The empirical study of the price mechanism in the Chinese housing market

In China, the history of the establishment of the private housing market is pretty short. Actually in less then two decades, the market has grown from almost the scratch to playing an important role in the economy. A great achievement! But many problems also exist. They need to be properly addressed and solved. Price problem---simply put, housing price is too high--- is one of them, and this paper is focused on it. Three basic questions are posed, i.e. (1) how to judge the housing affordability? (2) why the housing price is so high? (3) how to solve the housing price problem. The paper pays particular attention to answering the second question. Except the numerous news reports and surveys show that most of the ordinary city dwellers complained about the high housing price, the mathematical means, the four ratios, are applied to judge the housing affordability in Shanghai and Shenzhen. The results are very clear that the price problem is severe. So why? Something is wrong with the price mechanism. This research shows that mainly these five factors contribute to the price problem: the housing reform, the housing development model, the unbalanced housing market, the housing project financing and the poor governmental management. Finally the paper puts forward five suggestions to solve the housing price problem in first-hand private Chinese housing market. They include: the establishment of real estate information system, the creation of specific price management department, the government price regulation, the property tax and the legalization of "cushion money".

Architecture and other Architectures: Mapping the Production of Insular Space in the Pearl River Delta (China) and Jakarta (Indonesia)

Globalization is widely regarded as the rise of the borderless world. However in practice, true globalization points rather to a “spatial logic” by which globalization is manifested locally in the shape of insular space. Globalization in this sense is not merely about the creation of physical fragmentation of space but also the creation of social disintegration. This study tries to proof that global processes also create various forms of insular space leading also to specific social implications. In order to examine the problem this study looks at two cases: China’s Pearl River Delta (PRD) and Jakarta in Indonesia. The PRD case reveals three forms of insular space namely the modular, concealed and the hierarchical. The modular points to the form of enclosed factories where workers are vulnerable for human-right violations due to the absent of public control. The concealed refers to the production of insular space by subtle discrimination against certain social groups in urban space. And the hierarchical points to a production of insular space that is formed by an imbalanced population flow. The Jakarta case attempts to show more types of insularity in relation to the complexity of a mega-city which is shaped by a culture of exclusion. Those are dormant and hollow insularity. The dormant refers to the genesis of insular– radical – community from a culture of resistance. The last type, the hollow, points to the process of making a “pseudo community” where sense of community is not really developed as well as weak social relationship with its surrounding. Although global process creates various expressions of territorial insularization, however, this study finds that the “line of flight” is always present, where the border of insularity is crossed. The PRD’s produces vernacular modernization done by peasants which is less likely to be controlled by the politics of insularization. In Jakarta, the culture of insularization causes urban informalities that have no space, neither spatially nor socially; hence their state of ephemerality continues as a tactic of place-making. This study argues that these crossings possess the potential for reconciling venue to defuse the power of insularity.

The role of NGO in informal seed production and dissemination: The case of eastern Ethiopia

Seed is the basic input to crop production. Farmer-based seed production as an alternative agricultural technology transfer is increasingly given especial attention in developing countries where food insecurity is critical. This paper aims to assess the seed production and dissemination strategy among smallholder farmers in eastern Ethiopia that has been introduced by Hararghe Catholic Secretariat (a Non-GovernmentalOrganization). A survey of 160 households in four administrative districts and focus group discussions were used to collect data. While narratives helped understand the process, logistic regressionwas used to identify determinants of land allocation to seed production. Results indicate the crucial role of informal networks and social capital as facilitators of access to production inputs, information and knowledge. The informal seed supply system initiated by the NGO has a huge potential to benefit smallholder farmers by improving their access to higher-yielding varieties of various crops, thereby contributing to an increase in their wellbeing. However, the traditional practice of seed exchange, influenced by social relations, will remain uneconomical to seed producers. Thus, the paper suggests that this potential can be further exploited if some preconditions such as establishment of seed banks, investment in human capital, removal of the underlying constraints and creation of reliable seed markets are given emphasis.

