AfDB Gender in Employment: Case Study of Mali€¦ · A f r i c a n D e v e l o p m e n t B a n k 3...

A f r i c a n D e v e l o p m e n t B a n k

CONTENTS

1 – Introduction

2 – Country Backgroundand Context

3 – Methodology

4 – Analysis and PrincipalFindings

5 – Conclusions, PolicyImplications and ProposedFurther Research

Mthuli [email protected]+216 7110 2062

Charles Leyeka [email protected]+216 7110 2175

Désiré [email protected]+216 7110 2205

Executive Summary

1. Overview and Background. This re-port on Mali is the second of two countrystudies (the first concerns Botswana) thatexamine gender and employment in Africa.The study is informed by two broad deve-lopment frameworks. The first is the Millen-nium Development Agenda that views Mil-lennium Development Goal 3, GenderEquality and Women’s Empowerment, as acritical determinant in the attainment of allthe Millennium Development Goals (MDGs),and more broadly, poverty reduction. Thesecond is the AfDB’s Managing for Deve-lopment Results (MfDR), the organization’sblueprint for development effectiveness. Inresponse to two evaluations that concludedthat the Bank’s efforts to mainstream gen-der into its operations were weak, and en-couraged by the Bank’s Board and seniormanagement, this study comes at an op-portune moment to ensure that genderequality becomes one of the Quality-at-En-try standards central to the achievement ofits stated goals. Up-to-date data collectionand research efforts that go beyond thebiological to the social, political and econo-mic dimensions of gender inequality, are,therefore, timely and will contribute to ef-fective and efficient policy-making.

2. Objectives and Rationale. The threemain objectives of this study are to: 1) ex-plore representative data and qualitative in-formation on employment in various eco-nomic industries, formal and informalsectors; 2) analyze the underlying relations-hips between gender and employment andtheir determinants; and 3) produce a com-prehensive report in line with the ADF 11commitment to collect gender-disaggrega-ted data in two pilot RMCs and strengthencapacity to generate analytical studies.

3. Mali lags behind neighboring ECOWASstates and other countries in the region in

the attainment of most of the MDG indi-cators, especially the gender equality andempowerment goal. As well, it ranks lowon the Human Development Index (HDI),despite government policies and laws in-tended to promote gender equality. Suchlimited progress raises concern that thecountry may fall short of attaining the MDGof gender equality in 2015.

4. Methodology. Data for this study comefrom the third Enquête permanente emploiauprès des ménages (EPAM), a cross-sec-tional survey conducted in 2007 and ba-sed on a nationally representative sample.Using multivariate analysis, the study exa-mines: 1) gender inequality in various em-ployment sectors (agriculture, salaried, pri-vate informal and formal) and at variouslevels of income (no income, minimum in-come, francs CFA 29,000 to 50,000 andfrancs CFA 50,000 to 75,000) and 2) thedeterminants of these relationships, focu-sing on factors such as human capital,demographic characteristics, structural/economic variables, agency (political, eco-nomic and social), intergenerational as-pects and aspirations, and how these in-teract with sex.

5. Principal Findings. In Mali, men aremore likely than women to be salaried wor-kers, even when other potentially influentialfactors such as educational attainment,age, marital status, structural/economicfactors, region/place of residence, agency(political, economic and social), intergene-rational aspects and aspirations are takeninto account. This highlights decreasingpaid employment opportunities for wo-men, which stem from economic policyadjustments, and a growing disadvantagefaced by new labor market entrants. As inthe informal sector, men and women haveequal likelihood of access to the formalsector. This novel finding, which is contrary

AfDBChief Economist ComplexVolume 1 • Issue 1

12 April, 2011www.afdb.org

Gender in Employment: Case Study of Mali1

1 The paper was prepared by: Alice Nabalamba (Principal Statistician, ESTA.2) and Leslie Fox (Consultant,ESTA.2) under the supervision of Oliver Chinganya (Manager, ESTA.2). Ami Assignon provided French lan-guage editorial assistance. The paper was peer reviewed by: Ms. Yeshireg Dejene (Gender Expert, ORQR.4),Ms. Elena Maria Ferreras Carreras (Gender Expert, OSHD), Ms. Audrey Chouchane (Research Economist,EDRE), Mr. Etienne Porgo (Lead Education Officer, OSHD), Ms. Zeneb Toure (Gender Expert, ORQR.4), Ms.Gisela Geisler, (Gender Expert, ORQR.4), Ms. May Ali Babiker (Gender Expert, ORQR.4), Mr. Issahaku Bu-dali (Social Protection Specialist, KEFO), Mr. Mamadou Diagne (Country Economist, MLFO).

to conventional wisdom, requires furtherresearch. And while this evidence is en-couraging, Mali’s potential to attain theMDG related to gender inequality de-pends not only on the percentage of thefemale labor force that accesses the for-mal sector, but also the percentage thatremains there. Women’s earnings aremore likely to be in the lower income ca-tegories, while men’s earnings are morelikely to be in the higher income catego-ries. Women continue to be disadvanta-ged in access to high income levels.

6. Policy Implications. A stronger focuson education and training for women isneeded, but it must be directed towardquality and labor market relevance. Tomake women competitive and confidentlabor market participants, their schoolsubject choices and training/certificationbeyond school should result in their ha-ving skills that match demand rather thantraditional job norms.

7. Because agriculture remains the chiefemployer of the majority of the Malian po-pulation, emphasis must be placed on ma-king this sector economically profitable andsustainable. This must be done with a fo-cus on women, because currently, most ofthe benefits from the determinants aregreater for men. The interventions are well-known and include increasing access tocredit, information and extension services.Such interventions must be either bettertargeted or increased.

8. Policies to increase women’s earningscapability should be implemented to nar-row gender inequality and lift large num-bers of women out of poverty. Such po-licies would target equity in access toeducation, particularly secondary edu-cation; quotas for women in governmentand elected positions (e.g., Rwanda);economic empowerrment programmesthat provide a mix of access to produc-tive resources and access and opportu-nity in both the public and privatespheres. Affirmative action is an essentialfirst step to level the playing field, which,

through a combination of traditional cul-tural values and practices and modernpolicies that maintain the status quo, hasbeen tipped against women.

9. Research Recommendations andNext steps. The results of this study un-derscore the importance of consideringthe correlates of employment addressedhere as well as measures of householdwealth, family organization of work, ma-rital relations and ethnicity across fineemployment distinctions. This studyshould not be regarded as the ultimateassessment of the relative importanceof the factors examined here. Future re-search would benefit from employing al-ternative statistical methods to estimatethe relative effects of these variables, aswell as methods that account for fac-tors that could not be taken into account.Future research should also focus onanalyzing several waves of surveys in or-der to generate time series and tracehistorical trends. Finally, in Mali, where alabor force survey has been conductedevery 3 to 4 years, it may be beneficial tocombine several survey cycles to in-crease sample size and thereby improveestimates of employment indicators.

1 Introduction

1.1 Overview and Background

1.1.1 This report on Mali is the se-cond of two country studies (the otherconcerns Botswana) of gender and em-ployment in Africa. As the study terms ofreference (TORs) note, “Promoting gen-der equality in employment is an impor-tant cornerstone to advance women’seconomic empowerment in Africa andelsewhere.” Thus, the elimination of gen-der inequality in the labor market is acentral goal and one of the key objec-tives of development strategies designedto reduce poverty while achieving eco-nomic growth with equity. Indeed, closingthe gender gap in employment is one ofthe principal determinants in attaining

the Millennium Development Goals(MDGs), not just MDG 3, Gender Equa-lity and Women’s Empowerment.

1.1.2 Data collection and analysis thatfocus on gender inequality in sub-Saha-ran Africa are particularly timely given thecontinent’s need for gender-related sta-tistics to inform effective and efficient po-licy-making. However, earlier data col-lection and analysis efforts by the AfricanDevelopment Bank (AfDB, or the Bank),inter-alia, have used an analytical frame-work that relied on sex-disaggregateddata, itself a reflection of the assumptionthat the principal differences betweenmen and women are largely biological,and thereby discounting the influence ofsocial, political and economic factors.

1.1.3 A gender equality and women’sempowerment approach takes these lat-ter macro-level variables as a startingpoint and examines women’s and men’sroles at the household and societal levelin terms of power and how it is exerci-sed. It is, therefore, necessary to havegender statistics that adequately reflect“differences and inequalities in the situa-tion of women and men in all economic,social and political areas of life.”

1.2 Situating the Study in AfDB’s Gender EqualityAgenda

1.2.1 In 2007/2008, around the time ofthe ADF-11 replenishment, the Bank be-gan a series of major reforms in the wayit does business. Managing for develop-ment results (MfDR) became the principalframework within which a range of orga-nization-wide changes was launched. Theemphasis is on achieving results and onmeasuring this achievement. However,broad-based reform of Bank operationsand achievement of the new set of resultsset out in ADF-11, and under reformula-tion during ADF-12 negotiations, have ne-cessitated addressing a principalconstraint that has been identified inter-nally and through external reviews.


2

AfDBGender in Employment:

Case Study of Mali

Volume 1 • Issue 1 • 12 April, 2011 • www.afdb.org

AfDB


3

Gender in Employment: Case Study of Mali


AfDB

1.2.2 Two recent organization-wideevaluations―the Mid-term Review of thefirst Gender Action Plan and the OPEV-conducted Independent Evaluation ofQuality at Entry for ADF-11 Operationsand Strategies―concluded that theBank’s efforts to mainstream gender intoits operations were weak. The Bank, ledby the Quality Assurance and ResultsDepartment (ORQR) 4, and supportedby departments such as Statistics(ESTA), has worked to ensure that gen-der mainstreaming is integrated into allaspects of Bank operations. The findingsof the Mid-term Review of the GenderPlan of Action (GPOA), which served asthe basis for the up-dated Gender Planof Action, included the following:

♀60 to 70% of Bank projects do not in-clude gender equality as a goal;

♀Project log-frames miss gender-speci-fic indicators and outcomes;

♀Project and program designs lack ac-tivities to promote gender equality;

♀Project Appraisal Reports lack gen-der-disaggregated data; and,

♀Poverty analyses often exclude a gen-der dimension.1

1.2.3 The present ESTA-sponsoredstudy examines one of the most impor-tant dimensions of the gender equalityand women’s empowerment goal. Itcomes at an opportune moment, as theBank is strongly committed to ensuringthat gender equality becomes one of theQuality-at-Entry standards, and thus,central to the achievement of its statedresults.

1.3 Gender and Employment Study: The Rationale

1.3.1 Throughout the world and formuch of history, women have had dualroles as income generators (workers) andwives/mothers/caregivers, while men

have largely functioned as income gene-rators (Glick and Sahn 1998; Glick,2002). Although women's representa-tion in the workforce has increased dra-matically over the past 30 years, theycontinue to have most of the family andhousehold responsibilities. Even in thedeveloped world, significant inequalityremains. This duality in women’s liveshas resulted in gender inequality, not onlyin the household and the labor market,but also in women’s social position andwell-being.

1.3.2 At the global level, efforts to ad-dress gender inequality include the 1979Convention on the Elimination of AllForms of Discrimination Against Women,the 1994 Cairo International Conferenceon Population and Development, and the2000 United Nations Millennium Deve-lopment Summit. The outcome of thissummit was formulation of the eight Mil-lennium Development Goals (MDGs),which were adopted by all membercountries, to serve as a framework tofight poverty and promote development.The target date for attaining these goalsis 2015. One of the goals, elimination ofgender inequality, especially in educa-tion and employment, while important inits own right, is viewed as critical to theattainment of the remaining goals (UNI-CEF 2003). Yet ten years after the goalswere set and fewer than five years fromthe target date, sub-Saharan Africa lagsbehind its counterparts in the develo-ping world. It is against this backdropthat the African Development Bank hasundertaken pilot studies to examine gen-der inequality in employment in SSA, be-ginning with Botswana and Mali.

1.4 Purpose and Objectives

1.4.1 This study addresses “the limi-ted availability of gender statistics in theBank’s Regional Member Countries(RMCs) … identified as one of the major

constraints for making progress in policydevelopment, development planning andmonitoring progress.” To remedy the si-tuation, the Bank provides support toRMCs in the development of gender sta-tistics through the Statistical CapacityBuilding (SCB) initiative.2

1.4.2 Thus, the studies of Botswanaand Mali have three main objectives:

a. To explore existing representative hou-sehold sample surveys, standardizeddata and qualitative information on theemployment of men and women invarious industries and in the formaland informal sectors;

b. To conduct a detailed analysis of theunderlying relationships between sexand employment and the determi-nants of these relationships; and

c. To produce comprehensive analyticalreports of the findings in line with theADF 11 commitment to collect gen-der-disaggregated data in two pilotRMCs.

2 Country Background and Context

2.1 The Regional Context: African Employment Trends and Gender

2.1.1 Education has intrinsic benefits,but lower long-term economic rewards(resulting from prolonged schooling/limi-ted or unfavorable macro-economic po-licies) can reduce its value. Developmentstrategies in Africa are being formulatedunder demographic, social and macro-economic duress. First, the expansionof education has been accompanied bygrowing school-age populations, reflec-ting the region’s historically high fertility.Africa’s share of the global school-age

1 See, ORQR4, November 2010. Tracking Gender Equality Results and Resources: An AfDB Quality at Entry Tool, AfDB, Tunis.2 Multinational Statistical Capacity Building in Regional Member Countries for MDG Monitoring and Results Measurement.


4


Case Study of Mali


3 Mali’s population growth rate is among the highest in the ECOWAS region, after Benin, Burkina Faso, the Gambia, Niger, Senegal and Sierra Leone. Femalesand males comprise 50.6% and 49.4% of the Malian population, respectively.

population rose from 10% in 1950 to16% in 2000 (United Nations 2003). Thispopulation momentum has created highdependency ratios, that is, a large cohortof young people relying on a small num-ber of working adults.

2.1.2 Second, SSA has been under-going demographic transitions, such aschanges in age at marriage, family struc-ture, and more recently, declining fertility.More schooling leads to increased age atfirst marriage and to a greater preva-lence of non-marital unions and single- orfemale-headed households. Thesechanges, in turn, increase women’s pro-pensity to participate in the labor market.As well, declining fertility makes moretime available for non-child-rearing/-bea-ring activities.

