Multidimensional Analysis of the Determinants of Poverty Indicators in the Lake Victoria Basin(Kenya)

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1 IOSR Journal of Mathematcs (IOSR-JM) e-issn: , p-issn: Volume, Issue 3 Ver. V (May - Jun. 05), PP Multmensonal Analyss of the Determnants of Poverty Incators n the Lake Vctora Basn(Kenya) Anthony Nguny, Peter. N. Mwta, Romanus O. Ohambo, 3 Verana. G. Masana, Dean Kmath Unversty of Technology ( Kenya) P.O BO , Nyer, Jomo Kenyatta Unversty of Agrculture & Technology ( Kenya ) P.O BO , Narob 3, Unversty of Rwana(Hea Quarters) P.O Box 485, Kgal, Rwana 3, Afflate to Unversty of Dar es Salaam, P.O.Box 3506,Dar es Salaam,Tanzana Abstract: The stuy man obectve s to examne the multmensonal aspects of poverty n one Kenya s culturally verse regon of the Lake Vctora basn. The analyss usng ata collecte by IUCEA researchers n 007 an also the 009 census on househols n Kenya. Ths stuy nvestgates statstcal moels base on factors that characterze the emographc characterstc of nvuals, n etermnng the prectors of poverty for better polcy formulaton.. The research fnngs ncate that poverty measures o overlap to capture a percentage of the sample as poor. The analyss shows that eucaton, gener (beng male), martal status, assets (lvestock, water sources, an wall materals) an age of the hea of the famly have statstcally postve effects on the lkelhoo of an nvual fallng nto poverty. Keywors: Poverty, Demography, Augmente, Logstc, Assets I. Backgroun Informaton Accorng to the Worl Bank,(00).[], poverty s pronounce eprvaton n well-beng. Ths of course begs the questons of what s meant by well-beng an of what s the reference pont aganst whch to measure eprvaton. The obectve of the stuy was to look at the fferent factors that nfluence poverty n a househol, an the polcy formulaton that can be put n place n orer to acheve betterng lvng stanar for the members of the househol. Ths stuy base ts results on a multsplnary aspects on the fact that many stues on poverty n Kenya have been on regressng well known etermnants even though other factors may be able to gve an nformatve an smple to nterpret facts on poverty levels n the regon. One approach s to thnk of well-beng as the comman over commotes n general, so people are better off f they have a greater comman over resources. The man focus s on whether househols or nvuals have enough resources to meet ther nees, see, S. Puney(999) []. Typcally, poverty s then measure by comparng nvuals ncome or consumpton wth some efne threshol below whch they are consere to be poor. Ths s the most conventonal vew-poverty s seen largely n monetary terms-an s the startng pont for most analyss of poverty. A secon approach to well-beng (an hence poverty) s to ask whether people are able to obtan a specfc type of consumpton: Do they have enough foo? Or shelter? Or health care? Or eucaton? As cte n Ravallon an Ban (994); Kakwan (990),[3,4]. In ths vew the analyst goes beyon the more tratonal monetary measures of poverty: Nutrtonal poverty mght be measure by examnng whether chlren are stunte or waste; an eucatonal poverty mght be measure by askng whether people are lterate or how much formal schoolng they have receve, well artculate n Lpton an Ravallon (995),[5]. Perhaps the broaest approach to well-beng s the one artculate by Sen (999), [6], who argues that well-beng comes from a capablty to functon n socety. Thus, poverty arses when people lack key capabltes, an so have naequate ncomes or eucaton, or poor health, or nsecurty, or low self-confence, or a sense of powerlessness, or the absence of rghts such as freeom of speech. Vewe n ths way, poverty s a multmensonal phenomenon an less amenable to smple solutons. For nstance, whle hgher average ncomes wll certanly help reuce poverty, these may nee to be accompane by measures to empower the poor, or nsure them aganst rsks, or to aress specfc weaknesses such as naequate avalablty of schools or a corrupt health servce (Datt an Jollffe, 005). [7]. WHO (000),[8] note that poverty s relate to, but stnct from, nequalty an vulnerablty. Inequalty focuses on the strbuton of attrbutes, such as ncome or consumpton, across the whole populaton. In the context of poverty analyss, nequalty requres examnaton f one beleves that the welfare of nvuals epens on ther economc poston relatve to others n socety. Vulnerablty s efne as the rsk of fallng DOI: / Page

