Growth from 1997-

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1997 - sowing the seeds, nurturing growth and harvesting the rewards

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Moreover, whereas the growth of crops nurturing, the mastery of knowledge also requires reinforcement. A lazy farmer often neglects fertilizing the soil, only to find his crops in poor conditions and finally have a bad year. Similarly, a sluggish student also fails because he does not bother to consolidate what he acquires in class. In fact, learning does not happen once and for all. This has been proved by the experiences of those successful university students. They review their lessons constantly. They never hesitate to ask the teachers for solutions to their puzzles. They also do a lot of reading related to what they learn in class. Thus they have a better command of knowledge than those who do not take the trouble. As a consequence, the industrious students generally have a greater prospect of success.

熊彼特增长模型英文教程--香港浸会大学-工商管理学院课件

innovators!
• James M. Utterback:
If a company only focuses on current product rather
than improving the quality, it will be swept out of market!
• Peter Druke:
12/4/2020
熊彼特增长模型英文教程--香港浸会大学 -工商管理学院
Literature Review of Schumpeterian Model
• How economy grows in Schumpeterian growth theory?
12/4/2020
熊彼特增长模型英文教程--香港浸会大学 -工商管理学院
Literature Review of Schumpeterian Model
*Capital-based growth theory emphasizes on the idea that the accumulation of capital (material capital and human capital) is the most important power to push the advancement of technology and economy.
market.
12/4/2020
熊彼特增长模型英文教程--香港浸会大学 -工商管理学院
Literature Review of Schumpeterian Model
• Creative destruction is the motivation of the development of capitalism. Additionally, the Schumpeterian growth theory which we define here does not order the process of innovation to be a process of creative destruction. Under the vertical innovation framework, process of innovation is a process of creative destruction, so old goods will be replaced by new goods. Whereas, under horizontal innovation framework, both the old and new goods will exist in the market.

人类发展指数 (HDI) 的计算

成人识字率 (%)
综合毛入学率 (%)
人均国内生产总值
（按美元购买力平价）
极大值极小值
85 25
100
0
100
0
40,000 100
计算 HDI 此 HDI 计算演示使用了巴西的数据。
1. 计算预期寿命指数预期寿命指数用于测度一个国家在出生时预期
寿命方面所取得的相对成就。就巴西来说，其 2004 年的预期寿命为 70.8 岁，所对应的预期寿命指数为 0.764。
40
.400
30
成人识字指数 = 88.6 – 0 = 0.886 100 – 0
毛入学指数 = 86 – 0 = 0.857 100 – 0
教育指数 = 2/3 （成人识字指数）+ 1/3 (毛入学指数)
20 10
0
成人识字率 (%)
毛入学率 (%)
.200 0 教育指数
= 2/3 (0.886) + 1/3 (0.857) = 0.876
预期寿命指数 = 70.8 – 25 = 0.764 85 – 25
2. 计算教育指数
90 阈值 85 岁
80 70.8
70
60
50
40
30 阈值 25 岁
20
0.764
预期寿命（岁）
1.00 .800 .600 .400 .200 0 预期寿命指数
教育指数衡量的是一个国家在成人识字及
初、中、高综合毛入学率两方面所取得的相对
GER 指数
教育指数
体面生活人均国内生产总值 (GDP)
（按美元购买力平价）
GDP 指数
人类贫穷指数 (HPI-1)
维度指标

Financial Deepening, Inequality, and Growth

Review of Economic Studies(2006)73,251–2930034-6527/06/00100251$02.00 c 2006International Monetary FundFinancial Deepening,Inequality,and Growth:A Model-BasedQuantitative Evaluation1ROBERT M.TOWNSENDUniversity of Chicago and Federal Reserve Bank of ChicagoandKENICHI UEDAInternational Monetary FundFirst version received August2001;ﬁnal version accepted April2005(Eds.)We propose a coherent uniﬁed approach to the study of the linkages among economic growth,ﬁnancial structure,and inequality,bringing together disparate theoretical and empirical literature.Thatis,we show how to conduct model-based quantitative research on transitional paths.With analytical andnumerical methods,we calibrate and make tractable a prototype canonical model and take it to an ap-plication,namely,Thailand1976–1996,an emerging market economy in a phase of economic expansionwith unevenﬁnancial deepening and increasing inequality.We look at the expected path generated bythe model and conduct robustness experiments.Because the actual path of the Thai economy is imag-ined here to be just one realization of many possible histories of the model economy,we construct acovariance-normalized squared error metric of closeness andﬁnd the best-ﬁt simulation.We also con-struct a conﬁdence region from a set of simulations and formally test the model.We broadly replicate theactual data and identify anomalies.1.INTRODUCTIONWe propose a coherent uniﬁed approach to the study of the linkages among economic growth,ﬁnancial structure,and inequality.Of course,the relationship betweenﬁnancial structure and economic growth has long been studied both empirically and theoretically.Yet,on the one hand, empirical studies have been mainly focused on statistical relationships without a serious study of underlying mechanisms that generate the observations.On the other hand,most theoretical studies have depicted clean but simple mechanisms without serious consideration given to the models’quantitative predictions.The same dichotomy between theory and empirical work exists in the literature on inequality and growth.Early seminal empirical contributions focusing on growth andﬁnancial structure are those of Goldsmith(1969),McKinnon(1973),and Shaw(1973).A more recent empirical treatment is by King and Levine(1993).This body of empirical work establishes thatﬁnancial deepening is at least an intrinsic part of the growth process and may be causal—that is,repressedﬁnan-cial systems harm economic growth.Theoretical efforts at modelling growth and endogenous ﬁnancial deepening include studies by Townsend(1978,1983)and Greenwood and Jovanovic (1990)(hereinafter referred to as GJ).Their models posit costly bilateral exchange or intermedi-ation costs—for example,aﬁxed cost to enter the formalﬁnancial system and marginal costs to1.The views expressed in this paper are those of the authors and do not necessarily represent those of the FRB Chicago,the Federal Reserve System,or the IMF.251252REVIEW OF ECONOMIC STUDIESsubsequent transactions.Other theoretical contributions such as those of Bencivenga and Smith (1991)turn intermediation on and off exogenously and have an external effect that makes growth with intermediation higher.Saint-Paul(1992)features limited diversiﬁcation and multiple equi-librium growth paths,some with developedﬁnancial systems and specialized technologies and others with the opposite.In turn,Acemoglu and Zilibotti(1997)show that capital accumulation is associated with increasing intermediation and that better diversiﬁcation,which comes with higher levels of wealth,reduces the variability of growth.Likewise well known are seminal contributions on growth and inequality.Kuznets(1955) posited that growth is associated with increasing and eventual decreasing inequality.Interest and controversies,especially with respect to cross-country regressions,have continued ever since.A recent paper,Forbes(2000),conﬁrms previous regression studies that high(initial)inequality is associated with low subsequent long-run growth butﬁnds that the relationship is the opposite for the medium term.Resting separately from this strand of the empirical literature are the deservedly well-known theoretical contributions more motivated by Kuznets’s original assertion that growth may bring increasing,and eventually decreasing,inequality—namely,Banerjee and Newman (1993),Aghion and Bolton(1997),Piketty(1997),and Lloyd-Ellis and Bernhardt(2000).We have a concern about this dichotomy between theories and empirical studies.Although most of the theoretical models characterize economic growth withﬁnancial deepening and chang-ing inequality as transitional phenomena,typical empirical research employs regression analysis toﬁnd a coefﬁcient capturing the effect ofﬁnancial depth or inequality on growth.The implicit assumptions of stationarity and linearity are incorrect,even after taking logs and lags,if the vari-ables of actual economies lie on complex transitional growth paths,as they do in the theoretical ing artiﬁcial data generated by a canonical model that by construction displays tran-sitional growth withﬁnancial deepening and increasing inequality,we can sometimes replicate the typical empirical results of the literature:ﬁnancial deepening appears to lead to subsequent higher growth,and inequality to subsequent lower growth.But the statistical signiﬁcance is weak and sensitive to initial history,timeframe,the inclusion of covariates,and so on.Evidently re-gression coefﬁcients are not informative about the underlying true relationships.In pointing this out,we add to the list of concerns which have been raised in recent literature—for example,in Banerjee and Duﬂo(2000).Taking a more constructive tack,we show how to conduct quantitative research on transi-tional growth paths—that is,how to test a model and learn something about actual economies from potential rejections.Our canonical model is based on GJ.2As a prerequisite for a numerical study,we need to characterize analytical properties as much as possible.GJ characterizes primar-ily the initial and asymptotic economy but leaves the all-important transitions somewhat unclear. GJ also studies only the log utility function and does not take its characterization of the log case to data.Here we extend the model to include a wider class of constant relative risk aversion (CRRA)utility functions.We characterize much of the transitional dynamics analytically and,in doing so,provide new results.The seemingly non-convex technology of participation is shown, under some conditions,to be convexiﬁed by the optimal choice of portfolio shares between risky and safe assets.Consequently,savings and portfolio choice are uniquely determined depending on the wealth level.In particular,ironically,those risk-averse households and businesses without access are not condemned to low-yield(though safe)technologies but rather shift towards risky2.There are a few other model-based contributions to empirical work on growth and wealth inequality.Álvarez and Díaz(2002)study evolution of wealth inequality in a nonstochastic neoclassical growth model with minimum con-sumption requirements and apply it to the U.S.economy.It is a calibration study of growth and inequality of wealth,but the growth rate is not affected by inequality of wealth because of perfect capital markets and identical incomes among households.Likewise De Nardi(2004)focuses on savings and bequests to explain wealth inequality in the U.S.and Sweden.De Nardi’s is a steady-state calibration exercise based on an overlapping generation model.This also contains an excellent review of the inter-generational inequality literature.TOWNSEND&UEDA A MODEL-BASED QUANTITATIVE EV ALUATION253 enterprises,especially as their wealth approaches a critical value.These make clear the rich and potentially complicated dynamics not obvious in the original GJ formulation.Indeed,the single valuedness of savings and portfolio choice facilitates further research into transitional dynamics using numerical methods and weﬁnd,for example,overall inequality movement is not neces-sarily monotonic on the growth path—it can increase,then decrease,and then increase again as it moves slowly towards its asymptotic steady state.Financial deepening and growth are not monotonic either.With the model made tractable,we take it to an application,namely,Thailand between1976 and1996,an emerging market economy that was in a phase of economic expansion with uneven ﬁnancial deepening and increasing(and then decreasing)inequality.The Thai economy serves as a prototypical example of the growth and inequality phenomena,pervasive in other countries, that motivate the growth,ﬁnancial deepening,and inequality ing the Thai Socio-Economic Survey(SES),Jeong(2000)ﬁnds that growth and inequality are strongly associated withﬁnancial deepening.We emphasize,however,that our methods are not peculiar to Thailand and we hope to extend the analysis to other countries.In the spirit of the business cycle literature,we calibrate the parameter values.Thus,the benchmark parameters are set from several sources.Data on the yields of relatively safe assets or occupations—5·4%per year for agriculture—and idiosyncratic shocks for business come from the Townsend-Thai data.3Risk aversion is set at values typically found in theﬁnancial economics literature,and the real rate of interest implied by a preference for current consumption is set at 4%.Aggregate shocks are difﬁcult to pin down,and we take advantage of the average and the variation of the observed real growth rate of the per capita gross domestic product(GDP)over the sample period.The marginal cost of utilizing theﬁnancial system is set at low values,but the higherﬁxed cost of entry is such that,as in the Thai SES,6%of the Thai population would have had access to theﬁnancial system in1976,using a distribution of wealth estimated from the same1976SES data.The model is simulated at these and nearby values to deliver predicted paths,which can be compared to the actual Thai data.Note that we are calibrating a non-steady-state stochastic model.The business cycle liter-ature calibrates around stochastic steady states,not transitions.For example,Krusell and Smith (1998)ﬁnd that a pseudo-representative agent model can almost perfectly characterize the be-haviour of the macroeconomic aggregates of an actual incomplete market economy with wealth heterogeneity among agents.This is not the case here.This different outcome stems from the fact that they look at business cycle implications in a stochastic steady state and also their model does not contain a source of non-linearity,such as theﬁxed cost to participate in theﬁnancial system in our model.There is also a literature on transitions or out-of-steady-state dynamics devoted to the study of depressions,but the models are deterministic(e.g.Hayashi and Prescott,2002). Indeed,it is a challenging task to calibrate transitional paths in a stochastic environment,because there is no precedent sensible statistic criteria for goodness ofﬁt.Here,we examine theﬁt of the model in three ways:the expected path,the best-ﬁt prediction,and the conﬁdence region.First,we look at the expected path of the model.It is broadly consistent with the actual pat-tern of growth with increasing inequality along withﬁnancial deepening.However,although the computed expected participation rate and the Theil index(an inequality measure)in the model almost trace out a smoothed version of the actual Thai data,the computed expected GDP growth rate is lower than the actual Thai path.We vary the key parameters and conduct robustness experiments,exploring some trade-offs.3.See Townsend,Paulson,Sakuntasathien,Lee and Binford(1997).254REVIEW OF ECONOMIC STUDIESSecond,we look at the best-ﬁt path among1000simulations.Because the actual path of the Thai economy is imagined here to be just one realization of many possible histories of the model economy,the actual Thai path should differ from the expected path of the model.We construct a metric of closeness between a simulated path and the actual data,considering the growth rate,ﬁnancial deepening,and inequality variables over the entire1976–1996period to be one particu-lar realization.We pick the best-ﬁt simulation under this metric.The best-ﬁt simulation shows a reasonable match with GDP growth rate,but misses a sharp upturn of theﬁnancial deepening in the mid-1980’s as well as the eventual downturn of inequality in the1990’s.Finally,we examine whether the actual Thai data lie within a conﬁdence region generated by the model,using covariance-normalized mean squared error criteria constructed solely from the model-generated histories.The model imposes sharp restrictions on the data,and indeed,at the benchmark parameter values,the model is rejected.But this guides us in identifying where the modelﬁts well and where it does not.Of course,we are not unaware of the tension in this paper between the working out of the details of a structural and well-articulated but highly abstract version of reality,on the one hand, and its serious application to data on the other hand.But the goal here is to show that these two pieces can be brought together.This,we believe,is the way to make progress towards under-standing the true relationships among endogenousﬁnancial deepening,economic development, and changing inequality.2.MODEL2.1.NotationThe model is a simple,tractable growth model with aﬁnancial sector.It is assumed that a large number of people live in the economy and they are consumers and entrepreneurs at the same time.More speciﬁcally,there is a continuum of agents in the economy as if with names indexed on the interval[0,1].At the beginning of each period,they start with their assets k t.After they consume c t,some portion of the assets,they use the remaining assets(i.e.savings s t)to engage in productive activities.An individual can engage in two types of productive activities:a safe but low-return occupa-tion and a high-risk,high-return business.The safe projects are assumed to returnδand the risky businesses are assumed to returnθt+ t,whereθt∈ is a shock common to all the people and t∈E is an idiosyncratic shock,different among people.An individual does not have to stick tothe same projects over time and she/he can choose portionφt∈[0,1]of her/his savings to invest in high-risk,high-return projects.Aﬁnancial institution provides two services to its customers in this simple model.First, aﬁnancial institution offers insurance for idiosyncratic shocks by pooling funds.Second,a ﬁnancial institution selects projects when people apply for loans by inferring the true aggre-gate shocks,and tell them if they should stay in the relatively safe occupation or engage in the high-risk,high-return business.44.An alternative interpretation is that the household puts money on deposit but then borrows toﬁnance a project under advice from the bank.Average repayment is determined by eitherθt orδ.But if risky projects are undertaken, low t households repay less,as if receiving insurance,and high t households repay more,as if paying premia,so as to repayθt on average.That is,the debt repayment is allowed to vary with idiosyncratic shocks.Either way,we recognize that this speciﬁcation of theﬁnancial sector’s advantages may be extreme.One could imagine less-than-perfect risk sharing,constrained by default or private information considerations,for example.One could also imagine less-than-perfect information about forthcoming realizations,as the number of bank clients engaged in any given activity(or sector/region)may be limited and,in any event,past experience in a given activity is only a limited guide to future shocks. However,this speciﬁcation ofﬁnancial services does make the model tractable.TOWNSEND &UEDA A MODEL-BASED QUANTITATIVE EV ALUATION 255Financial services,however,are not free.They require a one-time cost q >0to start using them and a per-period cost (1−γ)∈[0,1]proportional to the investment amount.These costs can be thought of as combinations of intrinsic transactions costs and institutional impediments to a country’s ﬁnancial sector.5In summary,those who are not using ﬁnancial services accumulate assets according tok t +1=(φt (θt + t )+(1−φt )δ)s t ,(1)and those who are using ﬁnancial services accumulate according tok t +1=r (θt )s t ≡γmax {θt ,δ}s t .(2)We assume here formally that a ﬁnancial institution has indeed a real informational advan-tage despite marginal costs,and that the risky asset is potentially proﬁtable enough to attract positive investment,that is,the expected risky return dominates the safe return,even without advance information.6Assumption 1.E [r (θt )]>E [θt ]>δ>0.(3)An individual chooses whether she/he uses ﬁnancial service d t =1or not d t =0,savings s t ,and portfolio share of risky projects φt to maximize her/his expected lifetime utility E 1∞∑t =1βt −1u (c t ) (4)subject to the budget constraintc t =k t −s t −q 1d t >d t −1,(5)where β∈(0,1)denotes the discount rate and 1d t >d t −1denotes an indicator function,which takes value 1if an individual joins the ﬁnancial system at t (i.e.d t >d t −1)and takes value 0otherwise.7Though GJ restricts attention to the log contemporaneous utility,u (c t )=log c t ,we analyse as well the CRRA utility function u (c t )=c 1−σt /(1−σ)for σ>0,where σdenotes the degree of relative risk aversion.Because assets might accumulate unboundedly towards ∞with a series of good shocks or deplete towards zero with a series of bad shocks,we introduce three assumptions that ensure the consumer’s optimization problem (4)is well deﬁned.8Note that these assumptions place restrictions on parameter values for the calibration exercise.First,the cumulative distributions of θt and t are assumed to be time invariant and denoted as F (θt )and G ( t ),respectively,with compact supports:95.GJ and Townsend (1978)show this return is consistent with the return offered by a competing set of ﬁnancial intermediaries.We do not pursue decentralized interpretation further.Of course,this speciﬁcation of transactions costs begs for serious generalization,and extension to other kinds of costs,to distinguish among various possible kinds of involvement in the ﬁnancial system.We can allow heterogeneous costs across different households/businesses,and indeed we do allow costs to vary with education and urban/rural status below.6.This is Assumption C and part of Assumption A of GJ.7.In practice,d t will be 0for several periods and then jump to 1and stay there,that is,no one will ever exit in this transitional growth model,and either d t >d t −1or d t =d t −1.See below.8.Note that u (∞)=∞for σ≥1and u (0)=−∞for σ≤1.Three Assumptions 2–4together with some measur-ability requirements guarantee existence of the maximum and optimal decisions.Assumption 4also makes the economy grow perpetually.See the proofs in the working paper,Townsend and Ueda (2001).9.This assumption is also part of Assumptions A and B of GJ.256REVIEW OF ECONOMIC STUDIESAssumption2.Let =[θ,θ]⊂R++and F: →[0,1].Let E=[ , ]⊂R and G:E→[0,1],with E[ t]=0.We sometimes refer to total returnηt≡θt+ t∈[η,η],and its cumulative distribution is denoted as H: +E→[0,1].Second,the lifetime utility(4)must not explode.This is ensured by the following assump-tion,limiting the expected return,adjusted by risk aversion,to be smaller than1/β:10Assumption3.βE[(r(θ))1−σ]<1.Finally,as we focus on perpetual growth cases,it seems natural that the optimized lifetime utility have a real value bounded from below.A sufﬁcient condition is to make the safe return sufﬁciently high,that is,greater than1/β.Assumption4.βδ>1.2.2.Recursive formulationBecause it is difﬁcult to obtain analytical solutions that maximize the lifetime utility(4)for non-participants,we use numerical methods.More speciﬁcally,we use dynamic programming, transforming the original maximization problem at the initial date to a recursive maximization problem conditional on assets and participation status to theﬁnancial system in each period.11 Following the notation of GJ,we deﬁne V(k t)as the value for those who have already joined ﬁnancial intermediaries today,and W(k t)as the value for those who have not joined today but have an opportunity to do so tomorrow.Also,we introduce a pseudo W0(k t)as the value for those who are restricted to never-joining.Explicit forms of these value functions are the following: for participantsV(k t)=maxs t u(k t−s t)+βmax{W(k t+1),V(k t+1)}d F(θt)(6)subject to the wealth accumulation process(2); for non-participantsW(k t)=maxs t,φt u(k t−s t)+βmax{W(k t+1),V(k t+1−q)}d H(ηt)(7)subject to the wealth accumulation process(1);and for never-joinersW0(k t)=maxs t,φt u(k t−s t)+βW0(k t+1)d H(ηt)(8)subject to the same wealth accumulation process(1).We can also establish,as in GJ,that participants will never terminate membership:12W0(k t)≤W(k t)<V(k t).(9)10.Whenσ=1,Assumption3becomesβ<1.Although Assumption3applies to participants,βE[η1−σ]<1 is the analogue condition for non-participants by the same argument.This latter assumption is not necessary,because everyone eventually participates in theﬁnancial system,as is shown below.11.With some additional technical assumptions,we can establish the equivalence of solutions between these two formulations.See proofs in the working paper,Townsend and Ueda(2001).12.Again,see the proof in the working paper,Townsend and Ueda(2001).TOWNSEND&UEDA A MODEL-BASED QUANTITATIVE EV ALUATION257 This implies that V is the only relevant branch on the R.H.S.of the functional equation(6).Note that in the notation of GJ,the entering decision is made next period,not today.We can write an equivalent formulation in which the entering decision is made at the beginning of each period.We use this formulation to derive some analytical properties as well as to obtain numerical solutions.It is simply deﬁned asZ(k t)≡maxd t∈{0,1}{W(k t),V(k t−q)},(10)where V(k t−q)represents the value for new participants today.Then,the non-participant’s value W in the GJ formulation can be also written asW(k t)=maxs t,φt u(k t−s t)+βZ(k t+1)d H(ηt).(11)2.3.Solutions of value functions and policiesFor non-participants with value Z(k),the savings s and the portfolio shareφare functions ofwealth k,and these need to be obtained numerically.13Since the economy grows perpetually,wecannot apply a standard numerical algorithm,which requires an upper bound and a lower boundof wealth level k.Fortunately,the participant’s value V(k)and the never-joiner’s value W0(k) have closed-form solutions together with the associated optimal savings rate and portfolio share.We utilize these two boundary value functions V(k)and W0(k)to compute non-participant’s values W(k)and Z(k).The numerical algorithm is described in Appendix B.2.3.1.Solution V(k),the participant’s value function,and the associated policies.A participant’s value function(6)is easily obtained under log utilities,by guessing and verifying, as in GJ.V(k)=11−βln(1−β)+β(1−β)2lnβ+β(1−β)2ln r(θ)d F(θ)+11−βln k.(12)The saving rateµ,deﬁned asµ≡s/k,total savings divided by beginning-of-period wealth,is equal toβfor the log utility case.More generally for CRRA utilities(σ=1),we can also obtain the analytical formula for the value function V(k)and the optimal savings rateµ∗:V(k)=(1−µ∗)−σ1−σk1−σ(13)andµ∗=βE[r(θ)1−σ]1/σ.(14)2.3.2.Solution W0(k),the value function for those never allowed to join the bank,and the associated policies.Similarly,we can obtain analytical solutions for W0(k),the associated optimal savings rateµ∗∗,and the optimal portfolio share in the risky technologyφ∗∗.For CRRAutility,W0(k)=(1−µ∗∗)−σ1−σk1−σ(15)13.We omit time subscript t in the value functions because individuals face the same problem in each period given the current wealth level k.For detailed derivation of solutions in this section,see Townsend and Ueda(2001).258REVIEW OF ECONOMIC STUDIESandµ∗∗=βE[e∗∗(η)1−σ]1/σ,(16)where e∗∗(η)=φ∗∗η+(1−φ∗∗)δis the optimized per unit return.14 For the log utility,CRRA atσ=1,the value function isW0(k)=11−βln(1−β)+β(1−β)2lnβ+β(1−β)2ln e∗∗(η)d H(η)+11−βln k,(17)and the optimal savings isµ∗∗=β.3.ANALYTICAL CHARACTERIZATION3.1.Concavity of transitional value functionsTo simulate the economy,we need to make sure that we obtain optimal decisions by numerical methods.This would be difﬁcult if there were multiple optimal decisions on savings,portfo-lio share,and entry to theﬁnancial system.This might be the case if the value function were not globally concave functions.15Indeed,the value functions for non-participants might not be concave,because the entry cost is a one-timeﬁxed cost and this introduces a fundamental non-convexity.Put differently,the value function V(k)is strictly concave after entry and the value function W(k)may be strictly concave before entry,but still the outer envelope,the value func-tion Z(k),which determines the entry point,might not be concave(see Figure1).If Z(k)is not concave,W(k)is no longer assured to be concave by deﬁnition(11).However,with a concave period utility,an individual prefers to eliminate the non-concave part of lifetime utility,if possible.Fortunately,the risky asset is a natural lottery which allows some convexiﬁcation.Through the choice of her/his portfolio share in the risky asset,an individ-ual can“control”the randomness of her/his capital in the next period and hence her/his lifetime utility from the next period on.If there remained a non-concave part in the value function next period,increased variation in random returns from an individual’s investment would make her/his expected lifetime utility today bigger relative to non-random investment.Another way to see this result is to note that the outer envelope Z(k)is supposed to reﬂect discounted expected utility for each given wealth k,but evidently higher values are possible,if the envelope is non-concave, by a little more randomization in k today,something possible with greater stochastic investment yesterday.This need for randomization would imply that we have not found the optimal policy yet and that further iteration of the value function would be called for,so as to eliminate the non-concave part eventually.The result here is new to the literature on switching-state models,not only in the costly par-ticipation models of theﬁnancial system mentioned in the introduction,but also in labour-search models.For example,in Danforth(1979),there may exist multiple solutions for an unemployed person to select his/her consumption level as well as when and which job offer he/she accepts over time.In a two-sided match model,Mortensen(1989)shows that multiple equilibria arise when the technology of matching between workers andﬁrms exhibits increasing returns to scale. Gomes,Greenwood and Rebelo(2001),in a recent application of a labour-search model to the14.We can also show uniqueness of the optimal portfolio choiceφ∗∗,which can take on the boundary values0or 1(see Townsend and Ueda,2001).Conditions for the boundary values are given by(i)φ∗∗=0if E[η]<δ,that is,the safe return is sufﬁciently high(sufﬁcient condition)and(ii)φ∗∗=1only if E[1/ησ]≤1/βδ,that is,the safe return is sufﬁciently low(necessary condition).15.Proof of concavity is at best implicit in GJ,and single valuedness of the policy functions seems to be assumed implicitly in GJ though necessary for numerical computation here.。

