metadata: authors: 0: author: Costa,Valentina
1: author: Contreras Gonzalez,Ivette Maria
2: author: Palacios-Lopez,Amparo
count: Africa,World
docna: 0: docna: Never Too Young to Dream Big : Measuring
            Youth Aspirations in the Time of the COVID-19 Pandemic
docty: Policy Research Working Paper
abstracts: cdata!: This study explores the factors shaping
            the aspirations of youths in Ethiopia, Malawi, and Nigeria,
            specifically focusing on their academic goals; science,
            technology, engineering, or mathematics career interests;
            and migration intentions. The study draws on a robust
            dataset comprising 2,725 youth respondents aged
            15&#x2013;25 years, collected through high-frequency
            phone surveys conducted as part of the World
            Bank&#x2019;s Living Standards Measurement Study.
            Exploring the aspirations of youths in Sub-Saharan Africa is
            crucial, particularly given that by 2050, half of the
            region&#x2019;s population is expected to be younger
            than 25 years. The findings highlight significant gender and
            age disparities across youths, with female youths aged
            15&#x2013;18 showing higher educational aspirations than
            their male peers. However, female youths&#x2019;
            aspirations decline and become lower than those of male
            youths when they reach 19&#x2013;25 years old.
            Conversely, male youths aged 15&#x2013;18 and
            19&#x2013;25 report higher career and migration
            aspirations than their female peers. Additionally,
            educational background emerges as a pivotal factor
            influencing aspirations. The analysis shows that youths
            without formal education degrees are less inclined to aspire
            to higher educational, career aspirations, or migration.
            Moreover, family and community role models, along with
            individual attitudes, may contribute to shaping the
            aspirations of youths in these three countries. In
            conclusion, building on the findings, the paper formulates a
            comprehensive set of policy recommendations. These
            recommendations aim to align the skills and aspirations of
            youths with the prevailing labor market opportunities,
            ultimately working toward the reduction of youth
            unemployment and underemployment rates across these countries.
display_title: Never Too Young to Dream Big :
            Measuring Youth Aspirations in the Time of the COVID-19 Pandemic
listing_relative_url: /research/2024/06/34348193/never-too-young-dream-big-measuring-youth-aspirations-time-covid-19-pandemic
topicv3: Education m1327048 470,Aspirations and Poverty m1503845 3750,Household Surveys m1504071 2761,Youth Employment m1504211 2885,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2024-06-24T00:00:00Z
dois: 10.1596/1813-9450-10816
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 38
author_keys: 0: author_key: 1199429
1: author_key: 1464865
2: author_key: 1043558
model_extractions: Policy Research Working Paper 10816 Never Too Young to Dream Big Measuring Youth Aspirations in the Time of the COVID-19 Pandemic _Valentina Costa Ivette Contreras Amparo Palacios-Lopez_ Development Economics Development Data Group June 2024 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
Policy Research Working Paper 10816

# **Abstract**

This study explores the factors shaping the aspirations of youths in Ethiopia, Malawi, and Nigeria, specifically focusing on their academic goals; science, technology, engineering, or mathematics career interests; and migration intentions. The study draws on a robust dataset comprising 2,725 youth respondents aged 15–25 years, collected through high-frequency phone surveys conducted as part of the World Bank’s Living Standards Measurement Study. Exploring the aspirations of youths in Sub-Saharan Africa is crucial, particularly given that by 2050, half of the region’s population is expected to be younger than 25 years. The findings highlight significant gender and age disparities across youths, with female youths aged 15–18 showing higher educational aspirations than their male peers. However, female youths’ aspirations decline and become lower than those of male youths when they reach 19–25 years old. Conversely, male youths aged 15–18 and 19–25 report higher career and migration aspirations than their female peers. Additionally, educational background emerges as a pivotal factor influencing aspirations. The analysis shows that youths without formal education degrees are less inclined to aspire to higher educational, career aspirations, or migration. Moreover, family and community role models, along with individual attitudes, may contribute to shaping the aspirations of youths in these three countries. In conclusion, building on the findings, the paper formulates a comprehensive set of policy recommendations. These recommendations aim to align the skills and aspirations of youths with the prevailing labor market opportunities, ultimately working toward the reduction of youth unemployment and underemployment rates across these countries.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at vcosta@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility. worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Never Too Young to Dream Big: Measuring Youth Aspirations in the Time of the COVID-19 Pandemic

Valentina Costa (World Bank)<sup>1</sup> Ivette Contreras (World Bank) Amparo Palacios-Lopez (World Bank) Key words: Youth, aspirations, education, employment, migration, COVID-19, Sub-Saharan Africa JEL Codes: I24, J13, J16

> 1 <mark>Corresponding author: vcosta@worldbank.org . This work has been supported by the World Bank Multi-Donor Trust Fund for Integrated Household and Agricultural Surveys in Low and Middle-Income Countries (TF072496). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors thank Calogero (Gero) Carletto (Development Data Group, World Bank), Alemayehu Ambel (Development Data Group, World Bank), Kevin Robert McGee (Development Data Group, World Bank), Vanya Slavchevska (FAO), and Alberto Zezza (Development Data Group, World Bank) and all the participants to the IFAD Conference Jobs, Innovation and Value Chains in the age of Climate Change (June 2022) for their valuable comments on an earlier draft. Professional English editing was provided by Olutola Oni Jordan. Any remaining errors are the authors' sole responsibility.</mark>
# **1. Introduction**

_Far away there in the sunshine are my highest aspirations. I may not reach them, but I can look up and see their beauty, believe in them, and try to follow where they lead. Louisa May Alcott_ Aspirations, especially among youths, are powerful drivers of decisions related to education, jobseeking strategies, and the pursuit of better employment opportunities through migration. They are, therefore, crucial predictors of human capital achievement and economic outcomes, as emphasized by Genicot & Ray (2020). Thus, accounting for aspirations and life goals of youths sheds light on potential employment outcomes in adulthood and can enable policies to better align youths’ skills with the prevailing labor market opportunities. Despite their critical importance, youth aspirations remain largely unexplored, especially in Sub-Saharan Africa (SSA), which harbors the world's youngest labor force, as noted by Fox & Gandhi (2021) and where it is projected that half of the population will be younger than 25 years by 2050, as reported by the World Bank in 2022. These demographic trends underscore the urgent need for comprehensive data on the current status of youth aspirations in SSA countries to inform more effective policy making.

This paper aims to fill the data gap by providing estimates for three SSA countries and formulating policy recommendations that can effectively address the unique needs and aspirations of youths. The analysis is restricted to aspirations about completing university or post-graduate studies, pursuing careers in STEM (science, technology, engineering, or mathematics) fields, and considering migration for a representative sample of youths from Ethiopia, Malawi, and Nigeria.

Our findings show that youths aged 15-18 enrolled in school programs tend to have greater educational and career aspirations compared to their peers who are employed or not in education, employment, or training (NEETs). Moreover, enrollment in school seems to discourage the youngest youths from migrating and leaving their communities. Aspirations to migrate, instead, are higher among NEETs and employed youths, particularly those aged 19-25. Furthermore, youths with more years of schooling tend to aspire to pursue university education, STEM-related careers, and migration. Conversely, having worked at least once before the age of 18 may discourage educational and career aspirations while encouraging migration.

Additionally, being passionate about STEM subjects motivates youths to pursue higher education and STEM careers. Household income, professional prestige, and social recognition within the family and friends network also play crucial roles in influencing youth aspirations towards higher education and STEM careers. Youths from lower-income households are more inclined to aspire to migrate compared to those from higher-income backgrounds. Analyzing preferred migration destinations, youths express a stronger inclination towards urban areas, particularly capital cities or other cities, in pursuit of more appealing job opportunities, rather than considering rural areas as preferred destinations.

2
Moreover, we observe gender disparities in aspirations between male and female youths. Female youths aged 15-18 exhibit higher educational aspirations than their male peers. However, their aspirations decline and become lower than those of male youths when they reach 19-25 years old, irrespective of their activity. Conversely, male youths report higher career and migration aspirations than female youths across all ages and activities, except for young female students who demonstrate higher migration aspirations than young male students.

This paper presents two significant contributions to the existing literature. Firstly, it provides a comprehensive assessment of youth aspirations, utilizing data from nationally representative multi-topic household surveys conducted in three SSA countries. Our study offers robust insights into youth aspirations as they relate to occupational demand in SSA, providing valuable information for policy making aimed at aligning youth skills and migration desires with labor market opportunities. Secondly, by employing high-frequency phone surveys (HFPS) based on pre-pandemic sampling frames derived from nationally representative face-to-face household surveys supported by the World Bank’s Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA), the analysis links youth aspirations to a rich array of demographic and socio-economic characteristics. The inclusion of an aspiration module in household surveys allows for the tracking of aspirations over time and facilitates a deeper understanding of the youth and their families. This is crucial for comprehending how aspirations shape the future of young individuals and identifying the appropriate age for intervention in shaping aspirations, among other considerations.

The rest of the paper is organized as follows: section 2 presents a literature review that helps to conceptualize aspirations. Section 3 describes the data, survey instruments, sampling strategy and statistical analysis. Section 4 presents descriptive results, while section 5 examines the empirical results. Finally, section 6 sheds light on the policy implications of the study and concludes.

# **2. Conceptualizing Aspirations: A Literature Review**

The notion of aspiration, in its broadest sense, refers to the ambition of achieving something that may or may not be within the range of outcomes that the individual’s situation allows to achieve (La Ferrara, 2019). Aspirations in fact, are not expectations: we may aim or hope to achieve something, yet not necessarily realistically expect to achieve it. Gorard et al. (2012) distinguish between aspirations as “what an individual _hopes will happen_ in the future” and expectations defined as “what an individual _believes will happen_ in the future”. This distinction underscores that expectations are rooted in an individual's perception of their future performance, considering their socioeconomic background and past and current academic performance (Mickelson, 1990). In contrast, aspirations are not necessarily confined by specific socioeconomic realities (Khattab, 2015) . Nevertheless, they remain inherently social, influenced by circumstantial factors, and subject to evolution over time.

3
The foundational theories of anthropologist Arjun Appadurai and economist Debraj Ray have sparked a renewed interest in comprehending the impact of social factors on shaping aspirations. According to Appadurai (2004), individuals with limited means may begin with lower aspirations compared to their more affluent counterparts. This discrepancy arises because they believe that resource constraints will impede their progress in life or because they lack the resources needed to explore and understand their aspirations fully—a concept Appadurai refers to as ‘navigational capacity’. Their initial condition of scarcity of resources reduces their ‘capacity to aspire’ high. Due to lower occupational aspirations, the poor may choose lower levels of investment in education than the non-poor, investing, for instance, less in their children’s education, especially in daughters. This will perpetuate their state of economic disadvantage, increasing gender gaps and leading to a vicious circle of poverty (Appadurai, 2004).

