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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Evidence ofself-attribution bias from frequent acquirers. Unpublished working paper, Henry B, TippieCollege of Business, University of Iowa.○ Doukas, J., Petmezas, D., 2006. Acquisitions, overconfident managers and self-attributionbias.Unpublished working paper, Department of Finance, Graduate School of Business, OldDominion University.○ Malmendier, U., Tate, G., 2005. CEO overconfidence and corporate investment. Journal ofFinance 60, 2661-2700.10. The Board of DirectorsWeisbach, M., 1988, Outsider directors and CEO turnovers, Journal of Financial Economics 20, 431-460.Yermack, D., 1996, Higher market valuation of companies with a small board of directors, Journal of Financial Economics 40, 185-211.Rosenstein, S. and J. Wyatt, 1997, Inside Directors, Board Effectiveness, and Shareholder Wealth, Journal of Financial Economics 44, 229-250.Hermalin, B. and M. Weisbach, 1988, The determinants of board composition, Rand Journal of Economics 19, 589-606.Warner, J., R. Watts, and K. Wruck, 1988, Stock prices and top management changes, Journal of Financial Economics 20, 461-492.Johnson, Bruce, Robert Magee, Nandu Nagarajan and Henry Newman, 1985, An Analysis of the Stock Price Reaction to Sudden Executive Deaths: Implications for the Management Labor Model, Journal of Accounting and Economics 7, 151-174.11. Talent, Incentives, and Executive CompensationsBaumol, W., 1990, Entrepreneurship: Productive, Unproductive, and Destructive, Journal of Political Economy 98, 893-921.Murphy, K., A. Shleifer, and R. Vishny, 1991, The allocation of talent: Implications for growth, Quarterly Journal of Economics, 503-530.Jensen, Michael, and Kevin Murphy, 1990, Performance Pay and Top Management Incentives, Journal of Political Economy 98, 225-264.Core, John, Robert Holthausen and David Larcker, 1999, Corporate Governance, Chief Executive Officer Compensation, and Firm Performance, Journal of Financial Economics 51, 371-406.Rose, Nancy, and Andrea Shepard, 1997, Firm Diversification and CEO Compensation: Managerial Ability or Executive Entrenchment? RAND Journal of Economics 28, 489-514. 12. Corporate RestructuringDesai, H. and P. Jain, 1999, Firm performance and focus: Long-run stock market performance following spinoffs, Journal of Financial Economics 54, 75-101.Daley, L., V. Mehrotra and R. Sivakumar, 1997, Corporate focus and value creation: Evidence from spinoffs, Journal of Financial Economics 45, 257-281.Chen, P., V. Mehrotra, R. Sivakumar, and W. Yu, 2001, Layoffs, shareholders’ wealth, and corporate performance, Journal of Empirical Finance 8, 171-199.Servaes, H., 1996, The Value of Diversification During the Conglomerate Merger Wave, Journal of Finance 51, 1201-1225.Berger, P. and E. Ofek, 1996, Bustup Takeovers of Value-Destroying Diversified Firms,Journal of Finance 51, 1175-1200.Lamont, O. A. and C. Polk, 2002, Does diversification destroy value? Evidence from the industry shocks, Journal of Financial Economics 63, 51-77.Gillian, S., J. Kensinger, and J. Martin, 2000, Value creation and corporate diversification: the case of Sears, Roebuck & Co., Journal of Financial Economics 55, 103-137.Cusatis, P., J. Miles and J. Woolridge, Some new evidence that spinoffs create value, in NCF, 592-599.Mansi, S and D. M. Reeb, 2002 Corporate diversification: What gets discounted, Journal of Finance, 2167-2183Graham J. R., M. L. Lemmon and J. G. Wolf, 2002, Does corporate diversification destroy value? Journal of Finance , LVII, 695-720.Schoar, A, 2002, Effects of corporate diversification on productivity, Journal of Finance, LVII, 2379-2403.Campa, J. M. and S. Kedia, 2002, Explaining the diversification discount, Journal of Finance, 1731-1762.Aggarwal, R. and A. A. Samwick, 2003, Why do managers diversify their firms? Agency reconsidered. Journal of Finance, LVIII, 71-118.13. Risk ManagementGuay, W.R., 1999, The impact of derivatives on Þrm risk: An empirical examination of new derivative, Journal of Accounting and Economics 26 , 319-351Allayannis, G., and Weston, J.P., 2001, The use of foreign currency derivatives and firm market value, The Review of Financial Studies 14, 243-276.Guaya, W., and Kothari, S.P., 2003, How much do firms hedge with derivatives? Journal of Financial Economics 70, 423–461.Tufano, P., 1996, Who manage risks: An empirical examination of risk manage practices in gold mining industry, The Journal of Finance, 1097-1137.。

翻译——精选推荐

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。

2007 Nature Predicting antibiotic resistance

O p i n i O nPredicting antibiotic resistanceJosé L. Martínez, Fernando Baquero and Dan I. AnderssonAbstract | The treatment of bacterial infections is increasingly complicated because microorganisms can develop resistance to antimicrobial agents. This article discusses the information that is required to predict when antibiotic resistance is likely to emerge in a bacterial population. Indeed, the development of the conceptual and methodological tools required for this type of prediction represents an important goal for microbiological research. To this end, we propose the establishment of methodological guidelines that will allow researchers to predict the emergence of resistance to a new antibiotic before its clinical introduction.Conventional scientific wisdom dictates that evolution is a process that is sensitive to many unexpected influences, and is therefore essentially unpredictable. However, much of the microbiological research that is applied to public healthis based on the implicit belief that microbial variation, which leads to infectious diseases and antibiotic resistance (AR), is predictable and therefore can be controlled before it can cause clinical problems. The collection of genomic and proteomic data from microorganisms, the construction of microbial phylogenies, mathematical modelling and the development of advanced systems biology approaches1,2 (see glossary section) to analyse the biological interactions that lead to AR are based on the principle that predicting (and preventing) the emergence and dissemination of AR bacteria is possible. Preventing AR isan exercise in predictive evolution that requires an ability to predict the evolutionary trajectories of each relevant microbial pathogen. Current studies on AR tendto be based on the qualitative molecular analysis of the genetic elements and functions involved in expression of the AR phenotype. However to predict the emergence and dissemination of resistance, quantitative genetic studies based on the molecular phylogeny and the epidemiology of the genetic elements that are responsible for the AR phenotype3,4 and of the bacterial populations are required.The process by which organisms acquire AR can be operatively considered according to a hierarchical scheme (FIG. 1). First, it is necessary to consider the potential for becoming resistant — that is, the universe of all possible mutations and genes that could confer resistance. Here even small reductions in antibiotic susceptibility have to be considered because they might serve as intermediate stages in the path to acquiring highlevel resistance5. Second, it is necessary to consider the probability that such an adaptive event will occur.In other words, even when armed with knowledge of all the genetic elements that can potentially contribute to AR, our ability to predict emergence is incomplete because not all of these processes will occur with the same probability. These probabilities reflect the differences in the mutation or recombination rates of different domains of the bacterial genome or between genomes, the network of interactions between the genomes of pathogenic bacteria and other genomes (as well as with plasmids, bacteriophages and transposons that carry resistance genes), and changes in microbial physiology (fitness costs) that result from the acquisition of an AR phenotype6,7. Third, it is necessary to take into account the processes that actually result in the expression, selection, establishment and spread of a resistance trait. Thus, an accurate prediction of evolutionary trajectories requires better measurements and a more complete understanding of selection pressures, environmental variations and evolutionary constraints — in other words, what is the ecological dimension of AR? As the development and spread of AR in and between microbial populations represents an excellent experimental model for testing evolutionary principles in real time, each of these considerations requires an understanding of the basic mechanisms of bacterial evolution. An evolutionary analysis of AR also has practical implications for human health8,9, and a better understanding of the mechanisms involved in the emergence, dissemination and maintenance of AR among bacterial pathogens958 | DeCembeR 2007 | volume 5 /reviews/micro©2007Nature Publishing GroupSequenced genome/a Potentialityb Probabilityc ActualityFigure 1 | Conceptual framework for predicting antibiotic resistance in a bacterial population. a | The evolutionary process leading to antibi-otic resistance (AR) can be dissected according to a hierarchical scheme. On top, we have the ‘universe’ (or the ‘resistome’), which comprises of all the genetic elements that can potentially confer resistance. These include AR genes (purple) from the genomes of organisms that colonize different environments and that can be transferred by horizontal gene transfer (HGT) to pathogenic bacteria, and positions in the genome (blue circles) that could confer resistance after mutation. Genes that contribute to the intrinsic resistance of a pathogenic bacterium are also present (green). Note that all the mutations in a (sequenced) genome that can lead to resistance can be searched using the appropriate tools, a situation that is referred to as a closed scenario. By contrast, it is impossible to fully describe all the AR genes that are potentially present in different environ-ments, a situation that is referred to as an open scenario. b | The potential elements of resistance have different probabilities for eventual selection. First, a genetic event must occur. In the case of mutation, it has been found that mutation rates can increase under given conditions, in some strains and in some specific chromosomal locations. Gene amplification also increases the probability of mutation (1). In the case of AR genes, integra-tion into transfer elements (2,3) facilitates acquisition by pathogenic bacteria through HGT. HGT is favoured by integration of AR genes into broad-host vectors. The simultaneous transfer of several AR genes, acquired from one or several different organisms and integrated into the same gene-transfer element, increases the probability of selection (3). Fitness costs due to the presence of an AR gene, to the transfer element itself or to a mutation leading to AR reduce the probability of AR becoming established in the bacterial population. The probability of acquiring a given AR gene is zero if there is no ecological connection between the pathogen and the donor (4). The black arrows indicate the acquisition of AR genes by gene-transfer elements. The grey arrows indicate the transfer of AR genes between microorganisms. c | As a result of this evolutionary process only some of all potential AR elements, either mutations (red circle) or genes transferred by HGT, are found in the population (so-called actual-ity). Even low-probability AR mechanisms can be selected if the antibiotic selection pressures are strong enough.is an urgent public health priority. In this opinion, we propose the establishment of a methodological blueprint that will allow researchers to predict the emergence of resistance to a new antibiotic before the antibiotic is clinically introduced (BOX 1), an approach that is more relevant and complete than those currently in use.The resistance genesTwo types of genes can be defined depending on the genetic event that is required for acquiring an AR phenotype: genes that are acquired by horizontal gene transfer (HGT) and genes that are already encoded in the bacterial genome and that can confer AR following mutation 10 or activation by mobile elements 11.Acquired genes. Genes that are acquired through HGT and that confer resistance to human pathogens usually have an environmental, nonclinical origin 12. The diversity of microbial species in natural environments, including those thatencode AR genes, is high. The vast majority of microorganisms have never beencultivated, and those for which genomes have been sequenced only number in the hundreds. As such, it is difficult to predict how many AR genes are present in any given environment. In contrast to the scenario of mutationdriven AR (FIG. 1), accurately predicting the presence and ultimate fate of resistance genes in their natural environments and the possibility of their acquisition by pathogenic bacteria is currently impossible. So, efforts need to focus on the description of those resistance genes that have the highest probability of being acquired by pathogenic microorganisms.The term ‘resistome’13 has been coined to include all AR genes that are present in the environment and are potentially transferable to pathogenic bacteria, an idea that is based on the discovery that a large number of culturable antibioticproducing microorganisms can inactivate several antibiotics 14. To take this concept further, more powerful tools, including bioinformatics approaches for data mining and metagenomics , need to be harnessed to describe novel AR genes present in environments that are ecologically compatible with pathogenic bacteria 15,16. The usage of sequencebased and functional metagenomics 17 to screen for AR genes in various environments is labourintensive. Therefore, priorities need to be established, based primarily on the probability that an AR gene from a given environment might be transferred to pathogenic bacteria. The main contributing factor is the probability that in a given environment the microbial species harbouring the AR gene(s) coexists with the pathogenic microorganism, or with organisms that belong to itsgeneexchange community. For instance, extreme environments rarely host human pathogens, so the search for AR genes in these environments is of low priority. by contrast, the study of mesophilic environments (including the rhizosphere, sewage facilities, and animal and human microbiotas), in which human pathogens can proliferate, should have higher priority. Selection for, and further transfer of, AR genes from human commensals might be particularly relevant for topical and orally administered antimicrobial agents.nATuRe RevIewS | miCrobiologyvolume 5 | DeCembeR 2007 | 959© 2007Nature Publishing GroupThe increasing number of sequenced bacterial genomes offers the opportunity for predicting the presence of AR genes in the genomes of different bacteria, based on homology to previously characterized AR determinants. As a proof of concept, a recent study analysed the presence of metalloβlactamases (mbls) in the genomes of 12 different Rhizobiales species18. Fiftyseven open reading frames (oRFs) were classified as potential mbls. Four of these were functionally analysed and one oRF was shown to be a functional mbl. This research demonstrates how bioinformatics tools, linked to functional analysis, can be powerful for establishing the presence of AR genes in sequenced bacterial genomes.Native genes. As described above, bioinformatics tools can help us to define horizontally acquired genes that are encoded by bacterial pathogens and that contribute to the AR phenotype. However, to identify genes that are not homologous to previously described AR elements and that contribute to mutationdriven AR a different approach is needed. one method that has proven to be useful is the highthroughput screening of insertion mutants that present altered susceptibilities to antibiotics19. A step forward in this approach was the use of a transposon with an outwardreading promoter that allows the identification of silent AR genes in the genomes of bacterial pathogens20.The evolvability of known AR genes towards developing resistance to new antibiotics should also be explored by, for example, a combination of DnA shuffling and errorprone PCR21–24. Prediction is currently possible because clinically useful antibiotics belong to a limited number of chemical families. mechanisms of resistance against one antibiotic are likely to have some activity against other compounds in the same family. The extendedspectrumβlactamases (eSbls) provide an example of this phenomenon. mutations in the genes that encode penicillininactivating enzymes (such as Tem, SHv and Rob) can also confer resistance to thirdgeneration cephalosporins, or inhibitors of βlactamases. Studies to predict evolution from classicto extendedspectrum enzymes typically include serialpassage experiments, challenging a bacterial population that harbours classic enzymes with novel antibiotics. The efficiency of these assays is improved by increasing the mutation rate of the challenged organisms, either by using mutagenic procedures or hypermutagenic strains25.Frequently the ‘efficient’ evolution of anenzyme requires more than one mutation; assuch, evolvability can involve several eventsoccurring in a stepwise process. Thus, theselection of the initial mutations that lead toreduced antibiotic susceptibility should becarried out at low drug concentrations26,27.The order of appearance of mutations inthe ancestor gene might be crucial to reachan efficient extendedspectrum enzyme,which highlights the importance of determining evolutionary trajectories28(FIG. 2).Although several mutations can result in asimilar phenotype (the universe of possiblemutations, as described earlier), only somewill be present in a clinical setting, whichhighlights the relevance of the ecosystemfor predicting clinical AR. besides usingclassic methodologies, we must developmodels that better reflect infection andtreatment conditions, thus mimicking thepharmacokinetics of the antibiotics. Forexample, the selection of bacterial mutantsis usually achieved by exposing culturesto a single antibiotic, whereas in hospitalpractice the same strain is often challengedwith several antibiotics. In vitro studies that960 | DeCembeR 2007 | volume 5 /reviews/micro©2007Nature Publishing GroupTimeabNature Reviews | Microbiology use fluctuating environments — challenging with alternate antibiotics — have had better predictive success 29. The usage of in vivo models of infection for tracking the evolution of bacterial hypermutator strains has also been helpful for identifying which AR mutants are most likely to emerge during treatment 30.Recent work also highlights the need to expect the unexpected. For example, research has revealed that a variant of a gene that encodes an aminoglycoside acetyltransferase can also modify and inactivate quinolones 31,32. These findings indicate that antibioticinactivating enzymes are not as specific as previously thought and that secondary minor activities might be important in the evolution towards novel, unexpected resistance phenotypes. This potential to evolve towards novel AR phenotypes is not limited to known AR enzymes. As such, the chemical structure of each new antibiotic needs to be thoroughly analysed to detect all the ‘critical chemical points’ that cannot be modified without eliminating antimicrobial activity, and a catalogue of biochemical reactions that might produce these modifications should be established. bioinformatics analyses of sequenced bacterial genomes and metagenomics studies could provide a catalogue of genes that encode enzymes that catalyse these reactions, and that could potentially inactivate the antibiotic. Structural and mutational analysis of these enzymes docked to potential substrates in conjunction with evolvability challenges and selection experiments will prove helpful in the prediction of novel enzymatic activities.Evolution towards resistanceonce the ‘potential’ for acquiring AR has been defined, the next step is to analyse the probability that a genetic event, driven either by mutation 10 or by HGT 12, will result in an AR phenotype.Mutation. examples of an HGT process occurring during infection have been reported 33, including the generation of mosaic, βlactamresistant penicillin binding proteins in Streptococcus pneu-moniae 34 and Neisseria gonorrhoeae 35. However, the major cause of AR acquisition during the course of antibiotic treatment is mutation. The impact of mutation rates on the evolution of bacterial populations has only recently begun to be understood. The different members of a given bacterial species may havedifferent mutation rates 10, which are frequently clustered around discrete values 36 but that can form a broad continuum of mutation rates 37. Approximately 1% of all Escherichia coli isolates exhibit a hypermutation phenotype 38,39. The proportion of hypermutable organisms is much higher in chronic infections, including those associated with cystic fibrosis 40, chronic obstructive pulmonary disease 41 and chronic stomach infections 37.Hypermutable bacteria are significantly more likely to acquire an AR phenotype when compared to bacteria with lower mutation rates. Therefore, predictions that relate to the expression of a hypermutation phenotype by a particular pathogen are of value when assessing the likelyemergence of AR in that pathogen. Indeed, methods have been established that allow the identification of hypermutable bacteria 42,43. Although an understanding of the population biology of hypermutators is important for predicting AR, only a few bacterial pathogens have been analysed to establish the relevance of hypermutation during the infection process. The overall outcome from the few st udies that have been performed is that chronic infection, prolonged antibiotic therapy and, possibly, frequent hosttohost transmission favour the selection of hypermutators in a bacterial population, thus increasing the risk for the development of AR in that population.bacterial mutation rates can transiently increase depending on the environmental conditions 44. Starvation 45 and other situations that cause bacterial stress 46, including induction of the SOS response 47, all affect the mutation rate. Increased copy number of a gene, either because of its carriage on highcopynumber plasmids 10,48 or because of gene amplification 49,50, also increases the probability that the gene will mutate towards a new function that could be important in AR evolution. Finally, bacterial mutation rates that contribute towards the development of AR might vary considerably between in vitro and in vivo conditions. As such, to correctly assess the probability of resistance developing in a pathogen, it is important to perform mutationrate assays in the context of animal models and infected patients, in addition to laboratory growth media 51.Beyond HGT: modularization. HGT is the mechanism that underpins the acquisition of exogenous AR genes from the local environment. Studies on the molecular mechanisms of HGT have shown that theFigure 2 | Evolutionary trajectories in the emergence of resistance. Antibiotic resistance (AR) can be acquired by different mechanisms. However, the number of these mechanisms observed in pathogenic bacteria is limited and is determined by the ecological dimension of resist-ance. This limited number of mechanisms defines the evolutionary trajectories of microbial patho-gens to acquire resistance. a | In some scenarios, the acquisition of an AR phenotype (red) from a susceptible strain (green) results in a loss of bio-logical fitness. For some mechanisms that lead to AR, a mutation that compensates for this fitness loss can occur (white trajectory). The acquisition of an AR phenotype can also result in a strain that ultimately gains higher biological fitness than the susceptible parental strain (grey trajectory). b | When two mutations are needed for high-level AR resistance, the timing of when each mutation is acquired can be crucial. In this illustration, two mutations (red and yellow) confer a low-level resistance phenotype. If the yellow mutation occurs first, the population will remain at this low level of resistance without acquiring the addi-tional red mutation (black trajectory). If the red mutation occurs first, the population can main-tain this level of resistance (blue trajectory) or acquire the yellow mutation (green trajectory) to reach high-level resistance.nATuRe RevIewS | miCrobiologyvolume 5 | DeCembeR 2007 | 961© 2007Nature Publishing GroupglossaryEvolutionary trajectoriesAll the steps in evolution that are required to produce a given phenotype. Evolutionary trajectories depend on mutation, selection, drift, migration, ecological constraints and the previous history of the organism.EvolvabilityThe ability of an organism or a gene to evolve and change its genotype and, consequently, the phenotype it encodes. FitnessThe capability of an individual to survive and reproduce in a given ecosystem. Reproductive fitness is often measured as growth rate. Fitness is measured in comparison to other potential competitors.HypermutationA mutation rate that is significantly higher than that observed for most members of a bacterial species. IntegronA genecapture unit that is present in plasmids, chromosomes and transposons. It contains an integrase that allows the capture of gene cassettes flanked by conserved repetitive sequences, an integration site and a strong promoter that allows expression of the genes that are present in the cassettes.MetagenomicsMethodologies developed to describe all of the genetic elements that are present in a given ecosystem. T ypically these methodologies are based on nonculture procedures.ModularityAn attribute of a system that can be decomposed into a set of repeated, conserved cohesive entities that are loosely coupled.PersistenceA noninherited trait that is exhibited by a subpopulation of bacteria, characterized by slow growth coupled with an ability to survive antibiotic treatment.Phenotypic resistanceResistance that is not the consequence of a genetic event (mutation, horizontal gene transfer), but rather that is caused by the cells being in a particular physiological state. SOS responseA specific response to DNA damage that involves the blocking of DNA replication and cell division as well as the induction of genes that are involved in DNA repair, recombination and mutation.Synthetic biologyAn area of research that is based on the combination of biological modules to buildup novel biological functions and systems. In some aspects it is complementary to systems biology.System biologyThe study of the interactions between the components (modules) of biological systems that has the aim of understanding these systems as a whole, using quantitative models.elements involved in this process have a modular structure. Indeed, multiAR, due to the concerted acquisition of several different AR genes, is the result of combinatorial genetic evolution4,52. Four types of modules involved in the acquisition of AR can be described4: operative modules encode the basic, selectable function (in this case, AR genes); combinatorial modules can recombine, integrate or excise other modules (for instance, recruiting AR genes into integrons);translocative modules encode elements that mediate the transfer of other modules and multimodular structures to different locations in the same or between different DnA molecules (as transposons); and dispersive modules function to transfer modules from one organism to another (as plasmids or conjugative transposons).This modularity allows a nested evolutionary process for HGT. existence of a recruiting element (for example, a recombinase) allows the incremental addition of new operative modules to a DnA region. As the incoming DnA frequently contains new, interactive sequences, the process of module accretion increases the probability of recruiting new modules, making these DnA regions highly recombinogenic and ‘module promiscuous’. The cumulative collection of functional (AR) traits in thesemultimodular structures (integrons, transposons and plasmids) is the consequenceof such nested evolution. Consequently,the entire multimodular structure canfunction as a single transmissible element.The spread of these structures is favouredby environmental conditions — such asbacterial stress, which triggers the SoSsystem and thereby enhances HGT53 — andby the integration of these structures intobroadhostrange transmissible elements.whether or not bacterial stress due toanthropogenic pollution also favours HGTin natural environments54 is unknown andis the subject of ongoing research.Can the process of modularizationthat leads to the development of AR bepredicted? not every mobile element isequally distributed in every bacterial cloneof a species55. Furthermore, transposons,integrons and AR genes do not insert intoeach type of plasmid, transposon andintegron, respectively, at the same frequencies56–59. Finally, not every plasmid occurswith equal frequency in different bacterial species or subspecies. For instance,although there are several plasmids witha potential role in AR transmission, themore prevalent types of plasmids found inthe enterobacteriaceae have not changedsince the advent of the antibiotics era60.Prediction of AR dissemination that is dueto the process of modularization could bepossible because the number of transmissible elements, as well as the combinationof modules that is allowable in each ecosystem/bacterial species, might be low.The observed specific combinations ofmodules can be the result of two differentevolutionary processes. First, randomcombinations between all modules arepossible, and selection of the most adaptivepermutations will provide the observednonrandom associations. Some modelssuggest that even in the absence of a clearselective advantage, modularity mightincrease through the formation of newfunctionalities under nearneutral selectiveprocesses61. In this case, the fixation of asuccessful multimodular arrangement willbe due to positive directional selection asa result of the small benefits provided bythe functional interactions among a limitednumber of modules. Second, randomcombinations among all modules are notallowed because only some associationsbetween modules are possible owing tostructural and/or functional constraints. Inthis case, research on the rules that governthe interactions between modules is neededto predict the emergence of multimodularstructures. using this genome system architecture perspective62, it should be possibleto predict how identical modules combinein different ways in different geneticelements, allowing the construction ofcomplex multimodular arrangements63–65.The development of quantitative modelsof HGTelement evolution will gain fromthe experience of systems biology, whichfocuses on modelling network interactions between biological modules66. Theestablishment of comprehensive databases67that store searchable information on thefunction, structure and linkage patterns68of different modules would facilitate theprediction of how these modules mightcombine69,70 to achieve the selected bacterialphenotype. For such a prediction, bioinformatics approaches based on combinatoricsand fuzzylogic models, as well as the usageMicrobiologists need tobe extremely attentive to theemergence of novel mechanismsof resistance for the antibioticsthat are still widely effective inclinical settings.962 | DeCembeR 2007 | volume 5 /reviews/micro©2007Nature Publishing Group。

