Rural Policies and Poverty in Tanzania: an Agricultural Household Model-Based Assessment

Transcription

1 Caher de recherche/workng Paper Rural Polces and Poverty n Tanzana: an Agrcultural Household Model-Based Assessment Luca Tbert MarcoTbert Août/August 2012 Verson révsée/revsed : Novembre/November 2012 Luca Tbert: CIRPÉE & PEP, Unversté Laval luca.tbert@ecn.ulaval.ca Marco Tbert: Unversty of Florence marco.tbert@gmal.com Ths research s part of the PhD dssertaton by M. Tbert. We would lke to thank Donato Romano, Federco Peral, Alberto Zezza, Elsa Tcc, Abdelkrm Araar, John Cockburn and Bernard Decaluwé for ther valuable suggestons. The usual dsclamers apply. L. Tbert s grateful to PEP for fnancal support.

2 Abstract: The man objectve of ths study s to develop a robust and comprehensve tool to evaluate the effect on households welfare of dfferent agrcultural polces n Tanzana. Ths s done through a non-separable agrcultural household model where producton and consumpton decsons are consdered. In partcular, we look at labour market falure, snce ths s among the major constrants n a context lke rural Tanzana. Nonseparablty mples that producton and consumpton decsons are nterlnked and that labour allocaton s lkely to be determned by shadow wages rather than market wages. A two-stage estmaton strategy s adopted: the shadow prce of famly labour s frst estmated and then ncluded nto the producton and demand systems. The mpact of a number of agrcultural polces on poverty s then estmated. In partcular, we evaluate the mpact of polces establshed by the Agrcultural Sector Development Programme, as well as changes n food prces. Keywords: Agrcultural household models, poverty, agrcultural polces, Tanzana JEL Classfcaton: D12, O12, O13, Q12, Q18

3 1. Introducton and motvaton of the study The role of agrculture n economc development s a central topc snce the early stages of the development thought. Nowadays, t s broadly acknowledged that agrculture changes ts role accordng to the stages of development and that gnorng agrcultural growth n early stages of development.e. mplementng the so-called jump strateges s generally bound to falure. In addton, agrculture-based growth s generally more effectve than non-agrcultural-based growth n reducng poverty as shown by Ravallon and Datt (1996), Chrstaensen and Demery (2007) and Lgon and Sadoulet (2007). The assumpton behnd ths s that agrcultural growth favors lower ncome decles proportonally more than hgher decles. Moreover, many studes showed that farm productvty mprovements may also generate postve trcklng-down effects on non-farm actvtes n rural areas (Hazell and Haggblade, 1991; Haggblade et al., 2007). 1 The polcy mplcaton s that agrcultural development cannot be dsmssed n any poverty allevaton strategy and ths s partcularly true n countres stll domnated by rural economy. Buldng on ths lterature, ths research analyzes the potental mpacts of agrcultural growth n fghtng poverty focusng on the ncrease of household ncome and agrcultural producton, wth an emprcal applcaton to Tanzanan farmers. The choce of Tanzana as case study was made purposely. In fact, Tanzana s stll one of the poorest countres n the world and, although Tanzanan economy has grown rapdly snce the end of 90s wth GDP growng at 6.6 percent per year between 1998 and 2007, household ncome poverty dropped only slghtly both n rural and urban areas, decreasng only by 2.1 percentage ponts from 35.7 percent n to 33.6 percent n 2007 (Pauw and Thurlow, 2010). Tanzana economy s stll domnated by agrculture, specfcally small-scale farmng. Aggregate agrcultural output has grown sensbly snce the end of 90s. However, as recently dscussed by Pauw and Thurlow (2010), agrcultural growth was drven by large-scale farmers and growth was very uneven, affectng only a few regons of the country (Pauw and Thurlow, 2010). Therefore, t can be argued that the structure of agrcultural growth, favorng large-scale producers of tradtonal export crops, as well as the poor performance of food crops explan the neglgble mpact of agrcultural growth on poverty and nutrton. The Tanzanan government has recently recognzed the pvotal role of agrculture n reducng poverty. Agrcultural sector development s currently at a crtcal stage as new ntatves have been 1 As argued by Anrquez and Stamouls (2007, pp.16-17) agrcultural growth may help poverty reducton through four man channels: drectly, ncreasng the ncome and/or own consumpton of small farmers, and ndrectly, reducng food prces, [ ] ncreasng the ncome generated by the non-farm rural economy (through the ncrease n the demand for the goods and servces of the rural non-farm sector), and rasng employment and wages of the unsklled (beng agrculture typcally ntensve n unsklled labour). 2

