FemaleNon-CognitiveSkillsandCashCropAdoption:
EvidencefromRuralMalawi
MichaelFrese,NUSBusinessSchoolandLeuphanaUniversityLueneburg
MarkusGoldstein,WorldBank
TalipKilic,WorldBank
∗
JoaoMontalvao,WorldBank October2015
Abstract
This paper documents a robust positive correlation between female farmers’ non-cognitive skills and
cashcropadoption.WedosointhecontextofMalawi,oneofthepoorestcountriesintheworld,where
80percentofthepopulationpracticessmallholderfarming.Wefindthatthenon-cognitiveskillsofmale
farmersarenotassociatedwithcashcropadoptiononcewecontrolfortheirwives’non-cognitiveskills.
We also provide evidence that female non-cognitive skills are associated with critical inputs for the
successful adoption of cash crops, such as farm labor, fertilizer, and information on how to grow and
market cash crops. Finally, female non-cognitive skills are significantly more important in determining
tobacco adoption in patrilocal societies where women are at a relative social disadvantage, than in
matrilocalsocieties.Theseresultsareimportantforthedesignofpolicyinterventionstodevelopnoncognitiveskillsoffemalefarmersandtopromoteagriculturalcommercialization.
Keywords:CashCrops,Non-CognitiveSkills,Gender.
∗ Emails:[email protected],[email protected],[email protected],[email protected].
1.Introduction
The majority of smallholder farmers in developing countries specializes in crops for their own
consumption,eventhoughcashcrops,i.e.cropsgrownprimarilyformarketing,arethemajorsourceof
wealth.Theliteratureontechnologyadoption,reviewedbyFederetal(1985),FosterandRosenzweig
(2010), and Jack (2013), emphasizes that high-value crop adoption may be constrained by different
market failures, such as imperfect information about the profits or about how to manage the crop,
limited access to markets, incomplete insurance and credit solutions, and frictions in the markets for
inputs. However a minority of farmers is able to overcome these constraints. In this paper we ask
whether the ability to successfully grow cash crops is related to non-cognitive entrepreneurial skills,
suchastenacity,optimism,andperseverance.
Thereareseveralreasonswhynon-cognitiveskillsmayallowfarmerstoovercomethesemarketfailures
and cultivate cash crops. For example, tenacious farmers may be more willing to work through the
additional risks associated with cash crops, optimistic farmers may have greater (subjective)
expectationsabouttheprofitabilityofgrowingcashcrops,andpatientfarmersmaybemorewillingto
sacrifice present consumption in order to make the necessary up-front investments. In all these
examples, non-cognitive skills increase the expected profitability of cash crop adoption and thus the
personalmotivationtodoso.
Thepossibilitythatnon-cognitiveskillshelpthesuccessfuladoptionofcashcropshasatleasttwopolicy
implications.First,totheextentthattheseskillsaremalleable,suchevidenceisrelevantfordesigning
interventionsthataimtodevelopnon-cognitiveskillsamongsmallholderfarmers.Second,itmayalsobe
relevantfortargetingandmeasuringtheheterogeneousimpactsofinterventionsdesignedtoalleviate
moretraditionalconstraintstocashcropadoptionalongthenon-cognitiveskilldistribution.
A recent but growing body of evidence highlights the importance of non-cognitive skills in predicting
economicoutcomesindevelopedcountries,notablyinthelabormarket(seee.g.,Heckmanetal,2006;
LindqvistandVestman,2011).1Unfortunately,thescarcityofnon-cognitivedataindevelopingcountries
1
The literature uses different terms to refer to these skills, including non-cognitive skills, personality traits, soft
skills,non-cognitiveabilities,character,non-cognitiveaptitudes,andsocio-emotionalskills.Attheheartofthese
different terminologies lies the (mostly open) question of whether these personal characteristics are genetic or
learned, and thus sensitive to carefully designed education interventions. Almlund et al (2010) argue that these
characteristicsarelikelytobemoremalleableoverthelifecyclecomparedwithcognition.
hashamperedresearchprogressonthistopicinruralpoorcommunities.Onestrengthofthispaperis
theavailabilityofnon-cognitivedatafrombothmaleandfemalesmallholderfarmersinruralMalawi,as
wellastheircropportfoliochoicesandotherdetailedagricultureinformation,inadditiontothemore
standardsocioeconomicquestions.
Wefocusonthedecisiontogrowtobacco,ahighlyprofitablecashcrop,whichaccountsformorethan
60percentofMalawi’scashrevenues,70percentofwhichiscultivatedbysmallholderfarmers.While
thispaperisnottakingaviewastowhetherornothouseholdsshouldgrowtobacco,thisisacropthat
isexclusivelygrownfor(export)marketing.Tobaccothusprovidesuswithanunambiguousexampleofa
cash crop, which we use to shed light on the role of non-cognitive skills in determining agricultural
commercialization.
We find that farmers with higher non-cognitive skills are substantially more likely to grow tobacco.
Interestingly,wefindthatthenon-cognitiveskillsofmalefarmersarenotatallassociatedwithtobacco
adoptiononcewecontrolfortheirwives’non-cognitiveskills.Aonestandarddeviationincreaseinthe
non-cognitiveskillsoffemalefarmersisassociatedwitha6percentagepointincreaseinthelikelihood
of growing tobacco. This effect is not driven by differences in cognitive ability, literacy, health,
demographic characteristics, off-farm employment opportunities, wealth, or community level
determinantsoftobaccoadoption.
We also provide evidence that non-cognitive skills predict critical inputs to the successful adoption of
tobacco. Namely, they are strongly correlated with the amount of farm labor recruited, the use of
fertilizer,andaccesstoinformationabouthowtocultivateandselltobacco.Finally,wefindthatnoncognitive skills is a much stronger predictor of tobacco adoption for female farmers in patrilocal
communities where women are thought to enjoy relatively lower status and power, than they are for
femalefarmersinmatrilocalcommunities.
Thispaperisorganizedasfollows.Section2describesourdataandthemeasurementofnon-cognitive
skills. Section 3 presents our results linking spousal non-cognitive skills with household tobacco
adoption.Section4concludes.
2.DataandEmpiricalMethod
2.1.Sample
The data used in this paper comes from the second wave of the Malawi Third Integrated Household
Panel Survey (IHPS), collected in 2013. For rural households – a level at which the sample is
representative–thesurveyincludedadetailedagriculturequestionnaireaboutthelast2011-2012rainy
season. It also included a psychometric questionnaire developed by industrial and organizational
psychologists to measure the cognitive and non-cognitive skills of up to two household members that
haveasayinfarmmanagementdecisions.2InMalawi,apredominantlymatrilinealsociety,itiscommon
forwomentoshareresponsibilityandauthoritywiththeirhusbandsforfarmmanagementdecisions.In
the IHPS rural sample, about 73 percent of the couples report joint, rather than individual, decision
makingonfarmmanagement.3
Ourworkingsampleisbasedon479jointfarmmanagercouplesfrom139communities,onwhichwe
havepsychometricdataforbothspouses.4Thissampleallowsustoestimatetheindependenteffectof
each spouse’s non-cognitive skills on tobacco adoption. Tables A1 to A3 in the appendix compare
summary statistics between our working sample and the remaining 1,471 couples in the IHPS rural
samplewithmissingpsychometricdataforatleastoneofthespouses.TableA1focusesoncommunity
level characteristics, Table A2 on farm level characteristics, and Table A3 on characteristics related to
thehousehold,thewife,andthehusband.5
TableA1showsthatthecommunitiesinoursamplearenotrandomlyselected.Forexample,ontheone
handoursampleover-representscommunitiesonwhichthereisatobaccoclub(46percentversus31
percent),andanagriculturalextensionofficerresides(52percentversus38percent).Ontheotherhand
oursampleunder-representsmatrilocalcommunities(23percentversus49percent),onwhichthereisa
commercial bank or MFI (13 percent versus 17 percent). However once we control for districts these
differencesarenolongersignificant.TableA2andA3showthatoverallhouseholdsinoursampledonot
appear be strongly selected, even though some patterns of significant non-random selection are still
2
Farminghouseholdswereaskedtoidentify“whointhehouseholdmakesthedecisionsconcerningcropstobe
planted,inputuseandthetimingofcroppingactivities”foreachplotofland.Uptothreedecision-makerscouldbe
listed. We use the response to identify the spouses that have a say on farm management. Even though this
question was asked separately for each plot of household land, in practice the intrahousehold distribution of
decision-makingpowerisvirtuallyconstantacrossplotswithinagivenhousehold.
3
Thevastmajority(86%)ofsingleheadedhouseholdsareheadedbyawoman,halfofwhicharewidows.
4
What we refer to communities corresponds to sampled enumeration areas in the IHPS. In rural settings, an
enumerationareacorrespondstoonevillageorasmallgroupofcontiguousvillages.
