An Analsis f Tal Facr Prducivi Grwh in China s Agriculural Secr Shih-Hsun Hsu * Deparmen f Agriculural Ecnmics Nainal Taiwan Universi Taipei Taiwan Ming-Miin Yu Deparmen f Indusrial Engineering Da-Yeh Universi Chung-Hwa Taiwan Ching-Cheng Chang Insiue f Ecnmics Academia Sinica Taipei Taiwan. Paper prepared fr presenain a he American Agriculural Ecnmics Assciain Annual Meeing Mnreal Canada Jul 27-30 2003. Cprigh 2003 b Shih-Hsun Hsu Ming-Miin Yu and Ching-Cheng Chang. All righs reserved. Readers ma make verbaim cpies f his dcumen fr nn-cmmercial purpses b an means prvided ha his cprigh nice appears n all such cpies. * Please send all he crrespndences he fllwing address: Dr. Shih-Hsun Hsu Deparmen f Agriculural Ecnmics Nainal Taiwan Universi N. Rsevel Rad Secin 4 Taipei 06-7 Taiwan R.O.C. E-mail: m577@ccms.nu.edu.w
An Analsis f Tal Facr Prducivi Grwh in China s Agriculural Secr Shih-Hsun Hsu Ming-Miin Yu and Ching-Cheng Chang * Absrac A panel daa f 27 prvinces in China is used analze he prducivi grwh in China s agriculural secr ver he perid 984-999. We firs cmpue he upu-rienaed Malmquis prducivi indees and is decmpsiins using nn-parameric DEA (Daa Envelpmen Analsis) apprach. Tbi regressins are hen used idenif he majr deerminans f TFP grwh and is cmpnens. Resuls shwed ha he verall TFP grwh remains sluggish in China s agriculural secr. Gvernmen a plicies and invesmens n R&D have n e been ver effecive in prming prducivi efficienc and echnical prgress. On he her hand reginal facr seems be a ver impran deerminan n efficienc imprvemen and echnical innvain. Reginal dispariies als warran furher invesigains n her sci-ecnmic and gegraphic characerisics f prvincial agriculural prducin. Ke wrds: al facr prducivi agriculure China Malmquis prducivi indees * Shih-Hsun Hsu is a prfessr in he Deparmen f Agriculural Ecnmics a Nainal Taiwan Universi Taipei Taiwan. Ming-Miin Yu is an assisan prfessr in he deparmen f Indusrial Engineering a Da-Yeh Universi Chung-Hwa Taiwan. Ching-Cheng Chang is a Research fellw in he Insiue f Ecnmics a Academia Sinica and a prfessr in he Deparmen f Agriculural Ecnmics a Nainal Taiwan Universi Taipei Taiwan. Financial suppr frm Krea Insiue fr Inernainal Ecnmic Plic is graefull acknwledged. We alne are respnsible fr an errrs.
An Analsis f Tal Facr Prducivi Grwh in China s Agriculural Secr. Inrducin China is ne f he ms ppulus cunries in he wrld. There is cnsiderable speculain regarding he prducivi perfrmance f is agriculural secr. Esimaes f China s agriculural prducivi have been cnrversial. Differences in he esimain mehds and reliabili in he saisics creaed man debaes n he rend f China s agriculural prducivi. McMillan Whalle and Zhu (989) Wen (993) and Lin and Wen (995) prvide cmprehensive reviews n he al facr prducivi (TFP) grwh in China s farm secr during he refrm era. The shw ha he rapid TFP grwh parl cnribues he rural China s miracle grwh in he earl 980 s. Hwever hers argue ha TFP grwh has sagnaed afer 985 in spie f he fac ha upu has cninued grw a ver 5 percen per ear. Prducivi is generall defined in erms f he efficienc imprvemen and echnical change wih which inpus are ransfrmed in upus in he prducin prcess. Indees f prducivi herefre are simpl he rais f an aggregae upu inde an inde fr al facr use. The ms ppular frm fr esimaing TFP grwh in he pas is he Törnqvis inde. The Törnqvis inde calculaes TFP grwh based n infrmain cncerning prices and uses cs/revenue shares as 2
weighs aggregae inpus/upus. Hwever when calculaing he Törnqvis inde bserved upu is assumed be equivalen frnier upu. Cnsequenl decmpsiin f he TFP grwh in he mvemens wards (efficienc imprvemen) and shifs in he prducin frnier (echnical change) is n pssible. On he her hand he Malmquis inde has gained cnsiderable ppulari in recen ears since Färe e al. (994) appl he linear-prgramming apprach calculae he disance funcins ha make up he Malmquis inde. There are hree reasns fr his increasing ppulari. Firs f all since he daa envelpmen pe f analsis can be direcl applied calculae he inde he Malmquis inde has he advanage f cmpuainal ease. Secnd calculain f he Malmquis inde des n require infrmain n cs r revenue shares aggregae inpus r upus. Cnsequenl he Malmquis inde is less daa-demanding han he Törnqvis inde. Finall he Malmquis prducivi-change inde is mre general in ha i allws fr furher decmpsiin f TFP grwh in changes in efficienc and changes in echnlg. This furher decmpsiin is impran fr faciliaing a mulilaeral cmparisn ha ma help eplain and characerize he differences and similariies in grwh paerns fr differen regins in China. The purpse f his sud is wfld. Firs we inend use he Malmquis inde calculae TFP grwh in China s agriculural secr. A panel daa f 27 prvinces 3