The Long and Short of Quality Ladders

NBER WORKING PAPER SERIESTHE LONG AND SHORT (OF) QUALITY LADDERSAmit KhandelwalWorking Paper 15178/papers/w15178NATIONAL BUREAU OF ECONOMIC RESEARCH1050 Massachusetts AvenueCambridge, MA 02138July 2009I am especially grateful to my dissertation committee, Irene Brambilla, Penny Goldberg and Peter Schott, for guidance and support. I have benefited from conversations with Steve Berry, Ray Fisman, Juan Carlos Hallak, David Hummels, Kala Krishna, Chris Ksoll, Frank Limbrock, Alex Mcquoid, Nina Pavcnik, Siddharth Sharma, Gustavo Soares, Robert Staiger, Catherine Thomas, Daniel Trefler, Chris Udry, Eric Verhoogen, David Weinstein, Jeffrey Weinstein, and various seminar participants. Special thanks also to Amalavoyal Chari. All errors are my own. The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.© 2009 by Amit Khandelwal. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source.The Long and Short (of) Quality LaddersAmit KhandelwalNBER Working Paper No. 15178July 2009JEL No. F1,F15,F16ABSTRACTPrices are typically used as proxies for countries' export quality. I relax this strong assumption by exploiting both price and quantity information to estimate the quality of products exported to the U.S. Higher quality is assigned to products with higher market shares conditional on price. The estimated qualities reveal substantial heterogeneity in product markets' scope for quality differentiation, or their "quality ladders.'' I use this variation to explain the heterogeneous impact of low-wage competition on U.S. manufacturing employment and output. Markets characterized by relatively shorter quality ladders are associated with larger employment and output declines resulting from low-wage competition. Amit KhandelwalGraduate School of BusinessColumbia UniversityUris Hall 606, 3022 BroadwayNew York, NY 10027and NBERak2796@The Long and Short(of)Quality Ladders∗Amit Khandelwal†Columbia Business School&NBERFirst Draft:November2006This Version:July2009AbstractPrices are typically used as proxies for countries’export quality.I relax this strong assumption by exploiting both price and quantity information to estimate the quality of products exported to the U.S.Higher quality is assigned to products with higher market shares conditional on price.The estimatedqualities reveal substantial heterogeneity in product markets’scope for quality diﬀerentiation,or their“quality ladders.”I use this variation to explain the heterogeneous impact of low-wage competition onU.S.manufacturing employment and output.Markets characterized by relatively shorter quality laddersare associated with larger employment and output declines resulting from low-wage competition. Keywords:Quality Ladders;Low Wage Competition;Quality Specialization;Product DiﬀerentiationJEL Classiﬁcation:F1,F15,F161IntroductionThe quality of products manufactured by countries aﬀects many economic outcomes in international and development economics.Past studies have consistently found that product quality inﬂuences cross-border trade;richer countries consume and export higher quality products than developing countries.1The ability ∗I am especially grateful to my dissertation committee,Irene Brambilla,Penny Goldberg and Peter Schott,for guidance and support.I have beneﬁted from conversations with Steve Berry,Ray Fisman,Juan Carlos Hallak,David Hummels,Kala Krishna, Chris Ksoll,Frank Limbrock,Alex Mcquoid,Nina Pavcnik,Siddharth Sharma,Gustavo Soares,Robert Staiger,Catherine Thomas,Daniel Treﬂer,Chris Udry,Eric Verhoogen,David Weinstein,Jeﬀrey Weinstein,and various seminar participants. Special thanks also to Amalavoyal Chari.All errors are my own.†Uris Hall,Room606,3022Broadway,New York,NY10027,email:ak2796@,website: /faculty/akhandelwal/.1Support for the demand-side explanation,initially posited by Linder(1961),has been shown by Hallak(2006)and Verhoogen (2008).Studies by Hummels and Klenow(2005)and Schott(2004)provide systematic evidence that richer countries exportof developing countries to transition from low-quality to high-quality products is therefore seen by some as a necessary(but certainly not a suﬃcient)condition for export success and,ultimately,economic development.2 In addition,quality upgrading features prominently in current debates about the role of international trade in driving wage inequality.But while this research suggests a positive association between quality upgrading and income per capita,Verhoogen(2008)and Goldberg and Pavcnik(2007)argue that quality upgrading also aﬀects the variance of the income distribution through changes in the relative demand for skilled labor. Quality specialization may therefore partly explain why inequality has risen in developing countries following trade liberalizations,in contrast to Stolper-Samuelson predictions.Diﬀerences in the quality space may also aﬀect how closely countries’products compete with one another and therefore have implications for the impact of trade on industry employment and output Leamer(2006).These studies stress the importance of understanding why product quality varies across countries and over time,and how it is inﬂuenced by policy.The challenge faced by this literature is that product quality is unobserved.Research in the international trade literature has attempted to deal with this problem by using prices(or unit values)to proxy for quality.This approach,while convenient,requires strong assumptions since prices could reﬂect not just quality,but also variations in manufacturing costs.For example,in1999, the U.S.imported Malaysian and Portuguese women’s trousers(HS6204624020)at unit values(inclusive of transportation and tariﬀduties)of$146and$371,respectively.If prices are assumed to proxy perfectly for quality,Malaysian trousers would possess about half the quality of Portuguese trousers.However,the annual wage in the apparel sector for Malaysia and Portugal was$3,100and$5,700,respectively(UNIDO, 2005).So the diﬀerence in unit values may instead be a reﬂection of these diﬀerent factor prices.Why would a consumer ever purchase expensive Portuguese trousers if they,in fact,possess lower quality?One explanation is that a fraction of consumers have a preference for the horizontal attributes of Portuguese trousers(for instance,the cut or color patterns).Indeed,U.S.consumers imported more than82,000dozens of Malaysian trousers compared to only865dozens from Portugal.Idiosyncratic preferences for products’horizontal attributes can therefore break the direct mapping from prices to quality that has been traditionally assumed.This paper estimates the quality of U.S.imports using a procedure that relaxes the strong quality-equals-price assumption.The quality measures are derived from a nested logit demand system,based on Berry(1994),that embeds preferences for both horizontal and vertical attributes.3Quality is the vertical higher quality products.Baldwin and Harrigan(2007),Hallak and Sivadasan(2009),Kugler and Verhoogen(2008)and Johnson (2009)also document the role of product quality in inﬂuencing production and trade patterns.2Kremer(1993)provides microeconomic foundations for the quality production function and its implications for economic development(see also Verhoogen(2008)and Kugler and Verhoogen(2008)).Endogenous growth models that highlight the importance of product quality include Grossman and Helpman(1991).Hausmann and Rodrik(2003),Rodrik(2006)and Hidalgo et al.(2007)highlight the importance of export quality for economic performance.3Other studies within international trade that use a nested logit structure include Goldberg(1995)and Irwin and Pavcnikcomponent of the estimated model and captures the mean valuation that U.S.consumers attach to an im-ported product.The procedure utilizes both unit value and quantity information to infer quality and has a straightforward intuition:conditional on price,imports with higher market shares are assigned higher quality.Importantly,the procedure requires no special data beyond what is readily available in standard disaggregate trade data.It is also easy to implement;here,I estimate separate demand curves for approxi-mately hundreds of manufacturing industries.Moreover,the procedure recovers quality at theﬁnest level of product aggregation available(for the U.S.data,this is the ten-digit HS level).4The inferred qualities indicate that developed countries export higher quality products relative to developing countries.Thisﬁnding is consistent with Schott(2004)who uses unit values to proxy for quality.However,the estimates also reveal substantial heterogeneity in product markets’scope for quality diﬀerentiation,or quality ladders,which I measure as the range of qualities within the product market.In markets with a larger scope for quality diﬀerentiation,or a“long”quality ladder,unit values are relatively more correlated with the estimated qualities.In these markets,prices appear to be appropriate proxies for quality.In contrast,prices appear to be less appropriate proxies for quality in markets with a narrow range of estimated qualities(“short”ladder markets).This provides suggestive evidence that expensive imports in short-ladder markets coexist with cheaper rivals due to horizontal product diﬀerentiation.That is,although the average U.S.consumer attaches a low valuation to the expensive import,there is a fraction of consumers who still value the product.This heterogeneity underscores the drawback in invoking the quality-equals-price assumption,particularly for products characterized by short quality ladders.I use this heterogeneity in ladder lengths to demonstrate that quality specialization has important implications for the bor market.The public’s fear of globalization is often rooted in the vulnerability or, to use Edward Leamer’s terminology,the contestability of jobs.According to Leamer,the contestable jobs are those where“wages in Los Angeles are set in Shanghai”(Leamer(2006),p.5).A recent study by Bernard, Jensen,and Schott(2006)provides evidence that the probability of U.S.plant survival and employment growth are negatively associated with an industry’s exposure to import penetration,particularly from low-wage countries.5However,while low-wage competition negatively aﬀects output and employment growth, the impact is heterogenous across industries.For instance,between1980and the mid-1990s,electronics (SIC36)experienced greater low-wage import penetration than fabricated metals(SIC34)but experienced (2004)although these papers do not focus on the quality of imported products.4An alternative procedure developed by Hallak and Schott(2007)relies on the similar intuition to infer countries’export quality to the U.S.,but their methodology prevents estimating quality at theﬁnest level of disaggregation due to data limitations.5Other studies studying the negative relationships between trade and employment include Sachs and Shatz(1994),Free-man and Katz(1991)and Revenga(1992).Bernard,Jensen,and Schott(2006)explicitly connect the relationship between employment and trade with low-wage countries,deﬁned as nations with less than5percent of U.S.per capita GDP.I use their deﬁnition of low-wage countries in this paper(see Table1).a smaller employment decline.6Using a simple model developed in Section2,I demonstrate that the impact of low-wage competition on U.S.industries will vary with its quality ladder.My argument is related to a body of research that reject standard model predictions of factor price equalization(FPE).7These studies show that if countries inhabit diﬀerent cones of(quality)diversiﬁcation,with developing countries exporting low-quality products,then developed countries will be insulated from movements of wages in developing countries.However,if markets vary in their scope for quality diﬀerentiation,developed countries will experience heterogeneity in their exposure to developing countries.In long-ladder markets,developed countries can insulate themselves from the South by using comparative advantage factors(e.g.,skill,capital and/or technology)to specialize atop the quality ladder.In short-ladder markets,however,developed countries will be directly exposed to Southern competition because quality upgrading is infeasible.Thus,a market’s scope for vertical diﬀerentiation is important for understanding Leamer’s notion of contestable jobs.Iﬁnd robust support for this hypothesis by matching U.S.industry data and import competition to quality ladders constructed from the estimated qualities.Consistent with Bernard et al.(2006),Iﬁnd that industry employment is negatively associated with import penetration,particularly from low-wage countries. However,the empirical results conﬁrm that import penetration has a weaker impact on employment in industries with long quality ladders:a ten percentage point increase in low-wage penetration is associated with a6percent employment decline in an industry characterized by an average quality ladder length.A similar increase in competition in a long-ladder industry(one standard deviation above the mean)results in only a1.4percent employment decline.Diﬀerential impacts on industry output are similar.Importantly,the impact of import competition on short and long-ladder industries is similar in magnitude to the diﬀerential impact on low and high capital-intensive industries.In other words,even after controlling for the diﬀerential impact through traditional channels,such as capital and skill intensities(see Sachs and Shatz(1994)and Bernard et al.(2006)),the quality ladder remains an important determinant of an industry’s vulnerability to low-wage competition.Moreover,the heterogenous eﬀect is not precisely captured if one simply uses variations in unit values.These results complement the literature studying the relationship between quality specialization and labor markets.But while existing studies focus predominantly on developing countries(see Goldberg and Pavcnik(2007),Verhoogen(2008),Kaplan and Verhoogen(2005)),the evidence here suggests that quality specialization is also important for developed countries.Quality ladders may therefore help identify those markets that are likely to be contested by competition from low-wage countries.6One potential explanation is diﬀerences in capital intensity,but in1980,electronics was less capital intensive than fabricated metals.Indeed,this paper oﬀers evidence that capital intensity only partly explains the heterogeneity in U.S.employment outcomes due to import competition.7For instance,see Leamer(1987)and Schott(2003).The remainder of the paper is organized as follows.Section2oﬀers a simple model to illustrate that exposure to low-wage competition is greater in markets with short quality ladders.In Section3,I discuss the approach used to infer quality from trade data.The data and quality estimation results are presented in Section4.Section5applies the quality estimates to test the implications of quality specialization for U.S. employment.I conclude in Section6.2A Model of Contestable JobsThis section develops a simple model that delivers two comparative static results.First,the impact of foreign competition on domestic market shares is larger from low-wage countries.Second,the impact will depend on the market’s quality ladder length.I then use the empirical quality measures derived in Section3to assess the predictions of the model.The model is partial equilibrium and analyzesﬁrms in two regions,North(N)and South(S),where the Southernﬁrms freely export to the North.The wages in each country are determined by an outside sector and are therefore treated as exogenous:w N>w S.Each region has J homogenousﬁrms that compete by manufacturing horizontally and vertically distinct varieties.Following Krugman(1980)and Melitz(2003), horizontal diﬀerentiation is costless so in equilibrium,allﬁrms produce horizontally distinct varieties.But as in Flam and Helpman(1987),vertical(e.g.,quality)diﬀerentiation depends on a Ricardian-type comparative advantage given by region c’s technology,Z c.I assume that Northernﬁrms have access to better technology than the South:Z N>Z S.Firm j uses this technology to manufacture a variety subject to a marginal cost function that is increasing with quality(λj):w c+λ2j,for c∈{N,S}.82Z cThe consumers who live in the North have discrete choice preferences.Consumer n observes the domestic and Southern varieties and chooses the variety j that provides her with the highest indirect utility,V nj=θλj−αp j+ nj.(1) Quality is deﬁned as an attribute whose valuation is agreed upon by all consumers:holding pricesﬁxed, all consumers would prefer higher quality objects.The vertical component can be interpreted as the clarity or sharpness of a television screen or it can reﬂect the perceived quality that results from advertising.In either case,quality represents any attribute that enhances consumers’willingness-to-pay for a variety.An alternative interpretation is thatλrepresents a shift parameter in the variety’s demand schedule:holding price p jﬁxed,demand shifts out when the quality improves(Sutton,1991).The empirical identiﬁcation of quality relies on this latter intuition.The parameterθreﬂects the consumers’valuation for quality and,as shown below,represents a proxy for the market’s quality ladder in the model.8One can think of this marginal cost function as arising from aﬁxed-proportions technology that combines labor and capital (with the rental rate on capital being implicitly treated as one).in the proportion1toλ22ZHorizontal product diﬀerentiation is introduced in(1)through the consumer-variety-speciﬁc term, nj.Following standard practice in the discrete choice literature, nj is assumed to be distributed i.i.d. type-I extreme value.Unlike the vertical attribute,the horizontal attribute has the property that some people prefer it while others do not and on average,it provides zero utility.9Denote the mean valuation for variety j asδj≡θλj−αp j.Under the distributional assumption,the market share of variety j is given by the familiar logit formulam j=eδjkeδk.(2)Aﬁrm from region c maximizes proﬁts in the Northern market by choosing price and quality bysolving the following problemmaxp j,λjp j−w c−λ2j2Z ceδjkeδk(3)The market is characterized by monopolistic competition with a suﬃciently large number ofﬁrms that no oneﬁrm can inﬂuence the market equilibrium prices and qualities.The optimal price charged by variety j is therefore10p∗j=1α+w c+λ∗2j2Z c,∀j∈c(4)Under this pricing rule,ﬁrms charge a markup(1α)over their marginal cost.The optimal quality choice equates the marginal beneﬁt of choosing quality to its marginal cost:λ∗j=θZ cα,∀j∈c(5)Equations(4)and(5)indicate that allﬁrms within a region choose the same price and quality(but recall that allﬁrms diﬀerentiate their varieties in the(costless)horizontal dimension).I therefore drop the subscript j and index the representativeﬁrm’s choice in each region by N or S.Note also that the market share in(2) simpliﬁes to m c=eδcJ(eδN+eδS),c∈{N,S}and the aggregate market share in each region is M c=Jm c.The optimal price and quality choice imply that the mean valuation consumers attach to the representativeﬁrm in region c isδ∗c≡θλ∗c−αp∗c=θ2Z c2α−αw c−1,c∈{N,S}.(6)The Northernﬁrms manufacture the higher quality varieties since Z N>Z S.11Below,I verify that more advanced countries indeed export higher quality products using the newly proposed quality measures 9For example,comfort is a quality attribute since,ceteris paribus,all consumers prefer more comfort to less.An article of clothing’s fashion or style is a horizontal attribute since at equal prices,not all consumers would purchase the same style(e.g., stripes versus solids).10If aﬁrm takes into account the impact of its decision on the denominator in(2),the optimal price is given by p∗j=1α(1−m j)+w c+λ∗2j2Z c.As discussed in Anderson et al.(1992),monopolistic competition assumes there are a suﬃciently largenumber of varieties so that the market share of any one variety is negligible.The optimal price is therefore given by(4).11Since quality is a monotonic function of technology,prices are suﬃcient statistics for quality in this model.However,if Z N=Z S,all qualities would be identical,but the North would charge higher prices because of higher manufacturing costs. Thus,empirically,prices alone may confound diﬀerences in quality and quality-adjusted manufacturing costs.which provides a justiﬁcation for this assumption.These higher quality Northernﬁrms will also have larger market shares ifθ2(Z N−Z S)>α(w N−w S),(7)since this implies thatδ∗N >δ∗S.This condition in(7)holds if consumers’valuation for quality is suﬃcientlyhigh or the North’s technological prowess is suﬃcient to overcome its disadvantage in manufacturing costs. This assumption is consistent with substantial theoretical and empirical work arguing that higher quality, or more productive,ﬁrms have higher output(and market shares).12I deﬁne the market’s quality ladder as the diﬀerence between the highest and lowest quality(Gross-man and Helpman,1991).As discussed below,the empirical measures cannot separately identifyλfrom the consumers’valuation for quality(θ).I therefore deﬁne the market’s quality ladder asLadder(θ)≡θλ∗N−θλ∗S=θ2α(Z N−Z S).(8)The market’s quality ladder can be indexed byθand so as the valuation for quality increases,the quality ladder increases,or lengthens.The scope for quality diﬀerentiation will therefore vary according the con-sumers’valuations for quality in each market.13Moreover,as the quality ladder increases,the market sharegains are disproportionately distributed to the manufacturers of the higher quality(∂δ∗N∂θ>∂δ∗S∂θ).This simple model abstracts away from the endogenous“lengthening”of the ladder that may occur in a long-run equilibrium with technological progress or shifts in consumer preferences.Instead,I assume that the quality ladder isﬁxed which may be appropriate in the short to medium run and mitigate endogeneity concerns in the empirical analysis by assigning a market’s quality ladder its initial length.This assumption is consistent with the data which reports a persistence between a market’s initial ladder length and itsﬁnal period length.That is,on average,markets with initially“short”ladders are not“long”by the end of the sample,implying that the quality ladder length is an intrinsic attribute of a market characterizing its scope for quality diﬀerentiation.14I can now analyze how the aggregate Northern market share changes with Southern wages,and how this impact varies according to a market’s quality ladder length.Theﬁrst result shows that the North loses market share as Southern manufacturing wages decline:∂M NS =−M N M S∂δ∗SS>0(9)since∂δ∗SS=−α.(10)12For instance,see Melitz(2003)and Bernard et al.(2007).13The ladder length could also vary by changing Z N and the predictions of the model do not change.Hence,the contestable jobs hypothesis does not hinge on the source of the market’s scope for quality diﬀerentiation.14A market’s intrinsic scope for quality diﬀerentiation is closely related to escalation principle developed in Sutton(1998).Thus,Southernﬁrms become more competitive as its manufacturing costs falls and this gain comes at the expense of lower market shares for the Northernﬁrms.This comparative static is quite intuitive and is supported by existing empirical evidence.For instance,Bernard et al.(2006)show that output and employment for U.S.plants are negatively associated with import competition,but the impact is much larger when import competition originates from countries with less than5%of U.S.per capita GDP.Importantly,this model adds quality diﬀerentiation to show that the intensity of competition within a market depends on the quality ladder length.In particular,while(9)indicates that the North’s market share falls as Southern wages decline,it suﬀers a smaller loss in markets characterized by longer quality ladders(highθmarkets).This is seen by diﬀerentiating(9)with respect toθ:∂2M N ∂w S∂θ=−∂2δ∗S∂w S∂θM N M S+∂δ∗S∂w SM N∂M S∂θ+M S∂M N∂θ=−∂δ∗S∂w SM N M2S−M2N M S∂δ∗N∂θ−∂δ∗S∂θ=∂δ∗S∂w SM N M S(M N−M S)∂δ∗N∂θ−∂δ∗S∂θ=−θM N M S(M N−M S)(Z N−Z S)<0,(11)since M N>M S.This derivative shows that in long-ladder markets(highθ),the sensitivity of Northern market shares to Southern competitiveness is reduced.As a result,a decrease in the South’s wage results in a smaller decline of the North’s market share in long ladders.The model shows that trading with the South can generate a diﬀerential impact on two markets that are otherwise identical but vary according in their quality ladders.This result is related to more general models of international trade that predict a breakdown of FPE when countries are fully specialized in production.In contrast to a single-cone equilibrium,where endowments are such that all countries produce all goods,the conditions required for factor price equalization are not met in multi-cone equilibrium because countries specialize in varieties tailored to their endowments.15Schott(2004)has extended this analysis to within product specialization where endowment diﬀerences cause countries to specialize in diﬀerent segments of a product’s quality ladder.The model here sharpens this analysis by arguing that the scope for quality specialization varies across markets.3Empirical ImplementationThis section describes the procedure that infers quality using price and quantity information from standard disaggregate trade data.The estimated qualities are then used to verify predictions from the model.15For evidence in favor of the hypothesis that countries inhabit multiple cones of diversiﬁcation,see Leamer(1987),Davis and Weinstein(2001)and Schott(2003).The methodology is based on the nested logit framework by Berry(1994).The nested logit has the advantage over the logit in(1)because it partially relaxes the independence of irrelevant alternatives(IIA) property by allowing for more plausible correlation structures among consumer preferences.To understand why this is important,suppose a consumer is choosing between a Japanese wool shirt and an Italian cotton shirt.If a Chinese cotton shirt enters the market,a logit or CES framework would predict that the market shares for both imports would fall by the same percent.However,we might expect the Italian cotton shirt’s market share to adjust more than the Japanese shirt because the Chinese shirt is also cotton.The nested logit allows for more appropriate substitution patterns by placing varieties into appropriate nests.In order to delineate the nests,I rely on the structure of the U.S.trade data.Feenstra et al.(2002) have compiled U.S.import data which containﬁve-digit SITC industries that have been mapped to ten-digit HS products denoted by h.The products serve as the nests.An import from country c within a product is called a variety.I model consumer preferences for a single industry and therefore suppress industry subscripts.Fol-lowing Berry(1994),consumer n has preferences for country c’s import into HS product h(e.g.,variety ch) at time t.The consumer purchases the one variety that provides her with the highest indirect utility,given byV ncht=λ1,ch+λ2,t+λ3,cht−αp cht+Hh=1µnht d ch+(1−σ) ncht.(12)Quality is deﬁned asλ1,ch+λ2,t+λ3,cht since it reﬂects the valuation for variety ch that is common across consumers(notice that these terms are not subscripted by n).This quality term is decomposed into three components.Theﬁrst term,λ1,ch,is the time-invariant valuation that the consumer attaches to variety ch. The second term,λ2,t,captures for secular time trends common across all varieties.Theλ3,cht term is a variety-time deviation from theﬁxed eﬀect that is observed by the consumer but not the econometrician.This last term is potentially correlated with the variety’s c.i.f.unit value,p cht.The horizontal component of the model is captured by the random component, Hh=1µnht d ch+(1−σ) ncht.The logit error ncht is assumed to be distributed Type-I extreme value and explains why a variety that is expensive and has low quality is ever purchased.The former term interacts the common valuation that consumer n places on all varieties within product h,µnht,with a dummy variable d ch that takes a value of1if country c’s export lies in product h.This term generates the nest structure because if allows consumer n’s preferences to be more correlated for varieties within product h than for varieties across products.16 An“outside”variety completes the demand system.The purpose of the outside variety is to allow16As discussed in Berry(1994),Cardell(1997)has shown that the distribution of Hh=1µnht d ch is the unique distributionsuch that if is distributed extreme value,then the sum is also distributed type-I extreme value.The degree of within nest correlation is controlled byσ∈(0,1].Asσapproaches one,the correlation in consumer tastes for varieties within a nest approaches one and asσtends to zero,the nested logit converges to the standard logit model.。

