afdb gender in employment: case study and background. this re-port on botswana is the first of two...

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 – Study CountryBackground and Context

3 – Study 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 Botswana is the first of two coun-try studies (the second concerns Mali) thatexamine gender and employment in Africa.The study is informed by two broad deve-lopment frameworks. The first is the Mil-lennium Development Agenda that viewsMillennium Development Goal 3, GenderEquality and Women’s Empowerment, asa critical determinant in the attainment ofall the Millennium Development Goals(MDGs), and more broadly, poverty reduc-tion. The second is the African Develop-ment Bank’s (AfDB) Managing for Deve-lopment Results (MfDR), the organization’sblueprint for development effectiveness.In response to two evaluations that conclu-ded that the Bank’s efforts to mainstreamgender into its operations were weak, andencouraged by the Bank’s Board and se-nior management, this study comes at anopportune 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 eco-nomic dimensions of gender inequality,are, therefore, timely and will contribute toeffective and efficient policy- making.

2. Objectives and Rationale. The threemain objectives of the study are to: 1) ex-plore existing national representative sta-tistical and qualitative information on em-ployment of men and women in variouseconomic industries, formal and informalsectors; 2) analyze the underlying rela-tionships between gender and employ-ment and their determinants; and 3) toproduce a comprehensive report in line

with the ADF 11 commitment to generategender-disaggregated data in two pilotRMCs and strengthen capacity to gene-rate related analytical studies.

3. Botswana’s income per capita is one ofthe highest in sub-Saharan Africa (SSA). Adefining feature of the country is the highratio of female-to-male enrolment at thetertiary level. Yet, there is an inherent mis-match between educational developmentand growth in economic opportunities inthe country’s labor force. Thus, Botswanaoffers an interesting setting for studyingthe determinants of gender inequality inem ploym ent during the course of nationaldevelopment.

4. Methodology. Data for this study comefrom the 2005/2006 Botswana LaborForce Survey (BLSF), a cross-sectionalsurvey of a nationally representative sam-ple. Using multivariate analysis, the studyexamines: 1) gender inequality in agricul-ture, unpaid employment (family workers),paid employment, self-employment withemployees, self-employment without em-ployees, craft and related occupations,plant and machine operations, service oc-cupations, clerical jobs, legislature/mana-gerial, and professional occupations; and2) the determinants of these relationships,focusing on human capital, demographiccharacteristics, and some structural/eco-nomic factors.

5. Principal Findings and Conclusions.Overall, men are significantly more likelythan women to be in: (i) craft and relatedoccupations; (ii) self-employment withoutemployees; (iii) self-employment with em-ployees; (iv) legislature/managerial occu-pations; (v) professional occupations; (vi)

AfDBChief Economist ComplexVolume 1 • Issue 1

12 April, 2011www.afdb.org

Gender in Employment: Case Study of Botswana1

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), Mr. Wilberforce Mariki (Country Economist,ORSA), Ms. Zeneb Toure (Gender Expert, ORQR), Ms. Gisela Geisler, (Gender Expert, ORQR.4), Ms. MayAli Babiker (Gender Expert, ORQR.4), Mr. Issahaku Budali (Social Protection Specialist, KEFO).


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AfDBGender in Employment: Case Study of Botswana

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

AfDB

agricultural employment; and (vii) paidemployment. Women’s employment di-sadvantage is greatest in the most secureand prestigious occupational categories:legislature/managerial and professionaloccupations. These findings hold evenwhen other potentially influential variablessuch as age, educational attainment, ma-rital status, and structural/ economic fac-tors are taken into account. On the otherhand, men are less likely than women tobe employed in: (i) clerical occupations; (ii)unpaid family work; (iii) services; and (iv)plant and machine operations.

6. Comparisons of the determinants ofwomen’s and men’s employment acrossthe eleven industries and occupations in-dicate that the factors governing women’semployment differ from those that governmen’s behavior. As well, the determinantsof employment are complex and multifa-ceted. The disadvantage faced by womenin obtaining secure and profitable em-ployment presents a challenge for effortsaimed at addressing gender inequality inthe Botswana labor market.

7. Implications for Policy. The use of na-tionally representative data, coupled witha detailed discussion of the methodologyto facilitate replication elsewhere in theregion, is valuable for developing policiesand programs to achieve the MDG ongender equality in employment. The in-fluence that variables measuring humancapital, demographic and other factorshad on the results points to the need forwide-ranging policies that address multi-ple issues. Policies designed to promoteequity and eliminate poverty must reco-gnize the differences between women’sworlds of work, particularly with the aim ofincreasing profitability and career deve-lopment in occupations where womenare over-represented. A comprehensiveeffort to increase women’s employment inprofitable and secure jobs will be neces-

sary. To this end, policies that aim to en-sure that young female labor market en-trants’ academic qualifications match cur-rent labor market requirements should beenacted and enforced. Similarly, policiesthat facilitate women’s ability to obtainand hold profitable paid employment andthat ensure that self-employment is se-cure, profitable and sustainable are muchneeded. To this end, programs that facili-tate women’s access to capital or creditare imperative.

8. Research recommendations andnext steps. This study underscores theimportance of taking into account thecorrelates of employment in analyses ofgender differences in industries and oc-cupations. As well as the human capital,demographic and structural/economicfactors addressed here, measures ofhousehold wealth, income, family orga-nization of work, marital relations, aspi-rations, individual agency, inter gene ra-tional aspects and ethnicity mightusefully be included. Moreover, thisstudy should not be regarded as the ul-timate assessment of the relative im-portance of the factors that were exa-mined here. Future research wouldbenefit from employing alternative sta-tistical methods to estimate the relativeeffects of these influences, as well asmethods that account for factors thatcould not be taken into account. Fu-ture efforts should also focus on col-lecting and analyzing several waves ofsurveys in order to generate time seriesand trace historical trends.

1 Introduction

1.1 Overview and Background

1.1.1 This report on Botswana is thefirst of two country studies (the other is

on Mali) of gender and employment inAfrica. As the study terms of reference(TORs) note, “Promoting gender equalityin employment is an important corners-tone to advance women’s economic em-powerment in Africa and elsewhere.”Thus, the elimination of gender inequalityin the labor market is a central goal of de-veloping and developed countries alike,and one of the key objectives of deve-lopment strategies designed to reducepoverty while achieving economic growthwith equity. Indeed, together with elimi-nating gender disparities in schooling,closing the gender gap in employment isone of the principal determinants in at-taining the Millennium DevelopmentGoals (MDGs), not just MDG 3, GenderEquality and Women’s Empowerment.

1.1.2 Data collection and analysis thatfocus on gender inequality in sub-SaharanAfrica (SSA) are particularly timely giventhe continent’s urgent need for gender-re-lated statistics to inform effective and ef-ficient policy-making. However, earlierdata collection and analysis efforts by theAfrican Development Bank (AfDB, or theBank), inter-alia, have used an analyticalframework that relied on sex-disaggrega-ted data, itself a reflection of the assump-tion that the principal differences betweenmen and women are largely biological,thereby discounting the influence of so-cial, 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, whether at the household or so-cietal level in terms of power and how itis exercised. It is, therefore, necessary tohave gender statistics that adequatelyreflect “differences and inequalities in thesituation of women and men in all eco-nomic, social and political areas of life.”


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Gender in Employment: Case Study of Botswana


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1.2 Situating the Study in AfDB’s Gender Equality Agenda

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 princi-pal framework within which a range of or-ganization-wide changes was launched.The emphasis is on achieving results andon measuring this achievem ent. 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 both in-ternally and through external reviews.

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 such departments as Statistics(ESTA), has worked to ensure that gen-der mainstreaming is integrated into allaspects of Bank operations. The findingsfrom the Mid-term Review of the GenderPlan of Action, which served as the ba-sis for the up-dated GPOA (UGPOA) in-cluded the following:

♀60% to 70% of Bank projects do notinclude gender equality as a goal;

♀Project log-frames missed genderspecific indicators and outcomes;

♀Project and program designs lackedactivities to promote gender equality;

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

♀Poverty analyses often excluded agender dimension.1

1.2.3 This study by ESTA examinesone of the most important dimensions ofthe gender equality and women’s em-powerment goal. It comes at an oppor-tune moment, as the Bank is stronglycommitted to ensuring that gender equa-lity becomes one of the Quality-at-Entrystandards, and thus, central to the achie-vement of its stated goals and resultsagenda.

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 menhave largely functioned as income gene-rators (Glick and Sahn 1998; Glick 2002).Although women's representation in theworkforce has increased dramaticallyover the past 30 years, they continue toassume most of the family and house-hold responsibilities. Even in the develo-ped world where gender roles in the hou-sehold have evolved over severaldecades, significant inequality remains.This duality in women’s lives has resultedin gender inequality, not only in the hou-sehold and the labor market, but also inwomen’s social position and well-being.

1.3.2 At the global level, specific ef-forts to address gender inequality includethe 1979 Convention on the Elimination

of All Forms of Discrimination AgainstWomen to the 1994 Cairo InternationalConference on Population and Develop-ment, and the 2000 United Nations Mil-lennium Development Summit. The out-come of this summit was the formulationof the eight Millennium DevelopmentGoals (MDGs), which were adopted byall member countries, to serve as a fra-mework in the fight against poverty andpromote development. The target datefor attaining these goals is 2015. One ofthe goals, elimination of gender inequa-lity, especially in education and employ-ment, while important in its own right, isviewed as critical to the attainment ofthe remaining goals (UNICEF 2003). Yetten years after the goals were set and fe-wer than five years to the target date,sub-Saharan Africa lags behind its coun-terparts in the developing world. It isagainst this backdrop that the AfricanDevelopment Bank has undertaken pilotstudies to examine gender inequality inemployment in SSA, beginning withBotswana and Mali.

1.4 Study 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 majorconstraints 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:

1 See, ORQR.4, November 2010. Tracking Gender Equality Results and Resources: An AfDB Quality at Entry Tool, ORQR4, AfDB, Tunis.2 Multinational Statistical Capacity Building in Regional Member Countries for MDG Monitoring and Results Measurement.


