Do Education and Employment Play a Role in Youth’s Poverty Alleviation? Evidence from Morocco

Abstract

In Morocco, youth represent a large segment of society, but there are multiple structural constraints, such as unemployment, particularly among graduates, which exposes them to a great risk of poverty. Using data from the Household Consumption and Expenditure National Survey 2014, this article explores the determinants of youth poverty, focusing on the role of education and employment. Two indicators of poverty are used. The first one is a binary short-term indicator showing whether the young in a given household are poor or not at the threshold of 60% of the median annual expenditure. The second is a poverty measure of the long-term economic status or what is called the Wealth Index, computed using the Principal Component Analysis method. The results from both the logit and the quantile regressions show that being more educated constitutes a rampart against poverty for young people. By contrast, access to employment is not enough to guarantee a decent level of well-being. Moreover, there are no gender differences, but poverty seems higher among rural young and those between 15 and 19 years old compared to those who reside in the urban areas and who are between 20 and 29 years old, respectively. Youth poverty is also significantly associated with family/household characteristics such as education and employment of the other members and household size.

1. Introduction

If at any age, people can be affected by poverty and vulnerability, young people are more at-risk [1]. Youth poverty is therefore a serious global problem, especially given the number of young people and children living in absolute poverty in developing countries [2]. Moreover, youth is an important and critical transition stage towards adulthood, during which time individuals are supposed prepare in the best possible conditions for adulthood [3].

In Morocco, adolescents and young adults aged 15–29 years continue to constitute a significant segment of the population and face major challenges, as is the case for most countries in the MENA Region. Different diagnostic reports show that poor quality education, high rates of unemployment, costs of living and reduced disposable income are the main concerns of Moroccan youth, as indicated by themselves [4,5]. Although the issue of social and economic inclusion of Moroccan youth is still relevant, empirical evidence on youth poverty is very limited, mainly in comparison with other demographic groups. This paper aims to contribute to a better understanding of youth poverty determinants by investigating the role of education and employment in poverty alleviation. The results will be used to propose appropriate policy recommendations that should help to ensure sustainable youth well-being.

The role of employment in poverty reduction is evidenced by the adoption of the Global Employment Agenda of the ILO in 2003, with a large acceptance of the idea that employment and labor market issues are a decisive, although neglected, link between economic growth and poverty reduction [6]. In this context, special attention is paid to better integrating youth into the economy, as this group in particular faces specific barriers as their unemployment rate is significantly higher and their employment and working conditions are worse than those of their elders [7].

Regarding education, a negative relationship exists between the enrollment rate and several poverty indicators [8,9]. The key role of education has been underlined by Pavis et al. [10], who state that “simply finding a job is not enough to avoid social exclusion: even if they (youths) find a job, those with a low level of education can get stuck in low-paying jobs”. For this purpose, there are two well-established theories on the relationship between education and job assignment, namely human capital and queuing theories [11]. Both assume that education functions as a positional good in the labor market given that its value is relative to the educational attainment of other job seekers [12] and that educational achievement is the main factor that determines the ranking of applicants in the labor queue [13]. Hence, education can be the solution to decent work for youth in developing economies [14], even if this can lead to the phenomenon of overeducation [12].

Given these considerations, the research question investigated in this study is whether/how youth poverty can be alleviated through education and employment? We focus on the hypothesis that young people who are educated and have a job are less at risk of being poor. Additionally, education and employment of other adult household members can provide a safety net against youth poverty, as people living together tend to share the same standard of life.

The rest of this paper is structured as follows. Section 2 presents a review of the relevant literature, discusses the main poverty approaches and youth socio-economic characteristics that may be associated with being at risk of poverty, and gives an overview of the situation of youth in the Moroccan context. The data and the variables used are then outlined in Section 3, while Section 4 presents the results of the econometric models. The results of the analysis are discussed in Section 5, and Section 6 presents our conclusions and policy implications.

2. Literature Review

2.1. Theoretical Background

2.1.1. Poverty: Clarifying the Concept

The literature on the different conceptual approaches on poverty is extremely abundant and characterized by a very high level of ambiguity in its relation to economic theory [15]. This provides several ways of defining poverty, which leads to a different identification of the poor [16]. Considered globally as the opposite of well-being [17], poverty is generally defined as the lack of fulfillment of basic human needs [18] or of sufficient monetary/non-monetary resources to obtain an income or minimum consumption that allows the person to reproduce their livelihoods [19].

Hagenaars and de Vos [20] propose three types of definitions to apprehend poverty: absolute poverty, defined as a situation in which people are below a certain threshold, usually based on a basket of basic necessities; relative poverty, which considers poverty to be a situation in which resources are so low that one is excluded from the minimally acceptable way of life in the location one is living in; and subjective poverty, based on assessments of welfare obtained from opinions expressed by the target population. For his part, Sen [21] highlights the multidimensional nature of well-being and poverty. The Nobel Prize in Economic Sciences emphasizes the need to think of poverty beyond the monetary aspects alone, as the deprivation of basic capabilities.

While it is widely recognized that living standards are determined by a multitude of factors, monetary measures such as relative and absolute poverty remain popular. Relative poverty, which reflects a more income-distribution-oriented design, is the common approach used in Western Europe and at the (non-US) OECD and the most developed countries [22]. The poverty line is set at a constant proportion (typically around half) of a given distribution of income (the current mean, median, or some quintiles) and anyone whose income is below this threshold is considered as poor [23]. Unlike developed countries, which use income to measure poverty according to the monetary approach, consumption or expenditure data directly collected from households as a proxy of income are mostly used in developing countries, as expenditure is easier to track than income [24,25].

Recently, there has been a revival of interest in multidimensional and subjective well-being, although relative and absolute monetary approaches can still dominate poverty measurement [22]. Particularly, the multidimensional approach is now broadly recognized as important and a complement to monetary poverty. From this perspective, the Multidimensional Poverty Index (MPI) developed at Oxford University with the UNDP’s Human Development Report Office uses ten indicators to measure three critical dimensions of poverty at the individual level: education, health, and material living standards. The methodology underlying the MPI is based on Alkire and Foster [26] and offers a high degree of flexibility in the choice of indicators.

The majority of surveys do not collect data on the expenditures or income of households because this can be time-consuming and costly [27]. Moreover, sampling bias, under-reporting of income or expenditure, and difficulties in converting household products into money terms are also raised. For these reasons, an alternative tool for classifying households according to their socio-economic status was proposed for low- and middle-income countries. This method is an asset-based approach which allows a household-level composite index to be constructed called the Wealth Index. The indicator is calculated using easy-to-collect data on ownership of durable assets, housing characteristics, and access to services [28,29]. Data on these items are already collected in large-scale surveys and Population Census.

