metadata: authors: 0: author: Lopez-Acevedo,Gladys C.
1: author: Devoto,Florencia
2: author: Morales,Matías
3: author: Roche Rodriguez,Jaime Alfonso
count: Morocco
docna: 0: docna: Trends and Determinants of Female Labor
            Force Participation in Morocco : An Initial Exploratory Analysis
docty: Policy Research Working Paper
subtopic: Educational Sciences,Labor Markets,Rural Labor Markets,Gender and Development
teratopic: Social Protections and Labor,Rural Development,Education,Gender
abstracts: cdata!: The U-shape theory argues that at early
            stages of development, countries experience a reduction in
            the female labor force participation, eventually followed by
            a recovery. In Morocco, female labor force participation is
            now lower than it was two decades ago due to several factors
            that are discussed in the paper. There is also a persistent
            50-percentage-points gender gap in labor force participation
            rates, despite improvements typically related to development
            and female inclusion—such as a higher gross domestic product
            per capita, lower fertility rates, and better access to
            education. At the same time, urban job creation has not been
            able to offset rural job destruction nor the increase in the
            working age population for both genders. Using data from the
            Moroccan Labor Force Survey, the World Values Survey, and
            the Arab Barometer, probit models and a multinomial logit
            are estimated to explore the challenges affecting female
            insertion into the labor market. The findings show that
            higher educational attainment increases the probability of
            female participation, but this relationship has decreased
            over time, not being enough to offset other obstacles caused
            by other individual and household characteristics. Being
            married and the presence of other inactive women are found
            to decrease female participation. The educational level of
            the head of household (typically men) increases female
            inactivity, suggesting that potentially gender roles may
            drive women out of the labor market and slow the recovery in
            women’s participation.
geo_reg_key: 517202,517191
repnme: repnme: Trends and Determinants of Female Labor
            Force Participation in Morocco : An Initial Exploratory Analysis
display_title: Trends and Determinants of Female
            Labor Force Participation in Morocco : An Initial
            Exploratory Analysis
listing_relative_url: /research/2021/03/32934176/trends-determinants-female-labor-force-participation-morocco-initial-exploratory-analysis
topicv3: Labor Markets m1326488 855,Education m1327048 470,Gender m1327621 615,Jobs m1503931 2670,Labor Force Participation m1504050 2746
publishtoextweb_dt: 2021-03-22T00:00:00Z
dois: 10.1596/1813-9450-9591
historic_topic: Social Protections and Labor,Rural Development,Education,Gender
origu: EFI-MNA-POV-Poverty and Equity (EMNPV)
no_of_pages: 44
author_keys: 0: author_key: 428192
1: author_key: 1158005
2: author_key: 1915557
model_extractions: Policy Research Working Paper 9591 Trends and Determinants of Female Labor Force Participation in Morocco An Initial Exploratory Analysis _Gladys Lopez-Acevedo Florencia Devoto Matías Morales Jaime Roche Rodriguez_ Poverty and Equity Global Practice March 2021
Policy Research Working Paper 9591

# **Abstract**

The U-shape theory argues that at early stages of development, countries experience a reduction in the female labor force participation, eventually followed by a recovery. In Morocco, female labor force participation is now lower than it was two decades ago due to several factors that are discussed in the paper. There is also a persistent 50-percentage-points gender gap in labor force participation rates, despite improvements typically related to development and female inclusion—such as a higher gross domestic product per capita, lower fertility rates, and better access to education. At the same time, urban job creation has not been able to offset rural job destruction nor the increase in the working age population for both genders. Using data from the Moroccan Labor Force Survey, the World Values Survey, and the Arab Barometer, probit models and a multinomial logit are estimated to explore the challenges affecting female insertion into the labor market. The findings show that higher educational attainment increases the probability of female participation, but this relationship has decreased over time, not being enough to offset other obstacles caused by other individual and household characteristics. Being married and the presence of other inactive women are found to decrease female participation. The educational level of the head of household (typically men) increases female inactivity, suggesting that potentially gender roles may drive women out of the labor market and slow the recovery in women’s participation.

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 gacevedo@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 of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Trends and Determinants of Female Labor Force Participation in Morocco: An Initial Exploratory Analysis

Gladys Lopez-Acevedo, Florencia Devoto, Matías Morales, and Jaime Roche Rodriguez

## **JEL classification** : J16, J21, O54

**Keywords:** Morocco, Female, labor force participation.

This is a background paper for the World Bank Jobs Diagnostic of Morocco (2020). We thank Uche Eseosa Ekhator-Mobayode, Gordon Betcherman, Vasco Molini, and Federica Alfani for useful discussions and suggestions in the crafting of this paper. All remaining errors are the responsibility of the authors. To contact the authors please write to gacevedo@worldbank.org. Gladys Lopez-Acevedo<sup>1</sup> is a Lead Economist and Global Lead at the World Bank in the Poverty and Equity Global Practice Group (PEGP). Florencia Devoto, Matias Morales, and Jaime Alfonso Roche Rodriguez are consultants at the World Bank in the Poverty and Equity Global Practice Group (PEGP). The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Postal address: 2232 40<sup>th</sup> Pl NW, Washington DC, 20007, United States of America.
# **I. Introduction**

At 21.16 percent, the female labor force participation (FLFP) rate in Morocco ranked 180th out of a sample of 189 countries in 2018,<sup>1</sup> meaning that 78.4 percent of Moroccan women between 15 and 65 years old were neither employed nor looking for a job. Currently, the FLFP rate is lower than it was two decades ago, despite lower fertility rates and better access to education, factors usually associated with greater chances of employability.

While the education profile of the female population has improved, it has not translated into a greater share of educated women in the labor force. For instance, 40 percent of unemployed women have a college education (while 8 percent have no education), and among employed women in rural areas (half of the total employed women), 77 percent have no formal education and less than 1 percent have a college education.<sup>2</sup> Although women have better access to education, the participation rate among those educated has decreased over time. Female inactivity for women holding a superior education rose from 13 percent in 2010 to 19 percent in 2018 in rural areas and from 23 to 24 percent in urban areas in the same period. The overall low education profile of employed women signals constraints to participation, a possible mismatch between firms’ needs and their skills, and other cultural and social norms that could possibly limit the employment of women. Also, vulnerability in the form of unpaid or informal work is highly prevalent among employed women.