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1) To explore existing representative hou-sehold sample surveys, standardizeddata and qualitative information on theemployment of men and women invarious industries, and in formal andinformal sectors;

2) To conduct a detailed analysis of theunderlying relationships between sexand employment and the determi-nants of these relationships; and,

3) To produce comprehensive analyticalreports of the findings in line with theADF 11 commitment to collect gen-der-disaggregated data in two pilotRMCs and strengthen capacity for ge-nerating related analytical studies.

2 Study 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 macroeconomic poli-cies) can reduce its value. Developmentstrategies in Africa are being formulatedunder demographic, social and macroe-conomic duress. First, the expansion ofeducation has been accompanied bygrowing school-age populations, reflec-ting the region’s historically high fertility.Africa’s share of the global school-agepopulation 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 older age at firstmarriage and a greater prevalence of non-marital unions and single- or female-hea-ded households. These changes, in turn,increase women’s propensity to partici-pate in the labor market. Likewise, decli-ning fertility makes more time availablefor non-child-rearing/-bearing activities.

2.1.3 Third, urbanization of Africancountries has transformed economic ac-tivities 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 agriculturaland non-economic work has now assu-med an important place in the economy.A manifestation of this evolution is theshift from voluntary and unpaid farm la-bor at peak periods 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 occupa-tional sectors, particularly the more pro-fitable formal sector. At the same time,urbanization can weaken extended familyand social networks that otherwise mightease the pressure created by the incom-patibility between women’s outside workand their childcare and family obligations.In fact, 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 deve-lopment improves the economic status

of some who otherwise would not havebeen gainfully employed, it has implica-tions for gender economic equality. Inurban settings where childcare servicesare minimal or costly, women with moreeducation will have to make trade-offsbetween intermittently withdrawing fromformal work or paying heavily for theseservices. Such withdrawals from theworkforce can reduce women’s pros-pects for career advancement, and thuschallenge policy efforts to close the gen-der gap in employment.

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 theirprime productive years, is eroding theregion’s gains in human developmentand its future socio-economic resources.Added to tight development budgets,addressing the challenges of HIV/AIDS,hinders African governments’ efforts toprovide decent livelihoods for their citi-zens. As well, macro-economic forcesincreasingly define African labor markets.These forces include policy reforms fol-lowing the economic crises of the 1980sand 1990s and the ensuing privatizationof African labor markets (Eloundou-En-yegue and Davanzo 2003). Most coun-tries have barely recovered from thesecrises, while others are grappling withthe aftermath of the 2008 global econo-mic crisis.

2.2 The National Setting: Botswana’s Employment Profile

2.2.1 Botswana is a small country lo-cated in Southern Africa with a popula-tion size of 1.95 million3 by mid-2009




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with an annual population growth rateof 1.5% (AfDB Database, 2010). Agross national income per capita ofUS$6,470 in 2008 puts Botswanaamong the highest income countries inAfrica (AfDB Database, 2010). Despitethe economic downturn since 2008,Botswana maintains one of the highestper capita incomes in SSA. However, arecent assessment of labor supply anddemand signals tight competition in thelabor market arising from an imbalancein the pace of development of educa-tional and labor market opportunity(World Bank, 2010). The HIV/AIDS pan-demic has exacerbated the labor mar-ket supply and demand (World Bank,2010). With 38% of individuals (15-49)living with HIV/AIDS and mirroring theheavy HIV/AIDS burden in the region,Botswana is home to the largest per-centage of HIV/AIDS afflicted people inthe SSA.

2.2.2 At independence, Botswanalacked a skilled and educated laborforce (Siphambe 2000). The immediateresponse was heavy reliance on impor-ted skilled labor, which proved both ex-pensive and a disincentive to improvingthe country’s human resource base.

2.2.3 In view of the human capital de-ficit inherited at independence, the Bots-wana government placed a high pre-mium on education. This led to asubstantial historical rise in school enrol-ment at all levels since independence torecent levels of 109.7% and 80.2%, res-pectively, in gross primary and secondaryschool enrolment for 2006 (AfDB Data-base). Indeed, the ratio of female to maleenrolment at the tertiary level has alsobeen high at 1.150 in 2006, and increa-sing. This figure is the highest amongcountries with available comparative datain the region.

2.2.4 Such progress in female educa-tional attainment in Botswana is an ano-maly across the continent (not includingLesotho) and should, in principle, trans-late into gender parity in employment.The most recent statistics available fromILO and World Bank databases are in-consistent with this theoretical expecta-tion. On one hand, the female labor forceparticipation rate in recent years has been71% for both 2005 and 2006, increasingonly marginally to 72% in 2007 and re-maining stagnant in 2008. On the otherhand, the figure has remained higher at81% for males for the same period.

2.2.5 Siphambe (2000) attributes theincome inequality between women andmen to declines in the rates of return toeducation as Botswana’s economy de-veloped and the education system ex-panded leading to a mismatch betweendemand and supply for labor. As in muchof Sub-Saharan Africa, the heavy go-vernment emphasis on educational de-velopment has eclipsed the developmentand growth in the country’s labor marketstructures resulting in an unemploymentrate of about 20% for females and a re-latively lower rate of 15% for males(World Bank 2010). Thus, Botswana of-fers an interesting setting for studyingthe determinants of access to employ-ment by men and women in the courseof national development.

3 Study 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 pro-vides a general framework for unders-tanding the importance of education fordevelopment. The theory emphasizesthe primacy of abilities, education, ex-perience and skills for labor market suc-cess (Becker 1981; 1992; Mincer 1974).It hypothesizes that education is directlyrelated to participation and returns inthe labor market. Thus, women’s in-creased human capital and experienceshould facilitate their entry into the labormarket, and as gender inequality in edu-cation narrows, so should inequality inthe labor market. Extending the theoryto the modernization perspective, as theoccupational gender gap narrows, wo-men’s economic security and social sta-tus should improve (Goldin 1990). Suchexpectations have been buttressed bycross-country evidence showing aconsistent association between wo-men’s education and the labor marketreturns (King and Hill 1993). As a result,education has become central in natio-nal and international strategies that ad-dress women’s status and development(UNFPA 2002; UNICEF 2003; UnitedNations 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).


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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 continue tobe marginalized in low-skill jobs with limi-ted prospects for advancement becauseof employer discrimination and institutio-nal and labor market segmentation, butalso because of socialization (Anker1997; Anker and Heim 1997). Womenare presumed to self-select into less re-warding or less prestigious occupationsbecause they have been socialized tohave lower aspirations.

3.1.5 In contrast to the perspectivesreviewed above, a common explanationfor 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 on so-cio-cultural norms and values that exist atthe family, educational, occupational, andsocietal levels (Assie-Lumumba 2000;Birdsall and Sabot 1991; Boserup 1970;Stromquist 1990; Youssef 1972). Fami-lies, operating in accord with larger so-cietal norms and in anticipation of lowerreturns to educating their daughters, arepresumed to invest less in their daugh-ters’ than their sons’ education (Strom-quist 1990).

3.1.6 Relevance of TheoreticalPerspectives in African Labor Markets.Proponents of the cultural perspectivemaintain that patriarchal values trans-cending education, marriage, fertility, em-ployment structure, and developmentstage are the decisive factors in employ-ment gender inequality. This explanationis relevant in African societies where astrong kinship network co-exists withmale dominance. In such contexts, child-rearing is largely a female responsibility.

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 milieu in which indivi-duals/couples assess economicopportunities 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 economicroles. 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 ofSSA, where an expansion in the labormarket has not followed the educationaland demographic transitions, is parti-cularly salient. In countries with low la-bor demand but with rising educationalattainment among women, female com-petition for scarce, prestigious jobschallenges the neo-classical theory of amonotonic link between education andemployment. Instead, an inverted u-curve association (Standing 1983) or anegative relationship can prevail, as hasbeen reported in the African literature(Siphambe 2000 for Botswana). Themodernization theory assumes that de-velopment benefits men and womenimpartially, but the likelihood that indivi-duals, particularly women, will be uni-formly distributed across occupation

sectors may not be as automatic as thetheory 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 howcontemporary expansions in educationhave translated into employment pros-pects.

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 theBotswana Labor Force Survey (BLFS),conducted in 2005/2006 by the Bots-wana Central Statistics Office (CSO).



The 2005/2006 BLFS data are basedon a nationally representative sample of9,760 households obtained from atwo-stage stratified sampling proce-dure that yielded a total weighted po-pulation of 1,702,829 men and womenaged 7 and above. The populationcomprised of 53.1% or an estimated904,369 females and 46.9% or an es-timated 798,460 males. Using a hou-sehold and an individual questionnaire,data were collected on a wide range oftopics, including demographic charac-teristics of respondents and their hou-seholds, as well as indicators of em-

ployment, educational attainment andmarital status4.

3.2.2 Analytical Data

3.2.2.1 The BLFS pertain to individualsaged 7 or older living in private house-holds, excluding residents of temporarydwellings, such as tents, military barracksand school/institutional hostels. For thisstudy, an analytical data subset was crea-ted. First, the economically active variable(“econ”) was used to eliminate non-eco-nomically active persons. Next, respon-dents younger than age 12 were elimina-

ted in order to remove child workers. Thisalso eliminated most respondents whowere attending school at the time of thesurvey (ascertained by running frequen-cies on the variable “educ”). Given theconceptual framework of the study inwhich the role of human capital in em-ployment is examined, it is important thatthe analyses exclude people attendingschool. The remaining sample represen-ted an estimated population of 629,394economically active men and women.This data subset was further split by gen-der, yielding estimated populations of311,264 women and 318,130 men.


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3 Botswana’s population is comprised of 50.04% females and 49.96% males.4 For the questionnaires and a detailed discussion of the data collection, quality, coding, and classification of occupations, see the 2005/06 Botswana Labor

Force Survey (BLFS) Report 2008.

