The Impact of Flood Damages on Production of Iran s Agricultural Sector

Transcription

1 Middle-Eas Journal of Scienific Research 12 (7): , 2012 ISSN IDOSI Publicaions, 2012 DOI: /idosi.mejsr The Impac of Flood Damages on Producion of Iran s Agriculural Secor 1 2 Ghasem Norouzi and Mahsa Taslimi 1 Deparmen of Agriculural Economics, Qaemshahr Branch, Islamic Azad Universiy,Qaemshahr, Iran 2 Agriculural Economics, Islamic Azad Universiy, Qaemshahr Branch, Iran Absrac: According o saisics provided by he UN, among naural disasers, floods and sorms have he bigges losses and desrucive damages in oday s sociey. In jus a decade, he amoun of damages from flood and sorm was over 1705 housand dollars versus 1462 housand dollars of damage caused by earhquae. This paper invesigaes he damages caused by flood on agriculural producion by using Vecor Auo regressive (VAR) model. The resuls sugges ha damages caused by flood have significan and negaive impac on agriculural producion. The resuls of Impulse response funcions in agriculural producion show ha he shoc from he damages of flood has a negaive influence on agriculure secor in he shor ime and he mos influence of he shoc will occur in he medium erm. This rend will decrease gradually in he long erm. Analysis of variance decomposiion sin he agriculural secor indicaes ha he mos volailiy shocs can be explained from he damage. JEL: E23 Q54 c13 Q10 Key words: Flood Vecor Auo Regression model (VAR) Agriculural Secor INTRODUCTION his claim. The damages of flood include angible and inangible losses. Tangible losses are classified in direc According o he saisics obained by Unied and indirec losses. Direc angible losses can include he Naions Organizaion, among naural disasers, floods and followings: human damages and losses, flooded houses sorms have caused he mos damages in human and residenial and indusrial places, flooded farms and communiies. The level of damages arising from flood and losing of agriculure producions and animal damages, sorm only during a decade has been up o 1705 housand damages of infrasrucure insallaions such as roads and dollars agains 1462 housand dollars for damages arising bridges and power ransmission lines and newors of from earhquae. This affair is rue in regards o our waer and gas. In order o esimae he damages arising counry oo. During pas years 70% of annual credis of from a flood i is necessary o deermine a lile wasage plan of decreasing he effecs of naural disasers and afer separaing damage in any secor [1]. Invesigaed he saff of unexpeced evens (he saff of crisis) have naural disasers during he period and been spen for compensaing he damages of flood. indicaed ha in oal 4122 million dollars losses have (please rewrie his senence). Meanwhile i is necessary been creaed in agriculures secor (89% of oal losses), o be menioned ha due o he improvemen of 157 million dollars in residenial secor and 332 million consrucion mehods and observing he regulaions and dollars in infrasrucure secor [2]. Sudied he influence rules, he securiy of srucures and insallaions increases of naural disasers on macroeconomic variables. agains dangers such as earhquae, bu unforunaely He believes ha a clear decrease occurs in agriculural he naural rend of developmen in some counries such producion afer naural crisis such as sorm, flood and as Iran has caused he damage of environmen and drough. On he basis of he sudies of [3], Dominican naural resources and he damages of flood is in progress. sorm has caused damages o all producions of banana The growh level of 250% in damages of flood in he in 1979 and 75% of he foress of he counry and has counry during pas decade shows he confirmaion of decreased domesic gross producion abou 17% [4]. Corresponding Auhor: Ghasem Norouzi, Islamic Azad Universiy, Qaemshahr, Mazandaran, Iran. Tel:

