sustainability
Article Determinant of University Students’ Choices and Preferences of Agricultural Sub-Sector Engagement in Cameroon
Cynthia J. Mkong 1,*, Tahirou Abdoulaye 2, Paul Martin Dontsop-Nguezet 3, Zoumana Bamba 4, Victor Manyong 5 and Godlove Shu 1
1 Department of Agricultural Economics and Agribusiness, University of Buea, P.O. BOX 63, Buea, Cameroon; [email protected] 2 Social Science and Agribusiness, International Institute of Tropical Agriculture (IITA), B.P. 320, Bamako, Mali; [email protected] 3 Social Science and Agribusiness, International Institute of Tropical Agriculture (IITA), Kalemie, Democratic 570, Democratic Republic of the Congo; [email protected] 4 Country Representative, International Institute of Tropical Agriculture (IITA), Kinshasa, Democratic 4163, Democratic Republic of the Congo; [email protected] 5 Social Science and Agribusiness, International Institute of Tropical Agriculture (IITA), P.O. BOX 34441, Dar es Salam, Tanzania; [email protected] * Correspondence: [email protected]
Abstract: Although the agri-food sector has a huge potential to offer attractive employment op- portunities for Africa’s burgeoning youth, a negative perception of agriculture persists among Cameroonian youths, such as in many other African countries. The paper assesses the determinants of university students’ choices and preferences for agricultural sub-sector engagement in Cameroon. A multistage random sampling technique was used to select 550 students from two state universities. We used the SWOT analysis to evaluate students’ perceptions of challenges and opportunities within Citation: Mkong, C.J.; Abdoulaye, T.; the agricultural sector in Cameroon, the binomial probit analysis to assess the determinants of stu- Dontsop-Nguezet, P.M.; Bamba, Z.; dents’ choices of agriculture as a university major, and an ordered probit analysis to evaluate the Manyong, V.; Shu, G. Determinant of University Students’ Choices and determinants of students’ preferences of agricultural sub-sector engagement. Findings reveal that Preferences of Agricultural choice of agriculture as a university major is significantly determined by sex, pre-university farming Sub-Sector Engagement in Cameroon. experience, pre-university academic background, mother’s level of education, and household income. Sustainability 2021, 13, 6564. https:// Likewise, preference of agricultural sub-sector engagement is significantly determined by the stu- doi.org/10.3390/su13126564 dent’s level of study and location of a childhood home. Improving the attractiveness of, and working conditions in, the agricultural sector could increase youth engagement in agribusiness and rural Academic Editor: Donato Morea economic activities. The results also reinforce the need to increase household income in Cameroon, as this could lift families out of poverty and offer them more economic opportunities. Policies that Received: 12 April 2021 regulate levels of education can equally be used to allocate human resources into different agricultural Accepted: 26 May 2021 sub-sectors, subject to felt needs. Published: 9 June 2021
Keywords: youth; agriculture; university student; choice; preference; sub-sector engagement; Cameroon Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. 1. Introduction Agriculture remains an essential driver of pro-poor growth and the largest employer in Africa, even in the context of off-farm diversification [1–4]. The agricultural sector plays a crucial role in improving rural economies and reducing poverty in Africa [5]. In Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. Africa, 61% of the entire population is under 24 years of age, representing current and This article is an open access article future youth cohorts of a sobering magnitude [6,7]. The agri-food sector is therefore distributed under the terms and thought to have a huge potential to offer attractive employment opportunities for Africa’s conditions of the Creative Commons burgeoning youth [8–10] and is crucial to addressing the disproportionately high levels of Attribution (CC BY) license (https:// youth unemployment, underemployment, and poverty [10,11]. creativecommons.org/licenses/by/ Agriculture is the spine of Cameroon’s economy, employing up to 70% of its workforce 4.0/). and contributing 42% of its Gross Domestic Product (GDP), 30% of its export revenue, and
Sustainability 2021, 13, 6564. https://doi.org/10.3390/su13126564 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, 6564 2 of 18
23% of its added value [4,12]. Cameroon produces a variety of agricultural commodities both for export and for domestic consumption. However, the agricultural sector remains underdeveloped with the predominance of subsistence farming characterized by low yields per hectare, low rates of modern technology adoption, high vulnerability to climate, and weather risks, among others. The contribution of the youth to both agricultural and economic development in Africa can hardly be overestimated. Yet, a majority of African youths today do not have stable economic opportunities [13,14]; of Africa’s nearly 420 million youth aged 15–35, 33% are unemployed and discouraged, another 33% are vulnerably employed, and only 16% are in wage employment [13,15]. Again, over 70% of these youths subsist on less than US $2 per day, and the incidence of decent work deficits and poverty is particularly high among young