A Turning Point for the Service Sector in Thailand

Koonnathamdee, Pracha Working Paper A Turning Point for the Service Sector in Thailand ADB Economics Working Paper Series, No. 353 Provided in Cooperation with: Asian Development Bank (ADB), Manila Suggested Citation: Koonnathamdee, Pracha (2013) : A Turning Point for the Service Sector in Thailand, ADB Economics Working Paper Series, No. 353, Asian Development Bank (ADB), Manila, https://hdl.handle.net/11540/2315 This Version is available at: https://hdl.handle.net/10419/109460 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/3.0/igo A Turning Point for the Service Sector in Thailand Pracha Koonnathamdee No. 353 | June 2013 ADB Economics Working Paper Series A Turning Point for the Service Sector in Thailand This paper tests the hypothesis that the service sector is a growth engine in the Thai economy. While many developed countries maintain a positive association between the shares of the sector in output and per capita income, the opposite is true in Thailand. Using estimates from a fixed-effects model, the study confirms two waves of growth. In addition, total factor productivity and revealed comparative advantages are also discussed to determine the significant role of services and some service activities. About the Asian Development Bank ADB’s vision is an Asia and Pacific region free of poverty. Its mission is to help its developing member countries reduce poverty and improve the quality of life of their people. Despite the region’s many successes, it remains home to two-thirds of the world’s poor: 1.7 billion people who live on less than $2 a day, with 828 million struggling on less than $1.25 a day. ADB is committed to reducing poverty through inclusive economic growth, environmentally sustainable growth, and regional integration. Based in Manila, ADB is owned by 67 members, including 48 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance. Asian Development Bank 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines www.adb.org/economics Printed on recycled paper Printed in the Philippines ADB Economics Working Paper Series A Turning Point for the Service Sector in Thailand Pracha Koonnathamdee No. 353 June 2013 Pracha Koonnathamdee is Assistant Professor at the Faculty of Economics, Thammasat University, Thailand. Asian Development Bank 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines www.adb.org © 2013 by Asian Development Bank June 2013 ISSN 1655-5252 Publication Stock No. WPS135804 The views expressed in this paper are those of the author and do not necessarily reflect the views and policies of the Asian Development Bank (ADB) or its Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use. By making any designation of or reference to a particular territory or geographic area, or by using the term “country” in this document, ADB does not intend to make any judgments as to the legal or other status of any territory or area. Note: In this publication, “$” refers to US dollars. The ADB Economics Working Paper Series is a forum for stimulating discussion and eliciting feedback on ongoing and recently completed research and policy studies undertaken by the Asian Development Bank (ADB) staff, consultants, or resource persons. The series deals with key economic and development problems, particularly those facing the Asia and Pacific region; as well as conceptual, analytical, or methodological issues relating to project/program economic analysis, and statistical data and measurement. The series aims to enhance the knowledge on Asia’s development and policy challenges; strengthen analytical rigor and quality of ADB’s country partnership strategies, and its subregional and country operations; and improve the quality and availability of statistical data and development indicators for monitoring development effectiveness. The ADB Economics Working Paper Series is a quick-disseminating, informal publication whose titles could subsequently be revised for publication as articles in professional journals or chapters in books. The series is maintained by the Economics and Research Department. Printed on recycled paper CONTENTS ABSTRACT v  I. INTRODUCTION 1 II. BASIC FACTS 1 A.What is Thailand’s Service Sector? 