The study analyzed the demand for farm credit in Missouri. Results suggested that financial leverage, government payments, occupation of farm operators, average farm acreages, value of land and buildings and types of farm operation had significant influence on farm credit usage. The study highlighted the potential for credit rationing.Agricultural Finance,

Ashlock, Tara

Rimal, Arbindra

English

Research Papers in Economics

Explanation of Variation in Demand for Farm Credit in Missouri 
 
 
 
 
 
 
 
 
Tara Ashlock, Graduate Student 
Dept of Agriculture-Agribusiness 
Southwest Missouri State University 
206 Karls Hall, 901 South National Avenue 
Springfield, MO 65810 
(417) 836 5638,  E-Mail: tda216s@smsu.edu
 
 
 
 
 
Arbindra Rimal,  Ast. Professor 
Dept of Agriculture-Agribusiness 
Southwest Missouri State University 
206 Karls Hall, 901 South National Avenue 
Springfield, MO 65810 
(417) 836 5094,  E-Mail: arr412f@smsu.edu
 
 
 
 
 
 
Selected Paper prepared for presentation at the Southern Agricultural Economics Association 
Annual Meeting, Tulsa, Oklahoma, February 14-18, 2004 
 
 
 
 
Copyright 2004 by [authors].  All rights reserved.  Readers may make verbatim copies of this 
document for non-commercial purposes by any means, provided that this copyright notice 
appears on all such copies. 
 
