Socioeconomic and geospatial determinants of households’ food and non-food consumption dynamics within the West African Economic and Monetary Union

ContentslistsavailableatScienceDirect

Scientific African

journalhomepage:www.elsevier.com/locate/sciaf

Socioeconomic and geospatial determinants of households’

food and non-food consumption dynamics within the West African Economic and Monetary Union

Ibrahim Niankara

College of Business, Al Ain University, Abu Dhabi 112612, UAE

a rt i c l e i nf o

Article history:

Received 13 February 2023 Revised 12 May 2023 Accepted 18 May 2023

Editor: DR B Gyampoh JEL Codes:

C31 D1 L67 P46 R12 R2 Keywords:

Food security Household expenditure Private consumption spending Regional convergence in well-being Spatial inequalities

WAEMU

a b s t ra c t

Thisarticleexploresthecross-sectionalpropertiesofhousehold’sfoodandnon-foodcon- sumptionexpendituresconceptualizedasinter-dependentspatialrandomprocesseswithin theWestAfricanEconomicandMonetaryUnion(WAEMU).Tothisend,thepaperapplies spatio-temporaleconometricmethodstodataextractedfromthefirsteditionoftheHar- monized SurveyonHouseholdLivingConditions(EHCVM,2018/2019).Thefindingsfrom the unconditional distributionof householdeconomic wellbeing (measuredby nominal per-capita foodandnon-foodexpenditures)usingtheinequalityindicatorsoftheAtkin- sonandGinicoefficients,suggestthepresenceofcross-sectionalinequalitiesinhousehold foodandnon-foodwellnesswithinWAEMU.However,foodwellnessinequalityappeared lower thanthat ofnon-foodwellnessacrossthe 8 countries.Inaddition, theeconomic securityanalysis,basedontheindicesofWatts,Sen,andFoster(aplha=1),highlightim- portantspatialheterogeneityinhousehold’svulnerabilitytopovertywithin-and-between WAEMUcountrymembers.Moreover,estimationofthesemiparametricbivariateGaussian copulamodeloffoodandnon-foodconsumptionexpenditures,showstheirsignificantde- pendencewithhousehold’sdemographicandgeospatialcharacteristics,aswellastheeco- nomicandhealthcharacteristicsofthehouseholdhead.Indeed,amongotherkeyfindings, agenderbasedgradientinhousehold’sfoodandnon-foodwellnessatrespectively3.6and 4.4%isobservedinfavorofmaleheadedhouseholds;alongwithanUrban/Ruraldividein foodandnon-foodwellnessat20.7%and27.3%,respectively,infavorofurbanhouseholds.

Therefore, inlinewithgoals1,2,4,5,9and 10oftheUnited Nations2030Agendafor sustainabledevelopment,thepaperprovideskeyinsightsforevidencebasedpolicymak- ingwithinWAEMU.

© 2023TheAuthor(s).PublishedbyElsevierB.V.

ThisisanopenaccessarticleundertheCCBYlicense (http://creativecommons.org/licenses/by/4.0/)

Introduction

TheWestAfricanEconomicandMonetaryUnion(WAEMU)wasestablishedonJanuary10th,1994,bysevenWestAfrican States(Benin,BurkinaFaso, IvoryCoast,Mali, Niger,Senegal,andTogo) topromoteeconomic integration.Ithassince ex- pandedtoincludeGuineaBissau.WAEMUaimstoenhanceintegrationthroughcoordinationoffiscal,monetary,andsectoral policies,fosteringajointmarketframework.Theregioncoversanareaof3,507,006km²[49],andhasapopulationofover

E-mail address: Ibrahim.niankara@aau.ac.ae

https://doi.org/10.1016/j.sciaf.2023.e01724

2468-2276/© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

I. Niankara Scientific African 20 (2023) e01724 Table 1

Households final consumption expenditure (% of GDP) within WAEMU. Defined as “the market value of all final goods and services, including durable products purchased by households”.

Country Name 2015 2016 2017 2018 2019 2020 2021

Ivory Coast 65.78 67.07 68.41 69.33 68.68 66.13 65.91

Benin 75.17 73.22 72.63 70.45 68.45 68.06 ..

Burkina Faso 71.52 68.76 66.56 64.28 60.98 .. ..

Mali 78.42 76.67 75.87 74.76 73.97 73.41 73.25

Guinea-Bissau 85.28 87.15 87.00 75.58 79.70 81.60 ..

Niger 67.34 69.33 71.49 70.27 68.64 68.77 69.06

Senegal 72.62 71.59 70.40 69.46 68.31 68.82 68.53

Togo 71.75 77.83 76.43 75.75 74.00 72.04 74.02

WAEMU 73.49 73.95 73.60 71.24 70.34 71.26 70.15

Source: Author’s compilation based on World Development Indicators Data [53] .

120million people[51].TheunionutilizestheCFAfrancasitscommoncurrency,withmonetarypoliciesgovernedby the Central Bank of theWest Africanstates (BCEAO). Despitefacing challenges dueto the COVID-19 pandemic, WAEMU has demonstratedresilience,withprojectedregionalgrowthof6%in2022[24].

As highlighted inTable 1 below, private consumption plays a vital role inthe union’s Gross DomesticProduct (GDP) [52],contributingsignificantlytogrowth,employment,economicsecurity,andwell-being(keho,2019).BeforetheCOVID-19 pandemic, private consumption in theoverall Africancontinent wasamongthe fastestgrowing, witha 3.9% growthrate since 2010 reaching 1.4trillionUSDin 2015,andexpectedto reach2.1trillionUSD, by 2025[46]. TheJune 2022“global economic prospects” reportby theWorld Bank highlightsa deepeningeffectofCOVID-19 onprivate consumption within WAEMU,bringingitsaveragegrowthrateto-1.2%in2020fromits2019valueof1.5%;withanexpectedincreaseto3.7%in 2021,andaprojectedfurtherriseto3.6%in2023[52].