5
Reasonsformissingspousalinformationonpsychometricdatamayhavebeentiedtorespondentunavailability
atthetimeoftheinterview,aswellastoenumeratoreffortandoveralltimelineoffieldoperations.
presentwithincommunities.Takentogetherthisevidenceindicatesthatstrictlyspeakingoursisnota
randomsampleofruralcouples.Asaresult,ourinferencesonlyapplytothepopulationweexamine.6
2.2.DescriptiveStatistics
Tables1and2presentbasicdescriptivestatisticsforourworkingsample.Column1reportsmeansand
standarddeviations.Columns2and3reportoverallandwithin-communitymeandifferencesbetween
tobaccoadoptersandnon-adopters,togetherwiththeirassociatedp-values.Sincethemainanalysiswill
controlforcommunityfixedeffects,weconcentratethediscussiononwithin-communitydifferencesin
Column3.
Table1focusoncharacteristicsrelatedtotheorganizationoftheproduction.Fourpointsareofnote.
First,tobaccocultivationsignificantlycorrelateswithhigherprofitability.Specifically,growingtobaccois
associated with a 71 percent increase in annual net income from crop activities per hectare of land.
Second,consistentwiththenotionofimperfectmarketsforfoodthereislittlecropspecialization,with
virtuallyallhouseholdsgrowingmaizeirrespectiveofwhethertheygrowtobacco.Moreover,farmsize
for households that grow tobacco is 57 percent larger than for households that do not grow tobacco
(Fafchamps, 1992). Third, in line with the notion that tobacco is an input-intensive crop, tobacco
adopters use 28 percent more labor per hectare of cultivated land than non-adopters, and are 23
percentagepointsmorelikelytousefertilizer.Fourth,tobaccoadoptersare25percentagepointsmore
likelytohavereceivedadviceonhowtocultivateandselltobacco,suggestingthateventhoughtobacco
isawell-knowncropinMalawiitisnotnecessarilyeasytogrow.
Table 2 focuses on spousal and household characteristics. Irrespective of their gender, spouses in
tobacco households do not differ significantly on most individual characteristics from spouses in nontobacco households, with the exception that females in tobacco households are 11 percentage points
lesslikelytobeengagedinoff-farmwork.Thisisnotsurprisinggiventhattobaccoisalabor-intensive
crop and off-farm work is an alternative strategy to generate income. Moreover consistent with the
notion that tobacco is a very profitable income generating activity, households growing tobacco are
significantlywealthier,asmeasuredbythevalueofdurableassets,whichis120percenthigheramong
tobaccoadopters.
6
Reassuringly the results presented in this paper are robust to explicitly accounting for missing spousal
psychometric data. Specifically, we obtain qualitatively similar results when using weights constructed from the
inverseoftheestimatedprobabilityofinclusioninoursample.
2.3.Psychometrics
2.3.1.MeasuringNon-cognitiveSkills
Thepsychometricsquestionnaireincludestwenty-eightentrepreneurialpersonalityquestions.Example
questions are: “I can think of many times when I persisted with work when others quit” and “In
uncertaintimesIusuallyexpectthebest.”Responsestoallquestionsareorderedonafive-pointscale,
withoneindicating“stronglydisagree”andfiveindicating“stronglyagree”.Thefullsetofquestionsis
listedinTableA4intheappendix.
There are two basic concerns with using the responses to these questions to measure non-cognitive
entrepreneurial skills. First, subjective questions are prone to measurement error (Bertrand and
Mullainathan,2001;CunhaandHeckman,2008).Second,theresponsestothedifferentquestionsare
positively correlated with one another, which call for their aggregation into summary measures.7We
followedHeckmanetal(2013)andappliedexploratoryfactoranalysis(EFA)toaddressbothissues.
TheEFAestablishedthataone-dimensionalsummarymeasureofninequestionssufficestoexplainthe
variationintheentrepreneurialnon-cognitivedata,forboththewivesandthehusbands.8TheCronbach
alpha’sstatisticoftheseninequestionsequals0.71forwivesand0.69forhusbands.Wecomputethe
weighted average of the questions that were retained through EFA, whose weights equal their
correspondingfactorloadings.9Inordertogetamorenormallydistributedmeasureofentrepreneurial
personality, we follow the approach in Lindqvist and Vestman (2011), which first transforms the
resulting index into a percentile ranking and then converts it by taking the inverse of the standard
7
Apopularwaytosummarizetheseresponsesistoformsimpleunweightedaveragesofinterpretablegroupsof
questions(asindeMeletal2010).Theproblemwiththisapproachisthatitnotonlyusesarbitraryweights,but
more importantly it does not correct for measurement error, except through simple averaging (Heckman et al
2012).
8
Eightofthepersonalityquestionsarenegativelyworded,where“stronglydisagree”indicateshavingmoreofthe
underlyingpersonalitytrait.Apreliminaryanalysisrevealedthatquestionswordedinoppositedirectionsproduced
differentfactors.Weinterpretedthistobeartifactsofwordingdirectionduetopossibledifficultyinunderstand
negativelywordedquestions,andthusexcludedthesefromthefinalfactoranalysis(SchmittandStults,1985).A
Horn’s (1965) parallel analysis on the remaining questions was used to determine the optimal number of
compositeindexes.TheresultsaredisplayedinFigureA1intheAppendix.
9
ThisindexisaweightedaveragebasedonthequestionsdisplayedinTableA5intheappendix,whoseweights
correspondtotheirfactorloadingsofaone-factormodelafterobliquerotation.Weexcludedquestionsthatdid
nothaveloadingsatleast.3orhigher.Inordertoimprovetheprecisionofthemainestimatesinthispaper,we
alsoexcluded3questionsthatindividuallywerenotpredictiveoftobaccofarming.
normaldistribution.
The questions used to construct the measure of non-cognitive skills used in the analysis and their
corresponding weights are reported in Table A5 in the appendix. The first two questions measure
tenacity,whichistheabilitytopersistinthepursuitofgoalsdespitedifficulty.Thenextthreequestions
measureoptimism,whichistheabilitytoremainhopefulandconfidentaboutpositiveoutcomes.The
sixth and seventh questions measure patience, more specifically the ability to plan and save for the
future.Thelasttwoquestionsmeasurepassionforworkandorganization,respectively.
Figures 1A and 1B illustrate the association between each spouse’s non-cognitive skills and tobacco
adoption, obtained from nonparametric estimations. Standard error bands are presented in dashed
lines.Wecanseethatthelikelihoodoftobaccoadoptionismonotonicallyincreasingwithbothspouses’
non-cognitive skills. Figure 2 illustrates the association between the spouses’ non-cognitive skills
themselves.Spousesarematchedassortatively;highnon-cognitiveskillsmalesaremarriedtohighnoncognitivefemales(correlationcoefficient=0.54).Inthemainanalysisitwillthusbeimportanttocontrol
forbothspouses’noncognitiveabilities.10
2.3.2.MeasuringCognitiveSkills
Thepsychometricsquestionnairealsoincludedanarithmetictestandashort-termmemorytest,which
weusetoconstructameasureofcognitiveabilityforeachspouse.Thearithmetictestconsistedoffour
mental mathematical problems, and the short-term memory test consisted of forward and backward
digit span recall tests. 11An EFA established that a single index aggregating the number of correct
answerstothefourmathematicalproblems,andthemaximumnumberofdigitsrecalledineachdigit
span recall test, suffices to explain the bulk of the variation in these three measures. Their Cronbach
10
Dupuy and Galichon (2014) provide evidence that personality traits play an important role in the marriage
market. Specifically, using data from Dutch households, they show that sorting on personality traits explains 19
percentofthemarriagemarketequilibrium,incomparisonto26percentexplainedbyeducation.
11
Thedigitspanrecalltestisusedtomeasureworkingmemoryandhasbeenshowntobehighlycorrelatedwith
IQ(e.g.Colometal,2004).Initsforwardversion,theenumeratorstartsbyreadingaloudtwothree-digitnumbers
totherespondent.Afterreadingeachnumber,theenumeratoraskstherespondenttorepeatbackthenumber.If
the respondent remembers at least one of numbers, the number of digits is then incremented by one and the
testingcontinues,uptosevendigits.Theprocedureforthebackwardversionofthetestisessentiallythesame,
withtheexceptionthatrespondentsareaskedtorecallthedigitsinbackwardorder.Thearithmetictestsconsistof
thefourfollowingmentalmathproblems:(i)“Whatis20minus13?”,(ii)“Whatis200plus500?”,(iii)“Whatis3
multipliedby6”,and(iv)“Whatis400dividedby10?”.
alpha’sstatisticsequals0.73forthewivesand0.71forthehusbands.Allthreemeasuresarepositively
correlatedwithoneanother,andallthreeenterthecognitiveabilityindexwithsimilarweightsforboth
males and females. We have also normalized the resulting cognitive ability indexes for males and
femalesfollowingtheapproachusedfornoncognitiveability.