3.3.1 Dependent Variable: The main dependent variable in this study was employment.

3.3.1.1 Employment was measured as individuals who did some work during the “reference period for cashor in kind payment (referred to as paid employees); or, who were in self-employment for profit, or family gainas well as persons temporarily absent from these activities but definitely going to return to them (e.g. on leaveor sick)”. “Some work” was defined as one hour or more in the reference seven days prior to the survey. Threeeconomic sectors were considered: (i) industry, (ii) sectors and (iii) occupations.

a. The first outcome was industry, measured by all non-student economically active women and menemployed in: 1) agriculture; 2) paid employment; 3) unpaid employment (family worker); 4) self-employment withemployees; and 5) self-employment without employees.

i. Given that agriculture is the chief employer of the Botswana labor force, the first industry outcomewas agricultural employment and coded “1” if the respondent was employed in any agricultural activity and “0”if s/he was unemployed or employed in another industry.

ii. The second dimension of industrial employment distinguished between paid employment and non-paidemployment. Paid employment was coded “1” for employment in all forms of non-agricultural activity andcoded “0” if unemployed or involved primarily in agriculture.

iii. Unpaid employment was coded “1” for employment in all forms of non-agricultural activity and coded“0” if a person was unemployed or involved primarily in agriculture.

iv. Several forms of paid employment were considered: employment in service industry, self-employmentwith employees and self-employment without employees. Each of the paid employment categories were coded“1” if a respondent was engaged in these industries and “0” if not.

3.3 Measures: Dependent, Independent and Control Variables


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b. The second outcome was occupations in crafts and related occupations, plant and machine operators,service occupations, clerical jobs, and legislators, senior officials or managers, and professionals. Each of theoccupation categories was coded “1” if a respondent was engaged in them, otherwise “0.”

c. The third dimension distinguished between employment in the formal and informal sectors, and coded“1” if employed in the formal sector and “0” if not.

3.3.1.2 The coding of the occupations and definition of the informal sector were derived from the 1988International Standard Classification of Occupation (ISCO-88) and the 1993 System of National Accounts (SNA-1993), respectively, which in turn dictated the coding of the measures analyzed in the study (BLFS 2008).

3.3.1.3 Thus ten employment outcomes were examined. Detailed exploration of a few other economicdomains (e.g., elementary occupations) was not embarked upon due to the fact that the selected domainsadequately reflect the Botswana labor force spectrum.

3.3.2 Independent Variable

3.3.2.1 The main independent variable in the study is sex of the respondent. It is measured dichotomously andcoded “1” if male and “2” if female (the reference).

3.3.3 Control Variables

3.3.3.1 Drawing from the theoretical and past studies reviewed, the study controls include several correlatesof employment.

3.3.3.2 Human capital characteristics of men and women. The human capital characteristics were measuredby educational attainment and academic training. Educational attainment is measured at four levels: (i) primarylevel; (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 as “1” if respondent received any formof academic certification and “0” if not.

3.3.3.3 Demographic characteristics of men and women. The second set of correlates considers demographiccharacteristics such as family/household structural variables that affect women’s capacity to engage in paidemployment. The first, marital status measured, is whether the respondent is married (coded “1”), living together(coded “2”), separated/divorced/widowed (coded “3”), or single (coded “4”) as the reference. Marriage isexpected to create competing demands on women’s time between household/marital demands and workrelative while the reverse is expected for men. Conversely, separation/divorce/ widowhood are expected tocompel women in particular to seek employment. The second demographic variable measures whether therespondent is the household head, and the third measures whether the respondent is the spouse of thehousehold head. The former is hypothesized to be positively associated with employment, regardless of gender.On the other hand, being the wife of the household head can be a deterrent for outside work because ofhousehold demands and the potential reluctance of a husband that his wife should be employed outside thehome.

3.3.3.4 Structural/economic labor environment of women and men. The third set of correlates capturesstructural/economic labor environment as measured by economic migration and region of residence andindividual agency. Economic migration is measured by whether the respondent migrated for economic reasons(coded “1”) or for non-economic reasons (coded “0”) as the reference. Economic prospects will likely dependon regional-level employment opportunities. While urban settings generally offer good work opportunities, thismay not hold if urbanization is not matched by growth of economic structures.


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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 detailedclassifications that are possible with theBLFS data permit comprehensive ana-lyses of several dimensions of employ-ment. The ability to examine these veryfine distinctions using nationally repre-sentative data improves on the unders-tanding of employment behavior in thecountry as well as permitting generaliza-tion to the rest of the population. Themeasure of educational attainment isalso a strength in the data. And despitea considerable number of missing cases,academic certification is also an impor-tant factor in employment analyses.

3.4.2 Data are available for some ofthe classic correlates of employmentsuch as marital status and householdheadship. The data also include infor-mation on migration and allow regionalbreakdowns.

3.4.3 Thus the BLFS data complementthe World Bank - sponsored Income/Household Consumption Surveys in Re-gional Member Countries, which focusmainly on income. By facilitating detailedemployment breakdowns, the surveyhelps fill gaps in the African house holdsurvey data systems and permitstracking of progress on the MDGs des-igned to enhance women’s status andfight poverty.

3.5 Data Limitations

3.5.1 Duration of unemployment is im-portant in employment analyses (Mincer,1974), but measures for this factor werenot adequately captured in the BLFS.

3.5.2 Because of their potential in-fluence on time demands on mothersand on childcare needs, variables mea-suring fertility in terms of both family sizeand children’s ages are crucial in em-ployment analyses. The presence ofother adult females in the household canmediate the fertility-employment link. Intheir 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 recent a study of 21 SSAcountries of (a) the effect of having a firstbirth on a mother’s employment statusand (b) the influence of having anotheradult female in the household is mixed(Jah, 2010b).

3.5.3 Given the mixed evidence, thesetwo factors should be considered in em-ployment analyses. However, specific in-formation about fertility was not collectedin the 2005/06 BLFS. And because oftime constraints, data modifications thatcould have measured the presence ofother adult females were not attempted.Similarly, no information is available onaccess to substitute child care (formaland informal) and household help areabsent and should be collected in futuresurveys.

3.5.4 Socio-economic factors such ashousehold amenities and assets, 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-neration information. However, these va-riables are also absent. In SSA, spousalco-residence has been found to be im-portant in employment analyses, but wasnot asked in the survey. The dataset isalso limited in terms of attitudes and

other factors that can capture culturalattributes and individual agency.

3.5.5 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 Completelyat Random (MCAR),” that is, the missingdata are unrelated to the values of anyvariables.

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 regressionsthat 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 parameter


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estimates that would most likely have re-sulted in the complete dataset.

3.7 Analytical Strategy

3.7.1 This study examines gender ine-quality in employment in Botswana andthereby updates progress the country ismaking toward the attainment of theMGDs. The findings will also inform theAfDB’s programmes and its discussionswith its Regional Member Countries.

3.7.2 Gender inequality is analyzed inten employment outcomes. The analyseswere performed in two steps. In a firststep, the gross and net size (gross andnet extent) of gender inequality was mea-sured in a multivariate framework incor-porating the three sets of correlates dis-cussed above. Because the deter mi nantsfor men and women are apt to differ, inthe second step, key correlates were in-teracted with sex.

3.7.3 Maximum likelihood modelswere employed in an attempt to partlyovercome the problem of missing cases.Logistic regression was used to modelthe probability of each of the ten employ-ment outcomes as a function of gender,while controlling for several correlates.

3.8 Size of the Gender Inequality

3.8.1 To estimate the size of genderinequality in the Botswana labour force,five logistic regression models were runsequentially, incorporating the sets ofcorrelates outlined above. The first mo-del (model 1) estimates the gross gender

inequality for each employment out-come, adjusting only for age. Model 2adds human capital characteristics; andmodel 3, demographic influences; model4, structural economic controls; and mo-del 5 measures family background andaspirations. Thus, each model is morecomplex than its predecessor as equa-tion 1 shows, where P/1-P is the proba-bility that a man: as opposed to a wo-man is employed in a particularindustry/sector/occupation; β0 is the in-tercept (constant term); β1G is the para-meter estimating the gross gender ine-quality; β2H is the parameter estimatingthe influence of human capital; β3D is theparameter estimating the influence of de-mographic characteristics; β4E, the pa-rameter estimating the influence of struc-tural economic factors; and ε, theresidual or unexplained error term.

4 Study Analysis andPrincipal Findings

4.1 Descriptive Results

4.1.1 Of the 629, 394 economicallyactive adults, 26.2% reside in cities andtowns, 31.6% in urban villages, and41.7% reside in rural areas.

4.1.2 Figure 1 presents the distribu-tion of women and men across four in-

dustrial categories: paid employment,self-employment with and without em-ployees, unpaid family work, andland/farm ownership. Paid employmentaccounts for large majorities of the ac-tive labor force in cities and towns (85%)and urban villages (71%), comparedwith 44% of the active labor force in ru-ral villages. While the representation ofwomen and men is comparable in citiesand towns and urban villages, men do-minate paid employment in rural areas(28% versus 16%). Land/farm owner-ship (43%) is the major economic acti-vity in rural areas, engaging 23% of menand 19% women. Only 11% and 1% ofworkers own land/farm in urban villagesand cities and towns, respectively; thepercentages of women and men whoown land/farms is similar in both re-gions. Self-employment without em-

ployees is the third-ranking industry, ac-counting for 11%, 8% and 6% ofworkers in urban villages, rural villages,and cities and towns, respectively. Self-employment with employees and un-paid family work represent very smallpercentages of the active labor force inall regions.

4.1.3 Figure 2 shows the percentageof the labour force employed in the nineoccupational categories in cities and



5 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.


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Figure 1. Distribution of Economically Active Individuals Across Various Industries by Gender and Region, Botswana 2005/06

Source: Botswana Labour Force Survey (BLFS), 2005/2006.

towns. Service employment (17%) andelementary occupations (16%) are thechief employers, followed by clericalwork (14%), craft and related work(12%), and technical and associate pro-fessions (11%). Legislators, senior offi-cials or managers, and professionals,

respectively, comprise 11% and 8% ofworkers in cities and towns. The agri-cultural sector accounts for only 2% ofworkers in this region. Women consti-tute the majority of workers in elemen-tary occupations (12% out of 16%), ser-vices (10% out of 17%), clerical

occupations (9% out of 14%), and tech-nical and associate professions (6%).They are less likely than men to be plantand machine operators (1%), agricultu-ral workers(1%), legislators, senior offi-cials or managers (3%), or professio-nals (4%).

Figure 2: Distribution of the Economically Active Population Across Various Occupations in Cities and Towns by Gender, Botswana, 2005/2006


4.1.4 Figure 3 presents the distribu-tion of the nine occupational categoriesin urban villages. As in cities and towns,service employment (22%) and elemen-tary occupations (18%) are the chief em-ployers, followed by craft and relatedwork (12%) and agricultural work (12%). The remaining workers in this region aredistributed among clerical occupations(10%), technical and associate occupa-tions (7%), professional occupations(7%), legislative occupations (6%), andplant and machine operations (6%). Wo-men make up the majority in the service,

elementary, and clerical occupations,and of technicians and associate pro-fessionals. On the other hand, moremen than women are employed in craftsand related work and as plant and ma-chine operators.

