EVALUATING THE SOCIAL GAINS ASSOCIATED WITH TECHNOLOGICAL PROGRESS IN THE BRAZILIAN AGRICULTURE

1 EVALUATING THE SOCIAL GAINS ASSOCIATED WITH TECHNOLOGICAL PROGRESS IN THE BRAZILIAN AGRICULTURE Joaqum Bento de Souza Ferrera Flho 1 1 Introdução The share of agrculture n the Brazlan exports started to rse agan from 1994 on, after fallng consstently snce the mddle of the seventes. The rate of growth of agrcultural GDP has also been ncreasng at rates hgher than the natonal GDP, partcularly snce the end of the nnetes. Ths dynamsm of the Brazlan agrculture has as specal feature the change n the pattern of geographcal concentraton, wth new regons beng ncorporated to the process at fast rates. Ths change s accompaned by an ntense process of technologcal change, wth strong ncrease n the total factor productvty (TFP). The agrcultural sector s a key sector n the Brazlan economy n many dfferent aspects. Wth strong forward and backward lnkages, agrcultural GDP accounted for 10.3% of total Brazlan GDP n 2003 (IBGE, 2004), and rural populaton stll accounted for about 19% of total populaton n 2003. It s just natural, then, that those changes n the agrcultural sector have mportant mpacts n the economy as a whole. Due to ts partcular characterstcs, both n the labor market and as a food suppler, these mpacts are of complex nature, wth net results dependng n a great deal of the structural characterstcs of the economy. The analyss of the broad effects of technologcal change n agrculture s the goals of ths study. 2 Objectve The objectve of ths paper s to assess the effects of the technologcal progress n the Brazlan agrculture, both on the agrcultural sector and n the broad economy, usng a general equlbrum model of Brazl projected for poverty and dstrbutonal analyss. Of partcular nterest wll be the socal gans and the ncome dstrbuton effects assocated wth the process. 1 Professor, Escola Superor de Agrcultura Luz de Queroz, Unversdade de São Paulo. Emal: jbsferre@esalq.usp.br

2 3 Technologcal change and growth: the general equlbrum approach As notced by Frsvold (1997) the general equlbrum approach to the technologcal change process dffers from the tradtonal approaches of returns to nvestment n research that are focused, n general, n only one product, and n partal equlbrum. Accordng to that author, the set of hypothess used by those studes are restrctve n many aspects. In frst place, they assume that prces and producton of all other sectors are fxed. For example, t s assumed that the changes n the producton costs of corn won t affect the prces of wheat of chcken meats. Ths knd of hypothess, however, can be too strong for technologcal progress phenomena broad enough to affect other productve actvtes at the same tme. Ths s the case, for example, of a new knd of fertlzer or pestcde, whch wll affect smultaneously the productvty of many other agrcultural actvtes. When just one sector s analyzed, typcally only the supply curve of that sector s dsplaced, under the usual ceters parbus condtons. The general equlbrum approach allows the relaxaton of ths hypothess, allowng endogenous changes n prces and quanttes of all the other sectors n response to the technologcal change (TC) n one sector. Besdes that, the general equlbrum (GE) approach makes t possble the jont analyss both of vertcal effects (across actvtes placed at dfferent levels of the commercalzaton chan, as s the case of agrculture and the food ndustry) and horzontal effects (between actvtes n the same level of the producton chan), through the nput-output relatons n the economy, as well as the prmary factors (labor and captal) markets. The vertcal effects relate to lnkages between prmary producton and the nput and product markets. In GE models these relatons are explctly modeled n the productve sphere of the model, where the producton technology of all sectors s detaled. For example, n the model used n ths study (the TERM-BR model) prmary factors of producton combne to create a composte prmary factor through a Constant Elastcty of Substtuton (CES) functon whch, for ts turn, combne wth other nputs produced by other producton actvtes through a Leontef (fxed coeffcents) functon, to produce a certan level of output.

3 The horzontal effects are those lnkng the producton actvtes through relaton n producton and consumpton. In the producton sde the actvtes compete for prmary factors (land, labor and captal), usually n lmted supply. And, n the demand sde, the producton also substtutes n consumpton between domestc and mported products, and n producton for the domestc versus export markets. The GE approach takes explctly nto account all those aspects, and allows the understandng of the net results of many complex effects. 4 Methodology: the TERM-BR, a general equlbrum model for dstrbutve effects A computable general equlbrum (CGE) model of the Brazlan economy wll be used to assess the economc and dstrbutonal mpact of the Total Factor Productvty growth n Brazlan agrculture. The core CGE model s lnked to a mcro-smulaton model of Brazl, and has ts theoretcal structure based on prevous work of Ferrera Flho and Horrdge (2006), Ferrera Flho, Santos and Lma (2007) and Ferrera Flho and Horrdge (2008). The model used n ths paper s calbrated usng a database for the 2004 year. It s based on the Brazlan Natonal Accounts for 2004 and the Brazlan Natonal Household Survey (Pesqusa Naconal por Amostragens de Domcílos PNAD), for the year 2004 (IBGE, 2004). In what follows a descrpton of the man features of the model s presented. The core CGE model used here, the TERM-BR model, s a statc nterregonal model of Brazl based on the TERM 2 model of Australa (Horrdge, Madden and Wttwer, 2005). It conssts, n essence, of 27 separate CGE models (one for each Brazlan state), lnked by the markets for goods and factors. For each regon, each ndustry and fnal demander combnes Brazlan and mported versons of each commodty to produce a user-specfc constant elastcty of substtuton (CES) composte good. Household consumpton of these domestc/mported compostes s modeled through the Lnear Expendture System, whle ntermedate demand has a Leontef (fxed proportons) structure. Industry demands for prmary factors follow a CES pattern, whle labor s tself a CES functon of 10 dfferent labor types. These dfferent labor types are classfed accordng to wages, as a proxy for sklls. The 2 Versons of TERM have been prepared for Australa, Brazl, Fnland, Chna, Indonesa and Japan. Related materal can be found at www.monash.edu.au/polcy/term.htm.

