Social capital of entrepreneurs in a developing country
Citation for published version (APA):
Solano, G., & Rooks, G. (2018). Social capital of entrepreneurs in a developing country: the effect of gender on
access to and requests for resources. Social Networks, 54, 279-290.
https://doi.org/10.1016/j.socnet.2018.03.003
DOI:
10.1016/j.socnet.2018.03.003
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Published: 01/07/2018
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ContentslistsavailableatScienceDirect
Social
Networks
j ou rn a l h o m e p a g e :w w w . e l s e v i e r . c o m / l o c a t e / s o c n e t
Social
capital
of
entrepreneurs
in
a
developing
country:
The
effect
of
gender
on
access
to
and
requests
for
resources
Giacomo
Solano
∗,
Gerrit
Rooks
SchoolofInnovationSciences,EindhovenUniversityofTechnology,P.O.Box513,5600MB,Eindhoven,TheNetherlands
a
r
t
i
c
l
e
i
n
f
o
Articlehistory:
Availableonline4April2018 Keywords:
Developingcountries Gender
Personalnetworks Accesstoresources Requestsforresources Socialcapital Smallbusinesses
a
b
s
t
r
a
c
t
Thispaperaddressesgenderdifferencesinthesocialcapitalofentrepreneursinadevelopingcountry.
Socialnetworksareoftenanimportantassetforaccessingresources;however,theymayalsobea
lia-bilityindevelopingcountries,sinceentrepreneursareoftenexpectedtosupporttheircontacts.Usinga
recentsurveyamongurbanandruralUgandanentrepreneurs,wefocusonthefinancialresourcesthat
entrepreneurscanobtainfromtheircontactsontheonehand,andrequestsforfinancialsupportmade
totheentrepreneursfromthesecontactsontheotherhand.Ourresultsshowthattherearegender
differencesassociatedwithaccessto,andrequestsfor,financialresources.
©2018ElsevierB.V.Allrightsreserved.
1. Introduction
Female entrepreneurship in developing countriesis
increas-inglyreceivingattentionfromscholarsandpolicymakers(Minniti
and Naudé, 2010; Lindvert et al., 2017).Female entrepreneurs
canmake significantcontributions toinnovation and economic
growthindevelopingcountries(BrushandCooper,2012;deBruin
etal.,2007;Welteretal.,2007).However,femaleentrepreneurs
arestillan‘untappedsource’ofgrowth(Vossenberg,2013),given
thattheyfacemanybarriersrelatedtotheirgender,andthis
pre-ventsthemfromreachingtheirfullpotential(Jamali,2009;Lindvert
etal.,2017;Yetim,2008).Notwithstandingtheincreasing
atten-tionandpolicyinitiatives,anddespitetheimportanceoffemale
entrepreneurshipfordevelopingcountries,thereisstilla
signifi-cantgendergapwhenitcomestoentrepreneurshipindeveloping
countries(Vossenberg, 2013).Businessesowned bywomen are
generallymorelikelytounder-performorfail,duetoformaland
informalobstacles(RoomiandParrott,2008;Vossenberg,2016a,b).
Onepossiblereasonforthisgendergapisthedifferencebetween
thesocialnetworksofmaleandfemaleentrepreneursin
develop-ingcountries(Jamali,2009;Lindvertetal.,2017).Researchershave
longsinceacknowledgedthatentrepreneurialactivityis
embed-dedin network relationships(Dubiniand Aldrich,1991; Hoang
andAntoncic,2003).Thereisconsensusthatnetworksofpersonal
relationsareanimportantassetthatdeterminesthesuccessofa
∗ Correspondingauthor.
E-mailaddresses:giacomo.solano@gmail.com(G.Solano),g.rooks@tue.nl
(G.Rooks).
business,henceentrepreneurialnetworksareoftensaidto
consti-tuteaformofsocialcapital(Stametal.,2014).
Ingeneral,ithasbeennotedthatthetypeandamountofsupport
thatwomencanobtainfromtheirnetworksdiffersfromwhatmen
canobtain(vanEmmerik,2006).Thereisalackofsystematic
evi-denceonthedifferencesbetweenmaleandfemaleentrepreneurs
intheirsocialcapitalindevelopingcountries(Al-Dajanietal.,2015;
Mairetal.,2012;Lindvertetal.,2017;Myroniuk,2016).As
under-linedbyLindvertetal.(2017,759),“recentworkshaveincreasingly
questionedwhethertheoreticalframeworksonsocialcapitalfrom
matureeconomiccontextsapplytowomenentrepreneursin
devel-opingcountrycontexts,wherereligiousandculturalnormscould
beaprominenthindranceinleveragingsocialcapital”.
Moreover,mostresearchregardingnetworksofentrepreneurs
predominantlyfocusesonthepositive outcomesofnetworksin
developingcountries,namelytheresourcesthatanentrepreneur
can get from his/her contacts (Boso et al., 2013; Berrou and
Combarnous,2011,2012;Bruton etal.,2007;Fafchamps,2001;
FafchampsandMinten,1999,2002;FafchampsandQuinn,2016).
Thedownsidesofsocialnetworksbothindevelopingand
devel-oped countries have received less attention in the literature,
althoughvariousnegativeaspects derivedfromsocialnetworks
havebeenmentionedonoccasion(Barr,2002;Deguilhemetal.,
2017; Nordman, 2016; Nordman and Pasquier-Doumer, 2015;
O’Brien,2012;Portes,1998).Anecdotalevidence,early
anthropo-logicalresearch(Hunter,1962;KhalafandShwayri,1966)andafew
recentstudies(Albyetal.,2014;Grimmetal.,2013)suggestthat
excessiveclaimsonentrepreneursisanimportantissuein
develop-ingcountries,andthisislinkedtoascarcityofresources(Comola,
2016).Successfulentrepreneursfacedistributiveobligations.Once
abusinessbecomessuccessfulandgeneratesprofit,furthergrowth
https://doi.org/10.1016/j.socnet.2018.03.003
maybehinderedbecauseentrepreneursareexpectedtosupport
relatives,friends,andcommunitymembers.However,systematic
researchintothedownsidesofnetworksindevelopingcountriesis
scarce,especiallywithregardtotheSub-SaharanAfricancontext
(seeRooksetal.,2016).
Inthisarticlewecomparefemaleandmaleentrepreneursin
termsofboththebenefitstheycangetfromtheircontacts(access
toresources),andtheclaimstheymightreceivefromthesecontacts
(requestsforresources).Inotherwords,wefocusonaccessto,and
requestsfor,resourcesviatheentrepreneurs’contacts(i.e.,their
socialcapital).Wefocusonfinancialresourcesinparticular.
In developing countries, where the financial and legal
sys-tems arestill underdeveloped, financial resources are a critical
issuefor entrepreneurs(Beck and Demirguc-Kunt,2006; Cook,
2001).Siba’s(2016)elaborationonWorldDevelopmentIndicators
(WDI)showedthatthisissueisevenmorecriticalinsub-Saharan
Africa, where the formal borrowing rate is lower compared to
otherdevelopingareas(e.g.LatinAmericaandEastAsia).
Previ-ousstudiesunderlinedthattherearedifferencesbetweenmenand
womenintermsofaccessingfinancialcapital(AmineandStaub,
2009; Fletschner, 2009;Makena et al.,2014; Malmströmet al.,
2017;MarlowandPatton,2005;Mwobobia,2012;Lindvertetal.,
2017;Siba,2016).Byandlarge,women(entrepreneurs)havemore
difficultygettingformalfinancialsupport(e.g.,loans)thanmen
(entrepreneurs).FiguresfromtheInternationalFinance
Corpora-tionreport(IFC,2013)estimatedthat63–69%ofbusinessesowned
byawomanareunservedorunderservedbyfinancialinstitutions
indevelopingcountries.Womenarelesslikelythanmentohavea
bankaccountandtoborrowformally(Demirgüc-Kuntetal.,2015;
Siba,2016,ZinsandWeill,2016).Thisislinkedwith(formaland
non-formal)collateralrequirements.Forexample,studiesinKenya
(Makenaetal.,2014;Mwobobia,2012)highlightthat,although
for-mallymenandwomencanaccessloansequally,inpractice,women
facemoredifficultieswhentryingtoaccesscredit,astraditional
beliefsandgenderrolescontinuetoinfluenceresourceallocation.
