Drivers of migration and determinants of wellbeing among internal youth migrants in Ethiopia: Towns along Addis Ababa –adama route in focus

Abstract

The present study surveyed drivers of rural youth influx to urban areas and examined correlates of wellbeing among youth migrants domiciled in towns along important economic corridor of Ethiopia. In total, 694 (M = 418, F = 276) youth migrants aged 15–30 years and identified through multistage and purposive sampling techniques filled in a self-report questionnaire consisting of items probe profile and rating scales intended to uncover circumstantial and intentional activities of respondents. Descriptive statistics, Pearson's product momentum correlation, and multiple regression analysis were utilized to examine the data. The results show that most migrants are single and short distance migrants with secondary education and above. Both “push” and “pull” factors are found to be key factors driving youth to urban areas. The persistent challenges these youth migrants faced at destination areas include high living cost, housing problems and joblessness ˗˗ the existing Ethiopian urban area scenario, which is likely to be exacerbated by their very presence. Besides, the analysis of the relationship of circumstantial factors and intentional activities with indicators of wellbeing revealed a strong association of proactive coping behaviour with both indicators of participants' wellbeing (i.e., income and perceived subjective wellbeing). Sex and educational level are related with income, and perceived support from other is associated with perceived subjective wellbeing. The findings of the study provide additional evidence on drivers of youth migrants in developing countries and highlight some determinant factors that account for youth migrants' wellbeing. Implications of the study are discussed.

1. Background

Depending on the type of political and geographical boundaries crossed, migration can be classified as internal and international. Internal migration refers to movement of people within defined political boundaries, while international migration involves crossing national boundaries [1]. The number of international migrants has reached 272 million, which constitutes 3.5% of the world's population [2]. However, when spatial movement of people is considered, domestic migration or movement of people within their own country appears to be pervasive. It is estimated that 763 million people live either permanently or temporarily in rural and urban areas outside the region of their birth [3] (see Fig. 1, Fig. 2).

Fig. 1.

General theoretical model relating circumstantial factors and intentional activities to wellbeing.

Fig. 2.

Study sites (Addis Ababa-Adama corriodor).

Among internal migration, rural-to- urban migration is a relatively older and globally more widespread phenomenon. Internal migration, especially net migration to urban areas, drives the urbanization of a country and gives rise to urban population. This is particularly true in Africa, where urbanization is on the rise. Africa's urban population has been growing at a very high rate. It grew from about 27% in 1950 to 40% in 2015 and is projected to reach 60% by 2050 [4]. According to the data released by the United Nations about Ethiopia, the population of the country was predicted to be 114, 963, 588 in 2020, with 21% of the population living in cities. Annual total population growth and annual urban population growth were projected to be 2.5% and 4.63% in the same year. Ethiopia is predominantly a young country, with about 43% of the population under the age of 24 and a median age of 19.5.

Why do people migrate? Migration is a complex concept with a number of perspectives in the literature, including multiple causes of migration. Rural-urban migration is apparently explained by ‘push’ and ‘pull’ factors. Oftentimes, people are pushed by circumstances to leave their places, or they may be pulled by attractions in the region of their destinations. Some of the ‘push’ factors are negative home conditions that impel the decision to migrate (e.g., lack of job opportunities, lack of resources, unfavourable climatic condition, low crop yield, land shortage, and poor employment prospects). The ‘pull’ factors are attractions of the destination such as high wages, employment opportunities, and a wide range of amenities [5,6]. Thus, for an individual migrant, the decision to migrate results from both of these ‘push’ and ‘pull’ factors. However, the factors behind migration may differ across locations and types of migrations: rural-urban, rural-rural, urban-rural, urban-urban and circulatory.

In the context of Africa, various studies [e.g. Refs. [7,8]] identified some prominent factors that have led to increasing trends in rural-urban migration: the difference in economic growth between rural and urban areas, lower real income in rural areas, fewer job opportunities, disparities in social and health services, and climate change-related issues such as frequent droughts, desertification, and sea-level rise. Besides, rapid urbanization offers more job opportunities in cities and becomes an especially attractive destination for the youth. It appears that young people are the first to move and they have the greatest aspirations to move in response to calamities and slow onset of events. This cohort of the population is likely to be unemployed, less encumbered with family and responsibilities and more connected. Similarly, they have got better educational level, better ideas of where they can find opportunities and also do not want to have their dreams thwarted by existing situation.

The literature on internal migration in Ethiopia [e.g. Ref. [9]] elucidated certain features of the phenomenon. For instance, the authors have shown that internal migration has been increasingly from rural to urban areas, that people migrate for both “push” and “pull” reasons, and that age and education are key determinants of internal migration. As [9] indicated migrants are younger and better educated compared to non-migrants from the same origin and they mainly migrate in search of work.