rural women [16]. These poor prospects for young people partly arise from the rural economic stagnation observed in many developing countries. Africa’s youth problem, therefore, consists not only of officially registered unemployment, but also of official and hidden underemployment, vulnerable employment, and discouragement [17,18]. Youth in this study was defined using the definition of the African Union Charter that considers the age brackets 15–35. These youth possess qualities and strengths that could enhance agricultural productivity. However, a negative perception of agriculture persists among Cameroonian youth as it does among the youth of many African countries [19–26]. Agriculture in its present state appears to be so unattractive to young people that they are turning away from agricultural or rural futures [23,27–31]. This unattractiveness results from a growing divide between their economic, social, and lifestyle aspirations and the opportunities that agriculture offers [32]. Particularly for rural youth, their dream of a “good life” often lies far away from the rural areas. A key impediment to youth involvement in agriculture has therefore been chronic government neglect of small-scale agriculture and rural infrastructure [12,27]. The downgrading of farming and rural life, coupled with a lack of role models among young farmers, also appear as likely reasons for young Africans’ increasingly reluctance to pursue agriculture-based livelihoods [32–34]. Taking into account the sparse job opportunities, very low and unpredictable remuneration, and harsh working conditions, it is indeed not surprising that youth rarely mention farming as a “good job” [1,35]. Recognizing agriculture as an attractive option is even more challenging when eco- nomic and social restrictions related to access to productive resources are taken into account. Limited access of African youth to knowledge, information, and education, among others, has been identified as a major impediment to agricultural productivity in the developing world in general, and Africa in particular [11,36–39]. Increased rural-urban migration [40], falling levels of youth involvement in agricultural activities, and their unwillingness to undertake education in the field of agriculture, among others, further constrain agricultural development on the continent [6]. Consequently, in most African countries, the agricultural sector has experienced a shortage of qualified graduates to address skill gaps, transform the sector, and increase its performance in recent years [6,26,31,41]. More tragic is the fact that a majority of the few who brave the odds to major in an agriculture-related discipline fail to choose a career in primary agricultural production and transformation. These graduates prefer off-farm jobs within the sector, such as consultancy, marketing, teaching, or extension. This high concentration of graduates in the tertiary sub-sector has led to heavy competi- tion for the often few jobs available, thus increasing the rates of youth underemployment and unemployment. This unfortunate situation further discourages African youth from considering agriculture as a career option. This diagnosed falling youth interest, especially in the primary production and transformation sub-sectors, is a threat not only to Africa’s food security but that of the world, given that Africa contributes a significant quota to global food production [41]. Evaluating youth’s choices and preferences of agricultural sub-sector engagement will help inform policy-making geared towards revamping the declining youth interest in agriculture and rural economic activities, as well as curbing youth unemployment, underemployment, and discouragement in Africa. This paper as- Sustainability 2021, 13, 6564 3 of 18
sesses the determinants of university students’ choices and preferences of agricultural sub-sector engagement in Cameroon (the sub-sectors include primary production, transfor- mation, and agriculture-related services). Specifically, we determine students’ perception of inherent opportunities as well as the challenges inherent to Cameroon’s agricultural sector; assess the determinants of university students’ choice of agriculture as a university major in Cameroon; and assess the determinants of students’ preferences for particular sub-sectors on Cameroon’s agricultural value chain. The three sub-sectors refer to primary agricultural production, agricultural transformation, and agriculture-related services. We hypothesize that students’ socio-economic characteristics do not significantly influence the choice of agricultural sub-sector engagement.
2. Theoretical Framework Young people are presented with several options daily, from which they must make a choice. Drudgery and the lack of resources such as land, credit facilities, incentives, knowledge, capital, and information are some of the reasons why youth tend to be un- interested in agriculture [42]. The decline in youth participation in primary agricultural production and their preferences for the transformation and service sub-sectors is linked to their perception of the sector’s inherent opportunities and challenges. Their decision on sub-sector engagement is therefore influenced by both internal and external factors. These realities of Cameroonian youth are best captured in what has been termed in economics “the Theory of Choice” [43].