4.1.5 Figure 4 shows occupationalcategories by gender in rural areas. Agri-cultural work dominates, with 45% ofworkers, nearly half of whom are wo-men. Elementary occupations accountfor 22% of workers, with men outnum-bering women two to one. Very few

members of the rural labor force are em-ployed in other occupations.

4.1.6 In summary, the service sector isthe dominant economic activity in urbanareas (cities, towns and urban villagesalike), with women over-represented. Agri-culture dominates in rural settings, withmen slightly outnumbering women. Thepredominance of women in services andelementary occupations, and their low re-presentation in occupations deemed se-cure and profitable, will be examined inmore detail in the multivariate results.


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Figure 3. Distribution of Economically Active Population Across Various Occupations in Urban Villages by Gender, Botswana 2005/2006


Figure 4. Distribution of Economically Active Population Across Various Occupations in Rural Villages by Gender, Botswana, 2005/2006



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4.2 Multivariate Analyses and Principal Findings

4.2.1 To explain how gender relates toemployment, multivariate analysis is ne-cessary. A series of multivariate logisticregression models6 were run sequentiallyto quantify gross and net gender ine-quality in: 1) agricultural employment; 2)unpaid family work; 3) paid employment;4) self-employment with employees; 5)self-employment without employees,and in five occupations: 1) craft and re-lated occupations; 2) plant and machineoperators; 3) services; 4) clerical occu-pations; and 5) legislature/managerial,and professional occupations.

4.2.2 Gender Inequality in Industry.For each outcome, models of gross ine-quality (model 1) control only for age.Models 2 to 4 measure net inequalityand control sequentially for the three setsof correlates. Model 2 controls for humancapital (educational attainment and post-

secondary academic training). Model 3controls for demographic factors (maritalstatus, household headship, and whe-ther the respondent is the spouse of thehousehold head). Model 4 adds structu-ral and economic factors (economic mi-gration and region of residence).

4.2.3 Table 1 presents odds ratios forthe gross and net inequality between menand women in all the above employmentoutcomes. For brevity, the table reportsthe main effects of gender only (the inde-pendent variable),7 and not those of thecorrelates. The detailed results are annextables 1 to 20. (Annex tables are availableon the AfDB data portal).

4.2.4 Agricultural Employment. Menare 27% more likely than women to beagricultural workers (Model 1). When dif-ferences in human capital are taken intoaccount (Model 2), men are 23% more li-kely than women to be employed in agri-culture. And when demographic and

structural factors are taken into account(Models 3 and 4), this gender inequalitynot only persists, but is intensified, withmen being 39% more likely than womento be agricultural workers. These multi-variate results are consistent with thedescriptive findings.

4.2.5 Paid employment is the out-come considered to be more critical inaddressing gender inequality in the la-bour market.

4.2.6 Paid Employment. Model 1 in-dicates that men are 40% more likelythan women to be paid workers. Fur-ther, as reflected in Model 2, men’s oddsof paid employment rise when educationand academic certification are taken intoaccount. In Models 3 to 4, the differenceis slightly reduced, with men 29% and17% more likely than women to be paidworkers. Thus, while gender inequalitypersists, some of it is due to demogra-phic factors and region of residence.

6 The coefficients generated from the logistic regression analyses are called logits, denoting changes in the log odds for a male to be employed relative to a femalewith each unit increase in the explanatory (independent) variable. To facilitate interpretation, the coefficients are transformed into odds ratios (ORs) byexponentiation (i.e., Exp (β). An odds ratio of one implies no difference in the odds of employment between men and women. Odds ratios greater than onemean that men are more likely than women to be employed in the industry/occupation in question. Conversely, odds ratios smaller than one suggest that menare less likely than women to be employed in the industry/occupation in question.

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


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Table 1. Odds Ratios of Gross and Net Size of Gender Inequality in Various Industries and Occupations, Botswana, 2005/2006

Model 1 Model 2 Model 3 Model 4

Gross Gender Inequality Human Capital Demographic Structural/Economic Factors

Correlates OddsRatio

SE Sig OddsRatio

SE Sig OddsRatio

SE Sig OddsRatio

SE Sig

Agricultural Employment

Male 1.269 0.007 *** 1.227 0.007 *** 1.305 0.008 *** 1.386 0.008 ***

Female (ref) … … … … … … … … … … … …

Unpaid Family Work

Male 0.422 0.020 *** 0.419 0.020 *** 0.464 0.021 *** 0.499 0.021 ***

Female (ref) … … … … … … … … … … … …

Paid Employment

Male 1.399 0.005 *** 1.440 0.005 *** 1.291 0.006 *** 1.165 0.006 ***

Female (ref) … … … … … … … … … … … …

Self Employment With Employees

Male 2.200 0.015 *** 2.160 0.016 *** 2.333 0.017 *** 2.435 0.017 ***

Female (ref) … … … … … … … … … … … …

Self Employment Without Employees

Male 2.909 0.011 *** 2.964 0.011 *** 3.050 0.012 *** 2.720 0.012 ***

Female (ref) … … … … … … … … … … … …

Craft and Related Occupations

Male 9.462 0.020 *** 9.618 0.020 *** 8.842 0.021 *** 8.595 0.021 ***

Female (ref) … … … … … … … … … … … …

Plant and Machine Operators

Male 1.037 0.007 *** 1.034 0.007 *** 0.989 0.008 # 0.887 0.008 ***

Female (ref) … … … … … … … … … … … …

Services

Male 0.462 0.008 *** 0.496 0.008 *** 0.489 0.008 *** 0.499 0.008 ***

Female (ref) … … … … … … … … … … … …

Clerical Occupations

Male 0.417 0.011 *** 0.437 0.011 *** 0.420 0.012 *** 0.418 0.012 ***

Female (ref) … … … … … … … … … … … …

Legislature/Managerial and Professional Occupations

Male 1.796 0.009 *** 1.746 0.010 *** 1.739 0.011 *** 1.747 0.011 ***

Female (ref) … … … … … … … … … … … …

*** Significantly different from reference category (p<0.001) ** Significantly different from reference category (p<0.01) * Significantly different from reference category (p<0.05) # Significantly different from reference category (p<0.10)… Not applicable(ref) Reference categorySource: Botswana Labour Force Survey (BLFS), 2005/2006.

4.2.7 Unpaid family work. Model 1shows a sharp gross gender inequality inunpaid work—women are 58% more li-kely than men to be unpaid family wor-kers. Adjustments for human capital,demographic factors, and economic/structural correlates do notchange the substantive conclusions, a

result that is consistent with the descrip-tive findings.

4.2.8 Self-Employment with Em-ployees. According to Model 1, thegross inequality in self-employment withemployees in favour of men is substan-tial, and does not change when human

capital, demographic factors, and struc-tural/economic correlates are taken intoaccount.

4.2.9 Self-Employment without Em-ployees. Like self-employment with em-ployees, the gross and net gender ine-quality in self-employment without


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employees is large. The disadvantagefaced by women increases when the ef-fects of human capital and demographicfactors are taken into account, and onlyslightly decreases when structural oreconomic factors are considered.

4.2.10 Gross and Net Size of theGender Inequality in Occupations. Inthis section the analysis examines theextent of gender inequality in occupa-tion outcomes – plant and machine ope-rations, craft and related occupations,services, clerical occupations, and legis-lature/managerial, and professional oc-cupations.

4.2.11 Plant and Machine Opera-tors. According to Model 1, men areonly about 4% more likely than womento be plant and machine operators. Mo-reover, the direction of the inequalitychanges with adjustments for demogra-phic and structural/economic factors.Based on Model 4, men are 11% less li-kely to work as plant and machine ope-rators. Thus, much of the inequality inthis occupational sector tends to be as-sociated with demographic characteris-tics of individuals and economic/struc-tural factors. While consistent with thedescriptive results, this finding iscontrary to the literature, which suggeststhat men have a greater likelihood thanwomen of being plant and machine ope-rators. The results of the earlier studiesmay be tied to failure to fully control fordemographic and economic/structuralfactors.

4.2.12 Craft and Related Occupa-tions. Unlike the situation for plant andmachine operations, the gross inequalityin craft and related occupations is largeand to the advantage of men. The netinequality remains large even after ad-justments for the three set of correlates.

4.2.13 Services. Models 1 through 4indicate that women have a greater pro-bability of being employed in service oc-cupations compared with men—54%gross inequality (model 1) and 50% (mo-del 4) net inequality. These multivariateresults corroborate the descriptive fin-dings.

4.2.14 Clerical Occupations. Thesame picture emerges for clerical occu-pations, with the estimates in the gross(Model 1) and in the subsequent modelsindicating that men’s odds of clerical em-ployment are significantly lower thanthose of women. Again, these multiva-riate findings corroborate the descriptivefindings.

4.2.15 Legislature/Managerial, andProfessional Occupations. Model 1 in-dicates that men are about 80% more li-kely than women to hold legislative ormanagement and professional occupa-tions, the most secure and profitable thatwere examined in the survey. As well,the situation changes little with adjust-ments for human capital, demographicand structural/economic factors.

4.2.16 Summary of Initial Multiva-riate Findings. Overall, the multivariateresults are consistent with the descrip-tive findings for agricultural employ-ment, other industries and occupa-tions. Men are significantly more likelythan women to be agricultural workers.And among those not engaged in agri-culture, men are more likely than wo-men to be paid employees, self-em-ployed with or without employees, craftand related workers, and legislatorsand professionals. On the other hand,the descriptive and multivariate resultsconsistently show that women aremore likely than men to be service wor-kers and clerks.

4.2.17 The only difference between thedescriptive and multivariate resultsemerges in plant and machine opera-tions. While the descriptive results sug-gest that, regardless of region, men aremore likely than women to be employedin this category, this is due to failure toaccount for potentially demographic andstructural/economic factors. When thesefactors are considered in a multivariateframework, women are more likely thanmen to work as machine and plant ope-rators. This finding is contrary to theconventional belief that masculine phy-siology is more appropriate for this typeof work.

