Behavioral Field Experiments for the Study of HPAI Prevention and Control

Transcription

1 A Collaboratve Research Project Funded by: Implemented by: Behavoral Feld Experments for the Study of HPAI Preventon and Control Angelno Vcesza Afrca/Indonesa Team Workng Paper No. 2

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3 Pro-Poor HPAI Rsk Reducton Table of Contents PREFACE... II. INTRODUCTION TERMINOLOGY AND RELATED LITERATURE WHY CONDUCT EXPERIMENTS? WHAT ARE EXPERIMENTS? Generc dscusson Some examples of experments AN APPLICATION Motvaton The behavoral model Expermental desgn: treatments and protocol CONCLUSION REFERENCES...7 Page Lst of Fgure Fgure : Market Equlbrum (Q*, P*)... 0

4 Afrca/Indonesa Team Workng Paper Preface Snce ts re-emergence, HPAI H5N has attracted consderable publc and meda attenton because the vruses nvolved have been shown to be capable of producng fatal dsease n humans. Whle there s fear that the vrus may mutate nto a stran capable of sustaned human-to-human transmsson, the greatest mpact to date has been on the hghly dverse poultry ndustres n affected countres. In response to ths, HPAI control measures have so far focused on mplementng preventon and eradcaton measures n poultry populatons, wth more than 75 mllon brds culled n Southeast Asa alone. Untl now, sgnfcantly less emphass has been placed on assessng the effcacy of rsk reducton measures, ncludng ther effects on the lvelhoods of smallholder farmers and ther famles. In order to mprove local and global capacty for evdence-based decson makng on the control of HPAI (and other dseases wth epdemc potental), whch nevtably has major socal and economc mpacts, the UK Department for Internatonal Development (DFID) has agreed to fund a collaboratve, multdscplnary HPAI research project for Southeast Asa and Afrca. The specfc purpose of the project s to ad decson makers n developng evdence-based, pro-poor HPAI control measures at natonal and nternatonal levels. These control measures should not only be cost-effectve and effcent n reducng dsease rsk, but also protect and enhance lvelhoods, partcularly those of smallholder producers n developng countres, who are and wll reman the majorty of lvestock producers n these countres for some tme to come. To facltate the development of evdence based pro-poor HPAI control measures the project s desgned so that there are fve work streams: dsease rsk, lvelhood mpact, nsttutonal mechansms, rsk communcaton, and synthess analyss. Project teams are allocatng and collectng varous types of data from study countres and employng novel methodologes from several dscplnes wthn each of these work streams. So that efforts aren t duplcated and the outputs of one type of analyss feeds nto another the methodologes n each work stream wll be appled n a cohesve framework to gan complementartes between them based on unformty of baselnes and assumptons so that polcy makers can have consstent polcy recommendatons. The fgure below s the methodologcal framework used to depct how work stream outputs ft together. Ths bref dscusses the methodologes to be used when conductng the behavoral feld experments hghlghted n the methodologcal framework below. Methodologcal Framework Dsease Rsk -Base lne Rsk maps Suggest unt of analyss and level of aggregaton needed as nput nto the lvelhood mpact analyss and rsk management smulaton analyses - Rsk pathways - Dsease probablty models (qualtatve and quanttatve) -Spatal spread models Provdes parameters on potental spread Lvelhood Impact - CGE analyss - Mult-market analyss - Household level analyss - Nutrtonal analyss - Qualtatve analyss Provdes baselne parameters dsease rsk Provdes baselne parameters lvelhood mpact Synthess -Cost/beneft analyss of varous preventon/ control rsk management optons -Cost/effectve analyss of rsk management optons -Smulaton analyses capturng the effect of varous rsk management strateges on: a) bologcal effcacy of dsease b) economc effcency c) socal desrablty d) poltcal feasblty Insttutonal Challenges -Value chan analyss Provdes parameters regardng effectveness of nsttutons to mnmze mpact - Assessment of role and effectveness of varous nsttutons n control efforts; -Assessment of the costs and rsk reducton effects of varous polces, reforms and nsttutonal changes on dsease rsk to date; -Behavoral experments Provdes baselne parameters regardng effectveness of nsttutons to mnmze mpact Communcaton and advocacy, -Promoton of scence-based, dsease control decson-makng wth due consderaton of soco-ec onomc mpacts) - Analyss of () key stakeholders n poultry management n general and HPAI rsk reducton and () ther key decsons that need supportng. - Development of decson support tools sutable for varous stakeholders.

5 Pro-Poor HPAI Rsk Reducton Author Angelno Vcesza, Postdoctoral Fellow, Markets, Trade and Insttutons Dvson, Internatonal Food Polcy Research Insttute, 2033 K Street NW, Washngton, DC Acknowledgements I thank Ekn Brol, Laura Taylor and Máxmo Torero for nvaluable comments. All errors are my own. Dsclamer The vews expressed n ths report are those of the author(s) and are not necessarly endorsed by or representatve of IFPRI, or of the cosponsorng or supportng organzatons. Ths report s ntended for dscusson. It has not yet undergone edtng and does not contan a fnal desgn of the feld experments. Please contact author before ctng at a.vcesza@cgar.org. More nformaton For more nformaton about the project please refer to

