































   Advancements in Agricultural Development 
  Volume 5, Issue 3, 2024 
  agdevresearch.org 

 

1. Pablo Lamino, Assistant Professor, University of Florida, Gainesville, FL 32603, pablo.lamino@ufl.edu,                                   

 https://orcid.org/0000-0002-3941-4935  
2. Carla Millares Forno, Independent Researcher, 8100 Defiance Ave, Las Vegas, NV 89129, carlamillaresf@gmail.com,                 

 https://orcid.org/0000-0003-0482-9847  
3. Rafael Landaverde, Assistant Professor, Texas A&M University, 600 John Kimbrough Blvd, TAMU 2116, College Station, TX 

77843, rafael.q@ag.tamu.edu,  https://orcid.org/0000-0001-6489-04773  
4. Amy E. Boren-Alpízar, Associate Professor, Texas Tech University, Lubbock, TX 79409, amy.boren-alpizar@ttu.edu,           

 https://orcid.org/0000-0001-9002-4855  
25 

 

Rural Youth Migration Intentions in Ecuador: The Role of 
Agricultural Education Programs 

 
P. Lamino1, C. Millares-Forno2, R. Landaverde3, A. E. Boren-Alpízar4 

 
 

Article History 
Received: November 11, 2023 
Accepted: April 1, 2024 
Published: April 19, 2024 
 
 
Keywords 
rural migration; agricultural 
education; rural youth migration; 
Ecuador 
  

Abstract 
In the last decade, rural youth worldwide have grappled with a crisis 
marked by limited economic opportunities, inadequate services, and 
underdeveloped infrastructure in their home communities. This has 
driven a significant uptick in rural-to-urban migration, especially among 
young people in developing countries. Despite its lower urbanization rate 
compared to neighboring nations, Ecuador has seen a consistent rise in 
rural-to-urban youth migration. This trend is primarily attributed to 
environmental degradation, community conflicts, and rural areas' lack of 
educational and employment prospects. This study investigated the 
migration intentions of high school students in rural Ecuador, specifically 
those enrolled in agricultural and non-agricultural programs. The findings 
reveal that agricultural programs significantly influence youth migration 
intentions. Those in agricultural programs express an inclination to 
migrate. Moreover, students who have migrated before are more likely 
to migrate. These results underscore the importance of tailoring 
educational initiatives to inspire youth to explore opportunities within 
their rural communities. Future research should delve into the 
perspectives of rural youth and evaluate the effectiveness of agricultural 
education programs, contributing to a more comprehensive 
understanding of rural development and strategies for retaining youth in 
rural areas. 

 

mailto:pablo.lamino@ufl.edu
https://orcid.org/0000-0002-3941-4935
mailto:carlamillaresf@gmail.com
https://orcid.org/0000-0003-0482-9847
mailto:rafael.q@ag.tamu.edu
https://orcid.org/0000-0001-6489-04773
mailto:amy.boren-alpizar@ttu.edu
https://orcid.org/0000-0001-9002-4855


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Introduction and Problem Statement 
 
In the past decade, rural youth globally have been driven to migrate to urban areas due to 
limited income opportunities, restricted access to services, and inadequate community 
infrastructure (Food and Agriculture Organization [FAO], 2018). While rural-to-urban migration 
is not new, it has recently gained prominence among youth, particularly in low and middle-
income countries (Maunaye, 2013). For example, 50% of young people in Latin America have 
expressed a desire to leave their communities of origin and try their luck in urban centers (Baez 
et al., 2017). 

To address the urgent need for practical solutions, our research is focused on exploring the 
potential of agricultural education programs in rural Ecuador. This is important since education 
is a crucial factor in the migration dynamic (Corbett, 2007; Kodrzycki, 2001). Our investigation 
aims to shed light on how these programs influence youth to leverage local resources and 
enhance their lifestyles (Rodríguez-Vignoli & Rowe, 2018). These programs, designed to 
educate students on improved agricultural practices and inspire their application on local lands, 
have the potential to make a tangible impact (Rhoda, 1983). 