Figures3Aand3Billustratetheassociationbetweenthespouses’cognitiveabilitiesandthelikelihoodof
cultivatingtobacco.Wecanseethatthelikelihoodofgrowingtobaccoisnotpositivelyassociatedwith
spouses’ cognitive abilities. If anything, the correlation appears to be negative for husbands. Figure 4
shows that males and females are also positively assortatively matched with respect to cognitive
abilities(correlationcoefficient=0.46).
2.4.EstimatingTobaccoAdoption
We estimate the following specification for household!in community ! , where!!" is an indicator
variableforwhetherthehouseholdcultivatedtobaccoduringlastrainyseason,and!"#!" and!"#!" arethewife’sandhusband’snon-cognitiveskillsindexesdiscussedabove,respectively,
!!" = ! + !! !"#!" + !! !"#!" + !! !!" + !! + !!" .
(1)
!!" includes controls for characteristics related to the wife, the husband, their household, and their
farm,respectively.Spouselevelcontrolsincludethecognitiveskillsindexdescribedabove,indicatorsof
literacyandhealth,age,anddummyvariablesforwhetherthespousesworkoutsidethefarmandfor
whether they have migrated to the village. Household level controls include number of adults and
children in the household, log value of durable assets as a proxy for wealth, number of months of
adequatehouseholdfoodinthepast12monthsasaproxyforvulnerability,adummyequaltoonefor
Muslim households, and measures of distance between the household and the nearest road, the
nearesttobaccoauctionfloor,andthenearestagriculturalmarket.Farmlevelcontrolsincludelogfarm
size,elevation,precipitation,andanindexofoverallsoilquality.
Wealsoincludeenumerationareafixedeffects!! inordertocontrolforcommunitylevelfactorsthat
mayaffectthecostsandbenefitsoftobaccoadoption.Suchfactorscanincludetheexistenceoftobacco
clubs,auctionfloors,agriculturalmarkets,andagriculturalbasedprojects(includingextensionservices
relatedtotobaccocultivationandmarketing),theavailabilityofbothfarmlaborandnon-laborinputs,
non-farm employment opportunities, as well as agroecological characteristics. It also controls for
cultural characteristics that can determine female participation in cash crop, such social norms, tribal,
andkinshipsystems.12
The parameters of interest are!! and!! , which measure the effect size of a one-standard deviation
increase in the wife’s and the male’s noncognitive abilities, respectively, on the likelihood that the
householdgrowstobacco.Thestandarderrorsareadjustedforheteroskedasticityacrosshouseholds.
3.Results
3.1.TobaccoAdoption
Table 3 provides our main results on how spousal noncognitive abilities correlate with household
tobacco adoption. Columns 1 to 3 only control for spousal noncognitive abilities. We find that when
introduced separately in Columns 1 and 2, both spouses’ noncognitive abilities are positively
significantly correlated with the household propensity to grow tobacco (!! > 0and !! > 0). A one
standard deviation increase in female noncognitive ability is associated with a 5.9 percentage point
increase in the likelihood that the household grows tobacco (Column 1), compared to 4.8 percentage
pointsformalenoncognitiveability(Column2).
However, Column 3 shows that when both spouses’ noncognitive abilities are introduced jointly, only
the coefficient on female noncognitive ability is significantly different than zero. In this specification,
conditionalonmalenoncognitiveability,astandarddeviationincreaseinfemalenoncognitiveabilityis
associated with a statistically significant 4.7 percentage point increase in the likelihood that the
householdgrowstobacco.Conditionalonfemalenoncognitiveability,theestimatedeffectofastandard
deviation increase in male noncognitive ability on tobacco adoption is a statistically insignificant 2.1
percentagepoints.Hencepartoftheunconditionalcorrelationbetweenmalenoncognitiveabilityand
tobaccoadoptionreportedinColumn2isexplainedbythefactthathighnoncognitiveabilitymenare
marriedtohighnoncognitiveabilitywomen.13
12
Relianceonwithin-communityvariationinnon-cognitiveskillsacrossneighboringhouseholdcouldfailtoidentify
theeffectofspousalnon-cognitiveskillsontobaccofarmingifentirecommunitiesspecializeintobaccofarming,
leaving little variation after controlling for community fixed effects. This is not the case: within-community
variationintobaccofarmingissubstantial.
13
Whiletheevidenceindicatesthatspousesmatchassortativelyonnon-cognitiveskills,thespousalcorrelationin
thenon-cognitiveskillsindexesisfarfromperfect,withacorrelationcoefficientof0.54.Hence,conditionalonthe
InColumns4to8weaddcommunityfixedeffects,controlsrelatedtothecharacteristicsofthefarm,
the household, the wife, and the husband. The sensitivity of the estimates of interest (!! and !! )
relativetothatoftheR-squaredvaluetotheinclusionofthesecontrolsisinformativeofwhetherthese
estimatesarerobusttoomittedvariablebias.WefollowtheapproachdevelopedbyOster(2015)and
compute bounds for the coefficients of interest in the presence of unobservables driving tobacco
adoption.Thesearereportedatthebottomofeachcolumn.AscanbeenseenacrossColumns4to8,
the coefficient estimates on spousal noncognitive abilities are very robust to the inclusion of these
controls. The identification sets are tightly bounded, never include zero for the effect of female
noncognitiveability,andalwaysincludezerofortheeffectmalenoncognitiveability.Column4to8thus
provide solid evidence that our estimates are not explained by unobservables driving both tobacco
adoptionandspousalnoncognitiveabilities.
Our preferred specification is in Column 8 once all the controls are added. This shows that a one
standarddeviationincreaseinfemalenoncognitiveabilitycorrespondstoastatisticallysignificanthigher
propensitytogrowtobaccoof6.3percentagepoints.14Togaugethemagnitudeofthiscorrelationwe
notethat16percentofthehouseholdsinoursamplecurrentlygrowtobacco.Hencetheincreaseof6.3
percentage points associated with a one standard deviation increase in female noncognitive ability in
Column8correspondstoaabout40percentincreaseinthelikelihoodoftobaccoadoption.Incontrast,
the coefficient on male noncognitive ability is statistically indistinguishable from zero. Moreover, the
other coefficients in Table 3 show that female noncognitive ability is a much stronger predictor of
tobaccoadoptionthantheothermeasuresofhumancapital.Conditionalonbothspouses’noncognitive
abilitiesandontheothercovariates,theircognitiveabilities,literacylevels,andhealthstatushaveno
impactonthelikelihoodthatthehouseholdgrowstobacco.
Aremainingconcernhowever,isthattheresultscouldbedrivenbyreversecausality.Unfortunately,our
datasetdoesnotaffordaplausiblesourceofexogenousvariationinspousalnoncognitiveabilitiestobe
explored. Despite this caveat, our use of a rigorous factor analysis to measure noncognitive ability,
extensivecontrolvariablesrelatedtocharacteristicsofthespouses,theirhouseholds,andtheirfarms,
husband’s (wife’s) non-cognitive skills there is enough variation in the wife’s (husband’s) non-cognitive skills in
ordertoidentifytheindependenteffectofeachspouse’snon-cognitiveskillsontobaccoadoption.
14
Strictlyspeaking,someofthecovariatesincludedinspecificationarenotgoodcandidatesforcontrolvariables.
Forexample,householdwealthandvulnerabilityarelikelytobedeterminedbytobaccoadoption,anditispossible
that non-cognitive skills determines literacy. Yet, their inclusion does not affect the coefficients on spousal
noncognitiveskills,ourcoefficientsofinterest.
aswellastheinclusionofcommunityfixedeffects,suggestthecorrelationdocumentedinthispaperis
informativeofthecausaleffectofspousalnoncognitiveabilitiesontobaccoadoption.
3.2.InputUse
Havingestablishedarobustlargepositivecorrelationbetweenfemalenoncognitiveabilityandtobacco
adoption, we now examine whether female noncognitive ability also correlates with the use of inputs
required to successfully grow tobacco, such as farm labor, fertilizer, and information about how to
cultivateandselltobacco.Themotivationfordoingsoisthatwehavepreviouslyprovideddescriptive
evidence that access to these inputs is positively associated with tobacco adoption (Table 1). Hence
whetherfemalenoncognitiveabilityalsocorrelateswiththeseinputsprovidescomplementaryevidence
insupportofourmainfindingontheimpactoffemalenoncognitiveabilityontobaccoadoption.