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7 Pro-Poor HPAI Rsk Reducton. Introducton Economcs s the scence that studes human behavor as a relatonshp between ends and scarce means that have alternatve uses (Robbns, 945). Ths makes economcs a socal scence. Hstorcally, the method and subject matter of economcs have presupposed that t was a non-expermental (or "feld observatonal") scence more lke astronomy or meteorology than physcs or chemstry (Smth, 987). At approxmately the md-20th century professonal economcs began to change wth the ntroducton of the "laboratory experment" nto economc method. Ths allowed for at least two developments. Frst, the "laboratory approach" made t possble to ntroduce demonstrable knowledge nto the economsts' attempt to understand economc transactons (.e., markets, socal nteracton and so on). Second, ths approach brought to the economst drect responsblty for an mportant source of scentfc data generated by controlled processes that can be replcated and valdated by others. These developments nvted economc theorsts (n partcular, game theorsts) to submt to a new dscplne, but also brought an mportant new dscplne and standard of rgor to the data generatng process tself. Snce the developments of the md-20th century, the use of expermental methods n economcs has grown extensvely. In partcular, the "laboratory approach" to economcs has grown beyond the laboratory to nclude experments n the feld. For a detaled account on the past, present and future of feld expermentaton, see Levtt and Lst (2008). The purpose of ths paper s twofold. Frst, to dscuss the concept of a "feld experment" and ts strengths and weaknesses for studyng economc behavor. Second, to address a man applcaton (.e., example) of a feld experment n the context of the DFID-funded project on pro-poor polcy optons for the preventon and control of Hghly Pathogenc Avan Influenza (HPAI). The sample feld experment dscussed n ths paper only apples to Indonesa even though experments wll also be conducted n Ghana or Ngera. Snce the dscussons pertanng to the Afrca experments are at a very prelmnary stage, they are not addressed further n ths note; however, these experments are lkely to address ssues of compensaton and/or nsurance. The paper proceeds as follows. Secton 2 pns down some termnology. Secton 3 motvates the use of experments. Secton 4 dscusses the concept of an experment from a techncal standpont and summarzes some examples of prevous experments. Secton 5 dscusses the man applcaton for the case of HPAI n Indonesa. Fnally, secton 6 concludes. When economsts speak of data generatng processes, they typcally thnk of that part of the data generatng process that s verfable by means of acton-observable choces, (see Kőszeg and Rabn, 2008, for defntons). Such data (sometmes referred to as choce data") are usually analyzed usng the revealed preference approach (Samuelson, 948). The relatvely recent neuroeconomcs" lterature (.e., neuroscence appled to economcs) has sparked consderable debate on the extent to whch economsts should also focus on actonunobservable choces (also known as non-choce data") such as smles, bran mages, dopamne levels and so on. Advocates of "neuroeconomcs" argue that economsts have tradtonally only specfed and controlled part of the data generatng process by falng to look at non-choce data". Ths methodologcal debate s at the core of expermental methods n economcs. Namely, f non-choce data" matter, falng to account for them n the data generatng process results n a pror lack of control and ex post msspecfcaton. Ths paper wll focus on "choce data" unless otherwse noted. For further dscussons on ths debate, see Capln and Schotter (2008); partcularly, the lead artcle by Gul and Pesendorfer (2008).

8 Afrca/Indonesa Team Workng Paper 2. Termnology and related lterature The term "expermental economcs" tends to be used nterchangeably wth "behavoral economcs". However, t should be noted that these terms (.e., subfelds of economcs) need not be the same. Behavoral economcs (as advocated by the Russell Sage Foundaton's Behavoral Economcs Roundtable) s the combnaton of psychology and economcs that nvestgates what happens n markets n whch some of the agents dsplay human lmtatons and complcatons (Mullanathan and Thaler, 200). Expermental economcs consttutes the use of expermental methods (laboratory or feld) to address general economc questons that may or may not be related to psychology. The dstncton between the two felds has become more nuanced as both felds are ncreasngly usng expermental technques to address psychology-related questons n economcs. The term "feld experments" s used dfferently n expermental economcs (e.g., Harrson and Lst, 2004) than n (new) development economcs (e.g., Duflo, 2006). 2 Tradtonally, expermental economcs has been drven by laboratory experments (also known as "lab experments");.e., experments conducted wth students at academc nsttutons as subjects. Whle one of the man strengths of these experments s ther hgh degree of control over the data generatng process, lab experments have recently been crtczed for lack of generalzablty, partcularly, when addressng questons related to socal preferences (Levtt and Lst, 2007). 3 Ths crtque among others has drven expermental economsts dependng on the queston under consderaton to conduct lab experments n conjuncton wth feld experments. Feld experments n the context of Harrson and Lst (2004) are classfed nto three categores: () artefactual, () framed and () natural feld experments. Artefactual experments tend to be defned as "lab experments n the feld". They are feld experments n the sense that they are conducted wth feld partcpants (e.g., farmers), but the experment tasks are conducted as part of an artefactual envronment (smlarly to lab experments). Framed experments tend to be conducted n actual day-to-day settngs wth feld subjects who know that they are part of an experment. Fnally, natural experments are the same as framed experments wth the excepton that subjects typcally do not know that they are part of an experment. In other words, the experment tasks are performed as part of the subjects day-to-day (.e., natural) envronments. These feld experments tend to be behavoral n nature, because they are heavly concerned wth the behavoral foundatons that drve dentfed mpacts. Furthermore, these are "true experments" n the sense that the nterventons are typcally desgned a pror wth the am of establshng a proper counterfactual for purposes of estmaton and nference. New development economcs on the other hand has been drven by the "laboratory approach" appled to the feld data and thus, has manly been concerned wth randomzed trals conducted ex post to assess mpacts of nterventons that need not have been desgned as "experments" a pror. Furthermore, these trals need not always be concerned wth the behavoral processes (.e., theoretcal foundatons) drvng some of the dentfed mpacts; a crtque that has been voced by for example Mookherjee (2005). To the extent that randomzed trals were a pror desgned as experments to dentfy outcome as well as behavoral mpacts, they are the same as feld experments n the sense of the prevous paragraph. In fact, the reader wll note that the Harrson 2 The term (new) development economcs s dscussed further n Basu (2005) and Banerjee (2005). 3 Lab experments have been shown to be relatvely robust n other contexts (e.g., Brookshre et al., 987). 2