2.1.3 Third, the urbanization of Afri-can countries has transformed economicactivities and the meaning of paid work.Urbanization reduces reliance on sub-sistence agriculture and boosts the de-mand for consumer goods, thereby in-creasing the need for paid work. Indeed,urbanization affects not only the demandfor agricultural products, but also thefundamental nature of agricultural work.What used to be viewed as non-econo-mic work has now assumed an impor-tant place in the economy. A manifesta-tion of this evolution is the shift fromvoluntary and unpaid farm labor at peakperiods to paid labor.

2.1.4 Urbanization also indirectly in-fluences work opportunities. Urban labormarkets offer better economic prospectsthan do rural ones. However, whetherwomen reap the benefits depends ontheir representation in various sectors,particularly the more profitable formalsector. At the same time, urbanizationcan weaken extended family and socialnetworks that otherwise might ease the

pressure created by the incompatibilitybetween women’s outside work andtheir childcare and family obligations. Infact, childcare, domestic services, andactivities such as hair-braiding, garment-sewing and embroidery that were onceprovided free of charge now involve acost and fall under the umbrella of infor-mal economic activities. While this im-proves the economic status of somewho otherwise would not have beengainfully employed, it has implications forgender economic equality. In urban set-tings where childcare services are mini-mal or costly, women with more educa-tion will have to make trade-offs betweenintermittently withdrawing from formalwork or paying heavily for these services.Such withdrawals from the workforcecan reduce women’s prospects for ca-reer advancement, and thus challengepolicy efforts to close the gender gap inemployment.

2.1.5 Sub-Saharan Africa dispropor-tionately bears the global HIV/AIDS bur-den. In 2004, of the 36.9 million peoplewith HIV/AIDS, 23.6 million lived in SSA;two years later in 2006, the figure had ri-sen to 24.7 million. The epidemic, whichpredominantly affects adults in their primeproductive years, is eroding the region’sgains in human development and its fu-ture socio-economic resources. Added totight development budgets, the chal-lenges of addressing HIV/AIDS hindersAfrican governments’ efforts to providedecent livelihoods for their citizens. Aswell, macro-economic forces increasinglydefine African labor markets. Theseforces include policy reforms followingthe economic crises of the 1980s and1990s and the ensuing privatization ofAfrican labor markets (Eloundou-Enyegueand Davanzo 2003). Most countries havebarely recovered from these crises, whileothers are grappling with the aftermath ofthe 2008 global recession.

2.2 The National Setting: Mali’s Employment Profile

2.2.1 Mali is located in the Sahelianregion of West Africa. With a populationof 13.0 million in 2009, the country hasone of the highest annual growth rates inthe region: 2.4% (AfDB Database).3 AGNI per capita (US$) of 580 in 2008 putsMali among the low-income countries(AfDB Database). On the United NationsHuman Development Index (HDI), a com-posite measure of health (life expec-tancy), education, and income, Mali rosefrom 0.165 in 1980 to 0.309 in 2010(United Nations 2010). This is below theindex for Sub-Saharan Africa as a whole,which increased from 0.293 to 0.389.

2.2.2 Between 2000 and 2009, Mali’sGDP was lower and grew only marginallyrelative to the regional average of herneighboring Economic Community ofWest African States (ECOWAS) (Naba-lamba 2010). Reflecting the economiccrises, Mali’s GDP per capita fell signifi-cantly between 2008 and 2009 (Naba-lamba 2010). In this environment of ma-cro-economic duress, Mali’s capacity toprovide decent jobs to its citizens hasbeen severely constrained.

2.2.3 Gender parity in education andthe reduction of gender inequality in so-ciety are the most crucial MDGs. Thefirst is the most urgent and was to beachieved in 2005, ten years before thetarget date for the others. The ratio ofgirls to boys is used as an indicator tomeasure progress toward gender parityin education. Mali’s ratio in primary edu-cation rose from 0.801 in 2006 to 0.828in 2008, out-performing only Guinea andNiger among all ECOWAS memberstates for which data are available; in se-condary education, the ratio rose from0.608 to 0.639 (AfDB Database, 2010).

The percentage of seats held by womenin national parliaments is used as an in-dicator of the reduction of societal gen-der inequality. Among ECOWAS memberstates, Senegal has the highest percen-tage of parliamentary seats held by wo-men―around 20% in the 2006-to-2008period. Mali’s 10% for the same periodsurpasses the percentages of Nigeria,Cote d’Ivoire, The Gambia and Togo.

2.2.4 According to the InternationalLabor Organization labor market data-base, Mali’s employment-to-populationratio for individuals aged 15 or older isthe lowest among all the ECOWASstates, stalling at 0.47 during the 2006-to-2008 period.

2.2.5 Against this backdrop, a detai-led assessment of the 2007 Mali “En-quête permanente Emploi auprès desménages” (EPAM) is imperative to obtaina better appreciation of the determinantsof employment and to offer effective po-licy guidance for the AfDB.

3 Methodology

3.1 Conceptual Framework

3.1.1 Leading theories of labor forceparticipation have been grounded eitherin economic assumptions about the roleof human capital, modernization and ins-titutional segregation in labor market out-comes or in cultural perspectives em-phasizing discrimination.

3.1.2 Human capital theory providesa general framework for understandingthe importance of education for develop-ment. The theory emphasizes the primacyof abilities, education, experience andskills for labor market success (Becker1981; 1992; Mincer 1974). It hypothe-sizes that education is directly related toparticipation and returns in the labor mar-ket. Thus, women’s increased human ca-pital and experience should facilitate theirentry into the labor market, and as genderinequality in education narrows, so should

inequality in the labor market. Extendingthe theory to the modernization perspec-tive, as the occupational gender gap nar-rows, women’s economic security andsocial status should improve (Goldin1990). Such expectations have been but-tressed by cross-country evidence sho-wing a consistent association betweenwomen’s education and the labor marketreturns (King and Hill 1993). As a result,education has become central in nationaland international strategies that addresswomen’s status and development(UNFPA 2002; UNICEF 2003; United Na-tions 2000).

3.1.3 The theory of modernization,a variant of the human capital perspec-tive, relates employment outcomes to le-vel of development or industrialization.Modernization theorists regard labormarket expansion and increased laborsupply as by-products of the moderni-zation process. This expansion createsemployment opportunities for womenwho make further investments in theireducation to take advantage of the in-creased demand for labor. Ultimately, thegreater economic activity stemming fromeconomic progress and industrializationreduce gender inequality in all spheres ofsociety and thereby raise women’s socialstatus (Goldin, 1990).

3.1.4 Theories of occupational se-gregation further qualify the two neo-classical economic theories outlinedabove. Occupational segregation theo-rists suggest that women can continueto be marginalized in low-skill jobs with li-mited prospects for advancement be-cause of employer discrimination andinstitutional and labor market segmenta-tion, but also because of socialization(Anker 1997; Anker and Heim 1997).Women are presumed to self-select intoless rewarding or less prestigious occu-pations because they have been sociali-zed to have lower aspirations.

3.1.5 In contrast to the perspectivesreviewed above, a common explanation

for women’s limited labor market partici-pation, especially in the formal sector, isgender bias, which is presumed to ori-ginate in patterns of social organization(Collver and Langlois 1962) based onsocio-cultural norms and values thatexist at the family, educational, occupa-tional, and societal levels (Assie-Lu-mumba 2000; Birdsall and Sabot 1991;Boserup 1970; Stromquist 1990; Yous-sef 1972). Families, operating in accordwith larger societal norms and in antici-pation of lower returns to educating theirdaughters, are presumed to invest less intheir daughters’ than their sons’ educa-tion (Stromquist 1990).

3.1.6 Relevance of TheoreticalPerspectives in African Labor Mar-kets Proponents of the cultural pers-pective maintain that patriarchal valuestranscending education, marriage, ferti-lity, employment structure, and develop-ment stage are the decisive factors inemployment gender inequality. This ex-planation is relevant in African societieswhere a strong kinship network co-existswith male dominance. In such contexts,child-rearing is largely a female respon-sibility.

3.1.7 According to the neoclassicaleconomic perspective, notably humancapital, the gender-employment nexusextends beyond the absolute effect ofeducation to interactions with the demo-graphic (marriage, family size and struc-ture) and cultural milieux in which indivi-duals/couples assess economicop por tu nities and rewards and makeemployment decisions (Jah 2010a). Lo-wer long-term economic rewards (resul-ting from prolonged schooling and limi-ted or unfavorable macro-economicpolicies) can decrease the value of edu-cation. At the same time, delayed labormarket entry can reduce labor supplyand raise wages overall, and bring a sub-sequent rise in the demand for both edu-cation and labor. Increases in the numberof educated women can lead to accep-tance of women’s changing economic


5



AfDB


6

roles. However, this would not happen ifincreases in the number of educated wo-men lead to greater competition forscarce jobs.

3.1.8 An assessment of the moderni-zation perspective in the context of SSA,where an expansion in the labor markethas not followed the educational and de-mographic transitions, is particularly sa-lient. In countries with low labor demandbut with rising educational attainmentamong women, female competition forscarce, prestigious jobs challenges theneo-classical theory of a monotonic linkbetween education and employment.Instead, an inverted u-curve association(Standing 1983) or a negative relations-hip can prevail, as has been reported inthe African literature (Siphambe 2000 forBotswana). The modernization theory as-sumes that development benefits menand women impartially, but the likelihoodthat individuals, particularly women, willbe uniformly distributed across occupa-tion sectors may not be as automatic asthe theory implies.

3.1.9 The arguments of the occupa-tional segregation proponents are im-portant in Africa, where labor unions areweak, and markets are increasingly pri-vatized and informalized. Training andacquisition of skills in non-traditional fe-male occupations have been advocatedto narrow the labor market gender gap.However, it is not clear if or how contem-porary expansions in education havetranslated into employment prospects.

3.1.10 Thus, the relationship betweengender and employment is not asstraightforward as theory suggests. Fur-ther, these theories have typically beengenerated and tested in developed so-cieties. For instance, the thesis of de-

mographic incompatibility based on thenotion of competition between work andfamily roles, has received less attention inAfrica (see Jah 2010b and Shapiro andTambashe 1997 for exceptions). As no-ted earlier, the demographic, social andmacro-economic environments in whichdevelopment strategies in Africa arebeing devised and implemented are un-der duress. Demographic factors are li-kely to vary over an individual’s lifetime,yet women’s unique demographic rolesas wives and mothers are barely men-tioned by economic theorists even asthey highlight socialization and aspira-tions as determining factors in labor forcebehavior.

3.2 An Assessment of the Data

3.2.1 Data for this study are from the“Enquête permanente emploi auprès desménages” (EPAM),4 conducted in 2007by the Ministère de l’Emploi et de la For-mation Professionnelle, Agence Natio-nale pour l’Emploi (ANPE) et Départe-ment Observatoire de l’Emploi et de laFormation (DOEF). The 2007 EPAM dataare based on a nationally representativesample of 3,000 households, obtainedfrom a two-stage stratified sampling pro-cedure. Using a household and an indi-vidual questionnaire, data were collectedon a wide range of topics, including de-mographic characteristics of respon-dents and their households and indica-tors of income, employment, andeducational attainment. The individualquestionnaire was administered to allhousehold members aged 10 or older.The resulting sample was weighted tothe Mali national population aged 10 orolder in 2007. For the questionnaires anda detailed discussion of the data collec-tion, quality, coding, and classification of

occupations, see the “Gender in Em-ployment : Preliminary Results fromEPAM, Mali, 2007” Report (Nabalamba2010); and “Termes de référence pour laréalisation de l'enquête permanente em-ploi auprès des ménages (EPAM)” Re-port 2007, and related survey documen-tation5.

3.2.2 Analytical Data

3.2.2.1 The Mali EPAM data pertain toindividuals aged 10 or older living in pri-vate households, excluding residents ininstitutional settings (for example, mili-tary bases, hospitals, boarding institu-tions such as schools and prisons). Ho-wever, for this study, an analytical datasubset was created. First, respondentsyounger than 18 were eliminated in orderto remove child workers. This also elimi-nated most respondents who were at-tending school (ascertained by runningfrequencies). Given the conceptual fra-mework of the study in which the role ofhuman capital in employment is exami-ned, it is important that the analyses ex-clude people attending school. Next, theeconomic status variable (Etpop) wasused to eliminate non-economically ac-tive adults. The resulting dataset wasweighted to represent 5,178,725 eco-nomically active individuals, comprising41.8% of the Mali population aged 18 orolder.

3.2.3 Unlike the Botswana LaborForce Survey (BLFS), the Mali data per-mit an examination of income. Thus, in-come is also used to explore gender ine-quality. Unlike employment, this is acontinuous variable, measured inFRANCS CFA and ranging from zero to500,000 or more. To permit logistical re-gression analyses, the variable was divi-ded into five categories.


Case Study of Mali


4 EPAM 2007 is the third of three waves of cross-sectional employment surveys conducted in Mali since 2000.5 These documents are available from the Mali Ministère de l’Emploi et de la Formation Professionnelle, Agence Nationale pour l’Emploi, and DépartementObservatoire de l’Emploi et de la Formation.


7



AfDB

3.3.1 Dependent Variables: Two main dependent variables were analyzed: employment and income.

3.3.1.1 Employment was measured as individuals who did some work during the “reference period.”6 Severaleconomic sectors (as permitted by the data) were considered.

a. Given that the majority of the Malian population still lives in rural areas (Nabalamba, 2010) withagriculture as their main economic activity, the first outcome was agricultural employment, measured by activityin agriculture, fishing, livestock, etc., and coded “1”, with all other forms of non-agricultural employment as thereference (coded “0”).

b. The second dimension of employment distinguished between salaried and non-salaried employment.Salaried employment was coded s “1,” with non-salaried employment as the reference (coded “0”).

c. The third dimension distinguished between activity in the industrial sector coded “1,” with services asthe reference (coded “0”).

d. Fourth, the private informal sector (coded “1”) was distinguished from the public sector (the referencecoded “0”).

e. Fifth, formal employment (coded “1”) was distinguished from informal employment (the reference coded“0”).