GrowthHistory(宏观经济学-加州大学-詹姆斯·布拉

The Demographic Transition
• In the world today, not all countries have gone through their demographic transitions
– Nigeria, Iraq, Pakistan, and the Congo are projected to have population growth rates greater than 2% per year over the next generation
– sustained increases in the population and the productivity of labor followed
5-8
Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved.
– population growth accelerated – output per capita grew
5-4
Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved.
Table 5.1 - Economic Growth through Deep Time
The End of the Malthusian Age
• Over time, the rate of technological progress rose
– by 1500, it was sufficiently high so that natural resource scarcity could not surpass it

曼昆经济学原理中文第四版答案22

今天开始宏观经济学部分第22章《国民收入衡量》复习题：1. Explain why an economy’s income must equal its expenditure.答：每宗交易都由卖方和买方，所以经济中支出必然等于收入。

2. Which contributes more to GDP—the production of an economy car or the production of a luxury car? Why?答：豪华汽车市场价值高，所以对GDP贡献大。

（一对一比较）3. A farmer sells wheat to a baker for $2. The baker uses the wheat to make bread, which is sold for $3. What is the total contribution of these transactions to GDP?答：3元，即面包的市场价值，也即销售的最终产品。

4. Many years ago Peggy paid $500 to put together a record collection. Today she sold her albums at a garage sale for $100. How does this sale affect current GDP?答：对现今GDP不产生影响，因为它不是现今生产出来的。

5. List the four components of GDP. Give an example of each.答：消费-如购买CD。

投资-如公司购买一台电脑。

政府采购-如政府采购战机。

净出口-如美国卖小麦给俄罗斯。

6. Why do economists use real GDP rather than nominal GDP to gauge economicwell-being?答：因为实际GDP不受价格波动影响。

中级宏观经济学试题-chain-weighted(2)

In addition, the Commerce department has incorporated some new methodological procedures. For example, it has replaced the previously used straight-line method of estimating depreciation with one based on studies of the prices of used equipment and structures in resale markets.
Some of the more routine changes reﬂect the incorporation of new and revised source data. For example, the revisions to the data on non-durable consumption expenditures for 1993 and 1994 are based on newly available information on retail sales from the 1993 Annual Retail Trade Survey, while the revisions to data on nondurable goods are based on the results of a 1987 input-output analysis constructed by the Commerce Department. Revisions to the services consumption data are based on direct estimates of rental payments for tenant-occupied dwellings, taken from a 1991 Residential Finance Survey.

WHO儿童生长发育标准(2006年版+1997年版)