4 mplemented. In partcular, the Agrcultural Sector Development Programme (ASDP), launched n 2006, s the operatonal program that carres out the Agrcultural Sector Development Strategy (ASDS) as well as broader frameworks such as the Natonal Strategy for Growth and Reducton of Poverty and the Tanzana Development Vson 2025, whch endorse the Mllennum Development Goals (GoT, 2011). Hence, Tanzana seems to be a good case study to assess the effectveness of agrcultural polcy reform n stmulatng agrcultural growth and reducng poverty through the analyss of farmers producton and consumpton behavour and the assessment of polcy mpacts on households welfare. The man objectve of ths study s to provde robust fundaments to answer whch government nterventon amng at ncreasng agrcultural producton s most effectve at achevng natonal poverty objectves. The justfcaton of ths research s prmarly emprcal: t ams at developng a model to measure how changes n agrcultural producton affect households n terms of poverty, by prmarly lookng at behavoral changes affectng consumpton and producton outcomes. Gven that the bulk of the poor lves n rural areas and earns most of ts ncome from agrculture 2, effectve agrcultural polces are crtcal for reducng poverty. From the theoretcal vewpont, ths study s an addtonal contrbuton to the analyss of farmers behavour, and n partcular of producton and consumpton decsons wth mssng markets, whch call for a non-separable agrcultural household modellng strategy 3 (Sngh et al., 1986; de Janvry et al., 1991) 4. Gven the features of rural Tanzana (where labour s by far the most mportant nput n farm producton and the labour market s very thn), n our analyss we made the hypothess of labour market falure. Non-separablty (due to an mperfect labour market) mples not only that producton and consumpton decsons are nterlnked, but also that labour allocaton s lkely to be determned by shadow wages rather than market wages. To capture ths feature, a twostage estmaton strategy was adopted: frst the shadow prces of famly labour has been estmated and then used to estmate the producton and demand systems by means of a mult-nput multoutput producton system (translog) and a complete demand system (Almost Ideal Demand AID System). The producton and consumpton decsons taken under labour market falures are thus nterlnked through the famly labour (whch enters the producton system) and ts counterpart n the consumpton system, whch s lesure, both valued at the shadow wage as estmated n the frst stage. The models above are able to capture adjustments n producton and consumpton behavours resultng from specfc polcy nterventons whose mpacts are mmcked through smulatons. 2 Accordng to the most recent estmates (GoT, 2011), about 80 percent of the poor lve n rural areas and agrculture accounts for 75 percent of rural household ncomes. 3 As known, a separable agrcultural household model rests on the assumpton that hred labour and famly labour are perfect substtutes and that labour market works perfectly. 4 See Taylor and Adelman (2003) for a bref but exhaustve analytcal descrpton of agrcultural household models. 3

5 Takng nspraton from the agrcultural household models approach and the man features of Tanzanan rural economy, ths study proposes a partal equlbrum model whch estmates the welfare mpact of dfferent agrcultural polces and prce changes. In partcular, ths s done by modellng farm households responses n producton and consumpton decsons n a context wth falure n labour market. Wth regard to the welfare mpact of prce changes, most researches take nto account the effects of food prce change on both consumpton and producton (Ferrera et al., 2012). However, the behavoural effects n consumpton and producton decsons are rarely consdered accurately (f any). Ths s the case especally for the producton component. Under large prce changes (though n the short-to-medum term), neglectng second order effects n consumpton and producton decsons can bas the fnal mpact on household welfare. Smlarly, structural nterventons such as those concernng rrgaton or mechansaton can produce non-neglgble behavoural effects n producton decsons. Ths s why the approach we propose n ths study can be partcularly approprate for the knd of phenomena analysed hereafter. Fnally, to the best of our knowledge, there are no studes estmatng the substtuton effects n consumpton and producton n the SSA regon wth cross-secton data as effect of agrcultural polces and food prce changes. The study s organzed as follows. Secton 2 explans the estmaton strategy and the emprcal specfcaton of the agrcultural household model, and presents the estmates of the producton and demand systems and of the related elastctes. In secton 3 a number of mcro-smulatons of the mpact of prce changes and of rural polces are frst used to predct the change n agrcultural proft 5. Ths change s then plugged nto the demand model, and the new consumpton vectors (n real terms) are then generated. Results of these mcro-smulatons are fnally used to draw possble polcy mplcatons n terms of poverty reducton. Secton 4 summarzes the man fndngs of the study and the polcy mplcatons. 5 Takng nto account varatons n agrcultural proft followng prce changes, allows to assess properly the mpact of prce changes for farm households. Indeed, many studes recently assessed the mpact of food prce crss by focusng only on consumpton behavour and leavng out producton responses. Instead, ths study takes nto account the farm proft varaton (whch, for a farm household, s presumably postve n case of an ncrease n agrcultural prces), whch partly determnes the ncome effect of the Slutsky equaton. 4

6 2. Estmaton of Agrcultural Household Model 2.1. Theoretcal model In ths secton we specfy a statc model to estmate households producton and consumpton responses when labour market fals 6. Ths s the case of Tanzanan rural economy, whch calls for a non-separable agrcultural household modellng strategy. The farm household s assumed to maxmze utlty subject to technology, budget and market constrants. Drawng, partly, on Hennng and Hennngsen (2007a), the utlty functon to be maxmzed can be represented as follows: h m Uc (, c, Tl l; DR, ) (1) m a s s where c m and c a represent non-agrcultural and agrcultural consumpton goods, respectvely; T s total tme household avalable, l h s s on-farm household labour supply, l m s s off-farm household labour supply and T s lesure; D represents household characterstcs, and R household s h m l s ls resource endowment. The constrants to the maxmzaton problem are: h m qa qa ( ld, ld, x; Z) (2) m m m m h h p c p c w l ( p q w l w l p x) K (3) m m a a s a a d d x c c T l l l l (4) m h mh, mh, a 0; m 0; s s 0; s 0; d 0 Equaton (2) formalzes the producton technology, represented by a mult-nput mult-output producton functon, where q a s a vector of agrcultural goods produced by the farm household. Varable ntermedate nputs (x), on-farm famly ( l h d ) and hred ( l m d ) labour, and land as quas-fxed factor (Z) are used n the producton process. Equaton (3) s the budget constrant where p m s the prce of non-agrcultural (manufactured) goods; p a s the prce of agrcultural products; m w s the market wage n the agrcultural sector and w h s the shadow wage for agrcultural famly labour; and K represents all non-wage off-farm ncomes. Equaton (4) mposes the usual non-negatvty constrants for consumpton of food and nonfood goods, lesure, and demand for, and supply of labour. 6 We focused on labour market falures, because ths s one of the major constrants n a context lke that of rural Tanzana, where labour s the most mportant nput n farm producton. Focusng only on labour market falures, we dsregarded other mportant constrants (above all, credt constrants, cf. Feder et al., 1990), as well as other aspects of farmers decsons lke prce and producton rsk (Fafchamps, 1992), whch are dffcult to catch n a sngle-perod analyss. 5