1 B.Size, Growth, and Composition 3 C.Trade and Investment 8 III. SHARE OF OUTPUT MODEL 8 IV. TOWARD A POSSIBLE TURNING POINT 14 A.Share of Output Model Revisited 14 B.Total Factor Productivity 16 C.Revealed Comparative Advantage 16 D.Policy Recommendations 19 V. CONCLUDING REMARKS 21 REFERENCES 23 ABSTRACT Although Thailand’s service sector accounts for almost half of the national income and has a major stake in national employment, its contribution to the growth of the gross domestic product (GDP) fluctuates. Moreover, the share of the service sector in GDP is decreasing while many developed countries maintain a positive association between the shares of the sector in output and per capita income. This paper investigates this relationship by examining the gross provincial product of 76 provinces to test the hypothesis that the service sector is a growth engine in the Thai economy. Using the fixed-effects model, the estimates confirm two waves of growth. Total factor productivity and revealed comparative advantages are discussed to determine significant service activities. Wholesale and retail trade, tourism and travel-related activities, transportation, and construction all play major roles in contributing to Thailand’s economic growth. The government should continue to promote these services with unambiguous policies suitable for each region and province. Educational services also require more attention from pertinent agencies. Keywords: service sector, economic growth, structural change, Thai economy, economic development JEL codes: O14, O17, R11 I. INTRODUCTION As is true in every newly industrializing economy, the economy of Thailand is mixed. Decisions regarding the production of goods and services are made in both the private and public sectors. From the early 1970s to the mid-1990s, Thailand experienced significant economic growth. Between 1980 and 1990, the average growth of real gross domestic product (GDP) was about 7.6%, and the growth of exports was around 14%. Between 1990 and 1995, average growth in real GDP reached 8.4%, and average export growth was 14.2% (Salvatore 2011). Since 2000, however, Thailand has had an average real GDP growth of only about 4%. While Thailand is widely perceived as an economically developing country led by agricultural exports, the majority of the country’s income is driven by the manufacturing and the service sectors. Since 1993, the agriculture sector has contributed only 300–400 billion baht (B) per year to Thailand’s real GDP while in 2009, the service sector generated about B2 trillion or almost 50% of GDP mostly from private sector services (Table 1). Based on this pattern, the Thai economy is in the first phase of economic development. After resources shift from agriculture to manufacturing, there will be a final shift to tertiary production or services (Fisher 1939, Clark 1940). This paper analyzes the status of Thailand’s service sector and investigates whether it is a growth engine for the economy. II. BASIC FACTS Because the service sector is highly diverse, ranging from low-end services such as street vendors to high-end services in the financial and professional sectors, a clear definition is required. A. What is Thailand’s Service Sector? Like every country, Thailand has several definitions of services depending on derivation and terms of use. The National Economic and Social Development Board of Thailand defines the service sector as all economic activities except for those in the agriculture, manufacturing, and mining and quarrying sectors.1 Using this broad concept, Thailand defines its service sector as comprising no fewer than a dozen economic activities. Since 1991, the General Agreement on Trade in Services (GATS) has offered a different definition of the service sector and has published a service sector classification list (WTO 1995) that has become the standard for academics and scholars. A third classification method is the balance of payments, an International Monetary Fund definition used mainly for international trade and finance statistics. Because the National Economic and Social Development Board and GATS propose different definitions of the service sector, researchers and policymakers have a more difficult time studying service activities. For example, the national definition classifies hotels and restaurants as major service activities whereas the GATS recognizes each as services within tourism and travel (Table 2). Multiple definitions make data collection and systematic analysis difficult which in turn generates high transaction costs when researchers and policymakers need more information about particular services such as tourism or recreational services. 1 This is the International Standard Industrial Classification of All Economic Activities (ISIC) for objectively classifying economic data. 8 І ADB Economics Working Paper Series No. 353 The service sector has become increasingly important to Thailand because of its economic contribution and the employment it provides, but the inverse relation between its contribution to GDP and its contribution to employment merits a closer look from researchers and policymakers. Based on the information in Figure 1 and the World Factbook, starting in 2009 we can observe a turning point where the shares of the manufacturing sector decrease and those of the service sector increase. C. Trade and Investment From 2005 to 2010, Thailand had a trade deficit in services averaging about $8.9 billion that grew to almost $10 billion in 2011 (Table 5). In addition to transportation, royalties and licensing, communication services, and insurance