世界卫生组织儿童生长发育标准　WHO Child Growth Standards（2006 年版）安徽省妇幼保健所翻制　二○○七年八月目录0-4岁年龄别体重Weight-for-age0-4岁年龄别身长（高）　Length/height-for-age45-120厘米身长（高）别体重Weight-for-length/height头围Head circumference-for-age上臂围Arm circumference-for-age肩胛下皮褶厚度Subscapular skinfold-for-age三角肌皮褶厚度Triceps skinfold-for-age体块指数Body mass index-for-age (BMI-for-age)附：　5-6岁男孩年龄别体重　5-6岁女孩年龄别体重　5-6岁男孩年龄别身高　5-6岁女孩年龄别身高　120-138.5厘米男孩身高别体重　120-137厘米女孩身高别体重　更详细内容请参见：　http://www.who.int/childgrowth/standards/en/index.html儿童发育评价方法的等级评价比较　发育等级　离差法　标准差分（Ｚ分）法Z分对应的百分位百分位数法　上等　>x +2s>2 >P97.5> P97中上等　X+1s～ x+2s1～2 P85～Ｐ97.5P80 ～ P97中等　x±１s-1～1 P15～Ｐ85P20、～ P80中下等　x-2s～ x-1s-2～-1 P2.5～Ｐ15P3 ～ P20下等　< x-2s<-2 <P2.5< P3WHO（世界卫生组织）0-4岁年龄别体重标准（2006年版）　女.0－４岁　年.月　－3SD －2SD -1SD 均数　1SD 2SD 3SD 0.0 2.0 2.4 2.8 3.2 3.7 4.2 4.8 0.1 2.7 3.2 3.6 4.2 4.8 5.5 6.2 0.2 3.4 3.9 4.5 5.1 5.8 6.6 7.5 0.3 4.0 4.5 5.2 5.8 6.6 7.5 8.5 0.4 4.4 5.0 5.7 6.4 7.3 8.2 9.3 0.5 4.8 5.4 6.1 6.9 7.8 8.8 10.0 0.6 5.1 5.7 6.5 7.3 8.2 9.3 10.6 0.7 5.3 6.0 6.8 7.6 8.6 9.8 11.1 0.8 5.6 6.3 7.0 7.9 9.0 10.211.6 0.9 5.8 6.5 7.3 8.2 9.3 10.512.0 0.10 5.9 6.7 7.5 8.5 9.6 10.912.40.11 6.1 6.9 7.7 8.7 9.9 11.212.81.0 6.3 7.0 7.9 8.9 10.1 11.513.1 1.1 6.4 7.2 8.1 9.2 10.4 11.813.5 1.2 6.6 7.4 8.3 9.4 10.6 12.113.8 1.3 6.7 7.6 8.5 9.6 10.9 12.414.1 1.4 6.9 7.7 8.7 9.8 11.1 12.614.5 1.5 7.0 7.9 8.9 10.0 11.4 12.914.8 1.6 7.2 8.1 9.1 10.2 11.6 13.215.1 1.7 7.3 8.2 9.2 10.4 11.8 13.515.4 1.8 7.5 8.4 9.4 10.6 12.1 13.715.7 1.9 7.6 8.6 9.6 10.9 12.3 14.016.0 1.107.88.79.8 11.1 12.5 14.316.41.11 7.9 8.9 10.0 11.3 12.8 14.616.72.0 8.1 9.0 10.2 11.5 13.0 14.817.0 2.1 8.2 9.2 10.3 11.7 13.3 15.117.3 2.2 8.4 9.4 10.5 11.9 13.5 15.417.7 2.3 8.5 9.5 10.7 12.1 13.7 15.718.0 2.4 8.6 9.7 10.9 12.3 14.0 16.018.3 2.5 8.8 9.8 11.1 12.5 14.2 16.218.7 2.6 8.9 10.0 11.2 12.7 14.4 16.519.0 2.7 9.0 10.1 11.4 12.9 14.7 16.819.3 2.8 9.1 10.3 11.6 13.1 14.9 17.119.6 2.9 9.3 10.4 11.7 13.3 15.1 17.320.0 2.10 9.4 10.5 11.9 13.5 15.4 17.620.32.11 9.5 10.7 12.0 13.7 15.6 17.920.63.0 9.6 10.8 12.2 13.9 15.8 18.120.9 3.1 9.7 10.9 12.4 14.0 16.0 18.421.3 3.2 9.8 11.1 12.5 14.2 16.3 18.721.6 3.3 9.9 11.2 12.7 14.4 16.5 19.022.0 3.4 10.1 11.3 12.8 14.6 16.7 19.222.3 3.5 10.2 11.5 13.0 14.8 16.9 19.522.7 3.6 10.3 11.6 13.1 15.0 17.2 19.823.0 3.7 10.4 11.7 13.3 15.2 17.4 20.123.4 3.8 10.5 11.8 13.4 15.3 17.6 20.423.7 3.9 10.6 12.0 13.6 15.5 17.8 20.724.1 3.10 10.7 12.1 13.7 15.7 18.1 20.924.53.11 10.8 12.2 13.9 15.9 18.3 21.224.84.0 10.9 12.3 14.0 16.1 18.5 21.525.2 4.1 11.0 12.4 14.2 16.3 18.8 21.825.5 4.2 11.1 12.6 14.3 16.4 19.0 22.125.9 4.3 11.2 12.7 14.5 16.6 19.2 22.426.3 4.4 11.3 12.8 14.6 16.8 19.4 22.626.6 4.5 11.4 12.9 14.8 17.0 19.7 22.927.0 4.6 11.5 13.0 14.9 17.2 19.9 23.227.4 4.7 11.6 13.2 15.1 17.3 20.1 23.527.7 4.8 11.7 13.3 15.2 17.5 20.3 23.828.1 4.9 11.8 13.4 15.3 17.7 20.6 24.128.5 4.10 11.9 13.5 15.5 17.9 20.8 24.428.84.11 12.0 13.6 15.6 18.0 21.0 24.629.25.0 12.1 13.7 15.8 18.2 21.2 24.929.5男.0－４岁　年.月－3SD－2SD-1SD 均数　1SD 2SD 3SD 0.0 2.1 2.5 2.9 3.3 3.9 4.4 5.0 0.1 2.9 3.4 3.9 4.5 5.1 5.8 6.6 0.2 3.8 4.3 4.9 5.6 6.3 7.1 8.0 0.3 4.4 5.0 5.7 6.4 7.2 8.0 9.0 0.4 4.9 5.6 6.2 7.0 7.8 8.7 9.7 0.5 5.3 6.0 6.7 7.5 8.4 9.3 10.4 0.6 5.7 6.4 7.1 7.9 8.8 9.8 10.9 0.7 5.9 6.7 7.4 8.3 9.2 10.311.4 0.8 6.2 6.9 7.7 8.6 9.6 10.711.9 0.9 6.4 7.1 8.0 8.9 9.9 11.012.3 0.10 6.6 7.4 8.2 9.2 10.2 11.412.70.11 6.8 7.6 8.4 9.4 10.5 11.713.01.0 6.9 7.7 8.6 9.6 10.8 12.013.3 1.1 7.1 7.9 8.8 9.9 11.0 12.313.7 1.2 7.2 8.1 9.0 10.1 11.3 12.614.0 1.3 7.4 8.3 9.2 10.3 11.5 12.814.3 1.4 7.5 8.4 9.4 10.5 11.7 13.114.6 1.5 7.7 8.6 9.6 10.7 12.0 13.414.9 1.6 7.8 8.8 9.8 10.9 12.2 13.715.3 1.7 8.0 8.9 10.0 11.1 12.5 13.915.6 1.8 8.1 9.1 10.1 11.3 12.7 14.215.9 1.9 8.2 9.2 10.3 11.5 12.9 14.516.2 1.10 8.4 9.4 10.5 11.8 13.2 14.716.51.11 8.5 9.5 10.7 12.0 13.4 15.016.82.0 8.6 9.7 10.8 12.2 13.6 15.317.1 2.1 8.8 9.8 11.0 12.4 13.9 15.517.5 2.2 8.9 10.0 11.2 12.5 14.1 15.817.8 2.3 9.0 10.1 11.3 12.7 14.3 16.118.1 2.4 9.1 10.2 11.5 12.9 14.5 16.318.4 2.5 9.2 10.4 11.7 13.1 14.8 16.618.7 2.6 9.4 10.5 11.8 13.3 15.0 16.919.0 2.7 9.5 10.7 12.0 13.5 15.2 17.119.3 2.8 9.6 10.8 12.1 13.7 15.4 17.419.6 2.9 9.7 10.9 12.3 13.8 15.6 17.619.9 2.10 9.8 11.0 12.4 14.0 15.8 17.820.22.11 9.9 11.2 12.6 14.2 16.0 18.120.43.0 10.0 11.3 12.7 14.3 16.2 18.320.7 3.1 10.1 11.4 12.9 14.5 16.4 18.621.0 3.2 10.2 11.5 13.0 14.7 16.6 18.821.3 3.3 10.3 11.6 13.1 14.8 16.8 19.021.6 3.4 10.4 11.8 13.3 15.0 17.0 19.321.9 3.5 10.5 11.9 13.4 15.2 17.2 19.522.1 3.6 10.6 12.0 13.6 15.3 17.4 19.722.4 3.7 10.7 12.1 13.7 15.5 17.6 20.022.7 3.8 10.8 12.2 13.8 15.7 17.8 20.223.0 3.9 10.9 12.4 14.0 15.8 18.0 20.523.3 3.10 11.0 12.5 14.1 16.0 18.2 20.723.63.11 11.1 12.6 14.3 16.2 18.4 20.923.94.0 11.2 12.7 14.4 16.3 18.6 21.224.2 4.1 11.3 12.8 14.5 16.5 18.8 21.424.5 4.2 11.4 12.9 14.7 16.7 19.0 21.724.8 4.3 11.5 13.1 14.8 16.8 19.2 21.925.1 4.4 11.6 13.2 15.0 17.0 19.4 22.225.4 4.5 11.7 13.3 15.1 17.2 19.6 22.425.7 4.6 11.8 13.4 15.2 17.3 19.8 22.726.0 4.7 11.9 13.5 15.4 17.5 20.0 22.926.3 4.8 12.0 13.6 15.5 17.7 20.2 23.226.6 4.9 12.1 13.7 15.6 17.8 20.4 23.426.9 4.10 12.2 13.8 15.8 18.0 20.6 23.727.24.11 12.3 14.0 15.9 18.2 20.8 23.927.65.0 12.4 14.1 16.0 18.3 21.0 24.227.9WHO（世界卫生组织）0-4岁年龄别身长（高）标准（2006年版）女.0－２岁　年.月　－3SD －2SD -1SD 均数　1SD2SD 3SD0.0 43.6 45.4 47.3 49.1 51.052.9 54.70.1 47.8 49.8 51.7 53.7 55.657.6 59.50.2 51.0 53.0 55.0 57.1 59.161.1 63.20.3 53.5 55.6 57.7 59.8 61.964.0 66.10.4 55.6 57.8 59.9 62.1 64.366.4 68.60.5 57.4 59.6 61.8 64.0 66.268.5 70.70.6 58.9 61.2 63.5 65.7 68.070.3 72.50.7 60.3 62.7 65.0 67.3 69.671.9 74.20.8 61.7 64.0 66.4 68.7 71.173.5 75.80.9 62.9 65.3 67.7 70.1 72.675.0 77.40.10 64.1 66.5 69.0 71.5 73.976.4 78.90.11 65.2 67.7 70.3 72.8 75.377.8 80.31.0 66.3 68.9 71.4 74.0 76.679.2 81.71.1 67.3 70.0 72.6 75.2 77.880.5 83.11.2 68.3 71.0 73.7 76.4 79.181.7 84.41.3 69.3 72.0 74.8 77.5 80.283.0 85.71.4 70.2 73.0 75.8 78.6 81.484.2 87.01.5 71.1 74.0 76.8 79.7 82.585.4 88.21.6 72.0 74.9 77.8 80.7 83.686.5 89.41.7 72.8 75.8 78.8 81.7 84.787.6 90.61.8 73.7 76.7 79.7 82.7 85.788.7 91.71.9 74.5 77.5 80.6 83.7 86.789.8 92.91.10 75.2 78.4 81.5 84.6 87.790.8 94.01.11 76.0 79.2 82.3 85.5 88.791.9 95.02.0 76.7 80.0 83.2 86.4 89.692.9 96.1 女.2－５岁年.月　－3SD －2SD -1SD 均数　1SD 2SD 3SD 2.0 76.0 79.3 82.5 85.7 88.9 92.2 95.4 2.1 76.8 80.0 83.3 86.6 89.9 93.1 96.4 2.2 77.5 80.8 84.1 87.4 90.8 94.1 97.4 2.3 78.1 81.5 84.9 88.3 91.7 95.0 98.4 2.4 78.8 82.2 85.7 89.1 92.5 96.0 99.4 2.5 79.5 82.9 86.4 89.9 93.4 96.9 100.3 2.6 80.1 83.6 87.1 90.7 94.2 97.7 101.3 2.7 80.7 84.3 87.9 91.4 95.0 98.6 102.2 2.8 81.3 84.9 88.6 92.2 95.8 99.4 103.1 2.9 81.9 85.6 89.3 92.9 96.6 100.3 103.9 2.10 82.5 86.2 89.9 93.6 97.4 101.1 104.82.11 83.1 86.8 90.6 94.4 98.1 101.9 105.63.0 83.6 87.4 91.2 95.1 98.9 102.7 106.5 3.1 84.2 88.0 91.9 95.7 99.6 103.4 107.3 3.2 84.7 88.6 92.5 96.4 100.3 104.2 108.1 3.3 85.3 89.2 93.1 97.1 101.0 105.0 108.9 3.4 85.8 89.8 93.8 97.7 101.7 105.7 109.7 3.5 86.3 90.4 94.4 98.4 102.4 106.4 110.5 3.6 86.8 90.9 95.0 99.0 103.1 107.2 111.2 3.7 87.4 91.5 95.6 99.7 103.8 107.9 112.0 3.8 87.9 92.0 96.2 100.3 104.5 108.6 112.7 3.9 88.4 92.5 96.7 100.9 105.1 109.3 113.5 3.10 88.9 93.1 97.3 101.5 105.8 110.0 114.23.11 89.3 93.6 97.9 102.1 106.4 110.7 114.94.0 89.8 94.1 98.4 102.7 107.0 111.3 115.7 4.1 90.3 94.6 99.0 103.3 107.7 112.0 116.4 4.2 90.7 95.1 99.5 103.9 108.3 112.7 117.1 4.3 91.2 95.6 100.1 104.5 108.9 113.3 117.7 4.4 91.7 96.1 100.6 105.0 109.5 114.0 118.4 4.5 92.1 96.6 101.1 105.6 110.1 114.6 119.1 4.6 92.6 97.1 101.6 106.2 110.7 115.2 119.8 4.7 93.0 97.6 102.2 106.7 111.3 115.9 120.4 4.8 93.4 98.1 102.7 107.3 111.9 116.5 121.1 4.9 93.9 98.5 103.2 107.8 112.5 117.1 121.8 4.10 94.3 99.0 103.7 108.4 113.0 117.7 122.44.11 94.7 99.5 104.2 108.9 113.6 118.3 123.15.0 95.2 99.9 104.7 109.4 114.2 118.9 123.7男.0－２岁　年.月－3SD－2SD-1SD均数　1SD 2SD 3SD0.0 44.246.148.049.9 51.8 53.7 55.60.1 48.950.852.854.7 56.7 58.6 60.60.2 52.454.456.458.4 60.4 62.4 64.40.3 55.357.359.461.4 63.5 65.5 67.60.4 57.659.761.863.9 66.0 68.0 70.10.5 59.661.763.865.9 68.0 70.1 72.20.6 61.263.365.567.6 69.8 71.9 74.00.7 62.764.867.069.2 71.3 73.5 75.70.8 64.066.268.470.6 72.8 75.0 77.20.9 65.267.569.772.0 74.2 76.5 78.70.1066.468.771.073.3 75.6 77.9 80.10.1167.669.972.274.5 76.9 79.2 81.51.0 68.671.073.475.7 78.1 80.5 82.91.1 69.672.174.576.9 79.3 81.8 84.21.2 70.673.175.678.0 80.5 83.0 85.51.3 71.674.176.679.1 81.7 84.2 86.71.4 72.575.077.680.2 82.8 85.4 88.01.5 73.376.078.681.2 83.9 86.5 89.21.6 74.276.979.682.3 85.0 87.7 90.41.7 75.077.780.583.2 86.0 88.8 91.51.8 75.878.681.484.2 87.0 89.8 92.61.9 76.579.482.385.1 88.0 90.9 93.81.1077.280.283.186.0 89.0 91.9 94.91.1178.081.083.986.9 89.9 92.9 95.92.0 78.781.784.887.8 90.9 93.9 97.0男.2－５岁年.月－3SD －2SD -1SD 均数　1SD 2SD 3SD 2.0 78.0 81.0 84.1 87.1 90.2 93.2 96.3 2.1 78.6 81.7 84.9 88.0 91.1 94.2 97.3 2.2 79.3 82.5 85.6 88.8 92.0 95.2 98.3 2.3 79.9 83.1 86.4 89.6 92.9 96.1 99.3 2.4 80.5 83.8 87.1 90.4 93.7 97.0 100.3 2.5 81.1 84.5 87.8 91.2 94.5 97.9 101.2 2.6 81.7 85.1 88.5 91.9 95.3 98.7 102.1 2.7 82.3 85.7 89.2 92.7 96.1 99.6 103.0 2.8 82.8 86.4 89.9 93.4 96.9 100.4103.9 2.9 83.4 86.9 90.5 94.1 97.6 101.2104.8 2.10 83.9 87.5 91.1 94.8 98.4 102.0105.62.11 84.4 88.1 91.8 95.4 99.1 102.7106.43.0 85.0 88.7 92.4 96.1 99.8 103.5107.2 3.1 85.5 89.2 93.0 96.7 100.5104.2108.0 3.2 86.0 89.8 93.6 97.4 101.2105.0108.8 3.3 86.5 90.3 94.2 98.0 101.8105.7109.5 3.4 87.0 90.9 94.7 98.6 102.5106.4110.3 3.5 87.5 91.4 95.3 99.2 103.2107.1111.0 3.6 88.0 91.9 95.9 99.9 103.8107.8111.7 3.7 88.4 92.4 96.4 100.4 104.5108.5112.5 3.8 88.9 93.0 97.0 101.0 105.1109.1113.2 3.9 89.4 93.5 97.5 101.6 105.7109.8113.9 3.10 89.8 94.0 98.1 102.2 106.3110.4114.63.11 90.3 94.4 98.6 102.8 106.9111.1115.24.0 90.7 94.9 99.1 103.3 107.5111.7115.9 4.1 91.2 95.4 99.7 103.9 108.1112.4116.6 4.2 91.6 95.9 100.2 104.4 108.7113.0117.3 4.3 92.1 96.4 100.7 105.0 109.3113.6117.9 4.4 92.5 96.9 101.2 105.6 109.9114.2118.6 4.5 93.0 97.4 101.7 106.1 110.5114.9119.2 4.6 93.4 97.8 102.3 106.7 111.1115.5119.9 4.7 93.9 98.3 102.8 107.2 111.7116.1120.6 4.8 94.3 98.8 103.3 107.8 112.3116.7121.2 4.9 94.7 99.3 103.8 108.3 112.8117.4121.9 4.10 95.2 99.7 104.3 108.9 113.4118.0122.64.11 95.6 100.2 104.8 109.4 114.0118.6123.25.0 96.1 100.7 105.3 110.0 114.6119.2123.9WHO（世界卫生组织）0-2岁身长别体重标准（2006年版）　女.0－２岁　身长　－3SD －2SD -1SD 均数　1SD 2SD 3SD 45 1.9 2.1 2.3 2.5 2.7 3.0 3.345.5 2.0 2.1 2.3 2.5 2.8 3.1 3.446 2.0 2.2 2.4 2.6 2.9 3.2 3.546.5 2.1 2.3 2.5 2.7 3.0 3.3 3.647 2.2 2.4 2.6 2.8 3.1 3.4 3.747.5 2.2 2.4 2.6 2.9 3.2 3.5 3.848 2.3 2.5 2.7 3.0 3.3 3.6 4.048.5 2.4 2.6 2.8 3.1 3.4 3.7 4.149 2.4 2.6 2.9 3.2 3.5 3.8 4.249.5 2.5 2.7 3.0 3.3 3.6 3.9 4.350 2.6 2.8 3.1 3.4 3.7 4.0 4.550.5 2.7 2.9 3.2 3.5 3.8 4.2 4.651 2.8 3.0 3.3 3.6 3.9 4.3 4.851.5 2.8 3.1 3.4 3.7 4.0 4.4 4.952 2.9 3.2 3.5 3.8 4.2 4.6 5.152.5 3.0 3.3 3.6 3.9 4.3 4.7 5.253 3.1 3.4 3.7 4.0 4.4 4.9 5.453.5 3.2 3.5 3.8 4.2 4.6 5.0 5.554 3.3 3.6 3.9 4.3 4.7 5.2 5.754.5 3.4 3.7 4.0 4.4 4.8 5.3 5.955 3.5 3.8 4.2 4.5 5.0 5.5 6.155.5 3.6 3.9 4.3 4.7 5.1 5.7 6.356 3.7 4.0 4.4 4.8 5.3 5.8 6.456.5 3.8 4.1 4.5 5.0 5.4 6.0 6.657 3.9 4.3 4.6 5.1 5.6 6.1 6.857.5 4.0 4.4 4.8 5.2 5.7 6.3 7.058 4.1 4.5 4.9 5.4 5.9 6.5 7.158.5 4.2 4.6 5.0 5.5 6.0 6.6 7.359 4.3 4.7 5.1 5.6 6.2 6.8 7.559.5 4.4 4.8 5.3 5.7 6.3 6.9 7.760 4.5 4.9 5.4 5.9 6.4 7.1 7.860.5 4.6 5.0 5.5 6.0 6.6 7.3 8.061 4.7 5.1 5.6 6.1 6.7 7.4 8.261.5 4.8 5.2 5.7 6.3 6.9 7.6 8.462 4.9 5.3 5.8 6.4 7.0 7.7 8.562.5 5.0 5.4 5.9 6.5 7.1 7.8 8.763 5.1 5.5 6.0 6.6 7.3 8.0 8.863.5 5.2 5.6 6.2 6.7 7.4 8.1 9.064 5.3 5.7 6.3 6.9 7.5 8.3 9.164.5 5.4 5.8 6.4 7.0 7.6 8.4 9.365 5.5 5.9 6.5 7.1 7.8 8.6 9.565.5 5.5 6.0 6.6 7.2 7.9 8.7 9.666 5.6 6.1 6.7 7.3 8.0 8.8 9.866.5 5.7 6.2 6.8 7.4 8.1 9.0 9.967 5.8 6.3 6.9 7.5 8.3 9.1 10.067.5 5.9 6.4 7.0 7.6 8.4 9.2 10.268 6.0 6.5 7.1 7.7 8.5 9.4 10.368.5 6.1 6.6 7.2 7.9 8.6 9.5 10.569 6.1 6.7 7.3 8.0 8.7 9.6 10.669.5 6.2 6.8 7.4 8.1 8.8 9.7 10.770 6.3 6.9 7.5 8.2 9.0 9.9 10.970.5 6.4 6.9 7.6 8.3 9.1 10.0 11.071 6.5 7.0 7.7 8.4 9.2 10.1 11.171.5 6.5 7.1 7.7 8.5 9.3 10.2 11.372 6.6 7.2 7.8 8.6 9.4 10.3 11.472.5 6.7 7.3 7.9 8.7 9.5 10.5 11.573 6.8 7.4 8.0 8.8 9.6 10.6 11.773.5 6.9 7.4 8.1 8.9 9.7 10.7 11.874 6.9 7.5 8.2 9.0 9.8 10.8 11.974.5 7.0 7.6 8.3 9.1 9.9 10.9 12.075 7.1 7.7 8.4 9.1 10.0 11.0 12.275.5 7.1 7.8 8.5 9.2 10.1 11.1 12.376 7.2 7.8 8.5 9.3 10.2 11.2 12.4 76.5 7.3 7.9 8.6 9.4 10.3 11.4 12.5身长－3SD－2SD-1SD均数　1SD 2SD 3SD 78 7.5 8.2 8.99.7 10.6 11.7 12.978.57.6 8.2 9.09.8 10.7 11.8 13.079 7.7 8.3 9.19.9 10.8 11.9 13.179.57.7 8.4 9.110.0 10.9 12.0 13.380 7.8 8.5 9.210.1 11.0 12.1 13.480.57.9 8.6 9.310.2 11.2 12.3 13.581 8.0 8.7 9.410.3 11.3 12.4 13.781.58.1 8.8 9.510.4 11.4 12.5 13.882 8.1 8.8 9.610.5 11.5 12.6 13.982.58.2 8.9 9.710.6 11.6 12.8 14.183 8.3 9.0 9.810.7 11.8 12.9 14.283.58.4 9.1 9.910.9 11.9 13.1 14.484 8.5 9.2 10.111.0 12.0 13.2 14.584.58.6 9.3 10.211.1 12.1 13.3 14.785 8.7 9.4 10.311.2 12.3 13.5 14.985.58.8 9.5 10.411.3 12.4 13.6 15.086 8.9 9.7 10.511.5 12.6 13.8 15.286.59.0 9.8 10.611.6 12.7 13.9 15.487 9.1 9.9 10.711.7 12.8 14.1 15.587.59.2 10.010.911.8 13.0 14.2 15.788 9.3 10.111.012.0 13.1 14.4 15.988.59.4 10.211.112.1 13.2 14.5 16.089 9.5 10.311.212.2 13.4 14.7 16.289.59.6 10.411.312.3 13.5 14.8 16.490 9.7 10.511.412.5 13.7 15.0 16.590.59.8 10.611.512.6 13.8 15.1 16.791 9.9 10.711.712.7 13.9 15.3 16.991.510.010.811.812.8 14.1 15.5 17.092 10.110.911.913.0 14.2 15.6 17.292.510.111.012.013.1 14.3 15.8 17.493 10.211.112.113.2 14.5 15.9 17.593.510.311.212.213.3 14.6 16.1 17.794 10.411.312.313.5 14.7 16.2 17.994.510.511.412.413.6 14.9 16.4 18.095 10.611.512.613.7 15.0 16.5 18.295.510.711.612.713.8 15.2 16.7 18.496 10.811.712.814.0 15.3 16.8 18.696.510.911.812.914.1 15.4 17.0 18.797 11.012.013.014.2 15.6 17.1 18.997.511.112.113.114.4 15.7 17.3 19.198 11.212.213.314.5 15.9 17.5 19.398.511.312.313.414.6 16.0 17.6 19.599 11.412.413.514.8 16.2 17.8 19.6 99.511.512.513.614.9 16.3 18.0 19.8 100 11.612.613.715.0 16.5 18.1 20.0 100.511.712.713.915.2 16.6 18.3 20.2 101 11.812.814.015.3 16.8 18.5 20.4 101.511.913.014.115.5 17.0 18.7 20.6 102 12.013.114.315.6 17.1 18.9 20.8 102.512.113.214.415.8 17.3 19.0 21.0 103 12.313.314.515.9 17.5 19.2 21.3 103.512.413.514.716.1 17.6 19.4 21.5 104 12.513.614.816.2 17.8 19.6 21.7 104.512.613.715.016.4 18.0 19.8 21.9 105 12.713.815.116.5 18.2 20.0 22.2 105.512.814.015.316.7 18.4 20.2 22.4 106 13.014.115.416.9 18.5 20.5 22.6 106.513.114.315.617.1 18.7 20.7 22.9 107 13.214.415.717.2 18.9 20.9 23.1 107.513.314.515.917.4 19.1 21.1 23.4 108 13.514.716.017.6 19.3 21.3 23.6 108.513.614.816.217.8 19.5 21.6 23.9 109 13.715.016.418.0 19.7 21.8 24.2 109.513.915.116.518.1 20.0 22.0 24.4WHO（世界卫生组织）0-2岁身长别体重标准（2006年版）　男.0－２岁　身长　－3SD －2SD -1SD 均数1SD 2SD 3SD 45 1.9 2 2.2 2.4 2.7 3 3.345.5 1.9 2.1 2.3 2.5 2.8 3.1 3.446 2 2.2 2.4 2.6 2.9 3.1 3.546.5 2.1 2.3 2.5 2.7 3 3.2 3.647 2.1 2.3 2.5 2.8 3 3.3 3.747.5 2.2 2.4 2.6 2.9 3.1 3.4 3.848 2.3 2.5 2.7 2.9 3.2 3.6 3.948.5 2.3 2.6 2.8 3 3.3 3.7 449 2.4 2.6 2.9 3.1 3.4 3.8 4.249.5 2.5 2.7 3 3.2 3.5 3.9 4.350 2.6 2.8 3 3.3 3.6 4 4.450.5 2.7 2.9 3.1 3.4 3.8 4.1 4.551 2.7 3 3.2 3.5 3.9 4.2 4.751.5 2.8 3.1 3.3 3.6 4 4.4 4.852 2.9 3.2 3.5 3.8 4.1 4.5 552.5 3 3.3 3.6 3.9 4.2 4.6 5.153 3.1 3.4 3.7 4 4.4 4.8 5.353.5 3.2 3.5 3.8 4.1 4.5 4.9 5.454 3.3 3.6 3.9 4.3 4.7 5.1 5.654.5 3.4 3.7 4 4.4 4.8 5.3 5.855 3.6 3.8 4.2 4.5 5 5.4 655.5 3.7 4 4.3 4.7 5.1 5.6 6.156 3.8 4.1 4.4 4.8 5.3 5.8 6.356.5 3.9 4.2 4.6 5 5.4 5.9 6.557 4 4.3 4.7 5.1 5.6 6.1 6.757.5 4.1 4.5 4.9 5.3 5.7 6.3 6.958 4.3 4.6 5 5.4 5.9 6.4 7.158.5 4.4 4.7 5.1 5.6 6.1 6.6 7.259 4.5 4.8 5.3 5.7 6.2 6.8 7.459.5 4.6 5 5.4 5.9 6.4 7 7.660 4.7 5.1 5.5 6 6.5 7.1 7.860.5 4.8 5.2 5.6 6.1 6.7 7.3 861 4.9 5.3 5.8 6.3 6.8 7.4 8.161.5 5 5.4 5.9 6.47 7.6 8.362 5.1 5.6 6 6.57.1 7.7 8.562.5 5.2 5.7 6.1 6.77.2 7.9 8.663 5.3 5.8 6.2 6.87.4 8 8.863.5 5.4 5.9 6.4 6.97.5 8.2 8.964 5.5 6 6.5 7 7.6 8.3 9.164.5 5.6 6.1 6.6 7.17.8 8.5 9.365 5.7 6.2 6.7 7.37.9 8.6 9.465.5 5.8 6.3 6.8 7.48 8.7 9.666 5.9 6.4 6.9 7.58.2 8.9 9.766.5 6 6.5 7 7.68.3 9 9.967 6.1 6.6 7.1 7.78.4 9.2 1067.5 6.2 6.7 7.2 7.98.5 9.3 10.268 6.3 6.8 7.3 8 8.7 9.4 10.368.5 6.4 6.9 7.5 8.18.8 9.6 10.569 6.5 7 7.6 8.28.9 9.7 10.669.5 6.6 7.1 7.7 8.39 9.8 10.870 6.6 7.2 7.8 8.49.2 10 10.970.5 6.7 7.3 7.9 8.59.3 10.1 11.171 6.8 7.4 8 8.69.4 10.2 11.271.5 6.9 7.5 8.1 8.89.5 10.4 11.372 7 7.6 8.2 8.99.6 10.5 11.572.5 7.1 7.6 8.3 9 9.8 10.6 11.673 7.2 7.7 8.4 9.19.9 10.8 11.873.5 7.2 7.8 8.5 9.210 10.9 11.974 7.3 7.9 8.6 9.310.1 11 12.174.5 7.4 8 8.7 9.410.2 11.2 12.275 7.5 8.1 8.8 9.510.3 11.3 12.375.5 7.6 8.2 8.8 9.610.4 11.4 12.576 7.6 8.3 8.9 9.710.6 11.5 12.6 76.5 7.7 8.3 9 9.810.7 11.6 12.7身长－3SD－2SD-1SD均数　1SD 2SD 3SD 78 7.9 8.6 9.310.1 11 12 13.178.58 8.7 9.410.2 11.1 12.1 13.279 8.1 8.7 9.510.3 11.2 12.2 13.379.58.2 8.8 9.510.4 11.3 12.3 13.480 8.2 8.9 9.610.4 11.4 12.4 13.680.58.3 9 9.710.5 11.5 12.5 13.781 8.4 9.1 9.810.6 11.6 12.6 13.881.58.5 9.1 9.910.7 11.7 12.7 13.982 8.5 9.2 10 10.8 11.8 12.8 1482.58.6 9.3 10.110.9 11.9 13 14.283 8.7 9.4 10.211 12 13.1 14.383.58.8 9.5 10.311.2 12.1 13.2 14.484 8.9 9.6 10.411.3 12.2 13.3 14.684.59 9.7 10.511.4 12.4 13.5 14.785 9.1 9.8 10.611.5 12.5 13.6 14.985.59.2 9.9 10.711.6 12.6 13.7 1586 9.3 10 10.811.7 12.8 13.9 15.286.59.4 10.111 11.9 12.9 14 15.387 9.5 10.211.112 13 14.2 15.587.59.6 10.411.212.1 13.2 14.3 15.688 9.7 10.511.312.2 13.3 14.5 15.888.59.8 10.611.412.4 13.4 14.6 15.989 9.9 10.711.512.5 13.5 14.7 16.189.510 10.811.612.6 13.7 14.9 16.290 10.110.911.812.7 13.8 15 16.490.510.211 11.912.8 13.9 15.1 16.591 10.311.112 13 14.1 15.3 16.791.510.411.212.113.1 14.2 15.4 16.892 10.511.312.213.2 14.3 15.6 1792.510.611.412.313.3 14.4 15.7 17.193 10.711.512.413.4 14.6 15.8 17.393.510.711.612.513.5 14.7 16 17.494 10.811.712.613.7 14.8 16.1 17.694.510.911.812.713.8 14.9 16.3 17.795 11 11.912.813.9 15.1 16.4 17.995.511.112 12.914 15.2 16.5 1896 11.212.113.114.1 15.3 16.7 18.296.511.312.213.214.3 15.5 16.8 18.497 11.412.313.314.4 15.6 17 18.597.511.512.413.414.5 15.7 17.1 18.798 11.612.513.514.6 15.9 17.3 18.998.511.712.613.614.8 16 17.5 19.199 11.812.713.714.9 16.2 17.6 19.2 99.511.912.813.915 16.3 17.8 19.4 100 12 12.914 15.2 16.5 18 19.6 100.512.113 14.115.3 16.6 18.1 19.8 101 12.213.214.215.4 16.8 18.3 20 101.512.313.314.415.6 16.9 18.5 20.2 102 12.413.414.515.7 17.1 18.7 20.4 102.512.513.514.615.9 17.3 18.8 20.6 103 12.613.614.816 17.4 19 20.8 103.512.713.714.916.2 17.6 19.2 21 104 12.813.915 16.3 17.8 19.4 21.2 104.512.914 15.216.5 17.9 19.6 21.5 105 13 14.115.316.6 18.1 19.8 21.7 105.513.214.215.416.8 18.3 20 21.9 106 13.314.415.616.9 18.5 20.2 22.1 106.513.414.515.717.1 18.6 20.4 22.4 107 13.514.615.917.3 18.8 20.6 22.6 107.513.614.716 17.4 19 20.8 22.8 108 13.714.916.217.6 19.2 21 23.1 108.513.815 16.317.8 19.4 21.2 23.3 109 14 15.116.517.9 19.6 21.4 23.6 109.514.115.316.618.1 19.8 21.7 23.865 5.6 6.1 6.6 7.2 7.9 8.7 9.765.5 5.7 6.2 6.7 7.4 8.1 8.9 9.866 5.8 6.3 6.8 7.5 8.2 9.0 10.066.5 5.8 6.4 6.9 7.6 8.3 9.1 10.167 5.9 6.4 7.0 7.7 8.4 9.3 10.267.5 6.0 6.5 7.1 7.8 8.5 9.4 10.468 6.1 6.6 7.2 7.9 8.7 9.5 10.568.5 6.2 6.7 7.3 8.0 8.8 9.7 10.769 6.3 6.8 7.4 8.1 8.9 9.8 10.869.5 6.3 6.9 7.5 8.2 9.0 9.9 10.970 6.4 7.0 7.6 8.3 9.1 10.0 11.170.5 6.5 7.1 7.7 8.4 9.2 10.1 11.271 6.6 7.1 7.8 8.5 9.3 10.3 11.371.5 6.7 7.2 7.9 8.6 9.4 10.4 11.572 6.7 7.3 8.0 8.7 9.5 10.5 11.672.5 6.8 7.4 8.1 8.8 9.7 10.6 11.773 6.9 7.5 8.1 8.9 9.8 10.7 11.873.5 7.0 7.6 8.2 9.0 9.9 10.8 12.074 7.0 7.6 8.3 9.1 10.0 11.0 12.174.5 7.1 7.7 8.4 9.2 10.1 11.1 12.275 7.2 7.8 8.5 9.3 10.2 11.2 12.375.5 7.2 7.9 8.6 9.4 10.3 11.3 12.576 7.3 8.0 8.7 9.5 10.4 11.4 12.676.5 7.4 8.0 8.7 9.6 10.5 11.5 12.777 7.5 8.1 8.8 9.6 10.6 11.6 12.877.5 7.5 8.2 8.9 9.7 10.7 11.7 12.978 7.6 8.3 9.0 9.8 10.8 11.8 13.178.5 7.7 8.4 9.1 9.9 10.9 12.0 13.279 7.8 8.4 9.2 10.0 11.0 12.1 13.379.5 7.8 8.5 9.3 10.1 11.1 12.2 13.480 7.9 8.6 9.4 10.2 11.2 12.3 13.680.5 8.0 8.7 9.5 10.3 11.3 12.4 13.781 8.1 8.8 9.6 10.4 11.4 12.6 13.981.5 8.2 8.9 9.7 10.6 11.6 12.7 14.082 8.3 9.0 9.8 10.7 11.7 12.8 14.182.5 8.4 9.1 9.9 10.8 11.8 13.0 14.383 8.5 9.2 10.0 10.9 11.9 13.1 14.583.5 8.5 9.3 10.1 11.0 12.1 13.3 14.684 8.6 9.4 10.2 11.1 12.2 13.4 14.884.5 8.7 9.5 10.3 11.3 12.3 13.5 14.985 8.8 9.6 10.4 11.4 12.5 13.7 15.185.5 8.9 9.7 10.6 11.5 12.6 13.8 15.386 9.0 9.8 10.7 11.6 12.7 14.0 15.486.5 9.1 9.9 10.8 11.8 12.9 14.2 15.687 9.2 10.0 10.9 11.9 13.0 14.3 15.887.5 9.3 10.1 11.0 12.0 13.2 14.5 15.988 9.4 10.2 11.1 12.1 13.3 14.6 16.188.5 9.5 10.3 11.2 12.3 13.4 14.8 16.389 9.6 10.4 11.4 12.4 13.6 14.9 16.489.5 9.7 10.5 11.5 12.5 13.7 15.1 16.690 9.8 10.6 11.6 12.6 13.8 15.2 16.890.5 9.9 10.7 11.7 12.8 14.0 15.4 16.991 10.0 10.9 11.8 12.9 14.1 15.5 17.1 91.5 10.1 11.0 11.9 13.0 14.3 15.7 17.393 10.411.312.3 13.4 14.7 16.117.893.510.511.412.4 13.5 14.8 16.317.994 10.611.512.5 13.6 14.9 16.418.194.510.711.612.6 13.8 15.1 16.618.395 10.811.712.7 13.9 15.2 16.718.595.510.811.812.8 14.0 15.4 16.918.696 10.911.912.9 14.1 15.5 17.018.896.511.012.013.1 14.3 15.6 17.219.097 11.112.113.2 14.4 15.8 17.419.297.511.212.213.3 14.5 15.9 17.519.398 11.312.313.4 14.7 16.1 17.719.598.511.412.413.5 14.8 16.2 17.919.799 11.512.513.7 14.9 16.4 18.019.9 99.511.612.713.8 15.1 16.5 18.220.1 100 11.712.813.9 15.2 16.7 18.420.3 100.511.912.914.1 15.4 16.9 18.620.5 101 12.013.014.2 15.5 17.0 18.720.7 101.512.113.114.3 15.7 17.2 18.920.9 102 12.213.314.5 15.8 17.4 19.121.1 102.512.313.414.6 16.0 17.5 19.321.4 103 12.413.514.7 16.1 17.7 19.521.6 103.512.513.614.9 16.3 17.9 19.721.8 104 12.613.815.0 16.4 18.1 19.922.0 104.512.813.915.2 16.6 18.2 20.122.3 105 12.914.015.3 16.8 18.4 20.322.5 105.513.014.215.5 16.9 18.6 20.522.7 106 13.114.315.6 17.1 18.8 20.823.0 106.513.314.515.8 17.3 19.0 21.023.2 107 13.414.615.9 17.5 19.2 21.223.5 107.513.514.716.1 17.7 19.4 21.423.7 108 13.714.916.3 17.8 19.6 21.724.0 108.513.815.016.4 18.0 19.8 21.924.3 109 13.915.216.6 18.2 20.0 22.124.5 109.514.115.416.8 18.4 20.3 22.424.8 110 14.215.517.0 18.6 20.5 22.625.1 110.514.415.717.1 18.8 20.7 22.925.4 111 14.515.817.3 19.0 20.9 23.125.7 111.514.716.017.5 19.2 21.2 23.426.0 112 14.816.217.7 19.4 21.4 23.626.2 112.515.016.317.9 19.6 21.6 23.926.5 113 15.116.518.0 19.8 21.8 24.226.8 113.515.316.718.2 20.0 22.1 24.427.1 114 15.416.818.4 20.2 22.3 24.727.4 114.515.617.018.6 20.5 22.6 25.027.8 115 15.717.218.8 20.7 22.8 25.228.1 115.515.917.319.0 20.9 23.0 25.528.4 116 16.017.519.2 21.1 23.3 25.828.7 116.516.217.719.4 21.3 23.5 26.129.0 117 16.317.819.6 21.5 23.8 26.329.3 117.516.518.019.8 21.7 24.0 26.629.6 118 16.618.219.9 22.0 24.2 26.929.9 118.516.818.420.1 22.2 24.5 27.230.3 119 16.918.520.3 22.4 24.7 27.430.6 119.517.118.720.5 22.6 25.0 27.730.965 5.9 6.3 6.9 7.4 8.1 8.8 9.665.5 6.0 6.4 7.0 7.6 8.2 8.9 9.866 6.1 6.5 7.1 7.7 8.3 9.1 9.966.5 6.1 6.6 7.2 7.8 8.5 9.2 10.167 6.2 6.7 7.3 7.9 8.6 9.4 10.267.5 6.3 6.8 7.4 8.0 8.7 9.5 10.468 6.4 6.9 7.5 8.1 8.8 9.6 10.568.5 6.5 7.0 7.6 8.2 9.0 9.8 10.769 6.6 7.1 7.7 8.4 9.1 9.9 10.869.5 6.7 7.2 7.8 8.5 9.2 10.0 11.070 6.8 7.3 7.9 8.6 9.3 10.2 11.170.5 6.9 7.4 8.0 8.7 9.5 10.3 11.371 6.9 7.5 8.1 8.8 9.6 10.4 11.471.5 7.0 7.6 8.2 8.9 9.7 10.6 11.672 7.1 7.7 8.3 9.0 9.8 10.7 11.772.5 7.2 7.8 8.4 9.1 9.9 10.8 11.873 7.3 7.9 8.5 9.2 10.0 11.0 12.073.5 7.4 7.9 8.6 9.3 10.2 11.1 12.174 7.4 8.0 8.7 9.4 10.3 11.2 12.274.5 7.5 8.1 8.8 9.5 10.4 11.3 12.475 7.6 8.2 8.9 9.6 10.5 11.4 12.575.5 7.7 8.3 9.0 9.7 10.6 11.6 12.676 7.7 8.4 9.1 9.8 10.7 11.7 12.876.5 7.8 8.5 9.2 9.9 10.8 11.8 12.977 7.9 8.5 9.2 10.0 10.9 11.9 13.077.5 8.0 8.6 9.3 10.1 11.0 12.0 13.178 8.0 8.7 9.4 10.2 11.1 12.1 13.378.5 8.1 8.8 9.5 10.3 11.2 12.2 13.479 8.2 8.8 9.6 10.4 11.3 12.3 13.579.5 8.3 8.9 9.7 10.5 11.4 12.4 13.680 8.3 9.0 9.7 10.6 11.5 12.6 13.780.5 8.4 9.1 9.8 10.7 11.6 12.7 13.881 8.5 9.2 9.9 10.8 11.7 12.8 14.081.5 8.6 9.3 10.0 10.9 11.8 12.9 14.182 8.7 9.3 10.1 11.0 11.9 13.0 14.282.5 8.7 9.4 10.2 11.1 12.1 13.1 14.483 8.8 9.5 10.3 11.2 12.2 13.3 14.583.5 8.9 9.6 10.4 11.3 12.3 13.4 14.684 9.0 9.7 10.5 11.4 12.4 13.5 14.884.5 9.1 9.9 10.7 11.5 12.5 13.7 14.985 9.2 10.0 10.8 11.7 12.7 13.8 15.185.5 9.3 10.1 10.9 11.8 12.8 13.9 15.286 9.4 10.2 11.0 11.9 12.9 14.1 15.486.5 9.5 10.3 11.1 12.0 13.1 14.2 15.587 9.6 10.4 11.2 12.2 13.2 14.4 15.787.5 9.7 10.5 11.3 12.3 13.3 14.5 15.888 9.8 10.6 11.5 12.4 13.5 14.7 16.088.5 9.9 10.7 11.6 12.5 13.6 14.8 16.189 10.0 10.8 11.7 12.6 13.7 14.9 16.389.5 10.1 10.9 11.8 12.8 13.9 15.1 16.490 10.2 11.0 11.9 12.9 14.0 15.2 16.690.5 10.3 11.1 12.0 13.0 14.1 15.3 16.791 10.4 11.2 12.1 13.1 14.2 15.5 16.9 91.5 10.5 11.3 12.2 13.2 14.4 15.6 17.093 10.811.612.6 13.6 14.7 16.017.593.510.911.712.7 13.7 14.9 16.217.694 11.011.812.8 13.8 15.0 16.317.894.511.111.912.9 13.9 15.1 16.517.995 11.112.013.0 14.1 15.3 16.618.195.511.212.113.1 14.2 15.4 16.718.396 11.312.213.2 14.3 15.5 16.918.496.511.412.313.3 14.4 15.7 17.018.697 11.512.413.4 14.6 15.8 17.218.897.511.612.513.6 14.7 15.9 17.418.998 11.712.613.7 14.8 16.1 17.519.198.511.812.813.8 14.9 16.2 17.719.399 11.912.913.9 15.1 16.4 17.919.5 99.512.013.014.0 15.2 16.5 18.019.7 100 12.113.114.2 15.4 16.7 18.219.9 100.512.213.214.3 15.5 16.9 18.420.1 101 12.313.314.4 15.6 17.0 18.520.3 101.512.413.414.5 15.8 17.2 18.720.5 102 12.513.614.7 15.9 17.3 18.920.7 102.512.613.714.8 16.1 17.5 19.120.9 103 12.813.814.9 16.2 17.7 19.321.1 103.512.913.915.1 16.4 17.8 19.521.3 104 13.014.015.2 16.5 18.0 19.721.6 104.513.114.215.4 16.7 18.2 19.921.8 105 13.214.315.5 16.8 18.4 20.122.0 105.513.314.415.6 17.0 18.5 20.322.2 106 13.414.515.8 17.2 18.7 20.522.5 106.513.514.715.9 17.3 18.9 20.722.7 107 13.714.816.1 17.5 19.1 20.922.9 107.513.814.916.2 17.7 19.3 21.123.2 108 13.915.116.4 17.8 19.5 21.323.4 108.514.015.216.5 18.0 19.7 21.523.7 109 14.115.316.7 18.2 19.8 21.823.9 109.514.315.516.8 18.3 20.0 22.024.2 110 14.415.617.0 18.5 20.2 22.224.4 110.514.515.817.1 18.7 20.4 22.424.7 111 14.615.917.3 18.9 20.7 22.725.0 111.514.816.017.5 19.1 20.9 22.925.2 112 14.916.217.6 19.2 21.1 23.125.5 112.515.016.317.8 19.4 21.3 23.425.8 113 15.216.518.0 19.6 21.5 23.626.0 113.515.316.618.1 19.8 21.7 23.926.3 114 15.416.818.3 20.0 21.9 24.126.6 114.515.616.918.5 20.2 22.1 24.426.9 115 15.717.118.6 20.4 22.4 24.627.2 115.515.817.218.8 20.6 22.6 24.927.5 116 16.017.419.0 20.8 22.8 25.127.8 116.516.117.519.2 21.0 23.0 25.428.0 117 16.217.719.3 21.2 23.3 25.628.3 117.516.417.919.5 21.4 23.5 25.928.6 118 16.518.019.7 21.6 23.7 26.128.9 118.516.718.219.9 21.8 23.9 26.429.2 119 16.818.320.0 22.0 24.1 26.629.5 119.516.918.520.2 22.2 24.4 26.929.8。