Thoughtheoretically Keynes [27]Absolute IncomeHypothesis (AIH) laid thefoundations ofmodern consumptionthe- ories,withanemphasis onautonomousindividualconsumption, Duesenberry[15]proposed analternative viewbasedon a relative incomehypothesis(RIH) withinter-dependentindividuals’consumption.Thislatterview wassubsequentlydis- placed byboth, thelifecyclehypothesis (LCH)[34],andthepermanentincomehypothesis(PIH) (Friedman,1957),which suggestedtheinsignificanceofsocialinter-dependenceinconsumption.

As a result,much ofthe contemporaryempiricalconsumption analyzes inthe economicliterature focusesonthe AIH [8,10],theLCH[17,32]andthePIH[5,22],withadartinempiricalstudiesdealingtheRIH[14].Notonlythereexista gap intermsoflargescaleregionaleconomicbloclevelassessmentsofhouseholdfoodandnon-foodconsumptiondynamicsto drivebothregionalbloclevel,aswellasnational andsub-nationallevelpolicies;an integratedevaluationthatincorporate geospatial distributions, patterns, andkey socioeconomic control factors remains alsorare in the empirical consumption literature [54].Therefore,moreresearch intheseregardsareparticularlyimportanttobridge thehighlighted gap,butalso supporthouseholdfoodandnon-foodwellnessinlinewiththeUnitedNations(UN)2030agenda forsustainabledevelop- ment.

Hence, startingfromtheassumption that householdconsumption offoodandnon-fooditemsare manifoldandinter- twined, shaped by manyfactors that overlap not only atthe household level,but also athigher Mesoand Macroscale levels(BissetandTenaw,2022),thispaperproposesacomplementaryeconometricframeworkforunderstandinghousehold consumption,andthuseconomicwellbeing,basedonasystemicviewofconsumption.Theframeworkconceptualizescon- sumptionasaspatio-temporalrandomprocess,withdynamicallyevolvingmeanandvariancefunctionsthatareinfluenced by socio-demographic,andMicro-MesoandMacro-economicfactors.Inaddition,theframework dividesconsumptioninto its foodandnon-food dimensions, ensuring their distinctive butinter-dependent co-evolutionin time and space, within regionaleconomicsystems.

Therefore,withthespecificgoalofevaluatingandreportingonthemostrecentcross-sectionalpropertiesofthespatial randomprocess (SRP)ofhousehold’s(foodandnon-food)consumptionspending withinWAEMU;thepaperaddressesthe following research question:Is thereevidence ofcross-sectionalconvergencein household’s foodandnon-food wellness(con- sumptionspending)withintheWAEMUbloc?

To address this question, in what follows the paper first provide a brief review of the relevant literature on conver- genceandhouseholdconsumptionexpenditures;followedby adescriptionoftheadoptedresearch methodology;thenby a presentationofthedescriptiveandpredictivefindings inthenext section.Thesubsequent sectiondiscusses theresults’

implications;whilethelastsectionconcludestheanalysiswithsuggestionsforfutureresearch.

Literaturereview

Examiningtheconvergencehypothesis withineconomicandmonetaryunions froma newperspective [42],thisarticle builds on previous studies that have looked athousehold expenditures inAfrica andcross-country convergencethrough aggregatemacro-economicindicators[1,47],includingexchangerates[4],inflationrates[36],andgrowth[44].Nonetheless,

2

itisthefirsttofocusspecificallyontheconvergenceofhouseholdfoodandnon-foodconsumptionexpenditureswithinthe WAEMUzone.

Macro-economicstudiesofhouseholdconsumptionexpenditure

DespitenumerousstudiesexaminingthefactorsthatinfluencehouseholdconsumptionexpendituresinAfrica[10],such asthosethat focusontheEconomicCommunity oftheWestAfricanStates[25],pairsofcountriesinWestAfrica [16],or individualcountrieslikeIvoryCoast[26],Ghana[7],andSouthAfrica[19],thesestudiesprimarilyfocusonmacro-economic driversandneglecttoconsiderthemicro-characteristicsofhouseholdsthemselves.

For example,Chimere and Nwachukwu [10]employed a panel augmented mean group procedure to examine there- lationship between GDP per capita, domestic credit to the private sector, and household consumption in selected West Africancountries.Theirfindingsrevealedasignificantpositiveimpactofthesefactorsonhouseholdconsumption,although the magnitudeandsignificanceofthe estimatedcoefficientsvaried acrosscountries. Similarly, Keho[25] investigatedthe influenceofgovernmentspendingonhouseholdconsumptionwithintheEconomicCommunityofWestAfricanStates.The studyidentifieda substitutionrelationshipbetweengovernmentandprivateconsumption,withvaryingdegreesofsubsti- tutabilityacrosscountries.UsingFixed EffectsLeastSquaresDummyVariablePanelRegressionAnalysis,EkongandEffiong [16]explored the economic determinants of householdconsumption expenditures in Nigeriaand Ghana. The results sup- portedtheKeynesianhypothesis,showingthatgrossnationalincomeandinflationratepositivelyandsignificantlyaffected householdconsumptionexpenditure,whileinterestrateandsavingshadanegativeandsignificantimpact.Moreover,Keho [26] utilizedan AutoregressiveDistributed Lagsboundstestingapproachtoco-integration anddiscovereda long-termre- lationshipbetweenprivateconsumptionandits determinantsinIvoryCoast.The studyfoundthatcurrentincome,wealth, andgovernmentexpenditurepositivelyinfluencedprivate consumption,withcurrentincomeexhibitingthehighesteffect.

Incontrast,inflationrateandrealinterestrateondepositshadanegativeimpact.Theshort-termresultsdemonstratedthat incomeandwealthpositivelyinfluencedconsumptionexpenditure,whiletheeffectsofgovernmentconsumption,inflation, andinterestratewereinsignificant.Furthermore,BonsuandMuzindutsi[7]analyzedannualtimeseriesdatafrom1961to 2013inGhanausingamultivariateco-integrationapproach.Thestudyrevealedasignificantlong-termrelationshipbetween realhouseholdconsumptionandselectedmacroeconomicvariables,withamarginalpropensitytoconsumeof0.7971. Ad- ditionally,theanalysisshowedthat changesinpricelevelswere theonlyfactorsignificantly drivingshort-termhousehold consumption, witha notable impacton realexchange rateandeconomic growth.Finally,Habanabakize [19]conducteda studyusingquarterlytime seriesdatafrom2002to2020toinvestigatetheresponsivenessofhouseholdconsumptionex- pendituretopetrolprice,disposableincome,andexchangeratevolatilityinSouthAfrica.Thefindingsindicatedalong-run relationshipbetweenthevariables.Whilechangesinpetrolpricedidnothaveasignificantshort-termimpact,incomelevel andexchangeratevolatilitywerefoundtosignificantlydrivecurrentconsumptionexpenditures.