7 If non-separablty holds (e.g. due to an mperfect labour market), ths mples not only the nteracton of producton and consumpton choces, but also that labour allocaton s lkely to be endogenously determned by the shadow wage rate rather than the market equlbrum wage rate Emprcal Specfcaton 7 Startng from the emprcal approach proposed by Hennng and Hennngsen (2007a and 2007b), we estmated a non-separable agrcultural household model usng the 2008/2009 Tanzana Natonal Panel Survey Integrated Households Survey (TNPS-1) data. Dfferently from these and other authors (lke Jacoby (1994) and Abdula and Regm (2000)), who focused on labour supply, we used estmates of the non-separable model to assess the mpact on households welfare of dfferent agrcultural polces and prce changes. A two-stage estmaton strategy s adopted, estmatng frst the shadow prces of famly labour through producton functon modellng. After the shadow wage rate s ncluded nto a mult-nput mult-output producton system (translog) and a complete demand system (AID System), producton and consumpton functons are then estmated. The producton and consumpton decsons made when labour market fals are thus nterlnked by famly labour (whch enters the producton system) and ts counterpart n the consumpton system,.e. lesure, settng both prces at the shadow wage level as estmated n the frst stage. A smlar approach makes possble an assessment of the adjustments n producton (both n output and nput) and consumpton patterns as a result of changes n specfc decson varables. The shadow wage of household labour was estmated by followng the procedure proposed n Jacoby (1993). The Cobb-Douglas functonal form was used, despte ts lmtatons, because of ts easness of estmaton and nterpretaton. To assess the level and nteractons among dfferent farm products resultng from the mplementaton of alternatve agrcultural and prce polces, we estmated output supply and nput demand elastctes. Ths was done by estmatng a system of equatons derved from a restrcted proft-maxmzaton specfcaton. The producton technology s represented by a translog multnput mult-output proft functon, followng the methodology proposed, among others, by Fulgnt and Perrn (1990). On the consumpton sde, an AID System proposed by Deaton and Muellbauer (1980) was used to estmate the mpact of changes n prces and ncome on households consumpton behavour. 7 A detaled descrpton of emprcal specfcaton and estmaton strategy s reported n Annex 1. 6

8 2.3. Households Producton and Consumpton Behavour: Estmaton Results Shadow wage estmaton The OLS and IV estmates of the Cobb-Douglas producton functon are reported n table 1. The OLS regresson dsplays a hgh determnaton coeffcent (R 2 = ) and most of the OLS estmated coeffcents have the expected sgns. Most nputs have sgnfcantly postve effects on agrcultural output. All labour typologes have mpacts sgnfcantly dfferent from zero. Moreover, whle the mpact of hred female labour s greater than that of male hred labour, for famly labour the mpact s reversed 9. Ths s probably because of actvtes such as ploughng, whch are undertaken by men, contrbute margnally more to output than actvtes such as weedng and transplantng n whch females are largely engaged n Tanzana. Land qualty, rrgaton and mechanzaton have a sgnfcantly postve effect on farm producton. The household head s schoolng has not a sgnfcant mpact on agrcultural output, not supportng the wdely accepted role of human captal n mprovng farmers producton. Also household head s age s not sgnfcant. Infrastructures such as roads and markets have not statstcally sgnfcant effects on producton. However, the physcal nputs are lkely to be endogenous. Therefore, nstrumental varables (IV) are ncluded to estmate the producton functon 10. Durbn and Wu-Hausman tests are carred out to check for endogenety. Both tests suggest that the OLS model s rejected, whch ndcates the need for an IV procedure. Moreover, the Sargan test, checkng for the valdty of nstrumental varables (.e. uncorrelated wth the error term), confrms that nstruments are vald. Coeffcents from the IV estmate are used to estmate the household shadow wage. Table 2 reports the estmates of the household shadow wages obtaned usng equaton (A.2). The ch2-statstcs shows that the null hypothess of equalty between the margnal product and market wage rates can be rejected for both the men and women sample. 11 The samples fnally retaned to estmate (A.2) nclude men and women employed as household labourers and off-farm wage workers. Followng Jakoby (1993), the wage rate was nstrumented usng the worker s age and level of educaton, as well as the quadratc terms of these varables. Ths fndng supports the 8 In Annex 3 there are defntons, means and standard devatons of all varables used n man emprcal specfcatons. 9 Ths result s consstent wth the fndngs of Abdula and Regm (2000), but contrasts earler fndngs by Skoufas (1994) who found that n Inda famly female labour showed a greater mpact on output than famly male labour. 10 Set of nstruments used n the producton functon analyss: - Household composton varables: number of chldren (aged<15), number of elderly (aged>60), number of female adults (aged>14 and <61), number of male adults (aged>14 and <61); - Dstrct level prces and wages: prce of maze (logarthm), adult farm daly wage; - Dwellng characterstcs: home ownershp dummy (1 f own, 0 otherwse), cookng fuel dummy (1 f electrcty or gas, 0 otherwse), source of drnkng water dummy (1 f pped water nsde or outsde dwellng, 0 otherwse). 11 Ths fndng supports earler results by Jacoby (1993), Skoufas (1994) and Abdula and Regm (2000). 7

9 hypothess of nterdependence of producton and consumpton decsons by agrcultural households and justfes the use of a non-separable model. Table 1: Producton functon estmates () Dependent varable: loutput_value OLS IV Independent varables Coef. Coef. lhh_pestcdes a 0.072* lhh_norganc_fert a 0.171*** lhh_organc_fert a 0.019* 0.272** lhred_female a 0.152*** lhred_male a 0.066*** lhh_land 0.501*** 0.548*** lhh_tot_lab_f 0.054*** 0.102** lhh_tot_lab_m 0.102*** 0.108*** hh_head_age hhhead_age hh_head_sex * lterate land_qualty 0.330*** 0.353*** rrgaton 0.362** lroad lmarket lmechanzaton 0.088*** 0.047* cons 8.524*** 8.959*** R IV test results Durban: ch-sq(5)= Wu-Hausman: F(5,1837)=3.602 Sargan test: ch-sq(3)=9.113 p= p= p= () (***), (**), (*) sgnfcant at 0.01, 0.05 and 0.10 level respectvely a Varables consdered endogenous n the nstrumental varable estmaton. Source: Authors estmatons usng TNPS-1 Table 2: Test of the equalty between market and shadow wages (), a a Ch2-test b Men (n=529) *** Women (n=270) *** () *** statstcally sgnfcant at 1% a a and b b are coeffcents of equaton (A.3); b Null hypothess: Source: Authors estmatons usng TNPS-1 a,b 0, Producton 12 Coeffcents of the output shares, estmated through a translog producton system (cf. equaton (A.6)), as well as the proft functon (equaton (A.4)) are reported n Table 3. Proft share functons refer to (1) cereals, (2) fertlzers and pestcdes, (3) household labour and (4) hred labour, whle 12 Although we are aware of the presence of dfferent farmng systems n Tanzana, ths analyss was conducted gnorng such dfferences, snce a detaled analyss of dfferent farmng systems s beyond the scope of ths study. 8