The coding of occupations and the definition of the informal sector were derived from the 1988 InternationalStandard Classification of Occupations (ISCO-88) and the 1993 System of National Accounts (SNA -1993),respectively (BLFS 2008).

3.3.1.2 For income, four dimensions were measured:

a. Receipt of no income (coded “1”) was distinguished from all other forms of income (coded “0”). Forrespondents who received income, three categories were measured.

b. Minimum income (29,000 francs CFA or less and coded “1”) was distinguished from higher income levels(the reference coded “0”).

c. Income of 29,000 to 50,000 francs CFA (coded “1”) was distinguished from higher income levels (thereference coded “0”).

d. Income of 50,000 to 75,000 francs CFA (coded “1”) was distinguished from higher income levels (thereference coded “0”).

3.3.1.3 Thus, nine outcomes—five employment-related and four income-related— were examined. Attemptsto examine higher income levels were abandoned because the small proportions of the labor force earningincome at these levels yielded unstable estimates.

3.3.2 Independent Variable: The main independent variable is sex of the respondent. It is measureddichotomously and coded “1” if male and “2” if female (the reference).

3.3 Measures: Dependent, Independent and Control Variables

6 The reference period was the seven days before the survey.


8


Case Study of Mali


3.3.3 Control Variables: Drawing from the theoretical perspectives and past studies reviewed, the studycontrols for several correlates of employment.

3.3.3.1 Human capital characteristics. The human capital characteristics measured by educational attainmentand academic training. Educational attainment is measured at four levels: (i) primary; (ii) junior secondary; (iii)senior secondary; and (iv) non-formal/no-schooling, which serves as the reference. Academic certification(training) is measured as a dummy variable, coded “1” if respondent received any form of academic certificationand “0” if not.

3.3.3.2 Demographic characteristics. The second set of correlates considers family/household structuralvariables that affect women’s capacity to engage in paid employment. The first, marital status, is measured bywhether the respondent is married (coded “1”), living together (coded “2”), separated/divorced/widowed (coded“3”), or single (the reference coded “4”). Marriage is expected to create household demands on women’s timethat compete with work, while the effect for men is expected to be the reverse. Conversely, separation, divorceand widowhood are expected to compel women, in particular, to seek employment. The second demographicvariable measures whether the respondent is the household head, and the third measures whether therespondent is the spouse of the household head. The former is hypothesized to be positively associated withemployment, regardless of gender. On the other hand, being the wife of the household head can be a deterrentto outside work because of household demands and potential reluctance of a husband that his wife shouldbe employed outside the home.

3.3.3.3 Structural/Economic labor environment. The third set of correlates captures the structural/economiclabor environment as measured by economic migration, region of residence, and individual agency. Economicmigration is measured by whether the respondent migrated for economic reasons (coded “1”) or for non-economic reasons (the reference coded “0”). Economic prospects will likely depend on regional-levelemployment opportunities. While urban settings generally offer good work opportunities, this may not hold ifurbanization is not matched by the growth of economic structures.

3.3.3.4 Agency. Agency was measured by whether an individual is aware of and engages in political,economic and social activities in his/ her community, coded as “1” if yes and “0” if no (the reference). Agencyis expected to boost income and economic activity, especially among women.

3.3.3.5 Intergenerational aspects and aspirations. The fourth set of correlates concerns intergenerationalfactors and respondents’ career aspirations. Father’s employment in secure occupations is a proxy forintergenerational factors and the family’s socio-economic status, and is measured with a dummy variableindicating whether he was engaged in public and private formal (including non-governmental organizations)sector jobs and unionized jobs coded “1” and “0” if otherwise. A father’s employment in secure occupationsis presumed to be more influential in a son’s than a daughter’s access to profitable and secure occupations.Career aspirations is measured by whether the respondent indicated having ambitions for a future career,coded “1” if yes and “0” if not (the reference), expected to show a stronger and positive association withemployment prospects.

3.3.3.6 Interactions. Interactions between sex and several correlates were examined, based on theassumption that the effect of sex (the nature of the gender inequality) is apt to depend on the role of thesecorrelates. The number of interactions that could be examined was limited by the data available for eachcorrelate.


9



AfDB

3.4 Data Strengths

3.4.1 Much empirical research on therelationship between gender and em-ployment has focused on broad occu-pational classifications, in part, becauseof data unavailability. The more detailedclassification that is possible with theMali EPAM data permits comprehensiveanalyses of several dimensions of em-ployment. The ability to examine thesevery fine distinctions using nationally re-presentative data improves on the un-derstanding of employment behavior inthe country as well as permitting a ge-neralization to the rest of the population.The measure of academic certification isanother strength of the data. And despitea considerable number of missing cases,duration of unemployment is an impor-tant factor in employment analyses (Min-cer 1974).

3.4.2 Data are available for some clas-sic correlates of employment such asmarital status and household headship.The survey collected data on two mea-sures of educational attainment: highestlevel of school completed and academiccertification. The data include informationon migration and allow regional break-downs. In addition, data were collectedon political, economic and social agency,career aspirations, family background,intergenerational factors, and aspirations.

3.4.3 Thus, the Mali EPAM data com-plement the World Bank-sponsored In-come/ Household Consumption Surveysin Regional Member Countries, which fo-cus mainly on income. By facilitating de-tailed employment breakdowns, the sur-vey helps fill gaps in the Africanhousehold survey data systems and per-mits tracking of progress on the MDGsdesigned to enhance women’s statusand fight poverty.

3.5 Data Limitations

3.5.1 Because of their potential in-fluence on mothers’ time and on child-

care needs, variables measuring fertilityfamily size and children’s ages are crucialin employment analyses. The presenceof other adult females in the householdcan mediate the fertility-employment link.In their study of Guinea, Glick and Sahn(2000) found that having very young chil-dren constrains a mother’s ability to en-gage in paid employment. However, evi-dence from a recent study of 21 SSAcountries of (a) the effect of a first birth ona mother’s employment status and (b)the influence of another adult female inthe household is mixed (Jah, 2010b).

3.5.2 Given this mixed evidence,these two factors should be consideredin employment analyses. However, spe-cific information about fertility was notcollected in the 2007 Mali EPAM. Andbecause of time constraints, data modi-fications that could have measured thepresence of other adult females were notattempted. Similarly, no information isavailable on access to substitute childcare (formal and informal) and house-hold help, and should be collected in fu-ture surveys.

3.5.3 Socio-economic factors such ashousehold amenities and assets and theeducation and employment status ofspouses would be useful in determininghousehold members’ (including workingage women’s) need to work. As well,mother’s and father’s education andwork status provide important interge-nerational information. However, thesevariables are also absent. In SSA, spou-sal co-residence has been found to beimportant in employment analyses, butwas not asked in the survey. The datasetis also limited in terms of variables thatcan capture cultural attributes and indi-vidual agency.

3.5.4 But the most significant limita-tion derives from missing data. This oc-curs when respondents refuse to answera question, do not know the answer, oraccidentally skip an item. In the lattertwo cases, data are “Missing Completely

at Random,” that is, the missing dataare unrelated to the values of any varia-bles.

3.5.5 Because these missing valuesare a random sample of the full dataset,they can be ignored, and analysis of thedata yields results identical to those thatwould have been obtained from the fulldataset.

3.5.6 When a dataset is incomplete,the default is to analyze only cases withcomplete data and drop those with datamissing on any variables. The result is asubstantial reduction in sample size, andconsequently, statistical power. In thisstudy, the extent of missing data forsome measures was as high as 98%. Insuch circumstances, the outcome varia-ble may not be accurately measured,which results in estimated coefficientsand standard errors from the regressionthat are too low. Missing data because ofrefusals are not random and cannot beignored because simply eliminating themcould yield highly biased results. Specialtechniques are needed to address po-tentially non-random, Non-Ignorable mis-sing data.

3.6 Handling Missing Data

3.6.1 Imputation is often used tohandle missing data, because it isconceptually simple and retains samplesize. However, if missing data are non-random, imputation can bias parameterestimates (Allison 2000). To deal withmissing data, this study adopted themaximum likelihood estimation tech-nique. The utility of the maximum likeli-hood estimation technique permits useof observed data to calculate parameterestimates that would most likely have re-sulted in the complete dataset.

3.7 The Analytical Strategy

3.7.1 This study examines gender ine-quality in employment in Mali and therebyupdates the progress the country is ma-


10

king toward attainment of the MGDs.The findings will also inform the design ofthe AfDB’s programs and discussionswith its RCMs.

3.7.2 Gender inequality is analyzed innine employment and income outcomes.The analyses were performed in twosteps. In the first, the gross and net size(gross and net extent) of gender inequa-lity was measured in a multivariate fra-mework incorporating the six sets of cor-relates discussed above. Because thedeterminants for men and women areapt to differ, in the second step, key cor-relates were interacted with sex.

3.7.3 Maximum likelihood modelswere employed in an attempt to partlyovercome the problem of missingcases. Logistic regression was used tomodel the probability of each of the 12employment outcomes as a function ofgender, while controlling for several cor-relates.

3.8 The Size of the Gender Inequality

3.8.1 To estimate the size of genderinequality in the Malian labor force andin income distribution, seven logistic re-gression models were run sequentially,incorporating the sets of correlates out-lined above. The first model (model 1)estimates the gross gender inequalityfor each employment outcome, adjus-ting only for age. Model 2 adds humancapital characteristics; model 3, demo-graphic influences; model 4, structuraleconomic controls; model 5, measuresof political, economic and socialagency; and model 6, family/intergene-ration factors and aspirations. The final

model, model 7, considers interactionterms between gender and key corre-lates. Thus, as equation 1 shows, eachmodel is more complex than its prede-cessor. P/1-P is the probability that aman as opposed to a woman is em-ployed in a particular industry/sector/occupation; β0 is the intercept(constant term); β1G is the parameterestimating the gross gender inequality;β2H is the parameter estimating the in-fluence of human capital; β3D is theparameter estimating the influence ofdemographic characteristics; β4E, theparameter estimating the influence ofstructural economic factors; β5Ag, theparameter estimating the influence ofpolitical, economic and social agency;β6FAs, the parameter estimating the in-fluence of family/intergeneration factorsand aspirations; and β7IAs, the para-meter estimating the influence of corre-lates on the gender variable (interactionterms). The symbol ε refers to the resi-dual or unexplained variance.

4 Study Analysis andPrincipal Findings

4.1 Descriptive Results

4.1.1 The descriptive results for em-ployment and income are presented

graphically by gender and region of re-sidence (Bamako, the capital city; ur-ban villages and rural areas) in figures 1to 5.

4.1.2 Figure 1 presents the distribu-tion of women and men across the agri-cultural, industrial and services sectors.Employment in services is greater inBamako (79%) and other urban areas(76%) than in rural areas (57%). Whilethe representation of women and menin services is comparable in Bamako,women dominate this sector in other re-gions, particularly rural areas. On theother hand, agriculture (75%) is the ma-jor economic activity in rural areas, withmen dominating the sector (42%). In-dustry, the smallest sector, accountsfor 21%, 24% and 43% of workers inBamako, other urban areas and ruralareas, respectively. The high percen-tage for the industrial sector in ruralareas may seem surprising, but thismost likely reflects agricultural proces-

sing technology and the textile industry.Another important result is that whilewomen are minimally represented inthis sector in Bamako (4%) and otherurban areas (8%), they make up 27% ofindustrial workers in rural areas, subs-tantially outnumbering men (16%).


Case Study of Mali


7 The coefficients generated from the logistic regression analyses are called logits, denoting changes in the log odds for a man to be employed relative to a womanwith each unit increase in the explanatory variable. To facilitate interpretation, the coefficients are transformed into odds ratios (ORs) by exponentiation (Exp(β). An odds ratio of 1.00 implies no difference between men and women in the odds of employment in the selected industry/occupation. Odds ratios greaterthan 1.00 mean that men are more likely than women to be employed in the selected industry/occupation. Conversely, odds ratios less than 1.00 mean thatmen are less likely than women to be employed in the selected industry/occupation.


11



AfDB

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

Servi

ces

Indus

try

Agr

icultu

re

Servi

ces

Indus

try

Agr

icultu

re

Servi

ces

Indus

try

Agr

icultu

re

Bamako (Capital city) Other urban areas Rural areas

Male

Female

Total

Figure 1. Employment in the Agricultural, Industrial or Services Sector by Gender and Region, Mali 2007

4.1.3 Figure 2 shows the percentage ofthe labor force that is salaried as opposedto self-employed in the three regions. Self-employment is the chief economic activityin rural areas, accounting for 95% of wor-

kers 53% of whom are men. Self-employ-ment is also common in other urban areas(77%), but less so in Bamako (56%); inboth regions, women and men tend to beequally represented. Salaried employment

accounts for 44% of workers in Bamako,but only 23% and 5% of workers in otherurban areas and rural areas, respectively.In each region, men outnumber womenamong salaried workers.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

Servi

ces

Indus

try

Agr

icultu

re

Servi

ces

Indus

try

Agr

icultu

re

Servi

ces

Indus

try

Agr

icultu

re

Bamako (Capital city) Other urban areas Rural areas

Male

Female

Total

Figure 2. Salaried Employment and Self Employment by Gender and Region, Mali 2007

Source : Mali Enquête Permanente Emploi Auprès des Ménages (EPAM), 2007.


4.1.4 Figure 3 classifies the work-force into public sector and private in-formal sector employees. The predomi-nance of the private informal sector inthe Malian economy is reflected in theregional breakdowns—95%, 85% and74% of workers in rural areas, other ur-ban areas and Bamako, respectively,are engaged in private informal activi-ties. By contrast, public sector work israre in rural areas and uncommon evenin Bamako and other urban areas. The

respective contributions of this sector tothe labor economy are only 15% and26%, with trivial percentages of womenparticipating.

4.1.5 Figure 4 breaks down employ-ment by formal and informal sector.The percentages of workers in the for-mal sector—50% in Bamako, 30% inother urban areas and 15% in ruralareas—are higher than those notedabove for the public sector. And unlike

the public sector and across all re-gions, comparable percentages of wo-men and men are represented in formalsector work.