4.2.18 The analysis in the next sectionexamines the differential determinants ofwomen’s and men’s employment sta-tus that explain the inequality in paid andself-employment and in occupations.

4.3 Determinants of GenderInequality

4.3.1 The factors that influence em-ployment status differ for men and wo-men. Moreover, the association bet-ween a particular factor andemployment may not be the same formen and women, and thus, may contri-bute to gender inequality in differentways. For example, the same factormight have a positive association with aspecific aspect of employment for men,but a negative association for women,or vice versa. In Tables 2 to 6, the de-terminants of women’s and men’s em-ployment status are examined in an at-tempt to understand some of thesemechanisms. For conciseness, only thenet findings from the final and mostcomplex model (Model 4) are discus-sed. The full set of models (1 through 4)showing the separate determinants ofemployment for women and men are

presented in annex Tables 1 through20. (Annex tables 1-20 are available onthe AfDB data portal).

4.3.2 The regression results for va-rious industries and occupations indi-cate gender differences in employmentand provide initial evidence about thedrivers of gender inequality in Bots-wana.

4.3.3 Unpaid family work and PaidEmployment. Table 2, which presentsthe findings for unpaid family work andpaid employment, reveals substantivedifferences between women and men,and also between unpaid and paid em-ployment. Gender differences in seniorsecondary educational attainment, themarital status “living together,” and regionof residence are influential for unpaid fa-

mily work. Young age (12 to 19), seniorsecondary educational attainment, andthe marital status “living together” areimportant for paid employment. The sub-sequent analyses deal with the mecha-nisms that may result in gender-differen-tiated effects on paid and unpaidemployment. The results are examinedwithin the framework of theoretical ex-pectations.


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Table 2. Odds Ratios of Determinants of Unpaid Family Work and Paid Employment, Women and Men, Botswana 2005/2006

Unpaid Family Work Paid Employment

Model 4 Model 4

OddsRatio

SE Sig OddsRatio

SE Sig OddsRatio

SE Sig OddsRatio

SE Sig

Correlates Female Male Female Male

AGE

Youth (12-19 yrs) 3.539 0.062 *** 10.024 0.109 *** 0.762 0.026 *** 1.377 0.022 ***

Young Adults (20-29 yrs) 2.648 0.053 *** 4.953 0.105 *** 1.122 0.017 *** 1.937 0.016 ***

Adults (30-49 yrs) 1.283 0.047 *** 1.817 0.099 *** 2.002 0.014 *** 2.516 0.012 ***

Older Adults (50+ yrs) ( ref) … … … … … … … … … … … …

HUMAN CAPITAL

Primary Schooling 0.800 0.054 *** 0.853 0.067 * 2.967 0.019 *** 1.370 0.015 ***

Junior Secondary 0.871 0.047 * 0.592 0.063 *** 2.795 0.017 *** 1.235 0.014 ***

Senior Secondary 1.220 0.044 *** 0.650 0.063 *** 1.547 0.015 *** 0.976 0.012 *

No Schooling (ref) … … … … … … … … … … … …

Academic Training

Training 0.911 0.034 ** 0.515 0.055 *** 2.167 0.011 *** 1.546 0.011 ***

No Training (ref) … … … … … … … … … … … …

DEMOGRAPHIC FACTORS

Marital Status

Married 1.064 0.037 # 1.443 0.070 *** 0.673 0.012 *** 0.756 0.012 ***

Living Together 0.538 0.047 *** 1.329 0.066 *** 0.791 0.013 *** 1.195 0.013 ***

Separated, Divorced or Widowed

0.401 0.097 *** 0.000 0.105 ns 0.745 0.019 *** 0.580 0.026 ***

Household Head 0.395 0.034 *** 0.368 0.053 *** 1.610 0.010 *** 1.803 0.011 ***

Spouse of Household Head 1.208 0.029 *** 1.195 0.039 *** 0.772 0.013 *** 0.687 0.014 ***

Single (ref) … … … … … … … … … … … …

ECONOMIC & STRUCTURAL FACTORS

Economic Migration 0.245 0.044 *** 0.270 0.054 *** 2.718 0.009 *** 2.290 0.009 ***

REGION

Cities and Towns 0.610 0.035 *** 1.590 0.049 *** 2.409 0.011 *** 1.919 0.010 ***

Other Urban Areas 0.533 0.027 *** 1.343 0.040 *** 1.846 0.010 *** 1.428 0.011 ***

Rural Areas (ref) … … … … … … … … … … … …

*** Significantly different from reference category (p<0.001) ** Significantly different from reference category (p<0.01) * Significantly different from reference category (p<0.05) # Significantly different from reference category (p<0.10)ns Not significantly different from reference category (p<0.10 or better)… Not applicable(ref) Reference categorySource: Botswana Labour Force Survey (BLFS), 2005/2006.


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4.3.4 Human Capital Effects. Theresults for unpaid employment in Table 2are generally consistent with the humancapital perspective. Lower levels ofschooling, specifically primary and juniorsecondary level education as well as se-nior secondary education (men only), areassociated with lower odds of unpaidemployment among both women andmen. On the other hand, women with se-nior secondary education are more likelythan those with non-formal education orno schooling to be unpaid workers.

4.3.5 With regard to paid employ-ment, education benefits women, al-though the relationship is strongest atlower levels of schooling. This does notfully support the hypothesized “U” sha-ped link between education and em-ployment (Standing 1983). Moreover,this is particularly evident among men,who, as they acquire more education,become less likely than their peers wi-thout formal schooling to be paid wor-kers. Thus, the evidence in support ofthe positive role of human capital is lessthan compelling, and warns against re-lying on education as the main vehiclefor economic security.

4.3.6 Demographic Effects. Mar-riage and being the spouse of a house-hold head are associated with higherodds of unpaid employment for both wo-men and men, although the impact isconsiderably stronger for men. Conver-

sely, the odds for “living together” and“separated, divorced, or widowed” indi-cate that they impact gender inequality.The former hinders employment pros-pects for women and enhancing pros-pects for men; the latter hinders pros-pects for women, but is unrelated tomen’s employment behavior. The onlydemographic factor that appears to im-pact gender inequality in paid employ-ment is “living together,” which is asso-ciated with higher odds of men havingsuch employment, but lower odds forwomen.

4.3.7 Structural/Economic Factors.Migration and the reasons for doing sodo not impact women and men diffe-rently for either unpaid or paid employ-ment. By contrast, the relationship bet-ween region of residence and unpaidemployment does differ for men and wo-men: men who live in both cities andtowns and in other urban areas are morelikely to engage in unpaid work relative totheir rural counterparts, while the reverseholds for women.

4.3.8 Self-Employment with and wi-thout Employees. Table 3 shows factorsthat contribute to gender inequality inself-employment with and without em-ployees. The main influences are demo-graphic factors and region of residence.

4.3.9 Differences in women’s andmen’s region of residence are influential

for self-employment with employees, butnot for self-employment without em-ployees. Gender differences in demo-graphic factors are important for bothoccupational categories. “Living toge-ther” and “separated, divorced or wido-wed” are associated with gender ine-quality in self-employment withemployees. For self-employment withoutemployees, as well as differences in “se-parated, divorced or widowed,” diffe-rences in “marriage” and in the sex of thehousehold head are important.

4.3.10 Human Capital Effects. Theestimates in Table 3 indicate that edu-cation and training are associated withsignificantly higher odds of self-em-ployment with and without employees.The strength of the association, howe-ver, tends to decline with more educa-tion. In the absence of any differentialgender impact, no strong explanationfor the gender inequality in self-em-ployment with and without employeesis apparent.

4.3.11 Demographic Effects. Mar-riage boosts the odds of self-employ-ment with employees for both womenand men, and so does not appear tocontribute to gender inequality. Being thehousehold head or the spouse of thehousehold head is not a factor in ine-quality either, as it is negatively related toself-employment with employees forboth sexes.


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Table 3. Odds Ratios of Determinants of Self-Employment with Employees and Self-Employment without Employees, Women and Men,Botswana, 2005/2006

Self Employment with Employees Self Employment without Employees

Model 4 Model 4

OddsRatio

SE Sig OddsRatio

SE Sig OddsRatio

SE Sig OddsRatio

SE Sig


AGE

Youth (12-19 yrs) 0.097 0.137 *** 0.142 0.109 *** 1.318 0.042 *** 1.846 0.062 ***

Young Adults (20-29 yrs) 0.106 0.055 *** 0.478 0.037 *** 1.245 0.024 *** 1.201 0.037 ***

Adults (30-49 yrs) 0.428 0.034 *** 0.847 0.026 *** 0.824 0.017 *** 0.939 0.028 *

Older Adults (50+ yrs) (ref) … … … … … … … … … … … …

HUMAN CAPITAL

Primary Schooling 7.850 0.069 *** 2.977 0.039 *** 3.820 0.029 *** 2.392 0.037 ***

Junior Secondary 4.580 0.064 *** 1.752 0.040 *** 1.633 0.021 *** 1.654 0.032 ***

Senior Secondary 2.326 0.058 *** 1.769 0.034 *** 1.173 0.017 *** 1.352 0.026 ***

No Schooling (ref) … … … … … … … … … … … …

ACADEMIC TRAINING

Training 1.322 0.035 *** 1.808 0.023 *** 1.351 0.020 *** 1.155 0.026 ***

No Training (ref) … … … … … … … … … … … …

DEMOGRAPHIC FACTORS

Marital Status

Married 2.322 0.037 *** 1.996 0.024 *** 0.682 0.017 *** 1.113 0.028 ***

Living Together 0.872 0.062 * 1.351 0.031 *** 0.635 0.018 *** 0.803 0.028 ***


2.100 0.049 *** 0.419 0.100 *** 1.056 0.025 * 0.631 0.050 ***

Household Head 0.707 0.032 *** 0.831 0.027 *** 0.026 0.015 *** 0.678 0.030 ***

Spouse of Household Head 0.691 0.055 *** 0.827 0.039 *** 1.165 0.022 *** 0.615 0.034 ***

Single (ref) … … … … … … … … … … … …



REGION

Cities and Towns 1.246 0.037 *** 1.529 0.025 *** 0.964 0.018 * 0.377 0.027 ***

Other Urban Areas 0.979 0.034 ns 1.390 0.024 *** 0.844 0.014 *** 0.368 0.023 ***

Rural Areas (ref) … … … … … … … … … … … …


However, “separated, divorced, wido-wed” and “living together” widen the gapin favor of women. The picture is differentfor self-employment without employees.Marriage appears to intensify the ine-quality in favor of men by boosting theirlikelihood of being engaged in this in-dustry, while decreasing the likelihoodfor women by about 32%. By contrast,

“separated, divorced, widowed” andbeing the spouse of a household headnarrow the inequality in men’s favor byincreasing women’s likelihood of self-employment without employees andwhile reducing that of men.

