Invited paper presented at the 6th African

Conference of Agricultural Economists, September 23-26, 2019, Abuja,

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Impact of Agricultural Programs on Agripreneurship Performance in Nigeria: The

case of Fadama GUYS Training Program

1Adeyanju, D.F.*, 1Mburu, J., 2Mignouna, D. 1Department of Agricultural Economics, College of and Veterinary Science, University of Nairobi, Kenya 2International Institute of Tropical Agriculture, Ibadan, Nigeria. Email: [email protected] [email protected] [email protected]

Corresponding Author: [email protected]

Abstract Youth unemployment has become a global challenge. This is particularly serious in countries with high population such as Nigeria. Studies have shown that the agricultural sector has the capacity to employ over 70 per cent of the entire Nigeria population which implies that there are lots of unexplored opportunities in the sector. Thus, both government and private organizations have come to support youth in agribusiness by organizing youth-specific training programs in the field. In as much as there is a remarkable number of existing agricultural programs designed for across the country, there are few empirical proof on the impact such programs have on their agripreneurship performance. For this reason, this study empirically investigated the impact of agricultural programs on youth agripreneurship performance taking the case of Fadama Graduate Unemployed Youth and Women Support (GUYS) program. The study used both primary and secondary data. A total of 977 respondents comprising of both participants and non- participants were sampled across three states in Nigeria. Data were analysed using Propensity Score Matching method. The findings showed a significant, positive and robust impact of the program on youth agripreneurship performance as performance improved by up to 27 percent.

Keywords: Youth unemployment, Youth Entrepreneurship, Agripreneurship, Agribusiness Training

1.0 Introduction Youth bulge has become a controversial debate in . While a school of thought see it as an asset which could contribute immensely to and development, another see it as a ‘time-ticking bomb’ which might explode sooner than expected. The African Economic Outlook Report (2017) stated that about 60 to 70 percent of the entire African population are below 30 years of age. Also, according to UN Report (2015), about 230 million people are aged between 15 and 24 years, constituting close to 35 percent of the entire population. The same report stated that those aged between 25 to 34 years accounts for about 27 percent of the population. The short-term implication of this is that, countries with high population such as Nigeria cannot create enough job opportunities in the non-agricultural sector for young women and men (Muthomi, 2017) which has led to high rate of youth unemployment in the country. These unemployment and underemployment issues have created a wide economic gap between the Nigerian citizens which is particularly intense in the rural areas (Narendran & Ranganathan, 2015; Nwibo, Mbam, & Biam, 2016). According to the International Labour Organisation (2017), about 70 percent of young people in Sub-Saharan Africa (SSA) live in the rural areas which is characterized by high levels of , poor infrastructural development, food and nutrition insecurity and a number of other tough economic conditions. These conditions constrain young people from accessing basic amenities, timely information as well as quality and skill development opportunities which could aid them in changing both their economic and social status. Also, the labour markets in many SSA countries are either dysfunctional or skewed against the youths which has resulted in many young people, particularly in the rural areas, thriving on paltry sources of livelihoods (Lahiff, 1997). No wonder poverty remains prevalence even among the employed ones. The situation is similar in all African countries. One great challenge that Nigeria, like most African countries continue to struggle with is youth unemployment and its subsequent effect on labour output and the (Nyabam, Tarawali, & Ijie, 2018), which does not seem to have a good reflection on the country. A large number of young people graduate from higher institutions yearly with very little hope of getting a well-paid job (Inegbenebor & Ogunrin, 2011). This is because labour supply exceeds its demand particularly, in the formal sector as a result of high population. Thus, the informal sector has become an alternative source of sustainable employment for a large proportion of the population. According to Nyabam, et al. (2018), in comparison to the formal sector, the informal sector is increasingly becoming the biggest creator of job opportunities for many people. Nyabam, et al. (2018) suggested that rather than focusing on how to increase white-collar jobs as a strategy to address youth unemployment, more effort should be directed at how to develop youth entrepreneurship. To achieve this, the Agriculture sector has been found to be the most reliable, promising and sustainable sector since it holds numerous economic opportunities as well as the capacity to employ more than 70 percent of the entire population. On this note, agripreneurship is increasingly being adopted as a significant and valuable means to create job opportunities, improve the livelihoods and facilitate the economic independence of young people. According to Bairwa, Lakra, Kushwaha, Meena, & Kumar (2014), Agripreneurship holds numerous potential which can contribute to a range of economic and social development which includes employment and income generation, poverty alleviation and improvements in , nutrition, health as well as food security.