4.1.6 Figure 5 shows the distributionof women and men across various in-come levels in the three regions. Re-gardless of region, women are over-re-presented at low income levels, andunder-represented at higher income le-vels.


12


Case Study of Mali


Figure 3. Employment in the Public and Informal (Private) Sectors by Gender and Region, Mali 2007

Figure 4. Level of Employment in the Formal and Informal Sectors by Gender and Region, Mali 2007




13



AfDB

Figure 5. Distribution of Individuals Across Various Income Levels (in FCFA) from Main Economic Activity by Gender and Region, Mali 2007

8 Model significance is determined by looking at the information on “Testing Global Null Hypothesis” that Beta=0. Very small p-values for the Chi-Squaresindicate model significance and that at least one of the coefficients is not zero. Significance is evaluated at three levels: p<0.001 denoted by *** means therelationship is highly significant below the 99% level; p<0.01 denoted by ** means the relationship is significant at the 99 % level; and p<0.05 denoted by *meansthe relationship is significant at the 95% level; p<0.10 denoted by # means the relationship is marginally significant at the 90% level.

4.1.7 In summary, the service sectoris the dominant economic activity in ur-ban areas, with women over-represen-ted. Agriculture employment, in whichwomen are under-represented, is thedominant activity in rural areas. Industryis the smallest sector of the economy,especially in Bamako, which is surprisinggiven that it is the capital city. The privateinformal sector and self-employment ac-count for the largest shares of workers inMali.

4.2 Principal Findings from Multivariate Analyses

4.2.1 The descriptive results in sec-tion 4.1 do not explain how gender re-lates to employment and income. To dothis, multivariate analysis is necessary. Aseries of multivariate logistic regressionmodels was run sequentially to quantifygross and net gender inequality in: 1)agriculture, 2) salaried versus self-em-ployment, 3) public versus private infor-

mal employment, and 4) formal versusinformal employment. Finally, incomeinequality between women and menwas examined at four levels: the genderdifferential in the probability of an em-ployee receiving no income, and provi-ded an employee was salaried, genderdifferentials in: 1) minimum income(francs CFA 29,000 or less versus hi-gher income); 2) francs CFA 29,000 to50,000 versus higher income; and 3)francs CFA 50,000 to 75,000 versus75,000 or more.

4.2.2 Gross and Net Size of theGender Inequality in Employment. Foreach outcome, models of gross inequa-lity (model 1) control only for age.Controlling for duration of unemploy-ment in this basic model was abando-ned because of unstable estimates. Mo-dels 2 to 7 measure net inequality andcontrol sequentially for the six sets ofcorrelates. Model 2 controls for humancapital, including educational attainmentand professional/technical certification.

Model 3 controls for demographic fac-tors, including marital status, householdheadship and whether a respondent isthe spouse of the household head. Mo-del 4 adds structural and economic fac-tors, including economic migration andregion of residence. Model 5 considersagency measured in terms of social,economic and political awareness. Mo-del 6 controls for family/intergenerationalcharacteristics and individual aspira-tions. Model 7, the most complex, in-corporates possible interaction terms.8

The interactions considered are depen-dent on the distribution of women andmen for each outcome.

4.2.3 Table 1 presents odds ratios forthe gross and net inequality betweenmen and women in all the above em-ployment and income outcomes. Forbrevity, the table reports only the main ef-fects of gender, not those of the corre-lates. The detailed results appear in An-nex Tables 2 to 5. (Annex Tables can befound on the AfDB Data Portal.)


4.2.6 It is equally important to un-derstand how the correlates affect thegender-employment relationship in paidemployment, the outcome deemed morecritical in addressing gender inequality inthe labor market.

4.2.7 Salaried versus Non-SalariedEmployment. Model 1 indicates thatmen are significantly more likely than wo-men to be paid workers. Moreover, as re-flected in the estimates from models 2through 7, this difference persists re-gardless of adjustments for the othercorrelates.

4.2.8 Industry versus Services. Mo-del 1 indicates that men are 20% more li-kely than women to be employed in theindustrial sector. Even when adjustmentsare made to account for human capital,demographic, economic/structural, fa-mily/generational factors, and aspirationsor agency, this interpretation persists.But when interactions between genderand these correlates are considered, theassociation is reversed: men are less li-kely than women to be industrial sectorworkers. This reveals that men’s appa-rent advantage in access to the industrialsector (and disadvantage in access to

the service sector) reflects a failure tocontrol for mediating factors. The way inwhich gender relates to employment inthe industrial and service sectors isconditioned by a complex web of factorsthat will be examined in detail in “Genderand Employment: Mediating Influences.”

4.2.9 Private Informal Employmentversus Public Employment. The diffe-rential gender access to the informal sec-tor is important because, compared withthe public sector, it is relatively insecureand may not foster career growth or eco-nomic security (especially during retire-


14


Case Study of Mali


Table 1. Odds Ratios of the Size of Gender Inequality in Employment and Income, with Female as the Reference, Mali 2007

*** Significantly different from the reference category (p<0.001), ** (p<0.01), * (p<0.05), # (p<0.10), ns Not Significantly different from the reference category (p<0.10 or better), (ref): Reference category,... Not applicable. Source : Mali Enquête Permanente Emploi Auprès des Ménages (EPAM), 2007.

4.2.4 Agricultural Employment. Theodds ratios indicate that men are 58%more likely than women to be agricultu-ral workers (model 1). When differencesin human capital are taken into account(model 2), men are only 3% less likelythan women to be employed in agricul-ture.

4.2.5 These results suggest twoconclusions. First, the gender diffe-rences in agricultural employment ap-pear to be tied to human capital. Se-cond, and consistent with theoreticalexpectations, agricultural workers ge-nerally have little or no formal schoo-ling. However, the apparent impor-

tance of schooling in how gender re-lates to agricultural employment di-sappears when the other correlates areconsidered, particularly the demogra-phic factors. Based on the full model(model 7), men are significantly more li-kely than women to be agriculturalworkers.

Model 1 Model 3 Model 4 Model 5 Model 6 Model 7

Gross Gender Inequality

Human Capital Demographic Fac-tors

Economic/ Structural Factors

Individual Agency IntergenerationalFactors/Aspirations

Interactions

OddsRatio

SE Sig OddsRatio

SE Sig OddsRatio

SE Sig OddsRatio

SE Sig OddsRatio

SE Sig OddsRatio

SE Sig OddsRatio

SE Sig

EMPLOYMENT

Agricultural Employment

Men 1.584 0.002 *** 0.972 0.007 *** 3.375 0.014 *** 3.421 0.016 *** 3.431 0.016 *** 4.010 0.024 *** 6.814 0.039 ***

Salaried vs Non-Sala-ried Employment

Men 3.071 0.005 *** 2.076 0.009 *** 1.327 0.015 *** 1.583 0.015 *** 1.585 0.015 *** 3.557 0.028 *** 3.498 0.080 ***

Industry vs Services Men 1.203 0.004 *** 2.107 0.008 *** 1.695 0.014 *** 1.693 0.014 *** 1.711 0.014 *** 1.596 0.023 *** 0.507 0.035 ***

Private Informal Em-ployment vs PublicEmployment

Men 0.436 0.005 *** 0.733 0.009 *** 1.262 0.017 *** 1.254 0.017 *** 1.246 0.017 *** 1.368 0.026 *** 0.904 0.073 ns

Formal vs InformalEmployment

Men 0.892 0.003 *** 0.658 0.006 *** 0.639 0.010 *** 0.732 0.010 *** 0.853 0.018 *** 0.846 0.042 ***

INCOME

No Income Men 0.547 0.00 *** 0.972 0.01 *** 1.066 0.012 *** 0.963 0.01 ** 0.994 0.013 Ns 0.021 0.021 *** 3.29 0.04 ***

Minimum Income(<=29,000 francs CFA)

Men 0.248 0.003 *** 0.452 0.007 *** 0.565 0.013 *** 0.450 0.013 *** 0.448 0.013 *** 0.429 0.022 *** 0.322 0.046 ***

Income from 29,000 to50,000 francs CFA

Men 0.672 0.005 *** 0.811 0.010 *** 1.255 0.018 *** 1.243 0.018 *** 1.243 0.018 *** 0.884 0.029 *** 0.253 0.102 ***

Income from 50,000-75,000 francs CFA

Men 1.769 0.007 *** 0.638 0.012 *** 0.590 0.020 ns 0.536 0.021 ns 0.533 0.021 *** 2.552 0.042 ns 1.484 0.099 ***


15



AfDB

ment). Model 1 shows gross inequality inwomen’s likelihood versus that of men inaccess to the private informal sector,compared with the formal sector. Howe-ver, when interactions between genderand key variables are incorporated in themodel (model 7), no significant genderdifferences in employment in the privateinformal or public sector emerge. Thus,observed differential access to eithersector is explained by gender differencesin key correlates. This multivariate fin-ding is contrary to the differences thatappeared in the descriptive analysis (fi-gure 3).

4.2.10 Formal versus Informal Em-ployment. The likelihood of employmentin the formal relative to the informal sec-tor is a broader distinction than private in-formal employment versus public em-ployment. The multivariate results revealthat women’s likelihood of being formalsector employees is significantly lowerthan that of men. This is consistent withrecent evidence in the region (BotswanaStudy 2011).9

4.2.11 Gender and Employment:Mediating Influences. Tables 2 and 3show the detailed findings of each set ofcorrelates for the employment outcomesconsidered in the study. For concise-ness, only the gross estimates from mo-del 1 and the final net estimates frommodel 7 (full model) are presented anddiscussed. (See the annex tables for thedetailed results on the intermediate mo-dels, found on the AfDB data portal.)

4.2.12 Agricultural Employment andSalaried versus Non-Salaried Em-ployment. Table 2 presents the detailedestimates for the correlates of agricultu-ral employment (panel 1). Model 7 indi-

cates that younger men (aged 18 to 29)are more likely than men aged 55 or ol-der to be agricultural workers.

4.2.13 Men aged 40 to 54 are less li-kely than older men to be agriculturalworkers. The young age profile of wor-kers in this sector can be explained bythe physical demands of agricultural ac-tivity combined with low levels of me-chanization. Individuals aged 30 to 39years do not appear to be contributing togender inequality in this sector, perhaps(although it cannot be confirmed in thestudy) because women in this age rangeare in their reproductive years and areless likely than their male contemporariesto be actively involved in agriculture.

4.2.14 The relationship between agri-cultural employment and the human ca-pital variables is in the expected direc-tion. As individuals acquire moreschooling, they become less likely to en-gage in agricultural activity. Similarly, in-dividuals with higher levels of academiccertification are less likely to engage inagricultural activities, compared withpeople with lower or no certification. Ho-wever, this differs for women and men.The interaction reveals that at all levels ofacademic certification, women are morelikely than men to be agricultural workers.

4.2.15 The demographic correlatesthat were analyzed are related to agri-cultural employment, but in differingways. Being married and living togetherenhance agricultural activity, while sepa-ration/divorce/widowhood or being thespouse of the household head reducesit. Similarly, being the household headdepresses activity in this sector. Howe-ver, this is more so for female householdthan male household heads.

4.2.16 Economic migrants are signifi-cantly less likely to be engaged in agri-culture. And as expected, urban resi-dents, especially those in Bamako, areless likely to work in agriculture. Basedon the interaction terms, urban resi-dence reduces the likelihood of agri-cultural work more for men than wo-men.

4.2.17 Political, social and economicagency is inversely related to agricultu-ral employment. However, the interac-tion terms suggest that the dividends ofsuch agency may be greater for menthan women. Intergenerational factors(specifically, father’s occupation) andhaving career aspirations tend to steerindividuals toward the agricultural sec-tor, but again, gender differentials areapparent. Based on the interactionterms, father’s secure employment andhaving career aspirations benefitsons/men more than daughters/wo-men.

4.2.18 Salaried versus Non-SalariedEmployment. The estimates for thecorrelates of salaried versus non-sala-ried employment are shown in table 2,panel 2. Model 7 demonstrates genderinequality in men’s favor in this sector.Further, the likelihood of employmentin this sector rises with level of acade-mic certification. Men with higher levelsof certification are less likely than theirfemale counterparts to be engaged insalaried work, a finding that holds foreach of the three categories of certifi-cation considered. Demographic fac-tors reduce the likelihood of salariedemployment. However, male householdheads are substantially more likely thanfemale household heads to find salariedwork.

9 The AfDB 2011 Botswana study of gender and employment found that access to legislative positions and professional occupations is significantly greater formen as opposed to women, even when adjustments for several classic correlates of employment were made.


16


Case Study of Mali


Table 2. Odds Ratios of the Size of the Gender Inequality in Agricultural, Salaried and Industrial Sectors, Mali, 2007.

Panel 1 Panel 2 Panel 3

Agricultural Employment Salaried vs Non Salaried Industries vs Services

Model 1 Model 7 Model 1 Model 7 Model 1 Model 7

Gross Gender Inequality

Interactions Gross Gender Inequality

Interactions Gross Gender Inequality

Interactions

OddsRatio

SE Sig OddsRatio

SE Sig OddsRatio

SE Sig OddsRatio

SE Sig OddsRatio

SE Sig OddsRatio

SE Sig

SEXMale 1.584 0.002 *** 6.814 0.039 *** 3.071 0.005 *** 3.498 0.080 *** 1.203 0.004 *** 0.507 0.035 ***

Female (ref) ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...

AGE18-24 yrs 1.052 0.004 *** 2.405 0.037 *** 1.929 0.008 *** 0.325 0.048 *** 0.735 0.007 *** 0.620 0.043 ***

25-29 yrs 0.852 0.004 *** 2.087 0.035 *** 1.998 0.008 *** 0.912 0.041 * 0.658 0.007 *** 0.863 0.040 ***

30-39 yrs 0.774 0.004 *** 0.963 0.032 ns 2.180 0.007 *** 0.319 0.037 *** 0.704 0.006 *** 0.862 0.036 ***

40-54 yrs 0.898 0.004 *** 0.670 0.031 *** 1.490 0.007 *** 0.561 0.037 *** 0.864 0.006 *** 0.807 0.036 ***

55-99yrs (ref) ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...