  1Explanation of Variation in Demand for Farm Credit in Missouri. 
Abstract: Missouri farms that demand farm credit are diverse. They vary in size, value of land, 
value of farm products, commodities produced, government payments received, and 
occupational status of the operator. The study will analyze the demand for farm credit in 
Missouri. Preliminary results suggest that land value and net cash return from farm had positive 
influence on per capita demand for farm credit. Large and medium size farms with higher farm 
value and cash return were likely to be financing their operation through loans for real estate and 
working capital.   
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  2Introduction 
Despite government efforts, agricultural financial market inefficiencies still exist.  Many 
farmers do not have access to a variety of lenders offering competitive rates.  In many rural 
areas, where agricultural activities are more prevalent, there are few financial options for 
farmers.  This lack of competition causes market inefficiencies since some lenders can choose 
the terms of loans and be selective about to whom they lend (Collender, 1996).  Another cause of 
deficient competition is the manner in which the different lenders are segmented among 
borrowers.  Most large commercial farms satisfy their credit needs through insurance companies 
and the Farm Credit System (FCS), while part-time farmers seek credit at commercial banks.   
Many struggling, low-equity farms turn to the federal government (Collender, 1996).  This 
stratification of borrowers leads to other problems.  For example, beginning farmers find it 
difficult to satisfy their credit needs in the FCS, since the FCS targets older, more experienced 
farmers. 
  Belongia and Gilbert (1990) addressed the issues relating to federal farm credit and 
agricultural output including the evidence for the need for public credit programs.  They reported 
that  banks participated in credit rationing or discriminating borrowers based on non-price 
conditions.  Banks would prefer to lend to low risk borrowers than to farmers, which are 
associated with higher risk.  Tests for credit rationing showed that it predominantly existed in the 
private credit market.  Results demonstrated that as the growth in total credit slowed, the portion 
of farm credit supplied by the Farm Service Agency and Farm Credit System increased 
significantly.  Hence, the need for federal farm credit programs to correct the problems with 
credit rationing was evident.  The second issue addressed by the authors was the effect of federal 
credit on agricultural output.  The credit extended by federal programs is generally at a lower 
  3rate than the private sector and this can be construed as a subsidy to farmers.  This would tend to 
increase agricultural production.  However, borrowers may use federal credit for projects with 
higher returns than agriculture, hence federal farm credit programs would have little influence on 
farm output.  Therefore, a strong relationship between federal farm credit and an increase in 
agricultural production could not be proven.  
Dodson and Koenig (2001) examined the factors that affected the demand for direct farm 
ownership and farm operating loans from the Farm Service Agency (FSA).  Data were collected 
for each U.S. county to classify them as low-, medium-, or high-use using an ordinal probit 
approach.  A few characteristics were shown to affect the use of federal farm credit including 
share of farms with interest expenses, personal income of the operators, and type of farm 
operations (row crops vs. livestock).  The results of the study revealed that counties with more 
racial and ethnic minorities were less likely to be classified as high-use.  This would be contrary 
to the objectives of the FSA since they target disadvantaged farmers including minorities.   
Counties with a Farm Credit System (FCS) branch office located in or within five miles of the 
county were more likely to be high-use counties.  This may be caused by the FCS locating in 
areas were there are more borrowers.  Counties with a higher percent of farm land were more 
likely to be classified as high-use.  The application of credit rationing by banks could be the 
cause of farms turning to the federal government for their credit needs.  Banks do not want to 
increase their risk with a high percent of farm loans in their portfolio.  Counties with a higher 
percent of farms with debt were more likely to be a high-use county.  Private lenders want to 
avoid farms with a lot of debt since they are considered high risk investments.  Counties with 
fewer guaranteed borrowers were more likely to be classified as high-use.  This could be caused 
by two factors:  direct and guaranteed loans are substitutes and these counties show greater 
  4financial risk.  Dodson and Koeing reported that several characteristics of the counties did not 
show  effects on the demand for federal farm credit including farming as the primary occupation, 
farm-size based on sales, the presence of agricultural banks in the county, and Federal Crop 
Insurance Corporation (FCIC) loss rates. 
Missouri farms that demand farm credit are diverse. They vary in size, value of land, 
value of farm products, commodities produced, government payments received, and 
occupational status of the operator. The results from the study will help the lenders and the 
policy makers in identifying the variables that affect demand. Marketing programs targeting the 
farms with a specific set of attributes may be developed to manage the risk and reduce the 
chances of delinquencies. Lenders need to know what groups of farmers demand more 
agricultural loans to improve profits and lower costs.  
The objective of this study was to analyze the demand for farm credit in Missouri. It 
examined the effect of different factors such as land size, value of land, value of farm products, 
government payments received, and occupational status of the operator on the demand for farm 
credit in Missouri. 
Conceptual and Empirical Models 
  The goal of a farmer is to maximize profits.  Farmers are essentially price takers due to 
the competitive nature of the production agriculture market.  Farmers cannot influence the prices 
of commodities, but can affect the quantity of production and level of inputs.  The production 
function depicts the use of labor and capital (farm credit).  Therefore, farmers will choose the 
optimal level of labor and farm credit to maximize their profits. 
  Given the production technology the optimal use of inputs, land and farm credit, will 
solve the following profit maximization expression for a farmer i. 
  5(1)     π (x1, x2) = Pf (x1, x2) – w1x1 – w2x2
  where  P = output price;    f (x1, x2) = production function;  w1 = input price of x1(wage);  
x1 = labor; w2 = input price of x2 (interest);  x2 = capital. 
The optimized input demand for farm credit, therefore, is   
( 2 )        x 2 = f (P, w1, w2) 
  The proposed empirical model for the demand for farm credit in Missouri uses farm 