services have caused the majority of the deficit while travel services have been the major positive component since 2005. It is noteworthy that in 2010, the hotel and restaurant industry ranked second in employment in part due to the tourism industry. According to the Thomas White International website, in 2007, tourism and travel in Thailand contributed a staggering 6% of total GDP, more than in any other Asian nation. This concurs with data from the World Trade Organization (WTO) Service Profiles that show that in 2010 Thailand received a positive net trade balance of payments in travel equal to $14.644 million which ranked it first among the Asian countries studied. Moreover, Bangkok, has received "The World's Best City Award" for four consecutive years (2010–2013) in Travel & Leisure. Thailand’s inward foreign direct investment (FDI) in the service sector from 2005 to 2011 averaged $3 billion with a peak in 2007 of about $3.8 billion. The majority was in financial intermediation and real estate at about 88% of gross annual FDI (Table 6). Thailand’s sector has shown significant openness to trade in services by welcoming foreign investment. III. SHARE OF OUTPUT MODEL Based on the framework in Eichengreen and Gupta (2009), the relationship between the share of output in the service sector and per capita income in Thailand was examined using provincial data for the first time. Data for this study came from the National Economic and Social Development Board’s gross regional and provincial product (GPP). Provincial data are from 76 provinces and include 16 economic activities classified under the International Standard Industrial Classification Revision 3 and are available from 1995 to 2009. Table 7 presents these descriptive statistics. A Turning Point for the Service Sector in Thailand І 9 Table 5: Net Service Trade ($ million) 2005 2006 2007 2008 2009 2010 2011 Net service trade –6,862.95 –8,011.54 –7,937.09 –12,891.87 –6,377.33 –1,0551.10 –9,952.53 Transportation –9,812.65 –10,771.38 –11,692.15 –15,690.66 –11315 –16,500.13 –20,844.34 Freight –11,133.98 –11,884.91 –13,054.72 –17,131.15 –12,930.75 –17,745.62 –21,667.07 Passenger 1,410.09 1,646.65 2,211.38 2,642.95 2440.10 2163.80 2,145.01 Others –88.76 –533.13 –848.81 –1,202.47 –824.34 –918.31 –1,322.28 Travel 5,772.92 8,801.45 11,524.99 13,160.97 11,626.72 14,597.78 21,143.24 Government services n.i.e. 5.69 11.91 –19.63 97.26 48.84 –15.04 121.20 Other services –4,246.50 –7,633.03 –9,380.39 –12,579.06 –8,413.40 –10,819.98 –1,2974.30 Communication services –1,380.31 –1,519.23 –1,591.21 –1,955.20 –1,583.72 –2,078.81 –2,522.53 Construction services –58.54 –245.04 –123.35 –173.35 –310.70 –239.19 136.20 Royalties and licenses –1,659.40 –2,000.40 –2,234.51 –2,466.32 –2,102.38 –2,927.37 –2,943.93 Insurance services –1,380.31 –1,519.23 –1,591.21 –1,955.20 –1,583.72 –2,078.81 –2,522.53 Others 232.06 –2,349.13 –3,840.11 –6,028.99 –2,832.88 –3,495.80 –5,121.51 Note: Government services n.i.e (not included elsewhere) is a residual category covering government service transactions for goods and services (office supplies, furnishings, utilities, official vehicles and their operation and maintenance, and official entertainment) by embassies, consulates, military units and defense agencies, and personal expenditures incurred by diplomats, consular and military staff and their dependents in the economies in which they are located. Also included are transactions associated with general administrative expenditures and not included elsewhere. Source: Bank of Thailand. 10 І ADB Economics Working Paper Series No. 353 Table 6: Foreign Direct Investment by Economic Activity in Thailand, 2005–2011 ($ million) 2005 2006 2007 2008 2009 2010p 2011p Electricity, gas, and water supply Inward –87.71 353.83 33.20 200.43 221.92 –107.43 93.59 Outward 66.06 –106.82 4.17 –289.33 –68.91 –138.39 –112.59 Construction Inward 29.56 –93.79 29.96 –34.04 1.43 20.73 28.07 Outward 4.39 –29.98 –72.84 –44.20 –36.23 101.79 –203.41 Wholesale and retail trade; repair of motor vehicles, Inward 260.27 845.21 –262.52 131.58 344.86 29.95 –512.93 motorcycles, and personal and household goods Outward 229.57 133.13 –162.29 –936.55 24.56 –446.71 –983.42 Hotels and restaurants Inward 155.05 80.53 –43.31 450.25 118.42 –190.44 94.56 Outward –89.32 4.96 –127.98 –96.68 –169.47 –166.51 –10.42 Transport, storage, and communications Inward –29.94 124.97 166.77 –51.34 46.00 –31.99 12.83 Outward –9.96 –14.68 57.08 –60.42 51.10 18.59 –93.63 Financial intermediation Inward 3,269.45 691.65 2,815.04 1,765.99 274.15 2,332.01 1,662.11 Outward –231.93 –154.06 –2,337.52 –1,790.53 –1,755.14 –466.34 –3,368.91 Real estate, renting, and business activities Inward 73.28 1,419.06 1,103.16 1,202.53 767.96 802.40 905.14 Outward –7.50 –14.75 –272.90 335.77 –51.24 –96.49 –723.96 Gross foreign direct investment Inward 3,669.96 3,421.46 3,842.30 3,665.40 1,774.74 2,855.23 2,283.37 Outward –38.69 –182.20 –2,912.28 –2,881.94 –2,005.33 –1,194.06 –5,496.34 p = prediction. Source: Bank of Thailand. A Turning Point for the Service Sector in Thailand І 11 Table 7: Descriptive Statistics Variable No. of Observations Mean Std. Dev. Min Max Agriculture (million baht) 1,140 4,433.20 3,052.46 385 18,917 Non-agriculture (million baht) 1,140 41,292.96 117,045.70 2,653. 