The Wealth Index is now widely used in the literature [30], notably using datasets from DHS surveys [25,28,29,31,32,33]. Filmer and Pritchett [29] popularized the use of the statistical technique of principal component analysis (PCA) as an alternative to a simple sum of asset variables, which allows determining the weights in the overall index. Asset ownership indicates the long-term economic status of a household and is less dependent on short-term economic changes compared with other wealth or poverty measures [34]. PCA-based approaches for estimating wealth levels create a continuous variable which can be used in correlations or regression models. The higher the score of the index, the wealthier the household [35].

According to our available data, we use two poverty indicators in this paper: the relative monetary measure and the Wealth Index. The first one is a binary short-term indicator showing whether the young in each household are poor or not at the threshold of 60% of the median annual expenditure. The second is a poverty measure of the long-term economic status.

2.1.2. Determinants of Youth Poverty

Narayan and Petesch [36] argue that certainly there are differences in the way poverty is experienced by groups and in different countries but there are also noticeable similarities in these experiences. Limited resources force poor people to think in terms of very short time horizons and to make agonizing choices between feeding expenditures and schooling or obtaining medical treatment if needed. Therefore, it is crucial to identify in each context the major determinants of poverty, especially among the young, to be able to target the groups most at risk.

Dewilde and Raeymaeckers [37] found that all individual and collective factors such as education level, economic activity, age, family structure, and household size have a substantial, unidirectional, and significant effect on the risks of being poor. In addition to the protective effect of living in the family of origin mentioned by Iakōvou and Berthoud [38], families can continue to support their young people even when they leave home.

The role of institutional indicators and economic context as major determinants of poverty among young people is widely recognized [1,38,39]. Being employed, having a work partner, and living with the family of origin are the main factors that protect youth from falling into poverty [38]. Braziene and Merkys [1] affirm that high participation in the labor market rates and strong professional career growth are factors contributing to the economic independence and material wealth of individuals. Clearly, factors linked to human capital and the labor market, such as low education, unemployment and low wages, are the mean risk factors for poverty among young people [39].

A comparative study among youths in Egypt, Ethiopia, Jordan, and Tunisia conducted by Hlasny and AlAzzawi [40] revealed significant wealth gaps across urban-rural and educated-uneducated divides and found that parental education and the father’s occupation are important determinants of labor market outcomes and vulnerability. From selected countries in the MENA Region, Ozdamar and Giovanis [41] highlight that the dwelling characteristics and education contribute mostly to the youth’s multidimensional poverty, followed by health and employment. The authors argue that tackling the crucial issue of youth poverty remains one of the main challenges with respect to poverty reduction, especially in developing and underdeveloped countries. According to OCDE [42], poor quality education and high rates of unemployment remain the most important youth challenges in Morocco. Boudarbat and Ajbilou [43] believe there are patterns or drivers which reinforce youth economic exclusion in Morocco, including poor macro-economic performance, rapid urbanization, persistent poverty, poorly performing labor markets, and family dynamics. According to the World Bank [5], income deprivation keeps Moroccan youths in their parental homes, which is considered as protection against the risk of poverty. Other risk factors are the link between economic activity and the composition of the household and residential independence.

2.2. Empirical Background: Youth in the Moroccan Context

2.2.1. Moroccan Population Remains Very Young

Morocco is one of the pioneer countries in terms of demographic transition, both in Africa and in the Arab world [44]. Despite the spectacular decline in fertility, which has now reached the replacement level of 2.2 children per woman, adolescents and young adults aged 15–29 years will continue to constitute an significant segment of the population. The evolution in the shape of the pyramid from the end of the 1990s (Figure A1) indicates that their weight is continuously decreasing, going from 28.5% in 1994 to 26.3% in 2014, and it is expected to continue decreasing to reach 23.8% in 2024 and 19% in 2050, according to the latest demographic projections. However, their headcount continues to increase, from 2.6 million in 1960, to nearly 9 million in 2014. Moreover, data from the census conducted in 2014 show that a large proportion (82.9%) of young people are single (96.1% among males and 69.7% for females), which is more pronounced in cities than in rural areas (

Table A1. Distribution (%) of young people aged 15–29 years by marital status, gender, and residence area, Morocco, 2014.

Area of Residence			Gender		Total
			Male	Female
Urban	Marital status	Single	97.1	74.1	85.5
		Married	2.8	24.9	14.0
		Divorced	0.1	0.9	0.5
		Widowed	0.0	0.1	0.1
	Total		100.0	100.0	100.0
Rural	Marital status	Single	94.7	63.1	79.1
		Married	5.2	35.7	20.3
		Divorced	0.0	1.0	0.5
		Widowed	0.0	0.2	0.1
	Total		100.0	100.0	100.0
Total	Marital status	Single	96.1	69.7	82.9
		Married	3.8	29.2	16.6
		Divorced	0.1	0.9	0.5
		Widowed	0.0	0.1	0.1
	Total		100.0	100.0	100.0

Note: Authors, data from RGPH 2014 (www.hcp.ma) (accessed on 10 July 2022).

). Relative to marital status, the mean age of marriage was 28.5 years in 2014 (31.3 among males versus 25.7 for females), while it was 17 years in 1960.

For Morocco, this “youth bulge” is a demographic “gift” with enormous potential, along with several challenges. The World Bank [5] (p. vii) argues that “Young people can be engines of growth, as the source of innovation, productivity and consumption, to help achieving the demographic dividend. Yet, they need open and vibrant economies that provide plenty of opportunities into which their energies can be channeled”. The Integrated National Strategy of youth specifies that this demographic situation is both an opportunity and a challenge and integrating young people as a key factor of economic and social development requires the mobilization of significant human, material and financial resources [45]. However, besides that, the current cohort of young people in Morocco, as in the World, is the largest ever seen and this can be a threat to internal stability [46,47] by becoming an element of domestic conflict, political tension and uprisings rather than a positive contribution [48], specifically among urban and undereducated populations [49].

2.2.2. A Noticeable Decline in Youth Poverty versus an Increasing Gap with the National Level

Since Morocco’s independence, poverty has been marked by a general downward trend, while remaining at relatively high levels in rural areas. As can be seen from Figure 1, the incidence of poverty at the national level fell from 21.0% to 4.8% between 1985 and 2014 [50]. At the same time, youth poverty decreased from 24% to 10.2%; that is, in households with a per capita annual household expenditure that falls below the national poverty line. The gap concerning the national level is on an upward trend: 2.1 points in 2001 against 5.4 points in 2014. In fact, Morocco experienced a period of rapid economic growth between 1980 and 2010, during which Gross Domestic Product growth averaged 4%. This growth has been accompanied by several encouraging trends, such as increased integration with the global economy, increased levels of investment, decreased dependence on agriculture, and a fall in unemployment. Morocco has also achieved substantial gains in education in recent times, with broad access to basic education and improvements in the number of people attaining higher levels of education [5]. However, this gap suggests that the young population does not benefit from the fruits of growth in the same way as the total population.

