Global Stability Results for the Weak Vector Variational Inequality(2005)

中考英语经典科学实验与科学理论深度剖析阅读理解20题1<背景文章>Isaac Newton is one of the most famous scientists in history. He is known for his discovery of the law of universal gravitation. Newton was sitting under an apple tree when an apple fell on his head. This event led him to think about why objects fall to the ground. He began to wonder if there was a force that acted on all objects.Newton spent many years studying and thinking about this problem. He realized that the force that causes apples to fall to the ground is the same force that keeps the moon in orbit around the earth. He called this force gravity.The discovery of the law of universal gravitation had a huge impact on science. It helped explain many phenomena that had previously been mysteries. For example, it explained why planets orbit the sun and why objects fall to the ground.1. Newton was sitting under a(n) ___ tree when he had the idea of gravity.A. orangeB. appleC. pearD. banana答案：B。

Living a wellordered life is a skill that many strive for but few master. Its not just about keeping a clean house or a tidy schedule its about creating a lifestyle that supports your goals and wellbeing. Heres my take on how to manage lifes order, drawing from my personal experiences and observations.Embracing RoutineA structured routine is the backbone of an orderly life. Ive found that starting my day with a consistent morning ritual sets the tone for the rest of the day. Whether its a quick workout, a healthy breakfast, or a few moments of meditation, these activities help me feel prepared and focused. Its the predictability of routine that provides a sense of control over the chaos of life.Prioritizing TasksUnderstanding whats truly important is crucial. Ive learned to prioritize tasks based on urgency and importance, a method popularized by Stephen Covey. By categorizing tasks into four quadrants, I can focus on what truly matters and avoid getting bogged down by less critical activities. This approach has saved me countless hours and reduced unnecessary stress.Time ManagementEffective time management is key to maintaining order. I use a planner to map out my week, allocating time for studies, hobbies, and relaxation.Apps like Google Calendar or Todoist have also been instrumental in keeping me on track. By visualizing my commitments, I can better manage my time and avoid overcommitting.DeclutteringPhysical clutter can lead to mental clutter. Ive made it a habit to declutter my space regularly. Whether its a quick tidyup after school or a deep clean on weekends, a clean environment promotes clarity and focus. Marie Kondos concept of keeping only items that spark joy has been particularly influential in my approach to decluttering.Financial OrganizationManaging finances is another aspect of life that requires order. Ive started using budgeting apps to track my expenses and savings. This has helped me understand my spending habits and make more informed financial decisions. Setting financial goals and having a clear plan for achieving them has brought a sense of stability and security.Digital MinimalismIn todays digital age, managing the digital clutter is just as important as physical. Ive made a conscious effort to reduce screen time and limit my use of social media. By doing so, Ive found more time for meaningful activities and less distraction from my goals.SelfCareLastly, maintaining order in life is not just about external factors its also about internal wellbeing. Ive learned the importance of selfcare, whether its through regular exercise, a balanced diet, or simply taking time to unwind with a good book or a walk in nature. Taking care of my mental and physical health has a profound impact on my ability to manage lifes chaos.ConclusionIn conclusion, managing lifes order is a multifaceted endeavor that involves routine, prioritization, time management, decluttering, financial organization, digital minimalism, and selfcare. Its a continuous process of refinement and adaptation. By implementing these strategies, Ive found a greater sense of control and peace in my life. Its not about achieving perfection but about creating a life that supports your goals and allows you to thrive.。