HUMAN CAPITALNo Schooling (ref) ... ... ... ... ... ... ... ... ...

# of Years of Schooling 0.706 0.007 *** 0.635 0.011 *** 1.540 0.010 ***

# of Years of Schooling Squared 1.014 0.000 *** 1.028 0.001 *** 0.965 0.001 ***

Academic Certification No Academic Certification (ref) ... ... ... ... ... ... ... ... ...

CEP 13.631 0.057 *** 8.362 0.091 *** 1.395 0.022 ***

DEF/BEPC,CAP or BT 2.359 0.040 *** 57.256 0.058 *** 1.790 0.029 ***

DUTS/BTS/DUT/et autre niveaunBAC+2/+1, DES, Autres

1.504 0.064 *** 38.237 0.068 *** 3.509 0.039 ***

DEMOGRAPHIC FACTORSMarital StatusSingle (ref) ... ... ... ... ... ... ... ... ...

Married 2.409 0.033 *** 0.302 0.033 *** 1.102 0.028 ***

Living Together 3.614 0.036 *** 0.144 0.040 *** 1.112 0.034 **

Separated, Divorced or Widowed 0.511 0.051 *** 0.862 0.080 # 0.264 0.050 ***

Is not the Household Head (ref) ... ... ... ... ... ... ... ... ...

Is the Household Head 0.153 0.055 *** 0.227 0.070 *** 0.438 0.028 ***

Is not the spouse of Household Head(ref)

... ... ... ... ... ... ... ... ...

Is the Spouse of Household Head 0.711 0.038 *** 0.772 0.049 *** 0.364 0.035 ***

ECONOMIC/STRUCTURAL FACTORSMigrationNon Economic Migrant (ref) ... ... ... ... ... ... ... ... ...

Economic Migrant 0.397 0.024 *** 182.571 0.080 *** 1.222 0.018 ***

Rural Areas (ref)

Bamako (Capital) 0.081 0.057 *** 5.000 0.107 *** 0.746 0.020 ***

Other Urban Areas 0.105 0.026 *** 3.045 0.056 *** 1.148 0.017 ***

AgencyNot Politically, Economic and Socially Active (ref)

... ... ... ... ... ... ... ... ...

Political, Economic and Social Awareness

0.204 0.030 *** 2.136 0.049 *** 1.738 0.022 ***

INTERGENERATIONAL FACTORS/ASPIRATIONSFather's Occupation not Public, Pri-vate, Unionized NGOs (ref)

... ... ... ... ... ... ... ... ...

Father's Occupation is Public, Pri-vate, Unionized NGOs

1.546 0.025 *** 0.404 0.052 *** 0.839 0.028 ***

No Promising Career Ambitions (ref)

Promising Career Ambitions 1.104 0.021 *** 2.185 0.038 *** 0.781 0.023 ***

INTERACTION TERMSSex * CEP 0.093 0.061 *** 0.826 0.094 *

Sex * DEF/BEPC 0.090 0.044 *** 0.573 0.056 ***

DUTS/BTS/DUT/et autre niveaunBAC+2/+1, DES, Autres

0.793 0.069 *** 0.759 0.068 ***

Sex * Household Head 3.623 0.055 *** 3.491 0.071 ***

Sex * Economic Migration 0.012 0.084 ***

Sex * Bamako 0.149 0.073 *** 0.509 0.112 ***

Sex * Other Urban Areas 0.957 0.031 ns 1.210 0.061 ***

Sex * Agency 2.361 0.032 *** 1.332 0.046 ***

Sex * Father's Occupation 0.466 0.029 *** 3.883 0.053 *** 2.053 0.029 ***

Sex * Career Ambitions 0.471 0.026 *** 0.896 0.042 ** 2.498 0.029 ***

Father's Occupation * Agency 3.432 0.030 *** 0.258 0.038 *** 0.654 0.030 ***



17



AfDB

4.2.19 Economic migrants are signifi-cantly more likely to be engaged in sala-ried work. Similarly, residents of Bamakoand other urban areas are more likelythan rural residents to have salaried em-ployment. However, the interaction termsfor both economic migration and resi-dence in Bamako reveal that they bothyield greater economic dividends for wo-men than men. Conversely, residence inother urban areas benefits men morethan women.

4.2.20 Political, economic and socialagency facilitates salaried employment.The interaction term indicates that menbenefit more from agency than do wo-men. Father’s employment in secure oc-cupations is inversely related to an indivi-dual’s likelihood of salaried em ploym ent,but this inverse relationship holds morefor women than men. Having career as-pirations enhances the likelihood of sala-ried employment, but the interaction withsex suggests that women benefit morefrom agency than men do.

4.2.21 Industrial versus Service Sec-tor Employment. Table 2, panel 3shows how the correlates are related toemployment in the industrial versus theservices sector. According to the full mo-

del (model 7), men are less likely thanwomen to be industrial sector workers.The human capital factors suggest thatcertification enhances access to indus-trial sector jobs. Some of the demogra-phic factors enhance access to this sec-tor (marriage and living together), whileothers hinder access (separation/di-vorce/widowhood, being a householdhead or the spouse of the householdhead). Economic migration, residing inurban areas other than Bamako, and in-dividual agency foster access to indus-trial sector jobs. Data limitations precludean examination of gender dynamics re-lated to the four sets of correlates. Fa-ther’s secure employment and havingcareer aspirations are negatively relatedto employment in the industrial sector.

4.2.22 The interaction terms indicatethat women with promising career aspi-rations and whose fathers held securejobs are less likely than their male peersto be employed in industry.

4.2.23 Private Informal Employmentversus Public Employment. Panel 1 inTable 3 presents the estimates for thecorrelates of private informal employmentversus public employment. Contrary toconventional wisdom, women are not si-

gnificantly more likely than men to be in-formal sector workers. The estimate fornumber of years of schooling, and cor-roborated by Jah (2007), shows that in-creases in education tend to channel in-dividuals to the informal rather than theformal sector. Conversely, certificationsteers individuals away from the less se-cure private informal sector. However,the interaction terms indicate that at alllevels of certification, men are funneledtoward the private informal sector. Allmarital statuses tend to push individualstoward the private informal sector, whilebeing the head of the household or thespouse of the household head deters in-dividuals, particularly male householdheads, from working in the sector.

4.2.24 As expected, economic/struc-tural factors are negatively related to em-ployment in the private informal sector,but the lack of estimates for the interac-tion term precludes further examinationof gender relationships. Agency, father’ssecure occupation and career aspira-tions are all inversely linked to working inthe private informal sector. Additionally,the interaction terms reveal that father’ssecure occupation and career aspira-tions reduce the negative effect of wo-men’s overrepresentation.


18


Case Study of Mali


Table 3. Odds Ratios of the Gender Inequality in Private Informal and Formal Employment, Mali, 2007

Panel 1 Panel 2

Private Informal Employment vs. Public Employment Formal vs. Informal Employment

Model 1 Model 7 Model 1 Model 6

Gross Gender Inequality Interactions Gross Gender Inequality Intergenerational Factors/Aspirations

OddsRatio

SE Sig Odds Ratio SE Sig OddsRatio

SE Sig Odds Ratio SE Sig

SEX

Male 0,436 0,005 *** 0,904 0,073 ns 0,892 0,003 *** 0,846 0,042 ***

Female (ref) … … … … … … … … … … … …

AGENCY

55-99yrs (ref) … … … … … … … … … … … …

18-24 yrs 0.963 0.009 *** 0.423 0.048 *** 1.805 0.005 *** 0.820 0.034 ***

25-29 yrs 0.827 0.008 *** 0.405 0.043 *** 1.515 0.005 *** 0.820 0.032 ***

30-39 yrs 0.664 0.007 *** 0.797 0.039 *** 1.408 0.005 *** 0.475 0.029 ***

40-54 yrs 0.726 0.007 *** 0.427 0.038 *** 1.181 0.005 *** 0.755 0.029 ***

HUMAN CAPITAL

No Schooling (ref)

# of Years of Schooling 1.154 0.011 *** 0.796 0.008 ***

# of Years of Schooling Squared 0.982 0.001 *** 1.018 0.000 ***

Academic Certification

No Academic Certification (ref) … … … … … …

CEP 0.009 0.127 *** 1.755 0.050 ***

DEF/BEPC,CAP or BT 0.009 0.070 *** 8.212 0.033 ***

DUTS/BTS/DUT/et autre niveaunBAC+2/+1, DES,Autres

0.025 0.074 *** 5.159 0.046 ***

DEMOGRAPHIC FACTORS

Single (ref) … … … … … …

Married 1.861 0.033 *** 0.327 0.028 ***

Living Together 2.304 0.040 *** 0.351 0.032 ***

Separated, Divorced or Widowed 1.529 0.058 *** 1.936 0.038 ***

Not the Household Head (ref) … … … … … …

Is the Household Head 0.929 0.050 # 0.109 0.046 ***

Not Spouse of the Household Head (ref) … … … … … …

Is the Spouse of Household Head 0.583 0.044 *** 1.329 0.032 ***

ECONOMIC/STRUCTURAL FACTORS

Migration

Non Economic Migrant (ref) … … … … … …

Economic Migrant 0.240 0.020 *** 988.414 0.088 ***

Region of Residence

Rural Areas (ref) … … … … … …

Bamako (Capital) 0.665 0.021 *** 157.924 0.070 ***

Other Urban Areas 0.700 0.020 *** 1.498 0.029 ***

AGENCY

No Political, Economic and Social Awareness (ref) … … … … … …

Political, Economic and Social Awareness 0.674 0.029 *** 8.691 0.027 ***

INTERGENERATIONAL FACTORS/ASPIRATIONS

Father's Occupation not Public, Private, UnionizedNGOs (ref)

… … … … … …

Father's Occupation is Public, Private, UnionizedNGOs

0.245 0.042 *** 0.491 0.028 ***

Promising Career Aspirations 0.397 0.033 *** 3.615 0.025 ***

INTERACTION TERMS

Sex * CEP 26.538 0.131 *** 2.010 0.056 ***

Sex * DEF/BEPC 8.324 0.071 *** 1.317 0.033 ***

Sex * DUTS/BTS/DUT 4.961 0.076 *** 1.561 0.045 ***

Sex * Household Head 0.214 0.051 *** 16.711 0.049 ***

Sex * Economic Migration 0.051 0.077 ***

Sex * Bamako 0.826 0.062 **

Sex * Other Urban Areas 1.413 0.035 ***

Sex * Agency 1.766 0.036 *** 0.200 0.027 ***

Sex * Father's Occupation 0.123 0.044 *** 1.602 0.030 ***

Sex * Career Aspirations 1.636 0.037 *** 0.813 0.030 ***



19



AfDB

4.2.25 Formal versus Informal Em-ployment. Table 3, panel 2 presents theestimates for the correlates of formal ver-sus informal employment. Number ofyears of schooling is negatively associa-ted with formal sector employment,consistent with the results of recent stu-dies of the relationship in SSA (Jah2007). However, higher levels of certifi-cation facilitate access to formal sectorjobs. Given that men are less likely to beengaged in formal sector work, the in-teraction of sex and certification impliesthat the benefits of certification are grea-ter for men than women.

4.2.26 Marriage, living together andbeing a household head are negativelyassociated with employment in the for-mal sector, while being separated/divor-ced/widowed and being the spouse ofthe household head are positively relatedto formal sector work. The gendernuances stemming from interactions withsex can be explored only for householdheadship—being a male household headenhances formal sector prospects, com-pared with being a female householdhead.

4.2.27 The economic/structural corre-lates all behave in the expected direction,but residing in Bamako and economicmigration tend to intensify the disadvan-tage faced by men while residing in otherurban areas lessens their disadvantage.Agency is positively related to formal sec-tor work, particularly among women. Fa-ther’s employment in secure occupa-tions hinders formal sector prospects, adisadvantage that is worse for womenthan men. As anticipated, having careeraspirations enhances formal sector pros-pects, particularly for women.

4.2.28 Gross and Net Size of theGender Inequality in Income. Analysesof income disparities between womenand men were conducted at four levelsof income. First, the difference in the pro-bability of earning no income as opposedto earning some form of income was

quantified. Next, for those earning someincome, the probability of earning atthree progressive income levels was exa-mined: minimum (francs CFA 29,000 orless versus higher income; francs CFA29,000 to 50,000 versus higher income;and francs CFA 50,000 to 75,000 versushigher income. An analysis of income atlevels higher than francs CFA 75,000was abandoned because of the smallpercentage of workers at those levels.The findings cover the gross and net es-timates of gender inequality in income.For brevity, the lower portion of Table 1reports the main effects of gender only(the independent variable), not those ofthe correlates. (The detailed findings arereported in annex tables 7 to 10. AnnexTables can be found in the AfDB DataPortal.)

4.2.29 No Income. According to Table1, model 1, men are about 45% less li-kely than women to receive no incomefrom employment. Model 2, which takesaccount of differences in human capital,shows a sharp drop in inequality―menare only 3% less likely to earn no in-come. This suggests that much of theoverall inequality may be attributable todifferences in human capital. Subse-quent adjustment for differences in de-mographic factors actually reverses thedirection of the in equality: men are 6%more likely to receive no income fromemployment. This switch highlights theimportance of demographic factors inthe gender-income link.

4.2.30 In models 4 and 5, which incor-porate economic/structural factors andagency, respectively, the association re-verts to a negative direction, althoughthe effect of agency on the gender ine-quality is non-significant. Adjusting forintergenerational/family influences inten-sifies the inequality―men are significantlyless likely (92%) than women to receiveno employment income.

4.2.31 The full model, which considersgender interactions, shows the most dra-

matic results. Not only does the asso-ciation switch back to positive, but thesize of the inequality is substantially in-creased. Men are considerably more li-kely than women to receive no incomefrom economic activities. These resultsare consistent with the descriptive ana-lyses presented earlier. The strikingchanges in the behavior of the genderinequality estimate across the variousmodels highlights the importance ofconsidering as many measures as pos-sible of theoretically important explana-tions of gender inequalities in income.

