Youth perceptions of agriculture: influence of cognitive processes on participation in agripreneurship

Figures & data

Table 1. Description of variables used in the binary logistic model.

Variable code	Variable name and description	Mean	Mode category
Socio-economic variables
AGE	Respondent’s age (15–24 = 1)	0.54	18–22
GENDER	Respondent’s gender (female = 1)	0.62	Female
EDU	Respondent’s educational level (1 = secondary and/or tertiary)	0.89	Secondary education
MARRIED	Respondent’s marital status (1 = Married)	0.13	White/traditional/court marriage
INCOME	Respondent’s income band (1 = R0-R5000)	0.79	R0–R5000
DEPEN	Number of dependents	0.92	1 dependent
EMPL	Employment status (1 = employed)	0.31	Studying
P.SUPPORT	Level of perceived financial parental support (1 = more than 75%)	0.37	50–75%
Knowledge variables
RURAL	Living in rural area (1 = yes)	0.84	Rural abode
KNOWL	Knowledge of government initiatives (1 = yes)	0.47	No
AG.STUDIES	Enrolment in agricultural studies (1 = secondary and/or tertiary)	0.44	Secondary education

Source: Field survey 2017.

Table 2. Perception indicators included in the principal component analysis.

Perception indicators	Mean	Standard deviation
Individual perceptions
My role models have successful agricultural businesses	1.85	0.31
I can meet the laborious requirements of agripreneurship	1.74	0.45
I am able to develop a success agricultural business	1.82	0.76
Agriculture is an acceptable way of life to me	1.71	0.41
Average mean score	1.78
Economic perceptions
Agricultural remuneration is attractive	1.78	0.26
Agriculture is a profitable business	1.35	0.29
There are opportunities for promotion in agriculture	1.55	0.31
Farming is a business	1.04	0.17
Agriculture creates employment	1.09	0.25
Average mean score	1.36
Socio-cultural perceptions
Farming is not a clean job	1.99	0.4
Farming is suitable for old people	2.01	0.5
Agricultural professions are admirable	2	0.67
Average mean score	2.01

Notes: 0 = strongly agree; 1 = agree; 2 = indecisive; 3 = disagree; 4 = strongly disagree.

Source: Field survey 2017.

Table 3. Principal component analysis estimated using a covariance matrix.

Component matrix
	Components
	PC1 Economic perceptions	PC2 Economic and individual perceptions	PC3 Economic and individual perceptions
Farming is business	.730	.131	.051
There are opportunities for promotion for agriculture workers	.636	−.174	.547
Agricultural remuneration is attractive	.627	−.249	−.120
Agriculture creates employment	.508	−.153	−.460
Agriculture is an acceptable way of life to me	−.076	.645	.505
Employment opportunities are not scarce in agriculture	−.073	−.633	.535
Agriculture is a profitable business	.417	.606	.002
Summary indicators
Eigen values	1.774	1.318	1.070
Proportion of variance explained	25.347	18.822	15.281
Cumulative proportion of variance explained	25.347	44.169	59.450
Test for robustness
Kaiser-Meyer-Olkin:	0.86
Bartlett’s test:	0.04
Cronbach alpha:	0.86

Source: Field survey 2017.

Table 4. Variables in the equation.

	B	S.E.	Wald	Df	Sig	EXP(B)
Marital status	−1.362	0.705	3.734	1	0.053*	0.256
Parental financial support	1.421	0.771	3.396	1	0.065*	4.139
Agricultural studies	0.779	0.471	2.737	1	0.098*	2.179
Age	0.245	0.520	0.222	1	0.637	1.278
Monthly income	1.045	0.649	2.589	1	0.108	2.843
Principal component 1	0.577	0.269	4.608	1	0.032**	1.780
Principal component 2	−0.157	0.239	0.430	1	0.512	0.855
Constant	−0.014	0.383	0.001	1	0.971	0.986
Pseudo R-squared	0.151
Observations	120
Correct classification	78%

Notes: Variables significant at 1% (***), 5% (**), and 10% (*).

Source: Field survey 2017.