9 Pro-Poor HPAI Rsk Reducton and Lst (2004) taxonomy proposed prevously s general enough to capture defntons n ether dscplne, thus allowng for both behavoral and outcome mpacts. The crtque that randomzed trals lack theoretcal foundatons has drven new development economcs to become more behavoral n nature. The need for both dscplnes to deal wth crtques of generalzablty of dfferent types has pushed for a "new" approach that I would lke to term "behavoral development economcs". 4 Ths termnology s not ntended to take away from prevous work n ether game theory, expermental/behavoral or development economcs. In partcular, t s not my clam that development has not been behavoral and/or game-theoretc n nature. In fact, as dscussed by Bardhan (993), many novel theoretcal foundatons and methods were ntroduced to general economc thought by development economcs. Rather, by adoptng ths termnology I post that development and expermental economcs are ncreasngly convergng to a state n whch randomzed theory-testng feld experments (possbly complemented by laboratory experments) are seen as an operatonally meanngful way to () study economc behavor and mechansm (.e., nsttutonal) desgn and () generalze fndngs to nform polcy work. Unless otherwse stated, ths s how the term "feld experment" s used throughout ths paper. 4 To a certan extent, ths termnology was nspred by Camerer (2003) (ttled behavoral game theory") and Bertrand et al. (2004). 3

10 Afrca/Indonesa Team Workng Paper 3. Why conduct experments? The ratonale for conductng feld experments can be traced back to the process of quanttatve research. See for example the Mookherjee (2005) modfcaton of Haavelmo's (e.g., Haavelmo, 958) classfcaton, whch consttutes the followng stages: a. Stage : Emprcal descrpton of the relevant phenomenon, consstng of exploratory data analyss amed at helpng dentfy emprcal regulartes that need to be explaned by a sutable theory, and n addton the nature of assumptons that such a theory can make wthout gross volaton to the emprcal patterns. Stage 2: The formulaton of a relevant theory, ncludng dervaton of potentally observable (hence falsfable) mplcatons. Stage 3: The estmaton and testng of theores, a stage whch may lead back to modfcaton or replacement of the prevous theores, n an teratve back-and-forth process wth Stage 2. Stage 4: Use of the least unsuccessful theory--from the standpont of emprcal verfcaton for purposes of predcton and polcy mplementaton and evaluaton. The unfyng theme underlyng ths process s that economsts () observe choce (and perhaps, nonchoce) data and () seek to explan them. In some sense, ths process s herarchcal. If we do not observe data, there s nothng to be explaned. 5 So, t s sensble to ask where the observed data come from;.e., how were they generated and collected? Ths s manly how the expermental approach dffers from the classcal feld observatonal approach to economcs. Suppose we lve n a frst-best world n whch we () know and () observe the complete data generatng process for all varables, V * n for n. In such a world, we have perfect "predctve ablty", snce we know/observe all relevant varables and the complete data generatng process. However, we do not lve n a frst-best world. In fact, we lve n a second-best world n whch we () do not observe all varables and () do not know the true process that generated the data. We only know/observe a proper subset of all varables ( V V *) and somehow, we must use these to nfer a relatonshp between such varables. So, how do we proceed? Well, suppose that V can be parttoned nto X and Y. Then, the classcal (non-bayesan) way of proceedng s to:. Vew X and Y as random varables. 2. Assume exstence of some relatonshp between X, Y and some error term (also a random varable);.e., Y ~ (X, ). 3. Assume some form of exogenety on X, such as E ( X ) = Explore the relatonshp between X and Y (possbly, non-parametrcally). 5. Draw nference therefrom for purpose of predcton. In our second-best world (.e., when workng wth real data) pont 3 fals under many crcumstances * manly due to unobserved data (.e., the reducton of dmensonalty when movng from V to V ). The queston thus becomes how to proceed when ths assumpton s volated. The expermental approach dffers from the tradtonal approach manly n how and when one deals wth ths volaton. 5 For the sake of argument, the exposton here s delberately one-sded. However, t s generally accepted that there s contnuous two-way feedback between emprcs and theory (e.g., Samuelson, 2005). I.e., both nform each other. An example s Mookherjee's Stage 3 descrbed prevously. 4