It appeared that the predominant reason for migration is to seek a better and safer life. People who choose to migrate are motivated by economic intentions and the pursuit of wellbeing. Despite lack of commonly agreed definition of wellbeing, researchers [10,11] argue that wellbeing is the state of being healthy and leading contented livelihood, and it mainly encompasses the fulfilment of various human needs and the ability to pursue one's goals. It is argued that wellbeing may constitute at least two dimensions: material/economic wellbeing and quality of life or subjective wellbeing. The search for economic wellbeing means one's desire to be wealthy or being economically buoyant. On the other hand, subjective well-being (SWB) consists of three components: the presence of positive affect (happiness), cognitive dimension (life satisfaction) and absence of negative affect [12].

Drawing on a review of various studies on international migrants, Bab-Klimex, Karatzias and Maclean [13] pinpointed three major determinants of subjective wellbeing: a set point, circumstantial/contextual factors, and intentional activities. A set point is a factor related to relatively immutable intrapersonal, temperamental, and affective personality traits that change little over the lifespan. Circumstantial/contextual factors are “incidental but relatively stable factors of an individual's life” including life status variables such as marital status, occupational status, job security, income, health, and religious affiliations. Intentional activities refer to a broad category of determinants that involve the voluntary and effortful activities people do in their everyday lives. These activities may be cognitive, behavioural and volitional in nature. Intentional activities may include avoiding social comparison, developing coping strategies, nourishing social support, and committing to goals.

In Ethiopia, though its extent is not known, internal migration is thought to be larger than cross border flows [14,15]. As Carson [16] explicated, internal migration is common in Ethiopia, but it is less researched and understood than its international counterpart. It is also assumed that most of the domestic migrants are youth rural –urban migrants in search of better opportunities. They often arrive at urban areas with limited resources, less connections with people or with less life skills but with high expectation for success in life.

In the light of this background, the main objective of the current study was to analyse drivers of youth influx to urban areas in order to gain a better understanding of this cohort of population. To this end, the study explored post-migration living and working conditions among youth migrants domiciled in towns along an important economic corridor of Ethiopia. Specifically, the study: (i) explored the underlying push and pull factors of the youths under investigation from rural to urban areas, (ii) investigated challenges and opportunities the youth migrants have faced at destination sites, and (iii) scrutinized determinants of youth migrants’ wellbeing.

The study may contribute to the existing body of literature that seeks to understand drivers of youth migration in developing countries and factors that shape the wellbeing of migrants at destination sites. The findings of the study may complement and strengthen the existing body of knowledge on drivers of youth migration, and they may inform decision makers in addressing the driving factors of migration and in promoting youth migrants’ wellbeing and integration.

2. Review of literature

2.1. Drivers of migration: conceptualizing factors that induce and shape people's movement

People have been moving since time immemorial, oftentimes in search of better living conditions or to escape harsh situations in their homeland. In a plethora of research about explanatory factors of migration, different concepts such as ‘determinants’, ‘causes’ and ‘drivers’ have been used differently with their primacy in the literature changed over time. The use of drivers instead of determinants and causes is increasing with a growing interest in environmental effects of migration [17]. Drivers are generally defined as factors that induce, orient and sustain migration [18] and that can be external or internal to the migrants/households. There seems to be a consensus that there are forces which are often considered as drivers of migration and which lead to the inception and the perpetuation of migration. As van Hear et al. [18] indicated, drivers are factors which get migration to go and keep it going once it began, and they work in combination-in what can be termed as driver complexes – to shape the specific form and structure of population movements. Considering the factors for international migration, which may also be applicable to domestic migration, the authors have distinguished them into predisposing, proximate, precipitating and mediating factors.

Predisposing factors contribute to the creation of a context in which migration is more opportunistic. They may include structural disparities between the origin and destination of migrants that are shaped by the macro-political economy. Such predisposing factors are the outcomes of extensive processes such as globalisation, environmental change, urbanization and demographic transformation.

Proximate factors may include a downturn in the economic or business cycle, a turn for the worse in the security or human rights environments, or marked environmental degeneration, including the effects of climate change. In places of migrants’ destination, proximate factors might include opportunities that open up as a result of economic upturn. These factors may have a more direct bearing on migration and may derive from the working out of the predisposing or structural features.

Precipitating factors are those that actually trigger departure. They may be found in the economic sphere including financial collapse, a leap in unemployment, or the disintegration of health, education or other welfare services. They may also be located in the political or security sphere, and they include persecution, disputed citizenship, or outbreak of war. ‘Natural’ or environmental disasters can also be precipitating factors.

Mediating factors enable, facilitate, constrain, accelerate, diminish or consolidate migration. Facilitating factors include the availability and quality of transport, communication, information and the resources needed for the journey and transit period. Constraining factors include the absence of such infrastructure and the lack of information and resources needed for migration.

Thus, as Castelli [19] summed up, drivers of migration are much more complex and multifaceted, involving local realities, macro-level events, (largely independent from the individual), meso-elements (more closely related to the individual but not completely under the individual's control) and micro-level causes and intertwined with personal characteristic of the individuals.

In general, it appears that youth migrate to urban areas for a number of reasons. However, studies in Africa [ e.g. Refs. [7,20,21]] elucidated lack of employment opportunities in non-agricultural economy, search for a better job in urban areas, access to social services and infrastructure, educational opportunities, and perceived better livelihood in cities and towns as predominant factors for youth migration. For instance, a case study from Rwanda pointed out three critical factors that can encourage youth migration from rural to urban areas: the availability of social services in rural areas, which is likely to deter youths from migrating, and conversely, presumed stable jobs in cities, coupled with an inauspicious social environment in rural areas [21]. Urban growth in the neighbouring and potential migrant destination areas can also encourage youth to migrate.

