PRICE VOLATILITY INFLUENCE ON AGRICULTURAL INCOME INSTABILITY. Dmytro Bilodid

Transcription

1 PRICE VOLATILITY INFLUENCE ON AGRICULTURAL INCOME INSTABILITY by Dmytro Blodd A thess submtted n partal fulfllment of the requrements for the degree of Master of Arts n Economcs Natonal Unversty Kyv-Mohyla Academy Economcs Educaton and Research Consortum Master s Program n Economcs 2007 Approved by Mr. Serhy Korabln (Head of the State Examnaton Commttee) Program Authorzed to Offer Degree Master s Program n Economcs, NAUKMA Date

2 Natonal Unversty Kyv-Mohyla Academy Abstract PRICE VOLATILITY INFLUENCE ON AGRICULTURAL INCOME INSTABILITY by Dmytro Blodd Charperson of the Supervsory Commttee: Mr. Serhy Korabln, Economst, Natonal Bank of Ukrane In the paper I nvestgate how dfferent prce shocks, world and domestc, nfluence the stablty of farmers ncome n Ukrane. For ths purpose I computed the coeffcent of varaton (CV) of ncome, usng approach developed by Rapsomanks and Sarrs (2005) whch combnes prces varablty of crops cultvated wth ncome structure of farmers. Ths framework allows evaluatng the mpact of prce shocks on agrcultural producers profts. The results show that CV s vares for dfferent groups of frms dependng ether on type and regon of locaton of an enterprse. Investgatng the sources of ncome nstablty, I have found that t s caused mostly by the domestc factors. Trade lberalzaton would just slghtly decrease agrcultural ncome uncertanty.

3 TABLE OF CONTENTS INTRODUCTION..1 LITERATURE REVIEW 5 METHODOLOGY DATA DESCRIPTION.17 EMPIRICAL RESULTS.20 CONCLUSIONS...31 BIBLIOGRAPHY..33 APPENDIXES...35

4 LIST OF FIGURES Number Page Table4.1. Summary statstcs for corn prces 18 Table4.1. Summary statstcs for barley prces.. 18 Table4.1. Summary statstcs for wheat prces.. 18 Table4.1. Summary statstcs for sunflower seeds prces Table4.1. Summary statstcs for sugar prces Table 5.1. Transmsson coeffcent for barley prces Table 5.2. Transmsson coeffcent for barley prces Table 5.3. Transmsson coeffcent for barley prces Table 5.4. Transmsson coeffcent for barley prces Table 5.5. Transmsson coeffcent for barley prces Table 5.6. CV s of world prces and random components of the domestc prces...24 Table 5.7. Average shares of ncome (percent).25 Table5.8. Frm s producton CV s...27 Table 5.9. CV s of agrcultural ncome...28

5 ACKNOWLEDGMENTS The author wshes to express great grattude to hs advsor Professor Roy Gardner for hs nvaluable assstance and professonal gudance. I also want to thank all EERC Research Workshop members for very helpful comments durng perod of thess wrtng. Specal thankfulness s conveyed to Professor Tom Coupe for openness for dscusson and useful recommendaton and suggestons. I am very grateful to my sster Olha Blodd for moral support and to my frends: Dans Rozhn for hs advce regardng econometrc ssue and Oksana Lyashuk for her patence and nspraton.

6 GLOSSARY Income nstablty varaton of farmer s ncome from year to year or wthn a year ( v

7 Chapter 1 INTRODUCTION In the lght of globalzaton, developng and emergng economes are confronted wth a range of vtally mportant problems. On the one hand, the openng of borders promses them nternatonal captal nflow and experence; on the other hand, t enlarges these countres dependence on global stuaton, ncreases exposure to world prce shocks and decreases the opportuntes for protecton of domestc producers. One of the questons related to ths: does trade and prce lberalzaton add to domestc households ncome uncertanty? For Ukrane, the task of nvestgatng world prces nfluence on households ncome s extremely mportant consderng the next accesson of Ukrane to World Trade Organzaton (WTO). The accesson supposes tarffs to be reduced and gradually removed whch would pass new challenges and rsks to domestc players of such ndustres as metallurgy, machnery, energy, lght and food ndustres. The thess concentrates exactly on nfluence of global trade lberalzaton on agrcultural producers ncome. Frst, ncome from farm jobs s the man source of lvng for rural populaton that reduces durng years but stll accounts 32% (Ste of State Statstc Commttee ( )) of total populaton and most drastcally contrbutes to number of poor people as average wage of farmers s the lowest among all knds of economc actvty (by twce as less dependng on regon (Ste of State Statstc Commttee ( )). Income nstablty makes agrcultural households more uncertan, decreasng nvestments and agrcultural sector development. Ths uncertanty makes people to be less assured n ther future, 1

8 makng lvng n vllage not attractve and leadng to ndvdual s flow-out from rural regons whch n turn drves to substantal producton decreasng n rural regons, Second, the revenues from agrcultural sector are one of the sgnfcant sources of GDP and export performance (10% and 5% respectvely (Ste of State Statstc Commttee ( )). Thus, agrcultural prce volatlty s not desrable thng as farmer s underperformance would be reflected on prosperty of the whole economy. Besdes, agrculture actvty faces, much more frequently then some other sectors of economy, a lot of non-market (for example, land ownershp government polcy or hgh rural young labor force mgraton) related fluctuatons. Agrcultural prces have frequent fluctuatons whch brng uncertanty and farmers have to operate wth ndecson. Moreover, nowadays weather becomes even more unpredctable because of dramatc changes n clmate. Adamenko (2004) showed that n Ukrane for the perod frequency of appearance of such unfavorable weather condtons for harvest as drought, hal, flood ncreased much comparng to prevous 50 years. WTO accesson foresees admttance on commodty and servce markets; thus, we may expect that Ukrane wll become more a actve player n nternatonal trade and, correspondngly, wll be subjected to ncreasng mportance of global events. One of the essental questons here s: how lberalzaton, openness to world market and shocks on t wll nfluence prce volatlty on Ukranan agrcultural market and ncome stablty of dfferent agrcultural producers. Wll they face wth more prce uncertanty or not? Some authors have already nvestgated consequences of trade lberalzaton for Ukrane. Sagdova (2004) evaluated prce transmsson gans from the world to the Ukranan gran market; Sdorov (2005) examned WTO accesson mpact on poverty n Ukrane. It s not, however, clear how new 2