Robust Control and Estimation Robust control and estimation are critical components in the field of engineering and technology, playing a crucial role in ensuring the stability and performance of complex systems. These techniques are used to design control systems that can effectively handle uncertainties and variations in the system, making them robust to external disturbances and internal changes. This is particularly important in real-world applications where systems are subject to various environmental conditions, component variations, and unexpected disturbances. One of the key challenges in robust control and estimation is dealing with uncertainties in the system model. In many practical scenarios, the exact mathematical model of the system may not be known, or it may be subject to variations over time. Robust control and estimation techniques address this challenge by designing controllers and estimators that can accommodate these uncertainties, ensuring that the system remains stable and performs as desired despite the lack of precise knowledge about the system dynamics. From a practical perspective, robust control and estimation techniques are essential for ensuring the safety and reliability of critical systems. For example, in aerospace engineering, where the performance and stability of an aircraft are of utmost importance, robust control techniques are used to design flight control systems that can handle uncertainties in the aircraft dynamics and external disturbances such as turbulence. Similarly, in automotive engineering, robust control and estimation are used to design vehicle control systems that can adapt to varying road conditions and vehicle dynamics, ensuring a safe and smooth driving experience. Another important aspect of robust control and estimation is its role in addressing the trade-off between performance and robustness. In many control system design problems, there is a trade-off between achieving high performance, such as fast response and minimal overshoot, and ensuring robustness to uncertainties. Robust control and estimation techniques aim to strike a balance between these conflicting objectives, allowing the system to perform well under normal operating conditions while also being resilient to unforeseen disturbances and variations. Moreover, the development of robust control and estimation techniques has been greatly facilitated by advancements in the field ofoptimization and computational methods. Modern control system design often involves the use of sophisticated optimization algorithms to synthesize controllers and estimators that meet the desired performance and robustness specifications. These algorithms enable engineers to systematically explore the design space and find optimal solutions that would be impractical to obtain through manual trial-and-error methods. In conclusion, robust control and estimation play a vital role in ensuring the stability, performance, andreliability of complex engineering systems. These techniques are essential for addressing uncertainties in system models, designing controllers and estimators that can adapt to changing conditions, and striking a balance between performance and robustness. As technology continues to advance, the development of robust control and estimation techniques will remain a critical area of research, with far-reaching implications for a wide range of engineering applications.。

P rocedia - Social and Behavioral Sciences 58 ( 2012 ) 1158 – 11651877-0428 © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the 8th International Strategic Management Conference doi: 10.1016/j.sbspro.2012.09.10971Nizamettin Bayyurt. Tel.: +90-212-866-3300; fax: +90-212-866-3342. E-mail address : bayyurt@.tr.8th International Strategic Management ConferenceThe Impacts of Governance and Education on Agricultural Efficiency: An International AnalysisNizamettin Bayyurt abaFatih University, Istanbul, 34500, TurkeybFatih University,Institute of Social Sciences, Istanbul, 34500, TurkeyAbstractThe main aim of this study is to explain the interaction between governance, education and agricultural efficiency and to expose the impacts of governance and education on agricultural efficiency by a global context. Agricultural efficiency was measured as the ratio of agricultural outputs to agricultural inputs by Data Envelopment Analysisricultural land (km2), fertilizer (tons), the number of tractors, and labor. The output isproduced add value in agricultural area as USD currency. In this study, we combined DEA and a regression analysis in a worldwide context. For this purpose, in the first stage, we used DEA model (output-oriented, constant return to scale model) to analyze the agricultural efficiency of countries. And in the second stage, we used Panel Data Regression Analysis to find the effects of Worldwide Governance Indicators (WGI), education index, and countrytype. 