2.2.3. Education Performance

An analysis of education enrollment showed that educational attainment among young people is noticeably higher than that of other adults. As reflected in Figure 2, while 41% of the population did not have any formal diploma in 2014, only 14.2% of young people are currently suffering such a situation. On the other hand, 27.1% of young people have completed the lower secondary cycle (10.2% for adults), 24.2% have completed the upper secondary cycle (8.8% for adults) and 10.6% have a higher level (5.7% for adults). By gender, 19.4% of young women have no training certificate compared to 8.7% for young men. For tertiary education, 11.1% of young women have a higher level against 10% for their male counterparts.

2.2.4. Young People More Exposed to Unemployment, Especially Highly Qualified Ones

According to the HCP and World Bank [51], the Moroccan labor market is characterized by slow job growth and low quality of employment generated. For example, the volume of jobs created by the informal sector amounts to 28.7% annually. As a result, the productivity remains low, and workers lack sufficient mechanisms for social protection. However, the weakness is the limited inclusion, since youth and women are less integrated into the labor market.

Typically, the discussion on economic exclusion in Morocco has focused on youth unemployment and, more specifically, on women and graduates [5,43]. The participation rate of young people is marked by a noticeable decline. Indeed, at the national level, the participation rate of this category fell from 38.9% in 1999 to 35% in 2014, while the total population participation rate fell from 50.6% to 42.3% during the same period. By gender, the participation rate of young women aged 15–24 was 15.4% in 2014, down sharply from its level in 1999, which was 29.5%; for all women, it was reduced from 30.4% to 22.6% between 1999 and 2014. Young men have experienced the same downward trend; the participation rate fell from 66.8% in 1999 to 59,4% in 2014, while, overall, it fell from 79.3% in 1999 to 69.5% in 2014 (Figure 3).

This significant drop among the 15–24 age group is explained, among other things, by the decline in activity in general, due to younger people spending longer in education (the number of youth staying in school has more than doubled). This last observation should, however, be put into perspective. As indicated by the HCP, around 27.9% of Moroccans 15–24 years old are not working or investing in their future through training. These young people who are not in employment, education, or training, which is also referred to as the Youth “NEET” phenomenon, show very striking differences in terms of gender (11.4% among men and 45.1% among women are NEETs). Thus, young people are still largely excluded from the domestic economy

The labor force in Morocco is marked by a high unemployment rate but youths are more affected. In 2014, the unemployment rate of the total active population was 9.9%; this rate is 13.9% among 25–34 and 20.1% among young people aged 15–24. By gender, the unemployment rate for young women was 19.1% in 2014 against 20.3% for young men.

Table 1. Distribution of 15–24 years by type of activity and diploma, by sex, Morocco,2014.

	Without Diploma			Medium Diploma			Higher Diploma			Total
	Male	Female	Total	Male	Female	Total	Male	Female	Total	Male	Female	Total
Active	88.4	23.8	50.5	48.6	13.3	33.5	46.1	35	40.2	59.4	14.4	35.1
Inactive	11.6	76.2	49.5	51.4	86.7	66.5	53.9	65	59.8	40.6	85.6	64.9
Total	100	100	100	100	100	100	100	100	100	100	100	100
Active
Unemployed	10.1	5.8	8.9	23.2	28.5	24.1	40.1	51.3	45.3	18.4	20.9	19
Permanent employee	27.5	14.5	23.8	31.9	35.3	32.5	45.9	46.7	46.3	30.9	26.5	29.8
Occasional employee	10.2	1.4	7.7	7.4	1.1	6.3	1	−0.3	0.4	8.2	1	6.3
Self-employed	15.8	6.5	13.2	15.1	5.5	13.4	9.3	1.6	5.7	15	5.3	12.5
Unpaid	36.4	71.8	46.4	22.4	29.6	23.7	3.7	0.7	2.3	27.5	46.3	32.4
Total	100	100	100	100	100	100	100	100	100	100	100	100

Note: Authors, data from National Labor Survey 2014 (www.hcp.ma) (accessed on 20 April 2022).

shows the structure of young people in the labor force according to the situation in the profession. It can be noted that most (51.4%) of them are either unemployed (19%) or in an unpaid job (32.4%). Young employees represent 36.1% of the workforce, including 29.8% of permanent employees. Self-employment among young workers is 12.5%. By gender, while wage employment is the main status in the profession for young working men with 39.1%, unpaid employment ranks first for young working women (46.3%).

Unpaid employment declines as qualifications increase. Almost half (46.4%) of young workers without a diploma are in unpaid work. This rate declines to 23.7% for young people with medium qualifications and only 2.3% for young people with a higher qualification. The latter are the most affected by unemployment, with a rate of 45.3%. The unemployment situation for higher education graduates is much more alarming for young women as half of them (51.3%) are looking for work. Hence, in Morocco, the structural nature of youth unemployment is observed.

The economic participation of young people is characterized by a strong mismatch between education and employment, and weak social protection. Moreover, while the informal sector accounts for nearly one-third of the Moroccan economy and employs more than one-third of the workforce in the non-agricultural sectors, the under 35 years age group continues to account for a very large share of this sector (38%).

3. Data and Methods

Before we begin our investigation, we need to define what a young person is. There is no international consensus about defining the concept of “youth” and, in fact, it depends on several cultural, economic, and political factors, which explains the obvious disparities in this regard between different regions and within the same country [52]. At the international level, the 15–24 age group is used as the standard definition of youth. Indeed, for statistical purposes, United Nations defines youth, as “those persons between the ages of 15 and 24 years, without prejudice to other definitions by the Member States” [53] (p. 1). In Morocco, the HCP uses the UN definition of youth (15–24), while the Ministry of Youth and Sports adopts the 15–29 group.

This means that a definition of youth, which ends at the mid-twenties, fails to include large numbers of people who have not completed many (or, indeed, any) of the transitions to adulthood [54]. Because of this, studies which conceptualize youth as a process of transition often use the extended definition. One of the very few existing studies devoted specifically to youth poverty also adopts a higher upper age limit: in this case, 29 years [55]. Moreover, this extended definition of youth has already been used by the MYS from the first Consultation National Youth in 2001 and adopted by the report “Promoting Opportunities and Youth Participation” carried out in 2012. In our study, we choose to adopt the definition according to which a young person is any individual in the age group of 15–29 years old.

3.1. Data

For the empirical analysis, we use cross-sectional data from the most recent Household Consumption and Expenditure National Survey (HCENS) conducted by the High Commission for Planning (HCP) in 2014. This survey is a nationally representative survey of 15,970 households. A two-stage stratified random sampling technique was followed in drawing samples of HCENS 2014 under the master sample developed based on the Population and Housing Census 2004. In total, 75,691 members were investigated, including 19,695 young men and women (26%) in the 15–29 years age group. Basic information about all household members was collected. The information includes age, gender, education, occupation, economic activity and employment status, as well as housing conditions (water, electricity, sanitation, assets possession). This database also provides pieces of information on household expenditures, as well as absolute and relative monetary poverty status depending on its level of income, approached by food and non-food expenditures. In particular, the data allow classifying each household as poor or non-poor regarding the 60% of the median poverty line, which corresponds to 8071 Moroccan dirhams annually. Moreover, the microdata source is the most recent survey on household expenditure and consumption available in Morocco but does not include information about the household’s income.