In recent times, many stakeholders including development partners have come to support youth agripreneurship by coming up with several programs to help prospective agripreneurs acquire the required skills and capabilities to successfully run an agribusiness enterprise. An example of such programs is YCAD in Ekiti State, which was truncated shortly after the pioneering administration left. Many youths were trained under this program but, till date, no one can neither tell the impact of the intervention on the participants nor why the program ended the way it did, since no research has concentrated on this area. The implication of this is that the institutions offering these programs cannot tell if at all they make a difference afterwards or not. It is for this reason that more comprehensive research and tangible data on youth agricultural programs become inevitable, especially as this relates to different entrepreneurial framework and new business establishment. Most studies on entrepreneurial training programs are not sector specific. For instance, Karanja (2014) conducted an aggregated similar study, however he focused on self-employed youths. Also, Awogbenle & Iwuamadi, (2010); Chidiebere et al. (2014) explained the significance of education/training in fostering entrepreneurship development. However, the authors failed to provide empirical evidence to back this up. This study therefore addressed this research gap by exploring the impact of agricultural programs on agripreneurship performance thereby giving a more comprehensive and clearer picture of subject and helping to narrow the knowledge gap in the area. As part of IITA research recommendations on youth engagement in agribusiness, this study was selected to fill the research gap of lack of evidence to show the impact of training programs on youth agripreneurship. 1.1 Fadama GUYS program The Fadama GUYS program is one of the existing support programs specifically designed for unemployed youths. The program targets young people between the ages of 18-35years. It was introduced in 2017 with the major objective of empowering youths and motivating them to have profitable careers in agribusiness. This is done through an intensive training program which covers a wide range of agribusiness scope, ranging from Crop and Animal production, marketing, processing, financial management as well as risk management. It was funded under a tri-partite agreement between the World Bank, federal government and the state government. A total of 23 states was selected for the program which cuts across all the regions in Nigeria and about 300 unemployed youths were trained in each state. 2.0 Materials and Method The study was conducted in three states across Nigeria between January and March 2019. These include Abia, Ekiti and Kebbi States representing the South-eastern, South-western and North- western regions respectively. Abia state occupies a total land area of about 4,900 sq km. The estimated population as of 2016 was about 3,699,168 people (National Bureau of Statistics, 2011). Ekiti state is mainly an upland zone with a total land area of about 5,435 sq km. As of 2006, the state had a population of 2,398,957 people (National Bureau of Statistics, 2011) of which more than 75 percent were actively engaged in Agriculture. Kebbi state is located in the north-western part of Nigeria with a total land area of about 36,985 km sq out of which 12,600 km sq is cultivated for agricultural purposes. According to the National Bureau of Statistics (2011), the state had a total population of 3,256,541 following the 2006 population census. According to UNDP (2018), as of 2017, the unemployment rate in these states were of 39.6 percent, 18.6 percent and 11.6 percent respectively.