On the drivers of eco-innovations_ Empirical evidence from the UK

Research Policy 41 (2012) 862–870Contents lists available at SciVerse ScienceDirectResearchPolicyj o u r n a l h o m e p a g e :w w w.e l s e v i e r.c o m /l o c a t e /r e s p olOn the drivers of eco-innovations:Empirical evidence from the UKEfﬁe Kesidou ∗,Pelin DemirelUniversity of Nottingham,Nottingham University Business School,Jubilee Campus,NG81BB Nottingham,United Kingdoma r t i c l ei n f oArticle history:Received 27April 2010Received in revised form 30August 2011Accepted 15January 2012Available online 11 February 2012Keywords:Eco-innovationsEnvironmental regulations Organisational capabilitiesa b s t r a c tThe environmental economics literature emphasises the key role that environmental regulations play in stimulating eco-innovations.Innovation literature,on the other hand,underlines other important determinants of eco-innovations,mainly the supply-side factors such as ﬁrms’organisational capabilities and demand-side mechanisms,such as customer requirements and societal requirements on corporate social responsibility (CSR).This paper brings together the views of these different disciplines and provides empirical insights on the drivers of eco-innovations based on a novel dataset of 1566UK ﬁrms that responded to the Government Survey of Environmental Protection Expenditure by Industry in 2006.By applying the Heckman selection model,our ﬁndings indicate that demand factors affect the decision of the ﬁrm to undertake eco-innovations whilst these factors exhibit no impact upon the level of investments in eco-innovations.Hence,we suggest that ﬁrms initiate eco-innovations in order to satisfy the minimum customer and societal requirements,yet,increased investments in eco-innovations are stimulated by other factors such as cost savings,ﬁrms’organisational capabilities,and stricter regulations.Based on a quantile regression analysis,the paper offers interesting insights for policy makers,by showing that the stringency of environmental regulations affects eco-innovations of the less innovative ﬁrms differently from those of the more innovative ﬁrms.Crown Copyright © 2012 Published by Elsevier B.V. All rights reserved.1.IntroductionWhilst most researchers and policy makers are well acquainted with the concept of innovation,1eco-innovation is a new concept for which a standardised deﬁnition does not exist yet.OECD illustrates that eco-innovation differs from generic innovation on two signif-icant characteristics:“It is innovation that reﬂects the concept’s explicit emphasis on a reduction of environmental impact,whether such an effect is intended or not.And,it is not limited to innova-tion in products,processes,marketing methods and organisational methods,but also includes innovation in social and institutional structures”(OECD,2009,p.13).Additionally,the merit of eco-innovation has been highlighted by academics and policy-makers in the European Commission (see Kemp,2009about the MEI project)not only because of its beneﬁcial environmental impact but also due to the expected increased competitiveness of the ﬁrms and countries that eco-innovate (Arundel and Kemp,2009).∗Corresponding author.Tel.:+4401159515265;fax:+4401158466667.E-mail address:efﬁe.kesidou@ (E.Kesidou).1Innovation refers to “the implementation of a new or signiﬁcantly improved product (good or service),or process,a new marketing method,or a new organisa-tional method in business practices,workplace organisation or external relations”(OECD and Eurostat,2005,p.46).The UK environmental policy clearly sets out the urgent chal-lenges in meeting the increasing demands of the economy whilst moving towards a low emission and sustainable environment (DTI Energy White Paper,2007).Furthermore,“the transition to a low-carbon economy will bring challenges for competitiveness but also opportunities for growth’arising out of eco-innovations,which will offset some of the high costs of mitigation”(Stern Review,2006,p.16).On the way to an environmentally sustainable eco-nomic growth,integrating environmental and innovation insights and understanding where the UK stands in terms of its existing capabilities and potential for creating eco-friendly technologies is crucial.A number of contributions from the ﬁeld of innovation and envi-ronmental economics are seeking to determine the factors that drive eco-innovation.Studies leaning towards the ﬁeld of innova-tion indicate that demand factors in general (Horbach,2008),and collaboration with environmentally concerned stakeholders in par-ticular (Wagner,2007)play an important role for the generation of eco-innovations.On the other hand,insights from the manage-ment literature on corporate social responsibility (CSR)suggest that the societal pressure and demand for environmentally friendly products and processes may not necessarily lead to increasing investments in eco-innovation,but rather be limited to initiate a minimum investment in eco-innovation that will signal the com-mitment of the ﬁrm to ‘green issues’(Suchman,1995;Bansal and Hunter,2003;Potoski and Prakash,2003;Darnall,2006).0048-7333/$–see front matter.Crown Copyright © 2012 Published by Elsevier B.V. All rights reserved.doi:10.1016/j.respol.2012.01.005E.Kesidou,P.Demirel/Research Policy41 (2012) 862–870863Other scholars have highlighted the importance of technologi-cal and organisational capabilities in stimulating eco-innovations in manufacturingﬁrms(Horbach,2008).The implementation of environmental management systems(EMS)which facilitate eco-innovation is seen as the reﬂection of the strong organisational capabilities ofﬁrms in environmental management(Wagner,2007; Horbach,2008).Studies closer to theﬁeld of environmental economics underline the signiﬁcance of environmental regulation and standards that aim to combatﬁrms’pollution activities(Magat,1979;Malueg, 1989;Milliman and Prince,1989).Recently,increasing attention has been given to the role of regulation in enhancing investments in eco-innovations(Brunnermeier and Cohen,2003).Regulation is not seen as an undesirable cost-increasing factor but as a stimulator ofﬁrms’innovativeness that,in turn,would lead to aﬁrst-mover advantage in markets for eco-innovations(Porter,1991;Porter and Van Der Linde,1995a,b).Yet,an issue that is overlooked in these studies is related to the heterogeneity inﬁrms’innovation capabilities and their respective strategies for eco-innovation.Less innovativeﬁrms may adopt eco-innovation as a means to reduce production costs and comply with the minimum environmental standards,whilst more innovativeﬁrms may adopt eco-innovation in order to enter new markets(Grubb and Ulph,2002).As a result, the effectiveness of regulations forﬁrms could potentially differ depending on whether or not they are already ahead of their peers in eco-innovation investments and activities.This paper contributes to the literature by pooling together insights from the innovation literature on eco-innovation,the management literature on CSR strategy,and the environmental economics literature.The paper considers three key factors that potentially drive eco-innovation:(1)demand factors,(2)organisa-tional capabilities,and(3)stringency of environmental regulations.With respect to the demand factors and organisational capa-bilities,by applying the Heckman Selection model,we are able to distinguish between the factors that affect the decision of theﬁrm to conduct eco-innovation,and those factors that affect the level of investments in eco-innovation.The application of this method-ology enables us to examine the extent that demand factors–namely societal requirements of CSR and demand for environmen-tally friendly products–affect the decision of theﬁrm to conduct eco-innovation and/or the level of investment in eco-innovation. Additionally,we use quantile regression techniques to analyse whether environmental stringency affects eco-innovation differ-ently in less innovativeﬁrms and more innovativeﬁrms.The paper is structured as follows:in Section2,we review the theoretical and empirical literature on eco-innovation and for-mulate the research hypotheses.Section3presents the data and explains the methodologies that were applied.Section4presents the empirical results,whilst Section5discusses theﬁndings and the policy implications of the study.2.Theoretical framework and research hypotheses2.1.Demand factors:CSR and customer requirementsTheoretical insights from the innovation literature underline the critical role of demand pull factors for innovations.In the late 1980s,the linear model of innovation was replaced by new insights that emphasised the feedback mechanisms that take place at every stage of the innovation process(Kline and Rosenberg,1986).Con-sequently,numerous other studies from innovation management literature highlighted that not only producers participate and shape the innovation process but also consumers and users,universi-ties,as well as public and private institutes(Edquist,2004;Nelson, 1993;Von Hippel,1987).Innovation is now seen as a cumulative and interactive process integrating technology push and market pull(Dosi,1988;Lundvall,1992).However,the demand factors in eco-innovation concept have generally been overlooked.Only recently a small number of empir-ical studies indicate that demand factors play an important role for the creation of eco-innovations(Horbach,2008;Wagner,2007). In particular,Horbach(2008)shows that demand,namely expec-tations of increases in the turnover of theﬁrm,is an important determinant of eco-innovations in the case of German manufactur-ingﬁrms.Additionally,Wagner(2007)underlines the importance of active consumer associations for eco-innovation.His empirical study on German manufacturingﬁrms indicates that collaboration with predominantly environmentally concerned stakeholders–partly reﬂecting the activities of consumer protection associations –plays an important role for the generation of eco-innovative prod-ucts(Wagner,2007).Moreover,recent years have witnessed an increase in government efforts to endorse eco-innovations through the use of centralised‘green purchasing’plans(e.g.BIS,2009). Overall,this evidence pinpoints that demand factors and in par-ticular,calls for corporate responsibility and consumer demand for environmentally friendly products and processes,will affect the decision of theﬁrm to invest in eco-innovation.Additionally,the above studies examine the impact of demand factors upon a binary dependent variable that reﬂects the decision of theﬁrm to eco-innovate(eco-innovations=1vs.no eco-innovation=0).We still know very little about the impact of the demand factors upon the intensity of eco-innovations.Insights from the management literature on CSR suggest that demand factors may not affect the level of investment in eco-innovation.The CSR literature illustrates thatﬁrms align their practices with societal expectations in order to ensure the legiti-macy of their business(Iatridis,2009;Palazzo and Scherer,2006; Sethi,1975).Consequently,the adoption of CSR policies may reﬂect a reinforcing or reorienting of theﬁrm strategy through signalling commitment to‘green issues’and building the‘green’image of the company(Bansal and Hunter,2003;Darnall,2006;Potoski and Prakash,2003).As a result,ﬁrms may undertake only the mini-mum investment in eco-innovation to legitimise their practices and improve their‘green’image(Suchman,1995).Thus,CSR and cus-tomer demand for green products and processes may not bring the expected increasing investments in eco-innovations.Therefore,we test whether demand factors and in particular customer demand for environmentally friendly innovations and CSR will affect the decision of aﬁrm to invest in eco-innovation, as well as the level of investment in eco-innovation.Hypothesis1.Demand factors inﬂuence(1)the decision of the ﬁrm to invest in eco-innovation;and(2)the level of investment in eco-innovation.anisational capabilities:environmental management systems(EMS)The innovation literature focusing on eco-innovations has shown that increasing investments in eco-innovation are inﬂu-enced by the capabilities of theﬁrms(Kemp et al.,1992).In particular,thoseﬁrms that build organisational capabilities and practices such as source reduction,recycling,pollution preven-tion,and green product design are more likely to invest in eco-innovation(Georg et al.,1992;Winn and Roome,1993). Additionally,Florida et al.(2001)demonstrate that two types of organisational factors–organisational resources and performance monitoring systems–play an important role for the adoption of eco-innovations.Organisational environmental capabilities are often developed with the implementation of environmental management systems864 E.Kesidou,P.Demirel/Research Policy41 (2012) 862–870(EMS).2EMS are voluntary organisational frameworks that detail the procedures used to manage the impacts of the organisation on the natural environment(Darnall,2006).Their purpose is to continuously improve corporate environmental performance to get ahead of the existing government regulations to reduce emissions and waste disposal(Kollman and Prakash,2002).EMS are viewed as an indicator of the strong but latent organisational capabilities of theﬁrm in environmental management(Fryxell and Szeto,2002; Russo and Harrison,2005).A number of empirical studies from the innovation literature have found that implementation of EMS has a positive impact upon eco-innovation(Horbach,2008;Wagner,2007).Even though the implementation of EMS signals the building of organisational capabilities,management research on EMS has shown that solely external certiﬁcation does not boost eco-innovation due to the rather ostentatious organisational implementation of EMS by some ﬁrms(Boiral,2007;Fryxell and Szeto,2002;Rondinelli and Vastag, 2000;Russo and Harrison,2005).Hence,we investigate how the organisational environmental capabilities of theﬁrm affect its eco-innovativeness.anisational factors inﬂuence(1)the decision of theﬁrm to invest in eco-innovation and(2)the level of investments in eco-innovation.2.3.Stringency of environmental regulationsMost research on environmental economics is focussed on the effectiveness of various policy measures,namely regu-lations(ambient standards,technology-based standards,and performance-based standards)and market-based instruments (Pigouvian tax,subsidies,deposit/refund systems,and tradable per-mits),for limitingﬁrms’pollution activities(Callan and Thomas, 2009;Magat,1979;Milliman and Prince,1989;Malueg,1989). Presently the attention has shifted to the role that environmental regulations play in stimulating eco-innovations(Brunnermeier and Cohen,2003;Lanjouw and Mody,1996).Empiricalﬁrm-level stud-ies suggest that more stringent environmental regulations boost eco-innovations(Cleff and Rennings,1999;Frondel et al.,2008; Green et al.,1994).This hypothesis,initially formalised by Porter(1991)and Porter and Van Der Linde(1995a,b)has been empirically tested sev-eral times in different contexts and with different datasets as the interest to control emissions and environmental pollution heav-ily mounts on the industry and governments(Brunnermeier and Cohen,2003;Horbach,2008;Mazzanti and Zoboli,2006;Popp, 2006).The‘win-win’situation underlying the Porter hypothesis suggests that regulations can forceﬁrms to invest in environmen-tal research and development in order to cut down the costs of complying with environmental regulation standards.In turn,ﬁrms that undertake eco-innovations will be able to reduce their produc-tion costs and/or enter into expanding markets for eco-products. Indeed,regulations may be the only means to break out of exist-ing technological lock-ins and move towards eco-technologies that usually have higher costs at least in the short term(Arthur,1989; Klaassen et al.,2005).The majority of evidence on the relationship between envi-ronmental