Productivity Growth, Technical Progress and

Productivity Growth,Technical Progress and Efficiency Change in African AgricultureGuy Blaise Nkamleu*Abstract:The paper examines the economic performance of a large number of African countries using an international comparable data set and the latest technique for analysis.The paper focuses on growth in total factor productivity and its decomposition into technical change and efficiency change components.The analysis is undertaken using the data envelopment analysis(DEA).The present study uses data of16countries over the period1970–2001.It was found that,globally,during that period,total factor productivity has experienced a positive evolution in sampled countries.This good performance of the agricultural sector was due to good progress in technical efficiency rather than technical progress.The region suffered a regression in productivity in the1970s, and made some progress during the1980s and1990s.The study also highlights the fact that technical change has been the main constraint of achievement of high levels of total factor productivity during the reference period in sub-Saharan Africa.Contrariwise,in Maghreb coun-tries,technological change has been the main driving force of produc-tivity growth.Finally,the results indicate that institutional factors as well as agro-ecological factors are important determinants of agricultural productivity growth.Re´sume´:L’article analyse la performance e conomique d’un grand nombre de pays africains,en se servant d’une se rie de donne es inter-nationales comparables et de la toute dernie re me thode d’analyse.Il se penche sur la croissance de la productivite globale des facteurs de production et sa de composition en deux volets:e volution technologique et e volution de l’efficience.L’analyse repose sur la me thode dite de Cowbe enveloppe—Data Envelopment Analysis ou DEA(permettant de mesurer l’efficience a partir de donne es re elles).La pre sente e tude utilise les donne es de16pays sur la pe riode1970–2001.Il en ressort que, *University of Yaounde II and International Institute of Tropical Agriculture(IITA/ Cameroun),BP2008,Messa Yaounde,Cameroon.Tel:(237)223–74–34;fax:(237)223–74–37;e-mail:g.b.nkamleu@#African Development Bank2004,Published by Blackwell Publishing Ltd2004,9600Garsington Road,Oxford,OX42DQ,UK and350Main Street,Malden,MA02148,USA.203204G.B.Nkamleu d’une manie re ge ne rale,la productivite globale des facteurs de production ont affiche une bonne e volution dans les pays de l’e chantillon au cours de la pe riode conside re e.Cette bonne performance du secteur agricole e tait pluto t attribuable a une bonne progression de l’efficacite technique et non a des progre s productivite de la re gion a re gresse dans les anne es70,avant de remonter le ge rement dans les anne es80et90. L’e tude souligne e galement que l’e volution technologique a e te le principal obstacle a la re alisation de niveaux e leve s de productivite des facteurs en Afrique subsaharienne durant la pe riode conside re e.Par contre,dans les pays du Maghreb,l’e volution technologique a e te le principal moteur de la croissance de la productivite´.Enfin,les re sultats indiquent que les facteurs institutionnels et agro-e cologiques jouent un ro le de terminant dans la croissance de la productivite agricole.1.Introd uctionIn many parts of Africa,the major challenge facing agriculture is how to increase farm production to meet changing food needs without degrading the natural resource base.The agricultural sector is the most important in African economies employing as much as50–80per cent of the labour force(Johnston,1990).About two-thirds of the627million people living in sub-Saharan Africa(SSA)depend on agriculture or agriculture-related activities for their livelihoods(Ehui and Pender, 2003).It is estimated that throughout the region,there are236million agricultural poor,which represents60per cent of the agricultural population and80per cent of the total number of poor in the region (Dixon et al.,2001).Therefore,agriculture continues to remain import-ant in rural SSA and indicators of rural well-being are closely related to agricultural performance.In most African countries,because of its importance in overall GDP, export earnings and employment as well as its forward and backward linkages to the non-farm sector,growth in the agricultural sector will continue to be the cornerstone of poverty reduction.Increased agricul-tural productivity and growth,driven by technology and investments,has a powerful dynamic effect that benefits the poor throughout the econ-omy:directly through increased agricultural income and employment, and indirectly through increased food availability and lower food prices as well as through the demand created by increased agricultural incomes for non-farm goods and services produced by the very large,employment intensive non-agricultural rural economy.However,importation of food is still needed to curb the increasing gap between food demand and food production.As shown by several studies,one#African Development Bank2004Productivity Growth,Technical Progress and Efficiency Change205 of the most critical problems in Africa today is how to increase agricultural production to meet increasing food demand arising from an increase in population pressure(Mensah,1989;Timberlake,1990;Pretty,1995).The decline in food and agricultural per capita production over the years has become synonymous with the region’s stagnation,social decline and marginalization in the world.Unless renewed measures are taken by the governments and people of the region to dramatically increase agri-cultural production,there will be continued deterioration and stagnation.Given its importance,there is genuine concern among policymakers and researchers about the poor performance of SSA’s agricultural sector. Without exception,studies on developed countries’agriculture have shown substantial productivity increases,whereas the results for less developed countries have consistently shown productivity declines (Fulginiti and Perrin,1997).There is a substantial body of literature measuring agricultural productivity change in the developed countries(Kalirajan et al.,1996; Fare et al.,1994),while in sub-Saharan Africa,empirical studies to system-atically characterize the agricultural productivity in the region are scarce.In light of the general objective of attaining regional self-sufficiency in agricultural products,governments and institutions have sought strategies that would lead to higher levels of production.A key factor for a sustained increase of agricultural production is improvement of productivity,which is carried out through technical change and/or efficiency change.Many African farmers are still using low yielding agricultural technolo-gies,which lead to low productivity and production.Another relevant question for agricultural policymakers is whether the agricultural sector can be made more efficient,by achieving more output with the current input level,or by achieving the current output with less input usage than is currently observed.An important step in answering these questions is to understand the pathway of productivity and its components.The purpose of this study is to explore evolution of total factor productivity and its components in the African agricultural sector, using data envelopment analysis(DEA).The study used panel data on 16selected countries of the region,and is intended to explain the relative performance of the agricultural sector across regions and countries. 2.Theoretical Framework:Malmquist Data Envelopment AnalysisTechnical efficiency has received considerable attention in the economic literature in recent years.A variety of theoretical approaches,particu-larly yield gap and constraints methodologies,have been developed to #African Development Bank2004206G.B.Nkamleu investigate the failure of producers to achieve the same level of efficiency (Battese,1992).Over the past two decades,much progress has been made towards refining the frontier function methodology introduced by Farell in1957 (Farell,1957).Along with several methodological developments,there has been a considerable amount of empirical work,much of which use DEA and stochastic frontier production approaches(Lau and Yotopou-los,1971;Bagi,1982;Kopp and Diewert,1982;Russell and Young,1983; Taylor and Shonkwiler,1984;Huang and Bagi,1984;Dawson and Lingard, 1989;Ali and Chaudhry,1990;Bravo-Ureta and Rieger,1990;Defourny et al.,1992;N’gbo,1994;Kalirajan and Shand,2001;Bakhshoodeh and Thomson,2001;Wilson et al.,2001).More recently,a non-parametric method has been developed that calculates the total factor productivity index using an efficiency measure. This approach,when one has panel data,uses DEA-like linear programs and Malmquist total factor productivity index to measure productivity change,and to decompose this productivity change into technical change and technical efficiency change.In this paper,this method is employed.The method has the advantage that it is parameter free,we do not presuppose a parametric functional form.Specifying a functional form imposes restrictions on the structure of technology,which could give rise to specification error.Malmquist productivity indexes were introduced by Caves et al. (1982),who first developed these measures for varying return to scale (VRS)technologies,assuming overall efficiency and a translog technol-ogy for output distance functions.Though the authors could not provide direct estimates of the Malmquist index(MI),they noticed that the geometric mean of two MI was equivalent to a scaled Tornqvist-Theil productivity index.Subsequently,Fare et al.(1994)developed a non-parametric approach for estimating the Malmquist indexes,and showed that the component distance function could be derived using a DEA-like linear program method.Furthermore,they showed that the resulting total factor productivity indexes could be decomposed into efficiency change and technical change components.The method showed two main advantages. First,no assumption on the functional form of the underlying production technology was required.And second,unlike the Tornqvist TPF indexes, for the Malmquist indexes,data on output and input prices are not indispensable,hence making the method particularly suited for regions where price data are not readily available.The Malmquist TFP index is defined using distance functions. Distance functions allow one to describe a multi-input multi-output production technology without the need to specify a behavioural objective#African Development Bank2004such as cost minimization or profit maximization (Rao and Coelli,1998).One may define input distance functions and output distance functions.An input distance function characterizes the production technology by looking at a minimal proportional contraction of the input vector,given an output vector.An output distance function considers a maximal proportional expansion of the output vector,given an input vector.The output distance function is defined on the output set P (x ),as:d 0ðx ;y Þ¼min f :ðy = Þ2P ðx Þg ;where the output set,P (x )represents the set of all output vectors,y ,which can be produced using the input vector x .Extensive discussion on Malmquist indexes can be found in Fare et al .(1994).Here,we provide a brief summary of the discussion,and suggest interested readers to refer to the references above.Even though the method is easily accommodated to the multi-output,multi-input case,for clarity purposes the exposition is limited to the single-output,single-input and output-oriented case.Following Fare et al .(1994),the MI TFP change between a base period (s )and a period t can be written as:m 0y s ;x s ;y t ;x t ðÞ¼d s 0y t ;x t ðÞd s 0y s ;x s ðÞd s 0y t ;x t ðÞd t 0y t ;x t ðÞd s0y s ;x s ðÞd t0y s ;x s ðÞ 1=2;ð1Þwhere the notation d sy t ;x t ðÞrepresents the distance from the period t observation,to the period s technology.A value of m greater than one will indicate positive TFP growth from period s to period t .In (1),the term outside the square brackets measures the Farrell efficiency change between period s and t ,and the term inside measures technical change,which is the geometric mean of the shift in the technol-ogy between the two periods.Thus,the two terms in equation (1)are:Efficiency change ¼d s 0y t ;x t ðÞd s0y s ;x s ðÞTechnical change ¼d s 0y t ;x t ðÞd t 0y t ;x t ðÞd s 0y s ;x s ðÞd t0y s ;x s ðÞ1=2The efficiency change component is equivalent to the ratio of the Farrelltechnical efficiency in period t to the Farrell technical efficiency in period s ,under the constant return to scale (EFFCH crs ).This efficiency change component can be separated into a scale efficiency and pure technicalProductivity Growth,Technical Progress and Efficiency Change207#African Development Bank 2004208G.B.Nkamleuefficiency change.The pure technical efficiency is obtained by re-computing efficiency change under the variable return to scale(EFFCH vrs).The scale efficiency is therefore the ratio of efficiency under constant return to scale and the same efficiency under variable return to scale(EFFCH crs/ EFFCH vrs).The overall index in(1)represents the productivity of the production point(y t,x t)relative to the point(y s,x s),and a value larger than one depicts positive TFP growth between periods s and t.Empirical appli-cations require the computations of the four distance functions in(1). As suggested by Coelli(1996),the distance functions can be recovered by solving the following DEA-like linear programs:½d t0ðx t;y tÞ À1¼Max ; ;subject toÀ y itþY t !0x itÀX t !00!0;½d tþ1ðx tþ1;y tþ1Þ À1¼Max ; ;subject toÀ y i;tþ1þY tþ1 !0x i;tþ1ÀX tþ1 !00!0;½d t0ðx tþ1;y tþ1Þ À1¼Max ; ;subject toÀ y i;tþ1þY t !0x i;tþ1ÀX t !00!0;½d tþ1ðx t;y tÞ À1¼Max ; ;subject toÀ y itþY tþ1 !0x itÀX tþ1 !00!0;where is a NÂ1vector of constant and is a scalar with1* <1. À1is the proportional increase in outputs that could be achieved by the i th unit,with input quantities held constant.The above programs must be solved for each country in the sample in each period,and an extra three programs for each country to construct#African Development Bank2004the chained index.If we have T time periods,we must calculate(3TÀ2) LPs.Overall,for N firms and T periods,with the decomposition of the technical efficiency N(4TÀ2)LPs are solved(2016LP in this case). 3.Data SpecificationTo estimate the Malmquist indexes of efficiency and total factor produc-tivity,a panel data on16African countries from1970to2001was used. The countries concerned are listed in Table1below.Data consisted of information on agricultural production and means of production in the study countries.Record of agricultural production index(base1989–1991),rural population,number of tractors in use, fertilizer uses,agricultural areas were obtained from FAO statistic database.Specification of output and input in the analysis was as follows: Output*Agricultural production:To construct the output series,we followed the methodology suggested in Rao and Coelli(1998).Output aggre-gated for the year1990was used to compute output series.These1990 aggregated outputs were computed using international average pricesTable1:Sample countries used in the analysisColonial heritage Location Sahelian/non-SahelianHave/Have notexperience major civil warAlgeria French Maghreb Sahelian NoBurkina Faso French West Africa Sahelian NoCameroon French West Africa a Non-Sahelian NoCo te d’Ivoire French West Africa Non-Sahelian NoEgypt French Maghreb Sahelian NoGhana British West Africa Non-Sahelian NoKenya British East Africa Non-Sahelian NoMalawi British Austral Africa Non-Sahelian NoMali French West Africa Sahelian NoMorocco French Maghreb Sahelian NoMozambique Portugal Austral Africa Non-Sahelian YesNigeria British West Africa Non-Sahelian NoSenegal French West Africa Sahelian NoTunisia French Maghreb Sahelian NoUganda British East Africa Non-Sahelian YesZimbabwe British Austral Africa Non-Sahelian Noa Although Cameroon is politically part of Central Africa,it is more common in scientific studies to consider it as part of West Africa.Productivity Growth,Technical Progress and Efficiency Change209 #African Development Bank2004210G.B.Nkamleu (expressed in US dollars)derived using a Geary-Khamis method(see Rao,1993).The aggregates are based on the sum of price-weighted quantities of different agricultural commodities produced after deduc-tion of quantities used as seed and feed weighted in a similar manner.The resulting aggregates represent,therefore,disposable production for any use,except as seed and feed.The1990output series were then extended to cover the study period1970–2001,using the FAO produc-tion index number series.Input*Labour refers to the economically active population in agriculture for each year,in each country.The economically active population in agriculture is defined as all persons engaged or seeking employment in the agriculture,forestry,hunting or fishing sector,whether as employ-ers,own-account workers,salaried employees or unpaid workers.*Agricultural land:the sum of area under arable land(land under temporary crops,temporary meadows for mowing or pasture,land under market and kitchen gardens and land temporarily fallow);permanent crops(land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest,such as cocoa,coffee and rubber);and permanent pastures(land used perman-ently for herbaceous forage crops,either cultivated or growing wild). *Fertilizer:The sum of nitrogen,potash and phosphate content of various fertilizers consumed,measured in thousands of metric tons in nutrient units.*Tractors refer to total wheel and crawler tractors(excluding garden tractors)used for agricultural production.4.ResultsMean overall technical efficiencies(Table2),indicate an overall positive trend over time for the sample countries.However,countries did not have the same performance during the period.Some countries like Malawi and Co te d’Ivoire have experienced a big increase of overall technical efficiency during the period,while Burkina Faso experienced a negative trend.Recall that a value greater than unity represents an improvement of efficiency or productivity.Turning to the component measures(PechC and SechC),it appears that both pure and scale technical efficiency have contributed to the growth of overall efficiency. This suggests that,in the achievement of high levels of technical perform-ance over time,the technical efficiency is not a long-run constraint.#African Development Bank2004The positive evolution of the scale efficiency suggests that the agricul-tural sector succeeded in taking advantage of the growing size of the sector,while the improvement in pure technical efficiency over the study period is a significant finding and suggests that there was a learning process,as predicted by theories of intra-firm diffusion (Kalirajan and Shand,2001).Examining the trend of efficiencies offers another important insight into the performance over time.The evolution trend of the technical efficiency and its component is shown in Figure 1.Scale efficiency has experienced big season-by-season fluctuations,inducing big fluctuations in the overall technical efficiency.This situation may be due to the large difference between countries in performing scale efficiency change (Table 2).Turning to the Malmquist total factor productivity index,Table 3includes mean values of measures of change in total factor productivity index and its components (efficiency change and technical change).Means are given for the sample as a whole as well as by country.Looking at the sample as a whole,the change in total factor productivity of the agricultural sector of the countries studied has been positive.On average,total factor productivity has increased by 0.1per cent annually.An important question is:what is the main cause of that gain of productivity?The agricultural sector can improve the level of total factor productivity either by improving technical efficiency and/or by improvingTable 2:Mean technical efficiencies changeCountriesTechnical efficiency change Pure technical efficiency change Scale efficiency changeEffchCPechC SechC Algeria1.012 1.0190.994Burkina Faso 0.982 1.0000.982Cameroon 1.000 1.000 1.000Co te d’Ivoire 1.018 1.006 1.012Egypt 1.000 1.000 1.000Ghana 1.006 1.006 1.000Kenya 1.009 1.000 1.009Malawi 1.023 1.000 1.023Mali 1.009 1.009 1.000Morocco 1.0000.997 1.004Mozambique 1.002 1.0220.981Nigeria 1.000 1.000 1.000Senegal 1.013 1.000 1.013Tunisia 1.006 1.000 1.006Uganda 1.000 1.000 1.000Zimbabwe 1.009 1.001 1.009Mean1.0061.0041.002Productivity Growth,Technical Progress and Efficiency Change211#African Development Bank 2004technological level(shift in the production frontier).The component meas-ures of total factor productivity,EffchC and TechchC show that efficiency has been the main contributor of the success of total factor productivity. The average technical efficiency change was0.6per cent per year,while the technical change was negative(À0.5per cent per year).Table3:Mean total factor productivity changeCountries Technicalefficiency change TechnologicalchangeTotal factorproductivity changeEffchCTechchCTfpchC Algeria 1.012 1.017 1.030Burkina Faso0.9820.9670.950Cameroon 1.0000.9920.992Co te d’Ivoire 1.0180.993 1.011Egypt 1.0000.9980.998Ghana 1.0060.9920.998Kenya 1.009 1.004 1.013Malawi 1.023 1.002 1.024Mali 1.0090.9810.989Morocco 1.000 1.005 1.006Mozambique 1.002 1.007 1.009Nigeria 1.0000.9640.964Senegal 1.0130.987 1.000Tunisia 1.006 1.008 1.014Uganda 1.000 1.001 1.001Zimbabwe 1.009 1.005 1.014Mean 1.0060.995 1.001212G.B.Nkamleu#African Development Bank2004This suggests that,for the sampled countries,technical change hasbeen the main constraint of achievement of high levels of total factor productivity during the reference period.Also here,countries did not perform similarly.Countries that hadbeen good or average in increasing levels of technical efficiency experi-enced poor technical change.This was the case in countries like Co ted’Ivoire and Senegal.Overall,11out of the16sampled countries had increased efficiency more than technology(Table4).This is useful infor-mation and important in guiding efforts to increase agricultural produc-tion.Figure2shows the trend over time.This trend is characterized by an important season-by-season variation of the two components of the totalfactor productivity index.The technical change component has had more fluctuation,suggesting that promotion of technical change has not been constant during the period.Figures3and4show the rates of change in efficiency,technology and productivity,grouped by decade.It appears that during the1971–80period the region performed well in raising the efficiency of the agricul-tural sector.The average annual growth rate of technical efficiencyduring that period was2.3per cent,while the technical change was negative on average.The situation was reversed during the1980s andthe1990s,with a good score on technical change and a regression of technical efficiency.Table4:Comparison between technical efficiency change and tech-nological change>EffchC Countries EffchC>TechchCTechchC Algeria*Burkina Faso*Cameroon*Co te d’Ivoire*Egypt*Ghana*Kenya*Malawi*Mali*Morocco*Mozambique*Nigeria*Senegal*Tunisia*Uganda*Zimbabwe*Mean**¼YesThe results presented so far do support the notion that there is a difference between countries in performing efficiency and productivity change.It was therefore interesting to investigate the relationship between those changes and countries’particularities.We investigated what potential institutional and socio-political factors have affected the agricultural productivity performance in Africa.The relationship between total factor productivity and some measurable factors that may supposedly impact the productivity were investigated.The factors considered included:*Colonial heritage:countries were grouped according to their colonial heritage.*Political right and civil liberties:indexes of political freedom that ‘freedom house’has published for each sampled country was used.Each year,since1972,based on a series of checklists relating to political rights and civil liberties,freedom house has rated each coun-try as‘free’,‘partly free’or‘not free’.*Geographical location:It is expected that due to a difference in natural resource endowment,geographical location could have an impact on the performance of the agricultural sector.*Conflict:A dummy variable was used to characterize countries that have experienced a major civil war.Two countries were identified in this category(Mozambique and Uganda).However,it is recognized that this categorization is disputable,since the boundary between minor and major conflict has a subjective flavour.A Tobit model of the determinants of efficiency and productivity was run(Table5).Apart from the factors mentioned above,the illiteracy rate,the proportion of irrigated agricultural land,and the agricultural land in each country were also included in the model.Two major results came out of these estimations:1.The illiteracy rate is negatively related to productivity growth.Coun-tries with a high proportion of illiterates were also those performing weakly in productivity growth,suggesting that fighting illiteracy isanother means to push agricultural productivity.T a b l e 5:T o b i t m o d e l o f t h e d e t e r m i n a n t s o f t o t a l f a c t o r p r o d u c t i v i t y c h a n g e i n s a m p l e d c o u n t r i e sV a r i a b l eE f f i c i e n c y c h a n g eT e c h n i c a l c h a n g e T o t a l f a c t o r p r o d u c t i v i t y c h a n g eC o e f f i c i e n t t -s t a t i s t i c s C o e f f i c i e n t t -s t a t i s t i c s C o e f f i c i e n t t -s t a t i s t i c sC o n s t a n t 1.01220.10**1.005416.15**1.009812.98***%o f i r r i g a t e d l a n d À0.0253À0.86À0.0154À0.42À0.0391À0.86I l l i t e r a c y r a t e 0.00020.50À0.0016*À2.61**À0.0015À2.01**A g r i c u l t u r a l a r e a 5.3E –080.126.9E –080.131.5E –070.22D u m m y f o r p o l i t i c a l l y n o t -f r e e c o u n t r i e s À0.0043À0.360.00660.44À0.0009À0.05D u m m y f o r p o l i t i c a l l y f r e e c o u n t r i e s À0.0118À0.430.00500.15À0.0096À0.23D u m m y f o r S a h e l i a n c o u n t r i e s À0.0140À0.570.03371.120.02560.68D u m m y f o r f o r m e r F r e n c h c o l o n i e s À0.0002À0.010.07011.150.08651.14D u m m y f o r f o r m e r B r i t i s h c o l o n i e s À0.0046À0.120.05721.220.06621.13D u m m y f o r c o u n t r i e s w h i c h e x p e r i e n c e d m a j o r w a r À0.0186À0.680.10823.21**0.09352.22**D u m m y f o r M a g h r e b c o u n t r i e s 0.01420.44À0.0087À0.220.00070.01D u m m y f o r W e s t A f r i c a n c o u n t r i e s À0.0153À0.620.00680.23À0.0061À0.160.126030.89**0.155730.89**0.194630.89***L o g l i k e l i h o o d ¼311.41L o g l i k e l i h o o d ¼210.37L o g l i k e l i h o o d ¼103.92T o t a l s a m p l e ¼477T o t a l s a m p l e ¼477T o t a l s a m p l e ¼477*¼s i g n i f i c a n t a t 0.10;**¼s i g n i f i c a n t a t 0.05;***¼s i g n i f i c a n t a t 0.01.。

