Examining Farm Financial Management: How Do Small US Farms Meet Their Agricultural Expenses?
Abstract
:1. Introduction
2. Data and Methodology
2.1. Sampling and Data Collection
2.2. Conceptual and Empirical Model
3. Results and Discussion
3.1. Enterprise Diversity, Off-Farm Work, and Perceived Risks of Small Farms
3.2. Credit Constraints among Small Farms
3.3. Sources and Uses of Funds among Small Farms in the US
3.4. Factors Influencing the Extent of Use of Different Financing Sources
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
1 | Note that the definition of small family farm is based on the annual gross farm incomes of US farms defined by the US Department of Agriculture, which may not exactly correspond with the small farm context in developing countries or other emerging and advanced countries. |
2 | While we captured some methods such as personal savings in our study, we did not exactly distinguish formal and informal sources of funding in the analysis of this study due to data limitations and the lack of distinct separation between formal or informal means in the data. |
3 | While we acknowledge the possibility of interlinked decision in determining the extent of financing among these financing sources, we estimated individual equations separately for simplicity of interpretation. |
4 | The survey questionnaire used in this data did not collect details of expenses about where funds are used. With this limitation, we were not able to exactly distinguish types of expenses incurred, long-term vs. short term expenses, or operating expenses vs. investment. Specifically, the item states “considering your past year, what was the source of funds to pay for your expenses in agricultural activities (indicate the level of percentage in each)”, and it provides a source list and categorical choices for extent of use on each source. Therefore, we represented it by annual agricultural/farm spending and expenses in this paper. |
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Variables | Description of Variables | Mean | Std Dev. |
---|---|---|---|
Panel A: Explanatory variables | |||
Cred_cons | Whether farmer is credit constrained (=1 if constrained) | 0.58 | 0.5 |
Age below 35 | Age of principal operator below 35 (=1 id age below 35) | 0.05 | 0.22 |
Age 36 to 54 | Age of principal operator between 36 to 54 (=1 if age between 36 to 54) | 0.46 | 0.5 |
Age 55 to 64 | Age of principal operator between 55 to 64 (=1 if age between 55 to 64) | 0.27 | 0.44 |
Age 65 or above | Age of principal operator 65 and above (=1 if age 65 and above) | ||
High school | Education level of principal operator is high school (=1 if high school) | 0.16 | 0.37 |
College | Education level of principal operator is college (=1 if college education) | 0.6 | 0.49 |
Grad.Degree | Education level of principal operator has graduate degree (=1 if graduate) | ||
Male | Gender of principal operator (=1 if male) | 0.68 | 0.47 |
Married | Marital status of principal operator (=1 if married) | 0.92 | 0.31 |
Log hhinc | Gross annual household income (in US dollars, in log) | 11.03 | 1.07 |
hhsize | Number of family members | 2.69 | 1.49 |
Offfarm_hrs | Number of off-farm hours per week by the operator (farmer) | 16.33 | 20.96 |
Smart phone | Use of smartphone with internet access (=1 if smatphone use in farm activities) | 0.84 | 0.37 |
Continue plan | Expected to continue farming in 5 to 10 years (=1 if continue plan) | 0.85 | 0.36 |
Diverse_score | Agricultural enterprises diversification (score) | 2 | 1.29 |
Risk prcp | Farming operation’s level of risk perception (range: 1 to 5) | 3.1 | 1.18 |
Risk con | How much concern about risks in agricultural production, management, and marketing (range: 1 to 5) | 2.7 | 1.10 |
Panel B: Dependent variables | |||