Asaconsequence,womendonothavetheassetsthatbanks
nor-mallyrequiretosecurecredit(Makenaetal.,2014).Inaddition,
womenoftenlackthepiecesofinformationrequiredtogetloans
(Vossenberg,2016a,b)Forexample,FletschnerandMesbah(2011)
foundthatParaguayanwiveswerelesslikelytohaveknowledgeof
financialmarketsandinstitutionsthantheirhusbands.Finally,as
notedbyVossenberg(2016,15),“womenentrepreneursoften(...)
mayfacediscriminatorypractices,suchasbankingclerks
question-ingthelegitimacyandabilityofwomenentrepreneurstogrowa
businesswhenaskingforaloan”.Giventhis,thedifferencebetween
maleandfemaleentrepreneursintheroleofsocialnetworkswhen
itcomestofinancialresourcesseemsparticularlyimportant.
Theaimofthisarticleistoempiricallyinvestigategender
dif-ferencesintheformationofnetworksofsupportandrequestsin
relationtosmallbusinessactivitiesinadevelopingcountry,namely
Uganda(EastAfrica).We conductedalarge-scalesurveyin two
regions:anurbanarea(thecountrycapital,Kampala)andNakaseke
(amoreruralareainCentralUganda).Thisallowedustocompare
genderdifferencesinnetworksbetweenamoretraditional,
collec-tivisticcontext(theruralarea)andamoremodern,individualistic
context(theurbanarea).Indeed,asnoticedbyVossenberg(2013),
thecontextinwhichtheentrepreneurisembeddedisparticularly
importantwhenitcomestogenderdynamics.
2. Theory
2.1. Socialcapitalandaccessto/requestsforresources
Socialcapitalisabroadconceptwithmanydifferent
interpreta-tions(forreviews,see:Lin,2001;AdlerandKwon,2002;Akc¸omak
andterWeel,2009).InthisarticlewedrawuponPortes(1998),
whodefinedsocialcapitalasthe“abilityofactorstosecure
ben-efitsbyvirtueofmembershipinsocialnetworks”(Portes,1998,
6).Entrepreneurs–indevelopedanddevelopingcountries–can
obtainvariousresourcesfromtheirsocial connections,suchas:
information, finances and emotional support(see for example;
GreveandSalaff,2003;HoangandAntoncic,2003).However,as
notedbyPortes(1998),thecreationof,andparticipationin,social
networksisnotcost-free.1Whileentrepreneursmaygainaccess
toresourcesfromcontacts,converselythose relationsmayalso
involvecosts,andtheentrepreneurs’contactsmayinturntryto
obtainresourcesfromthem.
InthispaperweadoptPortes’(1998,8)double-edgedview2that
socialcapitalentailsboth“network-mediatedbenefits”–namely,
theresourcesthatapersoncanobtainfromhis/hercontacts–and
“claimsongroupmembers”–namely,theresourcesthataperson
maybe‘forced’togivetohis/hercontacts–.We therefore
dis-tinguishbetweenaccesstoresourcesthroughsocialcontactsand
requestsforresourcesonthepartofthesecontacts.
Asalreadyillustratedintheintroduction,wefocusonfinancial
supportfortwomainreasons.First,financialissuesand,in
particu-laraccesstofinancialresources,iscriticalwhenitcomestorunning
abusiness,especiallyindevelopingcountrieswhereresourcesare
limited(BeckandDemirguc-Kunt,2006).Second,previous
litera-turehasunderlinedthatthereisagenderdimensioninaccessto
financialresources(MarlowandPatton2005;Lindvertetal.,2017;
Vossenberg,2016a,b).
2.2. Genderdifferencesinsocialcapital
Theexistingliteraturehasrepeatedlysuggestedthatmaleand
femaleentrepreneurialnetworksdifferintermsoftheir
compo-sitionand structure(Agneessenset al.,2006; Aidisetal.,2007;
Bastani,2007;Moore,1990;Myroniuk,2016;LieblerandSandefur,
2002;vanEmmerik,2006).Inherpioneeringarticleon
determi-nantsofmen’sandwomen’spersonalnetworksintheUS,Moore
(1990)founddissimilaritiesbetweenmenandwomen:whereas
mendiscusspersonalmatterswithawiderrangeofcontacts
(co-workers,friends,relatives,etc.),womenaremorelikelytodiscuss
themwithrelativesandneighbourhoodfriends,andhavecloser,
and more homogeneous contacts in their personal-advice
net-works.
Lessisknownaboutthedifferencesbetweenmaleandfemale
entrepreneurswhenitcomestotheresourcestheycanobtainfrom
theirnetworks(Ahl,2006;Foss,2010).Inoneofthefewarticles
addressingthistopic,vanEmmerik(2006)foundthatmenwere
moreabletoaccessjob-relatedresourcesthroughtheircontacts
thanwerewomen.
2.2.1. Gender(egolevel)
Genderrolesareinfluencedbyculturalcontext,whichshapes
expectationsandrelationsbetweenmenandwomen(Acker,1992;
Baughn etal.,2006).In more traditionalsocieties, genderroles
causemen–entrepreneursandnon-entrepreneurs–totakeon
theresponsibilityofprovidingfinancialsupportfortheir‘group’
(Farré,2013;Jamali,2009;RismanandDavis,2013).
1Portes(1998,5)providedaclearexampletoexplainhisview:“Saying,for
exam-ple,thatstudentAhassocialcapitalbecauseheobtainedaccesstoalargetuition loanfromhiskinandthatstudentBdoesnotbecauseshefailedtodosoneglects thepossibilitythatB’skinnetworkisequallyormoremotivatedtocometoher aidbutsimplylacksthemeanstodoso.Definingsocialcapitalasequivalenttothe resourcesthusobtainedistantamounttosayingthatthesuccessfulsucceed”.
2Wetooktheexpression‘double-edgeviewofsocialcapital’fromLindvertetal. (2017).
Byandlarge,inUgandamenareexpectedtohavethe
finan-cialpowertotakecareoftheir(extended)family,clan,etc.(Otiso,
2006).Duetotheirroleasprovidersofresources,weexpectmale
entrepreneurstohavemorepeoplewhoaredependentonthemfor
financialsupport,andfewerpeoplewhocanhelpthemfinancially.
Ontheonehand,maleentrepreneursmayfacegreater
redistribu-tivepressure;asmen,theseentrepreneursmightfeelobligatedto
helpcertainpeople.Ontheotherhand,theymightbeless
atten-tivetoincludeintheirnetworkspeoplewhocanprovideaccess
tofinancialcapital.Forthesereasons,weexpectthat,whenthe
entrepreneurismale,heislesslikelytohaveaccesstofinancial
resourcesfromhiscontacts,andthatthecontactsaremorelikely
torequirefinancialsupportfromhim.
Hypothesis1a. whentheentrepreneurismale,contactsareless
likelytoprovideaccesstofinancialresourcestotheentrepreneur;
Hypothesis1b. whentheentrepreneurismale,contactsaremore
likelytorequestfinancialresourcesfromtheentrepreneur.
2.2.2. Gender(alter)
Most studies on women entrepreneurship have focused on
genderasanattributeoftheentrepreneur.Lessisknownabout
whether male or female contacts provide similar resources to
entrepreneurs(Klyver,2011).The literature suggeststhat male
contactsaremorelikelytoprovideinstrumentalsupportsuchas
financialresources,whilefemalecontactsaremorelikelyto
pro-videemotionalsupport(Klyver,2011;LieblerandSandefur,2002;
Plickertetal.,2007;ReevyandMaslach,2001).Thispatternhas
beenobservedinadevelopingcountryaswell.Intheirresearch
intotheSidama,anagro-pastoralistpopulationinsouthwestern
Ethiopia,Caudelletal.(2015)foundthatmalecontactswerethree
timesmorelikelytobementionedaslendersthanfemalecontacts.
Thisseemsrelatedtothefactthatwomengenerallyhaveless
economicpowerandcontroloverfinancesthanmen.Firstly,they
have less economic power due to limited property ownership,
smallersavings,and greaterdifficultyinaccessingformalcredit
(AmineandStaub,2009).Secondly,whentheydohavefinancial
resources,womenfacegreaterdifficultiesinmaintainingcontrol
over these resources,especially due to issues of controlin the
households(Agarwal,1997;Ateridoetal.,2013;Minniti,2010;ILO,
2017;Jamali,2009;Vossenberg,2016a,b).Thehusband–oranother
malerelativeinthehousehold(e.g.,father,brother)ifthewoman
isnotmarried–normallycontrolsthehouseholdassets.Arecent
ILOstudyonUganda(ILO,2017)confirmsthisbyhighlightingthe
factthatfemaleentrepreneursaremorelikelytokeepcontrolof
theirsavingswhentheyarewillingtohidetheirmoneyfromtheir
husbands.