Youth migrants in their destination areas face a number of constraints. These may include tenure insecurity in terms of rental arrangements in residential units as well as workplace insecurity due to eviction and confiscation, vulnerability and food insecurity due to lack of social networks that can provide them with informal safety net in times of crisis. Young women seem to be more disadvantaged than male youth migrants [22].

2.2. Conceptualizing wellbeing and its determinants

In the literature, despite discussions across various disciplines, there seems to be a lack of consensus on the concept of ‘wellbeing’. Besides, the terms wellbeing, quality of life, happiness, and life satisfaction are often used interchangeably in the literature [10]. Pollard and Lee [23] defined wellbeing as individual characteristics of an inherently positive state (happiness). It has also been defined on a continuum from positive to negative, such as how one might measure self-esteem. Wellbeing can also be defined in terms of one's context (standard of living), absence of well-being (depression), collective manner (shared understanding). Several other studies [e.g. Refs. [13,24]] emphasized individuals' subjective experiences of their own life/subjective wellbeing as important criteria of quality of life. Diener et al. [24] indicated that subjective well-being consists of three interrelated components: life satisfaction, pleasant affect, and unpleasant affect. Thus, affect refers to pleasant and unpleasant moods and emotions, whereas life satisfaction refers to a cognitive sense of satisfaction with life. Camfield [25] also pinpointed that subjective quality of life is not simply equated with happiness, but it is related to aspects of life people regard as important.

The multi-dimensional nature of wellbeing has been discussed in OECD [10], which delineated three pillars for its understanding. They are: (i) Material living conditions (or economic well-being), which determine people's consumption possibilities and their command over resources, (ii) Quality of life, which is defined as the set of non-monetary attributes of individuals that shape their opportunities and life chances, and has intrinsic value under different cultures and contexts, and (iii) The sustainability of the socio-economic and natural systems where people live and work, which is important for well-being to last over time.

In a positive psychology paradigm, the most discussed theoretical account of subjective wellbeing is that of the Sustainable Happiness Model (SHM) formulated by Lyubomirsky, Sheldon, and Schkade [26]. The model indicates that three factors: set point (inherent genetic predispositions), current life circumstances (demographic variables), and current intentional activities (volitional & effortful activities people do) account for the level of happiness of the individual. Initial estimates of the relative importance of the three factors in impacting chronic levels of happiness attribute 50% of happiness to genetic factors, 10% to circumstantial factors, and the remaining 40% to volitional or intentional activities factors.

Studies carried out following SHM on international migrants found out different results. For instance, regarding socio –demographic factors, many studies [e.g. Refs. [27,28]] found out that age and gender were not significant predictors of wellbeing. According to Uskul and Greenglass [28], marital status was found to be a significant predictor of wellbeing, that is, being married was associated with increased level of wellbeing. With respect to educational status, conflicting results were reported. Garcia et al. [29], for instance, found that higher levels of education are associated with increased levels of wellbeing, on the contrary Amit [30], reported that lower levels of education are associated with higher levels of wellbeing.

Concerning migration-related factors, variables such as duration of migration and age at migration did not predict subjective wellbeing of migrants, while household income and job status were positively correlated with wellbeing [27,30]. Personality factors such as greater sense of mastery, personal control, higher level of self – esteem, optimism and intentional activities such as comparison processes that involve comparing oneself with significant others back home and proactive coping strategies were associated with greater level of wellbeing [28,31].

Based on a systematic literature review and meta-analysis of studies on determinants of wellbeing among international economic migrants, Bak-Klimek et al. [13] revealed that social support and dispositional factors (e.g., optimism, self-esteem) were strongly related with wellbeing, whilst circumstantial factors such as income or duration of migration had weak and insignificant relationship with wellbeing.

2.3. Statement of the problem

The young population not only covers a significant share of Ethiopia's population but also comprises a considerable portion of rural urban migrants˗˗ a phenomenon that is growing from time to time. The interplay of socio-economic and political conditions in Ethiopia coupled with the unprecedented level of urbanization the country is experiencing may account for the influx of youth to urban areas. This trend will undoubtedly pose considerable socioeconomic challenges to urban areas. Studies examining the demographic profile of internal migrants in Ethiopia have shown somewhat conflicting results. On the one hand, there are studies [e.g. Ref. [32]] which assert that Ethiopian rural-urban migrants are educated individuals from agriculturally more productive and wealthier areas. According to these studies, rural-urban migration may be favoured by more educated young migrants as a strategy to secure white-collar jobs. On the other hand, other studies [e.g. Ref. [33]] claim that internal migrants are from poorest families (including female-headed households) who drop out of school to look for work in urban areas.