10 the other crops share (5) s estmated by dfference (all these shares sum to one). Furthermore, two fxed nputs (Z) are ncluded n the system: Z 1 s the area-weghted average number of rrgatons; Z 2 s the land nput measured as hectares cultvated by farms n the long rany season 2008/2009. Fnally, the proft functon ncludes also other varables 13 such as the use of mproved seeds, the ownershp of an ox-plough, the household head s educaton status (lterate or not) and the access to extenson servces to capture the effect of these other factors on producton. 14 All own-prce coeffcents have the expected sgn, except the one of household labour, but only those related to fertlzers and pestcdes and hred labour are sgnfcantly dfferent from zero. Ths means that the output supply and factor demand equatons are consstent wth underlyng proft maxmzaton theory. Land s hghly statstcally sgnfcant n the proft functon, whle rrgaton shows a complementary effect wth other nputs (fertlzers and pestcdes), but t s not sgnfcant n the proft functon 15. All dummy varables (.e. mproved seeds, ox-plough, household head educatonal attanment and extenson servces) have the expected sgns and are hghly sgnfcant (except extenson servces, whose coeffcent s not sgnfcant). 13 These varables were ncluded as dummes because of data lmtatons. As a result, they appear n the proft functon only, snce they were not nteracted wth prces (see, for example, Olson and Zoub, 2001). 14 Infrastructur varables, lke dstance to road or the presence of a market n the vllage, were not statstcally sgnfcant n any specfcaton. Therefore, they were dropped. 15 The null mpact of rrgaton may be explaned by the fact that rrgaton s lkely to be neffectve n reducng the effects of severe drought lke that occurred n Tanzana n 2008/2009. Ths s due by the fact that rrgaton n Tanzana s prevalently gravtatonal rrgaton and, hence, n case of severe droughts, the rvers dry up. Ths leads to nsuffcent water for rrgaton (a smlar result can be found n Chrstaensen et al., 2005). 9

11 Share (S ) functon Table 3: Restrcted parameters estmates of the translog proft functon () Intercept Cereals (LnP 1 ) Fertlzers& pestcdes (LnP 2 ) Prce of Household labour (LnP 3 ) Hred labour (LnP 4 ) Other crops (LnP 5 ) Irrgaton (LnZ 1 ) Land (LnZ 2 ) Cereals 0.438* ** * Fertlzers & pestcdes ** *** ** household labour * Hred labour * Proft Functon Intercept (LnP 1 ) (LnP 2 ) (LnP 3 ) (LnP 4 ) (LnP 5 ) (LnZ 1 ) (LnZ 2 ) 3.890*** 0.438** ** *** (LnP 1 ) 2 /2 (LnP 2 ) 2 /2 (LnP 3 ) 2 /2 (LnP 4 ) 2 /2 (LnP 5 ) 2 /2 LnP 1 LnP 2 LnP 1 LnP 3 LnP 1 LnP *** * ** LnP 1 LnP 5 LnP 2 LnP 3 LnP 2 LnP 4 LnP 2 LnP 5 LnP 3 LnP 4 LnP 3 LnP 5 LnP 4 LnP 5 LnP 1 LnZ LnP 1 LnZ 2 LnP 2 LnZ 1 LnP 2 LnZ 2 LnP 3 LnZ 1 LnP 3 LnZ 2 LnP 4 LnZ 1 LnP 4 LnZ 2 LnP 5 LnZ ** *** LnP 5 LnZ 2 (LnZ 1 ) 2 /2 (LnZ 2 ) 2 /2 LnZ 1 LnZ 2 mproved ox lterate extenson *** *** 0.216*** 0.313*** 0.649*** 0.195*** () (***), (**), (*) sgnfcant at 0.01, 0.05 and 0.10 level respectvely Source: Authors estmatons usng TNPS-1