4 model dstngushes 42 producng sectors (or ndustres), among whch 41 are sngleproduct ndustres and the agrcultural ( Agrculture ) ndustry dstrbutes ts output (accordng to a Constant Elastcty of Transformaton - CET constrant) between 11 agrcultural commodtes. Export volumes are determned by constant-elastcty foregn demand schedules. These regonal CGE models are lnked by trade n goods underpnned by large arrays of nter-regonal trade that record, for each commodty, source regon and destnaton regon, the values of Brazlan and foregn goods transported, as well as the assocated transport or trade margns 3. Users of, say, vegetables n São Paulo state substtute between vegetables produced n the 27 states accordng to ther relatve prces, under a CES demand system 4. Wth 27 regons, 42 ndustres, 52 commodtes, and 10 labor types, the model contans around 1.5 mllon non-lnear equatons. It s solved (n lnearzed form) wth the GEMPACK software. The CGE model s calbrated wth data from two man sources: a 2004 Brazlan Input-Output Matrx 5, and some shares derved from the Pesqusa Agrícola Muncpal (IBGE, 2004, avalable at http://bge.gov.br ). On the ncome generaton sde of the model, workers are dvded nto 10 dfferent categores (occupatons), accordng to ther wages. These wage classes are then assgned to each regonal ndustry n the model. Together wth the revenues from other endowments (captal and land rents) these wages wll be used to generate household ncomes. Each actvty uses a partcular mx of the 10 dfferent labor occupatons (sklls). Changes n actvty level change employment by sector and regon. Ths drves changes n poverty and ncome dstrbuton. Usng the expendture survey (POF, mentoned below) data the CGE model was extended to cover 270 dfferent expendture patterns, composed of 10 dfferent ncome classes n 27 regons. In ths way, all the expendture-sde detal of the mcro-smulaton dataset s ncorporated wthn the man CGE model. 3 The dmensons of ths margns matrx are: 52*2*2*27*27 [COM*SRC*MAR*REG*REG]. 4 For most goods, the nter-regonal elastcty of substtuton s farly hgh. To ease the computatonal burden, we assume that all users of good G n regon R draw the same share of ther demands from regon Z. 5 The 2004 Brazlan Input-Output database used n ths study was generated by the author based on the Brazlan Natonal Accountng System tables (avalable at http://www.bge.gov.br/servdor_arquvos_est/), snce the last offcal Input-Output table publshed by the Brazlan statstcal agency f from 1996.

5 The mcro-smulaton model uses two man sources of nformaton: the Pesqusa Naconal por Amostragem de Domcílos PNAD (Natonal Household Survey IBGE, 2004), and the Pesqusa de Orçamentos Famlares- POF (Household Expendture Survey, IBGE, 2006). The PNAD contans nformaton about households and persons. The man nformaton extracted from PNAD were wage by ndustry and regon, as well as other personal characterstcs such as years of schoolng, sex, age, poston n the famly, and other soco-economc detals. The POF, on the other hand, s an expendture survey that covers all the metropoltan regons n Brazl. It was undertaken durng 2003, and covered 48,470 households, wth the purpose of updatng the consumpton bundle structure. The man nformaton drawn from ths survey was the expendture patterns of 10 dfferent ncome classes, for all regons. One such pattern was assgned to each ndvdual PNAD household, accordng to each ncome class. After preparaton, the mcrosmulaton database comprses 283,363 persons (older than 15 years old) and 121,849 households. The CGE and the mcro-smulaton (MS) models are run sequentally, wth consstency between the two models assured by constranng the mcro-smulaton model to agree wth the CGE model. The CGE model s suffcently detaled, and ts categores and data are close enough to those of the MS model that the CGE model predcts MS aggregate behavor (that s also ncluded n the CGE model, such as household demands or labor supples) very closely. The role of the MS model s to provde extra nformaton about the varance of ncome wthn ncome groups, or about the ncdence of prce and wage changes upon groups not dentfed by the CGE model, such as groups dentfed by ethnc type, educatonal level, or famly status. Note that each household n the mcro data set has one of the 270 expendture patterns dentfed n the man CGE model. There s very lttle scope for the MS to dsagree wth the CGE model. The smulaton starts wth a TFP n Agrculture shock, and a new equlbrum calculated for 52 commodtes, 42 ndustres, 10 households and 10 labor occupatons, all of whch vary by 27 regons. Next, the results from the CGE model are used to update the MS model. At frst, ths update conssts bascally n updatng wages and hours worked for the 283,363 workers n the sample. These changes have a regonal (27 regons) as well as sectoral (42 ndustres) dmenson.

6 The model then relocates jobs accordng to changes n labor demand 6. Ths s done by changng the PNAD weght of each worker n order to mmc the change n employment. In ths approach, then, there s a true job relocaton process gong on. Although the job relocaton has very lttle effect on the dstrbuton of wages between the 270 household groups dentfed by the CGE model, t may have consderable mpact on the varance of ncome wthn a group. One fnal pont about the procedure used n ths paper should be stressed. Although the changes n the labor market are smulated for each adult n the labor force, the changes n expendtures and n poverty are tracked back to the household dmenson. A PNAD key lnks persons to households, whch contan one or more adults, ether workng n a partcular sector and occupaton, or unemployed, as well as dependents. In the model then t s possble to recompose changes n the household ncome from the changes n ndvdual wages. Ths s a very mportant aspect of the model, snce t s lkely that famly ncome varatons are cushoned, n general, by ths procedure. If, for example, one person n some household loses hs job but another n the same household gets a new job, household ncome may change lttle (or even ncrease). Snce households are the expendture unts n the model, we would expect household spendng varatons to be smoothed by ths ncome poolng effect. On the other hand, the loss of a job wll ncrease poverty more f the dsplaced worker s the sole earner n a household. 5 Poverty and ncome dstrbuton n Brazl n the 2004 reference year Despte the recent mprovement, ncome n Brazl s stll very concentrated. If household ncome s splt n ten groups, as dsplayed n Table 1, t can be seen that the frst fve ncome household groups (POF 1 to POF 5), whle accountng for 52.9% of populaton, get only 18.5% of total household ncome. The rchest household, on the other hand, whle accountng for just 10.9% of the populaton, get 43.7% of total household ncome. 6 Ths methodology was named by the authors as the quantum method n prevous work, and s descrbed n more detal elsewhere (see Ferrera Flho. and Horrdge, 2005). Here only the man deas are presented.

