Agents, Believability and Embodiment in Advanced Learning Environments Introduction to a Pa

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Heterogeneous agents

The role of simulation methods inEconomicsAlfonso NovalesDepartamento de Economía CuantitativaUniversidad ComplutenseJune20071. The issues•Widespread use of dynamic, stochastic, model economies has led to the need of using numerical methods to characterize the properties of a given theoretical economy.•There is still some misunderstanding regarding a correct use of numerical methods.•The non-specialist has sometimes the impression that simulation is more a caprice of the researcher than a real need.•Solution methods seem difficult to understand and replicate.•Besides, assignment of numerical values to key structural parameters is thought from the outside to be an arbitrary decision, which totally conditions the results.•Lastly, fundamental skepticism comes from considering whether results characterized by simulation can ever be compared to properties we learn about through a formal mathematical proof.1. The issues2. Why do we need to simulate?•Formulate economic questions in dynamic, stochastic setups to avoid biased answersAgents make decisions under uncertaintyThe consequences of their actions are felt over a number of periods. They might start only after some period of timeAnd influence some other variables, that will feedback into the economyExpectations formation mechanism is a crucial part of the structure of the model, and it decisively influences the characteristics of the model economy•Need to solve stochastic control problems:•Pontryagin’s principle, Bellman’s dynamic optimization principle, Kushner’s stochastic Lagrange multipliersSargent(1979)Macroeconomic Theory, Academic PressPhelps et al. (1969), Microeconomic Foundations of Employment and Inflation, NortonLucas (1983), Studies in Business Cycle theory, MIT PressLucas and Sargent(1981), Rational Expectations, 2 vol.,2. Why do we need to simulate?Same example: Bellman’s principle11ln():Tt t Tt Max C subject to C Aβ≤∑∑First solve:221ln():Max C subject to C A C ≤−that leads to:21C A C =−Then, solve:1121ln()ln():C Max C C subject to C Aβ+≤And taking into account the optimalrule at t=2:21C A C =−That is:1111ln()ln():C Max C A C subject to C Aβ+−≤Leading to the same solution as before2. Why do we need to simulate?The concepts•Maintained assumption: agents optimize objective functions subject to a set of constraints. These are: budget constraints, institutional constraints, …•State variables are taken as given by agents when making decisions (choosing values) on their control (decision) variables•Time-t decision may become or determine time-(t+1) state variables: investment and capital stock•Decision variables for some agents may be state variables for other agents: Public expenditures•We need a closed system: as many optimality conditions as decision variables•In the absence of uncertainty, linear-quadratic optimization problems Quadratic objective functions, linear constraintslead to a set of linear optimality (first-order) necessary conditions Together with transversality conditionsUnder uncertainty•The system of first order conditions is no longer a closed system, since it involves expectations of functions of future decision and state variables. Those functions are linear in a linear-quadratic problem•Need to make some assumption on the expectations formation mechanism: exogenous/endogenous•We may face expectations at different horizons, based on different information sets •Separation of control and estimation applies (certainty-equivalence principle): solve the deterministic control problem, and take expectations where needed afterwards •Current values of decision variables depend on past values of decision and state variables and current expectations of future exogenous variables•But not on current expectations of future decision variables, which are endogenous •Solution methods for linear models: Whiteman (1983), Linear Rational Expectations Models, U. of Minnesota Press.2. Why do we need to simulate?Partial equilibrium framework:Sargent(1979)•Simple problem: a firm maximizes present value of current and expected future profits by choosing current employment on the basis of past state (wages) and decision variables (employment, investment) and currentexpectations of future state variables (real wages, productivity)•If we add an assumption on expectations, we produce the optimal level of current decision variablesIt is the perception of rational economic agents on the future evolution of exogenous variables that matters, rather than actual future values Credibility of economic policyOptimal decision rules for private agents depend on the structure and parameter values used in policy rules (Lucas’critique)2. Why do we need to simulate?General equilibrium model•Each agent solves a specific optimization problem•Constraints and market clearing conditions are imposed on top of first order conditions for each agent’s problem•Equilibrium conditions should conceivable give us a closed system that we could solve for equilibrium paths of all endogenous variables•But we enter a new level of difficulty:We cannot assume any arbitrary perception about the stochastic process governing future pricesWe rather need to simultaneously solve for agents’decision variables together with equilibrium pricesBut if prices are endogenous, budget constraints are no longer linear ⇒the certainty-equivalence principle no longer applies2. Why do we need to simulate?Numerical solution methods•Not specific to equilibrium modelsMotivated by appearance of expectations of nonlinear functions of future state and decision variablesTogether with endogenous expectations mechanisms (rationality)They can accommodate frictions•When the equivalence between competitive equilibrium and central planning resource allocation mechanisms holds, it is helpful to obtain the solution to the competitive equilibrium modelBut that does not mean that numerical solution methods are specific to situations where the second welfare theorem holds•Not restricted to representative agent economies•Not restricted to rational expectations models (limited rationality being an interesting alternative)•Not specific to Macroeconomics•Not specific of business cycle theories, where productivity shocks are the main source of fluctuations2. Why do we need to simulate?3. What do we get out ofmodel simulations?• A theoretical, stochastic model can be seen as imposing a set of restrictions on the probability distribution of the vector stochastic process for therelevant variablesAnalytical structure of the modelParameter valuesMultivariate probability distribution for the vector of exogenous shocks•The solution to the model is the probability distribution of that vector stochastic process•That cannot usually be characterized analytically•Simulation methods provide us with an approximation: the frequency distribution for any characteristic of interest: impulse responses, a given regression coefficient, relative volatility, …3. What do we get out of modelsimulations?• A numerical solution: a set of time series, one for each variable in the economy, satisfying all optimality conditions, budget constraints, and equilibrium conditions•Model simulation is the procedure by which a numerical solution is computed•And we compute a large number of solutions realizations•Each of them can be used to compute the numerical value of any characteristic of the modelLeading to the sample frequency distributionEstimation theoryPossibility of testing3. What do we get out of modelSteady-state•Deterministic steady-state: constant rates of growth for per capita variables that can be maintained foreverThose constant growth rates may be zero in some economies•Exogenous versus endogenous growthExogenous growth. Causes for growth: exogenous productivity growing at a constant rateEndogenous growth. Causes for growth: tax rates, money growth, …•Stochastic steady-state: a set of time series fluctuating around a deterministic steady-state•In exogenous growth models,appropriately discounted variables remain constant in the deterministic steady-stateFluctuate around constant reference values in a stochastic steady-state3. What do we get out of model•In endogenous growth models,Per capita variables contain a unit root, even after discounting for any source of deterministic growth•Usually, policy analysis was performed only on steady-stateA policy intervention would take the economy outside steady-state: from an old toa new steady-stateLong-run effects were characterized, and alternative policies compared on the basis of those effects under a given policy loss functionNo reason why the analysis should be restricted to steady-stateOften, short-run effects run contrary to long.run effectsSo that there is some compensation, being unclear which effect dominatesImportant features: size of the policy effect, time needed to converge to the new steady-state (rather, to get close enough to it), utility discount factor•Sample characteristics that can be obtained: sample means, standard deviations, relative volatility, linear correlation coefficients, impulseresponses, regression coefficients (linear multipliers), VAR representations, decomposition of variance, spectral density matrices, VaR(value at risk)3. What do we get out of model•We can provide answers to probabilistic questions:What is the probability that such and such event would arise under each alternative policy considered?•The answer to which depends on:The structure of the model economythe chosen parameterizationthe probability distribution assumed for the vector of exogenous shocks3. What do we get out of modelInteresting statistical analysis•The standard deviation across the set of simulations of the marginal propensity to consume will not coincide with the one provided by the least-squares formula for a single realizationWhich will change with each simulationBut will the average of estimated least-squares deviations for βbe the same as the sample standard deviation (across the set of realizations for the numericalsolution)?The empirical distribution for a given characteristic does not need to be Gaussian even if innovations to exogenous stochastic processes are so. The modeleconomy is a nonlinear transformation between two multivariate stochasticprocessesThe stochastic dimension of the solution cannot be larger than that of the stochastic inputs (although the model is not linear…)3. What do we get out of model4. Calibrating/estimating a theoreticalmodel4.1 What is a reasonable model?•The question is not specific to simulation methods•Simplicity versus realism•Lucas (1980): Methods and problems of business cycle theories: “One of the functions of economic theory is to provide fully articulated artificialeconomic systems that can serve as laboratories in which policies that would beprohibitively expensive to experiment with in actual economies can be tested at a much lower cost…”“Insistence in the realism of an economic model subverts its potential usefulness in thinking about reality. Any model that is well articulated to give clear answersto the questions we put to it will necessarily be artificial, abstract, patentlyunreal…”4. Calibrating / Estimating•[...] "...Not all well-articulated models will be equally useful. Though we are interested in models because we believe they may help us to understand matters about which we are currently ignorant, we need to test them as useful imitations of reality, by subjecting them to shocks for which we are fairly certain how actual economies would react. The more dimensions on which the model mimics the answers actual economies give to simplequestions, the more we trust its answers to harder questions. This is the sense in which more realism in a model is clearly preferred to less".4. Calibrating / Estimating•The researcher is not interested in verifying if the model is correct, since he/she knows form the beginning that it is not.Should we test simple model economies and pretend we test general theories?• A simple, stylized model may be able capture a number of characteristics in actual data•Iterative method, from theory to data, and viceversa•Once we converge, we need to evaluate our degree of satisfaction with the limit reached: the number and relevance of empirical aspects that thetheoretical model can explain•Robustness: how the answer to a question of interest changes with local or global changes in the structure of the model (functional forms, parameter values, probability distributions for exogenous shocks)4. Calibrating / EstimatingQuestions of interest:•Is it possible to mimic a given empirical regularity using a particular model economy?•Power function over a class of models•How much of that regularity can be explained by impulses to a specific exogenous innovation?•Is it possible to reduce the discrepancy between the implications from the theoretical model and actual data by adding a given feature into the model?•How does the probability distribution for the endogenous vector stochastic process change after a change in the one for the exogenous vector?4. Calibrating / EstimatingExamples:•Can we use the neoclassical exogenous growth model to explain the empirical regularity, common to most countries, that the relative volatility of aggregate private consumption to output is less than 1?•To what extent can we reproduce the values of those volatilities using only technology shocks?•Does the goodness of fit of the model improve if we add shocks to preferences of individual consumers?•What effect does it have on the economy to announce a given time path for tax rates on income for the next four years, versus the alternative of maintaining continuous discretionality on them?•What is the effect on price volatility in the neoclassical, monetary growth model (Sidrauski(1967)) if the monetary authority quits the monetary rule of k%annual growth for the money supply to implement a policy of controlling interest rates?•Since the standard growth model predicts a high correlation between hours worked and productivity, which is contrary to empirical evidence, will incorporating a second sector, for human capital accumulation, improve upon this property of the model?4. Calibrating / Estimating•Many limitations that, in a first analysis, are associated with the methodology of analyzing models through simulation are more apparent than real:almost any model is able to mimic the value of an statistic of interest in an actual economy, just by appropriately picking parameter values,•Not always true, and nevertheless, that model might perform badly relative to other data characteristics. Use appropriate loss functions.different models can be found that are able to account for the sample value of a given statistic and, yet, have radically different implications relative to evaluating alternative economic policies•lack of identification is, in many occasions, just a reflection of the fact that the loss function used to rank alternative models includes too short a list ofarguments•different points in the parameter space able to produce a given data characteristic in a given model4. Calibrating / Estimating4.2 What is calibration?•Lack of a clear-cut definitionAssociating numerical values to coefficientsLike estimation, but …Not a consequence of a formal estimation procedure•Values for structural parameters are usually taken from estimates obtained with micro data: factor elasticities, degree of risk aversion, intertemporal elasticity ofconsumption•Or from some casual empirical characteristics of the economy under study•Some parameter values are chosen so that steady-state (nonlinear) expressions for some variables are equal to observed reference levels in actual data•Discount factors chose to match real interest rates•This comparison needs to take into account that time series data are often nonstationary4. Calibrating / Estimating• A subset of parameters are determined this way, and the rest can be used to validate the modelChoose them so as to replicate some empirical featuresBut this sounds a lot like simulated General Method of Moments (SGMM) estimation that minimizes a loss function based on comparison of moments inactual and in simulated dataSargent(1987): “…correlations and cross-correlations can contain more information on a given model than sample averages…”Standard deviations of structural shocks are usually chosen so that the volatility of a key variable, such as output, or the ratios of volatilities of consumption,investment or hours worked to output, match that of a real economy.4. Calibrating / Estimating4.3 Limitations in structuralparameter calibration•the range of values that can be considered for a key parameter such as the elasticity of intertemporal substitution is too wide for most purposes•some empirical analysis may differ from others in potentially substantial aspects, so that choosing parameter estimates from any one of them is not fully consistent, and produces significant parameter uncertainty•that some parameter values are chosen a priori implies a selection bias, since there are many empirical studies that could be used•marginal propensity to consume estimated in a cross section and in time series data have different interpretation4. Calibrating / Estimating4.4 Limitations in calibratingexogenous stochastic processes•hard to find information from a real economy concerning the stochastic structure of technology shocks, shocks in preferences, errors of controlling money growth or tax revenues, or the correlations among them.•in the simplest business cycle model, an AR(1) model is assumed for productivity shocks, so that the simulated output series exhibits apersistence similar to the GNP series in actual economies.•extreme care must be used when calibrating models so as not to achieve a spurious adjustment to data through ad-hoc assumptions.•exogenous perturbations are assumed to be independent when simulating.The ability of the economic authority to intentionally perturb policy variables, and establish some correlations between policy induced perturbations and observed exogenous shocks adds a new dimension to the analysis.4. Calibrating / Estimating4.5 Calibration versus formal estimation •From a Bayesian viewpoint, estimation is the solution to a problem of minimizing (the expected value of) a given loss function.•Discussing their relative properties does not make much sense without a reference to the corresponding loss functions.•Calibration, based in choosing values for some structural parameters as a function of long-run sample averages, can be interpreted to the light of a particular loss function•Alternative estimation procedures: Generalized Method of Moments(GMM), Maximum Likelihood (MV), Simulated Method of Moments (SMM)4. Calibrating / Estimating5. Incorporating parameter uncertainty/Evaluation proceduresStatistical evaluation througha loss function•Winter 1996 issue of the Journal of Economic Perspectives•Advantages:it avoids a possibly arbitrary election of parameter values,it provides a measure of dispersion that can be used to evaluate the goodness of fit of model to data.•Disadvantages,it needs a specific selection of moments to be used in fitting evaluation,there are finite sample biases, which may lead to spurious inference,the type of uncertainty which is imposed on the model by an estimation process does not necessarily reflect the uncertainty faced by a researcher whencalibrating a vector of parameters, which is specified more appropriately through Bayesian methods.5. Parameter uncertainty / ModelA Bayesian approach to simulation •Estimation by calibration is considered to be exact, disregarding the existence of uncertainty on parameter values•This practice is too restrictive: precise numerical results are provided for a given set of selected statistics, but no measure of uncertainty is presented.• A given belief on structural parameter values could be incorporated in the form of a prior probability distribution on the parameter space•Consider actual data statistics as fixed numbers, while the uncertainty in simulated data is used to provide a measure of how well the model fitsactual data. Rather than fixing their numerical values, empirical information on some parameters is used to build a probability distribution on theparameter space, and each simulation is computed with a different point drawn from that distribution5. Parameter uncertainty / Model•"The characteristics of a model reproduced in research must always come accompanied by indicators of the degree of uncertainty they embed, which is just a consequence of uncertainty on the right model specification, in the sense described by Leamer(1978). To adequately represent that uncertainty, it is necessary toincorporate uncertainty on parameter values directly in the simulation exercise"[Canova and de Nicolo(1995)].•After repeated simulations with different parameter vectors, we can compute either the size of the calibration tests, or the percentiles of the empirical distribution of the simulated statistic where the value of the statistic in actual data falls.•Actual and simulated data are used symmetrically, and one could either ask whether actual data could be generated by the model or whether the simulated data isconsistent with the distribution of the observed sample•The confidence interval criterion, and the difference of means, proposed by DeJong, Ingram and Whiteman (1996) measure the degree of overlap of the distributions of actual statistics and those obtained from the model5. Parameter uncertainty / ModelEvaluating procedures forsimulation models•Not even the need for model evaluation is uniformly accepted among calibrators.•Interpreting calibration as estimation with no error forces an informal evaluation of the distance between actual and simulated statistics: once parameter values have been chosen, uncertainty comes only from the exogenous stochastic processes.•The model establishes an exact relationship between endogenous variables and parameters, and we will not be able to conclude whether actual and simulatedstatistics are significantly different•"No attempt is made to determine the true model. All models are abstractions and are, by definition, false" [Kydland and Prescott (1982)].•"...the trust a researcher has in an answer given by the model does not depend on a statistical measure of discrepancy, but on how much he believes in the economic theory used and in the measurement undertaken" [Kydland and Prescott (1991)].5. Parameter uncertainty / Model•If parameters are estimated with sampling error, then it makes sense to use measures of dispersion for simulated statistics that reflect parameteruncertainty.• A quadratic distance between the vector of statistics computed from actual data and those obtained by simulation will asymptotically follow a chi-square distribution with degrees of freedom equal to the number of overidentifying restrictions.•The weighing matrix in the quadratic form should be the variance-covariance matrix of the vector of statistics being used, which can easily be estimated ⇒estimation by simulation methodology•Specially interesting to compare statistics from the joint probability distribution of subsets of variables, like impulse response functions, VAR representations, cross-correlations or coherence functions•Sources of dispersion in sample(simulated) statistics: i) parameter uncertainty, ii) sample variation in innovations, iii) model uncertainty, iv)numerical approximation in solution method5. Parameter uncertainty / Model6. Stability•Stability is crucial, but often neglected.•The analysis of stability is different in a stochastic, dynamic model than in its deterministic version.•It is also different in nature in endogenous than in exogenous growth models.•Besides, with the exception of the special cases that lead to linear models, we will be facing the stability of a non-linear, possibly stochastic system, for which general analytical conditions are unknown.•All we can do is build the best linear approximation to the model, and discuss stability in the approximated model.•Using stability conditions makes an important difference in terms of the behavior of the paths generated as solutions to the model.6. Stability•Since the approximation must be made around some specific reference (usually the steady state of detrended variables in exogenous growthmodels), conclusions can only be local.•Although such local analysis may be enough to characterize properties of fluctuations around steady state, it might not be enough when analyzing the effects of a policy intervention that takes the economy away from steady-state.•Some numerical solution methods are better equipped than others to handle stability.•Stability analysis provides useful information on the degree of determination of the model.Local indeterminacy: more than one possible trajectory leading to the steady state.Global indeterminacy, in that there is a multiplicity of steady states: Bubble equilibriumLack of solution6. Stability•Dealing with stability is not independent from dealing with expectations•Stability gets harder in endogenous growth models.The implied time series have a deterministic trend which can easily be taken care of, very much as in exogenous growth models,but they also have a unit root.Transitory shocks will have permanent effects even after detrending, i.e., after discounting for the deterministic steady state rate of growth.So, there is a fundamental lack of stationarity which cannot be handled by just normalizing variables to produce time series realizations for ratios of variables,not for their levels.But it is the levels of the relevant variables that are needed for welfare analysis of the kind used in policy evaluation6. Stability7. Some statistical issuesSome statistical issues•Non-parametric statistics is underutilized in Economics•The extent to which a given statistic (relative variance of investment, capital/labor ratio, etc.) behaves similarly under different parameterizations or models ⇒Not a problem of mean values•Evaluating differences to the light of the comparison of the mean and variance of a given statistic across simulations, assumes the empiricaldistribution of the statistic to be Gaussian, when it is rather unlikely that Normality of the exogenous shocks will be preserved by a non-linear model •Non-parametric tests even more interesting, since they are distribution-free.7. Some statistical issues•The convenience of using an average statistic to compare models is often questionable [use standard deviation appropriately]. But that distribution is often asymmetric, possibly multi modal.•Much more appropriate: compute measures of distance between the empirical distributions emerging from two different models or two different parameterizations of a same model economy.•Homogeneity tests between the (unknown) theoretical value of a given statistic under two parameterizations, or two different models, should be one-tailed. In most cases, there are theoretical reasons to believe that a given parameter vector, or feature of a model, or policy rule, will be more likely to account for a given stylized fact.• A theoretical model should not be expected to produce a time series for output, say, that matches the actual pattern observed in the US economy.7. Some statistical issues。

Analysis of Genetic Diversity and Population Structure

Agricultural Sciences in China2010, 9(9): 1251-1262September 2010Received 30 October, 2009 Accepted 16 April, 2010Analysis of Genetic Diversity and Population Structure of Maize Landraces from the South Maize Region of ChinaLIU Zhi-zhai 1, 2, GUO Rong-hua 2, 3, ZHAO Jiu-ran 4, CAI Yi-lin 1, W ANG Feng-ge 4, CAO Mo-ju 3, W ANG Rong-huan 2, 4, SHI Yun-su 2, SONG Yan-chun 2, WANG Tian-yu 2 and LI Y u 21Maize Research Institute, Southwest University, Chongqing 400716, P.R.China2Institue of Crop Sciences/National Key Facility for Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences,Beijing 100081, P.R.China3Maize Research Institute, Sichuan Agricultural University, Ya’an 625014, P.R.China4Maize Research Center, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100089, P.R.ChinaAbstractUnderstanding genetic diversity and population structure of landraces is important in utilization of these germplasm in breeding programs. In the present study, a total of 143 core maize landraces from the South Maize Region (SR) of China,which can represent the general profile of the genetic diversity in the landraces germplasm of SR, were genotyped by 54DNA microsatellite markers. Totally, 517 alleles (ranging from 4 to 22) were detected among these landraces, with an average of 9.57 alleles per locus. The total gene diversity of these core landraces was 0.61, suggesting a rather higher level of genetic diversity. Analysis of population structure based on Bayesian method obtained the samilar result as the phylogeny neighbor-joining (NJ) method. The results indicated that the whole set of 143 core landraces could be clustered into two distinct groups. All landraces from Guangdong, Hainan, and 15 landraces from Jiangxi were clustered into group 1, while those from the other regions of SR formed the group 2. The results from the analysis of genetic diversity showed that both of groups possessed a similar gene diversity, but group 1 possessed relatively lower mean alleles per locus (6.63) and distinct alleles (91) than group 2 (7.94 and 110, respectively). The relatively high richness of total alleles and distinct alleles preserved in the core landraces from SR suggested that all these germplasm could be useful resources in germplasm enhancement and maize breeding in China.Key words :maize, core landraces, genetic diversity, population structureINTRODUCTIONMaize has been grown in China for nearly 500 years since its first introduction into this second biggest pro-duction country in the world. Currently, there are six different maize growing regions throughout the coun-try according to the ecological conditions and farming systems, including three major production regions,i.e., the North Spring Maize Region, the Huang-Huai-Hai Summer Maize Region, and the Southwest MaizeRegion, and three minor regions, i.e., the South Maize Region, the Northwest Maize Region, and the Qingzang Plateau Maize Region. The South Maize Region (SR)is specific because of its importance in origin of Chi-nese maize. It is hypothesized that Chinese maize is introduced mainly from two routes. One is called the land way in which maize was first brought to Tibet from India, then to Sichuan Province in southwestern China. The other way is that maize dispersed via the oceans, first shipped to the coastal areas of southeast China by boats, and then spread all round the country1252LIU Zhi-zhai et al.(Xu 2001; Zhou 2000). SR contains all of the coastal provinces and regions lie in southeastern China.In the long-term cultivation history of maize in south-ern China, numerous landraces have been formed, in which a great amount of genetic variation was observed (Li 1998). Similar to the hybrid swapping in Europe (Reif et al. 2005a), the maize landraces have been al-most replaced by hybrids since the 1950s in China (Li 1998). However, some landraces with good adapta-tions and yield performances are still grown in a few mountainous areas of this region (Liu et al.1999). Through a great effort of collection since the 1950s, 13521 accessions of maize landraces have been cur-rently preserved in China National Genebank (CNG), and a core collection of these landraces was established (Li et al. 2004). In this core collection, a total of 143 maize landrace accessions were collected from the South Maize Region (SR) (Table 1).Since simple sequence repeat ( SSR ) markers were firstly used in human genetics (Litt and Luty 1989), it now has become one of the most widely used markers in the related researches in crops (Melchinger et al. 1998; Enoki et al. 2005), especially in the molecular characterization of genetic resources, e.g., soybean [Glycine max (L.) Merr] (Xie et al. 2005), rice (Orya sativa L.) (Garris et al. 2005), and wheat (Triticum aestivum) (Chao et al. 2007). In maize (Zea mays L.), numerous studies focusing on the genetic diversity and population structure of landraces and inbred lines in many countries and regions worldwide have been pub-lished (Liu et al. 2003; Vegouroux et al. 2005; Reif et al. 2006; Wang et al. 2008). These activities of documenting genetic diversity and population structure of maize genetic resources have facilitated the under-standing of genetic bases of maize landraces, the utili-zation of these resources, and the mining of favorable alleles from landraces. Although some studies on ge-netic diversity of Chinese maize inbred lines were con-ducted (Yu et al. 2007; Wang et al. 2008), the general profile of genetic diversity in Chinese maize landraces is scarce. Especially, there are not any reports on ge-netic diversity of the maize landraces collected from SR, a possibly earliest maize growing area in China. In this paper, a total of 143 landraces from SR listed in the core collection of CNG were genotyped by using SSR markers, with the aim of revealing genetic diver-sity of the landraces from SR (Table 2) of China and examining genetic relationships and population struc-ture of these landraces.MATERIALS AND METHODSPlant materials and DNA extractionTotally, 143 landraces from SR which are listed in the core collection of CNG established by sequential strati-fication method (Liu et al. 2004) were used in the present study. Detailed information of all these landrace accessions is listed in Table 1. For each landrace, DNA sample was extracted by a CTAB method (Saghi-Maroof et al. 1984) from a bulk pool constructed by an equal-amount of leaves materials sampled from 15 random-chosen plants of each landrace according to the proce-dure of Reif et al. (2005b).SSR genotypingA total of 54 simple sequence repeat (SSR) markers covering the entire maize genome were screened to fin-gerprint all of the 143 core landrace accessions (Table 3). 5´ end of the left primer of each locus was tailed by an M13 sequence of 5´-CACGACGTTGTAAAACGAC-3´. PCR amplification was performed in a 15 L reac-tion containing 80 ng of template DNA, 7.5 mmol L-1 of each of the four dNTPs, 1×Taq polymerase buffer, 1.5 mmol L-1 MgCl2, 1 U Taq polymerase (Tiangen Biotech Co. Ltd., Beijing, China), 1.2 mol L-1 of forward primer and universal fluorescent labeled M13 primer, and 0.3 mol L-1 of M13 sequence tailed reverse primer (Schuelke 2000). The amplification was carried out in a 96-well DNA thermal cycler (GeneAmp PCR System 9700, Applied Biosystem, USA). PCR products were size-separated on an ABI Prism 3730XL DNA sequencer (HitachiHigh-Technologies Corporation, Tokyo, Japan) via the software packages of GENEMAPPER and GeneMarker ver. 6 (SoftGenetics, USA).Data analysesAverage number of alleles per locus and average num-ber of group-specific alleles per locus were identifiedAnalysis of Genetic Diversity and Population Structure of Maize Landraces from the South Maize Region of China 1253Table 1 The detailed information about the landraces used in the present studyPGS revealed by Structure1) NJ dendragram revealed Group 1 Group 2 by phylogenetic analysis140-150tian 00120005AnH-06Jingde Anhui 0.0060.994Group 2170tian00120006AnH-07Jingde Anhui 0.0050.995Group 2Zixihuangyumi00120007AnH-08Zixi Anhui 0.0020.998Group 2Zixibaihuangzayumi 00120008AnH-09Zixi Anhui 0.0030.997Group 2Baiyulu 00120020AnH-10Yuexi Anhui 0.0060.994Group 2Wuhuazi 00120021AnH-11Yuexi Anhui 0.0030.997Group 2Tongbai 00120035AnH-12Tongling Anhui 0.0060.994Group 2Yangyulu 00120036AnH-13Yuexi Anhui 0.0040.996Group 2Huangli 00120037AnH-14Tunxi Anhui 0.0410.959Group 2Baiyumi 00120038AnH-15Tunxi Anhui 0.0030.997Group 2Dapigu00120039AnH-16Tunxi Anhui 0.0350.965Group 2150tianbaiyumi 00120040AnH-17Xiuning Anhui 0.0020.998Group 2Xiuning60tian 00120042AnH-18Xiuning Anhui 0.0040.996Group 2Wubaogu 00120044AnH-19ShitaiAnhui 0.0020.998Group 2Kuyumi00130001FuJ-01Shanghang Fujian 0.0050.995Group 2Zhongdouyumi 00130003FuJ-02Shanghang Fujian 0.0380.962Group 2Baixinyumi 00130004FuJ-03Liancheng Fujian 0.0040.996Group 2Hongxinyumi 00130005FuJ-04Liancheng Fujian 0.0340.966Group 2Baibaogu 00130008FuJ-05Changding Fujian 0.0030.997Group 2Huangyumi 00130011FuJ-06Jiangyang Fujian 0.0020.998Group 2Huabaomi 00130013FuJ-07Shaowu Fujian 0.0020.998Group 2Huangbaomi 00130014FuJ-08Songxi Fujian 0.0020.998Group 2Huangyumi 00130016FuJ-09Wuyishan Fujian 0.0460.954Group 2Huabaogu 00130019FuJ-10Jian’ou Fujian 0.0060.994Group 2Huangyumi 00130024FuJ-11Guangze Fujian 0.0010.999Group 2Huayumi 00130025FuJ-12Nanping Fujian 0.0040.996Group 2Huangyumi 00130026FuJ-13Nanping Fujian 0.0110.989Group 2Hongbaosu 00130027FuJ-14Longyan Fujian 0.0160.984Group 2Huangfansu 00130029FuJ-15Loangyan Fujian 0.0020.998Group 2Huangbaosu 00130031FuJ-16Zhangping Fujian 0.0060.994Group 2Huangfansu 00130033FuJ-17Zhangping Fujian0.0040.996Group 2Baolieyumi 00190001GuangD-01Guangzhou Guangdong 0.9890.011Group 1Nuomibao (I)00190005GuangD-02Shixing Guangdong 0.9740.026Group 1Nuomibao (II)00190006GuangD-03Shixing Guangdong 0.9790.021Group 1Zasehuabao 00190010GuangD-04Lechang Guangdong 0.9970.003Group 1Zihongmi 00190013GuangD-05Lechang Guangdong 0.9880.012Group 1Jiufengyumi 00190015GuangD-06Lechang Guangdong 0.9950.005Group 1Huangbaosu 00190029GuangD-07MeiGuangdong 0.9970.003Group 1Bailibao 00190032GuangD-08Xingning Guangdong 0.9980.002Group 1Nuobao00190038GuangD-09Xingning Guangdong 0.9980.002Group 1Jinlanghuang 00190048GuangD-10Jiangcheng Guangdong 0.9960.004Group 1Baimizhenzhusu 00190050GuangD-11Yangdong Guangdong 0.9940.006Group 1Huangmizhenzhusu 00190052GuangD-12Yangdong Guangdong 0.9930.007Group 1Baizhenzhu 00190061GuangD-13Yangdong Guangdong 0.9970.003Group 1Baiyumi 00190066GuangD-14Wuchuan Guangdong 0.9880.012Group 1Bendibai 00190067GuangD-15Suixi Guangdong 0.9980.002Group 1Shigubaisu 00190068GuangD-16Gaozhou Guangdong 0.9960.004Group 1Zhenzhusu 00190069GuangD-17Xinyi Guangdong 0.9960.004Group 1Nianyaxixinbai 00190070GuangD-18Huazhou Guangdong 0.9960.004Group 1Huangbaosu 00190074GuangD-19Xinxing Guangdong 0.9950.005Group 1Huangmisu 00190076GuangD-20Luoding Guangdong 0.940.060Group 1Huangmi’ai 00190078GuangD-21Luoding Guangdong 0.9980.002Group 1Bayuemai 00190084GuangD-22Liannan Guangdong 0.9910.009Group 1Baiyumi 00300001HaiN-01Haikou Hainan 0.9960.004Group 1Baiyumi 00300003HaiN-02Sanya Hainan 0.9970.003Group 1Hongyumi 00300004HaiN-03Sanya Hainan 0.9980.002Group 1Baiyumi00300011HaiN-04Tongshi Hainan 0.9990.001Group 1Zhenzhuyumi 00300013HaiN-05Tongshi Hainan 0.9980.002Group 1Zhenzhuyumi 00300015HaiN-06Qiongshan Hainan 0.9960.004Group 1Aiyumi 00300016HaiN-07Qiongshan Hainan 0.9960.004Group 1Huangyumi 00300021HaiN-08Qionghai Hainan 0.9970.003Group 1Y umi 00300025HaiN-09Qionghai Hainan 0.9870.013Group 1Accession name Entry code Analyzing code Origin (county/city)Province/Region1254LIU Zhi-zhai et al .Baiyumi00300032HaiN-10Tunchang Hainan 0.9960.004Group 1Huangyumi 00300051HaiN-11Baisha Hainan 0.9980.002Group 1Baihuangyumi 00300055HaiN-12BaishaHainan 0.9970.003Group 1Machihuangyumi 00300069HaiN-13Changjiang Hainan 0.9900.010Group 1Hongyumi00300073HaiN-14Dongfang Hainan 0.9980.002Group 1Xiaohonghuayumi 00300087HaiN-15Lingshui Hainan 0.9980.002Group 1Baiyumi00300095HaiN-16Qiongzhong Hainan 0.9950.005Group 1Y umi (Baimai)00300101HaiN-17Qiongzhong Hainan 0.9980.002Group 1Y umi (Xuemai)00300103HaiN-18Qiongzhong Hainan 0.9990.001Group 1Huangmaya 00100008JiangS-10Rugao Jiangsu 0.0040.996Group 2Bainian00100012JiangS-11Rugao Jiangsu 0.0080.992Group 2Bayebaiyumi 00100016JiangS-12Rudong Jiangsu 0.0040.996Group 2Chengtuohuang 00100021JiangS-13Qidong Jiangsu 0.0050.995Group 2Xuehuanuo 00100024JiangS-14Qidong Jiangsu 0.0020.998Group 2Laobaiyumi 00100032JiangS-15Qidong Jiangsu 0.0050.995Group 2Laobaiyumi 00100033JiangS-16Qidong Jiangsu 0.0010.999Group 2Huangwuye’er 00100035JiangS-17Hai’an Jiangsu 0.0030.997Group 2Xiangchuanhuang 00100047JiangS-18Nantong Jiangsu 0.0060.994Group 2Huangyingzi 00100094JiangS-19Xinghua Jiangsu 0.0040.996Group 2Xiaojinhuang 00100096JiangS-20Yangzhou Jiangsu 0.0010.999Group 2Liushizi00100106JiangS-21Dongtai Jiangsu 0.0030.997Group 2Kangnandabaizi 00100108JiangS-22Dongtai Jiangsu 0.0020.998Group 2Shanyumi 00140020JiangX-01Dexing Jiangxi 0.9970.003Group 1Y umi00140024JiangX-02Dexing Jiangxi 0.9970.003Group 1Tianhongyumi 00140027JiangX-03Yushan Jiangxi 0.9910.009Group 1Hongganshanyumi 00140028JiangX-04Yushan Jiangxi 0.9980.002Group 1Zaoshuyumi 00140032JiangX-05Qianshan Jiangxi 0.9970.003Group 1Y umi 00140034JiangX-06Wannian Jiangxi 0.9970.003Group 1Y umi 00140038JiangX-07De’an Jiangxi 0.9940.006Group 1Y umi00140045JiangX-08Wuning Jiangxi 0.9740.026Group 1Chihongyumi 00140049JiangX-09Wanzai Jiangxi 0.9920.008Group 1Y umi 00140052JiangX-10Wanzai Jiangxi 0.9930.007Group 1Huayumi 00140060JiangX-11Jing’an Jiangxi 0.9970.003Group 1Baiyumi 00140065JiangX-12Pingxiang Jiangxi 0.9940.006Group 1Huangyumi00140066JiangX-13Pingxiang Jiangxi 0.9680.032Group 1Nuobaosuhuang 00140068JiangX-14Ruijin Jiangxi 0.9950.005Group 1Huangyumi 00140072JiangX-15Xinfeng Jiangxi 0.9960.004Group 1Wuningyumi 00140002JiangX-16Jiujiang Jiangxi 0.0590.941Group 2Tianyumi 00140005JiangX-17Shangrao Jiangxi 0.0020.998Group 2Y umi 00140006JiangX-18Shangrao Jiangxi 0.0310.969Group 2Baiyiumi 00140012JiangX-19Maoyuan Jiangxi 0.0060.994Group 260riyumi 00140016JiangX-20Maoyuan Jiangxi 0.0020.998Group 2Shanyumi 00140019JiangX-21Dexing Jiangxi 0.0050.995Group 2Laorenya 00090002ShangH-01Chongming Shanghai 0.0050.995Group 2Jinmeihuang 00090004ShangH-02Chongming Shanghai 0.0020.998Group 2Zaobaiyumi 00090006ShangH-03Chongming Shanghai 0.0020.998Group 2Chengtuohuang 00090007ShangH-04Chongming Shanghai 0.0780.922Group 2Benyumi (Huang)00090008ShangH-05Shangshi Shanghai 0.0020.998Group 2Bendiyumi 00090010ShangH-06Shangshi Shanghai 0.0040.996Group 2Baigengyumi 00090011ShangH-07Jiading Shanghai 0.0020.998Group 2Huangnuoyumi 00090012ShangH-08Jiading Shanghai 0.0040.996Group 2Huangdubaiyumi 00090013ShangH-09Jiading Shanghai 0.0440.956Group 2Bainuoyumi 00090014ShangH-10Chuansha Shanghai 0.0010.999Group 2Laorenya 00090015ShangH-11Shangshi Shanghai 0.0100.990Group 2Xiaojinhuang 00090016ShangH-12Shangshi Shanghai 0.0050.995Group 2Gengbaidayumi 00090017ShangH-13Shangshi Shanghai 0.0020.998Group 2Nongmeiyihao 00090018ShangH-14Shangshi Shanghai 0.0540.946Group 2Chuanshazinuo 00090020ShangH-15Chuansha Shanghai 0.0550.945Group 2Baoanshanyumi 00110004ZheJ-01Jiangshan Zhejiang 0.0130.987Group 2Changtaixizi 00110005ZheJ-02Jiangshan Zhejiang 0.0020.998Group 2Shanyumibaizi 00110007ZheJ-03Jiangshan Zhejiang 0.0020.998Group 2Kaihuajinyinbao 00110017ZheJ-04Kaihua Zhejiang 0.0100.990Group 2Table 1 (Continued from the preceding page)PGS revealed by Structure 1) NJ dendragram revealed Group1 Group2 by phylogenetic analysisAccession name Entry code Analyzing code Origin (county/city)Province/RegoinAnalysis of Genetic Diversity and Population Structure of Maize Landraces from the South Maize Region of China 1255Liputianzi00110038ZheJ-05Jinhua Zhejiang 0.0020.998Group 2Jinhuaqiuyumi 00110040ZheJ-06Jinhua Zhejiang 0.0050.995Group 2Pujiang80ri 00110069ZheJ-07Pujiang Zhejiang 0.0210.979Group 2Dalihuang 00110076ZheJ-08Yongkang Zhejiang 0.0140.986Group 2Ziyumi00110077ZheJ-09Yongkang Zhejiang 0.0020.998Group 2Baiyanhandipinzhong 00110078ZheJ-10Yongkang Zhejiang 0.0030.997Group 2Duosuiyumi00110081ZheJ-11Wuyi Zhejiang 0.0020.998Group 2Chun’an80huang 00110084ZheJ-12Chun’an Zhejiang 0.0020.998Group 2120ribaiyumi 00110090ZheJ-13Chun’an Zhejiang 0.0020.998Group 2Lin’anliugu 00110111ZheJ-14Lin’an Zhejiang 0.0030.997Group 2Qianhuangyumi00110114ZheJ-15Lin’an Zhejiang 0.0030.997Group 2Fenshuishuitianyumi 00110118ZheJ-16Tonglu Zhejiang 0.0410.959Group 2Kuihualiugu 00110119ZheJ-17Tonglu Zhejiang 0.0030.997Group 2Danbaihuang 00110122ZheJ-18Tonglu Zhejiang 0.0020.998Group 2Hongxinma 00110124ZheJ-19Jiande Zhejiang 0.0030.997Group 2Shanyumi 00110136ZheJ-20Suichang Zhejiang 0.0030.997Group 2Bai60ri 00110143ZheJ-21Lishui Zhejiang 0.0050.995Group 2Zeibutou 00110195ZheJ-22Xianju Zhejiang 0.0020.998Group 2Kelilao00110197ZheJ-23Pan’an Zhejiang 0.0600.940Group 21)The figures refered to the proportion of membership that each landrace possessed.Table 1 (Continued from the preceding page)PGS revealed by Structure 1) NJ dendragram revealed Group 1 Group 2 by phylogenetic analysisAccession name Entry code Analyzing code Origin (county/city)Province/Regoin Table 2 Construction of two phylogenetic groups (SSR-clustered groups) and their correlation with geographical locationsGeographical location SSR-clustered groupChi-square testGroup 1Group 2Total Guangdong 2222 χ2 = 124.89Hainan 1818P < 0.0001Jiangxi 15621Anhui 1414Fujian 1717Jiangsu 1313Shanghai 1515Zhejiang 2323Total5588143by the software of Excel MicroSatellite toolkit (Park 2001). Average number of alleles per locus was calcu-lated by the formula rAA rj j¦1, with the standarddeviation of1)()(12¦ r A AA rj jV , where A j was thenumber of distinct alleles at locus j , and r was the num-ber of loci (Park 2001).Unbiased gene diversity also known as expected heterozygosity, observed heterozygosity for each lo-cus and average gene diversity across the 54 SSR loci,as well as model-based groupings inferred by Struc-ture ver. 2.2, were calculated by the softwarePowerMarker ver.3.25 (Liu et al . 2005). Unbiased gene diversity for each locus was calculated by˅˄¦ 2ˆ1122ˆi x n n h , where 2ˆˆ2ˆ2¦¦z ji ijij i X X x ,and ij X ˆwas the frequency of genotype A i A jin the sample, and n was the number of individuals sampled.The average gene diversity across 54 loci was cal-culated as described by Nei (1987) as follows:rh H rj j ¦1ˆ, with the variance ,whereThe average observed heterozygosity across the en-tire loci was calculated as described by (Hedrick 1983)as follows: r jrj obsobs n h h ¦1, with the standard deviationrn h obs obsobs 1V1256LIU Zhi-zhai et al.Phylogenetic analysis and population genetic structureRelationships among all of the 143 accessions collected from SR were evaluated by using the unweighted pair group method with neighbor-joining (NJ) based on the log transformation of the proportion of shared alleles distance (InSPAD) via PowerMarker ver. 3.25 (FukunagaTable 3 The PIC of each locus and the number of alleles detected by 54 SSRsLocus Bin Repeat motif PIC No. of alleles Description 2)bnlg1007y51) 1.02AG0.7815Probe siteumc1122 1.06GGT0.639Probe siteumc1147y41) 1.07CA0.2615Probe sitephi961001) 2.00ACCT0.298Probe siteumc1185 2.03GC0.7215ole1 (oleosin 1)phi127 2.08AGAC0.577Probe siteumc1736y21) 2.09GCA T0.677Probe sitephi453121 3.01ACC0.7111Probe sitephi374118 3.03ACC0.477Probe sitephi053k21) 3.05A TAC0.7910Probe sitenc004 4.03AG0.4812adh2 (alcohol dehydrogenase 2)bnlg490y41) 4.04T A0.5217Probe sitephi079 4.05AGATG0.495gpc1(glyceraldehyde-3-phosphate dehydrogenase 1) bnlg1784 4.07AG0.6210Probe siteumc1574 4.09GCC0.719sbp2 (SBP-domain protein 2)umc1940y51) 4.09GCA0.4713Probe siteumc1050 4.11AA T0.7810cat3 (catalase 3)nc130 5.00AGC0.5610Probe siteumc2112y31) 5.02GA0.7014Probe sitephi109188 5.03AAAG0.719Probe siteumc1860 5.04A T0.325Probe sitephi085 5.07AACGC0.537gln4 (glutamine synthetase 4)phi331888 5.07AAG0.5811Probe siteumc1153 5.09TCA0.7310Probe sitephi075 6.00CT0.758fdx1 (ferredoxin 1)bnlg249k21) 6.01AG0.7314Probe sitephi389203 6.03AGC0.416Probe sitephi299852y21) 6.07AGC0.7112Probe siteumc1545y21)7.00AAGA0.7610hsp3(heat shock protein 3)phi1127.01AG0.5310o2 (opaque endosperm 2)phi4207018.00CCG0.469Probe siteumc13598.00TC0.7814Probe siteumc11398.01GAC0.479Probe siteumc13048.02TCGA0.335Probe sitephi1158.03A TAC0.465act1(actin1)umc22128.05ACG0.455Probe siteumc11218.05AGAT0.484Probe sitephi0808.08AGGAG0.646gst1 (glutathione-S-transferase 1)phi233376y11)8.09CCG0.598Probe sitebnlg12729.00AG0.8922Probe siteumc20849.01CTAG0.498Probe sitebnlg1520k11)9.01AG0.5913Probe sitephi0659.03CACCT0.519pep1(phosphoenolpyruvate carboxylase 1)umc1492y131)9.04GCT0.2514Probe siteumc1231k41)9.05GA0.2210Probe sitephi1084119.06AGCT0.495Probe sitephi4488809.06AAG0.7610Probe siteumc16759.07CGCC0.677Probe sitephi041y61)10.00AGCC0.417Probe siteumc1432y61)10.02AG0.7512Probe siteumc136710.03CGA0.6410Probe siteumc201610.03ACAT0.517pao1 (polyamine oxidase 1)phi06210.04ACG0.337mgs1 (male-gametophyte specific 1)phi07110.04GGA0.515hsp90 (heat shock protein, 90 kDa)1) These primers were provided by Beijing Academy of Agricultural and Forestry Sciences (Beijing, China).2) Searched from Analysis of Genetic Diversity and Population Structure of Maize Landraces from the South Maize Region of China1257et al. 2005). The unrooted phylogenetic tree was finally schematized with the software MEGA (molecular evolu-tionary genetics analysis) ver. 3.1 (Kumar et al. 2004). Additionally, a chi-square test was used to reveal the correlation between the geographical origins and SSR-clustered groups through FREQ procedure implemented in SAS ver. 9.0 (2002, SAS Institute, Inc.).In order to reveal the population genetic structure (PGS) of 143 landrace accessions, a Bayesian approach was firstly applied to determine the number of groups (K) that these materials should be assigned by the soft-ware BAPS (Bayesian Analysis of Population Structure) ver.5.1. By using BAPS, a fixed-K clustering proce-dure was applied, and with each separate K, the num-ber of runs was set to 100, and the value of log (mL) was averaged to determine the appropriate K value (Corander et al. 2003; Corander and Tang 2007). Since the number of groups were determined, a model-based clustering analysis was used to assign all of the acces-sions into the corresponding groups by an admixture model and a correlated allele frequency via software Structure ver.2.2 (Pritchard et al. 2000; Falush et al. 2007), and for the given K value determined by BAPS, three independent runs were carried out by setting both the burn-in period and replication number 100000. The threshold probability assigned individuals into groupswas set by 0.8 (Liu et al. 2003). The PGS result carried out by Structure was visualized via Distruct program ver. 1.1 (Rosenberg 2004).RESULTSGenetic diversityA total of 517 alleles were detected by the whole set of54 SSRs covering the entire maize genome through all of the 143 maize landraces, with an average of 9.57 alleles per locus and ranged from 4 (umc1121) to 22 (bnlg1272) (Table 3). Among all the alleles detected, the number of distinct alleles accounted for 132 (25.53%), with an av-erage of 2.44 alleles per locus. The distinct alleles dif-fered significantly among the landraces from different provinces/regions, and the landraces from Guangdong, Fujian, Zhejiang, and Shanghai possessed more distinct alleles than those from the other provinces/regions, while those from southern Anhui possessed the lowest distinct alleles, only counting for 3.28% of the total (Table 4).Table 4 The genetic diversity within eight provinces/regions and groups revealed by 54 SSRsProvince/Region Sample size Allele no.1)Distinct allele no.Gene diversity (expected heterozygosity)Observed heterozygosity Anhui14 4.28 (4.19) 69 (72.4)0.51 (0.54)0.58 (0.58)Fujian17 4.93 (4.58 80 (79.3)0.56 (0.60)0.63 (0.62)Guangdong22 5.48 (4.67) 88 (80.4)0.57 (0.59)0.59 (0.58)Hainan18 4.65 (4.26) 79 (75.9)0.53 (0.57)0.55 (0.59)Jiangsu13 4.24 700.500.55Jiangxi21 4.96 (4.35) 72 (68.7)0.56 (0.60)0.68 (0.68)Shanghai15 5.07 (4.89) 90 (91.4)0.55 (0.60)0.55 (0.55)Zhejiang23 5.04 (4.24) 85 (74)0.53 (0.550.60 (0.61)Total/average1439.571320.610.60GroupGroup 155 6.63 (6.40) 91 (89.5)0.57 (0.58)0.62 (0.62)Group 2887.94 (6.72)110 (104.3)0.57 (0.57)0.59 (0.58)Total/Average1439.571320.610.60Provinces/Regions within a groupGroup 1Total55 6.69 (6.40) 910.57 (0.58)0.62 (0.62)Guangdong22 5.48 (4.99) 86 (90.1)0.57 (0.60)0.59 (0.58)Hainan18 4.65 (4.38) 79 (73.9)0.53 (0.56)0.55 (0.59)Jiangxi15 4.30 680.540.69Group 2Total887.97 (6.72)110 (104.3)0.57 (0.57)0.59 (0.58)Anhui14 4.28 (3.22) 69 (63.2)0.51 (0.54)0.58 (0.57)Fujian17 4.93 (3.58) 78 (76.6)0.56 (0.60)0.63 (0.61)Jiangsu13 4.24 (3.22) 71 (64.3)0.50 (0.54)0.55 (0.54)Jiangxi6 3.07 520.460.65Shanghai15 5.07 (3.20) 91 (84.1)0.55 (0.60)0.55 (0.54)Zhejiang23 5.04 (3.20) 83 (61.7)0.53 (0.54)0.60 (0.58)1258LIU Zhi-zhai et al.Among the 54 loci used in the study, 16 (or 29.63%) were dinucleotide repeat SSRs, which were defined as type class I-I, the other 38 loci were SSRs with a longer repeat motifs, and two with unknown repeat motifs, all these 38 loci were defined as the class of I-II. In addition, 15 were located within certain functional genes (defined as class II-I) and the rest were defined as class II-II. The results of comparison indicated that the av-erage number of alleles per locus captured by class I-I and II-II were 12.88 and 10.05, respectively, which were significantly higher than that by type I-II and II-I (8.18 and 8.38, respectively). The gene diversity re-vealed by class I-I (0.63) and II-I (0.63) were some-what higher than by class I-II (0.60) and II-II (0.60) (Table 5).Genetic relationships of the core landraces Overall, 143 landraces were clustered into two groups by using neighbor-joining (NJ) method based on InSPAD. All the landraces from provinces of Guangdong and Hainan and 15 of 21 from Jiangxi were clustered together to form group 1, and the other 88 landraces from the other provinces/regions formed group 2 (Fig.-B). The geographical origins of all these 143 landraces with the clustering results were schematized in Fig.-D. Revealed by the chi-square test, the phylogenetic results (SSR-clustered groups) of all the 143 landraces from provinces/regions showed a significant correlation with their geographical origin (χ2=124.89, P<0.0001, Table 2).Revealed by the phylogenetic analysis based on the InSPAD, the minimum distance was observed as 0.1671 between two landraces, i.e., Tianhongyumi (JiangX-03) and Hongganshanyumi (JiangX-04) collected from Jiangxi Province, and the maximum was between two landraces of Huangbaosu (FuJ-16) and Hongyumi (HaiN-14) collected from provinces of Fujian and Hainan, respectively, with the distance of 1.3863 (data not shown). Two landraces (JiangX-01 and JiangX-21) collected from the same location of Dexing County (Table 1) possessing the same names as Shanyumi were separated to different groups, i.e., JiangX-01 to group1, while JiangX-21 to group 2 (Table 1). Besides, JiangX-01 and JiangX-21 showed a rather distant distance of 0.9808 (data not shown). These results indicated that JiangX-01 and JiangX-21 possibly had different ances-tral origins.Population structureA Bayesian method was used to detect the number of groups (K value) of the whole set of landraces from SR with a fixed-K clustering procedure implemented in BAPS software ver. 5.1. The result showed that all of the 143 landraces could also be assigned into two groups (Fig.-A). Then, a model-based clustering method was applied to carry out the PGS of all the landraces via Structure ver. 2.2 by setting K=2. This method as-signed individuals to groups based on the membership probability, thus the threshold probability 0.80 was set for the individuals’ assignment (Liu et al. 2003). Accordingly, all of the 143 landraces were divided into two distinct model-based groups (Fig.-C). The landraces from Guangdong, Hainan, and 15 landraces from Jiangxi formed one group, while the rest 6 landraces from the marginal countries of northern Jiangxi and those from the other provinces formed an-other group (Table 1, Fig.-D). The PGS revealed by the model-based approach via Structure was perfectly consistent with the relationships resulted from the phy-logenetic analysis via PowerMarker (Table 1).DISCUSSIONThe SR includes eight provinces, i.e., southern Jiangsu and Anhui, Shanghai, Zhejiang, Fujian, Jiangxi, Guangdong, and Hainan (Fig.-C), with the annual maize growing area of about 1 million ha (less than 5% of theTable 5 The genetic diversity detected with different types of SSR markersType of locus No. of alleles Gene diversity Expected heterozygosity PIC Class I-I12.880.630.650.60 Class I-II8.180.600.580.55 Class II-I8.330.630.630.58。

Evidence on the Trade-Off between Real Activities Manipulation and Accrual-Based Earnings Management

Evidence on the trade-off between real activities manipulation and accrual-based earningsmanagementAmy Y. ZangThe Hong Kong University of Science and TechnologyAbstract: I study whether managers use real activities manipulation and accrual-based earnings management as substitutes in managing earnings. I find that managers trade off the two earnings management methods based on their relative costs and that managers adjust the level of accrual-based earnings management according to the level of real activities manipulation realized. Using an empirical model that incorporates the costs associated with the two earnings management methods and captures managers’ sequential decisions, I document large sample evidence consistent with managers using real activities manipulation and accrual-based earnings management as substitutes.Keywords:real activities manipulation, accrual-based earnings management, trade-off Data Availability:Data are available from public sources indicated in the text.I am grateful for the guidance from my dissertation committee members, Jennifer Francis (chair), Qi Chen, Dhananjay Nanda, Per Olsson and Han Hong. I am also grateful for the suggestions and guidance received from Steven Kachelmeier (senior editor), Dan Dhaliwal and two anonymous reviewers. I thank Allen Huang, Moshe Bareket, Yvonne Lu, Shiva Rajgopal, Mohan Venkatachalam and Jerry Zimmerman for helpful comments. I appreciate the comments from the workshop participants at Duke University, University of Notre Dame, University of Utah, University of Arizona, University of Texas at Dallas, Dartmouth College, University of Oregon, Georgetown University, University of Rochester, Washington University in St. Louis and the HKUST. I gratefully acknowledge the financial support from the Fuqua School of Business at Duke University, the Deloitte Foundation, University of Rochester and the HKUST. Errors and omissions are my responsibility.I.INTRODUCTIONI study how firms trade off two earnings management strategies, real activities manipulation and accrual-based earnings management, using a large sample of firms over 1987–2008. Prior studies have shown evidence of firms altering real activities to manage earnings (e.g., Roychowdhury 2006; Graham et al. 2005) and evidence that firms make choices between the two earnings management strategies (Cohen et al. 2008; Cohen and Zarowin 2010; Badertscher 2011). My study extends research on the trade-off between real activities manipulation and accrual-based earnings management by documenting a set of variables that explain the costs of both real and accrual earnings management. I provide evidence for the trade-off decision as a function of the relative costs of the two activities and show that there is direct substitution between them after the fiscal year end due to their sequential nature.Real activities manipulation is a purposeful action to alter reported earnings in a particular direction, which is achieved by changing the timing or structuring of an operation, investment or financing transaction, and which has suboptimal business consequences. The idea that firms engage in real activities manipulation is supported by the survey evidence in Graham et al. (2005).1 They report that 80 percent of surveyed CFOs stated that, in order to deliver earnings, they would decrease research and development (R&D), advertising and maintenance expenditures, while 55 percent said they would postpone a new project, both of which are real activities manipulation.1 In particular, Graham et al. (2005) note that: “The opinion of many of the CFOs is that every companywould/should take actions such of these [real activities manipulation] to deliver earnings, as long as the real sacrifices are not too large and as long as the actions are within GAAP.” Graham et al. further conjecture that CFOs’ greater emphasis on real activities manipulation rather than accrual-based earnings management may be due to their reluctance to admit to accounting-based earnings management in the aftermath of the Enron and Worldcom accounting scandals.Unlike real activities manipulation, which alters the execution of a real transaction taking place during the fiscal year, accrual-based earnings management is achieved by changing the accounting methods or estimates used when presenting a given transaction in the financial statements. For example, changing the depreciation method for fixed assets and the estimate for provision for doubtful accounts can bias reported earnings in a particular direction without changing the underlying transactions.The focus of this study is on how managers trade off real activities manipulation and accrual-based earnings management. This question is important for two reasons. First, as mentioned by Fields et al. (2001), examining only one earnings management technique at a time cannot explain the overall effect of earnings management activities. In particular, if managers use real activities manipulation and accrual-based earnings management as substitutes for each other, examining either type of earnings management activities in isolation cannot lead to definitive conclusions. Second, by studying how managers trade off these two strategies, this study sheds light on the economic implications of accounting choices; that is, whether the costs that managers bear for manipulating accruals affect their decisions about real activities manipulation. As such, the question has implications about whether enhancing SEC scrutiny or reducing accounting flexibility in GAAP, for example, might increase the levels of real activities manipulation engaged in by firms.I start by analyzing the implications for managers’ trade-off decisions due to the different costs and timing of the two earnings management strategies. First, because both are costly activities, firms trade off real activities manipulation versus accrual-based earnings management based on their relative costliness. That is, when one activity is relatively more costly, firms engage in more of the other. Because firms face different costs and constraints for the twoearnings management approaches, they show differing abilities to use the two strategies. Second, real activities manipulation must occur during the fiscal year and is realized by the fiscal year end, after which managers still have the chance to adjust the level of accrual-based earnings management. This timing difference implies that managers would adjust the latter based on the outcome of real activities manipulation. Hence, there is also a direct, substitutive relation between the two: if real activities manipulation turns out to be unexpectedly high (low), managers will decrease (increase) the amount of accrual-based earnings management they carry out.Following prior studies, I examine real activities manipulation through overproduction and cutting discretionary expenditures (Roychowdhury 2006; Cohen et al. 2008; Cohen and Zarowin 2010). I test the hypotheses using a sample of firms that are likely to have managed earnings. As suggested by prior research, earnings management is likely to occur when firms just beat/meet an important earnings benchmark (Burgstahler and Dichev 1997; DeGeorge et al. 1999). Using a sample containing more than 6,500 earnings management suspect firm-years over the period 1987–2008, I show the empirical results that real activities manipulation is constrained by firms’ competitive status in the industry, financial health, scrutiny from institutional investors, and the immediate tax consequences of manipulation. The results also show that accrual-based earnings management is constrained by the presence of high-quality auditors; heightened scrutiny of accounting practice after the passage of the Sarbanes-Oxley Act (SOX); and firms’ accounting flexibility, as determined by their accounting choices in prior periods and the length of their operating cycles. I find significant positive relations between the level of real activities manipulation and the costs associated with accrual-based earnings management, and also between the level of accrual-based earnings management and the costs associated with realactivities manipulation, supporting the hypothesis that managers trade off the two approaches according to their relative costliness. There is a significant and negative relation between the level of accrual-based earnings management and the amount of unexpected real activities manipulation, consistent with the hypothesis that managers “fine-tune” accruals after the fiscal year end based on the realized real activities manipulation. Additional Hausman tests show results consistent with the decision of real activities manipulation preceding the decision of accrual-based earnings management.Two recent studies have examined the trade-off between real activities manipulation and accrual-based earnings management. Cohen et al. (2008) document that, after the passage of SOX, the level of accrual-based earnings management declines, while the level of real activities manipulation increases, consistent with firms switching from the former to the latter as a result of the post-SOX heightened scrutiny of accounting practice. Cohen and Zarowin (2010) show that firms engage in both forms of earnings management in the years of a seasoned equity offering (SEO). They show further that the tendency for SEO firms to use real activities manipulation is positively correlated with the costs of accrual-based earnings management in these firms.2 Compared to prior studies, this study contributes to the earnings management literature by providing a more complete picture of how managers trade off real activities manipulation and accrual-based earnings management. First, it documents the trade-off in a more general setting by using a sample of firms that are likely to have managed earnings to beat/meet various earnings targets. The evidence for the trade-off decisions discussed in this study does not depend on a specific period (such as around the passage of SOX, as in Cohen et al. 2008) or a significant corporate event (such as a SEO, as in Cohen and Zarowin 2010).2 Cohen and Zarowin (2010) do not examine how accrual-based earnings management for SEO firms varies based on the costs of real and accrual earnings management.Second, to my knowledge, mine is the first study to identify a set of costs for real activities manipulation and to examine their impact on both real and accrual earnings management activities. Prior studies (Cohen et al. 2008; Cohen and Zarowin 2010) only examine the costs of accrual-based earnings management. By including the costs of real activities manipulation, this study provides evidence for the trade-off as a function of the relative costs of the two approaches. That is, the level of each earnings management activity decreases with its own costs and increases with the costs of the other. In this way, I show that firms prefer different earnings management strategies in a predictive manner, depending on their operational and accounting environment.Third, I consider the sequential nature of the two earnings management strategies. Most prior studies on multiple accounting and/or economic choices implicitly assume that managers decide on multiple choices simultaneously without considering the sequential decision process as an alternative process (Beatty et al. 1995; Hunt et al. 1996; Gaver and Paterson 1999; Barton 2001; Pincus and Rajgopal 2002; Cohen et al. 2008; Cohen and Zarowin 2010). In contrast, my empirical model explicitly considers the implication of the difference in timing between the two earnings management approaches. Because real activities manipulation has to occur during the fiscal year, but accrual manipulation can occur after the fiscal year end, managers can adjust the extent of the latter based on the realized outcomes of the former. I show that, unlike the trade-off during the fiscal year, which is based on the relative costliness of the two strategies, there is a direct substitution between the two approaches at year end when real activities manipulation is realized. Unexpectedly high (low) real activities manipulation realized is directly offset by a lower (higher) amount of accrual earnings management.Section II reviews relevant prior studies. Section III develops the hypotheses. Section IV describes the research design, measurement of real activities manipulation, accrual-based earnings management and independent variables. Section V reports sample selection and empirical results. Section VI concludes and discusses the implications of my results.II.RELATED LITERATUREThe extensive literature on earnings management largely focuses on accrual-based earnings management (reviewed by Schipper 1989; Healy and Wahlen 1999; Fields et al. 2001). A smaller stream of literature investigates the possibility that managers manipulate real transactions to distort earnings. Many such studies examine managerial discretion over R&D expenditures (Baber et al. 1991; Dechow and Sloan 1991; Bushee 1998; Cheng 2004). Other types of real activities manipulation that have been explored include cutting advertising expenditures (Cohen et al. 2010), stock repurchases (Hribar et al. 2006), sales of profitable assets (Herrmann et al. 2003; Bartov 1993), sales price reductions (Jackson and Wilcox 2000), derivative hedging (Barton 2001; Pincus and Rajgopal 2002), debt-equity swaps (Hand 1989), and securitization (Dechow and Shakespeare 2009).The prevalence of real activities manipulation as an earnings management tool was not well understood until recent years. Graham et al. (2005) survey more than 400 executives and document the widespread use of real activities manipulation. Eighty percent of the CFOs in their survey stated that, in order to meet an earnings target, they would decrease expenditure on R&D, advertising and maintenance, while 55 percent said they would postpone a new project, even if such delay caused a small loss in firm value. Consistent with this survey, Roychowdhury (2006) documents large-sample evidence suggesting that managers avoid reporting annual losses ormissing analyst forecasts by manipulating sales, reducing discretionary expenditures, and overproducing inventory to decrease the cost of goods sold, all of which are deviations from otherwise optimal operational decisions, with the intention of biasing earnings upward.Recent research has started to examine the consequence of real activities manipulation. Gunny (2010) finds that firms that just meet earnings benchmarks by engaging in real activities manipulation have better operating performance in the subsequent three years than do firms that do not engage in real activities manipulation and miss or just meet earnings benchmarks. Bhojraj et al. (2009), on the other hand, show that firms that beat analyst forecasts by using real and accrual earnings management have worse operating performance and stock market performancein the subsequent three years than firms that miss analyst forecasts without earnings management.Most previous research on earnings management examines only one earnings management tool in settings where earnings management is likely to occur (e.g., Healy 1985; Dechow and Sloan 1991; Roychowdhury 2006). However, given the portfolio of earnings management strategies, managers probably use multiple techniques at the same time. A few prior studies (Beatty et al. 1995; Hunt et al. 1996; Gaver and Paterson 1999; Barton 2001; Pincus and Rajgopal 2002; Cohen et al. 2008; Cohen and Zarowin 2010; Badertscher 2011) examine how managers use multiple accounting and operating measures to achieve one or more goals.Beatty et al. (1995) study a sample of 148 commercial banks. They identify two accrual accounts (loan loss provisions and loan charge-offs) and three operating transactions (pension settlement transactions, miscellaneous gains and losses due to asset sales, and issuance of new securities) that these banks can adjust to achieve three goals (optimal primary capital, reported earnings and taxable income levels). The authors construct a simultaneous equation system, in which the banks minimize the sum of the deviations from the three goals and from the optimallevels of the five discretionary accounts.3 They find evidence that some, but not all, of the discretionary accounts (including both accounting choices and operating transactions) are adjusted jointly for some of the objectives identified.Barton (2001) and Pincus and Rajgopal (2002) study how firms manage earning volatility using a sample of Fortune 500, and oil and gas, firms respectively. Both studies use simultaneous equation systems, in which derivative hedging and accrual management are simultaneously determined to manage earnings volatility. Barton (2001) suggests that the two activities are used as substitutes, as evidenced by the negative relation between the two after controlling for the desired level of earnings volatility. Pincus and Rajgopal (2002) find similar negative relation, but only in the fourth quarter.There are two limitations in the approach taken by the above studies. First, in the empirical tests, they assume that the costs of adjusting discretionary accounts are constant across all firms and hence do not generate predictions or incorporate empirical proxies for the costs. In other words, they do not consider that discretion in some accounts is more costly to adjust for some firms. Hence, these studies fail to consider the trade-off among different tools due to their relative costs. Second, they assume all decisions are made simultaneously. If some decisions are made before others, this assumption can lead to misspecification in their equation system.Badertscher (2011) examines overvaluation as an incentive for earnings management. He finds that during the sustained period of overvaluation, managers use accrual earnings management in early years, real activities manipulation in later years, and non-GAAP earnings management as a last resort. He claims that the duration of overvaluation is an important determinant in managers’ choice of earnings management approaches, but he does not model the3Hunt et al. (1996) and Gaver and Paterson (1999) follow Beatty et al. (1995) and construct similar simultaneous equation systems.trade-off between real activities manipulation and accrual-based earnings management based on their relative costliness, nor does his study examine the implication of the sequential nature of the two activities during the year.Two recent studies examine the impact of the costs of accrual-based earnings management on the choice of earnings management strategies. Cohen et al. (2008) show that, on average, accrual-based earnings management declines, but real activities manipulation increases, after the passage of SOX. They focus on one cost of accrual-based earnings management, namely the heightened post-SOX scrutiny of accounting practice, and its impact on the levels of real and accrual earnings management. Using a sample of SEO firms, Cohen and Zarowin (2010) examine several costs of accrual-based earnings management and show that they are positively related to the tendency to use real activities manipulation in the year of a SEO. Neither study examines the costs of real activities manipulation or considers the sequential nature of the two strategies. Hence, they do not show the trade-off decision as a function of the relative costs of the two strategies or the direct substitution between the two after the fiscal year end.III.HYPOTHESES DEVELOPMENTConsistent with prior research on multiple earnings management strategies, I predict that managers use real activities manipulation and accrual-based earnings management as substitutes to achieve the desired earnings targets. Unlike prior research, however, I investigate the differences in the costs and timing of real activities manipulation and accrual-based earnings management, and their implications for managers’ trade-off decisions.Both real activities manipulation and accrual-based earnings management are costly activities. Firms are likely to face different levels of constraints for each strategy, which will leadto varying abilities to use them. A manager’s trade-off decision, therefore, depends on the relative costliness of the two earnings management methods, which is in turn determined by the firm’s operational and accounting environment. That is, given the desired level of earnings, when discretion is more constrained for one earnings management tool, the manager will make more use of the other. This expectation can be expressed as the following hypothesis: H1: Other things being equal, the relative degree of accrual-based earnings management vis-à-vis real activities manipulation depends on the relative costs of each action.Accrual-based earnings management is constrained by scrutiny from outsiders and the available accounting flexibility. For example, a manager might find it harder to convince a high-quality auditor of his/her aggressive accounting estimates than a low-quality auditor. A manager might also feel that accrual-based earnings management is more likely to be detected when regulators heighten scrutiny of firms’ accounting practice. Other than scrutiny from outsiders, accrual-based earnings management is constrained by the flexibility within firms’ accounting systems. Firms that are running out of such flexibility due to, for example, their having made aggressive accounting assumptions in the previous periods, face an increasingly high risk of being detected by auditors and violating GAAP with more accrual-based earnings management. Hence, I formulate the following two subsidiary hypotheses to H1:H1a: Other things being equal, firms facing greater scrutiny from auditors and regulators have a higher level of real activities manipulation.H1b: Other things being equal, firms with lower accounting flexibility have a higher level of real activities manipulation.Real activities manipulation, as a departure from optimal operational decisions, is unlikely to increase firms’ long-term value. Some managers might find it particularly costly because theirfirms face intense competition in the industry. Within an industry, firms are likely to face various levels of competition and, therefore, are under different amounts of pressure when deviating from optimal business strategies. Management research (as reviewed by Woo 1983) shows that market leaders enjoy more competitive advantages than do followers, due to their greater cumulative experience, ability to benefit from economies of scale, bargaining power with suppliers and customers, attention from investors, and influence on their competitors. Therefore, managers in market-leader firms may perceive real activities manipulation as less costly because the erosion to their competitive advantage is relatively small. Hence, I predict the following: H1c: Other things being equal, firms without market-leader status have a higher level of accrual-based earnings management.For a firm in poor financial health, the marginal cost of deviating from optimal business strategies is likely to be high. In this case, managers might perceive real activities manipulation as relatively costly because their primary goal is to improve operations. This view is supported by the survey evidence documented by Graham et al. (2005), who find that CFOs admit that if the company is in a “negative tailspin,” managers’ efforts to survive will dominate their reporting concerns. This reasoning leads to the following subsidiary hypothesis to H1: H1d: Other things being equal, firms with poor financial health have a higher level of accrual-based earnings management.Managers might find it difficult to manipulate real activities when their operation is being monitored closely by institutional investors. Prior studies suggest that institutional investors play a monitoring role in reducing real activities manipulation.4 Bushee (1998) finds that, when4 However, there is also evidence that “transient” institutions, or those with high portfolio turnover and highly diversified portfolio holdings, increase managerial myopic behavior (e.g., Porter 1992; Bushee 1998; Bushee 2001). In this study, I focus on the average effect of institutional ownership on firms’ earnings management activities without looking into the investment horizon of different institutions.institutional ownership is high, firms are less likely to cut R&D expenditure to avoid a decline in earnings. Roychowdhury (2006) also finds a negative relation between institutional ownership and real activities manipulation to avoid losses. Unlike accrual-based earnings management, real activities manipulation has real economic consequences for firms’ long-term value. Institutional investors, being more sophisticated and informed than other investors, are likely to have a better understanding of the long-term implication of firms’ operating decisions, leading to more effort to monitor and curtail real activities manipulation than accrual-based earnings management, as predicted in the following subsidiary hypothesis:H1e: Other things being equal, firms with higher institutional ownership have a higher level of accrual-based earnings management.Real activities manipulation is also costly due to tax incentives. It might be subject to a higher level of book-tax conformity than accrual-based earnings management, because the former has a direct cash flow effect in the current period, while the latter does not. Specifically, when firms increase book income by cutting discretionary expenditures or by overproducing inventory, they also increase taxable income and incur higher tax costs in the current period.5 In contrast, management of many accrual accounts increases book income without current-period tax consequences. For example, increasing the estimated useful lives of long-term assets, decreasing write-downs for impaired assets, recognizing unearned revenue aggressively, and decreasing bad debt expense can all increase book income without necessarily increasing current-year taxable income. Therefore, for firms with higher marginal tax rates, the net present value of the tax costs associated with real activities manipulation is likely to be higher than that of accrual-based earnings management, leading to the following prediction:5Other types of real activities manipulation, such as increasing sales by discounts and price cuts, and sale of long-term assets, are also book-tax conforming earnings management.H1f: Other things being equal, firms with higher marginal tax rates have a higher level of accrual-based earnings management.Another difference between the two earnings management strategies that will influence managers’ trade-off decisions is their different timing. H1 predicts that the two earnings management strategies are jointly determined and the trade-off depends on their relative costliness. However, a joint decision does not imply a simultaneous decision. Because real activities manipulation changes the timing and/or structuring of business transactions, such decisions and activities have to take place during the fiscal year. Shortly after the year end, the outcome of the real activities manipulation is revealed, and managers can no longer engage in it. Note that, when a manager alters real business decisions to manage earnings, s/he does not have perfect control over the exact amount of the real activities manipulation attained. For example, a pharmaceutical company cuts current-period R&D expenditure by postponing or cancelling development of a certain drug. This real decision can include a hiring freeze and shutting down the research site. The manager may be able to make a rough estimate of the dollar amount of the impact on R&D expenditure from these decisions, but s/he does not have perfect information about it.6 Therefore, managers face uncertainty when they execute real activities manipulation. After the fiscal year end, the realized amount of the real activities manipulation could be higher or lower than the amount originally anticipated.On the other hand, after the fiscal year end but before the earnings announcement date, managers can still adjust the accruals by changing the accounting estimates or methods. In addition, unlike real activities manipulation, which distorts earnings by executing transactions6 Another example is reducing travelling expenditures by requiring employees to fly economy class instead of allowing them to fly business class. This change could be suboptimal because employees might reduce the number of visit they make to important clients or because employees’ morale might be adversely impacted, leading to greater turnover. The manager cannot know for certain the exact amount of SG&A being cut, as s/he does not know the number of business trips taken by employees during the year.。

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TARGET国际翻译研究杂志目录Target 1:1（1989） (2)Target 1:2（1989） (3)Target 2:1（1990） (3)Target 2:2（1990） (4)Target 3:1（1991） (5)Target 3:2（1991） (6)Target 4:1（1992） (7)Target 4:2（1992） (9)Target 5:1（1993） (10)Target 5:2（1993） (11)Target 6:1（1994） (12)Target 6:2（1994） (14)Target 7:1（1995） (15)Target 7:2（1995） (16)Target 8:1（1996） (18)Target 8:2（1996） (19)Target 9:1（1997） (20)Target 9:2（1997） (21)Target 10:1（1998） (23)Target 10:2（1998） (25)Target 11:1（1999） (26)Target 11:2（1999） (28)Target 12:1（2000） (29)Target 12:2（2000） (30)Target 13:1（2001） (31)Target 13:2（2001） (32)Target 14:1（2002） (34)Target 14:2（2002） (35)Target 15:1 （2003） (37)Target 15:2（2003） (38)Target 16:1 （2004） (40)Target 16:2（2004） (41)Target 17:1 （2005） (42)Target 17:2（2005） (44)Target 18:1 （2006） (45)Target 18:2 （2006） (46)Target 19:1（2007） (47)Target 19:2（2007） (49)Target 20:1（2008） (50)Target 20:2（2008） (51)Target 21:1（2009） (53)Target 21:2（2009） (54)Target 1:1（1989）On Target's Targets 1 Articles9 In Search of a Target Language: The Politics of Theatre Translation inQuebecAnnie Brisset29 Genre Analysis and the TranslatorCarl James43 Models of the Translation Process: Claim and RealityWolfgang Lörscher69 Wittgenstein, Translation, and SemioticsDinda L. GorléePlato, Bacon and the Puritan Apothecary: The Case of Nicholas95 CulpeperL.G. KellyForum111 Extending the Theory of Translation to Interpretation: Norms as aCase in PointMiriam ShlesingerReview Article117 Bibliographie: Traductions et CulturesJoséLambertReview 123 Paul Chavy. Traducteurs d'autrefois: Moyen âge et RenaissanceReviewed by Theo HermansMary Snell-Hornby (ed.) ZüriLEX '86 ProceedingsReviewed by R.R.K. HartmannTarget 1:2（1989）Articles129 Towards a Multi-facet Concept of Translation BehaviorWolfram WilssTranslation and Original: Similarities and Dissimilarities, I151 Kitty van Leuven-Zwart183 On Aboriginal Sufferance: A Process Model of Poetic TranslatingFrancis R. Jones201 Assessing Acceptability in Translated Children' BooksTiina Puurtinen215 La traduction, les langues et la communication de masse: Lesambiguïtés du discours internationalJoséLambertReview Article239 Verb Metaphors under TranslationGideon TouryReviews 249 James S. Holmes. Translated!: Papers on Literary Translation andTranslation StudiesReviewed by Hendrik van GorpYishai Tobin and Edna Aphek. Word Systems in Modern Hebrew:Implications and ApplicationsReviewed by Hannah Amit-KochaviPaul Nekemann (ed.). Actes du XIe Congrès mondial de la FIT: LaTraduction, notre avenirReviewed by Lieven D’hulstAlan Duff. TranslationReviewed by Francis R. JonesRevue de littérature comparée, numéro spécial: Le Texte étranger.L‘œuvre littéraire en traductionReviewed by Clem RobynsTarget 2:1（1990）Articles1 Typological Aspects of Translating Literary Japanese into German, I:Lexicon and MorphologyGötz WienoldThe Normative Model of Twentieth Century Belles Infidèles:23 Detective Novels in French TranslationClem RobynsA Statistical Method for Translation Quality Assessment43 Shouyi Fan69 Translation and Original: Similarities and Dissimilarities, IIKitty van Leuven-Zwart‗Die Seefahrt an den Nagel hängen‘? Metaphern beim Übersetzen und97 in der ÜbersetzungswissenschaftFrank G. KönigsForumNorms in Interpretation115 Brian HarrisReviews 121 Albrecht Neubert. Text and TranslationReviewed by Christina SchäffnerErika Fischer-Lichte, Fritz Paul Brigitte Schultze Horst Turk, eds.Soziale und theatralische Konventioinen als Problem derDramenübersetzungReviewed by Frank PeetersMary Snell-Hornby Translation Studies: An Integrated ApproachReviewed by Lieven D’hulstTarget 2:2（1990）ArticlesA Theoretical Account of Translation: Without a Translation Theory135 Ernst-August Gutt165 Linguistic Interference in Literary Translations from English intoHebrew of the 1960s and 1970sRachel Weissbrod183 Typological Aspects of Translating Literary Japanese into German, II:Syntax and Narrative TechniqueGötz Wienold199 Surely There Must Exist a Polish Equivalent: On the Inadequacy ofDictionary ExplicationsElżbieta TabakowskaTexttheorie und Translatorisches Handeln 219Hans J. VermeerReviews 243 Harald Kittel, ed. Die literarische Übersetzung: Stand undPerspektiven ihrer ErforschungReviewed by Dirk De GeestReiner Arntz, ed. Textlinguistik und Fachsprache: Akten desInternationalen übersetzungswissenschaftlichen AILA-SymposionsHildesheim, 13.-16 April 1987Reviewed by Wolfgang LörscherValerie Worth. Practising Translation in Renaissance France: TheExample of Étienne DoletReviewed by Paul ChavySherry Simon. L'inscription sociale de la traduction au QuébecReviewed by Clem RobynsNew Books at a Glance 255 Henry G. Schogt. Linguistics, Literary Analysis, and LiteraryTranslationLieven D’hulstMaarten Steenmeijier. De Spaanse en Spaans-Amerikaanse literatuurin Nederland (1946-1985)Ilse LogieTarget 3:1（1991）Articles1 World Knowledge in the Process of TranslationChristina SchäffnerCoincidence in Translation: Glory and Misery Again17 Robert de Beaugrande55 Computer-aided Translation: Where are the Problems?Albrecht Neubert65 Translation Anthologies: An Invitation to the Curious and a CaseStudyHelga Essman and Armin Paul Frank91 Scopos, Loyalty, and Translational ConventionsChristiane NordReviews 111 Candace Séguinot ed. The Translation ProcessReviewed by Hannah Amit-KochaviSusan Bassnett and André Lefevere, eds. Translation, History andCultureReviewed by Theo d’HaenHenri Van Hoof. Traduire l'anglais: Théorie et PratiqueReviewed by Michel BallardDanica Seleskovitch et Marianne Lederer. Pédagogie raisonnée del'interprétationReviewed by Jean DelisleBrian T. Fitch. Beckett and Babel: An Investigation into the Status ofthe Bilingual WorkReviewed by Rainier GrutmanNew Books at a Glance 129 La traduction plurielle. Textes réunis et présentés par Michel BallardLieven D’hulstDaniel Göske. Herman Melville in deutscher SpracheNorbert GreinerKlaus Martens. Die ausgewanderte ―Evangeline‖: Longfellowsepische Idylle im übersetzerischen TransferNorbert GreinerJean Delisle. The Language Alchemists: Société des traducteurs duQuébec (1940-1990)Rainier GrutmanAmparo Hurtado Albir. La notion de fidélité en traductionTarget 3:2（1991）ArticlesA False Opposition in Translation Studies: Theoretical versus/and137 Historical ApproachesDirk Delabastita153 Methodological Aspects of Interpretation (and Translation) ResearchDaniel Gile175 Names and Their Substitutes: Onomastic Observations on Astérix andIts TranslationsSheila Embleton207 Two Traditions of Translating Early Irish LiteratureMaria TymoczkoInstitutional Transmission and Literary Translation: A Sample Case225 Klaus MartensReviews 243 Christiane Nord. Textanalyse und Übersetzen: TheoretischeGrundlagen, Methode und didaktische Anwendung einerübersetzungsrelevanten TextanalyseReviewed by Werner KollerFrederick M. Rener. Interpretatio: Language and Translation fromCicero to TytlerReviewed by Antoine BermanPeter W. Krawutschke, ed. Translator and Interpreter Training andForeign Language PedagogyJean Delisle. Translation: An Interpretive ApproachSonja Tirkkonen-Condit and Stephen Condit, eds. Empirical Studiesin Translation and LinguisticsReviewed by Miriam ShlesingerMary Snell-Hornby and Esther Pöhl, eds. Translation andLexicography: Papers read at the EURALEX Colloquium held atInnsbruck 2-5 July 1987Reviewed by Guy A.J. TopsNew Books at a Glance 261 Bert Westerweel and Theo D'haen, eds. Something Understood:Studies in Anglo-Dutch TranslationDirk DelabastitaMyriam Salama-Carr. La traduction à l'époque abbasside: L'école deHunayn Ibn Ishāq et son importance pour la traductionMichel Ballard261 Andrzej Kątny, Hrsg. Studien zur kontrastiven Linguistik undliterarischen ÜbersetzungGerd FreidhofTarget 4:1（1992）Articles1 The Concept of Function of Translation and Its Application toLiterary TextsRoda P. Roberts17 On Constructing a Transfer Dictionary for Man and MachineJohn Laffling33 Sur le rôle des métaphores en traductologie contemporaineLieven D’hulstFilm (Adaptation) as Translation: Some Methodological Proposals 53Patrick Cattrysse71 Zum Aussagewert motivgeschichtlicher ÜbersetzungsstudienBärbel CzenniaForumNatural Translation: A Reply to Hans P. Krings97 Brian Harris105 Bilinguismus and Übersetzen: Eine Antwort an Brian HarrisHans P. KringsReview ArticleTranslation Theory Revisited111 Raymond van den BroeckReviews 121 Reiner Arntz and Gisela Thome, eds. Übersetzungswissenschaft.Ergebnisse und Perspektiven: Festschrift für Wolfram Wilss zum 65.GeburtstagReviewed by Dirk DelabastitaBasil Hatim and Ian Mason. Discourse and the TranslatorReviewed by Nils Erik EnkvistWolfgang Lörscher. Translation Performance, Translation Process,and Translation StrategiesReviewed by Donald C. KiralyArmin Paul Frank, Hrsg. Die literarische Übersetzung. Der langeSchatten kurzer Geschichten: Amerikanische Kurzprosa in deutschenÜbersetzungenReviewed by Jörn Albrecht and Johannes VolmertPeter Braun, Burkhard Schaeder and Johannes Volmert, eds. Internationalismen: Studien zur interlingualen Lexikologie undLexikographieReviewed by Frank PeetersNew Books at a Glance 139 Jerzy Tomaszczyk and Barbara Lwandowska-Tomaszczyk, eds.Meaning and LexicographyR.R.K. HartmannEija Ventola and Anna Mauranen. Tutkijat ja englanniksikirjoittaminenNils Erik EnkvistMaría Antonia Álvarez Calleja. Estudios de traducción(Inglés-Español): Teoría. Práctica. ApplicationesIlse LogieHenri Van Hoof. Histoire de la traduction en Occident: France,Grande-Bretagne, Allemagne, Russie, Pays-BasLieven D’hulstTarget 4:2（1992）ArticlesGood-bye, Lingua Teutonica? Language, Culture and Science in145 Europe on the Threshold of the 21st CenturyRoland PosnerThe Relations Between Translation and Material Text Transfer171 Anthony Pym191 Translation Policy and Literary/Cultural Changes in Early ModernKorea (1895-1921)Theresa Hyun209 On Two Style Markers of Modern Arabic-Hebrew Prose TranslationsLea Sarig223 The Cloze Technique as a Pedagogical Tool for the Training ofTranslators and InterpretersSylvie LambertReview ArticleA Theoretical Account of Translation: Without Translation Theory?237 Sonja Tirkkonen-ConditReviews 247 J.A. Henderson. Personality and the Linguist: A Comparison of thePersonality Profiles of Professional Translators and ConferenceInterpretersReviewed by Gideon TourySonja Tirkkonen-Condit, ed. Empirical Research in Translation andIntercultural Studies: Selected Papers of the TRANSIF Seminar,Savonlinna 1988Reviewed by Daniel GileAnnie Brisset. Sociocritique de la traduction: Théâtre et altérité auQuébec (1968-1988)Reviewed by Clem RobynsWilliam Luis and Julio Rodríguez-Luis, eds. Translating LatinAmerica: Culture as TextReviewed by Nadia LieNew Books at a Glance 261 Dan Maxwell and Klaus Schubert, eds. Metataxis in Practice:Dependency Syntax for Multilingual Machine TranslationJan DingsPatrice Pavis. Theatre at the Crossroads of Culture261 Sirkku AaltonenTarget 5:1（1993）ArticlesFrom ‗Is‘ to ‗Ought‘: Laws, Norms and Strategies in T ranslation1 StudiesAndrew ChestermanIs There a Special Kind of ―Reading‖ for Translation? An Empirical21 Investigation of Reading in the Translation ProcessGregory M. Shreve, Christina Schäffner,Joseph H. Danks and Jennifer GriffinArab Fatalism and Translation from Arabic into English43 Mohammed Farghal55 Rhetoric and Dutch Translation Theory (1750–1820)Luc Korpel71 Mixed Translation Patterns: The Ladino Translation of Biblical andMishnaic Hebrew VerbsOra (Rodrigue) SchwarzwaldReview Article89 Anthologies et HistoriographeJoséLambertReviews 97 Daniel Gouadec. Le traducteur, la traduction et l'entrepriseReviewed by JoséLambertSusan Bassnett-McGuire. Translation Studies (Revised Edition)Reviewed by John S. DixonGabriele Harhoff. Grenzen der Skopostheorie von Translation undihrer praktischen AnwendbarkeitReviewed by Christiane NordChristian Schmitt, Hrsg. Neue Methoden der SprachmittlungReviewed by Paul KussmaulBarbara Folkart. Le conflit des énonciations: traduction et discoursrapportéReviewed by Reine MaylaertsJelle Stegeman. Übersetzung und Leser: Übersetzung und LeserUntersuchungen zur Übersetzungsäquivalenz dargestellt an derRezeption von Multatulis ‗Max Havelaar‘ und seinen deutschenÜbersetzungenReviewed by Cees KosterSandor Hervey Ian Higgins. Thinking Translation. A Course inTranslation method: French to EnglishReviewed by Hans G. HönigMildred L. Larson, ed. Translation: Theory and Practice. Tension and InterdependenceReviewed by Anthony PymNew Books at a Glance 127 Kitty M. van Leuven Zwart Ton Naaijkens, eds. Translation Studies:The State of the Art. Proceedings of the First James S HolmesSymposium on Translation StudiesMichael SchreiberRainer Schulte John Biguenet, eds. Theories of Translation: AnAnthology of Essays from Dryden to DerridaLieven D’hulstIsabel Pascua Febles and Ana Luisa Peñate Soares. Introducción a losestudios de traducciónAnthony PymTarget 5:2（1993）Articles133 Underpinning Translation TheoryKirsten MalmkjærThe Distinctive Nature of Interpreting Studies149 Heidemarie Salevsky169 The Question of French Dubbing: Towards a Frame for SystematicInvestigationOlivier Goris191 The Grimm Tales in 19th Century DenmarkCay Dollerup215 Das Ende deutscher Romanübersetzungen aus zweiter HandWilhelm GraeberReview ArticleDiscourses on Translation: Recent, Less Recent, and to Come229 AndréLefevereReviews 243 Cay Dollerup and Anne Loddegaard, eds. Teaching Translation andInterpreting: Training, Talent and ExperienceReviewed by Rachel WeissbrodPeter Newmark: About TranslationReviewed by Christina SchäffnerLance Hewson and Jacky Martin. Redefining Translation: TheVariational ApproachReviewed by Michel BallardMarianne Lederer, éd.Études traductologiques en hommage à DanicaSéleskovitchReviewed by Annie BrissetJohn Laffling. Towards High-Precision Machine Translation : Basedon Contrastive TextologyReviewed by Anne-Marie Loffler-LaurianMichel Ballard. De Cicéron à Benjamin: Traducteurs, traductions,réflexionsReviewed by Jean DelisleMats Larsson Från tjeckiska till svenska: Översättningsstrategier förlitterärt talspråkReviewed by Werner KollerJames Hardin, ed. Translation and Translation Theory inSeventeenth-Century GermanyReviewed by Frederick M. RenerNew Books at a Glance 273 Werner Koller. Einführung in die Übersetzungswissenschaft, 4.,Völlig neu bearbeitete AuflageWolfram WilssBrigitte Schultze, Erika Fischer-Lichte, Fritz Paul and Horst Turk,eds.Literatur und Theater. Traditionen und Konventionen als Problem derDramen übersetzungFrank PeetersPhilip C. Stine, ed. Bible Translation and the Spread of the Church:The Last 200 YearsTheo HermansRosa Rabadán. Equivalencia y traducción: Problemática de laequivalencia translémica inglés-españolIlse LogieTarget 6:1（1994）ArticlesSemantic Models and Translating 1Paul KussmaulDid Adapa Indeed Lose His Chance for Eternal Life? A Rationale for15 Translating Ancient Texts into a Modern LanguageShlomo Izre'el43 Twelfth-Century Toledo and Strategies of the Literalist Trojan HorseAnthony PymForum67 Übersetzung * Translation * Traduction: An InternationalEncyclopedia of Translation StudiesReview Article81 Ideological Purity: Machine Translation's Pride or Pitfall?John LafflingReviews 95 Anthony Pym. Translation and Text Transfer: An Essay on thePrinciples of Intercultural CommunicationReviewed by Andrew ChestermanMarcel Thelen and Barbara Lewandowska-Tomaszczyk, eds.Translation and Meaning: Proceedings of the 1990 Maastricht-ŁódźDuo Colloquium I-IIReviewed by Franz PöchhackerHeidemarie Salevsky, Hrsg. Wissenschaftliche Grundlagen derSprachmittlungReviewed by Andreas PoltermannRadegundis Stolze. Hermeneutisches Übersetzen: LinguistischeKategorien des Verstehens und Formulierens beim ÜbersetzenReviewed by Frank G. KönigsRita Copeland. Rhetoric, Hermeneutics and Translation in the MiddleAges: Academic Traditions and Vernacular TextsReviewed by Douglas A. KibbeeCarmela Nocera Avila. Studi sulla traduzione nell'Inghilterra delSeicento e del SettecentoReviewed by Holger KleinChristiane Nord. Einführung in das funktionale Übersetzen: AmBeispiel von Titeln und ÜberschriftenReviewed by Katharina ReissPatrick De Rynck et Andries Welkenhuysen. De Oudheid in hetNederlands: Repertorium en bibliografische gidsReviewed by Arnoud WilsNew Books at a Glance 121 Cecilia Wadensjö. Interpreting as Interaction: OnDialogue-interpreting in Immigration Hearings and MedicalEncountersRuth MorrisCees W. Schoneveld, ed. ‗t Word grooter plas: maar niet zo ‗t was.Nederlandse beschouwingen over vertalen (1670-1760)Patrick De RynckChristiane Beerbom. Modalpartikeln als Übersetzungsproblem: Einekontrastive Studie zum Sprachenpaar Deutsch-SpanischReiner ArntzOther Books Received 127Target 6:2（1994）ArticlesA Framework for Decision-Making in Translation131 Wolfram WilssTranslation Studies in China: Retrospect and Prospect151 Fan ShouyiTranslating Allusions: When Minimum Change Is Not Enough177 Ritva Leppihalme195 Translating Literary Dialogue: A Problem and Its Implications forTranslation into HebrewRina Ben-ShaharReview Article223 Focus on the Pun: Wordplay as a Special Problem in TranslationStudiesDirk DelabastitaReviews 245 Wolfram Wilss. Übersetzungsfertigkeit: Annäherungen an einenkomplexen übersetzungspraktischen BegriffReviewed by John LafflingJean Delisle. La traduction raisonnéeReviewed by Robert LaroseJusta Holz-Mänttäri und Christiane Nord, Hrsg. TRADUCERENAVEM: Festschrift für Katharina Reiβ zum 70. GeburtstagReviewed by Luise Lieflander-KoistinenJohn Newton, ed. Computers in Translation: A Practical AppraisalReviewed by Frank Van EyndeAndré Lefevere, ed. Translation/History/Culture: A SourcebookReviewed by Luc KorpelLuc G. Korpel. Over het nut en de wijze der vertalingen: Nederlandse vertaalreflectie (1750-1820) in een Westeuropees kaderReviewed by Patrick De RynckNew Books at a Glance265 Tejaswini Niranjana. Siting Translation: History, Post-Structuralismand the Colonial ContextGurbhagat SinghWilliam A. Smalley.Translation as Mission: Bible Translation in theModern Missionary MovementAnneke de VriesMichael Hann. The Key to Technical Translation, 1-2Bruce W. Irwin and Erhard EydamClem Robyns, ed. Translation and the (Re)production of Culture:Selected Papers of the CERA Research Seminars in TranslationStudies 1989-1991John S. DixonOther Books Received 273Target 7:1（1995）Mirror Mirror on the Wall: An Introduction1 Daniel GilleArticles7 Stranger in Paradigms: What Lies Ahead for SimultaneousInterpreting Research?Miriam ShlesingerInterpreting Research and the ‗Manipulation School‘ of Translation29 StudiesAnne Schjoldager―Those Who Do…‖: A Profile of Research(ers) in Interpreting47 Franz PöchhackerUne approche asymptotique de la recherche sur l‘interprétation65 Birgit StrolzLa recherche en interprétation dans les pays d‘Europe de l‘Est: un e75 perspective personnelleIvana Čeňková91 Interpretation Research in JapanMasaomi Kondo and Akira Mizuno107 Development of Research Work at SSLM, Trieste (Italy)Laura Gran and Maurizio ViezziA Review of Conference Interpretation: Practice and Training119 Jennifer MackintoshOn The Relevance of Signed Languages to Research in Interpretation135 William P. IshamFidelity Assessment in Consecutive Interpretation: An Experiment151 Daniel Gille165 Interdisciplinary Research — Difficulties and BenefitsIngrid KurzReviews181 Sylvie Lambert and Barbara Moser-Mercer, eds. Bridging the Gap:Emperical Research in Simultaneous InterpretationFranz Pöchhacker181 Franz Pöchhacker. Simultandolmetschen als komplexes HandelnDaniel GilleOther Books Received 189Target 7:2（1995）Articles191 The Concept of Equivalence and the Object of Translation StudiesWerner KollerCorpora in Translation Studies: An Overview and Suggestions for223 Future ResearchMona Baker245 Quantitative and Qualitative Aspects of Corpus Selection inTranslation StudiesLuc van Doorslaer261 Text-Functions in Translation: Titles and Headings as a Case in PointChristiane Nord285 Headlining in Translation: English vs. Greek PressMaria SidiropoulouA Pragmatic Classification of LSP Texts in Science and Technology305 Susanne Göpferich327 Retranslation of Children's Books as Evidence of Changes of NormsMyriam Du-NourForumIntuition in Translation347 Vilen N. KomissarovReviews 355Dinda L. Gorlée. Semiotics and the Problem of TranslationReviewed by Elda WeizmanYves Gambier Jorma Tommola, eds. Translation and Knowledge:SSOTT IV — Scandinavian Symposium on Translation Theory (Turku,4–6.6.1992)Reviewed by Kirsten MalmkjærMary Snell-Hornby, Franz Pöchhacker and Klaus Kaindl, eds.Translation Studies: An InterdisciplineReviewed by Anthony PymRomy Heylen .Translation, Poetics, and the StageReviewed by Sirkku AaltonenCandace Whitman-Linsen. Through the Dubbing Glass: TheSynchronization of American Motion Pictures into German, Frenchand SpanishReviewed by Aline RemaelThomas O. Beebee. Clarissa on the Continent: Translation andSeductionReviewed by Wilhelm GraeberHelga Essmann. Übersetzungsanthologien: Eine Typologie und eine Untersuchung am Beispiel der amerikanischen Versdichtung indeutsch-sprachigen Anthologien, 1920–1960Reviewed by Hannah Amit-KochaviHans J. Vermeer. Skizzen zu einer Geschichte der Translation, Bd:1:Anfäange:von Mesopotamien bis GriechenlandRom und das frühe Christentum bis HieronymusReviewed by Heidemarie SalevskyKitty M. van Leuven-Zwart. Vertaalwetenschap: Ontwikkelingen en perspectievenReviewed by Theo HermansNew Books at a Glance 389 André Lefevere. Translation, Rewriting & the Manipulation ofLiterary FameHannah Amit-KochaviChristine Pagnoulle, éd. Les gens du passageMichel BallardPalma Zlateva Translation as Social Action: Russian and BulgarianPerspectivesAnikóSohárSiegfried Meurer, Hrsg. Die vergessenen Schwestern: FrauengerechteSprache in der BibelübersetzungAnneke de VriesTarget 8:1（1996）Articles1 There Is Always a Teller in a TaleGiuliana SchiaviThe Translator‘s Voice in Translated Narrative23 Theo Hermans49 Directionality in Translation Processes and PracticesSophia S.A. Marmaridou75 Some Thoughts About Think-Aloud ProtocolsCandace SéguinotThe Translation of English Passives into Arabic: An Empirical97 PerspectiveMohammed Farghal and Mohammed O. Al-Shorafat119 Translations, Paratextual Mediation, and Ideological ClosureUrpo KovalaForum149 A Case for Linguistics in Translation TheoryVladimir IvirOn Similarity159 Andrew ChestermanReview Article165 Venuti's VisibilityAnthony PymReviews 179 Elżbieta Tabakowska. Cognitive Linguistics and Poetics ofTranslationReviewed by Vladimir IvirMichel Ballard, dir. La traduction à l‘université: Recherches etpropositions didactiquesReviewed by Robert LaroseHeidrun Gerzymisch-Arbogast. ÜbersetzungswissenschaftlichesPropädeutikumReviewed by Hans G. HönigJuan C. Sager. Language Engineering and Translation: Consequencesof AutomationReviewed by Christina SchäffnerGideon Toury. Descriptive Translation Studies and beyondReviewed by Andrew ChestermanSherry Simon. Le Trafic des langues: Traduction et culture dans lalittérature québécoiseReviewed by Reine MeylaertsRadegundis Stolze. Übersetzungstheorien: Eine EinführungReviewed by Nelleke de Jong-van den BergTarget 8:2（1996）ArticlesLanguage, Translation and the Promotion of National Identity: Two211 Test CasesJudith Woodsworth239 Implicit Information in Literary Translation: A Relevance-TheoreticPerspectiveErnst-August Gutt257 Affective and Attitudinal Factors in Translation ProcessesJohanna LaukkanenA Translator' Reference Needs: Dictionaries or Parallel Texts?275 Ian A. Williams301 Translation of Modifications: About Information, Intention and EffectChunshen ZhuTowards a Model of Translation Proficiency325 Deborah CaoWhat Translators of Plays Think About Their Work341 Marja JänisForum365 Assumed Translation: Continuing the DiscussionVilen N. KomissarovReviews 375 Daniel Gile. Basic Concepts and Models for Interpreter andTranslator TrainingReviewed by Donald C. Kiraly and David B. SawyerJeanne Dancette. Parcours de traduction: étude expérimentale duprocessus de compréhensionReviewed by Wolfgang LörscherGeorges Mounin. Les Belles InfidèlesReviewed by Yves GambierPaul Kussmaul. Training the TranslatorReviewed by Jeanne DancetteHans G. Hönig Konstruktives ÜbersetzenReviewed by Luc van DoorslaerAntoine Berman. Pour une critique des traductions: John DonneReviewed by Reine MeylaertsNew Books at a Glance 395 Deanna L. Hammond, ed. Professional Issues for Translators andInterpretersPeter JansenMichael Schreiber. Übersetzung und Bearbeitung: ZurDifferenzierung und Abgrenzung des ÜbersetzungsbegriffsJuliane HouseAnneke de Vries. Zuiver en onvervalscht?: Een beschrijving voor bijbelvertalingen, ontwikkeld en gedemonstreerd aan de PetrusCanisius VertalingPaul GillaertsOther Books Received 403Target 9:1（1997）ARTICLES‗Acceptability‘ and Language-Specific Preference in the Distribution1 of InformationMonika Doherty25 Translating a Poem, from a Linguistic PerspectiveElżbieta Tabakowska43 Translat ing the Untranslatable: The Translator‘s Aesthetic,Ideological and Political ResponsibilityGillian Lane-Mercier69 Who Verbalises What: A Linguistic Analysis of TAP TextsSonja Tirkkonen-ConditCréativité et traduction85 Michel Ballard111 Cultural Agents and Cultural Interference: The Function of J.H.Campe in an Emerging Jewish CultureZohar ShavitLanguage and Translation in an International Business Context:131 Beyond an Instrumental ApproachChris Steyaert and Maddy JanssensFORUM。

刘颖会外文翻译原文及译文

大连民族学院国际商学院英文翻译2007级毕业论文外文翻译资料Microfinance's Latest Growing Pains小额信贷业的发展阵痛《Knowledge Wharton》February 2nd 2011《沃顿知识》杂志 2011年2月2日译者：刘颖会大连民族学院国际商学院国际经济与贸易072班2011年6月小额信贷业发展阵痛近期的小额信贷危机源于印度南部城市安得拉邦，当地过度负债、暴力催款和借款者被迫自杀等问题引发了民众对小额信贷行业的广泛指责，并强烈呼吁政府加强监管。

10月，印度政府对损害信贷、强行控制回款天数并拖累印度最大的盈利性小额信贷公司SKS股价暴跌的小额信贷机构实施管制。

1月19日，印度储备银行发布Malegam委员会报告，建议对印度小额信贷机构施加一系列新的监管措施，包括设置利率上限、贷款限额以及对借款人的收入进行规定。

有些观察家对此表示欢迎，而悲观人士则认为此举难以避免信贷紧缩和行业崩溃。

尽管现在要分析行业前景还为时尚早，但安得拉邦的危机着实引发了民众对全球小额信贷行业的热烈讨论和深刻反省。

近期在沃顿阿瑞斯高级管理教育学院小额信贷管理培训班上，讨论的焦点集中在过度信贷、高速的行业增长以及如何在追求利润的同时更好地实现小额信贷的设立宗旨。

小额信贷业经历了一场由坏账“大地震”所引发的“痛苦的觉醒”，26名来自全球各地的社会财富计划参与者之一Kamran Azim在一堂主题为小额信贷业的增长与可持续发展的讨论中如此比喻道。

Azim是创立于1996年的巴基斯坦拉合尔小额信贷机构Kashf 基金的运营总监。

他指出，过去20到30年间，小额信贷的方式方法几乎都没有发生过变化。

但现在，突然之间，这个行业经历了一场地震。

正如该培训计划中一门课程的导言所说：“面对不断加速的变革，人们趋向于依赖传统的方式进行商业发展。

然而，正是在这样的时刻，创新方显得尤为重要。

”此外，几名学员也指出，小额信贷行业必须在兼顾客户需求的同时通过创新的方式来巩固发展。

Universities in Evolutionary Systems(系统变革中的大学)

Universities in Evolutionary Systems of InnovationMarianne van der Steen and Jurgen EndersThis paper criticizes the current narrow view on the role of universities in knowledge-based economies.We propose to extend the current policy framework of universities in national innovation systems(NIS)to a more dynamic one,based on evolutionary economic principles. The main reason is that this dynamic viewﬁts better with the practice of innovation processes. We contribute on ontological and methodological levels to the literature and policy discussions on the effectiveness of university-industry knowledge transfer and the third mission of uni-versities.We conclude with a discussion of the policy implications for the main stakeholders.1.IntroductionU niversities have always played a major role in the economic and cultural devel-opment of countries.However,their role and expected contribution has changed sub-stantially over the years.Whereas,since1945, universities in Europe were expected to con-tribute to‘basic’research,which could be freely used by society,in recent decades they are expected to contribute more substantially and directly to the competitiveness ofﬁrms and societies(Jaffe,2008).Examples are the Bayh–Dole Act(1982)in the United States and in Europe the Lisbon Agenda(2000–2010) which marked an era of a changing and more substantial role for universities.However,it seems that this‘new’role of universities is a sort of universal given one(ex post),instead of an ex ante changing one in a dynamic institutional environment.Many uni-versities are expected nowadays to stimulate a limited number of knowledge transfer activi-ties such as university spin-offs and university patenting and licensing to demonstrate that they are actively engaged in knowledge trans-fer.It is questioned in the literature if this one-size-ﬁts-all approach improves the usefulness and the applicability of university knowledge in industry and society as a whole(e.g.,Litan et al.,2007).Moreover,the various national or regional economic systems have idiosyncratic charac-teristics that in principle pose different(chang-ing)demands towards universities.Instead of assuming that there is only one‘optimal’gov-ernance mode for universities,there may bemultiple ways of organizing the role of univer-sities in innovation processes.In addition,we assume that this can change over time.Recently,more attention in the literature hasfocused on diversity across technologies(e.g.,King,2004;Malerba,2005;Dosi et al.,2006;V an der Steen et al.,2008)and diversity offormal and informal knowledge interactionsbetween universities and industry(e.g.,Cohenet al.,1998).So far,there has been less atten-tion paid to the dynamics of the changing roleof universities in economic systems:how dothe roles of universities vary over time andwhy?Therefore,this article focuses on the onto-logical premises of the functioning of univer-sities in innovation systems from a dynamic,evolutionary perspective.In order to do so,we analyse the role of universities from theperspective of an evolutionary system ofinnovation to understand the embeddednessof universities in a dynamic(national)systemof science and innovation.The article is structured as follows.InSection2we describe the changing role ofuniversities from the static perspective of anational innovation system(NIS),whereasSection3analyses the dynamic perspective ofuniversities based on evolutionary principles.Based on this evolutionary perspective,Section4introduces the characteristics of a LearningUniversity in a dynamic innovation system,summarizing an alternative perception to thestatic view of universities in dynamic economicsystems in Section5.Finally,the concludingVolume17Number42008doi:10.1111/j.1467-8691.2008.00496.x©2008The AuthorsJournal compilation©2008Blackwell Publishingsection discusses policy recommendations for more effective policy instruments from our dynamic perspective.2.Static View of Universities in NIS 2.1The Emergence of the Role of Universities in NISFirst we start with a discussion of the literature and policy reports on national innovation system(NIS).The literature on national inno-vation systems(NIS)is a relatively new and rapidly growingﬁeld of research and widely used by policy-makers worldwide(Fagerberg, 2003;Balzat&Hanusch,2004;Sharif,2006). The NIS approach was initiated in the late 1980s by Freeman(1987),Dosi et al.(1988)and Lundvall(1992)and followed by Nelson (1993),Edquist(1997),and many others.Balzat and Hanusch(2004,p.196)describe a NIS as‘a historically grown subsystem of the national economy in which various organizations and institutions interact with and inﬂuence one another in the carrying out of innovative activity’.It is about a systemic approach to innovation,in which the interaction between technology,institutions and organizations is central.With the introduction of the notion of a national innovation system,universities were formally on the agenda of many innovation policymakers worldwide.Clearly,the NIS demonstrated that universities and their interactions with industry matter for innova-tion processes in economic systems.Indeed, since a decade most governments acknowl-edge that interactions between university and industry add to better utilization of scienti-ﬁc knowledge and herewith increase the innovation performance of nations.One of the central notions of the innovation system approach is that universities play an impor-tant role in the development of commercial useful knowledge(Edquist,1997;Sharif, 2006).This contrasts with the linear model innovation that dominated the thinking of science and industry policy makers during the last century.The linear innovation model perceives innovation as an industry activity that‘only’utilizes fundamental scientiﬁc knowledge of universities as an input factor for their innovative activities.The emergence of the non-linear approach led to a renewed vision on the role–and expectations–of universities in society. Some authors have referred to a new social contract between science and society(e.g., Neave,2000).The Triple Helix(e.g.,Etzkowitz &Leydesdorff,1997)and the innovation system approach(e.g.,Lundvall,1988)and more recently,the model of Open Innovation (Chesbrough,2003)demonstrated that innova-tion in a knowledge-based economy is an inter-active process involving many different innovation actors that interact in a system of overlapping organizationalﬁelds(science, technology,government)with many interfaces.2.2Static Policy View of Universities in NIS Since the late1990s,the new role of universi-ties in NIS thinking emerged in a growing number of policy studies(e.g.,OECD,1999, 2002;European Commission,2000).The con-tributions of the NIS literature had a large impact on policy makers’perception of the role of universities in the national innovation performance(e.g.,European Commission, 2006).The NIS approach gradually replaced linear thinking about innovation by a more holistic system perspective on innovations, focusing on the interdependencies among the various agents,organizations and institutions. NIS thinking led to a structurally different view of how governments can stimulate the innovation performance of a country.The OECD report of the national innovation system (OECD,1999)clearly incorporated these new economic principles of innovation system theory.This report emphasized this new role and interfaces of universities in knowledge-based economies.This created a new policy rationale and new awareness for technology transfer policy in many countries.The NIS report(1999)was followed by more attention for the diversity of technology transfer mecha-nisms employed in university-industry rela-tions(OECD,2002)and the(need for new) emerging governance structures for the‘third mission’of universities in society,i.e.,patent-ing,licensing and spin-offs,of public research organizations(OECD,2003).The various policy studies have in common that they try to describe and compare the most important institutions,organizations, activities and interactions of public and private actors that take part in or inﬂuence the innovation performance of a country.Figure1 provides an illustration.Theﬁgure demon-strates the major building blocks of a NIS in a practical policy setting.It includesﬁrms,uni-versities and other public research organiza-tions(PROs)involved in(higher)education and training,science and technology.These organizations embody the science and tech-nology capabilities and knowledge fund of a country.The interaction is represented by the arrows which refer to interactive learn-ing and diffusion of knowledge(Lundvall,Volume17Number42008©2008The AuthorsJournal compilation©2008Blackwell Publishing1992).1The building block ‘Demand’refers to the level and quality of demand that can be a pull factor for ﬁrms to innovate.Finally,insti-tutions are represented in the building blocks ‘Framework conditions’and ‘Infrastructure’,including various laws,policies and regula-tions related to science,technology and entre-preneurship.It includes a very broad array of policy issues from intellectual property rights laws to ﬁscal instruments that stimulate labour mobility between universities and ﬁrms.The ﬁgure demonstrates that,in order to improve the innovation performance of a country,the NIS as a whole should be conducive for innovative activities in acountry.Since the late 1990s,the conceptual framework as represented in Figure 1serves as a dominant design for many comparative studies of national innovation systems (Polt et al.,2001;OECD,2002).The typical policy benchmark exercise is to compare a number of innovation indicators related to the role of university-industry interactions.Effective performance of universities in the NIS is judged on a number of standardized indica-tors such as the number of spin-offs,patents and licensing.Policy has especially focused on ‘getting the incentives right’to create a generic,good innovative enhancing context for ﬁrms.Moreover,policy has also inﬂu-enced the use of speciﬁc ‘formal’transfer mechanisms,such as university patents and university spin-offs,to facilitate this collabo-ration.In this way best practice policies are identiﬁed and policy recommendations are derived:the so-called one-size-ﬁts-all-approach.The focus is on determining the ingredients of an efﬁcient benchmark NIS,downplaying institutional diversity and1These organizations that interact with each other sometimes co-operate and sometimes compete with each other.For instance,ﬁrms sometimes co-operate in certain pre-competitive research projects but can be competitors as well.This is often the case as well withuniversities.Figure 1.The Benchmark NIS Model Source :Bemer et al.(2001).Volume 17Number 42008©2008The AuthorsJournal compilation ©2008Blackwell Publishingvariety in the roles of universities in enhanc-ing innovation performance.The theoretical contributions to the NIS lit-erature have outlined the importance of insti-tutions and institutional change.However,a further theoretical development of the ele-ments of NIS is necessary in order to be useful for policy makers;they need better systemic NIS benchmarks,taking systematically into account the variety of‘national idiosyncrasies’. Edquist(1997)argues that most NIS contribu-tions are more focused onﬁrms and technol-ogy,sometimes reducing the analysis of the (national)institutions to a left-over category (Geels,2005).Following Hodgson(2000), Nelson(2002),Malerba(2005)and Groenewe-gen and V an der Steen(2006),more attention should be paid to the institutional idiosyncra-sies of the various systems and their evolution over time.This creates variety and evolving demands towards universities over time where the functioning of universities and their interactions with the other part of the NIS do evolve as well.We suggest to conceptualize the dynamics of innovation systems from an evolutionary perspective in order to develop a more subtle and dynamic vision on the role of universities in innovation systems.We emphasize our focus on‘evolutionary systems’instead of national innovation systems because for many universities,in particular some science-based disciplinaryﬁelds such as biotechnology and nanotechnology,the national institutional environment is less relevant than the institu-tional and technical characteristics of the technological regimes,which is in fact a‘sub-system’of the national innovation system.3.Evolutionary Systems of Innovation as an Alternative Concept3.1Evolutionary Theory on Economic Change and InnovationCharles Darwin’s The Origin of Species(1859)is the foundation of modern thinking about change and evolution(Luria et al.,1981,pp. 584–7;Gould,1987).Darwin’s theory of natural selection has had the most important consequences for our perception of change. His view of evolution refers to a continuous and gradual adaptation of species to changes in the environment.The idea of‘survival of theﬁttest’means that the most adaptive organisms in a population will survive.This occurs through a process of‘natural selection’in which the most adaptive‘species’(organ-isms)will survive.This is a gradual process taking place in a relatively stable environment, working slowly over long periods of time necessary for the distinctive characteristics of species to show their superiority in the‘sur-vival contest’.Based on Darwin,evolutionary biology identiﬁes three levels of aggregation.These three levels are the unit of variation,unit of selection and unit of evolution.The unit of varia-tion concerns the entity which contains the genetic information and which mutates fol-lowing speciﬁc rules,namely the genes.Genes contain the hereditary information which is preserved in the DNA.This does not alter sig-niﬁcantly throughout the reproductive life-time of an organism.Genes are passed on from an organism to its successors.The gene pool,i.e.,the total stock of genetic structures of a species,only changes in the reproduction process as individuals die and are born.Par-ticular genes contribute to distinctive charac-teristics and behaviour of species which are more or less conducive to survival.The gene pool constitutes the mechanism to transmit the characteristics of surviving organisms from one generation to the next.The unit of selection is the expression of those genes in the entities which live and die as individual specimens,namely(individual) organisms.These organisms,in their turn,are subjected to a process of natural selection in the environment.‘Fit’organisms endowed with a relatively‘successful’gene pool,are more likely to pass them on to their progeny.As genes contain information to form and program the organisms,it can be expected that in a stable environment genes aiding survival will tend to become more prominent in succeeding genera-tions.‘Natural selection’,thus,is a gradual process selecting the‘ﬁttest’organisms. Finally,there is the unit of evolution,or that which changes over time as the gene pool changes,namely populations.Natural selec-tion produces changes at the level of the population by‘trimming’the set of genetic structures in a population.We would like to point out two central principles of Darwinian evolution.First,its profound indeterminacy since the process of development,for instance the development of DNA,is dominated by time at which highly improbable events happen (Boulding,1991,p.12).Secondly,the process of natural selection eliminates poorly adapted variants in a compulsory manner,since indi-viduals who are‘unﬁt’are supposed to have no way of escaping the consequences of selection.22We acknowledge that within evolutionary think-ing,the theory of Jean Baptiste Lamarck,which acknowledges in essence that acquired characteris-tics can be transmitted(instead of hereditaryVolume17Number42008©2008The AuthorsJournal compilation©2008Blackwell PublishingThese three levels of aggregation express the differences between ‘what is changing’(genes),‘what is being selected’(organisms),and ‘what changes over time’(populations)in an evolutionary process (Luria et al.,1981,p.625).According to Nelson (see for instance Nelson,1995):‘Technical change is clearly an evolutionary process;the innovation generator keeps on producing entities superior to those earlier in existence,and adjustment forces work slowly’.Technological change and innovation processes are thus ‘evolutionary’because of its characteristics of non-optimality and of an open-ended and path-dependent process.Nelson and Winter (1982)introduced the idea of technical change as an evolutionary process in capitalist economies.Routines in ﬁrms function as the relatively durable ‘genes’.Economic competition leads to the selection of certain ‘successful’routines and these can be transferred to other ﬁrms by imitation,through buy-outs,training,labour mobility,and so on.Innovation processes involving interactions between universities and industry are central in the NIS approach.Therefore,it seems logical that evolutionary theory would be useful to grasp the role of universities in innovation pro-cesses within the NIS framework.3.2Evolutionary Underpinnings of Innovation SystemsBased on the central evolutionary notions as discussed above,we discuss in this section how the existing NIS approaches have already incor-porated notions in their NIS frameworks.Moreover,we investigate to what extent these notions can be better incorporated in an evolu-tionary innovation system to improve our understanding of universities in dynamic inno-vation processes.We focus on non-optimality,novelty,the anti-reductionist methodology,gradualism and the evolutionary metaphor.Non-optimality (and Bounded Rationality)Based on institutional diversity,the notion of optimality is absent in most NIS approaches.We cannot deﬁne an optimal system of innovation because evolutionary learning pro-cesses are important in such systems and thus are subject to continuous change.The system never achieves an equilibrium since the evolu-tionary processes are open-ended and path dependent.In Nelson’s work (e.g.,1993,1995)he has emphasized the presence of contingent out-comes of innovation processes and thus of NIS:‘At any time,there are feasible entities not present in the prevailing system that have a chance of being introduced’.This continuing existence of feasible alternative developments means that the system never reaches a state of equilibrium or ﬁnality.The process always remains dynamic and never reaches an optimum.Nelson argues further that diversity exists because technical change is an open-ended multi-path process where no best solu-tion to a technical problem can be identiﬁed ex post .As a consequence technical change can be seen as a very wasteful process in capitalist economies with many duplications and dead-ends.Institutional variety is closely linked to non-optimality.In other words,we cannot deﬁne the optimal innovation system because the evolutionary learning processes that take place in a particular system make it subject to continuous change.Therefore,comparisons between an existing system and an ideal system are not possible.Hence,in the absence of any notion of optimality,a method of comparing existing systems is necessary.According to Edquist (1997),comparisons between systems were more explicit and systematic than they had been using the NIS approaches.Novelty:Innovations CentralNovelty is already a central notion in the current NIS approaches.Learning is inter-preted in a broad way.Technological innova-tions are deﬁned as combining existing knowledge in new ways or producing new knowledge (generation),and transforming this into economically signiﬁcant products and processes (absorption).Learning is the most important process behind technological inno-vations.Learning can be formal in the form of education and searching through research and development.However,in many cases,innovations are the consequence of several kinds of learning processes involving many different kinds of economic agents.According to Lundvall (1992,p.9):‘those activities involve learning-by-doing,increasing the efﬁciency of production operations,learning-characteristics as in the theory of Darwin),is acknowledged to ﬁt better with socio-economic processes of technical change and innovation (e.g.,Nelson &Winter,1982;Hodgson,2000).Therefore,our theory is based on Lamarckian evolutionary theory.However,for the purpose of this article,we will not discuss the differences between these theo-ries at greater length and limit our analysis to the fundamental evolutionary building blocks that are present in both theories.Volume 17Number 42008©2008The AuthorsJournal compilation ©2008Blackwell Publishingby-using,increasing the efﬁciency of the use of complex systems,and learning-by-interacting, involving users and producers in an interac-tion resulting in product innovations’.In this sense,learning is part of daily routines and activities in an economy.In his Learning Economy concept,Lundvall makes learning more explicit,emphasizing further that ‘knowledge is assumed as the most funda-mental resource and learning the most impor-tant process’(1992,p.10).Anti-reductionist Approach:Systems and Subsystems of InnovationSo far,NIS approaches are not yet clear and systematic in their analysis of the dynamics and change in innovation systems.Lundvall’s (1992)distinction between subsystem and system level based on the work of Boulding implicitly incorporates both the actor(who can undertake innovative activities)as well as the structure(institutional selection environment) in innovation processes of a nation.Moreover, most NIS approaches acknowledge that within the national system,there are different institu-tional subsystems(e.g.,sectors,regions)that all inﬂuence each other again in processes of change.However,an explicit analysis of the structured environment is still missing (Edquist,1997).In accordance with the basic principles of evolutionary theory as discussed in Section 3.1,institutional evolutionary theory has developed a very explicit systemic methodol-ogy to investigate the continuous interaction of actors and institutional structures in the evolution of economic systems.The so-called ‘methodological interactionism’can be per-ceived as a methodology that combines a structural perspective and an actor approach to understand processes of economic evolu-tion.Whereas the structural perspective emphasizes the existence of independent institutional layers and processes which deter-mine individual actions,the actor approach emphasizes the free will of individuals.The latter has been referred to as methodological individualism,as we have seen in neo-classical approaches.Methodological indi-vidualism will explain phenomena in terms of the rational individual(showingﬁxed prefer-ences and having one rational response to any fully speciﬁed decision problem(Hodgson, 2000)).The interactionist approach recognizes a level of analysis above the individual orﬁrm level.NIS approaches recognize that national differences exist in terms of national institu-tions,socio-economic factors,industries and networks,and so on.So,an explicit methodological interactionist approach,explicitly recognizing various insti-tutional layers in the system and subsystem in interaction with the learning agents,can improve our understanding of the evolution of innovation.Gradualism:Learning Processes andPath-DependencyPath-dependency in biology can be translated in an economic context in the form of(some-times very large)time lags between a technical invention,its transformation into an economic innovation,and the widespread diffusion. Clearly,in many of the empirical case studies of NIS,the historical dimension has been stressed.For instance,in the study of Denmark and Sweden,it has been shown that the natural resource base(for Denmark fertile land,and for Sweden minerals)and economic history,from the period of the Industrial Revolution onwards,has strongly inﬂuenced present specialization patterns(Edquist& Lundvall,1993,pp.269–82).Hence,history matters in processes of inno-vation as the innovation processes are inﬂu-enced by many institutions and economic agents.In addition,they are often path-dependent as small events are reinforced and become crucially important through processes of positive feedback,in line with evolutionary processes as discussed in Section3.1.Evolutionary MetaphorFinally,most NIS approaches do not explicitly use the biological metaphor.Nevertheless, many of the approaches are based on innova-tion theories in which they do use an explicit evolutionary metaphor(e.g.,the work of Nelson).To summarize,the current(policy)NIS approaches have already implicitly incorpo-rated some evolutionary notions such as non-optimality,novelty and gradualism.However, what is missing is a more explicit analysis of the different institutional levels of the economic system and innovation subsystems (their inertia and evolution)and how they change over time in interaction with the various learning activities of economic agents. These economic agents reside at established ﬁrms,start-upﬁrms,universities,govern-ments,undertaking learning and innovation activities or strategic actions.The explicit use of the biological metaphor and an explicit use of the methodological interactionst approach may increase our understanding of the evolu-tion of innovation systems.Volume17Number42008©2008The AuthorsJournal compilation©2008Blackwell Publishing4.Towards a Dynamic View of Universities4.1The Logic of an Endogenous‘Learning’UniversityIf we translate the methodological interaction-ist approach to the changing role of universities in an evolutionary innovation system,it follows that universities not only respond to changes of the institutional environment(government policies,business demands or changes in scientiﬁc paradigms)but universities also inﬂuence the institutions of the selection envi-ronment by their strategic,scientiﬁc and entre-preneurial actions.Moreover,these actions inﬂuence–and are inﬂuenced by–the actions of other economic agents as well.So,instead of a one-way rational response by universities to changes(as in reductionist approach),they are intertwined in those processes of change.So, universities actually function as an endogenous source of change in the evolution of the inno-vation system.This is(on an ontological level) a fundamental different view on the role of universities in innovation systems from the existing policy NIS frameworks.In earlier empirical research,we observed that universities already effectively function endogenously in evolutionary innovation system frameworks;universities as actors (already)develop new knowledge,innovate and have their own internal capacity to change,adapt and inﬂuence the institutional development of the economic system(e.g., V an der Steen et al.,2009).Moreover,univer-sities consist of a network of various actors, i.e.,the scientists,administrators at technology transfer ofﬁces(TTO)as well as the university boards,interacting in various ways with indus-try and governments and embedded in various ways in the regional,national or inter-national environment.So,universities behave in an at least partly endogenous manner because they depend in complex and often unpredictable ways on the decision making of a substantial number of non-collusive agents.Agents at universities react in continuous interaction with the learn-ing activities ofﬁrms and governments and other universities.Furthermore,the endogenous processes of technical and institutional learning of univer-sities are entangled in the co-evolution of institutional and technical change of the evo-lutionary innovation system at large.We propose to treat the learning of universities as an inseparable endogenous variable in the inno-vation processes of the economic system.In order to structure the endogenization in the system of innovation analysis,the concept of the Learning University is introduced.In thenext subsection we discuss the main character-istics of the Learning University and Section5discusses the learning university in a dynamic,evolutionary innovation system.An evolution-ary metaphor may be helpful to make theuniversity factor more transparent in theco-evolution of technical and institutionalchange,as we try to understand how variouseconomic agents interact in learning processes.4.2Characteristics of the LearningUniversityThe evolution of the involvement of universi-ties in innovation processes is a learningprocess,because(we assume that)universitypublic agents have their‘own agenda’.V ariousincentives in the environment of universitiessuch as government regulations and technol-ogy transfer policies as well as the innovativebehaviour of economic agents,compel policymakers at universities to constantly respondby adapting and improving their strategiesand policies,whereas the university scientistsare partly steered by these strategies and partlyinﬂuenced by their own scientiﬁc peers andpartly by their historically grown interactionswith industry.During this process,universityboards try to be forward-looking and tobehave strategically in the knowledge thattheir actions‘inﬂuence the world’(alsoreferred to earlier as‘intentional variety’;see,for instance,Dosi et al.,1988).‘Intentional variety’presupposes that tech-nical and institutional development of univer-sities is a learning process.University agentsundertake purposeful action for change,theylearn from experience and anticipate futurestates of the selective environment.Further-more,university agents take initiatives to im-prove and develop learning paths.An exampleof these learning agents is provided in Box1.We consider technological and institutionaldevelopment of universities as a process thatinvolves many knowledge-seeking activitieswhere public and private agents’perceptionsand actions are translated into practice.3Theinstitutional changes are the result of inter-actions among economic agents deﬁned byLundvall(1992)as interactive learning.Theseinteractions result in an evolutionary pattern3Using a theory developed in one scientiﬁc disci-pline as a metaphor in a different discipline mayresult,in a worst-case scenario,in misleading analo-gies.In the best case,however,it can be a source ofcreativity.As Hodgson(2000)pointed out,the evo-lutionary metaphor is useful for understandingprocesses of technical and institutional change,thatcan help to identify new events,characteristics andphenomena.Volume17Number42008©2008The AuthorsJournal compilation©2008Blackwell Publishing。

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细胞分子生物学文章第十卷（2005），711-719 pl2005.7.15寄出200510.6收到脂质体：一项先进制造技术的概述新西兰，北帕默斯顿，专用邮袋11222，梅西大学Riddet中心，M.REZA MOZAFARI摘要：近几十年来，脂质体作为生物膜的理想模型，也是药物、诊断、疫苗、营养物和其他生物活性剂的有效载体，引起了广泛关注。

在不同背景下研究者们对脂质体学领域的文献报道广泛地不断地增加，这表明这一领域引人入胜。

自从大约40年前脂质体被介绍到科学界，许多技术和方法在或大或小的脂质体制造规模上得到发展。

这篇文章将在大体上提供脂质体制备方法优缺点的概览，特别强调在我们实验室开发的加热法，作为一种脂质囊泡快速生产的模式技术。

关键词：载体系统，加热法，脂质囊泡，脂质体学，制造技术引言脂质体科学技术是一个正在飞速发展的科学，举几个例子，它用于诸如药物递送，化妆品，生物膜的结构和功能，探索生命起源等领域。

这是由于脂质体有一些有利的特性，例如，它不仅能包含水溶性药物也能包含脂溶性药物，在体内识别特定靶向位点，在流动性、大小、电荷、层数的方面具有多样性。

脂质体作为生物膜模型的应用限于在实验室中研究，它们在生物活性剂的包载和递送的成功应用不仅取决于脂质体载体可以达到预期目的的优越性的示范，还取决于技术和经济可行性的规划。

对于递送应用，脂质体配方应该具有高包封率，窄粒度分布，持久稳定性和理想的释放特性（根据预期的应用）。

这些要求制备方法有产生脂质体的可能性，且脂质体可采用多种成分分子，例如：脂质/磷脂可提高脂质体稳定性。

除了上述特性，对于蛋白质、核酸之类敏感的分子/化合物的递送，脂质体也应该能保护复合制剂，防止其退化。

尽管在脂质体上进行了大量的研究开发工作，但只有少数脂质体产品已被批准为人类使用至今。

这也许有许多原因：一些脂质体配方的毒性，分子和化合物在脂质体中的低包封，脂质体载体的不稳定性，脂质载体的不稳定性，特别是大尺度的脂质体生产成本高。

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Agents, Believability and Embodiment in Advanced Learning EnvironmentsIntroduction to a Panel DiscussionAnton NijholtUniversity of Twente, PO Box 2177500 AE Enschede, the Netherlandsanijholt@cs.utwente.nlAbstractOn the world-wide-web we see a growing number of general HCI interfaces, interfaces to educational or en-tertainment systems, interfaces to professional environ-ments, etc., where an animated face, a cartoon character or a human-like virtual agent has the task to assist the user, to engage the user into a conversation or to educate the user. What to say about the effects a human-like agent has on a student’s performance? We discuss agents, their intelligence, embodiment and interaction modalities. In particular we introduce viewpoints and questions about roles embodied agents can play in educational environments.1. IntroductionThis short paper is meant to introduce the issues that underlie the introduction of embodied agents in learning environments. Embodied agents appear in different forms. We can just have a simple 2D talking face or a cartoon-like human figure on a web page or in a separate window making suggestions to the user, a desktop virtual reality environment where we have 3D avatars representing tutors or other learners or we can have an immersive Cave-like virtual reality environment where we can really experience interaction with a tutor, with objects and with other learners.2. Systems, Agents and IntelligenceBefore zooming in on some examples of embodied agents in learning environments it is useful to say some-thing about the impact of computer systems in general, the impact of intelligent agent-like systems, the impact of believability, trustworthiness, emotion and personality modeling, and the impact of animated and life-like char-acters on the behavior of a human user of the interface or system.Systems as Social Actors: Experiments have shown that when users engage with computer systems they at-tribute human characteristics to these systems. Not much intelligence has to be included in order to see this effect. Humans engage in social behavior toward computers. Studies and experiments show that users apply politeness norms to computers, they respond to computer personali-ties in the same way they respond to human personalities, they are susceptible to flattery and they apply gender stereotypes to computers (see e.g. Reeves & Nass [13]).Intelligent Software Agents: When we really aim at making a system more intelligent, as, for example, in intelligent tutoring systems, we may expect that apart from influencing the social behavior of the student toward the system, we have of course possibilities to steer a student’s learning behavior, but also the student’s cooperative, or motivational attitude can be influenced. This is even more true when we present the system or the interface to the system as some kind of actor (tutor) that knows, that reasons, that communicates and that displays consistent behavior in its environment. Agent technology is a research field that emerged in the 1990’s and that can be considered as a field in which exactly such actors have to be developed, although not necessarily in the context of human-computer interaction. There have been a lot of discussions about what is exactly an agent and is not every computer program an agent. Some researchers explain that the answer is no (see e.g. Franklin & Graesser [4]), other researchers have a pragmatic view: does the agent point of view helps us to develop ideas, helps us to become aware of possibilities and does it help us to communicate ideas. We don’t think it is wise to underestimate the value of a good metaphor. Without going into details and especially controversial details, we want to mention properties of software modules that are generally assumed to be present before being allowed to talk about them as agents: autonomy, reactive and proactive behavior and the ability to interact with other agents (or humans). For an agent to act appropriately in a domain it has been useful to have an internal model in which we distinguish beliefs (what the agent regards to be true, this may change in time), desires (the goals the agent has committed himself to) and intentions (short-term plans that it tries to execute).Interacting Personalities: Software shows itself to the (human) user in the interface. This interface, whatever its form, may aggressively push information, it may try to pull information from a user, it may try to sell, to cheat, to seduce, to persuade, to flatter, etc. We need to mention the notions of believability, trustworthiness and emotions. Believability is an important notion that has been emphasized by Joseph Bates in the early 1990’s. An agent is called believable, if some version of a personality shows in the interaction with a human. It does not neces-sarily mean that the agent is embodied although it is certainly true that in designing believable agents much can be learned from character-based artists that develop animate characters. In (Loyall [8]) requirements for be-lievable agents have been investigated and attempts are given to fulfill these requirements. The main requirements are: personality, emotion, self-motivation, change, social relationships, consistency of expression and, finally, a list of properties that help to create the illusion of life in an agent (reactive and responsive, situatedness, appearance of goals, etc.). Trustworthiness is an other issue. How does a system show its good will and does it build credibility? In a text-based system face-to-face interaction cues (facial expressions, gestures, intonation, posture and gaze) are not available.Embodied Agents: Now that we have discussed so-cial, intelligent and believable behavior, it is time to con-sider the role of embodiment. Do we need embodiment to display the previously mentioned kinds of behaviors and when we assume embodiment of an agent, what is the extra impact of this behavior, how does this show in the agent’s activities, and not less important, how can we use the embodiment as a multimedia modality to show in-formation (e.g., the sequence of actions to handle complex machinery), to support verbal communication, and to display nonverbal behavior of the agent? Several authors have investigated nonverbal behavior among humans and the role and use of nonverbal behavior to support human-computer interaction. See e.g. (Cassell [2]) for properties and impact of embodied conversational agents (with an emphasis on coherent facial expressions, gestures, intonation, posture and gaze in communication).3. Agents, Embodiment and LearningIn the previous sections we surveyed developments in computer science (artificial intelligence, agent technology and graphics) that make it possible to talk about software modules and use them in application domains as agents and as embodied agents that can take the form of a 2D or 3D talking face or an animated human-like body. Such agents are finding their way in learning environments. Are they pushed by the technology, are learners – having become accustomed to them in computer games – asking for them or do we have careful considerations about their use and careful experiments that evaluate their effectiveness in learning environments? And when we agree they can be effective, where and how to use them in a continuum between a constructivist and an instructionist approach? How should be their relation with teaching strategies such as tutoring, coaching, cognitive apprenticeship or Socratic dialogue? These questions need to be asked and answered. When we look at the current literature and survey the systems that have been designed and implemented in such a way that they allow experiments, two observations can be made First of all, several impressive research systems employing animated pedagogical agents have been built (see section 4). Sec-ondly, and not surprisingly, we must observe that the abundance of ideas and technological possibilities, the multi-disciplinarity that is required and the lack of re-sources to have really comprehensive research programs that involve both advanced technology and large-scale empirical study, have not made it possible to give text-book-like decisive answers on how to use animated peda-gogical agents, in what situations, and to achieve what goals. Nevertheless, with the observations on the abilities of animated agents in the previous section it is not diffi-cult to predict that researchers will employ these agents in their systems.Animated pedagogical agents have particular compe-tence. As a real teacher they can show how to manipulate objects, they can demonstrate tasks and they can employ gesture to focus attention. As such they can give more customized advice in a rich learning environment, probably leading to improved problem solving by the student. Lester et al. [7] use the term deictic believability for agents that are situated in a world that they co-inhabit with students and in which they use their knowledge of the world, their relative location and their previous actions to create natural deictic gestures, motions, and utterances. There are more possibilities using animated agents to broaden the bandwidth of tutorial communication. When the agents are sufficiently expressive they can increase the student’s enjoyment of the learning experience and the student’s motivation. An agent can be designed for emotive believability, showing contextually appropriate facial expressions and expressive movements, not only to support and enhance the communication but also emotion (appreciation, enthusiasm, concern, disagreement, etc.) appropriate to the context. Encouragement, avoiding a student’s frustration, conveying enthusiasm and making learning more fun are benefits that are mentioned when discussing the possibility to endow agents with emotive behavior and hence making it an interacting personality. As a result, students may spend more time using the (constructivist) learning environment, but also, as has been reported, there is a positive effect on student’s perception of their learning experience. Such animatedagents stimulate reflection and self-explanation and have a strong motivational effect. In Moreno et al. [9] a detailed report, including results on retention (recall of factual knowledge), problem-solving transfer (the ability to solve new problems based on similar principles) and motivation and interest, obtained by comparing learning in an animated agent-based environment with learning in a computer-based text environment, can be found.4. Embodied Agents: Learning EnvironmentsWe mention some projects that we think are illustrative for the work on embodied agents in educational envi-ronments. We would like to mention the Soar Training Expert for Virtual Environments (STEVE, see Johnson et al. [6]) as an example of an advanced immersive 3-D learning environment with a virtual animated agent. In STEVE an animated, 3D, pedagogical agent gives in-struction in procedural tasks in an immersive virtual en-vironment. STEVE is able to demonstrate and explain a sequence of actions, monitor the movements and ma-nipulations of the user, comment on them and suggest possible continuations to complete a task. In the JACOB project [3] a 3D agent walks and grasps objects in a par-ticular order to help students how to solve the problem of the Towers of Hanoi. The student interacts by performing actions as well as by using natural language. The ‘Design-A-Plant’ project [7] is an interactive learning environment in which Herman the Bug acts as an agent that helps student to learn about plants and their environment. Especially this project has been subject of careful experiments concerning constructivist learning yielding very interesting results. AutoTutor [5,12] is another tu-toring system that uses NL dialogues for tutoring. The dialogue is delivered using an animated agent. Intonation and facial expressions of the talking head have been in-corporated in order to present affective responses.5. Conclusions and DiscussionWe surveyed developments in computer science (artificial intelligence, agent technology and graphics) that make it possible to talk about software modules and use them in application domains as agents and even embodied agents that can take the form of a 2D or 3D talking face or an animated human-like body. Such agents are finding their way in learning environments. Are they pushed by the technology, are learners – having become accustomed to them in computer games – asking for them or do we have careful considerations about their use and careful ex-periments that evaluate their effectiveness in learning environments? And when we agree that they can be ef-fective, where and how to use them in a continuum be-tween a constructivist and an instructionist approach? How should be their relation with teaching strategies such as tutoring, coaching, cognitive apprenticeship or Socratic dialogue? These questions need to be asked and answered. When is it worth the trouble? Human-like agents raise expectations. The learner expects human-like concern, social and competent behavior whatever he or she as learner is doing, etc. Isn’t possible to increase the effect of computer-based learning environments without getting involved with creating models of emotion and personality of artificial embodied agents? Enough topics and approaches have been mentioned here to make a fruitful discussion possible.6. References[1] A.L. Baylor. Beyond butlers: Intelligent agents as mentors. Journal of Educational Computing & Research. To appear. [2] J. Cassell et al. (eds.). Embodied Conversational Agents. MIT Press, Cambridge, 2000.[3] M. Evers & A. Nijholt. Jacob, an agent for instruction in VR environments. Education and Information Technologies, T.A. Mikropoulos & I.D. Selwood (eds.), to appear.[4] S. Franklin & A. Graesser. Is it an agent, or just a program? 3rd Intern. Workshop on Agent Theories, Architectures, and Languages, Springer, Berlin, 1996.[5] A.C. Graesser et al. AutoTutor: A simulation of a human tutor. J. of Cognitive Systems Research, 1: 35-51, 1999.[6] W. L. Johnson et al. Animated Pedagogical Agents: Face-to-Face Interaction in Interactive Learning Environments. Intern. J. of Artificial Intelligence in Education (2000) 11, 47-78.[7] J.C. Lester et al. Deictic and emotive communication in animated pedagogical agents. In: Cassell et al., 2000.[8] A.B. Loyall. Believable Agents: Building interactive Per-sonalities. CMU-CS-97-123, Carnegie Mellon University.[9] R. Moreno. Life-like pedagogical agents in constructivist multi-media environments. EDMEDIA 2000, 741-746.[10] A. Nijholt & H. Hondorp. Towards communicating agents and avatars in virtual worlds. Proc. EUROGRAPHICS 2000, A. de Sousa & J.C. Torres (eds.), August 2000, Interlaken, 91-95 [11] A. Paiva & C. Martinho. A Cognitive Approach to Affecti-ve User Modeling. Proc. Affect in Interactions, Siena, 1999. [12] N. Person et al. The integration of affective responses into AutoTutor. In [11].13] B. Reeves & C. Nass. The Media Equation. New York, Cambridge University Press, 1996.。