The choice of these states was to ensure representation of at least three regions covered by the program. The study adopted a multistage sampling technique. In the first stage, three States were purposively selected. For representativeness, a state was considered in the Northern, Eastern and Western regions. Thus, Kebbi, Abia and Ekiti States were purposely and respectively selected in each region. In the second stage, the study population was divided into two strata: participants and non-participants. The third stage involve random selection of respondents from the two lists to make a total sample size of 977. Two sampling frame were used in gathering the respondents; the first frame consisted of a complete list of youths who were trained under the program in 2017 while the second was a list of community youths obtained in each local government where the training was conducted. The random selection of both the treatment and control group was done via random numbers generated using Microsoft Excel. Primary data was used for the study. The questions were programmed on Open data kit and data were collected using Phones and tablets by trained enumerators across the states. Information from the questionnaire formed the main data which was used to evaluate the study’s objective. The survey collected data from a total of 455 participants and 522 non-participants across the three states. The survey included detailed information on key variables, including youth capacity development, agripreneurial skills, business skills, motivation, agripreneurial intention, enterprise interest, knowledge gap as well as other relevant socioeconomic characteristics such as age, , education and marital status as the exogenous variables. 3.0 Method of Analysis 3.1 Entrepreneurship performance index The youth entrepreneurship (agripreneurship) performance index was developed based on Vuuren & Nieman (2004) entrepreneurial performance model. The model has been applied by Vuuren & Botha (2010); Pretorius at al. (2005); and Antonites (2003) as a multiplication construct shown in equation (i): E/P=f [M (E/S x B/S)] (i) Where: E/P is Entrepreneurship performance; M is Performance motivation E/S is Entrepreneurship skills B/S is Business skills. Based on this, it was concluded that upward or downward changes in entrepreneurship performance can be perceived as the multiplicative result of entrepreneurship skills, business skills and performance motivation. Hence, a positive entrepreneurial performance denotes improved entrepreneurship development. Botha (2006) classified entrepreneurial skills into three, namely: technical skills which includes technical-know-how; Business management skills and; personal entrepreneurial skills such as innovativeness, risk taking ability and creativity.

To measure agripreneurship performance, an index was constructed for each of the indicators in the model. Each indicator was assessed by 7 items measured on 5-point Likert-scale. The agripreneurship performance index was generated as the multiplicative function of the indexes. The performance index generated was continuous in nature. This is because of the preference for a continuous index against a binary one. 3.2 Propensity Score Matching This method was used to evaluate the impact of the program on youth agripreneurship performance across the three selected states and sensitivity analysis was carried out to address possible selection bias. In doing this, the following equations were estimated:

푇 퐶 Let 푌푖 and 푌푖 be the outcome variable for participants (treated) and Non-participants (control) respectively. The difference in the outcome between the two groups can be calculated using equation (1)

푇 퐶 ∆퐼 =푌푖 -푌푖 (1) 푇 푌푖 : Outcome of participants (Youth Entrepreneurship performance of the i-th individual, when he/she participates in the program),

퐶 푌푖 : Outcome of the Non-participants

∆퐼: Change in outcome which can be attributed to the program for the i-th individual.

Equation 1 can be expressed in causal effect notational form, by assigning 퐷푖=1 as a treatment variable taking the value 0 if a respondent did not receive treatment (non-participant) and 1 otherwise. Thus, the Average Treatment Effect can be expressed as:

푇 퐶 ATE=E (푌푖 |퐷푖 = 1)-E(푌푖 |퐷푖 = 0) (2) Where: ATE= Average Treatment Effect, the treatment effect on the outcome variables.

푇 E(푌푖 |퐷푖 = 1): Average outcomes for participant, (퐷푖=1). 퐶 E(푌푖 |퐷푖 = 0): Average outcome of non-participants, (퐷푖=0). The Average Effect of Treatment on the Treated (ATT) for the sample is given by:

푇 퐶 푇 퐶 ATT=E (푌푖 - 푌푖 ∣ 퐷푖 = 1) = E(푌푖 ∣퐷푖=1)-E(푌푖 ∣퐷푖=1) (3)

