Performing Department
(N/A)
Non Technical Summary
This project investigates the effect of climate change and soil quality on debt repayment potential of small and medium-sized producers (including minority farmers) and in-turn its effect on discrimination in FSA farm loans. Specifically, the two main objectives are: 1) examine the effect of climate change and soil quality on debt repayment potential segmented by small and medium-size producers; 2) investigate loan service discrimination (measured by loan application processing days) while controlling for farm debt repayment potential.The rationale of this project is to improve the profitability of small and medium-sized farms and examine factors that may result in a more resilient agricultural sector. Farm efficiency is impacted by the lack of access or delayed access to credit. Farm debt repayment potential is important for credit approval and processing procedures. Credit discrimination can affect a producer's decision-making and management practices, leading to producers misallocating resources, finances and labor.Climate change may affect farms' productive capacity, and farmers will need to adapt to changes in weather by adopting new crops, technologies, and management practices. The cost of adaptation is unclear, but farm credit will likely be an important way for producers to finance their investment in climate risk mitigation. Identifying the drivers of farm credit repayment that may be affected by climate change is essential to improving the resiliency of small and medium size farms in the US.
Animal Health Component
100%
Research Effort Categories
Basic
(N/A)
Applied
100%
Developmental
(N/A)
Goals / Objectives
The rationale of this project is to improve the profitability of small and medium-sized farms and examine factors that may result in a more resilient agricultural sector. Farm efficiency is impacted by the lack of access or delayed access to credit. Farm debt repayment potential (the farmer's ability to repay the debt) is important in the FSAs loan approval and processing procedures. Land attributes such as soil quality and climate may impact farm debt repayment potential. Black and minority producers, due to marginalization, may have land disadvantages that make them more exposed to climate change, such as poor soil quality or greater production sensitivity to temperature increases. Credit or loan discrimination can affect a producer's decision-making and management practices, leading to producers misallocating resources, finances, and labor. Misallocating resources may lead to unintended consequences regarding land use, environmental sustainability, and farm livelihood. As a result, credit discrimination can reduce the efficiency of the FSAs farm credit program, which affects taxpayers and producers.Climate change may affect farms' productive capacity, and farmers will need to adapt to changes in weather by adopting new crops, technologies, and management practices. The cost of adaptation is unclear, but farm credit will likely be an important way for producers to finance their investment in climate risk mitigation. Identifying the drivers of farm debt repayment that may be affected by climate change is essential to improving the resiliency of small and medium size farms in the US.This project will improve the profitability of small and medium-sized farms in three ways.Examining racial bias in FSA lending practices, specifically loan processing time. Racial discrimination (loan application processing days) will be investigated, and several control variables, such as farm soil quality and weather and climate, will be used to control farm debt repayment capacity. Removing racial bias will lead to more efficient lending practices that promote investment in small and medium-sized farms. Identifying climate and soil quality effects present across farmer attribute groups (e.g., race, beginning farmer, and gender) and examining the impact of climate change on the group. Due to historical discrimination, minority farmers may have land effects that make credit access difficult, such as lower average soil quality or a higher likelihood of weather events like flooding. Understanding these effects is essential for ensuring fair credit access that promotes investment in minority and beginning farmers.Examine the effect of climate change on minority farmers' debt repayment potential. The marginal effect of temperature increases and precipitation decreases from climate change projections on debt repayment potential will be examined. The effects discussed above may expose farmer-attribute groups to varying degrees of climate change risk. It is important to understand which groups may experience the largest decreases in debt repayment potential in the future and how climate change will affect credit access.
Project Methods
The project has two objectives: 1) Investigate discrimination measured by loan processing days while controlling for debt repayment potential; 2) examine the effect of climate change and soil quality on debt repayment potential segmented by producer type.This project uses farm credit loan, weather, climate, and soil data. The weather and soil data are extracted from geolocated farm locations linked to the credit data. The credit loan data contains producer credit information, producer attributes, and farm financial information. Once the data is prepared, a debt repayment model is created and estimated. This model estimates and the marginal effects are used to investigate the project objectives. The planned approach is outlined below in more detail.The proposed method is a two-step loan processing model that captures the effects of climate and soil quality on debt repayment potential and then models loan application completion days and loan processing days using multiple factors, including debt service potential. Debt repayment capacity (or potential) will be approximated by farm profit margin, which is available in the FSA data. This methodology allows for the examination of the marginal effects of weather and climate on debt repayment potential and the impact of farmer attributes such as race and beginning farmer indicators.Di=f(Ai,CRi,LTYi, LTRi, COLi, FEi, DSPi, Zi)--------------------(1)DSPi=f(ME, PPV, WEi, SQi)---------------------------------------(2)where Di is the days for a loan processing (days to make loans) for an individual i, Aiis the amount of loan requested by an individual farmer i, CRi is the FSA credit category of individual i, LTYi, COLi and LTRirepresent loan type (farm ownership and operating loans), collateral pledged, and loan term. FEirepresents fixed effects including year (dummy variable 2009-2021), a quarter (dummy variables for Q1-Q4), and regional location of the farm (dummy variables for 11 regional locations, namely Corn belt, Lake states, Northern Plains, Mountain, Pacific, Southeast, Southern Plains, Appalachia, Delta, Northeast and other). DSPi is the debt repayment capacity of farm i, MErepresents macroeconomic effects, which will be modeled using a binary recession variable. PPV is the commodity price volatility, which will be approximated using the IMF commodity price index. WEi represents weather effects measured at the field level and aggregated to the farm. They include transformations of precipitation and min and max temperature. SQirepresent soil quality variables measured at the field level and aggregated to the farm level, including soil organic matter. Finally, Zirepresents a vector of farmer attributes, including race, gender of the farmer, organization, participation in other USDA assistance programs, beginning farmer, the new borrower to FSA, and documentation requirements.Two time periods in the application process are investigated to assess possible discrimination in the direct farm loan program. The first period "completion days," is the days from an application's submission to when it is determined to be complete. This process involves the participation of both the borrower and the FSA staff. The second period, "processing days," starts when the application is determined to be complete and continues until an initial decision is made on loan (approval or rejection). Processing days only depend on FSA staff, and no further input is needed from the borrower or the farmer. At any time during this process, a borrower may withdraw the application.The dependent variable in the model, loan application completion days and loan processing days, are regressed on multiple explanatory variables, including race, loan specifications, FSA credit score category, collateral, loan term, and debt repayment potential. Race should not influence loan processing time if the FSA lending system is unbiased. Farmers with more debt repayment potential will have a lower risk of default, which is expected to reduce the time of processing the loan.The farm's debt repayment potential (using profit margins) will be regressed on the soil quality data and weather and climate data, with several derived variables. The marginal effects of the credit model Equation 1 will be examined to identify if minority farmers' loan applications take longer than other farmers. The impact of climate change on the debt repayment potential will be investigated using Equation 2.Climate change is expected to have varying effects on US crop production by region. Minority farmers and small and medium-sized farmers may be more exposed to climate change than other farmers. From Equation 2, a sensitivity analysis will be conducted that estimates the impact of climate scenarios on producers' creditworthiness, approximated by the farm debt repayment potential, by producer attribute group (race, gender, size, new farmer).