Ecuador, with a population of 17 million, has experienced a consistent surge in rural-to-urban 
migration among its youth since 2001 (Estévez, 2017). Recent estimates reveal that 10% of 
Ecuador's rural youth population intends to migrate to urban areas, enticed by perceived 
opportunities (Cisneros et al., 1988; Royuela & Ordóñez, 2018). In response to this 
phenomenon, our study seeks to bridge existing gaps by delving into the impact of agricultural 
education programs on youth migration decisions (Roth & Hartnett, 2018). Through our 
exploration, we aim to offer practical insights that can inform field practitioners and contribute 
to the formulation of effective strategies for addressing this pressing issue. 

Theoretical and Conceptual Framework  
 
The Theory of Planned Behavior (TPB), proposed by Ajzen (1991), was the framework used in 
this study. The TPB seeks to predict behavioral intentions centered on three principal 
components: attitudes toward behavior, subjective norms, and perceived behavioral control. 
Attitudes toward behavior are the degree to which a person has a positive or negative 
assessment of the behavior of interest. Subjective norms are the second component and are 
based on the perceived perception people around have about the behavior of interest. Finally, 
perceived behavioral control focuses on the person’s perception of the difficulty of performing 
the behavior of interest (Ajzen, 1991). Therefore, according to each component of the TPB, 
those who have a positive attitude toward migration, perceive the same attitude from their 
peers, and feel it is going to be an easy process, should have a solid intention to migrate 
(Yazdan-Panah & Zobeidi, 2017). However, it is important to notice that Ajzen (1991) argued 
that other variables could help to explain better and increase the model’s utility "if it can be 
shown that they capture a significant proportion of the variation in intention or behavior” (p. 
179).  

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This research study will replicate the TPB adaptation used by Lamiño Jaramillo et al. (2021), in 
which subjective norms are considered as the perception that other community members have 
regarding migration. Subjective norms are composed of social support, social participation, and 
disputes. The perceived migration behavioral control, the perceived difficulty of migrating, 
comprises access to extension activities, subjective expectations, and residential satisfaction. 
The attitude toward migration, the positive or negative idea of migration, comprises 
environmental impacts and interpersonal ties (Ajzen, 1985; De Jong, 2000; Lamiño Jaramillo et 
al., 2021; Yazdan-Panah et al., 2017).  

In the context of addressing rural youth migration, the TPB provides a comprehensive lens to 
analyze attitudes, community perceptions, and perceived challenges, offering a robust 
foundation for exploring the impact of agricultural education programs on migration decisions 
in Ecuador. Given the prevalence of rural-to-urban and international migration, it is essential to 
compare the migration drivers of students enrolled in agricultural-based programs with those in 
non-agricultural programs (Cisneros et al., 1988; Royuela & Ordóñez, 2018). By understanding 
the variables associated with youth migration behavior, implementers can strategically channel 
their developmental program efforts toward these crucial aspects, tailoring interventions based 
on the audience characteristics. This targeted approach holds the potential to subsequently 
mitigate youth intentions to migrate. Figure 1 shows how selected migration drivers were 
adapted to TPB variables. 

Figure 1 

Theory of Planned Behavior 

 

Note. This figure shows the relationship that each component has with the predictable 
behavior. Adapted from the study (Ajzen, 1991; Lamiño Jaramillo et al., 2021). 

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Purpose  
 

The study aims to identify and compare the migration intentions of high school students in 
agricultural programs (AGP) and non-agricultural programs (non-AGP) from two rural neighbor 
communities in Rumiñahui, Ecuador. The research questions answered in this study were: 
1. Compare students' intention to migrate by academic program. 
2. Determine the main and interaction effects of intention to migrate, country of origin, and 

academic program based on the different migration drivers. 
3. To predict the youth's intentions to migrate based on the academic program, gender, 

migratory background, and access to land. 
 

Methods  
 
For this quantitative study, a non-probabilistic convenience sample was used for the data 
collection. Researchers recruited Rumiñahui high school students from two neighboring rural 
communities that shared similar socio-economic characteristics. One group participated in a 
formal AGP (n = 95), located in Cotogchoa, and the other group was part of a non-AGP (n = 104) 
in Rumipamba. 

A 50-question paper-pencil survey was designed with five sections: demographic information, 
participants’ academic and agricultural background, migration drivers, and intention to migrate. 
For the “Migration Drivers” section, 34 5-point Likert-type questions (1 = Completely Disagree, 
2 = Disagree, 3 = Neither Agree nor Disagree Agree, 4 = Agree, 5 = Completely Agree) were 
designed to measure the following drivers: social participation, social support, access to 
extension activities, environmental impacts, interpersonal ties, disputes, residential 
satisfaction, and subjective expectations. The drivers were chosen based on existing literature 
(De Jong, 2000; Lamiño Jaramillo et al., 2021; Yazdan-Panah & Zobeidi, 2017). Finally, the 
average number of migration drivers gives the intention to migrate (Yazdan-Panah & Zobeidi, 
2017). 