3.2. Method

As discussed earlier, there is unanimity about the multidimensional nature of poverty, including employment and education as key factors. However, since our study aims to identify the impact of these variables on youth poverty, we choose two indicators of well-being/poverty that do not include education and employment in their construction to avoid endogeneity issues. The first one is the monetary relative poverty status, which reveals whether the household is poor or not according to the monetary approach. The second one is the household’s Wealth Index (hereafter WI) that we measure using the method proposed by [28,29]. The Wealth Index is an indicator with continuous distribution which can be used in correlations or regression models. It summarizes multi-dimensional information on housing characteristics and asset possession. The two indicators are measured at the household level. Since our analysis is based on individuals, we attribute the poor/non-poor status and the score of the Wealth Index observed at the household level to any household’s young member.

3.3. Dependent Variables

The first indicator is a binary variable based on the standard 60% relative poverty line, which is provided in the database as assessed by HCP (2014). It considers as “poor” (value = 1) young people who live in a household with per capita expenditure lower than the threshold (corresponding to 60% median expenditure), and “non-poor” (value = 0) otherwise. This captures a short-time situation of well-being/poverty, since it is based on the annual expenditure of households deflated by the household size.

The second indicator is the WI, which is based on household living standards using data on the household’s ownership of selected assets, such as televisions and bicycles, materials used for housing construction, and types of water and energy access and sanitation facilities. The higher the score of the index, the wealthier the household [28,29,35].

The WI is constructed using Principal Components Analysis (PCA), a technique for summarizing the information contained in many variables to a smaller number by creating a set of mutually uncorrelated components of the data. Filmer and Pritchett [29] argued that the first principal component explains the larger proportion of the total variance and is a good proxy to represent the wealth of the household. To create a WI in Morocco, we select and test 26 variables from the household dataset (

Table 2. List of variables used in PCA analysis.

Assets and Durable Goods	Household Utilities and Other	Housing Properties
- Has satellite dish - Has computer - Has television - Has Cooker - Has internet - Has refrigerator - Has mobile - Has Motorbike - Has car - Has telephone	- Water network - Electricity network - Kitchen - Toilet - Shower bath - Wastewater evacuation network - Water sources - Public network - Fountain - Well water	- House owner - Free house - House tenant - Hard housing - Hard floor - Room per person

Note: Authors, data from HCENS 2014 (www.hcp.ma) (accessed on 20 April 2022).

).

As the creation of the WI is an iterative process and to obtain the best results, we conducted a few rounds of PCA including or excluding certain variables based on the factor coefficient scores in the PCA outputs: KMO test (>0.6), Bartlett’s test (p-value < 0.001), variance explained by the first latent variable (Hjelm et al., 2017 [35]) and Cronbach’s Alpha (>0.7). The best result is based on 13 variables, with a first component explaining 30.5% of total inertia and Cronbach’s Alpha equal to 0.804. These variables are satellite dish, telephone, computer, internet, car, well water, electricity network, house owner, wastewater evacuation network, toilet, shower bath, hard housing, and hard floor. The scores are summed by household, and individuals (youths) are ranked according to the total score of the household in which they reside.

3.4. Independent Variables

The specific details of each explanatory variable used in the regression models are provided in

Table 3. List of explanatory variables for regression models.

Variable	Description	Code or Unit
Individual characteristics
Education level	The highest level attained by the individual	None = 1; Primary = 2; Lower secondary = 3; Upper secondary = 4; Higher = 5
Employment status	The status of the individual in the activity, it provides information on whether the individual is active occupied or not	No = 0; Yes = 1
Gender	Individual’s gender	Male = 1; Female = 2
Age	Age groups calculated in completed years (five-year age group)	15–19 = 1; 20–24 = 2; 25–29 = 3
Characteristics of the household
Education rate	Rate of persons in the household, other than the individual, with secondary education	Continuous variable
Employment rate	Rate of persons in the household, other than the individual, in a state of activity	Continuous variable
Location region	Individual’s residence area	Urban = 1; Rural = 2
Number of children	The number of children under the age of 15, who usually resides in the household	Discrete numerical variable
Household size	Number of the household members	Categorical variable (Less than 4 = 1; 4 = 2; 5 = 3; 6 or more = 4)
Marital status of the head	The marital status of the household head	Single = 1; Widowed = 2; Divorced = 3; Married = 4

below. The characteristics describing the individual include gender (male/female), age, educational attainment level (none, primary, lower secondary, upper secondary, and higher), and employment status (employed or not). We added variables for the following characteristics of the household structure: employment rate of other adult household members, secondary level attainment rate of other adult household members, household size, the number of children, the marital status of the head (single, widowed, divorced, and married), and the location region of the household (urban and rural).

3.5. Statistical Method

Two models, using similar independent variables, are estimated to examine the impact of education and employment on youth well-being. A logit model is used for the likelihood of a young person being monetary poor. The quantile regression model is established for WI to investigate the effects of the explanatory variables across the distribution (i.e., the quantiles) of youth well-being. To strengthen the latter model, the analysis of variance (ANOVA) was first conducted to determine whether there are any statistically significant differences between the WI mean among youths by different potential determinants.

For the first poverty indicator, Logit and Probit models, called binary regression models, are more appropriate and widely used [56]. One of the advantages of the binary models is eliminating the effect of atypical values in the distribution of the dependent variable [57]. As the focus in this paper is to identify the determinants of youth poverty, we use a logit model given by

$$Logit\left(p\right)=ln\frac{p}{1-p}={\alpha }_0+{\alpha }_1{X}_1+{\alpha }_2{X}_2+\dots +{\alpha }_{10}{X}_{10}$$

where p denotes the probability that the youth is poor; and $X_1,\dots ,X_{10}$ are the predictor variables. In applying the logit model in this paper, a value of 1 was assigned to poor youth (living in poor household), and a value of 0 to the contrary.

As our second poverty indicator WI is continuous, we chose the quantile regression model, a natural extension of the linear regression method, that allows the analysis of the effect of independent variables in the different quantiles of the distribution of the dependent variable [58,59]. The main advantage is the measure of relationships between variables outside of the mean of the data, making it useful in understanding outcomes that are non-normally distributed and that have nonlinear relationships with predictor variables [60]. Indeed, while a regression by the Ordinary Least Squares (OLS) method used in linear regression estimates how the independent variables are related to the average value of the dependent variable, quantile regression allows us to study the impact of the predictive variables’ average on its different quantiles [61]. This method avoids the use of constant parameters in the whole distribution [62] and can determine the existence of asymmetric effects in a household’s wellbeing [57].

