, the use of debt, and with bankruptcies. Significant differences appear in terms of government programs between the two regions. In Illinois the level of direct support per acre appears to be significantly higher than in Ontario. In terms of land values a paradox is found in that Ontario land values appear to be more highly correlated with the economics and level of support in Illinois and the United States. At the cursory level of analyses it appears that the farmland capitalization model is more supported in the U.S. than in Canada.

A Comparative Study of the Financial Performance of Grain and Oilseed Farms in Ontario and Illinois

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Paul N. Ellinger

Author Tools

Chat about Author