Field (2013) stated that an instrument can only be reliable if it is previously validated. For this 
study, seven experts were asked to examine the content and make suggestions to improve the 
instruments’ accuracy. The instrument was previously implemented in other Latin American 
countries to increase validity (Boren Alpízar et al., 2019; Lamiño Jaramillo et al., 2021). Expert 
recommendations included adapting the instrument to each country’s context to ensure the 
questions would be transferred and understood correctly. Cronbach's alpha was calculated to 
measure the constructs' reliability. For social participation, the reliability was .71, social support 
.70, access to extension activities .77, environmental impact .70, interpersonal ties .70, disputes 
.71, residential satisfaction .87, and subjective expectations .72. Results ranged from .70 to .87 
which means that they were acceptable (Rubin & Babbie, 2009).  

Data were collected with permission from the Human Research Protection Program (IRB2016-
697), transcribed and coded in Excel, and analyzed using the Statistical Package for Social 
Sciences (SPSS) v.26. To address missing values, which accounted for approximately 2% of the 

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total dataset and were determined to be missing at random, multiple imputations were 
performed. According to Enders (2017), multiple imputation is a suggested tool for behavioral 
science since it allows the researcher to tailor the missing handling data to match the goal of 
the statistical analysis. Ten multiple imputations were run, and the mean was used to create 
the corrected final database. Data were analyzed based on the objectives.  

Descriptive statistics were used to understand the study participants. For this study, the 
intention to migrate was measured in two ways, based on a 3-option question (“yes,” “I do not 
know,” “no”) and by averaging the seven migration drivers.  

For objective one, the 3-option question was used to compare agricultural and non-agricultural 
students' intention to migrate. An independent chi-square was conducted to compare 
migration intention depending on the academic program. 

For objective two, a 2 x 2 x 3 x 2 Factorial Multiple Analysis of Variance (MANOVA) was used to 
examine the main effects and interactions effects of the independent variables academic 
program ("AGP" vs. "non-AGP"), gender ( “male” vs. “female”), intention to migrate (“yes” vs. “I 
do not know” vs. “no”), and access to land (landowner Family vs. non-landowner family) on the 
seven migration drivers access to extension activities, environmental impacts, social 
participation, social support, interpersonal ties, disputes, and subjective expectations.   

For objective three, a multiple regression analysis was performed. A new variable was 
constructed by averaging the scores of the seven migration drivers. The Cronbach's alpha 
coefficient for this composite variable was calculated to be 0.74, indicating acceptable internal 
consistency. The predictors used in the regression model included academic program, gender, 
migratory background, and access to land. A 0.05 alpha was established a priori. 

Findings 
 
The sample contained 95 participants in an AGP and 104 in a non-AGP. Overall, 109 women and 
90 males participated in this study. In both groups, most participants were female, AGP (n = 54, 
56.80%), and non-AGP (n = 55, 52.9%). In AGP (n = 55, 57.9%) and non-AGP (n = 63, 60.6%), 
youth with a migration background comprised the majority. There was a greater number of 
students from non-landowners’ families (n = 108, 54.3 %) compared to landowners (n = 91, 45.7 
%) in both groups AGP (n = 49, 51.2%) and non-AGP (n = 59, 56.7%). Table 1 shows the general 
and specific distribution by gender, migratory background, and access to land based on 
academic programs. 

