Birol, Ekin; Asare-Marfo, Dorene; Ayele, Gezahegn; Mensa-Bonsu, Akwasi; Ndirangu, Lydia; Okpukpara, Benjamin; Roy, Devesh; and Yakhshilikov, Yorbol

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1 Investgatng the Role of Poultry n Lvelhoods and the Impact of HPAI on Lvelhoods Outcomes n Afrca: Evdence from Ethopa, Ghana, Kenya and Ngera By Brol, Ekn; Asare-Marfo, Dorene; Ayele, Gezahegn; Mensa-Bonsu, Akwas; Ndrangu, Lyda; Okpukpara, Benjamn; Roy, Devesh; and Yakhshlkov, Yorbol Poster presented at the Jont 3 rd Afrcan Assocaton of Agrcultural Economsts (AAAE) and 48 th Agrcultural Economsts Assocaton of South Afrca (AEASA) Conference, Cape Town, South Afrca, September 19-23, 2010

2 Investgatng the Role of Poultry n Lvelhoods and the Impact of HPAI on Lvelhoods Outcomes n Afrca: Evdence from Ethopa, Ghana, Kenya and Ngera Ekn Brol a *, Dorene Asare-Marfo a, Gezahegn Ayele b, Akwas Mensa-Bonsu c, Lyda Ndrangu d, Benjamn Okpukpara e, Devesh Roy a and Yorbol Yakhshlkov a a Internatonal Food Polcy Research Insttute, Washngton DC, USA b Ethopan Development Research Insttute, Adds Ababa, Ethopa c Unversty of Ghana Legon, Accra, Ghana d Kenya Insttute for Publc Polcy Research and Analyss, Narob, Kenya e Unversty of Ngera, Nsukka, Ngera *Contact author: e.brol@cgar.org Abstract In ths paper we nvestgate the role of poultry n the lvelhoods portfolos of households and the mpact of supply and demand shocks that may be caused by Hghly Pathogenc Avan Influenza (HPAI) on varous lvelhoods outcomes of households n four Sub-Saharan Afrcan (SSA) countres. The study countres nclude Ethopa and Kenya n East Afrca and Ghana and Ngera n West Afrca. These countres represent a spectrum of SSA countres n terms of dsease status, role of poultry sector and means of dsease spread. By usng natonally representatve household level secondary data and dscrete choce methods (probt model and zero nflated negatve bnomal model) we profle the household, farm and regonal characterstcs of those households who are most lkely to keep poultry, and those who are most lkely to be engaged n ntensve poultry producton,.e., keep larger household flocks. We estmate the mpact of the dsease outbreaks and scares/threats on lvelhood outcomes by usng matchng methods (.e., propensty score matchng). The results of ths study generate valuable nformaton regardng the role of poultry n the lvelhoods of small-scale poultry producng households and the lvelhood mpacts of HPAI nduced demand and supply shocks. Such nformaton s crtcal for the desgn of targeted and hence effcent and effectve HPAI control and mtgaton polces. Keywords: Hghly Pathogenc Avan Influenza (HPAI), demand shock, supply shock, lvelhoods, probt model, zero nflated negatve bnomal model, propensty score matchng 1

3 1. Introducton Poverty s both a cause and a consequence of nablty to cope wth shocks. The poor are often consdered more vulnerable to shocks because of the assumed lack of dversfcaton n ther ncome and/or asset portfolo. In low ncome countres of Sub-Saharan Afrca (SSA) ths vulnerablty of the poor to varous shocks s consdered to be of utmost mportance for polcy targetng. In the lmted lvelhoods dversfcaton that poor households tend to have, lvestock consttutes an mportant source of ncome and n general comprses the most mportant asset. The potental lvelhoods mpacts of a shock that affects the lvestock sector, partcularly the type of lvestock kept by the poorest and most vulnerable, such as poultry, s therefore of paramount mportance to polcy makers. In ths paper we assess the lvelhoods mpacts of a shock to the poultry sector n the form of a dsease, namely Hghly Pathogenc Avan Influenza (HPAI), n four dfferent countres n SSA. The study countres nclude Ethopa and Kenya n East Afrca and Ghana and Ngera n West Afrca. HPAI vrus has been crculatng n SSA snce February 2006, when the frst case was confrmed n Kaduna state, Ngera. Ths vrus has drectly or ndrectly affected the poultry sectors and overall economes of varous countres n SSA. Benn, Burkna Faso, Cameroon, Djbout, Ghana, Ivory Coast, Nger, Ngera, Sudan, Togo and Zmbabwe are among the countres affected drectly through sngle or multple outbreaks. SSA countres that were ndrectly affected nclude countres such as Ethopa, Kenya and South Afrca, whose poultry sectors suffered from scares, for example false alarms as a result of mass poultry loss to other dseases, and/or from HPAI threats, due to outbreaks n neghbourng countres. Wth fears of a human pandemc, substantal fundng has been channeled to prevent an avan flu outbreak and/or to control t n the event of an outbreak. In the 2006 meetngs n Bejng, multlateral donors and developed countres pledged US$1.9 bllon towards HPAI preventon and control programmes (World Bank, 2006). Ths fgure far exceeded the ntal target, hghlghtng the perceved mportance of ths ssue. Strengthenng of the dsease survellance and control systems n developng countres was a sgnfcant component of ths fund. Another sgnfcant part of the fund was earmarked for controllng the spread of the dsease, especally through the preservaton of lvelhoods so as to mprove reportng of the outbreak by the poor. In the specfc context of HPAI outbreaks (and also other anmal dseases), dsease control and lvelhoods preservaton are nextrcably lnked. The ncentve to report an outbreak, and thus facltate the mplementaton of control measures, s a functon of the effect of HPAI on lvelhoods. Ths lnk ratonalzes the system of compensaton for the loss of poultry from control measures,.e. n economc terms a supply shock. In ths paper we brng forth the elements of the HPAI shock that are more nuanced than the usual way polcy has dealt wth them, whch ncluded focusng solely on the supply shock effects. We emphasze that n economc terms t 2