This paper provides insight into the persistent challenge that Morocco faces in bringing women into the labor market. From a macroeconomic point of view, increased FLFP would put more labor inputs to use, thus improving the country’s productive capacity. From a

> 1 World Development Indicators 2020.

> 2 In urban areas, education levels of the employed are somewhat higher and more evenly distributed.

2
microeconomic point of view, the benefits are more nuanced. On the one hand, when women participate in the labor force, they gain a voice in society, and as a consequence, households invest more in education and health (Duflo, 2012). On the other hand, while higher FLFP may imply greater welfare, this may not always be true if working conditions are not ideal. Evidence from developing countries suggests that many women work in low-paid jobs or unsafe working conditions out of necessity.

We use 18 cross-sectional waves of the National Employment Survey between 2001 and 2018, three waves of the World Values Surveys between 2001 and 2011, and four waves of the Arab Barometer between 2006 and 2017. Our goal is to gain a better understanding of Morocco’s low FLFP and to initially explore the constraints and choices that women face regarding their labor market status. For example, a restricted demand for female employment may be associated with traditional attitudes toward women’s work, which pulls women out of the labor force—an understudied factor in Morocco.

The structure of the paper follows: Section II reviews the existing literature on the microdeterminants of FLFP, along with the role of values and identity in women labor. Section III describes Morocco’s position in context in the MENA region and the world. Section IV describes the data used and its sources. Section V describes the profile of active and inactive women and female workers in terms of participation, employment, and inclusion. Section VI describes the methodology used to summarize the interplay of multiple variables. Section VII presents the results. Finally, section VIII pulls the paper together with conclusions.

3
# **II. Academic Literature and Previous Studies**

## **_The micro-determinants of FLFP_**

At the microeconomic level, Killingsworth and Heckman (1986) review the empirical and theoretical literature on the determinants of female labor supply and its interactions with fertility, marriage, and divorce decisions in the United States, Canada, Great Britain, and Germany. They established three stylized facts of FLFP observed during the 20th century: (a) FLFP only decreases after age 20; (b) single women are always more likely to work than married women, but married women have experienced a sustained FLFP increase over the past 100 years; and (c) in 1980, women were over-represented in occupations such as whitecollar clerical and services jobs and under-represented in blue-collar jobs such as crafts, operatives, and non-farm labor.

Using data from the 1960 census, their Ordinary Least Squares (OLS) results indicate that higher FLFP is associated with higher educational attainment, being single, and having a source of “other income.”<sup>3</sup> As we do in this paper, they acknowledge that their results arise from an observed equilibrium between supply and demand for women’s skills and consequently, cannot be taken as causal relationships. However, the observed characteristics of labor markets in these 20<sup>th</sup> century Western countries may differ greatly from those in Morocco—especially for female inclusion, because of the different conditions in terms of development.

> 3 Income other than own earnings, including transfer income.

4
What does the literature show for FLFP constraints in Morocco? Overall, the evidence is limited and out of date. Using the Moroccan Living Standards Measurement Study 19901991, Assaad and Zouari (2002) find that marriage does not affect FLFP, but the presence of children reduces it, particularly in terms of private-sector employment. Spierings and Smits (2007) study FLFP in four Arab countries (data from Morocco corresponds to the 2004 Demographic and Health Survey (DHS)) and find that higher education is the most important explanatory variable. Verme et al. (2016) conclude that the nature of economic growth in Morocco and gender norms interact to determine FLFP. In particular, they claim that women holding secondary education are unable to find jobs, which encourages marriage and deters female labor supply. Previously mentioned works do not explore how demographic characteristics affect FLFP for different periods of time. Also, they do not pay attention to the attributes of other members inside the household that could also affect women’s decisions towards work **_._** Other literature streams highlight the importance of traditional roles on FLFP. The values in a society—beliefs and motivations other than economic factors—can shape individual behavior and play a role in determining female labor supply. A key manifestation of these traits is the individual’s attitude toward work and its relationship with gender. In Chile, for example, Contreras and Plaza (2010) find that as more women internalize conservative cultural values, they participate less in the labor market, offsetting positive human capital accumulation effects. Identity—that is, the link between certain social categories and the expectations about them—also affects individuals’ decisions (Akerlof and Kranton, 2000); 5
in MENA, women with strong “traditional”<sup>4</sup> identities have a 5-percentage-point lower probability of entering the labor market (Hayo and Caris, 2013).

# **III. FLFP in Context** **_Morocco in the world: The U-shape theory_**

The U-shape theory argues that as countries develop economically, they experience a reduction in FLFP, eventually followed by a recovery. At early stages of development, women participate extensively in economic activity by doing unpaid work in family farms and businesses. As economies develop, women tend to withdraw from the labor force as jobs migrate to industries and cities. A plausible explanation for a decrease in female participation is that households become less dependent on two incomes. Another one is that in many places a stigma is associated with women performing manufacturing tasks (Caraway, 2007). Also, it is more difficult to combine working outside the house along with childcare, which is usually considered “women’s work” (ILO, 2017). At later stages of development, female employment tends to rise again as sectors better aligned with women’s skills such as light manufacturing and services tend to develop (Goldin, 1994). Vasco et. al (2021) find that Between 2000 and 2018, Morocco’s FLFP rate fell from 24 to 21 percent, while GDP per capita grew 70 percent from $1,976 to $3,361,<sup>5</sup> with a population annual growth ranging between 1.1 and 1.4 percent. This joint movement fits the empirical regularity depicted in figure 1, which shows the resulting “U-shaped” relationship between development (GDP per capita) and FLFP rates for a cross section of countries in 2000 and 2018.

> 4 Those who respond that being a housewife is as fulfilling as working for pay.

> 5 World Development Indicators.

