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.

On the Evolution of the Spatial Economy with Multi-unit/Multi-plant Firms: The Impact of IT Development

This paper examines how the decline of communication costs between management and production facilities within firms and the decrease in trade costs of manufactured goods affect the spatial organization of a two-region economy with multi-unit/multi-plant firms. The development of information technology decreases the costs of communication and trade costs. Thus, the fragmentation of firms is promoted. Our result indicates that, with decreasing communication costs, firms producing low trade-cost products (such as consumer electronics) tend to concentrate their manufacturing plants in low wage countries. In contrast, firms producing high trade-cost products (such as automobiles) tend to have multiple plants serving to segmented markets, even in the absence of wage differentials.

The location of manufacturing firms and imperfect information in transport market

It is well know that transport charges are not symmetric: fronthaul and backhaul costs on a route may differ, because they are affected by the distribution of economic acitivities. This paper develops a two-regional general equilibrium model in which transport costs are determined endogenously as a result of a search and matching process. It is shown that economies or diseconomies of transport density emerge, depending on the search costs of transport firms and the relative importance of the possibility of backhaul transportation. It is found that the symmetry of the distribution of economic activity may break owing to economies of transport density when the additional search costs are small enough.

Some notes on the spatial representations

There are conventional methods to calculate the centroid of spatial units and distance among them with using Geographical Information Systems (GIS). The paper points out potential measurement errors of this calculation. By taking Indian district data as an example, systematic errors concealed in such variables are shown. Two comparisons are examined; firstly, we compare the centroid obtained from the spatial units, polygons, and the centre of each city where its district headquarters locates. Secondly, between the centres represented in the above, we calculate the direct distances and road distances obtained from each pair of two districts. From the comparison between the direct distances of centroid of spatial units and the road distances of centre of district headquarters, we show the distribution of errors and list some caveats for the use of conventional variables obtained from GIS.