7 Table 1. Poverty and ncome dstrbuton n Brazl. 2004. Proporton of populaton Proporton of ncome Share bellow poverty lne (FGT0) Household Contrbutons to FGT0 Average poverty gap (FGT1) Household contrbutons to FGT1 1 POF[1] (poorest) 14.2 2.2 0.86 0.14 0.53 0.09 2 POF[2] 14.1 4.1 0.64 0.09 0.19 0.03 3 POF[3] 20.9 9.9 0.20 0.04 0.03 0.01 4 POF[4] 7.5 4.7 0.04 0.00 0.01 0.00 5 POF[5] 11.1 8.4 0.02 0.00 0.00 0.00 6 POF[6] 7.3 7.0 0.00 0.00 0.00 0.00 7 POF[7] 9.8 12.7 0.00 0.00 0.00 0.00 8 POF[8] 5.3 9.3 0 0 0 0 9 POF[9] 4.7 12.1 0 0 0 0 10 POF[10] (rchest) 5.2 29.5 0 0 0 0 Natonal values 100.00 100.00 0.28 0.28 0.12 0.12 GINI 0.55 The poverty lne used n ths study was set as one thrd of the average household ncome. Based on ths poverty lne about 28% of the Brazlan households would be poor n 2004, or about 15,611,871 out of 55,707,000 households 7. The fgures n Table 1 also show how each POF group contrbutes to the Foster-Greer-Thorbecke (1984) (FGT, for short) overall measures of poverty: FGT0 the proporton of poor households (.e., below the poverty lne) and FGT1 the average poverty gap rato (proporton by whch household ncome falls below the poverty lne). It can be seen from Table 1 that the share bellow poverty lne s very hgh untl the thrd household ncome group, and that the poverty gap s very hgh among the poorest household group, around 53%. Actually, ths household group contrbutes to almost 75% to the natonal poverty gap. The poverty and ncome dstrbuton fgures also have mportant regonal dfferences nsde Brazl, a large country wth mportant regonal economc dfferentaton. These dfferences can be analyzed wth the ad of the fgures n Table 2. Table 2. Regonal poverty and ncome nequalty fgures. Brazl, 2004. Regons Macroregons (*) Regonal populaton share n total populaton Proporton of poor households n regonal populaton (FGT0) Regonal contrbuton to total FGT0 Regonal Average Poverty Gap (FGT1) Regonal Contrbuton to total Poverty Gap 7 The crteron used n ths study sets the value of the poverty lne n R$184.66, n 2004 values. Note that ths value s not drectly comparable to most other studes n the feld, snce t s computed based on an equvalent ncome bass, and not as the average household ncome, as many studes do.

8 1 Rondona N 0.83 0.26 0.00 0.09 0.00 2 Acre N 0.35 0.40 0.00 0.17 0.00 3 Amazonas N 1.76 0.37 0.01 0.16 0.00 4 Rorama N 0.21 0.44 0.00 0.22 0.00 5 Para N 3.77 0.40 0.01 0.17 0.01 6 Amapa N 0.32 0.39 0.00 0.17 0.00 7 Tocantns N 0.71 0.34 0.00 0.13 0.00 8 Maranhao NE 3.32 0.58 0.02 0.30 0.01 9 Pau NE 1.64 0.54 0.01 0.26 0.00 10 Ceara NE 4.40 0.51 0.02 0.23 0.01 11 RGNorte NE 1.64 0.47 0.01 0.21 0.00 12 Paraba NE 1.97 0.50 0.01 0.23 0.00 13 Pernambuco NE 4.59 0.49 0.02 0.22 0.01 14 Alagoas NE 1.65 0.57 0.01 0.27 0.00 15 Sergpe NE 1.07 0.39 0.00 0.16 0.00 16 Baha NE 7.55 0.46 0.03 0.20 0.01 17 MnasG SE 10.47 0.26 0.03 0.10 0.01 18 EspSanto SE 1.85 0.25 0.00 0.10 0.00 19 RoJanero SE 8.31 0.19 0.02 0.09 0.01 20 SaoPaulo SE 21.88 0.16 0.04 0.07 0.02 21 Parana S 5.59 0.17 0.01 0.07 0.00 22 StaCatar S 3.19 0.10 0.00 0.04 0.00 23 RGSul S 5.90 0.15 0.01 0.06 0.00 24 MtGrSul CW 1.23 0.22 0.00 0.09 0.00 25 MtGrosso CW 1.52 0.20 0.00 0.07 0.00 26 Goas CW 3.04 0.22 0.01 0.08 0.00 27 DF CW 1.25 0.22 0.00 0.11 0.00 Total Brazl 100-0.28-0.12 *Macro-Regons: N = North; NE = North-East; SE = South-East; S = South; CW = Center-West As t can be seen n Table 2, the most densely populated regons n Brazl are the Northeast regon (NE), wth 27.83% of total populaton, and the SE regon, wth 42.51% of total populaton n Brazl. The Northeast and North regons are those whch present the hgher relatve poverty levels, or share of regonal populaton bellow the poverty lne. When one takes nto account the sze of the populaton, however, Sao Paulo and Mnas Geras, both n the Southeast regons of Brazl appear, sde by sde wth Baha, as the most mportant contrbutors to the natonal headcount rato (FGT0) 8, as t can be seen from the ffth column n Table 2. Besdes that, Sao Paulo s also the most mportant regonal contrbutor to the poverty gap n the country. 8 Sao Paulo and Mnas Geras are two of the most ndustralzed states n Brazl.

9 Table 3 and Table 4 brng more nformaton regardng labor demand structure. Intally, Table 3 shows the structure of labor use by the producton sectors n Brazl. In ths table, the 42 ndustres have been aggregated to 5, for reportng purposes. The frst lne shows the upper lmt, n year 2004 Reas, of the value of each wage class. For example, the wage class OCC2 ncludes monthly wages rangng from R$130 to R$225, and so on. The last wage class, OCC10, ncludes all monthly wages hgher than R$1,500.00 n 2004 values 9. As t can be seen n the table, Agrculture accounts for about 50.2% and 47.8% of total use (wages) of the less sklled (lowest wages) workers n Brazl, respectvely wage classes OCC1 and OCC2, whle the other sectors account for a larger share of workers n the hgher wage classes. The Servces sector s another mportant sector for the employment of the poorest. Table 3. Use of labor by each aggregated actvty. Shares. Brazl, 2004. Wage classes Sectors OCC1 OCC2 OCC3 OCC4 OCC5 OCC6 OCC7 OCC8 OCC9 OCC10 Lmt (R$) 130 225 260 300 390 480 600 800 1500 open Agropec 0.502 0.478 0.169 0.213 0.172 0.128 0.107 0.075 0.051 0.059 ExtratMn 0.004 0.007 0.010 0.012 0.011 0.013 0.014 0.015 0.013 0.019 Manufact 0.067 0.051 0.093 0.100 0.136 0.164 0.159 0.157 0.147 0.125 FoodInd 0.023 0.026 0.038 0.040 0.046 0.046 0.042 0.036 0.028 0.014 Servces 0.401 0.435 0.689 0.634 0.634 0.646 0.677 0.719 0.760 0.784 Total 1 1 1 1 1 1 1 1 1 1 Table 4 brngs nformaton about the ncome composton of the household ncome classes n Brazl (POF1 to POF10, after the Pesqusa de Orçamentos Famlares POF, the expendture survey), the expendture unts n the model. As t can be seen, the ncome of the poorest households s mostly composed of wages comng from the poorer workers. The ncome of the poorest household (POF1), for example, s almost entrely composed of wages comng from the three lowest wage groups, the less sklled workers n the economy. Ths s an mportant aspect of the relaton between TC n agrculture and ts socal mpacts n Brazl. Agrculture pays a hgh share of the lowest wages n Brazl, 9 For the sake of reference, the monthly weghted average value of the mnmum wage n Brazl n 2004 was R$253.40 (4 months at R$240.0 and 8 months at R$260). Roughly speakng, then, OCC3 s around the lmt of the mnmum wage value. The PNAD reference month s September, when the mnmum wage was R$260.00, whch s the upper lmt of the thrd wage group.