is clleced ver he perid 984-999. Since he mehd cnsrucs a bes-pracice frnier frm he sample he resuls n nl allw us cmpare he paern f prducivi grwh and is cmpnens bu als idenif hse prvinces shifing he frnier ver ime (i.e. he "innvars"). Ne Tbi regressin analsis will be cnduced idenif he majr deerminans f TFP grwh and is cmpnens. Specificall he rle f gvernmen plicies invesmen in infrasrucure and educain in he prcess f TFP grwh will be invesigaed. The remaining f he paper is rganized as fllws. The ne secin briefl describes he mehdlg f measuremen f efficienc and prducivi. Secin 3 describes he daase. Secin 4 measures TFP grwh using he Malmquis inde apprach. Regressin resuls n he majr deerminans are als presened. Summar and cncluding remarks are presened in he las secin. 2. Malmquis TFP Inde Apprach The Malmquis prducivi inde (MPI) as prpsed b Caves Chrisensen and Diewer (982) allws ne describe muli-inpu muli-upu prducin wihu invlving eplici price daa and behaviral assumpins. The MPI idenifies TFP grwh wih respec w ime perids hrugh a quaniaive rai f disance funcins (Malmquis 953). Disance funcins can be classified in inpu disance 4
funcins and upu disance funcins. Inpu disance funcins lk fr a minimal prprinal cnracin f an inpu vecr given an upu vecr while upu disance funcins lk fr maimal prprinal epansin f an upu vecr given an inpu vecr. B using disance funcins he MPI can measure TFP grwh wihu cs daa nl wih quani daa frm muli-inpu and muli-upu represenains f echnlg. In his sud we use upu disance funcins. Befre frmulaing MPI we need sme basic cnceps and definiins. Assuming ha fr each ime perid = 2 T N i R and M i R dene respecivel an N inpu vecr and an M upu vecr fr perid (=2 T). The se f prducin pssibiliies is given b he clsed se {( ) can prduce } S = : () where he echnlg is assumed have he sandard prperies such as cnvei and srng dispsabili as described in Färe e al (994). The upu ses are defined in erms f S as: { : ( S } P ( ) = ). (2) Accrding Shephard (970) he upu disance funcin in fr an prducivi uni wuld be: { : ( ) P ( )} d ( ) = inf θ θ (3) where he subscrip sands fr upu riened. Färe and Lvell (978) shwed ha he disance funcin was he Farrell s reciprcal measuremen (Farrell 957). 5
6 This disance funcin represens he smalles facrθ b which an upu vecr is deflaed s ha i can be prduced wih a given inpu vecr under perid s echnlg. Tha is sa ) ( d prvides a sandardized average f disance f a uni in he perid frnier f prducin se when inpus are cnsan. The prducivi change using echnlg f perid as reference is as fllws: = ) ( ) ( ) ( d d M. (4) Similarl we can measure he MPI wih perid echnlg as references as fllws: = ) ( ) ( ) ( d d M. (5) In rder avid chsing an arbirar perid as reference Färe e al (994) specifies he MPI as he gemeric mean f he w indices abve: 2 ) ( ) ( ) ( ) ( ) ( = d d d d M. (6) The MPI frmula in inde (6) can be equivalenl rewrien and decmpsed in he fllwing w cmpnens: EFFCH = ( ) ( ) d d and (7) TECHCH = ( ) ( ) ( ) ( ) d d d d 2 /. (8) The EFFCH is he efficienc change inde and measures he upu-riened shif in
echnlg beween he w perids. When i is greaer (r less) han ne here eiss sme imprvemen (r deerirain) in he relaive efficienc f his uni. The erm TECHCH is he gemeric average f bh cmpnens and measures echnical change beween perid s and. The firs cmpnen in TECHCH measures he psiin f uni wih respec he echnlgies in bh perids. The secnd cmpnen als esimaes his fr uni. If he TECHCH is greaer (r less) han ne hen echnlgical prgress (r regress) eiss. Unfrunael i is impssible bserve he se f prducin pssibiliies S. Therefre he indices menined abve mus be esimaed. Varius mehdlgies have been used (Hjalmarssn Kumbhakar and Heshmai 996). Färe e al (994) use Daa Envelpmen Analsis (DEA) mehds esimae and decmpse he MPI. The DEA mehd is a nn-parameric apprach in which he envelpmen f decisin-making unis (DMU) can be esimaed hrugh linear prgramming mehds idenif he bes pracice fr each DMU. The efficien unis are lcaed in he frnier and he inefficien nes are envelped b i. Fur linear prgrams (LPs) mus be slved fr each DMU bain he disances defined in equain (3) and he are: [ d ( )] = ma i i φ λ φ s φ i Y λ 0 7