Table4presentstheresultsfromestimatingspecificationssimilartoequation(1)above,controllingfor
the same set of covariates and community fixed effects, but where the outcome is a dummy variable
thatequalsoneifduringthelastrainyseasonthehousehold(i)recruitedanamountoffarmlaborabove
thesamplemedianinColumn1,(ii)usedanynon-familyfarmlaborinColumn2,(iii)usedfertilizerin
Column 3, (iv) received advice on how to cultivate and sell tobacco in Column 4.15The dependent
variable in Column 5 is the number of farm tools owned by the household. We find that female
noncognitive ability is positively correlated with the overall amount of farm labor used by the
household,theuseoffertilizer,andwithwhetherthehouseholdreceivedadviceontobaccocultivation
and marketing techniques. These correlations are both statistically significant and economically
important.Specifically,everyadditionalstandarddeviationinfemalenoncognitiveabilityisassociated
witha12percentagepointincreaseinthelikelihoodthatthehouseholdusesabovemedianfarmlabor,
6 percentage point increase in the propensity to use fertilizer, and 9 percentage point increase in the
likelihoodofhavingreceivedadvice.
Toprobefurther,Table5presentstheresultsfromestimatingspecification(1)abovewhereeachofthe
elements of production is introduced sequentially, controlling for the same set of covariates and
communityfixedeffects.Thisapproachallowsustoexaminehowsensitivethecoefficientsonspousal
15
Amonghouseholdsthatreportedtohavereceivedadviceonhowtocultivateandselltobacco,thethreemain
sources of that advice were: (i) radio (58%), (ii) government agricultural extension service (21%), and (iii)
neighbor/relative (10%). In rural parts of Malawi, farmers often gather in groups to listen together to radio
programsonextensionandadvisoryandtodiscusswhattheyhaveheard.
noncognitive abilities are to the inclusion of these variables, and thus gauge the extent to which they
contribute to explain the overall effect of female noncognitive ability on tobacco adoption. At the
bottomofthetablewereporttheidentifiedsetsforthetwocoefficientsofinterestusingOster’s(2015)
biascorrectionapproach.
TheresultsinColumns2to4indicatethatthequantityoffarmlaborrecruited,fertilizeruse,andaccess
to information are all associated with a statistically and economically significant increase in the
propensity to grow tobacco. Conditional on spousal noncognitive abilities and on all controls and
communityfixedeffects,wefindthatthelikelihoodofgrowingtobaccois18percentagepointshigher
forhouseholdsthatrecruitfarmlaborinaquantityabovethesamplemedian(Column2),17percentage
points higher for households that use fertilizer (Column 3), and 13 percentage points higher for
householdsthatobtainadviceabouttobaccocultivationandmarketingtechniques(Column4).
Table5alsoconfirmsthattheeffectoffemalenoncognitiveontobaccoadoptionisinpartexplainedby
increasedaccesstotheseinputs.Specifically,whenwecontrolforfarmlaborinColumn2,fertilizeruse
in Column 3, and access to information in Column 4, the coefficient on female noncognitive ability
declinesby32percent,14percent,and19percent,respectively.Becauseoftherelativelylowsample
sizetheestimatedcoefficientsonfemalenoncognitiveabilityareonlystatisticallysignificantatthe10
percentlevelthroughoutColumns2to4.Howevertheyarewellabovezerointermsofmagnitude,and
infactthelowerboundsoftheirsassociatedidentificationsetsarenotclosetozero.Whenwecontrol
forallinputsinColumn6,thecoefficientonfemalenoncognitiveabilitydecreasesbyalmost60percent
andisnolongerstatisticallysignificant.
Insum,theevidenceinthissectionsuggeststhatfarmsmanagedbyhighnoncognitiveabilityfemales
arebetteratsecuringcriticalinputsthatinturnenablethemtosuccessfullygrowandmarkettobacco.
3.3.MatrilocalandPatrilocalCommunities
AuniqueaspectofMalawiisthatitispredominantlyamatrilinealsociety,whereinheritancefollowsthe
femalelineage.16Forthatreason,womenarethoughttoenjoygreaterstatusandpowerinmatrilineal
societies than in patrilineal societies, which are common across most of Africa. On the one hand it is
16
The community questionnaire administered to each of the 204 enumeration areas selected for nationally
representativeIHPSsample,revealsthatmatrilinealityisthemostcommonlineofdescentin196(96%)ofthose
enumerationareas.
possible that matrilineality eases women’s ability influence household decisions, which would
contributetoexplainthefindingthatfemalenoncognitiveabilitymattersfortobaccoadoption.17Onthe
other hand, it is also possible that personality skills such as tenacity, optimism, and patience may be
particularly effective for females in patrilocal societies where they face greater cultural resistance.
Distinguishing between these two hypotheses is important for the external validity of our results, and
thustheirpolicyrelevance.
BeyondtheprevalenceofmatrilinealityinMalawi,thereissubstantialvariationacrosscommunitiesin
theirculturalpracticesformarriages.Inparticular,themostcommontypeofmarriageismatrilocal(or
Chikamwini) in some communities, and patrilocal (or Chitengwa) in other communities. Under
matrilocality, men leave their home village to move to the village of their wives, whereas under
patrilocality, the women are the ones that move to the villages of their husbands. Women residing in
their home villages are likely to have greater power than women residing in their husbands’ villages,
sincetheyremainclosertotheirkin.Weexploitthisvariationinmarriagerulesacrosscommunitiesto
examinewhethercultureaffectsthepreviouslydocumentedcorrelationbetweenfemalenoncognitive
abilityandtobaccoadoption.
Inordertomeasurethedifferentialeffectoffemalenoncognitiveabilityontobaccoadoptionbetween
patrilocalandmatrilocalcommunities,weestimateaspecificationsimilartoequation(1)abovewhere
femalenoncognitiveabilityisinteractedwithadummythatequalsoneifthehouseholdislocatedina
matrilocalcommunity,andzeroifitislocatedinapatrilocalcommunity.Sincethematrilocalitydummy
is defined at the community level, and there is variation within districts in whether communities are
matrilocal,wereplacecommunityfixedeffectswithdistrictfixedeffects.Thesampleusedinthissection
includes all households with valid psychometric data for female farmers, irrespective of whether
psychometricdataisalsoavailablefortheirhusbands.Thejustificationfordoingsopertainstothesmall
size of the working sample used in the main analysis that makes it difficult to detect heterogeneous
effects. The specification thus includes all previous controls except male cognitive and noncognitive
abilities,whichwehavepreviouslyshownnottomatterfortobaccoadoption.
Despitetheirgeographicsimilarity,patrilocalandmatrilocalcommunitieswithindistrictsmaystilldiffer
on various characteristics other than culture and marriage rules. For example, patrilocal communities
17
Recent experimental evidence shows that women in matrilineal societies are more competitive that men,
whereastheoppositeholdinpatrilinealsocieties(Gneezyetal,2009;Andersenetal,2013).
mayhavebetter(orworse)marketconnections,oragreater(lower)historyofexposuretoagricultural
extension programs. If the effect of female noncognitive ability also varies across these same
characteristics, then any difference found between patrilocal and matrilocal communities may be
related not to culture, but to differences in these characteristics. In order to purge our estimate of
interest of the differential effect of female noncognitive ability on tobacco adoption from these
confoundingeffects,weadditionallycontrolforcommunitylevelcharacteristics,bothinisolationandin
interaction with female noncognitive ability. These controls include indicators for the presence in the
communityofatobaccoclub,anasphaltroad,anagriculturalextensionofficer,aselleroffertilizer,and
acommercialbankoranMFI.
Table6presentstheresults.InColumn1weonlycontrolforfemalenoncognitiveability.Forthissample
a one standard deviation increase in female noncognitive ability is associated with a 4.9 percentage
point increase in the likelihood of tobacco adoption. Column 2 additionally controls for an interaction
betweenthecommunitymatrilocaldummyandthefemalenoncognitiveabilitymeasure,aswellasthe
directeffectofmatrilocality.Wefindastrikinglylargeandsignificantdifferentialeffectbetweenthetwo
types of communities. In patrilocal communities, the effect of a one standard deviation increase in
female noncognitive on the propensity to grow tobacco is a statistically significant increase of 9.1
percentage points. The effect in matrilocal communities is the sum of the interaction term and the
effect in patrilocal communities, and is a precisely estimated zero. Columns 3 and 4 shows that this
resultisrobusttotheinclusionofdistrictfixedeffects,aswellascontrolsrelatedtothespouses,their
households, and their farms. Column 5 shows that the differential effect between patrilocal and
matrilocal communities is not masking heterogeneity in the effect of female noncognitive ability with
respecttootherobservablecommunitycharacteristics.
In summary, the evidence in this section suggests that culture strongly affects the key pattern
documented in this paper. The mean effect of female noncognitive ability on tobacco adoption is
entirely driven by communities with patrilocal marriage rules. That is, we find that it is in those
communitieswherewomenfacerelativelygreatersocialdisadvantage thatentrepreneurialpersonality
skillssuchastenacity,optimism,patience,andpassionforwork,areparticularlyhelpful.
