Policy Research Working Paper 9238
Once NEET, Always NEET?
A Synthetic Panel Approach to Analyze
the Moroccan Labor Market
Federica Alfani
Fabio Clementi
Michele Fabiani
Vasco Molini
Enzo Valentini
Poverty and Equity Global Practice
May 2020
Policy Research Working Paper 9238
Abstract
In many regions of the world, the persistent, and grow- employing a synthetic panel methodology in the context
ing, proportion of young people who are currently not in of labor market analysis, the paper describes how the con-
employment, education, or training is of global concern. ditions of individuals in this group has changed over time.
This is no less true of Morocco: about 30 percent of the One striking, and worrisome, pattern that emerges from
Moroccan population between ages 15 and 24 are currently the 2010 synthetic panel data is that, even after 10 years,
not in employment, education, or training. Drawing from a majority of the young population not in employment,
various rounds of Moroccan labor force surveys, this paper education, or training remained outside the labor market
contributes to understanding the complex dynamics of or education, with very little chance of moving out of their
labor markets in developing countries. First, it identifies situation. Their chronic stagnancy confirms the powerful
the socioeconomic determinants of Morocco’s young pop- effect that initial conditions have on determining young
ulation not in employment, education, or training. Second, people’s future outcomes.
This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to
provide open access to its research and make a contribution to development policy discussions around the world. Policy
Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at
vmolini@worldbank.org.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
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Once NEET, Always NEET?
A Synthetic Panel Approach to Analyze the Moroccan Labor Market
Federica Alfani Fabio Clementi Michele Fabiani Vasco Molini ∗
The World Bank University of Macerata University of Macerata The World Bank
Washington DC, USA Macerata, Italy Macerata, Italy Rabat, Morocco
Enzo Valentini
University of Macerata
Macerata, Italy
Keywords: synthetic panel, NEET, youth employment, gender equality, Morocco.
JEL Classification: C23, E24, J13, O55.
∗
Corresponding author. The authors acknowledge financial support from the World Bank. We thank colleagues from the
Haut Commissariat au Plan of Morocco (HCP) for their excellent data preparation.
1. Introduction
Using a synthetic panel methodology, this paper analyzes the evolution of Moroccan NEETs, an
acronym for young people (between 15 and 29, or 15 and 24, depending on the definition) who are
Not in Employment, Education or Training.
Appearing for the first time in the mid-1990s, the acronym was formulated to capture the social
situation of a growing category of the population: youth who do not build human capital in the
traditional way through work, education or training. Part of the reason the term was formulated is that
development experts began to recognize that the conventional metrics and predictors of success—
such as employment, academic education, and/or vocational training—no longer sufficiently captured
the multidimensional nature of the challenges faced by many young people during their schooling
years or during their transition from school to the labor market, nor the resulting long-term fragility
that marks their lives.
According to the most recent national census (RGPH, 2014), there are about 6 million Moroccans
between the ages of 15 and 24. Morocco is currently in a development window that demographers
call a “demographic bonus”. This means that the proportion of working-age people in the total
population is high, compared to the share of the population who are either younger or older than the
productive age bracket (15–64 years old).
HCP-WB (2017) estimates that Morocco’s demographic bonus will end in 2040 when the progressive
aging of the population, combined with increased life expectancy, begins to raise the share of the
population aged 65 and above. With such a high proportion of people in the productive age range,
and a low proportion of dependents, a demographic bonus therefore presents a country—for a limited
number of decades—with a one-time opportunity for accelerated economic growth.
The current age distribution of Morocco’s population represents such an opportunity, but it also
presents the major challenge of harnessing that human capital and putting it to the best use (HCP-
WB, 2017). Until now, the Moroccan labor market has not been able to absorb all of its new entrants
in an optimal way. Calculations based on subsequent rounds of the Moroccan Labor Force Survey
(see data section) indicate that of some 390,000 new entrants in the national workforce every year,
barely a third manage to find employment in the formal or informal sector.
2
Because of the importance of this age group, the NEET phenomenon has increasingly gained attention
in both developed and developing countries. In Morocco, by contrast, labor market analysis (see, for
example, Verme et al., 2016a, 2016b) has tended to focus on understanding what determines
unemployment or inactivity within the entire population, with no specific focus on those between 15
and 24 years old. The present paper aims at filling this informational gap by providing a first-of-its-
type comprehensive analysis of the characteristics and dynamics of NEETs in the last decade.
Examining NEET profiles is a relatively straightforward exercise using repeated cross-sections on
the labor force, but without access to longitudinal data, it is rather more complicated to construct
transition matrices that trace their movement in and out of the NEET condition. The Moroccan Labor
Force Surveys do have a panel component (50 percent of the sample) but it rotates every two years,
making it all but impossible to construct a transition matrix over a longer period than that.
Yet because of the extreme importance of gaining an understanding of the duration and persistence
of the NEET condition, our team worked diligently to overcome the data limitations of existing
methods by constructing a Synthetic Panel (SP) of individuals between 15 and 24 over a nine-year
period (2010-2018). To our knowledge, this represents a primer in the synthetic panel literature,
which has so far focused on welfare dynamics and only occasionally on labor market outcomes. 1 The
adaptation of synthetic panel methodology to the labor market also opens up the possibility of
conducting panel-type analyses in this field without relying on panel data sets, which are often scarce
in developing countries. An important advantage we intend to exploit in constructing the SP is the
possibility of comparing year-on-year estimates (for example, from 2010 to 2011) to the
corresponding panel component within the data.
The paper is organized in six sections. The first is the current Introduction. The second section profiles
NEETs, drawing comparisons in different countries around the world. Section 3 summarizes research
and findings related to the NEET phenomenon. Section 4 presents data and the methodology we
applied, while section 5 displays results. Section 6 draws conclusions.
1
Using a synthetic panel approach to study the German labor market during the 1993-2014 period, Burda and Seele
(2013) document the vital role played by part-time employment in reallocating a modest increase in total hours worked
over a large number of new workers, leading to net employment growth. Beblavy et al. (2013) undertake a synthetic panel
analysis of adult learning for cohorts aged 25 to 64 in 27 European countries, using the European Labour Force Survey
and studying the factors affecting educational attainment, participation in education and training.
3
2. Profiling the NEETs: Who Are They, and What Are Their Life
Conditions?
The transition from education to the world of work is one of the most important life decisions young
people face. Of particular concern are those who are neither employed, in school, nor in a training
program of any sort. Fully one-fifth of the global population between the ages of 15 and 24 fall in
this NEET category.
In 2019, the Organisation for Economic Co-operation and Development (OECD) released the latest
update of its own perspective on NEETs. The OECD defines NEETs as people aged between 15 and
29 who are neither enrolled in a formal educational or training program, nor in paid employment
(defined as at least one hour per week) during the relevant survey reference period. As Table 1 shows,
in 2018 the average NEET rate for the 15-29 years-old population across OECD countries is around
13 percent, ranging from 6.1 percent in Iceland to 26.5 percent in Turkey. Southern European
countries, Mexico and Turkey exhibit the highest NEET rates, whereas Northern and Central
European countries show the lowest rates. The 2018 OECD report highlights that in almost all OECD
countries, NEET rates are higher for women than for men; the OECD average rate for young women
is almost 4 percentage points higher than the rate for young men. In Mexico and Turkey, female rates
are around 25 percentage points higher than male rates. By contrast, Austria, Belgium, Canada,
Luxembourg, Portugal, and Switzerland show a NEET rate higher for males than for females yet the
difference is negligible.