regulations and eco-innovations comes from a small number of developed countries,namely the USA and Germany,and to a lesser extent from other European countries such as Italy,UK2European Union’s Environmental Management and Audit Scheme(EMAS)and ISO14001constitute the most diffused forms of formalised EMS and both schemes require third party certiﬁcation and investigation.Bansal and Hunter(2003)argue that these two schemes reinforce legitimacy which cannot be claimed through in-house EMS.and Denmark.The differences in stringency of environmental reg-ulations across countries are accompanied by different levels of development in their eco-innovation capabilities(Klaassen et al., 2005;Popp,2006).Eco-innovations respond heavily to national and local regulations and therefore,we anticipate stringency of environmental regulations to stimulate eco-innovations.Yet,we know very little about the response of different types ofﬁrms to environmental regulations.Firm heterogeneity with respect to innovative capabilities implies that someﬁrms may be ahead of others in their investments into environmental R&D. Thus,higher levels of environmental stringency may increase eco-innovation in less innovativeﬁrms,which assume a reactive strategy by adopting eco-innovations in order to reduce production costs of complying with environmental regulations.In contrast, more innovativeﬁrms,which are ahead of their peers in eco-innovations,may not respond to environmental stringency since they are already complying with the new regulations.Finally, highly innovativeﬁrms,which are more proactive,may increase their investments in eco-innovation for strategic reasons;in order to gain an advantage in product markets(Grubb and Ulph,2002). As a result,stricter regulations may not affect the investments of allﬁrms in eco-innovation uniformly.Hypothesis3.Stringency of environmental regulations affects the level of investments in eco-innovation differently for less innova-tiveﬁrms and more innovativeﬁrms.3.Data and methodology3.1.DataThe data we use in the empirical analysis was collected by the Department for Environment Food and Rural Affairs(DEFRA) in2006for the‘Government Survey of Environmental Protection Expenditure by Industry’.The objective of the survey was to gather ﬁrm-level data on environmental protection expenditure across industrial sectors in the UK.The survey is designed to collect information on operating and capital environmental expenditure, environmental management systems,environmental research and development expenditures,motivation for environmental expen-diture,and general information onﬁrm characteristics such as employment and turnover.From a random sample of7850manu-facturingﬁrms1599responded to the survey,which represents an approximately20.4%response rate.Because of missing responses to some of the variables,the sample that is used in the analysis is reduced to1566.We use environmental research and development expenditures (ECORD)as a proxy of eco-innovation.This variable measures the level ofﬁrms’R&D investments into eco-innovations and is more speciﬁc and precise compared to the majority of R&D based eco-innovation indicators found in the literature.Survey questions that investigate environmental R&D activities often askﬁrms‘whether or not’they conduct environmental research and development activities and not speciﬁcally‘how much’they invest into envi-ronmental research and development activities(Horbach,2008). Hence,theﬁrst advantage of the ECORD variable used in this study is its ability to indicate not only whetherﬁrms conduct environ-mental research and development but also to provide information on the level of such investments.The second advantage of the ECORD variable is that it is speciﬁcally aimed at R&D expenditures allocated for eco-innovations and not all types of innovations.Some previous studies have utilised theﬁrm’s total R&D as a proxy of eco-innovations even though these measures do not immediately indicate the rate of eco-innovations but assume a strong correla-tion between eco-innovations and generic innovations(Jaffe and Palmer,1997;Brunnermeier and Cohen,2003).E.Kesidou,P.Demirel/Research Policy41 (2012) 862–870865 Table1Variables and summary statistics.Variable Deﬁnition Mean Std ECORD Environmental research and development(£).7687134,186 CSR=1if theﬁrm invested in environmental protection because of parent company or owner policy/CSR.0.06aCust Req=1if theﬁrm invested in environmental protection because of customer environmental requirements.0.02aEMS=1if theﬁrm has implemented environmental management systems.0.26aEOC Environmental operating costs(£).512,29812,239,109 ECC Environmental capital costs(£).196,4784,135,495 AC Total abatement costs=EOC+ECC(£).708,92113,565,860 Equ Upgrade=1if theﬁrm invested in environmental protection because of equipment upgrade.0.13aCS Total cost savings resulting from environmental improvements(£).24,800265,313BP Total income obtained from the sale of by-products arising from environmental improvements(£).0.030.304 TOPCOM=1if theﬁrm is a Top Company b.0.03TURNOVER Company turnover in2006. 5.44E+07 4.55E+08 EMP Number of employees in2006.180606SIZE=ln(EMP). 3.94 1.39a The mean of a dummy variable represents the proportion or percentage of cases that have a value of1for that variable.b DEFRA selected Top Companies by ranking the top50companies by turnover and by number of employees.In addition the companies with over250employees in SICs 10–14(Mining and quarrying),23(Coke/petroleum/nuclear fuel),40and41(Energy&water supply)were included and theﬁve largest(by number of employees)companies for each SIC,were also selected as Top Companies.We measure the demand for eco-innovations with two indica-tors:(a)customer requirements(CUST REQ),which is a dummy variable that takes the value of1if aﬁrm has invested in environmental protection because of customers’environmental requirements;and,(b)corporate social responsibility(CSR),which is a binary variable that speciﬁes whether aﬁrm has invested in environmental protection because of parent company or owner policy on corporate social responsibility.Organisational capabilities related to eco-innovations are mea-sured with a dummy variable that indicates whether aﬁrm has implemented environmental management systems(EMS).In the literature,the stringency of environmental regulations is often proxied with abatement costs(i.e.the capital and operat-ing costs of complying with environmental regulations as in the US PACE survey),the number of pollution related inspections to companies(Brunnermeier and Cohen,2003),qualitative survey questions on whether existence(or anticipation)of regulations ‘prompt’product and process eco-innovations(Cleff and Rennings, 1999;Green et al.,1994)and also by considering the pre and post periods of a speciﬁc environmental legislation(Popp,2006).In this study we measure regulatory stringency with abatement costs by taking into account the environmental operating and capital expen-diture of theﬁrm(AC).Table1presents the main variables,their deﬁnition and some summary statistics.3.2.MethodologyHypotheses1and2are tested by applying a Heckman model (1979)whilst a quantile regression model is used for testing Hypothesis3.3.2.1.Heckman modelLet us use i=1,2,...,N to indexﬁrms.This equation speciﬁes the determinants of eco-innovation(ECORD i):ECORD∗i=˛x0i+ε0iHere x0i is a vector of the determinants of eco-innovation,˛is a vector of parameters of interest,andεi is an error term.Envi-ronmental research and development(ECORD)can be used as a proxy for eco-innovation but,this is possible only in cases where ECORD>0(i.e.ﬁrms spend on ECORD).However,manyﬁrms do not perform ECORD;in particular,1314out of1566ﬁrms do not undertake any environmental R&D.As a result,the dependent vari-able ECORD is left censored,with a lower threshold of zero.Hence,the above equation cannot be estimated with an OLS regression; this would produce inconsistent estimations of the˛coefﬁcients because of selection bias and truncation(Amemiya,1985).This is resolved by employing a two-step Maximum Likelihood Heck-man model byﬁrst estimating a selection equation,and then the outcome equation adjusting for selection bias(Greene,2003).In particular,the selection equation indicates whether or not aﬁrm performs ECORD:ECORD i=1if ECORD∗i>00if ECORD∗i≤0where ECORD∗i=ˇ0+ˇ1x1i+ε1iwhere ECORD∗i is the corresponding latent variable indicating that ﬁrms decide to perform ECORD if it is above the threshold level of zero.Hence,conditional onﬁrm i performing ECORD,3it is possible to observe the determinants of eco-innovation as follows:ln(ECORD i)=ln(ECORD)if ECORD∗i>0–if ECORD∗i≤03.2.2.Quantile regression modelThe quantile regression produces important insights about the different factors that determine investments in environmental research and development(ECORD)at different points on the conditional ECORD distribution(Koenker and Basset,1978).The Heckman model and other statistical techniques focus on the mean effect of the covariates.Hence,the quantile regression comple-ments the Heckman estimation by providing a detailed description of the conditional ECORD distribution across all quantiles.Note that,in this part of the study,we focus only on thoseﬁrms with positive ECORD investments.Ideally,one would combine a selection equation with the quantile regression model to correct for the sample selection problems as discussed in Section3.2.1.Whilst there are examples of quantile regression models that include a3Following previous studies,we use the logarithmic form of the environmen-tal research and development variable,since it is a highly skewed variable(Jaffe and Palmer,1997).The Histograms of ECORD and ln(ECORD)demonstrate that the logarithmic transformation corrects for the non-normality of the ECORD variable. Additionally,the Shapiro–Wilk test shows that the null-hypothesis of normality is rejected for ECORD(Prob>z:0.000)and accepted for ln(ECORD)(Prob>z:0.220). Note that the normality tests are conducted on the non-censored portion of the ECORD data(i.e.ECORD>0)which effectively enters into estimations in the second stage of the regression model.The missing values of ln(ECORD)generated in cases when ECORD=0are taken into account at theﬁrst stage of the model where we identify the factors forﬁrms to undertake ECORD or not.866 E.Kesidou,P.Demirel/Research Policy41 (2012) 862–870Table2Environmental R&D by size and sector.Allﬁrms ECORD>0ECORD mean Number ofﬁrms ECORD mean Number ofﬁrmsSizeMicro(1–9employees)2208426457 Small(10–49employees)118710619922127 Medium(50–249employees)10,68130742,04278 Large(≥250employees)67,751114193,09340SectorMining and quarrying63957331,12315 Food,beverages and tobacco products327223223,72532 Textiles,clothing and leather products21319316,51512 Wood and wood products32633616,7857 Pulp and paper products,printing680159636217 Coke,petroleum and nuclear fuel19,1451444,6726 Chemicals and man-madeﬁbres11,59822246,96054 Rubber and plastic products182814121,48612 Other non-metallic mineral products192854946511 Basic metals and metal products166515712,45021 Machinery and equipment42299330,25913 Electrical,medical and optical equipment466413130,55320 Transport equipment19488410,23116 Other manufacturing67075243,6008 Energy production and water228,54425710,9388Total1566252selection equation(Buchinsky,2001),this theoretically poses a con-ﬂict between the main equation and the selection equation sincethe Heckman model evaluates the results at the mean and not at dif-ferent quantiles.Hence,we omit the selection equation and simplyfocus on thoseﬁrms that invest in ECORD.3.2.3.Other methodological issuesFirstly,in order to address potential multicollinearity problems,we have checked the Pearson correlation matrix for dependent andindependent variables used in the analysis.The only two variablesthat are found to display high levels of signiﬁcant correlation istheﬁrm size(ln(EMP))and the abatement expenditure(ln(AC))variables(r=0.75,signiﬁcant at5%).In order to remove this correla-tion,we transformed the AC variable by dividing it with TURNOVER(AC/TURNOVER).The resulting correlation betweenﬁrm size andthe transformed AC variable is much lower(r=−0.0255)andinsigniﬁcant.Furthermore,the VIF inﬂation factor(VIF),1/(1−R2 k )test(Greene,2003)reveal an improvement in eliminatingmulticollinearity.4Secondly,since the data is cross sectional,problems of endo-geneity may arise especially with regard to issues of reverse causality between environmental research and development (ECORD)and abatement costs(AC).We were able to check empir-ically the potential problems of reverse causality by examining whether ECORD in2003is signiﬁcantly correlated with abate-ment costs in2006for a subset of64ﬁrms that appear in both years.The Spearman correlation analysis shows that ECORD in 2003is not signiﬁcantly correlated with AC/TURNOVER in2006 (r=0.187).5In contrast,ECORD in2006is signiﬁcantly correlated with AC/TURNOVER in2006(coefﬁcient=0.330,signiﬁcant at5%). This empirical test shows that the data does not suffer from serious endogeneity problems.We also tested the data for possible bias arising out of com-mon method variance(CMV).CMV is a type of spurious correlation which occurs amongst indicators or constructs when these derive4The VIF for the independent variables including the transformed AC variable (AC/TURNOVER)has dropped to1.13from the VIF value of1.46for the independent variables including abatement expenditure(ln(AC)).5We apply the Spearman non-parametric test because the AC/TURNOVER vari-able is not normally distributed and the data is not homoscedastic.from a common source.Podsakoff et al.(2003)have categorised the sources of CMV:(a)common rater effect,(b)item characteristics effect,(c)item context effect,and(d)measurement context effect. Note that CMV varies with the discipline of study.In particular,it has been shown that the levels of CMV are approximately15.8% in marketing,3.8%in other business areas,28.9%in psychology, and30.5%in education(Malhotra et al.,2006).The current study is based on the measurement of simple,objective,and unambiguous constructs–such as environmental expenditure,environmental management systems,environmental research and development, motivations for environmental expenditure,ﬁrm size and turnover –that we would expect to be associated with lower levels of CMV as it is in the case of‘other business areas’.Yet,we tested the survey for potential presence of CMV(Appendix A).Our results indicate that CMV is not a problem in this study.4.Empirical resultsEnvironmental research and development(ECORD)forﬁrms of different size and sector is presented in Table2.An ANOVA test on differences in mean values was conducted,comparing the mean of various size subgroups ofﬁrms.6A Kruskal–Wallis non-parametric ANOVA test of equality of population rank was carried out,comparing the medians of various sector subgroups ofﬁrms.7 Table2indicates that investment in environmental research and development is signiﬁcantly related to the size of theﬁrm,namely largeﬁrms spend on average£67,751per annum on ECORD,whilst micro,small,and mediumﬁrms invest considerably less.8Thisﬁrst ﬁnding conﬁrms that largeﬁrms are more likely to undertake envi-ronmental innovations compared to their smaller counterparts. Largeﬁrms’higher tendency to eco-innovate may be driven by their higher public visibility and the corresponding pressures they face from green/environmental public groups and the governments 6The assumptions of ANOVA,namely,independence,normality and homoscedas-ticity,were satisﬁed.7The non-parametric test was applied because ANOVA’s assumption of homoscedasticity was not satisﬁed.8The Bonferroni Post hoc ANOVA test was performed to determine the multiple comparisons between the mean differences of the various SIZE rgeﬁrms differ signiﬁcantly at the0.05level from other sub-groups.。