2 nto poverty n the future, even f the person s not necessarly poor now; t s often assocate wth the effects of shocks such as a rought, a rop n farm prces, or a fnancal crss. Vulnerablty s a key menson of wellbeng snce t affects nvuals behavor n terms of nvestment, proucton patterns, an copng strateges, an n terms of the perceptons of ther own stuatons. Accorng to the last Country Brefs, an estmate 3.8 mllon people n rural areas are between hghly to extremely foo nsecure. Foo an Agrculture Organzaton (FAO)/ Global Informaton an Early Warnng System on Foo an Agrculture (GIEWS ) an Famne Early Warnng System (FEWSNET) agree that, n the short term, Kenya s a hunger-prone country, whle WFP an IFPRI assess the long-term stuaton as alarmng an hunger as moerately hgh.. There s a long hstory of peroc shortfalls n foo supply n Kenya. Shortfalls occur all over the country or n parts of the country, an sometmes for two years n a row. In tmes of unfavorable weather, even the provnces normally characterze by a maze surplus (such as the Rft Valley) or margnally self-suffcent provnces (such as Western an Nyanza) may enter a maze efct stuaton. In aton, n areas characterze by chronc efcts (such as the Coast an Eastern an North Eastern provnces) the stuaton becomes acute. In many strcts n these areas, emergency relef becomes necessary. The hghest poverty rate was foun among people lvng n househols heae by farmers 46 percent (KNBS, 007a), []. By contrast, househols heae by someone workng n the government are least lkely to be poor; n these occupatons the poverty rate was 0 percent (993 94). Ths woul suggest that polces that am to reuce poverty through enhancng ncome-generatng capabltes shoul be targete towars the agrcultural sector. The relatonshp between poverty an eucaton s partcularly mportant because of the key role playe by eucaton n rasng economc growth an reucng poverty. The better eucate have hgher ncomes an thus are much less lkely to be poor. Kenyans lvng n househols wth an uneucate househol hea are more lkely to be poor, wth a poverty rate of 47 percent n 04 natonal poverty atlas.. Wth hgher levels of eucaton, the lkelhoo of beng poor falls conserably. Rasng eucaton attanment s clearly a hgh prorty to mprove lvng stanars an reuce poverty. The relatonshp between gener an poverty may also ncate another targetng strategy for poverty reucton. In Tanzana, about 35 percent of the populaton lves n househols heae by women. Perhaps surprsngly, the 007 ata show that the poverty rate was slghtly lower among female-heae househols (48 percent) than among male-heae househols (5 percent). In ths case, targetng nterventons base on the gener of the hea of househol woul not help to stngush the poor from the non-poor, Mark Schrener, [3]. II. Lterature Revew Poverty s a worlwe concern. Although there s a global concern towars poverty reucton, there s a lttle agreement on a sngle efnton an measurement of poverty (Kotler et al., 006; Laerch et al., 003), [4, 5]. Accorng to Kotler et al., (006),[4] an Laerch et al.( 003),[5], the problem of arrvng at one sngle efnton of poverty has been compoune by a number of factors. Poverty affects heterogeneous groups such that the concept of poverty s relatve epenng on fferent nterest groups an nvuals experencng t (Kotler et al., 006, Rank, 004), [4, 6]. The ffculty surrounng the efnton an measurement of poverty has often le poverty researchers an polcy makers to relate poverty to the concepts of mpovershment, eprvaton, the savantage, nequalty, the unerprvlege an the neey. Many researchers have authore many artcles on the ssue of poverty worlwe. The excepton beng the absolute poverty measures for the evelopng worl by Chen an Ravallon (007) [], whch serve to prove the latest evence for an Afrcan exceptonalsm that omnates the evelopment nees of toay. All evelopng country regons have shown marke mprovement n key ncators of poverty,health, economy, an foo, except for sub-saharan Afrca. For poverty, the global number of people lvng below the extreme poverty lne of $ per ay ecrease between 98 an 004 from,470 mllon to 969 mllon. The percentage of extremely poor fell from 40% to 8%. However, n sub- Saharan Afrca, the numbers almost ouble from 68 mllon to 98 mllon, an the percentage staye almost constant from 4% to 4%, Chen S, Ravallon M (007) [35]. For health, the lfe expectancy at brth n sub-saharan Afrca peake n 990 at 50 years but has snce eclne to 46 years, whle stealy rsng n all evelopng country regons to an average of 65 years, Jamson D.T, (006),[36]. Over the pero , sub-saharan Afrca s per capta measure of annual economc growth (gross omestc prouct) was a mere 0.%, whereas other evelopng country regons experence accelerate growth averagng 3.6%, Coller P (007), [37]. Foo proucton per capta grew by.3% per year between 980 an 000 n Asa, grew by 0.9% n Latn Amerca, an eclne by 0.0% n tropcal Afrca see, Dasgupta.P et al (004),[38]. There are bascally two approaches n moellng etermnants of poverty. The frst approach 5 s the employment of consumpton expenture per ault equvalent an regress t aganst potental explanatory varables (Gea et al, 00). Usng ths approach Arneberg an Peerson (00) report that househol DOI: / Page