6
<!-- Start of picture text --> °<br>BSS]“)7 Qo Oo Fo, 8,00<br>ont) S Y oe 6 Po ome?) °<br>S Dso ° ° & 8 6° g ° 2D° fe) ° role}fe) °Q 2<br>288es 0%,0 CA & QHo © FT. PegBy26<br>of F308 0% oP Ws GR Bice o<br>ang 00 3>.e® OAD BOSD B.SSs BS<br>oe oO OW Bi gn RRs. P60) DO 2 PESO<br>=agas 0 DO0 COROSTCOPPEGPBGap OA OoOO fo °<br>So °, °€00Ie) 0 20° Yo 9 oe]0 ® ° fe}<br>as Ovtorocco 000 @ 4<br>BEo S119 GonBregogpo1s %<br>@a22Nn re) fe) O<br>£6 0 0% vo<br>2 =° oS<br>°<br>56 7 8 9 10 11 12<br>Ln(GDP per capita (constant 2010 US$))<br>° 2000 fe} 2018<br>e Morocco 2000 e Morocco 2018<br>—-—— Fitted values — — — - Fitted values<br>Source: World Development Indicators. Each dot represents a different country<br><!-- End of picture text -->
# **_Morocco in the context of the Middle East and North Africa (MENA)_**

The Middle East and North Africa (MENA) region is characterized by historically low FLFP rates (Figure 2). In 2005, the unweighted average FLFP rate of MENA countries crossed 20 percent, where it has remained relatively stable, at 21 percent in 2019. However, FLFP varies widely across countries: in Algeria and Jordan, less than 15 percent of women participated in the labor market in 2019, while in Libya 34 percent did so. Morocco’s FLFP is slightly above the MENA average, but it is among the few countries in the region that have recorded a sustained drop, and the one that has experienced declining FLFP for the longest span of time—peaking at 26.3 percent in 2004. If this trend persists, in a few years Morocco’s FLFP rate could be below the region’s average.

Fertility rates have been previously found inversely related with FLFP by Mishra & Smith (2010). However, they state there is no consensus about this, since previous studies have found a positive relationship. For this study, fertility rates have experienced similar downward trajectories in MENA and Morocco, although at a lower level for the latter. Whereas for MENA this has been coupled with slightly higher FLFP, in Morocco, it has been coupled with lower FLFP since 2004. Therefore, a question that emerges is: Has Morocco’s FLFP rate reached a minimum in the U-shape curve—that is, the threshold after which it should start recovering—and if so, what conditions should be met for this to happen?

## **<u>Figure 2. Historically low FLFP in Middle East and North Africa</u>**

8
<!-- Start of picture text --> re)<br>o| 4 os<br>* g<br>Es A FSSGE MENA + E<br>wR lo <8<br>B | ow 2<br>S 24 gz » FSSGE Morocco =<br>G : x 2o<br>g “ "e<br>= J ©.eon " 2<br>FLFPR Morocco<br>Pu ®. An i. Ps 5<br>~ _ Seg- E——ST AnteSulsinieatatatebeeanaaanal teat poh AA BAA Aap re<br>2 FLEPR MENA $90900000 995 Se o~<br>1990 1994 1998 2002 2006 2010 2014 2018<br>Year<br>Source: World Development Indicators. FLFPR: Female labor force participation rate (% of female population ages 15-64); FSSGE<br>Female Secondary School Gross Enroliment (3 enrollment regardless of age); Fertility rate: Morocco(diamond): MENA\tnangle)<br><!-- End of picture text -->
(which in theory reflects productivity and hence wages), and the presence of other women in the household.<sup>7</sup> To explore the role of values in shaping women’s labor market decisions, we use two complementary data sources. The first is the Arab Barometer, which measures political, societal, and religious attitudes. The questionnaire distinguishes “Employed,” “Inactive,” and “Unemployed” individuals. While it contains basic demographic characteristics, it does not include some important data, such as household size, region, rural/urban household, or occupation. Importantly, it includes a question asking the level of agreement with the claim that “A married woman can work outside home if she wishes,” which we will use as a measure of the respondent’s attitudes toward women’s work. Interviewers conduct face-toface interviews in the respondent’s residence. There are four waves that include Morocco that are available: 2006-2009, 2012-2014, 2016-2017, and 2018-2019. The first three waves have a sample size of around 1,000 observations. The fourth one contains 1,560 women aged between 18 and 64.<sup>8</sup> The second data source is the World Values Survey, which aims to understand changes in beliefs, values, and motivations of people throughout the world. Morocco is included in three of the waves—2001, 2007, and 2011—each of which has about 1,200 observations, equally split between men and women, for a total of 3,648 individuals aged 18 or older. Questions measure cultural values, attitudes, and beliefs about diverse variables (such as gender, family, religion, poverty, education, health, and security). The surveys contain a labor status variable

> 7 Verme et al. (2016), facing the same limitation, argue that in informal economies earnings play a less relevant role in shaping labor supply decisions than in formal economies.

> 8 The unavailability of the variable regarding attitude toward female work in the 2018-2019 wave renders it useless for our purposes.

10
<!-- Start of picture text --> wo<br>2<br>S 78 Herne Keren gee.<br>7878 TT 76 76 16 76 75 75 : 74 eee cern ee<br>in . 72 mam 72 71<br>Te<br>+<br>Cc<br>Lo<br>ae<br>=<br>Qa<br>ow<br>as<br>iy<br>GS<br>HS<br>w @ © -© 9-9<br>NS @ 28 28 28 97 97 97 & & & -© -@-@—e¢<br>‘126 25 26 26 25 95 25 25 25 4 ee22 22<br>wo<br>= rsssSs<br>2001 2003 2005 2007 =«2009 += 2011 2013 2015 2017<br>Year<br>x Men @ Women<br>Source: HCP Enquete Nationele de Emploi, 2001-2018<br><!-- End of picture text -->
overall FLFP rate is due to the particularly low rate in urban areas—where 64 percent of the working age women in our sample lived in 2018. However, Arezki et al. (2020) suggest inactivity in MENA might be underestimated, so, if this is the case in Morocco, the gap between inactivity in rural and urban areas could be smaller.

The gender and geographical dimensions shape not only labor participation rates but, as a consequence, employment and unemployment rates. Female unemployment rates in rural areas are the lowest of the four sub-populations depicted, remaining at virtually zero. At the same time, unemployment rates are high in urban areas, along with high female inactivity (female unemployment rate in 2018 was 13 percentage points higher than male’s in urban areas), which hints that inactivity might be partly due to the “discouraged worker” phenomenon. It is worth noting that the urban/rural divide observed in labor market outcomes for women also holds true for men, whose employment rate is between 15 and 20 percentage points higher in rural than urban areas, and the unemployment rate is between 7 and 12 percentage points lower.