11 Pro-Poor HPAI Rsk Reducton Whle both approaches attempt to control for ths volaton, expermental approaches do so by usng a pror technques such as, control of the "data generatng process" by desgn of the experment as opposed to ex post methods such as nstrumental varables (e.g., Wooldrdge, 2002) or "randomzed trals" (e.g., Duflo, 2006; Banerjee et al., 2006). 6 A carefully desgned experment rds ex post econometrc analyss of most common problems faced by feld observatonal data sets. Ths s the prncple ratonale for conductng feld experments. However, n order to carefully desgn a feld experment, the expermenter needs a sold understandng of the frst-best (.e., true) data generatng process, whch brngs us back to Mookherjee's four-stage process for conductng quanttatve research. Namely, n order to carefully desgn an experment, t s essental to have a theoretcal model n mnd. And, n order to have a theoretcal model n mnd, t s mportant to have undergone Stage. So, experments should not be seen as stand-alone objects. Rather, experments should be seen as meanngful theory-testng nstruments that ft well nto the four-stage process of quanttatve research. Furthermore, f lnked to a well desgned model, they can provde generalzable results that nform polcy-makng. Fnally, three comments. Frst, theory-testng experments are not the only types of experments. In fact, Smth (982) draws the dstncton between theory-testng (.e., nomothetc) and heurstc experments among others. The latter are not theory-testng n the sense that they tend to be exploratory; e.g., to nform the development of new theory when certan models are scentfcally underdeveloped. Whle these types of experments have ther place n the four-stage process, they are not the type we have n mnd for polcy-makng. So, our man focus s on theory-testng experments. Second, the above dscusson llustrates the complementarty between surveys and experments. A good method for ganng an understandng of the status quo n Stage s to conduct baselne surveys (and perhaps, rapd apprasals). These methods nform the stylzed facts that drve the modelbuldng n Stage 2, whch n turn nforms the desgn of the experments. So, experments and surveys tend to be meanngful complements, partcularly, n the feld. Of course, surveys do not only serve a purpose n Stage of the research process. Thrd, experments should be conducted n the context of the four-stage process. Snce the desgn of an experment controls several aspects of the data generatng process, an experment n solaton can lead to non-generalzable results. Namely, f an expermenter does not carefully go through Stage and Stage 2 and thus, gans a wrong understandng of the data generatng process, the experment can suffer from "ex ante model msspecfcaton". Ths term s used to dstngush from standard ex post model msspecfcaton, whch occurs when specfyng an econometrc model once the data have been generated. 6 We crudely use the term "randomzed trals" to refer to the (new) emprcal technques for ex post mpact evaluaton such as the use of propensty score matchng to establsh counterfactuals (typcally, a control group), whch n turn s used to do for example dfference-n-dfference estmaton. For a textbook reference, see Wooldrdge (2002). 5

12 Afrca/Indonesa Team Workng Paper 4. What are experments? 4.. Generc dscusson Ths secton provdes a mcro-theoretc "defnton" of an experment. The dscusson s generc and apples to any type of experment regardless of the research queston under consderaton. We fnd t useful to set these theoretcal foundatons n generc terms, so that we can appeal to them when dscussng the applcaton. The dscusson draws heavly on Smth (987). In defnng a mcroeconomc system two components wll be dentfed: () an envronment and () an nsttuton. The envronment conssts of a set of N economc agents {,..., N }, a set of K commodtes (ncludng resources) {0,,..., K } and certan characterstcs of each agent, such as the agent's preference relaton endowment vector, technology (knowledge) endowment. Hence, the th agent s characterzed by the vector T, and a commodty e, =, T for whch the components are assumed to be defned on the K dmensonal commodty space. Hence, a mcroeconomc envronment s defned by the collecton of characterstcs e = ( e,..., e N ). The nsttuton specfes:. A language M = M,..., M N consstng of messages m = m,..., m N, where K m s an element of M, the set of messages that can be sent by agent. Note two thngs. Frst, m may refer to the fnal allocaton-determnng message. Second, the set of allowable messages M need not be unque across all. 2. A set of allocaton rules for each, = N H h ( m),..., h ( m). The rule h (m) states the fnal commodty allocaton to each as a functon of the messages sent by all agents whch precedes the allocaton. 3. A set of cost mputaton rules = N C c ( m),..., c ( m). The rule c (m) states the payment to be made by each agent n numerare unts (money) as a functon of the messages sent by all agents. 7 N 4. A set of adjustment process rules, G g ( t, t, T),..., g ( t, t, T). These rules consst of a () startng, () transton and () stoppng rule. = 0 0 Each agent 's property rghts n communcaton and n exchange are defned by I = M, h ( m), c ( m), g ( t0, t, T), whch specfes the messages that has the rght to send; the startng, transton and stoppng rules whch govern these communcaton rghts; and fnally the rght to clam commodtes or payments n accordance wth the outcome rules that apply to messages. A mcroeconomc nsttuton s defned by the collecton of all these ndvdual property rght characterstcs I,..., N = I I. A mcroeconomc envronment ( e ) and a mcroeconomc nsttuton ( I ) together defne a mcroeconomc system, S = ( e, I). Agent 's outcome behavor s defned by a 7 Note that C s redundant n that t could be ncluded n the defnton of H, but t wll be convenent n many applcatons (as when there are no ncome effects) to dstngush between commodty allocatons by H and payment mputatons by C. 6

13 Pro-Poor HPAI Rsk Reducton functon ( e I) characterstc whch yelds the allocaton-determnng message e, gven the property rghts of all agents defned by I. m sent by agent wth Ultmately, an experment becomes an emprcal exercse. So, n order to thnk about how the above dscusson can be appled to construct and conduct an experment, we must address two questons. The frst queston s what do we hope to learn from conductng the experment? Ths queston s relevant because t dctates () those observable characterstcs that we want to control or () those unobservables that we mght want to elct as part of the experment. The second queston s whch of the above-mentoned elements are observable n the feld? Among the observable elements of an economy are: () the lst of agents, () the lst of physcal commodtes and resources, () the physcal commodty and resource endowments of ndvdual agents, (v) the language and property rght characterstcs of nsttutons, and (v) outcomes (e.g., choces). What s unobservable are (v) preference relatons, (v) technologcal (knowledge, human captal) endowments, and (v) agent message behavor ( e I), =,..., N. The fact that certan varables n the mcroeconomc system are unobserved can pose a problem n applcatons dependng on the research queston the experment seeks to answer. For example, consder experments that test game-theoretc predctons. The usual way of constructng such experments s to () defne a model wth parametrc preferences, () apply the approprate soluton concept to derve testable hypotheses and () conduct an experment to test such predctons. However, as Ray and Zhou (200) and Cox (2004) among others have ponted out, f the theoretcal predcton dffers from the observed outcome, there could be two possble explanatons: ether ndvduals do not play accordng to the theory, or preferences are msspecfed. Notce that the extent to whch ths s problematc depends on the research queston under consderaton. For example, f the ntenton of the experment s to observe behavor under alternatve nsttutons, "stablty" of preferences whch would be an addtonal assumpton/premse would reduce the concern. On the other hand, f the am of the experment s to test a partcular game-theoretc soluton concept, ths becomes a serous concern. One of the ways of dealng wth ths ssue s to proceed by modelng preferences non-parametrcally;.e., to consder a revealed preference approach to games as dscussed by Ray and Zhou (200). However, ths approach can have ts lmtatons, (see Webull, 2004, for the so-called "Webull crtque"). Fnally, another approach s to attempt to emprcally valdate preferences by conductng (possbly tangental) experments that elct preferences such as experments on socal, rsk and tme preferences. Typcally, experments wll try to observe and explan outcome behavor (.e., a proxy for ( e I), call t ˆ ( e I) ) n the form of choce data, under alternatve nsttutons (.e., by changng I ). In some cases, the experment may be specfcally desgned to elct characterstcs of the envronment ( e ) such as ndvdual preferences. In other cases, preferences ( e ) are assumed to be stable and the experment seeks to characterze changes n outcome behavor (.e., the shape of (.) ) across changng nsttutons ( I ). ˆ 7