i X λ 0 λ 0 (9) [ d ( )] = ma φ i i φ λ s φ Y λ i 0 i X λ 0 λ 0 (0) [ d ( )] = ma i i φ λ φ s φ Yλ i 0 i X λ 0 λ 0 () [ d ( )] = ma φ i i φ λ s φ Y 0 i λ X i λ 0 λ 0 (2) Here K N M and T represen respecivel he al number f firms inpus upus and ime perids in he sample φ denes a scalar which represens he prprinal epansin f upu vecr given he inpu vecr λ [ λ λ 2 ] = denes he L λ K K vecr f cnsans which represen peer weighs f a firm i and i represen he M upu vecr and he N inpu vecr respecivel f firm i in perid Y and X represen respecivel he M K upu mari and N K inpu mari cnaining he daa fr all firms in perid. The nains fr 8
perid are defined in a similar fashin. Equains (9) and (0) measure he echnical efficienc f he i h firm in perid and respecivel. In equains () and (2) he i h bservain frm perid is cmpared he echnlg cnsruced using he perid daa and vice versa. 3. Daa This subsecin ffers mre deails n he definiin f inpus and upus and he daase used in his paper. Due he daa limiain ur empirical sud is based n a panel f aggregaed daa. The surce f daa cmes frm he Rural Saisical Yearbk f China. Because daa f Shanghai Hainan Chngqing and Tibe are n available unil ear 984 he panel cnains 27 agriculural prducing prvinces ver he perid 984-2000. In he empirical analsis he fllwing upu and inpus are used mdel he prducin echnlg. The upu variable used in ur TFP analsis is he al grss upu value f farming fresr animal husbandr and fisher. There are five inpu variables: number f rural labr irrigaed area machiner chemical ferilizer and elecrici cnsumpin. Sample means f hese variables are presened in Table. The upus are measured in 00 millin RMB f grss values deflaed b agriculural price inde. The numbers f rural labr are measured in 9
0000 persns. Irrigaed area is measured in 000 hecares. Agriculural machiner is measured in 0000 kilwa. Chemical ferilizer is measured in 0000 ns while elecrici cnsumpin is measured in 00 millin kilwa-hur. Table. Sample Means 984-992 993-2000 984-2000 Oupu (00 millin RMB) 9.7 98.48 56.50 Inpus: Labr (0000 persns) 03.53 04.24 03.86 Pwer (0000 kw) 885.93 390.34 23.30 Irrigaed (000 hecares) 568.9 785.52 670.84 Elecrici (00 millin kwh) 25.39 62.6 42.69 Ferilizer (0000 ns) 77.4 28.63 0.5 Affeced areas (000 hecares) 772.25 894.33 829.70 Fr furher cnsiderain f he effec f naural disaser n he prducivi f China s agriculure areas affeced b naural disaser measured in 000 hecares is als included in his sud. I is epeced ha he influence f areas affeced b naural disaser is negaivel relaed he prducivi. We can include his variable as a nn-discreinar inpu b impsing a resricin f he fllwing frm: z i Zλ 0 (3) where z i dene al areas affeced b naural disaser f he i h prvince/regin fr perid and mari Z denes he affeced areas f he full sample. Since his inpu variable has a negaive effec n prducivi we can als inver he measure as an 0
upu b he fllwing frm: z i Zλ 0. (4) Since he mehd cnsrucs a bes-pracice frnier frm he sample he resuls n nl allw us cmpare he paern f prducivi grwh and is cmpnens bu als idenif prvinces shifing he frnier ver ime (i.e. he "innvars"). Ne a regressin analsis will be cnduced idenif he surces f TFP grwh. Specificall he rle f gvernmen plicies invesmen in infrasrucure and educain in he TFP grwh prcess will be invesigaed. In he secnd sage f ur analsis we will idenif he facrs influencing he prducivi and efficienc perfrmance. The Tbi regressin is used regress he indees calculaed in he firs sage n sme caegries facrs. Due daa limiains pled daa f 35 bservains frm 27 prvinces during he perid 995-999 are used in he secnd sage. Table 2 liss variables used in he Tbi regressin mdel and heir definiins. Ms f he variables are bained frm China Saisical Yearbk. Ecepins include machiner-plugh area rais which cme frm he China Agriculure Yearbk.