Micro-economicstudiesofhouseholdconsumptionexpenditure

Relativelyfewerstudiesconsideredthemicroeconomicandsocio-demographicdriversofhouseholdconsumptionexpen- ditures,mostofwhicharesinglenationalcontext,andmakingnoreferencetocross-countryconvergence.

For instance,Mignounaetal.[33] investigated microeconomicfactors influencing householdconsumption expenditure in rural areas of Ghana and Nigeria. Employing ordinary least squares (OLS) and quantile regression (QR) methods, the studyanalyzedasampleof1400randomlyselectedyam-producinghouseholds.OLSresultshighlightedage,education,and householdsizeasimportantfactors.QRestimationrevealedadditionalsignificantfactors,includingmembershipinformal andinformalinstitutions,mainoccupation,familystructure,andfarmsize.Educationconsistentlyemergedasasignificant factorinboth regressionmethods,suggestingits potentialasapolicy toolforincreasing privateexpenditureandpromot- ing economicgrowth.Similarly, HoneandMarisennayya[23] aimedto assessconsumptionexpendituresofhouseholds in theAmhararegionofEthiopia.Datawascollectedthroughinterviewswith100randomlyselectedrespondents.Descriptive findingsindicatedarangeofmonthlyconsumptionlevels,withvaryingprioritiessuchasfoodoverclothing.Thestudyob- servedthathouseholdswithheadsworkingingovernmentinstitutionshadrelativelylowermeanconsumptionexpenditures comparedtothoseheadedbyself-employedindividuals.Multipleregressionanalysisrevealedadirectrelationshipbetween disposable income,familysize, andhouseholdconsumption expenditures,withdisposable incomeexerting a strongeref- fect.Moreover,Acharya[2]conductedastudytoinvestigatefactorsinfluencinghouseholdexpendituresinNepal.Thestudy utilizedacross-sectionalsampleof168householdsfromtheSidhalekruralmunicipalityofDhadingdistrict.Multiplelinear regressionandbivariateanalyzeswereemployedfordataanalysis.Thefindingsidentifiedthenumberofliterateindividuals ofworkingage,remittance-receiptstatus,andthegenderofthehouseholdheadaskeydriversofruralNepalesehouseholds’

consumptionexpenditure.Furthermore,Hastuti[21]examinedvariationsinhouseholdfoodand non-foodconsumptionex- penditures intheMakassarCityofTurkey.Utilizing adescriptive andexplanatoryresearch strategywithacross-sectional sample of327 respondents,including farmersand female farmers, the studyfound that households in the farmerssub- samplehadlargernon-foodexpenditurescomparedtofoodexpenditures.Conversely,therelationshipwasreversedamong thefemale farmerssub-sample.Thestudyalsoidentifiedhouseholdincome,householdhead’sgender, formaleducationof the spouse,householdsize, andregional differencesasthemain driversofchangesinhouseholdfoodandnon-foodcon- sumptionexpendituresinruralTurkey.

I. Niankara Scientific African 20 (2023) e01724

Consistentthereforewiththeabovereviewedliterature,andthecoreaimofWAEMUto fosterregionalintegrationbe- tween countrymembers, adoptingaconservative viewon theresearch questionraisedin theintroductionyields thefol- lowingtwohypothesesscenariosonhouseholdconsumptionbehaviorwithinWAEMU:

Ho1. Thereisasignificantcross-sectionalconvergenceinhouseholds’foodwellnesswithinWAEMU(i.e.atanycontemporaneous moment,household’sper-capitaexpenditureonfooditemswithinWAEMUisspatiallyhomogeneous)

Ha1. Thereisnosignificantcross-sectionalconvergenceinhouseholds’foodwellnesswithinWAEMU(i.e.atanycontemporaneous moment,household’sper-capitaexpenditureonfooditemswithinWAEMUisspatiallyheterogeneous)

Ho2. Thereisasignificantcross-sectionalconvergenceinhouseholds’non-foodwellnesswithinWAEMU(i.e.atanycontempora- neousmoment,household’sper-capitaexpenditureonnon-fooditemswithinWAEMUisspatiallyhomogeneous)

Ha2. Thereisnosignificantcross-sectionalconvergenceinhouseholds’non-foodwellnesswithinWAEMU(i.e.atanycontempo- raneousmoment,household’sper-capitaexpenditureonnon-fooditemswithinWAEMUisspatiallyheterogeneous)

Despite its cross-sectional design,to the best ofthe author’s knowledge,this studyis the first ofits kind toaddress convergence inhouseholdfoodandnon-foodconsumption expenditures,exploring their cross-sectional propertieswithin theWAEMUzone,througha spatio-temporalprocesslens,conditional ongeospatialaswellasmicro-economicandsocio- demographic factors. Inso doing,the presentstudybridges an importantgapbetweenthe streamofliterature on cross- country convergencein theWAEMU zone, andthat focusing onsheddinglights onhouseholdconsumption expenditures determinantswithinWAEMU.

Methods

Thedataandstudyvariables

Thecurrentlyuseddatainthisstudyareextractedfromthefirsteditionofthe“Enquetteharmoniséesurlesconditions de vie des ménages” or“Harmonized Survey on Householdslivingstandards” (EHCVM, 2018/2019), which isa nationally representativehouseholdlevelsurveyconductedwithinallWAEMUcountrymembers.Thesurveyispartofajointprogram by the worldbank andthe WAEMUcommissionthat aims toproduce standardized householdleveldatacovering Benin, BurkinaFaso,Chad,Coted’Ivoire,GuineaBissau,Mali,Niger,SenegalandTogo(seeFig.1).Itisimplementedintwostages, withineachcountrybytherespectiveNationalStatisticalInstitute,andcovershouseholds’representativeofthegeopolitical zonesatboth,ruralandurbanlevels.The firststagesamplingcorresponds totherandomselection ofenumerationareas, while thesecond stage samplingcorresponds to theselection ofhouseholds within eachenumeration area. Furthermore, to accountforconsumption seasonality,the surveywasfieldedin twowavesbetweenOctober2018 andJuly2019, using two main survey instruments(ahouseholdlevel questionnaire,anda communitylevel questionnaire)to producenation- allyrepresentativeestimatesforhouseholds’annualconsumptionspending,andarangeofdemographicandsocioeconomic characteristics fortheciviliannon-institutionalizedpopulationsineach countrymember.There isagrowing consensus in the value of usinghouseholdeconomic surveys toassess householdeconomic wellbeing [37,41], therefore,Table2 sum- marizes thecharacteristicsofthesurveysamplingdesign,whileTable3describesthekeyexperimentalfactors(variables) used.

Fig. 1. Regional level frequency count of study sample respondents within WAEMU.

4

Table 2

Study Data Sample Characteristics.

Country

WAVE 1 WAVE 2

Total raw Sample Size

Final Treated data Sample October 2018 to December 2018 April 2019 to July 2019 Final Sample

Size (n)

Final Retention Rate (%)

Urban Rural Urban Rural

Ivory cost 2744 3746 2531 3971 12,992 12,992 100%

Benin 1940 2057 2000 2015 8040 8012 99.65%

Burkina Faso 1577 1930 1572 1931 7070 7010 99.15%

Guinea Bissau 1015 1649 975 1712 5351 5351 100%

Mali 1338 1570 1414 2280 6603 6602 99.98%

Niger 715 2268 862 2179 6024 6024 100%

Senegal 1961 1607 1980 1608 7156 7156 100%

Togo 1034 1897 1236 2004 6171 6171 100%

WAEMU 59,407 59,318 99.85%

Table 3

Study variables definition and description.

Variables Name Variable Description

Country Household’s country of residence within WAEMU

Zae Household’s Agro-ecological Zone of residence within each country Residency Place of residency (rural/urban) of the household

Region Administrative region of residency of the household hhid Unique identification number (ID) of the household hhweight The probability weight of the household

hhsize The size (or number of people) in the household hgender The Gender of the household head

hage The Age of the household head, in years hmstat The marital status of the household head heduc The education level of the household head hLiteracyStat The literacy status of the household head

hdiploma The highest degree certification received by the household head hhandig Household head’s general health status (whether or not a major handicap) hSectEconAct Household head’s sector of economic activity

dali Household total expenditure on food consumption (measured in CFA franc) dnal Household total expenditure on non-food consumption (measured in CFA franc) deptot Household Overall nominal consumption expenditures in 2018 (in CFA franc) zref Official country level poverty threshold for the year 2018

pcexp Household real per-capita personal consumption expenditure in 2018 Source: Extracted from the 2018/2019 EHCVM, at https://phmecv.uemoa.int/nada/index.php/catalog.

Conceptualframework

The conceptualframework belowdefinesthe keyfactors assumedtodrivehouseholdfoodandnon-foodconsumption spending, asindicatorsof householdeconomicwell-being within WAEMU. Theframework stipulates that householdfood wellnessandnon-foodwellnessarejointlydrivenbyacombinationofexternalandinternalfactorstothehouseholds.Ex- ternally,householdeconomicwellbeingisassumedtobeinfluencedby:(i)overallregionalbloclevelinfluencesofmonetary policiesfromthecentralbankoftheWestAfricanStates;(ii)countrylevelfiscalandsocialpolicyeffectsfromnationalgov- ernance;(iii)decentralized,withincountrylevelpolicyeffectsfromlocaladministrativegovernance;and(iv)agro-ecologic zone levelrandom climate/environmentalinfluences. Internallyhowever,householdeconomic wellbeingis assumedtobe influencedbyobservedhouseholdcharacteristics,aswellasun-observedhouseholdcharacteristics(orhouseholdlevelran- dominfluences).

Econometricmodelandestimationprocedure

Attheregional(WAEMU)economicbloclevel,thespatio-temporalprocessgeneratinghouseholdfoodandnon-foodcon- sumptionspending,isexpressedas:C_{i jkzlt},whichrepresentshouseholdi’sconsumption type j,withinlocaladministrative region k, inagro-ecologicalzonez,insidecountry l,attimeperiodt.Overtime,C_{i jkzlt} isassumedtofollowa spatialran- domprocess, thusgeneratingspatio-temporaldatawithin thewholeWAEMUregionaleconomic bloc.Morespecificallyat anygivencross-sectional pointin timet,householdconsumptionC_{i jkzl} in theblocis assumedtofollowa randomlattice process,withspatial“snapshots” overacountablesubsetofspatialpolygons(orgeographicspaces).

Theoretically,thisviewallowsresearcherstostudythespatio-temporallatticeprocessinitsvariousdimensions,includ- ing its cross-sectionaldimension asspatial“snapshots”.Therefore,the presentresearch reliesonthe 2018cross-sectional snapshot,extractedfromthefirsteditionofthe(EHCVM,2018/2019),alongwithspatio-temporalstatisticalmodelingmeth-

I. Niankara Scientific African 20 (2023) e01724

ods, tostudyits mostrecentcross-sectionalproperties,andcharacterizethesocioeconomic mechanismsgivingrise toits variationswithinWAEMU.

Tothisend,theadventofincreasedcomputingpowerinrecentyears,haveeasedtheuseofnumericalmethodstoassess complex“ConditionalProbability” representationsofinter-dependentprocesses.Thispaperreliesthereforeonsuchnumeri- cal alternativetojointlymodelhouseholds’(foodconsumptionwiththeirnon-foodconsumption)expenditures,conditional onasetofdrivingfactors(includingsocioeconomic,demographicandspatial).Thisnumericalalternativetoclassicalmulti- level modelingofmarginallikelihoodfunctions, reliesoncopulafunctionstorepresenthouseholdi’s consumptiontype j, within local administrativeregion k, in agro-ecologicalzone z, inside country l, attime period“t”; as a spatialrandom process withconditionalmeanandvariancefunctionsexpressedasmixedlinearmodelswithknowncovariateswhoseco- efficientsareunknownandnon-random,togetherwithknownbasisfunctionswhosecoefficientsareunknownandrandom.

As describednext,thisformulationleap-frogsthecomputationalbottleneckcausedbyinvertingverylarge covariancema- tricesinmarginalprobabilitymodeling[50].

Bivariatecopulamodelofhouseholdfoodandnon-foodconsumptionspending

The copula method used in thisstudy provides a simple andefficient way to model multipleresponses in a regres- sion setting.Itfollowstheapproach outlinedin Niankara[39],andNiankaraetal.[40].In thiscontext,Y₁ representsthe household foodconsumption spending in WAEMUandY₂ represents householdspending on non-fooditemsin WAEMU.

BothY₁andY₂ arecontinuousvariablesthatmeasurehouseholds’foodandnon-foodwell-being,respectively.Theyhavea jointcumulativedistributionfunction,F(^y1, y₂

|

^z1,z₂)^,wherez1andz₂ arehouseholdconsumptioncovariatesaspreviously describedinFig.2.Thecopulaadditiverepresentationofthisfunctionis:

F

(

^{y1, y2}

|

^z1,z2

)

^=C

(

^F1

(

^y1

|

^z1

)

^{, F2}

(

^y2

|

^z2

)

^;

θ )

⁽¹⁾

Where F1(^y1

|

^z1)^andF2( ^y2

|

^z2) ^{are themarginal cumulative} distributionfunctionsofY1 andY2 respectivelyandtake values between0and1.C(.,.) ^{isa unique copulafunction that doesnot relyon thespecific marginal} distribution func- tions, while

θ

^{is a parameter of the copula function that measures the dependence between the two marginal distri-}

butions (Sklar, 1973; Kolev andPaiva, 2009). The marginal distributions ofY1 andY2 are defined using parametric den- sity functions, denoted by Fm(^ym

| μ

^m,

σ

^m,

v

m) ^{and fm}(^ym

| μ

^m,

σ

^m,

v

m) ^{for m}=1, 2 respectively. The parameters

μ

^m,

σ

^m

and

v

m are thelocation, scale,andshape parameters ofthe marginal distributions respectively(StasinopoulosandRigby, 2007).Thenumberofcoefficientsthat characterizeFmand fm dependsonthechosen copulafunction.TrivediandZimmer (2007)demonstratethat thereisa relationshipbetweenthecorrelation coefficient

θ

^{andKendall’s}

τ

^{coefficient,whichis}

a commonlyusedmeasure ofassociation intherange[−1,1].Inthisstudy,the performance oftheGaussian copula, rep- resented byC(^u,

v

;

θ

)=2(⁻¹(^u), ⁻¹(

v

);

θ

) ^for

θ

∈ [−1,1]; with Kendall’s

τ

= _π²arcsin(

θ

)^{and the} transformation functiontanh⁻¹(

θ

)^{,iscomparedtothatofthreeothercopula}specifications(Clayton,Joe,andFrankcopulas)forsensitivity analysis.

Specificationofthepredictorfunction

The general formof the predictorfunction under copulamodeling is expressed across all N householdsin the study sampleas:

η

ⁱ⁼

β

^{0+ K}

k=1

s_k

(

^zki

)

^,

∀

^{i=1...N (2)}

WhereNisthetotalnumberofhouseholdrespondents,

β

0∈Risthemodel’sintercept,z_kidenotesthek^thsub-vectorof thecompletecovariatevectorz_i,whichcontainsthefixedandrandomfactorsdescribedintheconceptualframework.TheK functionss_k(^zki)^{representgenericeffectswhicharechosen accordingtothetypeof}covariate(s)underconsideration.Each s_k(^zki)^isapproximatedbyalinearcombinationofJ_kbasisfunctionsb_kJ

k(^zki)^{andregressioncoefficients}

β

kJ_k∈R,thatis:

Jk

jk=1

β

kJkb_kJk

(

^zki

)

⁽³⁾

In this form, Eq. (3) implies that the vector of evaluations

{

^sk(^zki),...,s_k(^zkn)

}

^{T can be written as Z}k

β

k with

β

k= (

β

k1,...,

β

kJ_k)^{T anddesign matrix Z}k[i, J_k]= b_kJ

k(^zki). This allows the predictor function in Eq. (2) to be rewritten as:

η

⁼

β

^{01N+ Z1}

β

^{1+...+ Z}k

β

k (4)

Where 1_N is the N-dimensional vector of ones. This expression can be more compactly rewritten as

η

=Z

β

^{, with}

Z=(¹N, Z₁,..., Z_k) ^and

β

=(

β

0,

β

1^T,...,

β

K^T)^{T.In this}representation, thesmooth functionsmayrepresent linear,non- linear;randomandspatialeffects.Moreover,each

β

khasanassociatedquadraticpenalty

λ

k

β

K^TD_k

β

k,whichplaystherole ofenforcingspecificpropertiesonthek^thfunction,suchassmoothness.Smoothingparameter

λ

k∈[0,∞)^{controlsthetrade-} off betweenfit and smoothness,and playsthe role ofdetermining theshape ofSˆ_k(^zki)^.The overallpenalty canbe defined

Fig. 2. Conceptual framework of household economic wellbeing drivers within WAEMU.

as

β

^TDλ

β

^,withDλ=diag(⁰,

λ

1D₁,...,

λ

KD_K)^.Foridentificationpurposes,thesmoothfunctionsaremeancenteredfollow- ingtheprocedureinwood(2017).

Forvariableswithlinearparametriceffects(i.e.hgender,hmstat,heduc, hdiploma,hSectEconAct,residency),Eq.(4)be- comes Z^T_ki

β

k,andthe design matrixis obtainedby stacking allcovariate vectorsz_ki into Z_k. Forcontinuousvariables (i.e.

hhsize, hage)however,Z_ki, s_k(^Zki)^isapproximated by

J_k

j_k=1

β

kJ_kb_kJ

k(^Zki)^,wherethebkJ_k(^Zki) ^areknownspline basis.The smooth functions are represented using Eilers andMarx (1996)’s regression spline approach, withthe design matrix Z_k comprisingthebasisfunctionevaluationsfordescribingJ_kcurveswithpotentiallyvaryingdegreesofcomplexity.

Toincorporatethespatialeffectsintothiscopularegressionframework,the103localadministrativeregionswithinthe8 sampledcountrymembersoftheWestAfricanEconomicandMonetaryUnion,aresplitintodiscretecontiguousgeographic units,withspatialcoordinates usedthrougha Markovrandomfield sampling. Thislatterapproach isusedto incorporate

I. Niankara Scientific African 20 (2023) e01724

theinformationcontainedinneighboringhouseholdslocatedinthesameadministrativeregionswithinanycountryinside the WAEMUregionalbloc.Inthiscase,Eq.(4)becomesZ^T_ki

β

k,with

β

k=(

β

k1,...,

β

KR)^T representingthevectorofspatial effects. R=103denotes thetotal numberofsampledlocaladministrative regions,while z_ki makes up thecountry labels.

Thedesignmatrixlinkingeachhouseholdrespondentitothecorrespondingspatialeffectisdefinedforr=1,...103by:

Z_k[i, r]=

1i f thehousehold ibelongstotherthregion

0Otherwise (5)

Thesmoothingpenaltyisbasedontheneighborhoodstructureofthediscretecontiguousgeographicunits,sothathouse- holds fromspatially adjacentlocaladministrative regions sharesimilareffects. Specificallythe diagonalmatrixassociated withthequadraticpenaltyisgivenby:

D_k[r,q]=

_{−1i f r=q ∧ r ≈ q}

0i f r=q ∧ r ≈q Nri f r=q

(6)

Wherer ≈ qindicateswhetheranytwolocaladministrativeregionsrandqareadjacentneighbors,andNristhetotal numberof neighborsforthelocaladministrativeregions r.The resulting quadraticpenalty is equivalent to Rue and Held (2005)stochasticinterpretationthat

β

kfollowsaGaussianMarkovrandomfield.

Detailsofthemodel’sestimation

Relyingonthefullparametervector

δ

=(

β

μ^T1,

β

μ^T2,

β

σ^T1,

β

σ^T2,

β

υ^T1,

β

υ^T2,

β

_θ^T)^T,thecopula density functioninEq. (1)can be expressedas:

f

(

^y1i,y2i

| δ )

^=c

(

^F1

(

^y1i

| μ

1i

σ

1i

υ

1i

)

^{, F2}

(

^y2i

| μ

2i

σ

2i

υ

2i

)

^;

θ

i

)

^f1

(

^y1i

| μ

1i

σ

1i

υ

1i

)

^f2

(

^y2i

| μ

2i

σ

2i

υ

2i

)

^{; (7)}

Withcorrespondinglog-likelihoodfunction(Vatter&Chavez-Demoulin,2015),givenby:

( δ )

⁼ N

i=1

log

{

^c

(

^F1

(

^y1i

| μ

¹ⁱ

σ

¹ⁱ

υ

¹ⁱ

)

^{, F2}

(

^y2i

| μ

²ⁱ

σ

²ⁱ

υ

²ⁱ

)

^;

θ

ⁱ

) }

^+N

i=1

2

m=1

log

{

^fm

(

^ymi

| μ

^mi

σ

^mi

υ

^mi

) }

^{, (8)}

Maximumpenalizedlikelihoodestimationoftheparametersrequiresmaximizationof:

^p

( δ )

⁼

( δ )

⁻¹₂

δ

^TSλ

δ

⁽⁹⁾

WhereS_λ=diag(

λ

μ1D_μ1,

λ

μ2D_μ2,

λ

σ1D_σ1,

λ

σ2D_σ2,

λ

υ1D_υ1,

λ

υ2D_υ2,

λ

θD_θ)^with

λ

^definedas(

λ

1, ...,

λ

K)^T.Maximiza- tionofEq.(10)isachievedusingRadiceetal.(2016)trustregionalgorithm,withtheanalyticalscoreandHessianMatrixof

(

δ

)^definedas:

∂ ( δ )

∂β

μ1

= N

i=1

1

f1

(

^y1i

| μ

1i

σ

1i

υ

1i

) ∂

^f1

(

^y1i

| μ

1i

σ

1i

υ

1i

)

∂μ

1i

+ 1

c

(

^F1

(

^y1i

| μ

1i

σ

1i

υ

1i

)

^{, F2}

(

^y2i

| μ

2i

σ

2i

υ

2i

)

^;

θ

i

)

×

∂

^c

(

^F1

(

^y1i

| μ

1i

σ

1i

υ

1i

)

^{, F}2

(

^y2i

| μ

2i

σ

2i

υ

2i

)

^;

θ

i

)

∂

^F1

(

^y1i

| μ

1i

σ

1i

υ

1i

) ∂

^F1

(

^y1i

| μ

1i

σ

1i

υ

1i

)

∂μ

1i

∂μ

1i

∂η

1i

Z_μ1i, (10)

Asimilarapproachisusedtogetthefirstorderconditionswithrespectto

β

μ2,

β

σ1,

β

σ2,

β

υ1 and

β

υ1,whichhave the samestructuresasinEq.(10);with

∂ ( δ )

∂β

θ = N

i=1

1

c

(

^F1

(

^y1i

| μ

1i

σ

1i

υ

1i

)

^{, F}2

(

^y2i

| μ

2i

σ

2i

υ

2i

)

^;

θ

i

)

^×

∂

^c

(

^F1

(

^y1i

| μ

1i

σ

1i

υ

1i

)

, F₂

(

^y2i

| μ

2i

σ

2i

υ

2i

)

;

θ

i

)

∂θ

i

∂θ

i

∂η

θi

Z_θi

The scorevectorsandHessianmatricesfortheGaussiancopulafunctionsusedinthisanalysiswereverifiedusingthe toolsprovidedintheRlibrarynumDeriv(GilbertandVaradhan,2016).Uponreachingconvergenceofthetrustregionalgo- rithm,accurateestimatesofthemodelcoefficientsareobtainedusing

δ

∼ N(

δ

^ˆ, −Hˆ⁻¹p )^withHprepresentingthepenalized HessianMatrix.Formoreinformationaboutthetrustregionalgorithm,pleaserefertoMarraandRadice[31].

ImplementationofthemodelintheRstatisticalsoftware

Toidentifytheappropriatemarginal distributionsforthetwooutcomes,severalcandidateswere considered.Giventhe typicalright-skewnessofconsumptionexpendituredata,whicharegenerallynormalizedthroughlogtransformationforre- gressionmodeling,weconsideredamongthemarginaldistributioncandidates,thelog-normal“LN”,forbothfoodexpendi- tureandnon-foodexpenditure.ThenormalQ-QplotsofthenormalizedquantileresidualsasshowninFig.3belowsuggest theLNdistributiontobeagoodfitforboth.Adoptingthereforethelog-normalmargins,themodelwasthenfitunderfour differentcopulatypes(includingtheGaussian,Clayton,frank,andJoecopula)forsensitivityanalysis.Morespecifically,esti- mationoftheconditionalmean,varianceandcovariancefunctionsforthetwooutcomesoffoodandnon-foodconsumption spendingwereexplicitlyimplementedinRversion3.6.3,usingthe“GJRM()” package,version0.2–5.1[31].TheRcodesfor modelimplementationareincludedinthesupplementarymaterialsfortheinterestedreaders.

Fig. 3. Normal Q-Q plots and Histograms of the normalized quantile residuals of log food consumption spending (top panel), and log non-food consumption spending (lower panel).

Modelsensitivityanalysis

To ensure the econometric results are robust and free from misspecificationbiases, the parameters of the above de- scribed spatialbivariate copularegressionmodel ofhouseholdconsumption spending, are estimatedunderfour different specifications.The keyresultsfromthesespecificationsaresummarizedin Table4,forsensitivityandcomparative model performanceanalyzes.

First, it can be notedinthe table (from thelargest absolutegradient value, the observedinformation matrixpositive definiteness,andtheEigenvaluerange)thattheimplementedtrustregioniterationalgorithmshowssatisfactoryconvergence diagnostics outcomes forall four specifications. Additionally,the estimated correlation coefficients, have fairly consistent signs andmagnitudesacross all fourspecifications.Moreover, the95% confidenceintervalson thecorrelation coefficients are void of zero,suggesting their statistical significance,and theresulting inter-dependence between foodand non-food expendituresatthehouseholdlevelwithinWAEMU.Furthermore,theGaussian Copulaspecification,withitslowestvalues oftheAICandBIC criteriastoodout asthebestperforming specification,andisthereforechosen forresults’presentation purposesinthisstudy.

Following the estimation, the joint and independent cumulative probabilities that a given household fallsbelow the sampleaveragesoffoodconsumptionspending(dali)andnon-foodconsumptionspending(dnal)werecalculatedusingthe output ofthefittedbivariateGaussiancopularegressionmodelwithlog-normalmargins.Theresultingcumulative proba- bilityplots areshowninFig. 4,wheretheleft panel assumesfoodandnon-food wellnesstobe jointlyevolvingforeach household, whiletherightpanel assumesthetwoconsumption typestoevolve independentlyatthehouseholdlevel.The comparisonofthetwopanelsshowsthat thetwo probabilitydistributionsarespatiallynuanced,suggestingthereforethat accountingforthedependenceofthetwoconsumptionprocessesthroughcopulamodelingmattersimportantly.

Results

Descriptivefindingsfromthestudysample

Summarizedin Table5,thedescriptive statisticsshow that atotal of59,318householdsrespondedfullyto thesurvey within WAEMU.AtthespatiallevelwithinWAEMU,householdsareseentobedistributedwithvaryingfrequencies,across 8 countries, with103 regions, and 6 agro-ecological zones. For example,of the 59,318 households in the studysample, 21.9% (12,992)arefromIvoryCoast,followedby13.5%(8012) fromBenin,then by12.1%(7156)fromSenegal,11.8%(7010) from BurkinaFaso, 11.1%(6602) fromMali, 10.4% (6171) from Togo,10.2% (6024) fromNiger, andfinally 9% (5351)from Guinea-Bissau. Inaddition,58% (or34,425) ofhouseholdsliveinruralareas,while theremaining 42%(24,893) areurban households.

Withregardstothedirectcharacteristicsofthehouseholds,Table5showsthattheaveragehouseholdwithinWAEMU hadabout6members,withastandarddeviationofabout4peoplein2018.Onaverage,householdswithinWAEMUspent

I.NiankaraScientificAfrican20(2023)e01724

Table 4

Performance measures of the four specifications of the Spatial Bivariate Copula Regression Model.

Model

Specification AIC BIC Trust Region Iteration Algorithm Characteristics

Trust Region Iteration Algorithm Convergence diagnostics

Estimated Correlation coefficients, with 95% C. I.

