Spatial spillover effects in determining China's regional CO2 emission growth : 2007-2010

This paper proposes an alternative input-output based spatial-structural decomposition analysis to elucidate the role of domestic-regional heterogeneity and interregional spillover effects in determining China's regional CO2 emission growth. Our empirical results based on the 2007 and 2010 Chinese interregional input-output tables show that the changes in most regions' final demand scale, final expenditure structure and export scale give positive spatial spillover effects on other regions' CO2 emission growth, the changes in most regions' consumption and export preference help the reduction of other regions' CO2 emissions, the changes in production technology, and investment preference may give positive or negative impacts on other region's CO2 emission growth through domestic supply chains. For some regions, the aggregate spillover effect from other regions may be larger than the intra-regional effect in determining regional emission growth. All these facts can significantly help better and deeper understanding on the driving forces of China's regional CO2 emission growth, thus can enrich the policy implication concerning a narrow definition of "carbon leakage" through domestic-interregional trade, and relevant political consensus about the responsibility sharing between developed and developing regions inside China.

An Asian Triangle of Growth and Cluster-to-Cluster Linkages

It is expected that an Asian triangle of growth will be formed in the coming few decades. China, India and ASEAN surround the Asian triangle, which is home to many industrial clusters. Multinational corporations will link these clusters together. Regional integration will help them in this task by lowering the barriers of national borders. This paper explains the necessity of regional integration for cluster-to-cluster linkages in the Asian triangle of growth.

Export-Led Growth and Geographic Distribution of the Poultry Meat Industry in Brazil

This paper includes an examination of the sustainability of recent high growth in the poultry meat industry in Brazil. In addition, an assessment is made of the impact of increased production of poultry meat products on the development of local industries. Comparative studies of leading companies in the United States, Mexico, and Brazil reveal competitive advantages in the low costs of feedstuff and labor as well as disadvantages in the scale of business and management efficiency in the Brazilian poultry sector. Increases in domestic and foreign demand for Brazilian poultry meat have promoted development of the Brazilian poultry sector in local areas. The formation of industrial clusters is observed using regional data related to the location of slaughterhouses and the number of chickens farmed. Statistical analyses support observations made in this paper.

Agglomeration Economies in Japan: Technical Efficiency, Growth and Unemployment

This paper examines if the effects of agglomeration economies get manifested in technical efficiency and generate faster economic growth and higher (lower) levels of employment (unemployment). Using the prefecture level data for each of the two-digit groups of industries in Japan, the paper estimates region-specific technical efficiency index based on the stochastic frontier production function framework. The results of the factor analysis show that in most of the industry-groups (with a few exceptions) efficiency has a positive association with external scale variable(s). Though the relationship is not seen to be very strong, it would be equally erroneous to ignore the effect of agglomeration economies on efficiency. In the case of some of the light goods industries the agglomeration effect is relatively stronger. Further, economic growth varies positively with external scale variable(s) and unemployment rate tends to fall with respect to growth and concentration. All this tends to suggest that measures against industrial concentration may be counter-productive, particularly in the context of globalisation when countries are in dire need of raising productivity.

Interindustrial Structure in the Asia-Pacific Region: Growth and Integration, by Using 2000 AIO Table

Over the past 20 years Asian countries have achieved a certain degree of economic growth and at the same time deepened spatial interdependence. In January 2006, IDE completed the 2000 Asian International Input-Output Table, which covers eight major East Asian countries/regions as well as Japan and the United States. Given the dynamic changes in the economies of East Asia, this paper attempts to summarize the characteristics and their patterns of change in industrial structures and trade structures of the countries/regions in the Asia-Pacific region from the three viewpoints of time, space, and industry, by using the AIO table for 1985, 1990, 1995, and 2000.