Fund_sale | Extent of use of fund generated through sales of agricultural products to meet farm expenses (percentage) | 48.15 | 31.77 |
Fund_save | Extent of use of past savings to meet farm expenses(percentage) | 11.1 | 18.33 |
offfarminc | Extent of use of off-farm income to meet farm expenses (percentage) | 25.75 | 29.53 |
govpayinc | Extent of use of money from government incentives/payments to meet farm expenses (percentage) | 2.55 | 4.79 |
Fund_loan | Extent of use of credit/loans to meet farm expenses (percentage) | 7.05 | 17.47 |
Fund_other | Extent of use of other sources (besides listed bove) to meet farm expenses (percentage) | 4.5 | 13.66 |
Number of observations | 100 |
Variables | Agricultural Product Sales (fund_sale) | Past Savings (fund_save) | Off-Farm Income (offfarminc) | Government-Paid Income/Incentive (govpayinc) | Credit/Loan (fund_loan) | |||||
---|---|---|---|---|---|---|---|---|---|---|
(I) | (II) | (III) | (IV) | (V) | ||||||
Coef. | t-Score | Coef. | t-Score | Coef. | t-Score | Coef. | t-Score | Coef. | t-Score | |
Constant | 5.425 ** | 3.28 | 2.348 | 1.04 | −1.826 | −0.88 | −11.518 | −0.01 | −10.702 | −0.01 |
Credit constraint | −0.176 | −0.63 | −0.255 | −0.60 | 0.084 | 0.25 | −2.717 *** | −4.24 | −1.779 *** | −3.06 |
Age below35 | −1.230 * | −1.68 | −3.322 *** | −3.44 | 1.015 | 1.28 | −0.677 | −0.58 | −2.038 * | −1.80 |
Age 36 to 54 | 0.134 | 0.29 | −0.642 | −1.28 | 0.189 | 0.39 | 1.520 ** | 2.15 | 2.272 *** | 3.27 |
Age 55 to 64 | −0.414 | −0.87 | 0.007 | 0.01 | 0.015 | 0.03 | 3.561 *** | 4.18 | 2.967 *** | 3.48 |
Highschool education | −0.635 | −1.35 | 1.977 *** | 3.12 | −0.034 | −0.07 | 0.481 | 0.73 | −0.483 | −0.68 |
College education | −0.005 | −0.01 | 0.727 * | 1.67 | 0.117 | 0.30 | 1.118 ** | 2.19 | 1.541 *** | 2.69 |
Operator’s gender = male | 0.397 | 1.21 | 0.852 * | 1.92 | −0.264 | −0.67 | −1.369 ** | −2.26 | −1.154 ** | −2.39 |
Operator is married | −0.424 | −0.83 | −0.754 | −1.31 | 1.864 *** | 3.06 | −2.270 *** | −3.42 | −1.781 ** | −2.14 |
Farm household income (in log) | −0.002 | −0.02 | −0.327 * | −1.84 | 0.174 | 1.19 | −0.199 | −1.64 | −0.628 * | −1.64 |
Household size | −0.199 * | −1.84 | −0.167 | −1.28 | 0.165 | 1.53 | 0.044 | 0.31 | −0.112 | −0.83 |
Operator or spouse’s off-farm hours | −0.011 | −1.49 | −0.036 *** | −3.36 | −0.039 *** | −3.47 | −0.003 | −0.28 | ||
Land acreage holdings | −0.005 *** | −3.15 | ||||||||
Smartphone with internet | −0.201 | −0.54 | 1.178 *** | 2.68 | −0.094 | −0.24 | 0.490 | 0.82 | 1.755 *** | 2.67 |
Continuation plan | −0.251 | −0.46 | −0.237 | −0.34 | 1.919 *** | 3.12 | 17.221 | 0.02 | 16.951 | 0.02 |
Enterprise diversity score | 0.088 | 0.64 | 0.952 *** | 4.65 | −0.016 | −0.11 | 0.917 *** | 3.91 | 0.088 | 0.54 |
Perceived survival risk | −0.033 | −0.23 | 0.544 *** | 2.55 | −0.450 ** | −1.98 | 0.009 | 0.03 | −0.539 ** | −1.96 |
Risk concern level | −0.063 | −0.38 | −0.045 | −0.17 | 0.255 | 0.94 | −0.591 * | −1.76 | 1.019 | 1.08 |
Log likelihood | −348.11 | −219.93 | −285.51 | −125.499 | −162.457 | |||||
AIC | 10.142 | 6.58 | 8.403 | 3.958 | 4.985 | |||||
Number of observations | 100 | 100 | 100 | 100 | 100 |
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Omobitan, O.; Khanal, A.R. Examining Farm Financial Management: How Do Small US Farms Meet Their Agricultural Expenses? J. Risk Financial Manag. 2022, 15, 133. https://doi.org/10.3390/jrfm15030133
Omobitan O, Khanal AR. Examining Farm Financial Management: How Do Small US Farms Meet Their Agricultural Expenses? Journal of Risk and Financial Management. 2022; 15(3):133. https://doi.org/10.3390/jrfm15030133
Chicago/Turabian StyleOmobitan, Omobolaji, and Aditya R. Khanal. 2022. "Examining Farm Financial Management: How Do Small US Farms Meet Their Agricultural Expenses?" Journal of Risk and Financial Management 15, no. 3: 133. https://doi.org/10.3390/jrfm15030133