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of The 8thInternational Strategic Management ConferenceKey words: Agricultural Efficiency; Governance; Data Envelopment Analysis; Panel Data Regression.1. IntroductionAgricultural productivity is one of the most important problems of the world. High food prices, climate change, civil wars, and the global financial crisis bring very serious problems such as food safety, hunger and malnutrition in the world. Due to its importance the United Nations 2015 is "fight against hunger and poverty".Available online at © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the 8th International StrategicManagement Conference1159 N izamettin Bayyurt and Senem Yılmaz / P rocedia - Social and Behavioral Sciences 58 ( 2012 )1158 – 1165 There are lots of studies in literature concerning agricultural productivity. And also in recent years ithas begun to realiGovernance has become a hot topic on the critical role it plays in determining social welfare. In 2003,the former Secretary General of the United Nations, Kofi Annan, reflects a growing consensus when hestates that good governance is perhaps the single most important factor in eradicating poverty and promoting development. Not surprisingly, governance as a term has progressed from obscurity to widespread usage, particularly in the last decade. Governance is about the more strategic aspects of steering: the larger decisions about direction and roles. That is, governance is not only about where to go,but also about who should be involved in deciding, and in what capacity [Graham et al. (2003)].For measures of the quality of governance, the World(WGI, such as Voice and Accountability, Political Stability and Absence of Violence, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption) have been produced (WorldBank, 2011). Thesix WGI are recognized by many researchers as the most effective tools for assessing the status of governance in different countries.The main aim of this study is to explain the interaction between governance and agricultural productivity and to expose the impacts of governance on agricultural productivity by an internationalcontext using 64 countries over the period 2002-2008. For 64 countries, data are gathered from the WorldBank database.2.Literature ReviewThere are some researches that have been done on Agricultural Productivity Analysis in literature insome regions such as India [Dayal, E., 1984], Spanish Region [Millan and Aldaz, 1998], European Unionand Eastern Region [Serrao, A., 2003], MENA region [Jemma and Dhif, 2005], Nigeria [Fakayode et al. 2008], Vietnam [Minh and Long, 2008], etc.Lio and Liu (2008) analyzed 118 countries, whether a relationship exist between agricultural productivity and governance indicators for the years 1996, 1998, 2000 and 2002 in their study. Theyfound that when independent variables included in the model separately, the rule of law, control of corruption and government effectiveness increase agricultural productivity. When all of the variableswere included in the model at the same time while rule of law significantly increases the agricultural efficiency, political stability and voice and accountability have emerged a significant decrease in agricultural efficiency. In that study it is concluded that countries of which citizens respect to regulatoryquality have higher efficiency in agriculture. Low agricultural efficiency has been seen in more democratic countries is one the other important finding.Studies have been conducted on farmers' production differences of rich and poor countries. Why do farmers in poor countries cannot produce as much as farmers in rich countries? Schultz (1964) argues thatthe farmers in poor countries are poor, but effective. They are able to allocate their useful resources inrational ways, but do not reach high efficiency. The reason of this condition is explained as the inadequatesupply of modern agricultural technologies.Olson (1996) argued that due to the absence specialization and adequate institutional framework,many poor countries are only wasting money and resources. Individual rational behaviors can result withsocial inefficiencies because of institutional defects.Governance affects agricultural productivity through many channels. First, bad governance affectsefficiency of production by imposing unpredictable taxes (Camposs et al, 1999). Many countries withweak regulations and protectionist policies put high indirect taxes in agriculture. Krueger et al. (1991), in-1983, determined that the market-unfriendly macro-economic policies caused indirect taxes in agriculture by more than three times that of1160N izamettin Bayyurt and Senem Yılmaz / P rocedia - Social and Behavioral Sciences 58 ( 2012 )1158 – 1165 direct taxes. They also viewed that these policies have a deterrent effect in agricultural production. The governance infrastructure may affect agricultural performance in several ways. For instance, the government creates and maintains institutions that are crucial to the functioning of the market system. The protection of property rights and a judicial system administering justice and enforcing contracts strongly affect the incentives for production and investment. In addition, good governance supports a competitive and low-transaction-cost environment, which encourages agricultural innovation and stimulates the adoption of new technologies and forms of organization. The government acts as an important provider of rural infrastructure, public goods and services, and