Table 2. Variable definiins f he Tbi regressin mdel Smbl Definiins Mean Min. Ma. ALWR agriculure labr wage / al labr wage 70.50 49.04 90.2 EATR EDU agriculure and animal husbandr aes and a n he use f culivaed land / grss upu value f farming fresr animal husbandr and fisher ependiure fr peraing epenses f deparmens f culure educain / ppulain 0.0 0.0 0.07 20.42 56.88 865.7 FVSHARE R&D MACHINE TECL grss upu value f farming / grss upu value f all agriculure secr al funds and al ependiures f sae-wned research and develpmen insiuins / ppulain al machiner-plugh area / culivaed land area TECL= fr advanced-echnlg regin 0 fr lw-echnlg regin 58.5 42.06 78.5 70.4 5.6 440.70 3.2 0.4 67.92 _ 4. Empirical Resuls 4. Cmparisn f Prducivi Grwh Insead f presening he resuls fr each ear a summar descripin f average prducivi grwh f each prvince ver he enire sample perid 984-2000 and w sub-perids 984-992 993-2000 are presened. Table 3 prvides descripive saisics f he resuls which indicae ha here are sligh variains in prducivi change acrss he prvinces/regins. Recall ha he value greaer han ne indicaes increasing prducivi and less han ne implies diminishing prducivi frm 2
perid perid. The mean values f TFP change range frm 0.902.062 frm 0.902.042 and frm 0.93.062 fr he whle perid sub-perid and sub-perid 2 respecivel. The average TFP grwh ver he whle perid was -0. percen per annum. The mean value fr he s sub-perid is 0.995 and.003 fr he 2 nd sub-perid respecivel impling ha verall TFP grwh is imprving ver he w perids. This is ms likel due he cperain f equi share adped b he sae-wned farms afer 993 and he cnsrucin f a rural marke ssem afer 992. We als ne ha he TFP grwhs in ms f he easern regins (e.g. Beijing Tianjin Hebei Lianing Jilin Jiangsu Zhejiang Fujian Shandng Hubei Guangdng Ningia Xinjiang) are greaer han ne and n average slighl higher han hse in he her regins. This culd be due primaril he differences in sil quali irrigain and climaic cndiins. Ms f he wesern regins are n irrigaed while ms areas in he cenral and easern regins are irrigaed. Beijing he capial ci f China uperfrms all he her prvinces b a large margin fllwed b he Hebei prvince. Hwever if we divide he imeperid in w sub-perids Beijing s TFP grwh has been caugh up b Hebei during he 2 nd perid. Fujian and Ningia als shw grea imprvemens in TFP grwh during he 2 nd perid. On he her hand Gansu Guizhu Jiangi Tianjin and Xinjiang shw 3
significan deerirains ver ime. (Table 3 here) 4.2 Surces f Prducivi Grwh The TFP grwh can be decmpsed in w cmpnens efficienc change and echnical prgress. The firs cmpnen efficienc change (r efficienc imprvemen) measures he relaive deviain f each prvince frm is crrespnding frnier. The secnd cmpnen echnical prgress capures he mvemen f he frniers ver he sample perid. The decmpsiin resuls are illusraed in Tables 4 and 5 respecivel. We can use he decmpsiin invesigae surces f prducivi grwh. Frm Table 4 and Table 5 we bserve ha he average efficienc and echnical change ver he whle perid was.00 and 0.998 respecivel. This ells us ha frm he nainal perspecive efficienc prgresses b nl 0. percen wihu an significan prgress in echnlg during he sample perid. Frm he reginal perspecive we find ha Hebei prvince shws 2 percen imprvemen in efficienc while Shani prvince deeriraes b 2 percen in caching up wih he frnier. Ms f he her prvinces d n shw significan prgress r regress in efficienc. As fr he echnical prgress he nainal average ver he sample perid is 0.998 4
which indicaes a sligh regress. In cmparisn he 0. percen efficienc prgress he main cnribur he prducivi grwh in China s agriculural secr seems be cming frm efficienc prgress. Hwever if we eamine he reginal resuls Beijing shws a significan prgress b 7.6 percen fllwed b he 2.3 percen in Fujian prvince. The reginal discrepancies in echnical prgress are bviusl much larger han hse in efficienc imprvemen. I urns u ha a he reginal TFP grwh is largel deermined b he echnical prgress. Table 4 and Table 5 als shw ha a he nainal level he w cmpnens are alms idenical ver he w sub-perids. Hwever a he reginal level half f he prvinces eperience prgresses while he her half are eiher unchanged r shw regressive perfrmance. Therefre he reginal dispariies in bh efficienc and echnical prgress are enlarged ver ime. (Table 4 5 here) In he case f empral cmparisns we are als ineresed in cmbining annual changes in TFP in a measure ver a given perid. The inde cnsruced is knwn as a chain inde. The chained indees f TFP efficienc and echnical changes fr he whle sample perid are shwn in Figure. The efficienc change ends be ppsie agains echnical change in he firs sub-perid bu hen mve in he same direcin as he echnical change in he secnd sub-perid. TFP change alwas 5