The quantile regression model can be expressed as follows:

$$y_i={\alpha }_0{}^{\phi }+{\alpha }_i{}^{\phi }{x}_i+{\epsilon }_i{}^{\phi }$$

where $y_i$ is the dependent variable, $x_i$ is a vector of explanatory variables, ${\epsilon }_i$ is a disturbance term, and $\phi$ represents the quantile $(0<\phi <1$). As the quantile regression model requires a continuous variable as the dependent variable, $y_i$ is the WI, while the vector of explanatory variables $x_i$ are the same as in the logit model.

4. Results

4.1. Descriptive Statistics

In terms of relative monetary poverty, 18.3% of Moroccan young people are poor (

Table 4. Structure (%) of poverty status by area of residence and by gender, 2014.

Location Region			Poverty Status
			Non-Poor	Poor
Urban	Gender	Male	90.1%	9.9%
		Female	90.4%	9.6%
	Total		90.3%	9.7%
Rural	Gender	Male	69.0%	31.0%
		Female	67.2%	32.8%
	Total		68.1%	31.9%
Total	Gender	Male	82.1%	17.9%
		Female	81.3%	18.7%
	Total		81.7%	18.3%

Note: Authors. data from HCENS 2014 (www.hcp.ma) (accessed on 22 April 2022).

). By gender, the poverty rate is slightly higher among young women (18.7%) compared to young men (17.9%). According to the area of residence, poverty is much higher in rural communes (31.9%) than in urban ones (9.7%), the difference between males and females being slightly higher in rural areas (32.8% for females and 31% for males) compared to the cities (9.9% and 9.6%, respectively).

The structure of the WI by the area of residence shows that urban young people have a higher level of wealth than rural young people (

Table 5. Structure (%) of WI quintiles by area of residence and by gender, 2014.

		WI Quintiles
		1	2	3	4	5
Area	Urban	2.9%	9.0%	25.7%	35.0%	27.5%
	Rural	46.9%	36.5%	11.1%	3.7%	1.8%
Gender	Male	21.6%	20.6%	19.7%	21.8%	16.3%
	Female	11.5%	14.7%	21.5%	28.4%	24.0%
	Total	20.2%	19.8%	19.9%	22.7%	17.4%

Note: Authors. data from HCENS 2014 (www.hcp.ma) (accessed on 22 April 2022).

): 88% of young people living in urban areas are in the three higher quantiles of the WI, among them, 27.5% are in the richest quintile. For young people in rural areas, 46.9% are in the poorest quintile. In addition, only 5.5% of rural youth are in the fourth or fifth wealth quantiles. Concerning gender, the distribution of young men is almost uniform over all the quintiles of the WI, while young women are concentrated in the three higher quintiles of wealth (73.9%). These results show that globally, rural young people and young men are the categories most affected by poverty.

The analysis of the score of the WI according to the characteristics of young people shows that the level of wealth increases as the household size decreases (

Table 6. WI, Household size, age, per capita expenditure, employment rate, and education rate of young people by WI quintiles and poverty status.

	WI	Household Size	Per Capita Expenditure	Employment Rate	Education Rate
WI quantiles
Q1	7490.61	6.59	9383.37	0.30	0.05
Q2	22,907.47	6.26	13,485.70	0.30	0.12
Q3	37,992.05	5.30	21,844.04	0.28	0.31
Q4	53,911.30	5.39	16,878.92	0.27	0.26
Q5	68,808.80	5.34	15,854.05	0.28	0.27
Poverty status
Non-poor	40,362.60	4.61	17,833.53	0.28	0.23
Poor	26,110.96	5.91	5218.98	0.30	0.07
Total	37,748.90	3.14	15,520.06	0.28	0.20

Note: Authors. data from HCENS 2014 (www.hcp.ma) (accessed on 20 April 2022).

). Indeed, the mean household size in the richest quintile is 5.34 against 6.59 for the poorest quintile. In terms of per capita expenditure, young people in the third quintile of the WI have an annual expenditure of 21,844 MAD, compared to only 9383 MAD for the poorest 20%. The education rate of the household increases as the wealth increases: in the poorest quantile only 5% of members of the household have completed secondary education level, which is much lower compared to 31%, 26%, and 27% for the third, the fourth and the fifth quantiles, respectively.

As expected, there is consistency between the WI and the poverty status: the mean wealth score is higher for non-poor young people (40,362) compared to poor young people (26,110), which indicates that the first group has much better living standards than the second. This can be also observed in terms of expenditure (17,833 MAD and 5218 MAD, respectively). In addition, the non-poor are living in smaller and more educated households than the poor (4.61 against 5.91 and 23% against 7%).

Moreover, there are no significant differences in the employment rate of household members between the wealth quantiles on one hand, or between poor and non-poor on the other hand.

4.2. Determinants of Monetary Relative Poverty

A logit model was estimated to identify the variables that influence the probability of youths being poor. As shown in

Table 7. The results of the estimation of the logit regression model for the probability of a youth being labeled as poor.

Explanatory Variables	Estimate of Parameter	p-Value	Odds Ratio
Individual characteristics
Education level (base category: higher)
None	0.710	(0.000) ***	2.034
Primary	0.401	(0.003) ***	1.494
lower secondary	0.160	(0.225)	1.174
Upper secondary	0.313	(0.012) **	1.367
Employment status (base category: Yes)	−0.031	(0.469)	0.970
Gender (base category: Female)	−0.026	(0.545)	0.974
Age (base category: 25–29)
15–19	0.061	(0.095) *	1.063
20–24	−0.003	(0.949)	0.997
Characteristics of the household
Education rate	−2.894	(0.000 ***	0.055
Employment rate	−0.118	(0.043) **	0.889
Location region (base category: Rural)	−0.726	(0.000) ***	0.484
Number of children	0.338	(0.000) ***	1.402
Household size (base category: 6 or more)
Less than 4	−2.010	(0.000) ***	0.134
4	−1.130	(0.000) ***	0.323
5	−0.813	(0.000) ***	0.443
Marital status of the head (base category: Married)
Single	0.354	(0.000) ***	1.425
Widowed	0.488	(0.344)	1.629
Divorced	0.447	(0.020) **	1.563
Fit measures
χ2	6483.165 (0.000) ***
Nagelkerke R Square	0.266
Log-likelihood	15,249.105
Observations	19,695

Note: Authors, data from HCENS 2014 (www.hcp.ma) (accessed on 14 April 2022). Robust standard errors are reported in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%.

, the logit model is statistically significant (Chi square = 6483; p < 0.00). It must be pointed out that, except for youth employment status and gender, all the other variables are statistically significant at a 5% level. This suggests that the selected variables in this model are important determinants of youth poverty in Morocco. Given that the dependent variable of the logit model denotes if a young person is poor or not, the positive coefficient increases the probability that the young person is poor, and the negative coefficient decreases it.