Therefore,asmenhavemorefinancialpower,theyaremore
likelytokeepcontroloverhouseholdassets,andsincewomenhave
difficultyaccessingotherlending options,we expectmale
con-tactstobebetterabletoprovidefinancialsupportthanarefemale
contacts.Thus,wehypothesisedthat:
Hypothesis2a. malecontactsaremorelikelytoprovideaccessto
financialresourcestotheentrepreneurs;
Hypothesis2b. malecontactsarelesslikelytorequestfinancial
resourcesfromtheentrepreneurs.
2.2.3. Gender(egoandalter)
Relationshipsaregender-orientedastheychangebasedonthe
genderofthepersonsinvolved (Klyver,2011).Providingaccess
to,orrequestingfinancialsupportmaydependonwhetherthe
entrepreneurandthecontactareofthesameoroppositegender.
Homophilyreferstothetendencyofpeoplewithsimilarattributes,
suchasgender,tointeract(McPhersonetal.,2001).Thereisaclear
lackofresearchonhowgenderhomophilyinfluencesaccessto,and
requestsfor,resources,especiallyindevelopingcountries(Caudell
etal.,2015).However,previousstudiessuggestthatlending
net-worksarecharacterisedbyalackofhomophily(Caudelletal.,2015;
Fafchamps,1992;Platteau,1997).Thisseemstoapplyevenmore
readilytogenderhomophily.Asnotedabove,womenareless
eco-nomicallypowerfulthanmen,andtheyareusuallydependenton
men(theirhusband,theirfather,etc.).Therefore,itseemslesslikely
thatpeopleofthesamesexwouldprovideaccessto/requestsfor
finances:
Hypothesis3a. whentheentrepreneurand thecontactareof
thesamegender(male-maleorfemale-female),thecontactisless
likelytoprovideaccesstofinancialresources
Hypothesis3b. whentheentrepreneurandthecontactareof
thesamegender(male-maleorfemale-female),thecontactisless
likelytorequestfinancialresources
Tofurtherdisentangletheinteractionbetweenegoand alter
gender,wenowfocusonsituationswheretheentrepreneurand
thecontactareofdifferentgenders(male-femaleorfemale-male).
Asillustratedbefore,womengenerallylackfinancialpower,access
to,andcontrolover,finances(AmineandStaub,2009;ILO,2017;
Vossenberg,2016a,b).Thus,wehypothesisedthat:
Hypothesis4a. whentheentrepreneurisfemaleandthecontact
ismale,thelikelihoodthattheentrepreneurmaybeprovidedwith
accesstofinancialresourcesishighercomparedtotheopposite
situation(namely,whentheentrepreneurismaleandthecontact
isfemale);
Hypothesis4b. whentheentrepreneurisfemaleandthe
con-tactismale,thelikelihoodthattheentrepreneurmaybeasked
forresourcesislowercomparedtotheoppositesituation(namely,
whentheentrepreneurismaleandthecontactisfemale).
2.2.4. Genderandurbanisation
Previousliteratureunderlinesthatthewiderculturalcontext
(urbanvs.ruralareas)–intermsofindividualistic/lesstraditional
culture(urbanareas)versusacollectivistic/moretraditional(rural
areas)–influencesrelationshipsandresourcesexchange(Rooks
etal.,2012,2016).Inacollectivistictraditionalculture,whichisstill
dominantinruralareas(Oysermanetal.,2002;Otiso,2006),
gen-derrelationshipsareparticularlypowerfulininfluencingeconomic
andsocial life(Lauras-Lecoh,1990;McKenzie,2011;Onjalaand
K’Akumu,2016;Stoeltje,2015;Stone,2013;Vossenberg,2016a,b).
Weexpectthatinurbanareas,wherethecultureislesstraditional
andcollectivistic(MaandSchoeneman,1997),theeffectofegoand
altergenderisweaker.Therefore,weformulatethefollowingfour
hypotheses:
Hypothesis5a. theeffectofegogenderonaccesstoresourcesis
weakerintheurbanareacomparedtotheruralarea;
Hypothesis5b. theeffectofaltergenderonaccesstoresourcesis
weakerintheurbanareacomparedtotheruralarea
Hypothesis5c. theeffectofegogenderonrequestsforresources
isweakerintheurbanareacomparedtotheruralarea;
Hypothesis5d. theeffectofaltergenderonrequestsforresources
isweakerintheurbanareacomparedtotheruralarea
3. Methods
To test our hypotheses, we conducted a survey amongst
Ugandan entrepreneurs. Uganda is a very interesting place to
studyentrepreneurship,sinceentrepreneurialactivityinUganda
is relatively high (Balunywa et al., 2012). Over one in three
Table1
Demographiccharacteristics:comparisonbetweentheurbanandtheruralarea,andbetweenmaleandfemaleentrepreneurs.
Totalsample Kampala(urban district) Nakaseke(rural district) T-test Male entrepreneurs Female entrepreneurs T-test Numberofobservations 608 294 314 – 313 281 – Individualcharacteristics Age(mean) 34.1 33.4 34.9 1.66 34.1 34.2 0.09 %ofmales 47 48 47 −0.29 – – –
Yearsofeducation(mean) 9 9.8 8.2 −4.38*** 9.6 8.5 −2.99***
Businesscharacteristics
Businessage(mean) 7.3 6.3 8.2 2.87** 8.2 6.4 −2.75**
Numberofemployees(mean) 1.3 1.5 1.2 −0.52 1.7 0.8 −5.84***
%ofbusinessesinthetradesector 50.2 50 50.3 0.08 41.4 57.4 3.92***
%ofbusinessesintheservicessector 30.8 32.7 29.2 −0.93 29.3 32.7 0.89
%ofbusinessesintheproductionsector 11.7 14.6 9 −2.17* 20.4 4.5 −6.11***
%ofbusinessesinagriculturesector 7.3 2.7 11.5 4.24*** 8.9 5.5 −1.65
* p<0.05. ** p<0.01. ***p<0.001.
entrepreneurshiprateishigheramongstwomen,intheyounger
partofthepopulation(18–34yearsold),aswellasinthe
better-educatedmembersofsociety(Balunywaetal.,2012).
3.1. Samplinganddatacollection
Asasamplingframe,we usedtheCensusof Businessesand
Establishments(COBE)2011provided bytheUganda Bureauof
Statistics (UBOS) – the most updated list available. The COBE
was conducted in 2010–2011 and covered all businesses with
fixedestablishments,irrespectiveof theirdegreeofformality –
UBOSworkedautonomouslyfromtheUgandaRevenueAuthority–
(UgandaBureauofStatistics,2011).Duringthefieldwork,theUBOS
teamphysicallymovedupanddownthestreetsandregisteredall
businesses.
We selected entrepreneurs from two sampling sites: an
urbandistrict and a rural district.Then, we randomlyselected
entrepreneursfromtwooftheCOBElists,onefortheurbandistrict
andoneforaruraldistrict.TheurbandistrictwasKampala,the
cap-italofthecountry,withapopulationofapproximately1,500,000.
TheruralsamplingsitewastheNakasekedistrict,whichislocated
intheCentralRegion(oneofthefouradministrativeregionsof
Uganda),atapproximately150kmfromthecapital.TheNakaseke
districthasapopulationofapproximately94,800.
Theresearchonwhichthearticleisbasedwaspartofan
overar-chingresearchprojectthatfocusedonurbanandruraldifferences.
Thechoiceofincludingbothurbanandruralentrepreneurswas
basedonpreviousliterature.Rooksetal.(2016)showedthatthere
weredifferencesbetweenurbanandruralareasinUganda
con-cerningsocialcapital.Theauthorsfoundthattheeffectofnetwork
densityonaccesstoresourceswasweakerintheruralareathanin
theurbanarea.
We decidedto focus ontheCentral Region since, according
toUBOSstatistics(UgandaBureauofStatistics,2011),thisisthe
regionwherethemajorityofbusinessesarelocated;indeed,30%
ofall businessesin Uganda are locatedinthat area(59%if we
alsoconsiderKampala).Kampalawasselectedgiventhat,asitis
thecountry’scapital,itisthemostimportantUgandancity,and
becauseitishometo29%ofallbusinessesinthecountry(Uganda
Bureauof Statistics, 2011). Tomaximise thevariation between
urbanandruralareas,weselectedNakasekebecauseitisoneof
themostruraldistrictsoftheCentralRegion.3
3 Forexample,theNakasekedistricthasoneofthelowerpopulationdensitiesof
theregion(source:elaborationfromUBOSdata,www.ubos.org).