4.2.32 Minimum Income. As obser-ved for no income, the gross gender ine-quality in earning minimum income(francs CFA 29,000 or less) is negative(model 1). Men are about 75% less likelythan women to receive minimum incomefrom employment. Models 2 through 7indicate that the inequality changes littlewith progressive controls. According tothe final model that also allows for inter-actions, men are significantly less likely(68%) than women to earn minimum in-come from economic activities.

4.2.33 Income: francs CFA 29,000 to50,000. In the francs CFA 29,000 to50000 income range, the same com-plexity in the dynamics of the relations-hips is evident. When all the correlatesare considered, men are 75% less likelythan women to earn francs CFA 29,000to 50,000. Again, as observed for no in-come, demographic and intergeneratio-nal/family factors appear to be the mostcritical.

4.2.34 Income: francs CFA 50,000 to75,000. The estimates generated acrossthe seven models for the francs CFA50,000 to 75,000 category are also dy-namic, albeit slightly less so. Under mo-del l, the gross gender inequality in in-come is positive and large―men are77% more likely than women to earn in-come in this category. The final model(model 7) indicates that men are 48%more likely than women to earn between


20


Case Study of Mali


francs CFA 50,000 to 75,000 (the hi-ghest income bracket that could be ana-lyzed).

4.2.35 The demographic, economic/structural and intergenerational factorsare non-significant. This suggests thatat higher income levels (francs CFA50,000 to 75,000), differences in familyfactors and region of residence are notrelated to gender inequality in income, al-though they are influential at lower in-come levels. The sections that follow in-vestigate the estimates generated for thecorrelates, with particular emphasis onthe interactions.

4.2.36 Gender and Income: Media-ting Influences. Table 4 reports the fin-dings about the role of correlates on gen-der inequality in income. Again, forconciseness, the discussion focuses onthe final net estimates from model 7.

4.2.37 No Income. Panel 1 shows thatmen are significantly more likely thanwomen to earn no income from em-ployment. As individuals acquire moreyears of schooling, they are less likely toreceive no income. Similarly, as level ofacademic certification rises, the likeli-hood of receiving no income declines.The exception is at the lowest level ofcertification, where individuals are morelikely to receive no income from em-ployment than are those with no certifi-cation, mirroring the U-shaped natureof the relationship between human ca-pital and employment/earnings (Stan-ding 1983). However, based on the theinteractions, the disadvantage faced bymen eases.

4.2.38 Being the spouse of the house-hold head greatly increases the likelihoodof receiving no income. All the other ma-rital statuses and being the head of thehousehold decrease the likelihood of ear-ning no income. However, the interactionterm for sex and household headship in-dicates that male household heads aremore likely than their female counter-parts to receive no income.

4.2.39 Compared with people in ruralareas, Bamako residents are more likelyto receive no income, while residents ofother urban areas are less likely to do so.The interaction terms suggest that maleeconomic migrants and male residents ofBamako and other urban areas are gra-dually becoming less likely than their fe-male counterparts to receive no earningsfrom work.

4.2.40 People who are politically, eco-nomically and socially active are more li-kely than those who are not active to re-ceive no income from work. Moreover,women who are active in these spheresare more likely than their male counter-parts to earn no income.

4.2.41 Father’s secure employment in-creases the likelihood of receiving no ear-nings from work, particularly for women.Having career aspirations increases theodds of earning no income, an associationthat is stronger for men than for women.

4.2.42 Minimum Income

Panel 2 in table 4 shows the final esti-mates of the role of the correlates on gen-der inequality in minimum income (francs

CFA 29,000 or less). Men are 68% less li-kely than women to have earnings in thiscategory. As women and men acquiremore years of schooling, their odds ofearning minimum income decline by 22%.Women and men with the highest and lo-west levels of academic certifications areless likely than those with no certificationto earn minimum income. By contrast,those with DEF/BEPC, CAP or BT certifi-cation are more likely than those withoutacademic certification to earn minimumincome. However, at successively higherlevels of certification, women graduallybecome less likely than men to receive mi-nimum income.

4.2.43 All the marital statuses exami-ned decrease the odds of earning mini-mum income, while household headshipor being the spouse of the householdhead raises the odds. However, malehousehold heads are less likely than fe-male household heads to receive mini-mum income.

4.2.44 Economic migrants and resi-dents of Bamako and other urban areasare less likely than non-economic mi-grants and workers residing in rural areasto earn minimum income. This advantageis greater for women than men. Father’ssecure employment is associated with hi-gher odds of earning minimum income.As well, the odds are greater for men,suggesting that women tend to benefitmore than men do from family back-ground as measured by father’s occupa-tion. Career aspirations are also positivelyrelated to the odds of earning minimumincome, and the income disadvantage isgreater for women than men.


21



AfDB

Table 4. Odds Ratios of the Influence of Correlates on the Size of the Gender Inequality in Income, Mali, 2007.

Panel 1 Panel 2 Panel 3 Panel 4

No Income Minimum Income (29000 FCFA or less)

29000 to 50000 FCFA 50000 to 75000 FCFA

Model 7 Interactions Model 7 Interactions Model 7 Interactions Model 7 Interactions

OddsRatio

SE Sig OddsRatio

SE Sig OddsRatio

SE Sig OddsRatio

SE Sig

SEX

Homme 3.290 0.037 *** 0.322 0.046 *** 0.253 0.102 *** 1.484 0.099 ***

Femme (ref) ... ... ... ... ... ... ... ... ... ... ... ...

AGE

18-24 yrs 0.348 0.034 *** 1.055 0.035 # 10.530 0.052 *** 0.738 0.070 ***

25-29 yrs 0.257 0.032 *** 0.859 0.031 *** 2.891 0.043 *** 1.547 0.053 ***

30-39 yrs 0.348 0.029 *** 0.757 0.029 *** 3.018 0.039 *** 0.811 0.046 ***

40-54 yrs 0.266 0.029 *** 0.814 0.028 *** 1.514 0.039 *** 0.792 0.044 ***

Older Adults (55+ yrs) ref ... ... ... ... ... ... ... ... ... ... ... ...

CAPITAL HUMAIN

No Schooling (ref) ... ... ... ... ... ... ... ... ... ... ... ...

# of Years of Schooling 0.888 0.007 *** 0.782 0.003 *** 0.924 0.011 *** 1.470 0.016 ***

# of Years of Schooling Squared 1.007 0.000 *** 1.000 0.001 ns 0.986 0.001 ***

Academic Certification

No Academic Certification (ref) ... ... ... ... ... ... ... ... ... ... ... ...

CEP 2.147 0.061 *** 0.591 0.058 *** 658.105 0.128 *** 0.771 0.045 ***

DEF/BEPC,CAP or BT 0.355 0.043 *** 1.288 0.038 *** 0.860 0.058 ** 0.150 0.055 ***

DUTS/BTS/DUT/et autre niveau BAC+2/+1, DES, Autres 0.874 0.049 *** 0.254 0.061 *** 0.277 0.072 *** 0.073 0.058 ***

CARACTERISTIQUES DEMOGRAPHIQUES

Marital Status

Single (ref) ... ... ... ... ... ... ... ... ... ... ... ...

Married 0.275 0.040 *** 0.550 0.026 *** 0.276 0.042 *** 0.304 0.046 ***

Living Together 0.123 0.044 *** 0.434 0.030 *** 0.299 0.049 *** 0.147 0.056 ***

Separated, Divorced or Widowed 0.411 0.044 *** 0.142 0.049 *** 0.569 0.080 *** 0.090 0.094 ***

Not the Household Head (ref) ... ... ... ... ... ... ... ... ... ... ... ...

Is the Household Head 0.228 0.065 *** 9.670 0.050 *** 0.776 0.104 * 0.213 0.045 ***

Not Spouse of the Household Head (ref) ... ... ... ... ... ... ... ... ... ... ... ...

Is the Spouse of Household Head 2.163 0.042 *** 3.415 0.035 *** 5.245 0.057 *** 1.365 0.056 ***

ECONOMIC/STRUCTURAL FACTORS

Migration

Non Economic Migrant (ref) ... ... ... ... ... ... ... ... ... ... ... ...

Economic Migrant 0.996 0.047 ns 0.018 0.059 *** 0.109 0.096 *** 2.319 0.060 ***

Region of Residence

Rural Areas (ref) ... ... ... ... ... ... ... ... ... ... ... ...

Bamako (Capital) 1.819 0.057 *** 0.048 0.066 *** 0.009 0.197 *** 0.781 0.030 ***

Other Urban Areas 0.844 0.022 *** 0.536 0.034 *** 0.102 0.102 *** 0.661 0.032 ***

INITIATIVE

No Political, Economic and Social Awareness (ref) ... ... ... ... ... ... ... ... ... ... ... ...

Political, Economic and Social Awareness ... ... ... 2.455 0.037 *** 0.754 0.061 *** 7.093 0.064 ***

ASPECTS INTERGENERATIONNELS ET ASPIRATIONS

Father's Occupation is not Public, Private, Unionized NGOs (ref) ... ... ... ... ... ... ... ... ... ... ... ...

Father's Occupation is Public, Private, Unionized NGOs 1.088 0.024 *** 1.440 0.033 *** 23.045 0.058 *** 1.311 0.081 ***

No Promising Career Aspirations (ref) ... ... ... ... ... ... ... ... ... ... ... ...

Promising Career Aspirations 1.895 0.020 *** 1.909 0.029 *** 1.444 0.056 *** 0.528 0.052 ***

INTERACTIONS

Sex * CEP 0.114 0.069 *** 6.086 0.060 *** 0.001 0.130 ***

Sex * DEF/BEPC 0.654 0.050 *** 1.879 0.039 *** 0.076 0.061 ***

Sex * DUTS/BTS/DUT 0.854 0.052 ** 4.544 0.067 *** 0.339 0.075 ***

Sex * Household Head 1.842 0.069 *** 0.078 0.054 *** 2.067 0.103 ***

Sex * Economic Migration 0.101 0.070 *** 19.865 0.063 *** 9.025 0.100 *** 0.523 0.065 ***

Sex * Bamako 0.140 0.037 *** 5.563 0.072 *** 116.198 0.201 ***

Sex * Other Urban Areas 0.546 0.032 *** 2.091 0.038 *** 14.517 0.106 ***

Sex * Agency 0.482 0.032 *** 0.412 0.037 *** 0.165 0.055 *** 0.369 0.059 ***

Sex * Father's Occupation 1.386 0.026 *** 1.069 0.035 # 9.787 0.060 *** 1.390 0.082 ***

Sex * Career Aspirations 0.604 0.062 *** 0.729 0.032 *** 1.540 0.060 *** 2.411 0.056 ***

Father's Occupation * Agency 0.809 0.034 *** 0.283 0.031 *** 17.135 0.043 *** 0.386 0.048 ns



22


Case Study of Mali


4.2.45 Income: francs CFA 29,000 to50,000. Panel 3 in table 4 shows thatmen are less likely than women to haveearnings in the francs CFA 29,000 to50,000 range. Years of schooling andhaving the second or the highest levels ofcertification examined in this study de-crease the odds of earning income inthis range, while certification at the lo-west level substantially raises the odds.The interactions suggest that men withsome certification are less likely than theirfemale counterparts to have earnings inthis income bracket.

4.2.46 With the exception of being thespouse of the household head, all thedemographic factors decrease an indivi-dual’s odds of earning income between29,000 and 50,000 francs CFA. Femalehousehold heads are less likely than malehousehold heads to have earnings in thisrange. Economic migrants and residentsof urban centers including Bamako areless likely to earn income in the 29,000 to50,000 francs CFA range. However, maleeconomic migrants and male residents ofthese centers are more likely than theirfemale counterparts to earn incomes inthis range. Agency also decreases theodds of an individual earning income bet-ween 29,000 and 50,000 francs CFA,especially for men. On the other hand, in-tergenerational effects and career aspi-rations tend to increase the odds of re-ceiving income in this range, particularlyfor men.

4.2.47 Income: francs CFA 50,000 to75,000. Panel 4 of table 4 shows that theestimates of the correlates of income50,000 to 75,000 francs CFA (the hi-ghest level examined in this study) favormen. The prospects of earning income inthis range were enhanced by moreschooling, but the association was ne-gative for all levels of certification. The es-timates for the interactions were unstablebecause of the small number of womenwith earnings in this category. All the de-mographic factors except being thespouse of the household head are asso-

ciated with decreased odds of havingincome in this range.

4.2.48 Economic migration enhancesthe prospects of receiving earnings in thisrange, but the opposite holds for residingin Bamako and other urban areas. Theinteraction indicates that female econo-mic migrants tend to earn more than theirmale counterparts do. Agency and fa-ther’s secure employment increase the li-kelihood of having earnings in this cate-gory, but career aspirations tend todecrease the likelihood. Women with morepolitical, economic and social agency aremore likely than their male counterparts toreceive earnings in this range. On the otherhand, intergenerational and family factorstend to benefit men more than women.

4.3 Summary of Principal Findings

4.3.1 Employment

4.3.1.1 Agricultural Employment. Menare significantly more likely than womento be agricultural workers. While youngercohorts contribute to gender inequality infavor of men in this sector, older workerstend to narrow it. The young age profileof agricultural workers reflects the physi-cal demands of agricultural activity andits low degree of mechanization in Africa.Certification helps individuals accessmore profitable non-agricultural jobs, butthe advantage is greater for men thanwomen. Human capital facilitates men’saccess to non-agricultural work; the re-verse holds for women.

4.3.1.2 Being married and living toge-ther enhance agricultural activity, whilebeing the household head or spouse ofthe household head and separation/di-vorce/widowhood depress the likelihoodof engaging in agricultural activity. The ef-fect of household headship may be grea-ter for female household heads.

4.3.1.3 Economic migrants and urbanresidents, especially those in Bamako,

are less likely to work in agriculture, butthe economic benefits of this are greaterfor men than women. Consistent withtheoretical expectations, agency is in-versely related to agricultural employ-ment, and the dividends may be greaterfor women. Father’s secure employmentand having career aspirations tend tobenefit sons/men more than daugh-ters/women.

4.3.1.4 Salaried versus Non-SalariedEmployment. Men are significantly morelikely than women to be paid workers.The likelihood of access to the paid sec-tor rises with higher levels of certification,but less so for men than women.