森林生长模式之发展及应用

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國立中興大學森林系教授 Tel: 04-22854060 Fax: 04-22872027 E-mail: flfeng@.tw 聯絡地址：台中市國光路 250 號國立中興大學森林所研究生 Tel: 04-22840345ext334 E-mail: changwhh@ -1-
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2002 台灣林業 28(5):14-19.
(Korzukhin et al., 1996)。在過去數十年，實證模式在森林生長及收穫預測已有相當成功的表現，這項成功大多只是限定於預測木材纖維收穫的林木經營問題。實證模式在解決未來還是會繼續有其重要性。在處理過程發展的知識時，則需要瞭解變數之間的關係，而實證模式就會因其固有描述自然的能力而大受限制。然而，在面對森林生態系經營，則過程模式將會增加我們對森林生態系瞭解的能力。雖然，林業的過程模擬開始於 20-25 年前，但其成就受限於研究及推廣的不力，且早期發展一直受限於需要大量明確母數值及缺少精確的測驗程序。然而，儘管受到這些限制，過程模式可以提供我們瞭解單株、林分、地景等森林層級的狀態與機制的資訊。此外，過程模式也提供預測環境改變下森林之生長潛能。所以，若要獲得森林生態系經營決策上所需要之資訊，過程模式就能扮演非常重要的角色(Korzukhin et al., 1996)。三、森林生長模式分類 (一)實證模式(統計模式)：實證模式係建立依變數與獨立變數之統計關係而不用考慮基本的機制。實證模式也使用變數，但它假設這些變數的狀態在未來也會是以平均狀況值呈現。實證模式可說是一接近於未知的機制模式，典型的實證模式是具有合適次方的多項式。 (二)偽過程模式：描述某時間的某一性態值與其生長率或相對生長率關係，但只限於描述生物之生長行為，如考慮生長率為同化作用與異化作用差的函數。 (三)過程模式(機制模式)：過程模式模擬生物藉光合作用將 CO2、養分及水分轉換成為生物量的過程，過程模式可能使用生長季節降雨、光照時間和其他直接量測所得之量來推估生長量的變化與物種組成的動態變化(引自馮豐隆，1997)。 (四)混合模式(hybrid model)：過程模式將機制性的交感作用與環境相結合，因此，這種模式可以容易地反映出在環境狀況改變時過程模式參數的改變，但在另一方面，實證的生長模式亦有其功能，它可利用過去生長的統計資料，將決定生長的特殊因子納入，但這些因子在過程模式中卻不易模擬(Battaglia, 1998)；所以，混合模式結合過程模式與實證模式二者之優點，實現理論與實際結合之模擬。四、四種模式介紹及比較分析 (一)模式介紹 1、實證模式：在林學上，Spath 在十七世紀、Hossfeld 及 Smallan 在十八世紀時就已將數學模式應用在測計學及林木生長研究上，供作推定或預測之用，以表示各因子間量

GrowthHistory(宏观经济学加州大学詹姆斯·布拉

Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserv
Questions
• What policies can make economic growth faster?
• What are the prospects for successful and rapid economic development in tomorrow’s world?
Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserv
American Long-Run Growth, 1800-1973
• Many economists believe that official estimates of output per worker overstate inflation and understate real economic growth by 1 percent per year
Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserv
The Demographic Transition
• As material standards of living rise far above subsistence, countries undergo a demographic transition
Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserv

97年到现在的考研英语作文

考研英语作文：从1997年至今的演变与趋势Since 1997, the landscape of English writing for postgraduate entrance exams has undergone significant transformations, reflecting the evolving needs of society and the advancement of educational standards. This essay delves into the key shifts and prevailing trends in these essays, highlighting the adaptability and growth of students in meeting the challenges posed by the changing exam format.Firstly, there has been a marked increase in the complexity of topics and prompts. In the earlier years, the focus was primarily on straightforward descriptive or narrative writing, often centered around personal experiences or familiar topics. However, as the years progressed, the prompts became more analytical and argumentative, requiring students to delve into complex social, cultural, or technological issues. This shift not only tested students' language proficiency but also their ability to analyze, critique, and formulate coherent arguments.Secondly, the emphasis on critical thinking and creativity has become increasingly prominent. Examiners now seek essays that demonstrate a unique perspective, depth of analysis, and the ability to connect ideas across diverse fields. This trend encourages students to go beyond the surface level and engage with the material in a more nuanced and innovative manner.Furthermore, the integration of technological advancements into the examination process has been another noteworthy development. The advent of online testing platforms and automated scoring systems has transformed the way essays are submitted and evaluated. While this has brought about efficiencies in the grading process, it has also placed a higher demand on students to present their ideas in a clear, concise, and technically proficient manner.Lastly, the globalization of higher education has influenced the content and style of these essays. With increasing international participation in postgraduate programs, the essays now often incorporate a more international perspective, drawing parallels with globalissues and trends. This broadens the scope of the essays and prepares students for the cross-cultural challenges they may encounter in their academic careers.In conclusion, the evolution of English writing for postgraduate entrance exams since 1997 reflects the dynamic nature of higher education and the changing needs of society. The increasing complexity of topics, emphasis on critical thinking and creativity, technological advancements, and globalization have all contributed to shaping the essays into a more rigorous and comprehensive assessment tool. As students continue to adapt and excel in this evolving landscape, they are not only honing their writing skills but also developing the critical thinking and cross-cultural competencies that are essential for success in the modern academic world.**考研英语作文：从1997年至今的演变与趋势**自1997年以来，考研英语作文的格局发生了显著变化，反映了社会需求的演变和教育标准的提升。

1997年的两种英语读法

1997年的两种英语读法Title: Embracing the Diversity of 1997: Two Distinct Narratives.1997 was a year of significant transitions andhistorical moments, offering a rich tapestry of experiences and perspectives. As we delve into the dual narratives of this year, we are transported to a world where the threads of history, culture, and global events intertwine to create a dynamic tapestry.Narrative One: The Global Stage.1997 marked a pivotal moment in global history, with significant political and economic shifts that reshaped the world stage. The People's Republic of China marked a significant milestone with the return of Hong Kong to its motherland, a momentous occasion that symbolized China's growing influence and commitment to preserving itsterritorial integrity. This event was not just a politicalmilestone; it was also a cultural and emotional one, as families were reunited, and the city embarked on a new chapter in its history.On the economic front, 1997 saw the Asian Financial Crisis, a devastating event that shook the financial markets of Asia and beyond. It was a time of uncertainty and challenge, but also of resilience and recovery. This crisis exposed the fragility of global financial systems and highlighted the need for greater cooperation and regulation. It was a wake-up call that reminded the world of the interconnectedness of economies and the importance of collective action.Narrative Two: The Personal Journey.Beyond the global stage, 1997 was also a year of personal growth and discovery for many individuals. It was a time of exploration and experimentation, as people embraced new technologies, cultures, and ways of thinking. The internet was in its infancy, but it was alreadystarting to transform the way people communicated, accessedinformation, and interacted with the world.For many, 1997 was a year of transition and self-discovery. It was a time of finding one's voice andpursuing passions. It was a year of learning to navigatethe complexities of adulthood, whether it was taking on new responsibilities, pursuing career goals, or simply growing up and embracing the challenges of life.Conclusion.1997 was a year of dual narratives, a tapestry ofglobal events and personal experiences. It was a time of both transition and stability, of challenge and opportunity. As we reflect on this year, we are reminded of the interconnectedness of our lives and the impact of global events on our daily lives. We are also reminded of the resilience and adaptability of humanity, the ability to overcome challenges and emerge stronger.In conclusion, 1997 was a year that offered a rich palette of experiences and perspectives. It was a time ofgrowth and discovery, a time to embrace the diversity of our world and the beauty of our shared human experience. As we move forward, let us carry with us the lessons of 1997 and continue to shape a world that is more inclusive, understanding, and resilient.。