10 whch concentrates n the poorest households, what creates a strong lnk, from the ncome generaton sde, from changes n Agrculture and changes n the ncome of the poorest households. Ths aspect wll further explored later n ths text. Table 4. Household ncome composton accordng to worker s wage class. Brazl, 2004. 1 OCC1 2 OCC2 3 OCC3 4 OCC4 5 OCC5 6 OCC6 7 OCC7 8 OCC8 9 OCC9 10 OCC10 Total 1 POF[1] 0.244 0.333 0.404 0.019 0 0 0 0 0 0 1 2 POF[2] 0.098 0.142 0.119 0.196 0.213 0.231 0 0 0 0 1 3 POF[3] 0.049 0.105 0.127 0.095 0.11 0.146 0.268 0.1 0 0 1 4 POF[4] 0.028 0.063 0.102 0.07 0.091 0.134 0.215 0.249 0.048 0 1 5 POF[5] 0.019 0.05 0.061 0.053 0.078 0.121 0.215 0.169 0.233 0 1 6 POF[6] 0.01 0.036 0.04 0.043 0.064 0.101 0.167 0.188 0.351 0 1 7 POF[7] 0.004 0.017 0.022 0.025 0.037 0.064 0.114 0.134 0.427 0.156 1 8 POF[8] 0.002 0.011 0.013 0.014 0.024 0.04 0.085 0.095 0.35 0.368 1 9 POF[9] 0.001 0.004 0.006 0.007 0.009 0.019 0.041 0.052 0.235 0.627 1 10 POF[10] 0 0.001 0.002 0.002 0.003 0.005 0.011 0.014 0.073 0.89 1 (*) POF1 s the poorest, POF10 the rchest. 6 The smulaton: technologcal change n the Brazlan agrculture The hgh rate of TC n many sectors s a notceable feature of the Brazlan economy n the last years. Bonell and Fonseca (1998) found, for example, for the perod 1970/1997 a rate of growth of the Total Factor Productvty (TFP) of 1.7% a year for the aggregate of the economy. If only the 1995/1997 perod s consdered, however, that rate ncreases to about 2.75% a year what, accordng to those authors, would explan about 90% of GDP varaton n the perod. The hgh ncrease n PTF n agrculture was documented by Gasques et al (2004), who found an annual rate of 4.88% for the decade of nnety, a value whch rses to 6.04% a year for the perod 2000/2002. Even though not drectly comparable, these two studes gve some gudance for the smulatons mplemented n ths paper. The scenaro to be smulated here entals a dfferental gans n PTF for agrculture of 2% a year n relaton to manufacturng. For a fve years perod, the value to be smulated s a 10% ncrease n TPF n the Brazlan agrculture, above the trend n the total economy, startng n the 2004 base year.

11 7 Model closure A central feature of CGE models s the closure used. CGE models are large sets of equatons representng an economy whch, n general, don t have orgnally the same number of equatons and varables. The choce of the endogenous/exogenous set of varables to make a soluton feasble s called the closure of the model. Ths choce s crucal for the results, and gves the model a partcular character, whch represent s the modeler s vew of how the economy works. The closure chosen for ths study gves the model a short run flavor, gven the 5 years perod span to be smulated. The man aspects of the closure used are: The captal stock s kept fxed at sector level, wth the rate of return to captal adjustng endogenously to varatons n captal demand. In the labor market the closure s dfferentated for sklled and non-sklled types of labor. For the 10 occupatonal categores of the model, the frst 5 (less sklled workers) are deemed to be perfectly moble between sectors and regons. For these workers natonal employment s a postve functon of natonal real wages n the smulaton, representng the exstence of labor surplus n all regons of these types of workers. For the hgher wage groups (the last 5 wage groups) the total supply of labor s consdered fxed (exogenous) at natonal level. The adjustng varable n ths market s the real wage only, whch shall rse n expandng sectors n order to attract workers from contractng sectors. The model allows for lmted substtuton between dfferent types of labor. The total land stock s fxed, and used only by the agrcultural actvtes. The nomnal exchange rate s the model s numerare. In the above descrpton of the way the economy adjusts the short run flavor s mposed through the fxed quantty of factors stocks, notably land, captal and sklled labor. 8 Measurng the socal gans assocated wth TC n the Brazlan agrculture In partal equlbrum models the gans of TC are usually evaluated through varatons n the consumers and producers surplus. As mentoned before, these

12 measures don t take nto account the nterdependence between the many markets n the economy. The CGE models, on the other hand, on recognzng explctly the nterdependence of those markets allow the calculaton of broader measures of welfare varaton, as s the case of the money metrc measures of utlty varaton. Among the dfferent possble choces, a useful measure s the Hcksan Equvalent Varaton (EV) concept. Bascally the EV measures the amount of ncome whch would be equvalent, n welfare terms, to the change observed n the economy, or the TC n the case under study. In other words, the EV s meant to represent the amount of ncome that would have to be gven to (or taken from) an agent after a polcy shock to keep hm at the orgnal welfare poston, but after the ntroducton of the shocks. The consumer demand n the TERM-BR model s represented by the Lnear Expendture System (LES). The Money Metrc Utlty measure (MMU) must then be derved from the correspondng utlty functon. The utlty functon assocated s the Stone-Geary functon: β U = ( C γ ), where β = 1 and γ s the subsstence mnmum of each good, and C s the total consumpton of each good. Gven the utlty functon, the demand functon s obtaned through the ncome constraned maxmzaton process, resultng: ( Y P ) β C ( P, Y ) = γ + γ. P, where Y s the consumer s nomnal ncome and P the prce vector. The ndrect utlty functon s obtaned substtutng the demand functons n the utlty functon, resultng: V β β ( P, Y ) = [ Y γ ]. P *. And, fnally, solvng for Y the MMU P functon can be obtaned: P Y ( P, U ) = * U + γ. P β β. The EV then can be calculated as: 0 1 1 0 ( P, U ( P, Y )) Y EV = M, where, substtutng the expresson for U and rearrangng results:

13 β 1 1 0 0 [ Y γ. P ] [ Y. P ] 0 P EV =. γ. The superscrpts 0 and 1 1 P represent the tme perods before and after the polcy shock. Ths s the formula to calculate the EV n the model 10. 9 Results Ths secton brngs the results of the smulaton. The sector aggregaton to be used n ths paper can be seen n Table 5, bellow. Table 5. Sectors, products, occupatons and regons. Actvtes Products Margns Occupatons Regons Agrculture Coffee, Sugar Cane, Rce, Wheat, Soybean, Cotton, Corn, Lvestock, Natural Mlk, Poultry, Other Agrculture Trade OCC1 Rondona MneralExtr Mneral Extracton Transport OCC2 Acre PetrGasExtr Ol and gas OCC3 Amazonas MnNonMet Non metallc mnerals OCC4 Rorama IronProduc Iron ore OCC5 Para MetalNonFerr Non ferrous metals OCC6 Amapa OtherMetal Other metals OCC7 Tocantns MachTractor Machnes and tractors OCC8 Maranhao EletrcMat Electrc materal OCC9 Pau EletronEqup Eletronc equpment OCC10 Ceara Automobles Automobles RGNorte OthVecSpare Other vehcles and spare parts Paraba WoodFurnt Furnture and lumber Pernambuco PaperGraph Paper and graphc Alagoas RubberInd Rubber products Sergpe ChemcElem Chemcal elements Baha PetrolRefn Petrol Refnery MnasG VarousChem Other chemcal products EspSanto PharmacPerf Pharmaceutcals RoJanero Plastcs Plastcs SaoPaulo Textles Textles Parana Apparel Apparel StaCatar ShoesInd Shoes and leather products RGSul CoffeeInd Processed coffee MtGrSul VegetProcess Vegetable processng MtGrosso Slaughter Processed anmal products Goas Dary Dary DF SugarInd Sugar VegetOls Vegetable ols OthFood Other foods VarousInd Other ndustres 10 Actually, the model uses a lnearzed verson of ths formula.

14 PubUtlServ CvlConst Trade Transport Comunc FnancInst FamServc EnterpServ BuldRentals PublAdm NMercPrSer Publc utltes servces Cvl constructon Trade Transport Communcatons Fnancal nsttutons Servces to households Servces to busness Dwellngs Publc admnstraton Non mercaltle prvate servces As mentoned before, the scenaro to be analyzed comprses a 10% ncrease n PTF n the Brazlan agrculture, under the specfed set of hypothess about the factors markets closure. In what follows the results of the smulaton are presented. Intally, Table 6 brngs the results of some selected macroeconomc varables. Table 6. Model results, selected macroeconomc varables. Varable % varaton Real household consumpton 1.13 Government comsumpton 0 Exports quantum ndex 2.78 Imports quantum ndex 0.24 Real GDP 1.13 Average Real wage 1.01 Aggregated employment -0.01 Consumer Prce Index 0.02 GDP deflator 0.11 Exports prce ndex -0.57 Imports prce ndex 0 Land prce -4.44 As t can be seen from the above table, the smulated shock s mportant enough to generate aggregated mpacts n the model. The ncrease n TFP expands the producton possblty fronter of the economy, allowng an expanson n GDP. The results show a 1.13% ncrease n real terms. Ths ncrease s obtaned through ncreases n household consumpton (from the expendture sde) and n exports. Wth gven CIF mport prces and nomnal exchange rate value (numerare) the terms of change vary by exactly the same amount of the exports prce ndex, meanng that the TFP change n agrculture generates a fall n the terms of trade.

15 The average real wage ncreases, whle aggregate employment remans almost constant (a slght decrease). It s worth to remember that the labor market closure fxes the aggregated quantty of sklled labor. The ncrease n the real wage, then, s mostly due to the ncrease n the sklled workers wages, whch ncrease more than the Consumer Prce Index (CPI). These results can be seen n Table 7, bellow. Table 7. Model results. Wages and employment, by occupatonal class. Percent varatons. Wage class Nomnal wage Real wage Employment OCC1-1.33-1.25-0.63 OCC2-1.01-1.01-0.51 OCC3 0.32 0.27 0.14 OCC4 0.06 0.00 0.00 OCC5 0.31 0.24 0.12 OCC6 1.33 1.26 0 OCC7 1.47 1.40 0 OCC8 1.53 1.46 0 OCC9 1.63 1.55 0 OCC10 1.04 0.96 0 As t can be seen, employment varatons occur n the frst fve occupatonal groups, or the less sklled workers, wth a fall n employment of the less sklled due to the TC n agrculture. Ths fall happens manly due to a fall n employment of ths type of workers n agrculture, snce ths sector s responsble, n the database, for about 50.2% of the natonal wage bll of the lowest wages n Brazl (OCC1) n 2004 (see Table 3). The ncrease n TFP n agrculture ncreases the real GDP, as expected. Ths ncrease, however, does not translate n unform ncreases among all producng actvtes. As t can be seen n Table 8, the level of actvty of agrculture ncreases by 8.69%, whle employment falls by 2.54% n the same sector. Note that ths s the only one sector where both actvty level and employment vary n opposte drectons, what s due to the TC. Table 8. Model results, sector varables. Percent varaton. Sector Actvty level Employment Producton cost Agrculture 8.69-2.54-11.21 MneralExtr -0.25-1.12 0.05 PetrGasExtr -0.47-1.49 0.14 MnNonMet -0.25-0.51 0.48 IronProduc -0.36-1.43-0.03 MetalNonFerr -0.39-1.41 0.01 OtherMetal -0.35-0.71 0.41 MachTractor -1.00-1.54 0.34 EletrcMat -0.53-0.95 0.44 EletronEqup -0.87-1.45 0.39