12 The parameters that are shown n table 3 are used to estmate the prce elastcty of the quantty of output and nput. Elastctes can be easly used to assess the mpact of a prce change on the agrcultural output. The full set of prce elastctes for output supply and nput demand were computed usng sample means, accordng to equatons (A.7) and (A.8) (Table 4). Both supply of cereals and other crops are nelastc (below 1). Ths result s clearly affected by the relevance n the proft share of maze and other crops (such as tubers) whch are manly grown for self-consumpton purposes. Ths s also ndcaton of the poor dversfcaton n food consumpton preferences for farmers. In other words, the ncrease n a crop s prce does not nduce the farmer to produce more of that tem to sell t n the market and by substtutng ts consumpton wth somethng else. Avalablty of other food products s ndeed lmted, especally n the rural context. Supply cross-prce elastctes are postve, revealng a complementary relatonshp among the commodtes, a result n lne wth the fndngs n Ball (1988), Fulgnt and Perrn (1990) and Colby et al. (2000). Ths mples that the ncrease n the commodty prce leads new nputs to be drawn nto general producton, leadng, n turn, to an ncrease n the producton of other products. Moreover, ths suggests a low potental for dversfcaton n Tanzanan agrculture. The result that supply cross-prce elastctes are larger than own-prce elastctes suggests that producton s lkely to be drven by consumpton decsons. When one crop s prce ncreases farmers are dverted to consume less of that crop. They are lkely to be nduced to produce substantally more of the other crop n order to compensate for the lower consumpton of the frst product. Ths mechansm may be partcularly true for farmers engaged n only one crop (or category of crops) producton. At ths regard and based on the sample used for these estmates, we found that slghtly less than half percent of farmer households produce ether cereals or other crops. All cross-prce elastctes for nputs are negatve, whch means that nputs are complements: an ncrease n one nput prce, holdng other categores prces constant, decreases other nputs demand. Smlar results are reported n Fulgnt and Perrn (1990). The negatve cross-prce elastcty of nputs demand to household shadow wage, market wage and fertlzer prce shows that the combned use of household and market labour and other nputs ncreases agrcultural producton synergstcally. Indeed, the decrease of any of these three varable nputs nduces a lower demand of the other nputs. Also, the sze of nput elastctes wth respect to output prces reveals that the nput demand s more responsve to the prce of cereals than to that of other crops. Moreover, f we compare the cross-prce elastctes of fertlzers/pestcdes (FP) and household labour (HL) (e.g. FP, HL HLFP, ), we confrm the labour ntensve feature of the agrcultural producton system n Tanzana. Cross-prce elastctes of output supply to nput prces are always negatve: ths s

13 consstent wth the economc theory. Fnally, consstently wth the fact that household labour accounts for about 70% of total nput costs, ts own-prce elastcty results to be larger than for other nputs and hred labour. Quantty of Table 4: Output and nput prce elastcty matrx () Prce of Cereals Other crops Other nputs Household labour Hred labour Cereals 0.811*** 1.453*** *** *** *** Other crops 1.739*** 0.499* ** *** *** Other nputs 1.711*** 1.476*** *** *** *** Household labour 1.867*** 1.476*** ** *** *** Hred labour 1.546*** 1.392*** *** *** *** () Elastctes are estmated at the weghted average of output and nput shares. Z-stat for elastctes are calculated by bootstrappng wth replacement (after 1,000 replcatons). Note that (***), (**), (*) dentfy elastctes whch are statstcally dfferent from 0 at 0.01, 0.05 and 0.10 level respectvely Source: Authors estmatons usng TNPS Consumpton Table 5 shows that most of the estmated parameters of the AID system are hghly sgnfcant. Among demographc varables, household sze negatvely nfluences the expendture share of most food groups, whle the number of chldren has a postve and sgnfcant mpact only on the consumpton share of fsh, ol and fats and meat, eggs and dary and a negatve and sgnfcant effect on the consumpton share of pulses and dry and lesure. The sgn of own prce and ncome (expendture) elastctes, reported n Table 6, are consstent wth theory and ther magntudes are wthn the expected ranges. Other cereals, fsh, meat, eggs and dary, and non-food consumpton are very senstve to own prce changes, whle starches mostly consumed by the poor are qute unresponsve to ther own prce. Except for starches, ncome elastctes are postve for all commodty groups. Meat, eggs and dary, other cereals, sugar and sweets, ol and fats, beverages, fsh and non-food have expendture elastctes above one, ndcatng that these commodtes are superor goods and that the det among the poor s lmted to qute a few food tems. On the contrary, as ncome ncreases consumers tend to spend proportonately less on maze and pulses, vegetables and frut, and salt and spces. Fnally, ncome elastcty assocated to lesure s lower than one; ths result may ndcate that, as ncome ncreases, people allocate more tme on work actvtes and reduce ther tme for lesure. Ths s true partcularly n rural areas. 12

14 () () Table 5: AID System estmates Prce (LnP ) of Intercept Maze Other cereals Starches Sugar& sweets Pulses& seeds Vegetables Meat, &fruts eggs, dary Fsh Ol& Fats Salt& spces Beverages Lesure Non-food Number Household Normalzed chldren sze (Ln) expend(ln) Maze 0.313*** 0.017*** 0.010*** 0.009*** 0.005*** *** *** 0.008*** *** *** *** *** *** Other cereals *** *** 0.011** *** *** 0.029*** *** 0.005* *** *** 0.031*** *** 0.028*** Starches 0.242*** 0.009*** 0.011** 0.037*** 0.004* *** 0.026*** ** *** 0.001* *** *** *** *** Share Equaton (S) of Sugar& *** ** 0.004*** ** *** ** *** sweets Pulses& 0.106*** *** *** *** 0.017*** *** *** *** *** *** seeds Vegetables 0.059*** *** 0.020*** *** *** 0.032*** *** 0.028*** ** *** *** & fruts Meat, 0.093*** *** 0.029*** 0.026*** *** *** *** 0.016*** *** * *** *** 0.045*** 0.005*** *** eggs, dary Fsh 0.020* 0.008*** *** ** *** 0.016*** *** 0.004** ** *** * *** Ol & fats * *** 0.005* *** 0.005** *** *** 0.004** 0.009*** *** 0.009*** 0.001*** *** Salt & spces 0.024*** * * ** *** *** 0.002* *** *** () Beverages *** *** *** 0.006** *** *** 0.062*** *** 0.016*** Lesure 0.430*** 0.015*** *** ***-0.002*** *** *** *** *** 0.001*** *** 0.081*** *** *** 0.068*** *** (***), (**), (*) sgnfcant at 0.01, 0.05 and 0.10 level respectvely; () Household head employment status, household head educaton status and urban/rural dummes are ncluded n the demand system Source: Authors estmatons usng TNPS-1