4.3.1.5 The demographic factors consi-dered in this analysis hinder access topaid employment. However, male hou-sehold heads are substantially more likelythan female household heads to find paidwork. As expected, economic migrantsand urban residents, including those inBamako, are significantly more likely thanrural residents to have paid work. Exclu-ding “other” urban areas, the advantageis for women.

4.3.1.6 Agency facilitates access to paidemployment, but men tend to benefitmore from agency. Father’s secure em-ployment is inversely related to an indivi-dual’s access to paid employment, butthis relationship holds more for womenthan men. Having career aspirations en-hances paid employment prospects,with women benefiting more than men.

4.3.1.7 Industry versus Services.Contrary to evidence in the literature andconventional wisdom, men are less likelythan women to work in the industrial sec-tor. This finding emerges only when thefull set of correlates is considered. Thus,the relationship between gender and em-ployment in the industrial and servicesectors is influenced by a complex webof human capital, demographic, econo-mic, structural, intergenerational, agency,and aspirational factors. Women with ca-


23



AfDB

reer aspirations and those whose fathershad secure employment are less likelythan their male counterparts to be em-ployed in industry.

4.3.1.8 Private Informal Employmentversus Public Employment. The diffe-rences in gender representation in the in-formal sector is important because, com-pared with the public sector, it is relativelyinsecure and so may not support careergrowth or economic security (especiallyduring retirement) in ways that eliminategender inequalities. The most compellingfindings emerge after interactions bet-ween sex and key variables are incorpo-rated in the final model (model 7). Thesefindings emphasize the importance of fo-cusing on gender differences in the de-terminants of employment that werehighlighted in the Botswana study (AfDB,2011). The full model reveals no signifi-cant gender differences in access to theprivate informal or public sector. Thus,the differences in access to these sectorsare tied to gender differences in the keycorrelates.

4.3.1.9 The results for number of years ofschooling, and corroborated in earlierstudies (for instance, Jah 2007), showthat contemporary expansion in educa-tion tends to channel individuals to the in-formal rather than the formal sector.Conversely, and as expected, certifica-tion steers individuals away from the lesssecure private informal sector. However,the interaction terms indicate that at alllevels of certification, men are funneledtoward the private informal sector. Thenet outcome of certification is to reducewomen’s over-representation in this sec-tor. All marital statuses tend to push in-dividuals toward the private informal sec-tor, while being the head of thehousehold or the spouse of the house-hold head deter individuals, particularlymale household heads, from being en-gaged in the sector.

4.3.1.10 The economic/structural fac-tors, as expected, are negatively relatedto access to the private informal sector,but lack of estimates for the interactionterms precludes further examination ofthe association with gender. Agency, fa-ther’s secure employment, and havingcareer aspirations are all inversely linkedto private informal sector work. Additio-nally, women’s overall economic disad-vantage arising from their over-repre-sentation in the informal sector islessened, as father’s secure employ-ment and having career aspirations re-duce the effect of women’s over-repre-sentation.

4.3.1.11 Formal versus Informal Em-ployment. Men are less likely than wo-men to be employed in the formal sector.This is contrary to recent evidence fromBotswana (Botswana Study, 2011).10

This finding highlights the importance ofconsidering as many distinctions acrosssectors as possible in survey designsand data analysis. To tease out some ofthe nuances of the inequality, the corre-lates of employment considered in thisstudy should be critically examined.

4.3.1.12 Academic certification benefitsmen more than women and so reducemen’s initial disadvantage. Conversely,economic migration, residing in Bamako,agency and having career aspirations alltend to favor women more than menwith respect to enhancing formal sectoradvantage. A cautionary note is warran-ted. Although these findings are encou-raging, the implications for Mali’s poten-tial to attain the MDG related to genderequality depends on the proportions ofthe female labor force that actually ac-cess and remain in the formal sector. In-deed, the study finds that male house-hold heads and those whose fathersworked in secure occupations havegreater formal sector prospects relativeto women.

4.3.2 Income

4.3.2.1 No Income. Men are considera-bly more likely than women to receive noincome from economic activities. Themultivariate results are consistent withthe descriptive findings. The strikingchanges in the gender inequality esti-mate across the various models high-light the importance of considering asmany measures of theoretically importantexplanations as possible in gender ana-lyses of income.

4.3.2.2 The evidence for human capitalfactors is consistent with theoretical ex-pectations. Individuals are less likely toreceive no income as they acquire moreschooling. Similarly, as level of certifica-tion rises, the likelihood of receiving noincome declines. The only exception isat the lowest levels of certification, atwhich individuals are more likely to re-ceive no income from employment thanare individuals with no form of certifica-tion (Standing 1983). But based on theinteractions, the disadvantage faced bymen is diminishing, so that more wo-men are earning no income than pre-viously. Being the spouse of the house-hold head tends to greatly increase thelikelihood of receiving no income, whilethe other marital statuses and being thehead of the household decrease the li-kelihood of earning no income. However,further explorations indicate that malehousehold heads are more likely thantheir female counterparts to receive noincome.

4.3.2.3 Minimum Income. Based onthe final model, men are significantly lesslikely (68%) than women to earn mini-mum income. However, as women andmen acquire more certification, womengradually become less likely than men toreceive minimum income. An importantimplication is that the acquisition of hi-gher levels of certification would reduce

10 The AfDB Botswana Study (2011) found that access to legislative positions and professional occupations is significantly greater for men than women evenwhen adjustments for several classic correlates of employment were made.


24


Case Study of Mali


women’s greater propensity to receiveminimum income.

4.3.2.4 All the marital statuses exami-ned decrease the odds of having mini-mum income, while household headshipand being the spouse of the householdhead raise the odds of receiving incomein this range. However, male householdheads are less likely than female heads toreceive minimum income.

4.3.2.5 Economic migrants and resi-dents of Bamako and other urban areasare less likely to earn minimum incomethan are non-economic migrants and re-sidents of rural areas. This advantage isgreater for women than men. Unexpec-tedly, father’s secure employment en-hances the odds of receiving minimumincome, especially among men. Thus,women tend to benefit more than menfrom this intergenerational effect. Howe-ver, having career aspirations increasesthe odds of earning minimum income.

5 Conclusions, PolicyImplications and Proposed Future Research

5.1 Conclusions

5.1.1 The main aim of this study wasto examine the relationship between gen-der and employment in various econo-mic sectors in Mali. The conceptual fra-mework was guided by the literature onthe relationship between the employ-ment of women and men and a widerange of factors that bear on economicopportunities: human capital, demogra-phic, structural/economic, intergenera-tional and individual agency.

5.1.2 Employment

5.1.2.1 Agricultural Employment.Agriculture dominates the Malian eco-nomy. As expected, men are significantlymore likely than women to be agricultu-

ral workers, and younger people aremore likely than older people to work inthis sector. The correlates examined inthis study explain much of the inequality.Years of schooling, household headship,separation/divorce/widowhood, beingthe spouse of the household head, eco-nomic migration, residence in Bamakoand other urban areas, and civic aware-ness/agency are negatively related toagricultural activity. Conversely, and sur-prisingly, certification (particularly at lowerlevels), marriage, living together, father’semployment in secure occupations andhaving career aspirations are positivelyrelated to agricultural activity. These fin-dings signal Malians’ growing difficultyin accessing jobs outside the more pro-fitable non-agricultural sector. Male hou-sehold heads and men with moreagency are more likely than their femalecounterparts to be agricultural workers.On the other hand, for men, all levels ofcertification, residence in Bamako andother urban areas, fathers’ secure em-ployment and having career aspirationsreduced the likelihood of being agricul-tural workers, compared with their fe-male peers.

5.1.2.2 Salaried Work. Men are alsomore likely than women to be salariedworkers. Compared with workers aged50 to 59, workers in all age groups areless likely to be salaried. This points tothe decreasing paid employment oppor-tunities stemming from economic policyadjustments and the growing disadvan-tage faced by new labor market entrants.As expected, increasing levels of certifi-cation, economic migration, residing inBamako and other urban areas andagency enhance an individual’s likelihoodof having a salaried job. Father’s securejob and all the demographic factors de-crease prospects for salaried employ-ment. However, academic certification,economic migration, residence in Ba-mako and having career aspirations nar-row women’s disadvantage in securingsalaried employment, while householdheadship, residence in other urban

areas, agency and father’s secure occu-pation reinforce their disadvantage.

5.1.2.3 Industrial Sector. Contrary tothe situation in the agricultural and sala-ried sectors, women are more likely thanmen to work in the industrial sector.While this could plausibly be explained bythe globalization of economic activities,people aged 55 or older are more likelythan all other age groups to work in theindustrial sector. Schooling, certification,marriage, living together, economic mi-gration, residence in other urban areasand agency all enhance participation inthe industrial sector. By contrast, sepa-ration/divorce/widowhood, householdheadship, spouse of the householdhead, residence in Bamako, father’s se-cure employment and having career as-pirations are associated with lower oddsof participation. However, men with ca-reer aspirations and whose father hadsecure employment are more likely thantheir female counterparts to be indus-trial workers.

5.1.2.4 Private informal sector. Nogender differences emerge in employ-ment in the private informal sector―menare as likely as women to work in thissector. People aged 55 or older are morelikely than younger age groups to haveprivate informal employment. Contraryto years of schooling (which works in theopposite direction), certification, house-hold headship, spouse of the householdhead, economic migration, residence inBamako and other urban areas, agency,father’s secure job and having career as-pirations are all negatively related to em-ployment in the private informal sector.Conversely, marriage, separation/di-vorce/ widowhood and living togetherenhance participation in this sector. Theresults for marriage are contrary to his-torical evidence (Jah 2010b) that findsmarriage to be unrelated to overall em-ployment in the mid-1990s and negati-vely related to it in the early 2000s. Suchinconsistencies call for the continued useof historical as opposed to snapshot evi-


25



AfDB

dence in employment analyses. Despitethe absence of gender differences in ac-cess to the private informal sector, the in-teraction terms reveal that certification,agency and career aspirations enhancemen’s access to this sector relative towomen’s, while household headship andfather’s secure employment dampen ac-cess.

5.1.2.5 Formal Sector. Unexpectedlyand contrary to conventional wisdom,men are less likely than women to workin the formal sector. This finding is novel,but also inconsistent with evidence fromother countries in the region (Appleton,Collier and Horsnell for Cote d’Ivoire;AfDB 2011 for Botswana; Glick andSahn 1997 for Guinea; Jah 2010 for Ca-meroon), although the inconsistency canbe explained partly by timing and partlyby research design. Again, people aged55 or older are more likely than otherage groups to work in the formal sector.As anticipated, certification, economicmigration, residence in Bamako andother urban areas, agency and promisingcareer enhance the odds of participa-tion in the formal sector. The same holdsfor separation/divorce/widowhood andbeing the spouse of a household head.On the other hand, marriage (and incon-sistent with Jah 2010b), living together,household headship and father’s secureemployment tend to hinder formal sectorparticipation. However, these associa-tions vary by gender. Certification, hou-sehold headship, residence in other ur-ban areas and father’s secureemployment reduce women’s advantageover men. By contrast, economic migra-tion, residence in Bamako, agency andhaving career aspirations reinforce wo-men’s advantage.

5.1.2.6 A cautionary note is warranted.Although the formal sector evidence isencouraging, Mali’s potential to attainthe MDG related to gender equality de-pends on the proportions of the femalelabor force that actually enter and re-main in the formal sector.

5.1.3 Income

5.1.3.1 No Income. Net of the corre-lates included in the analyses, men areconsiderably more likely than women toreceive no income from economic activi-ties. These multivariate findings areconsistent with the descriptive results.People aged 55 or older are more likelythan younger individuals to receive noincome, perhaps because they are reti-red. The evidence conforms with theo-retical expectations. As schooling andcertification levels rise, so does the like-lihood of receiving income from work. Allthe demographic factors (excludingspouse of the household head), econo-mic migration and residence in urbanareas other than Bamako decrease the li-kelihood of earning no income.

5.1.3.2 Surprisingly, residing in Bamako,father’s secure occupation, and havingcareer aspirations appear to increase thelikelihood of earning no income. Basedon the results of the interactions, certifi-cation, economic migration, residence inBamako and other urban areas, agencyand positive career aspirations decreasemen’s disadvantage. Unexpectedly, hou-sehold headship and father’s secure em-ployment tend to reinforce the male ad-vantage. These unexpected findings maybe due to an increasing squeeze in thejobs previously available to men and tothe fact that female household heads,and women in West Africa generally, aremore enterprising.

5.1.3.3 Minimum Income. Men are si-gnificantly less likely than women to earnminimum income. Individuals in all agegroups except 18 to 24 are less likely toreceive minimum income. More schoo-ling, certification (except the middle le-vels), marriage, living together, separa-tion/divorce/widowhood, economicmigration and residence in Bamako andother urban areas are negatively relatedto earning more income. On the otherhand, household headship or being thespouse of the household head, agency,

father’s secure employment and havingcareer aspirations increase the odds ofearning minimum income. Men with cer-tification are more likely than men wi-thout certification as well as women withcertification to receive minimum income.Economic migration, residence in Ba-mako and other urban areas and father’soccupation increase men’s odds of ear-ning minimum income more than is thecase for women. Conversely, householdheadship, agency and promising careergoals decrease men’s likelihood of ear-ning minimum income.

5.1.3.4 Income: 29,000 to 50,000francs CFA. Men are considerably less li-kely than women to earn 29,000 to50,000 francs CFA. People younger thanage 55 are more likely than older indivi-duals to have earnings in this incomebracket. Likewise, CEP certification,being the spouse of the household head,father’s secure occupation and havingcareer aspirations increase the odds ofearning 29,000 to 50,000 francs CFA. Bycontrast, years of schooling, higher levelsof certification, almost all the demogra-phic and all of the economic/structuralfactors and agency reduce the odds ofearning income in this range. However,men with certification and agency areless likely than their counterpart femalesto have this level of income. Conversely,men who are household heads, econo-mic migrants, residents of Bamako andother urban centers, who have careeraspirations and whose father had secureemployment are more likely than their fe-male peers to receive earnings in thisrange.