Overall, these patterns reveal the existence of two different labor markets: one in rural areas, where employment and participation are higher for both genders, and another in urban areas, where inactivity is extremely high for women and relatively low but increasing for men.

Beyond the urban/rural divide, education plays an important role in determining the labor status of individuals.

12
<!-- Start of picture text --> &<br>+<br>Le<br>5.<br>Luo 7 7<br>Qu 63 61 61 62 62 63 63 63 63 63 .63<br>ae<br>$2<br>_<br>ew§°.2o)a” @33 9°49 37 %8 38 38 37 36 % 36 36 835 36 36 36%.349.829 .29<br>Sw2<br>83‘ 02© -@-0-0-92 02 01 .010 -©02 -©92 ©02 -@92 002 02© 02© 992 -©02 @-@-%-02 02 23* ©<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br>Year<br>@ Inactivity © Unemployment rate @ Employment<br><!-- End of picture text --> <!-- Start of picture text --> 8<br>ha oo» o 0 000 © 0 © © © e © *%-o 0<br>= 8 8 9 79 8 81 8 81 81 62 82 62 63 62 69 82 82<br>rae]5"<br>5<br>o8<br>Boo<br>53<br>£35238 28-9-8_ 26 24 25 dg 2 BB pre25_ 24<br>5 © 0-0-6@¢ © -e-e-*- @<br>15 15 16 16 15 18 16 15 15 45 44 14 14 14 44 43 14 14<br>8. assSs a |<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br>Year<br>@ Inactivity © Unemployment rate @ Employment<br><!-- End of picture text -->

<!-- Start of picture text --> Q<br>2<br>oe 79 8 €1 81 8 8 7g BOM 77 = ©---0..@...9...9--0--0<br>Zec23 7% 75 74 74 74 75 75<br>Bungn<br>$2<br>a<br>£8<br>Be<br>5 wo<br>57 2<br>nwoTM eee ie21 eee.22 22 22ee22<br>16 16 16 16 16 17 17 18 18<br>8 @-e_0'_06 05 0 ©05 -o05 -0.05 0.05 -0-0-05 05 050- 0-095 95 -©05 0 5-0-9.05 4<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br>Year<br>@ Inactivity © Unemployment rate @ Employment<br><!-- End of picture text --> <!-- Start of picture text --> it)<br>2<br>2e<br>S<br>ZugO eee<br>By}a8 SY eee6 6 59 Gr eo 62 62 62 62 G2 SO59 -e-@-59 59 ee6 59<br>oO<br>Bo<br>SeEX.<br>ave}<br>=So ee ee ee eee33 34 39 33<br>lo}2a % a a1 2 ~9 29 2% 29 3 3 8 32 Ss)<br>£Ao @-@-@-e-e17-7 47 «(47 4 13 13.13<br>= Oe 8 6 ee -0 Fe<br>2<br>. ia|<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br>Year<br>@ Inactivity © Unemployment rate @ Employment<br><!-- End of picture text -->
<!-- Start of picture text --> =<br>FaE<br>g2<br><<br>&<br>3<br>55 SE<br>a}<br>ae<br>©<br>i; re — : 5 i : =<br>s 3 os ° 3 o s °<br>z z<br>2001 2010 2018<br>HEE Employes ME Unemployed SM inactive (Inactive in school<br><!-- End of picture text --> <!-- Start of picture text --> =<br>FaE<br>g2<br><<br>&<br>3<br>55 SE<br>a}<br>5<br>© = : re = F rs = ; i<br>3 s 3 os ° 3 o s °<br>z z z<br>2001 2010 2018<br>HEE Employes ME Unemployed SM inactive (Inactive in school<br><!-- End of picture text -->
<!-- Start of picture text --> ~ wy 0.10<br>0.04 0.16 0.18<br>0.03 0.060.02 0.32<br>=6 0.07oor Cen<br>= 008 067 “<br>no] =<br>a<br>5<br>Ss ot 0.85 082<br>2 0.69 0.07<br>= on oy 0.08<br>a 0.51 047 0.03<br>N<br>0.23 0.25<br>o<br>2001 2018 2001 2018 2001 2018 2001 2018<br>No education Fund. Secondary Superior<br>GE 4c, SR in Const’) «=ES Senvic.<br><!-- End of picture text --> <!-- Start of picture text --> ~<br>—5 0.57 054 0.55 054<br>5 0.69<br>no] 0.74<br>a<br>5<br>os oT 0.11<br>© 0.13 0.19 on<br>=<br>wn 0.05 on<br>N 0.20 0.16 0.30<br>“ 0.19 0.18 — one<br>0.10 0.11 0.10 0.10<br>o 0.04 0.05 nn 0.02 nna nine<br>2001 2018 2001 2018 2001 2018 2001 2018<br>No education Fund. Secondary Superior<br>GE 4c, SR in Const’) «=ES Senvic.<br><!-- End of picture text -->
<!-- Start of picture text --> © 113<br>0.67 1.56<br>5 °<br>e 0.42 121<br>s O78<br>6 ial BY<br>zg * 055 1.24 288<br>2 1.41 2.03 2.49<br>=<br>N<br>3.51 a 2.85 AGh 539 2.84<br>Oo<br>2001 2010 2018 2001 2010 2018<br>Rural Urban<br>GE «No education (Func. $= Secondary [I superior<br><!-- End of picture text --> <!-- Start of picture text --> N<br>no] U-U<br>2 wo<br>Ss = 0.32<br>£ sayy<br>E ayy ons<br>& 0.29<br>=<br>g 7 0.34<br>5 ofA 0.26<br>2 0.24<br>s oa: 131 0.17 0.20<br>Ss 4 1.00<br>0.31 O32 0.33<br>0.27 0.23 0.26<br>Oo<br>2001 2010 2018 2001 2010 2018<br>Rural Urban<br>GE «No education (Func. $= Secondary [I superior<br><!-- End of picture text -->
trade (12 percent)—all sectors with low labor productivity, LFS (2018). While a large share of women are salaried workers (73 percent), most do not have a formal job (only 24 percent declare to be enrolled in social security). The average uneducated, employed woman is 41 years old, lives in a household of 4 people where one out of two times the head is a man, has one child, and is slightly more likely to be married than single (40 percent against 34 percent). A larger share of women working in personal services, compared to the average uneducated employed woman, are divorced or widowed, which could signal that these women select into a low-return activity out of necessity.