underges he same endenc wih echnical change. This again indicaes ha echnical change shuld be he main driving frce behind TFP grwh in China s agriculure secr..2.5. SCORE.05 0.95 0.9 0.85 MAL-IND EFFCH TECHCH 0.8 984 987 990 993 996 999 YEAR Figure. Cumulaive Indees f Malmquis Inde Efficienc Change and Technical Change in China s Agriculure 984-999. T cnclude hree empirical findings can be drawn frm he analsis. Firs China s TFP grwh in agriculure cmes mainl frm echnical prgress raher han frm efficienc imprvemen. Secnd TFP grwh rae in he easern regin uperfrms he her pars f China. Third reginal dispariies seem be wrsened ver ime. Differences in sil quali irrigain and climaic cndiins culd be he main reasns. A regressin analsis will fllw s ha he reasns 6
behind hese findings can be invesigaed. 4.3 Regressin Analsis T furher invesigae he deerminans f prducivi grwh in China s agriculure we hphesize a se f influenial facrs based n previus lieraure. In ur regressin mdels he dependen variables are he scres f TFP change efficienc change and echnical change respecivel. As fr he eplanar variables pries fr effecive a rae (EATR) are used invesigae he impacs f agriculural a plic b dividing prvincial gvernmen agriculural a revenues wih heir crrespnding nminal grss agriculural prducs. Per capia ependiures n research and develpmen (R&D) in prvincial level is used represen he effr f R&D invesmens while per capia ependiure n peraing css f culural and educain deparmens (EDU) is used represen human capial invesmens. Agriculure labr wage relaive al labr wage (ALWR) is used eamine wheher changes in labr prducivi will affec TFP grwh while he share f farming in al agriculure upu value (FVSHARE) inends capure he imprance f specializain in prducivi change. Since reginal difference in echnical change has a significan impac n verall TFP change (Ma and K 997) we als add a dumm variable (TECL) in each regressin equain. This dumm variable represens differen level f echnlgies adped in agriculural prducin. 7
A value f ne indicaes a higher endwmen f advanced echnlgies and zer herwise. The Tbi esimaes f prducivi change efficienc change and echnical change equains are repred in Table 6. Table 6. Tbi Regressin Resuls fr Facrs Affecing TFP Efficienc and Technical Change Based n he Panel f 27 Prvinces 995-999. TFP Change Efficienc Change Technical Change Inercep.9592 0.8590 2.950 (2.592)*** (8.8206)*** (3.8383)*** ALWR 0.0223 0.0003 0.0330 (2.362)** (0.0265) (3.4083)*** EATR.6726 2.4046 4.0259 (0.667) (0.2396) (0.40) EDU 0.000-0.000 0.0003 (0.0948) (-0.0599) (0.232) FVSHARE 0.059-0.0043 0.022 (.6529)** (-0.435) (2.2003)** R&D 0.0006-0.000 0.000 (.57) (-0.2204) (.85)** MACHINE -0.043-0.0020-0.022 (-.7355)** (-0.2359) (-2.5625)*** TECL 0.3203-0.0672 0.477 (.5945)* (-0.3243) (2.3634)*** Nes: Numbers in parenheses are ne-ailed- saisics. Aserisks *** ** * indicae significan a % 5% and 0% level respecivel. Table 6 shws ha TFP grwh is psiivel relaed ALWR (significan a % level) FVSHARE (significan a 5% level) and TECL (significan a 0% level). EATR EDU and R&D hugh psiivel relaed prducivi grwh are n 8
significan. The negaive sign n MACHINE is unepeced. These resuls impl ha he main deerminans f TFP grwh in China s agriculure are relaive labr prducivi and level f specializain and iniial echnlg endwmen. Invesmens n R&D human capial and machineries have n e shwn an significan influence. Resuls fr he efficienc change are als given in clumn 2 f Table 6. The resuls shw ha all seven facrs cann eplain efficienc change in China s agriculural prducin. Again ependiures n educain R&D and machineries fail imprve efficienc. Resuls fr echnical change mdel are given in clumn 3 f Table 6. The resuls shw ha ALWR and TECL (significan a % level) FVSHARE and R&D (significan a 5% level) are psiivel relaed echnical change. The cefficiens n EATR EDU are n significan. The negaive sign f MACHINE is again unepeced. Man researchers have cnfirmed ha here eiss a huge surplus labr in rural China which calls fr an urgen plic implemenain. Therefre ecess labr n land ma deer machiner frm imprving farmers efficienc and prducivi The resuls als shw a number f ineresing pins. Higher agriculure labr wage relaive verall wage level ma als induce echnlg prgress. 9
