Does Agricultural Trade Liberalization Help the Poor in Tunisia? A Dynamic General Equilibrium Approach

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1 Does Agrcultural Trade Lberalzaton Help the Poor n Tunsa? A Dynamc General Equlbrum Approach Nada Belhaj Hassne 1 Bernard Décaluwé 2 Abstract Computable General Equlbrum (CGE) models have ganed contnuously n popularty as an emprcal tool for assessng the mpact of trade lberalzaton on growth, poverty and equty. In recent years, there have been attempts to extend the scope of CGE trade models to the analyss of the nteracton of agrcultural growth, poverty and ncome dstrbuton. Conventonal models gnore however the channels lnkng techncal change n agrculture, trade openness and poverty. Ths study seeks to ncorporate econometrc evdence of these lnkages nto a dynamc sequental CGE model, to estmate the mpact of alternatve trade lberalzaton scenaros on welfare, poverty and equty. The analyss uses the latent class stochastc fronter model n nvestgatng the nfluence of nternatonal trade on agrcultural technologcal change and productvty. The estmated productvty gans nduced from a more opened trade regme are combned wth a general equlbrum analyss of trade lberalzaton to evaluate the drect welfare benefts of poor farmers and the ndrect ncome and prces outcomes. These effects are then used to nfer the mpact on poverty usng the tradtonal top-down approach and the Tunsan household survey. Key words: Openness, Agrculture, Productvty, Poverty, Latent class model, CGE modellng. JEL classfcaton: C24, C33, D24, F43, I32, Q17. 1 Assocate Professor, Unversty of Nabeul, Tunsa; PEP; LEGI-EPT; LEAD. Tel: , fax: , Courrel: nada@hbhgts.com. 2 Professor, Unversty Laval, Quebec Canada; PEP ; CEPII, CIRPEE. Tel : , fax: Courrel: bdec@ecn.ulaval.ca. 1

2 1. Introducton The gradual openng of agrcultural markets durng the recent wave of globalzaton focused poltcal and economc attenton on the lnks between nternatonal trade, agrcultural development and poverty. More lberalsed trade regmes have been advocated worldwde for ther growth and welfare enhancng effects, as they are assumed to facltate the transfer of new technology and to boost productvty (Wnters, 2002; Clne, 2004; Nssanke and Thorbecke, 2006). Beyond ts drect beneft to rural lvelhoods, growth n agrcultural productvty stmulates lnkages to the non-farm rural economy, causng economc growth and rapd poverty reducton (Hwa, 1988; Datt and Ravallon, 1998; Irz et al., 2001, Thrtle et al., 2003; Bravo-Ortega and Lederman, 2005; Chrstaensen et al., 2006; Ravallon and Chen, 2007; Self and Grabowsk, 2007). The growng nterest on the economcs of trade reforms has generated a correspondng ncrease n the number of emprcal approaches nvestgatng the mpact of trade polcy on nequalty and poverty. Appled General Equlbrum (AGE) models are wdely used because of ther ablty to produce dsaggregated results at the mcroeconomc level, wthn a consstent macroeconomc framework. Even though most of the smulatons show welfare gans from the removal of trade barrers, the estmated benefts greatly dverge across the studes (Bouët, 2006). The dffculty of assessng the true poverty mpacts of trade reforms s n part explaned by the complexty of the dynamc mplcatons of external trade lberalzaton. In most studes the long-run productvty mechansms tend to be treated n rather ad hoc ways. CGE frameworks generally deal poorly wth the productvty effects of nternatonal technology spllovers. Whle these dynamc responses to nternatonal openness are gradually beng ncorporated n some CGE applcatons, the most nfluental CGE frameworks n the polcy debate are at qute some dstance from fully ntegratng these forces (Vos, 2007). Ths study attempts to make a contrbuton to the exstng lterature n fllng that gap. The analyss tempts to explore the short and long run effects of alternatve trade lberalzaton scenaros on agrcultural and economc growth and to synthesze poverty and nequalty mplcatons. The method used here s based on two lnks, one connectng trade openness to farmng performance, and another connectng agrcultural productvty to economc growth, 2

3 poverty and equty. Our approach frst seeks to nvestgate the key parameters that can serve as a bass for estmatng dynamc agrcultural productvty gans from ncreased trade, and then ncorporate econometrc evdence of the productvty lnkages nto a dynamc sequental CGE model, to arrve at a comprehensve calculaton of alternatve trade lberalzaton scenaros on commodty and factor prces, as a bass for then calculatng the correspondng mpact on poverty and nequalty. Ths methodology wll be appled to explore the potental ncome and dstrbutonal mplcatons of agrcultural trade lberalzaton n the Tunsan country. Tunsa has taken steps towards greater ntegraton n the global economy as t s about to start mplementng a new agreement on trade n agrcultural products under the EU-Medterranean partnershp and the Doha round of the WTO agreement on agrculture. Agrculture s an economcally and socally mportant sector n Tunsa, although hghly dstorted due to trade barrers and protectve polces. As ths country press ahead wth lberalzaton wthn the framework of the Barcelona-Agreement, speculatons have arsen regardng the mpact of lberalzaton n acceleratng agrcultural development va technology transfer. The adopton of new technologes and the subsequent ncrease n agrcultural productvty are reasonably expected to offer a route out of poverty through generatng employment opportuntes and ncreasng wages rates n the rural areas. There s a concern that technologcal progress may be based n favour of sklled and educated labour and tends to be labour savng. Hence, techncal change may exasperate wage nequalty slowng the pace of poverty reducton. Furthermore, f the poor are mostly n unsklled small farmers and are not suffcently equpped to take advantage of the advances, poverty wll be unaffected, or maybe even worsened (Wnters, 2004; Wnters et al., 2004). In an attempt to shed some lght on these ssues, ths paper examnes the role of nternatonal trade n promotng technology transfer and stmulatng farmng productvty growth and nvestgates the nfluence of agrcultural productvty on lvelhoods of the poor, askng whether technologcal development s propoor. The paper starts by sketchng a conceptual framework for explorng the effects of nternatonal trade on agrcultural productvty n Tunsa and ts man tradng partners n the Medterranean. For ths purpose we measure techncal effcency (TE) and Total Factor Productvty (TFP) ndexes usng the latent class stochastc fronter model to account for cross-country heterogenety n producton technologes. Then we present an emprcal 3