_Employed women with secondary education._ They are scattered among service sectors— chiefly employed in health and education services (25 percent); wholesale and retail sale (13 percent); government administration (10 percent); finance, insurance, and real estate (10 percent); and the textile industry (10 percent). The share of salaried work is higher among this group (87 percent), as is the share of formal employees (60 percent). It is also noteworthy that the public sector employs a significant portion of this group—25 percent against only 5 percent for the uneducated employed women—and that the remaining 75 percent are employed in the private sector. These women are 36 years old, on average, live in a household with four members, where 70 percent have a male head, and 50 percent are single.

_Employed women with superior education._ They work mostly in three sub-sectors: health and education services (43 percent); finance, insurance, and real estate (17 percent); and public administration (12 percent). They also display the largest share of formal employment (80 percent), as expected. Most of them are salaried (90 percent), and they are almost equally divided between the private (54 percent) and the public sectors (45 percent). Educated women are slightly younger than uneducated women, being on average 37 years old. Almost 17
<!-- Start of picture text -->
- OU UU UU<br>co<br>oO<br>3$0.70<br>2ge 0.82 0.74<br>=oO<br>ot<br>g<br>oa<br>w<br>Jow 0.20<br>0.12<br>0.06 0.08 0.10<br>o<br>2001 2010 2018<br>HE Salaried GE independent/Employer<br>GE Family worker = Trainee<br><!-- End of picture text -->

<!-- Start of picture text -->
- 0.0 UY ame)<br>0.05 0.04 00<br>0.15 0.13 ons<br>oc ©<br>oO<br>$<br>ao]2eg ©<br>oO=<br>5s * 0738 0.81 0.82<br>g<br>oa<br>w<br>J<br>o<br>2001 2010 2018<br>HEE Salaried HER independent/Employer<br>GEE Family worker = Trainee<br><!-- End of picture text -->
The fact that 70 percent of employed women in rural areas work as unpaid family workers fits the notion that rural women do not switch job status even when switching families: when single they work for their fathers and when married they work for their husbands. Not surprisingly, only 3 percent of these women have a formal job.

# **_Evolution of female employment_**

How has female employment evolved over the past 20 years? Most female job creation has taken place in urban areas. Between 2001 to 2018, the number of employed women in urban areas increased by 32 percent, a net increase of 280,000, while jobs performed by women in rural areas remained almost stable (net variation of -1 percent). New jobs created in urban areas absorbed 10 percent of the increase in the female active population, which experienced a large net increase of 2.7 million women over the period. The pace of job creation observed in urban areas was thus not dynamic enough to preserve the levels of female employment observed at the beginning of the period: female urban employment rates declined from 15 percent to 13.7 between 2001 and 2018. Maintaining the same level of women’s employment would have required net job creation 45 percent higher, or equivalent to 410,000 additional female employments, instead of the observed 280,000 over the period.

In rural areas, the demographic dynamics are quite different. The female working age population increased by 0.5 million women in rural areas (as opposed to 2.7 million in urban areas), outnumbering the slow pace of rural job creation (figure 9 panel a). The number of employed women fell by 20,000, equivalent to 4 percent of the increase in the female rural active women. The decline in female employment rates was, therefore, more accentuated in rural areas—starting at 33 percent in 2001, reaching a peak of 38 percent between 2004 and 2006, before declining steadily thereafter to 29 percent in 2018.

19
<!-- Start of picture text --> ”<br>5<br>=<br>==<br>oS<br>5<br>@<br>So<br>N<br>8<br>s~<br>2<br>=<br>a<br>°<br>Rural Urban Total<br>HE Working-age Women HEE Employed Women<br><!-- End of picture text --> <!-- Start of picture text --> ~ So<br>58<br>ES<br>=<br>no] So<br>as<br>=<br>38<br>So<br>Za<br>3"<br>8<br>><br>BO<br>Qa<br>g<br>Se<br>38<br>ec38<br>2.<br>Eggo<br>é¢<br>: Rural Urban Total<br>WS «2009-2001 (MMM 2018-2009 [EMM 2018-2001<br><!-- End of picture text -->
in informal jobs as unpaid family workers. The job destruction/creation dynamics observed in rural areas shows that the likelihood for low-skilled women to find a job is decreasing significantly. These two sectors are shrinking, while the few job opportunities being created are in areas requiring higher education.

Most of the female jobs created in urban areas between 2010 and 2018 were concentrated in four sub-sectors, which fully offset the decline observed in the textiles industry. While female textile employment decreased by 25 percent, employment in these sub-sectors—finance, insurance & real state, wholesale and retail trade, housekeeping services and education & health services—increased by 67, 32, 32, and 19 percent, respectively. Female employment increased in total by about 10 percent in urban areas between 2010 and 2018, with an additional 122,000 women being employed.

# **_Female inclusion in the labor market_**

Declining labor force participation trends are not gender specific, as male labor participation rates also decreased. However, increased inactivity has affected males and females quite differently. The decrease in male participation exclusively concerns young men, both in rural and urban areas (figures 10 panel c and 10 panel d), which, in turn, correlates with larger school enrollment rates in secondary and superior education. Labor force participation of adult men (24-65) remained high and stable over the period.