1,074,500 Manufacturing (million baht) 1,140 16,918.32 42,069.44 92 260,337 Services (million baht) 1,140 23,410.76 85,680.50 2,439 84,9739 GPP total (million baht) 1,140 45,637.11 116,885.50 3,383 1,075,643 Population (1,000 persons) 1,140 833.29 810.73 145 6,866 Per capita income at 1988 prices (baht) 1,140 48,194.58 59,846.07 9,137 413,657 Service share (%) 1,140 55.49 16.34 11 89 Agriculture share (%) 1,140 22.19 13.01 0.10 58.97 Manufacturing share (%) 1,140 20.12 21.33 2.46 86.97 Non-agriculture share (%) 1,140 77.80 13.01 41.03 99.89 Log per capita income 1,140 10.38 0.79 9.12 12.93 GPP = gross provincial product Source: Author's calculations using data from National Economic and Social Development Board. Scatter plots4 are used to compare the share of services in GPP and the log of per capita income. The plots are shown in Figure 4 in four categories. Plot (1) displays all provinces except Bangkok and vicinity and Phuket. The relationship appears wave-like with an increasing trend in the service share when income is low and a decreasing trend when income is high. This relationship differs from a major assumption in economic development: the service sector grows as income increases. Plot (2) shows Bangkok and vicinity and presents a parabolic function. Plot (3) is for Phuket, Thailand’s largest island, and confirms the conventional assumption that service output and income are directly related. Although Phuket and Bangkok seem to be outliers in our model, by including these outliers plot (4) still maintains a wave-like shape. Therefore the panel data model uses 76 provinces from 1995 to 2009 with a total of 1,140 observations and hypothesizes the wave-like shape as shown in Figure 4 plot (4). Because of the limitations of a bounded share as discussed in Eichengreen and Gupta, the relationships were estimated in quartic form. Before determining the equation for the estimation, the relationships between the shares of GPP and per capita income in each of the three sectors were tested using the Lowess plots as stated in Eichengreen and Gupta. The agricultural share of output declines as income increases while the manufacturing share of output rises as income increases. The service share of output generally decreases as income increases except for the lowest and the highest income groups. This information is relevant to the fact stated in Figure 1. The Lowess plots for the manufacturing share of GPP are similar to the plots from Eichengreen and Gupta, but the declining trend has not yet appeared. 4 The plots presented in Figure 4 are uncontrolled for time and spatial dimensions; nevertheless, they help explain the nature of the data used in the model. 12 І ADB Economics Working Paper Series No. 353 Figure 4: Scatter Plot of the Service Share of Gross Provincial Product and Log per capita Income GPP = gross provincial product. Note: There are no controls for any time or spatial dimensions. Source: Author’s calculations using National Economic and Social Development Board data. The fixed-effect model with robust standard errors was run with the service sector’s percentage of GPP as the dependent variable. The independent variables were the four powers of the natural log of real per capita income and a dummy variable for structural change in the Thai economy. The dummy variable may be seen as post-financial crisis development factors. Fixed-effect models control for the effects of time-invariant variables with time-invariant effects, i.e., the variable has the same effect across time such as gender, race, and some institutional factors. Therefore, the equation was determined as follows:   θDαY αYαYαY ε The estimates are displayed in Table 8. All models confirm the hypothesis of a quartic functional form and two waves of service sector growth. The service sector share in GPP and per capita income in model I (base case) and the relationship between the service sector share in GPP and per capita income in model II (with a dummy variable) were then plotted together in Figure 5. This figure exhibits two types of relationships based on estimates from both models. Each relationship pattern indicates that there is a possibility for two waves of service sector growth in Thailand and also implies that the service sector is a growth engine for the Thai economy. This finding is relevant to previous 0 50 100 0 50 100 910 11 12 13 910 11 12 13 (1) = (4)-(3)-(2) Bangkok and Vicinity (2) Phuket (3) Total (4) Services Sector Share of GPP Log of per Capita Income A Turning Point for the Service Sector in Thailand І 13 studies using