3 characterstcs an eucaton are the man factors whch affect lvng stanar n Ertrea. However, they treat eucaton as a lnear an contnuous varable. Moreover they fn out that transfer payment from relatves abroa s a sgnfcant contrbutor to the welfare of a socety. From ther analyss they conclue that eucaton s the most mportant factor for the way out of poverty. However, ther approach suffers from the common problems of consumpton as beng ncator of welfare an the assumpton that consumpton of the poor an non poor are both etermne by the same process (Okw, 999). The secon approach s to rectly moel poverty by employng a screte choce moel. The practce of screte choce moels n the analyss of etermnants of poverty has been popular approach 6 (for nstance, Fafack(00) for Burkan faso, Kabubuo-Marara (00) for Kenya; Amueo_Dorantes(004) for Chle; Grootaert(997) for Cote D vore; Gea et al (00) for Kenya; Charlette- Guear an Mesple-Somps (00) for Cote `vore, Goae an Ghazouan (00) for Tunsa; Roubau an Razafnrakoto,003). The analyss then procees by employng bnary logt or probt moel to estmate the probablty of a househol beng poor contonal up on some characterstcs. In some cases also the househols are ve nto three categores: absolute poor, poor an non poor an then employ orere logt or orere logt moel to entfy the factors whch affect the probablty a househol beng poor contonal up on set of characterstcs. In ths stuy we apply the crete choce moel as scusse by many researchers n kenya but also look at the augmente moel propose by Datt. G an Jollffe.D. (005),[34] Common nces evelope by the Unte Natons Development Programme are the human evelopment nex compose of three measures of evelopment (per capta gross omestc prouct, lfe expectancy, an lteracy) or the human poverty nex compose of measures of eprvaton n the evelopment nces (chl an young ault mortalty, llteracy, an lack of water an santaton) Unte Natons Development Programme (006),[37].In the stuy of the lake Vctora basn,we look at the aspects of the asset component as a measure of poverty an artculate the best polcy measures that can be taken nto conseraton to reuce poverty n the area. Poverty stues n Kenya have focuse on a scusson of nequalty an welfare base on lmte house level ata (Arne, 98; Hazlewoo, 98; House an Kllck, 98) [7,8,9]. One recent comprehensve stuy on the subect s that of Gea et al. (00), [0], whch eals wth measurement, profle an etermnants of poverty. The stuy employs a househol welfare functon, approxmate by househol expenture per ault equvalent. The authors runs two categores of regresson, usng overall expentures an foo expentures as epenent varables. In each of the two cases, three equatons are estmate whch ffer by type of epenent varable. These epenent varables are: total househol expenture, total househol expenture gap (the fference between the absolute poverty lne an the actual expenture) an the square of the latter. A smlar set of epenent varables s use for foo expenture, wth the explanatory varables beng entcal n all cases. Gea et al. (00), [0], ustfe ther choce of ths approach (compare to a logt/probt moel) as follows; Frst, the two approaches (screte an contnuous choce-base regressons) yel bascally smlar results; also the expenture as a bnary varable has certan nherent weakness. One obvous weakness s that, unlke the logt/probt moel, the level of the regresson about poverty. Secon, the maor assumpton of the welfare functon approach s that consumpton expenture are negatvely assocate wth absolute poverty at all expenture levels. Thus factors that ncrease consumpton expenture reuce poverty. However, ths basc assumpton nees to be taken cautously. For nstance though ncreasng welfare, rasng the level of consumpton expenture of househols that are alreay above the poverty lne oes not affect the poverty level (for example measures by the heacount rato). Notwthstanng such weakness, the approach s wely use Gea et al. (00),[0] entfe the followng as mportant etermnants of poverty: unobserve regon-specfc factors, mean age, sze of househol, place of resence (rural versus urban), level of schoolng, lvestock holng an santary contons. The mportance of these varables oes not change whether the total expenture, the expenture gap or the square of the gap s taken as the epenant varable. The only notceable change s that the szes of the estmate coeffcents are enormously reuce n the expenture gap an n the square of the expenture gap specfcatons. Moreover, except for the mnor changes n the relatve mportance of some of the varable, the pattern of coeffcent agan funamentally remans unchange when the regressons are run wth foo expentures as epenant varable. Another recent stuy on the etermnant of poverty s Oyug (000),[], whch s an extenson to earler work by Greer an Thorbecke (986b,a).[,3]. The later stuy use househol calore consumpton as the epenant varable an a lmte number of househol characterstcs as explanatory varables. An mportant aspect of Oyug s stuy s that t analyse poverty both at mcro (househol) an meso (strct) level, wth the meso level analyss beng the nnovatve component of the stuy. The explanatory varable (househol characterstcs) nclue: holng area lvestock unt, the proporton of househol members able to rea an wrte, househol sze, sector of economc actvty (agrculture, manufacturng/nustral). The results of the probt DOI: / Page