21
<!-- Start of picture text --> for)<br>85 oo<br>BN<br>2<br>co<br>©<br>gw oecegcppgaetenecsaseeneetetttttteeeeasettt t tt<br>8.26® Doe TSme “Se -<br>3<br>En<br>ire<br>ass Ss a |<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br>Year<br>——— 15-24 =~ 25-44 eres 45-65<br><!-- End of picture text -->

<!-- Start of picture text --> for)<br>n<br>G8 co<br>at<br>2<br>2°<br>go<br>>tSn ——<br>2 --- ~+-------- 22-2 ----<br>EN<br>2 pees nsresasressprs serosa 27ST Tre tteseeesegeseenete<br>assSs a |<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br>Year<br>——— 15-24 =~ 25-44 eres 45-65<br><!-- End of picture text -->

<!-- Start of picture text --> Bx<br>2Zos&<br>s<br>2s<br>20oo<br>ass Ss a |<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br>Year<br>——— 15-24 =~ 25-44 eres 45-65<br><!-- End of picture text -->

<!-- Start of picture text --> ex ET<br>So=<br>Qns<br>=<br>2s —<br>on©<br>SN<br>assSs a |<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br>Year<br>——— 15-24 =~ 25-44 eres 45-65<br><!-- End of picture text -->
(Percentage of women in each labor status)

||**2001**|**Rural**<br>**2010**|**2018**|**2001**|**Urban**<br>**2010**|**2018**|
|---|---|---|---|---|---|---|
|Employed|32.9|36.2|29|15.1|14.7|13.7|
|Unemployed|.54|.743|.73|4.96|3.64|4.39|
|Inactive|66.5|63.1|70.3|79.9|81.6|81.9|
|Paid workers|17.3|25.2|29.4|93.2|94.7|96|
|Formal workers|.549|1.75|3.05|32.9|44.3|48.1|
|Public Salaried|.|7.09|10.7|.|25.3|22|
|Private Salaried|.|88.1|82.8|.|65.6|68.2|

Source: HCP Enquête National sur l’emploi. Paid workers category includes wage workers, independent, employers and others; it excludes apprentices and cooperatives workers. Formal workers is the share of employed that have health insurance (CNSS). Public and Private categories -unavailable in 2001- are calculated as shares of employed (they do not sum up to 100 because of a third “other” marginal category).

In urban areas, the decline in female participation rates is less accentuated, smoother, and spread across age groups (figure 10 panel b). A phenomenon specific to urban areas is the increase in female unemployment compared to the steady decline in unemployment for men between 2001 and 2018. At the same time, female employment rates remained stable at around 14 to 15 percent throughout the period (table 1). It is worth noting, however, that the quality of jobs women accessed suffered a slight deterioration—with the number of remunerated, formal, and private sector salaried jobs marginally lower for women relative to men. The dynamics of job creation also reveals that the likelihood for a woman to be employed in a new job fell over the period. Around 21 percent of jobs created over 20012007 in urban areas employed a woman, while only 13 percent did so over 2007- 2018.

# **VI. Methodology**

In the previous sections, we profile different groups of women regarding their labor status and demographics, and we show the evolution of these variables over time. In the following equation, we summarize how the multiple variables interact to determine the inactivity of 23
women in urban and rural areas in two points in time, 2001 and 2018, using a Probit estimation: where **X** i includes age, squared age, number of kids under 10 years old in the household, a 4- categories education variable, education level of head of household, a dummy variable that equals “1” if the woman has never been married, number of inactive women in the household and its square. These data come from the LFS of 2001 and 2018. We report the marginal effect of each covariate using the average values in the sample.

We also estimate two models to shed light on the role that values play in determining female labor force status. The first one is also a Probit model as in Equation 1. The data source is the World Values Survey. **X** i includes 10- Moroccan regions fixed effects, a dummy that takes the value of “1” if the individual is married, age and squared age, dummies for a 3- categories educational variable, and number of children. Our variable of interest is a dummy that equals “1” if an individual agrees with the claim “Men should have more right to a job than women if jobs are scarce,” which we associate with conservative or traditional traits.<sup>9</sup> We estimate this model for men and women separately, and we report the marginal effect evaluated at the means of the regressors in our sample.

> 9 We drop from the estimation those individuals who do not answer and those who do not know. We set the dummy to a value of zero for those individuals who disagree or whose answer is “Neither.”

24
We estimate this model only with women.<sup>10</sup> The data source is the Arab Barometer, and **X** i includes dummies for 7 age brackets, a dummy variable that equals “1” if a woman is married, and dummies for five categories of education and years fixed effects.<sup>11</sup> Our variable of interest is a dummy that equals “1” if women agree with the claim “A married woman can work outside home if she wishes,” which we will interpret as signaling a positive attitude toward female work.

# **VII. Results: The determinants of female labor supply**

Since the descriptive statistics show that urban and rural areas function as different markets for labor, we estimate the model in equation (1) separately for women in urban and in rural areas in 2001 and 2018 and show the resulting marginal effects in figure 10. During this time period, there are no important qualitative changes in the influence of the covariates and only a few important quantitative changes. We highlight the effect of each covariate in our results:

## **_First, higher educational attainment means more participation, although this relationship_**

**_has decreased over time._** The effect of women’s education differs greatly across areas and educational levels, although, especially in rural areas, more education does not necessarily translate into greater FLFP. In 2001, having fundamental education in rural areas increased the probability of remaining OLF compared to women with no education, contrary to the results obtained for urban areas where fundamental education slightly reduces the probability. In 2001, in rural and urban areas, secondary education decreased the probability

> 10 Further research can estimate this model, including only men’s answers to assess their attitude toward female work.

> 11 Strictly speaking, these fixed effects correspond to three waves: 2006-2009, 2012-2014, and 2016-2017.

25
of remaining OLF. For 2018, the coefficient for rural areas is not significant, while for urban it remains negative although with a slight decrease.

Holding superior education in urban and rural areas greatly increases the probability of joining the labor force in both 2001 and 2018, signaling that the type of skills demanded requires some specialization. These results differ from those in figure 7, where more education is not necessarily correlated with more employment. However, when controlling for other characteristics, more education is correlated with higher participation.

The same qualitative results can be found in figure A.1 in the Appendix, where the sample is limited to women over age 25 (instead of restricting the sample to those currently enrolled in school). In this sense, the higher the education level of women in Morocco (especially above fundamental education), the lower the probability they will remain inactive. However, the correlation has decreased over time, and the share of active women has been decreasing among educated women.

# **_Second, the better the education of the head of household, the more likely that a woman_**

**_will remain OLF._** These effects acquire greater importance over time, especially in rural households, where the effects became stronger in 2018. Indeed, the presence of a head of household holding higher education renders the probability of a woman in a rural household remaining inactive almost 20 percentage points higher compared to if the household head has no formal education.