Conversely, Ray (2006) and Genicot & Ray (2017) argue that the poor do not lack the capacity to aspire high; rather, their aspirations are influenced by societal changes, notably income inequality. However, the unattainability of their aspirations can ensnare them in a trap, as described by Ray (2006) and Genicot & Ray (2017). When high aspirations remain unfulfilled, they can breed discouragement or frustration. For instance, raising aspirations through economic growth or increased educational attainment, without creating corresponding opportunities for these aspirations to be realized, can lead to frustration. This frustration, in turn, may demotivate individuals, prompting them to seek alternative sources of aspiration in different aspects of life, such as religion. In more extreme cases, it can even drive individuals toward dark alternatives, including crime or terrorism (Ray, 2016; Ray, 2006; Genicot & Ray, 2017; Genicot & Ray, 2020).

To understand the factors that shape youth aspirations, Sumberg & Okali (2013) identify two broad categories of social constraints and opportunities within the ‘opportunity space’ of each individual: (1) _structural factors_ encompassing economic conditions, personal safety, social status, and social identity, and (2) _relational factors_ including social norms, role models, and interactions with neighbors or peers. Empirical studies have demonstrated that structural factors, such as the place of residence, living standards and life experiences, can profoundly affect youth aspirations (Du & Wong, 2019; Mau & Li, 2017). Specifically, career aspirations, defined as representative of an individual’s ideal occupational choice (Davey & Stoppard, 1993), are likely to be related to a wide range of personal and situational variables, including sex, socioeconomic status, educational aspirations, and social support.

Structural factors are also crucial in the initial formation of migration aspirations as well as in their realization. In particular, difficulties to find a job, education and age are important factors in people’s desire to migrate. Young people and those with secondary education or higher degrees are the most likely to want to migrate (Tjaden, et al., 2018). Migali and Scipioni (2019) also find that male individuals aged 15-19 have a higher likelihood of aspiring to migrate when compared to other age groups, while women are less likely to aspire to migrate (Migali & Scipioni, 2019). This is consistent with the nonlinear evolution of educational aspirations of youths over time, suggesting that youth aspirations evolve with the circumstances (Favara, et al., 2018).

4
Additionally, according to Van Hear (2014), migration is intricately linked to the resources potential migrants can accumulate, with their ability to mobilize these resources strongly influenced by their socio-economic background or class. Consequently, individuals with higher socio-economic status may harbor greater professional or material ambitions, which can stimulate migration aspirations. However, migration aspirations often follow an inverted U-shaped pattern, being lower among both the poorest and the wealthiest, and higher within middle-income groups (de Haas, 2020) and may vary by context. Dustmann & Okatenko (2024) have found a positive relationship between wealth and migration aspirations in Sub-Saharan Africa and Asia, while it is slightly negative and not statistically significant in Latin America and in the Middle East and North Africa region. For instance, unemployment is one of the key drivers of migration aspirations among young individuals from the Middle East and North Africa to Europe (Dibeh, et al., 2019).

Additional evidence supports the influence of relational factors, such as role models (Beaman, et al., 2012; Macours & Vakis, 2014; Riley, 2019; Breda, et al., 2020) as well as neighbors or peers (Bernard & Taffesse, 2014; Gagete-Miranda, 2020; Galiani, et al., 2021) on youths' awareness of opportunities, aspirations, and subsequent life outcomes. Influential figures such as parents, peers, teachers, and community leaders can play a crucial role in shaping youths' ‘capacity to aspire’, serving as both role models and sources of information that can inspire them to reevaluate their goals and escape from what are often referred to as ‘aspiration traps’ (Fruttero, et al., 2021). For instance, in Ghana, societal norms strongly discourage women from pursuing physically demanding jobs like cocoa farming, as there is a prevailing perception that such work makes them appear too masculine and could potentially hinder their chances of finding a spouse (Boateng & Löwe, 2018).

Furthermore, the evidence underscores that relation factors also influence migration aspirations, such as the example of other youths or family members who have already migrated. Established migration histories and pathways, connections with current or former migrants, and the receipt of remittances can foster migration aspirations. This is particularly pronounced among young people in Sub-Saharan Africa, specifically in countries like Nigeria, Malawi, and Uganda. According to Esipova et al. (2018), residents of Sub-Saharan Africa are the most likely worldwide to express a desire to migrate permanently. Approximately one-third of adults in the region convey a wish to relocate to another country. Nigeria, in particular, stands among the top 10 countries with a substantial proportion of its population aspiring to migrate elsewhere, with 48% expressing this inclination (Esipova, Pugliese, & Ray, 2018). On the contrary, the quality of interpersonal relations in the family can be decisive for migration aspirations and youths with a strong sense of attachment to or actively engage in their current society may be less inclined to leave, particularly in relation to ethnic factors (Bastianon, 2019).

Structural and relational factors serve to confine youth aspirations within a specific opportunity space and elevating aspirations out of the opportunity does not inherently guarantee improved outcomes, but it may even yield adverse effects when not aligned with corresponding opportunities. Empirical evidence, in line with Ray's theory, indicates that beyond a certain 5
threshold significantly divergent from one's initial conditions, aspirations cease to exhibit a meaningful correlation with enhanced outcomes. This phenomenon may be attributed to the onset of frustration or other complex factors at play (Janzen, et al., 2017; Ross, 2019; Bloem, 2021).

# **3. Methods**

# **3.1. Data and Survey Instruments**

Recent surveys targeting youth have incorporated questions aimed at understanding their career aspirations and life goals amid the backdrop of the COVID-19 crisis. The ‘Youth Aspirations and Employment Module’ was designed based on household surveys<sup>2</sup> and implemented in World Bank's Living Standards Measurement Study (LSMS) High Frequency Phone Surveys (HFPS) in Ethiopia, Malawi and Nigeria<sup>3</sup> to collect information about (i) education history, including the level of education already achieved (note that in multi-topic household surveys, some of this information is partially captured within the Education Module), (ii) work history, such as the age at which respondents began working, their previous work experiences, and sources of income, (iii) future plans, which encompass intended activities following the completion of education or training programs, and (iv) career aspirations, covering desired occupations, role models, adherence to social norms, and interest in migration.

Understanding youth aspirations can be complicated due to measurement issues. Firstly, all empirical measures of aspirations rely on self-reported survey questions. Secondly, the measures of aspirations may vary in the underlying concepts they capture. While most authors agree on aspirations being multidimensional, the specific survey questions used to capture them differ in formulation, potentially yielding diverse results and policy implications. A key distinction among survey questions is whether respondents are asked about their aspirations without any constraints or limitations (Roy, et al., 2018; Ross, 2019; Favara, M., 2017).

This analysis examines three key dimensions (i) educational aspirations, which were assessed using the question ‘ _Imagine you had no constraints and could study for as long as you liked or go back to school if you have already left. What level of formal education would you like to complete?’_ ; (ii) occupational aspirations, determined through the question ‘ _When you are about 30 years old, what job or "dream" job would you like to be doing?’_ ; and (iii) migration aspirations, gauged by asking _‘Would you consider leaving your community to look for better job opportunities?’_

> 2 The ILO SWTS questionnaire (2009), Module 2; the World Bank Young Basotho's Aspirations and Challenges Survey (2019); and Young Lives, Round 4, Ethiopia (2013-2014).

> 3 <mark>World Bank. Ethiopia - High Frequency Phone Survey 2020-2023. Ref: ETH_2020-2023_HFPS_v13_M. Dataset downloaded from h</mark> <u>ttps://microdata.worldbank.org/index.php/catalog/3716</u> <mark>on January 2, 2024; Malawi National Statistical Office (NSO) (Government of Malawi). Malawi - High-Frequency Phone Survey 2020-2024 (HFPS-COVID-19 2020-2024). Ref: MWI_2020-2024_HFPS_v18_M. Downloaded from</mark> <u><mark>https://microdata.worldbank.org/index.php/catalog/3766</mark></u> <mark>on January 2, 2024; National Bureau of Statistics. Nigeria COVID-19 National Longitudinal Phone Survey (COVID-19 NLPS) 2020-2021, Phase 1. Dataset downloaded from</mark> <u>https://microdata.worldbank.org/index.php/catalog/3712</u> <mark>on January 2, 2024.</mark>

6
In addition to the information collected using this youth module, the analysis incorporates data from the latest rounds of nationally representative face-to-face LSMS-ISA surveys conducted in Ethiopia, Malawi, and Nigeria before the emergence of the COVID-19 pandemic. This inclusion allowed us to integrate respondents' demographic characteristics into our analysis.<sup>4</sup>

# **3.2. Sampling Strategy**

The ‘Youth Aspirations and Employment Module’ for High-Frequency Phone Surveys (HFPS) was implemented in a single round of data collection in Ethiopia, Malawi, and Nigeria from April to July 2021. The youth sample was randomly selected from households that had previously participated in HFPS rounds. Within each household, a youth respondent aged between 15-25 years was randomly chosen for the interview, and proxy respondents were not permitted. To ensure that the individual weights were representative of youth aged 15-25 years in their respective countries, household weights were adjusted using sample-based reweighting techniques.<sup>5</sup> A consistent methodology was adopted in sampling, weighting, and implementing the survey across the countries, facilitating cross-country comparisons. In the case of Nigeria, data collection occurred in April 2021. The sample comprises 52% male respondents and 48% female respondents. Among the respondents, 44% fall in the 15 to 18 years age group, 36% are aged between 19 to 22 years, and 20% are in the 23 to 25 years age bracket. Notably, male respondents tend to be younger than their female counterparts. In Malawi and Ethiopia, data collection took place in May and June 2021, and both countries reported a similar distribution of respondents by sex and age.

# **3.3. Statistical Analysis**

A set of logit regressions was used for estimating the probability of personal, household and community factors to have a significant association on the aspirations of youth aspirations for higher education (complete university degree or post-graduate studies), working on STEM-related jobs and for migrating to seek better life opportunities. The core specification is estimated for the entire sample of youth aged 15-25 years old _i_ interviewed at time _t_ =2021. The specification includes covariates captured in the LSMS-ISA pre-COVID-19 surveys denoted by the sub-index _t_ - _1_ .

A similar model is replicated for three different outcomes: educational aspirations (m=1), career aspirations (m=2), and migration aspirations (m=3). The main specification is presented as Equation 1.

> 4 The sampling frame for these phone surveys was the most recent nationally representative LSMS-supported national longitudinal household survey. This enables a methodologically sound comparison with the pre-COVID-19 survey data. This paper uses data from the latest rounds of LSMS surveys from three countries: Ethiopia (September/ December 2018 and June/August 2019), Malawi (April 2019 to March 2020), and Nigeria (July/August 2018 and January/February 2019).