essential information for agriculture for farmers. The government also determines macroeconomic policies that affect both agricultural production and investment. In some countries, agricultural development has been seriously hindered by market-unfriendly policies that are characteristics of bad governance.The majority of individuals will lead the efforts for the protection of property in a country where the rule of law is weak. Most of the resources of a society where corruption is widespread devote to unearned incomes rather than productive activities. Agricultural Organizations, agricultural projects, irrigation units are usually encountered the most corrupted units in countries. Corruption is an obstacle on agricultural development (World Bank, 2007).However, in some cases, poor governance would cause high efficiency and good governance may result in low efficiency. The best known example for that is "Grease the Wheels" hypothesis. In countries with a slow and inefficient bureaucracy, corruption increases efficiency (Huntington, 1968). Political stability may not provide economic efficiency at all times. Because many reforms accelerating the economic efficiency, were made in times of crisis (Binswaeger and Deininger, 1997).3.Methodology3.1.Analytical TechniquesIn this study firstly, agricultural productivity as the ratio of agricultural outputs to agricultural inputs is measured by Data Envelopment Analysis (DEA) which is an efficiency measurement technique.2), fertility (tons), the number of tractorsoutput is produced add value in agricultural area as USD currency. gricultural efficiencies by using DEA (output-oriented, assuming constant returns to scale technology) [Charnes et. al., 1978]stage.3.2.Data Envelopment AnalysisData Envelopment Analysis (DEA) is a linear programming based nonparametric method for measuring the relative efficiency of Decision Making Units (DMUs). DEA creates a frontier function by comparing the ratios of multiple inputs to multiple outputs of similar units taken from the measured observations (Charnes, Cooper, and Rhodes 1978). It was first proposed by Charnes et al. (1978) based on the work of Farrell (1957). Since it was first proposed with CCR model by Charnes et al (1978), some extensions of the model have been developed. Over the years this methodology has been applied across a variety of sectors. An important advantage of DEA is that it is independent of the units measuring inputs and outputs allowing great flexibility in specifying the outputs/inputs to be studied. This is very important in the context of this study as the input and output variables have different units of measurement.Two models in DEA have been largely utilized in efficiency measurements (i) input-oriented and (ii) output-oriented models. With input-oriented DEA, the linear programming model is configured to1161N izamettin Bayyurt and Senem Yılmaz / P rocedia - Social and Behavioral Sciences 58 ( 2012 ) 1158 – 1165 determine how much the input use of a country could achieve the same output level. With this model, thepossible reduction in the levels of the inputs conditional to fixed outputs is found. In contrast, by output-oriented DEA, the linear programme is configured to determine a countryfixed inputs. In the context of this study, output based efficiency measures are suitable for the country level inputs in our data. It is important to use a DEA output based model to measure how much output can be produced from a given level of inputs. The envelopment surface will differ depending on the scale assumptions that describe the model. Two scale assumptions are generally employed: constant returns to scale (CRS), and variable returns to scale (VRS). The latter comprises both increasing and decreasing returns to scale. CRS reflects the fact that output will change by the same proportion as inputs are changed (e.g. doubling of all inputs will double output). VRS reflects the fact that production technology may demonstrate increasing, constant and decreasing returns to scale. In this study we use CRS model. An output oriented CCR DEA model in the literature, can be expressed below for m inputs, s outputs and n DMUs:rj i all for s s mi s x x n j s y y ts s s Max ri j i nj jijikr nj jrj krk mi si r ikk ,,0,,, (1)0,...,1,0.)(1111The DMU kkis 1. If it is less than 1, DMU k is inefficient. The efficiency frontier defined by the above CCR model reveals constant returns to scale (CRS) (Cook and Zhu, 2005). As an extension of CCR DEA model, Banker et al. (1984) referred as BCC model for variable returns to scale (VRS). 