4 analyss show that all varable use are mportant etermnants of poverty n rural areas an at the natonal level, but that there are mportant exceptons for urban areas. In the probt moel, however, n the orer of mportance the key etermnants of poverty are: beng able to rea an wrte, employment n off-farm actvtes, beng engage n agrculture, havng a se-busness n the servce sector, source of water an househol sze. Regon of resence appears to be equally mportant n etermnng poverty status n the two approaches. Although the two approaches not employ the same explanatory varables, ths comparson ponts to the possblty of arrvng at fferent polcy conclusons from the two approaches Oyug (000),[]. 3. Area of Stuy III. Methoology Fgure : The Lake Vctora Basn on the Kenyan se. The stuy ste consttutes three strcts of the Lake Vctora basn. Some of the raw ata on Househol Demography, collecte urng the frst year work of the proect enttle Mathematcal Technques for Foo Crops Balance Sheet an Foo Securty Incators n Lake Vctora Water She, see [4] s use n ths stuy. The ranom samplng approach was employe to select the stuy areas an sample responents n whch the subects selecte were suppose to meet the stuy nees. A total of 4 househols n each of the three strcts (Kura, Saya an Ksumu) n Kenya were surveye usng structure questonnare, ntervew sessons, focus group scusson an observaton. A lst of househol heas (whch s the samplng frame from whch a probablty sample s selecte) were supple by respectve sub-locaton amnstratons. These lsts were each use to select 4 househols from each sub-locaton by employng smple ranom samplng technque. Ths metho of sample selecton s free form bas; t has gven every househol hea n each sub-locaton a chance of beng nclue n the sample for ths stuy. The stuy also makes use of ata obtane from the 009 Populaton an Housng Census conucte by the Kenya Natonal Bureau of Statstcs. The survey questonnare collecte nformaton on househol an emographc characterstcs, eucaton, assets, employment, ncome, an expentures an assets n the househols. The questonnare nclue nformaton on househol members an was amnstere to all househols n the country, wth the excepton of North Eastern Provnce. Although the census not collect nformaton on ncome an expentures, t proves nformaton on a number of characterstcs that have been shown to be strong correlates of poverty.. Such characterstcs nclue assets, eucaton an the househol sze. DOI: / Page

5 3. Specfcaton of the Regresson Moel When poverty s efne as the current consumpton efct, a househol s categorze as poor f the value of per capta consumpton of ts members s lower than the poverty lne. Therefore, t s logcal to search for poverty prectors base on varables that correlate wth per capta househol consumpton. These varables can be obtane by estmatng a moel of consumpton correlates, where the left-han se s per capta consumpton an the rght-han se s a set of varables that s thought of correlatng wth househol consumpton. Dfferent from etermnants moel, n correlates moel the enogenety of the rght-han se varables s not a concern,see Datt an Jollffe, 005). [37]. Once the set of the rght-han se varables has been etermne, a stepwse regresson proceure s employe to estmate the moel. The stepwse estmaton proceure s use because n the en we want to obtan a manageable number of varables that can be relatvely easly collecte n practce an at the same tme meanngfully use to prect househol consumpton level an poverty status. 3.3 The Augmente moel The usual approach concernng poverty measurements has hstorcally been to moel poverty Drectly.The consumpton moel can be escrbe as the basc moel.futhermore the moel of consumpton c, the etermnants of per capta consumpton at the househol level n the smplest form of a moel s as follows logc = β x () e where x s a set of househol characterstcs an e s a ranom error term.. It has the feature that the margnal effects of the etermnants of consumpton are constant across househols. It s however arguable that there s heterogenty across househols an the margnal effects themselves epen on househol characterstcs. Ths concern leas us to conser the augmente moel that allows for a range of nteracton effects an nvual specfc margnal effects (β ); where β = ' β x e an hence logc Ths elvers a moel wth heterosceastc errors, logc = β x e * = ' x x x e * e β () e, whch s easly allowe for estmatng the varance matrx of the moel parameters. The moel has a generalze quaratc form whch s a numercally equvalent secon orer approxmaton to any arbtrary twce fferentable functon (Fahrmer an Kaufmann, 985). [5]. 3.4 Specfcaton of the Poverty logstc Moel Choosng an approprate moel an analytcal technque epens on the type of varable uner nvestgaton. Regresson eal wth cases where the epenent varable of nterest s a contnuous varable whch we assume, perhaps after an approprate transformaton, to be normally strbute. But n many applcatons, the epenent varable of nterest s not on a contnuous scale; t may have only two possble outcomes an therefore can be represente by an ncator varable takng on values 0 an. In ths stuy, the epenent varable Y was efne to have two possble outcomes:. The househol s poor ( ). The househol s not poor ( 0 ) These two outcomes are coe an 0 respectvely. Ths shows that the epenent varable s chotomous an t can be represente by a varable takng the value wth probablty π an the value 0 wth probablty π. Such a varable s a pont bnomal varable, that s, a bnomal varable wth n = tral, an the moel often use to express the probablty π as a functon of potental nepenent varables uner nvestgaton s the logstc regresson moel. Therefore, to sort out whch explanatory varables are most closely relate to the epenent varable, nne factors are consere. Ths metho nvolves a lnear combnaton of the explanatory or nepenent varables. Thus, the stuy s moele wthn the framework of above mentone theores an the moel use by ths stuy to etermne factors affectng poverty status s gven equaton (3). DOI: / Page