Table 1: NEET Rates for 15- to 29-Year-Olds in OECD Countries
Country 2006 2011 2018 Country 2006 2011 2018
Iceland 5.1 9.8 6.1 Estonia 11.4 15.2 12.7
Netherlands 6.2 6.9 7.0 Poland 17.4 15.5 12.7
Malta 13.6 12.1 7.3 United States 12.9 15.9 12.7
Switzerland 10.0 21.9 8.1 Belgium 13.9 13.9 12.8
Luxembourg 8.6 7.2 8.4 OECD average 14.3 15.9 13.2
Norway 7.9 8.5 8.7 Israel 30.0 27.6 13.3
Sweden 10.5 9.1 8.9 Hungary 17.0 18.5 13.5
Germany 13.6 11.0 9.2 Cyprus 11.9 14.8 14.9
Slovenia 10.8 10.7 9.7 Slovak Republic 19.1 19.1 15.1
Japan 12.0 11.7 9.8 Croatia 15.8 19.1 15.6
Czech Republic 14.1 12.7 10 France 15.2 16.4 16.1
New Zealand 12.0 14.3 10.2 Romania 16.5 19.5 17.0
Lithuania – 18.0 10.5 Bulgaria 23.9 24.7 18.1
4
Australia 11.4 11.5 10.8 Chile – 21.8 18.4
Denmark 6.2 11.0 10.8 Spain 15.9 24.3 19.1
Austria 12.0 10.3 11.1 Mexico 23.2 24.0 20.9
Latvia 14.4 19.6 11.2 Greece 16.7 21.6 21.5
Portugal 12.4 15.3 11.6 Colombia – 20.1 22.7
Ireland 10.4 21.9 11.7 Costa Rica – 20.1 23.1
Canada 12.0 13.4 11.9 Italy 20.1 23.2 23.9
Finland 10.4 11.8 11.9 Brazil – 19.3 24.9
United Kingdom 15.1 15.5 12.6 Turkey 42.6 34.6 26.5
Source: OECD Family Database, Indicator CO3.5 “Young people not in education or employment”,
http://www.oecd.org/els/family/database.htm.
Notes: For 2011, data for Switzerland refer to 2009, for Lithuania to 2010, and for Colombia and Costa Rica to 2013. For 2018,
data for Japan refer to 2014, and for Chile to 2017. The OECD average excludes Chile and Korea.
As shown in Figure 1, NEET rates are generally higher for young people in their 20s than for those
in their teens. In the OECD countries, on average about 18 percent of those aged between 25 and 29
years old, compared to less than 6 percent of 15-19 year-olds, are NEET. This difference may be the
result of the expansion of upper secondary education in many OECD countries. Furthermore, as the
OECD reports, NEETs are less likely to live with their parents than non-NEETs. About 50 percent of
NEETs, compared to about 75 percent of non-NEETs, live with their parents.
Caring for children and/or living with a partner can also make a substantial difference. About 26
percent of NEETs, but only 9 percent of non-NEETs, live with a partner and one or more children—
an almost 3:1 ratio. The reason is likely that parenthood compels young people to devote their time
and energy to childcare rather than education or working outside the home. Parenthood with no
partner makes even more of a difference. Single (non-partnered) young women account for just 1
percent of non-NEETs, compared to 5 percent of NEETs—a 5:1 ratio versus 3:1. Caring for children,
especially all by oneself, often forces a young person to stay at home instead of attending school or
working outside (OECD, 2016).
5
Figure 1: NEET Rate by Age Group and Gender
Source: OECD (2019), Youth Not in Employment, Education or Training (NEET) Indicator. doi: 10.1787/72d1033a-en (accessed on
December 20, 2019).
International Labour Organization (ILO) data allow us to enlarge our view to other regions of the
world. ILO defines youth as “all persons between the ages of 15 and 24 (included)” and employees
as “all persons of working age who during a specified brief period, such as one week or one day, were
in the following categories: a) paid employment (whether at work or with a job, but not at work); or
b) self-employment (whether at work or with an enterprise, but not at work)”. People are defined as
being in training if engaged “in a non-academic learning activity through which they acquire specific
skills intended for vocational or technical jobs”. Finally, vocational and technical training includes
only programs that are solely school-based.
As Table 2 shows, nearly 22 percent of youth worldwide are NEET, about 77 percent of whom are
women. This underscores the observation that deeply ingrained social norms drive the unequal labor
market outcomes between men and women. 2
2
ILO (2017) analyzes in depth the drivers of gender disparities in educational attainment and labor market outcomes, and
the constraints that influence these disparities.
6
Table 2: NEET Rates for 15- to 24-Year-Olds, by Region
NEET rates, latest years
Region Female share
Total Male Female
World 21.8 9.8 34.4 76.9
Developing countries 12.1 8.0 16.0 66.1
Emerging countries 25.2 9.6 41.8 80.3
Developed countries 13.1 11.3 14.9 55.7
Northern Africa 26.1 16.7 36.0 67.6
Sub-Saharan Africa 15.5 11.2 19.0 61.4
Latin America and the Caribbean 19.4 11.9 27.0 68.6
Northern America 16.3 14.1 18.6 55.8
Arab States 18.2 9.9 27.1 71.8
Eastern Asia 3.7 2.8 4.7 61.8
South-Eastern Asia and Pacific 18.0 13.4 22.6 61.5
Southern Asia 28.6 5.8 53.3 89.5
Northern, Southern and Western Europe 12.3 12.2 12.4 49.2
Eastern Europe 15.6 13.8 17.4 54.5
Central and Western Asia 23.4 14.8 32.1 67.5
Source: International Labour Organization, 2017.
Notes: the table shows the NEET rate in different regions of the world, using youth population-weighted averaging. The number of
countries with available data in different regions are as follows: World (98), developing countries (12), emerging countries (46),
developed countries (40), Northern Africa (3), Sub-Saharan Africa (16), Latin America and the Caribbean (16), North America (2),
Arab states (5), Eastern Asia (4), South-Eastern Asia and the Pacific (8), Southern Asia (6), Northern, Southern and Western Europe
(27), Eastern Europe (7), and Central and Western Asia (4). The most recent year is 2015, with 67 observations. There are 15
observations for 2014 and 16 observations for 2009–2013.
Figure 2 narrows the focus to Morocco and other Middle East and North Africa region countries. The
MENA region, in particular the North African part, seems to perform worse than many other regions
both in the developed and developing world. Although its peers are not doing particularly well,
Morocco has even higher NEET rates than most. In 2017, Morocco was the worst performer among
MENA countries that were not in a situation of conflict or state fragility (in other words, excluding
the Republic of Yemen, Iraq and the West Bank and Gaza). Its NEET rate is two percentage points
above that of the Arab Republic of Egypt, and four and five points higher than that of Tunisia and
Algeria, respectively.
7
Figure 2: NEET Rates in MENA Countries
50.
37.5
25.
12.5
0.
Yemen Iraq West Morocco Egypt Tunisia Lebanon Algeria Saudi Arabia U.A. Emirates
Bank/Gaza
Source: ILOSTAT, https://ilostat.ilo.org/.
3. Research Findings on the Status of NEETs Worldwide
As mentioned in the introduction, a quantitative investigation of the condition and dynamics of
NEETs in Morocco is rather new, compared to other parts of developing world where new studies
have already become available. Employing longitudinal data, Ranzani and Rosati (2013) present
evidence concerning the extent, characteristics, and evolution of the NEET phenomenon in Mexico
over a 10-year period. In addition, they investigate the existence and extent of state dependence by
disentangling unobserved heterogeneity from genuine state dependence. For example, they find that,
compared to other NEETs, female and lower-educated youth are more likely to remain in this status
than be employed.
Looking at Turkey, Bilgen Susanli (2016) examines the determinants of its NEET picture, drawing
on data from the Household Labor Force Surveys over the 2004-2013 period. A logit analysis
indicates that gender and educational attainment are key factors in explaining who is or is not in
NEET. A greater number of household members who are in employment is associated with a lower
likelihood of NEET. Transition matrixes analysis reveals that NEET status remains highly persistent
despite the substantial fall over the sample period. In South Africa, Akinyemi and Mushunje (2017),
investigating the determinants of rural youth participation in agricultural activities, show that 21
percent of youth are NEET, 77 percent of them in the 20–24 age bracket. Variables such as age,
government funding and parent participation in farming increase the likelihood of young people’s
participation in agricultural activities. By contrast, being married, having young children, and
receiving social grants reduce the likelihood.