Specializain and echnlgical endwmen have psiive cnribuins n TFP grwh and echnical prgress bu n n efficienc. In her wrds prvinces wih higher farm wage bill and rel mre n farming upus appear fser he adpin f echnlgical innvain. This is n surprising given ha hse prvinces lcaed in mre advanced-echnlg regin als ehibi mre echnical prgresses. Such a paern hins a agglmerain ecnmies in advanced-echnlg. Higher educain spending will n necessaril lead higher TFP grwh while prvinces wih larger ependiures n R&D end have mre echnical prgress han hers. Therefre spending n educain is less effecive a fsering agriculural innvains han spending n R&D. Machiner-plugh area rai (MACHINE) appears be negaivel relaed prducivi efficienc and echnical change. This resul ma arise frm adping he duble-inde mehd in he rural area. The ucme f his mehd is inefficienc in using culivaed area prduce unsuiable grain crps. Higher densi in land use ma induce a higher machiner-plugh area rai bu i ma fail imprve prducivi and efficienc simulaneusl. Effecive agriculural a rae (EATR) is psiivel relaed TFP efficienc and echnical change hugh n significanl s. The pssibili ha relaivel high effecive agriculural a revenues are he ucmes f TFP grwh raher han he resuls warrans furher invesigain. 20
5. Cnclusins This sud prvides empirical evidence abu he prducivi f agriculure secr in China ver he perid 984-999. The w-sage esimain prcedure is applied. In he firs sage we cmpue he upu-rienaed Malmquis prducivi indees and is decmpsiins using nn-parameric DEA apprach. In he secnd sage Tbi regressins are used idenif he majr deerminans f TFP grwh and is cmpnens. A panel f 27 prvinces is used in ur esimain. The resuls indicae ha nainal TFP remains unchanged ver he enire perid bu sme prgresses in he laer perid is bserved. Reginal resuls sugges ha he majr surce f grwh cmes frm echnical prgress. Furher aenins shuld be paid he reginal discrepancies in TFP grwh and wh here is ver lile imprvemen n efficienc given he fac ha marke ssem has been inrduced in he agriculural secr. In he secnd sage we invesigae he influence f gvernmen plicies and invesmen in R&D and educain n efficienc imprvemen echnical change and prducivi grwh. Resuls shw ha he prducivi changes were psiivel relaed he rai f agriculure labr wage al labr wage rai f grss upu value f farming al agriculure grss upu value and a dumm variable f 2
prvinces placed in advanced-echnlg regin. Effecs n TFP grwh f agriculure a and ependiures n educain and R&D are psiive bu n significan. The negaive sign n machiner-plugh area rai is als unepeced bu ma be eplained b he surplus labr prblem in he rural area and he adpin f he duble inde sraeg b he Chinese agriculural auhriies. The analses in his sud shwed ha he verall TFP grwh remains sluggish in China s agriculural secr. Gvernmen a plicies and invesmens n R&D have n e been ver effecive in prming prducivi efficienc and echnical prgress. On he her hand reginal facr seems be a ver impran deerminan n efficienc imprvemen and echnical innvain. Reginal dispariies als warran furher invesigains n her sci-ecnmic and gegraphic characerisics f prvincial agriculural prducin. Our resuls ech hse prvided b Ma and K (997) n prducivi grwh in Chinese agriculure afer rural ecnmic refrms which cnclude ha advanced-echnlg prvinces had higher prducivi and echnlg grwhs han lw-echnlg prvinces in agriculural prducin. Besides he iniial echnlg endwmen relaive wage bill and specializain are als crucial TFP grwh. 22