> 5 For details on reweighting techniques, please consult the World Bank Guidelines on Sampling Design available at <u>https://documents1.worldbank.org/curated/en/742581588695955271/pdf/Guidelines-on-Sampling-Design.pdf</u>

7
)) = f�β0 + β1Xmit + β2Zmi(t−1) + β3Tmi(t−1) � (1) mm mm mm mm cc logit (Pr(YY = 1|XX , ZZ , TT + δδ

The statistical analysis proceeds in several steps. We first start with educational aspirations (m=1) and estimate the probability of university or post-degree aspirations (Y1i) by country, and within countries in a set of logit regressions for exploring its correlation with (1) youth’s characteristics (X1it), i.e., sex, age group, highest level of education, age at which they started working, type of dream job; (2) household characteristics Z1i(t−1), i.e., age of the oldest child of the household, highest level of education and current occupation of the adult head of the household, and income quintile; and finally (3) community characteristics T1i(t−1), i.e., place of residence (urban and rural) and role models in the community. We observe the independent associations of each set of personal, family and community variables with educational aspirations. are fixed effects at the country level. cc δδ We extend this step analysis to explore career and migration aspirations. Regarding career aspirations (m=2), we estimate the probability of reporting a STEM-related occupation as dream job<sup>6</sup> (Y2i) by country, and within countries in a set of logit regressions using individual-level weights for exploring its correlations with (1) youth’s characteristics X2i(t−1), i.e., sex, age group, educational level, age at which started working, interest in the job and youth likelihood to reach the dream job (self-efficacy); (2) household characteristics Z2i(t−1), i.e., household size, adult head of the household engaged in STEM-related jobs, and income quintile; and finally (3) community characteristics T2i(t−1), i.e., place of residence (urban and rural), highly regarded job by friends and parents and role models in the community.

Lastly, we explore how personal characteristics, household characteristics and exposure to migration correlates with the desire to migrate (Y3i), in a set of multivariate logit regressions (m=3). In addition, we explore the correlation of youth’s characteristics X3i(t−1), household characteristics Z3i(t−1), and exposure to migration with the desired location selected by the youth that expressed their desire to migrate T3i(t).

For all set of logit regressions, household and community data are obtained from previous LSMSISA pre-COVID-19 surveys in Ethiopia, Malawi, and Nigeria. Standard errors are clustered at the enumeration-areas (EA) level.

# **4. Descriptive Results**

# **4.1. Educational levels and aspirations**

> 6 The STEM-related occupations are defined following the PISA25 definition that includes science and engineering professionals, health professionals, science technicians and associate professionals, and formation and communication technology (ICT) professionals (OECD, 2021).

8
We estimate that approximately half of the youth population in our study is enrolled in educational programs, while the other half is either employed or neither working nor studying (NEETs) as of May/June 2020. Nigeria stands out with the highest percentage of youths in school, accounting for 51 percent of the youth population, compared to 50 percent in Malawi and 45 percent in Ethiopia. Additionally, on average, 45 percent of youths have completed secondary education, ranging from 65 percent in Nigeria to 28 percent in Ethiopia and 14 percent in Malawi. The remaining 26 percent of youths have not received any formal education. There are significant disparities across countries as well, ranging from 55 percent in Malawi to 34 percent in Ethiopia and 11 percent in Nigeria. Only 6 percent of the youth population have completed either tertiary or university degrees ( **Table A2 in the Annex** ). Furthermore, we observe higher educational aspirations in countries where youths have reported greater levels of education. For instance, in Nigeria, 85 percent of youths aspire to complete a university degree, compared to 74 percent in Malawi and 71 percent in Ethiopia ( **Table A2 in the Annex** ). This difference in aspirations may be attributed not only to levels of education but also to the age at which youths enter the workforce in these countries. Approximately 29 percent of youths in Malawi and 20 percent of youths in Ethiopia have worked at least once before the age of 18, compared to 18 percent of youths in Nigeria ( **Table A2 in the Annex** ).

Therefore, we explore educational aspirations comparing high-school youths aged 15-18 with college youths aged 19-25 in relation to their activity as of May/June 2020. **In general, the desire to pursue university or post-graduate studies is greater among youths aged 15-18 compared to the older youths** , regardless of whether they are employed, enrolled in school, or NEETs. Furthermore, we observe that **enrollment in educational programs is the strongest predictor of high educational aspirations among youths regardless of their age** _._ Conversely, being engaged in working activities may discourage youths from aspiring to higher levels of education.

We further observe gender disparities in educational aspirations between female and male youths. **In general, female youths aged 15-18 show higher educational aspirations than their male peers, but their aspirations decline and become lower than male youths when they reach 1925 years old, regardless of their activity.** For example, among youths currently enrolled in educational programs, on average, 88 percent of female youths aged 15-18 express a desire to complete a university degree or pursue post-graduate studies, compared to 85 percent of their male peers. Female youths aged 15-18 maintain higher educational aspirations than their male peers even when employed, while no difference is observed among NEETs ( **Figure 1** ). Conversely, on average, 95 percent of male youths aged 19-25 currently attending school aspire to complete a university degree or post-graduate studies, compared to 91 percent of their female peers. Male college-aged youths demonstrate higher educational aspirations than their female counterparts even when employed or NEETs ( **Figure 2** ). This decline in educational aspirations among female youths may reflect frustration stemming from unfulfilled aspirations during high school age (Genicot & Ray, 2017). Such frustration might have demotivated them as they reached 19-25 years old, prompting them to seek alternative sources of aspiration in various aspects of life.

9
These findings are consistent at the country level, but with some exceptions. For example, in Ethiopia and Nigeria, male NEETs and male students aged 15-18 exhibit greater educational aspirations than their female peers ( **Figure 1** ). Conversely, in other countries, female youths aged 19-25 display greater educational aspirations than male counterparts. Moreover, in Malawi, female NEETs and female students aged 19-25 express higher educational aspirations than their male peers ( **Figure 2** ).

**Figure 2. Share of youths aged 15-18 who would like to complete university or post-graduate studies, by sex and current activity.**

**Figure 1. Share of youths aged 19-25 who would like to complete university or post-graduate studies, by sex and current activity** _._

<!-- Start of picture text --> Ethiopia Malawi Nigeria Average Ethiopia Malawi Nigeria Average<br>100% 100% 95%<br>91%<br>88%<br>90% 85% 90%<br>82%<br>79% 79%<br>80% 80%<br>69%<br>70% 70% 65%<br>62%<br>59% 59%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Male Female Male Female Male Female Male Female Male Female Male Female<br>NEETs Currently at school Working NEETs Currently at school Working<br><!-- End of picture text --> Note. Sampling weights used. T-tests of equality of means were conducted across men and women within each country. Only average shares are reported for the sake of clarity.

Source: Own calculations based on Ethiopia - High Frequency Phone Survey (2020-2023); Malawi - High-Frequency Phone Survey 2020-2024; Nigeria COVID-19 National Longitudinal Phone Survey 2020-2021. Datasets downloaded from https://microdata.worldbank.org/index.php/catalog/hfps/?page=1&ps=15&repo=hfps on January 2, 2024

# **4.2. Employment status and career aspirations**

We estimate that overall, 25 percent of youths is employed as of May/June 2020 and 30 percent of them is not working in their ideal job. Nigeria and Ethiopia report the highest proportions of youths who are not currently engaged in their ideal work activity, accounting for 41 percent of youths in Ethiopia and 31 percent of youths in Nigeria. This share decreases to 10 percent in Malawi ( **Table A2 in the Annex** ). Nonetheless, approximately 81 percent of youths believe they can achieve their dream job, with percentages ranging from 86 percent of youths in Ethiopia and Nigeria to 61 percent in Malawi ( **Table A2 in the Annex** ). Furthermore, about 70 percent of youths know someone from their community who holds their desired occupation, ranging from 72 percent and 69 percent in Nigeria and Ethiopia respectively to 64 percent in Malawi ( **Table A2 in the Annex** ).

10
We further observe that approximately 22 percent of youths who are not working in their ideal job would like to work in STEM-related fields ( **Table A2 in the Annex** ). We explore aspirations to work in STEM-related fields comparing high-school youths aged 15-18 with college youths aged 19-25 in relation to their activity as of May/June 2020. As observed in relation to educational aspirations, **the inclination towards STEM-related careers is greater among youths aged 1518 compared to older youths** , regardless of their school enrollment, employment or NEET status. Furthermore, **we observe that the willingness to work in STEM-related jobs is the highest among young students aged 15-18 (Figure 3) and young NEETs aged 19-25 (Figure 4).** Being engaged in working activities, instead, is the weakest predictor of career aspirations in STEM.

Moreover, we observe gender disparities in career aspirations between female and male youths. **In general, female youths exhibit lower career aspirations than male youths, regardless their age and activity.** For instance, 37 percent of male students aged 15-18 would like to work in STEM-related jobs, compared to 26 percent of their female peers. Similarly, male NEETs and employed youths aged 15-18 report higher career aspirations compared to their female peers ( **Figure 3** ). Male youths continue to exhibit higher career aspirations than their female peers as they grow up. For instance, 21 percent of male NEETs aged 19-25 would like to work in STEMrelated jobs, compared to 11 percent of their female peers ( **Figure 4** ).

These findings are consistent at the country level, with only few exceptions. For example, in Ethiopia, female employed youths aged 15-18 show greater career aspirations than their male employed peers ( **Figure 3** ). Similarly, female students aged 19-25 also demonstrate higher career aspirations than their male peers in Malawi ( **Figure 4** ).

**Figure 3. Share of youths aged 15-18 who would like to work in STEM-related jobs, by sex and current activity.**

**Figure 4.  Share of youths aged 19-25 who would like to work in STEM-related jobs, by sex and current activity.**

<!-- Start of picture text --> 100%<br>100% Ethiopia Malawi Nigeria Average Ethiopia Malawi Nigeria Average<br>90%<br>90%<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 37% 40%<br>30% 26% 30%<br>20% 18% 16% 15% 11% 20% 21% 15%<br>11% 11% 10%<br>10% 8%<br>10%<br>0%<br>Male Female Male Female Male Female 0%<br>Male Female Male Female Male Female<br>NEETs Currently at school Working<br>NEETs Currently at school Working<br><!-- End of picture text --> Note. Sampling weights used. T-tests of equality of means were conducted across men and women within each country. Only average shares are reported for the sake of clarity. Source: Own calculations based on Source: Own calculations based on Ethiopia - High Frequency Phone Survey (2020-2023); Malawi - High-Frequency Phone Survey 2020-2024; Nigeria COVID-19 National Longitudinal Phone Survey 2020-2021. Datasets downloaded from https://microdata.worldbank.org/index.php/catalog/hfps/?page=1&ps=15&repo=hfps on January 2, 2024 11
# **4.3. Migration aspirations**

When facing challenges in securing employment and educational aspirations, youths can contemplate relocating to other towns, cities, or even other countries. Exploring their aspirations to migrate offers valuable insights into the broader life goals of young individuals, especially in terms of their efforts to enhance their long-term personal situations (Van Mol, 2016). We estimate that overall, 70 percent of youths would like to migrate, with broad differences across countries, going from 85 percent in Nigeria and 84 percent in Malawi to 34 percent in Ethiopia. This finding is in line with Esipova et al. (2018)’s study, which includes Nigeria among the top 10 countries with a significant share of the population expressing a desire to migrate to another country.