5.1.3.5 Income: 50,000 to 75,000francs CFA. The results show substantialgender inequality in favor of men withrespect to earnings between 50,000 to75,000 francs CFA (the highest incomethat could be analyzed in this study). Em-ployees in all age categories except 25 to29 are less likely than those aged 55 orolder to have earning in this range. Unex-pectedly, the odds of having earnings at


26


Case Study of Mali


this level, while enhanced by more yearsof schooling, are reduced by more certi-fication. With a few exceptions, the de-mographic factors examined here arealso associated with low odds of earning50,000 to 75,000 francs CFA.

5.1.3.6 Consistent with expectations,economic migration, agency and father’ssecure employment enhance prospectsof having earnings in this range. Surpri-singly, the opposite holds for residence inBamako and other urban areas and ha-ving career aspirations. The fact that ur-ban residence is not associated with highearnings can be linked to competitionand unfavorable economic policy adjust-ments by African governments in thewake of recent economic crises in the re-gion. Further evidence shows that theprospects of earning income within thisrange are enhanced more for femalethan male economic migrants. As well,compared with their male counterparts,women with political, economic and so-cial agency are more likely to have ear-nings in this range. On the other hand, in-tergenerational and family factors tend tobenefit men more than women.

5.2 Policy Implications

5.2.1 The foregoing conclusions havepolicy implications for accelerating ef-forts toward the provision of secure andprofitable jobs to both women and men,and ultimately, for attaining the genderequality MDG. Mali must critically exa-mine the factors that emerge as influen-tial in this study:

5.2.2 With Respect to Employment

5.2.2.1 Greater attention to educationis needed, but it must focus on quality,and especially for women, labor marketrelevance. Further, to make women com-petitive and confident labor market par-ticipants, it is urgent to promote schoolsubject choices and training/certificationthat meet demand rather than traditionalthan job norms.

5.2.2.2 Policies that target marriageand the family as they relate to the orga-nization of work are also urgent. Thismust be done by examining the suc-cesses and failures of relevant measuresalready in effect.

5.2.2.3 Given that the dividends of re-sidence in urban areas (the recipients ofspillovers from Bamako) besides Ba-mako are greater for men than women,efforts to promote female migrants’ suc-cess in the labor market hold promisetoward closing economic gender ine-quality.

5.2.2.4 Because agriculture remains thechief employer of the majority of the po-pulation, greater emphasis must be pla-ced on making this sector profitable andsustainable. This must be done inconjunction with a focus on women, asmost of the benefits from the determi-nants examined in this analysis are grea-ter for men. The interventions are well-known―for instance, increased accessto credit, information and extension ser-vices―they simply must be either bettertargeted and increased.

5.2.3 With Respect to Income

5.2.3.1 Women are more likely thanmen to have earnings in the lower-in-come, while men are more likely thanwomen to have earnings in the higher-in-come categories. Thus, women conti-nue to be disadvantaged in terms of in-come, which reinforces gender inequalityin the labor market and challenges gen-der equity efforts. Policies that increasewomen’s earning capability should beimplemented, including policies that tar-get equity in access to education, parti-cularly secondary and higher levels, quo-tas for women in government andelected positions (for example, Rwanda),economic empowerment programs thatprovide access to a mix of resources,and policies that ensure equal accessand opportunity in both the public andprivate spheres. Affirmative action poli-

cies are an essential first step to levelinga playing field that, through a combina-tion of traditional cultural values andpractices and more modern policies thatmaintain the status quo, has been tippedagainst women.

5.3 Proposed Further Research

5.3.1 Contrary to past evidence in theliterature and conventional wisdom, menare less likely than women to work in theindustrial sector. And inconsistent withrecent findings for Botswana (AfDB2011) and entrenched gender inequalityin paid employment, men are 1ess likelyto be employed in the formal sector. Ho-wever, these findings emerge only whenthe data are adjusted for the full set ofcorrelates that were included in thisstudy. This reveals the multi-faceted na-ture of the dynamics of gender inequalityin employment. Moreover, the way inwhich the correlates are associated withgender is not straightforward. A numberof methodological/analytical issuesemerge from this study:

5.3.1.1 It is necessary to consider therole of as many correlates of employ-ment (intergenerational factors, family/social organization of work, husband’sattitudes, impact of urban areas otherthan Bamako, attitudes and ambitions,agency and civic awareness) as possiblein survey design to support rigorous ana-lyses across various economic sectors,in particular the formal sector, to deepeninsight about the employment behaviorof women and men.

5.3.1.2 The finding that demographicfactors hinder access to salaried em-ployment raises the question of family-work incompatibility. This calls for quali-tative and quantitative research into therole of formal and non-formal (throughextended networks within and outsideof households) daycare and householdhelp in women’s employment behavior.5.3.1.3 Ethnicity should be part of future


27



AfDB

surveys and research to determine therole of ethnic background in the gender-employment nexus.

5.3.1.4 The evidence generated is equi-vocal. This is not the conclusive assess-ment of the relative importance of eachof the factors examined here. Future re-search should explore decompositionmethods to estimate the relative weightof the various theoretical explanations toenhance effective policy targeting.

5.3.1.5 A growing literature indicatesthat failure to account for theseconfounding factors in African employ-ment analyses can alter researchconclusions (Jah 2007; 2010a; 2010b).Fixed effects modeling in SAS, capableof handling these unmeasured in-fluences (Allison 1996), should be em-ployed to determine if this methodwould yield different interpretations.

5.3.1.6 Beyond the caveats mentioned

in the data section, another limitation ofthe study must be noted. The analysesare based on cross-sectional data, andtherefore, the evidence is a snapshot ofthe situation at the time of data collectionand is liable to temporal fallacy (Thornton2001). To monitor the progress of deve-lopment efforts, and in the absence oflongitudinal data, the AfDB and partnersshould consider facilitating countrycross-sectional surveys across severalperiods.


28


Case Study of Mali


© 2

011

- A

fDB

- L

ayou

t, E

RC

U/Y

AL

REFERENCES

African Development Bank Group. 2010. AfDB Da-tabase.

Allison, P. 2000. “Multiple Imputation for Missing Data.”Sociological Methods and Research 28:301-309.

Allison, P.D. 1996. Survival Analysis Using the SASSystem: A Practical Guide, Cary, NC: SAS InstituteInc.

Anker, R. 1997. “Theories of Occupational Segrega-tion by Sex: An Overview.” International Labour Re-view 136(3):315-339.

Anker, R. and C. Heim. 1985. “Why Third World Ur-ban Employers Usually Prefer Men.” InternationalLabour Review 124(1):73-91.

Appleton, S., P. Collier, and P. Horsnell. 1990. “Gen-der, Education, and Employment in Cote d’Ivoire.Social Dimensions Of Adjustment in Sub-SaharanAfrica.” Working Paper No. 8. Washington DC: TheWorld Bank.

Assié-lumumba, N. T. 2000. “Educational and Eco-nomic Reforms, Gender Equity, and Access toSchooling in Africa.” IJCS 3:9:56.

Barrett, C.B., T. Reardon, and P. Webb. 2001. ‘‘Non-farm Income Diversification and Household Liveli-hood Strategies in Rural Africa: Concepts, Dyna-mics, and Policy Implications.’’ Food Policy26:315-31.

Beauchemin, C. and P. Bocquier. 2004. ‘‘Migrationand Urbanization in Francophone West Africa: AnOverview of the Recent Empirical Evidence.’’ UrbanStudies 41:2245–72.

Becker, G.S. 1981. A Treatise on the Family. Cam-bridge, MA: Harvard University Press.

Becker, G. 1992. “Fertility and the Economy.” Jour-nal of Population Economics 5:185-201.

Birdsall, N. and R.H. Sabot. 1991. “Introduction” inN. Birdsall and R.H. Sabot (eds.). Unfair Advantage:Labor Market Discrimination in Developing Coun-tries. Washington DC: The World Bank.

BLFS 2008. 2005/06 Labor Force Survey Report.Central Statistics Office, Department of Printing andPublishing Services, Private Bag 0081, Gaborone.

Boserup, E. 1970. Women’s Role in Economic De-velopment, London: George Allen and Unwin.

Collver, A. and E. Langlois. 1962. The Female LaborForce in Metropolitan Areas: An International Com-parison Berkeley: The University of California.

Bourdet, Y., A.G. Doumbia and I. Persson. 2010.“Inégalités de Genre, Croissance et Lutte Contre laPauvreté au Mali.” Asdi. Available at:www.sida.se/publications.

Eloundou-Enyegue, PM and A.E Calves. 2006. “TillMarriage Do Us Part: Education and Remittances

from Married Women in sub-Saharan Africa.” Com-parative Education Research 50(1):1-20.

Eloundou-Enyegue, P.M. and J. Davanzo. 2003.‘‘Economic Downturns and Schooling Inequality, Ca-meroon, 1987–1995.’’ Population Studies 57:183–197.

Glick, P. 2002. “Women’s Employment and Its Rela-tion to Children’s Health and Schooling in DevelopingCountries: Conceptual Links, Empirical Evidence,and Policies.” Cornell Food and Nutrition Policy Pro-gram Working Paper No. 131.

Glick, P. and D. Sahn. 2001. “ Intertemporal FemaleLabor Force Behavior in a Developing Country: WhatCan We Learn from a Limited Panel?” Labour Eco-nomics 12(1):23-45.

Glick, P., and D. Sahn, 1998. “Maternal Labor Sup-ply and Child Nutrition in West Africa.” Oxford Bul-letin of Economics and Statistics 60(3): 32-355.

Glick, P. and D. Sahn. 1997. “Gender and EducationImpacts on Employment and Earnings in West Africa:Evidence from Guinea.” Economic Development andCultural Change 45(4):793-823.

Goldin, C. 1990. Understanding the Gender Gap:An Economic History of American Women. Oxford:Oxford University Press.

Greenhalgh, S. 1991. “Women in the Informal En-terprise: Empowerment or Exploitation?” ResearchDivision Working Papers No. 33. The PopulationCouncil.

Jah, F. 2010a “Sources of Inequality within the Ca-meroonian Labor Market: Socio-demographic ver-sus National Economic Contexts.” Manuscript Pre-sented at the Annual PAA Conference in Dallas,Texas, 2010.

Jah, F. 2010. “Educational Expansion, DemographicTransitions and Implications for Women’s Employ-ment in sub-Saharan Africa (1991-2005): A Multi-method and Multi-country Analysis.” DissertationSubmitted to Cornell University, Ithaca, NY 14650.

Jah, F. 2007. “Learning to Labor: What Happens toAfrican Women’s Employment in the Course of Edu-cational Transitions, 1991-2005” The Current10(2):29-48. Published by Public Policy Journal ofthe Cornell Institute for Public Affairs.

King, E. M. and M. A. Hill (eds.), Women’s Educationin Developing Countries: Barriers, Benefits and Po-licies. John Hopkins University Press, Baltimore,1993.

Krishnan, P. 1996. “Family Background, Educationand Employment in Urban Ethiopia. Oxford Bulletinof Economics and Statistics (58)1:0305-9049.

Mincer, J. 1974. Schooling, Experience and Ear-nings. New York: National Bureau of Economic Re-search.

Nabalamba, A. 2010. “Gender in Employment. Pre-liminary Results from “Enquete Permanente EmploiAupres Des Menages, 2007 (EPAM). AfDB.

Naude, W. and P. Serumaga-Zake. 2001. “An Analy-sis of the Determinants of Labor Force Participationand Unemployment in South Africa’s North-WestProvince.” Development Southern Africa 18(3):261-278.

Ntuli, M. 2007. “Determinants of South African Wo-men’s Labour Force Participation, 1995-2004. IZADiscussion Paper No. 3119. Available at SSRN:http://ssrn.com/abstract=1031715.

Shapiro, D. and B.O. Tambashe. 1997. “Education,Employment, and Fertility in Kinshasa and Prospectsfor Changes in Reproductive Behavior. PopulationResearch and Policy Review 16(3) :259-287.

PRB 2010. World Population Data Sheet. PopulationReference Bureau. Available online at: http://www.prb.org/Publications/Datasheets.aspx. Acces-sed December 2010.

Siphambe, H. 2000. “Education and the Labour Mar-ket in Botswana.” Development Southern Africa17(1):105-116.

Standing, G. 1983. “Women’s Work Activity and Fer-tility.” Pp. 517-546 in Bulatao, R.A. and R.D. Lee(eds.). Determinants of fertility in developing coun-tries. New York: Academic Press.

Stromquist N. P. 1990. “Women and Illiteracy: The In-terplay of Gender, Subordination and Poverty.” Com-parative Education Review 34 (1):95-111.

Thornton, A. 2001. “The Development Paradigm,Reading History Sideways, and Family Change.” De-mography 38:449-465.

UNFPA. 2002. State of World Population 2002. Peo-ple, Poverty, and Possibilities. United Nations Po-pulation Fund, New York, U.S.A. URL: http://www.unfpa.org.

UNICEF 2003. The State of the World’s Children2004. Girls, Education and Development. United Na-tions Children’s Fund (UNICEF). Available online at:www.unicef.org.

United Nations. 2010. The Human Development In-dex. Available online at : http://hdrstats.undp.org/en/countries/profiles/ MLI.html. Accessed January2011.

United Nations. 2005. The Millennium DevelopmentGoals Report. Available online at http:// www.un.org/millenniumgoals. Accessed December 2010.

United Nations 2000. United Nations Millennium De-claration [on line]. Available at http://www.un.org/mil-lenniumgoals. Accessed December 2010

United Nations. 2005. The Millennium DevelopmentGoals Report. Available online at http:// www.un.org/millenniumgoals. Accessed December 2010.

Vijverberg, W.P.M. 1993. “Educational returns forWomen and Men in Cote d’Ivoire.” The Journal ofHuman Resources 28(4):933-974.

World Bank. 2010. World Development Indicators,CDROM. Washington, DC: The World Bank.

AfDB Gender in Employment: Case Study of Mali€¦ · A f r i c a n D e v e l o p m e n t B a n k 3...

Documents

Transcript of AfDB Gender in Employment: Case Study of Mali€¦ · A f r i c a n D e v e l o p m e n t B a n k 3...