GDP data5 that described two waves of service sector growth: the study by Eichengreen and Gupta (2009) and the study by Park and Shin (2012). Table 8: Coefficient Estimates for the Relationship between Service Share of Gross Provincial Product and Per Capita Income Model I Model II Log per capita income 6,520.90** 6,255.68** (2,846.99) (2,952.33) Log per capita income, squared –857.13** –817.42** (388.75) (403.47) Log per capita income, cube 49.63** 47.07* (23.47) (24.38) Log per capita income, quartic –1.07** –1.01* (0.53) (0.55) Dummy for 2001 –2.57*** (0.43) Constant –18,370.25** –17,734.84** (7,777.60) (8,059.30) Province fixed effects yes yes Observations 1,140 1,140 Number of provinces 76 76 Prob > F 0.0004 0.0000 R-squared 0.49 0.46 Note: Robust t statistics are in parentheses. *, **, *** indicate coefficient with significance at 10%, 5%, and 1%, respectively. Source: Author's calculations. Figure 5: Service Share of Gross Provincial Product and Log per capita Income Based on Quartic Functional Form GPP = gross provincial product. Source: Author’s calculations using National Economic and Social Development Board data. 5 Using GDP data, Eichengreen and Gupta (2009) and Park and Shin (2012) assume no resources move between countries. This study assumes no resources move between provinces. In the real world, there is labor/human capital movement not only within a country but also among countries. 30 40 50 60 70 Services Sector Share in GPP 910 11 12 13 Log of Real per Capita Income Base case With dummy 14 І ADB Economics Working Paper Series No. 353 The first wave takes place when a province moves from lower to middle-income status, and the second takes place when a province moves from middle to high-income status. Therefore there will be two turning points. Figure 5 displays information that is especially important for Thailand.  After a first turning point, provinces will experience a reduced service share in GPP as incomes move toward higher levels. Moreover, per capita income in the bottom 10% is log per capita income less than 9.516 which is equal to per capita income of B13,577 a year (1988 prices). All of the lower per capita income provinces are located in the northeastern region.6  The high-income provinces have two distinct relationship patterns that could explain why the service sector is a growth engine.  The first possible turning point for the estimates in model II occurs when per capita income in the highest 8% of the population is equal to the log per capita of income greater than 12 and equals per capita income of B163,169 a year (1988 prices). There are only seven provinces7 with these characteristics. They contain industrial parks and are either near the capital or a marine port. In this model, the service sector would be a growth engine for the Thai economy.  For the base case, our estimates show the possible turning point would be a point after log per capita income greater than 13, or per capita income greater than B442,000 per year (1988 prices). In the base case, the Thai economy would depend mainly upon the manufacturing sector rather than services for growth. IV. TOWARD A POSSIBLE TURNING POINT A. Share of Output Model Revisited Thailand’s service sector could potentially experience a second wave of growth, particularly in high-income provinces near Bangkok. In this section, specific service activities are investigated in order to offer public policy advice. The service sector is then assessed comparing model II with the dependent variables of private service sector results and 12 other service activities. Before doing so, data on 12 service activities were tested in scatter plots to reveal the relationship between the share of the GPP and log per capita income. The plots indicated that each service activity may not be evidence for a quartic function and also has several outlying points. Therefore, estimating the share of each service in GPP using the model discussed above is not statistically significant except for wholesale and retail trade, construction, and education. The relationship between the private service sector share and log per capita income is almost the same as the relationship seen in Figure 5 including a possible turning point for the service sector share in GPP at high per capita income levels. This confirms that government services such as public administration, defense, and compulsory social security play a lesser role in per capita income. Based on the scatter plots, the share of government services in GPP has a negative relationship with the log of per capita income. Construction and related engineering services and wholesale and retail trade have a positive relationship between the 6 The lowest per capita incomes are in Amnatcharoen, Buriram, Chaiyaphum, Kalasin, Mahasarakham, Mukdahan, Nakhonphanom, Nongbualamphu, Roi-et, Sakonnakhon, Sisaket, Surin, and Yasothon. 