4 framework n whch nternatonal trade and technology transfer provde two potental sources of productvty growth for countres behnd the technologcal fronter. The analytcal framework for the analyss of the poverty and nequalty mplcatons of the trade nduced productvty gans conssts of a dynamc CGE model. The model structures and assumptons are dscussed n secton 3. Secton 4 revews the data, whle secton 5 reports some emprcal results and draws some conclusons. 2. Econometrc model 2.1 Productvty Measurement: Panel Data Specfcaton of a Latent Class Stochastc Fronter Model The analyss of nternatonal agrcultural productvty and effcency has been subject to extensve research. The conceptual approaches to measurng agrcultural productvty rely on the dvsa ndex and the producton fronters, adoptng alternatve non-parametrc and parametrc technques. The dvsa ndex and the non-parametrc methods have been challenged n the lterature as the frst does not provde sources of productvty growth, and the second s determnstc and does not allow for stochastc shocks n the producton process (Kumbhakar 2004). The parametrc stochastc fronter models have the advantage of controllng for such random events and of dstngushng the statstcal nose effects from techncal neffcency. Based on the econometrc estmaton of the producton fronter, the effcency of each producer s measured as the devaton from the best practce technology. Evenly productvty change s computed as the varaton over tme of the producer s dstance from the fronter and s decomposed nto techncal change, scale economes, and changes n techncal effcency (Sena 2003; Kumbhakar 2004). Estmaton of these models hnges, however, on the restranng belef that all producers use a common technology. It s nevertheless unlkely that unts n dfferent countres or regons operate under the same technology. Comparsons of nter-country producton functons rase then the ssue of accommodatng the technologcal dfferences n the stochastc fronter models, gven that the effects of unmeasured heterogenety mght be msgudedly labeled as 4

5 neffcency (Green 2003; Kumbhakar and Tsonas 2003; Moutnho et al. 2003; Corral and Alvarez 2004; Kumbhakar 2004; Orea and Kumbhakar, 2004). In an attempt to overcome ths drawback, two approaches have been proposed n the recent analyses. One method s to splt the sample of observatons nto several groups accordng to some exogenous sample separaton nformaton. Alternatvely, a cluster analyss may be appled to the dependent varable, and then a technologcal reference s estmated for each group (Kolar and Zardkooh 1995; Grfell and Lovell 1997; Mester 1997). Ths two stage procedure fals, however, to explot the nter-group nformaton n estmatng the separate stochastc fronters. The latent class stochastc fronter models have been recently desgned as better suted to modelng technologcal heterogenety. These models combne the stochastc fronter approach wth a latent sortng of ndvduals nto dscrete groups and enable to control for heterogenety through the smultaneous estmaton of the probablty of class membershp and a mxture of several technologes. Ths sngle stage approach s proved to outperform the two stage procedure that precludes the effcent utlzaton of nformaton regardng one partcular class to estmate the other class fronters (Green 2001b, 2002, 2003; Caudll 2003; Kumbhakar 2004; Orea and Kumbhakar 2004). We use a panel data specfcaton of the latent class stochastc fronter approach to study ntercountry agrcultural productvty performance n the Medterranean regon and to nvestgate the factors drvng productvty growth. Comparsons of cross-country productvty provde useful nsghts on the relatve poston of each country n terms of potental agrcultural producton, and on the factors explanng the nter-country dversty of performance. The latent class stochastc fronter framework posts that there s a latent sortng of the producers nto J dscrete unobserved groups, each usng a dfferent producton technology. The model appears as a fnte mxture of stochastc fronter models, where the technology for the j th group s specfed as: ln( y t ) = ln f ( xt, j ) + ν t j ut j β (1) 5

6 subscrpt ndexes producers (or countres) (: 1 N), t (t: 1 T) ndcates tme and j (j: 1,, J) represents the dfferent groups. β j s the vector of parameters for group j, and y t and x t are, respectvely, the producton level and the vector of nputs. For each class (or group), the stochastc nature of the fronter s modeled by addng a two-sded random error term v t j, whch s assumed to be ndependent of a non-negatve neffcency component u t j. In order to estmate (1) by the maxmum lkelhood method we assume the nose term v t j to 2 follow a normal dstrbuton N(0, σ ) and the neffcency term u ν t j to be a non-negatve normal random varable. j The recent lterature contans few applcatons of the latent class stochastc fronter model (Green 2001a, 2002; Caudll 2003; Corral and Alvarez 2004; Orea and Kumbhakar 2004; Tak 2004; El-Gamal and Inanoglu 2005). Most of these models specfy the neffcency component as..d half normal and do not nvestgate the effect of the exogenous factors on techncal effcency. Orea and Kumbhakar (2004) suggest remedyng ths shortcomng by modelng the dependence of the effcency term on a set of exogenous varables. Followng these authors, we adopt the scaled specfcaton for u t j by wrtng t as 3 : ( ln( zt )'δ j ) ωt j u = (2) t j exp Where, z t s a vector of country s specfc control varables assocated wth neffcences, δ j s a vector of parameters to be estmated, and ω t j s a random varable wth a half normal dstrbuton. In a latent class model, the uncondtonal lkelhood for country s obtaned as a weghted average of ts j-class lkelhood functons, wth the probabltes of class membershp used as the weghts: J LF = LFj Pj (3) j:1 Where, LF and LF j are respectvely the uncondtonal and condtonal lkelhood functons for country, and P j s the pror probablty of belongng to class j, as assgned by the researcher for ths country. The salent feature of the latent class model s that the class 3 See Wang and Schmdt, (2002) and Alvarez et al., (2006) for the dscusson of the practcal advantages of models wth the scalng property. 6