Besides, studies on rural-urban migration in Ethiopia [e.g. Refs. [5,34]] mainly focus on causes and consequences of migration. Most of the studies employed the case study method to examine the situation of migrant household heads at destination urban areas. Despite the pervasive nature of the youth on the move, there are few studies targeting drivers of youth migration from the perspectives of migrants themselves. Moreover, the literature seems to have ignored the well-being of domestic youth migrants. Thus, the current study seeks to examine fundamental issues of internal youth migrants including the main drivers of rural-urban youth migrants, migrants’ diversities and characteristics, and determinants of their wellbeing in selected research sites. In investigating the correlates of wellbeing, the researchers have developed the following theoretical model based on the reviewed literature.

2.4. Research questions

The study seeks to answer the following specific questions.

• 1.What are the underlying drivers of internal youth migration?
• 2.What are the attributes of internal youth migrants in the study sites?
• 3.To what extent do selected circumstantial factors and intentional activities influence the wellbeing of youth migrants?
• 4.Between circumstantial factors and intentional activities, which ones contribute more to the variability of youth migrants' wellbeing?

3. Methods

3.1. Research design

The study employed cross-sectional survey research design, which involves gathering data from participants at a time. The cross-sectional design has the advantage of measuring current attitudes or practices, enabling researchers to look at numerous characteristics at once and to garner information in a short period of time [35].

3.2. Description of study areas

The study covered the towns and cities along Addis Ababa - Adama road, which ultimately connects Ethiopia with the sea port of Djibouti. The towns are Dukem, Bishoftu, Modjo and Adama, which are often labelled as ‘economic corridor’ of the country with a number of private and public enterprises and industries. For instance, the industrial parks such as Bole Lemi I and II, Qilinto, Dukam, and Adama industrial parks are positioned along this economic corridor. The Ethio-Djibouti Railway, the Addis Ababa-Adama Expressway, and the Modjo Dry Port (the first inland dry port in the country) with their multimodal transportation capacity lie along this corridor. Against these socioeconomic backdrops, rapid urbanization and influx of people in search of job seem to be evident along this economic corridor. The cities are located to the southeast of Addis Ababa, with Adama being the farthest with 99 km from the capital.

3.3. Participants and selection procedure

The population of the study encompasses rural-urban or urban-urban youth migrants aged 15-30, who moved to the urban study areas in the last five years. In defining youth, different age ranges have been adopted by different global and regional organizations and countries [36]. The UN secretariat/UNESCO/ILO considers youth as persons between 15 and 24 years of age, while the UN- Habitat extends the upper limit to 32. The African Youth Charter considers a cohort of people aged 15–35 as youth. The Ethiopia National Youth Policy defines youth as regiment of the society who are between 15 and 29 years [37]. The present study adopts this national definition by extending the upper limit to 30 years.

In order to identify the target samples, multistage sampling technique was utilized. At the first stage, the labour-intensive industry hubs, namely, Dukem and Adama were selected purposefully. Secondly, taking into account the engagement of migrants in economic activities, the following categories of migrants were considered purposefully: small informal business operators, micro and small enterprise operators, employees of companies, employees of small private businesses, and public organization employees. Next, with the exception of those engaged in public organizations, which account for 10% of the total participation, 20–25% of participants in the remaining sectors were selected out of the initial 800 proposed samples using the availability sampling method. With the above premise trained data collectors were deployed to various places in the selected cities/towns and gathered information from 780 respondents. However, during data check and screening, consumable data were reduced to 694 primarily due to incomplete and/or arbitrarily filled-in cases. Thus, the analysis was based on the data generated from 694 participants drawn from small self-business operators (168 or 24.6%), micro and small business operators (112 or 16.1%), employees at companies (143 or 20.6%), employees at private-owned businesses (179 or 25.8%), public organization workers (61 or 8.8%), and others (31 or 4.5%).

In the current study, in order to secure the consent of the participants, the purpose of the study was clearly explained to them by trained data collectors. The proposal of the study was also approved by Ethical Review Board of the School of Humanities and Social Sciences (SoHSSs), Adama Science and Technology University (ASTU).

3.4. Data gathering tools

In order to collect data, a battery of self-report questionnaire consisting of three parts was utilized. The first part consists of items intended to identify the demographic profile of respondents, while the second part contains items intended to describe the process of migration (including items intended to measure the main reasons for migration and the support migrants received from significant others) and outcomes of migration (challenges faced, incomes earned, and main sources of livelihood). The third part of the questionnaire consists of standard Likert-type scales: Personal Wellbeing Index-Adult (PWI-A) and Proactive Coping subscale of the Proactive Coping Inventory (PCI).

The Personal Wellbeing Index-Adult (PWI-A) is a multi-item indicator of subjective wellbeing that examines participants' level of life satisfaction along seven domains: standard of living, personal health, success in life, personal relationships, personal well-being, community-connectedness, and future well-being. These aspects of satisfaction should collectively indicate people's satisfaction with their life as a whole [38].