We explore migration aspirations comparing high-school youths aged 15-18 with college youths aged 19-25 in relation to their activity as of May/June 2020. As observed in relation to educational and career aspirations, **the desire to migrate is greater among youths aged 15-18 compared to older youths, regardless of their school enrollment, employment or NEET status (Figures 5 and 6).** We further observe that **aspirations to migrate are higher among NEETs and employed youths in both age groups, with this trend being particularly pronounced for those youths aged 19-25** . Conversely, **being enrolled in school seems to be a factor that may encourage youths to remain in their community for a longer period** . These findings hold true across both age groups ( **Figures 5 and 6** ).

Additionally, we find gender disparities in migration aspirations between female and male youths. **Male NEETs and employed youths exhibit a stronger inclination towards considering migration compared to their female peers** . However, **female students show higher migration aspirations than male students** . These patterns persist across both age groups and across all countries, except for students in Nigeria and Ethiopia ( **Figures 5 and 6** ). Male students aged 1518 and 19-25 have greater migration aspirations than their female student peers in Nigeria ( **Figures 5 and 6** ). Similarly, migration aspirations are higher for male students aged 19-25 than their female student peers in Ethiopia ( **Figure 6** ). Moreove <mark>r, in Malawi and Nigeria, the highest migration aspirations are among employed youths in both age groups, while in Ethiopia, the highest migration aspirations are among employed youths aged 15-18 and NEETs youths aged 19-25</mark> ( **Figures 5 and 6** ).

12
**Figure 5.  Share of youths aged 15-18 considering migrating, by sex and current activity.**

**Figure 6. Share of youths aged 19-25 considering migrating, by sex and current activity.**

<!-- Start of picture text --> Ethiopia Malawi Nigeria Average Ethiopia Malawi Nigeria Average<br>100% 100%<br>87%<br>90% 90%<br>80% 80%<br>71%<br>70% 69% 64% 64% 67% 71% 67% 70% 60% 62%<br>60% 60% 57% 58%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Male Female Male Female Male Female Male Female Male Female Male Female<br>NEETs Currently at school Working NEETs Currently at school Working<br><!-- End of picture text --> Note. Sampling weights used. T-tests of equality of means were conducted across men and women within each country. Only average shares are reported for the sake of clarity. Source: Own calculations based on Source: Own calculations based on Ethiopia - High Frequency Phone Survey (2020-2023); Malawi - High-Frequency Phone Survey 2020-2024; Nigeria COVID-19 National Longitudinal Phone Survey 2020-2021. Datasets downloaded from https://microdata.worldbank.org/index.php/catalog/hfps/?page=1&ps=15&repo=hfps on January 2, 2024.

<mark>Regarding migration destinations, our findings show that</mark> **<mark>both youths aged 15-18 and 19-25 prefer relocating to urban areas, such as capital cities and towns, or other countries rather than rural areas.</mark>** <mark>This preference is particularly evident among the youngest youths, while it appears more varied among the oldest youths. Specifically,</mark> **<mark>youths aged 15-18 are more inclined to consider migrating abroad, whereas youths aged 19-25 show a higher interest in moving to capital cities</mark>** <mark>.</mark> <mark>We further observe gender disparities in migration destinations between female and male youths.</mark> **<mark>Male youths aged 15-18 are more inclined to desire to migrate than their female peers, especially towards urban areas</mark>** <mark>. On average, 44 percent of male youths in this age group express a desire to migrate to another country, compared to 36 percent of their female peers.</mark> **<mark>However, as they age, the situation changes, and female youths aged 19-25 become more willing to migrate than their male peers.</mark>** <mark>For instance, 56 percent of female youths in this age group express a desire to move to capital cities, compared to 46 percent of their male peers.</mark> These trends hold at the country level, but with some specificity. Youths are more likely to migrate to the capital city in Ethiopia, to cities/towns in Malawi, and to another country in Nigeria. These preferences are consistent across both age groups.

13
**Figure 7.  Share of youths aged 15-18 considering migrating, by sex and intended destination.**

**Figure 8. Share of youths aged 19-25 considering migrating, by sex and intended destination.**

<!-- Start of picture text --> 100% Ethiopia Malawi 100% Ethiopia Malawi<br>Nigeria Average Nigeria Average<br>90% 90%<br>80% 80%<br>70% 70%<br>60% 57% 60% 56%<br>53%<br>50% 50% 46%<br>44% 44%<br>40%<br>40% 36% 40%<br>31%<br>30% 25% 30% 26% 26%<br>20% 14% 15% 13% 13% 20% 14% 14% 15%<br>9%<br>10% 10%<br>0% 0%<br>Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female<br>Capital city City/town Another country Rural No preference Capital city City/town Another country Rural No preference<br><!-- End of picture text --> Note. Each respondent was able to select more than one destination. T-tests of equality of means were conducted across men and women within each country. Only average shares are reported for the sake of clarity. Source: Own calculations based on Source: Own calculations based on Ethiopia - High Frequency Phone Survey (2020-2023); Malawi - High-Frequency Phone Survey 2020-2024; Nigeria COVID-19 National Longitudinal Phone Survey 2020-2021. Datasets downloaded from https://microdata.worldbank.org/index.php/catalog/hfps/?page=1&ps=15&repo=hfps on January 2, 2024

# **5. Empirical analysis and discussion**

# **5.1. Main findings and comparison with other studies**

In line with the framework proposed by Sumberg & Okali (2013), we delve into the factors that impact aspirations related to completing university and post-graduate studies, pursuing STEMrelated careers, and planning to migrate. These determinants encompass both structural and relational factors, which include personal, household, and community characteristics.<sup>7</sup>

**In the absence of barriers, our analysis reveals that female youths, particularly in low-income households, exhibit a lower likelihood of aspiring to complete university or post-graduate degrees and pursuing STEM-related careers compared to their male counterparts.** Specifically, female youths in Ethiopia are 11% less likely to aspire to complete university or postgraduate studies, and in Nigeria, they are 9% less likely ( **Table 1** ). Additionally, female youths in Ethiopia are 7% less inclined to desire careers in STEM-related fields compared to males, while in Malawi, the difference is 6% ( **Table 2** ). Moreover, our findings indicate that youths from the

> 7 We computed robustness checks for all logit regressions to counterprove our results and these checks include various means such as using different estimators, different weight functions, different standard errors, inclusion or exclusion of the constant term, and omission of some regressors (Bianco & Martínez, 2009; Cramer, 2007; Wooldridge, 2002). To test the robustness of our results, we conducted two different checks. First, we followed Wooldridge (2002) to include logistic disturbances in the full aspirations models and re-estimated them by omitting some regressors. In particular, we carried out some leave-out analysis by estimating individual, household and community characteristics. Results confirm the robustness of our results by simple simulations of the logit regressions for different disturbance distributions. Moreover, we performed test for non-distortion. Inspecting the rural sample, we further observed results that confirm the robustness of our results. We observed some variability only for selected results in the migration regression. All results are available under request.

14
wealthiest households in Nigeria are 11.2% more likely to aspire to STEM-related careers than those from the poorest households ( **Tables 2** ). These findings are in line with the existing literature (Mau & Li, 2017; Sadler, et al., 2012). Favara (2017) recognizes that a gender bias in aspirations is a potential mechanism for the perpetuation of gender inequality over time and this bias is particularly pronounced among girls from the poorest households. Specifically, girls residing in impoverished households are 12 percentage points less likely to aspire to attend university compared to boys in similar circumstances.

**We also observe that aspirations related to education and careers tend to diminish with age, especially among youths actively engaged in the labor market.** For instance, youths in the 2325 age group are less inclined to express a desire to pursue STEM-related professions compared to their younger counterparts aged 15-18. Specifically, the eldest youths show a 16.7% and 16.6% decreased likelihood of aspiring to work in STEM-related fields in Ethiopia and Malawi, respectively ( **Table 2** ). These trends remain consistent and statistically significant for youths aged 19-22 across all countries, with the effect becoming more pronounced as they get older. We further observe that older siblings within households are 1.7% less likely to aspire to complete university or post-graduate studies when compared to their younger counterparts ( **Table 1** ).

**Our findings also shows that early entry into the labor market during childhood (before 14 years old) has a significant impact on the aspirations of youths.** In Nigeria, youths who began working at an early age are 9.7% less likely to aspire to complete university or post-graduate studies compared to those who entered the labor market at later ages ( **Table 1** ). This likelihood increases to 17.5% in Ethiopia and 18.8% in Malawi. These patterns remain consistent and statistically significant in Ethiopia and Malawi even when youths started working between the ages of 15 and 18. The early participation in the labor market also influences career aspirations in STEM fields. In Ethiopia, youths who began working between the ages of 15 and 18 are 30.2% less likely to aspire to work in STEM-related jobs than those who entered the labor market later ( **Table 2** ). This probability decreases to 7.8% in Malawi, while it is not statistically significant in Nigeria.

These results are consistent with the current literature. Research by Akosah-Twumasi et al. (2018) suggests that younger youths are more likely to express their ideal career choices in ways that reflect what they aspire to become as they grow up. However, as youths age, their aspirations tend to adapt to their changing circumstances, and they become more inclined to view their career choices as dynamic outcomes influenced by their developmental stages and the prevailing environmental contexts. This perspective aligns with the findings of Dalton et al. (2018), who observed that older entrepreneurs tend to harbor lower aspirations compared to their younger counterparts who are just starting their businesses. Moreover, in Sub-Saharan African countries, the eldest youths within households often assume responsibilities related to income-generating activities and the care of younger siblings. These additional responsibilities may have a negative impact on their educational and career aspirations (Evans, 2010).

15
**Moreover, we find that educational aspirations tend to increase with the number of years of education completed and the desire to pursue STEM-related careers.** Specifically, youths who have attained tertiary education are 48.1% more likely to aspire to complete university or postgraduate degrees in Ethiopia than those without completed educational degrees ( **Table 1** ). In Nigeria, this probability drops to 29%. These findings are consistent and statistically significant across all countries for youths who have completed primary and secondary education and the magnitude of their marginal effects on educational aspirations tends to increase for higher educational degrees.

Additionally, youths interested in pursuing STEM-related careers are more likely to aspire to higher educational degrees and, consequently, are inclined to study for more years. In Ethiopia, youths interested in STEM-related jobs are 23.6% more likely to aspire to complete higher educational degrees compared to those with different career aspirations ( **Table 1** ). In Malawi, this probability is 8.9%, although not statistically significant, and it is not statistically significant in Nigeria. Similarly, young people with primary or secondary degrees who aspire to attend university are more inclined to desire careers in STEM-related fields. In Ethiopia, they are 11.8% more likely to aspire to work in STEM-related jobs compared to those who have completed primary or secondary school but expressed lower educational aspirations than their current education. This probability is 6.6% in Malawi and 10.2% in Nigeria ( **Table 2** ).

However, having high educational aspirations may not always serve as a strong incentive for young people to pursue longer periods of education, especially when their households lack the economic resources to support them. Additionally, there is a significant issue concerning the quality of education systems in many SSA countries, often characterized by poor quality and limited access to educational resources. Consequently, the number of years of education completed may not necessarily translate into better employment prospects, even in urban areas where a more educated youth population resides and seeks work (Fox & Gandhi, 2021). This situation can contribute to a widening gap between educational aspirations and the current level of education achieved, ultimately leading to unmet career aspirations and generating frustration among young people.