15 Table 6: Expendture prce and expendture elastcty matrx () Prce of Maze Other cereals Starches Sugar & sweets Pulses & seeds Vegetables &fruts Meat, eggs, dary Fsh Ol & fats Salt & spces Beverages Lesure Non food Expend Maze *** 0.166*** 0.150*** 0.069*** 0.041* ** 0.098*** * 0.007** *** ** Other cereals 0.077** *** *** 0.152** 0.192*** 0.299*** *** ** *** 0.271** 1.309*** Starches 0.238*** 0.230*** *** 0.073*** *** 0.410*** ** 0.023*** *** 0.277*** Sugar & sweets 0.164*** *** *** ** * *** ** 1.273*** Quantty of Pulses & seeds *** *** *** 0.308*** ** *** 0.125* *** Vegetables & fruts Meat, eggs, dary *** 0.257*** *** *** *** ** 0.360*** * ** *** *** 0.346*** 0.315** ** *** *** 0.197*** *** ** *** *** 0.499*** 1.397*** Fsh 0.189*** *** ** * 0.751*** 0.426*** *** *** *** *** Ol & fats *** *** 0.184** *** * *** *** *** 0.279* 1.613*** Salt & spces 0.132** ** * ** 0.166* *** *** 0.521** Beverages ** *** *** *** *** *** 1.476*** 1.405*** Lesure 0.089*** *** 0.029*** *** ** *** *** 0.004*** * *** *** 0.819*** Non food *** 0.061* *** *** ** 0.136*** * *** *** *** 1.841*** () Elastctes are estmated at the weghted average of expendture shares. Z-stat for elastctes are calculated by bootstrappng wth replacement (after 1,000 replcatons). Note that (***), (**), (*) dentfy elastctes whch are statstcally dfferent from 0 at 0.01, 0.05 and 0.10 level respectvely Source: Authors estmatons usng TNPS-1 14

16 3. Smulaton of Agrcultural Polces and Prce Changes 3.1. Descrpton of agrcultural polces and prces varaton smulatons The estmated model was used to assess the mpacts on households welfare of the full mplementaton of phase 1 of the Agrcultural Sector Development Program (ASDP) (launched n 2006/2007 and expected to be concluded n 2012/2013) and the change n output prces. These scenaros are lkely to have been affectng Tanzanan households, partcularly n rural areas. Gven the top-rank prorty of the former n Tanzanan Government s objectves, the smulatons wll focus prmarly on the polcy reform, applyng t to the status quo (baselne) wthout consderng other changes that took place snce the model s reference year,.e However, there are changes that cannot be gnored due to ther non trval mpacts on households welfare. We smulated n fact the prce fluctuatons that took place snce the begnnng of the food crss up to the date of the analyss (November 2011), alone and ther combned effect wth of polcy reform. The ASDP s part of the Agrcultural Sector Development Strategy (ASDS) and the broader Natonal and Global Polces, ncludng the Natonal Strategy for Growth and Reducton of Poverty (most commonly known as MKUKUTA), the Tanzana Development Vson 2025, and the Mllennum Development Goals. Prorty actons wthn the ASDP are ncreasng the use of modern nputs and technologes (.e. rrgaton, mproved seeds, eroson control, chemcal fertlzers, ox-ploughs), mprovng support servces (ncludng agrcultural research and extenson servces), and provdng better agrcultural marketng nfrastructures as well as formal and nformal credt nsttutons. In ths secton we wll assess the mpacts on poverty of a number of polces ncluded n the Agrcultural Sector Development Programme, namely 16 (Table 7):, ncreasng the proporton of farm households usng mproved seeds from 17.9 percent to 35 percent (Smulaton 1), ncreasng the proporton of farm households usng mechanzaton (ox-plough) from 7.5 percent to 30 percent (Smulaton 2), ncreasng the percentage of farmers havng access to crop extenson servces from 22.6 percent to 55 percent (Smulaton 3). The whole package of ASDP measures descrbed above has been also smultaneously smulated n Smulaton 4: ths allows the assessment of how effectve the agrcultural polces desgned by Tanzanan Government are and to compare ther achevements wth the outcome targets set by the Agrcultural Sector Development Programme (.e. leadng to a 24% headcount rato n rural areas). 16 Other nstruments ncluded n the ASDP were not smulated snce n the proft model the varables related to these measures were not statstcally sgnfcant (e.g., varables regardng SACCOs and rural credt, or varables regardng nfrastructure as the dstance to a market and to a prncpal road).

17 To assess the dfferent mpacts of these polces on poverty, four dfferent targetng profles were hypotheszed 17. They can be grouped n two categores. The frst category adopts ncome crtera and compares a targetng orented to the poorest groups of populaton 18 ( pro-poor ), versus a polcy mplementaton orented to those who have the hghest probablty 19 of beng drectly nterested by the polces ( no targetng ). Of course, under ths pro-poor scenaro we made the hypothess that the polcy-maker perfectly knows the ncome dstrbuton of Tanzanan populaton. Ths s unrealstc, but t wants to represent unquely the top-performng reference f effectve proxy-means test schemes are mplemented. The second group dentfes two targetng profles accordng to land sze: one orented to smallholders and one orented to farmers ownng relatvely more land ( non-smallholders ) 20, 21. Another set of smulatons refers to changes n the prce of outputs. The mpact of the ncrease n cereals prces occurred over the last years s assessed n Smulaton 5 22, enterng both the consumpton (negatve effect) and producton (postve effect). Accordng to exstng data 23, n November 2007 Tanzana showed the lowest cereals prces over the second half of 2000s; n September 2009 (whch s also the last month of our dataset), nstead, cereals prces peaked at ther hghest value (already reached n February 2008). Therefore we consdered the prce changes 24 that took place between May 2007 and September 2009; specfcally, we smulated a (weghted) prce ncrease of cereals by around 132.6% n the aforementoned perod. Smulaton 6 takes nto account the prce change for cereals snce October 2009 (the month followng the end of our questonnare) 17 A lmtaton n our approach s that we consder only polcy mpacts, whle there are mportant ssues related for nstance to the mplementaton costs that we do not take nto account. For example a pro-poor targetng wll probably be more expensve than a random polcy mplementaton. 18 Accordng to ths approach, the smulated polces progressvely nclude households from the poorest quntles wth hgher agrcultural proft/ncome rato up to the rankng poston where the polcy-specfc target has been acheved. By followng ths approach, not necessarly easy to be put n place, we wanted to target the poorest households whose ncome mostly comes from the agrcultural sector. 19 For ths purpose we estmated probt and tobt models wth the varables to be smulated (.e. mproved seeds, oxplough, extenson servces) as dependent varables. In ths case, households were selected accordng to the probablty predcted by these bnomal models, startng from households not endowed wth those nputs but showng the hghest probablty of havng them, up to the rankng poston where the polcy-specfc target s acheved. 20 It would be msleadng to defne these farmers as large farmers. Indeed, those are farmers who cultvate on average less than 5 ha of land. 21 For smallholders and non-smallholders targetng profles, we dentfed the groups of households wth land sze lower and greater than 2 ha respectvely. Wthn these groups, we selected those whch have not these nputs (mproved seeds, ox-plough and extenson servces) and choose randomly the polcy benefcares up to the polcy-specfc target s acheved. 22 For the producton system we took nto account varatons n the prce of maze and rce as proxy of the prce of the cereals category, whle for the demand system we used the change n the prce of maze for the maze category and the prce of rce as proxy of the prce for the other cereals category. Gven the lack of regonal data, we used prce changes referrng to Dar es Salaam. We are aware that ths may overestmate the effect of prce ncrease. Moreover, we made the hypothess that changes n consumpton and producton prces are equal, whch may be qute a strong assumpton, especally under large and rapd prce ncreases. 23 Cereals prces data are from FAO/Global Informaton and Early Warnng System on Food and Agrculture (GIEWS) onlne dataset (FAO, 2011). 24 Cereals prces were reported back at ther value n May