Later, an eighth domain (an optional item), which focused on religion and spirituality, was added to the scale. The PWI-A used in the present study, however, consisted of the seven domains, measured on an 11-point end defined Likert scale, with numerical ratings ranging from 0 (extremely dissatisfied) to 10 (extremely satisfied). Domain scores were summed up to yield a perceived subjective wellbeing score representative of how the participant is satisfied with life as a whole. An additional item was included to probe participants’ overall satisfaction with their life and to look for construct validity, but it is not part of the sum score. The psychometric properties of the PWI-A are well established in a series of studies conducted on Western and non-Western samples [[38], [39], [40], [41]]. Example questions are “How satisfied are you with your standard of living?” “How satisfied are you with what you are achieving in life?”

Proactive coping behaviour (indicator of intentional activity) of the participants was assessed by Proactive Coping Subscale of Personal Coping Inventory (PCI) developed by Greenglass, Schwarzer, & Taubert [42]. This 14-item subscale enquires a person's reactions to various situations. It is a self-report measure that is scored on a 4-point Likert-type scale that ranges from 1 (not at all true) to 4 (completely true). Example items are “I like challenges and beating the odds” and “I turn obstacles into positive experiences”. It is an exclusive measure of proactive coping that combines autonomous goal setting with self-regulatory goal attainment cognitions and behaviour with high internal consistency (α 85) in various studies [42,43].

To ease understanding for participants, the PWI-A and PCI subscales were translated from English into Amharic by independent bilingual professionals from Adama Science and Technology University, using forward-backward translation procedure.

Perceived level of support was assessed by three items corresponding to categories of assistance or support (tangible support˗˗ concrete material/financial support, emotional support˗˗ warmth and nurturance, and informational support˗˗ provision of advice and guidance) that one can receive from significant others. Based on the basics of support developed by the researchers, the items are a 3-point Likert-type, ranging from low (1) to high (3).

Prior to the final study, the Amharic version of the self-report questionnaire was reviewed by two pertinent professionals from School of Humanities and Social Sciences, Adama Science and Technology University, for validity. The questionnaire was then piloted on 60 participants from Adama city. Based on the reviewers’ comments and the pilot results, some modifications such as deletion of and/or rephrasing items were made.

3.5. Data analysis

In order to examine the data, descriptive statistics, Cronbach's alpha, Pearson's product momentum correlation, and multiple regression analyses were employed. To examine the degree to which migrants' wellbeing is shaped by circumstantial factors (demographic and migration related factors) and intentional activities, the ordinary least square method was used. Specifically, to assess the amount of variability explained by the predictors, coefficient of determination (R²) was employed, and to determine the magnitude of the path effects, standardized path coefficient estimates were applied.

Prior to the analyses, basic assumptions of multivariate data analysis such as normality and linearity were examined. Based on skewness and kurtosis analyses and visual inspection of histograms, normal Q-Q plots, and box plots, the data were found to show approximately normal distributions. The assumption of linearity was also met.

4. Results

4.1. Descriptive evidence

The participants of the study were rural-urban or urban –urban migrants who came to the study sites during the past five years. Of these migrants, 587 (84.6%) were from Oromia, followed by 51 (7.3%) from Amhara, and 44 (6.3%) from South Nations and Nationalities peoples (SNNPs) Region. The remaining small number of participants, i.e., 12 (1.7%) were from other regions that include Gembela, Benishangul Gumuz and Tigray including the two chartered cities (Addis Ababa and Dire Dawa.

The demographic characteristics of the respondents are displayed in Table 1. Among the participants, 418 (60.2%) were males and 276 (39.8%) were females. In terms of age, 393 (56.6%) were in the age range between 25 and 30 years, 263 (37.9%) from 19 to 24 years, and the remaining 58 (5.5%) were in the age group between 15 and 18 years. In terms of educational qualification, a large portion of the participants, i.e., 245 (35.3%) attended secondary school (grade 9–10), followed by 184 (26.5%) with preparatory level of education (grade 11–12) and technical and vocal education level. Those who have attended the second cycle of primary education (grade 5–8) and university degrees were 128 (18.4%) and 104 (15%), respectively. With respect to marital status, the majority of the participants, i.e., 444 (64%) were single/unmarried, and married participants, i.e., 235 (33.9%).

Table 1.

Demographic characteristics of respondents.

Categories		Number	%
Sex	Male	418	60.2
	Female	276	39.8
	Total	694	100
Age in year	15–18	38	5.5
	19–24	263	37.9
	25–30	393	56.6
	Total	694	100
Educational Level	Informal education	8	1.2
	Primary I (G1-4)	25	3.6
	Primary II (G 5–8)	128	18.4
	Secondary (G 9–10)	245	35.3
	Prep & TVETs	184	26.5
	BA/Bsc & above	104	15.0
	Total	694	100
Marital status	Unmarried	444	64.0
	Married	235	33.9
	Divorced	13	1.9
	Widowed	2	0.3
	Total	694	100

The participants’ zones of origin are presented in Table 2. A large number of participants, i.e., 276 (39.8%) migrated from Arsi Zones (Arsi & West Arsi), followed by 201 (39.4%) came from Shoa Zones (East, North & West Shoa of Oromia), where the study sites are located. The remaining 54 (7.8%) migrants were from Wellega (East and West Wellega, Horoguduru Wellega and Qellam Wellega).

Table 2.

Respondents’ zones of origin.