The results indicate that the risk of poverty decreases as the level of education increases. Non-educated young people and those with a primary education level are, respectively, 2 and 1.5 times more likely to be poor compared to those with a higher education level. This risk decreases to 1.2 and 1.4 times for lower and upper secondary levels, respectively. Additionally, the education rate of the household members has the greatest effect (−2.9) on decreasing the probability of being poor: an increase by one additional unit in the proportion of household members who have a secondary education level or more reduces the likelihood of falling into poverty by 5.5%.

Being employed does not bring significant differentials (p > 0.05) in the prediction of the probability of being poor. However, the results show that the rate of other employed members in the household has a significant effect (p < 0.05) on the status of youth poverty. In this regard, the odds of falling into poverty decrease by 11% when the young live in a household with many working members.

Considering the location region, the probability of being poor in urban areas decreases significantly compared to the rural region. In married-headed households, young people are at a lower risk of poverty compared to single, widowed, or divorced-headed. Likewise, the results show that the probability of poverty increases as the size of the household increases. Living in a household with less than four members decreases by 87% the odds of being poor compared to households with six or more members. In addition, each additional increase in one member in the number of children is associated with a 40% increase in the odds of youth being poor. Finally, gender is not statistically significant (p > 0.1) and young girls and young men face a similar risk of poverty.

4.3. Determinants of Poverty Measured by WI

The one-way ANOVA test was carried out to examine the significance of the association between the wealth score and the independent factors. First, the ANOVA finding shows the difference in the mean wealth of youths according to WI for poorest, poorer, middle, richer, and richest quantiles. The mean is, respectively, 7490, 22,907, 37,992, 53,911, and 68,808, which indicates that, for example, the wealth average in the richest group is 9.2 times higher compared to the poorest one. The one-way ANOVA test shows that this difference is statistically significant at 1% level.

The results shown in

Table 8. ANOVA results.

Factors	Mean WI	F-Value
WI (base category: Q5)	68,808	120,591.607 (0.000) ***
Q1	7490
Q2	22,907
Q3	37,992
Q4	53,911
Education level (base category: higher)	46,032	649.571 (0.000) ***
None	25,606
Primary	30,803
lower secondary	40,367
Upper secondary	45,246
Employment status (base category: No)	38,189	6.002 (0.014) **
Yes	37,362
Gender (base category: Female)	37,664	0.319 (0.572)
Male	37,839
Age (base category: 25–29)	38,005	4.310 (0.013) **
15–19	37,144
20–24	38,156
Location region (base category: Rural)	18,718	18,488.929 (0.000) ***
Urban	49,804
Household size (base category: 6 or more)	34,949	132.185 (0.000) ***
Less than 4	42,309
4	39,869
5	40,919
Marital status of the head (base category: Married)	33,580	72.197 (0.000) ***
Single	38,895
Widowed	33,625
Divorced	42,888

Note: Authors, data from HCENS 2014 (www.hcp.ma) (accessed on 14 April 2022). Statistical significance is reported in parentheses. ** significant at 5%; *** significant at 1%.

indicate that all the variables have a WI differential at a 5% level of significance, except for the gender of the youth. The findings show that the youth’s education level is positively associated with the WI: the score increases as education level increases, with the highest mean observed among higher educated youths (46,032) and the smallest mean among those with no education (25,606). Concerning youth employment status, the mean wealth decreases when youth are employed. The mean wealth of those who were not working is 38,189 compared to 37,362 for the youths who were working.

The mean wealth increases as the age of youth increases. The score in the youngest age group (15–19) was the lowest. The mean wealth for the age groups 15–19, 20–24, and 25–29 were 37,144, 38,156, and 38,005, respectively. The results show that the difference in wealth is statistically significant according to the youth’s place of residence and the marital status of the head of the household. Finally, according to the household size, the mean wealth increased as size decreased. The mean wealth was highest in households with no more than three members with an average wealth of 42,309.

The results of the quantile regressions are presented in

Table 9. Results of the quantile regression.

	q = 0.2		q = 0.4		q = 0.6		q = 0.8
	BETA	Sig.	BETA	Sig.	BETA	Sig.	BETA	Sig.
Individual characteristics
Education level (base category: higher)
None	−2238.7	0.000	−2573.1	0.000	−2447.0	0.000	−1725.2	0.000
Primary	−1992.6	0.000	−1966.0	0.000	−1585.5	0.000	−869.0	0.001
lower secondary	−1236.5	0.000	−818.9	0.000	−255.5	0.001	−411.0	0.000
Upper secondary	−250.5	0.000	0.0	0.000	0.0	0.000	0.0	0.000
Employment status (base category: Yes)	−187.5	0.468	189.8	0.554	97.0	0.755	71.4	0.806
Gender (base category: Female)	−835.2	0.002	−997.2	0.003	−406.4	0.209	−560.3	0.063
Age (base category: 25–29)
15–19	−747.2	0.038	−1199.4	0.007	−443.3	0.006	−1181.6	0.003
20–24	−189.4	0.560	−216.9	0.590	−45.0	0.908	−749.8	0.040
Characteristics of the household
Education rate	2837.0	0.000	3169.0	0.000	2263.4	0.000	677.0	0.000
Employment rate	2452.0	0.000	790.6	0.317	301.9	0.694	−840.4	0.240
Location region (base category: Rural)	29,398.5	0.000	32,894.0	0.000	36,637.3	0.000	37,891.3	0.000
Number of children	−1560.5	0.000	−2132.4	0.000	−1354.3	0.000	−1587.5	0.000
Household size (base category: 6 or more)
Less than 4	964.6	0.000	1267.0	0.000	769.9	0.000	411.0	0.000
4	703.6	0.000	643.2	0.000	641.8	0.000	0.0	0.872
5	681.5	0.007	816.0	0.001	817.2	0.000	411.0	0.000
Marital status of the head (base category: Married)
Single	−915.1	0.009	−679.5	0.112	−191.3	0.647	−433.7	0.267
Widowed	−621.8	0.851	−5475.6	0.173	−4054.4	0.301	−7600.3	0.039
Divorced	3058.6	0.012	3337.7	0.024	3010.7	0.037	1126.1	0.406

Note: Authors, data from HCENS 2014 (www.hcp.ma) (accessed on 14 April 2022). Statistical significance is reported in sig. Column (<0.1 significant at 10%; <0.05 significant at 5%; <0.01 significant at 1%).

. Globally, the estimated parameters confirm the findings of the logit model shown before (Table 7). Particularly, the level of education of the young and the aged, the education rate of the household’s adults, the household size, as well as the location region are highly significant at all quantiles of the distribution.

Figure 4 shows graphically the results of the estimated quantile regressions for each of the independent variables. The dotted black line shows the value of the coefficients for each of the quantile regressions, and the blue shaded area indicates the 95% confidence interval. The red solid horizontal line shows the regression coefficient value by the OLS method, with two horizontal dotted lines in the same color, which indicates the confidence interval of the regression at 95%. It must be noted that there are significant differences between the results of the models if the parameters estimated by the quantile regression are outside the confidence intervals of the OLS regression [62].