5.Conclusion
Work on labor markets and outcomes in developed countries has shown that non-cognitive skills are
important for, among other outcomes, occupational choice and earnings. And some of this work
(Heckman et. al. 2006) finds that the gradient of the effect of non-cognitive skills with respect to
earnings is steeper for women. We provide evidence from a rural, developing country context that is
consistent with this work. Among couples in Malawi, female non-cognitive skills are significantly
associatedwiththeadoptionoftobacco,ahighlyprofitablecropthatisexclusivelyproducedforselling
inexportmarkets.Onemainchannelthroughwhichthesenon-cognitiveskillsseemtoworkisthrough
theuseofproductiveinputsincludinghigherlevelsoflabor,fertilizerandagriculturaladviceservices.
These findings have clear implications for agricultural growth and poverty reduction. The adoption of
high value crops is critical for agricultural growth and economic growth more broadly. Non-cognitive
skillsseemtoplayanimportantroleinadoptionandinprocuringthenecessaryinputsforsuccess.Thus,
interventionsthatdeveloptheseskillsarelikelytohavesignificantpayoffsforbothhouseholdincomes
andagriculturalproductivity.
These findings also deepen our understanding of how gender matters for household outcomes. These
are households where men and women farm together. When we control for both the husband and
wife’snon-cognitiveskills,herskillsaresignificantlycorrelatedwithfarmingtobacco,whilehisarenot.
Thisindicatesthatprogramsseekingtoincreaseagriculturalproductivityneedtotakeintoaccountall
farmers in the household, not just the titular household head. In addition, our finding that women’s
non-cognitiveskillsmatterparticularlyinpatrilocalcommunities,wherewomenareatarelativesocial
disadvantagesuggeststhattheseskillsmaymattermorewhenlocalandsocialinstitutionsarestacked
against women’s empowerment. A finding that is consistent with recent evidence in developed
countries documenting a greater importance of non-cognitive skills for individuals with low socioeconomicbackgrounds(Carneiroetal,2011;KuhenandMelzer,2015).Giventhestructureofourdata
aswellasthestructureofagriculturalproductioninMalawi,wecannotprovidedefinitiveevidencefor
this,butthisindicatesanintriguingareaforfutureresearch.
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Table1.DescriptiveStatisticsonFarmCharacteristics,byTobaccoAdoptionStatus
Means,standarddeviationsreportedinparentheses,p-valuesinbrackets
Differencesbetweentobaccoandnon-tobacco
households[p-value]
(1)Mean(SD)
(2)Unconditional
(3)WithEA
fixedeffects
Logannualnetcropincome[MWK1,000perha]
3.86
(.919)
.618
[.000]
.707
[.000]
Growsmaize[yes=1]
.981
(.136)
.006
[.643]
.000
[.999]
Numberofcrops
3.44
(2.45)
.991
[.001]
1.23
[.000]
Logfarmsize[hectares]
.410
(.813)
.746
[.000]
.567
[.000]
Labor[person-daysperhectare]
116.4
(106.2)
-7.84
[.471]
32.5
[.023]
Usesnon-familylabor[yes=1]
.428
(.495)
.062
[.317]
.116
[.190]
Usesfertilizer[yes=1]
.817
(.387)
.171
[.000]
.232
[.000]
Receivedadviceonhowtogrow/selltobacco[yes=1]
.408
(.492)
.443
[.000]
.250
[.000]
Soilquality[score=1-7]
4.90
(2.00)
.335
[.071]
-.212
[.032]
Elevation[inmeters]
946.0
(311.1)
190.9
[.000]
-.661
[.927]
Totalrainfallpast12months[inmm]
856.7
(158.1)
-80.2
[.000]
-8.42
[.111]
Notes:ThedatacomesfromtheagriculturequestionnaireoftheIHPS2013,whichwasadministeredtohouseholdswhoreportedtoownorcultivateaplot
duringthe2012/13rainyseason.Thesampleusedisallhouseholdswithhusband-wifepairsintheIHPSruralsamplewithvalidnoncognitivedataforboth
spouses,whichcomprises479households.Ahouseholdisselectedintoourworkingsampleifbothspousesrespondedtothepsychometricquestionnaire.
Column 1 reports the mean and standard deviation of each variable on the full sample. Columns 3 reports the mean difference on each characteristic
betweenhouseholdswithpsychometricdataforbothspousesandhouseholdswithpsychometricdatamissingforatleastoneofthespouses,withthepvaluesadjustedforheteroskedasticityreportedinbrackets.Column4reportsthatsamedifferenceandassociatedrobustp-valuesconditionalondistrict
fixedeffects.Thevariablesoilqualityisthesumof7indicatorvariablesthatequal1ifthesoilisunconstrainedonnutrients’availability,retentioncapacity,
rootingconditions,oxygenavailabilitytoroots,excesssalts,toxicity,andworkability.
Table2.DescriptiveStatisticsonSpousalandHouseholdCharacteristics,byTobaccoAdoption
Means,standarddeviationsreportedinparentheses,p-valuesinbrackets
Differencesbetweentobaccoandnon-tobacco
households[p-value]
(1)Mean(SD)
(2)Unconditional
(3)WithEA
fixedeffects
36.0
(12.9)
.640
(.480)
.906
(.292)
.499
(.501)
.183
(.387)
41.9
(14.4)
.807
(.395)
.929
[.257]
.437
(.497)
.368
(.483)
2.89
(1.24)
2.82
(1.67)
.074
(.262)
8.68
(2.73)
10.2
(2.02)
4.17
(.911)
1.86
(.782)
2.98
(.790)
3.41
(.810)
2.77
[.088]
-.036
[.556]
.034
[.285]
.071
[.253]
-.125
[.001]
2.53
[.152]
-.079
[.141]
.038
[.143]
-.102
[.088]
-.144
[.009]
.038
[.799]
.215
[.248]
.004
[.912]
.453
[.124]
.265
[.272]
-.009
[.929]
.105
[.210]
.245
[.001]
.126
[.093]
3.49
[.113]
-.014
[.858]
-.011
[.772]
.040
[.627]
-.110
[.016]
3.54
[.118]
-.025
[.742]
.019
[.504]
-.047
[.499]
-.085
[.226]
.058
[.759]
.146
[.593]
-.009
[.299]
1.20
[.019]
.470
[.130]
.002
[.970]
.088
[.204]
.059
[.212]
-.022
[.452]
PanelA.Wifecharacteristics
Age
Literate[yes=1]
Healthy[yes=1]
Migratedtovillage[yes=1]
Engagedinoff-farmwork[yes=1]
PanelB.Husbandcharacteristics
Age
Literate[yes=1]
Healthy[yes=1]
Migratedtovillage[yes=1]
Engagedinoff-farmwork[yes=1]
PanelC.Householdcharacteristics
Numberofadults
Numberofchildren
Muslim[yes=1]
Valueofdurableassets[inlogsofMWK]
Numberofmonthsoffoodsecurity
Distancetonearesttobaccoauctionfloor[inlogs]
DistancetonearestADMARCoutlet
Distancetonearestagriculturalmarket
Distancetonearestpopulationcenterwith>20,000
individuals[inlogs]
Notes: ThedatacomesthehouseholdquestionnaireoftheIHPS2013.Thesampleusedisallhouseholdswithhusband-wifepairsintheIHPSruralsample
withvalidnoncognitivedataforbothspouses,whichcomprises479households.Ahouseholdisselectedintoourworkingsampleifbothspousesresponded
to the psychometric questionnaire. Column 1 reports the mean and standard deviation of each variable on the full sample. Columns 3 reports the mean
differenceoneachcharacteristicbetweenhouseholdswithpsychometricdataforbothspousesandhouseholdswithpsychometricdatamissingforatleast
oneofthespouses,withthep-valuesadjustedforheteroskedasticityreportedinbrackets.Column4reportsthatsamedifferenceandassociatedrobustpvaluesconditionalondistrictfixedeffects.