SN	Zonal administration	Number	%
1	Arsi and West Arsi	276	39.8
2	Shoa (East, North & West Shoa of Oromia)	201	29,0
3	Wellega (all zones)	54	7.8
4	Jimma	25	3.6
5	Hararge (East & West Hararge)	22	3.2
4	Other Zones scattered in different regions	116	16.7
	Total	694	100

The survey was meant to identify the underlying factors responsible for participants’ influx to study urban sites. The results of the survey are presented in Table 3. Accordingly, the study revealed that the following factors are the major drivers of youth migrants to urban sites: (i) lack of employment opportunities in their localities, (ii) seeking better job opportunities in destination areas, (iii) wide prospects of participation in informal economy, (iv) pursuit of better livelihood, (v) search for higher education/training institutions, and (vi) attractions by industries in urban areas. These factors are equally important irrespective of gender as shown in Table 3. In other words, the factors are sequentially important for both male and female participants in similar fashion.

Table 3.

Factors of youth migration.

	Major migration factors	Total frequency	Male frequency	Female frequency
1	Lack of job in migrants' localities	475	174	233
2	Better job opportunities in urban area	227	100	177
3	Opportunities in informal economy in urban areas	114	49	65
4	Search for better livelihood	98	45	53
5	Search for higher education/training institutions	84	36	48
6	Availabilities of industries in urban area	82	34	48

The study also examined the post-migration situations of youth migrants. The challenges migrants faced in the urban destination areas are presented in Table 4. Accordingly, the major and persistent challenges reported include: high living cost, housing problem, and inability to find job. The challenges are more or less similar across genders. Besides, it was found out that the vast majority of respondents (77%) were not registered and 75% of them did not get any training in destination towns/cities.

Table 4.

Persistent challenges migrants faced at destination sites.

SN	Persistent challenges	Total frequency	Male frequency	Female frequency
1	High living cost	462	192	270
2	Housing problem	348	129	219
3	Inability to get job	261	106	155
4	Foodstuff shortage	48	19	29
5	Utility (water & electricity) shortage	40	14	26
6	Cultural conflict	16	5	11
7	Shortage of social services (health & education)	13	6	7

4.2. Inferential analysis

The bivariate correlations, means, standard deviations and measures of internal consistency among the variables considered in investigating factors influencing indicators of wellbeing (perceived subjective wellbeing and monthly income) are presented in Table 5. The bivariate correlations are indicated by Pearson's product momentum correlation coefficient, while measures of internal consistency of scales are shown by Cronbach alpha (α). Among background factors, weak negative relations between sex and age, r (692) = -0.192, P < .01 and between sex and income, r (692) = −0.112, P < .05 were obtained. Besides, weak positive correlations between sex and ‘who decided’, r (680) = 0.098, P < .05 and subjective wellbeing measure, r (692) = 0.075, P < .05 were obtained, while sex relations with other demographic factors, migration related factors, and proactive behaviour failed to reach a significance level. Age was positively correlated with marital status, r (677) = 0.327, P < .01, education level, r (692) = 0.202, P < .01, duration, r (679) = 0.261, P < .01, income, r (692) = 0.190, P < .01, migration history, r (692) = 0.078, P < .05, support level, r (385) = 0.114, P < .05, and subjective wellbeing r (694) = 0.091, P < .05, but it was negatively correlated with ‘who decided’, r (680) = = −120, P < .05.

Table 5.

Bivariate correlation, mean (M), standard deviation (SD), and internal consistencies (Cronbach's α) of the study variables.

	M	SD	Α value	Sex	Age	MSt	EdL	BP	WhD	Du	MH		SL	PC	SWB	INC
Sex	.4	.49		1
Age	2.51	.60		−.192** (692)	1
MSt	1.38	.54		.043	.327** (677)	1
EdL	3.27	1.11		−.007	.202** (692)	.065	1
BP				.021	.032	.066	.084*	1
WhD				.098* (680)	−.120* (680)	−.071	−.027	.006	1
Du				−062	.261** (679)	.152* (664)	.064	.010	.067	1
MH				−.014	.078* (692)	.053	.061	.026	.110* (680)	.388	1
SL	6.83	1.69	.72	.016	.114* (385)	−.015	.102* (385)	.075	.016	.091	−.040		1
PC	47.36	5.46	.71	−.025	.060	.049	.053	.051	−.078* (680)	.036	.017		.176**	1
SWB	52.27	10.31	.77	.075* (692)	.091* (692)	−.006	.077* (692)	.10** (692)	−.029	−.061	−05		.484** (385)	.286** (692)	1
INC	2451.89	2110.4		−.112** (692)	.190** (692)	.093* (692)	.237** (692)	.021	.018	.132* (679)	.094* (692)		.096	.135** (692)	.051	1

Notes: *p < .05, **p < .01 (two tailed).