4 s extremely mportant to treat an HPAI outbreak as both a demand shock (.e., reducton n demand due to consumer panc and assocated fall n the prce/value of poultry and eggs) and a supply shock (.e., reducton n brd supply as a result of brd mortalty from dsease or from control measures). The former s generally non-localzed and, more mportantly, can occur even n the absence of an outbreak snce t s a percepton based consumer response. The demand shock s also often dscrete, and evdence from several countres suggests that the mpact of a demand shock far outweghs that of a supply shock caused by dsease mortalty or mortalty from control measures such as cullng. Charactersaton of the shocks as supply and demand shocks, compounded wth the fact that HPAI spread s essentally transboundary, provdes us wth the frst set of ratonale for lookng at the set of four SSA countres as a group. The two study countres n East Afrca, namely Ethopa and Kenya, have not yet experenced any outbreaks, however, they share a physcal border wth each other and wth Sudan, where several HPAI outbreaks have occurred, thereby mplyng nformal trade effects. The two study countres n West Afrca, namely Ghana and Ngera, have both experenced outbreaks and are effectvely neghbors from a dsease spread standpont beng on the same brd flyways. Though scence of the channels of spread (trade and/or flyways) s stll not defntve, ether or both of the two channels are consdered mportant n the spread of the dsease. Regardng the frst channel,.e. the trade lnkage between Kenya and Ethopa, the current levels of trade (most of whch s nformal and/or undocumented) s often taken as a bass for downplayng the nterdependence n dsease transmsson. Ths reasonng, we argue, s based on gnorng a very mportant dynamc,.e. the endogenous ntaton or expanson of trade followng an outbreak. If Ethopa has an outbreak and Kenya has not and f lvelhoods n Ethopa are affected sgnfcantly, tradng of brds out of Ethopa wll be a ratonal response, at least n the short run. Smlarly, f both Kenya and Ethopa have an outbreak or are affected through a demand lnk channel, arbtrage wll materalze wth the transfer of brds towards hgh compensaton areas through nformal tradng. The study countres represent a spectrum regardng HPAI status and the mportance of poultry n small-scale producers lvelhoods outcomes. In Ngera, HPAI s consdered endemc; Ghana has experenced three outbreaks, and n Kenya and Ethopa, where HPAI outbreaks have not yet occurred, scares and threats of ths dsease have sgnfcantly affected the poultry sectors. The countres also vary n terms of varous factors, ncludng the sze and structure of the poultry sector; relance of the poor on poultry, and the levels of dversfcaton n ncome sources and n assets that determne the capacty to cope wth shocks. The results of our analyses show some nterestng and mportant results from a polcy perspectve. Our relance on natonally representatve data provdes an ex post vndcaton by revealng the sgnfcant nter-regonal dspartes n household s ncome and asset portfolo. Most localzed studes lookng at the effect of these shocks are case study based (.e., on one area or regon of the study country) and can therefore not be treated as generalsable. Further, 3

5 the datasets that we use allow us to look at the whole ncome and asset portfolo, thereby provdng a more accurate measure of the mpact of the dsease. If for example one looked only at the mpact of HPAI on the ncome from poultry wthout accountng for ts role n the whole ncome stream, the effects can be grossly naccurate and can even be exaggerated. Contrary to our ex ante conjecture, we fnd that surprsngly poor households are sgnfcantly dversfed n the four study countres, though the regonal dfferences are sgnfcant. When lvelhoods portfolos are dversfed, any dosyncratc shock would have only lmted effect partcularly f the contrbuton of the lvelhoods actvty that s affected by the shock to the ncome and asset portfolo s small. Ths turns out to be true n case of poultry for most regons n the study countres. Once agan the regonal dfferences n mpacts need attenton. More mportantly, our results hghlght the mportance of the nature of the shock. An dosyncratc shock to a sector (such as poultry) mples neglgble covarance wth other sectors (such as other lvestock or crop producton). Hence, gnorng the shocks to upstream and downstream lnkages, the draw upon the other sectors n the portfolo could be overestmated. Yet, n the short to medum run the evdence from the SSA countres show that a shock to an mportant lvestock actvty undertaken by the poor wll not have a sgnfcant effect, on average. Ths s an mportant result but does not mply that earmarkng of funds for preservng lvelhoods s not mportant n the context of Afrcan countres. As long as poor are loss averse and effects on lvelhoods s non-zero, there exsts a sgnfcant potental for small effects on lvelhoods to translate nto frst order effects on dsease control. The mportance of our fndngs les n understandng the followng ponts. We fnd that the lvelhood strateges of the poor n SSA are characterzed by sgnfcant dversfcaton but ther ablty to cope wth rsks s contngent on the nature of shock (dosyncratc versus generalzed). Further, n all countres there are sgnfcant regonal dfferences wth lvelhood hotspots for poultry. A balance sheet perspectve on households wll lkely dentfy hotspots for other sectors n each country. Polces need to target these areas, though our demand shock perspectve makes all areas vulnerable to a dsease outbreak or threat. The mportance of nformaton management (n regonal, natonal or even nternatonal context) can therefore not be overstated. Fnally, our fndngs pont at the mportance of lvelhoods dversfcaton as a polcy n tself. Much of the dversfcaton observed n the four study countres has been fostered by the households themselves and there are sgnfcant gans to be made from enhancng portfolo dversfcaton. Ths s exemplfed by lmted non-farm sector n many regons of the study countres, where government polces and nsttutons have a dsproportonately larger role to play. In the context of the lterature ths paper contrbutes n dfferent ways. There has been an ncreasng number of studes whch nvestgated the economy-wde, nter-sectoral or sector-wde mpacts of HPAI n several SSA countres (e.g., You and Dao, 2007; Dao, 2009; Dao et al., 2009; Schmtz and Roy, 2009; Thomas et al., 2009; Thurlow, 2009). Some 4