7 membershp s unknown to the analyst; the probabltes n ths formulaton reflect the uncertanty that the researchers mght have about the true parttonng n the sample. To constran these probabltes to sum to unty, we parameterze P j as a multnomal logt model: Pj = exp( λ j' q ) exp( λ j' q ) j (4) Where, q s a vector of country s specfc and tme-nvarant varables that explan probabltes and λ j are the assocated parameters. The overall log lkelhood functon for the sample s then gven by: N ln LF = ln LF (5) :1 Varous algorthms for the maxmum lkelhood estmaton have been proposed. The conventonal gradent methods and the expectaton maxmzaton (EM) algorthm are among the most used approaches (Greene 2001a; Caudll 2003; Kumbhakar 2004; Orea and Kumbhakar 2004). Usng the parameters estmates and Bayes' theorem, we compute the condtonal posteror class probabltes from: LFj Pj Pj = (6) LFj Pj j It appears from ths settng that the sample s classfed nto dfferent groups by usng the goodness of ft of each estmated fronter, namely LF j, as addtonal nformaton to dentfy whch class generates each observaton. Every country s assgned a specfc class accordng to the hghest posteror probablty.e., country s classfed nto group k (:1 J) f P k = max Pj. Furthermore, the estmated posteror probabltes help to compute the j effcency scores. Gven that there are J groups, the latent class model estmates J dfferent fronters from whch the neffcences of the producers can be computed by two methods. The frst method estmates techncal effcency usng the most lkely fronter (the one wth the hghest posteror probablty) as a reference technology. Ths approach results n a somewhat 7

8 arbtrary selecton of the reference fronter that can be avoded by evaluatng the weghted average effcency score as follows: J lntet = Pj lntet j (7) j:1 Where, TE = exp( u ) s the techncal effcency of country usng the technology of t j class j as the reference fronter. t j The model can be fully specfed by the selecton of the approprate number of classes. Snce estmaton wth too few or too many classes may result n based estmates, the Schwarz Bayesan Informaton Crtera (SBIC), and the Akake Informaton Crtera (AIC) have been proposed n the lterature to address the class sze ssue. These crterons are expressed as: SBIC ( J ) 2 LF( J ) + K( J ) ln( n) = (8a) ( J ) 2LF( J ) 2K( J ) AIC = + (8b) Where, LF(J) s the value of the lkelhood functon wth J classes, K(J) s the number of ndependent parameters to be estmated and n s the number of observatons. The decson rule s to take the model wth the lowest AIC or SBIC. Once ths model s estmated, t s possble to assess the rate of total factor productvty change from the results. The components of productvty can be dentfed from the parametrc decomposton of stochastc output growth. TFP growth s defned as the dfference between the rate of growth of output and the rate of growth n nput use and can be computed from 4 : TFP = TC + TE+ Scale (9) where ln f TC =, t ut TE = t j, and Scale ( ε ) j 1 ε k j x = k ε j k. ε j s the sum of all the nput elastctes 5 ε kj. 4 See Kumbhakar and Lovell (2000) for the tr-partte decomposton of productvty growth. 5 Snce nput elastctes vary across groups, productvty change estmates from equaton (9) are group-specfc. Uncondtonal productvty measures can be obtaned as a weghted sum of these estmates. 8

9 Equaton (9) decomposes TFP growth nto a scale component, whch measures a scale effect when nputs expand over tme; a techncal change component, whch measures the rate of outward shft of the condtonal best-practce fronter; and effcency mprovement Internatonal trade and agrcultural productvty growth From the estmated latent class model we obtan TE and TFP measures for each country. We turn then to examnng the role of nternatonal trade n promotng technology transfer, as well as n facltatng productvty growth and catch up wth the fronter technology. Studes by Grffth et al. (2004) and by Cameron et al.(2005) emphasze the mportance of technology transfer, nternatonal trade and human captal for productvty growth n countres behnd the technologcal fronter. In these models technology gap s used to capture the potental for technology transfer, and s ncluded as both a level and an nteracton term to capture an effect on the speed of technology transfer. Followng these authors we derve an equaton for agrcultural productvty growth as: GTFP t = α + α H θ IT 3 1 t 1 t 1 GAP t 1 + α IT 2 t 1 + μ' X t 1 + θ GAP 1 + υ t t 1 + θ H 2 t 1 GAP t 1 + (10) where GTFP t s the growth rate of agrcultural TFP of country at tme t, H s the human captal level of the country, IT s a measure of nternatonal trade, GAP s the technology gap, and X s vector of control varables ncludng nsttutonal factors 6. constant and υt s an error term. α s a country-specfc Human captal and nternatonal trade enter n equaton (10) both separately and n nteracton wth the technology gap. The trade nteracton captures the effect of nternatonal ntegraton on productvty growth through the speed of technology transfer, whle the human captal nteracton reflects a country s capacty to adopt advanced technology. We expect the countres that le further behnd the fronter to experence hgher rates of productvty growth. Technology gap ndcates the devaton of country fronters from the best practce technology labeled as metafronter (Battese et al., 2004). We estmate the metafronter by takng the outer * envelop of the group specfc fronters, f ( x, ) max f ( x β ) β =. Then we measure the t t, j j 6 TFP n equaton (7) can be consdered as the emprcal counterpart of GTFP. 9