Table 1: Variables definition and Measurement

Variable definition and Codes Measurement

Dependent variables

Participation in the program (PART_TRAINING) Dummy 1 if Yes, 0 if No Youth agripreneurship performance (AGRIC_PERF) Continuous

Independent variables

AGE Continuous, age of respondents Years of Education (EDUC) Continuous, years of respondent education GENDER Dummy, Gender of respondents 1 if male, 0 if female Head of Household Gender (HHGENDER) Dummy, Gender of respondents head of household 1 if male, 0 if female Dummy, Employment status of respondents 1 if Employed, 0 if Employment status (EMPLOYSTAT) Unemployed Intention to start agribusiness (INTENT) Dummy, Established an agribusiness enterprise 1 if Yes, 0 if No Residence (RES) Categorical, Current residence of respondents Asset index (ASTINDEX) Continuous, Asset index Years of Education OF Household head (HOHEDUC) Continuous, years of household head education Number of literate Household members (HHLIT) Continuous, number of household members that are literate Farm ownership (FARM_OWN) Dummy, Farm ownership 1 if Yes, 0 if No

4.0 Results and Discussion 4.1 Factors influencing youth participation in the FADAMA GUYS program This study adopted the logit model in estimating the propensity scores which were utilized in matching the treated and control groups as well as to identify the factors which affects participation in the program. As shown in Table 2, the model is fit for the proposed matching exercise. A low pseudo R2 of 0.28 implies that overall, the characteristics of the youths which participated in the program were not so distinct from that of the non-participants and this was similar to the result obtained by Ahmed & Haji (2014), the authors explained that a low pseudo R2 is good as it facilitates finding a good match between the treated and the control group. Also, the likelihood ratio is significant at p < 0.01 with a chi-square distribution implying that the model is fit for the analysis. From the results, almost all the variables apart from gender of the household head and years of formal education significantly influence the likelihood of youth participation in the program. Age was significant at 1 per cent and positively correlated with the likelihood of participating in the program. The marginal effect implies that the likelihood of participation will increase by a

factor of 0.018 as age increases by 1year. This may likely be because of the educational system in Nigeria. Hence, youths between 18 and 24years are more likely to be studying and more dependent on their family for means of livelihood unlike the older youths which may be either in the labor market or searching for employment or means of livelihood. This is in line with the findings of Ayinde, et al (2016) that a positive relationship exist between the age and the level of youth participation in development projects. Gender was negative and significant at 5 per cent. The results showed that there was a negative relationship between gender and participation in the program. The implication of this is that male youth are less likely to participate in the program compared to female. This is similar to the findings of Judith (2014) and Senkondo, Msangi, & Hatibu (2004). Respondents’ years of formal education was also found to be positively and significantly related to the likelihood of participation. As shown by the marginal effect, youths with more years of schooling are more likely to participate in the program. In other words, as the year of education increases by 1 year, the likelihood of participating increases by 0.020. The possible reason may be because educated people are more likely to be more informed and updated about such programs than those who are not educated. This result concurs with the findings of Ayinde et al. (2016). According to these authors, higher level of education is a vital means of accessing information. Also, Amaza and Tashikalma (2003) posited that education is capable of influencing people towards embracing innovations. However, contrary to the findings of these authors, Sudarshanie (2015) found a negative connection between years of education and participation in training programs. He found that youths with more years of education are less likely to participate in agricultural related programs due to their preference for white collar jobs. Contrary to the findings of Adesina & Eforuoku (2017), household size was found to negatively and significantly influenced youth decision to participate in the program. This may partly be explained by the possibility of large household having existing family business. For instance, the eastern part of Nigeria is well known for manufacturing products and in most cases, it is run as a family business. However, this does not imply that it is so in all cases. Having more literate people in the family increased the likelihood of participating in the program. Again, more educated people may increase family exposure and knowledge about the need and importance of skill acquisition through training. Hence, families with more literate people are more likely to think alike in terms of educational programs and interventions. Also, they are more likely to be well-informed on such programs since they are exposed to different educational channels. This corroborates the findings of Ayinde et al. (2016) that accessibility to information can be hastened by literacy level. The result of the marginal effect also showed that the literacy level of the household head increases the likelihood of participating in the program by 0.004. This may be because the household heads are more exposed and understand the importance of training which influences the youths’ perception and decision about training programs. Results also showed that asset ownership has a significant and positive relationship with participation. Assets like electronic gadgets (phones, laptops, modems) are direct means through/by which youths can access information regarding training programs. Hence, ownership of these assets may influence youth participation in training programs if they are willing. However, high productive asset index, as shown by the result, reduces the likelihood of