14 Afrca/Indonesa Team Workng Paper 4.2 Some examples of experments Many expermental applcatons have appeared n the economcs lterature. One study that s most closely related to the experments proposed n ths paper s by Bennett and Wlls (2007). Bennett and Wlls report choce experments that estmate the value that socety places on changes to the sze of the badger populaton. The study was undertaken n the context of the possble need to reduce the badger populaton by cullng to help control bovne tuberculoss n cattle. The study found that people were concerned about the problem of bovne tuberculoss n cattle, as reflected n ther wllngness to pay to control the dsease; however, people dd not lke the dea of a polcy that ntentonally klled large numbers of badgers, as reflected n ther relatvely hgh wllngness to pay not to have such a polcy. To the extent of our knowledge, very few experments n the publshed lterature have explored people s preferences towards and the effect thereof on the preventon and/or control of anmal dseases (n partcular, HPAI). However, there have been numerous studes assessng () agents' preferences and () nteractons between preferences and alternatve nsttutons that can be appled to the context under consderaton. Below, we dscuss further examples of experments n two broad categores. The frst s rsk (and tme) experments and the second s contngent valuaton (choce) experments. The choce of these two categores s not random, as these are the types of experments we plan to conduct n the context of ths project. Some of the most celebrated rsk experments n a feld context are those of Bnswanger (980). Bnswanger conducted rsk experments wth households n rural Inda usng real monetary payoffs and found that at hgh stakes, most ndvduals are rsk averse despte wealth levels. Snce, rsk experments have become extremely mportant n economcs and wth good reason: most models vew rsk preferences as an mportant determnant of behavor. Consequently, t s mportant to elct rsk preferences n order to be able to control for them when dong estmaton and nference. 8 In the case of the HPAI project, we see rsk as an mportant factor as well, snce occurrence of the dsease n tself s random and thus, rsky. Furthermore, an HPAI outbreak puts household lvelhoods at rsk. Ths s another way n whch rsk mpacts people's behavor durng an outbreak. The second set of experments are n the class of stated preference methods (SPMs), whch encompass both contngent valuaton and choce experments. SPMs have been appled n many contexts to assess consumers' wllngess to pay (.e., demand) for attrbutes of dfferent goods such as food safety, food qualty and generc product characterstcs. One such study was conducted by Alfnes et al. (2006). 9 These authors looked at consumers' wllngness to pay for the color of salmon. They found that consumers use color of salmon as a qualty ndcator and that they are wllng to pay sgnfcantly more for salmon fllets wth normal or above-normal redness, as compared wth paler salmon fllets. Our experments wll be smlar n the sense that they wll gauge consumers' wllngness to pay for market cleanlness. Furthermore, we wll also explore the extent to whch producers are wllng to lose some surplus (durng perods of market closure) n order to provde the attrbute of market cleanlness. 8 Recently, Andersen et al. (2008) showed that t s mportant to elct both rsk and tme preferences. 9 For addtonal references, see the revew by Brol et al. (2008). 8

15 Pro-Poor HPAI Rsk Reducton 5. An applcaton 5. Motvaton Ths secton dscusses prelmnary detals for a potental feld experment n Indonesa related to the DFID-funded project on pro-poor polcy optons for the preventon and control of HPAI. We start by motvatng the man queston n terms of proposed polcy optons n Indonesa. Then, we dscuss the behavoral model underlyng the experments as well as some detals on the expermental desgn and protocol. The basc nformaton for the "market closures" s based on dscussons wth several Indonesan stakeholders, ncludng but not lmted to Dr. Elly Sawtr Sregar, head of the HPAI Campagn Management Unt that s part of the Indonesan Department of Agrculture (DEPTAN). These dscussons are ongong. As part of ther Natonal Strategc Plan for the contanment and eradcaton of HPAI, the Indonesan government s plannng to mplement market closures. The basc dea behnd a market closure s the followng: At a regularly scheduled pre-announced date and tme, the market n queston wll be closed for thorough cleanng. For example, n the case of a lve-brd market ths would ental removng all brds and cages from the market and thoroughly cleanng both the premses and the cages pror to re-nstallaton. The government s concerned wth the economc ramfcatons of the proposed polcy. Namely, once the polcy s ntroduced, t wll affect both consumers and producers. Specfcally, three key economc questons arse: 0. How wll ths polcy affect consumers' and producers' surplus? 2. Gven the magntude of these economc losses, what are the expected changes n behavor both on the consumer and the producer sde? 3. If the polcy changes nduce ncentves towards perverse behavor, how can the government secure that these are mnmal n terms of economc dsturbance? The standard (.e., neoclasscal) approach to markets s to model consumers and producers whose aggregate demand and supply schedules gve rse to an equlbrum prce n the market (e.g., Mas- Colell et al., 995). The typcal graph that most readers are famlar wth from ntroductory economcs textbooks s gven n fgure. However, the basc dea s the followng: If we assume that consumers and producers have well-defned preferences, then ratonalty and optmalty manfest themselves n aggregate form as well-defned demand and supply functons. Each pont on the demand curve ndcates consumers' aggregate wllngness to pay for a partcular quantty of the commodty n queston. Smlarly, the supply curve ndcates producers' aggregate wllngness to accept at any gven quantty. Markets exst because they have benefts (n terms of ncreased welfare) assocated wth them. One way to measure welfare s by means of consumers' and producers' surplus. These concepts are best explaned usng fgure. Consder pont A n fgure. Suppose that wth ths pont there s assocated the followng prce-quantty combnaton, ( P A, QA). Compare P A wth * P. Clearly, * P A > P. How can we use ths to formulate a welfare measure? Well, note that even though the aggregate consumer pays a prce of * P n equlbrum, the demand curve ndcates that at quantty 0 The DFID-funded project as a whole s concerned wth other consequences such as epdemologcal ones. The schedules n ths fgure can rgorously be derved; see Mas-Colell et al. (995). 9