ThedatacollectiontookplaceinJanuary2016.Weinterviewed
608respondents,294entrepreneursintheurbanarea(Kampala)
and314intheruralarea(Nakaseke).Inalmostallcases,theselected
respondentswerewillingtoparticipateinthestudy.InKampala
therewere9refusals,whileinNakasekeonlyonepersondeclined
toparticipate,makingforaresponserateof98.3%.Whena
per-sonrefusedtobeinterviewed,orwhenthebusinesswasnolonger
present,4wereplaceditwiththenearestavailableequivalent.5
Face-to-faceinterviewswerecarriedoutbyeightexperienced
interviewers. Theywere given a three-daytraining program to
helpthemunderstandthequestionnaireandfamiliarizewiththe
datacollectionsoftware(QuestionPro).Afterthetrainingsessions,
apilotcollectionwasundertakeninwhich16respondentswere
interviewed.
Respondentswereinterviewedontheirbusinesspremises.The
interviewswere sometimes interrupted,for instancewhen the
entrepreneurhad toattendtobusiness.Theylastedan average
of25–35min.Aftereach interview therespondentwasgiven a
notebookasatokenofappreciation.
3.2. Sample
Table1presentsthedemographiccharacteristicsofour
respon-dents.ConsistentwithtrendsintheUgandanpopulationpresented
intheGEM(GlobalEntrepreneurshipMonitor)report(Balunywa
etal., 2012), thesampleconsistsof a slightmajority offemale
entrepreneurs(53%);therespondentsare34yearsoldonaverage,
with9yearsofeducation.Respondentsintheurbansampleare
bet-tereducatedthantheircounterpartsintheruralsample(number
ofyearsofeducation:Murban=9.8;Mrural=8.2;t=−4.38,p<0.01).
Similarly, male entrepreneurs are better educated than their
femalecounterparts(numberofyears ofeducation:Mmale=9.6;
Mfemale=8.5;t=−2.99,p<0.01). We alsofoundthat 65% ofthe
urbansampleconsistedofentrepreneurswhowereborninrural
areasandwho,atsomepoint,decidedtomovetoKampala.
Theentrepreneursinoursampleownsmall,butrather
well-established, businesses. On average, they started about seven
years ago(four, ifwe considerthemedian) andtheyhave one
employee(Table1).TheseresultsareinlinewiththeUBOSfigures
4Thelistdisplayedthelocationofthebusiness,andnotthedescriptionofthe
businessinitself.Onlyafewtimes–inabout1%ofthecases–didwegotothe indicatedlocationandfindoutthattherewasnobusinesspresent.
5Wereplacedthebusinesswiththeclosestone,firstontheoppositesideofthe
street,and,ifnotpossible,onthesameside.Inthisway,webelievethatwereplaced theoldbusinesswithasimilarone,sincefrequentlybusinessesinthesameareahave similarcharacteristics(e.g.,size,macro-sector).
Table2
Businesssectors.ComparisonbetweensurveysampleandUBOSstatistics(percentages).
Totalsample Kampala(urbandistrict) Nakasekea(ruraldistrict)
SurveySample(2016) UBOS(2011) SurveySample(2016) UBOS(2011) SurveySample(2016) UBOS(2011)
Trade 50.2 61.5 50 60.6 50.3 59.7 Service 30.8 29.4 32.7 30.9 29.2 31.5 Production 11.7 7.3 14.6 8.2 9 6.4 Agriculture 7.3 1.8 2.7 0.3 11.5 2.3 Total 100 100 100 100 100 100 N 608 454,766 294 133,663 314 137,541 *p<0.05;**p<0.01;***p<0.001.
aThisdatareferstoCentralRegion(excludingKampala),giventhatUBOSdataonlyfromNakasekedistrictisnotavailable.
Table3
Networkcharacteristicsofthesampledividedintotwosamplingsites(mean).
Totalsample Kampala(urban district) Nakaseke(rural district) T-test Male entrepreneurs Female entrepreneurs T-test Wholenetwork(0–15) 4.9 4.8 5 0.75 5.3 4.6 −3.37***
Personal-advicenetworksize(0–5) 1.1 1.1 1.1 −0.30 1.3 0.9 −4.48***
Business-advicenetworksize(0–5) 2.5 2.5 2.5 0.78 2.7 2.4 −2.62**
Requestnetworksize(0–5) 1.3 1.2 1.4 1.15 1.3 1.3 0.03
Density(0–1) 0.7 0.6 0.7 3.76*** 0.6 0.6 1.52
%ofmales 56 54 57 1.05 69 42 −12.99***
%ofkin 50 47 53 1.49 43 59 6.01***
Homophily 55 55 55 0.15 64 49 −8.56***
Numberofpeoplementioned 2983a 1414 1569 – 1495 1426 –
aThenumberofcontactswhencomparingmaleandfemaleentrepreneursis2921. **p<0.01.
***p<0.001
(UgandaBureauofStatistics,2011),whichshowthatUgandan
busi-nessesarerathersmall(twoemployeesonaverage),andbetween
twoand fiveyears old.Thesizeof thebusinessdoesnotdiffer
betweenKampalaandNakaseke;however,businessesinKampala
are,onaverage,morerecentthanthoseinNakaseke(numberof
yearssincestart-up:Murban=6.3;Mrural=8.2;t=2.87,p<0.01).As
forgenderdifferences,businessesownedbymaleentrepreneurs
areonaverageolderthanthoseownedbyfemaleentrepreneurs
(numberofyearssincestart-up:Mmale=8.2;Mfemale=6.4;t=2.75,
p<0.01).
Mostofthebusinessesinoursampleareeitherintrade-related
industriesorservices.Together,theyrepresentmorethan80%of
thebusinessesinoursample.ThisisinlinewithUBOSdata(Uganda
BureauofStatistics,2011)showingthatthemajorityofbusinesses
inUgandaareinthetradesector.IfwecomparetheKampalaand
Nakasekedistricts,businessesinKampalaarelesslikelytobeinthe
productionandagriculturalsectorscomparedtothoseinNakaseke
(Table 1).Our sampleis consistentwithUBOSfigures
concern-ingbusinesssectors,includingwhensortingthesamplebetween
KampalaandNakasekedistricts(Table2presentsthecomparison
betweensurveysampleandUBOSfigures).Finally,comparedto
femaleentrepreneurs,maleentrepreneursarelesslikelyto
oper-ateinthetradesector(Mmale=41.4;Mfemale=57.4;t=3.9,p<0.001)
andmorelikelytooperateintheproductionsector(Mmale=20.4;
Mfemale=4.5;t=−0.6.1,p<0.001).
3.3. Questionnaire
Weusedthreenamegeneratorstomeasuretheentrepreneurs’
socialnetworks:twotoassesspersonal-adviceandbusiness-advice
networkties(together,theadvicenetwork),andonetoassessthe
number ofpeoplerequesting resourcesfromthe entrepreneurs
(therequestnetwork).Tomeasurethepersonal-advicenetwork,
weaskedthefollowingquestion:“Fromtimetotime,most
peo-plediscussimportantpersonalmatterswithotherpeople.Looking
backoverthelastsixmonths,whoarethepeoplewithwhomyou
havediscussedanimportantpersonalmatter?”Forthe
business-advicenetwork namegeneratorwe asked,“From time totime,
entrepreneursseekadviceonimportantbusinessmatters.
Look-ingbackoverthelastsixmonths,whoarethepeoplewithwhom
youhavediscussedanimportantbusinessmatter?”Tomeasurethe
requestnetworkweasked,“Lookingbackoverthelastsixmonths,
couldyoumentionthenamesofpeoplewhoaskedyouforfinancial
support,freegoods,servicesorajob?”
Foreveryoneofthethreenamegenerators,respondentswere
asked to list a maximum of five names (Burt, 1984).By
com-biningthethreenamegenerators,wecollected2983names,i.e.
approximately5peopleperrespondent.6Thepersonsmentioned
constitutedtheentrepreneur’s socialnetworks.Thesearerather
close-knit,especiallyinruralareas.
Foreachpersonidentified(contacts),weaskedabouttheir
gen-derandtheirrelationshipwiththeentrepreneur(relative,7friend
orjobcontact).8Mostofthecontactsweremaleand,largely,most
wererelatives.Thecomposition ofthenetworkswassimilarin
urbanandruralareas(Table3).Wealsomappedtherelationship
betweenaltersbyaskingtherespondent(ego),“Dothesetwo
per-sonsknoweachotherquitewell?9”(Responsecategories:yes/no).
3.4. Dependentvariables:accesstofinancialresourcesand
requestsforfinancialresources
Foreverycontact,entrepreneurswereaskedtoindicatewhether
theycouldobtainfinancialresourcesfromthiscontactoriftheyhad
receivedrequestsforfinancialsupportfromthiscontact.
6Somecontacts(26%)werementionedmorethanonce.Wecountedthemasone. 7Weincludedbothcloseandextendedfamilyasrelatives.Wedefinedarelative
asapersonbelongingtothesamefamilyastherespondent.Wedefinedfamilyasa groupofpeoplerelatedbybloodormarriage.
8Wedecidedtofocusonlyongenderandrelationshipwithego,sincewewanted
toexplorein-depththeexchangeofresources(seealsobelow),forwhichweasked7 questionsforeachalter.Toavoidobtainingaquestionnairethatwouldbetoolengthy –sinceourrespondentswereclearlylosingattentionandwillingnesstoanswerafter 20min–,weonlyincludedthesepiecesofinformationasnameinterpreters.
9Duringtheinterviews,wemadeitclearthat‘knowquitewell’meantthatthey
Table4
Numberandpercentageofcontactswhoaskedforsupportfromtheentrepreneurs.
Totalsample (N=2983) Kampala(urban district,N=1414) Nakaseke(rural district,N=1569) T-test Male entrepreneurs (N=1492) Female entrepreneurs (N=1428) T-test Financialsupport 1707(57%) 798(56%) 909(58%) 0.8 887(59.5%) 799(56%) −1.94*
Freegoodsorservices 972(32%) 441(31%) 531(34%) 1.55 475(31.8%) 475(33.3%) 0.82
Job 143(5%) 94(7%) 49(3%) −4.51*** 98(6.6%) 45(3.2%) −4.29***
Atleastonekindofsupport 2125(71.3%) 1040(73.6%) 1085(69.2%) −2.68** 1120(75.1%) 973(68.1%) −4.20*** * p<0.05.
** p<0.01. ***p<0.001.
Table5
Numberandpercentageofcontactsthatareabletosupporttheentrepreneur.
Totalsample(N=2983) Kampala(urban district,N=1414) Nakaseke(rural district,N=1569) T-test Male entrepreneurs (N=1495) Female entrepreneurs (N=1428) T-test Financialsupport 1208(40.5%) 574(40.5%) 634(40.4%) −0.06 592(39.6%) 591(41.4%) 0.98 Information 1669(55.9%) 806(56.9%) 863(55%) −1.03 821(54.9%) 813(56.9%) 1.10
Introductiontootherpeople 665(22.3%) 290(20.5%) 375(23.9%) 2.25* 339(22.7%) 322(22.6%) −0.08
Freelabour 647(21.7%) 383(27%) 264(16.8%) −6.81*** 322(21.5%) 303(21.2%) −0.21
Atleastonekindofsupport 2633(88.2%) 1309(92.3%) 1324(84.4%) −6.80*** 1342(89.8%) 1236(86.6%) −2.69** * p<0.05.
** p<0.01.
First,theentrepreneurwasaskedwhattypeofresourcescould
beobtainedfromaspecificcontactviathequestion,‘What
sup-portfor the businesscan you getfrom this person?’Then, the
entrepreneurwasaskedaboutrequestsforresourcesonthepartof
thesespecificcontactsthroughthequestion,‘Whatkindofsupport
dideachcontactaskofyou?’
Following the definition of social capital, we did not ask
the entrepreneurs about actual resources received, but rather
aboutpotentialaccesstoresources(namely,resourcesthatcould
begained). However, note that, when it came to investigating
claimsfrom contacts,we asked whetherthecontact had
actu-allyrequestedresourcesinthepast.Earlierfieldworkconducted
bytheauthorsshowedthatentrepreneurswerepronetogiving
socially desirable answers when asked what kind of resources
thecontactmight obtainfromthem.Thus, wedecided torefer
to actual requests for resources. On the contrary, Rooks et al.
(2016)foundthatsocialdesirabilityissueswerelessproblematic
whenaskingwhattypeofresourcescouldbeobtainedfroma
con-tact.
Wecreatedtwovariables:(1)accesstoresources:this
dichoto-mousvariableindicatesthatacontactisabletoprovidefinances;
(2)requestsforresources:thisdichotomousvariableindicatesthat
theentrepreneurhasbeenaskedbythecontactforfinancial
sup-port.
3.5. Independentvariables
3.5.1. Gender(alterlevel)
Toaccountforgenderdifferences,weincludedthegenderofthe
contacts(‘0’forfemaleand‘1’formale).
3.5.2. Gender(egolevel)
To account for the different network composition between
maleand femaleentrepreneurs, we includedthegender ofthe
entrepreneur(‘0’forfemaleand‘1’formale).
3.5.3. Homophily(ego-alterlevel)
Toaccountforsituationswheretheentrepreneurandthe
con-tactarethesamegenderweincludedthevariablehomophily(‘0’
fordifferentgenderand‘1’forsamegender).
3.6. Controlvariables
3.6.1. Kinship(alterlevel)
Eachcontactwasclassifiedbasedonwhetherornotheorshe
wasa relative(includingpartner/spouse).We createdadummy
variablelabelled‘relative,’usingallothercategories(friendshipand
business-onlyrelationship)asareference.
3.6.2. Urbanisation(egolevel)
To account for differences between urban and rural areas
we inserted an urbanisation variable, indicating whether the
entrepreneurwaslivingintheurbanorruralarea(reference
cate-gory).
3.6.3. Networksize(egolevel)
Toaccountfordifferencesinnetworksize(WellmanandFrank,
2001),weaddedtwoadditionalvariablestocontrolfortheimpact
ofnetworksize:[1]advicenetworksize,whichisthetotalnumberof
contactsmentionedinthepersonalandbusinessadvicenetworks,
and[2]requestnetworksize,namelythenumberofuniquecontacts
mentionedbytherespondentinthe‘requests’namegenerator.
3.6.4. Density(egolevel)
Previousresearchunderlinesthefactthatnetworkdensitymay
influencetheexchangeofresources(Burt,2001;ShaneandCable,
2002).Densityshowshowcloselyanetworkofrelationshipsisknit
and,morespecifically,howwellanentrepreneur’scontactsmight
knoweachother.Wecalculatedthevariabledensityasthenumber
ofactualties,outofthenumberofpossibletiesinthenetwork
(namely,ifeverycontactmentionedhadarelationshipwithevery
othercontactmentioned).
3.6.5. Yearsofeducation(egolevel)
Weincludedthenumberofyearsofeducationasavariableto
controlfortheconfoundingeffectsofhumancapital,sincehigher
levelsofhumancapitalaregenerallyassociatedwithgreatersocial
resources(vanTilburg,1998).
3.6.6. Age(egolevel)
Agemayaffectnetworkcompositionandsocialsupport(Moore,
Table6
CorrelationsbetweenVariables.
1 2 3 4 5 6 7 8 9 10 11
1 Accesstoresources 1
2 Requestforresources −0.53*** 1
3 Urbanisation 0.00 0 1
4 Kinship 0.01 0.12*** −0.06* 1
5 Gender(alter) 0.08*** 0.07*** −0.03* −0.18*** 1
6 Homophily −0.06** 0.01 −0.01 −0.24*** 0.00 1
7 Request-networkcontact −0.39*** 0.43*** −0.02 0.18*** −0.05** 0.02 1
8 Advicenetworksize 0.12*** −0.04* −0.01 −0.18*** 0.05** 0.09*** −0.13*** 1
9 Requestnetworksize −0.17*** 0.15*** −0.05 0.06** 0.00 −0.02 0.38*** 0.19*** 1
10 Density 0.01 0.01 −0.18*** 0.16*** −0.03 0.01 0.03 0.27*** 0.21*** 1 11 Gender(ego) −0.02 0.03 0.01 −0.05** 0.27*** 0.15*** −0.02 0.18*** 0.00 −0.06 1 12 Age −0.02 −0.01 −0.07 0.08*** 0.01 0.00 0.04* 0.00 0.06 0.01 0.00 13 Yearsofeducation 0.07** 0.01 0.18*** −0.09*** 0.01 0.00 −0.06** 0.12* −0.06 −0.03 0.12* 14 Maritalstatus 0.03 0.03 −0.16*** 0.01 0.02 −0.02 0.01 −0.07 0.08* 0.01 −0.20*** 15 Numberofchildren 0.02 −0.01 −0.20*** 0.07*** −0.01 −0.01 0.03 0.04 −0.09 0.05 0.03 16 Businesssize −0.04* 0.00 0.02 −0.07*** 0.08*** 0.05** −0.03 0.21*** 0.05 0.09* 0.25*** 17 Businessage −0.03 −0.01 −0.12* 0.02 0.05* 0.01 0.01 0.08 0.02 0.08* 0.11** 18 Sector:Production 0.03 0.02 0.08* 0.04* 0.07*** 0.05* 0.05* 0.05 0.12** 0.02 0.24*** 19 Sector:Service −0.02 −0.01 0.04 0.07 0.06 −0.01 −0.02 0.00 −0.03 0.02 −0.04 20 Sector:Trade 0 0.0 0.00 −0.05* −0.06** −0.03 −0.01 −0.06 −0.02 −0.05 −0.16*** 21 Sector:Agriculture 0.01 −0.04** −0.17*** 0.02 0.01 −0.01 −0.03 0.04 −0.05 0.05 0.07 12 13 14 15 16 17 18 19 20 21 12 Age 1 13 Yearsofeducation −0.35*** 1 14 Maritalstatus 0.46*** −022*** 1 15 Numberofchildren 0.60*** −0.32*** 0.33*** 1 16 Businesssize −0.01 0.08 −0.07 0.06 1 17 Businessage −0.64*** −0.34*** 0.25*** 0.51 0.12** 1 18 Sector:Production 0.02 −0.03 −0.02 0.01 0.17*** 0.14*** 1 19 Sector:Service −0.03 0.04 −0.04 0.03 0.05 −0.10** −0.24** 1 20 Sector:Trade −0.05 0.07 0.04 −0.13** −0.27*** 0.14 0.37*** −0.37*** 1 21 Sector:Agriculture 0.12*** −0.15*** 0.02 0.20*** 0.23*** 0.27*** −0.11** −0.19** −0.28*** 1
Note:whenthecorrelationisbetweentwovariablesattheegolevel(e.g.densityandage),thecorrelationswerecalculatedatanegolevel.Otherwise,correlationswere calculatedatanalterlevel.
*p<0.05. **p<0.01. ***p<0.001.
theageoftherespondent–specificallytherespondent’sexactage
atthetimeoftheinterview–asacontrolvariable.
3.6.7. Maritalstatus(egolevel)
Giventheimportanceofintra-householdrelations(ILO,2017;
Vossenberg, 2013), we included the marital status of the
entrepreneur(‘0’forunmarriedand‘1’formarried).
3.6.8. Numberofchildren(egolevel)
Toaccountforfamilycomposition,wealsoincludednumberof
children.
3.6.9. Businesssize(egolevel)
Asaproxyforbusinesssuccess(Freseetal.,2007),weincluded
businesssize(i.e.numberofemployees).Weinsertedthe
logarith-micversioninthemodelbecausetheoriginalvariableresultedin
askeweddistribution.
3.6.10. Businessage(egolevel)
Tocontrolforthedatethatthebusinesswasstarted,weincluded
theageofthebusiness(innumberofyears).Weinsertedthe
loga-rithmicversioninthemodelbecausetheoriginalvariableresulted
inaskeweddistribution.
3.6.11. Sector(egolevel)
Tocheckforanyeffectstemmingfromsectordifferences,we
createdthreevariables:[1]production(whetherornotabusinessis
inthemanufacturingorconstructionsector);[2]services(whether
ornotabusinessisintheservicessector)[3]trade(whethera
busi-nessisretailorwholesale).Thereferencecategoryisagriculture
(whetherornotabusinessisintheagriculturalsector).
3.6.12. Request-networkcontact(alterlevel)
Tocontrolfortheeffectofwhichnamegeneratorthecontact
hadbeenmentionedin,weincludedavariableindicatingwhether
thecontactwasmentionedintheadvicenetwork(‘0’)orinthe
requestnetwork(‘1’).
4. Results
Inthissection,wefirstillustratethedescriptiveresultsofour
research,focusingonthedifferentformsofsupportaskedforand
accessed.Then,inordertotesttheabove-formulatedhypotheses,
wepresenttheresultsoftwoseparatemultilevellogistic
regres-sions.
4.1. Descriptiveanalyses
Table 3 displays the results of the descriptive analyses.
Entrepreneurs have a limited number of people in their
net-works (fewer than five people, on average). On average,
maleentrepreneursmentioned significantlymorecontactsthan
femaleentrepreneurs,(Mmale=5.3Mfemale=4.6;t=−3.4,p<0.001).
The same holds for the personal-advice network (Mmale=1.3
Mfemale=0.9;t=−4.5,p<0.001)andthebusiness-advicenetwork (Mmale=2.7Mfemale=2.4;t=−2.6,p<0.01).Bycontrast,maleand
Table7
MultilevelLogisticRegressionAnalysis.
Accesstoresources Requestsforresources
ModelI ModelII(ModelI+
interactionego gender*altergender)
ModelI ModelII(ModelI+
interactionego gender*altergender)
 SE  SE  SE
Context(entrepreneur)
Urbanisation 0.00 0.21 0.00 0.21 0.03 0.19 0.03 0.19
Relationallevel(alter)
Kinship 0.72*** 0.13 0.72*** 0.13 0.59*** 0.13 0.59*** 0.13
Gender(alter) 0.52*** 0.13 – – −0.41*** 0.13 – –
Request-networkcontact −3.23*** 0.20 −3.23*** 0.20 2.94*** 0.16 2.94*** 0.16
Homophily −0.44*** 0.13 – – 0.16 0.13 – –
Networkcharacteristics(entrepreneur)
Advicenetworksize 0.14** 0.05 0.14* 0.05 −0.04 0.05 −0.04 0.05
Requestnetworksize −0.06 0.07 −0.06 0.07 0.08 0.70 0.08 0.70
Density 0.20 0.29 0.20 0.29 0.14 0.28 0.14 0.28
Individuallevel(entrepreneur)
Gender(ego) −0.45* 0.22 – – 0.23 0.3 – – Age −0.01 0.01 −0.01 0.01 −0.01 0.01 −0.01 0.01 Yearsofeducation 0.03 0.03 0.03 0.03 0.05 0.04 0.05 0.04 Maritalstatus −0.01 0.24 −0.01 0.24 0.34 0.23 0.34 0.23 Numberofchildren 0.35 0.23 0.35 0.23 −0.18 0.21 −0.18 0.21 Firmlevel Businesssize −0.44** 0.16 −0.44** 0.16 0.15 0.15 0.15 0.15 Businessage −0.17 0.15 −0.17 0.15 0.04 0.14 0.04 0.14 Sector:Production 0.38 0.49 0.38 0.49 −0.43 0.45 −0.43 0.45 Sector:Service −0.42 0.44 −0.42 0.44 −0.19 0.40 −0.19 0.40 Sector:Trade −0.46 0.44 −0.46 0.44 −0.14 0.40 −0.14 0.40 Interactions Female(alter)*Female(ego) – – 0.01 0.26 – – −0.08 0.24 Male(alter)*Female(ego) – – 0.97*** 0.26 – – −0.65** 0.26 Male(alter)*Male(ego) – – 0.07 0.18 – – −0.25 0.18 Constant −1.18 0.69 −0.63 0.69 −2.74*** 0.65 −2.50*** 0.66 Nobservations 2548 2548 2548 2548 Nentrepreneurs 491 491 491 491 SD(u) 1.65 1.65 1.39 1.39 Loglikelihood −1327.66 −1327.66 −1200.52 −1200.52 Wald(df) 317.25***(18) 317.25***(18) 401.65***(18) 401.65***(18) * p<0.05. ** p<0.01. ***p<0.001.
Themajorityofcontactsaremales(onaverage,56%ofnetwork
contacts).Homophilyisadrivingfactorwhenitcomestoincluding
apersoninthenetwork,giventhat55%ofthecontactsinthe
net-workareofthesamegender.Comparedtofemaleentrepreneurs,
maleentrepreneurshaveahigherpercentageofmales(Mmale=69
Mfemale=42;t=−13.0,p<0.001),andalowerpercentageof
rela-tives(Mmale=43Mfemale=59;t=−6.0,p<0.001),intheirnetworks.
Entrepreneurs’networksareratherclose-knit–densityisequal
to0.6–,meaningthatthepeoplewithinthenetworkarelikelyto
knoweachother.Thereisnodifferencebetweenmaleandfemale
entrepreneurs.Networksofentrepreneursintheruralareaandin
theurbanaresimilar,exceptfornetworkdensity.Entrepreneursin
theruralareahavedensernetworksthanthoseintheurbanarea
(Murban=0.6Mrural=0.7;t=−3.8,p<0.001).
Tables4and5presentthenumberofrespondentswhoeither
provided access toresources, or requested resources from the
entrepreneurs.Inapproximately 70%of cases,thecontacts had
askedforsupportfromtheentrepreneur.Financialsupportisthe
mostoftenrequested(57%).88%ofthecontactswereableto
pro-videtheentrepreneurwithsomeformofsupport,althoughless
thanhalfwereabletoprovidefinancing.
Maleentrepreneurshave(onaverage)ahigherpercentageof
contactsintheirnetworkwhohadaskedforatleastoneformof
support(Mmale=75.1Mfemale=68.1;t=−4.2,p<0.001)compared
tofemaleentrepreneurs.Thisdifferenceholdsforfinancialsupport
andrequestsforajob,butnotrequestsforfreegoodsandservices.
Similarly,male entrepreneurshave a higherpercentage of
con-tactsintheirnetworkwhoprovidethemwithaccesstoresources
(Mmale=89.8Mfemale=86.6;t=−2.7,p<0.001)incomparisonwith
femaleentrepreneurs.However,nosignificantdifferencesemerge
foreachformofsupport.
Asforurbanandruralentrepreneurs,thoseintheurbanarea
haveahigherpercentageofbothcontactswhohadaskedforatleast
oneformofsupport(Murban=92.3Mrural=84.3;t=−6.8,p<0.001)
and contactswho had provided them withaccess toresources
(Murban=73.6Mrural=69.2;t=−2.7,p<0.01).
4.2. Mainanalysis
Inordertotestourhypotheses,werantwoseparatemultilevel
logisticregressions.Ourdataconsistsofmultipletiesper
respond-ingentrepreneur,andsoitischaracterizedbyanestedstructure.To
dealwiththenestedstructureofthedata,weappliedamultilevel
logisticregressionmodel(SnijdersandBosker,1999).Table6shows
thecorrelationsofthevariablesincludedinthemodels.The
cor-relationsbetweenindependentvariablesaregenerallylow,apart
fromthosebetweenageandnumberofchildren,ageandbusiness
age,businessageandnumberofchildren.However,sincethese
highvaluesofcorrelationswerenotaboutthethreemain
inde-pendentvariables(egogender,altergenderandhomophily),we
decidedtokeepthemascontrolvariables.Furtheranalysis(not
reportedhere)showsthattheeffectsdidnotchangewhenweran
themodelswithoutnumberofchildrenandbusinessage(i.e.the
Table8
MultilevelLogisticRegressionAnalysis–Interactionsbetweenurbanisation,andegoandaltergender.
Accesstoresources Requestsforresources
ModelIII(ModelI+ interactionego gender*urbanisation)
ModelIV(ModelI+ interactionalter gender*urbanisation)
ModelIII(ModelI+ interactionego gender*urbanisation) ModelIV(ModelI+ interactionalter gender*urbanisation)  SE  SE  SE Context(entrepreneur) Urbanisation 0.20 0.28 0.13 0.25 −0.33 0.23 −0.26 0.23
Relationallevel(alter)
Kinship 0.71*** 0.13 0.72*** 0.13 0.59*** 0.13 0.59*** 0.13
Gender(alter) 0.52*** 0.13 0.63*** 0.17 −0.42*** 0.13 −0.68*** 0.17
Request-networkcontact −3.23*** 0.2 −3.23*** 0.20 2.94*** 0.16 2.94*** 0.16
Homophily −0.44*** 0.13 −0.43*** 0.13 0.17 0.13 0.15 0.13
Networkcharacteristics(entrepreneur)
Advicenetworksize 0.14** 0.05 0.14** 0.05 −0.03 0.05 −0.04 0.05
Requestnetworksize −0.06 0.07 −0.06 0.07 0.07 0.70 0.08 0.70
Density 0.20 0.29 0.20 0.29 0.14 0.28 0.14 0.28
Individuallevel(entrepreneur)
Gender(ego) −0.27 0.27 −0.45* 0.22 −0.09 0.26 0.23 0.20 Age −0.01 0.01 −0.01 0.01 −0.01 0.01 −0.01 0.01 Yearsofeducation 0.03 0.03 0.03 0.03 0.03 0.02 0.05 0.04 Maritalstatus −0.02 0.23 −0.01 0.24 0.36 0.26 0.34 0.23 Numberofchildren 0.36 0.23 0.35 0.23 −0.19 0.21 −0.19 0.21 Firmlevel Businesssize −0.44** 0.17 −0.44** 0.16 0.13 0.15 0.14 0.15 Businessage −0.17 0.15 −0.17 0.15 0.04 0.14 0.05 0.14 Sector:Production 0.45 0.49 0.39 0.49 −0.46 0.45 −0.46 0.45 Sector:Service −0.37 0.44 −0.42 0.44 −0.29 0.40 −0.22 0.40 Sector:Trade −0.39 0.44 −0.46 0.44 −0.25 0.40 −0.16 0.40 Interactions egogender*urbanisation −0.41 0.39 – – 0.75* 0.36 – – altergender*urbanisation – – −0.24 0.24 – – 0.56* 0.25 Constant −0.34 0.70 −0.26 0.69 −2.46*** 0.65 −2.56*** 0.65 Nobservations 2548 2548 2548 2548 Nentrepreneurs 491 491 491 491 SD(u) 1.65 1.65 1.38 1.39 Loglikelihood −1327.12 −1327.66 −1198.42 −1197.86 Wald(df) 317.63***(19) 317.25***(19) 402.80***(19) 403.37***(19) *p<0.05. **p<0.01. ***p<0.00.
Theresultsofthemultilevellogisticregressionsarepresented
inTable7.InModel1ofTable7,wetestedHypotheses1–3,
formu-latedinthetheorysection.
Hypotheses1aand1baddressegogender.Hypothesis1ais
sup-ported bythe results, which show thatcontacts are less likely
toprovideaccess tofinancialresourceswhen theentrepreneur
ismale(=−0.48;p<0.01).Thisimpliesthattheoddsofamale
entrepreneurhavingaccesstofinancialresourcesareabout36%
lowerthan thatof afemale entrepreneur.Hypothesis1b states
thatcontactsaremorelikelytoaskforfinancialresourceswhen
theentrepreneurismale.Thishypothesisisnotsupportedbyour
results.
OurresultssupportHypotheses2aand2b,whichrefertoalter
gender.Amalecontactismorelikelytoprovideaccesstofinancial
resources(=0.65;p<0.001),asstatedbyHypothesis2a,andless
likelytoaskforfinancialsupport(=−0.28;p<0.01),asstatedby
Hypothesis2b.Thisimpliesthattheoddsthatamalecontactwill
providefinancialsupportare68%higherthanafemalecontact.The
oddsofamalecontactrequestingfinancialsupportare34%lower
comparedtoafemalecontactaskingforsupport.
Hypotheses3aand3baddressgenderhomophily.The
hypothe-ses stated that, when the entrepreneur and the contact are
of the same gender, the contact is less likely to both provide
accesstofinancialresourcesandalsorequestresourcesfromthe
entrepreneur.Our resultsonly supportHypothesis3athat
gen-derhomophilynegativelyaffects accesstoresources(=−0.43;
p<0.001).Thisimpliesthatwhenentrepreneurandcontacthave
thesamegender,theoddsthatacontactprovidesfinancialsupport
are35%lower.Hypothesis3bisnotsupportedbyourresults.
In Model 2 (Table7), we takeinto account theinteractions
betweencontactgenderandentrepreneurgender.Inordertotest
Hypotheses4aand4b,weinsertedallpossibleinteractions,10
tak-ingthecombination offemale(contact)-male(entrepreneur)as
a referencecategory.Asfor Hypothesis4a,wefoundthatmale
contacts provided a female entrepreneur withaccess to
finan-cialresourcesmoreoftenthanafemalecontactprovidesamale
entrepreneurwithaccess(=0.97;p<0.001).Asforrequestsfor
resources(Hypothesis4b),incomparisonwiththereference
situa-tion(i.e.,femalecontactandmaleentrepreneur),amalecontact
is less likely to ask for resources from a female entrepreneur
(=−0.65;p<0.01).Therefore,bothHypotheses4aand4bare
sup-ported.
Asforcontrolvariables,themostinterestingresultsregard
kin-ship.Relativesaremorelikely toprovidefinancialsocialcapital
(=0.84;p<0.001).However,ifthecontactisarelative,heorsheis
morelikelytoaskforfinancialresourcesaswell(=1.13;p<0.001).
Theresultsalsoshowthaturbanisationdoesnotplayarolein
eitheraccesstofinancialresourcesorrequestsforresources.