3.3. Data and VariablesData on 64 countries over the time period of 2002 through 2008 are used in the empirical analysis. Our country selection process depends on data availability in World Bank. The variables used in the first stage for DEA analysis given below. Output:Value added: Produced add value in agricultural area as USD currency, Inputs:Agricultural land (land): It is estimated by the arable land used for farming, forestry, and production activities. It is measured in km2.Fertilizers: It refers to the sum of pure weight of nitrogen, phosphate, potash, and complex fertilizers which were used for agriculture. It is measured in tons.1162N izamettin Bayyurt and Senem Yılmaz / P rocedia - Social and Behavioral Sciences 58 ( 2012 )1158 – 1165 Machinery (tractors): It is considered as capital input for the agricultural production activities such as plowing, irrigation, draining, harvesting, farm product processing, etc. It is measuredin one unit of tractor.Labor (labor): Participants in the economically active population in agriculture, i.e. employment in agriculture as a percentage of total employment.Since the 1990s, development researchers and practitioners have focused on good governance as both a means of achieving development and a development objective in itself. The World Bank has defined good governance as epitomized by predictable, open and enlightened policy making; a bureaucracy imbued with a professional ethos; an executive arm of government accountable for its actions; and a strong civil society participating in public affairs; and all behaving under the rule of law. In response to the growing demand for measures of the quality of governance, a number of aggregate governance indicators have been produced, such as the W ide Governance Indicators (WGI).The WGI rank countries with respect to six aspects of good governance: Voice and Accountability, Political Stability and Violence, Government Effectiveness, Rule of Law, Regulatory Quality, and Control of Corruption.The Worldwide Governance Indicators are based on several hundred variables produced by 25 different sources, including both public and private (commercial) data providers. The WGI cover 213 countries and territories (Thomas, 2008).The Worldwide Governance Indicators are defined as follows:Voice and accountability: captures perceptions of the extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media.Political stability and absence of violence: measures the perceptions of the likelihood that the government will be destabilized or overthrown by unconstitutional or violent means, including domestic violence and terrorism.Government effectiveness: captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies.Regulatory quality: captures perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development.Rule of law: captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence.Control of corruption: captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as "capture" of the state by elites and private interests (see /governance/wgi/pdf/).primary, secondary and tertiary gross enrolment. In agricultural economics literature edon agricultural productivity have been discussed much. So we include education into our model together with the WGI., we constructed the following linear regression model: For the panel regression analysis dependent variable is country agricultural efficiency and independent variables are six governance indicators, country education index and country type (developed or developing). Analysis has been run for developing and developed countries separately as well.1163N izamettin Bayyurt and Senem Yılmaz / P rocedia - Social and Behavioral Sciences 58 ( 2012 ) 1158 – 1165 18765Re 43210t Edu Edu ContCorr RuleLaw gQual GovEffec PolStab Acc Voice efficiency t t tt t t t tDurbin-Watson result (d=0,60) shows us the regression has autocorrelation, and the VIF value (VIF>10) shows us the high multicollinearity between independent variables. For this reason, we have the following modifications for all the variables in the model like 1.t Efficiency t Efficiency t EfficiencyWhere 70.02/1dSo the model tested in the study isAdditionally, a second education term is used in the model that is Edu t-2 to reflect the lagged effect of education level of a country on agriculture.4. ResultsFixed Effect panel data analysis does not analyze the country type data since it is a categorical data. So for the overall data we run random effects panel regression (tablo 1). Last column of the table shows the results of this analysis. Regulatory quality, education and country type (developed or developing) are three significant variables in the model. Regulatory quality has a positive effect on agricultural efficiency. Education has negative coefficient which shows the negative relationship between efficiency and education. The result can be interpreted as when the education level becomes high, educated people tend on their own fields and to be away from agricultural activities. On the other hand, country type has positive coefficient which shows the positive relationship between efficiency and development level of a country. It is evident that the agricultural productivity in developing