16 Afrca/Indonesa Team Workng Paper Q A, the aggregate consumer s wllng to pay P A. So, the aggregate consumer gets a surplus snce n equlbrum she pays a lower prce than she would have been wllng to pay for ths partcular quantty. We can reason smlarly for quanttes to the rght of Q A up to the equlbrum quantty. Consumers' surplus s thus ndcated by the area marked CS ;.e., the area of the trangle ( ) * named AEP. A smlar argument wth regard to the aggregate supply curve yelds the concept of producers' surplus ( PS ). A market s assocated wth these two welfare measures, whch together may be referred to as total surplus ( TS = CS PS ). 2 Fgure : Market Equlbrum (Q*, P*) * Q Now, what happens f a market s closed? Suppose a market operates over tme (e.g., on a daly bass). Then, we can assocate wth t a daly surplus of partcular day. A daly market closure mples a surplus loss of TS t, where the subscrpt t denotes a TS t for every t. Two thngs are worth notng here. Frst, the daly surplus need not be the same across all tme perods. Second, the total surplus conssts of two components: consumers' and producers' surplus. In other words, when there s a closure, both partes lose. Under the assumpton of scarcty, lost surplus nduces economc agents to make up for economc losses n other markets. In the case of market closures n Indonesa, we mght expect agents to fnd nformal (.e., unobserved) markets n whch to engage n alternatve economc actvty. In other words, the government polcy mght nduce agents to resort to nformal economc actvty, whch may be even less hygenc than those that the government s tryng to avod. It s n ths sense that the proposed polcy acton can nduce perverse economc ncentves, snce t may reduce the probablty of dsease contanment. How then we can we deal wth ths behavoral ssue? Recall that as dscussed by Smth (982), message behavor (.) s a functon of two varables, the envronment ( e,.e., preferences) and the nsttuton ( I ). So, accordngly f we observe ˆ (.), we can focus on one or both of ts 2 See MWG95 for a formal defnton of these surpluses and how to calculate them. 0

17 Pro-Poor HPAI Rsk Reducton determnants. We can try to buld nsttutons such that perverse ncentves from the government polcy are mnmzed. Alternatvely, we can study the extent to whch agents' preferences wll lead to mnmal perverse behavor. On the nsttutonal sde, one opton s to close the market on a date and at a tme when the surplus loss s expected to be mnmal. For example, a perod durng whch there s typcally mnmal actvty n the market. However, ths may be nfeasble. A second opton s to ncrease montorng of nformal actvty on days of market closure. Ths n turn may be costly. So, t s worth askng to what extent agents' preferences may be such that the resultng perverse behavor wll be mnmal. Ths s where our feld experments wll play a role. The proposed feld experments ask whether producers have an ncentve to comply wth the market closure due to consumers' wllngness to pay for "clean" markets. Namely, f consumers are wllng to pay for cleaner markets, ths wll be reflected n a prce premum for producers. Ths has two mplcatons: Frst, consumers wll be wllng to ncur loss of consumers' surplus (durng perods of market closure) n order to get "cleaner" products. Second, producers wll be wllng to lose surplus (durng perods of market closure) n order to gan a prce premum n other perods, as long as the premum s suffcent to compensate for the loss. 5.2 The behavoral model The tradtonal approach to consumer theory has been to assume that goods are the drects object of utlty;.e., the arguments over whch we have preference orderngs. Instead, n the Lancaster (966) framework t s the propertes or characterstcs of goods from whch utlty s derved. In other words, Lancaster assumes that consumpton s an actvty n whch goods, sngly or n combnaton, are nputs and n whch the output s a collecton of characterstcs. The appeal of ths approach for the queston under consderaton s that we can vew a partcular product or collecton of products (such as poultry or poultry products) as possessng the same (set of) characterstc(s); n ths case, the "clean market" characterstc. 3 Accordngly, we can gauge consumers' wllngness to pay for ths attrbute and producers' wllngness to accept losses (durng market closure) to supply t. The relatonshp between the level of actvty k, y k, and the goods consumed n that actvty are lnear and objectve, so that f x s the j th commodty we have j x j = a yk, k jk () and the vector of total goods requred for a gven actvty vector s gven by x = Ay. (2) 3 As for the termnology, we recognze that poultry suppled n formal markets have most of the tme come from many dstnct orgns, typcally va traders. So, we fnd the clam that "market closure" wll lead to "safe poultry" too bold. However, t s not unreasonable to clam that poultry sold n a market subject to "market closure" has come from a cleaner market envronment n the sense that such market contans cleaner storng and slaughter facltes. So, we call ths the "clean market" or "market cleanlness" attrbute of poultry as opposed to the "food safety" attrbute.