学术英语(第二版)医学教师用书Unit 8

UNIT 8 Principles of Biomedical EthicsTeaching ObjectivesAfter learning Unit 8, Ss are expected to accomplish the following objectives:To know the boundaries between medical research and practice To have a clear understanding of the moral principles and behavioral guidelines for the biomedical research and medical practice To understand the boundaries drawn between medical research and practice To know three basic ethical principles of research involving human subjects To learn the requirements when basic principles are properly applied in research To be more prepared for a life-or-death decision in medical practice To get more insights into the ethical justification of dilemmas in medical practice To know some building blocks in medical terminologyTo be familiar with expressions used to define key termsTo further develop awareness of formal and informal language To get familiar with the Cornell note-taking system To know the two approaches to medical decisions: traditional paternalistic mode and more recent collaborative modeTo learn how to develop a strong conclusion To know the format requirements of the reference listTo be able to make a reference list according to style requirements To be aware of the balance between medical authority and patients ’ autonomyProfessionalknowledgeReadingAcademic vocabulary anddiscourseViewingSpeakingWritingResearchingTeaching Activities and ResourcesPart 1 ReadingText ALead-inSuggested teaching plan1. To draw Ss’ attention and to raise their awareness of the importance ofbiomedical ethics, T is advised to relate the discussion of this unit to the real-world happenings.Before starting the class,search the media for the latest news reports,either at home or abroad,about controversial events in medicine community or healthcare settings.2. Start the class by doing Task / Lead-in and relate the content of the video clip toyour findings in the pre-class searching.Key to the task2) Death4) Patient rightsScriptWell,advancements in medical science have afforded us the opportunity to live decades longer than in previous generations.For every new possibility offered, we now face an equal number of challenges and we find ourselves confronting decisions that are unprecedented in human history.When does life begin?When should life end? How do we define death when we have the ability to keep people technically alive,or we should say,technologically alive long after their discrete body parts no longer function? Welcome to “Matter and Beyond . ” I’m your host MaryLynn Schiavi.In this program we’re going to explore issues around medical science that are forcing us to define life, death, quality of life, patient rights, and confront the moral and ethical questions that arise when facing critical healthcare decisions.3. Introduce the topic of Text A as a natural continuum of Lead-in .Text Comprehension1. Make good use of Lead-in video clip as it serves as a perfect introduction to thetopic of this unit. Elaborate on the connection of its content with the latest events in the real world. Naturally, ask Ss how medicine differs from other branches of natural science, especially when human subjects are involved in the research. Here are some hints:2. Analyze the text and lead Ss to discuss, integrating Task 2 / Critical reading andthinking / Text A into analysis and discussion. The presentation topics should be assigned to individual Ss for preparation at least one week in advance. Ask other Ss to preview the text with the guidance of presentation topics.3. Integrate Task 2 / Language building-up / Text A when a careful definition ofkey terms is covered.4. When analyzing the text, ask Ss to pay special attention to the sentences listed inLanguage focus below.5. If time allows, ask Ss to do Task 1 / Critical reading and thinking / Text A inabout five minutes. Check out the task by asking one or two Ss to read their answers. This is done to get an overview about the text.Language focus 1. … described in a formal protocol that sets forth an objective … (P185, Para.2)set forth 是动词词组，表示用清晰、具体的方式解释或描述，多用于正式的书面语中。

13-Beijing II 07 WHO(revised)

Functions
Comparing national experiences
International Differences
(Quantitative and Qualitative)
disease prevalence genetic social cultural healthcare systems health professional practices icines pharmaceutical formulations drug monitoring practices
Combinations database - example
Advantages of computerized signal generation
Necessary for huge databases Automatic, no time loss Objective, unbiased Reproducible Flexible (adjustable)
Capacity building : multi regional, multi lingual trainings, regional centres of excellence in PV Local evidence for the need of pharmacovigilance Economics of it: facts and figures for political commitment Draft protocol developed for baseline data
15 No of countries 10 14 5 0 0 1 2 3 13 12 10 6 9 4 0 4 5 6 7 No of submissions 2 8 2 9 10+ 9

ANTIMICROBIAL AGENTS

专利名称：ANTIMICROBIAL AGENTS发明人：CHALLIS, Gregory Leonard,GRIFFITHS, Daniel John,MASSCHELEIN, Joleen SolangeLiesbet申请号：GB2017/051124申请日：20170421公开号：WO2017/182828A1公开日：20171026专利内容由知识产权出版社提供专利附图：摘要：The invention provides novel compounds of formula (I) and their pharmaceutically acceptable salts, metabolites, isomers (e.g. stereoisomers) and prodrugs. Such compounds are effective in the treatment of infections caused by Gram-negative bacteria such as Acinetobacter baumannii. In formula (I), X is O, NR (where R is either H or C alkyl, e.g. CH), or CH; R is H, F, CI, Br, I, or CH; R is H, or OH; R and R are independently selected from H and OH, or R and R together are =O; R is H, F, CI, Br, I, or CH; R is H, OH, or -OC(O)NR' (where each R' is independently H or Calkyl, e.g. CH), preferably R is H, OH or -OC(O)NH; R is a 5- or 6-membered, saturated or unsaturated, carbocyclic ring optionally substituted by one or more substituents, or R is an optionally substituted straight-chained or branched C alkyl group (e.g. C alkyl group); R is a straight-chained or branched C alkyl group (e.g. C alkyl group), a C cycloalkyl group, or an optionally substituted aryl or heteroaryl group; and each --- independently represents an optional bond (i.e. each of C-C, C-C, C-C, C-C, C-C and C-C are independently either C-C (single) or C=C (double) bonds).申请人：THE UNIVERSITY OF WARWICK地址：University House Kirby Corner Road Coventry Warwickshire CV4 8UW GB 国籍：GB代理人：DEHNS更多信息请下载全文后查看。

The Forms of Capital

The Forms of CapitalPierre Bourdieu 1986.The social world is accumulated history, and if it is not to be reduced to a discontinuous series of instantaneous mechanical equilibria between agents who are treated as interchangeable particles, one must reintroduce into it the notion of capital and with it, accumulation and all its effects. Capital is accumulated labor (in its materialized form or its …incorporated,‟ embodied form) which, when appropriated on a private, i.e., exclusive, basis by agents or groups of agents, enables them to appropriate social energy in the form of reified or living labor. It is a vis insita, a force inscribed in objective or subjective structures, but it is also a lex insita, the principle underlying the immanent regularities of the social world. It is what makes the games of society – not least, the economic game –something other than simple games of chance offering at every moment the possibility of a miracle. Roulette, which holds out the opportunity of winning a lot of money in a short space of time, and therefore of changing one‟s social status quasi-instantaneously, and in which the winning of the previous spin of the wheel can be staked and lost at every new spin, gives a fairly accurate image of this imaginary universe of perfect competition or perfect equality of opportunity, a world without inertia, without accumulation, without heredity or acquired properties, in which every moment is perfectly independent of the previous one, every soldier has a marshal‟s baton in his k napsack, and every prize can be attained, instantaneously, by everyone, so that at each moment anyone can become anything. Capital, which, in its objectified or embodied forms, takes time to accumulate and which, as a potential capacity to produce profits and to reproduce itself in identical or expanded form, contains a tendency to persist in its being, is a force inscribed in the objectivity of things so that everything is not equally possible or impossible.[1] And the structure of the distribution of the different types and subtypes of capital at a given moment in time represents the immanent structure of the social world, i.e. , the set of constraints, inscribed in the very reality of that world, which govern its functioning in a durable way, determining the chances of success for practices.It is in fact impossible to account for the structure and functioning of the social world unless one reintroduces capital in all its forms and not solely in the one form recognized by economic theory. Economic theory has allowed to be foisted upon it a definition of the economy of practices which is the historical invention of capitalism; and by reducing the universe of exchanges to mercantile exchange, which is objectively and subjectively oriented toward the maximization of profit, i.e., (economically) self-interested, it has implicitly defined the other forms of exchange as noneconomic, and therefore disinterested. In particular, it defines as disinterested those forms of exchange which ensure the transubstantiation whereby the most material types of capital – those which are economic in the restricted sense –can present themselves in the immaterial form of cultural capital or social capital and vice versa. Interest, in the restricted sense it is given in economic theory, cannot be produced without producing its negative counterpart, disinterestedness. The class of practices whose explicit purpose is to maximize monetary profit cannot be defined as such without producing the purposeless finality of cultural or artistic practices and their products; the world of bourgeois man, with his double-entry accounting, cannot be invented without producing the pure, perfect universe of the artist and the intellectual and the gratuitous activities of art-for-art‟s sake and pure theory. In other words, the constitution of a science of mercantile relationships which, inasmuch as it takes for granted the very foundations of the order it claims toanalyze – private property, profit, wage labor, etc. – is not even a science of the field of economic production, has prevented the constitution of a general science of the economy of practices, which would treat mercantile exchange as a particular case of exchange in all its forms.It is remarkable that the practices and assets thus salvaged from the …icy water of egotistical calculation‟ (and from science) are the virtual monopoly of the dominant class – as if economism had been able to reduce everything to economics only because the reduction on which that discipline is based protects from sacrilegious reduction everything which needs to be protected. If economics deals only with practices that have narrowly economic interest as their principle and only with goods that are directly and immediately convertible into money (which makes them quantifiable), then the universe of bourgeois production and exchange becomes an exception and can see itself and present itself as a realm of disinterestedness. As everyone knows, priceless things have their price, and the extreme difficulty of converting certain practices and certain objects into money is only due to the fact that this conversion is refused in the very intention that produces them, which is nothing other than the denial (Verneinung) of the economy. A general science of the economy of practices, capable of reappropriating the totality of the practices which, although objectively economic, are not and cannot be socially recognized as economic, and which can be performed only at the cost of a whole labor of dissimulation or, more precisely, euphemization, must endeavor to grasp capital and profit in all their forms and to establish the laws whereby the different types of capital (or power, which amounts to the same thing) change into one another.[2]Depending on the field in which it functions, and at the cost of the more or less expensive transformations which are the precondition for its efficacy in the field in question, capital can present itself in three fundamental guises: as economic capital, which is immediately and directly convertible into money and may be institutionalized in the forms of property rights; as cultural capital, which is convertible, on certain conditions, into economic capital and may be institutionalized in the forms of educational qualifications; and as social capital, made up of social obligations (…connections‟), which is convertible, in certain conditions, into economic capital and may be institutionalized in the forms of a title of nobility.[3]资本可以以三种基本的外表来呈现自身，这取决于它起作用的场域，而且那多或少昂贵的转换预先决定了资本在被讨论的场域中是否有效，例如，经济资本能直接无碍地转化为金钱并能以财产权的形式被体制化；文化资本在某些特定情况下可以被转化为经济资本并能以教育资格证明（educational qualifications）的形式被体制化；社会资本由社会责任[obligations]（即联系connection）组成，在特定情况下能被转换成经济资本，且能以荣誉头衔的形式被体制化。

antibiotic agents

Antibacterial agents∙amikacin AMK阿米卡星∙〖所属类别〗抗微生物药物/抗生素/氨基糖苷类∙amoxicillin AMX阿莫西林∙〖所属类别〗抗微生物药物/抗生素/青霉素类∙ampicillin AMP氨苄西林∙〖所属类别〗抗微生物药物/抗生素/青霉素类∙ampicillin/sulbactam SAM∙Sulbactam舒巴坦∙〖所属类别〗抗微生物药物/抗生素/β-内酰胺酶抑制剂∙azithromycin AZM阿奇霉素∙〖所属类别〗抗微生物药物/抗生素/大环内酯类∙azlocillin AZL阿洛西林∙〖所属类别〗抗微生物药物/抗生素/青霉素类∙aztreonam ATM氨曲南∙〖所属类别〗抗微生物药物/抗生素/其它β-内酰胺类∙carbenicillin CAR羧苄西林∙〖所属类别〗抗微生物药物/抗生素/青霉素类∙cefaclor CEC头孢克洛∙〖所属类别〗抗微生物药物/抗生素/头孢菌素类∙cefadroxil CFR头孢羟氨苄∙〖所属类别〗抗微生物药物/抗生素/头孢菌素类∙cefalexin LEX头孢氨苄∙〖所属类别〗抗微生物药物/抗生素/头孢菌素类∙cefalotin CEF头孢噻吩∙〖所属类别〗抗微生物药物/抗生素/头孢菌素类∙cefamandole FAM头孢孟多∙〖所属类别〗抗微生物药物/抗生素/头孢菌素类∙cefapirin HAP头孢匹林∙〖所属类别〗抗微生物药物/抗生素/头孢菌素类∙cefazolin CFZ头孢唑林∙〖所属类别〗抗微生物药物/抗生素/头孢菌素类∙cefdinir CDR头孢地尼∙〖所属类别〗抗微生物药物/抗生素/其它抗菌抗生素∙cefditoren CDN头孢托仑∙〖所属类别〗抗微生物药物/抗生素/其它抗菌抗生素∙cefepime FEP头孢吡肟∙〖所属类别〗抗微生物药物/抗生素/头孢菌素类∙cefetamet FET头孢他美∙〖所属类别〗抗微生物药物/抗生素/头孢菌素类∙cefixime CFM头孢克肟∙〖所属类别〗抗微生物药物/抗生素/头孢菌素类∙cefmetazole CMZ头孢美唑∙〖所属类别〗抗微生物药物/抗生素/头孢菌素类∙cefonicid CID头孢尼西∙〖所属类别〗抗微生物药物/抗生素/头孢菌素类∙cefoperazone CFP头孢哌酮∙〖所属类别〗抗微生物药物/抗生素/头孢菌素类∙cefotaxime CTX头孢噻肟∙〖所属类别〗抗微生物药物/抗生素/头孢菌素类∙cefotetan CTT头孢替坦∙〖所属类别〗抗微生物药物/抗生素/头孢菌素类∙cefoxitin FOX头孢西丁∙〖所属类别〗抗微生物药物/抗生素/头孢菌素类∙cefpodoxime CPD头孢泊肟∙〖所属类别〗抗微生物药物/抗生素/头孢菌素类∙cefprozil CPR头孢丙烯∙〖所属类别〗抗微生物药物/抗生素/头孢菌素类∙cefradine RAD头孢拉定∙〖所属类别〗抗微生物药物/抗生素/头孢菌素类∙ceftazidime CAZ头孢他啶∙〖所属类别〗抗微生物药物/抗生素/头孢菌素类∙ceftibuten CTB头孢布烯∙〖所属类别〗抗微生物药物/抗生素/头孢菌素类∙ceftizoxime ZOX头孢唑肟∙〖所属类别〗抗微生物药物/抗生素/头孢菌素类∙ceftriaxone CRO头孢曲松∙〖所属类别〗抗微生物药物/抗生素/头孢菌素类cefuroxime CXM ∙chloramphenicol CHL氯霉素∙〖所属类别〗抗微生物药物/抗生素/酰胺醇类∙cinoxacin CIN西诺沙星∙〖所属类别〗抗微生物药物/合成抗菌药/喹诺酮类∙ciprofloxacin CIP环丙沙星∙〖所属类别〗抗微生物药物/合成抗菌药/喹诺酮类∙clarithromycin CLR克拉霉素∙〖所属类别〗抗微生物药物/抗生素/大环内酯类∙clinafloxacin CLX克林沙星∙〖所属类别〗抗微生物药物/合成抗菌药/喹诺酮类∙clindamycin CLI克林霉素∙〖所属类别〗抗微生物药物/抗生素/其它抗菌抗生素∙co-amoxiclav AMC∙daptomycin DAP达托霉素∙〖所属类别〗抗微生物药物/抗生素/其它抗菌抗生素∙dicloxacillin DCX双氯西林∙〖所属类别〗抗微生物药物/抗生素/青霉素类dirithromycin DTM ∙doxycycline DOX多西环素∙〖所属类别〗抗微生物药物/抗生素/四环素类∙enoxacin ENX∙〖药品通用名〗依诺沙星∙〖所属类别〗抗微生物药物/合成抗菌药/喹诺酮类∙erythromycin ERY∙〖药品通用名〗红霉素∙〖所属类别〗抗微生物药物/抗生素/大环内酯类∙fleroxacin FLE∙〖药品通用名〗氟罗沙星∙〖所属类别〗抗微生物药物/合成抗菌药/喹诺酮类∙fosfomycin FOF∙〖药品通用名〗磷霉素∙〖所属类别〗抗微生物药物/抗生素/其它抗菌抗生素gatifloxacin GAT ∙gentamicin GEN∙〖药品通用名〗庆大霉素∙〖所属类别〗抗微生物药物/抗生素/氨基糖苷类∙grepafloxacin GRX∙〖药品通用名〗格帕沙星∙〖所属类别〗抗微生物药物/其它类∙imipenem IPM∙〖药品通用名〗亚胺培南∙〖所属类别〗抗微生物药物/抗生素/其它β-内酰胺类∙kanamycin KAN∙〖药品通用名〗卡那霉素∙〖所属类别〗抗微生物药物/抗生素/氨基糖苷类∙levofloxacin LVX∙〖药品通用名〗左氧氟沙星∙〖所属类别〗抗微生物药物/其它类linezolid LZD∙lomefloxacin LOM∙〖药品通用名〗洛美沙星∙〖所属类别〗抗微生物药物/合成抗菌药/喹诺酮类loracarbef LOR∙meropenem MEM∙〖药品通用名〗美罗培南∙〖所属类别〗抗微生物药物/抗生素/其它β-内酰胺类methicillin MET ∙mezlocillin MEZ∙〖药品通用名〗美洛西林∙〖所属类别〗抗微生物药物/抗生素/青霉素类∙minocycline MIN∙〖药品通用名〗米诺环素∙〖所属类别〗抗微生物药物/抗生素/四环素类moxalactam MOX∙moxifloxacin MXF∙〖药品通用名〗莫西沙星∙〖所属类别〗抗微生物药物/合成抗菌药/喹诺酮类∙nafcillin NAF∙〖药品通用名〗萘夫西林∙〖所属类别〗抗微生物药物/抗生素/青霉素类∙nalidixic acid NAL∙〖药品通用名〗萘啶酸∙〖所属类别〗抗微生物药物/合成抗菌药/喹诺酮类∙netilmicin NET∙〖药品通用名〗奈替米星∙〖所属类别〗抗微生物药物/抗生素/氨基糖苷类∙nitrofurantoin NIT∙〖药品通用名〗呋喃妥因∙〖所属类别〗抗微生物药物/合成抗菌药/硝基呋喃类norfloxacin NOR ∙ofloxacin OFX∙〖药品通用名〗氧氟沙星∙〖所属类别〗抗微生物药物/合成抗菌药/喹诺酮类oxacillin OXA∙penicillin PEN∙〖中文通用名〗青霉素∙〖所属类别〗抗微生物药物/抗生素/青霉素类∙piperacillin PIP∙〖药品通用名〗哌拉西林∙〖所属类别〗抗微生物药物/抗生素/青霉素类∙piperacillin/tazobactam TZP∙quinupristin/dalfopristin Q/D∙[英汉药名关联词典]Quinupristin∙〖药品通用名〗奎奴普丁∙〖所属类别〗抗微生物药物/其它类∙[英汉药名关联词典]Dalfopristin∙〖药品通用名〗达福普汀∙〖所属类别〗抗微生物药物/其它类∙rifabutin RFB∙利福布丁[治疗爱滋病引起的鸟结核分支杆菌综合征]∙〖药品通用名〗利福布汀∙〖所属类别〗抗微生物药物/抗结核病药∙rifampicin RIF∙[英汉药名关联词典]Rifampicin∙ Isoniazid and Pyrazinamide Tablets∙〖药品商品名〗异福酰胺片∙〖英文通用名〗Rifampicin+Pyrazinamide+Isoniazid∙〖中文通用名〗利福平+吡嗪酰胺+异烟肼rifapentine RFP∙sparfloxacin SPX∙〖药品通用名〗司帕沙星∙〖所属类别〗抗微生物药物/合成抗菌药/喹诺酮类∙spectinomycin SPT [英汉通用词典]spectinomycin∙大观霉素∙〖药品通用名〗大观霉素∙〖所属类别〗抗微生物药物/抗生素/氨基糖苷类∙streptomycin STR∙〖药品通用名〗链霉素∙〖所属类别〗抗微生物药物/抗生素/氨基糖苷类∙teicoplanin TEC∙〖所属类别〗抗微生物药物/抗生素/其它抗菌抗生素∙[英汉医学词典]Teicoplanin∙替考拉宁[新糖肽类抗生素]∙telithromycin TEL∙泰利霉素<抗生素类药>tetracycline TET∙ticarcillin TIC∙〖药品通用名〗替卡西林∙〖所属类别〗抗微生物药物/抗生素/青霉素类∙ticarcillin/clavulanic acid TIM∙tobramycin TOB∙〖药品通用名〗妥布霉素∙〖所属类别〗抗微生物药物/抗生素/氨基糖苷类trimethoprim TMP∙trimethoprim/sulfamethoxazole SXT∙[英汉药名关联词典]Trimethoprim∙〖药品通用名〗甲氧苄啶∙〖所属类别〗抗微生物药物/合成抗菌药/甲氧苄啶类trovafloxacin TVA ∙[英汉医学词典]sulfamethoxazole∙ [英汉药名关联词典]Sulfamethoxazole∙〖药品通用名〗磺胺甲噁唑∙〖所属类别〗抗微生物药物/合成抗菌药/磺胺类∙vancomycin VAN∙〖药品通用名〗万古霉素∙〖所属类别〗抗微生物药物/抗生素/其它抗菌抗生素∙β-lactamase inhibitors∙clavulanic acid CLA∙棒酸,克拉布兰酸,克拉呋酸,克拉维酸∙[英汉药名关联词典]ClavulaniC Acid∙〖药品通用名〗克拉维酸∙〖所属类别〗抗微生物药物/抗生素/β-内酰胺酶抑制剂∙sulbactam SUL∙〖药品通用名〗舒巴坦∙〖所属类别〗抗微生物药物/抗生素/β-内酰胺酶抑制剂∙tazobactam TZB∙〖药品通用名〗三唑巴坦∙〖所属类别〗抗微生物药物/抗生素/青霉素类Antifungal agents∙amphotericin B AMB∙两性霉素B∙[英汉药名关联词典]Amphotericin B∙〖药品通用名〗两性霉素B∙〖所属类别〗抗微生物药物/抗真菌药∙clotrimazole CLT∙〖药品通用名〗克霉唑∙〖所属类别〗抗微生物药物/抗真菌药∙flucytosine 5FC∙〖药品通用名〗氟胞嘧啶∙〖所属类别〗抗微生物药物/抗真菌药∙fluconazole FLC∙氟康唑,大扶康∙itraconazole ITC∙〖药品通用名〗伊曲康唑∙〖所属类别〗抗微生物药物/抗真菌药ketoconazole KTC ∙nystatin NYT∙〖药品通用名〗制霉菌素∙〖所属类别〗抗微生物药物/抗真菌药terbinafine TRB ∙voriconazole VRC∙伏立康唑<抗真菌药>∙Antiviral agents∙aciclovir ACV∙〖药品通用名〗阿昔洛韦∙〖所属类别〗抗微生物药物/抗病毒药∙cidofovir CDV∙〖药品通用名〗西多福韦∙〖所属类别〗抗微生物药物/抗病毒药∙didanosine ddI [英汉医学词典]Didanosine∙地达诺新,双脱氧腺苷[抗艾滋病药]∙[英汉药名关联词典]Didanosine∙〖药品通用名〗去羟肌苷∙〖所属类别〗抗微生物药物/抗病毒药∙famciclovir FCV∙泛西洛维[抗病毒药]∙[英汉药名关联词典]Famciclovir∙〖药品通用名〗泛昔洛韦∙〖所属类别〗抗微生物药物/抗病毒药∙foscarnet FOS [英汉医学词典]Foscarnet∙膦甲酸(抗病毒药)ganciclovir GCV∙indiavir IDV∙lamivudine 3TC [英汉医学词典]lamivudine∙拉米夫定∙[英汉药名关联词典]Lamivudine∙〖药品通用名〗拉米夫定∙〖所属类别〗抗微生物药物/抗病毒药∙penciclovir PCV [英汉医学词典]Penciclovir∙喷西洛维[治带状疱疹药]∙[英汉药名关联词典]Penciclovir∙〖药品通用名〗喷昔洛韦∙〖所属类别〗抗微生物药物/抗病毒药ritonavir RTV∙saquinavir SQV [英汉医学词典]saquinavir∙沙奎那韦<抗病毒药>∙[英汉药名关联词典]Saquinavir∙〖药品通用名〗沙奎那韦∙〖所属类别〗抗微生物药物/抗病毒药stavudine d4T∙valaciclovir VCV [英汉医学词典]valaciclovir∙缬昔洛韦∙[英汉药名关联词典]Valaciclovir∙〖药品通用名〗伐昔洛韦∙〖所属类别〗抗微生物药物/抗病毒药∙zalcitabine ddC [英汉医学词典]Zalcitabine∙扎西他宾,唑西他宾[抗艾滋病药]∙[英汉药名关联词典]Zalcitabine∙〖药品通用名〗扎西他滨∙〖所属类别〗抗微生物药物/抗病毒药∙zidovudine ZDV[英汉医学词典]Zidovudine∙叠氮胸苷,齐多夫定,叠氮脱氧胸苷∙[英汉药名关联词典]Zidovudine∙〖药品通用名〗齐多夫定∙〖所属类别〗抗微生物药物/抗病毒药BACKGROUND AND SCOPE OF THE JOURNALThe Journal of Antimicrobial Chemotherapy was founded in 1975 and is owned by the British Society for Antimicrobial Chemotherapy, which exists to facilitate the acquisition and dissemination of knowledge in the field of antimicrobial chemotherapy. The Journal is published monthly and is among the foremost international journals in antimicrobial research. Our readership includes representatives of academia, industry and health services, and includes those who are influential in formulary decisions.The Journal features original articles and brief reports on the laboratory aspects and clinical use of antimicrobials including antibacterial, antiviral, antifungal, and antiprotozoal agents. In addition to a wealth of primary papers, the journal carries review articles offering in-depthdiscussion on matters of topical concern, and lively leading articles offer incisive coverage of recent advances and controversies. Other sections include: antimicrobial practice (articles on practical prescribing and formulary issues), correspondence (a section for scientists to present early observations of their work and to comment on published articles) and book reviews (helpful reviews of relevant, newly published titles).Submissions to JAC have nearly doubled over the last 5 years, and it has therefore become necessary to increase the rejection rate to at least 50%. Manuscript evaluation time (i.e. time from submission to first decision) is typically 4-5 weeks, and the time from acceptance to publication is approximately 4-5 weeks (papers are published online ahead of print, which greatly reduces publication times). The journal has an impact factor of 3.611 and is ranked 19/84 and 10/41 in the Microbiology and Infectious Disease categories, respectively, of the ISI Journal Citation Reports (2005).EDITORIAL OFFICE CONTACT INFORMATIONJAC Editorial Office11 The Wharf16 Bridge StreetBirminghamB1 2JSUKTelephone:+44 121 633 0415Fax:+44 121 643 9497GENERALMaterial offered for publication must be original, unpublished and not under simultaneous consideration by another journal. Any previouspublication of the material (including conference proceedings, letters to journals and brief communications) must be declared. For these purposes the posting of essentially raw data on a website without significant analysis, is not considered to represent prior publication.Please note that we discourage the unnecessary fragmentation of research into 'least-publishable' units.ETHICSPublication ethicsJAC is a member of the Committee on Publication Ethics (COPE), and strives to adhere to its code of conduct and guidelines. For further information see /.Ethics approval and patient consentIn reports of investigations in humans or animals, authors must explicitly indicate (in the appropriate section of the Methods) their adherence to ethical standards and note the approval of an ethics committee when this is relevant. Where approval from an ethics committee has been obtained authors must provide the approval/reference number. Articles in which adherence to ethical/animal care standards is not explicitly acknowledged will be returned to the authors. For human studies, written informed consent must be obtained from each participant and authors must include a statement indicating that such consent has been given. Patients must not be referred to by their own initials or hospital numbers. Work offered for publication in the Journal must conform to the standards for experimentation and care set down in the UK Animals (Scientific Procedures) Act of 1986 and the Code of Practice for the Housing and Care of Animals used in Scientific Procedures 1989. Authors who are in doubt about complying with these provisions should contact the Editorial Office.Financial supportIt is important that readers should be informed about who has funded particular research, as well as the role, if any, of the funders in the research. This information must always be included and should be disclosed in the ‘Acknowledgements’ section. In addition, ongoing support for any of the authors should also be stated.Transparency declarationsIt is important for readers of JAC to be made aware of any interests of authors that could be perceived as having influenced the views they have expressed. Only when appropriately informed can readers make a judgment regarding the potential for interests to influence a particular article.In the interests of openness, ALL papers submitted to JAC must include a ‘Transparency declarations’ section (which should appear after the ‘Acknowledgements’ section). We suggest authors concentrate on transparency declarations of a financial nature, although relevant non-financial disclosures can also be made. Authors should consider making a declaration if they answer 'Yes' to either of the following questions:1. Have you in the period of research leading up to this publication accepted any of the following from an organization (including government departments or granting bodies) that may in any way be financially affected by the conclusions of your article (e.g. reimbursement for attending a symposium, a fee for speaking, a consultancy fee, funds for research other than directly for this work, funds for a member of staff, any other substantial material benefit)?2. Do you directly own any stocks or shares in a company that might be financially affected by the conclusions of your article?Authors should either include appropriate declarations or state ‘None to d eclare’. Importantly, the declarations should be kept as concise as possible, should avoid giving financial details (e.g. sums received, numbers of shares owned etc.), and should be restricted to declarations that are specific to the paper in question. Authors will of course need to consider whether or not the transparency declarations need to be amended when revisions are submitted. A suitable declaration might be as follows:'CD has received funds for speaking at symposia organized on behalf of Panacea Ltd and has also received funds for research from Panacea. CD is a member of the Panacea advisory board for fantastazole.’The burden of responsibility rests with all authors, who must ensure that appropriate declarations are included. The corresponding author will be responsible for obtaining the relevant information from all of their co-authors. By signing a submission form each author is stating that they have made any necessary transparency declaration. All authors shouldcarefully consider the embarrassment and potential damage to their reputation that could result should they fail to declare an interest that is revealed subsequently.All papers submitted from January 2006 onwards must include a transparency declarations section; papers that do not include such a section will not enter the review process; they will be returned to the corresponding author so that the appropriate section can be added. Following resubmission the paper will then be progressed to peer review.From now until January authors will be encouraged to include transparency declarations, both on submission and at the revision decision stage. Failure to include a transparency declaration section will result in the following statement appearing in the event of publication: ‘No declarations were made by the author(s) of this paper’. Authors will have one last chance to amend this statement at the proof stage.AUTHORSHIPThe authorship of the paper should be confined to those who have made a significant contribution to the design and execution of the work described.Author signed submission formsPlease note that the Journal requires the original signatures of ALL authors. This is the only way in which the Journal can be certain that all authors agree with the submission. The names of any persons from whom signed consent cannot be obtained should not be included on the author list. We regret that this includes cases in which a potential author has died prior to manuscript submission or where contact with a potential author has been lost for any other reason. In these instances the authors should acknowledge the contribution of these individuals in the Acknowledgements section. The Journal reserves the right to remove the name of any author from the author list if their signed consent cannot be obtained for any reason.Changes in authorshipThe author list of any submission should be decided upon and fixed BEFORE submission. Other than in exceptional circumstances the Journal does not allow addition or removal of author names after submission. A satisfactory explanation for any proposed changes in authorship will be required and ALL authors will be required to supply new signed consent forms thatreflect the changes. We will also require a signed consent form from any person whose name has been removed indicating that they agree to the removal of their name from the author list. Owing to the complexity of these rules we strongly advise authors to fix the author list before submission and not to attempt to make changes later.'Umbrella' groups and authorshipMany large collaborative studies (frequently resistance surveys) are organized under a group name which represents all the participants. JAC will not accept a group name as an 'author' of an article. All articles must have at least one named individual as author. Authors who wish to acknowledge the umbrella group from which the data originate should first list the author(s) of the article and follow this with 'on behalf of the GROUP NAME'. If necessary the names of the participants may be listed in the Acknowledgements section.RESPONSIBILITIES OF THE CORRESPONDING AUTHORAs the representative of the authors, the corresponding author must ensure that all authors are given access to submitted and revised versions of papers. The corresponding author is responsible for the collation of the authors signatures on submission letters and also the collation and communication of proof corrections to the Journal. The corresponding author should be the signatory of the copyright licence form. As the authors' nominated representative, the corresponding author will be held primarily accountable for any failure to comply with the Instructions to authors or generally accepted standards of good practice. This does not absolve other authors of responsibility, however.The corresponding author will act as the primary contact for correspondence regarding the paper, and as such authors should take care not to appoint a corresponding author likely to be absent for extended periods (such as a sabbatical) during the consideration of the paper as this is likely to cause unacceptable delays.Please note that papers submitted via Manuscript Central must be submitted through the account of the corresponding author listed on the paper, not through the account of one of the other authors or the account of a third party who is not on the author list. In addition, the authors listed during the submission process on the Manuscript Central website must fully match the author list of the actual submitted article.TOPPERMISSIONSAuthors must:obtain permission from the original publisher and, if possible, the original author (i.e. the corresponding author of the article from which the figure/table has come) for reproducing/modifying figures/tables.include a statement indicating that permission has been obtained in the relevant legend/footnote.get permission for print AND electronic use.provide the Editorial Office with copies of any relevant paperwork. SUBMISSION OF PAPERSWhere to submitAll material to be considered for publication should be submitted in electronic form via the Journal's online submission system at:/oup/jacGiven that you can produce a file of your paper through a word processing package of some description, you only need the three following items to access and use the system: access to the website via a web browser, Adobe Acrobat Reader (which can be downloaded free of charge from/) and an e-mail account. For more guidance see the section Online submission details.In addition to submitting your paper online you should simultaneously provide a written statement, signed by all the authors indicating that you have complied with the stipulations in the Instructions to authors.A copy with the original signatures must be faxed to the Editorial Office as soon as possible after online submission. A blank form is available on the Instructions and Forms page of the JAC Manuscript Central website. If at any stage during consideration the authorship of the article changes, the authors must supply a signed statement from ALL the authors (includingany whose names are being removed) explicitly indicating the nature of the changes and their agreement.Be aware that the Journal will NOT publish any article until it has received both:1.The appropriate author submission letter bearing the necessary original signatures;2. A copyright licence form bearing an original signature (a fax is NOT acceptable). Papers in pressIn-press and submitted papers that are important for the review of your paper should also be uploaded when you submit your paper online. Authors should be aware of the issues of redundant/duplicate publication. For further information, please see the following Editorial:Reeves DS, Wise R, Drummond CWE. Duplicate publication: a cautionary tale. J Antimicrob Chemother 2004; 53: 411-2.Supplementary dataPlease note that it is also possible to include files containing supplementary data. The supplementary data (for example large tables of MICs, or a questionnaire) can be lodged with the version of the paper published online as an extra resource for readers. Please contact the Editorial Office if you would like further details.Assignment of copyrightPapers are considered on the understanding that after acceptance and before publication the authors will grant an exclusive licence to publish to the British Society for Antimicrobial Chemotherapy.Article formatAll documents should be double spaced, with wide margins. A clear, legible single font (which is readily available internationally) and point size should be employed throughout. For symbols, please use the 'insert symbol' function and ONLY select characters from the 'normal text' subset. All submitted articles should be line numbered (using continuous line numbers). To do this in Word, use File, Page Setup, Layout, Line Numbersand select continuous line numbering. Please DO NOT insert page numbers (as the pdf proof created by the online submission system will automatically be page numbered).Original articles and Brief reports must have a structured synopsis. The headings for the structured synopsis are as follows: Objectives, Patients and methods (or Methods), Results, and Conclusions. Authors should also provide 3-5 keywords. Very general terms such as 'bacteria' and 'human' and terms already present in the title should be avoided, as should non-standard abbreviations.Original articles. There is no length limit for this format; however, papers must be written as concisely as possible. Original articles are divided into the following sections: Synopsis (250 words maximum), Introduction, Materials (or Patients) and methods, Results, Discussion, Acknowledgements and References. Repetition of content between sections must be avoided. A combined Results and Discussion section is acceptable.Brief reports. These should have the same format as Original articles, but should have no more than two figures/tables, should have a maximum of 10 references and should not exceed 1500 words of text.Antimicrobial practice. Short articles on topics related to the use of antimicrobials, format as for Brief reports.Correspondence.Letters on topics of concern or interest in the field of antimicrobial chemotherapy, particularly arising from papers or letters already published in the Journal. These should be addressed to the Editor-in-Chief and must not exceed 800 words, one figure or table and six references.Case reports.JAC will publish Case reports that are of sufficient calibre and potential importance, and they should be submitted in the form of Correspondence (see above).Systematic review articles.There is no length limit for this format. They should be summaries of previously published literature that use explicit methods to identify, select and appraise relevant research, and that use appropriate statistical methods to combine the valid studies. They should include a structured synopsis (with appropriate headings; these may differ from the headings used for Original articles etc.).Review articles.There is no length limit for this format. These generally aim to give an overview of a field suitable for a wide audience, and they should include a synopsis (250 words maximum). Most reviews are invited.We are pleased to consider unsolicited reviews, but authors are encouraged to consult the Editor-in-Chief in advance of writing to avoid duplicating commissioned material.Leading articles. These are usually in the region of 800-1000 words and may contain the expression of opinion as well as fact. They should address a topical subject, perhaps taking a particular viewpoint and throwing new light on a current debate. A leading article should include a short synopsis (150 words maximum) that should convey the topics and ideas the article covers. Those wishing to contribute a Leading article are encouraged to contact the Editor-in-Chief to discuss their ideas before writing to prevent clashes with any articles already in the pipeline.STYLEGeneralIn addition to reading the information provided here, authors should consult a recent issue of the Journal for the layout and conventions used.The past tense should be used throughout for description of the results of the paper, the present tense should be used when referring to previously established and generally accepted results.Where possible SI units should be used.SpellingBritish spelling should be used. Spelling should follow that of the Oxford Dictionary for Scientific Writers and Editors and where this gives no guidance the Concise Oxford Dictionary. Spelling of drug names should conform with that given in the latest edition of the British National Formulary (published by the British Medical Association and the Royal Pharmaceutical Society of Great Britain and available online at/webnf/lform1/bnf/index.html).AbbreviationsNon-standard abbreviations should be defined at the first occurrence and introduced only where multiple use is made. The following abbreviations may be used without definition:Organizations∙ATCC American Type Culture Collection∙CDC Centers for Disease Control (and Prevention) (USA)∙CDSC Communicable Disease Surveillance Centre (UK)∙HPA Health Protection Agency (UK)∙MRC Medical Research Council (UK)∙NCCLS National Committee for Clinical Laboratory Standards (as of 2005: CLSI Clinical and Laboratory Standards Institute)∙NIH National Institutes of Health (USA)∙WHO World Health OrganizationGeneral∙AIDS acquired immunodeficiency syndrome∙AMP, ADP, A TP etc. adenosine 5'-phosphate, adenosine 5'-diphosphate, adenosine 5'-triphosphate∙bp base pair(s)∙BSA bovine serum albumin∙cfu colony-forming unit(s)∙CNS central nervous system∙cpm counts per min∙CSF cerebrospinal fluid∙Da dalton(s)∙DNA, cDNA deoxyribonucleic acid, complementary DNA∙dpm disintegrations per min∙ELISA enzyme-linked immunosorbent assay∙g acceleration due to gravity∙h hour(s)∙HEPES 4-(2-hydroxyethyl)-1-piperazine-ethanesulphonic acid∙HPLC high-performance (or high-pressure) liquid chromatography∙ID50 50% infective dose∙IU international unit(s)∙kg kilogram(s)∙L litre(s)∙LD50 50% lethal dose∙m metre(s)∙MBC minimum bactericidal concentration∙MIC minimum inhibitory concentration∙min minute(s)∙MLD minimum lethal dose∙mol. wt molecular weight∙MOPS 4-morpholinepropanesulphonic acid∙nt nucleotide(s)∙P probability∙PAGE polyacrylamide gel electrophoresis∙PBS phosphate-buffered saline∙PCR polymerase chain reaction∙PFGE pulsed-field gel electrophoresis∙pfu plaque-forming unit(s)∙ppm parts per million∙RNA ribonucleic acid∙rpm revolutions per min∙RT-PCR reverse transcriptase PCR∙s second(s)∙SDS sodium dodecyl sulphate∙sp./spp. species (singular)/species (plural)∙Tris 2-amino-2-hydroxymethylpropane-1,3-diol∙U unit(s)Dosage and routes of administrationDosage frequencies should be given in full in English at each occurrence. Abbreviations are not permitted. Routes of administration other than intramuscular (im) and intravenous (iv), which may be abbreviated after definition, should be given in full in English.Pharmacokinetics∙AUC area under the concentration-time curve∙AUC0-24 and AUC0-∞ AUC from 0-24 h and AUC from 0 h to infinity∙CL clearance∙CL CR creatinine clearance∙CLN R non-renal clearance∙CL R renal clearance∙C max maximum concentration of drug in serum∙k el elimination rate constant∙k ss residence rate constant at steady state∙t½ half-life∙T max time to maximum concentration of drug in serum∙V volume of distribution∙V max maximum rate of metabolism∙V ss volume of distribution at steady statePharmacodynamics。