Fur-thermore,inpartialcontrastwithotherliteratureonthetopic(e.g.
Bhagavatulaetal.,2010;Rooksetal.,2016;ShaneandCable,2002),
10Homophilywasnot insertedinthemodelforobviousproblems of
densityhasnoeffectonaccesstofinancialresources.Similarly,
despitethefactthatclose-knitnetworkshavealsobeen
associ-atedwithnegativeoutcomes(seeforexample,Shinnaretal.,2011),
densitydoesnotaffectrequestsforfinancialresources.
To test whether theeffect of ego and alter gender differed
betweentheurbanandtheruralarea(Hypotheses5a–d),weran
twomodels(Table8).Oneincludedtheinteractioneffectsbetween
urbanisation and ego gender(Table8, Model 3), and theother
includedtheinteractionbetweenurbanisation andalter gender
(Table 8, Model 4).11 Running the modelswith theinteraction
effects,wefoundthatthevaluesoftheinteractionswere
signif-icantonlyfor‘requestsforresources’.Therefore,bothHypothesis
5aandHypothesis5b,whichrefertoaccesstoresources(inthe
urbanincomparisonwiththeruralarea),arenotconfirmed.
Asforrequestsforresources,inourresults(Table8,ModelIII),
whentheentrepreneurismaleandlivesintheurbanarea,theeffect
onrequestsforresourcesispositive(combinedeffect:0.33)and
strongerthantheeffectoftheentrepreneurbeingmaleintherural
area(=−0.09).ThisresultcontradictsHypothesis5c,whichstated
thattheeffectofegogenderonrequestsforresourceswasweaker
intheurbanareathanintheruralarea.
Thesamereasoningappliestotheinteractionbetweenalter
gen-derandurbanisation(Table8,ModelIV).Whenthecontactismale
andlivesintheurbanarea, theeffectonrequestsforresources
ispositive (combinedeffect: 0.53) andstronger than theeffect
ofthecontactbeingmaleintheruralarea(=0.23).Therefore,
Hypothesis5discontradicted.
Overall,ourhypothesesthatgenderislesscriticalintheurban
areathanintheruralareaarenotsupported.Actually,theresults
showtheoppositewhenitcomesto‘requestsforresources’;the
effectofegoandaltergenderisstrongerintheurbanareathanin
theruralarea.
5. Discussionandconclusions
Inthisarticle,weaddressedgenderdifferencesinsocialcapital
amongUgandanentrepreneurs.Wefoundthatgenderinfluences
thelikelihoodofaccessingfinancialcapitalthroughcontactsand
receivingrequestsoffinancialsupportfromthesecontacts.
Specif-ically,whentheentrepreneurisaman,hiscontactsarelesslikelyto
provideaccesstofinancialresources.Asforthegenderofthe
con-tact,femalecontactsaremorelikelytoaskforfinancialsupport,
andtheyarealsolessabletoprovideentrepreneurswithfinancial
support.Furthermore,malecontactsaregenerallymorelikelyto
provideaccesstofinancialresourcestofemaleentrepreneursthan
theotherwayaround(femalecontactstomale entrepreneurs).
Asituationinwhichamalecontactrequestsfinancialresources
fromafemaleentrepreneurislesslikelytohappenthanasituation
whereafemalecontactasksamanforresources.
Ourresultssuggestthatfemaleentrepreneursgenerallyhave
lessfinancialpowerthanmen,duetodifficultiesinaccessing
finan-cialresourcesvialoansbyformalinstitutions,andalackofcontrol
overfinancesinthehousehold(Aidisetal.,2007;Demirgüc-Kunt
etal., 2015;Fletschner, 2009; Minniti, 2010; ILO, 2017; Jamali,
2009;Vossenberg,2016b;WorldBank,2015).Asaconsequence,
mostofthetime,requestingmoneyfromtheirhusbandsortheir
malerelativesseemstheonly optionfor womenentrepreneurs
whowishtoobtainfinancialsupport(Jamali,2009;Vossenberg,
2016a,b).
Wealsocomparedgenderdifferencesinurbanandruralareas.
Our resultssuggest that urbanisation doesnot matterwhen it
comestoexchangingresources.Urbanisationisnotassociatedwith
11 Fortheinterpretationofinteractioneffects,see:GrotenhuisandThijs(2015).
eitheraccesstoresourcesorrequestsforresources.Ourhypothesis
thatgenderislesscriticalinurbanareascomparedtoruralareas
isnotsupported.Apossibleexplanationissuggestedbythe
liter-atureonrural-urbanmigration(Mukwayaetal.,2011;Belletal.,
2015).Giventhehighnumberofpeoplemovingfromruraltourban
areasinUganda,culturaldifferencesbetweenurbanandruralareas
aremoreorlessblurred.Therelevanceofthisphenomenonisalso
confirmedinourstudy,since65%oftheentrepreneursinoururban
samplewerebornoutsideKampala.
Ourresearchconfirmsthattheroleofthefamilyiscriticalfor
entrepreneurshipindevelopingcountries.Inlinewithsome
pre-viousstudies(Arregleetal.,2015;BerrouandCombarnous,2012;
Egbert,2009;HoffandSen,2016),thisstudymakesitclearthatthe
roleofthefamilyisnotalwayspositive.Ontheonehand,relatives
aremorelikelytoprovideaccesstofinancialresources,butonthe
otherhand,theyarealsomorelikelytoaskforfinancialsupport.
Lastbutnotleast,ourfindingsshowthatasignificantpartof
aUgandanentrepreneurs’networkscanprovetobealiability.For
entrepreneursinUganda,beingembeddedinanetworkofrelations
oftenimpliesthattheyareexpectedtosupporttheircontacts.
Thisarticlecontributestotheliteratureonsocial capitaland
entrepreneurshipbyconnectingthesewithgenderandfocusingon
requestsforresources.Firstly,ourstudycontributestofillingthe
gapregardingdifferencesbetweenmaleandfemaleentrepreneurs’
socialcapitalindevelopingcountries,atopicthatcertainlydeserves
moreattention(Lindvertetal.,2017;Myroniuk,2016).Secondly,
thearticleconstitutesoneofthefirstattemptstoidentifythe
deter-minantsofrequestsforresources.Inparticular,ourresultsshow
thatthere isa seriousissuerelatedtothepressureenduredby
entrepreneurs,whoareexpectedtoprovidesupporttosomeof
theircontacts.Ourresearchsuggestspossiblelimitationspresentin
previousliterature,whichhasoftenadoptedanoveremphaticview
ofsocialnetworksintermsofsocialcapital(see:O’Brien,2012;
Portes,1998).
Theresultsofthisarticlearelimited,whichleavesroomfor
fur-therresearch.First,sincewestudiedUgandanentrepreneurs,we
arenotsurewhetherornot,andtowhatextent,ourresultsmight
begeneralisedtoothercontexts.Inthisregard,apossiblefuture
researchavenuemightbetoconductsimilarresearchstudiesin
otherdeveloping countries, or in developedWestern countries.
Combinedwithourresearch,thiscouldshedfurtherlightonthe
determinantsofrequestsforresourcesfromthecontactsthatmake
upanentrepreneurs’socialcapital.
Second,weemployedadichotomousdefinitionoftheexchange
ofresourcesbetweentheentrepreneur(ego)andeachhisorher
contacts(alter):contactsareabletoprovidefinancialsupportor
theydonot;contactsaskforfinancialsupportortheydonot.Inthis
way,wedidnottakeintoaccountpossibledifferencesconcerning
theamountoffinancialsupportextendedorrequested.
Third,inordertolimitthelengthoftheinterviews,wecollected
limitedinformationconcerningcontactcharacteristics.However,
moredetailedinformationcanbegathered,suchaswhetherornot
thepersonwastherespondent’spartner;thecontact’sage;the
contact’sjobposition.Otherpossibleinformationtocollectmight
bethesocial statusofthecontacts,assuggestedbyBerrouand
Combarnous(2011).Indeed,thecontact’ssocialstatusmight
influ-encethelikelihoodofhimorherbeingabletoprovidefinancial
supportoraskforit.
Lastly,wefocusononeruralcontextonly,butruralareasmay
welldifferfromoneanother.TheNakasekeareais(relatively)close
tothecountry’scapital.Therefore,itisnotclearhowwellthisarea
representsotherruralareasthataremoredistantfromthecapital.
Furtherstudiescouldthereforeexplorevariationsbetweenrural
areaswhenitcomestotheentrepreneur’ssocialcapital.
Inconclusion,thisarticlehasillustratedthattheeffectofsocial