18 Afrca/Indonesa Team Workng Paper Each consumpton actvty k produces a fxed vector of characterstcs, z. The relatonshp between the level of actvty k, y, and the th characterstc z s lnear or k k k k z = b y (3) z = By. (4) It s assumed that the coeffcents a jk and b k are objectvely determned so that they hold for all ndvduals. In other words, these coeffcents are determned by the ntrnsc propertes of the goods and characterstcs themselves and possbly by the context of technologcal knowledge n socety. Consder ths model n relaton to the applcaton under consderaton. In ths case, a partcular consumpton actvty y k could be "consumng from a tradtonal Indonesan market". The assocated commodty could be "pounds of chcken" and the assocated characterstc could be whether or not the market s subject to a market closure,.e., "market cleanlness". It s further assumed that the ndvdual possesses an ordnal utlty functon U (.) on characterstcs z, U (z) and that he wll choose a stuaton that maxmzes z. U (z) s assumed to satsfy the standard propertes of a neoclasscal utlty functon (.e., ncreasng, twce contnuously dfferentable and regular strctly quasconcave). Note that n ths model, the relatonshp between the collectons of characterstcs avalable to the consumer, z, whch are drect ngredents of preferences and welfare, and the collecton of goods avalable, x, whch represent the relatonshp wth the rest of the economy, s ndrect through the actvty vector y. Suppose that the relatonshp between goods and actvtes s one-to-one. Then, we can rewrte x = Ay as y = A x such that z = By can be expressed as z = Cx, where C = BA we can wrte the consumer problem n smpler form as follows:. As a result, max U ( z) z s.t. px k, wth z = Cx, z, x 0, (5) whch can further be rewrtten as max U ( z) z s.t. pc z k, wth z 0. (6) For smplcty, suppose that k, 2 z, p wth 2 2 transpose) and p ( p, p ) and C = c2 c22. = 2 c c 2 T z = ( z, z2) (where the T stands for Gven these smplfyng suppostons, the consumer choce problem becomes: maxu( z, z2) s.t. ( pc p2c2) z ( pc 2 p2c22) z2 k, wth z, z2 { z, z 2 } 0, (7) 2

19 Pro-Poor HPAI Rsk Reducton wth necessary (and suffcent by assumpton of regular strct quasconcavty) frst-order condtons: L z L z 2 = U ( pc p2c2) = 0 = U 2 ( pc 2 p2c22) = 0 L = k ( pc p2c2) z ( pc 2 p2c22) z2 = 0, (8) (9) (0) where L stands for the Lagrangan, U stands for the Lagrange multpler and U. z By the Implct Functon Theorem, t can be shown that there exst Marshallan demand functons for * * the characterstcs, whch can be expressed as z z ( p, p, k; c ) and z z ( p, p, k; c ) for, j =,2 = 2 j. Recall that c j relates the characterstcs back to the goods n queston. 2 = 2 2 j So far, we have dscussed the attrbutes approach for the consumer sde. Smlarly, we can thnk of producers supplyng goods that have certan characterstcs, say z and z 2. Under such condtons we can thnk of a market where equlbrum prce s determned based on demand for and supply of certan product characterstcs; n ths case, "market cleanlness". 5.3 Expermental desgn: treatments and protocol We antcpate conductng two types of experments n Indonesa as part of ths project. The frst set s a set of rsk and tme experments as dscussed n Andersen et al. (2008). 4 As dscussed prevously, we expect preferences for rsk to play an mportant role n how economc agents react to an occurrence of HPAI. The second set of experments whch consttute the man experments of nterest wll assess () whether there exsts demand (.e., wllngness to pay) for "market cleanlness" and () whether supplers are wllng to lose some surplus n order to supply "market cleanlness" and charge a hgher prce n the future. A comparson of these two measures wll gve an dea of the extent to whch the proposed market closures mght nduce consumers and producers to engage n nformal market actvty. These man experments wll be valuaton experments n the sense that they wll attempt to put a monetary value on agents' preferences for the attrbute of nterest;.e., market cleanlness. 5 There s a vast lterature on (contngent) valuaton and choce experments, spannng several felds ncludng economcs. The typcal ssues encountered n ths lterature are: () how to mnmze overestmaton due to the hypothetcal nature of such experments (n partcular, how to make them ncentve compatble), () how to frame such experments and () how to do ex post estmaton and nference based on stated preference data once the expermental data have been collected. Frst, we dscuss a potental way of conductng these experments. Then, we use ths to elaborate on the 4 For a typcal rsk experment, please see Andersen et al. (2008) and/or the references cted wthn. 5 We also antcpate nteractng these valuaton treatments wth other varables of nterest such as nformaton. Pror to statng/revealng preference, t s necessary to be well nformed about the specfc attrbute of nterest. So, varyng degrees of nformaton s an mportant aspect of study. 3