6 of these studes are lnked wth household data through mcro-smulaton routnes to assess the mpact at the household level. Important as these effects are, they do not assess effects at the household level or do so n a summary (for example households clubbed nto decle groups). Most mportantly, these studes cannot dfferentate across households based fully on ther ncome and asset portfolo. The number of studes whch nvestgated the mpact of HPAI on small-scale, household level producers lvelhoods has however been scant (e.g., Bush, 2006; Kman et al., 2006; UNDP 2006; Obayelu, 2007; UNICEF/AED, 2008). These studes were manly based on qualtatve and/or quanttatve data generated through rapd assessment technques conducted as case studes n selected states or regons of the study countres. We argue that the area/regon specfc case studes and qualtatve methods both have sgnfcant lmtatons when producng estmates of the mpact of the shock on lvelhoods. These locaton specfc case studes can present a very based pcture and do not generate polcy prescrptons for resource allocaton, a very mportant requrement n developng economes under strct budget constrants. The same crtque apples to qualtatve methods. Startng from the pror that poultry plays a consderable role n household level producers varous lvelhoods outcomes, such as cash ncome, wealth, food and nutrton securty, and nsurance aganst shocks, to name a few (see e.g., Kush et al. 1998; Ktaly 1998; Tadelle and Ogle 2001; Tadelle et al. 2003; Njenga 2005; Houndonougbo 2005; Aboe et al., 2006; Blacke, 2006; Akllu et al., 2008; Chnombo et al., 2008) we see a mert n conductng a detaled nvestgaton of the mpact of HPAI on small-scale, household level poultry producers lvelhoods by usng rgorous quanttatve methods. The evdence from all four study countres clearly shows that a great majorty of the poultry populatons of these countres are managed by household level producers, wth mnmal or no bosecurty measures (see e.g., Alemu et al., 2008; Anng et al., 2008; Ob et al., 2008; Omt and Okuthe, 2008). Informaton regardng the role of poultry n the lvelhoods of small-scale poultry producng households and the lvelhood mpacts of HPAI nduced demand and supply shocks s crtcal for the desgn of targeted and hence effcent and effectve control and mtgaton polces. In ths paper we am to fll ths gap by usng natonally representatve household level data from the study countres to answer the followng questons: () Who are the poultry keepers? Are they poor? Do they have dversfed ncome and/or asset portfolos? Wthn a country where are they located,.e. are there sgnfcant regonal dfferences? () Among the poultry keepers what s the ntensty of partcpaton n poultry producton? Who are the farmers who partcpate n poultry wth greater ntensty? In quanttatve terms we examne these by assessng the flock szes of the poultry keepers. 5

7 () Together () and () mply that we can hypothesze the characterstcs and locaton of poultry producers n the study countres who are lkely to bear the brunt of the dsease. (v) What s the effect of the dsease outbreaks and scares/threats on lvelhood outcomes? How can we assess ths effect n the absence of actual data on affected households? Answers to these research questons are expected to assst n the desgn of effcent, effectve, and equtable nterventons for mtgaton and control of HPAI n the study countres, wth possble mplcatons for smlar countres n the rest of SSA. The rest of the paper s organzed as follows. The next secton provdes background nformaton regardng the HPAI status n each study country and summarses the documented evdence on poultry demand and supply shocks caused by HPAI outbreaks and scares n these countres. Secton 3 explans the econometrc models used to tackle the research questons. Secton 4 ntroduces the data sources and presents descrptve statstcs. Secton 5 reports the results of the analyss and the fnal secton concludes the paper wth mplcatons for HPAI preventon and control polces. 2. Background: HPAI status and economc mpacts In ths paper we study two West Afrcan countres, namely Ngera and Ghana, whch have experenced multple HPAI outbreaks. In Ngera, there have been several HPAI outbreaks snce February 2006, affectng 27 out of 36 states, the most recent outbreak occurred n July 2008 (Ob et al., 2008). Accordng to the records of the World Bank-funded Avan Influenza Control Program, between February 2007 and January 2008, N623,077,880 (US$4,215,683) has been pad to compensate farmers whose brds were culled. No nformaton s avalable on the costs of cullng, dagnostc testng of samples, cleanng and dsnfecton, and other admnstratve costs (Ob et al. 2008). Regardng the mpacts of HPAI on the poultry sector, a study conducted by the UNDP n 2006, rght after the ntal outbreaks, revealed that the offcal confrmaton of HPAI n Ngera caused ntal panc, resultng n the total boycott of poultry and poultry products. Consequently, wthn two weeks, egg and chcken sales declned by 80.5%, due to demand shock, and up to four months after, prces had not recovered up to 50% pre-hpai levels. The study found that although the hghest brd mortalty rates occurred n commercal farms, overall small-scale, household level poultry producers, especally those n rural areas, as well as medum scale farmers were most severely affected by the HPAI outbreaks, snce these smaller scale producers lack necessary assets for recovery and often do not qualfy for compensaton (especally vllage extensve, small-scale poultry producng households). Affected backyard producers suffered up to a 100% ncome loss, whle non-affected producers also wtnessed ncome losses as hgh as 68.2% (UNDP, 2006; Ob et al., 2008). State level studes conducted n Ngera found that HPAI resulted n a 57% drop n the chcken prces n the Kwara state (Obayelu, 2007). The household level demand shock was as hgh as 80%, and supply shock resulted n 75% of poultry farmers to stop orderng of new supples of brds and to opt out of poultry farmng altogether. Accordng to Obayelu (2007) small 6