10 technology gap as the rato of the output for the fronter producton functon for group j relatve to the potental output defned by the metafronter, GAP t f ( xt, β j ) =. f * ( x, β ) t 3. Poverty mplcatons of trade lberalzaton: a dynamc CGE model The effects of trade lberalzaton and agrcultural productvty change on poverty are assessed usng a dynamc sequental general equlbrum approach ncludng product dfferentaton. The basc features of the model are nspred from the prototype model of Van der Mensbrugghe (2005), Rattsø and Stokke (2005), Dao et al. (2005) and from Chemngu and Dessus (1999), Decaluwé et al. (1999), Bchr et al. (2002), Annab et al. (2005) and Chemngu and Thabet (2006). The model s calbrated to data from a Tunsan socal accountng matrx for It dstngushes 38 producton sectors, ncludng 25 agrcultural and food actvtes wth 13 urban ndustres and servces. Factors of producton are classfed as captal, land, labor and natural resources. Land s further dfferentated accordng to the perennal features of the crops, the rrgaton ntensty and the varetes grown. Labor s classfed by the level of qualfcaton (sklled and unsklled). Insttutons nclude households, companes, government and foregn tradng partners. The household bloc s desegregated nto rural and urban households. The tradng partners are decomposed nto European Unon countres and rest of the world The model structure a. Producton structure The model s producton functons are of the nested structure. Perfect complementarty s assumed between value added and the ntermedate consumptons n each sector. Value added s a Cobb Douglas (CD) functon of labor, land, captal and natural resources. Labor s a CES bundle of sklled and unsklled labor. Land s also decomposed by type n a CES nest. Land s agrculture specfc and labor s assumed to be fully moble. Captal and natural resources are assumed to be sector specfc. The model ncorporates product dfferentaton by varety and qualty. 10

11 b. Demand structure In the demand sde, the preferences across sectors are represented by the LES (Lnear Expendture System) functon to account for the evoluton of the demand structure wth the changes n ncome level. The consumpton choces wthn each sector are a nestng of CES functons. The subutlty specfcatons are an augmented verson of the Dxt-Stgltz structure of preferences desgned to capture the partcular status of domestc goods, together wth product dfferentaton accordng to geographcal orgn as well as horzontal and vertcal dfferentaton. Qualty enters as a utlty shfter wth the horzontal dfferentaton between varetes. Total demand s made up of fnal consumpton, ntermedate consumpton and captal goods. Sectoral demand of these three compounds follows the same pattern as fnal consumpton. c. Overvew of the model Ignorng the dfference between foregn and domestc goods, we assume that consumer s utlty depends on consumpton of the output of several ndustres () each of whch contans a large number of dfferentated varetes (ω) produced by heterogeneous frms. We assume that the upper ter of utlty determnng consumpton of the dfferent goods s LES and that the lower ter of utlty determnng consumpton of varetes takes the CES form, α ( C ) C U = mn (11) C s a consumpton ndex defned over consumpton of ndvdual varetes, q (ω), wth dual prce ndex, P, defned over prces of varetes, p (ω), C 1 ρ ( θ ( ω) ( ω) ) ρ = q dω, ω Ω ( ω) ( ω) 1 1 σ 1 σ p P = ω θ d (12) ω Ω σ 1 wth ρ σ of varety (ω). Let Q I C =, σ the elastcty of substtuton between any two goods and ( ω) θ the qualty and P I P the aggregate ndustry good and prce respectvely. The optmal consumpton and expendture decsons are gven by 7 : q ( ω) = p ( ω) P I σ σ 1 ( θ ( ω) ) QI 1 σ 1 p ( ω) ( P (13) I σ, r ω) = ( θ ( ω) ) RI 7 See Meltz (2001) and Bernard et al.(2006) for a smlar formulaton. 11

12 wth p ω) q ( ω) = r ( ω) ( and R I = PI QI The producton sde of the model follows Meltz (2003) and Bernard et al. (2006) n that producton nvolves a fxed and varable cost every perod, and only varable costs move systematcally wth frm productvty. Producton requres multple factors of producton whose ntensty of use vares across ndustres. We assume that the cost functon takes the followng Cobb Douglas form: q k β CT = F + wk A k F ω the fxed cost, A the total factor productvty (TFP), wth ( ) (14) w k the factor prces, k ncludng sklled and unsklled wages, and β the factors shares n margnal cost. As shown n equaton (13) demand for a product varety depends upon the own varety prce, the prce ndex for the product and the prce ndces for all other products. We assume that frms are small enough relatve to the ndustry that they have no power to nfluence the ndustry prce ndex. The prce of frm s varety n one product market only nfluences the demand for ts varetes n other product markets through the prce ndces. Therefore, the frm s nablty to nfluence the prce ndces mples that ts proft maxmzaton problem reduces to choosng the prce of each product varety separately to maxmze the profts derved from that product varety (Bernard et al., 2006). Factors demand may be expressed as: x k = β k ( A) 1( w ) k β k 1 h w h β h (15) d. Productvty dynamcs Productvty growth s generated through technology adopton and own nnovatons. Technology adopton s assumed to combne the gap to the technologcal leader, defnng the learnng potental through mtaton; human captal, ndcatng the ablty to explot foregn technology; and the level of foregn trade whch represents the channel transmttng the new technology to domestc producers. The equaton for productvty growth can be specfed n the followng form: 12