4.3.12 Structural/Economic Factors.In general, economic migration is asso-

ciated with higher odds of self-employ-ment without employees for both sexes.However, male and female economic mi-grants have significantly low odds of self-employment with employees. Inequalitywithin this type of employment is inten-sified against women residing in otherurban areas, relative to cities and townsor rural areas. On the other hand, no dif-


19

ferential effect emerges in cities andtowns, where the odds of self-employ-ment with employees are high for bothwomen and men.

4.3.13 Craft and Related Occupa-tions and Plant and Machine Opera-tions. Table 4 shows that for crafts andrelated occupations and for plant andmachine operations, the underlying fac-tors in gender inequality are varied des-pite some overlap. For craft and relatedoccupations, differences in all three levelsof educational attainment are influential,while only the difference in senior secon-dary level is influential for plant and ma-chine operations. The differences in allthe three marital statuses are influential ingender inequality in craft and related oc-cupations. For plant and machine opera-tions, only the difference in the “living to-gether” status is influential in favor of men.Being a household head increases theodds of employment in craft and relatedoccupations and plant and machine ope-rations for both women and men. Thus,household headship does not contributeto gender inequality in either occupationalcategory. On the other hand, urban resi-dence is associated with increased oddsof employment in plant and machine ope-rations among women and probably has

the most important influence on genderinequality in this occupational category.

4.3.14 Human Capital Effects. Themultivariate results in Table 1 show gen-der inequality in men’s favor in craft andrelated occupations and in plant andmachine operations. However, the re-sults in Table 4 reveal that educationalattainment at all levels is positively rela-ted to women’s employment in craftand related occupations, but has a ne-gative effect on men’s employment inthis occupation. As well, for women,the likelihood of employment in craftand related occupations sharply in-creases with each additional level ofeducation. With respect to plant andmachine operations, the collective ana-lysis (Table 1) indicates a slight gendergap in favor of men. However, in thegender-differentiated results (Table 4),this relationship is reversed in favor ofwomen with senior secondary school.The finding that senior secondary edu-cation increases women’s odds of em-ployment in this occupation while redu-cing the odds for men may indicate thatmen with higher levels of educationmove to other occupation categories. Atlower levels of education, there appearsto be no evidence of gender inequality.

4.3.15 Demographic Effects. Mar-riage and living together appear to re-duce women’s odds of employment incraft and related occupations, while in-creasing the odds for men. This isconsistent with earlier results (Figures 2and 3 and Table 1), although the ad-vantage is confined to urban areas. Bycontrast, separation/divorce/widow-hood increase women’s odds of em-ployment in craft and related occupa-tions, but reduce the odds for men.Household headship is not likely to in-fluence inequality, since it is related tohigher odds of employment in theseoccupations for both sexes. For em-ployment in the plant and machineoperation occupations, marriage andseparation/divorce/widowhood signifi-cantly reduce the odds for both menand women, and therefore, have noimpact on the female advantage re-ported in Table 1. Similarly, householdheadship benefits men and women al-most equally in employment in plantand machine operations. The influentialdemographic factor in gender inequa-lity for employment in plant and ma-chine operations is living together,which is associated with decreasedodds for women, but increased oddsfor men.



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Table 4. Odds Ratios of Determinants of Employment in Craft and Related Occupations and Plant and Machine Operations, Women and Men,Botswana, 2005/2006

Craft & Related Plant & Machine Operators

Model 4 Model 4

OddsRatio

SE Sig OddsRatio

SE Sig OddsRatio

SE Sig OddsRatio

SE Sig


AGE

Youth (12-19 yrs) 0.000 0.044 ns 0.000 0.044 ns 1.577 0.032 *** 2.455 0.027 ***

Young Adults (20-29 yrs) 3.500 0.082 *** 1.476 0.028 *** 1.448 0.021 *** 2.413 0.021 ***

Adults (30-49 yrs) 1.770 0.071 *** 1.699 0.021 *** 1.808 0.017 *** 1.917 0.017 ***

Older Adults (50+ yrs) (ref) ... ... ... ... ... ... ... ... ... ... ... ...

HUMAN CAPITAL


Junior Secondary 8.233 0.187 *** 1.014 0.025 ns 0.687 0.019 *** 0.512 0.017 ***


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

ACADEMIC TRAINING

Training 2.130 0.047 *** 2.067 0.017 *** 0.282 0.019 *** 0.190 0.022 ***

No Training (ref) ... ... ... ... ... ... ... ... ... ... ... ...

DEMOGRAPHIC FACTORS

Marital Status

Married 0.349 0.078 *** 1.845 0.019 *** 0.596 0.016 *** 0.448 0.017 ***

Living Together 0.337 0.083 *** 1.471 0.021 *** 0.713 0.017 *** 1.056 0.015 ***


2.458 0.061 *** 0.899 0.052 * 0.835 0.022 *** 0.469 0.037 ***

Household Head 1.557 0.054 *** 1.226 0.022 *** 1.277 0.013 *** 1.259 0.015 ***

Spouse of HouseholdHead

0.871 0.018 *** 0.584 0.019 ***

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

ECONOMIC AND STRUCTURAL FACTORS


REGION

Cities and Towns 1.282 0.049 *** 2.913 0.019 *** 2.351 0.015 *** 0.412 0.016 ***

Other Urban Areas 0.873 0.051 ** 2.481 0.019 *** 1.708 0.013 *** 0.538 0.013 ***

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


4.3.16 Structural/Economic Factors.Table 4 also shows that economic mi-gration is not significantly related to em-ployment in craft and related occupa-tions or in plant and machine operationsfor either women or men. However, resi-dence in cities and towns raises the oddsof employment in craft and related oc-cupations for both women and men. The

opposite is observed for other urbanareas where men’s odds of working incraft and related occupations are increa-sed, while the odds for women are re-duced.

4.3.17 Services and Clerical Occu-pations. The data in Table 1 and Figures2 to 4 indicate that compared with wo-

men, men are less likely to be employedin service and clerical occupations. Thegender-differentiated results in Table 5confirm the gender inequality in favor ofwomen in the service sector, and showthat this be attributed to youth (aged 12to 19). Older women and men haveequal odds of holding service sectorjobs, and thus, older age groups do not


21



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contribute to the advantage enjoyed bywomen in general. However, women’sadvantage in clerical occupations is at-tributable to older age groups. Compa-red with women aged 50 or older, theodds of clerical employment are signifi-cantly high for women in the 20 to 49age range, but significantly low for youn-ger women. Men in all age groups up to49 are significantly are less likely to workin clerical jobs compared with men aged50 or older.

4.3.18 Human Capital Effects. Theresults in Table 5 suggest that humancapital factors have no bearing on wo-men’s advantage in service and clericaloccupations, since for both sexes, all le-vels of education are associated with si-gnificantly higher odds of employmentin such jobs, compared with the odds forpeople who have no schooling. However,for both sexes, the odds of clerical em-ployment, while still significantly high, de-cline at higher levels of attainment.

4.3.19 Demographic Effects. Thegender-disaggregated results for bothservice and clerical occupations shownin Table 5 further highlight the influence ofdemographic factors on gender inequa-lity. Marriage and being the spouse of thehousehold head have no effect on wo-men’s greater odds of employment inservices occupations; the odds ratios forboth sexes are significantly low. “Livingtogether” is associated with significantlyhigh odds of employment in service oc-cupations for both women and men. Onthe other hand, the odds of services em-ployment are significantly low for womenwhose marital status is separation/divor-ced/widowhood, but significantly highfor men with this marital status. House-hold headship is not related to women’slikelihood of holding a services job, butmen who are household heads have si-gnificantly high odds. The overall in-fluence of the two sets of demographicfactors is to reduce women’s advantagein service occupations.

4.3.20 With regard to clerical occupa-tions, marriage and household headshiphave no impact on inequality, becausefor both sexes, the former is associatedwith low odds of employment, while thelatter is associated with significantly highodds. The odds of clerical employmentare significantly low for women who areseparated/divorced/widowed, but signi-ficantly high for their male counterparts.Being the wife of the household head isnot significantly related to clerical em-ployment, but the odds for husbands ofhousehold heads are significantly high.

4.3.21 Structural/Economic Factors.Economic migration reduces women’slikelihood holding service occupations,while increases the likelihood for men.However, structural or economic factorshave no effect on gender inequality inclerical occupations, as both male andfemale economic migrants have highodds of employment in this occupationcategory.