Table 3. Esimaes f Malmquis TFP Change in China s Agriculure 984-999 b Prvince/Regin 984 985 986 987 988 989 990 99 992 993 994 995 996 997 998 999 984-99 984-92 993-99 Anhui 0.970 0.964 0.982 0.898 0.96 0.972 0.797.4.09 0.969.07.07 0.938.082 0.859 0.932 0.978 0.972 0.986 Beijing.359.585.20 0.669 0.607.53.9.345 0.997 0.973 0.974 0.820 0.939.720 0.89 0.932.076.6.025 Fujian 0.808 0.899.008.009.032 0.998.39.064.084.2.7.086.057.040 0.946 0.975.023.005.048 Gansu.037 0.98.0.028 0.878 0.96.084.033.003 0.970 0.955.002 0.955.035 0.934 0.952 0.989.002 0.972 Guangdng.03.020.024.003.04.039.070.087 0.924.02.054.062.040.079 0.943 0.933.020.022.09 Guangi 0.96 0.949 0.985 0.96.02 0.967.020.078.004.006.066.03.088 0.943 0.89 0.960 0.995 0.994 0.995 Guizhu 0.797.050 0.90 0.989 0.886 0.962.252 0.864.026 0.920 0.93 0.993 0.90.80 0.653 0.950 0.954 0.97 0.933 Hebei 0.787 0.926.005.03 0.97 0.998.040 0.988 0.942.035.02 2.395 0.399.047 0.964 0.944.035 0.963.27 Heilngjiang 0.849.079 0.927.060 0.86.049.064.075 0.963.88.042.08 0.952 0.857 0.869 0.954 0.988 0.992 0.983 Henan 0.86 0.893.64 0.93 0.990.007 0.976 0.969.042 0.977.8.05.05.008 0.936 0.947 0.992 0.979.007 Hubei 0.892 0.979 0.975 0.880.060.042 0.997.052.058.043.30.047.068 0.925 0.922 0.939.00 0.993.0 Hunan 0.886 0.953.06 0.935 0.986 0.987.069.00.05 0.994.009.059.032 0.957 0.908 0.986 0.987 0.983 0.992 InnerMnglia 0.970 0.884 0.966.056 0.890.054.023 0.982 0.966.08 0.890.076 0.878.072 0.938 0.989 0.978 0.977 0.980 Jiangsu 0.728.024 0.977.003 0.96.053 0.99.5.04.088.7.048 0.937.02.88 0.775.008 0.995.025 Jiangi 0.96 0.962 0.976 0.958 0.984.044.46.050.052.040 0.994.044 0.999 0.939 0.928 0.898 0.996.00 0.977 Jilin 0.795 0.970.08.002 0.834.88.099.05.085.096.004.34 0.907.069 0.955 0.894.00.0.008 Lianing 0.655.04 0.994.0 0.889.022.9.072.29.00.096.45 0.993.055 0.96 0.995.03.000.029 Ningia 0.988 0.962 0.838.058.36 0.694 0.800 0.977.068 0.899 0.9 2.382 0.696.20 0.624 0.82.05 0.967.076 Qinghai.097 0.949.54 0.978 0.529 0.864 0.988.234 0.853 0.874 0.954.030 0.75.22 0.925 0.945 0.957 0.96 0.952 Shaani 0.894.03.003 0.983 0.968 0.97.022.024.2 0.962 0.985.4 0.964.08 0.893 0.946 0.993.00 0.983 Shandng 0.846 0.954.027 0.979 0.954.0.209 0.99.07.226.024.042.000 0.952 0.936 0.976.009 0.999.022 Shani 0.635 0.899 0.955.06 0.996 0.972 0.883.080.048.005 0.966.086 0.928.08 0.936 0.947 0.967 0.948 0.993 Sichuan 0.902 0.98.009 0.984 0.973 0.993.60.009 0.984 0.990.024.07 0.985 0.934 0.942 0.948 0.990 0.999 0.977 Tianjin 0.940.055.4 0.898 0.756.5.020 0.98.082 0.997 0.92 0.954.055.026 0.878 0.929.003.033 0.966 Xinjiang.20 0.965.035.024 0.944.062 0.994.079.003.040 0.999 0.99.003.069 0.89 0.994.03.025 0.998 Yunnan 0.94 0.898.057.030 0.95.008 0.978 0.925 0.965 0.974 0.979 0.963 0.995.035.2 0.836 0.976 0.969 0.985 Zhejiang 0.73 0.994 0.980 0.955 0.986.004.077.045.64.025.02.058.03.0 0.936.024.005 0.99.024 Annual Average 0.902 0.993.05 0.979 0.932.022.042.044.029.06.04.38 0.943.062 0.93 0.937 0.999 0.995.003 23
Table 4. Esimaes f Efficienc Change in China s Agriculure 984-999 b Prvince/Regin 984 985 986 987 988 989 990 99 992 993 994 995 996 997 998 999 984-99 984-92 993-99 Anhui.090 0.960.033 0.908.020 0.925 0.70.098.0 0.908 0.992.028 0.963.95 0.967 0.992 0.993 0.983.006 Beijing.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000 Fujian 0.990 0.92 0.933.004.52 0.93.039.02.049.000.000.000.000.000.000.000.00.002.000 Gansu.75.02.043.08 0.964 0.937.07.038.024 0.952 0.937 0.948 0.946.25.03 0.959.02.035 0.983 Guangdng.205.04 0.98 0.986.052.000 0.950.052 0.896 0.942.08 0.984.00.066.03 0.939.008.08 0.996 Guangi.074 0.926.049 0.95.45 0.974 0.877.092.07 0.978.025 0.965.23.002 0.927.033.00.02.008 Guizhu.000.000.000.000.000.000.000.000.000.000.000.000 0.998.000 0.964.037.000.000.000 Hebei 0.922 0.883.095.022.099 0.926 0.995 0.992 0.985 0.987.082.657 0.560.22.025 0.938.08 0.99.053 Heilngjiang.000.000.000.000.000.000.000.000.000.000.000.000.000.000 0.977 0.987 0.998.000 0.995 Henan 0.970 0.882.23 0.908.078 0.936 0.875 0.925.062 0.93.062 0.992.035.7.004 0.966 0.997 0.983.05 Hubei.000.000.000 0.907.03 0.978 0.88 0.995.0 0.958.20 0.956.068 0.924 0.993.058 0.997 0.986.0 Hunan 0.978 0.96.060 0.956.037 0.962 0.948 0.987.063 0.957 0.970 0.992.04.08 0.996.005 0.998 0.995.002 InnerMnglia.000 0.923.00.072.000.000.000.000.000.000 0.940.05 0.870.204.000.000.002.00.004 Jiangsu 0.99.035 0.959 0.982.090 0.933 0.896.070.033 0.986.09 0.940 0.937 0.996.26 0.826 0.994 0.99 0.999 Jiangi.07 0.986.003 0.985.03.03.000.000.000.000.000.000.000.000.000.000.00.002.000 Jilin.000.000.000.000.000.000.000.000.000.000.000.000 0.973.028.000.000.000.000.000 Lianing 0.863 0.93.9.054 0.964 0.985.08.006.04 0.920.032.052 0.995.005.000.000.002.003.00 Ningia.09 0.992 0.793.035.700 0.658 0.923 0.864.35 0.897.092.297 0.888 0.928 0.927.023.0.03.007 Qinghai.