6 3.5 Logstc Regresson Analyss The functon has been scusse by many researchers lke [6]. It s gven by; exp( g) f g = = exp g exp g (3) when moelng a Bernoull ranom varable wth multvarate, one rectly moels the probabltes of group membershp, as follows; P Y = x = exp 0 x = = (4) where g n Equaton 3 s gven by g = 0 ; (5) To llustrate, the applcablty of the logstc functon, the bol curve n the fgure shows that the logstc functon puts more weght on the tals than the normal strbuton. Author (04) Fgure : Stanarze Normal an Logstc CDF s The logstc moel s boune between zero an one, ths property estmates the possblty of gettng estmate or precte probabltes outse ths range whch woul not make sense. Also wth a proper transformaton, one can get a lnear moel from the logstc functon. [6] uses the logt functon of the Bernoull strbute response varable. Transformng Equaton 4 as n [6] we have ; Logt P Y = = x P Y = = x = loge P Y = = x DOI: / Page

7 exp 0 = log e exp 0 = log e exp 0 = = = = = (6) 0 the functon n Equaton 6 s a generalze lnear moel (GLM) wth nepenent varables. The motvaton to the use of logstc moel s that t follows the propertes of the GLM. Lets efne the hypothetcal populaton proporton of cells for whch Y = as = P Y = = x. Then the theoretcal proporton of cells for whch Y = 0 s = P Y = 0 = x. We estmate by the sample proportons of cells for whch Y =. In the GLM context, t s assume that there exsts a set of prector varables,,,,, that are relate to Y an therefore proves atonal nformaton for estmatng Y. For mathematcal reasons of atvty an multplcty, logstc moel s base on lnear moel for the log os n favour of Y =. log e = = (7) thus = (8) =0 where of unknown parameters. The logstc regresson (logt lnk), g = log = logt e an g thus the nverse of the logt functon n terms of ; s gven by; Ths moel can be rewrtten as g g = ; = = exp 0 =0 = x logt( ) = (9) DOI: / Page

8 IV. Results an Dscusson 4. Emprcal stues of the Stepwse Regresson Moel an the Augmente Regresson Moel For several of the explanatory varables, there are observatons wth mssng ata an have constructe ummy varable that take a value of one f the househol s mssng ata for a partcular varable(whle the value of that varable tself s set as zero). In ths way, we reuce the potental of sample selecton bas, an we o not mss out on useful nformaton from househol wth some val ata for most varables. Per capta consumpton s use as the basc measure of nvual welfare. The use of per capta consumpton mposes the assumptons that there are no economes of househol sze n consumpton an that househol composton oes not matter, an therefore, the estmate parameters must be nteprete wth cauton. There may also be some concern of potental bas n parameter estmates ue to enogenety of omtte varables. If these factors are sgnfcant etermnants of welfare, the error term wll not converge to zero n probablty lmt an the parameter estmate for the nvual explanatory varables wll be nconsstent. To control ths, nteractons term effects are nclue n the moel. Whle the augmente equaton offers a farly general approach to moelng welfare, ths generalty comes wth the potental cost of overparameterzng the moel wth the full set of nteracton terms, there are an k k( k ) exploson of parameters. Begnnng wth a k-parameters n the basc moel, there are parameters n the augmente equaton. A moel wth numerous parameters s lkely to suffer from multcollnearty. In the vew of these ffcultes; we use the stepwse regresson as our basc moel so as to lmt them to only those sgnfcant n the moel. see Mcheal. H.K et al.(005),[39] Table : Stepwse an augmente moelng of the log per capta consumpton Varables Descrpton Stepwse moel Augmente moel Coeffcent t-rato Coeffcent t-rato Hhsze (.) (***) Hh sze -0.08(*) (**) Gener Hh (hea) (.) Lan sze (acre) 0.584(.) (*).03 6 Hh (hea)age 0,588(***) Hh (hea) age (**) Hh Aveage n school (.) (**) Proucton(kg) per year (***) (*).676 :8 Hh sze* Hh Aveage n school (*) -.39 (Sgnfcance coes: *** 0.00, **0.0, * ) (Hh-Househol: A omestc unt consstng of members of a famly who lve together) Table represents both the stepwse regresson moel an the augmente moel. The null hypothess, that nteractons n the augmente moel are ontly equal to zero s convncngly reecte. Thus, there s no support for the stanars are unform across househols. The househol sze has sgnfcant negatve (though nonlnear) effects on welfare. Ths nverse relaton between househol sze an the log per capta consumpton s a common fnng n the lterature (Lanouw an Ravallon, 995; Lpton, 00), [7,8]. The measure of per capta consumpton as use n the stuy s the total foo consumpton,non-foo an othe expenses of the househol. Each of these components of consumpton s well ocumente n more etals n the basc report of well-beng n Kenya 005/06.thus consumpton s crtcally epenent on the unerlyng assumpton regarng economes of househol sze an equvalent scales. Eucaton varable emerge as a strong etermnant of welfare. In both moels the average years of schoolng specfe on ts own have sgnfcant postve effects on per capta consumpton. However, once the moels have been augmente wth nteractons, several nteracton terms n schoolng are foun to be sgnfcant. For example, the margnal return to school s foun to be ncreasng wth househol sze as well as ecreasng wth the number of the years n school. We fn a strong postve sgnfcance effects on the average number of years n eucaton for the famly. The moels ncate strong postve effects on househol f the famly s eucate. Ouro et al. (004),[9] argue that eucaton an skll acquston are crtcal factors for explanng the pattern of rural poverty. Eucaton contrbutes to the process of moulng atttunal sklls an evelopng techncal sklls, an also facltates the aopton an mofcaton of technology [9]. The stuy fns that famly that owne lan (for proucton) has a sgnfcant postve effect on per capta consumpton of the househol, DOI: / Page