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Table 5: Odds Ratios of Determinants of Employment in Service and Clerical Occupations, Women and Men, Botswana, 2005/2006

Service Occupations Clerical Occupations

Model 4 Model 4

OddsRatio

SE Sig OddsRatio

SE Sig OddsRatio

SE Sig OddsRatio

SE Sig

Female Male Female Male

AGE

Youth (12-19 yrs) 1,402 0,028 *** 0,923 0,038 * 0,301 0,067 *** 0,154 0,075 ***

Young Adults (20-29 yrs) 1,407 0,020 *** 1,077 0,027 ** 1,107 0,034 ** 0,398 0,042 ***

Adults (30-49 yrs) 1,239 0,016 *** 1,106 0,023 *** 1,688 0,031 *** 0,657 0,035 ***

Older Adults (50+ yrs) (ref) … … … … … … … … … … … …

HUMAN CAPITAL

Primary Schooling 0,956 0,022 * 2,137 0,026 *** 15,748 0,059 *** 9,193 0,047 ***

Junior Secondary 1,538 0,019 *** 2,578 0,024 *** 11,361 0,058 *** 6,013 0,045 ***

Senior Secondary 1,529 0,017 *** 1,430 0,023 *** 2,636 0,060 *** 1,569 0,047 ***

No Schooling (ref) … … … … … … … … … … … …

Academic Training

Training 0,592 0,014 *** 0,529 0,017 *** 1,086 0,016 *** 0,273 0,025 ***

No Training (Ref) … … … … … … … … … … … …

DEMOGRAPHIC FACTORS

Marital Status

Married 0,949 0,014 *** 0,702 0,020 *** 0,867 0,019 *** 0,484 0,030 ***

Living Together 1,033 0,015 * 1,121 0,019 *** 1,020 0,021 ns 0,777 0,031 ***


0,674 0,023 *** 1,134 0,043 ** 0,771 0,035 *** 1,269 0,060 ***

Household Head 1,001 0,012 ns 1,205 0,018 *** 1,295 0,016 *** 1,451 0,028 ***

Not Head of Household 0,956 0,015 ** 0,869 0,022 *** 0,975 0,022 ns 1,156 0,034 ***

Single (ref) … … … … … … … … … … … …

ECONOMIC /STRUCTURAL FACTORS

Economic Migration 0,886 0,011 *** 1,135 0,014 *** 1,181 0,014 *** 1,076 0,022 ***

Region

Rural Areas (ref) … … … … … … … … … … … …

Cities and Towns 1,147 0,014 *** 2,122 0,018 *** 2,240 0,019 *** 5,574 0,030 ***

Other Urban Areas 1,384 0,011 *** 2,416 0,016 *** 1,724 0,019 *** 3,102 0,031 ***

4.3.22 Legislature/Managerial, andProfessional Occupations and Agri-cultural Employment. Table 6 presentsthe determinants of access for womenand men to legislature/managerial, andprofessional occupations and to agricul-tural employment. As might be expected,the factors that contribute to gender ine-

quality in the two occupational categoriesdiffer greatly. While for agricultural em-ployment, only two factors are associa-ted with gender inequality (senior secon-dary educational attainment and “livingtogether”), seven factors are associatedwith gender inequality in the legisla-ture/managerial, and professional occu-

pations, the largest number of influentialfactors for any occupation category. 4.3.23 Human Capital Effects. Edu-cation raises the odds of employment inlegislature/managerial, and professionaloccupations, but the effect is strongestat the primary school level and declinessubstantially for both sexes, but particu-


larly women, as they acquire moreschooling. On the other hand, the certi-fication is unrelated to men’s employ-ment in these occupations, but is asso-ciated with higher odds for women. Thedifferential gender effect of certificationsuggests that credentialism may be ope-rating for women but not for men.

4.3.24 Demographic Effects. In Table6, the positive relationship between mar-riage and employment in legislature/ma-nagerial and professional occupationsfor women can be interpreted as redu-cing gender inequality in favor of men.Marriage is associated with significantlyhigh odds of employment in professionaloccupations for women, but significantlylow odds for men. This finding appearsto be contrary to conventional wisdom,but it is consistent with a recent studythat of the sources of change in wo-men’s employment since the early 1990s

(Jah 2010a, 2010b). In this study, re-gression decompositions to apportionand quantify the relative weight of thedifferent sources of change in the 21Sub-Saharan countries studied revealedthat marriage, with fertility operatingthrough marriage, and not human capi-tal, was the driver of the observedchanges in women’s employment in 20of the 21 countries.

4.3.25 This finding, which demonstratesthe importance of nuances within maritalrelations interacting with larger culturalnorms that govern the organization of workin the region, deserves continued scrutinyin gender and employment research.

4.3.26 Separation/Divorce/Widowhoodhas the same effect as marriage, per-haps partly because more educated wo-men tend to put a high premium on ca-reer development, and partly because

prolonged schooling and postsecondaryeducation limit the marriage market forwomen. While marriage and the dissolu-tion of marriage tend to reduce men’sadvantage in professional employment,the growing incidence of “living together”and male household headship act in theopposite and reinforce men’s advantage.This is understandable in Botswanawhere male household headship entailsfinancial obligations for the household.

4.3.27 Structural/Economic Factors.Table 6 further reveals that structural andeconomic factors—economic migrationand region of residence—on which eco-nomic opportunity depend are also si-gnificantly related to employment in pro-fessional occupations. Urban residenceis associated with significantly high oddsof employment in legislature/managerial,and professional occupations for wo-men, but significantly low odds for men.


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Table 6: Odds Ratios of Determinants of Access to Legislature/Managerial, and Professional Occupations, Women and Men, Botswana, 2005/2006

Legislature/Managerial and Professional Occupations Agricultural Occupations

Model 4 Model 4

OddsRatio

SE Sig OddsRatio

SE Sig OddsRatio

SE Sig OddsRatio

SE Sig

Female Male Female Male

AGE

Youth (12-19 yrs) 0.000 0.025 ns 0.000 0.044 ns 0.442 0.033 *** 0.549 0.025 ***

Young Adults (20-29 yrs) 0.281 0.033 *** 1.257 0.044 *** 0.356 0.022 *** 0.308 0.020 ***

Adults (30-49 yrs) 0.606 0.028 *** 1.359 0.038 *** 0.301 0.015 *** 0.272 0.015 ***

Older Adults (50+ yrs) ( ref) … … … … … … … … … … … …

HUMAN CAPITAL


Junior Secondary 4.272 0.065 *** 5.144 0.129 *** 0.347 0.020 *** 0.809 0.017 ***


No Schooling (ref) … … … … … … … … … … … …

ACADEMIC TRAINING

Training 3.958 0.022 *** 87090913 1.766 ns 0.344 0.024 *** 0.446 0.017 ***

No Training (Ref) … … … … … … … … … … … …

DEMOGRAPHIC FACTORS

Marital Status

Married 2.126 0.021 *** 0.766 0.024 *** 2.198 0.017 *** 1.469 0.016 ***

Living Together 0.713 0.036 *** 1.015 0.034 ns 1.121 0.019 *** 0.704 0.017 ***


1.430 0.036 *** 1.079 0.077 ns 1.747 0.021 *** 1.688 0.030 ***

Household Head 0.801 0.044 *** 2.192 0.036 *** 0.961 0.015 ** 0.855 0.015 ***

Spouse of Household Head 0.461 0.034 *** 0.908 0.060 # 1.323 0.019 *** 1.182 0.017 ***

Single (ref) … … … … … … … … … … … …



Region

Cities and Towns 1.291 0.024 *** 0.918 0.025 *** 0.050 0.033 *** 0.053 0.026 ***

Other Urban Areas 1.176 0.024 *** 0.685 0.027 *** 0.193 0.014 *** 0.225 0.013 ***

Rural Areas (ref) … … … … … … … … … … … …


4.4 Summary of Principal Findings

4.4.1 In summary, compared withmen, women have made limited inroadsin legislature/managerial, and professio-nal occupations, the more secure and

prestigious category (Table 1). Increasededucation, in particular, while benefittingboth sexes, appears to have greater re-wards for men.

4.4.2 The overall comparison of wo-men’s and men’s employment across

the ten industries and occupations hashighlighted the multifaceted and com-plex nature of the determinants of indivi-dual employment. In particular, it has re-vealed some of the nuances that may bedriving gender inequality. The showingthat women with secondary education


25

are more likely than their peers withoutformal education to be engaged in un-paid employment imply that women maynot be benefitting fully from more edu-cation. This is consistent with Si-phambe’s (2000) findings that, compa-red with men, women face tightercompetition in the labor market. Simi-larly, the gender-differentiated analyses ofemployment in services and clerical oc-cupations reinforce the imbalance in thedevelopment of educational and em-ployment opportunities and the compe-tition faced by labor market entrants re-ported by previous studies (Siphambe2000). The analyses also emphasize thecontinued importance of adopting a lifecourse approach in studies that examineemployment behavior.

5 Conclusions, PolicyImplications and Proposed Further Research

5.1 Conclusions

5.1.1 The main aim of this study wasto examine the relationship between gen-der and employment in various industriesand occupations in Botswana. Theconceptual framework was guided bythe literature on the relationship betweenemployment of women and men and awide range of variables that influenceeconomic opportunities: human capital,demographic characteristics, and struc-tural and economic factors.

5.1.2 The multivariate analyses of theassociations between gender and em-ployment in Botswana corroborate thefew studies available for the country (seeSiphambe 2000). Additionally, the ana-lyses reveal interesting patterns in whatthis study views as initial evidence onthe sources of gender inequality in Bots-

wana, a country where labor marketcompetition is stiff, and employment op-portunities are limited (Siphambe 2000).

5.1.3 The second aim of the studywas to quantify the gender inequality invarious economic sectors. Overall, thestudy finds men to be substantially morelikely than women to be: (i) craft and re-lated workers; (ii) self-employed withoutemployees; (iii) self-employed with em-ployees; (iv) legislature/managerial andprofessional workers; (v) agricultural wor-kers; and (vi) paid workers. On the otherhand, men are substantially less likelythan women to be employed in: (i) cleri-cal occupations; (ii) unpaid family work;(iii) services; and (iv) plant and machineoperations. Thus, the findings corrobo-rate recent African literature showing wi-despread gender inequality in employ-ment, and in particular, theoverrepresentation of women in servicesand clerical work (Jah 2010a; Glick andSahn 2001; Greenhalgh 1991; Siphambe2000).

5.1.4 Having established the pre-sence of gender inequality in employ-ment in various economic sectors, thestudy addressed a second question:how the determinants of employment inthese sectors vary by gender.

5.1.5 Because the determinants ofemployment behavior are apt to differ bysex, separate analyses were conductedfor women and men. The regression re-sults indicate that the gender differencesin employment may, indeed, be linkedwith demographic and economic/struc-tural factors that govern employment be-havior, and to a lesser extent, individualhuman capital. The differences, however,depend on the specific economic sector.For instance, while no substantive genderdifferences were observed for unpaid fa-

mily work, for paid employment, genderdifferences are related to age, higher edu-cational attainment, and living together.5.1.6 Similarly, for self-employmentwith employees, gender differencesemerge in the associations with living to-gether, separation/divorce/widowhood,and residing in other urban areas. Gen-der differences also exist in self-employ-ment without employees, although in ad-dition to separation/divorce/widowhood,marriage and being the spouse of thehousehold head are significant.

5.1.7 For craft and related occupa-tions, gender differences are evident inassociations with primary schooling,marriage, living together, separation/di-vorce/widowhood and residence in otherurban areas. For plant and machine ope-rations, the important gender differencespertain to senior secondary schooling, li-ving together and residence in cities andtowns.