9 condtons, ncreased exposure to nternatonal market wll affect on domestc prce nstablty, whether they make farmer s ncome more uncertan or not. In my thess, I wll nvestgate consequences of access expanson to Ukranan market of world agrcultural producers for domestc prce stablty and agrcultural producers ncome varaton. Frst, I wll run autoregressve model (VAR) (I expect that Ukranan prces have nfluence on the world prces) for each culture prce to estmate elastctes of transmsson of world prces to domestc prces and to compute condtonal varances and covarances through varance decomposton. Then, I wll fnd ncome varances and coeffcents of varaton (CV) of ncome, usng calculated elastctes and the data of farmers ncome structure; and evaluate them n order to nvestgate to what extent ncreased exposure to nternatonal prces may nfluence natonal producer ncome stablty. For the research, I wll use monthly data on prces for man agrcultural products (barley, corn, wheat, sugar beets and sunflower seeds). Domestc prces for these products are gathered by the analytc centre of Ukragroconsult ( Internatonal prces are gathered by IFC ( Annual ncome structure of Ukranan agrcultural producers s presented n specal statstc form 50-SG, whch have been gvng by agrcultural producers to state statstcal bodes. As a result, I expect to fnd to what degree prosperty of dfferent groups of natonal agrcultural producers depends on ther producton prce volatlty, namely, how and what shocks, domestc or world, especally n vew of forthcomng WTO accesson, more or less, nfluence on farmers ncome volatlty. The rest of the thess s organzed as follows. Chapter 2 deals wth theoretcal and emprcal ssues concernng causes and effects of market volatlty on agrcultural household s ncome. In Chapter 3 the theoretcal framework s 3

10 gven. Data descrpton can be found n Chapter 4. Chapter 5 provdes emprcal results and polcy mplcatons. Chapter 6 contans conclusons. 4

11 Chapter 2 LITERATURE REVIEW Any economc actvty s accompaned by a huge number of uncertantes. Rsk factors vary from prce vagueness to problems wth supplers. Agrculture s one of the busnesses whch s mostly exposed to dfferent market and non-market fluctuatons. Harwood (nd) defnes followng sources of rsk for households: producton or yeld rsk, prce or market rsk, nsttutonal rsk, human or personal rsk and fnancal rsk. Exstence of these rsks mples appearance of varous shocks whch may harmfully nfluence on agrcultural producers, ncreasng uncertanty and leadng to great varaton of farms ncome from year to year. Importance of ncome stablty s emphaszed by Mshra at al. (2002) who stress: The economc well-beng of farm household s nfluenced by varablty of ts ncome, whch can hamper ts ablty to mantan consumpton and accumulate wealth. However, Grao at al. (1974) comparng two groups of farmers n Mnnesota wth dfferent level of ncome varablty (farmers were dvded by coeffcents of varaton of ncome) emprcally show that ncome nstablty does not matter for consumpton. However, nvestment s nfluenced by ncome uncertanty. Evaluatng ncome varaton level mpact on consumpton, the authors use 5 dfferent models and conclude that ncome nstablty have lttle mpact on consumpton behavor. For example, all models estmate small dfference n long-run margnal propensty to consume for two groups. As for nvestment, the authors analyze estmates from two alternatve measures of nvestment: total fxed nvestment and nvestment n machnery and equpment. Both models show, for example, that savng mpact s larger for 5

12 stable group whch means that nvestment s less responsve to savng for farmers n unstable group. But adverse mpact of rsk factors on ncome stablty may be elmnated or reduced by mplementaton of dfferent measures, farmers have developed a lot of strateges of managng rsks they face. Accordng to Harwood (nd), agrcultural producers use dversfcaton n order to reduce rsk. It ncludes ncome dversfcaton, namely partcpatng n more than one actvty; crop dversfcaton, as growng varous crops. Also, farmers conclude dfferent types of contracts, such as future optons contracts and producton contracts whch guarantee them earnngs n the future. One of the most effectve and very often used means of rsk management s fnancal nstruments nsurance, credt and leasng nputs. But householders may have problems wth these methods to mplement as dversfcaton can be lmted due to clmatc or market condtons and fnancal nstruments may be not accessble because of not developed fnancal markets as we observe n Ukrane. Carter (1997) dstngushes covarate and dosyncratc shocks whch add to farmers economc uncertanty. Idosyncratc shocks are that shocks that nfluence on partcular household e.g. llness, crop reducton. In developng countres, almost all farms especally small producers are exposed to such crop or ncome shocks. However, adverse mpact of dosyncratc shocks on wealth and partcularly ncome stablty can be elmnated by applyng dfferent measures. Kochar (1995), analyzng agrcultural farmer s ncome n Inda, shows that households are relatvely well protected from dosyncratc shocks. Rsk assocated wth such shocks can be reduced by nvestment nto more ncome stable but low-yeld agrcultural technques rather than more proftable but less stable n returns agrcultural nvestments. One more way of managng such shocks s ncrease market hours of work. 6