9 The age of the househol hea shows that the expecte lfe cycle n the stepwse moel ncreases poverty status by 5%, also the quaratc term of the age whch s nonlnear shows a eclne n the lfe cycle phenomenon of hgh earnng capacty wth greater experence an smoothng of consumpton over lfe cycle. There have been smlar fnng by other authors though usng a fferent technques, (Datt an Jollffe, 005; Mwabu et al., 000; Oyug, 000), [7,30,3]. Table 3: Sutablty of the moels as ncator of poverty Moels R Stanar error Stepwse Augmente Emprcal stues of the Logstc Moel Ths metho prects poverty rectly because of the nature of the epenent varable. There are two thngs that nee to be reterate. Frst, the epenent varable takes values the values of when the responent s poor an 0 otherwse.ths means n nterpretng the estmaton result t s mportant to remember that a postve coeffcent means that the varable s correlate postvely wth the poor. Secon, precte value of the epenent varable s the probablty of the observaton to be poor. A logt moel has been estmate to elct the factors nfluencng welfare status of househols. The moel uses current welfare status of househol as the chotomous epenent varable. poverty varable s efne on the bass of the varable etermnant of poverty ncate below. The varables n ths case are: Y Poverty of househol ( = Poor, an = 3 0 Non-Poor) Househol sze Square of househol sze Gener of househol hea ( = male, an 0 = female) lan sze(acres) 4 Eucaton of HH hea ( = Prmary level an above, 0 = No Eucaton) Age of Hh (hea) Square of Age of Hh (hea) Per capta aggregate proucton (No. of Kgs) The logstc moel was ftte to the ata to test the relatonshp between the lkelhoo of a househol beng poor or non-poor. The logstc regresson analyss was carre out by stepwse metho, an the result showe that The optmal moel Z R = (0) Accorng to the moel, equaton 0, the log of the os of a househol beng poor was negatvely relate to sze of the househol p=0.0), whch accorng to lterature, Pay (003) [3] note that househol sze was negatvely correlate to poverty an Deaton an Paxson (995) [3] foun that foo requrement ncrease n relaton to the number of persons n househol. The non-lnear component of the househol sze s postvely correlate to poverty. Ths s a common fnng n the lterature, see [7] an [8]. The log of os of the gener of the hea of the househol was postvely relate to the poverty ( p = 0.05). The age of the househol hea shows the expecte lfe expectancy. In our moel, househol lvng stanar ncreases wth the age of the househol hea upto the optmal age of aroun 60 years but ecreases wth the quaratc term whch s sgnfcant p = Ths s consstent wth hgher earnng wth greater experence. DOI: / Page