8 scale commercal producers and backyard poultry farmers suffered the most ncome losses as a result of HPAI. A more recent study conducted by UNICEF and AED n Kano and Lagos states found that HPAI shocks resulted n substantal losses n employment n the poultry sector, as well as sharp decreases n prces of poultry. In Kano, the prce of chcken n the markets dropped by as much as 90%, whle n Lagos the prce fell by 81.25% (UNICEF/AED, 2008). Anecdotal evdence from Ghana suggests that durng the 2006 outbreaks n the neghborng countres, the magntudes of supply and demand shocks were large. In terms of supply shocks, poultry producers could not sell ther produce and due to the ncreasng costs of keepng poultry (e.g., feedng and mantanng costs) they had to dspose of ther produce as quckly as possble and hence they sold at extremely low prces. For example a crate of eggs was sold at 63.3% of ts normal prce (Anng et al., 2008). In terms of demand shocks, Mnstry of Food and Agrculture of Ghana reported that the scare of the brd flu alone led to a drastc reducton n the demand for poultry and poultry products. It was recorded by the Ghana Natonal Assocaton of Poultry Farmers that poultry consumers reduced ther demand by 40% durng these HPAI scares (GNAPF, 2006). There have been three actual outbreaks of HPAI n Ghana n 2007 (Anng et al., 2008). There s no publshed nformaton avalable on the demand and supply shocks and changes n prces after the outbreaks. There s, however, anecdotal nformaton on the numbers of farmers who have gone bankrupt due to the loss of markets as a result of the ban on poultry and the reductons n the demand for poultry products, durng and sometme after the outbreaks. For example, accordng to the Poultry Farmers Assocaton, the total number of ther broler producng members was reduced by 95%, whereas ther broler chckens was reduced by 83%. The number of ther egg producng members also fell, though at a lower rate of 30%. At the country level, however, the total number of egg producers plummeted by 66.7%. These fgures provde some ndcators of the supply and demand shocks suffered by poultry farmers n Ghana (Anng et al., 2008). In ths paper we also study two eastern Afrcan countres, namely Kenya and Ethopa, whch have not had actual HPAI outbreaks to date. These two countres however have experenced HPAI scares and threats, whch affect the poultry sector and the household level lvelhoods through the demand shocks they cause. Both of these countres are hghly susceptble to the ntroducton of HPAI. Kenya s located along a mgratory route of wld brds, whereas both of these countres, and especally Ethopa, share a border wth neghbourng Sudan, where the vrus s present and llegal trade actvtes across the borders are paramount (Alemu et al., 2008; Omt and Okuthe, 2008). Gven the susceptblty of these two countres to HPAI, we wanted to understand ex ante lvelhoods mpact of a possble HPAI outbreaks and the role of poultry n the households lvelhoods. In Kenya there was a major HPAI scare that took place September 2005 through March 2006 (Omt and Okuthe, 2008), whch was ntated by msguded reports by the meda, compounded by HPAI actual outbreaks n neghbourng Sudan. Kman et al. (2006) assessed the demand and supply shocks caused by ths scare to be hghly sgnfcant. Accordng to ths study, 7

9 as a result of ths scare, 25% of farmers prematurely culled ther brds and all farmers ntervewed reduced ther flock szes between 2 to 39 percent due to varous reasons related to the scare (e.g., premature sellng, postponement or cancellaton of day-old-chcks, and unavalablty of new chcks as hatcheres reduced producton). The prces of poultry and poultry products were also affected by the HPAI scare. The prce of broler chcken fell by 15% per kg, and the prces of eggs fell by 15.3% per tray. The demand and supply shocks caused by the scare also reduced the prces of ndgenous eggs and chckens, by 7.2% per tray and 26.5% per kg, respectvely (Kman et al., 2006). The overall fnancal losses assocated wth the HPAI scare are estmated to be Ksh2.3 bllon (US$30.7 mllon) (Omt and Okuthe, 2008). Fnally, n Ethopa there was an HPAI scare n 2006, due to a false alarm n a state run poultry multplcaton centre. Ths scare caused a massve demand shock, whch subsequently led to sharp falls n poultry prces (Alemu et al., 2008). Bush (2006) reports that ths scare led to a demand shock, especally n urban areas, whch decreased poultry demand by %. As a result of reducton n urban demand and the consequent over-supply of local markets, the prces of chcken sold at the local markets dropped by 50-60%. The scare, however dd not affect egg supply, demand and prce (Bush, 2006). 3. Methodology As stated n the ntroducton, n order to understand the mpact of HPAI on lvelhoods, we frst profle the characterstcs of the households who choose poultry producton as a lvelhoods actvty; and among those, we profle the characterstcs of those households who are engaged n more ntensve poultry producton. To nvestgate these ssues we estmate probt and zero nflated count data models, respectvely. Followng these, we measure the lvelhoods mpacts of the HPAI supply and demand shocks on households who are engaged n poultry and who are engaged n ntensve poultry. For the latter analyss we use the propensty score matchng approach. Informaton on the poultry keepng and ntensve poultry keepng households profles, as well as the nformaton on the lvelhoods mpacts these households may suffer, are expected to ad n the desgn of targeted nterventons. The econometrc models used n ths paper are explaned nto greater detal below Determnants of partcpaton n poultry producton Household level partcpaton n poultry as a lvelhoods actvty s modelled followng the random utlty framework proposed by McFadden (1974). A non-separable farm household model s assumed gven that a great majorty of small-scale poultry producers n the study countres are non- or sem-commercal producers who manly produce for own household consumpton (see Sngh et al., 1986; de Janvry et al. 1991). A reduced form of the model for a poultry producer wth mssng markets for poultry products descrbes the overall welfare of the 8

10 household to be a functon of the household (H) and farm level (F) characterstcs as well as regonal factors (R), such as market ntegraton and densty of poultry. That s: U U ( H, F, R ) (1) let U () denote the maxmum utlty level the household can acheve gven ts constrants, f the household partcpates n poultry actvty. Let U () denote maxmum constraned utlty otherwse. Both of these utlty levels assume optmal choces of producton and consumpton. In the random utlty model, the utlty the household derves from undertakng poultry actvty conssts of two parts, an observable part and an unobservable one (McFadden, 1974). The utlty levels the household derves from partcpatng n poultry producton and otherwse respectvely: U ( ) and U U ( ) ( ) U ( ) (2) The household chooses to partcpate n poultry producton f and only f the utlty the household derves from partcpatng n the poultry actvty s hgher than that of not partcpatng n t. That s, U or ( ) U ( ) U ( ) U ( ) (3) The level of utlty derved from poultry actvty s not observable, however the household s actual choce s. For the dchotomous choce case the household s choce to partcpate n poultry producton can be characterzed by a varable I, such that 1 f U ( ) U ( ) I 0 f U ( ) U ( ) (4) The household takes a decson on whether or not to partcpate n poultry producton. The soluton to ths partcpaton decsons yelds of the household s optmal partcpaton choce I*, where the probablty of observng a household s partcpaton n poultry actvty s gven by Pr( ) Pr( I 1) Pr( U ( ) U ( )) M ( U ( ) U ( ) ) (5) where t s commonly assumed that both error terms are normally dstrbuted wth mean zero and constant varance, and where M s ther cumulatve dstrbuton functon that s assumed to have a standard normal dstrbuton. In ths study, therefore, whether or not a household decdes to partcpate n poultry producton mplcate a dchotomous, bnary choce. Equaton (5) can be estmated wth a unvarate Probt model for a bnary outcome of takng part n ths lvelhoods actvty. 9