16 Automobles -0.25-0.30 0.52 OthVecSpare -1.43-2.00 0.32 WoodFurnt 0.15 0.29 0.28 PaperGraph 0.14 0.30 0.44 RubberInd -0.23-0.42 0.21 ChemcElem 0.29 1.10-0.73 PetrolRefn -0.01-0.04 0.37 VarousChem -0.58-1.14 0.23 PharmacPerf -0.07-0.15 0.55 Plastcs -0.17-0.26 0.62 Textles 0.95 1.79-0.10 Apparel 0.63 0.72 0.62 ShoesInd 0.39 0.46 0.04 CoffeeInd 1.17 1.98-1.93 VegetProcess 6.70 12.16-3.42 Slaughter 8.41 16.83-3.09 Dary 6.08 6.92-6.00 SugarInd 2.33 7.70-0.86 VegetOls 3.20 13.96-0.92 OthFood 1.64 2.92-2.12 VarousInd 0.10 0.19 0.71 PubUtlServ 0.09 0.43 1.45 CvlConst -0.05-0.09 0.76 Trade -0.04-0.06 0.99 Transport -0.16-0.25 0.84 Comunc 0.11 0.46 1.44 FnancInst 0.29 0.60 1.50 FamServc 0.51 0.64 0.67 EnterpServ -0.23-0.33 0.87 BuldRentals 0.02 0.46 2.00 PublAdm 0.00 0.00 1.01 NMercPrSer 1.09 1.13 0.69 *- Only ntermedate nputs, does not nclude prmary factors costs. Agrculture produces n the model eleven commodtes, wth dfferent producton varaton outcomes, as can be seen n Table 9. In ths table, the last column shows the total value exported of each commodty as a share of total use n Brazl n 2004. As t can be seen, coffee, wheat and soybeans have sgnfcant shares of ther total use exported n that year. Not by concdence these are the commodtes that expand producton the most, gven the hgh export elastcty assumed n ths study. The other commodtes, whch are drected more to the domestc market, show a smaller ncrease n producton. Table 9. Agrcultural commodtes. Producton and export varaton %) and exported share. Commodty Producton Exports (value) Exported share n total use Coffee 13.90 24.95 0.54 SugarCane 2.79-0.01 PaddyRce 6.57-0.00 Wheat 14.57 29.75 0.21 Soybean 12.61 27.65 0.38 Cotton (n seed) 4.48-0.01 Corn 5.85 39.51 0.12 Lvestock 8.91 49.52 0.04

17 NaturMlk 6.84-0.00 Poultry 9.50 22.61 0.06 OtherAgrc 7.85 47.74 0.05 Notce also that the food ndustry n general experences a strong push n ts actvty level, as well as a reducton n ts producton cost (Table 8). Ths llustrates the fact that the gans n TFP n agrculture are transmtted n the commercalzaton chan, beneftng the other sectors whch have n agrcultural products ts man nputs. The above results suggest that labor composton n the economy must change after the shock, snce dfferent sectors have dfferent labor demand structure, whch stll vares across regons. The smulaton results for regonal employment and regonal Gross Domestc Product (GDP) can be seen n Table 10. Table 10. Regonal employment and GDP. Percent varaton. Regon Employment Regonal GDP Rondôna -0.06 0.75 Acre -0.24 0.31 Amazonas -0.01 0.67 Rorama -0.43 0.24 Para 0.08 0.83 Amapá 0.19 0.93 Tocantns -0.06 0.78 Maranhão 0.17 0.87 Pauí 0.21 1.10 Ceara 0.25 1.68 Ro Grande Norte -0.05 0.62 Paraíba 0.05 0.83 Pernambuco -0.02 0.90 Alagoas -0.57 0.31 Sergpe 0.05 0.81 Baha -0.15 0.49 Mnas Geras 0.07 1.17 Espírto Santo 0.30 1.68 Ro de Janero -0.11 1.04 São Paulo -0.12 1.18 Paraná 0.50 2.58 Sta. Catarna 0.11 1.77 Ro Grande Sul 0.02 1.31 Mato Grosso Sul -0.02 1.28 Mato Grosso 0.37 2.20 Goás -0.23 0.97 DF -0.12 1.39 The results for employment at regonal level are a wage bll weghted average of employment varaton at ndustry level n each regon. As t can be seen from Table

18 10, the results are mxed, wth some states ncreasng and others decreasng employment. Ths net result depends on the labor composton n each actvty, as well as on the share of each actvty n the regons. Note also n the same table that regonal GDP ncreases n every state, even n those where there s a fall n employment, and ncreases more n those states where agrculture has a relatvely hgher share n total regonal value added. It was seen before n Table 7 that the fall n employment happens manly n the less sklled labor types, what s, of course, a consequence of the structure of labor demand n agrculture. Ths s, then, a clear example of the effects of technologcal progress n a sector whch demands proportonately more unsklled labor than the other sectors n the economy. The ncrease n the TFP n agrculture tends to save all producton factors, but proportonately more the more ntensve n use, generatng a negatve dstrbutve effect (as wll be seen later). In terms of socal gans evaluaton, however, there s stll another effect whch must be taken nto account, namely the varaton n the prce of food whch, as s well known, accounts for an mportant share of the consumpton bundle of the poorest households. Beng the consumpton bundle partcular to each ncome group, a specfc analyss for each ncome group s requred n order to correctly nclude ths effect. The model allows ths calculaton, snce the consumpton bundle by type of household s explctly modeled wth nformaton from the Expendture Surveys (POF). The results can be seen n Table 11. Table 11. Income varaton by household ncome group. Percent varaton. Income group Nomnal ncome Consumer Prce Index Real ncome POF[1] 2.00-1.17 3.17 POF[2] 0.10-0.99 1.09 POF[3] 0.27-0.74 1.01 POF[4] 0.56-0.53 1.09 POF[5] 0.71-0.46 1.17 POF[6] 0.90-0.33 1.23 POF[7] 0.96-0.13 1.09 POF[8] 1.00 0.1 0.9 POF[9] 0.95 0.01 0.94 POF[10] 0.78 0.24 0.54 As dscussed before, household ncome s calculated n the model trackng back from the ndvdual ncomes after the polcy shock, aggregatng to each

19 household the ncome of all the workers belongng to t 11. As t can be seen n Table 11 the household nomnal ncome ncreases n all ncome groups, rrespectve to the fall n employment observed prevously 12. The second column n the table shows the household specfc Consumer Prce Index. As t can be seen, the lower ncome households, whch usually expend a large share of ther ncome on food, show a larger fall n the CPI, due to the ncrease n PTF n agrculture. Ths doesn t happen n the three hghest ncome groups, whch show a postve varaton n CPI. But the real ncome varaton, whch s the dfference n the percentage ncrease n the nomnal ncome and the CPI, s postve for all households. Model results show, then, that even though the TFP ncrease n agrculture reduces the employment of the least sklled (and poorest) occupatonal group n Brazl, the benefts generated by the fall n prces and the general equlbrum employment effects on the economy tend to overcome that effect. Model results, then, pont to a generalzed gan n the economy. Agan, ths s a general equlbrum effect that can be only captured n ths knd of models. The above nformaton can also be analyzed from a regonal perspectve, as shown n Table 12. Table 12. Model results. Nomnal ncome, Consumer Prce Index and Real Income, by regon. Percent varaton. Regon Nomnal ncome Consumer Prce Index Real Income Rondôna 0.46-0.32 0.78 Acre -0.16-0.34 0.18 Amazonas 0.16-0.43 0.59 Rorama -0.28-0.36 0.08 Para 0.35-0.55 0.90 Amapá 0.60-0.49 1.09 Tocantns 0.26-0.27 0.53 Maranhão 0.06-0.66 0.72 Pauí -0.11-0.60 0.49 Ceara 0.58-0.16 0.74 Ro Grande Norte 0.30-0.33 0.63 Paraíba -0.23-0.60 0.37 Pernambuco -0.25-0.44 0.19 Alagoas -1.58-0.44-1.14 Sergpe 0.05-0.52 0.57 Baha -0.15-0.40 0.25 11 The PNAD database allows dentfyng the lnk between persons and households. 12 Remember that the household ncome s an add-up of dfferent occupatonal wage groups, as shown n Table 4. Besdes that, one of the hypothess of ths work s that government transfers to households, whch n 2004 accounts to 19.7% of total household ncome and s concentrated n the poorest, s updated (ncreases) by the nomnal GDP growth. Ths s, of course, an arguable hypothess.