Table3:SpousalNoncognitiveSkillsandTobaccoAdoption
Dependentvariable=1ifhouseholdgrowstobacco,=0otherwise
OLSestimates,standarderrorsreportedinparentheses
.059***
(.015)
.048***
(.016)
(2)Husband
only
.021
(.018)
.047***
(.018)
(3)Both
spouses
-.001
(.022)
.052**
(.024)
(4)EAfixed
effects
.007
(.021)
.057**
(.024)
(5)Farm
controls
.006
(.021)
.060**
(.025)
(6)Household
controls
.063
(.062)
-.016
(.061)
-.020
(.026)
.008
(.022)
.067***
(.026)
(7)Wife
controls
Yes
Yes
Yes
Yes
Yes
[.063,.065]
[.002,.012]
.505
479
.061
(.071)
-.049
(.062)
-.042
(.029)
.066
(.065)
.005
(.062)
-.006
(.026)
.012
(.023)
.063**
(.030)
(8)Husband
controls
Controls
Yes
Yes
Yes
Yes
No
[.067,.069]
[-.004,.008]
.494
479
Husband’snoncognitiveskills
Yes
Yes
Yes
No
No
[.060,.061]
[-.007,.006]
.481
479
Unconditional
Wife’scognitiveability
Yes
Yes
No
No
No
[.057,.057]
[-.006,.007]
.468
479
Wifeisliterate[yes=1]
Yes
No
No
No
No
[.049,.052]
[-.017,-.001]
.420
479
Wifeishealthy[yes=1]
(1)Wifeonly
Husband’scognitiveability
No
No
No
No
No
.026
479
Husbandisliterate[yes=1]
No
No
No
No
No
.016
479
Wife’snoncognitiveskills
Husbandishealthy[yes=1]
No
No
No
No
No
.024
479
EAfixedeffects
Farmcontrols
Householdcontrols
Wifecontrols
Husbandcontrols
Identifiedset[Oster2015biascorrection]:
Wifenoncognitiveability
Husbandnoncognitiveability
R-squared
Observations
Notes:***denotessignificanceat1%level,**at5%level,*and10%level.Standarderrorsadjustedforheteroskedasticity.Thesampleusedisallhouseholdswithhusband-wifepairsinthe
IHPS rural sample with valid noncognitive data for both spouses. Each column corresponds to a separate regression. The dependent variable in all columns is a dummy that equals 1 if
household grows tobacco, and 0 otherwise. Column 1 only controls for the wife’s noncognitive skills. Column 2 only controls for husband’s noncognitive skills. Column 3 controls for both
spouses’ noncognitive skills. Column 4 further controls for enumeration area fixed effects. Columns 5 to 8 further sequentially control for farm, household, wife, and husband level
characteristics.Farmcontrolsincludelogfarmsize,elevation,totalrainfall,andanindexofsoilqualitythatequalsthesumof7dummyvariablesthatequal1ifthesoilisunconstrainedon
nutrients’availability,retentioncapacity,rootingconditions,oxygenavailabilitytoroots,excesssalts,toxicity,andworkability.Householdcontrolsincludenumberofadultsandchildreninthe
household,adummyequalto1ifthehouseholdisMuslim,logvalueofdurableassets,numberofmonthsinthepast12monthswithsufficienthouseholdfood,anddistancesbetweenthe
householdandthenearesttobaccoauctionfloor,ADMARC,agriculturalmarket,populationcenterwithatleast20,000individuals.Wifeandhusbandlevelcontrolsincludeforeachspousethe
normalizedindexofcognitiveability,adummythatequals1ifthespouseisliterate,ahealthindicatorthatequals1ifthespousereportsnotbechronicallyill,age,amigrantdummythat
equals1ifthespousehasmigratedtothevillage,andadummyequalto1ifthespouseengagedinoff-farmworkduringthepast12months.
Table4:SpousalNoncognitiveSkillsandInputUse
EAfixedeffects
Meanofdependentvariable
Yes
Yes
.500
.119***
(.040)
-.021
(.032)
(1)Farmlabor
abovemedian
(yes=1)
.523
Yes
Yes
.428
-.030
(.037)
.012
(.031)
(2)Anynonfamilylabor
(yes=1)
479
.463
Yes
Yes
.816
.058**
(.030)
-.014
(.028)
(3)Fertilizer
(yes=1)
479
.601
Yes
Yes
.412
.089***
(.033)
.063**
(.028)
(4)Advice
(yes=1)
479
.525
Yes
Yes
6.79
.435
(.296)
.210
(.256)
(5)FarmTools
OLSestimates,standarderrorsreportedinparentheses
Controls(farm,household,wife,husband)
.519
479
R-squared
479
Husband’snoncognitiveskills
Wife’snoncognitiveskills
Observations
Notes: *** denotes significance at 1% level, ** at 5% level, * and 10% level. Standard errors adjusted for heteroskedasticity. The sample used is all households with
husband-wifepairsintheIHPSruralsamplewithvalidnoncognitivedataforbothspouses.Eachcolumncorrespondstoaseparateregression.Thedependentvariablein
Columns 1 to 4 are dummy variables that equal 1 if during the last rainy season the household used above-median farm labor (Column 1), any nonfamily family labor
(Column 2), fertilizer (Column 3), and received advice on how to cultivate and sell tobacco (Column 4). The dependent variable in Column 5 is the sum of 8 dummies
indicating household ownership of the following 8 farm tools: hoe, slasher, axe, sprayer, panga knife, stickle, treadle pump, and watering can. All columns control
enumerationareafixedeffects,andthefullsetofcontrolsforcharacteristicsrelatedtothefarm,thehousehold,thewife,andthehusband.Farmcontrolsincludelogfarm
size, elevation, total rainfall, and an index of soil quality that equals the sum of 7 dummy variables that equal 1 if the soil is unconstrained on nutrients’ availability,
retentioncapacity,rootingconditions,oxygenavailabilitytoroots,excesssalts,toxicity,andworkability.Householdcontrolsincludenumberofadultsandchildreninthe
household, a dummy equal to 1 if the household is Muslim, log value of durable assets, number of months in the past 12 months with sufficient household food, and
distances between the household and the nearest tobacco auction floor, ADMARC, agricultural market, population center with at least 20,000 individuals. Wife and
husbandlevelcontrolsincludeforeachspousethenormalizedindexofcognitiveability,adummythatequals1ifthespouseisliterate,ahealthindicatorthatequals1if
thespousereportsnotbechronicallyill,age,amigrantdummythatequals1ifthespousehasmigratedtothevillage,andadummyequalto1ifthespouseengagedinofffarmworkduringthepast12months.
Table5.SpousalNoncognitiveSkillsandTobaccoAdoption,ConditionalonInputUse
Adviceonhowtocultivate/selltobacco[yes=1]
Fertilizer[yes=1]
Anynon-familylabor[yes=1]
Farmlaborabovemedian[yes=1]
Malenoncognitiveskills
Femalenoncognitiveskills
.012
(.023)
(1)
.063**
(.030)
Yes
Yes
.532
[.038,.043]
[.006,.016]
479
.002
(.046)
.175***
(.044)
.016
(.023)
(2)
.043*
(.027)
Yes
Yes
.521
[.052,.054]
[.005,.015]
479
.166***
(.050)
.015
(.023)
(3)
.054**
(.026)
Yes
Yes
.519
[.048,.051]
[-.010,.004]
479
.130***
(.051)
.004
(.023)
(4)
.051*
(.027)
Yes
Yes
.505
[.064,.065]
[.002,.012]
479
-.000
(.004)
.012
(.024)
(5)
.063**
(.027)
Yes
Yes
.554
[.015,.026]
[-.001,.010]
479
.001
(.004)
.119**
(.050)
.147***
(.051)
-.029
(.047)
.147***
(.044)
.010
(.023)
(6)
.026
(.025)
Dependentvariable=1ifhouseholdcultivatestobacco,0otherwise
Linearprobabilitymodelestimates,robuststandarderrorsreportedinparentheses
Farmtools
Yes
Yes
.522
479
EAfixedeffects
Controls(farm,household,wife,husband)
R-squared
Identifiedset[Oster2015biascorrection]:
Wifenoncognitiveability
Husbandnoncognitiveability
Observations
Notes:***denotessignificanceat1%level,**at5%level,*and10%level.Standarderrorsadjustedforheteroskedasticity.ThesampleusedisallhouseholdswithhusbandwifepairsintheIHPSruralsamplewithvalidnoncognitivedataforbothspouses.Eachcolumncorrespondstoaseparateregression.Thedependentvariableinallcolumnsisa
dummythatequals1ifhouseholdgrowstobacco,and0otherwise.Allcolumnscontrolenumerationareafixedeffects,andthefullsetofcontrolsforcharacteristicsrelatedto
thefarm,thehousehold,thewife,andthehusband.Farmcontrolsincludelogfarmsize,elevation,totalrainfall,andanindexofsoilqualitythatequalsthesumof7dummy
variables that equal 1 if the soil is unconstrained on nutrients’ availability, retention capacity, rooting conditions, oxygen availability to roots, excess salts, toxicity, and
workability.Householdcontrolsincludenumberofadultsandchildreninthehousehold,adummyequalto1ifthehouseholdisMuslim,logvalueofdurableassets,numberof
months in the past 12 months with sufficient household food, and distances between the household and the nearest tobacco auction floor, ADMARC, agricultural market,
populationcenterwithatleast20,000individuals.Wifeandhusbandlevelcontrolsincludeforeachspousethenormalizedindexofcognitiveability,adummythatequals1if
thespouseisliterate,ahealthindicatorthatequals1ifthespousereportsnotbechronicallyill,age,amigrantdummythatequals1ifthespousehasmigratedtothevillage,
andadummyequalto1ifthespouseengagedinoff-farmworkduringthepast12months.
Table6.HeterogeneousImpactsofFemaleNoncognitiveSkillsRelatedtoCommunityCharacteristics
Districtfixedeffects
MatrilocalCommunity
Wife’snoncognitiveskillsXMatrilocalCommunity
No
No
.049***
(.012)
(1)Baseline
No
No
No
-.101***
(.024)
-.088***
(.024)
.091***
(.017)
(2)Matrilocal
.157
No
No
Yes
-.052
(.033)
-.061***
(.024)
.075***
(.019)
(3)Districtfixed
effects
.227
No
Yes
Yes
-.038
(.033)
-.056**
(.024)
.068***
(.019)
(4)Controls
758
.248
Yes
Yes
Yes
-.017
(.035)
-.053**
(.026)
.141**
(.068)
(5)Other
heterogeneity
Dependentvariable=1ifhouseholdcultivatestobacco,=0otherwise
OLSestimates,standarderrorsreportedinparentheses
Controls(farm,household,wife,husband)
No
.056
758
Heterogeneousimpactrelatedtocommunitycharacteristics
.019
758
Wife’snoncognitiveskills
R-squared
758
758
Observations
Notes:***denotessignificanceat1%level,**at5%level,*and10%level.Standarderrorsadjustedforheteroskedasticity.ThesampleusedisallhouseholdswithhusbandwifepairsintheIHPSruralsamplewithvalidnoncognitivedataforthewife.Eachcolumncorrespondstoaseparateregression.Thedependentvariableinallcolumnsisa
dummythatequals1ifhouseholdgrowstobacco,and0otherwise.Column1onlycontrolsforthewife’snormalizedmeasureofnoncognitiveability.Column2controlsfor
(bothinisolationandininteraction)thewife’snormalizedmeasureofnoncognitiveability,andadummythatequals1ifthemostcommonmarriagetypeinthecommunityis
matrilocal,and0ifpatrilocal.Column3addsdistrictfixedeffects.Column4addscontrolsrelatedtothecharacteristicsofthefarm,thehousehold,thewife,andthehusband.
Column 5 adds the community level controls, both in isolation and interaction with female noncognitive ability. The community level controls include log population, and
indicatorvariableforwhetherornotcommunitymembersareexpectedtopaytothevillageheadmaneverytimetheybuy/sellland,and5dummyvariablesthatequal1ifthe
followingarepresentinthecommunity:tobaccoclub,commercialbankorMFI,fertilizerdealer,andanagriculturalextensionofficer
0
Probability household grows tobacco
.1
.2
.3
.4
Figure1A:FemaleNoncognitiveSkillsandTobaccoAdoption
-2
-1
0
Female noncognitive ability
1
2
0
Probability household grows tobacco
.1
.2
.3
.4
Figure1B:MaleNoncognitiveSkillsandTobaccoAdoption
-2
-1
0
Male noncognitive ability
1
2
Notes: Each figure plots predicted values from a Kernel-weighted locally weighted regression of an indicator
variable that equals one if the household produces tobacco on the corresponding spouse’s noncognitive skills
index smoothed with a (Epanechnikov) kernel regression. Both ability measures have been truncated at +/−2
standarddeviations.Dashedlinesrepresent95%confidenceintervals.
Figure2:PositiveAssortativeMatingonNoncognitiveSkills
-1
Female noncognitive ability
-.5
0
.5
1
-2
-1
0
Male noncognitive ability
1
2
Notes: The figure plots predicted values from a Kernel-weighted locally weighted regression of the wife’s
noncognitiveskillsonthehusband’snoncognitiveskillsindexsmoothedwitha(Epanechnikov)kernelregression.
Thehusband’snoncognitiveabilityhasbeentruncatedat+/−2standarddeviations.Dashedlinesrepresent95%
confidenceintervals.
Figure3A:FemaleCognitiveAbilityandTobaccoAdoption
0
Probability household grows tobacco
.1
.2
.3
.4
-2
-1
0
Female cognitive ability
1
2
0
Probability household grows tobacco
.1
.2
.3
.4
Figure3B:MaleCognitiveAbilityandTobaccoAdoption
-2
-1
0
Male cognitive ability
1
2
Notes: Each figure plots predicted values from a Kernel-weighted locally weighted regression of an
indicator variable that equals one if the household produces tobacco on the corresponding spouse’s
cognitive ability index smoothed with a (Epanechnikov) kernel regression. Both ability measures have
beentruncatedat+/−2standarddeviations.Dashedlinesrepresent95%confidenceintervals.
Figure4:PositiveAssortativeMatingonCognitiveAbilities
-1
Female cognitive ability
-.5
0
.5
1
-2
-1
0
Male cognitive ability
1
2
Notes:ThefigureplotspredictedvaluesfromaKernel-weightedlocallyweightedregressionofthewife’s
cognitive ability on the husband’s ncognitive ability index smoothed with a (Epanechnikov) kernel
regression. The husband’s cognitive ability has been truncated at +/−2 standard deviations. Dashed lines
represent95%confidenceintervals.
TableA1.SampleSelectiononCommunityCharacteristics
Means,standarddeviationsreportedinparentheses,p-valuesinbrackets
Differencesbetweenhouseholdswith
psychometricdataforbothspousesand
householdswithpsychometricdatamissingforat
leastoneofthespouses[p-values]
(1)Mean(SD)
(2)Unconditional
(3)Withdistrict
fixedeffects
Matrilocal[yes=1]
.485
(.500)
-.256
[.000]
-.040
[.083]
Population[inlogs]
7.90
(1.28)
.138
[.316]
-.007
[.928]
Asphaltedroad[yes=1]
.208
(.406)
.070
[.077]
.054
[.028]
Tobaccoclub[yes=1]
.310
(.463)
.148
[.002]
.025
[.412]
CommercialbankorMFI[yes=1]
.173
(.378)
-.043
[.031]
-.016
[.503]
Fertilizerdealer[yes=1]
.212
(.409)
.067
[.104]
.021
[.504]
Agriculturalextensionofficer[yes=1]
.379
(.485)
.137
[.004]
-.004
[.897]
Villageheadmantaxeslandtransactions[yes=1]
.269
(.443)
-.013
[.739]
.004
[.870]
Notes:ThedatacomesfromthecommunityquestionnaireoftheIHPS2013,whichdefinesa“community”byavillageorgroupofvillagesinruralareas
thatarefoundwithinthecorrespondingenumerationarea,andwhichshouldberepresentativeoftheenumerationareasasawhole.Thesampleusedis
allhouseholdswithhusband-wifepairsintheIHPSruralsamplewithvaliddataforalltheremainingvariablesusedintheanalysis.Thissamplecomprises
1,794 households, 446 of which are part of our working sample. A household is selected into our working sample if both spouses responded to the
psychometric questionnaire. Column 1 reports the mean and standard deviation of each variable on the full sample. Columns 3 reports the mean
difference on each characteristic between households with psychometric data for both spouses and households with psychometric data missing for at
least one of the spouses, with the associated p-values clustered at the enumeration area level reported in brackets. Column 4 reports that same
differenceandassociatedrobustp-valuesconditionalondistrictfixedeffects.Thefirstvariableisdummythatequals1forcommunitieswherethemost
commonformofmarriageismatrilocal,and0ifpatrilocal.Thesecondvariableisthesizeofthepopulationinthecommunitymeasuredinlogs.Thethird
variableisadummythatequals1ifthesurfaceofthemainaccessroadinthecommunitiesistar/asphalt.Thefourthtoseventhvariablesareindicators
forwhetheratobaccoclub,acommercialbankorMFI,afertilizerdealer,andanagriculturalextensionofficerarepresentinthecommunity,respectively.
Thelastvariableisadummythatequals1ifthecommunitymembersexpectedtopaytothevillageheadmanwhentheybuyorsellland.