13  α 5 = α H + α Trade + α GAP + α H * GAP + Trade* GAP (16) where  s the proportonal change n productvty, H s the educaton level, Trade total trade, and GAP s the technology gap. As ncreased openness may lead to skll based productvty growth, we nvestgate ths effect through the followng CES specfcaton of aggregate labor demand. Followng Rattsø and Stokke (2005) aggregate labor demand s specfed as: L 1 ρl / β ρ ρ l l / β ρl [ γ A UL A SL ] ρl 1, γ 2, + = (17) The drecton and degree of technologcal bas s ntroduced through the parameter β, whch gves the elastcty of the margnal productvty of sklled relatve to unsklled labor wth respect to labor augmentng techncal progress. For β equal to zero, techncal change s neutral and does not affect the relatve effcency of the three labor types. Wth a postve value of β techncal change favors sklled workers, whle negatve values mply that mprovements n technology are based towards unsklled labor. The reduced form specfcaton of technologcal bas s assumed to be an ncreasng and convex functon of adopton relatve to nnovaton: 2 α 4 + α 5 β = 1 (18) α1 + α 2 The proportonal change n factor demand s affected by productvty varaton n the followng way: xˆ k = qˆ + k ( 1) wˆ k + β hwˆ h β β Aˆ (19) h k h h h 3.2. Parameters estmaton The prevous model forms the theoretcal bass of our emprcal analyss. Dfferent types of parameters are requred to allow the emprcal mplementaton of the model. We wll estmate the model s behavoral parameters econometrcally. 13

14 The effect of trade openness on agrcultural productvty s captured through the estmaton of equaton (10). To nvestgate whether trade openng nduces skll-based techncal change and whether t affects wage nequalty we need to estmate the parameters n equaton (17). Assumng compettve labor markets, and usng the CD specfcaton for value added and the CES form for labor demand; the relatve wages may be wrtten as a functon of the relatve productvty and the relatve labor supply as follows: w ln w s u 2 TRADE SL = δ + α 1 ln AL + ( ρ 1) ln (20) I UL where w s and w u are the sklled and unsklled wages respectvely. Equaton (20) wll be estmated usng the non lnear least square approach Income dstrbuton and poverty Ths secton dscusses ncomes dstrbuton and attempt to provde a bref overvew on the methodology used to analyze the external choc effects on poverty. The common poverty measures can be formally characterzed n terms of per capta ncome and relatve ncome dstrbuton as follows: ( ( p) ) P = P Y,L (21) where Y s per capta ncome and L(p) s the Lorenz curve. P denotes the poverty measure whch we assume to belong to the Foster-Greer-Thorbecke class (1984): z θ z y Pθ = f ( y)dy, where θ s a parameter of nequalty averson, z s the poverty z 0 lne, y s ncome, and f(.) s the densty functon of ncome. P0, P1 and P2 are respectvely the headcount rato, the poverty gap and the squared poverty gap. We follow Decaluwé et al.(1999), by adoptng specfc ntra-group ncome dstrbutons n order that conform to the dfferent soco-economc characterstcs of the groups, and by endogenzng the poverty lne and the resultng poverty ncdence among the dfferent socoeconomc household groups. The analyss of the poverty mpacts of agrcultural trade lberalzaton and productvty growth s based on smulatons of the model descrbed earler usng the SAM for 2001 as 14

15 base. The model calbraton s based on the SAM and the econometrc results obtaned from the prevous secton. Snce adaptng to a trade polcy shock s nether mmedate nor costless, settng a dynamc analyss s useful n studyng the dfferent adjustment perods,.e. the short- and medum-run mpacts. The analyss s conducted usng a sequental dynamc set-up, where captal stock s updated endogenously wth a captal accumulaton equaton, whereas technologcal change s updated exogenously between perods. The model s desgned of such way to capture the drect and ndrect effects of agrcultural trade lberalzaton on commodty and factor prces as a bass for then calculatng the correspondng mpact on poverty. The model ncorporates econometrc evdence of the tradeproductvty lnkages. The poverty mplcatons of alternatves trade lberalzaton scenaros are nferred usng the tradtonal top-down approach. We frst smulate the CGE model to generate full vector of commodty and factor prces owng to polcy experment. These are then fed nto a mcrosmulaton framework to conduct a detaled analyss of ncome dstrbuton and poverty at the household level usng the Tunsan household survey of Data Our study requres an mportant database: 4.1. The econometrc analyss The applcaton s based on panel data at the natonal level for agrcultural producton n nne Southern Medterranean Countres (SMC) nvolved n partnershp agreements wth the Eurpean Unon (EU) such as: Algera, Egypt, Israel, Jordan, Lebanon, Morocco, Syra, Tunsa and Turkey; and fve EU Medterranean countres wth demonstrated performance n agrcultural producton: France, Greece, Italy, Portugal and Span durng the perod Our data set ncludes observatons on the man crops grown n these countres, nputs use, nternatonal trade, human captal, agrcultural research effort, land dstrbuton, land qualty, clmatc condtons, nsttutonal factors, per capta ncome, and ncome nequalty. These varables are grouped n fve sets to estmate the stochastc producton functon n (1); the parametrc functon of the neffcency component n (2); the class probabltes n (4); and the productvty change equaton n (10). The data are the FAO (FAOSTAT), World Bank (WDI), AOAD, Eurostat, CEPII, AMAD, ASTI, UN-WIDER, Barro and Lee (2000), Pardey 15