credit usage as a proxy variable to represent the demand for farm credit.  Usage of farm credit is 
calculated by dividing the number of farm with interest expenses by the total number of farms in 
a county. Several farm characteristics were examined that were believed to have an effect on the 
demand for farm credit in Missouri.  The variables used in the regression models are listed and 
explained in Table 1.  The empirical model is expressed as follows.   
(3) USEi = " +  $1INTVSi + $2GPAYi + $3POFi + $4POOi + $5PCFi + $6LVLi + $7D1i + 
$8D2i + $9LT25i + $10CPVSALi + εi ;   i= 1 to 114 
  Dependent Variable 
  Demand for farm credit in Missouri was examined by creating a usage variable (USE i) as 
a dependent variable. USE i was a ratio of number of farms with interest expenses in a county to 
the total number of farms. The sources of farm credit included Farm Credit Systems, Farm 
Service Agencies, and Commercial Banks. Although the USE i may not be a true reflection of the 
demand for farm credit in the light of rapid structural change in the farm sector, it is a better 
proxy variable given the data constraints. 
  Explanatory Variables 
  A number of variables were hypothesized to influence farm credit usage at the county 
level.   The degree of financial leverage or the indebtedness of farm borrowers would be 
  6expected to positively influence whether a county is a higher user of farm credit (Dodson and 
Koenig, 2001). To evaluate the influence of financial leverage the ratio of interest expense to 
sales of agricultural products (INTVSi) was included in the estimations. The financial leverage is 
expected to be directly related with the farm credit usage.  GPAYi was the average government 
payments received per farm. It was expected that government payment programs would 
stimulate land purchases hence increasing the need for credits.  POFi was the number of farm 
operators whose primary occupation was farming, while POO was the total of those with main 
occupations other than farming.  It was expected that higher the number of fulltime farmers 
higher would be the usage of farm credits. PCFi was the average farm size in acres.  LVLi was 
the average market value of farm land and buildings.  D1 and D2 were the dummy variables used 
to capture structural changes in the agricultural sector between each census period. The dummies 
could also measure the effect of interest rate changes over the period. It is hypothesized that 
estimated coefficients for the dummy variables would be negative reflecting the inverse 
relationship between demand for farm credit and interest rates. LT25i was the number of farms 
with sales less than $25,000 and may offer insight into how differences in farm size affect the 
demand for loans.  CPVSAL was sales from cattle and calves as a percentage of total farm sales. 
Livestock operation dominates much of Missouri agriculture.  This variable will provide as much 
needed insight into the role of the livestock sector in the demand for farm credit. 
Data Sources 
The model is estimated with data compiled from primarily the Census of Agriculture 
conducted by the National Agricultural Statistics Service, a division of the U.S. Department of 
Agriculture.  The Census of Agriculture is a detailed survey of U.S. farms taken every five years.  
The farms included in the survey had or were expected to have $1,000 in sales of agricultural 
  7products during the year of the survey.  The data was compiled for each county, state, and for the 
United States.  For this study, data used were 114 Missouri counties.   This is a pooled data 
including 114 counties for three time periods:1987, 1992, and 1997.   
Data for each Missouri county was assembled and organized using Microsoft Excel.  The 
empirical model was evaluated using the SAS System.  The functional forms used for analysis of 
the data were linear regression and double log.  The results of the double log form have the 
benefit of showing elasticities of the farm credit usage with respect to each of the independent 
variables. 
Model Estimation Results 
  The overall fit of the model was good with an R-square of 0.7229 and an adjusted R-
square of 0.7139 for linear model and 0.7336 and 0.7250 for double log model.  Nine of the ten 
explanatory variables were statistically significant at less than 10% thus demonstrating a 
powerful explanation of the countywide variation in farm credit usage in the state of Missouri.  
The independent variable INTVSi was significant at 99% level of confidence and the coefficient 
was positive. As expected, the financial leverage was directly related with farm credit usage. 
Every 10 percent increase in the financial leverage  resulted in 1.6% increase in farm credit 
usage.    The variable GPAYi had a positive coefficient and was significant at 99% confidence 
level.  Receipt of government payments increased the use of debt.   This may be explained by 
tendency of farmer to purchase more land to take advantage of the government payments. Every 
10% increase in the average government payments increased the usage of farm credit by 6%. 
POFi was positively related with farm credit usage as expected and significant at 99%.   When an 
operator’s occupation was strictly farming the only way to increase assets was by taking 
additional debt.  The independent variable PCFi represented average farm size and was positive 
  8and significant at a 99% level of confidence.  As average farm size increased the percent of 
farms with interest expense also increased because farmers have to take additional debt to pay 
for additional land.  LVLi was significant at 99% confidence level and had a negative coefficient.  
Higher valued farm assets including land and buildings would be more productive, and yield 
higher return on investments than lower valued assets.  Farmers owning higher valued assets 
were able to pay off debts and lower the debt usage.  D1 and D2 were both negative and were 
significant at 95% and 90% levels of confidence, respectively.  Percent of farms with interest 
expenses in 1987 and 1992 were significantly lower than base year, 1997 indicating growth in 
debt usage over time. As discussed above, the dummy variables may have also captured the 
effects of other variables which were not included in the empirical model including interest rates.  
LT25i was negative and significant at 95%.  This suggested that smaller farms demanded less 
debt than larger farms.  Larger farms needed more financing for their larger operations while 
small farms may have relied on owner financing. It also highlighted the potential for credit 
rationing by FCS and commercial banks. Big farmers have relatively easy access to farm loans 
than the small farms. The last variable CPVSALi was significant at the 99% confidence level and 
had a negative coefficient.  On one hand, livestock farms were generally less reliant on farm 
loans.  On the other side, commercial banks generally targeted farms with permanent asset base 
such as land. Beef production does not require fertile land of higher value.  
 