The coding scheme was as follows: Sex: 0 = male, 1 = female; Age: 1 = 15–18, 2 = 19–24, 3 = 25–30; Marital status (MSt): 1 = single, 2 = married; Educational Level(EdL): 0 = informal education,1 = Grade 1–4, 2 = grade 5–8, 3 = Grade 9–10, 4 = grade 11–12 & Technical and vocational education (TVET) level,5 = first degree holders & above; M = Mean; SD= Standard deviation; Birthplace (Oromia region = 1& other region = 0); WhD- Who decided(self = 1& others = 2); Du-duration; MH- migration history(those his history = 1; others = 0) SL- Support level; PC= Proactive coping; SWB= Subjective Wellbeing; INC= Income (monthly).

Figures in parenthesis are degrees of freedom.

Marital status relationships reach significance levels only with duration, r (664) = 0.152, P < .05 and income, r (692) = 0.093, P < .05. Educational level was strongly positively correlated with income, r (692) = 0.237, P < .01, while its relation with support level, r (385) = 0.102, P < .01, birthplace, r (692) = 0.084, P < .05, and subjective wellbeing, r (692) = 0.077, P < .05 was positive although weak.

Among migration-related factors, birthplace was positively correlated with only subjective wellbeing, r (692) = 0.10, P <. 01; however, its relation with other variables of the study failed to reach significance level. ‘Who decided’ was positively correlated with migration history, r (680) = 0.110, P < .01 and negatively with proactive coping, r (682) = .-0.078, P < .05. Duration and migration history were weakly positively correlated with income, r (679) = 0.132, P < .05; r (692) = 0.094, P < .05, respectively, while their relation with other variables failed to reach the level of significance. Level of support was strongly correlated with subjective wellbeing, r (385) = 0.484, P < .01 and proactive coping, r (385) = 0.176, P < .01.

With respect to intentional activities, strong positive correlations were observed between proactive and subjective wellbeing, r (692) = 0.286, P < .01 and between proactive and income, r (692) = 0.135, P < .01). Relationship between subjective wellbeing index and income, proxy of economic wellbeing failed to reach the level of significance.

Concerning measures of internal consistency, all utilized scales: measure of support level, proactive coping, and subjective wellbeing index demonstrated traditional acceptable internal reliability levels (α ranged from .71 to .77).

4.3. Determinants of wellbeing

In order to identify factors influencing the wellbeing of youth migrants, multiple regression analyses were conducted. Both indictors of wellbeing: income (proxy of economic wellbeing) and ‘perceived subjective wellbeing’ were regressed on demographic factors, migration-related factors and intentional activities separately. As shown in Table 6, a small but significant amount of variability in participants' income (R² = 0.12, P < .01) was explained by demographic factors, migration-related factors and intentional activities taken together. However, when standardized path coefficient estimates were considered, only the path effects of sex (β = - 0.129, P <. 05), education level (β = 0.203, P <. 01), and proactive behaviour (β = 0.136, P <. 01) were found significant.

Table 6.

Regression results for predicting measures of wellbeing from demographic factors, migration-related factors and indicators of intentional activities.

Variables	Income	Subjective wellbeing
Sex	−.129*	−.021
Age	.069	.064
Marital status	.068	−.060
Educational level	.203**	.078
Birth region	.048	.073
Migration history	.039	.012
‘Who decided’	−.002	−.014
Duration	−.018	−.094
Support level	.050	.455**
Proactive coping behaviour	.136**	.207**
R2 =	.12	.32
F	(10,358) = 4.945**	(10,358) = 16.88**

Notes: **P < .01; *P < .05 (two tailed).

Multiple linear regression was also calculated to predict the amount of variability in participants' subjective wellbeing based on demographic factors, migration-related factors and subsumed intentional activities. The results are depicted in Table 6. A significant regression equation was found (F _(10,358) = 16.88, P < .01) with R² of 0.32. This means that, taken together, all factors considered in the study explained about 32% of variability in respondents’ subjective wellbeing. However, when the standardized path coefficient is taken into account, only the influences of support level (β = 0.455, P < .01) and proactive coping behaviour (β = 0.207, P < .01) were found significant. The influences of all demographic factors and migration -related factors failed to reach significance levels.

5. Discussion and conclusion

The main purpose of the current study was to examine the factors that lead to the influx of young people to the urban centres along an important economic corridor of Ethiopia and analyse determinants of migrants' wellbeing at destination sites. In investigating influences of migrants’ economic and subjective wellbeing, circumstantial factors consisting of demographic and migration -related factors and intentional activities that comprise level of proactive coping behaviour and support received from significant others were considered.

The survey results showed that the dominant share of youth migrants to the study sites is from the nearby zone administrations (Arsi & Shoa zones). In other words, the stream of youth migrants to the urban areas under consideration is dominated by short -distance migrants. Most of them are over 18 years old, single and have secondary education and above. Factors such as the ability to maintain family ties, availability of social network in vicinal urban areas and better idea about opportunities in nearby towns/cities may encourage migrants to opt for short distance migration. Besides, being educated and single (fewer burdens with family responsibilities) may well fuel aspirations for migration.

The shortage of jobs in their locality on the one hand, and the availability of better job opportunities in cities/towns, prospect of participation in the informal economy, the search for a better life in urban areas, the search for higher education/training institutions and the presence of industries in urban areas, on the other hand, are salient drivers of youth migration to urban centres. The findings are consistent with other studies [e.g. Refs. [20,21]] conducted in other African countries. As in the case of some of African contexts, lack of job in non-farm sectors and social amenities in rural areas are push factors, while better opportunities for job or participation in informal economy and access to social services are seen as pull factors for domestic youth migration in the study sites.