Capturing Emergent Semantics from Social Annotation Systems

The ongoing growth of the World Wide Web, catalyzed by the increasing possibility of ubiquitous access via a variety of devices, continues to strengthen its role as our prevalent information and commmunication medium. However, although tools like search engines facilitate retrieval, the task of finally making sense of Web content is still often left to human interpretation. The vision of supporting both humans and machines in such knowledge-based activities led to the development of different systems which allow to structure Web resources by metadata annotations. Interestingly, two major approaches which gained a considerable amount of attention are addressing the problem from nearly opposite directions: On the one hand, the idea of the Semantic Web suggests to formalize the knowledge within a particular domain by means of the "top-down" approach of defining ontologies. On the other hand, Social Annotation Systems as part of the so-called Web 2.0 movement implement a "bottom-up" style of categorization using arbitrary keywords. Experience as well as research in the characteristics of both systems has shown that their strengths and weaknesses seem to be inverse: While Social Annotation suffers from problems like, e. g., ambiguity or lack or precision, ontologies were especially designed to eliminate those. On the contrary, the latter suffer from a knowledge acquisition bottleneck, which is successfully overcome by the large user populations of Social Annotation Systems. Instead of being regarded as competing paradigms, the obvious potential synergies from a combination of both motivated approaches to "bridge the gap" between them. These were fostered by the evidence of emergent semantics, i. e., the self-organized evolution of implicit conceptual structures, within Social Annotation data. While several techniques to exploit the emergent patterns were proposed, a systematic analysis - especially regarding paradigms from the field of ontology learning - is still largely missing. This also includes a deeper understanding of the circumstances which affect the evolution processes. This work aims to address this gap by providing an in-depth study of methods and influencing factors to capture emergent semantics from Social Annotation Systems. We focus hereby on the acquisition of lexical semantics from the underlying networks of keywords, users and resources. Structured along different ontology learning tasks, we use a methodology of semantic grounding to characterize and evaluate the semantic relations captured by different methods. In all cases, our studies are based on datasets from several Social Annotation Systems. Specifically, we first analyze semantic relatedness among keywords, and identify measures which detect different notions of relatedness. These constitute the input of concept learning algorithms, which focus then on the discovery of synonymous and ambiguous keywords. Hereby, we assess the usefulness of various clustering techniques. As a prerequisite to induce hierarchical relationships, our next step is to study measures which quantify the level of generality of a particular keyword. We find that comparatively simple measures can approximate the generality information encoded in reference taxonomies. These insights are used to inform the final task, namely the creation of concept hierarchies. For this purpose, generality-based algorithms exhibit advantages compared to clustering approaches. In order to complement the identification of suitable methods to capture semantic structures, we analyze as a next step several factors which influence their emergence. Empirical evidence is provided that the amount of available data plays a crucial role for determining keyword meanings. From a different perspective, we examine pragmatic aspects by considering different annotation patterns among users. Based on a broad distinction between "categorizers" and "describers", we find that the latter produce more accurate results. This suggests a causal link between pragmatic and semantic aspects of keyword annotation. As a special kind of usage pattern, we then have a look at system abuse and spam. While observing a mixed picture, we suggest that an individual decision should be taken instead of disregarding spammers as a matter of principle. Finally, we discuss a set of applications which operationalize the results of our studies for enhancing both Social Annotation and semantic systems. These comprise on the one hand tools which foster the emergence of semantics, and on the one hand applications which exploit the socially induced relations to improve, e. g., searching, browsing, or user profiling facilities. In summary, the contributions of this work highlight viable methods and crucial aspects for designing enhanced knowledge-based services of a Social Semantic Web.

Development of an ArcGIS extension to model urban climate factors

The possibility to develop automatically running models which can capture some of the most important factors driving the urban climate would be very useful for many planning aspects. With the help of these modulated climate data, the creation of the typically used “Urban Climate Maps” (UCM) will be accelerated and facilitated. This work describes the development of a special ArcGIS software extension, along with two support databases to achieve this functionality. At the present time, lacking comparability between different UCMs and imprecise planning advices going along with the significant technical problems of manually creating conventional maps are central issues. Also inflexibility and static behaviour are reducing the maps’ practicality. From experi-ence, planning processes are formed more productively, namely to implant new planning parameters directly via the existing work surface to map the impact of the data change immediately, if pos-sible. In addition to the direct climate figures, information of other planning areas (like regional characteristics / developments etc.) have to be taken into account to create the UCM as well. Taking all these requirements into consideration, an automated calculation process of urban climate impact parameters will serve to increase the creation of homogenous UCMs efficiently.

