ChinaCFA! Learning Board

过犹不及事缓则圆地方经济增长目标约束与全要素生产率余泳泽

摘要: MANAGEMENTWORLDNo.72019ABSTRACTSOFSELECTEDARTICLESTargetofLocalEconomicGrowthandTotalFactorProductivityYuYongze,LiuDayongandGongYuInthetransitioneconomyrepresentedbyChina,themanagementmodeofeconomicgrowthtargetismainlyby“in⁃tervention”.Thismodeinevitablyleadstotheresultsthatthelocalgovernmenteconomicbehaviorwillbeinfluencedsignificantlybythe“interference”witheconomicgrowthtargets.Thispaperteststheeffectoflocaleconomicgrowthtargetsonthetotalfactorproductivitythroughatheoreticalmodel,whichbasedonthesampleoftheeconomicgrowthtargetdatafromChina's230prefecture-levelmunicipalgovernmentworkreports.Theresultsshowthat:(1)Wheneco⁃nomicgrowthtargetmakingadoptsthe“margin”setmode,thewholefactorproductivitylevelofthiskindofprefec⁃ture-levelcityishigherandwhengrowthinthetargetlanguageusehardconstraintsvocabularysuchas“above”,“makesure”,“to”andsoon,thenthesecitieshavealowertotalfactorproductivity.Theeffectofhardconstraintontotalfactorproductivityismorereflectedintechnicalefficiency(TE);(2)Theeconomicgrowthtarget“layerbylay⁃er”hassignificantlyinhibitedtheimprovementoftotalfactorproductivity,whichhasbeenmoreserioussince2006;(3)Ifacityusessoftconstraintsoneconomicgrowthtargetsetmode,it'stotalfactorproductivitywillbeimprovedduetoabetteractualcompletion;(4)Theintermediatemechanisminspectionshowsthattheeconomicgrowthtargets“layerbylayer”reducethetotalfactorproductivitylevelthroughthesurgeofinvestment,increasingthechangeoftheinvestmentstructureliketherealestateandthedistortionofpublicexpenditurestructure.Theaboveempiricalconclu⁃sionisstillrelativelystableonthebasisofconsideringendogenousproblems.ThisresearchcangivesomeimportantcluestounderstandingaseriesofuniquephenomenaofChina'seconomicoperation,suchastheproblemofGDP,thequestionofrepeatedconstructionandsoon.ArtificialIntelligence,StructuralTransformationandLaborShareGuoKaimingArtificialintelligence(AI)isastrategictechnologythatleadsanewwaveoftechnologicalrevolutionandstructur⁃altransformation.ThepaperstudiestheeffectsofAIonstructuraltransformationandfactorincomeshares.BasedontheassumptionsthatAIisageneral-purposetechnologywiththenewinfrastructure'scharacteristics,maysubstitutetoeithercapitalorlabor,andappliestoindustrieswithdifferentdegrees,webuildamulti-sectordynamicgeneralequi⁃libriummodel,whichshowsthattherisesinAIservicesorAI-specifictechnologyinducefactorstomovebetweensec⁃tors,withthedirectiondependingonthesectoraldifferencesintheoutputelasticityofAIandthesubstitutionelastici⁃tybetweenAIandtraditionalproduction.Suchaprocessofstructuraltransformationalsocausesthevariationsinla⁃borshare.Wederivethetheoreticalconditionsthatdeterminethedirectionofstructuraltransformationandchangeinlaborshare.WealsodiscussthepolicyimplicationsabouthowtoadvanceAItechnologytoachievehigh-qualitydevel⁃--202 ...

MANAGEMENT WORLD
No.7 2019
ABSTRACTS OF SELECTED ARTICLES
Target of Local Economic Growth and Total Factor Productivity
Yu YongzeLiu Dayong and Gong Yu
In the transition economy represented by Chinathe management mode of economic growth target is mainly byin⁃
tervention. This mode inevitably leads to the results that the local government economic behavior will be influenced
significantly by theinterferencewith economic growth targets. This paper tests the effect of local economic growth
targets on the total factor productivity through a theoretical modelwhich based on the sample of the economic growth
target data from China's 230 prefecture-level municipal government work reports. The results show that1When eco⁃
nomic growth target making adopts themarginset modethe whole factor productivity level of this kind of prefec
ture-level city is higher and when growth in the target language use hard constraints vocabulary such asabove
make suretoand so on then these cities have a lower total factor productivity. The effect of hard constraint on
total factor productivity is more reflected in technical efficiencyTE2The economic growth targetlayer by lay⁃
erhas significantly inhibited the improvement of total factor productivitywhich has been more serious since 2006
3If a city uses soft constraints on economic growth target set modeit's total factor productivity will be improved
due to a better actual completion4The intermediate mechanism inspection shows that the economic growth targets
layer by layerreduce the total factor productivity level through the surge of investmentincreasing the change of the
investment structure like the real estate and the distortion of public expenditure structure. The above empirical conclu
sion is still relatively stable on the basis of considering endogenous problems. This research can give some important
clues to understanding a series of unique phenomena of China's economic operationsuch as the problem of GDPthe
question of repeated construction and so on.
Artificial IntelligenceStructural Transformation and Labor Share
Guo Kaiming
Artificial intelligenceAIis a strategic technology that leads a new wave of technological revolution and structur
al transformation. The paper studies the effects of AI on structural transformation and factor income shares. Based on
the assumptions that AI is a general-purpose technology with the new infrastructure's characteristicsmay substitute to
either capital or laborand applies to industries with different degreeswe build a multi-sector dynamic general equi⁃
librium modelwhich shows that the rises in AI services or AI-specific technology induce factors to move between sec
torswith the direction depending on the sectoral differences in the output elasticity of AI and the substitution elastici
ty between AI and traditional production. Such a process of structural transformation also causes the variations in la
bor share. We derive the theoretical conditions that determine the direction of structural transformation and change in
labor share. We also discuss the policy implications about how to advance AI technology to achieve high-quality devel
-- 202

页次:18/1818页 第 [1] 页 第 [2] 页 第 [3] 页 第 [4] 页 第 [5] 页 第 [6] 页 第 [7] 页 第 [8] 页 第 [9] 页 第 [10] 页 第 [11] 页 第 [12] 页 第 [13] 页 第 [14] 页 第 [15] 页 第 [16] 页 第 [17] 页 第 [18]

 

 

 

  • 300098 积分
  • 69999 文章数
  • 4765 发帖数

 

Ta的文章(4765)发表文章

最新评论

QQ|小黑屋|手机版|Archiver|ChinaCFA Learning ORG ( 京ICP备13009572号 )

GMT+8, 2019-10-4 14:59 , Processed in 0.033055 second(s), 11 queries , File On.

Powered by Discuz! X3.4

© 2001-2017 Comsenz Inc.

返回顶部