13 Farmers also need to deal stands wth covarate shocks whch brng damage to all farmers n a whole economy or even larger regon, such as weather and prce fluctuatons. Rsks of such shocks cannot be fully removed by methods descrbed above as they demand specal programs of the government and ts support. Agrculture, perhaps, the only one actvty whch depends from weather at such great extent as t does. Weather unpredctablty causes great yeld varaton from year to year leadng to ncome nstablty; because of ths uncertanty households cannot effcently allocate resources when decdng how much to consume, save and nvest. Rozenzweg and Bnswanger (1993) nvestgate emprcally how ranfall frequency n rural Inda affects the wealth of agrcultural producers wth dfferent types weather rsk. They conclude that weather s crucal exogenous part of rsk for farmers to analyzed. It s one of the factor that mostly adds to output nstablty, that s, ncreases ncome nstablty. As weather s spatally covarant rsk t s hard for households to apply protectve actons aganst t and they have to change ther ex ante producton decsons whch leads proft and welfare losses. Besdes, weather s one of the man reasons for appearng another covarate shocks, namely prce shocks. Jabara and Thompson (1982) emphasze that uncertanty wth prce on nternatonal market especally concerns agrcultural commodtes due to weather factor. Dramatc changes n weather condtons cause producton shocks, postve or negatve, leadng to substantal prce fluctuaton n partcular economy or even all over the world. But t s dffcult to say whether t brngs net loss of proft and correspondently welfare because, especally n terms of world economy, somebody gans, somebody loses from antcpated prce shock. Fafchamps (2000) dscusses the consequences of prce nstablty for farmers n developng countres. He emphaszes n developng countres, households are much less protected from prce varaton, especally exporters from world 7

14 prce fluctuatons. Developed countres e.g. European Unon nsulate ther farmers, trggerng varous support programs or subsdzng them durng perods of unfavorable market condtons. Also, prce rsk affects agrcultural producers n two ways: drectly, ncreasng or decreasng ther output profts and ndrectly, nfluencng the whole economy. For developng countres, ths ndrect mpact s much larger as ther young economes are very vulnerable to dfferent shocks. The author analyzes the results for the agrcultural producer s revenues of appearance prce shock n the economy wthout nternatonal trade. He shows that wth exstence of downward slopng demand for agrcultural products, negatve producton shock caused by bad harvest and resulted n supply decrease wll be compensated by prce ncrease and farmers wll not suffer from proft fall. Increase n supply leads to decrease n prces but n case of non-stochastc unt elastc demand drop n proft wll be zero. Another deal wth nternatonal trade, as one country may experence good harvest another may not. For these countres, prce shock gong after producton shock s exogenous as t s not correlated wth ther output and they suffer from t because lower prces decrease ther profts. However, the lucky country may gan from trade as t compensates drop n prce by larger volumes of sales. There s large number of papers devoted to the consequences of trade lberalzaton for country s growth rate, nvestment, producer s welfare etc. For example, Blake, Mckay and Morrsey (2001) nvestgate the mpact of trade lberalzaton on Uganda agrcultural producers usng General Equlbrum model. They fnd that expanded trade openness wll have overall postve effect, make agrcultural prces to rse and ncrease farmer s welfare. Comparng results of ths and another artcles, one may conclude that mpact for every country s unque and to the great extent depends on specal condtons whch exst n every partcular economy especally on government polcy. Choosng optmal system of tarffs and subsdes government may avod detrmental effect of world shocks on economy (Reynolds (1963); Lankosky (1997); Aello (2003); Sugyarto, Blake, Snclar (2003)), but 8

15 nsuffcently consdered nterventons for some sectors may add even more to domestc prce nstablty than nternatonal shock (World Bank(2005)). However, undesrable effect mght happen after negatve prce shock even f polcy s recognzed as approprate (Dehn (2000)). However, much less researches nvestgate how trade lberalzaton may nfluence farmer s ncome stablty whether ncreased exposure to world prce shocks add to ther ncome uncertanty or not. Rapsomanks and Sarrs (2006), usng mcroeconomc approach, try to fnd out how wll change farmer s ncome stablty after ncreased trade openness for dfferent groups of households n Ghana, Peru and Vetnam. They compute household s ncome varances and coeffcents of varaton whch allow to ndcate the level of ncome varablty and to capture whether t depends on the world or domestc prce shocks. They fnd that nfluence of nternatonal prces on ncome s small and the man source of ncome nstablty s domestc prces. Also, I would lke to consder papers whch analyze the effects of trade lberalzaton on Ukrane. Sagdova (2004) nvestgates the possble affect of world gran prces on Ukranan gran prces. She estmates the level of ntegraton between the world gran market and Ukranan gran market through mechansm transmsson. The author fnds how fast the world prces wll be transmtted to the domestc market, speed of adjustment to equlbrum or, by other words the level of absorpton of future world shocks by the Ukranan economy. The results show that ntegraton s nsuffcent and lmted as 87.5% of the potental shock wll be absorbed durng 8 month whereas n the world practce tme needed for ths s about 6 month. Sydorov (2005) estmates poverty effects n Ukrane after WTO accesson. The examnes tarff reducton mpact on household s ncome usng 9