2 Middle-Eas J. Sci. Res., 12 (7): , 2012 Table 1: Damages caused by flood in Iran Year Flood damages (dollar) Source: official websies. Found ha naural disasers in developing counries have deeper influences han developed counries bu some inds of naural disasers occurring wih moderae severiy have even had useful influences and cause economic growh such as flood ha had posiive influence on economic growh. In his aricle i I ried o invesigae he influences of flood on he producion of agriculural secor. So he losses of flood in agriculural secor are esimaed wih affirmaion of agriculural producion from he ind of Cobb-Douglas using he mehod of Vecor Auo Regression (VAR) and maing seady-sae clear. The following able is he value of damages caused by flood in Iran in las hree decades: Mehodology: The Vecor Auo Regression mehod (VAR) is one of he opions of Box Jenins heory which is similar o simulaneous equaions [5]. This model was seriously criicized by Chrisopher Sims and he opion of VAR offered by him. He believes ha a heory canno provide necessary limiaions for deermining srucural models [6]. There are some endogenous variables in VAR and each one is explained by is pas amouns and he lags amouns from all oher endogenous variables of he model. Two ime series x and y for wo variables are as follows [7]: n = 0 + j=1 j j + i=1δ j i + 1 X a BX Y u n = 0 + j=1 j j + i=1λ j i + 2 Y a AX Y u The model VAR is esimaed from OLS. The answers of his model depend on enered variables and he lag lengh. In relaion o saically of invesigaed variables he exisence of non-saic variables inensifies he probabiliy of creaing spurious regression and he relaions of coinegraion. So i is necessary o es he exisence of accumulaed vecor or vecors in VAR included non-saic series. In order o evaluae he model using he mehod of VAR, i is necessary firs o invesigae he saionary of variables: (6) (7) If he variables are in he level of saic hen paern becomes clear afer invesigaing he saionary of variables. 2- If he variables are saic in firs order difference hen firs he paern VAR becomes clear as menioned before and hen he convergence of hem will be invesigaed. Here he coinegraion es of Johansen has been used [8]. VAR of formula 8 is wrien wih m variables as formula 9: =δ+α 1 i =δ+ j=1 j j+ Y Y... A Y v A Y v = j=1 j j + Y AY v 1 1 j=1 j j Y = BY + B Y + v (8) (9) For maing he formulaion simple, inercep is eliminaed. Also i is hypohesized ha all variables of i have coinergraion order 1 or 0. The paern above is wrien as below: in which: (10) B = (I A A... A ) (11) 1 2 B = (A A... A ) (12) j j+1 j+2 j+ The equaion of 12 is similar o he paern of error correcion and if he whole variables of i have coinergraion order 1 he variables y j will be saic. Now wih his hypohesis ha here is coinegraion beween variables and B y j is saic i is possible o esimae he paern consisenly. In his aricle in order o invesigae he effecs of flood on agriculure secor a funcion of oal agriculural producion has been used as follows: a 1 i y = (K )(AL) 0<a<1 (13) y: oal producion of agriculure secor, K: capial level in agriculure secor, A: he index of echnical progress and AL: effecive labor in agriculure secor, is ime, is he elasiciy of producion in relaion wih capial and 1- is he elasiciy ofproducion in relaion wih effecive labor. Wih dividing wo sides of above funcion by effecive labor, per capia producion funcion is compued as follows: 922

3 Middle-Eas J. Sci. Res., 12 (7): , 2012 a y = (14) Wih insering he relaion 22 in 17 we will have: y y y = y = 1 Iny lny ( Ln y Lny ) 1 = 1 Iny lny [ aln Lny ] s So ha y is per capia producionand sy S I y = = = = i AL AL AL K = AL is he capial in relaion wih effecive labor. Wih hypohesis ha in he framewor of adapive expecaions (he opimal per capia producion) is as formula 15: (15) I is possible o ransform i o he logarihmic form as formula 16: (16) By subsiuing formula 14 by formula 16 we will have: Also pariy o S=I, S=sy and I= d/d we can wrie: (17) (18) i is he same per capia invesmen in erms of effecive labor. The growh rae of per capia invesmen in erms of effecive labor () is as follows mahemaically: K=sy (19) In seady-sae he capial relaion o effecive labor in is seady-sae is. In his siuaion real invesmen is equal o subsiued invesmen. The growh rae of per capia invesmen of effecive labor is zero. I means K=0 so: sy = (20) Wih aing logarihm from boh sides we have: ln(sy) = In + In (21) Using he relaions 18 and 19 we have: ln = lni ln (22) lny lny = [ (Ln i ln ) Lny ] (23) 1 1 Wih simplifying we have: lny = (1 ) lny + Lni ln (24) 1 y is per capia producion, i is per capia invesmen and is amorizaion. Since naural damages (DMG) lead o decreasing he value of agriculure asses, in equaion 25 he subsiued variable of per capia damages (dmg) is used insead of amorizaion in he framewor of final model below for indicaing he influence of flood damage on agriculure secor: lny = + lny + Ln(inv) + (dmg) + (25) In he final model above, y is he value of per capia producion in agriculure secor, inv is per capia invesmen value in his secor and dmg is per capia damages of flood and he effec of oher variables has been summarized in inercep. The quaniy value of producion and invesmen in agriculure secor has been aen from he naional accouns of cenral ban and for evaluaing he employmen in agriculure secor he average percen employmen in his secor (close o 23%) and oal acive populaion of he counry (aen from he naional accouns of cenral ban) have been used and he per capia variables have been compued. The daa relaed o flood damages also has been colleced from insurance fund of agriculure producs. In his aricle he daa of ime series has been used annually. RESULTS AND DISCUSSION In order o evaluae he model, firs we invesigaed he saionary of paern variables using augmened Dicey-Fuller es and he resuls have been indicaed in Table 2. All relaed variables are saic in firs order differencing. As menioned earlier, in order o evaluae he paern using VAR i is necessary firs o invesigae he saionary. Invesigaion of considered variables indicaed ha all variables are in saic firs order differencing. 923