Sample size (n) Number of

Trust region iterations before smoothing parameter estimation

Number of Loops for smoothing parameter estimation

Number of Trust region iterations within smoothing loops

Largest absolute gradient value

Observed information matrix is positive definite

Eigenvalue range

(M1): Gaussian Copula Model

specification 1,223,743,982 1,223,749,241

5 2 7 2.523 ×10 ⁻⁶ yes [287.4703,

9,468,943,432]

σˆ F = 0.525 (0.523, 0.527) σˆ NF = 0.558 (0.556, 0.560) θˆ F,NF= 0.512 (0.508, 0.516) τˆ F,NF= 0. 344 (0.341, 0.347)

59,318

(M2): Clayton Copula Model Specification

1,225,762,397 1,225,767,656

6 2 13 1.184 ×10 ⁻⁵ yes [346.0261,

8,841,230,076]

σˆ F = 0.536 (0.534, 0.538) σˆ NF = 0.564 (0.562, 0.566) θˆ F,NF = 0.721 (0.710, 0.732) τˆ F,NF = 0.259 (0.256, 0.262)

59,318

(M3): Joe Copula Model Specification

1,225,764,221 1,225,769,480

6 2 11 8.507 ×10 ⁻⁶ yes [235.5932,

8,325,727,061]

σˆ F = 0.537 (0.535, 0.539) σˆ NF = 0.571 (0.569, 0.573) θˆ F,NF= 1.640 (1.630, 1.650) τˆ F,NF= 0.258 (0.255, 0. 262)

59,318

(M4): Frank Copula Model Specification

1,223,946,498 1,223,951,756

4 2 9 6.685 ×10 ⁻³ yes [94.26285,

9,694,130,080]

σˆ F = 0.531 (0.529, 0.533) σˆ NF = 0.564 (0.561, 0.566) θˆ F,NF = 3.730 (3.690, 3.770) τˆ F,NF= 0.359 (0.356, 0.362)

59,318

Note: Numbers in parenthesis are respectively: the 95% Confidence Intervals (C.I.) on the correlation coefficients.

∗is for significance at 0.05; ^∗∗is for 0.01; while ^∗∗∗is for 0.001.

Source: Author’s own, using the data extracted from the EHCVM 2018/2019, at https://phmecv.uemoa.int/nada/index.php/catalog .

10

Fig. 4. spatial distribution of the joint probabilities (in%) that a given household’s per-capita food and non-food consumption spending fall below their respective sample averages within the WAEMU regional bloc in 2018. These have been calculated using the output from the fitted spatial bivariate copula model.

1202,643CFAfrancs(withastandarddeviationof1010,395CFAfrancs)onfoodconsumption;and1141,136CFAfrancs(with astandarddeviationof1569,584CFAfrancs)onnon-foodconsumptionin2018.

The demographic characteristics of household’s heads in Table 5 show that they were predominantly males, 81% (or 48,045), withan average ageof45.63years (withstandard deviationof 14.66years). Withregardsto maritalstatus, the datashowsthat58.8%(or34,857)ofhouseholdheadsidentifiedasmonogamous,18.7%(or11,106)identifiedaspolygamous, 12.5% (or 7424) aspreviously married, andfinally 10% (or 5931) asnever married. Based onreligiousdenomination, the majorityofhouseholdheadsreportedbeingMuslims(58.3%or34,555),followedby28.4%(or16,850)identifyingthemselves asChristians,thenby8.4%(or4975)reportingtobeanimists,andfinally5%(or2938)reportingotherfaiths.

Withregardstosocioeconomiccharacteristics,only48.6%(or28,853)ofhouseholdheadswereidentifiedasliterate.The greatest majorityofrespondents reportedhavingno education(60.1%or35,658), followed by18.1% (or 10,736)reporting a primaryeducation,thenby 16.9%reportingasecondaryeducation,withonly4.9%(or2882) reportinga post-secondary orhighereducationlevel.Withregardstoeducational certificate,thegreatestmajority(75%or44,473)reportedhavingno certificate,10.9%(or6459)reportedaprimaryschoolcertificate,5.7%(or3401)reportedasecondaryschoolcertificate,4%

(or 2395)reportedatmostahighschoolcertificate,2.6% (or1548)reportedatmosta twoyearsdiploma certificate,and finally1.8%(or1042)reportedatleasta4yearsBachelorcertificate.

With regards to physical health conditionas a labor market enabler, 93.1% (or 55,223) of householdheads reported havingnohandicap,although6.9%(or4095)reportedhavingamajorhandicap.Withregardstolabormarketparticipation, 10.8% (or6432)ofhouseholdheadsreportednotbeingactiveintheWAEMUlabormarket,themajority46.5%(or27,554) reportedtheirmaineconomicactivitytobeintheprimarysectorofWAEMU’seconomy,10.4%(or6158)reportedtheirmain activityinthesecondary sector,20.9%(or12,398) reportedtheirmainactivityinthetertiary sectorofWAEMU’seconomy, andfinally11.4%(or6776)reportedbeingspecificallyengagedincommerce.

Descriptivestatisticsofthedependentvariables

Corroborating the discussion in the model implementation section, the distributional properties of the two outcome variables,assummarizedinTable6belowshowthathouseholdper-capitaexpendituresonfoodandnon-foodconsumptions are bothrightskewedinlevels,since theirmeanvalues,liewell abovetheir respectivemedianvalues.However, applying thelogarithmfunctiontotheseoutcomes,yieldstheirloggedversionswithfairlyequalmeanandmedianvalues,suggesting theirsuccessfulnormalization.ThesenumericalresultsinTable6arefurthervalidatedbythegraphicalfindingsinFigs.(5) and(6)below.

InequalityandpovertyanalysiswithinWAEMUasof2018

Relying on workingprinciplesfrom welfare economics, inequality andpoverty can be analyzed in anyeconomic sys- tem, usingan objectiveindicator ofeconomicwell-being.Thispaperuses theabove householdper-capitaexpenditureon foodconsumptionasa“food-wellness” indicator,andtheper-capitaexpenditureonnon-foodconsumption asa“non-food wellness” indicator.Theresultsoftheanalysisoftheseindicatorsarenowpresentedbelow.

Analysisoftheequitabledistributionoffoodandnon-foodwellnesswithinWAEMU

Toaddress theequitable(or inequitable)nature offoodandnon-foodwellnesswithin WAEMUin2018, thispaperre- lieson theLorenzcurvesproduced usingthe‘LC’function inZeileis [55].As showninFig.7below,thesecurvessuggest that non-foodwellness(gray curve)wasrelativelymoreequitably distributedamonghouseholdswithin WAEMUin2018, than foodwellness(redcurve).TheseLorenzcurves basedgraphicalresultscorroboratesthe findingsfromthe numerical inequality measures inTable7,whereboththeAtkinsonandGinicoefficientsforhousehold’sfoodwellnessarerelatively