移栽期对云烟97_上部烟叶质量的影响

移栽期对云烟97上部烟叶质量的影响张岚清1,高华峰2,张军刚2,李玲美2,桑应华1,申苗苗1,李春乔2,李祖红2,卢君龙2,刘彦中1,罗以贵1∗㊀(1.云南农业大学烟草学院,云南昆明650201;2.云南省烟草公司曲靖市公司,云南曲靖655000)摘要㊀为缓解滇东北烟区上部烟叶成熟时低温造成的影响,提高上部叶的产质量,采用田间试验的方法,研究不同移栽时间对云烟97的上部烟叶农艺性状㊁生育期㊁表观特征㊁叶片组织结构㊁酶活性㊁经济性状㊁鲜干比㊁外观质量的差异,分析其对烟株生长发育以及产值量的影响㊂结果表明,4月21 25日移栽处理大田生育期最短,为113d ,该处理可提高上部烟叶SOD ㊁PPO 酶活性,降低NR 酶活性和MDA 含量,鲜干比㊁栅栏组织和海绵组织的比值均最小,成熟度最好,增加上部烟叶开片度及抗逆性,有利于烟株的生长发育,产量及产值最高,比4月26 30日处理初烤烟叶产量提高521.42kg /hm 2,产值增加11529.42元/hm 2,上等烟的比例达50.71%㊂因此,移栽期为4月21 25日时更有利于改善宣威烟区烤烟上部叶组织结构,提高上部烟叶的抗逆性㊁产量及外观质量㊂关键词㊀烤烟上部叶;移栽期;表观特征;叶片组织结构;酶活性;产质量中图分类号㊀S 572㊀㊀文献标识码㊀A㊀㊀文章编号㊀0517-6611(2023)21-0037-06doi :10.3969/j.issn.0517-6611.2023.21.009㊀㊀㊀㊀㊀开放科学(资源服务)标识码(OSID):Effects of Transplanting Periods on Quality of Upper Flue-cured Tobacco Leaves of Yunyan 97ZHANG Lan-qing 1,GAO Hua-feng 2,ZHANG Jun-gang 2et al ㊀(1.College of Tobacco,Yunnan Agricultural University,Kunming,Yunnan 650201;2.Qujing Tobacco Company of Yunnan Province,Qujing,Yunnan 655000)Abstract ㊀In order to alleviate the effects of low temperature on tobacco leaf ripening in the upper tobacco-growing area of northeast Yunnan and to improve the yield and quality of the upper leaves,field experiment was used to study the effects of different transplanting time on the ag-ronomic traits,growth period,apparent characteristics,leaf structure,enzyme activity,economic traits,fresh-dry ratio and appearance quality of Yunyan 97tobacco leaves.Effects on the growth and yield and quality of tobacco plants were researched.Results showed that the transplan-ting treatment of field growth period from April 21to April 25was 113d,which was the smallest.This treatment increased the SOD,PPO en-zyme activities,decreased NR enzyme activities and MDA content,had the smallest fresh-dry ratio and the ratio of palisade tissue to sponge tissue,and the optimal maturity.Increasing the opening degree and stress resistance of the upper tobacco was conducive to the growth and de-velopment of tobacco plants and the yield and output value were highest in this transplanting pared with the treatment of trans-planting from April 26to April 30,the yield of primary flue-cured tobacco increased by 521.42kg /hm 2,the output value increased by 11529.42yuan /hm 2,and the proportion of high-grade tobacco reached 50.71%.Therefore,when the transplanting period was from April 21to April 25,it was more beneficial to improve the tissue structure of the upper leaves of flue-cured tobacco in Xuanwei tobacco-growing area,and to improve the stress resistance,yield and appearance quality of the upper leaves.Key words ㊀Upper leaf of cured tobacco;Transplanting period;Apparent characteristics;Leaf tissue structure;Enzyme activity;Yield and quality基金项目㊀云南省烟草公司科技计划重点项目提高滇东北烟区上部烟叶烘烤质量关键技术研究与应用 (云烟科 2020 39号)㊂作者简介㊀张岚清(1997 ),女,云南大理人,硕士研究生,研究方向:烟草原料生产与加工㊂∗通信作者,副教授,硕士,硕士生导师,从事烟草原料生产与加工研究㊂收稿日期㊀2022-10-14;修回日期㊀2023-07-13㊀㊀烤烟的上部叶包括顶叶及上二棚叶2个部分,在烤烟的大田生产过程中,上部烟叶占烤烟总产量的30%~40%,在烟草大田生产过程中占有重要地位[1]㊂然而,云南为低纬度高原季风气候,烤烟生产具有两头低温影响,中间高温不足的特点,低温影响时常发生,是云南中高海拔地区烤烟生产的主要气象灾害之一,严重影响烤烟产质量[2]㊂滇东北烟区烤烟种植面积大,主要有曲靖和昭通两大烟区,该地区平均海拔2000m 以上,成熟期时常受到秋季低温影响,严重影响了鲜烟叶的素质,尤其是对上部烟叶的影响较大,烘烤过程中易出现大量挂灰现象[3]㊂因此,为预防低成熟期低温冷害对烤烟上部叶的影响,探明烤烟在滇东北烟区的适宜移栽期具有重要意义㊂烟草是典型的喜温作物,对温度极敏感,烤烟在大田期生长发育的最适宜温度为25~28ħ,成熟期不低于17ħ,低于10~13ħ时会抑制烟株正常生长,持续时间长会使烟叶品质低劣,当温度降低至2~3ħ时,会导致烟叶严重脱水,生理指标紊乱,严重时会导致植株死亡[4]㊂关于移栽期对烤烟生长发育及产质量的影响已有较多研究报道㊂蒋代兵等[5]研究了云烟87采用膜下小苗移栽,且移栽时间较常规早10d,避开上部叶成熟时的不利气候条件,减轻了上部叶病害发生,保障了烟叶品质,提高其可用性㊂白应香等[6]研究显示,与其他移栽时间相比,云烟87在4月27日移栽可有效促进烟株的生长,增加干物质积累量,提高烟叶的经济性状㊂李小勇等[7]研究表明,在同一播种条件下,适当推迟移栽期,烟株的生育期明显缩短,株高显著增加,最大叶面积逐渐增加㊂目前,移栽期对上部叶的表观特征㊁叶片组织结构的影响鲜有报道㊂鉴于此,笔者于2020年研究了移栽期对上部烟叶农艺性状㊁生育期㊁表观特征㊁叶片组织结构㊁酶活性㊁经济性状㊁鲜干比㊁外观质量的影响,为滇东北烟区适宜移栽期的选择提供参考依据㊂1㊀材料与方法1.1㊀试验地概况㊀试验于2020年在云南省曲靖市宣威市热水镇(103ʎ46ᶄ25ᵡE,26ʎ04ᶄ11ᵡN)进行,该地土壤类型为壤土,试验地土地平整,排灌方便㊂栽烟前取土样1kg 分析土壤养分,试验地土壤基本养分含量如下:有机质49.34g /kg,全氮2.11g /kg,全磷1.51g /kg,全钾3.97g /kg,水解性氮163.17mg /kg,有效磷24.79mg /kg,速效钾159.80mg /kg,安徽农业科学,J.Anhui Agric.Sci.2023,51(21):37-42㊀㊀㊀pH 7.55㊂其中,pH 测定按水土比为2.5ʒ1㊂1.2㊀试验材料㊀试验品种为云烟97㊂1.3㊀试验设计㊀移栽时间设置4个不同处理:YZ1处理为4月11 15日,YZ2处理为4月16 20日,YZ3处理为4月21 25日,YZ4处理为4月26 30日㊂各处理均设3次重复,每小区种植烟株100株,4行区,随机区组排列,打顶时抹去杈烟㊂适熟采烤当天,分别在各处理的3次重复小区内,随机取35株烟草的上部叶(倒数4片叶)35片为研究对象㊂初烤烟叶取样分别采集每个处理的上部初烤烟叶B2F 等级2kg 检测烟叶外观质量㊂1.4㊀栽培措施管理㊀试验田采用膜下小苗移栽技术,行株距120cm ˑ60cm㊂各处理按当地常规施肥纯氮97.5kg /hm 2,N ʒP 2O 5ʒK =1ʒ1ʒ3,基肥为烟草专用复合肥(N ʒP 2O 5ʒK =14ʒ10ʒ24)621.9kg /hm 2,商品有机肥(总养分ȡ5.0%,有机质ȡ50%)1500kg /hm 2,钙镁磷肥(P 2O 5ȡ16%)220.5kg /hm 2,追肥硝酸钾(13.5-0-44.5)75kg /hm 2,硫酸钾(K 2Oȡ51%)200.25kg /hm 2㊂其他生产管理均按照当地操作执行,烘烤工艺为曲靖市烘烤工艺㊂1.5㊀测定项目与方法1.5.1㊀烟株农艺性状㊂按照YC /T 142 2010进行调查测定㊂各处理每小区于现蕾期㊁打顶后调查记录1次烟株的株高㊁叶数㊁茎围㊁节距㊁最大叶长和叶宽等农艺性状㊂1.5.2㊀生育期调查记载㊂从移栽开始分别记录烟株各生育期的时间㊂1.5.3㊀原烟外观质量㊂调查记录初烤烟叶的外观质量,包括成熟度㊁颜色㊁色度㊁油分㊁叶片结构㊁身份等㊂1.5.4㊀经济性状测定㊂烘烤结束后,对各小区产量及上中下等烟比例进行称量,根据交售价格进行产值及均价计算㊂1.5.5㊀上部鲜烟叶表观特征㊂表观特征包括上部鲜烟叶片数㊁开片度以及成熟度(尚熟㊁适熟㊁完熟)㊂1.5.6㊀叶片组织显微结构测定㊂观察栅栏组织㊁海绵组织等,组织结构观察测定技术[8]㊂1.5.7㊀酶活性测定㊂各处理取样5片鲜烟叶,测定酶活性,主要包括丙二醛(MDA)㊁硝酸还原酶(NR)㊁超氧化物歧化酶(SOD)和多酚氧化酶(PPO),采用苏州格锐思生物科技有限公司试剂盒进行测定㊂1.5.8㊀上部叶鲜干重比值㊂上部叶鲜干比为上部叶鲜重(g)/上部叶干重(g)㊂1.6㊀数据处理㊀采用Excel 和SPSS 22.0软件进行数据统计分析㊂2㊀结果与分析2.1㊀不同处理对现蕾期和打顶后烟株农艺性状的影响㊀由表1可知,现蕾期株高以YZ1处理最高,为106.60cm,且显著高于YZ2处理,YZ1㊁YZ3㊁YZ4处理间差异不显著;有效叶数以YZ1处理最多,为20.47片,显著多于YZ2㊁YZ4处理,其次是YZ3;YZ2㊁YZ3㊁YZ4处理间茎围无显著差异,但都显著高于YZ1处理,YZ1处理仅为6.08cm;节距YZ1㊁YZ3㊁YZ4处理间无显著差异,但都显著高于YZ2处理,YZ2处理仅为4.75cm;YZ2㊁YZ3㊁YZ4处理间的最大叶宽无显著差异,但都显著高于YZ1处理(仅为17.21cm);最大叶长各处理间无显著性差异㊂打顶后,YZ2㊁YZ3处理的最大叶长显著高于YZ1处理,YZ1与YZ4处理间无显著差异㊂其余性状各处理间无显著差异㊂综合来看,现蕾期YZ1㊁YZ3㊁YZ4处理的农艺性状均优于YZ2处理㊂打顶后各处理间农艺性状相差不大㊂表1㊀不同处理对现蕾期和打顶后烟株农艺性状的影响Table 1㊀Effects of different treatments on agronomic characters of tobacco plants at budding stage and after topping时期Stage处理编号Treatment code株高Plant heightcm有效叶数Effective leavesʊ片茎围Stem girth cm节距Node distancecm最大叶长Maximum leaf lengthʊcm最大叶宽Maximum leaf widthʊcm现蕾期Budding stageYZ1106.60a20.47a6.08b5.21a47.55a17.21bYZ281.30b17.13b 7.61a4.75b 52.97a22.99aYZ398.62ab18.73ab 7.82a5.27a50.07a22.85aYZ4100.66ab 17.53b7.69a5.74a49.36a20.55ab 打顶后After toppingYZ196.97a18.00a8.75a5.39a58.70b 23.95aYZ291.93a17.80a8.72a5.19a66.29a27.57aYZ393.13a17.07a9.02a5.47a64.33a27.63aYZ489.20a 17.00a 8.85a 5.27a63.74ab26.83a㊀注:同列同一时期不同小写字母表示在0.05水平差异显著㊂㊀Note:Different lowercases in the same column of the same period indicated significant differences at 0.05level.2.2㊀不同处理对烤烟生育期的影响㊀从表2可以看出,YZ1处理进入各个生育期的时间均较早;YZ2处理进入团棵期和旺长期的时间较晚,进入现蕾期㊁中心花开放期㊁脚叶成熟期㊁顶叶成熟期的时间与YZ4处理相当;YZ3处理进入各个生育期的时间虽晚于YZ1处理,但早于YZ2㊁YZ4处理㊂各处理大田生育期为113~123d,其中YZ3处理最短,为113d,YZ2处理最长,为123d㊂2.3㊀不同处理对上部叶原烟外观质量的影响㊀由表3可知,在上部叶各处理中,成熟度表现基本一致;颜色均为橘黄;从油分来看,YZ3和YZ4处理的油分略高于YZ1和YZ2处理;从叶片结构上看,YZ1和YZ4处理结构稍密,YZ2和YZ3处理结构为尚疏松,优于YZ1和YZ4处理;从身份上来83㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀安徽农业科学㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀2023年看,YZ4处理叶片较其他处理略厚㊂综上所述,YZ3处理的外观质量表现更佳㊂表2㊀不同处理对烤烟生育期的影响Table 2㊀Effects of different treatments on growth periods of flue-cured tobaccos处理编号Treatment code移栽期Transplan-ting date团棵期Rosette stage旺长期Vigorous growth period现蕾期Budding stage 中心花开放期Central flowering stage脚叶成熟期Mature stage of bottom leaf顶叶成熟期Mature stage of top leaf大田生育期Field growth stageʊdYZ104-1105-1805-2906-2106-2807-0608-07118YZ204-1606-0506-1707-0207-0707-1408-17123YZ304-2105-2806-1206-3007-0407-1108-12113YZ404-2605-2906-1507-0207-0707-1408-18114表3㊀不同处理对上部叶原烟外观质量的影响Table 3㊀Effects of different treatments on the appearance quality of upper tobacco leaves处理编号Treatment code成熟度Maturity 颜色Color 色度Chromacity油分Oil content叶片结构Leaf structure身份Status YZ1成熟橘黄中稍有稍密稍厚YZ2成熟橘黄中稍有尚疏松稍厚YZ3成熟橘黄中有尚疏松稍厚YZ4成熟橘黄中有稍密厚2.4㊀不同处理对烤后烟叶经济性状的影响㊀由表4可知,各处理之间YZ3处理的烟叶产量㊁产值与其他3个处理间存在显著差异㊂各处理间在均价㊁上等烟比例㊁中等烟比例㊁下等烟比例间均无显著差异㊂各处理均价为21.43~22.72元/kg㊂YZ3处理的产量显著高于YZ1㊁YZ2㊁YZ4,YZ4处理产量最低,YZ3处理产量比YZ4处理高521.42kg /hm 2㊂各处理产值为38399.02~49928.43元/hm 2,YZ3处理产值最高,YZ4处理产值最低,YZ3处理产值比YZ4处理高11529.41元/hm 2㊂各处理上等烟比例依次为YZ3处理>YZ1处理>YZ4处理>YZ2处理㊂表4㊀不同处理对烤后烟叶经济性状的影响Table 4㊀Effects of different treatments on economic characters of flue-cured tobacco leaves处理编号Treatment code产量Yield kg /hm 2产值Output value 元/hm 2均价Average price 元/kg上等烟比例Proportion of first-class tobaccosʊ%中等烟比例Proportion of middle-class tobaccosʊ%下等烟比例Proportion of low-class tobaccosʊ%YZ12015.75b43194.63b21.43a39.96a46.23a13.81aYZ21797.34b40838.92b22.72a39.03a48.35a12.62aYZ32276.42a49928.43a21.93a50.71a37.48a11.81aYZ41755.00b 38399.02b 21.88a 39.48a 54.25a6.27a㊀注:同列不同小写字母表示在0.05水平差异显著㊂㊀Note:Different lowercases in the same column indicated significant differences at 0.05level.2.5㊀不同处理对上部叶表观特征的影响㊀由表5可知,各处理成熟度均为适熟,各处理间上部叶片数无显著差异㊂YZ3处理开片度最大,为0.43,显著高于YZ1处理,但与YZ2㊁YZ4处理间差异不显著㊂表5㊀不同处理对上部叶表观特征的影响Table 5㊀Effects of different treatments on appearance characters ofupper leaves处理编号Treatment code成熟度Maturity 上部叶片数Upper leavesʊ片开片度Leaf opening degreeYZ1适熟 6.53a0.41bYZ2适熟 6.67a0.42ab YZ3适熟6.67a0.43aYZ4适熟 6.40a 0.42ab ㊀注:同列不同小写字母表示在0.05水平差异显著㊂㊀Note:Different lowercases in the same column indicated significant differ-ences at 0.05level.2.6㊀不同处理对叶片组织结构的影响㊀由图1~5可知,海绵组织由厚到薄依次为YZ3处理>YZ4处理>YZ1处理>YZ2处理,YZ3处理显著高于YZ2处理,YZ1㊁YZ2㊁YZ4处理间差异不显著;栅栏组织由厚到薄依次为YZ3处理>YZ1处理>YZ4处理>YZ2处理,处理间差异不显著;叶片厚度由厚到薄依次为YZ3处理>YZ4处理>YZ2处理>YZ1处理,YZ3处理显著高于YZ1㊁YZ2㊁YZ4处理;栅栏和海绵组织比各处理间差异不显著,其中YZ3处理比值最小㊂2.7㊀不同处理对酶活性的影响㊀由图6~9可知,不同处理间多酚氧化酶(PPO)活性依次为YZ3处理>YZ4处理>YZ2处理>YZ1处理;不同处理间超氧化物歧化酶(SOD)活性依次为YZ3处理>YZ2处理>YZ4处理>YZ1处理;各处理丙二醛(MDA)含量为1.72~1.89mmol /g ,其中YZ2和YZ3处理含量较低;各处理硝酸还原酶(NR)活性依次为YZ1处理>9351卷21期㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀张岚清等㊀移栽期对云烟97上部烟叶质量的影响YZ3处理>YZ2处理>YZ4处理㊂2.8㊀不同处理对上部鲜烟叶的鲜干比的影响㊀由图10可知,YZ4处理的上部叶鲜干比显著高于YZ1㊁YZ2㊁YZ3处理,而且YZ4处理>YZ2处理>YZ1处理>YZ3处理,YZ3处理的上部叶鲜干比最小,说明YZ3处理的上部叶含水量较少,烟叶干物质积累最高㊂注:A.YZ1处理;B.YZ2处理;C.YZ3处理;D.YZ4处理㊂Note:A.YZ1treatment;B.YZ2treatment;C.YZ3treatment;D.YZ4treatment.图1㊀不同处理上部鲜烟叶叶片组织显微结构Fig.1㊀Microstructure of fresh tobacco leaf tissue in the upper part of eachtreatment注:不同小写字母表示在0.05水平差异显著㊂Note:Different lowercases indicated significant differences at 0.05level.图2㊀不同处理对烟叶栅栏组织厚度的影响Fig.2㊀Effects of different treatments on palisade tissue thicknessof flue-cured tobaccoleaves注:不同小写字母表示在0.05水平差异显著㊂Note:Different lowercases indicated significant differences at 0.05level.图3㊀不同处理对烟叶海绵组织厚度的影响Fig.3㊀Effects of different treatments on spongy tissue thicknessof flue-cured tobacco leaves3㊀讨论鹿莹等[9]研究发现,不同的移栽时间会显著影响烤烟生长过程,与5月2日移栽相比,延迟移栽时间会缩短伸根期㊁旺长期以及成熟期时间,最终缩短整个大田生育期㊂王德权注:不同小写字母表示在0.05水平差异显著㊂Note:Different lowercases indicated significant differences at 0.05level.图4㊀不同处理对烟叶叶片厚度的影响Fig.4㊀Effect of different treatments on leaf thickness of flue-cured tobaccoleaves注:不同小写字母表示在0.05水平差异显著㊂Note:Different lowercases indicated significant differences at 0.05level.图5㊀不同处理对烟叶栅栏组织/海绵组织比的影响Fig.5㊀Effects of different treatments on the ratio of palisade tis-sue to sponge tissue of flue-cured tobacco leaves等[10]研究表明,在同一移栽方式下,适宜推迟移栽期可缩短伸根期和旺长期,从而减少烤烟生育期㊂从生育期看,适宜推迟移栽期,各处理间相应生育期的时间差距在不断减小,4㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀安徽农业科学㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀2023年注:不同小写字母表示在0.05水平差异显著㊂Note:Different lowercases indicated significant differences at 0.05level.图6㊀不同处理对NR 活性的影响Fig.6㊀Effects of different treatments on NRactivity注:不同小写字母表示在0.05水平差异显著㊂Note:Different lowercases indicated significant differences at 0.05level.图7㊀不同处理对PPO 活性的影响Fig.7㊀Effects of different treatments on PPOactivity注:不同小写字母表示在0.05水平差异显著㊂Note:Different lowercases indicated significant differences at 0.05level.图8㊀不同处理对SOD 活性的影响Fig.8㊀Effects of different treatments on SOD activityYZ1与YZ4处理移栽时间相差15d,但最终大田生育期仅相差4d,这表明当移栽时间适当推后,适宜的温度有利于烤烟生长发育,从而缩短了生育期㊂在农艺性状方面,YZ2处理烤烟现蕾期农艺性状最差,这可能是由于移栽后遇到春末干旱,不利于烟株生长发育;YZ3和YZ4处理的烤烟现蕾期农艺性状与YZ1处理相差不大,这可能是因为推迟移栽,烟苗注:不同小写字母表示在0.05水平差异显著㊂Note:Different lowercases indicated significant differences at 0.05level.图9㊀不同处理对MDA 含量的影响Fig.9㊀Effects of different treatments on MDAcontent注:不同小写字母表示在0.05水平差异显著㊂Note:Different lowercases indicated significant differences at 0.05level.图10㊀不同处理对上部叶鲜干比的影响Fig.10㊀Effects of different treatments on fresh /dry weight ratioof upper leaves根系健壮发达,栽后还苗快㊂研究表明[11-15],烟叶的外观品质是其内在质量的外观表现,烤后烟叶的颜色㊁油分㊁身份受烟叶内含物及内部组织结构的影响㊂该试验各处理初烤烟叶外观质量成熟度以YZ3处理移栽效果最好,说明过早或过晚移栽均会影响烟叶的外观质量㊂各处理中,YZ3处理的烟叶产量㊁产值与其他3个处理间存在显著差异,各处理在均价㊁上等烟比例㊁中等烟比例㊁下等烟比例上没有显著差异,说明过早过晚移栽对产量㊁产值影响较大,这与郭建等[16]的研究结果一致㊂从表观特征上看,各处理成熟度均为适熟,上部叶片数无显著差异;YZ3处理开片度最大,为0.43,且显著高于YZ1处理,与YZ2㊁YZ4处理间差异不显著,这可能是由于移栽期的不同,烟株在生长过程中温度㊁水分等生态条件有差异,导致烟叶组织细胞在发育过程中出现差异㊂移栽期对烤烟叶片组织的影响,主要体现在对栅栏组织和海绵组织的影响㊂由于各移栽期的气候不同,叶片栅栏组织和海绵组织发生变化[17]㊂栅栏组织与海绵组织的厚度最大值均出现在YZ3处理,最小值均出现在YZ2处理,叶片厚度最厚是YZ3处理,最薄是YZ1处理,YZ3处理栅栏组织和1451卷21期㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀张岚清等㊀移栽期对云烟97上部烟叶质量的影响海绵组织厚度比最小,说明YZ3处理较YZ1㊁YZ2㊁YZ4处理上部叶成熟度好㊂王寒等[18]研究表明,烤烟提前移栽,质体色素含量㊁硝酸还原酶(NR)活性和淀粉酶活性在烟叶成熟前期均保持升高趋势,其中硝酸还原酶活性较大值出现较晚,能有效延长烟叶的功能期,但过早移栽叶绿素含量和硝酸还原酶活性会在烟叶成熟后期偏高,碳氮代谢不协调,移栽时间延迟酶活性和质体色素含量较低,有利于提高初烤烟叶产量㊂多酚氧化酶(PPO)是鲜烟叶组织中一种重要酶类,超氧化物歧化酶(SOD)能清除烟株代谢过程中和各种环境胁迫下产生的活性氧和自由基,保护细胞免受氧化损伤㊂在该试验中,各处理多酚氧化酶(PPO)活性依次为YZ3处理>YZ4处理>YZ2处理>YZ1处理,各处理超氧化物歧化酶(SOD)活性依次为YZ3处理>YZ2处理>YZ4处理>YZ1处理,各处理丙二醛(MDA)含量依次为YZ1处理>YZ4处理>YZ2处理=YZ3处理,各处理硝酸还原酶(NR)活性依次为YZ1处理>YZ3处理>YZ2处理>YZ4处理㊂YZ3处理上部叶的PPO㊁SOD活性较高,硝酸还原酶活性较最早移栽的YZ1处理低,丙二醛含量最低,说明适当推迟移栽期,烟株上部叶成熟后期的超氧化物歧化酶和多酚氧化酶活性较高,硝酸还原酶活性较低,丙二醛含量较低,碳氮代谢较协调且抗逆性较强㊂过早或过晚移栽都会影响烟株的生长发育,导致干物质积累少,从而影响烟叶的产质量㊂该研究结果表明,YZ3处理的上部叶鲜干比最小,上部烟叶干物质积累最优,而其余3个移栽处理的鲜干比较大㊂4　结论在云南宣威烟区,YZ3(4月21 25日移栽)处理大田生育期最短,为113d,说明YZ3处理有利于烟株的生长发育; YZ3处理上部叶SOD㊁PPO酶活性提高,NR酶活性和MDA 含量降低,说明YZ3处理碳氮代谢较协调㊁抗逆性较强,能有效提升烤后烟叶的质量;YZ3处理的烟株上部叶开片度高于其他处理,上部初烤烟叶的外观质量优于其他3个处理,鲜干比最小,说明YZ3处理能提高烟叶干物质的积累;YZ3处理产量及产值较高,比YZ4处理分别高521.42kg/hm2和11529.42元/hm2,且YZ3处理的上等烟率达50.71%,说明YZ3处理能有效提高烟叶的产质量㊂综上所述,YZ3(4月21 25日移栽)处理更有利于改善云南宣威烟区烤烟上部叶组织结构,提高上部烟叶的抗逆性㊁产量及外观质量㊂参考文献[1]许自成,黄平俊,苏富强,等.不同采收方式对烤烟上部叶内在品质的影响[J].西北农林科技大学学报(自然科学版),2005,33(11):13-17.[2]贺升华,任炜.烤烟气象[M].昆明:云南科技出版社,2001:200-259.[3]李蒙,张明达,朱勇,等.云南烤烟低温冷害风险区划[J].气象科学, 2014,34(3):294-298.[4]刘国顺.烟草栽培学[M].北京:中国农业出版社,2003:45-49.[5]蒋代兵,苏秀芳,谢凤标,等.移栽期及移栽方式对龙岩烤烟生长发育及烟叶品质的影响[J].农学学报,2021,11(4):19-27.[6]白应香,胡锦,段建军,等.移栽期对黔西南烟区云烟87田间生长及经济性状的影响[J].作物研究,2021,35(2):141-145.[7]李小勇,肖荣贵,胡蓉花,等.不同播种期和移栽期对烤烟生长发育㊁产量和质量的影响[J].江西农业学报,2019,31(7):81-86.[8]徐向丽,宋卫武,周文辉,等.初烤烟叶柔软性检测及细胞结构比较[J].湖南农业科学,2019(3):76-79.[9]鹿莹,梁晓芳,管恩森,等.移栽时间对烤烟光合特性㊁产量和品质的影响[J].中国烟草科学,2014,35(1):48-53.[10]王德权,孙延国,杜玉海,等.移栽时间与方式对烤烟生长发育及产量㊁品质的影响[J].作物杂志,2021(2):87-95.[11]苏鹏飞,李亚纯,沈雪婷,等.移栽时间与方式对烤烟生长及产质量的影响[J].贵州农业科学,2016,44(11):51-55.[12]冯吉,孙光伟,陈振国.不同移栽方式和移栽期对烤烟产量和质量的影响[J].安徽农业科学,2018,46(30):51-53.[13]陈义强,范坚强,郑湖南,等.不同移栽期对云烟85 烟叶质量风格特色的影响[J].甘肃农业大学学报,2014,49(4):47-57.[14]付美夫.重庆烟区不同移栽期㊁覆盖方式对烤烟品质的影响[J].山东农业科学,2014,46(7):66-71.[15]木潆,杨志坚,和强,等.不同移栽期烤烟膜下小苗移栽比较试验[J].安徽农学通报,2014,20(7):62-63.[16]郭健,程晓东,李洪亮,等.不同移栽期对烤烟生长和产量产值的影响[J].吉林农业,2017(18):86-87.[17]齐飞,刘国顺,史宏志,等.移栽期对烤烟化学成分及成熟烟叶组织结构的影响[J].中国烟草学报,2011,17(3):37-41.[18]王寒,陈建军,林锐峰,等.粤北地区移栽期对烤烟成熟期生理生化指标和经济性状的影响[J].中国烟草学报,2013,19(6):71-77.24㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀安徽农业科学㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀2023年。