11 3.2. Determnants of poultry flock sze The Posson model for count data s used to model the household s decson regardng the number of brds to keep (Greene, 1997). The probablty of rasng k number of poultry gven n ndependent possbltes s represented by the bnomal dstrbuton n k nk P( Y k) p (1 p) (6) k n n! where and p s the probablty of keepng k number of poultry k. k k!( n k)! Statstcal theory states that a repetton of a seres of bnomal choces, from the random utlty formulaton, asymptotcally converges to a Posson dstrbuton as n becomes large and p becomes small. k n k n k e lm p (1 p ) (7) n k k! where p / n and s the mean of dstrbuton, such as the mean number of poultry kept per household. Ths formulaton allows modellng of the probablty that a household chooses to rase a number of poultry k gven a parameter (the sample mean). Each household makes a seres of dscrete choce decsons on whether or not to rase poultry on the farm, resultng n the number of poultry kept. Accordngly, Posson specfcaton s used to model the ncrease n household utlty from an addtonal brd rased. The Posson regresson model s the development of the Posson dstrbuton presented n equaton (7) to a non-lnear regresson model of the effect of ndependent varables x on a scalar dependent varable y. The densty functon for the Posson regresson s y e f ( y / x ) (8) y! where the mean parameter s the functon of the regressors x and a parameter vector s gven by E ' y / x exp( x ) and y 0,1,2,... (9) where ' exp( x ) exp( 0 ) exp( 1x1 ) exp( 2 x2 )... exp( k xk ) (10) Also note that E[ y / x ]/ x j log E[ y / x ] j (11) E[ y / x ] x j That s the coeffcents of the margnal effects of the Posson model can be nterpreted as the proportonate change n the condtonal mean f the j th regressor changes by one unt. Fnally the Posson model sets the varance to equal to the mean. That s 10

12 V ( y / ) (, ) exp( ' x x x ) (12) Ths restrcton of the equalty of the mean and varance n the Posson dstrbuton s often not realstc as t has been found that the condtonal varance tends to exceed the mean resultng n over-dsperson problem (Cameron and Trved, 1986; Grogger and Carson, 1991; Wnkelmann, 2000). If over-dsperson problem exsts, the condtonal mean estmated wth a Posson model s stll consstent though the standard errors of are based downwards (Grogger and Carson, 1991). A more generalzed model to account for the over-dsperson problem s based on the negatve bnomal probablty dstrbuton expressed as y ( y ) f y y ( /, ) (13) ( 1) ( ) where exp( ' ) y 0,1,2... (14) x and 0 characterses the degree of over-dsperson, or the degree to whch the varance dffers from the mean. Cameron and Trved (1990) have proposed a regresson-based test for over dsperson, whch tests for the sgnfcance of the parameter as compared to the Posson model (Greene, ). The test s based on the hypothess that the Posson model, ( y E[ y]) E[ y] has mean zero and that under both the null and the alternatve hypotheses the Posson model gves consstent estmates of E[ ]. The test s based on the hypotheses H0 vs. H : Var[ y ] y : Var[ y ] g( ) (15) 1 In ths study, the test of equalty of the mean and varance fals to hold, for all of the study countres. Therefore Negatve Bnomal Model s consdered. However, n each study country there are many zero observatons for households who dd not keep poultry n the survey year n whch the data were collected. Consequently the Zero-nflated Negatve Bnomal (ZINB) model was estmated to account for both the over-dsperson and the excess zeros (Long, 1997; Greene, 1998). In ZINB, for each observaton, there are two possble data generaton processes; the result of a Bernoull tral determnes whch process s used. For observaton, Process 1 s chosen wth probablty and Process 2 wth probablty. Process 1 generates only zero counts, whereas Process 2, generates counts from a negatve bnomal model: (16) The probablty of s (17) 11

13 When the probablty depends on the characterstcs of observaton, s wrtten as a functon of, where s the vector of zero-nflated covarates and s the vector of zeronflated coeffcents to be estmated. The functon F that relates the product (whch s a scalar) to the probablty s called the zero-nflated lnk functon, and t can be specfed as ether the logstc functon or the standard normal cumulatve dstrbuton functon (the probt functon) (Greene, 1998). The mean and varance of the zero-nflated negatve bnomal model (ZINB) are: (18) To test whether ZINB model fts better than Negatve Bnomal to the data for each study country we performed Vuong test. Ths test s for nested models and s used to determne whch zero-nflated model explans the data better (Vuong, 1989). The test favors the ZINB model for all countres, suggestng that there s a separate process for households decson to keep poultry and the number they decde to keep Estmatng lvelhoods mpact of HPAI by usng the propensty score matchng method Snce we do not have natonally representatve data on the same households from before and after the HPAI outbreaks and scares we use an ex ante evaluaton method as proposed by Chmera & Taber (2000) and Todd & Wolpn (2005). The man feature of ths approach s the fact that all the factual outcomes are about non-treated ndvduals, that s, none of them has yet been exposed to the polcy (n ths case, HPAI outbreak or shock) that the analyst s to evaluate. The matchng procedure s between an ndvdual who we observe (or estmate) the outcome as non-treated, and an ndvdual j that mmcs the outcome ndvdual would have under the new 1 0 polcy/shock. Then t must be Y,.e.: the factual outcome for ndvdual j under the status Y j quo polcy regme must be equal the one of ndvdual under the new polcy/shock (hereafter referred to as the treatment). The estmaton of an average treatment effect n observatonal studes can produce based results when we use a non-expermental estmator. The typcal problem n ths type of studes s that the assgnment of subjects to the treatment and control groups s not random and therefore the estmaton of the average treatment effect s usually based as a result of the exstence of confoundng factors. For that reason, the matchng between treated and control subjects becomes dffcult when there s an n-dmensonal vector of characterstcs. The matchng approach s one possble soluton to the selecton problem and has become a popular approach to estmate causal treatment effects (Calendo and Kopeng, 2005). Its basc dea s to fnd n a large group of nontreated ndvduals or households who are smlar to the partcpants n all relevant pre-treatment characterstcs X. That beng done, dfferences n outcomes of ths well selected and thus adequate control group and of treated group can be attrbuted to the treatment. Snce condtonng on all relevant covarates s lmted n case of a hgh dmensonal vector X ( curse of dmensonalty ), Rosenbaum and Rubn (1983) suggest the use of so-called 12

