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.

Regional differences regarding land tenancy in rural Rwanda, with special reference to sharecropping in a coffee production area

This paper examines land tenancy systems and tenant contracts in Rwanda, with respect to socioeconomic contexts. Our research in southern and eastern Rwanda produced data suggesting that land borrowing with fixed rents has been generally practiced, and that rent levels have been low in comparison to expected revenues from field production. In the western areas of coffee production, however, the practice of sharecropping has recently appeared. This system is advantageous to landowners, as they are able to acquire half of the harvests; in addition, the fixed rent levels in this region are much higher than those of other regions. In the southern and eastern regions, because land borrowing with fixed rents has been the only tenancy pattern and rent levels have remained low, the economic situation should be interpreted in the context of a continuing traditional Rwandan land tenure system. In contrast, in the western coffee production area, the soaring of fixed rents and the emergence of sharecropping have been brought about by high pressures for land use, which were caused not only by a population increase but also by the development of cash crop production and the existence of a labor exchange system. The increase in rent levels has therefore been offset by a corresponding increase in agricultural productivity.

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.