10 There s a strong ntergeneratonal effect on eucaton. Parental eucaton has a strong postve correlaton on househol welfare. Foo proucton was expecte to be ncrease extensvely through expanson of areas uner utlzaton. The moel ncates lan sze ncrease foo securty wth even though ( p > 0.05). The moel, the log os of lan sze n postvely relate to poverty ( p = 0.05). In other wors, the larger the sze of lan the ncrease to proucton. The proucton (kg) of the househol, the log of the os ncates that a unt ncrease of foo proucton mprove the foo poverty status of the househol by.4%, wth ( p > 0.05). Prectors Table 4: Prectors SE () z p -value e (Os ratos) Sze of Hh (numbers) Square of househol sze Gener of Hh hea (-male, female) Lan sze(acres) Eucaton of Hh hea ( = Prmary level an above, 0 = No Eucaton) Age of of Hh hea *.0000 Square of Age of Hh hea *.060 Per capta aggregate proucton (No *.040 of Kgs) (Dsperson parameter for bnomal famly taken to be ) 4.3 Evaluaton of the logstc regresson moel The overall moel evaluaton s sa to prove a better ft to the ata f t emonstrates an mprovement over the ntercept only moel (also calle the null moel). An ntercept only moel serves as a goo baselne because t contans no prectors. Accorng to ths moel, all observatons woul be precte to belong n the largest outcome category. An mprovement over ths baselne s examne by usng three nferental statstcal tests. Table 5: Statstcal nference table Statstcal test Statstcs Test f Lkelhoo rato test Hosmer-Lemeshow Wal test The statstcal sgnfcance of nvual regresson coeffcent.e. ( s) s teste usng the Wal chsquare statstc. Accorng to table 5, the varables are sgnfcant prectors of poverty ( p < 0.05). Gooness-of-ft statstcs assess the ft of a moel aganst actual values. The nferental gooness-of-ft test s the Hosmer-Lemeshow (H-L) test that yels a ( p <. of an was nsgnfcant 0.05) Suggestng that the moel fts the ata well. In other wor s, the null hypothess moel of a goo moel ft to ata was tenable. The lkelhoo rato test yels a p > whch (5) (5) of an was sgnfcant at 0.05 also gve a goo ft for the moel an thus the null hypothess was also tenable for the moel. p DOI: / Page

11 Table 6: 95% confence nterval for one unt change n t Sze of Hh (Number) , Square of househol sze 0.000, Gener of Hh hea (-Male, 0-Female) 0.099, Lan sze 0.456, Prmary level an above, 0 = No Eucaton.0770,.566 Eucaton of Hh hea ( = Age of Hh hea , Square of Age of Hh hea 0.00,0.004 Per capta aggregate proucton (kg) , The full moel s: Z F = () We wsh to test H : = = The reuce moel s: 0 0 = H : 0 A Z R 0 = Table 7: Devance analyss of the moel Moel Null Devance f Resual Devance f Full moel Reuce moel Therefore, we o not reect the hypothess, an conclue that the reuce moel s a better moel than the full moel. 4.4 Comparson of the two moels usng the confuson matrx The confuson matrx s commonly use to compare two moels on how goo the precte responents. In our stuy the followng matrx were obtane: Table 8: Logstc moel Incator observe Table 9: Augmente moel Incator observe The confuson matrx nforms us that the logstc moel s better for prectng poverty than the augmente moel snce t has a hgh precton of accurate responents than the augmente. 4 DOI: / Page

12 4.5 Housng contons 4.5. Roofng Materal as measure of poverty Maorty of the responent represente by 78% stay n corrugate ron sheet houses, followe by wth glass thatche houses at 6%, there are also about % houses roofe wth tles, another.5% wth asbestos an the other wth about 3% roofe by other materals, ths factor may not gve a goo ncator of poverty but f looke from the perspectve of the whole house bulng materal we wll be able to see that ths ncator can be able to gve some ncaton of poverty. Fgure 3: Roofng Materals 4.5. Wall materal as a measure of poverty The Maorty of houses are walle usng mu an woo whch represents 6%, 9% are mae of brcks, 7% are walle wth mu an cement an the others about 3% are walle wth other materals lke tmber an stone whch ncates that even combne wth roofng materals ths area poverty s very hgh. Fgure 4: Wall Materals DOI: / Page

13 4.5.3 Man water sources as a measure of poverty Accorng to [8] about. bllon people lack access to mprove water sources, whch represents 7% of the global populaton. In orer to acheve the mllenum goals, many efforts nees to be one n the areas to ensure the people have clean an safe water. The area maorty about 80% only get water from rvers, lake an streams whch many tmes are not clean. [33] also argues that lmte access to basc servces such as to runnng water, santaton on ste, gr electrcty an health care servces s an mpement to escapng from poverty. 4.6 Informaton regarng lvestock 4.6. Poverty aganst Ingenous cattle Fgure 5: Water Sources Table 8: Inegenous cattle Ch-square Test Tests Value.f Asy. Sgnfcance Pearson ch-square a Lkelhoo rato test Lnear by Lnear assocaton N of val cases 444 a 0 cells 0% have expecte count less than 5. The mnmum expecte count s 7.5 In the table 7, we can see that ch square test (3) = at p < Snce the p-value s less than 0.05, we reect the null hypothess an say that there s statstcally sgnfcant assocaton between poverty an the rearng of the ngenous cattle n the regon. The sample sze requrement for ch-square test of nepenence s satsfe snce zero cells (0 %) has expecte count less than Poverty aganst Goat Table 9: Goat Ch-square Test Tests Value.f Asy. sgnfcance Pearson ch-square a Lkelhoo rato test Lnear by Lnear assocaton N of val cases 444 0% have expecte count less than 5. The mnmum expecte count s 5.3 DOI: / Page