**Furthermore, our findings reveal that the capacity to aspire varies significantly among young individuals. Academic and career aspirations are notably influenced by interactions within the social environment and the constraints and opportunities within the family context** . As shown in **Table 2** , youths from the wealthiest households in Nigeria are 11.2% more likely to aspire to work in STEM-related jobs. However, it is important to note that these findings are not statistically significant in Ethiopia and Malawi. Additionally, residing in households where the adult head has obtained a secondary degree tends to encourage youths to be 5.6% more likely to aspire to complete university or post-graduate studies in Nigeria, compared to youths from households with lower levels of education. This probability increases to 20.8% in Ethiopia if the adult head of the household has obtained the university degree ( **Table 1** ). There has been a proliferation of work that suggests how family background can shape youths’ educational and career expectations (i.e., Du & Wong, 2019; Mau & Li, 2017; among others). Living standards

16
and life experiences can weigh heavily on what one gets to know and perceives as possible (Fruttero, et al., 2021). Initial low aspirations are frequently observed among youths from disadvantaged backgrounds, especially among girls living in poverty. However, when examining household size, we did not find any statistically significant correlation with youths' career aspirations across all countries. This is notable, as previous research has suggested that household size plays a significant role in shaping youths' educational aspirations and their participation in income-generating activities ( **Table 2** ).

**We also observe that cultural values and social expectations play a significant role in shaping youths' career aspirations in STEM fields and influencing their academic choices.** The social context and recognition from their closest family and friends' network can have a substantial impact on their decision to aspire to work in STEM-related jobs. In Ethiopia, for instance, youths who consider "professional prestige" as a crucial aspect of their dream job are 22.1% more likely to aspire to work in STEM-related fields. However, it it important to note that this influence is not statistically significant in Malawi and Nigeria.

The importance of social expectations and prevailing gender norms in changing individual beliefs and promoting positive behaviors has been quite substantially documented (see, for example Rao & Walton, 2004; La Ferrara, 2019; Akosah-Twumasi, et al., 2018). Empirical studies show that role models (Beaman, et al., 2012; Macours & Vakis, 2014; Riley, 2019; Breda, et al., 2020; Nguyen, 2008) as well as neighbors or peers (Bernard & Taffesse, 2014; Gagete-Miranda, 2020; Galiani, et al., 2021; Akosah-Twumasi, et al., 2018) influence one’s aspirations and outcomes. Youths who are influenced by interpersonal factors highly value the opinions of family members and significant others; they therefore consult with and depend on these people and are willing to compromise their personal interest (Guan, et al., 2015).

**Our findings also show that self-efficacy plays a critical role in shaping youths' career aspirations, especially in STEM fields.** Beyond family contexts and socio-cultural expectations, individual attitudes have a significant impact on their career aspirations. Interestingly, youths who lack confidence in their ability to realize their dream job are less likely to aspire to work in STEMrelated fields. In Malawi, they are 9.7% less likely, in Ethiopia, 9.1% less likely, and in Nigeria, 3.7% less likely ( **Table 2** ). This is particularly true for female youths who may perceive STEM as a male-dominated sector. Research suggests that self-efficacy, rather than actual academic achievement, is a key determinant of youths' career choices and work-life decisions. Self-efficacy empowers young individuals to confidently face new challenges and work towards achieving their aspirations (Pinquart, et al., 2004; Kabeer, 1999). When young individuals have a strong belief in their ability to meet the educational requirements and excel in occupational roles, it broadens the range of career options they seriously consider and become interested in. They tend to prepare themselves better educationally for various occupations and exhibit greater determination in pursuing challenging career paths. Without an understanding of an individual's self-efficacy, it becomes challenging to comprehend and measure youth aspirations because their observed choices and preferences may align with various sets of expectations (Manski, 2004).

17
**Finally, we find that social role models significantly shape and reinforce individuals' selfbeliefs of efficacy through their example.** Gender differences in perceived occupational selfefficacy have been observed, with male youths having a strong sense of self-efficacy for both traditional male-dominated and female-dominated occupations. In contrast, female youths tend to have a weaker sense of self-efficacy in occupations dominated by men, including STEM-related jobs (Bandura, et al., 2001). Math interest and science self-efficacy are identified as crucial predictors of STEM career aspirations, particularly among male youths (Mau & Li 2017). Additionally, there is evidence of greater gender differences in STEM learning experiences, parental involvement, and STEM self-efficacy among students from collectivist cultures compared to those from individualist cultures (Mau, et al. 2020). Early interest in STEM subjects at the beginning of high school is another significant factor predicting STEM career interest at the end of high school (Sadler, et al. 2012).

**Table 1. Youths’ aspirations to complete university degree or higher, marginal effects from logit regressions**

|**Main variables**|**Pooled sample**|**Ethiopia**|**Malawi**|**Nigeria**|
|---|---|---|---|---|
|**Characteristics of theyouths**|||||
|Youth is female|-0.079***<br>(0.02)|-0.113***<br>(0.038)|-0.0004<br>(0.034)|-0.101***<br>(0.032)|
|Primary education|0.190***<br>(0.0400)|0.291***<br>(0.0754)|0.234***<br>(0.0395)|0.0814<br>(0.0824)|
|Secondary education|0.352***|0.393***|0.269***|0.320***|
||(0.0348)|(0.0652)|(0.0440)|(0.0732)|
|Tertiary education|0.358***|0.481***|-|0.290***|
||(0.0431)|(0.0651)|(-)|(0.0754)|
|Started working <14 years|-0.119***<br>(0.0275)|-0.175**<br>(0.0845)|-0.188***<br>(0.0449)|-0.0977***<br>(0.0335)|
|Started working 15-18 years|-0.092***<br>(0.024)|-0.133**<br>(0.054)|-0.171***<br>(0.045)|-0.05<br>(0.034)|
|Started working 19-22 years|-0.037<br>(0.0354)|-0.083<br>(0.065)|-0.151***<br>(0.053)|0.022<br>(0.057)|
|Ideal job is STEM related work|0.096***|0.236***|0.089*|0.042|
||(0.029)|(0.061)|(0.047)|(0.032)|
|Oldest youth|-0.017*|0.015|-0.011|-0.022|
||(0.01)|(0.019)|(0.014)|(0.014)|
|**Characteristics of the adult head of the household**|||||
|Primary education|0.07***|0.077|0.02|0.056*|
||(0.021)|(0.054)|(0.04)|(0.032)|
|Tertiary education|0.047|0.208***|-0.024|-0.018|
||(0.057)|(0.063)|(0.154)|(0.061)|
|Wage job employee|0.086***<br>(0.029)|0.091<br>(0.057)|0.024<br>(0.044)|0.108**<br>(0.044)|
|Observations|2,613|831|852|916|

Note. Results are presented in margins and clustered standard errors in parentheses. Individual-level survey weights used. Statistical significance at: *** p<0.01, ** p<0.05, * p<0.1. Enumeration areas fixed effect included. The variable ‘Started working [age]’ captures the age of the first job, but includes youths who came back to study or are NEETs after having worked for a while. In Malawi, we excluded 14 observations from the regression analysis because they had completed tertiary education, which resulted in perfect collinearity with the dependent variable. This happened because all the youths who had completed tertiary education in Malawi expressed aspirations to pursue a university degree or higher. Likewise, for 18
the same reason, we excluded youths who had already completed a university degree or above from the regressions. This included 75 observations from the pooled sample, as well as 50, 7, and 18 observations from Ethiopia, Malawi, and Nigeria, respectively. These exclusions were made to address the issue of perfect collinearity, as these individuals all shared the common aspiration of furthering their education beyond the current level, making them collinear with the dependent variable. We also controlled for the following variables: Age, Started working 23-25 years, Secondary education, Working in agriculture, Own account worker, Consumption Quintiles, Urban area. Results for these variables were either not statistically significant or not discussed in the analysis. Full results are available under request.

**Table 2. Youths’ aspiration to work in STEM-related jobs, marginal effects from logit regressions.**

|**Main variables**|**Pooled sample**|**Ethiopia**|**Malawi**|**Nigeria**|
|---|---|---|---|---|
|**Characteristics of theyouths**|||||
|Youth is female|-0.054**|-0.071*|-0.06**|-0.074|
||(0.028)|(0.038)|(0.03)|(0.046)|
|19-22 years|-0.109***|-0.113**|-0.089**|-0.117***|
||(0.03)|(0.055)|(0.036)|(0.044)|
|23-25 years|-0.147***|-0.166***|-0.044|-0.167***|
||(0.033)|(0.059)|(0.054)|(0.046)|
|Educational gap|0.116***|0.118***|0.066*|0.102*|
||(0.031)|(0.041)|(0.04)|(0.052)|
|Started working 15-18 years|-0.046|-0.302***|-0.079*|0.048|
||(0.038)|(0.111)|(0.047)|(0.055)|
|Started working 19-22 years|-0.146**|-0.086|-0.181**|-0.162|
||(0.069)|(0.103)|(0.074)|(0.115)|
|Youth likelihood to reach the dream job (self-efficacy)|-0.05*|-0.091*|-0.097***|-0.032|
||(0.03)|(0.051)|(0.03)|(0.061)|
|Job that family and friends regard highly|0.120***|0.221***|0.03|0.055|
||(0.033)|(0.05)|(0.047)|(0.051)|
|**Characteristics of the household**|||||
|Quintile 2|0.084*|0.037|-0.037|0.112*|
||(0.044)|(0.076)|(0.056)|(0.065)|
|Observations|2,688|881|873|934|

Note. Results are presented in margins and clustered standard errors in parentheses. Individual-level survey weights used. Statistical significance at: *** p<0.01, ** p<0.05, * p<0.1. Enumeration areas fixed effect included. The Educational gap is measured as the difference between aspiration to complete university or higher degree and the current education of the youth at the time of the interview. We also controlled for the following variables: Started working <14 years, Started working 23-25 years, Interesting job to do, Working in STEM related jobs, Size of the HH, and Urban area, Consumption Quintiles 3, 4 and 5. Results for these variables were either not statistically significant or not discussed in the analysis. Full results are available under request.

**Regarding aspirations to migrate, we find that female youths generally have lower aspirations to migrate compared to their male counterparts.** Specifically, female youths are 11.6% less likely to express a desire to migrate than their male peers. This pattern is in line with the literature (i.e., Migali and Scipioni, 2019) and consistent across all the countries examined, although there are some variations. For example, in Nigeria, the gender difference is more pronounced, with female youths being 16.8% less likely to aspire to emigrate to another country ( **Table 5** ).