14 balancng scores b(x),.e. functons of the relevant observed co-varants X such that the condtonal dstrbuton of X gven b(x) s ndependent of assgnment nto treatment. Ths s the condtonal ndependence assumpton (CIA). One possble balancng score s the propensty score,.e. the probablty of partcpatng n a treatment gven observed characterstcs X. The matchng procedures based on ths balancng score are known as propensty score matchng (PSM). Besdes CIA, a second assumpton of matchng requres that treatment observatons have comparson observatons nearby n the propensty score dstrbuton. Ths s the common support or overlap condton and ensures that persons wth the same X values have a postve probablty of beng both partcpants and non-partcpants (Heckman et al., 1999). The common support thus represents the area where there are enough of both, control and treatment observatons. The common support regon allows effectve comparsons of outcomes between the treated and control groups. Assumng the CIA holds and that there s overlap between both groups, the average 1 0 treatment effect can then be estmated. One deally wants to estmate Yt Y t, whch s the dfference of the outcome varable of nterest at tme t between two groups, denoted by the superscrpts 1 and 0. However, the econometrcan s unable to estmate Δ n ths way because a household cannot smultaneously be n treatment and control group. The econometrcan s thus forced to measure the average treatment effect (ATE) gven the observable data: 1 0 ATE E( Yt T 1) E( Yt T 0) (19) When data are generated through a properly mplemented random expermental desgn, the expectatons of the treatment and comparson groups are equal because the groups are composed of randomly allocated members (households), ensurng that the dstrbuton of observable and unobservable characterstcs of the groups are equvalent n a statstcal sense. Wth a randomzed desgn, the selecton bas equals zero, whch establshes that the estmate of the ATE provdes an unbased estmate of ts mpact. Randomzed experments are not always possble (such as n the case of estmaton of the mpacts of HPAI on lvelhoods) or plausbly mplemented, so that absence of selecton bas s a credble assumpton. Hence, econometrcans are often forced to estmate the average treatment effect on the treated households (ATT), gven a vector household characterstc, X.: ATT E( X, T 1) E( Yt Yt X, T 1) E( Yt X, T 1) E( Yt X, T 0) (20) To estmate potental effects of HPAI ncdence, propensty scores are used to match households wth smlar observable characterstcs, varyng only the treatment, whch n ths case s havng poultry (and therefore beng susceptble to HPAI). Households are matched to each other condtonal on a set of observable household characterstcs. A probt model s estmated usng a vector of household characterstcs to obtan predctons of household propensty scores. Heckman, et al. (1998) observe that the PSM have lower bas when X ncludes varables that affect both program partcpaton and the outcome. The household-level characterstcs (e.g., household demographcs, assets, regonal characterstcs such as locaton, poverty status, number 13

15 of ncome sources, etc.) ncluded n the model are therefore those that have a hgh probablty of nfluencng partcpaton n poultry producton, as well as outcome varables ncludng lvelhoods ndcators such as lvestock ncome and wealth. Accordng to ths method of matchng, the two groups, whch nclude treatment group of households representng the result of the HPAI demand or supply shocks, and a control group representng the status quo (f no HPAI shocks occurred), should dffer only n terms of ther poultry ownershp characterstcs. In ths study we smulated sx counterfactual scenaros to estmate possble mpact of HPAI on lvelhood ndcators (ncome and asset wealth) for poultry producng households. These scenaros consder the lvelhoods mpacts of both demand (Scenaro 4) and supply shocks (all other scenaros), as well as the mpact of the supply shocks on poultry keepers of dfferent scales. Specfcally, small-scale poultry producers are allocated nto two groups across study countres, wth smaller small-scale producers representng those poultry producers wth one brd to 25 th percentle number of brds, and more ntensve larger small-scale producers havng more than 25 th percentle number of brds but less than 500 brds, where 500 s the cut off pont for smallscale household level poultry keepng n the study countres (see Alemu et al., 2008; Anng et al., 2008; Omt &Okuthe, 2008; Ob et al., 2008). Across scenaros, scenaro 2 consders the mpact of HPAI on smaller small-scale producers, whereas scenaros 3 and 6 consder the mpact of HPAI on larger small-scale producers. Moreover, ntegraton of our mpact assessment wth the dseases rsk maps developed by Stevens et al (2009) enables us to measure the lvelhoods mpacts n dfferent rsk areas (scenaros 5 and 6). Scenaro 1 assumes a country-wde shock where all poultry producng households n the study country experence total loss (.e., 100% loss) of ther poultry flock due to HPAI. In ths scenaro outcomes of households wth poultry are compared to those wthout poultry.scenaro 2 nvestgates the mpact of HPAI on smaller small-scale poultry producers. The assumpton s that only those households wth smaller small-scale flocks are affected by HPAI and they lose all (100%) of ther flocks. Scenaro 3 assumes only larger small-scale producers are adversely affected by HPAI, and they lose some of ther brds and are left wth a flock sze smlar to that of the smaller small-scale producers. Scenaro 4 assesses the mpact of a prce shock caused by HPAI. We assume ths shock would be countrywde. We looked at the mpact of a prce shock on the lvelhoods outcomes of those chcken producers who sell poultry. Of those households who sell chcken we compared households who get hgher prces (above medan chcken prce n each country) to those who get lower (below medan) prces. Scenaros 5 and 6 use the dsease spread map developed by (Stevens et al, 2009), whch shows the lkelhood for spread of HPAI n each study country, assumng that the dsease has been ntroduced for those countres where there s no HPAI. In scenaro 5 households located n the areas wth hgh HPAI spread rsk are assumed to be affected by HPAI and loose 100% of the brds. Smlar to the frst scenaro, poultry producng households are compared to those wth no poultry; however n ths scenaro only those households n the hgh rsk areas are matched. Fnally, n scenaro 6 we use the dsease spread rsk map to dentfy medum rsk areas n each 14