# **_Third, being never married makes it less likely that the woman will remain OLF._** While in

urban areas remaining single reduces the probability of being OLF by 30 percent, in rural areas, it does so by 20 percent. The marginal effect of being single falls slightly in both areas.

26
**_Fourth, the presence of other inactive women in the household increases the probability_**

**_of a woman being inactive._** These effects increase over time in both rural and urban areas. If the presence of other women made it easier to join the labor force—for instance, because of more help with child caring and domestic duties—a negative correlation would be expected, signaling that low FLFP is a result of supply-side constraints. Since this is not the case, we hypothesize that FLFP is constrained by either attitudes towards women’s work within households or a lack of job opportunities for women.

**_Fifth, the higher the number of children under 10 in a household, the more likely the woman will remain OLF._** This effect holds in both urban and rural areas. The fact that the number of children is negatively correlated with inactivity (positively correlated with participation) is also at odds with the notion that childcare deters FLFP. Marginal changes hold steady over time.<sup>12</sup>

**_Sixth, a person’s age—and hence experience—is of little significance for whether a_**

**_woman joins the labor force._** This holds in rural and urban areas. This finding is consistent with the fact that women tend to be employed in low-skilled occupations where experience might not be rewarded. The marginal effect, which is negligible, holds steady over time.

**Figure 11. Drivers of females being out of the labor force (OLF) – Without women currently studying.** <u>(Marginal Effects on the probability of female inactivity)</u>

> 12 Additional specifications of the model using dummies for having at least one child 0-2, 3-6 and 7-10 in the household were tested obtaining the same negative correlation. This result is atypical and not conclusive because it varies according to the data source (see results of Figure 15).

27
<!-- Start of picture text --> HHH Sup a<br>HHH Sec. =<br>HHH Fund. fA-<br>N Women OLF in HH CY<br>N Children 7<br>Never Married |<br>Superior |$i\<br>Secondary ES]<br>Fund. {AE<br>155,65] a<br>[45,54] |<br>[35,44] a<br>[25,34] ‘Ay<br>A -3 +2 ) ml 2<br><!-- End of picture text -->

<!-- Start of picture text --> HHH Sup a<br>HHH Sec. a<br>HHH Fund. [-#-<br>N Women OLF in HH |ee|<br>N Children {7<br>Never Married |<br>Superior |[i\ —<br>Secondary rf.<br>Fund. [4<br>155,65] a<br>[45,54] fll<br>[35,44] is<br>[25,34] ‘A<br>-4 -3 -.2 -1 () a 2<br><!-- End of picture text -->

<!-- Start of picture text --> HHH Sup (4.<br>HHH Sec. (4<br>HHH Fund. (4<br>N Women OLF in HH f<br>N Children |<br>Never Married Pe<br>Secondary aaa<br>Fund. i]<br>[55,65] ie<br>[45,54] |<br>[35,44] |<br>[25,34] ff]<br>“A -3 0-2 od 0 a 2<br><!-- End of picture text -->

<!-- Start of picture text --> HHH Sup (4<br>HHH Sec. [4<br>HHH Fund. [4<br>N Women OLF in HH |ee<br>N Children Si<br>Never Married |<br>Secondary Ld<br>Fund. ca<br>[55,65] |g<br>[45,54] Hi<br>[35,44] ; |<br>[25,34] ff]<br>-4 -3 -.2 -1 () a 2<br><!-- End of picture text -->
<!-- Start of picture text --> 4 0.06 0.06 O05 0.05 0.04 0.06<br>_ 0.10<br>2 0.26<br>£ :<br>oo 0.39<br>2 0.45 wn<br>5<br>Be<br>3<br>S= a 0.78 0.86<br>o<br>Male —- Female Male —- Female Male Female<br>Married Divorced/Widowed Never Married<br>ME cree MEE Disagree MEN Neither<br><!-- End of picture text --> <!-- Start of picture text --> Be) an<br>= ’ ine 0.19 021<br>=<br>ga<br>a3<br>5 O16<br>ca} 0.24<br>co<br>3 0.65 059<br>g<br>5%<br>o 0.24 0.26<br>Ww=o<br>= aS ace T=ans ness<br>Female Male<br>EEE Employed = OLF ENN Unemployed<br><!-- End of picture text -->
<!-- Start of picture text --> ae3 0.09 0.09 O08— 0.12<br>®<br>ge=<br>z 031<br>s_ 0.57 o4: _<br>co:<br>3<br>zs<br>2:a<br>=]8 0.60<br>a 0.51<br>a 0.45<br>=oo Li 0.34_<br>52<br>Ww<br>°<br>Disagrees: Agrees Disagrees Agrees<br>Female Male<br>HE Employed = LF = EE) Unemployed<br><!-- End of picture text --> <!-- Start of picture text --> =a a<br>ens 0.21 o.25a 031a5 0.26—<br>2<br>co- 0.06 0.15<br>5<br>: 0.56<br>zs 055<br>5 t<br>Es2 0.63 nso0.59<br>2 ~<br>en=<br>3a 0.23_ 0.20<br>5<br>o<br>Disagrees: Agrees Disagrees Agrees<br>Female Male<br>EE Employed = OF = Unemployed<br><!-- End of picture text --> <!-- Start of picture text --> Bea re<br>o 0.09<br>=" 0.16<br>HiES 0.25<br>Be 0.09<br>a=]Cca<br>SHa 0.20<br>coo<br>é 0.94 o79<br>S st O74<br>8<br>a<br>xe<br>is}<br>2<br>=a nay<br>al 0.06 0.12<br>° Disagrees: Agrees Disagrees Agrees<br>Female Male<br>EE Employed = LF = Unemployed<br><!-- End of picture text -->
<!-- Start of picture text --> Gea<br>2 0.12 a0 oe 0.14<br>o<br>z O31<br>oGa= o 053 O41 0.33<br>g<br>7]<br>5%<br>3 _<br>5eo 0.36__ :<br>a<br>o<br>oDisagrees Agrees Disagrees Agrees<br>Female Male<br>EE Employed = OF «= Unemployed<br><!-- End of picture text --> <!-- Start of picture text --> Be<br>3<br>o<br>z<br>oO2vl ee 0.03 013<br>g<br>3 0.46 oso<br>5<br>& -<br>Bn5<br>2 0.26 _<br>3 0.20<br>°<br>Disagrees Agrees Disagrees Agrees<br>Female Male<br>EEE Employed = OF ER Unemployed<br><!-- End of picture text --> <!-- Start of picture text --> Bea ams)<br>]<br>2 0.14<br>a2<br>= 0.09<br>a<br>aoa<br>cwg ogs 0.22<br>% 0.72<br>5<br>& o.70<br>3<br>en 0.49<br>5<br>2<br>2 0.12 0.14<br>=) Disagrees: Agrees Disagrees Agrees<br>Female Male<br>EE Employed = oF «=ER Unemployed<br><!-- End of picture text -->
<!-- Start of picture text --> se |<br>Married |:<br>-.05 0 .05 a<br><!-- End of picture text -->