14 The table 8, shows the relatonshp between poverty an goat rearng s also statstcally sgnfcant as we can see from the ch square test() = 85.3 at p < 0.05.The sample sze requrement for chsquare test of nepenence s satsfe snce zero cells (0 %) has expecte count less than Poverty aganst Sheep Table 0: Sheep Ch-square Test Tests Value.f Asy. sgnfcance Pearson ch-square a Lkelhoo rato test Lnear by Lnear assocaton N of val cases 444 a 0 cells 0% have expecte count less than 5. The mnmum expecte count s 9.80 Table 9 ncates also that n the regon there exsts a relatonshp between poverty an sheep rearng whch s statstcally sgnfcant wth ch square test (8) = at p < 0.05.The sample sze requrement for ch-square test of nepenence s satsfe snce zero cells (0 %) has expecte count less than 5. Number of total lvestock unts owne reuce househol poverty rates, mplyng that assets are mportant etermnants of poverty. Ths fnng s consstent wth earler fnngs for Kenya [0, an 30]. V. Concluson The man obectve of the stuy even wth ffcultes of obtanng expenture an ncome ata househol precse ata an to fnng varables that prect poverty n rural areas of Kenya s acheve. In the stuy we explore the two methos, augmente regresson moel an the logstc regresson moel, on prectng poverty.the logstc moel was better snce t was able to prect correctly all responents, whle the augmente moel ha a precton rate of about % of not prectng correctly the responents n the consumpton moel. However, snce our am s to prect the poor for polcy mtgaton we focus on the metho that proves us wth the most accurate precton. In prectng the poor the logstc moel s the best of the consumpton moels. Further, we also notce that the varables wth the strongest ether postve or negatve are Lan, eucaton, sze of the househol, age of househol hea an gener. Furthermore, house characterstcs, access to faclty an assets play sgnfcant role. Thus, f we want to roughly assess whether a househol s more lkely to be poor or not n the regon, t woul be better to gather nformaton on assets ownershp, eucaton level an consumpton patterns as they are the best ncators that shoul be use to tell the status of poverty n a househol. Conserng the current populaton growth rate of about.5 percent per annum, there s nee for a general overvew of the polces to boost economc growth an measures to ensure reucton of poverty to the maorty of Kenyans. Ths shoul be combne wth promoton of famly plannng to ensure that economc gans an reuce buren on househols, as a result of free or subsze servces (e.g. n eucaton an health), o not translate to hgher populaton growth. There s also nee for targete nvestments n nfrastructure such as roas, rural electrfcaton, safety net programmes an provson of water, especally n the margnal areas. The polces on poverty levels n the lake regon uner the PRSP s three pllar strategy of rasng the ncome opportuntes for the poor shoul focus mostly on agrculture, snce the macroeconomc envronment s mportant n etermnng the prouctvty whch s key to poverty reucton. References []. Worl Bank (WB), Worl evelopment ncators 00, Techncal report Lcense: CC BY 3.0 IGO, Worl Bank, Washngton, DC, 00. Avalable at: []. S. Puney, On some statstcal methos for moellng the ncence of poverty, Oxfor Bulletn of Economcs an Statstcs, vol. 6, no. 3, pp , 999. [3]. M. Ravallon an B. Ban, How robust s a poverty profle?, Worl Bank Economc Revew, vol. 8, no., pp , 994. [4]. N. Kakwan, Poverty an economc growth wth an applcaton to cote lvore, LSMS Workng Paper 63, Worl Bank, Washngton, D.C, 990. [5]. M. Lpton an M. Ravallon, Hanbook of Development Economc, vol. 3 of B. Amsteram: Elsever, 995. [6]. A. Sen, Development as Freeom. Oxfor: Oxfor Unversty Press, 999. [7]. G. Datt an D. Jollffe, Poverty n egypt: Moelng an polcy smulatons, Economc Development an Cultural Change, vol. 53, pp , January [8]. WHO, Energy an proten requrements, Tech. Rep. 74, Worl Health Organzaton, Geneva, 000. [9]. M. Ravallon, "poverty lnes n theory an practce",lvng stanars measurements surveys, tech. rep., Worl Bank, 998. [0]. A. M. P. Prescott, Ncholas, a poverty profle of camboa, Dscusson Paper 373, Worl Bank, Washngton, DC, 997. DOI: / Page

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