16 study country (Stevens et al, 2009). Smlar to scenaro 3, ths scenaro assumes only larger small-scale producers are adversely affected by HPAI and they lose some of ther brds and are left wth a flock sze smlar to that of the smaller small-scale producers, however n ths scenaro only those households n the medum rsk areas are matched. These scenaros are summarzed n Table 1. Table 1: Descrpton of HPAI scenaros for poultry-keepng at the household level Scenaro 1 Scenaro 2 Scenaro 3 Scenaro 4 Scenaro 5* Scenaro 6* Descrpton of smulated mpact Treatment group Control group 100% loss of poultry flock All households wthout poultry All households wth poultry 100% loss of small-scale poultry flocks All households wthout poultry Small-scale poultry keepers (1 to x** brds) 75-85% loss n large-scale poultry flock Small-scale poultry keepers (1 to x brds) Large-scale poultry keepers (x- 500 brds) 50% reducton n poultry prce Poultry keepers who sold at low prces Poultry keepers who sold at hgh prces 100 % loss of poultry flock n hgh-rsk areas All households wthout poultry All households wth poultry 75-85% loss n large-scale poultry flock n medumrsk areas Small-scale poultry keepers (1 to x brds) Large-scale poultry keepers (x to 500 brds) *For scenaros 5 and 6, country level dsease spread maps (Stevens et al. 2009) were used to allocate locatons (dstrcts, provnces or zones) nto hgh HPAI spread rsk and medum HPAI spread rsk areas; ** x represents the 25 th percentle number of brds n each study country 4. Data sources and descrptve statstcs 4.1. Data sources In ths study we rely on the latest natonally representatve data from each study country. There are two advantages to usng natonally representatve data to study the role of poultry n the households lvelhoods and the mpact of HPAI thereon. Frst, havng natonally representatve data enables us to nvestgate the regonal or locaton-related varatons, such as urban vs. rural areas or hgh HPAI rsk vs. low HPAI rsk regons, whch targeted case studes may not allow for. Second, the data sets used n ths study are from studes whose am s to montor the changes n the welfare (poverty) levels n the study countres through tme. Consequently, these studes have collected detaled data on the households varous sources of ncome and lvelhoods strateges, as well as on the type and quantty of assets owned by the households. Therefore these data sets allows us to nvestgate n detal the role of poultry (both as a source of ncome and as an asset) n the entrety of the households ncome and asset portfolos. Regardng the sources of data used n ths study, for the West Afrcan countres we used the Lvng Standards Measurement Study (LSMS) survey data. For Ngera we use Ngeran Lvng Standard Survey, (NLSS, ), whch was collected by the Natonal Bureau of Statstcs, the World Bank and Natonal Plannng Commsson. For Ghana we use the Ghana Lvng Standards Survey (GLSS, ), whch was conducted by Ghana Statstcal 15

17 Servce wth fnancal assstance from the World Bank. The data used for Kenya comes from the Kenya Integrated Household Budget Survey, (KIHBS, ), mplemented by Kenya Natonal Bureau of Statstcs and the Human Resources Socal Servces Department of the then Mnstry of Fnance and Plannng. Fnally for Ethopa we used the data from the Household Income and Consumpton (HICE) survey conducted n , collected by the Ethopan Central Statstcal Authorty. Each one of these studes collected data on the number of poultry kept by the sampled households n the study year, and n the case of Kenya, Ngera and Ghana, on the number of poultry sold and the prce sold at. For Ethopa, we reled on monthly producer prce data collected n 2004/05 by the Central Statstcal Authorty to derve the value of poultry owned by the households Descrptve Statstcs Accordng to the natonally representatve data, 30% of all Ngeran households engage n smallscale poultry producton, whereas ths fgure s 35% for Ghanaan households, and 42% and 43% for Ethopan and Kenyan households, respectvely. Table 2 presents these statstcs also for urban and rural households. Table 2. Percentage of poultry producng households, average flock sze and percentage of poultry ncome n total ncome All households Rural households Urban households ETHIOPIA % households that keep poultry Avg. flock sze of poultry keepers KENYA % households that keep poultry Avg. flock sze of poultry keepers % poultry ncome n total ncome for poultry keepers GHANA % households that keep poultry Avg. flock sze of poultry keepers % poultry ncome n total ncome for poultry keepers NIGERIA % households that keep poultry Avg. flock sze of poultry keepers % poultry ncome n total ncome for poultry keepers Accordng to these results, Ngera supports the smallest proporton of urban poultry producng households, whereas n Ethopa poultry producton s almost as popular n urban areas as t s n rural areas. Across the study countres, the largest flocks are kept by Ngeran poultry producng households wth almost 17 brds, and the smallest flocks are kept by Kenyan poultry producng households wth two brds. 16

18 In ths study, total annual household ncome ncludes salares from ndustry employment (agrculture, mnng, manufacturng, servces, and so on); ncome from lvestock and crop sales; and remttances, rent ncome, and other reported ncome. On average, poultry (lve brd) and egg sales contrbute 4.1 percent to the poultry-producng household s total annual household ncome n Ghana, whereas ths fgure s as low as 2.1% n Kenya and as hgh as 5.61% n Ngera. In Ethopa HICE data dd not nclude nformaton on the amount of lve brds and eggs sold by the households, therefore we could not calculate the share of ncome from poultry n total ncome for ths country. For poultry producng households, the share of poultry ncome n total ncome and the number of brds kept across ncome quntles are reported n Table 3. The fgures for Ngera, Kenya and Ghana reveal that the flock sze ncreases and the share of ncome obtaned from poultry decreases wth ncome quntles. That s poorer households rely on poultry more to provde some of ther lvelhoods and they have fewer brds compared to ther wealther counterparts. In Ethopa, however, the average flock sze s smlar across ncome quntles, and snce we do not have nformaton on the amount of lve brds and eggs sold by the households we cannot calculate the share of ncome from poultry for ths country. Table 3. Average flock sze and percentage of total poultry ncome n total ncome across ncome quntles, poultry keepng households 1 st quntle 2 nd quntle 3 rd quntle 4 th quntle 5 th quntle ETHIOPIA Average flock sze KENYA Average flock sze % ncome from poultry GHANA Average flock sze % ncome from poultry NIGERIA Average flock sze % ncome from poultry Results 5.1. Role of poultry n household lvelhoods Determnants of partcpaton n poultry producton Household, farm level and regonal factors that affect households decson regardng whether or not to partake n poultry producton are nvestgated wth a probt model, as explaned n secton 3.1 above. Probt models were estmated for each country and the results of these models are reported n Table A1 n the appendx. For detals of the country level models please see the country reports (Ayele et al., 2010; Mensa-Bonsu et al., 2010; Ndrangu et al., 2010 and 17