Summary and Conclusion 
Missouri farms that demand farm credit are diverse. They vary in size, value of land, 
value of farm products, commodities produced, government payments received, and 
occupational status of the operator. This study analyzed the demand for farm credit in Missouri 
  9using the usage of farm credit as a proxy to the actual demand for loans. Results suggested that 
financial leverage, government payments, occupation of farm operators, average farm acreages, 
value of land and buildings and types of farm operation had significant  influence on farm credit 
usage. Large and medium size farms with higher farm value and cash return were likely to be 
financing their operation through loans for real estate and working capital.  
  
References: 
Belongia, Michael T. and R. Alton Gilbert.  “The Effects of Federal Credit Programs on Farm 
Output.” American Journal of Agricultural Economics 72.3 (1990):769-73. 
 
Collender, Robert. “Can Federal Action Improve Efficiency in the Market for Farm Loans?” 
Economic Research Service.  Bulletin No. 724-01.  March 1996. 
 
American Bankers Association “Condition of Farm Banks in 2002.”  http://www.aba.com
 
Dodson, Charles B., and Steven R Koenig. “Explaining Regional Demand for Federal Farm 
Credit Programs: An Ordinal Probit Approach.” Annual Meetings of the American Agricultural 
Economics Association. Chicago, 2001. 
 
Economic Research Service. http://www.ers.usda.gov
  
Missouri Department of Agriculture. Agricultural Income & Finance Outlook, Economic 
Research Service. September 25,  2001. 
 
Stam, Jerome et al. “Agricultural Income and Finance Annual Lender Issue” Economic Research 
Service. www.ers.usda.gov
 
U.S. Department of Agriculture. Credit in Rural America, Economic Research Service. 
 
 
 
 
 
 
  10 
Table 1.  Explanation of variables 
Variable names  Explanation 
USE  Number of farms per county paying interest/total number of farms per county
INTVS  Interest per farm ($1,000)/market value of agricultural products sold per farm 
($1,000) 
GPAY  Government payments received per farm ($) 
POF  Farm operators with primary occupation of farming 
POO  Farm operators with primary occupation other than farming 
PCF  Average farm size in acres 
LVL  Average estimated market value of land and buildings per acre (dollars) 
D1  Dummy variable 1:  1=1987, 0 otherwise 
D2  Dummy variable 2:  1=1992, 0 otherwise 
LT25  Number of farms with sales below $25,000 
CPVSAL  Sales of cattle and calves as a percentage of market value of agricultural 
products sold 
 
 
 
 
 
 
 
 
 
  11 
Table 2.  Descriptive statistics of the variables used in the study 
 
Variable 
names 
 
Mean 
Standard 
deviation 
 
Minimum 
 
Maximum 
USE 0.4449783  0.1465610 0 0.8017241
INTVS 0.0877275  0.0804815 0.0166232 0.6778351
GPAY 5961.46  3805.64 357.0 24566.0
POF 424.3918129  176.4303548 17.0 1010.0
POO 451.4181287  230.7346752 60.0 1464.0
PCF 292.5331376  139.1931244 0.4207921 987.3520599
LVL 13402.89  60247.31 354.0 444769.0
D1 .3529412  0.4786261 0 1.0
D2 0.3343109  0.4724422 0 1.0
LT25 3016.08  12924.31 51.0 151993.0
CPVSAL 499.6100743  2576.66 0.0014909 25562.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  12 
 
Table 3.  Results from the farm credit usage model: Linear regression 
Variable names  Parameter 
Estimate 
P-Values Elasticities  P-Values 
Intercept 0.27821  <.0001  0.28834 0.6296 
INTVS .095574  <.0001 0.16904 <.0001 
GPAY 0.00000437  0.0045 0.06815 0.0001 
POF 0.00029744  <.0001  0.42174 <.0001 
POO 0.00010776  0.2990  0.12499 0.1264 
PCF 0.00034455  <.0001  -0.06893 0.2531 
LVL -0.00004715  <.0001  -0.19520 <.0001 
D1 -0.01988  0.0686  -0.13702 <.0001 
D2 -0.01398  0.1010  -0.09534 <.0001 
LT25 -0.00016946  0.0435  -0.43099 <.0001 
CPVSAL -0.08103  0.0018  -0.02208 0.1436 
 
 
 
 
 
 
 
 
  13

EXPLANATION OF VARIATION IN DEMAND FOR FARM CREDIT IN MISSOURI

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