Appendix

Appendix: Notes on Data for Key Variables

(1) Road infrastructure

Availability, level of details, and types of data on road infrastructure vary among GMS members, necessitating some procedure of making the data consistent and comparable across the GMS members. Therefore, our quantitative analysis used road density for GMS members where road inventory data are available and density of freight carriage for those where road inventory data are not available but administrative data on freights are available. For Cambodia, there are no geographically disaggregated data on road inventory. 1995 data provided by the Committee for Development of Cambodia (CDC) was the only disaggregated data by province made available to the authors. This information was extrapolated by the available aggregate road length figures for the subsequent years in calculating road density by province. For Lao PDR, data on road inventory and density by province were provided directly by the Department of Roads, Ministry of Communication, Transport, Post and Construction, upon the request of the authors. For Thailand, road inventory data from Department of Highways, Ministry of Transport are disaggregated only by the route of national highways which run through multiple provinces. These data are adjusted by the estimated provincial shares based on the GIS-based “Road Inventory of ASEAN Highways” developed by UNESCAP in calculating road density by province. For Myanmar and Viet Nam, there exist no official data on road length. Instead, various administrative data included in the transport section of the statistical yearbooks were combined to calculate the density of freight carriage by state/province. For Yunnan Province, road density by region was calculated from the road inventory data available in the transport section of the provincial statistical yearbooks.

Distinction between cross-border and domestic road infrastructure was made for each pair of GMS members based on the location of international crossing points as presented in Table A1 [ PDF 98.6KB | 1 page(s) ]. For example, Cambodia’s cross-border and domestic road infrastructure with respect to Lao PDR is represented by road density of Stung Treng Province and that of all the other provinces (which do not share border with the other GMS members), respectively. Likewise, Lao PDR’s cross-border and domestic road infrastructure with respect to Cambodia is represented by road density in Champassack Province and all the other provinces (which do not share a border with the other GMS members), respectively. Where there is more than one province with shared borders with a neighbor country, the corresponding cross-border road infrastructure is represented by the average of the road density in such provinces. Likewise, domestic road infrastructure is represented by the average of the road density in the remaining provinces.

“Local border points” as opposed to “international cross-border points”, as often referred to by public institutions in GMS, are the borders where only the residents in immediately neighboring provinces/states can cross borders and trade freely. While some of these borders might carry noticeable but unrecorded trade volumes, their traffic would mainly be limited to those immediate neighboring provinces/states and therefore, of limited economic impact on the subregion as a whole. Because the focus of this paper is on the impact of road infrastructure on the entire GMS economies, it makes sense to focus on the international crossing points and leave out local border points. This treatment also seems to be a convenient way of making quantitative analysis consistent between the road infrastructure data and the officially recorded trade data that are the only available data in any reasonable time series.

(2) Distance

Data on distance between each pair of GMS members were taken from Oldfield (2004) as summarized in Table A2 [ PDF 80.4KB | 1 page(s) ].

(3) Export environment; import environment; and FDI environment.

Table A3 [ PDF 80.7KB | 1 page(s) ] summarizes the proxies selected for these variables and the assignment of dummy variables (in parentheses).

(4) Transport cost:

Finding reliable and usable data on transport cost has proved difficult. Some attempt was made to look for directly observed transport costs by destination in GMS that may exist with shipping or logistics companies. However, the only available data relates mainly to sea transport and for a limited number of years and origin-destination. Part of the reason is that insurance is still difficult to obtain for long-distance land transport in the region due to various procedural and security uncertainties involved.

Use of proxy data for transport costs such as CIF/FOB ratios was considered but this also proved problematic. First, government authorities normally record export values in FOB and import values in CIF. The FOB value of imports recorded in balance of payment statistics is only available at the country-aggregate level, not by trading partners. The usual short-cut practice for recording FOB import in the balance of payment statistics seems to involve dividing CIF value by a certain assumed ratio such as 1.08 or 1.10. An alternative for finding FOB import value would be to use trade data of the exporting countries. But this does not appear to work for the GMS because there exist large discrepancies between the recorded values of exporter countries and those of corresponding importer countries. Even in international database such as IMF-DOTS, there are many missing or unreliable trade data for countries with weak statistical capacity such as Cambodia, Lao PDR, and Myanmar. Data from the trading partners such as People’s Republic of China and Thailand are substituted with adjustment of some assumed CIF/FOB factors.

A further attempt was made to collect CIF/FOB ratios for some representative goods being traded between each pair of GMS members using the UNCOMTRADE database. However, very few time-series data by country pair are available other than for Thailand-People’s Republic of China. Even for this series, the derived CIF/FOB ratios for major trade commodities do not look stable from year to year – presumably due to unreliable customs coverage – and proved unusable.

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