**Additionally, our analysis reveals that aspirations to migrate tend to decline with age.** In Malawi, youths aged 23-25 years are 10.8% less likely to express a desire to migrate compared to the youngest age group, which includes those aged 15-18 years ( **Table 4** ). When considering

19
specific migration destinations, we find that in Nigeria, the likelihood of aspiring to migrate to the capital city decreases by 12.1% among youths aged 23-25 years in comparison to the youngest age group (15-18 years). However, this trend is not statistically significant in Ethiopia( **Table 5** ). **Furthermore, we observe that youths with more years of education are more inclined to aspire to migrate.** Specifically, each additional year of education increases the probability of aspiring to migrate by 15.9% in Ethiopia and 12.3% in Nigeria. These findings are consistent across all countries and align with existing literature on the topic. According to Tjaden, Auer, & Laczko (2018), young people with secondary or higher degrees are most likely to migrate to find better job opportunities. Mussa (2020) provides evidence that youths migrate in search of better returns for their skills and human capital or try to change their lives in rural areas.

**Our findings also show that youths residing in urban areas are less likely to aspire to migrate in search of better opportunities.** Specifically, in Malawi, youths living in urban areas are 19.5% less likely to consider relocating to another city, but 9.8% more likely to contemplate moving to another country when compared to their rural counterparts ( **Table 4** ). This suggests that urban areas may offer certain advantages or opportunities that make youths less inclined to migrate within the country but more open to international migration. African rural youths are not interested in farming at all due to reasons including the perceptions of farming life, hence abandon agriculture (Leavy & Smith, 2010). However, this finding is not statistically significant across the countries.

**Work, job satisfaction and interest also play an important role in aspiring to migrate.** Working, particularly in the dream job, is a statistically significant factor that explains the willingness to migrate. We observe that in Nigeria, youths who are currently employed, are 22.7% less likely to move to rural areas and 15% less likely to migrate to towns ( **Table 5** ). Specifically, youths currently employed in their ideal job in Ethiopia are 23.7% less likely to move to another town ( **Table 3** ), and 21.2% are less likely to desire to migrate outside Nigeria ( **Table 5** ). Moreover, youths who aim to work for interest or passion are 4.6% more willing to move to cities or towns, possibly in search of more interesting and better job opportunities outside their current community ( **Table A3 in the Annex** ). On the contrary, we find that youths who are currently neither working nor studying (NEETs), are 20.4% more likely to desire migrating in Ethiopia ( **Table 3** ) and 12.8% more likely to move to capital city and 6.4% to rural areas in Malawi ( **Table 4** ). We posit that factors such as employment, job satisfaction, and passion may serve as anchors tying them to their place of origin, potentially dampening their aspirations to migrate. However, it is important to note that these results do not achieve statistical significance across all countries.

**Finally, we show that youths’ aspirations to migrate can be influenced by the family context**

**and community role models.** The occupation of the head of the household can impact youths’ aspirations to migrate. For instance, if the adult head of the household works in agriculture, youths in Ethiopia are 40.1% less likely to consider moving to towns, while youths in Nigeria are 11.4% less likely to contemplate moving to rural areas ( **Tables 3 and 5** ). We further observe that the level of education of the adult head of the household may have a positive effect on youths’ aspirations to migrate in search of better opportunities. Moreover, aspiring to pursue a job highly esteemed by

20
family and friends makes youths 14.2% more inclined to desire migration to towns in Malawi ( **Table 4** ). Additionally, having role models in the community can serve as a source of inspiration for youths. Knowing someone in the community who has achieved their dream job makes youths 8.4% less likely to contemplate moving to the capital city in Malawi ( **Table 4** ).

We further observe statistically significant impact of remittances on youths’ aspirations to migrate, but with some differences on the migration destinations across countries. If the households receive remittances from abroad, youths are less interested to migrate. Specifically, youths are 22.7% less likely to move to capital city in Malawi ( **Table 4** ) and 29.4% less likely to move to another city or 18.4% to rural areas in Nigeria ( **Table 5** ). Conversely, in case of the households receiving remittances from family members within the same enumeration area (EA), we find that youths are more likely to migrate, especially to capital city – 22.9% in Ethiopia ( **Table 3** ), to rural areas – 7.4% in Malawi ( **Table 4** ) and to towns – 12.7% in Nigeria ( **Table 5** ). Finally, the lack of money may increase youths’ aspirations to migrate by 11.7% in Nigeria and to migrate abroad by 13.6% in Ethiopia and by 13.8% in Malawi.

**Table 3. Youth’s migration aspirations in Ethiopia, marginal effects from logistic regressions**

|**Main variables**|**Migrate**|**Moving**<br>**to**<br>**capital**<br>**city**|**Moving**<br>**to**<br>**town/city**|**Moving**<br>**to**<br>**rural**<br>**area**|**Moving**<br>**to**<br>**another**<br>**country**|
|---|---|---|---|---|---|
|**Characteristics of the youths**||||||
|Youth is female|-0.05|0.143**|-0.107**|-0.03|-0.011|
||(0.047)|(0.07)|(0.05)|(0.03)|(0.062)|
|Years of education|0.016***|0.004|-0.005|0.0006|-0.006|
||(0.004)|(0.007)|(0.005)|(0.001)|(0.005)|
|Currently working (either paid job or farming)|0.26***|0.0174|0.076|-|-0.117|
||(0.065)|(0.120)|(0.098)|(-)|(0.07)|
|Currently neither working or attending school|0.204***|0.028|0.011|-0.007|-0.014|
||(0.063)|(0.112)|(0.076)|(0.04)|(0.088)|
|Currently doing the ideal job|-0.02|-0.036|-0.237*|-|0.067|
||(0.09)|(0.152)|(0.134)|(-)|(0.139)|
|Lack of money|-0.028|0.007|-0.017|-0.026|0.136**|
||(0.049)|(0.085)|(0.057)|(0.034)|(0.065)|
|STEM related work|-0.052|0.193|-0.029|0.026**|-0.130|
||(0.060)|(0.120)|(0.083)|(0.010)|(0.107)|
|**Characteristics of the adult head of the household**||||||
|Wage job employee|-0.029|0.102|-0.014|0.05***|0.0110|
||(0.060)|(0.085)|(0.049)|(0.016)|(0.067)|
|Working in agriculture|-0.141|-0.081|-0.405**|-|-|
||(0.116)|(0.197)|(0.159)|(-)|(-)|
|**Characteristic of the household**||||||
|Quintile 4|-0.155*|0.016|0.07|-|0.142**|
||(0.088)|(0.160)|(0.092)|(-)|(0.072)|
|Remittances from family within the country|-0.269**|0.011|0.047|-|-0.189*|
||(0.124)|(0.165)|(0.108)|(-)|(0.098)|

21
|Remittances from abroad within the same enumeration area (EA)|-0.047|-0.180**|0.066|0.056**|0.069|
|---|---|---|---|---|---|
||(0.058)|(0.091)|(0.061)|(0.022)|(0.067)|
|Remittances from family within the same enumeration area (EA)|0.099*<br>(0.051)|0.23***<br>(0.09)|-0.097<br>(0.068)|0.009<br>(0.007)|-0.046<br>(0.053)|
|Observations|881|311|311|141|237|
|Note. Results are presented in margins and clustered standard errors in parent<br>at:<br>***<br>p<0.01,<br>**<br>p<0.05,<br>*<br>p<0.1.<br>In Ethiopia, the number of youths who expressed a desire to relocate to rural<br>individuals in these categories respectively. Consequently, several observa<br>presence of perfect collinearity with the dependent variable. We also contr<br>community that has your dream job, Years of education, Own account wo<br>Remittances from abroad. Results for these variables were either not statist<br>available under request.|heses. Individ<br>Enumeratio<br>areas and to a<br>tions had to b<br>olled for the<br>rker, Quintile<br>ically signific|ual-level surve<br>n<br>areas<br>nother country<br>e excluded fr<br>following vari<br>2, Quintile 3<br>ant or not disc|y weights use<br>fixed<br>was quite li<br>om the regre<br>ables: Age,<br>, Quintile 5 (<br>ussed in the|d. Statistical<br>effect<br>mited, with o<br>ssion analysi<br>Knowing som<br>richest), Urb<br>analysis. Ful|significance<br>included.<br>nly 6 and 28<br>s due to the<br>eone in the<br>an area and<br>l results are|

**Table 4. Youth’s migration aspirations in Malawi, marginal effects from logistic regressions**

|**Main variables**|**Migrate**|**Moving**<br>**to capital**<br>**city**|**Moving to**<br>**town/city**|**Moving**<br>**to rural**<br>**area**|**Moving**<br>**to**<br>**another**<br>**country**|
|---|---|---|---|---|---|
|**Characteristics of the youths**||||||
|23-25 years|-0.108*|-0.018|0.027|-0.059**|-0.067|
||(0.064)|(0.063)|(0.069)|(0.026)|(0.05)|
|Years of education|0.007*|0.011**|0.003|-0.001|-0.004|
||(0.004)|(0.005)|(0.007)|(0.002)|(0.004)|
|Currently neither working or attending school|0.0825|0.128*|0.043|0.064**|-0.097|
||(0.053)|(0.073)|(0.063)|(0.026)|(0.068)|
|Currently doing the ideal job|-0.152*|-0.187|0.153|0.043|-|
||(0.084)|(0.199)|(0.152)|(0.065)|(-)|
|Knowing someone in the community that has your<br>dream job|0.075***|-0.084**|0.018|-0.005|0.014|
||(0.022)|(0.039)|(0.046)|(0.02)|(0.032)|
|Lack of money|0.017|-0.035|-0.021|0.057**|-0.14***|
||(0.035)|(0.04)|(0.041)|(0.027)|(0.043)|
|Job that family and friends regard highly|0.01|0.142**|0.034|-|-0.069|
||(0.051)|(0.061)|(0.083)|(-)|(0.056)|
|**Characteristics of the adult head of the household**||||||
|Wage job employee|0.024|-0.022|-0.063|0.051**|-0.0003|
||(0.035)|(0.051)|(0.051)|(0.024)|(0.034)|
|**Characteristics of the household**||||||
|Quintile 2|0.047|-0.122*|0.027|0.013|-0.0003|
||(0.04)|(0.067)|(0.077)|(0.032)|(0.073)|
|Quintile 3|0.034|-0.031|0.122**|-0.003|-0.111*|
||(0.042)|(0.064)|(0.059)|(0.033)|(0.064)|
|Quintile 4|0.018|0.034|0.124*|0.006|-0.135**|
||(0.038)|(0.076)|(0.066)|(0.035)|(0.055)|
|Quintile 5 (richest)|-0.014|0.012|0.164**|0.031|-0.17***|
||0.034|-0.031|0.122**|-0.003|-0.111*|
|Urban area|-0.061|-0.086|-0.19***|-0.022|0.098**|
||(0.037)|(0.061)|(0.054)|(0.031)|(0.040)|
|Remittances from abroad|0.178|-0.227*|-0.13|0.01|0.123|

22
||(0.12)|(0.118)|(0.11)|(0.051)|(0.094)|
|---|---|---|---|---|---|
|Remittances from family within the same||||||
|enumeration area (EA)|-0.064|-0.014|-0.084|0.075**|0.038|
||(0.044)|(0.059)|(0.051)|(0.038)|(0.046)|
|Observations|873|726|726|655|710|

Note. Results are presented in margins and clustered standard errors in parentheses. Individual-level survey weights used. Statistical significance at: *** p<0.01, ** p<0.05, * p<0.1. Enumeration areas fixed effect included. In Malawi, the number of youths who expressed a desire to relocate to rural areas and to another country was quite limited, with only 3 6 and 143 individuals in th ese categor ies respectively . Consequently, several observations had to be excluded from the regression analysis due to the presence of perfect collinearity with the dependent variable. We also controlled for the following variables: Youth is female, 19-22 years, Currently working (either paid job or farming), Interesting job to do, STEM related work, Years of education, Working in agriculture, Own account worker, Remittances from family within the country and Remittances from abroad within EA. Results for these variables were either not statistically significant or not discussed in the analysis. Full results are available under request.