4 Middle-Eas J. Sci. Res., 12 (7): , 2012 Table 2: The resuls of saionary of he variables s Criical value a 1 difference s Observed value a 1 difference Criical value a level Observed value a level sae Variable Saic a inercep Log y Saic a inercep Log inv Saic a inercep Log dmg, and indicae criical numbers in he level 1%, 5% and 10%, respecively. Table 3: Deermining opimum inerval for model VAR Schwarz Bayesian crieria Lags (1,1) (1,2) (1,3) Source: findings of he research Table 4: The race resuls of Johansen s coinegraion es Probabiliy Criical Observed he alernaive Null a 95% value a 95% value hypohesis hypohesis r 1 r= r 2 r r 3 r 2 Source: findings of research Table 5: The Maximum Eigenvalue resuls of Johansen s coinegraion es Probabiliy Criical Observed he alernaive Null a 95% value a 95% value hypohesis hypohesis r=1 r= r=2 r r=3 r 2 Source: findings of research Then in order o mae he paern clear he model opimum order has been deermined using he crierion of Schwarz-Byssian. The resuls have been indicaed in Table 3. The inerval having he leas amoun of saisics daa is opimum. In which he opimal lag has been deermined in (1,1). The convergence of variables was invesigaed afer deermining opimal lag using Johansen s coinegraion es. I is provided by wo saisical daa iled wo marices which represen Trace and Maximum Eigen value Marix. The null hypohesis says ha here is no co-inegraion of vecors; i means ha here is no any long run relaionship beween he variables and he alernaive hypohesis says ha here is co-inegraion of vecors. The level which Table 6: he resuls of evaluaion of he model wih VAR Variable Coefficien Sandard error Log y(-1) Log inv Log dmg he null hypohesis can be rejeced a indicaes he numbers of vecors ha are co-inegraed. Tes resuls are presened below: As can be seen in able 4 and 5, he null hypohesis has been rejeced a he level and vecors have proved he exisence of coinergraion and long erm balance relaions. The resuls of long erm relaion beween variables in he framewor of able 6 and normalized vecor in proporion wih he firs endogenous variable are as follows: On he basis of resuls obained, he variable of invesmen in agriculure secor and agriculure producion wih lag has posiive and significan relaion wih agriculure producion. Also he variable of damage of flood has negaive bu significan relaion wih agriculure producion. Impulse Response Funcions: This index indicaes he response of one variable o he shoc and indicaes ha how ha shoc is eliminaed during he ime. For compuing, he shocs are enered wih he size of one sandard deviaion from a considered variable and hen we observe he obained response during he ime which will be done in 10 periods in fuure. The evaluaion of impulse response funcions of agriculure producion variables was done for he nex 10 period, which will analyze he behaviors and reacions o he incoming shocs by he relevan variables in hree periods of shor erm, miderm and long erm. The resuls are as follows: 924