16 Computable General Equlbrum model. Hs man result s that 10% decrease n tarff wll reduce overall poverty. 10

17 Chapter 3 METHODOLOGY As my research s focused on nvestgatng of varaton agrcultural frm s ncome I need the exact formula for the coeffcent of varaton (CV) of ncome. I wll use the emprcal framework developed by Rapsomanks and Sarrs (2006). Consder the followng expresson for normalzed devaton of total ncome from ts mean (under the assumpton that the quanttes produced by the frm n perod t are ndependent of the prces faced by the frm n the same perod): where Y α agrіcultural part of іncome, α - share of agrculture n the total frm s ncome, s - average shares of each agrіcultural product n agrcultural ncome, q - normalzed quantty of product produced (the normalzaton s made by dvdng the amount Q produced n any perod by the average value of producton) p normalzed prce of product (whch s defned as the prce P of the product n a perod dvded by the average value of prce) 11

18 Gven (1), the square coeffcent of varaton can be wrtten as: CV 2 ( Y ) = 2 α s s E[( p q + p + q )( p q + p + q )] (2) j j j j j j Assumіng normalіty of prces and quantіty term, the terms that may be іncluded nto expresson (1) are those whch contan of even number n the products of the prce and quantty terms. So (2), may rewrtten as: CV j 2 ( Y ) 2 = α j p q + q q )] j s s E[( p p q q j j j + p p j + p q + j (3) Coeffcent of varaton of ncome s just the square root of (3) Let s denote by σ the coeffіcent of varіaton of productіon of the th crop produced by the frm, by κ j the correlaton coeffcent between the w producton of the th crop and the jth other crop produced by the frm, by v the coeffіcent of varaton of the world prce of the th prоduct, by ρ j the correlaton coeffcent of world prіces of the th and jth products (f they are tradable), by v the coeffcent of varіaton of the random component u t of the domestіc prce of the th product, and by ψ j the correlaton coeffcent between the random components u t of the domestc prces of the th and jth products. The terms n (3) can be evaluated as follows: E( p p q q ) = (ϕ ϕ ρ ν ν + ψ ν ν ) k σ σ (4) j j j w j w j j j j j (5) E ( p q ) = 0 (6) j E( q q ) = k σ σ (7) j j j 12

19 The parameter φ can be nterpreted as the elastcіty of transmsson of world prce to domestc prce (prces are n logarthms) and shows to whіch extent the domestc and the world prces are ntegrated. Generally, by Law of One Prce (Granger (1969), Engle and Granger (1987)) the relatіonshp between domestc crop prce followng: d Pt and world crop prce w Pt wth transfer costs c s the (8) If such lnk between two prces, exsts markets are consdered as ntegrated but ths case rarely occurs. But f the prоbablty dstrbutons of the two prces were fоund to be ndependent, then one may say that there s no market ntegraton and no prce transmsson. Emprcally, ths relatonshp may be defned by the followng specfcaton: (9) where d p t domestc prce of crop, w p t nternatonal prce of crop, ϕ - transmsson coeffcent, ut - error term. So, by ths equaton, n the long-run n presence of tradablty and transfer costs crop prces n the domestc market are defned by the world prces. Calculaton of ϕ allows to compute CV of ncome for dfferent groups of frms and to determne how they are exposed to dfferent prce shocks. Also, 13

20 puttng ϕ equal to zero t s possble to capture the factors whch take place only on the domestc market such as producton. And f contrary to ths stuaton ϕ s set equal to 1 and varance of domestc error term equal to zero t wll stmulate crcumstances when the domestc prces are equal to the world prces or by the words to get stuaton when domestc frms are exposed to the world prce shocks. In the emprcal applcatons, (9) can be extended to multvarate case or to Vector Autoregressve model (VAR), assumng that domestc and nternatonal prces tme seres are generated by stochastc process and ther seres are statonary. Generally, a VAR model descrіbes the evoluton of a set of n varіables measured over the same sample perod (t = 1,..., T) as a lnear functon of only ther past evoluton. The varіables are сollected n a n x 1 vector y t, whch has as the th element y,t the tme t observaton of varable y A (reduced) j-th order VAR, denoted VAR(j), s where c s a n x 1 vector of constants (ntercept), a s a n x n matrx (for every = 1,..., j) and ε t s a n x 1 vector of error terms called mpulses or nnovatons satsfyng: E( ε t ) =0 - every error term has mean zerо; E ( ε t ε`) = Ω - the contemporaneous covarance matrx of error terms s Ω (a n x n postve defnte matrx); 14

21 ` E( ε ε t t k ) =0 - for any non-zero k there s no correlaton across tme; n partcular, no seral correlaton n ndvdual error terms. The k-perods back observaton y t-k s called the k-th lag of y. Usually, VAR s constructed for models wth two or more varables wth one equaton for each varable n whch current observaton of each varable depends on ts own lags and lags of other varables. Each varable affects another, so n VAR there s no need to dstngush between exogenous and endogenous varables. VAR can be estmated by OLS equaton by equaton whch s consstent as ` ε s are assumed to be ndependent of the hstory of y. VAR for prces wll be the followng: (10) (11) where a s are parameters and ε s are error terms. Parameters and errors estmaton allows to forecast and to compute future means and varances of the prces. Assumng that economc agents behave aсcordng to VAR predctons both nterrelated and separate mpact of nternatonal and domestc prce volatlty on domestc prce can be evaluated. Also, Granger causalty test (Granger (1969)) whch examіnes whether the lags of one varable enter nto the equaton of another varable can show presence (absence) of dependence between іnternatonal and domestc prces. Let s assume, for example, that domestc prce nternatonal prce w p t but at the same tme d p t does not Granger cause d p t depends on p w t. Ths means that mpulses of the former are dependent and some share of volatlty n the domestc prce belongs to the shocks n world prce and not conversely. Ths relatonshp has the followng vew: 15

22 ; where ϕ s prce elastcty and can be nterpreted as n (9); ω t s part of a domestc prce shock caused not by changes n the world prce; Contrastng to elements n (10) and (11), n ths equaton zero mean and constant varance. w ε t and ϕ have 16

23 Chapter 4 DATA DESCRIPTION The data set that emprcal nvestgaton of ths paper s based on contans economc actvtes of agrcultural frms from all regons of Ukrane. The data comes from the statstcal form 1-agreeculture collected by the State Statstcs Commttee.Ths content gves a possblty to dvde the sample by terrtory belongng n order to determne specal condtons of workng n some regon. Totally, there are fve large regons: west (Chernvets'ka oblast', Ivano- Frankvs'ka oblast', Khmel'nyts'ka oblast', L'vvs'ka oblast', Rvnens'ka oblast', Ternopl's'ka oblast', Volyns'ka oblast', Zakarpats'ka oblast'), North (Zhytomyrs'ka oblast', Chernhvs'ka oblast', Kyvs'ka oblast', Sums'ka oblast'), Centre (Vnnyts'ka oblast', Cherkas'ka oblast', Poltavs'ka oblast', Krovohrads'ka oblast', Dnpropetrovs'ka oblast'), East (Kharkvs'ka oblast', Luhans'ka oblast', Donets'ka oblast') and South (Khersons'ka oblast', Mykolavs'ka oblast', Zaporz'ka oblast', Odes'ka oblast', Avtonomna Respublka Krym). The number of frms s 10,000. The ndcators of nterest are total frm s gan and sales proceeds from each crop produced for the perod of 4 years, begnnng n 2002 and endng n 2005, that allows to capture the share of agrcultural actvty and porton of each culture n total ncome. Accordng to ths nformaton, frms are dvded dependng on ths share n total revenue. Also, frms are classfed n the three groups dependng on the sze: small, medum and large. As crteron for the selecton, the quantty of assets n money terms serves. For estmatng of prce transmsson coeffcents, correlaton coeffcents and varances, monthly data on domestc crop (wheat, barley, sunflower, corn, sugar beet) prces are used gathered by the analytc centre of 17

24 Ukragroconsult ( and gven by the Insttute for Economc Research and Polcy Consultng ( for the perod , 96 observatons n all. The prces are n US dollars per ton. Internatonal prces data s obtaned from IMF survey ( Table 4.1. Summary statstcs for corn prces Varable Obs Mean Std. Dev. Mn Max World Domestc Table 4.2. Summary statstcs for barley prces Varable Obs Mean Std. Dev. Mn Max World Domestc Table 4.3. Summary statstcs for wheat prces Varable Obs Mean Std. Dev. Mn Max World Domestc

25 Table 4.4. Summary statstcs for sugar prces Varable Obs Mean Std. Dev. Mn Max World Domestc Table 4.5. Summary statstcs for sugar prces Varable Obs Mean Std. Dev. Mn Max World Domestc

26 Chapter 5 EMPIRICAL RESULTS Obtanng of the results conssts of three steps: VAR estmaton and prce specfcaton computaton; calculaton of the ncome structures and producton specfcatons of the frms; computaton of the fnal results. Step 1 At ths step, I estmated VAR models for each prce seres and calculated prce specfcaton (coeffcents of varatons, correlaton) needed for computng coeffcents of varaton of ncome. VAR estmaton Frst, n order to get transmsson coeffcent for every crop I have to estmate VAR model for these products. However, to be used n VAR model prce seres should be statonary. To check for statonarty, I used Dckey-Fuller (DF) test of presence of a unt root. If the value of DF statstcs (Taustatstcs) s more than the crtcal value t shows the presence of a unt root and a seres s not statonary. In the opposte case, a prce seres s statonary. But f prce seres s not statonary n levels, t only means that prces are not ntegrated of the same order and takng logarthms and dfferences may make them statonary. Then, for seres beng statonary I defned how many lags VAR model for each crop contans, estmated the models (regressons (10) and (11) n the methodology part), tested for stablty, autocorrelaton and Granger causalty. Test results for each crop are presented n Appendxes A- E. If everythng was clear I predcted resduals for each regresson. The model for every product contans the two regressons. The frst regresson determnes how domestc prce depends on ts own lags and the lags of the 20

27 world prce, the second regresson defned the same for the world prce. So, for each crop I got two seres of resduals and then regressed resduals from the equaton for domestc prce on resduals from the equaton for the world prces by the expresson (12) n the methodology part. The result s prce transmsson coeffcent estmaton. Now, let s consder VAR model for every crop separately. Barley Tau-statstcs s less than crtcal values, thus I reject hypothess about presence of unt root and I conclude that process s non-statonary. Therefore, logarthms of prces are taken whch, however, are also nonstatonary, but dfferences pass the test for statonarty. Model wth dfferences of logarthms has three lags. Also, the egenvalues le nsde the unt crcle, thus, VAR model satsfes stablty condton. Granger causalty test shows that domestc prce does not help to predct the world prce; however, the world prce Granger causes the domestc prce. Testng for autocorrelaton, I do not reject H o at 5% sgnfcance level of no autocorrelaton (P-value are large). Coeffcent of prce transmsson s calculated regressng resduals from the equaton for domestc prces on resduals from the equaton for the world prces s n the the table 5.1: Table 5.1. Transmsson coeffcent for barley prces Coeffcent, φ P-value The result shows that t s not statstcally dfferent from zero that probably means: the lags n the world prce for barley have no essental nfluence on lags of the domestc prce. 21

28 Corn For ths crop, prce seres n levels, both the world and domestc, are not statonary. DB statstcs shows that I have to take the frst dfferences. Frst dfferences of logarthms of corn prces do not have a unt root, so I can use t n calculatons. VAR model for ths product has two lags. Test for stablty shows that characterstcs roots are smaller than one n absolute value, so the system s stable. Test for Granger causalty shows that world prce nfluences on the domestc and vce versa. Testng for autocorrelaton, I do not reject H o at 5% sgnfcance level of no autocorrelaton (P-value are large). Estmated prce transmsson coeffcent (Table 5.2) s nsgnfcant. Ths result mples that lags n the world prce have no effect on lags n the domestc prce. Table 5.2. Transmsson coeffcent for corn prces Coeffcent, φ P-value Sugar beets Testng for unt root presence, I conclude that seres n levels are not statonary. Therefore, I took frst dfferences of logarthms whch, as DF test shows, pass test for statonarty. Estmated model contans 1 lag. Testng for stablty, I nferred that seres are stable because all characterstc roots are smaller than one n absolute value. Test for Granger causalty shows that both domestc and world prce have an mpact on each other. Testng for autocorrelaton, I do not reject H o at 5% sgnfcance level of no autocorrelaton (P-value are large). Prce transmsson coeffcent (Table 5.3.) s not statstcally dfferent from zero, thus, the world prce lags do not nfluence on the domestc prce lags. 22

29 Table 5.3. Transmsson coeffcent for sugar beets prces Coeffcent, φ P-value Sunflower seeds Test for staonarty shows that seres are not statonary. However, frst dfferences of logarthms do not have a unt root. The VAR model specfcaton contans two lags. Test for stablty shows that characterstcs roots are smaller than one n absolute value, so the system s stable. Test for Granger causalty shows that world prce nfluences on the domestc and vce versa. Testng for autocorrelaton, I do not reject H o at 5% sgnfcance level of no autocorrelaton (P-value are large). Prce transmsson coeffcent (Table 5.4.) s not statstcally dfferent from zero, the world prce lags do not nfluence on the domestc prce lags. Table 5.4. Transmsson coeffcent for sunflower seeds prces Coeffcent, φ P-value Wheat Testng for unt root presence, I conclude that seres n levels are not statonary. DB statstcs shows that I have to take the frst dfferences. Frst dfferences of logarthms of wheat prces do not have a unt root, so I can use t n calculatons. VAR model for ths product has two lags. Test for stablty shows that characterstcs roots are smaller than one n absolute value, so the system s stable. Test for Granger causalty shows that both domestc and 23

30 world prce have an mpact on each other. Testng for autocorrelaton I do not reject H o at 5% sgnfcance level of no autocorrelaton (P-value are large). Prce transmsson coeffcent (Table 5.5.) s not statstcally dfferent from zero, the world prce lags do not nfluence on the domestc prce lags. Table 5.5. Transmsson coeffcent for wheat prces Coeffcent, φ P-value In Table 5.6. calculated coeffcents of varaton of the world prces and random components of the domestc prces are presented. CV s computed as a rato of the standard devaton to the mean. Random components are estmated from the regresson (9) of the methodology part. Table 5.6. CV s of world prces and random components of the domestc prces Crop CV(world) СV(random) barley corn sugar beeets sunflower seeds wheat The table shows the largest varablty exhbts prces of sunflower seeds. Step 2 At ths step I computed ncome structures (more specfcally, share of every crop n frm s ncome) and producton specfcaton for each group (coeffcents of varaton (CV) of producton, correlaton coeffcents between productons of dfferent products) of the frms. 24

31 Share of every crop n frm s ncome s computed as a rato of ncome from a culture producton to a total ncome of a frm. Average shares for every group are presented n the Table 5.4. Table 5.7. Average shares of ncome (percent) Frm type Small Medum Large Regon West barley corn sugar beets sun seeds wheat Regon North barley corn sugar beets sun seeds wheat Regon Centre barley corn sugar beets sun seeds wheat Regon East barley corn sugar beets sun seeds wheat Regon South barley corn sugar beets sun seeds wheat The table shows average share of ncome for the perod of four years from cultvaton of fve crops for each group. Analyzng regon dfferences, t s 25

32 clear that n the West the man ncome sources for the enterprses are cultvaton and sellng of sugar beets and wheat, n the North such role belongs to barley and wheat, n the Centre almost equal shares (totally 66%) of ncome brng to farmers three crops: sugar beets, sun seeds and wheat, n the East the most sgnfcant parts of ncome have such cultures as sun seeds and wheat (totally 65%), the same stuaton as n the East s observed n the South but n ths regon average share of barley ncreases but share of sugar beets decreases. So, n almost each regon wheat ranks very mportant role n ncome structure of the farmers, n four regons (North, West, Centre and South) t consttutes the largest fracton of enterprse ncome. Also, n each regon (except Centre), besdes wheat, some other crop amounts the sgnfcant part of ncome. For the West ths s sugar beet, for the North t s barley, for the East and the South such crop s sunflower seed. Ths culture may be ndcated as product of specalzaton for some partcular regon. Analyss of the table n the context of frm sze shows that shares of product of specalzaton decrease wth ncrease of frm sze and shares of nonspecalzaton products, correspondngly, rse. Ths means that large frms n Ukrane use strategy of ncome dversfcaton (they, however, have more possbltes for ths) n order to reduce rsk of bad harvest. Calculated CV s of producton are presented n the Table 5.5. For dstrbutons of postve-valued random varables, t allows comparson of the varaton of populatons that have sgnfcantly dfferent mean values (wkpeda.org). By other words, CV computaton gves possblty to evaluate how producton of every crop vares from year to year for each group. Table5.8. Frm s producton CV s Frm type Small Medum Large Regon West barley corn sugar

33 sun seeds wheat Regon North barley corn sugar sun seeds wheat Regon Centre barley corn sugar sun seeds wheat Regon East barley corn sugar sun seeds wheat Regon South barley corn sugar sun seeds wheat Analyss of ths table allows to determne producton of whch crops vares greatly from year to year or, by other words, whch cultures add to ncome uncertanty due to producton shocks most sgnfcantly and whch regons are exposed to these shock to great extent comparng wth other regons. So, for all regons and frm groups the most unpredctable s cultvaton of wheat. Step 3 After computaton of all components (formulas (4)-(7) n the methodology part) needed for coeffcent of varaton calculaton, at ths step I have to get 27

34 the fnal results of the paper. The average CV s of ncome are presented n the table 5.6. Table 5.9. CV s of agrcultural ncome Frm type Small Medum Large Regon West Actual ncome CV s due to prce and producton shocks Agrcultural ncome CV s due to domestc market and producton shocks Smulated Agrcultural ncome CV s due to world prces and producton shocks Regon North Actual ncome CV s due to prce and producton shocks Agrcultural ncome CV s due to domestc market and producton shocks Smulated Agrcultural ncome CV s due to world prces and producton shocks Regon Centre Actual ncome CV s due to prce and producton shocks Agrcultural ncome CV s due to domestc market and producton shocks Smulated Agrcultural ncome CV s due to world prces and producton shocks Regon East Actual ncome CV s due to prce and producton shocks Agrcultural ncome CV s due to domestc market and producton shocks Smulated Agrcultural ncome CV s due to world prces and producton shocks Regon South Actual ncome CV s due to prce and producton shocks Agrcultural ncome CV s due to domestc market and producton shocks Smulated Agrcultural ncome CV s

35 due to world prces and producton shocks Frst, I would lke to menton that my results may slghtly underestmate truth CV s of ncome as I do not use n my research data on crops (buckwheat, pea, soy bean etc.), whch consttute share of farmer s ncome less than 1%. The obtaned results show that actual CV s of agrcultural ncome n Ukrane range from not sgnfcant (5%) to relatvely large (20%). It means that some frms profts vares from year to year by 5% and some have varablty of ncome of 20%. The pattern demonstrates that ndex depends on the sze of a frm. In each regon, a group wth the largest frms possesses the hghest coeffcent of varaton. The fact that larger frms face wth hgher ncome uncertanty may be explaned by the results from the table 5.4. Accordng to them, large frms dversfy ther ncome so as to reduce prce and producton rsks. However, results show that dversfcaton does not work n Ukrane because such frms have hgher ncome volatlty. Also, such output may be explaned by absence of approprate nfrastructure n Ukrane. Very often, agrcultural frms, especally large, have no sutable place where to save producton and vehcles to delver t to a buyer. These mperfectons force them to sell producton rght away after harvest at low prce acceptng condtons of a contract of a buyer or to run a rsk of havng spoled producton n the future. Comparng regons, I conclude that enterprses n the East and South are more than others exposed to prce and producton volatlty. As on average sgnfcant share of ncome of the frms n these regons consttute wheat and sun seeds (Table 5.4.), so, these cultures more than others add to ncome nstablty. Also, producton CV s of wheat are hgh but sun seeds CV s are low, t means that large share of shocks from wheat s due to producton shocks but sun seeds prce shocks have more adverse effect. Enterprses n other regon have about equal CV s of ncome. 29

36 For all regons and farms groups, the results desgnate that about all ncome nstablty frms suffer s caused by the domestc factors, prce and producton shocks. It means that government polcy, system of donatons and prce support amed to stablze prces and producton on the agrcultural market has no desrable effect. Results of smulaton the stuaton when Ukranan farmers are fully exposed to the world prce shocks show that for all regons and groups ncome nstablty slghtly decreases. It means that ntegraton of the Ukranan agrcultural market wth the world would decrease ncome uncertanty of farmers, stablze prces and smooth producton shocks. 30

37 Chapter 6 CONCLUSIONS The paper was devoted to the nvestgaton of the ssue of prce volatlty mpact on farmers ncome. To be more precse, the research tred to answer the queston of whch prce shocks, domestc or world, ncome stablty of Ukranan agrcultural producers s nfluenced by and whether ncreased exposure to the world markets wll reduce or make hgher ncome uncertanty. In the nvestgaton, the explct formula for ncome varance computaton s used. In the formula, producton specfcatons and ncome structures of the agrcultural producers are combned wth prce transmsson coeffcents and other prce specfcaton of the crops. Prce transmsson coeffcents are estmated for every by VAR model. The man fndng of the VAR estmaton s that for each culture there s no essental relatonshp between lags of the domestc prce and lags of the world prce. Also, the formula gves possblty to smulate stuaton when frms are exposed to domestc shocks or stuaton when enterprses are drectly exposed to the world prce shocks. In order to capture specfc regon and frm characterstcs, data set of enterprses s dvded by regonal belongng and by frm sze, dependng on the amount of assets. Then, the average coeffcent of varaton for each group s computed. In Ukrane, the actual coeffcent of varaton of ncome for agrcultural producers vares from 5% to 20% whch relatvely large range, as 5% s not essental ndex but 20% s rather sgnfcant. These results, however, do not contradct to the results of prevous nvestgatons. Income varablty ncreases wth sze of a frm. As the largest frms more often than other types use dversfcaton strategy the results conclude that n Ukrane dversfcaton does not help farmers to reduce ncome nstablty. In 31