WHO儿童生长发育标准2006年版1997年版

世界卫生组织儿童生长发育标准WHO Child Growth Standards（2006 年版）安徽省妇幼保健所翻制二○○七年八月目录0-4 岁年龄别体重Weight-for-age0-4 岁年龄别身长（高）Length/height-for-age45-120 厘米身长（高）别体重Weight-for-length/height头围Head circumference-for-age上臂围Arm circumference-for-age肩胛下皮褶厚度Subscapular skinfold-for-age三角肌皮褶厚度Triceps skinfold-for-age体块指数Body mass index-for-age (BMI-for-age)附：5-6 岁男孩年龄别体重5-6 岁女孩年龄别体重5-6 岁男孩年龄别身高5-6 岁女孩年龄别身高120-138.5 厘米男孩身高别体重120-137 厘米女孩身高别体重更详细内容请参见：http://www.who.int/childgrowth/standards/en/index.html儿童发育评价方法的等级评价比较发育等级离差法标准差分（Z 分）法Z 分对应的百分位百分位数法上等>x +2 s >2 >P97.5 > P 97中上等X+1s～x +2s 1～2 P85～P97.5 P80 ～P 97中等x±1s-1 ～1 P15～P85 P20、～P 80 中下等x-2 s～x -1 s -2 ～-1 P2.5 ～P15 P3 ～P 20下等< x -2 s <-2 <P2.5 < P 3女. 0－4岁WH（O世界卫生组织）0-4 岁年龄别体重标准（2006 年版）男.0 －4岁年. 月－3SD －2SD -1SD 均数1SD 2SD 3SD 年. 月－3SD －2SD -1SD 均数1SD 2SD 3SD 0.0 2.0 2.4 2.8 3.2 3.7 4.2 4.8 0.0 2.1 2.5 2.9 3.3 3.9 4.4 5.0 0.1 2.7 3.2 3.6 4.2 4.8 5.5 6.2 0.1 2.9 3.4 3.9 4.5 5.1 5.8 6.6 0.2 3.4 3.9 4.5 5.1 5.8 6.6 7.5 0.2 3.8 4.3 4.9 5.6 6.3 7.1 8.0 0.3 4.0 4.5 5.2 5.8 6.6 7.5 8.5 0.3 4.4 5.0 5.7 6.4 7.2 8.0 9.0 0.4 4.4 5.0 5.7 6.4 7.3 8.2 9.3 0.4 4.9 5.6 6.2 7.0 7.8 8.7 9.7 0.5 4.8 5.4 6.1 6.9 7.8 8.8 10.0 0.5 5.3 6.0 6.7 7.5 8.4 9.3 10.4 0.6 5.1 5.7 6.5 7.3 8.2 9.3 10.6 0.6 5.7 6.4 7.1 7.9 8.8 9.8 10.9 0.7 5.3 6.0 6.8 7.6 8.6 9.8 11.1 0.7 5.9 6.7 7.4 8.3 9.2 10.3 11.4 0.8 5.6 6.3 7.0 7.9 9.0 10.2 11.6 0.8 6.2 6.9 7.7 8.6 9.6 10.7 11.9 0.9 5.8 6.5 7.3 8.2 9.3 10.5 12.0 0.9 6.4 7.1 8.0 8.9 9.9 11.0 12.3 0.10 5.9 6.7 7.5 8.5 9.6 10.9 12.4 0.10 6.6 7.4 8.2 9.2 10.2 11.4 12.70.11 6.1 6.9 7.7 8.7 9.9 11.2 12.8 0.11 6.8 7.6 8.4 9.4 10.5 11.7 13.01.0 6.3 7.0 7.9 8.9 10.1 11.5 13.1 1.0 6.9 7.7 8.6 9.6 10.8 12.0 13.3 1.1 6.4 7.2 8.1 9.2 10.4 11.8 13.5 1.1 7.1 7.9 8.8 9.9 11.0 12.3 13.7 1.2 6.6 7.4 8.3 9.4 10.6 12.1 13.8 1.2 7.2 8.1 9.0 10.1 11.3 12.6 14.0 1.3 6.7 7.6 8.5 9.6 10.9 12.4 14.1 1.3 7.4 8.3 9.2 10.3 11.5 12.8 14.3 1.4 6.9 7.7 8.7 9.8 11.1 12.6 14.5 1.4 7.5 8.4 9.4 10.5 11.7 13.1 14.6 1.5 7.0 7.9 8.9 10.0 11.4 12.9 14.8 1.5 7.7 8.6 9.6 10.7 12.0 13.4 14.9 1.6 7.2 8.1 9.1 10.2 11.6 13.2 15.1 1.6 7.8 8.8 9.8 10.9 12.2 13.7 15.3 1.7 7.3 8.2 9.2 10.4 11.8 13.5 15.4 1.7 8.0 8.9 10.0 11.1 12.5 13.9 15.6 1.8 7.5 8.4 9.4 10.6 12.1 13.7 15.7 1.8 8.1 9.1 10.1 11.3 12.7 14.2 15.9 1.9 7.6 8.6 9.6 10.9 12.3 14.0 16.0 1.9 8.2 9.2 10.3 11.5 12.9 14.5 16.2 1.107.88.79.8 11.1 12.5 14.3 16.4 1.10 8.4 9.4 10.5 11.8 13.2 14.7 16.51.11 7.9 8.9 10.0 11.3 12.8 14.6 16.7 1.11 8.5 9.5 10.7 12.0 13.4 15.0 16.82.0 8.1 9.0 10.2 11.5 13.0 14.8 17.0 2.0 8.6 9.7 10.8 12.2 13.6 15.3 17.1 2.1 8.2 9.2 10.3 11.7 13.3 15.1 17.3 2.1 8.8 9.8 11.0 12.4 13.9 15.5 17.5 2.2 8.4 9.4 10.5 11.9 13.5 15.4 17.7 2.2 8.9 10.0 11.2 12.5 14.1 15.8 17.8 2.3 8.5 9.5 10.7 12.1 13.7 15.7 18.0 2.3 9.0 10.1 11.3 12.7 14.3 16.1 18.1 2.4 8.6 9.7 10.9 12.3 14.0 16.0 18.3 2.4 9.1 10.2 11.5 12.9 14.5 16.3 18.4 2.5 8.8 9.8 11.1 12.5 14.2 16.2 18.7 2.5 9.2 10.4 11.7 13.1 14.8 16.6 18.7 2.6 8.9 10.0 11.2 12.7 14.4 16.5 19.0 2.6 9.4 10.5 11.8 13.3 15.0 16.9 19.0 2.7 9.0 10.1 11.4 12.9 14.7 16.8 19.3 2.7 9.5 10.7 12.0 13.5 15.2 17.1 19.3 2.8 9.1 10.3 11.6 13.1 14.9 17.1 19.6 2.8 9.6 10.8 12.1 13.7 15.4 17.4 19.6 2.9 9.3 10.4 11.7 13.3 15.1 17.3 20.0 2.9 9.7 10.9 12.3 13.8 15.6 17.6 19.9 2.10 9.4 10.5 11.9 13.5 15.4 17.6 20.3 2.10 9.8 11.0 12.4 14.0 15.8 17.8 20.22.11 9.5 10.7 12.0 13.7 15.6 17.9 20.6 2.11 9.9 11.2 12.6 14.2 16.0 18.1 20.43.0 9.6 10.8 12.2 13.9 15.8 18.1 20.9 3.0 10.0 11.3 12.7 14.3 16.2 18.3 20.7 3.1 9.7 10.9 12.4 14.0 16.0 18.4 21.3 3.1 10.1 11.4 12.9 14.5 16.4 18.6 21.0 3.2 9.8 11.1 12.5 14.2 16.3 18.7 21.6 3.2 10.2 11.5 13.0 14.7 16.6 18.8 21.3 3.3 9.9 11.2 12.7 14.4 16.5 19.0 22.0 3.3 10.3 11.6 13.1 14.8 16.8 19.0 21.6 3.4 10.1 11.3 12.8 14.6 16.7 19.2 22.3 3.4 10.4 11.8 13.3 15.0 17.0 19.3 21.9 3.5 10.2 11.5 13.0 14.8 16.9 19.5 22.7 3.5 10.5 11.9 13.4 15.2 17.2 19.5 22.1 3.6 10.3 11.6 13.1 15.0 17.2 19.8 23.0 3.6 10.6 12.0 13.6 15.3 17.4 19.7 22.4 3.7 10.4 11.7 13.3 15.2 17.4 20.1 23.4 3.7 10.7 12.1 13.7 15.5 17.6 20.0 22.7 3.8 10.5 11.8 13.4 15.3 17.6 20.4 23.7 3.8 10.8 12.2 13.8 15.7 17.8 20.2 23.0 3.9 10.6 12.0 13.6 15.5 17.8 20.7 24.1 3.9 10.9 12.4 14.0 15.8 18.0 20.5 23.3 3.10 10.7 12.1 13.7 15.7 18.1 20.9 24.5 3.10 11.0 12.5 14.1 16.0 18.2 20.7 23.63.11 10.8 12.2 13.9 15.9 18.3 21.2 24.8 3.11 11.1 12.6 14.3 16.2 18.4 20.9 23.94.0 10.9 12.3 14.0 16.1 18.5 21.5 25.2 4.0 11.2 12.7 14.4 16.3 18.6 21.2 24.2 4.1 11.0 12.4 14.2 16.3 18.8 21.8 25.5 4.1 11.3 12.8 14.5 16.5 18.8 21.4 24.5 4.2 11.1 12.6 14.3 16.4 19.0 22.1 25.9 4.2 11.4 12.9 14.7 16.7 19.0 21.7 24.8 4.3 11.2 12.7 14.5 16.6 19.2 22.4 26.3 4.3 11.5 13.1 14.8 16.8 19.2 21.9 25.1 4.4 11.3 12.8 14.6 16.8 19.4 22.6 26.6 4.4 11.6 13.2 15.0 17.0 19.4 22.2 25.4 4.5 11.4 12.9 14.8 17.0 19.7 22.9 27.0 4.5 11.7 13.3 15.1 17.2 19.6 22.4 25.7 4.6 11.5 13.0 14.9 17.2 19.9 23.2 27.4 4.6 11.8 13.4 15.2 17.3 19.8 22.7 26.0 4.7 11.6 13.2 15.1 17.3 20.1 23.5 27.7 4.7 11.9 13.5 15.4 17.5 20.0 22.9 26.3 4.8 11.7 13.3 15.2 17.5 20.3 23.8 28.1 4.8 12.0 13.6 15.5 17.7 20.2 23.2 26.6 4.9 11.8 13.4 15.3 17.7 20.6 24.1 28.5 4.9 12.1 13.7 15.6 17.8 20.4 23.4 26.9 4.10 11.9 13.5 15.5 17.9 20.8 24.4 28.8 4.10 12.2 13.8 15.8 18.0 20.6 23.7 27.24.11 12.0 13.6 15.6 18.0 21.0 24.6 29.2 4.11 12.3 14.0 15.9 18.2 20.8 23.9 27.65.0 12.1 13.7 15.8 18.2 21.2 24.9 29.5 5.0 12.4 14.1 16.0 18.3 21.0 24.2 27.9女.0 －2 岁WH（O世界卫生组织）0-4 岁年龄别身长（高）标准（2006 年版）男.0 －2 岁年. 月－3SD －2SD -1SD 均数1SD 2SD 3SD 年. 月－3SD －2SD -1SD 均数1SD 2SD 3SD0.0 43.6 45.4 47.3 49.1 51.0 52.9 54.7 0.0 44.2 46.1 48.0 49.9 51.8 53.7 55.60.1 47.8 49.8 51.7 53.7 55.6 57.6 59.5 0.1 48.9 50.8 52.8 54.7 56.7 58.6 60.60.2 51.0 53.0 55.0 57.1 59.1 61.1 63.2 0.2 52.4 54.4 56.4 58.4 60.4 62.4 64.40.3 53.5 55.6 57.7 59.8 61.9 64.0 66.1 0.3 55.3 57.3 59.4 61.4 63.5 65.5 67.60.4 55.6 57.8 59.9 62.1 64.3 66.4 68.6 0.4 57.6 59.7 61.8 63.9 66.0 68.0 70.10.5 57.4 59.6 61.8 64.0 66.2 68.5 70.7 0.5 59.6 61.7 63.8 65.9 68.0 70.1 72.20.6 58.9 61.2 63.5 65.7 68.0 70.3 72.5 0.6 61.2 63.3 65.5 67.6 69.8 71.9 74.00.7 60.3 62.7 65.0 67.3 69.6 71.9 74.2 0.7 62.7 64.8 67.0 69.2 71.3 73.5 75.70.8 61.7 64.0 66.4 68.7 71.1 73.5 75.8 0.8 64.0 66.2 68.4 70.6 72.8 75.0 77.20.9 62.9 65.3 67.7 70.1 72.6 75.0 77.4 0.9 65.2 67.5 69.7 72.0 74.2 76.5 78.70.10 64.1 66.5 69.0 71.5 73.9 76.4 78.9 0.10 66.4 68.7 71.0 73.3 75.6 77.9 80.10.11 65.2 67.7 70.3 72.8 75.3 77.8 80.3 0.11 67.6 69.9 72.2 74.5 76.9 79.2 81.51.0 66.3 68.9 71.4 74.0 76.6 79.2 81.7 1.0 68.6 71.0 73.4 75.7 78.1 80.5 82.91.1 67.3 70.0 72.6 75.2 77.8 80.5 83.1 1.1 69.6 72.1 74.5 76.9 79.3 81.8 84.21.2 68.3 71.0 73.7 76.4 79.1 81.7 84.4 1.2 70.6 73.1 75.6 78.0 80.5 83.0 85.51.3 69.3 72.0 74.8 77.5 80.2 83.0 85.7 1.3 71.6 74.1 76.6 79.1 81.7 84.2 86.71.4 70.2 73.0 75.8 78.6 81.4 84.2 87.0 1.4 72.5 75.0 77.6 80.2 82.8 85.4 88.01.5 71.1 74.0 76.8 79.7 82.5 85.4 88.2 1.5 73.3 76.0 78.6 81.2 83.9 86.5 89.21.6 72.0 74.9 77.8 80.7 83.6 86.5 89.4 1.6 74.2 76.9 79.6 82.3 85.0 87.7 90.41.7 72.8 75.8 78.8 81.7 84.7 87.6 90.6 1.7 75.0 77.7 80.5 83.2 86.0 88.8 91.51.8 73.7 76.7 79.7 82.7 85.7 88.7 91.7 1.8 75.8 78.6 81.4 84.2 87.0 89.8 92.61.9 74.5 77.5 80.6 83.7 86.7 89.8 92.9 1.9 76.5 79.4 82.3 85.1 88.0 90.9 93.81.10 75.2 78.4 81.5 84.6 87.7 90.8 94.0 1.10 77.2 80.2 83.1 86.0 89.0 91.9 94.91.11 76.0 79.2 82.3 85.5 88.7 91.9 95.0 1.11 78.0 81.0 83.9 86.9 89.9 92.9 95.92.0 76.7 80.0 83.2 86.4 89.6 92.9 96.1 2.0 78.7 81.7 84.8 87.8 90.9 93.9 97.0女.2 －5 岁男.2 －5 岁年. 月－3SD－2SD -1SD 均数1SD 2SD 3SD 年. 月－3SD －2SD -1SD 均数1SD 2SD 3SD 2.0 76.0 79.3 82.5 85.7 88.9 92.2 95.4 2.0 78.0 81.0 84.1 87.1 90.2 93.2 96.3 2.1 76.8 80.0 83.3 86.6 89.9 93.1 96.4 2.1 78.6 81.7 84.9 88.0 91.1 94.2 97.3 2.2 77.5 80.8 84.1 87.4 90.8 94.1 97.4 2.2 79.3 82.5 85.6 88.8 92.0 95.2 98.3 2.3 78.1 81.5 84.9 88.3 91.7 95.0 98.4 2.3 79.9 83.1 86.4 89.6 92.9 96.1 99.3 2.4 78.8 82.2 85.7 89.1 92.5 96.0 99.4 2.4 80.5 83.8 87.1 90.4 93.7 97.0 100.3 2.5 79.5 82.9 86.4 89.9 93.4 96.9 100.3 2.5 81.1 84.5 87.8 91.2 94.5 97.9 101.2 2.6 80.1 83.6 87.1 90.7 94.2 97.7 101.3 2.6 81.7 85.1 88.5 91.9 95.3 98.7 102.1 2.7 80.7 84.3 87.9 91.4 95.0 98.6 102.2 2.7 82.3 85.7 89.2 92.7 96.1 99.6 103.0 2.8 81.3 84.9 88.6 92.2 95.8 99.4 103.1 2.8 82.8 86.4 89.9 93.4 96.9 100.4 103.9 2.9 81.9 85.6 89.3 92.9 96.6 100.3 103.9 2.9 83.4 86.9 90.5 94.1 97.6 101.2 104.8 2.10 82.5 86.2 89.9 93.6 97.4 101.1 104.8 2.10 83.9 87.5 91.1 94.8 98.4 102.0 105.62.11 83.1 86.8 90.6 94.4 98.1 101.9 105.6 2.11 84.4 88.1 91.8 95.4 99.1 102.7 106.43.0 83.6 87.4 91.2 95.1 98.9 102.7 106.5 3.0 85.0 88.7 92.4 96.1 99.8 103.5 107.2 3.1 84.2 88.0 91.9 95.7 99.6 103.4 107.3 3.1 85.5 89.2 93.0 96.7 100.5 104.2 108.0 3.2 84.7 88.6 92.5 96.4 100.3 104.2 108.1 3.2 86.0 89.8 93.6 97.4 101.2 105.0 108.8 3.3 85.3 89.2 93.1 97.1 101.0 105.0 108.9 3.3 86.5 90.3 94.2 98.0 101.8 105.7 109.5 3.4 85.8 89.8 93.8 97.7 101.7 105.7 109.7 3.4 87.0 90.9 94.7 98.6 102.5 106.4 110.3 3.5 86.3 90.4 94.4 98.4 102.4 106.4 110.5 3.5 87.5 91.4 95.3 99.2 103.2 107.1 111.0 3.6 86.8 90.9 95.0 99.0 103.1 107.2 111.2 3.6 88.0 91.9 95.9 99.9 103.8 107.8 111.7 3.7 87.4 91.5 95.6 99.7 103.8 107.9 112.0 3.7 88.4 92.4 96.4 100.4 104.5 108.5 112.5 3.8 87.9 92.0 96.2 100.3 104.5 108.6 112.7 3.8 88.9 93.0 97.0 101.0 105.1 109.1 113.2 3.9 88.4 92.5 96.7 100.9 105.1 109.3 113.5 3.9 89.4 93.5 97.5 101.6 105.7 109.8 113.9 3.10 88.9 93.1 97.3 101.5 105.8 110.0 114.2 3.10 89.8 94.0 98.1 102.2 106.3 110.4 114.63.11 89.3 93.6 97.9 102.1 106.4 110.7 114.9 3.11 90.3 94.4 98.6 102.8 106.9 111.1 115.24.0 89.8 94.1 98.4 102.7 107.0 111.3 115.7 4.0 90.7 94.9 99.1 103.3 107.5 111.7 115.9 4.1 90.3 94.6 99.0 103.3 107.7 112.0 116.4 4.1 91.2 95.4 99.7 103.9 108.1 112.4 116.6 4.2 90.7 95.1 99.5 103.9 108.3 112.7 117.1 4.2 91.6 95.9 100.2 104.4 108.7 113.0 117.3 4.3 91.2 95.6 100.1 104.5 108.9 113.3 117.7 4.3 92.1 96.4 100.7 105.0 109.3 113.6 117.9 4.4 91.7 96.1 100.6 105.0 109.5 114.0 118.4 4.4 92.5 96.9 101.2 105.6 109.9 114.2 118.6 4.5 92.1 96.6 101.1 105.6 110.1 114.6 119.1 4.5 93.0 97.4 101.7 106.1 110.5 114.9 119.2 4.6 92.6 97.1 101.6 106.2 110.7 115.2 119.8 4.6 93.4 97.8 102.3 106.7 111.1 115.5 119.9 4.7 93.0 97.6 102.2 106.7 111.3 115.9 120.4 4.7 93.9 98.3 102.8 107.2 111.7 116.1 120.6 4.8 93.4 98.1 102.7 107.3 111.9 116.5 121.1 4.8 94.3 98.8 103.3 107.8 112.3 116.7 121.2 4.9 93.9 98.5 103.2 107.8 112.5 117.1 121.8 4.9 94.7 99.3 103.8 108.3 112.8 117.4 121.9 4.10 94.3 99.0 103.7 108.4 113.0 117.7 122.4 4.10 95.2 99.7 104.3 108.9 113.4 118.0 122.64.11 94.7 99.5 104.2 108.9 113.6 118.3 123.1 4.11 95.6 100.2 104.8 109.4 114.0 118.6 123.25.0 95.2 99.9 104.7 109.4 114.2 118.9 123.7 5.0 96.1 100.7 105.3 110.0 114.6 119.2 123.9WH（O世界卫生组织）0-2 岁身长别体重标准（2006 年版）女.0 －2 岁身长－3SD －2SD -1SD 均数1SD 2SD 3SD 身长－3SD －2SD -1SD 均数1SD 2SD 3SD 45 1.9 2.1 2.3 2.5 2.7 3.0 3.3 78 7.5 8.2 8.9 9.7 10.6 11.7 12.945.5 2.0 2.1 2.3 2.5 2.8 3.1 3.4 78.5 7.6 8.2 9.0 9.8 10.7 11.8 13.046 2.0 2.2 2.4 2.6 2.9 3.2 3.5 79 7.7 8.3 9.1 9.9 10.8 11.9 13.146.5 2.1 2.3 2.5 2.7 3.0 3.3 3.6 79.5 7.7 8.4 9.1 10.0 10.9 12.0 13.347 2.2 2.4 2.6 2.8 3.1 3.4 3.7 80 7.8 8.5 9.2 10.1 11.0 12.1 13.447.5 2.2 2.4 2.6 2.9 3.2 3.5 3.8 80.5 7.9 8.6 9.3 10.2 11.2 12.3 13.548 2.3 2.5 2.7 3.0 3.3 3.6 4.0 81 8.0 8.7 9.4 10.3 11.3 12.4 13.748.5 2.4 2.6 2.8 3.1 3.4 3.7 4.1 81.5 8.1 8.8 9.5 10.4 11.4 12.5 13.849 2.4 2.6 2.9 3.2 3.5 3.8 4.2 82 8.1 8.8 9.6 10.5 11.5 12.6 13.949.5 2.5 2.7 3.0 3.3 3.6 3.9 4.3 82.5 8.2 8.9 9.7 10.6 11.6 12.8 14.150 2.6 2.8 3.1 3.4 3.7 4.0 4.5 83 8.3 9.0 9.8 10.7 11.8 12.9 14.250.5 2.7 2.9 3.2 3.5 3.8 4.2 4.6 83.5 8.4 9.1 9.9 10.9 11.9 13.1 14.451 2.8 3.0 3.3 3.6 3.9 4.3 4.8 84 8.5 9.2 10.1 11.0 12.0 13.2 14.551.5 2.8 3.1 3.4 3.7 4.0 4.4 4.9 84.5 8.6 9.3 10.2 11.1 12.1 13.3 14.752 2.9 3.2 3.5 3.8 4.2 4.6 5.1 85 8.7 9.4 10.3 11.2 12.3 13.5 14.952.5 3.0 3.3 3.6 3.9 4.3 4.7 5.2 85.5 8.8 9.5 10.4 11.3 12.4 13.6 15.053 3.1 3.4 3.7 4.0 4.4 4.9 5.4 86 8.9 9.7 10.5 11.5 12.6 13.8 15.253.5 3.2 3.5 3.8 4.2 4.6 5.0 5.5 86.5 9.0 9.8 10.6 11.6 12.7 13.9 15.454 3.3 3.6 3.9 4.3 4.7 5.2 5.7 87 9.1 9.9 10.7 11.7 12.8 14.1 15.554.5 3.4 3.7 4.0 4.4 4.8 5.3 5.9 87.5 9.2 10.0 10.9 11.8 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8.7 9.5 10.5 96 10.9 11.9 12.9 14.1 15.5 17.0 18.868.5 6.2 6.7 7.3 8.0 8.8 9.7 10.7 96.5 11.0 12.0 13.1 14.3 15.6 17.2 19.069 6.3 6.8 7.4 8.1 8.9 9.8 10.8 97 11.1 12.1 13.2 14.4 15.8 17.4 19.269.5 6.3 6.9 7.5 8.2 9.0 9.9 10.9 97.5 11.2 12.2 13.3 14.5 15.9 17.5 19.370 6.4 7.0 7.6 8.3 9.1 10.0 11.1 98 11.3 12.3 13.4 14.7 16.1 17.7 19.570.5 6.5 7.1 7.7 8.4 9.2 10.1 11.2 98.5 11.4 12.4 13.5 14.8 16.2 17.9 19.771 6.6 7.1 7.8 8.5 9.3 10.3 11.3 99 11.5 12.5 13.7 14.9 16.4 18.0 19.971.5 6.7 7.2 7.9 8.6 9.4 10.4 11.5 99.5 11.6 12.7 13.8 15.1 16.5 18.2 20.172 6.7 7.3 8.0 8.7 9.5 10.5 11.6 100 11.7 12.8 13.9 15.2 16.7 18.4 20.372.5 6.8 7.4 8.1 8.8 9.7 10.6 11.7 100.5 11.9 12.9 14.1 15.4 16.9 18.6 20.573 6.9 7.5 8.1 8.9 9.8 10.7 11.8 101 12.0 13.0 14.2 15.5 17.0 18.7 20.773.5 7.0 7.6 8.2 9.0 9.9 10.8 12.0 101.5 12.1 13.1 14.3 15.7 17.2 18.9 20.974 7.0 7.6 8.3 9.1 10.0 11.0 12.1 102 12.2 13.3 14.5 15.8 17.4 19.1 21.174.5 7.1 7.7 8.4 9.2 10.1 11.1 12.2 102.5 12.3 13.4 14.6 16.0 17.5 19.3 21.475 7.2 7.8 8.5 9.3 10.2 11.2 12.3 103 12.4 13.5 14.7 16.1 17.7 19.5 21.675.5 7.2 7.9 8.6 9.4 10.3 11.3 12.5 103.5 12.5 13.6 14.9 16.3 17.9 19.7 21.876 7.3 8.0 8.7 9.5 10.4 11.4 12.6 104 12.6 13.8 15.0 16.4 18.1 19.9 22.076.5 7.4 8.0 8.7 9.6 10.5 11.5 12.7 104.5 12.8 13.9 15.2 16.6 18.2 20.1 22.377 7.5 8.1 8.8 9.6 10.6 11.6 12.8 105 12.9 14.0 15.3 16.8 18.4 20.3 22.577.5 7.5 8.2 8.9 9.7 10.7 11.7 12.9 105.5 13.0 14.2 15.5 16.9 18.6 20.5 22.778 7.6 8.3 9.0 9.8 10.8 11.8 13.1 106 13.1 14.3 15.6 17.1 18.8 20.8 23.078.5 7.7 8.4 9.1 9.9 10.9 12.0 13.2 106.5 13.3 14.5 15.8 17.3 19.0 21.0 23.279 7.8 8.4 9.2 10.0 11.0 12.1 13.3 107 13.4 14.6 15.9 17.5 19.2 21.2 23.579.5 7.8 8.5 9.3 10.1 11.1 12.2 13.4 107.5 13.5 14.7 16.1 17.7 19.4 21.4 23.780 7.9 8.6 9.4 10.2 11.2 12.3 13.6 108 13.7 14.9 16.3 17.8 19.6 21.7 24.080.5 8.0 8.7 9.5 10.3 11.3 12.4 13.7 108.5 13.8 15.0 16.4 18.0 19.8 21.9 24.381 8.1 8.8 9.6 10.4 11.4 12.6 13.9 109 13.9 15.2 16.6 18.2 20.0 22.1 24.581.5 8.2 8.9 9.7 10.6 11.6 12.7 14.0 109.5 14.1 15.4 16.8 18.4 20.3 22.4 24.882 8.3 9.0 9.8 10.7 11.7 12.8 14.1 110 14.2 15.5 17.0 18.6 20.5 22.6 25.182.5 8.4 9.1 9.9 10.8 11.8 13.0 14.3 110.5 14.4 15.7 17.1 18.8 20.7 22.9 25.483 8.5 9.2 10.0 10.9 11.9 13.1 14.5 111 14.5 15.8 17.3 19.0 20.9 23.1 25.783.5 8.5 9.3 10.1 11.0 12.1 13.3 14.6 111.5 14.7 16.0 17.5 19.2 21.2 23.4 26.084 8.6 9.4 10.2 11.1 12.2 13.4 14.8 112 14.8 16.2 17.7 19.4 21.4 23.6 26.284.5 8.7 9.5 10.3 11.3 12.3 13.5 14.9 112.5 15.0 16.3 17.9 19.6 21.6 23.9 26.585 8.8 9.6 10.4 11.4 12.5 13.7 15.1 113 15.1 16.5 18.0 19.8 21.8 24.2 26.885.5 8.9 9.7 10.6 11.5 12.6 13.8 15.3 113.5 15.3 16.7 18.2 20.0 22.1 24.4 27.186 9.0 9.8 10.7 11.6 12.7 14.0 15.4 114 15.4 16.8 18.4 20.2 22.3 24.7 27.486.5 9.1 9.9 10.8 11.8 12.9 14.2 15.6 114.5 15.6 17.0 18.6 20.5 22.6 25.0 27.887 9.2 10.0 10.9 11.9 13.0 14.3 15.8 115 15.7 17.2 18.8 20.7 22.8 25.2 28.187.5 9.3 10.1 11.0 12.0 13.2 14.5 15.9 115.5 15.9 17.3 19.0 20.9 23.0 25.5 28.488 9.4 10.2 11.1 12.1 13.3 14.6 16.1 116 16.0 17.5 19.2 21.1 23.3 25.8 28.788.5 9.5 10.3 11.2 12.3 13.4 14.8 16.3 116.5 16.2 17.7 19.4 21.3 23.5 26.1 29.089 9.6 10.4 11.4 12.4 13.6 14.9 16.4 117 16.3 17.8 19.6 21.5 23.8 26.3 29.389.5 9.7 10.5 11.5 12.5 13.7 15.1 16.6 117.5 16.5 18.0 19.8 21.7 24.0 26.6 29.690 9.8 10.6 11.6 12.6 13.8 15.2 16.8 118 16.6 18.2 19.9 22.0 24.2 26.9 29.990.5 9.9 10.7 11.7 12.8 14.0 15.4 16.9 118.5 16.8 18.4 20.1 22.2 24.5 27.2 30.391 10.0 10.9 11.8 12.9 14.1 15.5 17.1 119 16.9 18.5 20.3 22.4 24.7 27.4 30.6 91.5 10.1 11.0 11.9 13.0 14.3 15.7 17.3 119.5 17.1 18.7 20.5 22.6 25.0 27.7 30.965 5.9 6.3 6.9 7.4 8.1 8.8 9.6 93 10.8 11.6 12.6 13.6 14.7 16.0 17.565.5 6.0 6.4 7.0 7.6 8.2 8.9 9.8 93.5 10.9 11.7 12.7 13.7 14.9 16.2 17.666 6.1 6.5 7.1 7.7 8.3 9.1 9.9 94 11.0 11.8 12.8 13.8 15.0 16.3 17.866.5 6.1 6.6 7.2 7.8 8.5 9.2 10.1 94.5 11.1 11.9 12.9 13.9 15.1 16.5 17.967 6.2 6.7 7.3 7.9 8.6 9.4 10.2 95 11.1 12.0 13.0 14.1 15.3 16.6 18.167.5 6.3 6.8 7.4 8.0 8.7 9.5 10.4 95.5 11.2 12.1 13.1 14.2 15.4 16.7 18.368 6.4 6.9 7.5 8.1 8.8 9.6 10.5 96 11.3 12.2 13.2 14.3 15.5 16.9 18.468.5 6.5 7.0 7.6 8.2 9.0 9.8 10.7 96.5 11.4 12.3 13.3 14.4 15.7 17.0 18.669 6.6 7.1 7.7 8.4 9.1 9.9 10.8 97 11.5 12.4 13.4 14.6 15.8 17.2 18.869.5 6.7 7.2 7.8 8.5 9.2 10.0 11.0 97.5 11.6 12.5 13.6 14.7 15.9 17.4 18.970 6.8 7.3 7.9 8.6 9.3 10.2 11.1 98 11.7 12.6 13.7 14.8 16.1 17.5 19.170.5 6.9 7.4 8.0 8.7 9.5 10.3 11.3 98.5 11.8 12.8 13.8 14.9 16.2 17.7 19.371 6.9 7.5 8.1 8.8 9.6 10.4 11.4 99 11.9 12.9 13.9 15.1 16.4 17.9 19.571.5 7.0 7.6 8.2 8.9 9.7 10.6 11.6 99.5 12.0 13.0 14.0 15.2 16.5 18.0 19.772 7.1 7.7 8.3 9.0 9.8 10.7 11.7 100 12.1 13.1 14.2 15.4 16.7 18.2 19.972.5 7.2 7.8 8.4 9.1 9.9 10.8 11.8 100.5 12.2 13.2 14.3 15.5 16.9 18.4 20.173 7.3 7.9 8.5 9.2 10.0 11.0 12.0 101 12.3 13.3 14.4 15.6 17.0 18.5 20.373.5 7.4 7.9 8.6 9.3 10.2 11.1 12.1 101.5 12.4 13.4 14.5 15.8 17.2 18.7 20.574 7.4 8.0 8.7 9.4 10.3 11.2 12.2 102 12.5 13.6 14.7 15.9 17.3 18.9 20.774.5 7.5 8.1 8.8 9.5 10.4 11.3 12.4 102.5 12.6 13.7 14.8 16.1 17.5 19.1 20.975 7.6 8.2 8.9 9.6 10.5 11.4 12.5 103 12.8 13.8 14.9 16.2 17.7 19.3 21.175.5 7.7 8.3 9.0 9.7 10.6 11.6 12.6 103.5 12.9 13.9 15.1 16.4 17.8 19.5 21.376 7.7 8.4 9.1 9.8 10.7 11.7 12.8 104 13.0 14.0 15.2 16.5 18.0 19.7 21.676.5 7.8 8.5 9.2 9.9 10.8 11.8 12.9 104.5 13.1 14.2 15.4 16.7 18.2 19.9 21.877 7.9 8.5 9.2 10.0 10.9 11.9 13.0 105 13.2 14.3 15.5 16.8 18.4 20.1 22.077.5 8.0 8.6 9.3 10.1 11.0 12.0 13.1 105.5 13.3 14.4 15.6 17.0 18.5 20.3 22.278 8.0 8.7 9.4 10.2 11.1 12.1 13.3 106 13.4 14.5 15.8 17.2 18.7 20.5 22.578.5 8.1 8.8 9.5 10.3 11.2 12.2 13.4 106.5 13.5 14.7 15.9 17.3 18.9 20.7 22.779 8.2 8.8 9.6 10.4 11.3 12.3 13.5 107 13.7 14.8 16.1 17.5 19.1 20.9 22.979.5 8.3 8.9 9.7 10.5 11.4 12.4 13.6 107.5 13.8 14.9 16.2 17.7 19.3 21.1 23.280 8.3 9.0 9.7 10.6 11.5 12.6 13.7 108 13.9 15.1 16.4 17.8 19.5 21.3 23.480.5 8.4 9.1 9.8 10.7 11.6 12.7 13.8 108.5 14.0 15.2 16.5 18.0 19.7 21.5 23.781 8.5 9.2 9.9 10.8 11.7 12.8 14.0 109 14.1 15.3 16.7 18.2 19.8 21.8 23.981.5 8.6 9.3 10.0 10.9 11.8 12.9 14.1 109.5 14.3 15.5 16.8 18.3 20.0 22.0 24.282 8.7 9.3 10.1 11.0 11.9 13.0 14.2 110 14.4 15.6 17.0 18.5 20.2 22.2 24.482.5 8.7 9.4 10.2 11.1 12.1 13.1 14.4 110.5 14.5 15.8 17.1 18.7 20.4 22.4 24.783 8.8 9.5 10.3 11.2 12.2 13.3 14.5 111 14.6 15.9 17.3 18.9 20.7 22.7 25.083.5 8.9 9.6 10.4 11.3 12.3 13.4 14.6 111.5 14.8 16.0 17.5 19.1 20.9 22.9 25.284 9.0 9.7 10.5 11.4 12.4 13.5 14.8 112 14.9 16.2 17.6 19.2 21.1 23.1 25.584.5 9.1 9.9 10.7 11.5 12.5 13.7 14.9 112.5 15.0 16.3 17.8 19.4 21.3 23.4 25.885 9.2 10.0 10.8 11.7 12.7 13.8 15.1 113 15.2 16.5 18.0 19.6 21.5 23.6 26.085.5 9.3 10.1 10.9 11.8 12.8 13.9 15.2 113.5 15.3 16.6 18.1 19.8 21.7 23.9 26.386 9.4 10.2 11.0 11.9 12.9 14.1 15.4 114 15.4 16.8 18.3 20.0 21.9 24.1 26.686.5 9.5 10.3 11.1 12.0 13.1 14.2 15.5 114.5 15.6 16.9 18.5 20.2 22.1 24.4 26.987 9.6 10.4 11.2 12.2 13.2 14.4 15.7 115 15.7 17.1 18.6 20.4 22.4 24.6 27.287.5 9.7 10.5 11.3 12.3 13.3 14.5 15.8 115.5 15.8 17.2 18.8 20.6 22.6 24.9 27.588 9.8 10.6 11.5 12.4 13.5 14.7 16.0 116 16.0 17.4 19.0 20.8 22.8 25.1 27.888.5 9.9 10.7 11.6 12.5 13.6 14.8 16.1 116.5 16.1 17.5 19.2 21.0 23.0 25.4 28.089 10.0 10.8 11.7 12.6 13.7 14.9 16.3 117 16.2 17.7 19.3 21.2 23.3 25.6 28.389.5 10.1 10.9 11.8 12.8 13.9 15.1 16.4 117.5 16.4 17.9 19.5 21.4 23.5 25.9 28.690 10.2 11.0 11.9 12.9 14.0 15.2 16.6 118 16.5 18.0 19.7 21.6 23.7 26.1 28.990.5 10.3 11.1 12.0 13.0 14.1 15.3 16.7 118.5 16.7 18.2 19.9 21.8 23.9 26.4 29.291 10.4 11.2 12.1 13.1 14.2 15.5 16.9 119 16.8 18.3 20.0 22.0 24.1 26.6 29.5 91.5 10.5 11.3 12.2 13.2 14.4 15.6 17.0 119.5 16.9 18.5 20.2 22.2 24.4 26.9 29.8。