countries is lagging far behind that of the developed countries. This should be a result of cross-country heterogeneity in tangible assets and technologies. In developed countries, agricultural productivity is of importance and is supported by Research and Development studies and uses technological agriculture, whereas old-type agricultural activities is commonly used in others.The analyses were repeated for developed and developing countries separately (tablo 1). Hausman test specifies random effects model for eapursue in both models. But, while there is a positive effect of regulatory qualities on agricultural efficiency in developing countries, this is not validated in developed countries. This result indicates developing countries can increase their efficiencies in agriculture by ruling regulatory qualities, permitting and promoting the development of private sectors. None of the other variables in the model were found affecting significantly agricultural efficiency of countries28)1.(7)1.(6)1.(5)1Re .(Re 4)1.(3)1.(2)1.(101.t Edut Edu t Edu t ContCorr t ContCorr t RuleLaw t RuleLaw t gQual t gQual t GovEffec t GovEffec t PolStab t PolStab t Acc Voice t Acc Voice t Efficiency t Efficiency1164N izamettin Bayyurt and Senem Yılmaz / P rocedia - Social and Behavioral Sciences 58 ( 2012 )1158 – 1165Tablo1: Random-effects GLS regression resultsDeveloped Developing OverallB Z B Z B Z Constant 0.812 1.67 0.238 3.78 0.242 3.9 PolStab -0.055 -1.21 -0.007 -0.23 -0.019 -0.72 RegQual 0.046 0.93 0.095* 1.89 0.070* 1.76 RuleLaw 0.085 1.21 -0.033 -0.55 -0.044 -0.9 VoiceAcc -0.029 -0.71 -0.016 -0.34 -0.035 -0.99 Corruption 0.030 0.82 -0.071 -1.22 -0.027 -0.75 GovEffec 0.129 0.21 0.250 0.6 -0.021 -0.52Edu 0.049 1.25 -0.075 -1.58 0.250 0.73Edu t-2-0.802* -1.87 -0.242** -2.11 -0.247** -2.47 CountryType 0.082** 3.06 Number of obs 320 100 220Number of groups 64 20 44R-sq: within 0.0026 0.1689 0.0133between 0.257 0.0016 0.287overall 0.166 0.0222 0.178Wald chi2(9) 18.68 14.7 19.12Prob > chi2 0.028 0.0653 0.0142Hausman chi2 9.35 2.19prob>chi2 =0.32 Prob>chi2 =0.97**: significant at 5% , *: significant at 10%References[1] Binswanger,H.ve Deininger,K.Literature, 35, 1997, issue 4, ss.19582005, /a/aea/jeclit/v35y1997i4p1958-200August 2010.[2] Charnes, A., Cooper, W.W., Rhodes, E., (1978), Measuring the Efficiency of Decision Making Units, EJOR, Vol.2, pp.429-444.[3]Development, Vol.27, 1999, Issue:6, pp.10591067., /science/article/B6VC6-3X5H9TD-1B/2/15393eefc6f3cea1e6b5f5153c61c146, Retrieved: July 24, 2010.[4] Dayal, E.,(Mar., 1984), Agricultural Productivity in India: A Spatial Analysis, Annals of the Association of AmericanGeographers, Vol. 74, No. 1, pp. 98-123.[5]Fakayode, B.S., Omotesho, O.A., Tsoho, A.B., and Ajayi, P.D., (2008), An Economic Survey of Rural Infrastructures andAgricultural Productivity Profiles in Nigeria, European Journal of Social Sciences, Vol. 7, No. 2, pp. 158-171.1165 N izamettin Bayyurt and Senem Yılmaz / P rocedia - Social and Behavioral Sciences 58 ( 2012 )1158 – 1165[6] Fare, R., Grosskopf, S., Lindgren, B., Rots, P., (1992), Productivity changes in Swedish pharmacies 1980-1989: Anonparametric Malmquist approach, J. Product. Anal, Vol: 3, pp. 85-101.[7] Graham, J., Amos, B., Plumptre, T.,(20Governance, Ottawa, Canada./jspui/bitstream/123456789/11092/1/Principles%20for%20Good%20Governance%20in%20the%2021st%20Century.pdf?1 68[8]Huntington, S.P. Political Order in Changing Societies, New Haven, CT: Yale University Press, 1968.[9] /governance/wgi/pdf/va.pdf).[10]Jemma, M.M.B., and Dhif, M.A., (Dec 19-21, 2005), Agricultural Productivity and technological gap between MENA regionand some European countries: A Meta Frontier Approach, ERF(Economic Research Forum) 12th Annual Conference: Reform-Made to Last, Cairo,Egypt, Conference Paper No: 122005023.[11]Krueger,A., Schiff, M. ve Valdes, A., Political Economy of Agricultural Pricing Policy, Baltimore, MD: Johns HopkinsUniversity Press, 1991.[12]Lio, M. and Liu, M-C.,(2008), Governance and Agricultural Productivity: A Cross-National Analysis, Food Policy, Vol. 33, pp.504-512.[13]Governance, Vol. 6, 2004, issue 1, January, pp. 7590, /a/spr/ecogov/v6y2004i1p75-90.html[14]Millan, J.A. and Aldaz, N., (1998), Agricultural Productivity of the Spanish Regions: a Non-Parametric Malmquist Analysis,Applied Economics Vol.30, No.7, pp. 875-884.[15]Minh, N.K. and Long, G.T., (Sept., 2008), Measuring Agricultural Production Efficiency In Vietnam: An Application of DataEnvelopment Analysis (DEA), Vietnam Development Forum, Working Paper 0813,.vn/workingpapers/vdfwp0813.[16] cPerspectives, Vol.10, 1996, pp. 324, /jep/index.php[17]Serrao, A., (July 27-30, 2003), Agricultural Productivity Analysis of European Union and Eastern Regions, AmericanAgricultural Economics Association Annual Meeting, Montreal, Canada.[18]-/sebin/q/r/What%20Do%20the%20Worldwide%20Governance%20Indicators%20Measure.pdf。