 

 

 

 

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Table 1 

Summary of Sociodemographic Information 
Characteristics Total (N = 199) AGP (n = 95) non-AGP (n = 104) 
 f % F % F % 
Gender       

Male 90 45.2 41 43.2 49 47.1 
Female 109 54.8 54 56.8 55 52.9 

Intention to Migrate       
  Yes 41 20.6 21 22.1 20 19.2 
  I do not know 59 29.6 21 22.1 38 36.5 
  No 99 49.7 53 55.8 46 44.2 

Migratory Background       
Migrated 119 59.8 55 57.9 64 61.5 
Never Moved 80 40.2 40 42.1 40 38.5 

Access to Land       
Landowners 91 45.7 46 48.4 45 43.3 
Non-Landowners 108 54.3 49 51.2 59 56.7 

 
To answer objective one, four Chi-Square tests of independence were conducted to compare 
the intention to migrate (yes = 1, I do not know = 2, and no = 3) based on academic programs 
("AGP" and "non-AGP"), gender (“male” and “female”), migratory background (“migrated” and 
“never moved”), and access to land (“landowners” and “non-landowners”). The Chi-square 
results indicated a significant association between the intention to migrate with academic 
programs (χ2 (2) = 5.02, p = 0.04) and migratory background (χ2 (2) = 11.26, p < 0.01).  

Overall, 55.8% of the youth in the agricultural program did not intend to migrate, while 22.1% 
said yes, and 22.1% were undecided. For non-AGP, 44.2 % of students did not intend to 
migrate, 36.5% were undecided, and 19.2% considered migration an option. The strength of the 
association was low, with a Cramer's value of 0.16 (Cohen, 1988). Table 2 details the results of 
the Chi-square for intention to migrate based on academic programs. 

Table 2 

Chi-Square Results for Intention to Migrate Based on Academic Program (N = 199)   
Intention to 
migrate  

AGP (n = 95) Non-AGP (n = 104) 
c2 (2) Cramer's V 

n % n % 
Yes  21 22.1 20 19.2 5.02* 0.16 
I don't know  21 22.1 38 36.5 

  

No  53 55.8 46 44.2 
  

Note. *p < .05 
 
For migratory background, 40.3% of youth who had migrated before intended to migrate, while 
22.7% said yes, and 33.0% were undecided. For youth who never moved, 63.7% did not intend 

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to migrate, 18.8% were undecided, and 17.5% considered migration an option. The strength of 
the association was medium, with a Cramer's value of 0.24 (Cohen, 1988). Table 3 details the 
results of the Chi-square for intention to migrate based on migratory background. Gender (χ2(2) 
= .82, p = 0.66) and landowner status (χ2 (2) = 3.08, p = 0.21) were not statistically associated 
with youth intention to migrate. 

Table 3 

Chi-Square Results for Intention to Migrate Based on Migratory Background (N = 199)   
Intention      
to migrate  

Migrated (n = 119) Never Moved (n = 80) 
c2 (2) 

Cramer's V 
n % n % 

Yes  27 22.7 14 17.5 11.26* 0.24 
I don't know  44 37.0 15 18.8 

  

No  48 40.3 51 63.7 
  

Note. *p < .05 
 
For objective two, a 2 x 2 x 3 x 2 Factorial MANOVA was used to examine the main effects and 
interactions effects of independent variables academic program (“AGP” vs “non-AGP”), gender 
(“male” vs “female”), migration intentions (“yes” vs “I do not know” vs “no”) access to land 
(“landowner family” vs “non-landowner family”) on the seven migration drivers access to 
extension activities, environmental impacts, social participation, social support, interpersonal 
ties, disputes and subjective expectations.  

The dataset was tested for linearity, normality, and homogeneity of variance assumptions. The 
person correlations results showed a linear relationship among the variables, which indicates 
that the linearity assumption has been met. The normality assumption was also met, as 
skewness and kurtosis values ranged within +/- 2, and histograms showed a normal 
distribution. However, the box’s Test of Equality of Covariance Matrices was significant and 
violated the assumption of equal covariance matrices, so Wilks’ Lambda was considered for the 
multivariate test interpretations. 

The analysis revealed a significant medium effect for the academic program variable, Wilks’Λ = 
.76, F(7,148) = 6.64, p < .001, η2=. 24, for the seven migration variables. Additionally, the 
intention to migrate variable showed a statistically significant small effect, Wilks’Λ = .83, F(14, 
296) = 2.01, p = .02, η2= .17. However, there was no significant effect found for gender, Wilks’Λ 
= .95, F(7, 148) = 1.12, p = .35, η2= .05, migratory background, Wilks’Λ = .96, F(7, 148) = 1.05, p 
= .40, η2= .04, or access to land, Wilks’Λ = .95, F(7, 148) = 1.28, p = .26, η2= .05. Table 4 
provides a comprehensive presentation of the findings of the independent variables that 
demonstrated statistical significance. 