participating in the training program by 0.021. This may be because people who have lots of productive assets already have something lucrative they are engaged in and hence, do not see the need or have the time to spare for a training program. This is in accordance with the findings of Ayinde et al. (2016) who found that as the number of productive assets increases, youths’ intention to attend agricultural training changes. Farm ownership and other agricultural enterprises was positive and significantly influenced youth participation in program. This may be because those that are already engaged in agribusiness have access to land and basic resources required to start their own agribusiness and only aspire to improve their skills. Many studies have identified lack of access to land as one of the major factors hindering youths from engaging in agriculture (Adesina & Eforuoku, 2017 ; Ovwigho & Ifie, 2011) . Hence, their major need will range from resource mobilization to agribusiness expansion. They will therefore seek knowledge on how they can expand their enterprise and this may likely influence their decision to participate in agricultural training programs than other categories.

TABLE 2: LOGIT MODEL FOR PROGRAM SELECTION

Variables Coef. Std. Err. z P>z ME AGE 0.073 0.023 3.190 0.001 0.018*** GENDER -0.340 0.188 -1.810 0.071 -0.084* EDUC(Years) 0.079 0.032 2.470 0.014 0.020** EMPLOYSTAT 0.801 0.227 3.530 0.000 0.198*** HHSIZE -0.093 0.031 -2.940 0.003 -0.023*** HOHGENDER 0.212 0.281 0.750 0.451 0.052 HHLIT 0.104 0.034 3.050 0.002 0.026*** HOHEDUC(Years) 0.018 0.027 0.640 0.522 0.004 AST_OWN 1.079 0.188 5.740 0.000 0.260*** ENT_OWN 0.537 0.222 2.420 0.016 0.133** RES 0.526 0.193 2.730 0.006 0.129*** INTENT 0.712 0.076 9.310 0.000 0.177*** ASTINDEX -0.083 0.037 -2.260 0.024 -0.021** _cons -6.246 0.807 -7.740 0.000

Source: survey data (2019) Prob Note: * , **, *** Significance levels at 10 , 5, and 1 per cent respectively >Ch 2 Log Likelihood: -489.8 i = Pseudo R2: 0.28 0.00 0, LR Chi2 (13) = 370.14

4.2 PSM Results 4.2.1 Selection of matching algorithm According to Radicic et al ( 2014), Haji & Legesse (2017), Caliendo & Kopenig (2008) and (Sianesi, 2002), a good matching estimator must fulfil a set of criteria which include;  Low pseudo R-square after matching

 Large number of insignificant variables after matching  Large number of matched sample size  Low mean Standardized Bias (SB). Following this criteria, the estimator that best fulfils this criteria for this analysis was the Nearest Neighbor matching (NNM) estimator. According to (Caliendo & Kopeinig, 2008), NNM estimator is the most straightforward matching estimator. It has been widely applied in impact evaluation studies (Almeida & Bravo-Ureta, 2017; Adebayo, et al., 2018 ). Augurzky & Kluve (2007) found that the compared to other algorithms, the NNM algorithm produced a better balancing of covariates after assessing the performance of matching algorithms when selection into treatment is strong. Table 2: Comparison of the performance matching algorithms