As expected, a higher education level significantly improves the wealth of Moroccan youth throughout the entire spectrum of poverty. As indicated by the OLS results, on average, the WI decreases as we move from higher educated young people to upper secondary (−250), to lower secondary (−1236), to primary (−1993), and finally to non-educated young people (−2239). However, the quantile regression reveals the range of disparities of this effect across the quantile distribution of the WI: differences between higher education level and the other levels are more marked at the lower end (1st quantile) compared to the upper end (5th quantile). It is noteworthy that, in the case of the upper secondary level, the effect of education level on the wealth completely disappears, beginning from the 3rd quantile (0.4). This means that from this point of the distribution of the wealth, the difference between having an upper secondary level and a higher level is no longer significant.

The education rate among the household’s adult members has a positive significant outcome on the wealth of the youth. This effect is higher in poor quantiles (2837 for 0.2 quantiles and 3169 for 0.4 quantiles); that is, wealth increases in poorer quantiles as the education rate increases. However, the effect of education rate on wealth is expected to decrease in rich quantiles (677 for 0.8 quantiles). The perceived advantage from the presence of more educated adults is therefore changing in inverse proportion to quantiles.

Youth poverty differs greatly depending on their family structure. On the one hand, wealth is likely to be higher for households with small sizes. This result is more significant in poorer quantiles, where a higher effect is observed. For example, compared to households with six or more members, households with less than four members have an increase in the wealth score of 465 in the 1st quantile and 411 in the 5th quantile. On the other hand, the number of children living in the household has a significant negative effect on the youth’ wealth. Living in a household with many children decreases the well-being of the youth, the effect of an additional child in the household is −1560 in the poorest quintile and −1587 in the richest one. It must be pointed out that this effect is more marked in the middle classes (−2132 for the 0.4 quantiles).

Regarding the employment rate in the household, the results show a significant effect for the poorest quantile compared to the others. An increase by one additional point in the employment rate increases the wealth by 2452 points.

Age has a positive and significant association with the standard of living of households in all studied quantiles. The negative sign of the coefficients of the 15–19 age group reflects that wealth is especially lower than the other age groups. In addition, since the estimated parameters are inside the confidence interval of the OLS model, there is no significant effect of age difference across the quantile distribution. Finally, the results show strong regional patterns of higher wealth levels in the urban areas and lower wealth in rural ones.

In sum, the results from both the logit model, ANOVA, and the quantile regression show that being more educated constitutes a rampart against poverty for the young person. By contrast, access to employment is not enough to guarantee a decent level of economic well-being. Moreover, there are no gender differences in youth poverty, but this seems higher among rural young and 15–19 years old compared to the those who reside in urban areas and are 20–29 years old, respectively. Youth poverty is also significantly associated with family/household characteristics such as education and employment of the other members and household size. This study also reveals that using quantile regression procedures to estimate wealth determinants among young people produces more reliable results than those emerging from conditional mean estimations (linear regression).

5. Discussion

Morocco is a young population with a growing youth workforce that does not fully contribute to the expected demographic dividend [51]. Even though they should be shaping the future and despite multiple government programs in favor of their education, training and professional insertion, young people still represent the specific fringe of society that experiences the most structural constraints, such as low skill levels and unemployment. Given the greater educational achievements compared to their elders, the way to adulthood from the family of origin to that of procreation, from the school to the labor market, from dependency to financial and residential independence seems to be very complicated to achieve for young people in Morocco.

Moroccan youth are mostly inactive, a phenomenon more urban than rural and more female than male. This can be explained, in part, by the housewives’ phenomenon, which represents 40%, and global average years of schooling being much higher than before, particularly in cities. Consequently, this inactivity delays their financial or residential independence (75% of young are living in their parental houses, with no significant differences according to the education level). Indeed, “with increasing levels of participation in higher education, young people are spending longer dependent on the state or their families for financial support, and without earned incomes of their own” [53] (p. 22). In terms of wellbeing, despite a significant decrease in poverty over the last few decades globally, youth poverty remains much higher and is currently double the national rate.

In this paper, we explore the determinants of poverty alleviation, focusing on the role of education and employment, using two poverty indicators: namely, monetary relative poverty (at 60% median expenditures) and WI. Our results, based both on logit and quantile regression models, confirm the assumption that improving the level of education will tend to significantly increase the standard of living among young people. Education proves to be decisive, both at the individual and household levels: poverty decreases as the youth are more educated and the rate of school attendance in the household is higher. In other words, the education level of the young people and the presence in the household of other adults who have achieved at a least secondary education reinforces the protection against a low standard of living. The role of education as a key mechanism to accede to prosperity is also present in the collective perception of young people: when asked about the factors of upward social mobility, 76% of young people cite education [63].

The quantile regression analysis highlights that the return of education is bigger for the lowest quantiles and decreases as the quantile increases. This positive effect seems to disappear beginning from the upper secondary level: there is a threshold (0.4 quantile) at which education inequalities between upper secondary and higher levels no longer have an impact on poverty among the young. Since higher education corresponds to a higher wealth, thus we can conclude that completing the upper secondary education, which corresponds to the level of qualification in Morocco, i.e., at which young people start to learn a certain number of skills and competencies, allows achieving a higher standard of a living conditions similar to the higher educated young.

Analyzing the role of education in poverty alleviation can be rather complicated because of their close bidirectional link. On this point, Torres-Munguía and Martínez-Zarzoso [64] argue that the regression models provide estimated correlations between the covariates used and wealth, which do not necessarily imply causality, in particular, for independent variables that could be endogenously determined. This is generally recognized for education and academic performances, since poverty may prevent individuals from benefiting as much from the available schooling as those who are better off [65]. In fact, young people begin their life with unequal opportunities, some of them grow up with disadvantaged backgrounds while others come from wealthy families. The family of origin’s living standards can therefore impact the attained education level of its child.

It should be noted also that in Morocco, unemployment affecting young graduates or the highly educated is structural and affects graduates of universities more than those of engineering high schools [66]. Several factors have been identified as explaining these findings which defies the laws of the market and the theory of human capital. The problem of the training-employment mismatch remains the main determinant and has both a quantitative and qualitative nature [67]. On the one hand, young people refuse precarious employment or jobs that do not correspond to their educational levels, knowing that companies prefer job flexibility and low wages, even if it means recruiting those who are less educated. However, on the other hand, companies consider that the quality of the skills does not meet their particular job requirements. The HCP Enterprise survey, conducted in 2014, reveals that nearly a third of enterprises (30.2%) insist on the serious problem of skills mismatch in the Moroccan labor market, which widely exceeds the level observed in the MENA Region (20.4%) or at the World level (20.5%) as indicated by World Bank enterprises surveys (2019).