心理契约的内容、维度和类型

1997 年 Herriot 和 Manning 等以管理者代表组织用关键事件技术和比例分层抽样方法对英国各地区各行业的 184 名管理者和 184 名员工的心理契约内容进行研究结果发现两类心理契约中的组织责任项目有 1 培训 2 公正 3 关怀 4 协商 5 信任 6 友善 7 理解 8 安全 9 有恒一致 10 薪资 11 福利 12 工作稳定等 12 个类别员工责任项目有 1 守时 2 务业
Shapiro 和 Kessler 2000
[16]
703 名管理者和 6953 名雇员 1.同等报酬 2.同等福利 3.报酬与责任挂钩 4.随着生活水平的提高增加工资 5.必要工作培训 6.新知识新技能培训 7.组织支持 8.长期工作保障 9.良好职业前景
Kickul 和 Lester (2001,2002) [17,18]
Rousseau 1990 用典型相关分析 Canonical Correlation Analysis 对所调查的 129 名 MBA 毕业生心理契约的 7 项雇主责任和 8 项雇员责任的进行维度分析结果抽出两对典型变量第 1 对变量反映的是雇员以加班职责外工作为代价以换取组织提供的高额报酬绩效奖励培训和职业发展是以经济交
依照存在主体的不同员工和企业之间的心理契约可分为员工心理契约和组织心理契约两大类别正如人们所了解的那样在每一类别的心理契约中又都分别含有组织对员工的责任简称组织责任或雇主责任和员工对组织的责任简称员工责任或雇员责任两个方面[2] 由于组织心理契约的主体难以确定因此迄今为止的心理契约探讨主要针对员工心理契约进行即探讨员工对个人与企业关系中对以相互责任义务为主要内容的关于相互付出与回报的主观约定[3]