 

 

 

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Table 4 

Multivariate Analysis of Variance for Migration Drivers 
Source F p η2 
Academic Program 6.64 .01** .24 
Gender  1.12 .38 .05 
Migratory Background 1.05 .46 .04 
Access to Land 1.28 .18 .05 
Intention to migrate  2.02 .02* .17 

Note. *p < .05; ** p < .001; None of the significant independent variable interactions were 
significant 
 
To better understand the interaction effects of independent variables on migration drivers, a 
set of univariate Analyses of Variances (ANOVAs) was conducted as a complementary test to 
the significant MANOVA. The results showed that academic program had a significant main 
effect on the migration driver, interpersonal ties, F(1,154) = 4.62, p = .03, and η2 = 0.6. AGP 
students (M = 2.80, SD = .85) have more extension activities than non-AGP students (M = 2.53, 
SD = .99). Additionally, academic program had a significant effect on the access to extension 
activities construct, F(1,154) = 43.53, p < .001, and η2 =24. AGP (M = 3.11, SD = .87). AGP (M = 
3.11, SD = .87) students have more access to extension activities than non-AGP students (M = 
1.92, SD = .86). The intention to migrate also showed a significant effect on the access to 
extension activities construct, F(2,154) = 3.99, p = .04, and η2 = 0.4. Table 5 summarizes the 
significant ANOVAs. 

Table 5 

Univariate Analysis of Variance for Interpersonal Ties and Access to Extension Activities as a 
Function of Main and Interaction Effects of Academic Program and Intention to Migrate 
Source F P η 2 
 Interpersonal Ties 
Academic Program 4.62 .03* .06 
 Access to Extension Activities 
Academic Program  43.53   .01** .24 
Intention to Migrate 3.99 .02* .04 

Note. *p < .05; ** p < .001; The variables environmental impacts, social support, social 
participation, and disputes were not significant for any source. 
 
A post hoc test was conducted for the intention to migrate construct because it was the only 
independent variable with more than two levels. The researchers assumed that the sample 
sizes per condition were equal; therefore, Bonferroni was implemented. This test is well-known 
for being conservative and has strong statistical power (Field, 2013). The Bonferroni test 
showed that the migration option “yes” (M = 2.80, SD = 1.03) had a significant difference with 
the option “I do not know” (M = 2.31, SD = 1.05). However, there was not a significant effect 

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with “no” (M = 2.49, SD = 1.04), and either between the options “no” and “I do not know” for 
access to extension activities.  

To achieve objective three, the intention to migrate variable was created by averaging the 
seven migration drivers. This variable was then used as the dependent variable for multiple 
regression analysis. Four predictors were considered in the analysis: students' academic 
program, gender, migratory background, and access to land. In addition to predicting the 
intention to migrate, regression was used to confirm the findings from objective one.  

The assumptions analysis showed that the residuals were independent, as assessed by Durbin-
Watson statistics of 1.97. We also found that the data were homoscedastic, as evaluated by a 
plot of studentized residuals versus unstandardized predicted values. There was no 
multicollinearity, as evaluated by tolerance values greater than 0.1. Furthermore, no 
studentized deleted residuals were over ±3 standard deviations, and no leverage values greater 
than 0.2 or values for Cook's distance were above 1. 

The prediction model for the intention to migrate variable was significant, F(4,198) = 4.42, p = 
.02, R2 = .08, adj. R2 =.07. The significant predictors were academic program (B = -.18, SE= .06, β 
= -.22, p = .01) and migratory background (B = .12, SE = .06, β = .15, p = .04). Similar to the 
results from the Chi-square, gender, and landowner were not statistically significant predictors. 
The intention to migrate attitude among AGP students was .18 higher than that of non-AGP. 
Students who had migrated before had .12 higher attitude intention to migrate attitude. Table 
6 shows the findings of the multiple regression. 