Production Networks and Spatial Economic Interdependence: An International Input-Output Analysis of the Asia-Pacific Region

The Asia-Pacific Region has enjoyed remarkable economic growth in the last three decades. This rapid economic growth can be partially attributed to the global spread of production networks, which has brought about major changes in spatial interdependence among economies within the region. By applying an Input-Output based spatial decomposition technique to the Asian International Input-Output Tables for 1985 and 2000, this paper not only analyzes the intrinsic mechanism of spatial economic interdependence, but also shows how value added, employment and CO2 emissions induced are distributed within the international production networks.

Is South Sulawesi a Center of Growth in Eastern Indonesia? : Japanese ODA Strategy Revisited

Japanese ODA, especially that undertaken by JICA, has targeted South Sulawesi Province as a core area of development in eastern Indonesia, with hope that the economic growth of South Sulawesi will bring about spillover effects in other regions. This paper tests the validity of the strategy using a framework of Vector Autoregressive model. The results show that South Sulawesi’s economy Granger causes other regions in eastern Indonesia, but not vice versa, implying that South Sulawesi drives the development of other regions in eastern Indonesia. Further analysis shows that the development of the agricultural sector in South Sulawesi potentially has the highest spillover effects than other sectors and that the magnitude of spillover effect from South Sulawesi on eastern Indonesia is higher than other economically important regions, such as Eastern Java and Kalimantan.

Chapter 4 A Review of the Availability of and Studies on Spatial Economic Data in Laos

Spatial data are being increasingly used in a wide range of disciplines, a fact that is clearly reflected in the recent trend to add spatial dimensions to the conventional social sciences. Economics is by no means an exception. On one hand, spatial data are indispensable to many branches of economics such as economic geography, new economic geography, or spatial economics. On the other hand, macroeconomic data are becoming available at more and more micro levels, so that academics and analysts take it for granted that they are available not only for an entire country, but also for more detailed levels (e.g. state, province, and even city). The term ‘spatial economics data’ as used in this report refers to any economic data that has spatial information attached. This spatial information can be the coordinates of a location at best or a less precise place name as is used to describe administrative units. Obviously, the latter cannot be used without a map of corresponding administrative units. Maps are therefore indispensible to the analysis of spatial economic data without absolute coordinates. The aim of this report is to review the availability of spatial economic data that pertains specifically to Laos and academic studies conducted on such data up to the present. In regards to the availability of spatial economic data, efforts have been made to identify not only data that has been made available as geographic information systems (GIS) data, but also those with sufficient place labels attached. The rest of the report is organized as follows. Section 2 reviews the maps available for Laos, both in hard copy and editable electronic formats. Section 3 summarizes the spatial economic data available for Laos at the present time, and Section 4 reviews and categorizes the many economic studies utilizing these spatial data. Section 5 give examples of some of the spatial industrial data collected for this research. Section 6 provides a summary of the findings and gives some indication of the direction of the final report due for completion in fiscal 2010.

Assessing agglomeration economies in the Yangzi River Delta, China : a bayesian spatial econometric approach

This paper estimates the elasticity of labor productivity with respect to employment density, a widely used measure of the agglomeration effect, in the Yangtze River Delta, China. A spatial Durbin model is presented that makes explicit the influences of spatial dependence and endogeneity bias in a very simple way. Results of Bayesian estimation using the data of the year 2009 indicate that the productivity is influenced by factors correlated with density rather than density itself and that spatial spillovers of these factors of agglomeration play a significant role. They are consistent with the findings of Ke (2010) and Artis, et al. (2011) that suggest the importance of taking into account spatial dependence and hitherto omitted variables.

Spatial patterns of manufacturing clusters in Vietnam

The formation of industrial clusters is critical for sustained economic growth. We identify the manufacturing clusters in Vietnam, using the Mori and Smith (2013) method, which indicates the spatial pattern of industrial agglomerations using the global extent (GE) and local density (LD) indices. Spatial pattern identification is extremely helpful because industrial clusters are often spread over a wide geographical area and the GE and LD indices—along with cluster mapping—display how the respective clusters fit into specific spatial patterns.