16 et al. (2006), and Kaufmann et al. (2007) databases as well as from the dfferent reports of the FEMISE, FAO, CIHEAM and ESCWA. The varables used to estmate the stochastc producton fronter consst of thrty sx agrcultural commodtes belongng to sx product categores (fruts, shell-fruts, ctrus fruts, vegetables, cereals, and pulses) and fve nputs (cropland, rrgaton water, fertlzers, labor and machnes) 8. The agrcultural product categores nclude the man produced and traded commodtes n the Medterranean regon. Substantal protecton measures exst n the form of entry prces, customs tarffs, quotas, and safeguard clauses. These measures am at restrctng the exchange of commodtes consdered as a potental source of strong competton n the Medterranean basn, and for whch greater openness may have serous domestc economc and socal consequences. The data for the nput use by crop for each country are constructed accordng to the nformaton collected from recently publshed reports from the sources above. All the nput and output varables are measured n quantty. The neffcency effect model and the productvty growth equaton ncorporate an array of control varables representng openness to trade, human captal, land holdngs, agrcultural research effort, land qualty, and nsttutonal qualty. Three dfferent measures are used to proxy the degree of openness of each country, the rato of agrcultural exports plus mports to agrcultural value added (AGVA), agrcultural trade barrers, and the share of agrcultural machnery and equpment mports n AGVA. Agrcultural commodtes are currently protected wth a complex system of ad-valorem tarffs, specfc tarffs, tarff quotas, and are subject to preferental agreements. The determnaton of the approprate level of protecton s a farly complex task. The MacMaps database constructed by the CEPII provdes ad-valorem tarffs, and estmates of ad-valorem equvalent of appled agrcultural protecton, takng nto account trade arrangements (Bouët et al. 2004). Our data on agrcultural trade barrers are drawn from ths database. 8 We construct aggregate output and nput ndces for each product category usng the Tornqvst and EKS ndexes. For each country and n each product category k, we compute tornqvst output and nput ndexes, takng alternatvely all the countres j (j ) as numerare, usng the followng formula: ( ωh + ωhj ) 2 k yh Tj Π h k y = where y h and y hj are outputs (or nputs) of h-th agrcultural commodty n countres hj and j respectvely, and ω h and ω hj are the h-th output (nput) shares. We use the Eltetö-Köves-Szulc (EKS) procedure whch defnes the quantty ndex for product k and country as the geometrc weghted average of I k k a these ndces: Q ( ) j = Π Tj where a j s the share of country j n the total producton of the k-th commodty j:1 (ncludng countres 1,,I only). See Hallak (2003) and Rao et al. (2004) for a smlar procedure. 16

17 The use of the share of agrcultural machnery and equpment mports as a measure of nternatonal trade s explaned by the fact that foregn technology dffuses manly through captal goods, the productvty effects of openness mght then be sutably captured by ths varable. Human captal s measured by average years of schoolng n the populaton over age 25 and s ncluded to capture the labor qualty and the ablty to absorb advanced technology. Land qualty, land fragmentaton and the dstrbuton of agrcultural holdngs are often cted as sources of neffcency n agrculture (Vollrath, 2007). The neffcency model ncludes land qualty, whch s measured by the percent of land under rrgaton; land fragmentaton, whch s controlled for by the percent of holdngs under fve hectares; and nequalty n operatonal holdngs measured by the land Gn coeffcent to capture these effects. Agrcultural research effort s measured by publc and prvate R&D expendtures. Insttutonal qualty ncludes varous nsttutonal varables consdered as ndcators of a country s governance, namely, poltcal stablty, government effectveness, and control of corrupton. These varables reflect the ablty of the government to provde sound macroeconomc polces and mpartal authorty whch protects property rghts and enforces contracts. Regardng the determnants of the latent class probabltes, we consder country averages of fve separatng varables related to natural and modern nput endowments as well as to clmatc condtons. The varables ncluded n the class probabltes are total number of wheel and crawler tractors, total appled fertlzers, total agrcultural land, average farm sze, and ranfall levels. Tractors and fertlzers help to dentfy countres endowed wth modern producton factors. Average farm sze captures the dfferences n the scale of agrcultural holdngs across countres and dstngushes countres wth mportant small farms (Vollrath, 2007). Total agrcultural land and ranfall levels capture the nfluence of resources endowments and clmatc condtons on class membershp The CGE analyss The constructon of the Socal Accountng Matrx requres very detaled observatons at the sectoral level. An mportant desegregaton of the agrcultural sector s needed for the analyss purposes. The requred data concerns sectoral outputs, value added, ntermedate nputs, consumpton, nvestment, mport, export, taxaton and factoral ncome dstrbuton. Other data could prove to be necessary wth the progresson of the study. Data are taken from varous sources: I.N.S. the natonal statstcal agency of Tunsa, the dfferent reports of the Mnstry of Fnance and Plannng and of the Mnstry of agrculture. 17