18 to November 2011 (the last month for whch data were avalable), whch corresponds to a (weghted) decrease by around 25%. Fnally, Smulaton 7 combnes the complete ASDP package of measures smulated (Smulaton 4) wth the prce trend consdered n Smulaton 6. Ths allows the assessment of the true mpact of the ASDP polces on poverty. Snce the ncrease n prces affects all populaton quntles, smulatons of prce changes (Smulatons 5 and 6) were carred out wthout any targetng (e.g., pro-poor versus non pro-poor, or smallholders versus non-smallholders) Poverty estmates 25 Before gong nto the detals of poverty results, we should remnd that the ASDP polces are nowadays stll far from ther full mplementaton. The results shown below thus represent the potental effectveness of the ASDP polces under ther full mplementaton. As the nformaton on the costs assocated to each of the polces proposed hereafter are not readly avalable, the analyss (for the moment) does not take nto account the effectveness per shllng spent and the results are then not normalzed by the cost of the polcy. These smulatons show that, consderng sngle agrcultural polces wthn the ASDP package, mechanzaton (Sm2,.e. ox-plough adopton) s the most effectve polcy for ncreasng farm profts and, as a result, reducng poverty (both headcount rato and poverty gap). Ths s true partcularly under the pro-poor targetng scheme (rural HCR deceases by 2.6 percentage ponts). The adopton of mproved seeds sgnfcantly reduces rural poverty by around 1.1 percentage ponts under both ncome targetng approaches (whle poverty gap reducton s larger n case of a pro-poor targetng), and by 1 percentage pont under non-smallholders targetng, whle t has not any statstcally sgnfcant mpact when the smallholders are targeted. In case of pro-poor targetng, the mplementaton of the whole ASDP package has a strong mpact on headcount rato and poverty gap reducton (n rural areas nearly 4.7 and 2.1 percentage ponts respectvely). The results are far more mpressve f the polces are carred out n a more realstc scenaro that takes nto account the actual changes n food prces snce the tme the survey was carred up to November 2011 (Sm 7). Here, the mplementaton of the whole ASDP package reduces rural HCR by 8.6 percentage ponts n rural areas. Poverty gap decreases by 3.1 percentage ponts. Anyway, even wth a pro-poor targetng, the smulated results are lower than the expectatons of the Tanzanan government about the mpact of ASDP n reducng poverty. 25 Poverty estmates, as well as sgnfcance tests and domnance curves were carred out by usng the DASP program (Araar and Duclos, 2009). 17

19 Wthout targetng, ASDP s, obvously, less effectve n reducng headcount rato (3.1 percentage ponts n rural areas and a null mpact n urban areas), but t has, however, a strong mpact n reducng poverty. Ths suggests that, n a context lke Tanzana, regardless of targetng, agrculture can be a key n reducng poverty. In addton, a pro-poor targetng mples a trade-off: a more effectve poverty reducton mples a hgher mplementaton cost due to targetng. If solated, the prce changes that took place between October 2009 and November 2011 (Sm 7) led to a reducton by 3.4 percentage ponts n the headcount rato (4 percentage ponts n rural areas) and a decrease by 1 percentage pont n poverty gap. Fnally, the ncrease n food prces had a huge mpact on households welfare. The mpact on poverty of an ncrease n cereals prces as the one occurred snce the onset of the food prce crss n 2007 (Sm 5) s mpressve. Indeed, ceters parbus food prces ncrease led HCR from 26 to 32.9 percent. In addton, the food prce jump ncreased poverty gap from 7.3 (smulated poverty gap n May 2007) percent to 9.4 percent (poverty gap at base run). For farm households the ncrease n food prces had mnor effect on poverty. However, n general, the ncrease of farm proft followng the hke n food prces dd not succeed n neutralsng the negatve effect on real consumpton. Ths has an mmedate polcy mplcaton: that s the government should ntervene to reduce prce movements to prevent adverse effects n terms of poverty and ncome dstrbuton. Surprsngly, a targetng orented to those who have relatvely more land results n a more effectve headcount rato reducton as well as poverty gap reducton. Seemngly, ths result s n contrast wth the wdely accepted statement that focusng on smallholders, nstead of large farmers, leads to better results n terms of poverty reducton. Actually, non-smallholders targetng does not refer to large farmers, whch are not sgnfcantly represented n the sample, but to farmers ownng relatvely more land than smallholders, but wth an absolute low endowment of land ( nonsmallholders have, on average, less than 5 ha of land). In addton, ths s related to the crucal role played by land n farmng: land s ndeed the lmtng factor for smallholders, whose very low land endowment 26 prevents the possblty of ncreasng agrcultural producton and proft. Apparently, those endowed wth fewer land have greater dffcultes n gettng out poverty as they lve further from the poverty lne, whle targetng polces to those who have relatvely more land leads to better results n terms of poverty reducton. ASDP polces are less effectve n ncreasng farm profts of those owng fewer lands: the estmaton of the producton system showed the mportance of land as a major nput n farm producton (cf. secton 2.3.2) and perhaps ths s a lmtng factor n ASDP mplementaton. All ths clearly appears n Fgure 1 where proft varaton due to the mplementaton of the full ASDP package under the smallholders targetng scheme s 26 Smallholders have, on average, 1.24 ha of land. 18