Table 6 

Multiple Regression Predicting Students’ Intention to Migrate 
Predictors    B SE    Β 
Intercept 3.37 .17  
Academic Program   .18 .06   .22** 
Gender   .09 .06   .11 
Migratory Background   .12 .06   .15* 
Landowner  -.02 .06  -.03 

Note. **p <.001; *p <.05 
 

Conclusions, Discussion, and Recommendations  
 
According to the Migration Data Portal (MDP; 2023), migration in Latin America and the 
Caribbean has increased dramatically due to economic, climate, and natural resource pressures. 
Recent reports on migration in this region indicate that men migrate more than women (Boyd, 
2021; MDP, 2023). However, this study found that sex was not an influential factor in young 
Ecuadorians' intentions to migrate. These results align with recent statistics that demonstrate a 
global increase in migration among women for professional and economic reasons, mirroring 
statistics historically achieved only by men (Boyd, 2021; MDP, 2023). These findings open 

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several opportunities for research and action focused on the needs of migrant women, such as 
preventing the high incidence of discrimination experienced by migrant women compared to 
their male peers (MDP, 2023).  

Young Ecuadorians with a migratory history are more attracted to migration than those who 
have never left their communities. These results agree with those of Nouwen et al. (2015), who 
found that young people of migrant origin are more willing to migrate in search of new areas of 
employment and will drop out of school to do so. The literature indicates that young migrants 
face more challenges at an educational and professional level, including more difficulty in 
inserting themselves into a new educational system and indecisiveness about future 
professional endeavors (Kalalahti et al., 2017; Van Caudenberg et al., 2020). Contrary to the 
results of Lamiño Jaramillo et al. (2021), which found that youth from agricultural programs had 
more intentions to migrate than those from other educational programs, this study found no 
differences in intentions to migrate between youth who study agricultural-related sciences and 
those who study other technical or professional areas. In both groups, approximately 20% of 
the participants are undecided about migration and could be influenced to migrate or to remain 
in their current location. Recognizing the importance of this undecided group, it is critical to 
prioritize their needs and concerns in strategies aimed at reducing youth migration. This 
presents an opportunity to implement specific programs such as awareness campaigns, 
counseling services, and vocational guidance initiatives. By working with this group and 
providing them with relevant information and realistic alternatives to migration, these 
interventions can give individuals the power to make informed choices about their future, 
ultimately reducing overall youth migration rates. 

To improve the negative perception of working in agriculture and promote it as a viable and 
fulfilling career path, it is essential for practitioners to actively engage in promoting agricultural 
education (Rhoda, 1983). Collaboration with educational institutions, industry stakeholders, 
and government bodies is key to amplifying the message about the pivotal role of agriculture in 
securing future food, fiber, and energy supplies. By partnering with educational institutions, 
practitioners can contribute to developing comprehensive agricultural education programs. 
These programs should not only focus on imparting technical skills but also emphasize the 
broader significance of agriculture in sustaining communities and meeting essential needs. 
Industry stakeholders and government bodies can support these programs by providing 
resources, advocating for policy changes, and showcasing successful role models in the 
agricultural sector. However, the effectiveness of these efforts depends on establishing robust 
monitoring and evaluation mechanisms. Practitioners should prioritize the implementation of 
systematic assessments to measure the impact of agricultural education programs on 
influencing youth migration decisions. Regular reviews, based on participant feedback and 
adaptability to changing circumstances, are crucial for ensuring interventions' continued 
relevance and effectiveness. 

The results of this study are not representative of the entire country of Ecuador. Still, they offer 
relevant insight on integrating and promoting efforts targeting youth intending to migrate. 
Other studies have shown the potential of agricultural education to reduce young people's 

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intentions to migrate (Rhoda, 1983). When encouraging agricultural education as a future 
career path, the growing and acute trend of the unwillingness of young people to work in 
agriculture must be considered (Girdziute et al., 2022; Lamiño Jaramillo et al., 2023), or the 
perception of migration as a better option will lead to migratory behavior (Migali & Scipioni, 
2018). Finally, similarly to (Lamiño Jaramillo et al., 2021), this study continues the development 
of national profiles on youth migration that could serve to inform local, national, and regional 
initiatives contributing to inform inclusive and effective mechanisms to cultivate the future 
agricultural workforce for future generations in low- and middle-income countries. 

Acknowledgments 
 
Author Contributions: P. Lamino - methodology, software, formal analysis, investigation, 
resources, data curation, writing-original draft, writing- review & editing, and visualization; C. 
Millares Forno - conceptualization, methodology, investigation, and writing-original draft; R. 
Landaverde - conceptualization, investigation, writing-original draft, and writing- review & 
editing; A.E. Boren-Alpízar- conceptualization, writing-original draft, and writing- review & 
editing 

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