20 Mnas Geras 0.84-0.02 0.86 Espírto Santo 1.75 0.37 1.38 Ro de Janero 0.85 0.29 0.56 São Paulo 0.91 0.16 0.75 Paraná 1.93 0.44 1.49 Sta. Catarna 0.90 0.15 0.75 Ro Grande Sul 0.66-0.03 0.69 Mato Grosso Sul 1.01-0.10 1.11 Mato Grosso 1.55 0.06 1.49 Goás 0.72-0.11 0.83 DF 1.15 0.54 0.61 The results above turn evdent the compostonal effect of the regonal consumpton bundle over the real ncomes. The prce of the consumpton bundle tends to fall more n the North and Northeast regons of Brazl, snce n these regons there s proportonally a concentraton or poor households, whose members are counted among the lowest occupatonal wage groups. It s n these regons, then, that the fall n the prce of food has greater nfluence upon the real ncomes. In the other regons of the country, where the hgher ncome households concentrate relatvely more, the CPI actually ncreases, as s the case of the states of Sao Paulo, Ro de Janero, Paraná and Santa Catarna. Ths effect s caused by the ncrease n the prce of the other (nonfood and mported) goods n the consumpton bundle, a general equlbrum effect caused by the TFP ncrease n agrculture. Stll, t calls attenton the results for Alagoas state, where the (negatve) nomnal ncome effect domnates the prce bundle effect, generatng a fall n regonal real ncome. Ths result s partcular lnked to the sugar cane producton, whch n the database s responsble for about 7% of total state value of producton, and s relatvely ntensve n the least sklled workers. And, fnally, as mentoned before, the use of a CGE model allows the calculaton of broad money metrc measure of welfare varaton, specfcally the Hcksan Equvalent Varaton (EV). Ths measure s a synthess of all the multple effects generated by the PTF ncrease n agrculture, a net welfare measure caused by the polcy shock n the economy. The smulated 10% ncrease n TPF n agrculture, then, would be assocated to a R$12,996.00 mllons gan n 2004 values. Ths amount would correspond to 0.67% of the Brazlan GDP n 2004 13, or a gan of about 0.11% of GDP per year. Accordng to the EV defnton ths would be the money value that would keep the economc agents n Brazl n the same welfare level f the TFP n 13 The value of the Brazlan GDP n 2004 was R$1,937,183 mllons.

21 agrculture dd not have happened n the way t was smulated here. Ths gan would be around R$2.6 bllons per year. As a yardstck for comparson, the budget of Embrapa n 2004 was R$923 mllons, a value whch falls to R$740 mllons n the 2000-2004 perod average. Ths s a hgh socal gan. Of course, not all of t can be attrbuted to the research n scence and technology n Brazl, snce part of those gans arses as a result of spllovers from nvestments n other countres. It can be expected, however, that a substantal share of those gans are assocated to the domestc nvestments n research. Stll to gve a perspectve for those values, Araujo et al (2002) estmated that the return to nvestment n research n the Sao Paulo state s around R$12 for each R$1 nvested. Ths value s close to that found by Grlches (1975) for the Amercan agrculture. Evenson, Pray and Rosegrand (1999), however, found a lower value of R$5 to R$6 for Inda. 10 Poverty and ncome dstrbuton results As dscussed prevously, the mcro-smulaton model uses nformaton from PNAD and allows the trackng of the effects of TC n Agrculture on poverty and ncome dstrbuton n Brazl. The results can be seen n Table 13. Table 13. Poverty and ncome dstrbuton results. Percent varaton. Household Income class Average real ncome GINI Proporton of poor households (headcount rato) Average poverty gap (FGT1) Index 1 POF[1] 2.00-0.70-0.31 2 POF[2] 0.10-0.39 2.49 3 POF[3] 0.27-1.40 10.04 4 POF[4] 0.56 9.91 42.25 5 POF[5] 0.71 27.97 97.08 6 POF[6] 0.90 196.58 896.91 7 POF[7] 0.96 502.78 67559.20 8 POF[8] 1.00 0 0 9 POF[9] 0.95 0 0 10 POF[10] 0.78 0 0 Orgnal values - 0.55 0.28 0.12 (base year) Percentage change 0.35-0.29 1.35 The results n Table 13 show how some ncome and poverty ndcators n Brazl would change due to the smulated TFP n the Brazlan agrculture. As t can be seen, results show an aggregate fall n poverty (number of households bellow the poverty lne, or headcount rato), wth a 0.29% reducton n the headcount rato. Ths

22 would amount to 45,162 less poor households, or less 189,059 people. The fall n poverty s more ntense n the poorest households, and concentrates n the thrd household ncome group 14. The poverty gap, on the other hand, also reduces for the poorest households, but ncreases for all the others. Ths s reflected n a 1.35% ncrease n the aggregate poverty gap, meanng that n average there s an ncrease n the dstance of the average ncomes of the poor to the poverty lne. Interestngly enough, model results also pont to a worsenng of the GINI ndex, whch s a measure of ncome dstrbuton n the economy. Ths s assocated to the fall n employment of the two least sklled (lowest wages) households, as shown n Table 7. The results on poverty and ncome dstrbuton can also be seen n regonal terms, as shown n Fgure 1. As t can be seen n the fgure, the number of poor households actually ncreases n the poorest North and Northeast regons (except for Ceara and Ro Grande do Norte), and decreases n the other regons. The aggregated result, then, s lnked to the fall n poverty n the rchest states n Brazl, as t can be seen from the absolute number of households leavng poverty n Sao Paulo, for example. Change n poor households, by regons. 3 2 1 30000 20000 10000 % change 0-1 -2-3 -4 1 Rondona 2 Acre 3 Amazonas 4 Rorama 5 Para 6 Amapa 7 Tocantns 8 Maranhao 9 Pau 10 Ceara 11 RGNorte 12 Paraba 13 Pernambuco % change absolute numbers 14 Alagoas 15 Sergpe 16 Baha 17 MnasG 18 EspSanto 19 RoJanero 20 SaoPaulo 21 Parana 22 StaCatar 23 RGSul 24 MtGrSul 25 MtGrosso 26 Goas 27 DF Fgure 1. Model results. Change n number of poor households, by regon. Percentage change. 14 The very hgh numbers n poverty varaton for households n class 4 and above are meanngless, snce they represent a very hgh percent varaton on a very small base value. 0-10000 -20000-30000 -40000-50000 absolute numbers

23 Ths s an mportant ssue assocated to the phenomenon, whch, even though s poverty decreasng n aggregate, would tend to ncrease the actual regonal employment dspartes n Brazl. Ths result, of course, could be dfferent f dfferent regonal TFP change measures were used n the smulatons. Ths nformaton, however, s not avalable, but would be very mportant for a more refned analyss of the process. The same occurs wth a more detaled descrpton of the TC n Brazl. In ths paper t s assumed to be Hcks neutral, or non-based, what s also an arguable hypothess. Feld observaton suggests that TC n Brazl s actually labor savng, and s lkely to vary regonally, dependng on a myrad of varables and crcumstances. 11 Fnal remarks As dscussed n ths paper, the TFP ncrease n agrculture has complex general equlbrum effects whch dstrbute unevenly between the dfferent actors n the socety. The results here found suggest some ponts of partcular nterest. Intally, t was shown that the employment of the less sklled workers would be negatvely affected by the change, what s caused by the partcular labor demand structure n the Brazlan agrculture. Ths s an extremely mportant aspect of the problem, and deserves a lttle more attenton. The technologcal shock appled to the model s neutral (non-based) from the standpont of factor use. The dynamc actvtes n the Brazlan agrculture, however, appear to demand, n the dynamc regons, relatvely less low sklls labor and more hgh sklls labor than n the less dynamc regons. Ths observaton suggests that, n fact, the technologcal progress n the Brazlan agrculture s labor savng, and not neutral. Even though ths hypothess rased before by Ferrera Flho (2004) stll demands more emprcal assessment, ts consequence would be a worsenng of the negatve effect here observed for the less sklled employment. Another aspect assocated to the abovementoned s related to the spllover of the TFP ncrease n agrculture to the other sectors n the economy. Just as the agrcultural sector, the other sectors n the upstream poston n the commercalzaton chan (food and other sectors to whch agrcultural nputs are mportant) would also be benefted n terms of ncreasng ther actvty level. On the contrary of agrculture,

24 however, these sectors would show an ncrease n ther employment levels, compensatng n part the fall observed n agrculture. Model results for employment are mxed across regons, wth some states ncreasng aggregate employment and other states decreasng. The aggregated regonal results, however, hde the fact that the effect on employment s partcularly negatve for the less sklled workers n every state, wth mportant falls n the Northeast regons, the poorest n the country. The TFP n agrculture s transmtted through the commercalzaton chan, promotng also a regonal redstrbuton of ncome, gven the heterogenety of the spatal dstrbuton of the economc actvty n the Brazlan terrtory. Wth the food and agrcultural related ndustres concentrated n the South-Southeast Brazl, one of the effects of the TFP n agrculture s to decrease less qualfcaton jobs employment n agrcultural regons and ncrease more qualfed employment n regons where food and agrcultural related ndustres are concentrated. Despte ths last aspect of the problem, the results here found suggest that the postve net effect of the TFP ncrease n agrculture on welfare would be caused manly by the fall n food prces, whch would beneft the poorest the most. Actually, as shown by Alves (2004) the technologcal change n the Brazlan agrculture nduced by the research system has had mportant role n the reducton of food prces n the country. And, fnally, t s worth notng that the approach used n ths paper doesn t take nto account the frctonal effects of the adjustments n the labor market, whch are mportant n realty. As a statc model, the results show the fnal state of the economy n comparson to the ntal one, but gve mportant hnts regardng the consequences of TFP ncrease n the Brazlan agrculture. Ths phenomenon reduces the employment of the unsklled workers proportonately more than the other types of workers, and ths happens not as a consequence of based technologcal change (not smulated here), but due to the labor demand structure n agrculture. Wth TC the agrcultural sector wll demand less and less unsklled workers, and would reduce ts role as the most mportant employer of that knd of labor. The ssue of whch polcy nstruments should be used to accommodate that phenomenon becomes relevant n a medum run perspectve. Polces that turn labor a less expensve producton factor would assume a promnent role here. Ths s especally true for the less sklled labor groups, whch are better substtutes for captal

25 (machnery and equpment) n the producton process. Sklled labor s, n a certan sense, complementary to captal and modern producton technques, what can be nferred by the partcular producton structure of some actvtes n the tradtonal and dynamc agrcultural regons. In ths context, the queston of adaptaton of the Brazlan legal system to cope wth ths aspect of the problem would consttute a research program n tself, and the hypothess to be examned would be to what extent s the labor legslaton dstortng the relatve prces of labor and captal n Brazlan agrculture. 12 References Alves, E.A. Presdente, fque bravo com a Embrapa. O Estado de São Paulo, edção de 23 de Dezembro de 2004. Horrdge, Madden and Wttwer (2005), The mpact of the 2002-2003 drought on Australa, Journal of Polcy Modelng, vol. 27, ssue 3, pages 285-308. O Crescmento da Agrcultura Paulsta e as Insttuções de Ensno, Pesqusa e Extensão numa Perspectva de Longo Prazo. Relatóro Fnal de Pesqusa à FAPESP. Snt. 2002. Bonell, R; Fonseca, R. Ganhos de Produtvdade e de Efcênca: Novos Resultados para a Economa Braslera. IPEA. Texto para Dscussão no. 557. 43 p. Ro de Janero, 1998. Carvalho, M.A. Desempenho da Agrcultura Braslera no Comérco Exteror. snt. 16 p. 2004. Deaton, A; Muellbauer, J. Economcs and Consumer Behavor. Cambrdge Unversty Press. 1983. Evenson,R.E; Pray,C,E; Rosegrant,M.W. Agrcultural Research and Productvty n Índa. Research Report 109. IFPRI, 1999. Ferrera Flho; J.B.S. Mudança Tecnológca e a Estrutura da Demanda por Trabalho na Agrcultura Braslera. In: Workshop sobre Trabalho na Agrondústra Açucarera. S.n.t. Praccaba, Novembro, 2004. Ferrera Flho; J.B.S; HORRIDGE, J.M. The Doha Round, Poverty and Regonal Inequalty n Brazl. In: Hertel, T.W; Wnters, A. (eds). 2006. Puttng Development Back nto de Doha Agenda: Poverty Impacts of a WTO