**Table 5. Youth’s migration aspirations in Nigeria, marginal effects from logistic regressions**

|**Main variables**|**Migrate**|**Moving to**<br>**capital city**|**Moving to**<br>**town/city**|**Moving to**<br>**rural area**|**Moving**<br>**to**<br>**another**<br>**country**|
|---|---|---|---|---|---|
|**Characteristics of the youths**||||||
|Youth is female|-0.116***|0.0244|0.00910|0.008|-0.17***|
||(0.031)|(0.047)|(0.045)|(0.043)|(0.042)|
|23-25 years|-0.005|-0.121*|-0.01|0.081|-0.04|
||(0.046)|(0.069)|(0.063)|(0.061)|(0.067)|
|Years of education|0.012***|0.01**|-0.013**|-0.011***|0.003|
||(0.003)|(0.005)|(0.006)|(0.004)|(0.005)|
|Currently working (either paid job or farming)|0.008|0.017|-0.151**|-0.227***|-0.028|
||(0.047)|(0.069)|(0.063)|(0.067)|(0.057)|
|Currently doing the ideal job|0.04|-0.008|0.054|0.093|-0.212*|
||(0.073)|(0.09)|(0.11)|(0.089)|(0.120)|
|Lack of money|0.117***|0.054|-0.01|0.08*|0.032|
||(0.037)|(0.049)|(0.049)|(0.045)|(0.049)|
|Job that family and friends regard highly|-0.065*|0.029|-0.004|-0.003|-0.074|
||(0.036)|(0.054)|(0.055)|(0.051)|(0.063)|
|**Characteristics of the adult head of HH**||||||
|Years of education|0.003|-0.0005|-0.003|-0.01***|-0.003|
||(0.002)|(0.004)|(0.003)|(0.0035)|(0.004)|
|Wage job employee|-0.077**|-0.018|-0.036|-0.024|0.088|
||(0.036)|(0.059)|(0.056)|(0.054)|(0.063)|
|Working in agriculture|0.031|-0.034|-0.032|-0.114**|-0.029|
||(0.031)|(0.051)|(0.053)|(0.045)|(0.054)|
|**Characteristics of the household**||||||
|Quintile 2|-0.054|-0.058|-0.011|-0.041|0.136**|
||(0.046)|(0.063)|(0.068)|(0.071)|(0.057)|
|Quintile 3|0.0003|-0.085|-0.132*|-0.158**|0.212***|
||(0.04)|(0.069)|(0.068)|(0.067)|(0.068)|
|Quintile 4|-0.039|-0.031|-0.178**|-0.28***|0.069|
||(0.048)|(0.071)|(0.074)|(0.071)|(0.071)|
|Quintile 5 (richest)|-0.094*|-0.024|-0.109|-0.191**|0.231***|
||(0.049)|(0.084)|(0.082)|(0.090)|(0.077)|

23
|Remittances from abroad|-0.011|-0.116|-0.294***|-0.184*|-0.153|
|---|---|---|---|---|---|
||(0.074)|(0.115)|(0.111)|(0.111)|(0.118)|
|Remittances from abroad within the same||||||
|enumeration area (EA)|0.0102|0.108*|0.057|-0.086**|0.001|
||(0.037)|(0.065)|(0.071)|(0.041)|(0.058)|
|Remittances from family within the same||||||
|enumeration area (EA)|0.068**|-0.042|0.127***|0.116**|0.087|
||(0.031)|(0.053)|(0.048)|(0.046)|(0.056)|
|Observations|934|802|802|802|802|

Note. Results are presented in margins and clustered standard errors in parentheses. Individual-level survey weights used. Statistical significance at: *** p<0.01, ** p<0.05, * p<0.1. Enumeration areas fixed effect included. We also controlled for the following variables: 19-22 years, Currently neither working or attending school, Interesting job to do, STEM related work, Own account worker, Quintile 2, and Remittances from family within the country, Knowing someone in the community that has your dream job, and Urban area. Results for these variables were either not statistically significant or not discussed in the analysis. Full results are available under request.

# **5.2. Limitations of this study**

There are several limitations to our study. Firstly, aspirations are subjective measures and may be subject to bias. Individuals may have limited capacity to predict how their aspirations might change in response to success or failure. The process of revising aspirations can be slow, especially when social expectations and norms impede the flow of information and the revision of expectations (Favara, 2017). Furthermore, as discussed, measurement issues can pose challenges in comprehending youth aspirations, potentially affecting the quality of the collected data and subsequent policy recommendations. Different sets of questions and tools may yield different results. Lastly, we must acknowledge that youth aspirations are subject to change over time. This means that cross-sectional data cannot establish cause-and-effect relationships. Aspirations are endogenous, meaning they evolve as youths either achieve or fail to attain their goals, leading to the setting of new objectives.

# **6. Policy implications and conclusions**

This paper investigates the factors that influence the educational, career, and migration aspirations of young individuals in Ethiopia, Nigeria, and Malawi. We have used data from dedicated surveys designed to capture youth aspirations, along with information on their educational and employment backgrounds. Drawing from our findings, we present policy recommendations aimed at aligning youth’s educational and career aspirations with concrete strategies to mitigate youth unemployment and underemployment.

**Firs of all, we find that aspirations are context dependent** . Our study highlights that both career and academic aspirations are significantly shaped by interactions within the social environment, and the constraints and opportunities within the family context. Additionally, cultural values and social expectations play a crucial role in influencing youth’s aspirations, particularly impacting

24
female youths, who are less inclined to aspire to complete university or post-graduate degrees and pursue careers in STEM-related fields compared to their male counterparts.

Therefore, as reported by Appadurai (2004) and Ray (2006), altering one’s social network and values with higher aspiration anchors can enable youths to aspire, especially female youths. Role models are a powerful influence on shaping aspirations (Beaman et al., 2012, Bernard et al., 2019, Golan and You, 2020, Macours and Vakis, 2014, Mckenzie et al., 2021). This means, for instance, enabling the aspirations of young women to grow through the promotion of successful female leaders in local government and female role models. Another implication can be fostering passion and interest about STEM-related subjects among youths aged 15-18 years, especially female youths in male-dominated occupations. These interventions could also play an important role in aspirations to migrate.

**Secondly, we observe that aspirations tend to align with the level of education** . Our study reveals that youths with primary or secondary school education exhibit a higher interest in completing a university degree, pursue careers in STEM-related fields, and consider migration for improved life opportunities compared to youths with no educational background. Nonetheless, it is important to note that educational aspirations may not always align with the demands of the labor market, and their realization can be hindered by limited economic resources. This situation may lead to either overqualified youths working in unsatisfactory jobs or unemployed youths with relatively low educational qualifications.

Policies related to budget-relaxing interventions can reduce skills mismatch and favor the entrance of under and unemployed youths in the labor market. These include, for instance, cash transfers (Chiapa et al., 2012, García et al., 2019, Kosec and Mo, 2017, Macours and Vakis, 2014, Whetten et al., 2019), micro-credit (Garcia et al., 2020) and early childhood interventions (Zou et al., 2020) for youths from low-middle income households to keep them in school as long as they want and reduce the potential education gap at 23-25 years old.

Moreover, policies should ensure that young people can access to accurate information regarding educational opportunities and the corresponding labor market demands. Effective career guidance and counseling are essential components, necessitating the development of accessible and functional education and labor market information systems. These systems should provide regular and up-to-date information on educational outcomes and employment prospects (Lorenceau, et al., 2021) . This approach not only ensures that youths can make well-informed decisions but also equips them with the employability skills that employers seek. For instance, in many Sub-Saharan African countries, programs have successfully imparted these skills after regular school hours, proving to be cost-effective models for addressing the skills deficit in general education programs (Fox & Gandhi, 2021). While these programs have aided youth in starting businesses, their medium-term success remains to be fully evaluated.

**Finally, enhanced data collection regarding aspirations is crucial for the targeted design of policies aimed at addressing specific questions** . Currently, there is a scarcity of studies on

25
aspirations, and many of the existing ones employ such diverse methodologies that meaningful comparisons become challenging. As a result, there is a lack of comprehensive data regarding the aspirations of the general population, especially among young individuals. A key contributing factor to this data gap is the absence of standardized instruments and guidelines for measuring youth aspirations. Incorporating an aspirations module into household surveys can help capture aspirations over time and establish connections with household characteristics. Collecting individual-level data within nationally representative multi-topic household surveys enables the identification of various attributes concerning young people and their families. This, in turn, enriches our comprehension of how aspirations impact the future prospects of youth.

However, it is crucial to acknowledge that the measurement of youth aspirations requires ongoing research and refinement. Rigorous testing of assessment tools and their adaptation to local contexts are essential steps, as they can incentivize countries to comprehensively assess the state of youth aspirations. This comprehensive understanding is vital for elucidating how aspirations influence future outcomes, including how they may evolve with age, respond to external shocks, and adapt in light of available coping mechanisms. Furthermore, delving into the connections between current youth aspirations and family-related factors, such as the educational achievements of their parents or the socio-economic background of the household, can significantly contribute to a more nuanced policy discourse aimed at aligning aspirations with viable job opportunities.

# **7. References**

Akosah-Twumasi, P. et al., 2018. A Systematic Review of Factors That Influence Youths Career Choices—the Role of Culture.. _Front. Educ,_ p. 3:58..

Appadurai, A., 2004. The Capacity to Aspire: Culture and the Terms of Recognition. In: _Culture and Public Action._ Washington: World Bank, pp. 59-84.

Bandura, A., Barbaranelli, C., Caprara, G. V. & Pastorelli, C., 2001. Self-efficacy beliefs as shapers of children's aspirations and career trajectories. _Child Development ,_ p. 72(1): 187–206..

Bastianon, C. D., 2019. Youth Migration Aspirations in Georgia and Moldova. _Migration Letters,_ 16(1), pp. 105-121.

Beaman, L., Duflo, E., Pande , R. & Topalova, P., 2012. Female Leadership Raises Aspirations and Educational Attainment for Girls: A Policy Experiment in India. _Science,_ p. 335(6068): 582– 586.

Beaman, L., Duflo, E., Pande, R. & Topalova, P., 2012. Female Leadership Raises Aspirations and Educational Attainment for Girls: A Policy Experiment in India.. _Science,_ p. 582–586..

Bernard, T. & Taffesse, A., 2014. Aspirations: An Approach to Measurement with Validation Using Ethiopian Data. _Journal of African Economies,_ p. 23(2): 189–224.