19 Okpukpara et al., 2010). The probt models were used to predct each household s lkelhood of beng a poultry keeper. Those households wth above 50% probablty of beng a poultry keeper were consdered as predcted poultry-keepers and those wth below 50% probablty of beng a poultry keeper were coned as predcted non-keepers of poultry. Household, farm and locaton characterstcs of predcted poultry-keeper households were compared to those of predcted nonkeepers. The sgnfcant results of these comparsons are reported n Table 4. Table 4. Household predcted to be poultry keepers have Household, farm and regonal characterstcs ETHIOPIA KENYA GHANA NIGERIA Larger households More adult women n the household More chldren n the household Older household heads NS Less educated household heads More ncome sources Other lvestock producton (small) Other lvestock producton (large) Crop producton Less off farm employment/ncome Lower ncome per capta Income below extreme poverty lne NS NS x Hgher lvestock wealth NS Hgher overall wealth (house, land, lvestock) NA NS Rural locaton When compared to the predcted non-keepers of poultry, households who are predcted to be poultry keepers have sgnfcantly larger households. Ths s as expected snce as the number of people n a household ncreases, both the household food and nutrton securty needs and the household labour avalablty ncreases. In all countres, households wth hgher proporton of adult women and chldren are more lkely to be engaged n poultry keepng. Ths result s also as expected snce prevous studes have shown that women and chldren tend to be nvolved n the rearng and sellng of poultry (e.g. Akllu et al., 2007; Sonaya, 2007). Chldren, especally n rural areas, often own one or two brds to meet ther costs of school materals (Halemaram et al, 2006), whereas women are wdely recognsed to be the most mportant stakeholders n vllage level poultry keepng n Afrca, ownng over 70% of all household level poultry (Alder, 1996; Gueye, 1998; Gueye, 2000). In all of the countres households wth less educated heads and n majorty of the study countres households wth older heads are sgnfcantly more lkely to keep poultry. The former result can be explaned by the fact that n the study countres household-level poultry producton s a low-nput, low output actvty, whch does not requre hgh levels of skll and educaton (see e.g., Alemu et al., 2008; Anng et al., 2008; Omt &Okuthe, 2008; Ob et al., 2008). Smlarly, households wth older heads are more lkely to keep poultry, snce t s a low labour-ntensve lvelhoods actvty whch older households may be able to undertake. 18

20 Regardng lvelhoods portfolos of households, t s found that n all countres households wth more dversfed lvelhoods portfolos,.e., those wth a hgher number of ncome sources, and those engaged n other agrcultural lvelhoods strateges (e.g., other lvestock and/or crop producton) are more lkely to be poultry keepers. Snce poultry contrbutes very small proporton of household ncome, as dscussed n secton 4.2., ths result s as expected. Moreover varous prevous studes have found that poultry producton s often complementary wth crop producton, snce farm manure and crop land area are nputs to poultry producton as provdng feed and area for scavengng/roamng. In fact prevous studes found that households who own hgher numbers of plots and/or larger areas are more lkely to keep lvestock (see, e.g., Wadsworth, 1991; Klen et al., 1997). Moreover, households who own other lvestock are also more lkely to be engaged n poultry producton snce several studes have found that poultry s the frst step n the lvestock ownershp ladder (e.g., Gueye 2000; Akllu et al., 2008). Therefore overall, households who are predcted to be poultry keepers have dversfed agrcultural lvelhoods strateges, and consequently ther lvelhoods outcomes are more lkely to be the reslent towards shocks and stresses whch may be cause by HPAI outbreaks and scares (Ells, 2000; Iyama, 2006). In all of the study countres predcted poultry keepers are found to have a lower number of household members wth non-agrcultural ncome and/or lower off farm ncomes. These results reveal that t s the more agrcultural, subsstence or sem-subsstence orented farm households who are engaged n poultry keepng. Ths result s also supported by the fndng that households located n rural areas are more lkely to be poultry keepers, as suggested by the descrptve statstcs presented n Table 2 above, snce off-farm ncome opportuntes are fewer n rural areas. Moreover, households who have lower ncome per capta are more lkely to be poultry keepers. Ths result s also expected snce household level poultry keepng s often coned as a lvelhoods actvty favoured by the poor due to ts hgh return rate compared to ts low nput nvestment requrements. The mpact of havng ncome below the poverty lne on household s lkelhood of beng poultry keeper s however mxed across countres. In Kenya households who are below the poverty lne are more lkely to keep poultry whereas the opposte holds for Ngera. For Kenya ths result s consstent wth the fndng that that larger households wth hgher adult female ratos are more lkely to have ncomes below the poverty lne and to engage n poultry keepng (KPIA, 2009). In Ngera ths fndng can be explaned by the fact that n ths country to partake n poultry producton some mnmum level of fnancal nvestment s needed, whch may not be affordable by the households whose ncomes are below the poverty lne. Fnally n terms of wealth, we see that n all countres except Ghana households who have hgher lvestock wealth (market value of lvestock owned) are more lkely to keep poultry. Ths result s as expected snce households who have other lvestock are more lkely to own poultry as poultry s the frst step n lvestock ladder as mentoned above. Kenyan and Ngeran households who are wealther n terms of other assets (e.g., house and land) are also more lkely to keep poultry, possbly due to the complementartes between poultry producton and crop producton. 19

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