Our results can reflect different social and economic situations and must therefore be nuanced. One situation concerns young men or women from poor or vulnerable families, who manage, despite obstacles, to succeed after having completed a medium or high school course. There is therefore a certain return on investment in the sense of [68]. Thus, they succeeded in capitalizing on the acquired educational resources by entering the labor market, where they can occupy well-paid jobs with inherent social benefits. Some of them can also achieve residential independence by establishing in turn their own family. Others prefer to stay with their original family, enjoying certain financial independence. This situation is related to marital status, for example, most newlyweds’ youth reside separately from the family of origin, while the duration of dependence of young single people on the family is prolonged [63]. To these can be added another category: those of young people from socially advantaged families who achieve high levels of education that allow them to find decent work and who manage to maintain the initial level of wealth or improve it. Again, there is a return on investment.

Another possible situation concerns young people who have been able to acquire educational capital due to the support and sacrifices of parents, but who find themselves jobless or NEETs in desperation by neither looking for work nor pursuing any training, enabling them to break the vicious cycle of unemployment. Without labor market income, the well-being of this educated group can be just an illusion because it is more related to the family solidarity that intervenes to provide a minimum safety-net to its members, even if this solidarity can take different forms according to times and places. In many societies, whether modern or traditional, family solidarity remains a key societal pillar and a defense mechanism against poverty, especially among young people during the transition to adulthood, and when welfare policies are absent or insufficient [63,69,70,71,72]. This is the case in Morocco where 55% of youth state that family solidarity continues operating. The most obvious forms of family solidarity consist of providing needy members with transfers in cash or in-kind and providing them with various non-monetary services [71]. In Morocco, it is noteworthy that inter-generational support not only guarantees young people a decent standard of living but also enables them to access school and extend the duration of their studies. Confronted with the very high unemployment and the absence of a friendly and attractive market, some young people, especially graduates, tend to extend their studies beyond what is required on the labor market by obtaining a “Master” or a “Doctorate” degree with the hope that this may be what will allow them to access a well-paid and protected job.

Another important result is that among young people, having a job is not enough to guarantee higher decent living conditions. Whether they are graduates or not, women or men, coming from urban or rural families, young workers are at the same risk of being caught in the poverty trap as those who do not work. Therein arises the question of remuneration for work, the level of wages and social protection raised by Aassve, Iacovou, and Mencarini [53] (p. 22), who note that “changes to youth labor markets over recent decades mean that when young people do enter the labor market, they may spend considerable periods without a job, or in low wage or insecure employment”. The results of the latest ILO report [73] reveal that persistent working poverty rates underscore the need for social protection systems to help shore up income security. However, when one considers income poverty, it seems that living in a household with a significant number of employed persons is a factor of monetary well-being. Additionally, at ages between 15 and 29, youth in Morocco are much more likely to be in the family home, so parents’ employment/wealth is the more important factor (one reviewer). This confirms the family solidarity enjoyed by young people from other economically occupied household members who thus provide support to compensate for the deficit recorded by the youngest members.

Youth’s empowerment is a key factor in improving their integration toward agency, autonomy and future aspirations achievement [74,75]. However, empowering young people is necessary but not sufficient. As recommended by the United Nations [76] (p. 4), “social inclusion is necessary to eradicate poverty and deprivations among all youth, but such inclusion will require structural measures that go beyond individual empowerment”.

6. Conclusions and Recommendations

This paper investigated the role of education and employment, among other drivers, on youth poverty alleviation in Morocco. The data used in this study are taken from the Household Consumption and Expenditure National Survey (HCENS) 2014, conducted by HCP. The cross-sectional analysis highlights the importance of education (as measured by the education level of the youth and the secondary education rate among his/her household) in determining the risk of poverty. Improving the level of education will tend to significantly increase the standard of living among young people. The relative importance of educational attainment, however, varies across wealth quintiles. The return of educational attainment is more marked for the poorest quantiles and decreases as the quantile increases. Moreover, the effect of higher education level seems to disappear beginning from the upper secondary level. Starting from the middle class (0.4 quantiles), education inequalities between upper secondary and higher levels no longer have an impact on poverty among the young. Accordingly, expanding access to education and decreasing educational inequality among poor youth should arguably be accorded a larger weight in policies to reduce the risk of poverty in Morocco. Additionally, since a higher education level does not bring significant wealth increase for middle and rich classes compared to upper secondary level, there should be an emphasis on adequately paid, quality job creation that matches the higher levels of attained education.

The results also explain that the employment status of young people does not have a significant effect on his/her well-being. The results raise the issue of the “youth working-poor” in combination with the unemployment issue; obtaining a job does not mean escaping poverty. This then leads to the need for further investigation into the types of jobs in which Moroccan youth are involved. A policy implication would be that the quality of jobs (wages, social protection, skills matching) should be considered in parallel with the quantity of jobs as a tool to deal with youth poverty. The results highlight the importance of the overall level of employment among the household/family on the status of youth poverty. The probability of falling into poverty decreases when the young live in a household with many working members.

Another core message of this paper is the importance of family/household characteristics for reducing the risk of poverty. The results highlight the role played by intra-household solidarity in mitigating youth poverty. In addition to the household’s employment and educational attainment levels, youth poverty is significantly associated with family/household characteristics such as household size, location region, and the number of children in the household. The results show that the probability of being poor is increased when the size of the household is increased. Similarly, the risk of poverty tends to rise significantly with the number of dependent children. Additionally, there is a positive association between living in a rural area and being at risk of poverty. There is a need to review the family planning programs to keep the family size small. Additionally, besides encouraging family solidarity, there needs to be a basis for youth to receive support from society (unemployment allocation, guaranteed annual income).

Since poverty is multifaceted, addressing youth poverty in Morocco requires integrated policy and programming solutions that provide equal opportunities to recent generations, regardless of their gender, social origins, and geographic location. This can be reached through quality education and strong academic and vocational orientation systems that facilitate the transition to the labor market. It is also necessary to reinforce equal access to services for all youth and the eradication of discrimination.

From a methodological standpoint, this study reveals that using quantile regression procedures to estimate poverty risk determinants among young people produces more reliable results than those emerging from conditional mean estimations (linear regression). We see that the education level of the youth, their marital status, household size, the number of household members being educated, or economically active and residence area interact to determine differences in estimated coefficients between the quantiles. The positive effect of being more educated, living in a small household with more educated members matters for the poorest, while a higher proportion of employed members and those residing in cities make a difference for the richest. The significance of these covariates indicates the importance of implementing pro-poor policies with household structure by size, education level of its members, especially the young people, and residential perspectives that will help to target young people, both males and females, in a rural and large family with less education and employment resources, instead of using a global and uniform approach.

Finally, as has already been emphasized by other authors [61,64], there are some limitations to our study concerning the use of cross-sectional data in assessing the effect of covariables on poverty, which omit the impact of the trajectory of individuals. It is therefore necessary to investigate the evolution of poverty determinants over time through a quantile regression methodology or to privilege the cohort/panel approach.