26
Bianco, A. M. & Martínez, E., 2009. Robust testing in the logistic regression model. _Computational Statistics & Data Analysis,_ 53(12), pp. 4095-4105.

Bloem, J., 2021. Aspirations and Real Estate Investments in Rural Myanmar. _Journal of Economic Inequality._ Boateng, E. S. & Löwe, A., 2018. _Aspirations matter: what young people in Ghana think about work,_ s.l.: Overseas Development Institute.

Breda, T., Grenet, J., Monnet , M. & Van Effenterre, C., 2020. Can female role models reduce the gender gap in science? Evidence from classroom interventions in French high schools. _IZA Discussion Paper 13163._ Carling, J., 2017. How Does Migration Arise?. In: _Ideas to Inform International Cooperation on Safe, Orderly and Regular Migration._ Geneva: IOM.

Cramer, J., 2007. Robustness of Logit Analysis: Unobserved Heterogeneity and Mis-specified Disturbances. _Oxford Bulletin of Economics and Statistics,_ Volume 69, pp. 545-555.

Dalton, P. S., Ruschenpohler, J. & Zia, B., 2018. Determinants and Dynamics of Business Aspirations : Evidence from Small-Scale Entrepreneurs in an Emerging Market. _Policy Research Working Paper No. 8400._ Davey, F. & Stoppard, J., 1993. Some factors affecting the occupational expectations of female adolescents. _Journal of Vocational Behavior,_ pp. 43, 235–250.

de Haas, H., 2020. Paradoxes of migration and development. In: T. B. &. R. Skeldon, ed. _Routledge Handbook of Migration and Development._ Abingdon: Routledge: s.n., pp. 17-31.

Dibeh, G., Fakih, A. & Marrouch, W., 2019. Labor market and institutional drivers of youth irregular migration in the Middle East and North Africa region. _Journal of Industrial Relations,_ 61(2), pp. 225-251.

Dustmann, C. & Okatenko, A., 2024. Out-migration, wealth constraints, and the quality of local amenities. _Journal of Development Economics,_ Volume 110, pp. 52-63.

Du, X. & Wong, B., 2019. Science career aspiration and science capital in China and UK: a comparative study using PISA data. _International Journal of Science Education._ Esipova, N., Pugliese, A. & Ray, J., 2018. _More than 750 Million Worldwide Would Migrate if They Could._ [Online] Available at: https://news.gallup.com/poll/245255/750-million-worldwide-migrate.aspx Evans, R., 2010. Children’s Caring Roles and Responsibilities within the Family in Africa. _Geography Compass,_ Volume 4, pp. 1477-1496.

Favara, M., 2017. Do Dreams Come True? Aspirations and Educational Attainments of Ethiopian Boys and Girls. _Journal of African Economies,_ p. 26(5): 561–583.

27
Favara, M., Chang, G. & Sánchez, A., 2018. _No longer children: What do Young Lives children do when they grow up? Transitions to post-secondary education and the labour market,_ Oxford: Young Lives.

Fox, L. & Gandhi, D., 2021. Youth emaployment in sub-Saharan Africa. _AGI Working Paper #28._ Fruttero, A., Muller, N. & Calvo-Gonzalez, O., 2021. The Power and Roots of Aspirations : A Survey of the Empirical Evidence.. _Policy Research working paper,no. WPS 9729._ Gagete-Miranda, J., 2020. _An Aspiring Friend Is a Friend Indeed: School Peers and College Aspirations in Brazil,_ Milan: Bocconi University.

Galiani, S., Gertler, P. J. & Undurraga, R., 2021. Aspiration adaptation in resource-constrained environments. _Journal of Urban Economics,_ p. 123(103326).

Genicot, G. & Ray, D., 2017. Aspirations and Inequality. _Econometrica,_ p. 85(2): 489–519.

Genicot, G. & Ray, D., 2020. Aspirations and Economic Behaviors. _Annual Review of Economics,_ p. 12: 715–746.

Gorard, S., See, B. H. & Davies, P., 2012. The impact of attitudes and aspirations on educational attainment and participation. _Retrieved from https://www.jrf.org.uk/sites/default/files/jrf/migrated/files/education-young-people-parentsfull.pdf ._ Guan, Y. et al., 2015. Differences in career decision-making profiles between american and chinese. _J. Cross-Cult Psychol.,_ p. 856–872..

Jacob, B. A. & Wilder, T., 2010. Educational expectaions and attainment. _National Bureau of Economic Research._ Janzen, S., Magnan, N., Sharma, S. & Thompson, W. M., 2017. Aspirations Failure and Formation in Rural Nepal. _Journal of Economic Behavior & Organization,_ p. 139(2017): 1–25.

Kabeer, N., 1999. Resources, agency, achievements: reflections on the measurement of women’s empowerment. _Development and Change,_ 30(3), pp. 435-464.

Khattab, N., 2015. Students’ aspirations, expectations and school achievement: what really matters?. _British Educational Research Journal,_ 41(5), pp. 731-748 .

La Ferrara, E., 2019. Presidential Address: Aspirations, Social Norms, and Development. _Journal of the European Economic Association,_ p. 1687–1722.

Leavy, J. & Smith, S., 2010. Future farmers: youth aspirations, expectations and life choices. _Future Agricultures Discussion Paper,_ p. 1–15.

Lorenceau, A., Rim, J.-Y. & Savitki, T., 2021. Youth aspirations and the reality of jobs in Africa. _OECD Development Policy Papers,_ Volume 38, pp. 1-21.

28
Lybbert, T. & Wydick, B., 2018. Poverty, Aspirations, and the Economics of Hope. _Economic Development and Cultural Change ,_ Volume 66, p. 709–753.

Macours, K. & Vakis, R., 2014. Changing Households’ Investment Behaviour through Social Interactions with Local Leaders: Evidence from a randomized transfer program. _Economic Journal,_ p. 124(576): 607–633.

<mark>Malawi National Statistical Office (NSO) (Government of Malawi). Malawi - High-Frequency Phone Survey 2020-2024 (HFPS-COVID-19 2020-2024). Ref: MWI_2020-2024_HFPS_v18_M. Downloaded from https://microdata.worldbank.org/index.php/catalog/3766 on January 2, 2024;</mark> Manski, C., 2004. Measuring expectations. _Econometrica,_ 72(5), pp. 1329-1376 .

Marjoribanks, K., 2003. Family background, individual and environmental influences, aspirations and young adults' educational attainment: A follow-up study. _Educational Studies,_ 29(2-3), pp. 233-242.

Mau, W., Chen, S.-j., Li, J. & Johnson, E., 2020. Gender Difference in STEM Career Aspiration and Social- Cognitive Factors in Collectivist and Individualist Cultures. _Administrative Issues Journal Education Practice and Research,_ pp. 10. 30-46.

Mau, W.-C. & Li, J., 2017. Factors Influencing STEM Career Aspirations of Underrepresented High School Students. _The Career Development Quarterly,_ pp. 66: 246-258.

Mickelson, R. A., 1990. The attitude–achievement paradox among black adolescents. Sociology of Education. 63(1), p. 44–61 .

Migali, S. & Scipioni, M., 2019. Who's About to Leave? A Global Survey of Aspirations and Intentions to Migrate. _International Migration._ Mussa, E. C., 2020. Youth Aspirations, Perceptions of Farming, and Migration Decisions in Rural Sub-Saharan Africa: Further Empirical Evidence From Ethiopia. _ZEF-Discussion Papers on Development Policy No. 296._ <mark>National Bureau of Statistics. Nigeria COVID-19 National Longitudinal Phone Survey (COVID19 NLPS) 2020-2021, Phase 1. Dataset downloaded from</mark> <u>https://microdata.worldbank.org/index.php/catalog/3712</u> <mark>on January 2, 2024</mark> Nguyen, T., 2008. Information, Role Models and Perceived Returns to Education: Experimental Evidence from Madagascar.. _Working paper, MIT, Cambridge, MA._ OECD, 2021. _PISA 2025 Foreign Language Assessment Framework,_ s.l.: OECD Publishing.

Pinquart, M., Juang, L. P. & Silbereisen, R. K., 2004. The role of self-efficacy, academic abilities, and parental education in the change in career decisions of adolescents facing German unification.. _Journal of Career Development,_ 31(2), pp. 125-142.

Rao, V. & Walton, M., 2004. _Culture and Public Action._ Stanford, CA: Stanford University Press.

29
Ray, D., 2006. Aspirations, Poverty and Economic Change. In: _Understanding Poverty._ Oxford: Oxford University Press, pp. 409-22.

Ray, D., 2016. Aspirations and the Development Treadmill. _Journal of Human Development and Capabilities,_ p. 7(3): 309–323.

Riley, . E., 2019. Role models in movies: the impact of Queen of Katwe on students' educational attainment. _Revise and resubmit at the Review of Economics and Statistic._ Ross, P., 2019. Occupation Aspirations, Education Investment, and Cognitive Outcomes: Evidence from Indian Adolescents. _World Development,_ p. 123: 104613.

Ross, P. H., 2019. Occupation Aspirations, Education Investment, and Cognitive Outcomes: Evidence from Indian Adolescents. _World Development,_ p. 123: 104613.

Roy, S., Morton, M. & Bhattacharya, S., 2018. Hidden human capital: Self-efficacy, aspirations and achievements of adolescent and young women in India. _World Development,_ p. 111: 161–180.

Sadler, P. M., Sonnert, G., Hazari, Z. & Tai, R., 2012. Stability and volatility of STEM career interest in high school: A gender study. _Science Education,_ pp. 96(3), 411–427.

Siwatu, G. O. et al., 2020. _Impact of COVID-19 on Nigerian households : 5th round results (English).,_ Washington, D.C. : World Bank Group: s.n.

Sumberg, J. & Okali, C., 2013. Young People, Agriculture, and Transformation in Rural Africa: An “Opportunity Space” Approach. _Innovations, 8, ,_ Volume 8, p. 259–269.

Tabe-Ojong, M. P. J. & Nshakira-Rukundo, E., 2021. Religiosity and parental educational aspirations for children in Kenya. _World Development Perspectives,_ 23(100349).

The World Bank, 2022. _The World Bank in Africa._ [Online] Available at: <u>https://www.worldbank.org/en/region/afr/overview</u> [Accessed 15 August 2022].

Tjaden, J., Auer, D. & Laczko, F., 2018. Linking Migration Intentions with Flows: Evidence and Potential Use. _International Migration._ Van Hear, N., 2014. Reconsidering migration and class. _International Migration Review,_ 48(1), p. S100–S121.

Van Mol, C., 2016. Migration aspirations of European youth in times of crisis. _Journal of Youth Studies,_ pp. 1303-1320.

Wooldridge, J., 2002. _Econometric Analysis of Cross Section and Panel Data._ Cambridge: The MIT Press.

<mark>World Bank. Ethiopia - High Frequency Phone Survey 2020-2023. Ref: ETH_20202023_HFPS_v13_M. Dataset downloaded from h</mark> <u>ttps://microdata.worldbank.org/index.php/catalog/3716</u> <mark>on January 2, 2024;</mark> 30