Youth migrants are also faced with a number of challenges at destination urban centres. In connection with this, the results of the study elucidated that high living costs, housing problem, inability to find decent job and lack of registration and support system at the end of their journey are the major obstacles for this cohort of population.

With respect to migrants' wellbeing, the results of the study revealed that a small but significant percentage of variability in economic wellbeing (income) is explained by demographic factors, migration-related factors and intentional activities taken together. From these factors, sex, educational level and proactive coping behaviour are found to be important in influencing income of youth migrants. However, for subjective wellbeing, a fairly large amount of its variability is explained by demographic factors, migration-related factors, and intentional activities. Nonetheless, when their contributions are delineated, only indicators of intentional activities (support level from significant others & proactive copping behaviour) are found to have significant influence on participants' subjective wellbeing. This means that all demographic factors (sex, age, marital status & educational level) and demographic-related factors (birth region, migration history ‘who decided’ & duration of stay) failed to influence participants' subjective wellbeing. Similar to previous studies (e.g. Refs. [13,28]], the current study corroborated the importance of intentional activities such as support received from significant others and proactive coping behaviour of a person. Specifically, the influence of proactive coping behaviour on both indicators of wellbeing (economic & subjective wellbeing) signifies the role of being proactive, that is, striving for improvement in one's life and environment instead of primarily reacting to a past or anticipated adversity. Being endowed with cognitions and behaviour of autonomous goal setting with self-regulatory goal attainment seems to be a key psychological trait for wellbeing among youth domestic migrants.

Like most African countries, Ethiopia is experiencing rapid urbanization. Though the country is predominantly an agrarian society, urban population is growing rapidly. Besides natural population growth and reclassification of areas, rural –urban/urban-urban migration seems to play a crucial role in the country's rapid urbanization phenomenon. The young population comprises a considerable share of domestic migration, a growing and prevailing scenario posing challenges as well as opportunities for sustainable development. As the wide education coverage the country has attained over the years enables the youth to become more educated, they are more motivated to flow into urban centres. The nature and welfare consequences of youth migration appear distinct from those of other age groups as the migration event overlaps with many other psychosocial transitions the youth are supposed to undergo. In addition, for this cohort of population, migration adds another layer of complexity to the conditions of entry into the world of adulthood. These young people also arrive at urban destinations with a lot of susceptibility, low experience, and low financial and social capital but with high aspiration for improved life.

The results of the present study elucidated that the interplay of ‘push’ factors (factors associated with the area of origin) and ‘pull’ factors (factors associated with the area of destination) is the driving factor behind the influx of youth to the urban areas under consideration. This seems require intervention at both ends. These youth migrants have also been challenged by high living cost, housing problem, and inability to find decent jobs, problems which seem to be further accentuated by their mere presence in the urban areas. Besides, proactive coping behaviour is found to be an important determinant of both wellbeing indicators (income & perceived subjective wellbeing), while some demographic factors such as educational level attained and sex are important correlates of income. Meanwhile, support received from others is linked with perceived subjective wellbeing. These need to be considered during interventions.

In view of the above conclusions, the researchers propose the following recommendations.

• •The actual or perceived difference between rural-urban areas in terms of opportunities, incomes and social services appear to be the main cause for youth migration. Thus, balanced growth through viable regional planning strategies is recommended for the mutual development of the towns and their hinterlands at different hierarchical scales in order to check or slow down the outflow of rural young people. .
• •Zone administrations, specifically of those zones which proximate with important urban centres, need to diversify economic opportunities in farm and non-farm sectors so that job opportunities are widened for educated youth in their localities.
• •A policy must be put into place as a mechanism to track the influx of youth migrants into urban centres and to increase the preparedness of urban administrations for the rural-urban migration.
• •To take advantage of the opportunities that the youth migrants bring with them and to address consequential pressure, city administrations need to be proactive, launch some form of registration and support system for youth migrants, and organize training and other viable activities to enhance the coping skills of their migrants and improve their behaviours.

5.1. Limitations of the study and implications for future research

In spite of its substantive theoretical and practical inferences, this study has certain drawbacks. First and foremost, the cross-section survey research design used in the study, particularly to examine predictors of participants' wellbeing, did not explain their causality rather as suggested association among the variables. This means that the determinants of the wellbeing model utilized in the current study should not be perceived/interpreted as casual relationships, but as links that suggest casual ordering which need to be confirmed by longitudinal research. Secondly, ‘material wellbeing’ is narrowly operationalized considering only monthly income. Things such as expenditure/consumption and wealth were not taken into account in delineating material wellbeing. Thus, for the future research, the researchers suggest a longitudinal research design and a broader measure of wellbeing.

Author contribution statement

Habtamu Kebu Gemeda; Oumer Beriso Metaksa; Mesay Mulugeta: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability statement

Data will be made available on request.

Declaration of interest's statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.