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000 Shaani.044.029.022 0.985.046 0.92 0.97 0.976.072 0.93 0.952.047 0.940.040 0.947 0.994 0.990.00 0.976 Shandng 0.954 0.926.078 0.963.049 0.9.098 0.950.027.49 0.962 0.973.024.026.002 0.99.005 0.995.08 Shani 0.722 0.857.05.07.44 0.90 0.834.072.00 0.962 0.943.036 0.98.47.007 0.946 0.983 0.973 0.994 Sichuan.000.000.000.000.000.000.000.000.000.000.000.000.000 0.938.038.007 0.999.000 0.998 Tianjin.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000 Xinjiang.059.000.000.000.000.000.000.000.000.000.000 0.92.043.04.000.000.004.007.00 Yunnan.036 0.926.063.044 0.972.024 0.932 0.98.044 0.938 0.933 0.899 0.990.68.26 0.847 0.997 0.995 0.999 Zhejiang 0.939 0.970.008 0.94.072 0.945 0.992.022.27.000.000.000.000.000.000.000.00.002.000 Annual Average 0.999 0.968.09 0.996.063 0.957 0.959.003.03 0.977.006.026 0.974.043.009 0.983.00.000.002 24
Table 5. Esimaes f Technical Change in China s Agriculure 984-999 b Prvince/Regin 984 985 986 987 988 989 990 99 992 993 994 995 996 997 998 999 984-99 984-92 993-99 Anhui 0.890.004 0.95 0.989 0.943.050.22.05 0.990.067.025.077 0.974 0.906 0.888 0.939 0.989 0.995 0.982 Beijing.359.585.20 0.669 0.607.53.9.345 0.997 0.973 0.974 0.820 0.939.720 0.89 0.932.076.6.025 Fujian 0.86 0.977.080.005 0.896.093.095.04.034.2.7.086.057.040 0.946 0.975.023.004.048 Gansu 0.883 0.970 0.969 0.927 0.90.025.066 0.995 0.980.09.020.057.00 0.920 0.923 0.993 0.979 0.969 0.992 Guangdng 0.84 0.980.044.07 0.964.039.26.033.03.083.036.079.03.02 0.93 0.994.05.008.024 Guangi 0.895.025 0.939.00 0.892 0.993.63 0.987 0.986.029.040.049 0.968 0.942 0.96 0.929 0.988 0.988 0.988 Guizhu 0.797.050 0.90 0.989 0.886 0.962.252 0.864.026 0.920 0.93 0.993 0.903.80 0.677 0.96 0.954 0.97 0.93 Hebei 0.854.049 0.98 0.992 0.883.078.044 0.996 0.957.049.08.445 0.72 0.933 0.940.006 0.992 0.975.05 Heilngjiang 0.849.079 0.927.060 0.86.049.064.075 0.963.88.042.08 0.952 0.857 0.890 0.966 0.990 0.992 0.988 Henan 0.887.02 0.959.005 0.98.077.6.048 0.98.050.053.060 0.98 0.903 0.932 0.98 0.998.000 0.994 Hubei 0.892 0.979 0.975 0.970 0.96.066.3.057.047.089.009.095.000.002 0.928 0.888.006.009.002 Hunan 0.906 0.992 0.959 0.978 0.95.026.27.05 0.955.038.040.068.08 0.885 0.9 0.98 0.99 0.990 0.992 InnerMnglia 0.970 0.958 0.957 0.985 0.890.054.023 0.982 0.966.08 0.947.060.00 0.890 0.938 0.989 0.977 0.976 0.979 Jiangsu 0.793 0.989.09.022 0.88.29.06.04.068.03.024.5.000.026 0.977 0.939.05.005.026 Jiangi 0.902 0.975 0.973 0.973 0.972.030.46.050.052.040 0.994.044 0.999 0.939 0.928 0.898 0.995.008 0.977 Jilin 0.795 0.970.08.002 0.834.88.099.05.085.096.004.34 0.933.040 0.955 0.894.00.0.008 Lianing 0.758.0 0.888.053 0.922.037.099.066.022.088.06.088 0.998.050 0.96 0.995.009 0.995.028 Ningia 0.970 0.969.056.022 0.774.055 0.867.3 0.94.002 0.834.836 0.784.304 0.673 0.794.00 0.976.032 Qinghai.097 0.949.54 0.978 0.529 0.864 0.988.234 0.853 0.874 0.954.030 0.75.22 0.925 0.945 0.957 0.96 0.952 Shaani 0.856.002 0.98 0.998 0.926.054.5.050.037.054.035.063.025 0.978 0.943 0.953.004.002.007 Shandng 0.886.03 0.953.07 0.909.0.0.042 0.990.067.064.07 0.976 0.928 0.934 0.985.004.004.004 Shani 0.880.048 0.909 0.990 0.87.068.058.007 0.953.045.025.049.0 0.943 0.929.00 0.987 0.976.000 Sichuan 0.902 0.98.009 0.984 0.973 0.993.60.009 0.984 0.990.024.07 0.985 0.995 0.908 0.94 0.99 0.999 0.980 Tianjin 0.940.055.4 0.898 0.756.5.020 0.98.082 0.997 0.92 0.954.055.026 0.878 0.929.003.033 0.966 Xinjiang.058 0.965.035.024 0.944.062 0.994.079.003.040 0.999.077 0.962.027 0.89 0.994.00.08 0.999 Yunnan 0.909 0.970 0.995 0.987 0.942 0.985.049.008 0.924.039.049.070.006 0.886 0.95 0.988 0.983 0.974 0.993 Zhejiang 0.760.025 0.972.05 0.99.062.086.023.033.025.02.058.03.0 0.936.024.004 0.988.024 Annual Average 0.902.026 0.999 0.984 0.878.067.087.042 0.998.04.009.093 0.964.024 0.903 0.954 0.998 0.998 0.998 25
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