5 Middle-Eas J. Sci. Res., 12 (7): , 2012 Response funcion of agriculure producion variable o shoc from producion of agriculure secor: he shoc from he producion of agriculure secor during he firs period causes increasing of producion o he level This effec causes increasing of in he fifh period. This descending rend coninues unil i reaches o in enh period. In oher words, he shocs and he flucuaions from he producion of agriculure secor mae producion of agriculure secor in he shor ime o increase abou Bu in he medium and long erm his rend decreases and come o in long erm. The response funcion of he producion o shoc from he invesmen of agriculure secor: he producion response of agriculure secor o he incoming shoc in he shor ime was a firs in he level bu i will increase gradually and reaches o in miderm. Bu his rend decreases again and reaches o in he long erm. In oher words, he shoc from invesmen will mae he producion in he medium erm increase. The response funcion of he variable of agriculure secor producion o he shoc from he damages of flood: producion response o he shoc in he shor ime has been a firs a he level bu i will increase gradually and will reach o in he medium erm. Bu his rend will decrease again and will reach o in he long erm. In oher words, he shoc from he damages of flood has negaive impac on agriculure secor in he shor ime and causes he producion o decrease. Bu he mos impac of he shoc will occur in he medium erm and causes he producion o grow. This rend will decrease gradually in he long erm. Analysis of Variance Decomposiion: In his secion he resuls of analysis variance decomposiion forecas for a period of 10 year can be inerpreed. In his mehod, flucuaions of differen variables can be divided ino variables of he paern and he relaive imporance of a variable can be seen in he behavior of oher variables [5]. Thus, he conribuion of each variable can be measured based on he changes in oher variables over he ime. The resuls are shown in Table 8: Wih he help of variance decomposiion, he conribuion of each variable on changes of oher variables was evaluaed. The resuls indicae ha he mos conribuions of flucuaions in he shor ime is relaed o flood damages wih he amoun 97.46% and Table 7: The impulse response funcions for he variable Of agriculure per capia producion Period Log y Log inv Log dmg Source: findings of research Table 8: Variance decomposiion for he variable of agriculure per capia producion Period Log ind Log invid Log open Source: Findings of research hen he producion of agriculure secor wih 2.49% and he invesmen of agriculure secor wih he amoun 0.04%. In he medium erm abou 82% of flucuaions of agriculure producion are explained by shoc from flood damage and abou 7% are explained by he invesmen of agriculure secor and finally abou 10% are explained by he variable of agriculure secor producion. Also in he long erm hese figures will reach respecively o 63%, 20% and 17%. Recommendaions: The allocaion of necessary credis for compensaing he creaed damages and prevening agains he occurrence of nex floods regarding o his poin ha he reurning period of he flood is every 25 year. Wih regard o he fac ha unforunaely every year he naural disasers impose many financial and bodily losses on differen secors of economic and especially agriculure, he exisence of agriculure producs insurance is necessary for compensaing hese losses. 925

6 Middle-Eas J. Sci. Res., 12 (7): , 2012 The coninuaion of producion and he rend of 3. Rasmussen, T.N., Macroeconomic Implicaions invesmen for he mos of wea farmers are no of Naural Disasers in he Caribbean. IMF woring possible wihou he exisence of proper insurance paper, pp: 04/224. sysem. A he same ime he insurance fund of 4. Fombay, T., Y. Ieda and N. Loayza, The agriculure producs has been founded for Growh afermah of Naural Disasers, he world compensaing he damages of flood, drough and he ban. oher naural disasers. For providing beer services 5. Abassinejad, H. and A. Shafiee, Is money really and developing his ind of insurance in he counry neural in he Iranian economy? Journal of Economic he necessary paricipaion of he governmen and Research, (68): insurance companies is fel seriously. 6. Abrishami H. and M. Mehrara, Pracical economerics: nd modern approaches, 2 ediion, REFERENCES Tehran Universiy Publicaions, Tehran 7. Sims, C.A., Macroeconomics and Realiy, 1. Kazem, J.M., Comparaive sudy of Economerica, 48: compensaion mehods of damages of naural 8. Noferesi Mohammad, Common roo and disasers in Iran and he world, season leer of co-addiion in economerics. Ocober-november, Insurance Indusry, (1): Rasa publicaions 2. Charveria, C., Naural Disasers in Lain 9. Seddiqih, R and Lavler.a, Pracical America and Caribbean: An overview of Ris. Iner- economerics, ranslaion by sh. Shirin Bahsh, American Developmen Ban Research Deparmen, Avaye Nur publicaions. Woring Paper, pp: