Issues and Challenges in Collecting Freight Flow Statistics in ThailandSompong Sirisoponsilp, Ph. D.

Director of Transportation InstituteChulalongkorn University, Bangkok, Thailand

1. Introduction

In 2007 Thailand has officially launched its first and ambitious national logistics development strategy designed to boost its competitiveness in the global competition. Under the vision to establish a world-class logistics system to support Thailand as Indochina’s trade and investment center, the strategy was drafted by the National Economic and Social Development Board (NESDB), the nation’s influential think-tank agency, has identified specific initiatives to be implemented during the period 2007-2011 divided into 5 five strategic agenda:

1. Business Logistics Improvement 2. Transport and Logistics Network Optimization 3. Logistics Service Internationalization 4. Trade Facilitation Enhancement 5. Capacity Building

It was well recognized during the course of the strategy development that the country lacked comprehensive and complete statistics that would provide a clear picture of the underlying characteristics of domestic and international freight flows. Consequently, the strategy “to establish a data system and a mechanism for planning and monitoring the performance of Thailand’s logistics strategies” was proposed under the strategic agenda “Capacity Building”. Specific strategic initiative listed under this strategy is the endeavors to collect detailed data on commodity flows

In the past the development of transportation infrastructures in Thailand has basically relied on the annual statistics assembled and disseminated by the Information and Communication Technology Centre under the Office of the Permanent Secretary at the Ministry of Transport. Although the original data set contained information on freight flows by mode, by commodity type, and by origin-destination pair (at the provincial level), the publicly reported statistics narrowly show the amount of freight traffic by mode and commodity type. The brief version of these statistics can be easily accessed at http://www.news.mot.go.th/motc/portal/graph/index_ebook.html but those who require the data in greater detail must directly contact the Information and Communication Technology Centre for their customized data needs. The statistics for water, rail, and air freight movements have been collected and compiled from official document associated with the movements and therefore have been regarded as relatively reliable but nevertheless have suffered from a drawback that they reflect terminal-to-terminal rather than the ultimate origin and destination of the movements. The statistics on road freight traffic were customarily derived from the roadside interview surveys conducted annually by the Department of Land Transport. However, during the financial crisis that seriously hit the country in 1997 the severe budget constraint forced the Department to cease its field survey activities afterwards. The annual road freight statistics from the year 1997 on have then been simply extrapolated from the past statistics.

A number of government agencies have recognized the current shortfall of the country’s statistics on road freight flows and have launched some attempts to resolve the

issues. Over the last five years, the Transportation Institute has had fortunate opportunities to work with these agencies in determining appropriate ways to collect and analyze statistics on freight flows. In addition, the Transportation Institute has internally commissioned research projects to gather information on the cross-border trade and freight movements. The purpose of this report is to present our own experience with these major research tasks performed to gather freight flow information in Thailand. The discussion begins with the nature and type of surveys involved followed by the presentation on the difficulties and issues encountered during the undertakings.

2. Commodity Flow Survey

The U.S. Commodity Flow Survey (CFS) was initiated in 1993. The survey has been designed to provide the national and state level data on domestic freight movements by collecting information from shippers about their outbound shipments. The surveys covered establishments classified by the North American Industry Classification System (NAICS) in manufacturing, mining, wholesale, and selected retail and service industries. The CFS has been conducted every five years through a partnership between the Census Bureau and the Bureau of Transportation Statistics as part of the Economic Census. Specific items of freight movement information provided by the surveys include the type, origins and destinations, values, weights, modes of transport, distance, and ton-miles of commodities shipped. The most recently available data are from the survey carried out in 2007. The 2012 Commodity Flow Survey planning is now under way.

Besides the U.S., Sweden and Japan have also attempted to carry out their own CFS in the fashion that is as comprehensive as the US. In Sweden, the Swedish Institute for Transport and Communications Analysis in cooperation with the Swedish National Rail Administration, the Swedish Civil Aviation Administration, the Swedish Maritime Administration, the Swedish National Road Administration and the Swedish Agency for Innovation systems is the sponsoring agency while the Statistics Sweden is the implementing agency of the survey. Prior to the full-scale survey in the year 2001, experimental surveys were conducted twice in spring 1996 and the fourth quarter of 1998. The study is carried out at approximately three-year intervals and the latest survey was performed in 2004/2005. The methodology adopted in the survey is similar to the one used in the US.

The CFS in Japan began in 1970 and since then has been carried out every five year. The survey has been organized by The Ministry of Land, Infrastructure, Transport and Tourism (MLIT) as the National Logistics Survey. The establishments covered in the survey include those in mining, manufacturing, wholesale, and warehousing industries.

Given their extensive experience with census surveys, the National Statistical Office (NSO) was assigned by the NESDB as the responsible agency for the CFS in Thailand. Over the past few years, the NSO conducted experimental commodity flow surveys with the technical advice provided by the Transportation Institute. In the latest attempt, the NSO adopted the sampling methodology following the one employed in the U.S. 2002 CFS.

The survey covered shippers with more than 10 workers in a variety of industries including agriculture, mining, manufacturing, wholesale & retail trade, and storage classified according to the ISIC (International Standard Industrial Classification of All Economic Activities: ISIC Rev. 3.0). To avoid double counting of movements each sampled

establishment was generally asked to report only outbound shipments. However, as it would be very costly to collect information about outbound shipments from individual farms, the information about the movements of primary agricultural products from farms, such as rice or sugarcane, were indirectly collected from surveying the inbound shipments to the establishments in agro-industrial sector that require these primary agricultural products as their business inputs.

To capture the seasonality of freight movements, the survey covered the entire year and the survey period was divided into four quarters. The survey then collected the information on a sample of shipments from each sampled establishment during a one week period in each calendar quarter. The selection of sample was based on a three-stage design process similar to the one adopted in the U.S. CFS. The first stage involved sampling of establishments classified by geography location, industry, and size of establishment from the establishment list used in the most recent Industrial (Manufacturing) census. For each selected establishment, the second stage identified the reporting week for each quarterly survey. As each establishment was required to report a maximum of 35 shipments, the third stage is concerned with the sampling of the shipments for each reporting week. Shipment data collected during the one week period of the survey include:

Number of shipments in the week Value and weight of shipments Commodity type Origin - Destination Mode of transport Port of exit for export

With the objective to ensure harmonization of survey results with other official trade statistics, it was decided during the survey design that commodities were classified using the Harmonized System framework. The commodities were accordingly grouped into 20 categories as follows.

Live animals & Animal products Vegetable products Animal or Vegetable Fats Prepared Foodstuffs Mineral Products Chemical Products or allied industries Plastic & Rubber Hides & Skins Wood & articles of wood Wood & Pulp Products Textile & Textile Articles Footwear, Headgear Articles Of Stone, Plaster, Cement, Asbestos Pearls, Precious Or Semi-Precious Stones, Metals Base Metals & Articles Thereof Machinery & Mechanical Appliances Transportation Equipment Instruments - Measuring, Musical Arms and Ammunition; parts and accessories thereof Miscellaneous

A number of valuable lessons have been learned from these experimental CFS attempts. In particular, we found from these surveys that the data contained major gaps that required serious attention in the future. These gaps basically included:

Out-of-scope shipments: A number of factors contributed to the out-of-scope shipments. Firstly, major industries including forestry, fishery, and construction were not incorporated in the survey (Chokchairungroj, 2010). Secondly, due to resource constraints, relatively small establishments were excluded from the surveys. Fortunately, the degree of out-of-scope in this particular may be fairly small because these small establishments usually have negligible amount of shipments. Thirdly, according to the survey design which basically focused on the outbound shipments, the surveys could not pick up imports and transit shipments which have both the origin and destination in countries other than Thailand.

Under-reported freight volumes: Freight volumes were likely under-reported due to three specific causes. Firstly, during the survey it was found that a large proportion of goods sold by some establishments were picked up by their customers. As our survey focused on the outbound freight movements from establishments, a large number of establishments who were the sellers reported only the shipments that they delivered to their customers and failed to report the portion of freight movements that were picked up by their customers. This resulted in the substantial underestimates of freight movements. Secondly, some establishments indirectly refused to participate in the survey by simply reporting no shipments during the reporting periods. Lastly, the manufacturing of certain products especially those involved substantial movements i.e. cements are concentrated to a very few number of companies. It was found that that for certain products none of these large manufacturers were not selected and surveyed due to the random selection process in which each establishment in the population having the same probability of being selected, The quantity of movements of these products was therefore vastly under-reported.

The thesis by Chokchairungroj (2010) represented an initial research work attempting to develop methodology for systematically fixing the above deviation in the CFS data. The survey data deviation fell into two types of errors namely: Sampling errors and Non-sampling errors. The sampling errors were determined by 1) the out-of-scope and 2) the deviation from the sampling plan determined by comparing the actual size (defined by the number of total employees) of the surveyed establishment with that used as the sampling frame. It was viewed that underreported freight volumes were probably the major source of non-sampling errors in this survey. The non-sampling errors for each commodity group were simply determined from the difference between the “expected” freight movements and the “reported” ones. In the analysis of non-sample errors, statistical measures and techniques were applied to systematically separate the set of survey responses with complete records and those with incomplete records. The data obtained from the establishments with completer records were subsequently used to statistically develop the relationship reflecting the effect of the number of employees and the freight volumes shipped (or received) by a firm. The developed relationship was eventually applied to predict the total freight volume likely shipped (or received) by each survey respondent regarded as an incomplete record. The non-sampling errors were eventually computed from the difference between the “predicted” freight volume and the “reported” one. The commodities covered in the study included consumer goods, food stuffs, construction materials, steel, machinery, plastic products, chemicals, cement, tapioca, and sugar.3. Roadside Interview Survey

In 2008/2009, the Transportation Institute was commissioned by the Department of Land Transport to carry out an ambitious project to collect data on freight movements entering and leaving ten major provinces in Thailand (see Figure 1).

Figure 1: Survey target provinces

For each target province, the survey stations were positioned on major highways linking the principal city of that province to other areas of the country. This particular survey eventually employed a total of 30 survey stations. Given the amount of budget allocated to the project, only two days of data were collected at each survey station except stations located in Bangkok and Chonburi where four days of data were collected. To capture the effect of seasonality of freight movements, in all target provinces other than Bangkok and Chonburi the two days of data collection were divided into one day in the harvest period and one day outside of the harvest period. In light of the tight measure banning heavy trucks during the

weekdays in Bangkok and the prevailing characteristics of imports and exports through the province Chonburi where the nation’s largest seaport was located, the patterns of freight movements during weekdays and weekends in these two provinces were expected to be quite different. As a result, the four days of data collection in Bangkok and Chonburi were separated into the harvest period and outside of harvest period with data being collected in one weekday and one weekend day in each period.

At all survey stations the 24-hour data collection was carried out. Traffic volumes were recorded and selected trucks were intercepted while enroute and direct interviews were subsequently conducted to gather shipment information including

• Weight and value of shipment• Commodity type• Origin and destination • Truck types• Time of shipping• Type of transport (private or hired)

The classification of commodity in this survey was an expansion of the one adopted by the Commodity Flow Survey described in the earlier section. Given the requirements to harmonize the survey results with the statistics previously disseminated by the Ministry of Transport, the original classification adopted by the CFS was further disaggregated into 37 commodity types including

Paddy rice Corn Rice Chemical Machinery Food Consumer goods Electronics Flowers and trees Soil stone sand Coal Sugar Fuel Fertilizer Cement Flour Other agricultural products Paper products Wood products Plastic products Rubber products Vegetables and fruits Cassava Wood Para rubber Automobile Mineral Metal and nonmetal

Construction material Wood fuel and agricultural residue Aquatic animals Live animals Textiles Steel Sugarcane Veterinary food Others

In addition to shipment information, the survey took this valuable opportunity to gather information related to truck drivers which have been considered as one of the critical elements of the truck transportation system in Thailand. The data about truck drivers collected during the survey included driving experience, education level, income, and transportation/logistics-related training experience.

Even the survey results satisfactorily fulfilled the specific objective of the project commissioned set forth by the Department of Land Transport. However, with regard to the establishment of comprehensive database of freight movements in Thailand, the survey results suffered from the following specific shortfalls.

As the survey locations were positioned on the major highways connecting the principal cities of ten target provinces, the survey results offered relatively limited coverage and would not be the representative of all road freight movements in Thailand. The survey that would provide complete statistics covering total freight movements in Thailand would definitely require considerably high budget that well exceeds the typical amount allocated to the Department of Land Transport. In particular, the survey results were good in showing the movements entering and leaving the ten principal cities of the target provinces. Movements with neither origin nor destination lying in these principal cities were detected by chance only if they happened to pass through the survey stations. Given the limited number of survey stations located only at major highways, the survey results contained a bias favoring long-distance movements that normally travelled along major highways. Short-distance deliveries that use small rural roads were not covered in the survey.

According to the laws, the Police and the Department of Land Transport are the only agencies that have the authority to intercept the vehicles traveling on the highways for the direct interviews. Our surveys were kindly assisted by the local police officers however the officers usually selected the vehicles at their own convenience rather than following the theoretical random selection process which would require substantial survey resources.

It should be noted that the shipment information collected during the survey was useful not only for developing origin-destination matrix illustrating the freight flows between the ten target provinces aggregated at the provincial level but also for determining the efficiency of truck movements in Thailand. The paper by Rongviriyapanich et al. (2010) reported the analysis to explore the efficiency of truck transportation in Thailand based on the information gathered in the survey. The efficiency of truck transportation was assessed from

three specific measures. The first measure was investigated by analyzing “the distribution of travelled distances by truck type and by commodity”. The results revealed that a large fraction of small trucks transported cargo over a relatively long distance. This reflects an inefficient practice resulting in unnecessarily high trucking cost. The second aspect of truck transportation efficiency was measured from “the load factors by truck type” defined as the ratio of the average cargo weight to vehicle weight capacity. While the loading efficiency of truck transportation in Thailand seemed to be higher than those achieved in other developed countries, the data also revealed that a large proportion of truck traffic was overloaded, confirming the presence of the problem of overloaded trucks in Thailand. The third measure was determined by “the degree of empty running by truck type”. It was found that the empty running in Thailand constituted a larger percentage of truck movement than those experienced in the UK. Nevertheless, the average distance of empty trips was lower than that of loaded movements, indicating the recognition of truck operators in the need to minimize empty running.

With our involvement with both the CFS and RS, we recognized that the CFS and RS as described earlier have each own strength and shortcomings. Nevertheless we found that the results of the two survey methods were complementary in certain occasions. We therefore foresaw a need for research works that make use of the results from these two pioneering survey undertakings to derive a better set of statistics. The thesis by Hirun (2010) represented such research works. This research aimed to develop a methodology for combining readily available CFS and RS data to produce a more complete and reliable origin destination matrix of freight movements. The methodology proposed two methods utilizing the strengths of each survey method. It was viewed that the CFS provided a better representation of distribution pattern while the RS yielded better information on marginal total volumes of relatively long distance movements. The first method was the Trip Length Distribution Adjusting (TLDA), which systematically adjusted the CFS trip length distribution to meet the marginal totals provided by the RS. The second method was the Gravity Model Approach (GMA), which utilized the friction functions derived from the CFS data to adjust the RS data matrix.

4. Cross Border Traffic

Theoretically, the information about cross-border freight traffic can be best gathered from all commercial documents that are to be submitted for customs formalities. The development of cross-border database should therefore rely on the information gathered by the Customs Office.

Presently, a number of government agencies in different Ministries such as the Customs Department of the Ministry of Finance and the Department of Foreign Trade of the Ministry of Commerce have actively reported the statistics about the cross-border freight traffic. However, one must exercise some care when considering these statistics because there exist certain problems with these publicly available statistics. The first problem lies in the discrepancies in the statistics reported by different agencies which are likely contributed by two causes. The first cause of the problem is the difference in the definition of reporting time period. The Customs Department has normally adopted the calendar year as the basis for reporting while the statistics disseminated by other agencies may be based on the Fiscal year. The second cause of problem arises due to the time lag during which the submitted documents are processed by the Customs Department. As a result, the other agencies may

not receive the most updated data from the Customs Department at the time they decide to publicly release their statistics. As all other agencies must rely on the data provided by the Customs Department, it is therefore recommended that statistics released by the Customs Department are the most up-to-date ones. Nevertheless, the statistics made publicly available by the Customs Department still has had two shortcomings. The first shortcoming is that the statistics have been usually reported in the aggregate form lacking detailed shipment information normally required by transportation analysis. For example, the origin and destination of the shipments are reported only at the country level while the information at the city (or province) level will be more useful in most analysis. Fortunately the Customs Department provides service for those requiring statistics in greater detail subject to certain service charges varying with the elapsed time required to process the data.

The second shortcoming of the present publicly available statistics on cross-border freight movement is that the statistics reflect only the trades that go through official customs formalities. Recently, the Transportation Institute has completed projects related to cross-border trade and transportation, for example the project entitled “The Development Trade and Transport Information Database for the Lower Mekong Riparian Countries”. The key objective of these projects is to collect data related to cross-border trade and transport between Thailand and other Mekong riparian countries namely Laos, Cambodia and Vietnam. The study carried out field surveys at the border crossing points as well as in-depth interview with key persons and agencies related to cross-border trade and transport at border of Thailand. The survey data indicated that at present the cross-border trade and freight movements between Thailand and its neighboring countries have been carried out through three channels:

Permanent Border Crossing Points: The movements of people, freight and vehicles through this type of border crossing points are normally overseen by Customs and Immigration officials.

Temporary Border Crossing Points: This type of border crossing is established for a particular purpose or activity and with a specific duration.

Traditional Border Crossing Points: These border crossing points are established with the approval of the provincial governors of the two neighboring countries and are under the control of local security authorities namely territory defense guards, scout troopers etc.

The study found that cross-border trade has been performed in two manners, official and informal. Official cross-border trades consisting of import and export, and transit cargoes are normally transported and processed via permanent or temporary border crossing points following the Customs Act B.E. 2480. On the other hand, informal cross-border trade is exempt from customs formalities and based on the traditional system which existed before the advent of customs in the modern state. This type of trade can be carried out via permanent and traditional border crossing points. Since this type of trade does not require official document, it is not included in the official trade statistics. So far, there has not been any systematic research works to seriously estimate the volumes and values of the informal cross-border trades. However as estimated by some Customs officers namely Khemmarat Customs House, Chiang Khong Customs House, the amount of the informal cross-border trade likely accounts for 50 – 70% of the official trade of each border province. Furthermore, it was found that the cross-border trades in all traditional border crossing has been rising (Sukdanont, 2010).

5. Conclusions

Given our experience described above, we view that a number of further works have to be carried out to resolve a number of issues before a comprehensive and reliable database of freight movements in Thailand can be satisfactorily established.

The very first and probably the most important one is that the government should be aware of the important role the availability of reliable data plays in assisting the policy makers in developing effective transportation-related decisions. In the past the financial support for data collection efforts has been relatively limited and has unfortunately been allocated on the adhoc basis. The available data are fragmented and lack the detail necessary for in-depth transportation planning and analysis. Without reliable data on freight transportation demand, large sum of public money can be wasted on badly-planned transportation infrastructures. As no single data collection activity can address all data requirements, it is timely for the government to draw up an integrated plan for the establishment of the country’s freight movement database to coordinate necessary data collection efforts. The plan should take into account the emerging data needs caused by the evolving environments affecting transportation and logistics landscape. The requirements and issues at all levels (city, regional, national, and international) faced by all stakeholders and users of freight movement data should be incorporated into the plan. The plan reflect the joint strategy for data collection and should clearly specify and prioritize data collection and related synthesis activities to be seriously implemented on the continuous basis. For each activity, the plan should clearly identify the implementing agencies, the timing and frequency, and the financial requirements.

References:

Chokchairungroj, Sukrit. Analysis of the Deviation of Commodity Flow Survey Data in Thailand. Master Thesis, Department of Civil Engineering, Faculty of Engineering, Chulalongkorn University, 2010.

Hirun, Wirach. Estimating Freight Origin Destination Matrices Using Combined Commodity Flow Survey and Roadside Survey Data. Doctoral Thesis, Department of Civil Engineering, Faculty of Engineering, Chulalongkorn University, 2009.

Rongviriyapanich, Terdsak, Sirisoponsilp, Sompong and Hirun, Wirach. Efficiency of Long-distance Truck Transportation in Thailand. Paper presented at the 2nd International Conference on Logistics and Transport, Queenstown, New Zealand, 2010. Sukdanont, Sumalee. Cross-border Trade and Transport between Thailand and Laos People’s Democratic Republic along the Mekong River. Paper presented at the 3rd International Conference on Transportation and Logistics, Fukuoka, Japan, 2010.