疫苗生产的细胞培养

由于使用了各种不同的处理方法如dna酶处理细胞残余dna的量和大小可进一步下降要评价已加入的生产工艺或考虑要加入的生产工艺以其减少dna应注意由于改变工艺而引起潜在的产品质量的影响如免疫原性?残余物与添加剂残余细胞dnarcdna1感染活性细胞dna中含有感染性病毒基因1dna病毒插入的病毒基因或染色体外基因2逆转录病毒的前病毒dna2致肿瘤性oncogenicity1病毒基因一旦引入细胞可能被扩增继而产生病毒颗粒250pg多瘤病毒dna即可致小鼠发生肿瘤1pg的逆转录病毒前病毒dna可使小鼠发生肿瘤为何对dna表示担忧原因1细胞可能含有1致肿瘤基因2整合在细胞基因中的病毒基因2生产疫苗用的细胞基因会带入疫苗终产品中3产品中的dna通过注射进入使用者体内导致的肿瘤病原体感染结果1感染2诱发肿瘤1肿瘤基因的表达2原癌基因的激活3肿瘤抑制基因的关闭失去功能3突变基因的插入激活抑制功能丧失正负调节功能的破坏去除或和灭活rcdna1在生产过程中任何减少rcdna的量2在生产过程中任何减小rcdna片断的大小3在生产过程中使用任何化学物质对rcdna生物活性的灭活betapropiolactone的灭活作用dna酶的消化处理经口或鼻途径给药的产品其dna应与nranca磋商但肠溶产品应严格考虑
批次不能保证获得的细胞的一致性。
原代细胞制备用动物 SPF (specific-pathogen-free)
“closed” “封闭群” animal husbandry without any introduction of new animals fromoutside the maintained group prevents introduction of geneticallytransmittedviral elements (e.g., retroviral proviruses).

中国能源统计年鉴

15.5 15.3 13.5
15.3 16.1 10.6
2006 2007 2008 2009
12.7 14.2 9.6 9.1
7.4 6.5 5.4 5.4
14.6 14.5 5.6 7.1
9.6 8.4 3.9 5.2
注:国内生产总值增长速度按可比价格计算，能源生产增长速度和能源消费增长速度采用等价值总量计算。
8.8 11.6 11.3 4.1 3.8
3.0 3.6 5.0 6.1 2.2
9.5 10.6 9.6 7.3 6.2
5.4 7.2 7.4 4.2 1.8
1991 1992 1993 1994 1995
9.2 14.2 14.0 13.1 10.9
0.9 2.3 3.6 6.9 8.7
9.1 11.3 15.3 10.7 8.6
1.55 1.51 1.19
1.53 1.60 0.93
1.56 1.52 1.19
14.6 14.4 5.6 7.2
0.58 0.46 0.56 0.59
1.15 1.02 0.58 0.78
0.76 0.59 0.41 0.57
1.15 1.01 0.58 0.79
速度采用等价值总量计算。
ction and consumption are calculated by coal equivalent.
5.1 5.2 6.3 5.8 6.9
1996 1997 1998 1999 2000
10.0 9.3 7.8 7.6 8.4
3.1 0.3 -2.7 1.6 2.4
7.2 5.1 2.7 6.3 9.4
3.1 0.5 0.2 3.2 3.56.5 4.7

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Comparison of TAAS Results: New American Schools (NAS) to Non New American Schools
1
Limitations of Analysis
64 of 75 schools in NAS designs were
participants for 2 years or less Schools were self selecting: no randomization of schools to treatments For the 1999-2000 school year, only Roots & Wings remains a viable design No attempt to control for the degree or quality of program implementation of NAS designs 2
None
3 MS 2 HS 1 AS
6 MS 1 HS
2 MS 6 HS
4
Schools by NAS Design
NAS Design Modern Red (16) 1st Year Woodlawn Hills Whittier MS Jefferson HS 2nd Year Green Miller Woodlawn Connell Harris Lowell Rogers Wheatley 3rd Year Foster Japhet Knox Maverick Travis 4th Year
7
TAAS Analysis
Controlled for effects of: – Type of NAS Design – Years participating in NAS Design – Years of teaching experience – Percent of minority teachers
Increase in Increase in Increase in Increase in Increase in Increase in Increase in Math for Reading for Math for Reading for Writing for Math for Reading for 3rd Grade 3rd Grade 4th Grade 4th Grade 4th Grade 5th Grade 5th Grade Non-NAS NAS SAISD ELOB Modern Red Roots/Wings Co-NECT 11.3 6.2 8.0 5.2 8.0 5.7 4.8 8.9 4.6 6.1 -4.0 11.8 2.4 4.5 7.6 7.2 7.3 5.8 4.2 8.1 6.0 1.3 -1.3 -0.4 -0.6 -0.4 -0.8 -3.2 0.7 1.7 1.3 1.2 1.2 1.2 5.1 6.4 2.8 4.1 23.1 1.8 1.5 -3.2 -2.8 -3.0 -2.9 4.8 -1.9 -3.8 -9.3
13
Non-NAS NAS SAISD ELOB Modern Red Roots/Wings Co-NECT
Gain for 5th Grade
25 20 15 10 5 0 -5 -10 Math Reading
14
Non-NAS NAS SAISD ELOB Modern Red Roots/Wings Co-NECT
Percent Passing Math for 6th Grade Non-NAS Excl. Magnet NAS SAISD ELOB Modern Red 81.0 69.8 70.8 72.0 68.2 68.7 Percent Passing Reading for 6th Grade 74.5 66.5 67.0 67.9 57.8 69.8 Percent Passing Math for 7th Grade 78.6 64.2 66.6 68.0 66.5 65.3 Percent Passing Reading for 7th Grade 68.3 64.8 67.0 67.2 63.6 66.2 Percent Passing Math for 8th Grade 83.8 67.8 69.3 71.0 67.8 67.9 Percent Passing Reading for 8th Grade 86.7 71.9 73.9 75.5 72.9 73.5 Percent Passing Writing for 8th Grade 88.4 73.6 75.9 77.3 73.6 77.3
Hirsch Kelly King Margil Rodriguez Ruiz Stewart Washington
Expeditionary Learning/Outward Bound (11) CO-NECT (11)
Twain MS Jefferson HS
King MS Houston HS
Accelerated Schools (3)
5
Expenditures by NAS Design
NAS DESIGN Accelerated CO-NECT ELOB Modern Red Roots&Wings Totals 0 $650,000 $ 400,000 $ 250,000 $ 291,450 $ 586,000 $ 869,034 $ 1,013,447 $2,759,931 1995 - 1996 1996 - 1997 1997 - 1998 1998 - 1999 $ 90,000 $ 673,848 $ 600,000 $ 1,590,000 $ 1,349,000 $4,302,848 Totals $90,000 $965,298 $1,586,000 $2,709,034 $2,362,447 $7,712,779
NAS Background Data
3
Schools by Design
Modern Red Roots/Wings Number of Elementary Schools ELOB Co-NECT No Design
9
25
4
4
22
Number of Secondary Schools
6 MS 1 HS
Comparison of 1999 TAAS Passing Rates by Grade and Subject
19
1999 TAAS Pass Rates: Elementary
Percent Percent Passing Passing Math for Reading for 3rd Grade 3rd Grade Non-NAS NAS SAISD ELOB Modern Red Roots/Wings Co-NECT 76.8 67.0 70.5 64.7 72.5 65.4 70.8 79.1 72.1 74.6 66.1 81.1 69.4 74.9 Percent Percent Passing Passing Math for Reading for 4th Grade 4th Grade 80.3 74.6 76.6 75.2 75.8 73.7 76.9 79.5 74.1 76.0 81.0 76.9 73.0 72.6 Percent Percent Percent Passing Passing Passing Writing for Math for Reading for 4th Grade 5th Grade 5th Grade 84.4 80.0 81.6 81.7 78.2 80.7 79.5 85.5 79.5 81.6 92.3 81.0 77.4 80.0 76.2 72.9 74.1 79.6 74.1 71.7 72.1
Analyzed Higher Order Thinking Skills
portion of TAAS exam for 1999
8
Result of TAAS Analyses
9
Gain Scores from 1998 to 1999
10
Growth from 1997-1998 to 1998-1999: Elementary
6
TAAS Analysis
Gains by grade level on TAAS: 1998 to
1999 Percent Passing by Subject by grade level for 1999 Statistical adjustment for prior achievement Longitudinal TLAAS Analysis of NAS compared to Non-NAS Schools
Roots and Wings (25)
Arnold Austin Huppertz Madison
Pershing Riverside Pk Tynan White
Barkley Bowden Bowie Burnet Cameron Carvajal Cotton Crockett DeZavala Bonhamwth from 1997-1998 to 1998-1999: Secondary
Increase in Increase in Increase in Increase in Increase in Increase in Increase in Math for Reading for Math for Reading for Math for Reading for Writing for 6th Grade 6th Grade 7th Grade 7th Grade 8th Grade 8th Grade 8th Grade Non-NAS Excl. Magnet NAS SAISD ELOB Modern Red 8.4 16.1 9.9 9.6 7.7 8.4 -1.8 9.0 3.6 3.0 -0.6 4.0 4.9 12.9 9.1 8.6 11.1 11.8 -0.3 7.0 2.6 0.9 5.9 4.1 11.5 14.3 10.4 10.5 10.8 15.2 3.7 10.0 7.5 7.0 7.7 9.7 6.0 13.0 10.4 9.9 6.8 17.9