Cabral (2018), focusing on Senegal, shows that about 40 percent of young people are NEET. In his
analysis, the key factors affecting the probability of being NEET are the existence of a physical and
8
mental disability, age and gender of the person, education, occupational and marital status of the
household head, as well as household income.
Research by Abayasekara and Gunasekara (2019) using 2016 Labour Force Survey data reveals some
of the risk factors that predispose young people to become NEET in Sri Lanka. Using binomial and
multinomial logistic regression models, the results indicate that the risk factors center on being
female, belonging to an ethnic and religious minority, being in the 20–24 age group, having very low
or very high levels of education, being English-illiterate, belonging to a low‐income household or to
a male-headed household, having young children, and living in a more remote area. The authors also
offer important policy recommendations for how to reduce Sri Lanka’s NEET rate and engage more
youth in education and in the labor force.
Looking at developed countries, Quintano, Mazzocchi and Rocca (2018) analyze the determinants of
the NEET condition in Italy through a step-by-step procedure. They first determine the main
characteristics of being NEET, then focus on specific homogeneous clusters of NEETs. The
decomposition of the clusters into different probabilities of being NEET enables the effect of various
personal characteristics to be verified. Using a bivariate selection probit model based on the
propensity to look for a job against the condition of being inactive, the authors assess the influence
of unobserved factors on the professional condition of young people. The results confirm the crucial
role of education, as well as the importance of economic and social disparities between men and
women in the Italian territorial districts.
4. Data and Methodology
1. Data and descriptive statistics
In this paper, we make use of the “Enquête nationale sur l’emploi”, a nationally representative labor
force survey conducted by the Moroccan Haut-Commissariat au Plan (HCP). Its main objective is
identifying the volume of active population as well as the main demographic, cultural and socio-
professional characteristics of workers. The data set may also be used to measure the Moroccan
population’s access to basic social services.
The survey has been conducted every year since 1999, using a comprehensive questionnaire covering
both urban and rural areas. The sampling frame follows a two-stage stratification strategy in the
country’s urban and rural areas and regions, which in 2013 were consolidated into 12 from an original
16. On average, every year the sample comprises about 80,000 households, of which 60,000 reside
9
in urban areas and 20,000 in rural areas. A team of HCP enumerators conducts each survey round
through direct household interviews, using a computer-assisted personal interviewing (CAPI)
technique. The survey also contains a rotating panel component that can be used to examine the
persistence and dynamics of labor market status. This rotating panel component, however, is available
only for about half of the sample for two adjacent years—specifically, 2010/2011 through to
2017/2018. In this paper, we focus our analysis on the period ranging from 2010 to 2018. 3
Table 3 presents descriptive statistics of control variables for 2010 and 2018. We observe that
between 2000 and 2018, the NEET rate remained high, hovering around 30 percent, with little sign
of decline. In 2018, 28 percent of young Moroccans (about 2 million people) could be classified as
NEET. The percentage of youth who have secondary education or are pursuing any type of education
beyond high school has increased over time. In particular, secondary education moved from 23 to 30
percent between 2010 and 2018, with tertiary education rising from 6.7 percent in 2010 to 13.8
percent in 2018.
Table 3: Descriptive Statistics of Selected Control Variables (48,024 observations in 2010 and 55,280 in 2018)
2010 2018
Variable
Mean Min Max Mean Min Max
NEET (1 = yes) 32.4 0 1 28.4 0 1
HH member is female (1 = yes) 49.7 0 1 49.5 0 1
HH member is 20-24 years old (1 = yes) 55.7 0 1 54.2 0 1
HH member is single 87.8 0 1 88.3 0 1
HH member is married 11.8 0 1 11.2 0 1
HH member is widower\divorced 0.4 0 1 0.4 0 1
HH living in rural area 46.2 0 1 40.3 0 1
HH living in most developed regions 68.7 0 1 50.5 0 1
No education 12.5 0 1 5.1 0 1
Koranic school 1.3 0 1 0.7 0 1
Primary school 56.2 0 1 50.6 0 1
Secondary school 23.2 0 1 29.7 0 1
Tertiary education 6.7 0 1 13.8 0 1
Asset index (normalized) 37.4 0 1 43.6 0 1
HH living in rural accommodation (1 = yes) 35.8 0 1 26.9 0 1
HH living in villa (1 = yes) 1.3 0 1 1.3 0 1
HH living in apartment (1 = yes) 7.3 0 1 10.3 0 1
HH living in traditional house (1 = yes) 3.3 0 1 2.6 0 1
HH living in modern house (1 = yes) 47.3 0 1 55.5 0 1
HH living in shanty (1 = yes) 5.0 0 1 3.4 0 1
Source: authors’ own elaboration based on the Enquête nationale sur l’emploi (ENE).
3 We excluded 2016 from the sample because, in the available data set, we could not find a set of variables regarding
family background that we could subsequently use in the econometric analysis. In any case, in 2016 the NEET rate was
29 percent, very similar to both previous and subsequent years.
10
Figure 3 shows that the age distribution of male NEETs did not change substantially between 2007
and 2018, as it did for females, especially for younger ones. The rapid increase in enrollment rates of
young women in secondary and tertiary education explains this marked difference. However, when
the same females approach the age at which they would normally be entering the labor market—their
early 20s—this positive improvement they experienced in their early educational enrollment rates has
all but been erased. The NEET rate for females age 23 to 24 is virtually the same in 2018 as it was in
2007, more than a decade earlier.
Figure 3: Percentage of the Moroccan Population Who are NEET: Males and Females by Age
80%
70%
60%
50%
40%
30%
20%
10%
0%
15 16 17 18 19 20 21 22 23 24
Age
Males, 2007 Females, 2007
Males, 2018 Females, 2018
Source: authors’ own elaboration based on the Enquête nationale sur l’emploi (ENE).
Figure 4 focuses on 2018 only but provides a more comprehensive snapshot of labor market outcomes
by age and gender. We also represented the shares of those in education or working: the differences
between men and women are quite striking. The disadvantaging of females starts very early on. In
2018, just 19 percent of girls aged 15 were NEET compared to more than 30 percent in 2007, a
marked improvement. In 2018, of the remaining 81 percent of 15-year-old girls, 77 percent were in
school and 4 percent were working. At 24 years old, however, more than 70 percent of women in
2018 were NEET, compared to just 19 percent of 15-year-olds.
By contrast, in 2018 about 5 percent of boys aged 15 were NEET—not much different from the 7
percent or so who were NEET in 2007. In comparative terms, that 5 percent figure for NEET young
boys is not hugely different from the 19 percent of 15-year-old girls who were NEET in 2018. What
is more significant is that when men reached 24 years old in 2018, about 21–22 percent of them were
11
NEET compared to more than 70 percent of their female counterparts—a very large difference that
points to severe retrenchment on the women’s side. It can be seen that, as age increases, the male-
female gap widens markedly. Additionally, although this widening of the male-female gap holds both
in 2007 and in 2018, in relative terms it is far worse in 2018 than in 2007, because of the great gains
the girls made in early education but then lost as they entered their 20s. In 2018, about 10 percent of
women aged 24 were in school, and fewer than 20 percent were working, compared to about 20
percent of their male counterparts in school and about 60 percent working.
Both Figures 3 and 4 portray a situation for Morocco not dissimilar from other MENA region
countries, where the share of employed youth is higher among young men than young women, and
the share of NEET is higher for young women. Interestingly, both in Morocco and in the rest of the
region (Doss et al., 2018), when the NEET information is crossed with marital status, married
women—either with or without children—represented a significant percentage of female NEETs.
When this married group is removed from the NEET calculation, however, the share of female NEETs
(for example, those who are neither married nor have children) becomes comparable to the share of
NEET men.
Figure 4: School-to-Work Transition for Population Aged 15 to 24 years old, 2018
Male Female
100% 100%
NEET
80% 80%
School only
60% 60%
40% 40% NEET
School only
20% 20%
Work only
Work only
0% 0%
15 16 17 18 19 20 21 22 23 24 15 16 17 18 19 20 21 22 23 24
Age Age
Source: authors’ own elaboration based on the Enquête nationale sur l’emploi (ENE).
2. Logit regression
Following Bilgen Susanli (2016), we first estimate the probability of being NEET using a simple logit
model based on a set of individual (e.g. age, gender, and level of education) and household
characteristics, geographical location, and housing conditions.
������������������������������������������������������������(������������������������������������ ) = ������������������������ ������������������������������������ + ������������������������������������ . (1)
The model calculates the probability that the dependent variable acquires value 1:
������������ [������������������������������������ℎ = 1|������������ = ������������] = ������������(������������������������������������ℎ = 1), (2)
12
where ������������(������������������������������������ℎ = 1) represents the probability of observing the condition of success for the i-th
individual given a particular value of X.
3. Synthetic panel
While the current panel data module allows the creation of year-by-year transition matrixes, any
longer-term analysis of labor-force transition is not feasible. However, a majority of the analyses of
the Moroccan labor market conducted so far (HCP-WB, 2017) indicate that the duration of inactivity
or unemployment tends to be particularly long. The need for a longer-term perspective to gauge
Moroccan labor market outcomes encouraged us to adapt a methodology originally developed for
analyzing poverty dynamics, the so-called synthetic panel (Dang et al., 2014, and Dang and Lanjouw,
2013). 4 The synthetic panel approach, using repeated cross-sections, produces transition matrices the
results of which tend not to be significantly different from those one might have produced by using a
real panel.
The approach builds on the “out-of-sample” imputation methodology described in Elbers et al. (2003)
for small-area estimation of poverty (often referred to as “poverty maps”) which, for the first time in
the literature, we adapt for analyzing movements in and out of the NEET condition. Following the
approach employed by Dang et al. (2014), we adapt it to our analysis as follows:
• We estimate a logit model for a binary dependent variable (0 = non-NEET, 1 = NEET) in the
first round of cross-sectional data (2010) using a specification which includes only time-
invariant covariates.
• Parameter estimates from this model are then applied to the same time-invariant regressors in
the second survey round (2018) to provide an estimate of the (unobserved) first period’s
NEET/non-NEET condition for the individuals surveyed in that second round.
4 Synthetic panels differ from pseudo-panel data in two major ways: first, as few as two rounds of repeated cross-sections
are required to construct the synthetic panels, and second, these panels are created at a more disaggregated level than
pseudo-panels. They are broadly related to the literatures on survey-to-census imputation (for example, Elbers et al.,
2003) and survey-to-survey imputation (see, for example, Dang et al., 2017). Recent applications and/or validations of
synthetic panel methods against actual panel data include Bierbaum and Gassmann (2012), Ferreira et al. (2013), Martinez
et al. (2013), Garbero (2014), Cancho et al. (2015), Dang and Ianchovichina (2018), Dang and Lanjouw (2018), Dang
and Dabalen (2019) and Hérault and Jenkins (2019).
13
• We then conduct an analysis of transitions in and out of the NEET condition based on the
NEET/non-NEET condition observed in the second round, along with the estimates from the
first round.
This method produces lower- and upper-bound estimates of transitions in and out of the NEET
condition that can be expected to sandwich true transition estimates obtained from actual panel data
sets. 5
More formally, the linear projection of the log-odds of the event that NEET equals 1 in each round is
given by the following logit model, where ������������������������������������ is a vector of time-invariant characteristics, 6
������������
������������������������������������������������������������(������������������������������������ ) = ln �1−������������
������������������������
� – for 0 < ������������ = ������������������������(NEET = 1) < 1 – is the log-odds, ������������������������������������ denotes an error
������������������������
term and ������������ runs from 1 to 2, representing the two rounds of cross-sectional surveys (that is, the 2010
and 2018 waves, respectively, of the Enquête nationale sur l’emploi):
′
������������������������������������������������������������ (������������������������������������ ) = ������������������������ ������������������������������������ + ������������������������������������ . (3)
Using model estimates, inferences on movements in and out of NEET are based on the directly
observed condition of an individual in round 2, and the estimated condition for the same individual
in round 1. For instance, the estimates of transitions in and out of the NEET condition are respectively
given by:
^
2
������������������������ �������������������������������������������������1 = 0 ⋂ ������������������������������������������������2 = 1� (4)
and
^
2
������������������������ �������������������������������������������������1 = 1 ⋂ ������������������������������������������������2 = 0�, (5)
where the superscript 2 denotes the estimated round 1 NEET/non-NEET condition for individuals
sampled in the second round. By contrast, the fraction of individuals who are neither in education nor
in employment or training in both survey rounds is given by:
^
2
������������������������ �������������������������������������������������1 = 1 ⋂ ������������������������������������������������2 = 1�, (6)
5 It should be noted that the terms “lower bound” and “upper bound” do not refer to bounds on the proportions of NEETs,
but to bounds on their mobility. This means that lower-bound estimates can indeed give higher proportions of NEETs
than upper-bound estimates—which, instead, tend to understate mobility (Dang et al., 2014, p. 115). Therefore, in what
follows, “lower” and “upper” will refer to the two bounds on mobility; for immobility, “lower” is the upper bound and
“upper” is the lower bound.
6 Given the data, the predictors that we include in the round 1 and 2 logit models are the ones that best adhere to the time
invariance assumption: individual’s sex, age, and relationship to the household head of the person.
14
whereas for individuals staying either in education or in employment or training in both rounds, the
immobility probability can be written as:
^
2
������������������������ �������������������������������������������������1 = 0 ⋂ ������������������������������������������������2 = 0�. (7)
To use the proposed methodology, the following two assumptions need to be satisfied (Dang et al.,
2014). In the first instance, the underlying population must be the same in all rounds of the survey;
this assumption is necessary to justify the use of time-invariant individual characteristics to predict
the NEET/non-NEET condition. Secondly, the correlation between the error terms of the logit model
in the two rounds is assumed to be non-negative according to Dang et al. (2014), and this assumption
can usually be made because negative correlation of the error terms is unlikely to happen on a large
scale. In other words, although for particular individuals we might see some negative correlation, the
kind of factors leading to such a correlation are unlikely to apply to an entire population all at the
same time. Because NEET rates are calculated preferably for youth defined as persons aged 15 to 24,
in our empirical analysis below these two assumptions will best be met by restricting the cross-
sectional sample to individuals aged 15 to 24. This range refers to the age in round 1; the round 2 age
range is adjusted upwards accordingly. The age-restricted sample size is 48,110 individuals for round
1 (that is, the 2010 wave); for round 2 (that is, the 2018 wave), the corresponding size is 47,186
individuals.
Based on these two assumptions, we estimate an upper bound on transitions in and out of NEET
condition by assuming—as in Dang et al. (2014)—no correlation between the error terms in the two
rounds. The practical implementation of the estimation of upper bounds proceeds along the following
lines:
1. Using data from round 1, we estimate the logit model:
′
������������������������������������������������������������(������������������������1 ) = ������������1 ������������������������1 + ������������������������1 (8)
̂1
and obtain the estimated coefficients ������������ ′
and the predicted residuals ������������̂������������1 .
2. For each individual in round 2, a random draw with replacement is taken from the empirical
distribution of residuals ������������̂������������1 , subsequently denoted ������������̃������������2
1 ; the estimated NEET/non-NEET
condition in the first round for each individual ������������ in the second round is predicted through: 7
� ������������(������������������������2������������ ̂′ 2 2
������������������������������������������������ 1 ) = ������������1 ������������������������1 + ������������̃������������1 , (9)
7 The superscripts “U” and, later, “L” refer to upper- and lower-bound estimates, respectively, of transitions in and out of
NEET condition.
15
from which we obtain:
� ������������(������������������������2������������
� ������������2������������
0 if ������������������������������������������������ 1 )<0
������������������������������������������������ 1 =� . (10)
�
1 if ������������������������������������������������ ������������(������������2������������ ) ≥ 0
������������1
3. Movements in and out of the NEET condition as well as immobility probabilities are
calculated using (10) and the observed NEET/non-NEET condition of individuals in round 2
via Equations (4) to (7).
4. Steps 1 to 3 are repeated ������������ times, and the average over all replications is taken.
Sensitivity analyses carried out using different numbers of replications suggested that precision gains
beyond 50 replications are modest. 8 Therefore, for the following analysis, estimates are based on 50
replications.
A lower bound of mobility is provided by assuming perfect correlation between the error terms in the
two rounds (Dang et al., 2014). Precisely, lower-bound estimates for transitions in and out of NEET
condition are obtained as follows:
̂1
1. Using data from round 1, we estimate Equation (8) to obtain the predicted coefficients ������������ ′
.
2. Using data from round 2, we estimate the logit model:
′
������������������������������������������������������������(������������������������2 ) = ������������2 ������������������������2 + ������������������������2 (11)
and obtain the predicted residuals ������������̂������������2
2.
3. The estimated NEET/non-NEET condition in round 1 for each individual in round 2 is
̂1
predicted by using data from round 2, the predicted coefficients ������������ ′
from round 1, and the
individual’s own residual in round 2, ������������̂������������2
2 , via the equation:
� ������������(������������������������2������������ ̂′ 2 2
������������������������������������������������ 1 ) = ������������1 ������������������������1 + ������������������������̂������������2 , (12)
�������������
������������
where scalar ������������ = ������������
�
1
is chosen to ensure the standard deviation of the imputed round 1
������������2
�������������1 ; from Equation (12), we obtain:
residuals distribution equals ������������
� ������������(������������������������2������������
� ������������2������������
0 if ������������������������������������������������ 1)<0
������������������������������������������������ 1 =� . (13)
�
1 if ������������������������������������������������ ������������(������������2������������ ) ≥ 0
������������1
8 The results of these analyses are not shown in the paper but are available upon request from the authors.
16
4. Movements in and out of NEET condition as well as immobility probabilities are calculated
using (13) and the observed NEET/non-NEET condition of individuals in round 2 via
Equations (4) to (7).
In this case, steps 1 to 3 do not have to be replicated since the prediction errors for each individual
are used.
In summary, the only difference between the lower- and upper-bound estimates arises from the
residual that is added to the prediction of the NEET/non-NEET condition, as can been seen by
comparing Equations (9) and (12). The lower-bound estimate simply adds the same residual to the
linear prediction that an individual has in round 2, thereby inducing perfect correlation between the
residuals. The upper-bound estimate takes a random draw from all individual residuals in round 1,
resulting in no correlation between the residuals in the first and second round.
5. Results
1. Characteristics of the NEET Category
Results of the logit regression analysis for all individuals in the age group 15-24 are presented in the
following figures 9 and the marginal effects are presented in the Appendix. We estimate a logit model
for the NEET binary dependent variable (NEET = 1, non-NEET = 0) assuming that the probability
of a positive outcome is determined by the standard normal cumulative distribution function. Results
show a clear profile of the NEETs, already provided in the descriptive analysis. In particular, the
NEET category is composed mainly of young women 20 to 24 years old, with low levels of education,
and typically married. This profile will be analyzed in greater detail when gender-specific results are
presented.
With reference to Figure 5, results show that the likelihood of being a NEET increases with age
because of the complexity of transitioning from school to the labor market that all too frequently leads
9 For the logit regression model, we used the 2010, 2011, 2015, 2017 and 2018 survey rounds. The choice depended on
whether of a complete set of data required to perform the analysis was available. Complete regression results are saved
in the appendix. In the following graphs, significant results are those where the bar is away from the zero value and the
symbol describing the confidence interval does not include the zero value. All the independent variables are dummies or
categorical variables: female (1 = female, 0 = male); age group (1 = 19-24, 0 = 15-19); marital status (married or
widower/divorced, with being single as the omitted reference case); education attainment (Koranic school, primary
school, secondary school and tertiary education, with no-education as the omitted reference case); housing characteristics
(villa, apartment, traditional Moroccan house, modern Moroccan house, with rural house as the omitted reference case)
and two geographical areas, namely urban or rural regions (with urban as the omitted reference case), and macro-regions
(less developed regions and more developed regions, with the former as the omitted reference case).
17
to unemployment as the person gets older. Moreover, the results confirm that the area of residence
matters significantly; people living either in big towns or in rural areas are less likely to become
NEET than those living in medium-sized towns. In big towns, there are many more chances to
continue studying or to find a job, while in rural areas young people are often involved, depending
on the season, in family-based farming activities.
The effect of education is as expected: all other things being equal, higher levels of education are
associated with a lower probability of being NEET. This effect is particularly pronounced in the case
of tertiary education (see also the marginal effects in the appendix). As expected, household well-
being also matters substantially. The asset index constructed by aggregating various household
assets 10 is also negatively associated with the probability of having a NEET in the household: the
wealthier the household, the higher the chances are that young members will continue on to higher
education or find a job. Coming from less affluent families, on the other hand, can in practice virtually
preclude the possibility of continuing to study beyond a certain level, or may affect access to the jobs
market.
Finally, the education level of the household head, and sector of activity, both significantly impact
the probability of having NEETs in the family. Again, higher levels of household head education are
negatively associated with the presence of NEETs at home. In addition, as previously discussed for
rural areas, whenever the family (and household head) are active in agriculture, young members tend
also to be active in that sector. This explains the negative and significant marginal effect of the
variable household head employed in agriculture.
10 With this asset index, suggested by Filmer and Pritchett (2001), principal-components analysis was used to calculate
the weights of the index. The first principal component—the linear combination capturing the greatest variation among
the set of variables—can be converted into factor scores, which serve as weights. The rationale for using this index is that
it captures the household’s permanent welfare dimension better than simple consumption data and can provide more
reliable rankings among households.
18
Figure 5: Results of Logit Regression
Source: authors’ calculations using Labor Force Surveys 2010-2018.
Figure 6 shows the results by dividing the sample into males and females; we perform this analysis
to understand whether specific individual characteristics affect the outcomes differently. Since the
regressors are the same as those used in the whole sample, it is worth commenting only on the key
aspects that differ in the two subgroups. The first point to mention is the difference in the impact of
marital status between males and females: married men have a lower probability of being NEET,
while the opposite can be observed for women. These results suggest that, once married, it is even
harder for women to enter the labor market. They often must rely economically on their husbands’
income.
Differences between men and women can also be found for other variables such as the educational
level: all other things equal, education matters more for women than men in determining the risk of
becoming NEET. Interestingly, the household head education variables remain significant and
negative for women only, while for men they become insignificant, suggesting that family
background matters more for women than for men. This is further confirmed by the size of the
marginal effect of the asset index, as well as by most of the housing quality variables: in absolute
value, they are bigger than those of men and always negative and significant.
19
Finally, results by area of residence show different effects for men and women. For men, this has the
positive effect of reducing the probability of being NEET, while for women the effect is the opposite.
This may be because in rural areas men can often find work in agriculture or undertaking jobs that
require high levels of physical effort. This excludes many women from that labor market.
Figure 6: Logit Regression, by Gender
Source: authors’ calculations using Labor Force Surveys 2010-2018.
2. Synthetic Panel and Transition Matrices
In the preceding section, we captured a static snapshot of the NEET condition, but it is also important
to understand the dynamics of the phenomenon, especially the probability of moving in or out of
NEET, remaining NEET, or remaining non-NEET, and for how long. As mentioned earlier, the lack
of panel data that can cover a period longer than one year limits the possibility to undertake a
meaningful analysis: one year is not enough, for example, to gauge whether or not a person is stuck
in the NEET condition. A longer time span is clearly needed. The adaptation of a synthetic panel
method to our analysis enabled us to overcome this limitation.
The following section will first present some validation results, notably showing that the estimated
model can replicate observed transition matrixes. The section will then show the 2010-2018 transition
20
matrix results, and finally, examine the performance of specific subsamples, such as men and women,
and urban and rural.
Table 4 compares the percentages of NEETs in 2010 and 2018 derived from the cross-section data
sets, the actual panel data tracking the same individuals in four survey rounds—notably 2010– 2011
and 2017–2018—and the synthetic panel based on cross-sectional data in 2010 and 2018. For 2010,
there are no separate estimates of NEET rates based on the synthetic panel, because they are obtained
using the share of individuals in the 2018 sample estimated from those who were NEET in 2010 and
2018, then adding the share of individuals falling into the NEET condition over the considered period.
Table 4: NEET Percentage Rates in 2010 and 2018: Comparison of Cross-Section, Actual Panel and Synthetic
Panel Lower- and Upper-bound Estimates
Year Lower-bound estimate Cross-section Actual panel Upper-bound estimate
31.41 31.82
2010 - -
(30.96; 31.86) (31.14; 32.51)
46.66 46.92
2018 46.66 46.66
(46.20; 47.12) (46.33; 47.52)
Notes: results are restricted to the sample of individuals aged 15 to 24 in 2010 and aged 23 to 32 in 2018. “Lower” is the upper
bound and “Upper” is the lower bound. Synthetic panel upper-bound estimates are based on 50 replications. Individual-level
sampling weights are applied; 95 percent confidence intervals are given in parentheses.
Two key elements emerge from this comparison. First, NEET rates for both 2010 and 2018 calculated
on the cross-section and the actual panel data almost overlap. Second, the upper- and lower-bound
estimates of the synthetic panel for 2018 are remarkably close to the NEET rates observed in both the
cross-section and the actual panel. This represents an encouraging result that gives a positive first
indication of the quality of the synthetic panel.
The next step is represented by the analysis of the transitions in and out of the NEET condition based
on the 2010/2018 synthetic panel. For this purpose, a transition matrix was created. The rows in table
5 indicate the proportion of individuals in the 2018 sample estimated to be in one of four groupings:
a. In NEET both in 2010 and 2018 (NEET, NEET); b. Moved out of NEET during that time period
(NEET, non-NEET); c. Fell into the NEET condition (non-NEET, NEET); or d. Were never in NEET
between 2010 and 2018 (non-NEET, non-NEET). These four options are exhaustive, and therefore
each column adds up to 100 percent.
21
Table 5: Transition Matrix Based on the Synthetic Panel 2010/2018
Status in 2010, 2018 Lower-bound estimate (%) Upper-bound estimate (%)
NEET, NEET 46.66 35.95
NEET, non-NEET 4.35 17.82
Non-NEET, NEET 0.00 10.71
Non-NEET, non-NEET 48.98 35.51
Notes: results are restricted to the sample of individuals aged between 15 and 24 in 2010 and between 23 and 32 in
2018. “Lower” and “Upper” refer to the two bounds on mobility; for immobility, “Lower” is the upper bound and
“Upper” is the lower bound. Upper-bound estimates are based on 50 replications. Individual-level sampling weights
are applied.
The estimated NEET rates for 2018 can be directly derived from the transition matrix by adding up
the share of people in two groups: (NEET, NEET) and (non-NEET, NEET). For instance, the lower-
bound estimate of the NEET rate in 2018 amounted to 46.66 percent, which is the estimate displayed
in the respective cell in Table 4. Similarly, the upper-bound estimate consists of the chronically NEET
(35.95 percent) and the (non-NEET, NEET) group (10.71 percent), resulting in the estimate displayed
in the respective cell in Table 4 (46.66 percent).
The results suggest that the NEET condition tends to persist, as is hinted at in the paper’s title.
Comparing the share of NEET in both periods to those who were NEET in 2010, we found that those
NEET in 2010 had a 70 to 90 percent probability of remaining NEET after 10 years, and only a 10 to
30 percent probability of escaping from the condition. This impression of substantial immobility is
confirmed by the complementary results of the non-NEETs in 2010. The chances after 10 years of
staying non-NEET are 80 to 100 percent; indeed, the risk of becoming NEET is between 0 and 20
percent. Ten years is quite a long period and yet we observed very little mobility during that time.
While it is beyond the scope of this paper to investigate the causes of this inertia, some indications
can be already drawn from the results of the static analysis—notably, that the person’s gender, the
household head’s level of education, and the well-being of households can all play a crucial role in
explaining the labor market trajectory of young people. These initial conditions, we conjecture, tend
to determine whether the young family member will start off as NEET—we can see this in the
previous section—but they also can give some indication of his/her capacity to move or not move out
of NEET.
To further explore the persistence and dynamics of the NEET condition in the Moroccan labor market,
the same exercise is repeated for different population subgroups separately: male/female, wealthy
and not, and levels of household head education (in Appendix). Table 6 summarizes NEET dynamics
22
for males (left-hand side columns) and females (right-hand side columns). The share of NEET young
women in 2010 is already four times bigger than that of young men. Therefore, since the labor market
is particularly immobile, after 10 years these women have an 80 to 90 percent probability of remaining
NEET, with little chance of improvement. The men’s results confirm that for them is less of a
problem: most of them are non-NEET in 2010 and remain that way after 10 years.
Table 6: Transition Matrix Based on the Synthetic Panel 2010/2018 by Gender
Male Female
Status in 2010,
2018 Lower-bound Upper-bound Lower-bound Upper-bound
estimate (%) estimate (%) estimate (%) estimate (%)
NEET, NEET 18.31 3.20 74.23 67.75
NEET, non-NEET 0.14 14.69 8.45 20.90
Non-NEET,
0.00 15.11 0.00 6.48
NEET
Non-NEET, non-
81.55 67.01 17.32 4.87
NEET
Notes: results are restricted to the sample of individuals aged between 15 and 24 in 2010 and between 23 and 32 in
2018. “Lower” and “Upper” refer to the two bounds on mobility; for immobility, “Lower” is the upper bound and
“Upper” is the lower bound. Upper-bound estimates are based on 50 replications. Individual-level sampling weights
are applied.
We also divided the sample into two groups: those in the bottom 80 percent and those in the top 20
percent of the asset index distribution (Table 7). Here also, we noticed some differences between the
two groups. First, as expected, there is a higher prevalence of NEETs among households in the bottom
80 percent than in the top 20. Second, among the top 20 percent, there is less persistence in the NEET
condition, and there are fewer chances to become a NEET in 2018 if the person was non-NEET in
2010.
Table 7: Transition Matrix Based on the Synthetic Panel 2010/2018 for Top 20 Percent and Bottom 80 Percent
Asset Index
Asset index < 80% Asset index > 80%
Status in 2010,
Lower-bound Upper-bound Lower-bound Upper-bound
2018
estimate (%) estimate (%) estimate (%) estimate (%)
NEET, NEET 32.52 16.50 16.37 7.94
NEET, non-
0.00 11.42 0.00 15.75
NEET
Non-NEET,
0.00 16.02 0.00 8.44
NEET
Non-NEET, non-
67.48 56.06 83.63 67.87
NEET
23
Notes: results are restricted to the sample of individuals aged between 15 and 24 in 2010 and between 23 and 32 in 2018. “Lower” and
“Upper” refer to the two bounds on mobility; for immobility, “Lower” is the upper bound and “Upper” is the lower bound. Upper-bound
estimates are based on 50 replications. Individual-level sampling weights are applied.
Some further validation checks were undertaken by means of comparison between transition
matrices—both for the overall population and by population subgroups—using true panel data and
the estimated synthetic panels for each of the consecutive year-pairs of data (2010/2011 to
2017/2018). 11 As we expected, the lower-bound estimates underestimate mobility—understating
movements into and out of the NEET condition, and overstating the extent to which people remain
NEET or non-NEET—while the upper-bound estimates overestimate mobility. Encouragingly, the
“truth” (true rate) tends to lie about midway between these bounds. We find, thus, that our approach
does indeed present bounds within which the “truth” can be observed.
6. Discussion and Conclusions
Several decades ago, Morocco embarked on a long-term process of political, social, economic, and
environmental reforms, culminating in the adoption of the New Constitution in 2011. Because of
these reforms, progress has been made, in particular, in the reduction of absolute poverty, better
access to basic public services, and the considerable development of public infrastructures. The social
landscape, however, remains marked by challenges yet to be met, particularly in terms of social
cohesion. These challenges could pose the risk of exclusion of the most fragile components of
Moroccan society, in particular women and young people. Young people unemployed, outside the
school system and not undergoing any training—the so-called NEETs—form about 30 percent of the
Moroccan population between ages 15 and 24. That number looks even grimmer when compared to
NEET rates in the MENA region, already characterized by particularly high rates of NEETs (ILO,
2019).
Subsequent rounds of Moroccan labor force surveys have already presented the opportunity to
develop a clearer profile of NEETs in the country. What is missing, however, are the data to examine
how the circumstances of people in the NEET condition have evolved over a reasonably long period
of time. Our adaptation of the synthetic panel methodology to this specific issue enables us to
overcome the data limitations and to provide helpful insights into NEETs dynamics in the last decade.
The first part of our analysis showed the key determinants of the NEET condition. As expected,
individual characteristics play an important role. The probability of becoming NEET is higher for
11 For space considerations, we just summarize the findings here; the full set of results is provided in the Appendix.
24
women—particularly those married and/or with children—and for young men and women with lower
levels of education. A higher concentration of NEETs is also more likely in medium-sized towns than
in big towns or rural farming areas. In big towns, it is easier for young people to continue schooling
or find a job, while in rural areas every household member is typically involved in some farming
activities. The family context also influences the probability of being NEET. Higher parental
education and better economic conditions tend—all other things being equal—to decrease the
probability of young household members becoming NEET.
In the second part, we presented the results on NEET dynamics using a synthetic panel method, a
primer in this literature. After validating the results using the available panel subsample in the data,
we proceeded by estimating a transition matrix that tracks how those who were between ages 15 and
24 in 2010 evolved over a 10-year period. The results are far from encouraging: the vast majority of
those who were in NEET in 2010 tended to remain outside both the labor market and education even
after 10 years, with very little chance of moving out.
Likewise, those in non-NEET tended to remain as they were after 10 years, confirming not only a
general impression of immobility within the Moroccan labor market, but also the crucial importance
of initial conditions: to avoid the NEET condition in 2018, the best option is to have started in 2010
with a job or in school. While at first glance this might look like a tautological statement, it in fact is
not. It confirms the importance of initial conditions—for example, being female, or coming from a
relatively disadvantaged family—in determining future outcomes, and the profound effect of the lack
of corrective mechanisms and interventions within the Moroccan political economy that might help
to change these results along the line.
These preliminary results can already provide some initial suggestions for policy intervention.
On one side, the recommendation is to work on prevention since, as we have seen, initial conditions
tend to largely condition a young person’s future trajectory. For the young, prevention means
improving the quality of education, reducing the likelihood of early dropouts, and financially
supporting those whose initial disadvantaged background might preclude their continuing with formal
academic studies or vocational skills training.
Ex-post interventions are likely to be more costly, but this does not lessen their urgency. In this regard,
one important aspect we have stressed throughout the paper is the persistent disadvantaged position
of women. This is true for those we analyzed as NEET (that is, between ages 15 and 24) but it also
25
applies to those older than 24. According to the latest figures, more than 8 million Moroccan women
are not active in the labor market. Among these, almost 2 million have more than a secondary level
of education—truly a dramatic wastage and underutilization of human capital into which a costly
educational investment has already been made. Developing incentives and providing services to
encourage them to enter or remain in the labor market—and to undo the deeply entrenched social
norms that undervalue women’s education and their potential to contribute productively to the
economy—should be a top priority in the country, and indeed, there are signs that this is becoming
increasingly recognized as a matter of national urgency.
26
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29
Appendix A: Additional Tables
Table A.1: Marginal Effects of the Logit Model: Full, Male and Female Models
N2010_2018 M2010_2018 F2010_2018
Total Male Female
coef se coef se coef se
2011 -0.00719 0.00412 -0.00404 0.00378 -0.0117 0.00777
2015 -0.00546 0.00437 0.00472 0.00417 -0.0264** 0.0081
2017 0.0432*** 0.00398 0.0290*** 0.00378 0.0535*** 0.00703
2018 0.0357*** 0.004 0.0270*** 0.00381 0.0385*** 0.00709
Female 0.298*** 0.0023
20-24 years 0.182*** 0.0023 0.102*** 0.00217 0.278*** 0.00423
Married 0.282*** 0.00516 -0.0827*** 0.00267 0.407*** 0.00538
Widower\Divorced 0.143*** 0.0222 -0.00879 0.0345 0.194*** 0.0257
Rural area -0.0112** 0.00366 -0.0446*** 0.00339 0.0569*** 0.00703
Large cities -0.0297*** 0.00242 -0.0175*** 0.00223 -0.0444*** 0.00445
Primary school -0.0218*** 0.00464 -0.0631*** 0.00484 0.0517*** 0.00854
Secondary school -0.227*** 0.00356 -0.118*** 0.00348 -0.343*** 0.00691
Higher education -0.212*** 0.00239 -0.0966*** 0.00205 -0.367*** 0.00574
Asset index -0.140*** 0.00774 -0.0654*** 0.00683 -0.226*** 0.0141
Modern house 0.0406*** 0.00379 0.0362*** 0.00382 0.0495*** 0.00764
Apartment 0.0000634 0.00621 0.0354*** 0.00687 -0.0369*** 0.011
Tradition house 0.0639*** 0.00768 0.0298*** 0.00758 0.115*** 0.0126
Bidonville 0.0308*** 0.00652 0.0424*** 0.00703 0.0260* 0.0118
HH characteristic
Unemployed 0.0772*** 0.00593 0.0784*** 0.00714 0.0603*** 0.0096
Industry 0.0475*** 0.00756 0.0533*** 0.00869 0.0403** 0.0125
Construction 0.0382*** 0.00555 0.0402*** 0.00629 0.0358*** 0.00995
Services 0.0348*** 0.00379 0.0308*** 0.00368 0.0418*** 0.00764
Primary school -0.00594 0.0032 0.0115*** 0.0031 -0.0351*** 0.00622
Secondary school -0.0115** 0.004 0.00971* 0.00391 -0.0485*** 0.00755
Higher education -0.0229*** 0.00386 0.00105 0.00369 -0.0629*** 0.00736
Table A.2: Transition Matrix: Synthetic vs. Actual Panel Data (2010/2011 through to 2017/2018)
Status in 2010, 2011 Lower-bound estimate (%) Actual panel (%) Upper-bound estimate (%)
24.52
NEET, NEET 33.81 16.80
(23.89; 25.15)
7.30
NEET, non-NEET 0.00 11.36
(6.93; 7.70)
7.86
Non-NEET, NEET 0.00 17.02
(7.47; 8.26)
60.32
Non-NEET, non-NEET 66.19 54.82
(59.60; 61.04)
Status in 2011, 2012 Lower-bound estimate (%) Actual panel (%) Upper-bound estimate (%)
24.11
NEET, NEET 33.88 16.76
(23.46; 24.77)
6.51
NEET, non-NEET 0.00 10.68
(6.13; 6.92)
8.16
Non-NEET, NEET 0.00 17.12
(7.76; 8.59)
61.21
Non-NEET, non-NEET 66.12 55.44
(60.45; 61.96)
30
Status in 2012, 2013 Lower-bound estimate (%) Actual panel (%) Upper-bound estimate (%)
21.70
NEET, NEET 33.21 16.36
(21.07; 22.34)
5.71
NEET, non-NEET 0.00 10.82
(5.37; 6.07)
6.28
Non-NEET, NEET 0.00 16.85
(5.93; 6.65)
66.32
Non-NEET, non-NEET 66.79 55.97
(65.59; 67.04)
Status in 2013, 2014 Lower-bound estimate (%) Actual panel (%) Upper-bound estimate (%)
24.41
NEET, NEET 32.75 16.33
(23.74; 25.09)
5.90
NEET, non-NEET 0.00 10.97
(5.53; 6.28)
6.17
Non-NEET, NEET 0.00 16.42
(5.80; 6.55)
63.53
Non-NEET, non-NEET 67.25 56.28
(62.76; 64.29)
Status in 2014, 2015 Lower-bound estimate (%) Actual panel (%) Upper-bound estimate (%)
23.56
NEET, NEET 32.48 16.01
(22.88; 24.26)
5.65
NEET, non-NEET 0.00 10.73
(5.29; 6.04)
6.28
Non-NEET, NEET 0.00 16.47
(5.90; 6.69)
64.50
Non-NEET, non-NEET 67.52 56.79
(63.69; 65.30)
Status in 2017, 2018 Lower-bound estimate (%) Actual panel (%) Upper-bound estimate (%)
24.30
NEET, NEET 32.17 15.72
(23.84; 24.78)
4.92
NEET, non-NEET 0.00 10.53
(4.69; 5.16)
6.56
Non-NEET, NEET 0.00 16.45
(6.29; 6.83)
64.22
Non-NEET, non-NEET 67.83 57.30
(63.69; 64.74)
Notes: for each year-pair of data, results are restricted to the sample of individuals age 15 to 24 in round 1; the round 2 age range is adjusted upwards
accordingly. “Lower” and “Upper” refer to the two bounds on mobility; for immobility, “Lower” is the upper bound and “Upper” is the lower bound.