<!-- Start of picture text --> me 1<br>Married ee<br>-.05 0 .05 A<br><!-- End of picture text -->
We find that men’s labor force status is not very sensitive to any of the covariates (relative to the effect these same variables have on women’s status), which we interpret as male labor force supply being inelastic. In the case of women, though, we find larger effects: for every additional child in the household it is 3.7 percentage points more likely that a woman will remain inactive; acquiring secondary education increases the probability by 6.6 percentage points compared to having no education; acquiring college education does not have a significant effect. Although we cannot control for urban/rural areas in this estimation because of the nature of the data, higher education does not increase participation of women at the national level, even after controlling for regions in which they live. More importantly, those women who agree with the claim are 5.8 percentage points more likely to remain out of the labor force than those who disagree. In other words, the stigma associated with female work deters FLFP, even after controlling for all demographic characteristics.

Second, we complement this finding by estimating the Multinomial Logit Model in Equation 2. Figure 16 shows the marginal effects of different education levels along with the effect of agreeing with the statement “Women can work outside the home if they wish” on the probability of working for individuals in each of three labor statuses (Employed, Inactive (labeled OLF), Unemployed).

Although we cannot claim causality at this point, the results show that agreeing is positively correlated with the chances of being employed in 6.7 percentage points, about the same magnitude as having fundamental education (relative to no education at all). It is also correlated with the probability of being unemployed (this is, still participating) by 2.8 percentage points, and negatively with the probability of remaining inactive by 9.5 percentage points (figure 16).

33
<!-- Start of picture text --> z }<br>oO<br>2 )<br>3 '<br>fe] ®<br>fo<br>“nn6 Jt a<br>#<br>g<br>+<br>ww<br>”<br>Elementary Preparatory Secondary Superior Agrees<br>Effects with Respect to<br>© Employed OLF & Unemployed<br><!-- End of picture text -->
The U-shape theory argues that economies experience a reduction in FLFP as they develop, and that women withdraw from the labor force when jobs migrate from rural to urban areas, with an eventual recovery when sectors such as light manufacturing and services develop. In this sense, a question that emerges is: Has Morocco’s FLFP rate reached a minimum in relation to the U-shape theory—that is, the threshold after which it should start recovering— and if so, what conditions should be met in order for this to happen? Also, what can be done to help Morocco benefit from an upward shift of the curve as the transition in manufacturing and services takes place in the development process—which would facilitate higher FLFP rates for any income level? So far, Morocco has yet to see this benefit, as evidenced by the fact that the FLFP rate in 2018 was lower than in 2000.

This paper finds answers by profiling Morocco’s workers, as demographics play a vital role in determining participation rates. While both female and male overall labor force participation rates have declined over the past two decades, a 50-percentage-point gender gap persists, following different dynamics in urban and rural areas. For men, this decline is mostly present in youth mainly because of education enrollment, while for women this phenomenon touches all ages and is not necessarily correlated with education enrollment. These gender and demographic dimensions shape not only labor force participation rates but also employment and unemployment.

For instance, employment rates are between 15 and 20 percentage points higher in rural than urban areas, and unemployment rates are between 7 and 12 points lower for both genders. This is a consequence of urban areas not generating enough jobs for women in Morocco to offset agricultural job destruction. Female unemployment remains close to zero in rural areas, while the most recent data (2018) show an urban female unemployment rate 13 percentage 35
points higher than that of males, suggesting the increase of inactivity might be caused by discouraged women.

As for inactivity, the reasons women give for staying out of the labor force vary across education levels and marriage status. Married women report childcare and domestic duties as their main reasons, while single women report other voluntary reasons. When incorporating the education variable to the analysis, women with less education state family opposition and domestic chores as the main constraints, which is related to the fact that social norms worsen FLFP. In rural areas, the non-educated population has the highest labor participation. For women with higher education, one-third are unemployed, which may be related to the lack of suitable jobs for educated workers.

Overall, these patterns reveal the existence of two different labor markets: one in rural areas where employment and participation are higher for both genders, and another in urban areas where inactivity is extremely high for women and relatively low but increasing for men.

Using data from the LFS we find there are individual and household characteristics keeping women out of the labor force, besides the precondition of an overall decrease in labor force participation rates for both genders, since urban job creation in Morocco has not offset agricultural job destruction. Being married, and the educational attainment of the head of the household—which are variables related with traditional gender roles—are found to deter FLFP. This is also reflected in the later multinomial logit where we find a positive correlation between women who agree that women should be able to work outside home if they wish are and the higher probability of being employed and lower of remaining inactive, suggesting there might be a social stigma toward female keeping women out of the labor force.

36
For the case of those who agree that men should have more right to a job, according to data from the World Values Survey combined with other demographic characteristics from the LFS, we find that men’s labor force status is not very sensitive to any of the covariates, relative to the effect on women’s, which we interpret as male labor force supply being inelastic. The effect of the number of children in the household is found counterintuitive and positively related with FLFP (maybe due to the need of more income) when using LFS, but negative when using the World Values Survey, which reinforces the self-reported reason of childcare as a cause to remain out of the labor force but is not conclusive on our analysis. Finally, and in line with the literature on the topic, higher educational attainment in women increases their probability to participate in the labor force.

37