20 substantally lower than the one smulated under the non-smallholders program. Ths s verfed along the whole dstrbuton of household welfare, ncludng below the poverty lne. Gven the structural constrants faced by farmers wth smaller land sze, the ASDP package results to be sgnfcantly less effectve on agrcultural producton when smallholders are targeted and, consequently, changes n poverty gap as well as HCR are lower than under the non-smallholders targetng approach. Indeed, t s well-known that access to land s a crucal ssue n many Sub-Saharan Afrcan countres 27 and partcularly n Tanzana, a country abundant n arable land but wth a very stcky land market. Ths calls for a renewed effort n land reform 28. To sum up, ths has two man polcy mplcatons: () there s a need for a new land reform facltatng the access to land for smallholders 29, and () n absence of a land reform, f the polcy objectves are the reducton n the overall poverty, t s better to target relatvely larger farmers. As shown n Fgure 2, under the non-smallholder smulaton, the rankng of the poverty mpact assocated to the dfferent scenaros s found to be robust accordng to a farly large range of poverty lnes as well as to poverty ndces of class 1 and 2. More specfcally, we can conclude that consumpton dstrbuton under 2007 cereal prces (Sm5) always domnates all dstrbutons smulated under the non-smallholders targetng. On the contrary, under the pro-poor targetng results are less robust. Consumpton dstrbuton derved wth 2007 cereal prces domnates all dstrbutons except for poverty lnes lower than the offcal threshold the one under 2011 cereal prces together wth the ASDP package (Sm7). The poverty curve assocated to the ASDP package (Sm4) s found to domnate the one estmated wth 2011 cereal prces (Sm6) untl around the offcal poverty lne; after that threshold the two curves cross and the domnance result s then reversed. Fnally, wth regard to poverty gap, under the pro-poor targetng the dstrbuton under 2011 cereal prces together wth the ASDP package (Sm7) domnates the one under 2007 cereal prces (Sm5) for poverty lnes lower than around 1.3 tmes the offcal threshold. In addton, the ASDP package (Sm4) produces robustly better results n terms of poverty reducton than the 2007 cereal prces scenaro (Sm5) for poverty lnes lower than around 70% of the offcal lne. 27 The problem of access to land n Afrca has recently become agan a hot topc n the development agenda (see, for example, de Janvry and Sadoulet, 2005; Dennger, 2009). 28 The Tanzanan government has recently reckoned the problem and the Tanzanan Mnstry of Lands, Housng and Human Settlement Development s now nvolved n a sgnfcant number of projects to mplement Tanzana s land law reform, whch has been enforced snce May However, ts mplementaton s slow and geographcally uneven, and not much s known about how the reform affected the dstrbuton of land. 29 It s evdent that a land reform n the context of Tanzanan agrculture may represent a wn-wn opton, whch mproves equalty as well as effcency. 19

21 Table 7: Poverty estmatons () Headcount rato (%) Poverty gap (%) Smulaton () Pro-poor No targetng Pro-poor No targetng Urban Rural All Urban Rural All Urban Rural All Urban Rural All Base year Sm * 32.03* * 32.03* * 8.90* * 9.25* Sm * 30.86* * 31.38* * 8.28* * 8.82* Sm * * 9.30* Sm * 29.18* * 30.57* * 7.65* * 8.66* Sm * 30.34* 26.01* 12.13* 30.34* 26.01* 3.56* 8.46* 7.30* 3.56* 8.46* 7.30* Sm * 33.60* 29.47* 16.21* 33.60* 29.47* 4.20* 9.60* 8.32* 4.20* 9.60* 8.32* Sm * 29.00* 25.86* 16.05* 31.12* 27.54* 3.78* 7.72* 6.78* 4.13* 8.79* 7.68* Smulaton Smallholders Non-Smallholders Smallholders Non-Smallholders Urban Rural All Urban Rural All Urban Rural All Urban Rural All Base year Sm * 32.13* * 9.16* Sm * 32.55* * 31.60* * 9.18* * 8.96* Sm Sm * 36.85* 32.19* * 30.66* * 9.08* * 8.69* Sm * 30.34* 26.01* 12.13* 30.34* 26.01* 3.56* 8.46* 7.30* 3.56* 8.46* 7.30* Sm * 33.60* 29.47* 16.21* 33.60* 29.47* 4.20* 9.60* 8.32* 4.20* 9.60* 8.32* Sm * 32.49* 28.53* 16.02* 30.95* 27.40* 4.14* 9.31* 8.08* 4.11* 8.79* 7.68* () * the dfference between to the base run s fgure s statstcally dfferent from zero (at 5%); () Sm1=mproved seeds; Sm2=mechanzaton; Sm3=crop extenson servces; Sm4=ASDP package; Sm5=2007 cereal prces; Sm6=2011 cereal prces; Sm7=2011 cereal prces & ASDP Source: Authors estmatons usng TNPS-1