TableA2.SampleSelectiononFarmCharacteristics
Means,standarddeviationsreportedinparentheses,p-valuesinbrackets
Differencesbetweenhouseholdswith
psychometricdataforbothspousesand
householdswithpsychometricdatamissingforat
leastoneofthespouses[p-values]
(1)Mean(SD)
(2)Unconditional
(3)WithEA
fixedeffects
Logannualnetcropincome[MWK1,000perha]*
3.78
(1.07)
.110
[.034]
-.012
[.838]
Growstobacco[yes=1]
.139
(.346)
.028
[.136]
.016
[.439]
Growsmaize[yes=1]
.965
(.183)
.021
[.008]
.007
[.408]
Numberofcrops
3.35
(2.19)
.121
[.323]
.317
[.016]
Farmsize[hectares]
2.80
(18.6)
-1.00
[.076]
-.740
[.195]
Labor[person-daysperhectare]
124.4
(269.6)
-10.6
[.254]
-22.4
[.256]
Usesnon-familylabor[yes=1]
.392
(.488)
.048
[.062]
.055
[.067]
Usesfertilizer[yes=1]
.799
(.401)
.028
[.228]
.024
[.249]
Receivedadviceonhowtogrow/selltobacco[yes=1]
.306
(.461)
.135
[.000]
.106
[.000]
Soilquality[score=1-7]
5.18
(1.63)
-.370
[.000]
-.097
[.113]
Elevation[inmeters]
925.0
(326.4)
27.8
[.094]
-.614
[.936]
Totalrainfallpast12months[inmm]
829.4
(119.0)
36.3
[.000]
4.01
[.236]
Notes:ThedatacomesfromtheagriculturequestionnaireoftheIHPS2013,whichwasadministeredtohouseholdswhoreportedtoownorcultivateaplot
duringthe2012/13rainyseason.Thesampleusedisallhouseholdswithhusband-wifepairsintheIHPSruralsamplewithvaliddataforalltheremaining
variables used in the analysis. This sample comprises 1,895 households, 479 of which are part of our working sample. A household is selected into our
workingsampleifbothspousesrespondedtothepsychometricquestionnaire.Column1reportsthemeanandstandarddeviationofeachvariableonthe
fullsample.Columns3reportsthemeandifferenceoneachcharacteristicbetweenhouseholdswithpsychometricdataforbothspousesandhouseholds
withpsychometricdatamissingforatleastoneofthespouses,withthep-valuesadjustedforheteroskedasticityreportedinbrackets.Column4reports
thatsamedifferenceandassociatedrobustp-valuesconditionalondistrictfixedeffects.Thevariablesoilqualityisthesumof7indicatorvariablesthat
equal1ifthesoilisunconstrainedonnutrients’availability,retentioncapacity,rootingconditions,oxygenavailabilitytoroots,excesssalts,toxicity,and
workability.
TableA3.SampleSelectiononSpousalandHouseholdCharacteristics
Means,standarddeviationsreportedinparentheses,p-valuesinbrackets
Differencesbetweenhouseholdswithpsychometric
dataforbothspousesandhouseholdswith
psychometricdatamissingforatleastoneofthe
spouses[p-values]
(1)Mean(SD)
(2)Unconditional
(3)WithEA
fixedeffects
35.6
(13.3)
.621
(.485)
.921
(.270)
.540
(.498)
.184
(.388)
41.7
(14.7)
.789
(.408)
.920
(.271)
.496
(.500)
.368
(.482)
2.85
(1.20)
2.71
(1.66)
.144
(.351)
8.68
(1.59)
10.1
(2.04)
4.03
(.934)
1.83
(.749)
2.95
(.813)
3.27
(.863)
-.456
[.506]
.026
[.308]
-.019
[.209]
-.087
[.036]
.002
[.923]
.207
[.786]
.023
[.276]
.012
[.372]
-.079
[.003]
-.000
[.985]
.060
[.347]
.145
[.099]
-.092
[.000]
.003
[.982]
.106
[.321]
.183
[.000]
.038
[.350]
.047
[.259]
.179
[.000]
-1.72
[.039]
-.023
[.421]
-.029
[.098]
-.087
[.003]
-.024
[.319]
1.71
[.067]
-.006
[.813]
.005
[.775]
-.057
[.049]
.015
[.618]
.073
[.325]
.019
[.853]
.001
[.912]
.110
[.522]
.093
[.135]
.059
[.118]
.050
[.116]
.036
[.297]
.055
[.116]
PanelA.Wifecharacteristics
Age
Literate[yes=1]
Healthy[yes=1]
Migratedtovillage[yes=1]
Engagedinoff-farmwork[yes=1]
PanelB.Husbandcharacteristics
Age
Literate[yes=1]
Healthy[yes=1]
Migratedtovillage[yes=1]
Engagedinoff-farmwork[yes=1]
PanelC.Householdcharacteristics
Numberofadults
Numberofchildren
Muslim[yes=1]
Valueofdurableassets[inlogsofMWK]
Numberofmonthsoffoodsecurity
Distancetonearesttobaccoauctionfloor[inlogs]
DistancetonearestADMARCoutlet
Distancetonearestagriculturalmarket
Distancetonearestpopulationcenterwith+20,000
[inlogs]
Notes:ThedatacomesthehouseholdquestionnaireoftheIHPS2013.Thesampleusedisallhouseholdswithhusband-wifepairsintheIHPSruralsamplewith
valid data for all the remaining variables used in the analysis. This sample comprises 1,794 households, 446 of which are part of our working sample. A
household is selected into our working sample if both spouses responded to the psychometric questionnaire. Column 1 reports the mean and standard
deviationofeachvariableonthefullsample.Columns3reportsthemeandifferenceoneachcharacteristicbetweenhouseholdswithpsychometricdatafor
bothspousesandhouseholdswithpsychometricdatamissingforatleastoneofthespouses,withthep-valuesadjustedforheteroskedasticityreportedin
brackets.Column4reportsthatsamedifferenceandassociatedrobustp-valuesconditionalondistrictfixedeffects.
TableA4.EntrepreneurialPsychologySurveyQuestions
1.Iplantaskscarefully
2.Imakeupmymindquickly
3.Isaveregularly
4.IlookforwardtoreturningtomyworkwhenIamawayfromwork
5.IcanthinkofmanytimeswhenIpersistedwithworkwhenothersquit
6.Icontinuetoworkonhardprojectsevenwhenotheropposeme
7.Iwouldliketojuggleseveralactivitiesatthesametime
8.Iwouldrathercompleteanentireprojecteverydaythancompletepartsofseveralprojects
9.Ibelieveitisbesttocompleteonetaskbeforebeginninganother
10.Itisdifficulttoknowwhomyrealfriendsare
11.InevertryanythingthatIamnotsureof
12.Apersoncangetrichbytakingrisks
13.ItisimportantformetodowhateverI'mdoingaswellasIcanevenifitisn'tpopularwithpeoplearoundme
14.Partofmyenjoymentindoingthingsisimprovingmypastperformance
15.WhenagroupIbelongtoplansanactivity,Iwouldratherdirectmyselfthanjusthelpoutandhavesomeone
elseorganizeit
16.ItryharderwhenI'mincompetitionwithotherpeople
17.Itisimportanttometoperformbetterthanothersonatask
18.Ienjoyplanningthingsanddecidingwhatotherpeopleshoulddo
19.Ifindsatisfactioninhavinginfluenceoverothers
20.Iliketohavealotofcontrolovertheeventsaroundme
21.Themostimportantthingsthathappensinlifeinvolveswork
22.MyfamilyandfriendswouldsayIamaveryorganizedperson
23.InuncertaintimesIusuallyexpectthebest
24.Ifsomethingcangowrongforme,itwill
25.I'malwaysoptimisticaboutmyfuture
26.Ihardlyeverexpectthingstogoonmyway
27.Irarelycountongoodthingshappeningtome
28.OverallIexpectmoregoodthingstohappentomethanbad
Notes:Responsestoallquestionsareonafive-pointscale,withoneindicating“stronglydisagree”andfive“stronglydisagree”.
TableA5.FactorLoadingsofaOne-FactorModelAfterObliqueRotation
(1)Females
(2)Males
IcanthinkofmanytimeswhenIpersistedwithworkwhenothersquit
.615
.574
Icontinuetoworkonhardprojectsevenwhenotheropposeme
.595
.544
InuncertaintimesIusuallyexpectthebest
.439
.427
I'malwaysoptimisticaboutmyfuture
.407
.342
OverallIexpectmoregoodthingstohappentomethanbad
.331
.367
Iplantaskscarefully
.441
.482
Isaveregularly
.339
.268
IlookforwardtoreturningtomyworkwhenIamawayfromwork
.419
.510
MyfamilyandfriendswouldsayIamaveryorganizedperson
.495
.459
Cronbach’salphastatistic
.712
.694
Notes:Factorloadingsbasedontheexploratoryfactoranalysiswithdirectquartiminrotationareshown.
FigureA1.Horn’sParallelAnalysis
0
1
Eigenvalue
2
3
4
(a)Husbands
0
5
Observed
10
Number of factors
Adjusted
15
Random
0
1
Eigenvalue
2
3
4
(b)Females
0
5
Observed
10
Number of factors
Adjusted
15
Random
Notes: Eigenvalues adjusted for sampling error were computed using 5,000 randomly generated datasets with the same
th
numberofquestionsandobservationsasouroriginalsample.Theycorrespondtothe95 percentileeigenvaluesfromthe
randomdata.Forbothmaleandfemalemanagers,wecanseethatonlytheeigenvalueassociatedwiththefirstfactoris
substantially above one. The next two largest eigenvalues are just marginally above one. Furthermore weak- and crossloadingproblem.