From evolution to co-evolution – An empirical study of the role of innovation, policy and public research in industrial dynamics

In the past decades since Schumpeter’s influential writings economists have pursued research to examine the role of innovation in certain industries on firm as well as on industry level. Researchers describe innovations as the main trigger of industry dynamics, while policy makers argue that research and education are directly linked to economic growth and welfare. Thus, research and education are an important objective of public policy. Firms and public research are regarded as the main actors which are relevant for the creation of new knowledge. This knowledge is finally brought to the market through innovations. What is more, policy makers support innovations. Both actors, i.e. policy makers and researchers, agree that innovation plays a central role but researchers still neglect the role that public policy plays in the field of industrial dynamics. Therefore, the main objective of this work is to learn more about the interdependencies of innovation, policy and public research in industrial dynamics. The overarching research question of this dissertation asks whether it is possible to analyze patterns of industry evolution – from evolution to co-evolution – based on empirical studies of the role of innovation, policy and public research in industrial dynamics. This work starts with a hypothesis-based investigation of traditional approaches of industrial dynamics. Namely, the testing of a basic assumption of the core models of industrial dynamics and the analysis of the evolutionary patterns – though with an industry which is driven by public policy as example. Subsequently it moves to a more explorative approach, investigating co-evolutionary processes. The underlying questions of the research include the following: Do large firms have an advantage because of their size which is attributable to cost spreading? Do firms that plan to grow have more innovations? What role does public policy play for the evolutionary patterns of an industry? Are the same evolutionary patterns observable as those described in the ILC theories? And is it possible to observe regional co-evolutionary processes of science, innovation and industry evolution? Based on two different empirical contexts – namely the laser and the photovoltaic industry – this dissertation tries to answer these questions and combines an evolutionary approach with a co-evolutionary approach. The first chapter starts with an introduction of the topic and the fields this dissertation is based on. The second chapter provides a new test of the Cohen and Klepper (1996) model of cost spreading, which explains the relationship between innovation, firm size and R&D, at the example of the photovoltaic industry in Germany. First, it is analyzed whether the cost spreading mechanism serves as an explanation for size advantages in this industry. This is related to the assumption that the incentives to invest in R&D increase with the ex-ante output. Furthermore, it is investigated whether firms that plan to grow will have more innovative activities. The results indicate that cost spreading serves as an explanation for size advantages in this industry and, furthermore, growth plans lead to higher amount of innovative activities. What is more, the role public policy plays for industry evolution is not finally analyzed in the field of industrial dynamics. In the case of Germany, the introduction of demand inducing policy instruments stimulated market and industry growth. While this policy immediately accelerated market volume, the effect on industry evolution is more ambiguous. Thus, chapter three analyzes this relationship by considering a model of industry evolution, where demand-inducing policies will be discussed as a possible trigger of development. The findings suggest that these instruments can take the same effect as a technical advance to foster the growth of an industry and its shakeout. The fourth chapter explores the regional co-evolution of firm population size, private-sector patenting and public research in the empirical context of German laser research and manufacturing over more than 40 years from the emergence of the industry to the mid-2000s. The qualitative as well as quantitative evidence is suggestive of a co-evolutionary process of mutual interdependence rather than a unidirectional effect of public research on private-sector activities. Chapter five concludes with a summary, the contribution of this work as well as the implications and an outlook of further possible research.

The role of institutions as actors influencing Uganda’s cassava sector

We aim at mapping out a detailed framework that reveals the proportionate flow of cassava and its products along the value chain (VC). Furthermore, we aim at establishing the role of institutions and the linkages between institutions and other VC actors that influence the cassava VC in Uganda. We use both primary and secondary data obtained from four regions in Uganda. Results show that farmers, processors, transporters, traders, consumers and institutions are the major actors. There are four categories of institutions, viz, government, non-government, community based organisations and international agencies. Roles performed by institutions include: development and enforcement of policies, Research and Development (R&D), capacity building, and creation of market access linkages for cassava and its products. Findings reveal that there is no clear nexus and no coordination among farmers/producers, processors, traders, transporters and consumers. However, institutions are well coordinated and play various roles along the VC to influence the dynamics of actors. Policy-wise it is important to establish strong private-public partnerships to bridge the impaired linkages between the actors (farmers/producers, processors, traders, transporters, and consumers) and institutions. Strong partnerships are envisaged to reduce the associated transaction costs amongst the actors.