3. Materials and Methods 3.1. Sampling Design and Nature of Data The study was carried out in the West and South West Regions of Cameroon between August and December 2018. A structured questionnaire was used to obtain cross-sectional data on students’ socio-economic characteristics such as age, sex, the current level of education, perceived inherent opportunities, pre-university farming experience, the main occupation of the sponsor/mentor/father, father’s level of education, and mother’s level of education, among others. A multistage random sampling technique was used to select university students from the University of Dschang and the University of Buea in the West and South-West Regions of Cameroon, respectively. These two universities were purposefully selected because they both register the highest enrolment in agriculture-related disciplines per year, based on their mixed cultural setting and relatively lower cost of living. These make them convenient destinations for students from across the nation. With the help of admission records for the 2018/2019 academic year from the different faculties, 550 students (275 studying agriculture-related courses and 275 studying non-agricultural courses— technology, engineering, and biomedical sciences) were randomly selected. We treat a university as the principal sampling unit and a university major as the secondary sampling unit. According to the registration records, the number of under- graduate students admitted in agriculture and non-agricultural courses (technology and biomedical sciences) in the 2018/2019 academic year in both universities amounted to 1820, comprising 800 at the University of Buea and 1020 at the University of Dschang. Of the 800 undergraduate students in the University of Buea, 215 majored in agriculture-related courses and 585 majored in non-agricultural courses. Similarly, of the 1020 undergraduate students admitted to the University of Dschang, 209 majored in agriculture-related courses and 811 majored in non-agricultural courses. The number of admitted postgraduate students in the 2018/2019 academic year from both universities was 480, with 216 at the University of Buea and 264 at the University of Dschang. Again, of the 216 postgraduate students at the University of Buea, 110 ma- jored in agriculture-related courses and 106 majored in non-agricultural courses. Of the 264 postgraduate students at the University of Dschang, 128 majored in agriculture-related courses and 136 majored in technology and other professional courses. The Probability Sustainability 2021, 13, 6564 4 of 18
Proportional to Size (PPS) technique was used to ensure that all levels of education (under- graduate and postgraduate) and all university majors were properly represented in the sample as presented in Table1. The total sample size of 550 students, therefore, consisted of 275 students majoring in agriculture and 275 students majoring in non-agricultural courses. The survey in 20I8 completed interviews of 550 students, with a response rate of about 86%. Table 1. Sample Distribution in the Study Area.
Agricultural Non-Agricultural Total Under-Graduate Post-Graduate Under-Graduate Post-Graduate University of Dschang 103 47 109 41 300 University of Buea 109 16 106 19 250 TOTAL 212 63 215 60 550
3.2. Empirical Framework The strengths, weaknesses, opportunities, and threats (SWOT) analysis has become a fundamental tool for businesses, organizations, and sectors to evaluate their position in the market and is widely used to analyze their internal and external conditions [44]. The four components identify either internal or external considerations. Strengths refer to the internal elements of an organization that facilitate reaching its goals, while weaknesses are those internal elements that interfere with business, organizational, or sectoral success. Opportunities—external aspects that help an organization reach its goals—are not only positive environmental aspects but also opportunities to address gaps and initiate new activities. Threats, on the other hand, are aspects of the organization’s external environment that are barriers or potential barriers to reaching its goals [45–48]. The SWOT analysis was used in the study to evaluate students’ perceptions of challenges and opportunities within the agricultural sector in Cameroon [49]. The decision of the ith student to choose agriculture or not depends on an unobserv- able utility index Ii (also known as latent variables) that is determined by one or more explanatory variables, say student’s age xi, in such a way that the larger the value of the index Ii, the greater the probability of student choosing agriculture as a major. The index is expressed in Equation (1) as: Ii = β1 + β2Xi (1)
where there is a critical or threshold level of the index, called Ii*, such that if Ii exceeds Ii*, the student will choose agriculture. Otherwise, he/she will not. The threshold Ii*, like Ii, is not observable, but if we assume that it is normally distributed with the same mean and variance, it is possible not only to estimate the parameters of the index given above but also to attain some information about the unobservable index itself. Given the assumption of normality, the probability that Ii* is less than or equal to Ii can be computed from the standardized normal CDF as:
Pi = P(Y = 1|X) = P(Ii∗ ≤ Ii) = P(Zi ≤ β1 + β2Xi) = f (β1 + β2Xi) (2)
where P(Y = 1/X) means the probability that an event occurs given the value(s) of the X, and where Zi is the standard normal variable. The probability assumes a value of 1 when the student chooses agriculture and 0, otherwise. Xi includes socioeconomics characteristics such as age, sex, the current level of educa- tion, religious background, location of the childhood home, perceived inherent opportuni- ties, pre-university farming experience, the main occupation of the sponsor/mentor/father, pre-university educational background, pre-university contact with agricultural experts and pre-university academic performance, father’s level of education, mother’s level of education, student’s birth position, and household income. Among the models used for the dichotomous dependent variable, the Linear Prob- ability Model (LPM) differs from the probit and logit models because it is estimated by Sustainability 2021, 13, 6564 5 of 18
the ordinary least squares (OLS) technique, which allows for a binary response using regression analysis. Addressing the limitations of the LPM (fitted probabilities that can be less than 0 or greater than 1 and the constant partial effect of explanatory variables) calls for the employment of either the probit or logit model. Probit and logit models are structurally, methodologically, and statistically similar [50]. The former, however, uses the cumulative distribution function (CDF) to explain the behavior of a dichotomous dependent variable, while the latter uses a logistic distribution. The use of either model is thus discretionary given that parameter estimates will likely vary between them because of the varying scales. The binary probit model is most useful in cases where we want to model the event proba- bility for a categorical response variable with two outcomes. The categorization of students into “agricultural” and “non-agricultural” is based on the dichotomous outcome of the decision of enrolment, which characterizes the dependent variable. Because the dependent variable (choice) is binary, the binomial probit model (Equation (2)) was used to determine the factors that influence agricultural and non-agricultural students’ choice of agriculture as a major subject in Cameroon. Similarly, rank-ordered data are often collected in stated preference surveys where respondents are asked to rank hypothetical alternatives (rather than choose a single alter- native) to better understand their relative preferences. Despite the widespread interest in collecting data on, and modeling people’s preferences for, choice alternatives, rank-ordered data are rarely collected in school enrolment surveys and very little progress has been made in the ability to rigorously model such data and obtain reliable parameter estimates. The ability to estimate rank-ordered probit models offers a pathway for better utilizing rank- ordered data to understand preferences and recognize that choices may not be absolute in many instances [51]. Given that within any preference category (first, second, or third), each stage chosen is an ordered response; the efficacy of the rank-ordered probit modeling methodology is applied in this study to understand preferences for alternative agricul- tural sub-sector engagement (Equation (3)). The methodology offers behaviorally intuitive model results with a variety of socio-economic and demographic characteristics, including, age, sex, level of education, religious background, location of a childhood home, passion for a sub-sector, pre-university farming experience, occupation of sponsor/mentor/parents, pre-university educational background, contact with agricultural experts, pre-university academic performance, influencing preference for primary agricultural production, trans- formation, or related services. The functional form for the chosen pattern in each preference category can be represented as follows:
∗ 0 n yn = β x + εn (3)
∗ where yn is the latent and continuous measure of stage of engagement chosen by student n, xn is a vector of explanatory variables describing the students’ socio-demographic characteristics (such as those used in the binomial probit model above), and β0 is the vector of parameters. The observed discrete stage of engagement yn, is determined from the model below,
1 i f − ∞ ≤ y∗ ≤ µ1 (no engagement) 2 i f µ1 < y∗ ≤ µ2 (engagement in primary production) Y = (4) n 3 i f µ 2 < y∗ ≤ µ3 (engagement in transformation) 4 i f µ3 < y∗ ≤ µ4 (engagement in related services)
where the µi’s are thresholds at different levels to be estimated with β. Students’ ordering choices depend on certain measurable factors, x, and unobservable factors ε. In principle, they could respond to the questionnaire with their y∗ if asked to do so. They would choose the value that most closely represents their preference. With the normal distribution of error terms, the probability of coded responses varies with the orders given. Sustainability 2021, 13, 6564 6 of 18
Probability of no engagement (y = 1):