5.1.8 For service occupations, signifi-cant gender differences in the determi-nants of employment emerge for age (12to 19), primary schooling, householdheadship, separation/divorce/widow-hood and economic migration. For cleri-cal occupations, gender differences inthe determinants of employment involveage (both youth and adults), training, li-ving together, separation/divorce/wi-dowhood, and being the spouse of thehousehold head.

5.1.9 Understandably, agricultureshows the least gender differentiation inthe determinants of employment in thissector. By contrast, the determinants ofemployment in the most prestigious sec-tors— legislature/managerial, and pro-fessional occupations—reveal the mostgender differentiation. Significant genderdifferences are evident for a host of de-



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8 The African evidence is mixed. In West Africa, the relationship between marriage and women’s employment is positive (Glick and Sahn 1997 for Guinea; Appletonet al. 1990 for Cote d’Ivoire), but negative in East Africa (Krishnan 1996 for Ethiopia) and in South Africa (Naude and Serumaga-Zake 2001; Ntuli 2007). If thispattern holds for the rest of the region, the marriage-employment relationship appears to be conditioned by geography. Marriage increases the likelihood ofwomen’s employment in West Africa but impedes it in the Eastern and South African sub-regions. This sub-regional effect, in turn, can be linked to culturaldifferences in family systems and nuptiality. Further, past studies have been limited to rural (Naude and Serumaga-Zake 2001) or urban data (Glick and Sahn1997; Shapiro and Tambashe 1997), or a particular subgroup (Krishnan 1996). Few studies (Glick and Sahn 1997; Shapiro and Tambashe 1997; Siphambe 2000)have distinguished between occupational sectors. The mixed evidence can also be tied to design issues and the use of differing statistical approaches.

9 Most studies that examine how gender relates to employment in SSA have shown low labor force attachment for women (Appleton et al. 1990; Glick and Sahn1997; Krishnan 1996; Siphambe 2000; Naude and Serumaga-Zake 2001; Ntuli 2007; Vijverberg 1993). However, the reasons for this low attachment areinconsistent, with some studies supporting (Appleton et al. 1990; Glick and Sahn 1997) and others refuting (Siphambe 2000; Vijverberg 1993) the theory ofhuman capital. The studies by Siphambe (2000) in Botswana and Vijverberg (1993) in Cote d’Ivoire assert that women are disadvantaged in the labor marketand this is despite their greater educational attainment relative to men (Siphambe 2000). This assertion, which is tied to the hypothesis of discrimination (Birdsalland Sabot 1991), has been disputed by other authors who contend that African labor markets are the “least discriminatory in the world” and point to women’slower education (Appleton et al. 1990; Glick and Sahn 1997; Naude and Serumaga-Zake 2001; Ntuli 2007).

terminants of employment: young adults,adults, senior secondary schooling, trai-ning, cities and towns, other urban areas,and almost all the demographic factors(marriage, living together, and separa-tion/divorce/widowhood).

5.1.10 Thus, the descriptive, multiva-riate and gender-disaggregated analysesall underscore the importance of demo-graphic and structural/economic factorson the gender inequality in employment.The significant results for demographicfactors, specifically marriage, are consis-tent with recent findings for SSA obtai-ned from more sophisticated decompo-sition analyses of the sources of changein women’s employment (Jah 2010b).

5.1.11 Raising the status of womenthrough employment has been a lynch-pin in the international developmentagenda. Yet, little empirical evidence onthe relationship between marriage andlabor force participation is available inthe African literature, partly due to datascarcity.8 Similarly, most studies that exa-mine how human capital mediates thegender-employment relationship in SSAhave shown persistently low labor forceattachment for women.9 Such mixed evi-dence from past studies, some of whichare dated or based on small, non-repre-

sentative samples, is of limited utility inassessing contemporary progress to-ward reaching the MDGs in SSA.

5.1.12 This study attempted to over-come some of the limitations of past stu-dies. As well, the aim was to make acontribution to the literature on genderand employment in SSA in particular, anddeveloping countries as a whole. First,this appears to be the first empirical studyto examine employment outcomes for alarge, nationally representative samplethat permits more detailed occupationaldistinctions (10 in total) than have here-tofore been possible. Therefore, it com-plements the income studies that use theLiving Standards Surveys in SSA. Se-cond, it is one of the few studies in SSAthat uses nationally representative data toquantify gender inequality in employmentand to then examine the determinants ofthis inequality. Third, it refines theoreticalexplanations through consideration of de-mographic and structures/economic fac-tors that are associated with employ-ment. Despite missing cases, theapplication of appropriate statistical esti-mation methods allowed robust associa-tions between key variables to emerge.The results suggest that in the absence ofmissing cases, even stronger associa-tions could be expected.

5.2 Policy Implications

5.2.1 The foregoing conclusions havepolicy implications for the provision ofsecure and profitable jobs to both menand women, and ultimately, for attainingthe gender equality MDG.

5.2.2 The study’s use of representativedata and detailed description of the me-thodology, which facilitates replication el-sewhere in the region, are particularly va-luable in designing development policiesand programs that aim to achieve genderequality in employment. The broadenedconceptualization beyond human capitalthat considers demographic factors andeconomic and structural factors signalsthe importance of wide-ranging policies,rather than unidirectional educational po-licies. Policies designed to promote equityand eliminate poverty must recognize thedifferences between women’s worlds ofwork, particularly with a view to enhancingprofitability and career development in oc-cupations where women are over-repre-sented as well as under-represented. Thefollowing section refers to the economicsectors where policy directives are mostcritical.

5.2.3 Paid Employment and Legis-lature and Managerial Occupations.


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The disadvantage faced by women inobtaining employment in the more se-cure and profitable industries and occu-pations presents a challenge for reducinggender inequality in the labor market. Acomprehensive effort is needed, one thatfocuses on both human capital and de-mographic influences rather focusingunilaterally on one or the other. To thisend and in terms of:

5.2.3.1 Human capital policies that fo-cus on quality of education, and espe-cially for women, labor market relevance,should be enacted and enforced. Poli-cies should aim to ensure that young fe-male labor market entrants’ academicqualifications match current labor marketrequirements.

5.2.3.2 Demographic influences thatfacilitate women’s ability, regardless oftheir marital status, to obtain and remainin profitable paid occupations should bedesigned and implemented.

5.2.4 Self-Employment with and wi-thout Employees. The fact that men aremore likely than women to be self-em-ployed, with or without employees,points to, among other factors, women’sinability to secure capital. Accordingly,policies that facilitate women’s access tocredit are imperative. Further, policies toensure that all forms of self-employmentare secure, profitable and sustainableare needed. Based on the data regardingself-employment without employees,measures that reduce the constraintsposed by marriage, and to some de-gree, being a female household head arealso desirable.

5.2.5 The hurdle faced by women re-lative to men in self-employment withoutemployees stems mostly from demogra-phic factors, with human capital factorsnot appearing to be critical. The situationis different for self-employment with em-ployees. Participation in this sector is as-sociated with region of residence in ad-dition to being unmarried but cohabitingwith a partner. This calls for policy focuson both sets of factors.

5.2.6 Craft and Related Occupa-tions and Plant and Machine Opera-tions. Beyond ensuring that craft and re-lated occupations and plant and machineoperations are profitable, safe and securefor all women, policy efforts must be di-rected toward increasing craft-related em-ployment for married and cohabiting wo-men and women residing in urban areasother than cities and towns. Likewise,with respect to plant and machine opera-tions, policy emphasis must be put onnon-married cohabiting women.

5.3 Proposed Further Research

5.3.1 Although the results do not pre-sent unequivocal evidence about thesources of gender inequality, these initialfindings point to where and what to lookfor in future analyses of the determinantsof employment. The study provides up-dated data on gender inequality in em-ployment and a rationale for further ana-lysis of its determinants. The resultssuggest that gender inequality in em-ployment is rooted in a web of demo-graphic factors and economic/structuralinfluences on prevailing employment op-

portunities and human capital. Howe-ver, the analysis does not constitute ir-refutable proof of the relative importanceof each factor. Decomposition methodswould be necessary to estimate relativeeffects by apportioning the determinantsinto differences in human capital versusdifferences in demographic and econo-mic/structural factors.

5.3.2 Moreover, the mix of demogra-phic, economic/structural and humancapital factors that are linked to genderinequality likely include unobserved fa-mily factors that pertain to both indivi-dual and community attributes. A gro-wing literature indicates that failure toaccount for these confounding unmea-sured influences in African employmentanalyses can alter substantive researchconclusions (Jah, 2010b). The logisticregressions used thus far cannot ad-dress potential variations in unmeasuredfixed factors of individual and family. Ho-wever, fixed effects modeling in SAS canhandle these unmeasured factors (Alli-son 1996), and therefore, should beconsidered for future analyses to deter-mine if this method would lead to diffe-rent interpretations of the Botswanadata.

5.3.3 Another limitation is that theanalyses are based on cross-sectionaldata. The results, therefore, pertain toonly one point in time and are liable totemporal fallacy (Thornton 2001). In theabsence of longitudinal data, the AfDBshould support comparable cross-sec-tional surveys across several periods tomonitor the progress of development ef-forts.


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REFERENCES

Allison, P. 2000. “Multiple Imputation for MissingData.” 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 Printingand Publishing Services, Private Bag 0081, Gabo-rone.

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.

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 Supply and Child Nutrition in WestAfrica.” Oxford Bulletin of Economics and Statistics60(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 Enter-prise: Empowerment or Exploitation?” Research Di-vision Working Papers No. 33. The Population Council.

Jah, F. 2010a “Sources of Inequality within the Ca-meroonian Labor Market: Socio-demographic versusNational Economic Contexts.” Manuscript Presentedat the Annual PAA Conference in Dallas, Texas, 2010.

Jah, F. 2010b. “Educational Expansion, DemographicTransitions and Implications for Women’s Employ-ment in sub-Saharan Africa (1991-2005):

A Multi-method and Multi-country Analysis.” Disser-tation Submitted to Cornell University, Ithaca, NY14650.

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.

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. Population Research and Policy Review 16(3):259-287.

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 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 December2010.

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.

World Bank. 2010. Botswana: Country Brief. Wash-ington, DC: The World Bank.

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