18 5. Estmaton Results Ths secton summarzes the man results derved usng the emprcal applcaton of the methodologes descrbed n secton The latent class model Ths emprcal applcaton nvolves bascally a three-step analyss of agrcultural productvty performance across Medterranean countres. Frst, a Cobb Douglas parameterzaton of the technology fronter s employed and the latent class model of equaton (1) s estmated usng maxmum lkelhood va the EM algorthm 9. Second, effcency and productvty levels and growth are computed for each country. Thrd, the technology gap among the dfferent countres s measured and the determnants of agrcultural TFP growth are nvestgated focusng on the role of trade openness n speedng the catch up process. Table 1 presents the results of estmatng the nput elastctes of the producton fronter. TABLE 1: LATENT CLASS MODEL PARAMETER ESTIMATES: TOTAL POOL CLASS 1 CLASS 2 CLASS 3 CLASS4 Producton Fronter Land Water Labor Fertlzers Captal Tme Intercept Irrgaton Land fragmentaton Average holdng Machnery Tertary σ² γ= σ u ²/σ² Irrgaton Total fertlzers Total machnery HDI Land GINI Intercept Log-lkelhood Number of Obs ** 0.377** 0.162* 0.036* 0.041* 0.023* 0.97* * 0.028** * * ** ** * * * 0.113** * 0.174** 0.235** 0.347** * 0.036** 1.766* * * ** ** ** * 0.011** * 0.021* 0.022** 1.359* 0.192* 0.187** 0.152* ** 0.071** 1.98* Effcency term ** 0.053** * ** * ** Probabltes * * ** ** ** 1.042* ** 0.188** 0.239* 0.195* 0.196** 0.088* 2.91* * 0.024* * * * ** 9 The estmaton procedure was programmed n Stata

19 Notes: the varables n the producton fronter and effcency functon are n natural logarthm. The sgnfcance at the 10% and 1% levels s ndcated by * and ** respectvely. A negatve sgn n the neffcency model means that the assocated varable has a postve effect on techncal effcency. For the producton functon, we obtan farly reasonable estmates. The nput elastctes are globally postve and sgnfcant at the 10% level. The dfferences of the estmated factor elastctes among classes seem to support the presence of technologcal dfferences across the countres. Water and cropland have globally the largest elastcty, ndcatng that the ncrease of Medterranean agrcultural producton depends manly on these nputs. Water appears among the most mportant producton factors n the pooled crop producton model and n the commodty models, ndcatng that Medterranean crops are hghly water ntensve and water s the most lmtng and precous nput n ths regon. Labour and machnery seem also to be mportant factors n crop producton. Fertlzers, although sgnfcant n some specfc commodty models, appear to have a lmted effect on Medterranean producton. Ths may be explaned by the fact that farmers n some regons tend to use fertlzers as complementary nput to organc manure whch s much less expensve. In addton to producton elastctes, the estmated technology fronters provde a measure of techncal change. A postve sgn on the tme trend varable reflects techncal progress. Sgnfcant shfts n the producton fronter over tme were found n the pooled and specfc commodty models, ndcatng gans n techncal change for the selected countres. The estmated coeffcents of the neffcency functon provde some explanaton of the effcency dfferentals among the selected countres. All the varables proved sgnfcant at the 10% level and have globally the expected sgns. Internatonal trade seems to exert a sgnfcant mpact on mprovng effcency n the Medterranean farmng sector. Educatonal attanment, land qualty, agrcultural research effort and nsttutonal factors appear also to contrbute to enhancng effcent nput use. As expected, the unequal dstrbuton of agrcultural land and to a lesser extent land fragmentaton have sgnfcant adverse effects on effcent resource use. The examnaton of the estmaton results of the latent class probablty functons shows that the coeffcents are globally sgnfcant, ndcatng that the varables ncluded n the class 19

20 probabltes provde useful nformaton n classfyng the sample. We had no apror expectaton about the sgn of these coeffcents, as postve values on the separatng varables coeffcents n one class ndcate that hgher values of these varables ncrease the probablty of assgnng a country nto ths class, whle negatve parameters suggest that the probablty of class membershp decrease wth an ncrease of the correspondng varables. Table 2 summarzes the estmated pror and posteror class probabltes as well as the groupng of countres between the dfferent classes n the pooled and specfc commodty models. The posteror class probabltes are, on average, very hgh (70 percent or more). The classfcaton resultng from these probabltes show globally that Algera, Israel, Jordan, Lebanon, Portugal and Tunsa belong to the same group characterzed by relatvely low agrcultural producton levels, a smlar pattern of specalzaton based on a strong presence of fruts and vegetables, sgnfcant land nequalty and hgh fragmentaton of holdngs. The second group formed by Greece, Morocco and Syra shows hgher producton levels and more equtable land dstrbuton. The remanng groups nclude Egypt, France, Italy Span and Turkey. The average producton level of these countres s sgnfcantly larger than that n other classes, whle land fragmentaton and land nequalty are much lower. These countres show a common croppng pattern n whch cereal crops account for an mportant part. TABLE 2- PRIOR AND POSTERIOR PROBABILITIES CLASS COUNTRIES PRIOR PROB. POST. PROB FRUITS Algera, Egypt, Jordan, Morocco, Portugal, Syra, Tunsa, Turkey France, Greece, Italy Span, Israel, Lebanon France Algera, Portugal, Jordan, Lebanon, Tunsa Greece, Morocco, Syra Egypt, Italy, Israel, Span, Turkey CITRUS SHELL FRUITS Egypt, Greece, Israel, Jordan, Lebanon, Morroco, Portugal Algera, France, Syra, Tunsa Italy, Span, Turkey VEGETABLES Jordan, Lebanon, Portugal Algera, Egypt, Greece, Israel, Italy, Morocco, Span, Syra, Turkey France, Tunsa CEREALS 1 Algera, Israel, Jordan, Lebanon, Portugal, Tunsa