Matching Algorithm Number of Pseudo R2 after Matched sample Mean SB Insignificant matching size variables after matching Nearest Neighbor Matching 1 11 0.018 919 6.8 2 11 0.008 919 4.9 3 11 0.011 919 5.2 4 11 0.016 919 6.3 Kernel Matching No Bandwidth 11 0.013 919 5.5 0.06 11 0.013 919 5.5 0.1 11 0.012 919 5.8 0.25 8 0.020 919 8.5 0.5 5 0.064 919 16.1 Caliper Matching 0.06 11 0.018 919 6.8 0.1 11 0.018 919 6.8 0.25 11 0.018 919 6.8 0.5 11 0.018 919 6.8 Source: survey data (2019) using pstest 4.2.2 The Quality of Matching This step involves verifying whether the condition for common support has been satisfied. Basically, it is assumed that the likelihood of receiving training lies between 0 and 1. Therefore, it is necessary to ensure that respondents with the same value of covariate, x have common likelihood of either participating and not (Haji & Legesse, 2017). Based on the NNM algorithm, the common support region was restricted to a region defined by a distance of two matches. Figure 1 clearly shows that the common support condition was fulfilled since the propensity score distribution of both groups had enough overlap even though a few cases was lost to the common support restriction (Table 3).

0 .2 .4 .6 .8 1 Propensity Score

Untreated Treated: On support Treated: Off support

Figure 1: Common Support Graph for NNM Algorithm Source: survey data (2019) plotted using psgraph Tables 3 and 4 show that a good matching quality was obtained. The mean bias after matching was reduced to 4.9% from 36.1% which implies that there was a reduction of 86%. This was within the recommended percentage (3-5 percent). The pseudo R-square after matching was 0.008. All these are indications that the matching quality is good. The implication of this is that it can adequately balance the observable characteristics of the treated group and the matched control group. Hence, the result was used to analyze the impact of the training program on the outcome variable among the group of youths that have similar observable characteristics for the basis of comparing their outcomes. Table 3: Robustness of Results and Matching Quality Criteria

SBBefore SBAfter % reduction Cases lost % of cases Critical (%) (%) in SB to CS lost to CS value of Entrepreneurship gamma performance 36.1 4.9 86 58 6 2.8-2.85 Source: Field survey data (2019) using pstest Table 4: Chi-square test for joint significance

Sample Ps R2 LR chi2 p>chi2 Unmatched 0.277 374.17 0.000 Matched 0.008 9.18 0.759

Source: Field survey data (2019) using pstest

4.2.3 Covariate Balancing Tests for Selection Bias After Matching Using NNM Algorithm A balancing test was conducted to verify if the differences in the covariates between the two groups have been eliminated. This is important to ensure that the control group is a good counterfactual (Caliendo and Kopeinig, 2008). Table 4 shows that most of the variables (11 out of 13) were insignificant after matching, which was not the case before the matching exercise. Also before matching, the percentage bias in covariate was between 0.8% and 95.8%. However after matching, the values were in line with the critical value (≤ 20 percent) recommended by Rosenbaum & Rubin (1983). Thus, matching created balanced covariate between the samples of the treated and control groups.

Table 5: Covariates balancing test results after matching

Unmatched Mean %Reduction Variable Matched Treated Control %bias Bias t-value p- values

AGE Unmatched 27.33 24.33 69.9 10.92 0.000 Matched 26.80 26.61 4.5 93.5 0.590 0.554

EDUC(Years) Unmatched 14.48 13.77 24.1 3.780 0.000 Matched 14.30 14.11 6.2 74.2 0.920 0.360 SEX Unmatched 0.65 0.68 -6.2 -0.960 0.337 Matched 0.68 0.69 -1.6 73.9 -0.230 0.819

HHSIZE Unmatched 5.63 6.49 -26.1 -4.030 0.000 Matched 5.82 6.02 -5.9 77.3 -1.030 0.301

