Risk Management and Financial Literacy Practices of Rural Kentucky Farmers

Variable	Definition	Level of measurement	Notes
Financial Literacy**	Total number of correct responses to financial knowledge questions	Ratio (0–10)	Higher scores indicate greater financial literacy
Risk Management Practices*	Number of risk management strategies actively used on the farm	Ratio (0–18)	Higher scores indicate broader adoption of risk strategies
Age***	Self-reported age of farmer respondent	Ratio	Measured in years
Gender***	Self-identified gender	Nominal	Coded (0 = Male, 1 = Female, 2 = Other/Prefer not to say)
State***	State of residence	Nominal	Coded (States 1–50, alphabetically)
Education level***	Highest level of education attained	Ordinal	High school = 1 to Graduate certification = 6, and Other professional certification (trade) = 7 (or similar scale)
Income level***	Self-reported household income (participant estimate)	Ratio	Measured in dollars
Years of experience***	Total years of farming experience	Ratio	Measured in years
Credit score***	Self-reported credit score (participant estimate)	Ratio	Measure of credit score
Acres farmed***	Total acres farmed	Ratio	Measured in acres
Land ownership***	Ownership status of farmland operated	Nominal	Coded (0 = inherited, 1 = purchased, 2 = rented, 3 = mix
Commodities farmed***	Type of farm based on commodities farmed	Nominal	Coded (0 = crops, 1 = livestock, 2 = both crops and livestock)

* Question from Tudor et al.'s (2014) instrument.

** Question from Houts and Knoll's (2020) instrument.

*** Demographic question.

Source: own elaboration.

Table 2.

Response counts for categorical demographic variables

Gender
Male	63
Female	49
Farm type (Commodities farmed)
Crops	8
Livestock	61
Both crops and livestock	43
Highest education level
High school/GED	31
Associates	7
Bachelors	40
Master's	24
Doctoral	3
Graduate certification	2
Other professional certification (Trades)	5
Method of land ownership
Purchased	40
Rented	4
Inherited	16
Mix (purchased, rented, and/or inherited)	52

[i] Source: own elaboration.

Table 3.

Descriptive statistics for demographic variables (scaled) (n = 112)

Statistics	Age	Income level (estimate)	Credit score (estimate)	Years of experience (farming)	Acres farmed
Modea	33.000	150 000.000	800.000	20.000	100.000
Median	43.000	110 000.000	755.000	25.000	184.500
Mean	47.205	145 814.286	687.571	28.920	294.052
Std. deviation	16.170	160 849.560	215.849	17.799	480.613
Shapiro-Wilk	0.921	0.552	0.581	0.929	0.510
P-value of Shapiro-Wilk	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001
Minimum	23.000	13 000.000	0.000	3.000	1.000
Maximum	78.000	1.250×10⁺⁶	850.000	70.000	4 000.000

a The mode is computed assuming that variables are discrete.

Source: own elaboration.

Table 4.

Descriptive statistics for financial literacy scores and risk management scores (n = 112)

Statistics	Financial literacy	Risk management score
Mode	8.000	2.000
Median	8.000	2.000
Mean	7.813	2.946
Std. deviation	1.630	2.320
Shapiro-Wilk	0.917	0.747
P-value of Shapiro-Wilk	< 0.001	< 0.001
Minimum	2.000	1.000
Maximum	10.000	11.000

[i] Source: own elaboration

Table 5.

RQ2 model summary – Risk management score

autocorrelation	statistic	p
M₀	0.000	0.000	0.000	2.320	−0.027	2.041	0.826
M₁	0.709	0.503	0.437	1.741	−0.095	2.183	0.342

[i] M₀ includes no independent variables except for the intercept; M₁ includes age, gender (coded), non-college, undergraduate degrees, income level (estimate), years of experience (farming), credit score (estimate), acres farmed, inherited, purchased, rented, crops, livestock.

[ii] Source: own elaboration.

Table 6.

RQ2 ANOVA – Risk Management Score

	Model	Sum of squares	df	Mean square	F	p
M₁	Regression	300.782	13	23.137	7.637	< 0.001^*
	Residual	296.897	98	3.030
	Total	597.679	111

* p < 0.05.

Source: own elaboration.

Table 7.

RQ2 coefficients table – Risk management score

Model	Variable	Unstandardized	Standard error	Standardized	t	p	Tolerance	VIF
M₀	(Intercept)	2.946	0.219		13.438	< 0.001
M₁	(Intercept)	3.396	1.004		3.382	0.001
	Age	−0.025	0.018	−0.173	−1.405	0.163	0.333	3.004
	Gender (coded)	−0.438	0.358	−0.094	−1.223	0.224	0.856	1.169
	Non-college	−0.971	0.480	−0.196	−2.022	0.046*	0.537	1.861
	Undergraduate degrees	−0.017	0.426	−0.004	−0.039	0.969	0.611	1.637
	Income level (estimate)	−2.851×10⁻⁷	1.114×10⁻⁶	−0.020	−0.256	0.799	0.850	1.177
	Years of experience (farming)	0.023	0.016	0.176	1.449	0.151	0.345	2.898
	Credit score (estimate)	1.363×10⁻⁴	8.388×10⁻⁴	0.013	0.163	0.871	0.833	1.201
	Acres farmed	0.003	3.663×10⁻⁴	0.589	7.768	< 0.001*	0.881	1.135
	Inherited	−0.617	0.527	−0.094	−1.172	0.244	0.796	1.256
	Purchased	0.062	0.394	0.013	0.158	0.875	0.761	1.315
	Rented	0.549	0.923	0.044	0.595	0.553	0.923	1.084
	Crops	1.589	0.699	0.177	2.273	0.025*	0.834	1.199
	Livestock	−0.702	0.376	−0.151	−1.867	0.065	0.772	1.295

[i] *p < 0.05.

[ii] M₀ includes no independent variables except for the intercept; M₁ includes age, gender (coded), non-college, undergraduate degrees, income level (estimate), years of experience (farming), credit score (estimate), acres farmed, inherited, purchased, rented, crops, livestock.

[iii] Source: own elaboration.

References

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Articles in this issue

DOI: https://doi.org/10.17306/j.jard.2026.3.00024r1 | Journal eISSN: 1899-5772 | Journal ISSN: 1899-5241

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Language: English

Page range: 289 - 297

Accepted on: Aug 13, 2026

Published on: Sep 30, 2026

Published by: Poznań University of Life Science

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

farming,

Related subjects:

Business and economics,

Political economics,

Political economics, other,

Business management,

Business management, other,

Life sciences,

Life sciences, other,

Social sciences,

Political science,

Political science, other

© 2026 Randa Morris, Michelle Preiksaitis, published by Poznań University of Life Science
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 81 (2026): Issue 3 (September 2026)