20 Afrca/Indonesa Team Workng Paper above mentoned ssues and explan how we plan to mtgate them as part of our expermental desgn. Consder the followng queston: to what extent do consumers exhbt a wllngness to pay for market cleanlness when consumng poultry products? How can we possbly answer such a queston? Well, one way s to conduct a wllngness to pay (stated preference) experment. Suppose we have a sample of 00 respondents that have been (randomly) selected for ths partcular treatment. One potental way of elctng ther wllngness to pay s to proceed as follows:. Calbrate an ntal value of a partcular poultry product at P 0 accordng to the current market prce or n the absence thereof, a randomly assgned ntal prce from a dscrete unform dstrbuton wth reasonable support. 2. Based on such ntal prce, ask the agent whether or not he or she s wllng to pay P 0 for the product wth the characterstc of nterest. 3. If the answer s Yes, then proceed to the next hgher prce, say j, where j =,2,..., J. 4. Contnue ths process untl the answer to the queston s No. Suppose the answer s No on the Kth teraton. Then, the value P 0 K becomes the consumers wllngness to pay. The above process n tself s relatvely smple. However, the ssues mentoned above arse mmedately, f ths procedure s appled as s. Frst, the fact that the queston s hypothetcal (.e., non-bndng and not affectng payoffs), may not gve the respondent suffcent ncentve to answer truthfully. Ths s contrary to the case n whch he or she were actually forced to purchase the product at the specfed prce. To deal wth ths ssue, Lusk et al. (2008) and Alfnes et al. (2006) among others have attempted to make the consumers choce bndng by requrng the consumer to buy the product once hs or her choce has been made. Furthermore, once the consumer choce s (possbly) bndng, he or she has an ncentve to le about K. So, these researchers have also spent consderable tme tryng to ensure that the rankng elctaton mechansm used s ncentve compatble. Second, the fact that the queston s often posed for products that may not necessarly exst at the tme the study s beng conducted, requres careful framng and elctaton of the respondent s preference statement. Tran and Wlson (2007) (and the numerous references wthn) suggest an approach called pvotng that bulds on an actual choce that a respondent recently made. An example would be the followng: Suppose we ask the respondent to descrbe a recent purchase of poultry at a tradtonal market and how much he or she pad for t. Then, based on that, the expermenter would pvot and ask about a choce that s slghtly dfferent; namely, one n whch the same product s purchased from a market that has been closed (.e., regularly thoroughly cleaned). The man advantage of pvotng (.e., teratng from a famlar choce) s that t ncreases the realsm of the task by relatng the (hypothetcal) stated preference choce wth an actual choce that the respondent made n a revealed preference envronment. So, ths n part takes care of framng as well. Fnally, f the above consderatons are taken nto account when elctng partcpants stated preferences, ex post, the expermenter now n the role of econometrcan s faced wth the task of estmatng wllngness to pay usng stated preference data, possbly combned wth revealed preference data. Ths gves rse to endogenety, namely, a dependence between the stated P 0 P 0 4

21 Pro-Poor HPAI Rsk Reducton preference attrbutes and unobservables. Tran and Wlson (2007) propose an estmaton technque that deals wth ths ssue of lack of ndependence. Gven these consderatons, we can thnk of a revsed wllngness to pay experment that conssts of the standard model adapted for elements of Lusk et al. (2008) and Tran and Wlson (2007). Suppose agan that we have a sample of 00 respondents. Then, we could conduct an experment by performng the followng steps:. Ask the partcpant regardng a recent experence where he or she purchased a poultry product from a partcular market. 2. From ths, nfer a benchmark for the partcpant. 3. Iterate from ths benchmark (smlarly to Tran and Wlson, 2007) to present the subject wth a new task that s somewhat famlar: purchasng a poultry product from a partcular market that has been closed. 4. Now, apply conjont rankng analyss (smlarly to Lusk et al., 2008) to have the partcpant value and rank the poultry product from the closed versus the non-closed market. 6 The rankng can be done n terms of prces and nvolves the random event that the choce becomes bndng. The prces (.e., rankng) and probabltes of wnnng the lottery n whch case the choce becomes bndng are proportonal such that they nduce ncentve compatblty;.e., the hghest ranked alternatve has the hgher probablty of wnnng. The lottery can be mplemented as a wheel smlar to that used n game shows such as Wheel of Fortune. Dfferent peces of the pe ndcate dfferent probabltes. 5. Implement the lottery and determne the respondent s wllngness to pay. These fve steps now consttute a wllngness to pay experment, to be appled wth consumers. Smlarly, we can envson a wllngness to accept/lose experment wth producers. We can then analyze the data (perhaps, usng a method smlar to Tran and Wlson, 2007) to compare the responses to see f the net surplus justfes the proposed polcy. It should be noted that whle the above may seem detaled, t s just a bref dscusson of a potental experment. In partcular, several addtonal detals need to be sorted out pror to mplementaton of these experments. Some of these are: () the dfferent treatments to be mplemented (e.g., we can vary the type of the poultry product, the nformaton provded to respondents, the prcng and the rankng mechansm), () the locaton and frequency of the experments (.e., whch regons, whch markets, whch agents, durng what tmeframes), () the sample szes per treatment n order to acheve suffcent power (e.g., we can envson 00 respondents per treatment) and (v) the best way (.e., level and method) to reward subjects for ther partcpaton n the experments. We expect these detals to be sorted out soon based on the pendng dscussons wth stakeholders. 6 We can also envson a non-dchotomous choce n whch we vary the frequency of the market closure; e.g., consder a market that was closed for t hours/week, t+ hours/week, where =,2,. 5

22 Afrca/Indonesa Team Workng Paper 6. Concluson Ths paper dscusses the concept of a feld experment, the motvaton for conductng a feld experment as well as an applcaton of a proposed feld experment to be conducted as part of the DFID-funded project on pro-poor polcy optons for HPAI Preventon and Control n Indonesa. The desgns of these feld experments are under constructon, snce consultatons wth several relevant stakeholders are stll n progress. Any questons and/or comments should be drected to a.vcesza@cgar.org; especally, f one would lke to cte ths work. 6