ENT_OWN Unmatched 0.43 0.10 82 13.00 0.000 Matched 0.38 0.38 0 100 0.000 1.000

RES Unmatched 0.65 0.63 4.4 0.680 0.498 Matched 0.64 0.70 -13.1 -202.1 -1.890 0.060

FARM_OWN Unmatched 3.67 2.55 95.8 14.90 0.000 Matched 3.55 3.54 1.4 98.5 0.220 0.828

ASTINDEX Unmatched 4.68 4.35 12.9 1.990 0.046 Matched 4.60 4.60 0 99.6 0.010 0.994

HOHEDUC(Years) Unmatched 14.76 14.02 21.1 3.300 0.001 Matched 14.49 14.13 10.3 51.4 1.660 0.096 HHLIT Unmatched 2.84 2.82 0.8 0.130 0.895 Matched 2.73 2.57 5.8 -586.5 0.830 0.407

EMPLOYSTAT Unmatched 0.45 0.18 60.3 9.480 0.000 Matched 0.38 0.40 -5.7 90.6 -0.730 0.467 AST_OWN Unmatched 0.69 0.45 49.1 7.630 0.000 Matched 0.66 0.69 -6 87.7 -0.870 0.384 HIHSEX Unmatched 0.93 0.88 16.9 2.620 0.009 Matched 0.92 0.93 -3 82.4 -0.480 0.630 Source: survey data (2019) using pstest 4.2.4 Sensitivity Analysis

To ensure that the impact estimate was valid and that participation in the program was not affected by unobservable covariates, we conducted a sensitivity analysis to account for hidden bias which may arise due to the presence of unobserved variables which influences the assignment of both groups (Rosenbaum, 2002). Thus, it becomes to know the critical value of gamma at which the casual inference of a significant impact on the outcome variable may be questioned (Haji & Legesse, 2017). For this analysis, the critical value of gamma was between 2.8 and 2.85 which implies that the unobserved variable would have to increase the odds ratio of participating by up to 185% before the impact estimate can be negated. Thus, it can be concluded that the impact estimate is robust against hidden bias and can be attributed purely to the training program. 4.2.5 Impact of the progarm on youth agripreneurship performance The result of the ATT for the outcome variable (Youth agripreneurship performance) is presented in table 6. The result shows that agricultural training program had a significant (p < 0.01) and positive impact on youth agripreneurship performance. From the table, it is shown that the youth agripreneurship performance of participants improved by up to 27 percent. Table 6: Impact of FADAMA GUYS program on Youth Entrepreneurship Performance

Variable Sample Treated Control Difference S.E T-Stat AGRIC_PERF ATT 69.77 54.77 15.00 3.54 4.23*** Source: Field survey data (2019) Note: *** Level of Significance at 1 per cent 5.0 Conclusion and Policy Recommendation Based on the result obtained from the impact estimation, there is a significant and positive relationship between youth agripreneurship performance and agricultural programs. Also, there was a significant difference between the performance of participants and non-participants which could invariably be attributed to the program. As shown by the result, the performance of youths who participated in training improved by up to 27 per cent compared to those who did not. To validate this result, the outcome of the sensitivity anlaysis showed that the impact result was insensitive to unobserved selection bias. Thus, validating the robustness of the result. Based on this, it can be concluded that participation in Agricultural training programs is essential to improve the agripreneurship performance of young people. The agricultural sector has been identified as one of the sectors that can help to reduce youth unemployment in Nigeria. As a result, many youths have started engaging in agriculture as a means of livelihood in recent times. However, certain skills are required if agriculture is to be taken as a business. Thus, investing in agricultural training programs can help youths to kick-start their career in agribusiness as well as substantially improved their agripreneurship performance at the same time. This will also go a long way in reducing youth unemployment rate in Nigeria. Therefore, it is recommended that government and other stakeholders should invest in agricultural training program, particularly those that are meant for young people.

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