Synthetic panel upper-bound estimates are based on 50 replications. Individual-level sampling weights are applied; 95 percent confidence intervals
are given in parentheses.
31
Table A.3: Transition Matrix by Gender: Synthetic vs. Actual Panel Data (2010/2011 through to 2017/2018)
Male Female
Status in 2010, 2011
Lower-bound Upper-bound Lower-bound Upper-bound
Actual panel (%) Actual panel (%)
estimate (%) estimate (%) estimate (%) estimate (%)
6.12 43.59
NEET, NEET 13.41 0.91 54.20 32.64
(5.63; 6.66) (42.57; 44.61)
6.64 7.99
NEET, non-NEET 0.00 5.22 0.00 17.46
(6.13; 7.19) (7.45; 8.56)
6.66 9.10
Non-NEET, NEET 0.00 12.50 0.00 21.55
(6.14; 7.21) (8.53; 9.71)
80.58 39.32
Non-NEET, non-NEET 86.59 81.37 45.80 28.34
(79.72; 81.41) (38.32; 40.33)
Male Female
Status in 2011, 2012
Lower-bound Upper-bound Lower-bound Upper-bound
Actual panel (%) Actual panel (%)
estimate (%) estimate (%) estimate (%) estimate (%)
6.20 42.43
NEET, NEET 13.51 0.92 54.30 32.70
(5.71; 6.74) (41.37; 43.50)
5.58 7.46
NEET, non-NEET 0.00 5.18 0.00 16.24
(5.07; 6.15) (6.91; 8.05)
6.68 9.68
Non-NEET, NEET 0.00 12.60 0.00 21.60
(6.16; 7.25) (9.07; 10.32)
81.53 40.43
Non-NEET, non-NEET 86.49 81.31 45.70 29.46
(80.65; 82.38) (39.38; 41.49)
Male Female
Status in 2012, 2013
Lower-bound Upper-bound Lower-bound Upper-bound
Actual panel (%) Actual panel (%)
estimate (%) estimate (%) estimate (%) estimate (%)
5.83 38.34
NEET, NEET 14.03 1.06 52.75 31.87
(5.36; 6.35) (37.30; 39.38)
4.69 6.78
NEET, non-NEET 0.00 5.35 0.00 16.37
(4.25; 5.17) (6.27; 7.32)
5.40 7.20
Non-NEET, NEET 0.00 12.97 0.00 20.88
(4.94; 5.90) (6.68; 7.75)
84.08 47.69
Non-NEET, non-NEET 85.97 80.62 47.25 30.88
(83.27; 84.85) (46.64; 48.74)
Male Female
Status in 2013, 2014
Lower-bound Upper-bound Lower-bound Upper-bound
Actual panel (%) Actual panel (%)
estimate (%) estimate (%) estimate (%) estimate (%)
6.88 42.81
NEET, NEET 13.99 1.19 51.65 31.52
(6.34; 7.47) (41.73; 43.89)
5.68 6.13
NEET, non-NEET 0.00 5.62 0.00 16.29
(5.18; 6.21) (5.60; 6.70)
5.46 6.91
Non-NEET, NEET 0.00 12.79 0.00 20.12
(4.97; 6.00) (6.38; 7.48)
81.98 44.16
Non-NEET, non-NEET 86.01 80.39 48.35 32.06
(81.10; 82.83) (43.08; 45.24)
32
Male Female
Status in 2014, 2015
Lower-bound Upper-bound Lower-bound Upper-bound
Actual panel (%) Actual panel (%)
estimate (%) estimate (%) estimate (%) estimate (%)
6.56 40.73
NEET, NEET 14.33 1.28 50.81 30.81
(6.01; 7.17) (39.65; 41.82)
4.84 6.47
NEET, non-NEET 0.00 6.08 0.00 15.52
(4.35; 5.38) (5.95; 7.04)
5.68 6.89
Non-NEET, NEET 0.00 13.05 0.00 20.00
(5.15; 6.26) (6.36; 7.47)
82.91 45.91
Non-NEET, non-NEET 85.67 79.58 49.19 33.67
(81.98; 83.81) (44.81; 47.01)
Male Female
Status in 2017, 2018
Lower-bound Upper-bound Lower-bound Upper-bound
Actual panel (%) Actual panel (%)
estimate (%) estimate (%) estimate (%) estimate (%)
8.10 41.13
NEET, NEET 14.89 1.59 49.66 30.03
(7.69; 8.53) (40.37; 41.90)
4.89 4.96
NEET, non-NEET 0.00 6.78 0.00 14.27
(4.56; 5.23) (4.63; 5.31)
6.16 6.97
Non-NEET, NEET 0.00 13.30 0.00 19.63
(5.80; 6.54) (6.59; 7.37)
80.85 46.94
Non-NEET, non-NEET 85.11 78.33 50.34 36.07
(80.24; 81.45) (46.17; 47.72)
Notes: for each year-pair of data, results are restricted to the sample of individuals age 15 to 24 in round 1; the round 2 age range is adjusted upwards
accordingly. “Lower” and “Upper” refer to the two bounds on mobility; for immobility, “Lower” is the upper bound and “Upper” is the lower bound.
Synthetic panel upper-bound estimates are based on 50 replications. Individual-level sampling weights are applied; 95 percent confidence intervals
are given in parentheses.
Table A.4: Transition Matrix by Education levels
No education Education
Status in 2010, 2018
Lower-bound estimate (%) Upper-bound estimate (%) Lower-bound estimate (%) Upper-bound estimate (%)
NEET, NEET 34.50 17.39 27.22 13.96
NEET, non-NEET 0.00 10.76 0.00 13.03
Non-NEET, NEET 0.00 17.11 0.00 13.26
Non-NEET, non-NEET 65.50 54.75 72.78 59.75
Notes: Results are restricted to the sample of individuals age 15 to 24 in 2010 and 23 to 32 in 2018. “Lower” and “Upper” refer to the two
bounds on mobility; for immobility, “Lower” is the upper bound and “Upper” is the lower bound. Upper-bound estimates are based on 50
replications. Individual-level sampling weights are applied.
33