21 Greece, Morocco, Syra Egypt, France, Italy, Span, Turkey PULSES Israel, Jordan, Lebanon Algera, Greece, Italy, Portugal, Morocco, Syra, Span, Tunsa, Egypt France, Turkey Israel, Jordan Morocco, Portugal, Syra, Tunsa Algera, Greece, Lebanon France, Egypt, Italy, Turkey, Span TOTAL POOL Average effcency scores and TFP changes are reported n Table 3. The results show consstent productvty ncreases n the Medterranean agrcultural sector, on average, wth Turkey regsterng the best average rate of productvty gan (8.31%). Sgnfcant dfferences n techncal effcency and productvty performance are, however, apparent among commodty groups and countres. On average, over the perod under consderaton, EU countres exhbted better effcency levels and hgher productvty growth rates than SMC. TABLE 3: EFFICIENCY SCORES AND TFP GROWTH FRUITS CITRUS SHELL VEGETABLES CEREALS PULSES POOL TE a TFPG b TE TFPG TE TFPG TE TFPG TE TFPG TE TFPG TE TFPG ALGERIA EGYPT FRANCE GREECE ISRAEL ITALY JORDAN LEBANON MOROCCO PORTUGAL SPAIN SYRIA TUNISIA TURKEY a: Techncal effcency score, b: TFP growth. Varaton of performance across countres opens the possblty of nvestgatng the factors contrbutng to productvty mprovement and facltatng the catchng up process between hgh-performng and low-performng countres. To tackle ths ssue, we frst measure the technology gap rato (TGR) and then estmate the model n equaton (10) that lnks the TFP 21

22 growth rate to a host of varables, ncludng trade openness, human captal, R&D, and nsttutonal factors. TABLE 4: IMPACT OF INTERNATIONAL TRADE ON AGRICULTURAL TFP GROWTH MACHINERY IMPORTS TRADE VOLUMES TRADE BARRIERS Human captal Internatonal Trade GAP H*GAP IT*GAP Land GINI Land fragmentaton Land qualty R&D Cont. of Corrupton Gov. effectveness Poltcal stablty 0.065** 0.912*** *** ** *** ** * 0.003* 0.007* 0.03*** ** 0.232*** *** ** ** -0.02* * 0.052* 0.003* 0.033* 0.014** ** *** *** *** 0.163*** *** * 0.031* 0.011* 0.004* 0.016* N. of observatons R² adjusted Notes: Dependant varable s GTFP. H*Gap: the product of human captal (H) and technology gap (GAP), IT*GAP: the product of nternatonal trade (IT) and technology gap. H, IT, GAP and the nteracton terms are nstrumented by ther second lags. *, ** and *** denote statstcal sgnfcance at the 10%, 5% and 1% levels respectvely. These estmates provde nterestng nsghts nto the agrcultural productvty dynamcs. The results hghlght the role of nternatonal trade n promotng technology transfer and pont to the mportance of educaton n facltatng the assmlaton of foregn mprovement of technology. The fndngs suggest that nternatonal tradng opportuntes would have larger benefts n countres wth favourable nternal factors relatng to more equtable dstrbutons of land, better land qualty, sgnfcant R&D and postve nsttutonal condtons. 5.2 CGE analyss The exstng SAMs for Tunsa are unlkely to adequately reflect the structural features of the natonal agrcultural sector, we therefore comple a new SAM. Buldng a completely new SAM requres however gatherng a huge amount of data; we use a top-down approach to carry 22

23 out the complaton of the new SAM. Our procedure follows two man steps. Frst, we construct a Macro SAM from natonal accounts. Second, we dsaggregate the Macro SAM by actvty and commodty to generate a Mcro SAM. The dsaggregaton manly relates to agrculture and agr-food processng commodtes and s mplemented usng the natonalaccounts tables and dfferent complementary sources such as the surveys conducted by the Natonal Insttute of Statstcs (INS), the Mnstry of agrculture. Ths step s carred out n order to match wth the commodty structure of the Tunsan household expendtures, and n a way that s consstent wth the natonal accounts and coeffcents from a pror SAM. As the data dscrepances n the mcro matrx may cause unbalances, we apply the cross-entropy approach to generate a balanced SAM table. We are currently runnng the model smulatons. 6. SOME CONCLUSION Proponents of globalzaton dentfy strong benefts from trade lberalsaton n terms of resource allocaton, economc growth and poverty allevaton. Despte the controversy that surrounds the trade ssues, there s wdespread acceptance that relatvely open polces contrbute sgnfcantly to development. The exstng emprcal lterature has been relatvely successful n examnng the assocaton between trade openness, growth and poverty; t has however much less to say about the lnk to agrcultural productvty gans. For poverty reducton, however, even f the effects of trade on ndustry and economc growth are mportant, agrcultural productvty would have the most drect effect. The analyss of ths paper has focused on the mpact of trade lberalzaton on agrcultural productvty n Tunsa and a panel of ts man tradng partners. Agrculture s a vtal sector n these economes as t represents an mportant source of ncome and output and employs a large segment of mpovershed populaton. The crtcal rural dmenson of poverty n Tunsa ponts to a central role for the agrcultural sector n poverty eradcaton. Agrculture was subject to varous protecton mechansms that have dstorted market ncentves and resulted n neffcent allocatons of resources. As Tunsa proceeds wth more plans for trade lberalzaton, attenton has focused on the potental effects on agrcultural productvty and poverty reducton towards evaluatng the potental gans for ths country n the context of globalzaton. 23

24 The dstngushng aspect of ths study s the attempt to account for heterogenety n cross country agrcultural producton n the estmates of techncal effcency and productvty change. The methodology follows the latent class stochastc fronter models. Estmates support the presence of technologcal dfferences across countres. Medterranean crops appear to be hghly water ntensve whch lmts productve capacty gven the scarcty of water n the regon. Openng up to foregn trade and drect nvestment seems to facltate catchng up wth the best practce technology, provdng substantal support for the vew that openness promotes productvty growth through technology transfers. Agrcultural productvty gans would lead to both faster growth and lower ncome nequalty. The work s stll under progress; we are presently runnng the CGE model smulatons to evaluate the mpact of trade nduced agrcultural productvty growth on poverty and nequalty. 24

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