38 the context of regon dfferences frms n the East and South more than others suffer from ncome nstablty. As sgnfcant share of frms ncome belong to wheat and sun seeds, prce and producton shocks of these crops sgnfcantly add to ncome nstablty. In Ukrane, for all groups of farmers the orgn of almost all ncome varablty are domestc factors. The reasons of ths fact are small from the world to domestc prce transmsson and not effectve prce stablzaton polcy of the Ukranan government concernng the agrcultural market. Market lberalzaton would slghtly decrease ncome uncertanty of agrcultural producers. Increased exposure to the world markets wth nonexstence of effcent prce stablzaton polcy may be expressed nto declne of ncome nstablty. The results of the paper can have some mportant polcy mplcatons to the Ukranan government: stablzaton and support polcy should be revewed, drect donaton mechansm should be replaced by the polcy whch has long-run prospectve (nfrastructure creaton etc.), drect nstruments of prce stablzaton exstng now should be replace by those whch promote competton on the market; government support should be concentrated on large frms, n the South and n East, as such type enterprses and frms n these regon more than others suffer from ncome uncertanty; In further research t would be nterestng to compute coeffcents of varatons n other former Sovet Unon countres and to compare them wth the results of the paper. Also, t would be useful to evaluate ncome nstablty after market lberalzaton whch s expected followng WTO accesson. 32

39 BIBLIOGRAPHY Adamenko, T. (2004), Change of agro clmate condtons and ther nfluence on gran farmng n Ukrane, APK-Inform, Aello, F. (2003), Does Lberalzaton Reduce Instablty? Look at the Interests of Exportng Development Countres, paper presented at 2003 Internatonal Agrcultural Polcy Reform and the WTO: Where are We Leadng? Blake, A., A. McKay and O. Morrsey (2001), The Impact on Uganda of Agrcultural Trade Lberalzaton, Unversty of Nottngham, CREDIT Research Paper 01/07. Dehn, J. (2000), The Effect on Growth of Commodty Prce Uncertanty and Shocks, Polcy Research Workng Paper nr Washngton, DC: World Bank Engle, R. and C.W.J. Granger (1987), Contegraton and Error Correcton: Representaton, Estmaton and Testng, Econometrca, vol.5, nr.2, Fafchamps, M. (2000), Farmers and Prce Fluctuatons n Poor Countres Department of Economcs, Unversty of Oxford. Granger, C.W.J. (1969), Investgatng Causal relatonshps by econometrc models and cross spectral methods. Econometrca Grao, J., W. Tomek and D. Mount (1974), Effect of Income Instablty on Farmer s Consumpton and Investment, The Revew of Economcs and Statstcs, vol.56, nr.2, p Harwood, J.(1999), Managng Rsk n Farmng: Concept, Research and Analyss, Agrcultural Economc Report nr Jabara, C. L. and R. L. Thompson (1982), The Optmal Tarff for a Small Country Under Internatonal Prce Uncertanty, Oxford Economc Papers, vol. 34, nr.2, p Kochar, A.(1995), Explanng Household Vulnerablty to Idosyncratc Income Shocks, The Amercan Economc Revew, vol. 85, nr.2, p Lankosky, J. (1997), Envronmental Effect of Agrcultural Trade Lberalzaton and Domestc Agrcultural Polcy Reforms, UNCTAD Dscusson Paper nr.126. Mshra A. (2002), Income, Wealth and Economc Well-Beng of Farm Households, Agrcultural Economc Report nr Rapsomanks, G. and A. Sarrs (2006), The Impact of Domestc and Internatonal Commodty Prce Volatlty on Agrcultural Income Instablty, UNU-WIDER Dscusson Paper nr. 2006/04. 33

40 Reynolds, C. W. (1963), Domestc Consequences of Income Instablty, The Amercan Economc Revew, vol. 53, nr. 2, p Rozenzwveg, M. and H. Bnswanger (1993), Weather Rsk and the Composton and Proftablty of Agrcultural Investments, The Economc Journal, vol. 103, nr. 416, p Sagdova, G. (2004), Prce Transmsson n Gran Market: case of Ukrane, EERC Master s Paper. Ste of State Statstc Commttee, Sugyarto, G., A. Blake and M. T. Snclar (2003), Trade Lberalzaton wth Labor Market Dstortons: the Case of Indonesa, paper presented at the Sxth Annual Conference on Global Economc Analyss, June 12-14, 2003, Shevenngen, The Nethelands. Sydorov, V. (2005), Poverty Effects on Ukrane s WTO Accesson, EERC Master s Paper. World Bank (2005), Managng Food Prce Instablty n an Envronment of Market Lberalzaton, Washngton, DC: World

41 APPENDIX A TEST RESULTS OF BARLEY PRICES Checkng dfference of logarthm of domestc prce for a unt root Dckey-Fuller test for unt root Number of obs = Interpolated Dckey-Fuller Test 1% Crtcal 5% Crtcal 10% Crtcal Statstc Value Value Value Z(t) MacKnnon approxmate p-value for Z(t) = Checkng dfference of logarthm of domestc prce for a unt root Dckey-Fuller test for unt root Number of obs = Interpolated Dckey-Fuller Test 1% Crtcal 5% Crtcal 10% Crtcal Statstc Value Value Value Z(t) MacKnnon approxmate p-value for Z(t) = Defnng the number of lags Selecton order crtera Sample: 6 96 Number of obs = lag LL LR df p FPE AIC HQIC SBIC * * * * * Endogenous: D.ln_d D.ln_w

42 Exogenous: _cons Checkng for stalty Egenvalue stablty condton Egenvalue Modulus All the egenvalues le nsde the unt crcle VAR satsfes stablty condton Checkng for autocorrelaton Lagrange-multpler test lag ch2 df Prob > ch H0: no autocorrelaton at lag order Checkng for Granger causalty Granger causalty Wald tests Equaton Excluded ch2 df Prob > ch D_ln_d D.ln_w D_ln_d ALL D_ln_w D.ln_d D_ln_w ALL