7 Those are Chachoengsao, Chonburi, and Rayong in the eastern region; Pathumthani, Samutsakhon, Samutprakan in the Bangkok metropolitan area; and Phranakhonsriayuthaya in the central region. A Turning Point for the Service Sector in Thailand І 15 share of output and per capita income, while education services have a negative relationship (Figure 6). Figure 6: Share of Gross Provincial Product and Per Capita Income in Construction, Trade, and Education GPP = gross provincial product. Source: Author’s calculations using National Economic and Social Development Board data. Eichengreen and Gupta found that wholesale and retail trade has a negative relationship with income while our estimates found the opposite. Wholesale and retail trade in Thailand is around 10%–20% of GPP which indicates two waves of growth, but including street vendors and flea market merchants from the informal economy would make the data more complete. The relationship between construction and income is linked to the stability of Thailand’s real estate and infrastructure. The plots indicate that returns from construction must increase in order for income to increase in each middle-income province. From the estimates, wholesale and retail trade and construction are clearly the two waves of service sector growth that imply a growth engine for the Thai economy. The relationship between the share in GPP and per capita income for education services predicted by the model is downward sloping. This is completely different from the Group II plots in Eichengreen and Gupta. It should be noted that the average annual expenditure on education is about B1.2 billion (1988 prices) per province or B1,376 (1988 prices) per person. When the average GPP grows faster than the rate of growth in expenditures, the share in GPP will decrease, i.e., it will have a negative relationship with per capita income. Educational expenditures may be under-estimated, especially for special education services or offsite tutoring. Agencies involved with education should investigate why this relationship is a converse one when in most developed countries the relationship is positive. 12 14 16 18 20 22 Wholesale and Retail Trade Share in GPP 910 11 12 13 Log of per capita income 0 2 4 6 8 Education Share in GPP 910 11 12 13 Log of per capita income Construction share in GPP 910 11 12 13 Log of per capita income 10 5 0 –5 16 І ADB Economics Working Paper Series No. 353 B. Total Factor Productivity Although plots for the relationship between the service sector and per capita income indicate the possibility for service sector growth, the components of its growth can be determined by total factor productivity (TFP). The latest TFP study for Thailand was done in 2009 by the National Economic and Social Development Board. It calculated the TFP for eight economic activities: agriculture, mining, manufacturing, electricity, construction, retail trade, transportation, and services and other activities (NESDB 2009). Among these services, transportation was the most significant with a positive TFP between 1982 and 2007 except during the 1997 financial crisis. This may be due to the country’s improvement in logistics, mainly in road, air, and sea transportation. The TFP for retail trade is positive after 1999 while for services and other activities it is positive after 2002 (Table 9). It is probable that income from retail trade is more than its recorded high as income from the informal economy is not recorded. Although TFP indices have been positive in services and in other activities since 2002, the reason is still unclear because the activities have been cumulated. It indicates only a reason for growth; if it continues, we may expect a real turning point in service sector growth. In terms of TFP, retail trade and transportation should be major service activities for Thai economic growth. C. Revealed Comparative Advantage Although trade in services and FDI implies high levels of openness in Thailand’s service sector, it does not imply anything about competitiveness. If countries have information about their competitiveness in trade and investment, they can better implement trade policies and negotiate suitable agreements. Using the revealed comparative advantage (RCA) index8 established by Balassa (1965), important service activities in Thailand were examined. If the RCA for a service is greater than 1, it means that country has a level of competitiveness above the world average and a comparative advantage in that service. The opposite is true for an RCA less than 1. If any services in Thailand have a comparative advantage, Thailand will gain from trade in those services, and they could be a growth engine for the Thai economy. RCAs were calculated for selected economies using the WTO International Trade Statistics on commercial services including transportation, tourism and travel, and other services such as business services. Table 10 presents the RCAs for these services from 1990 to 2009. In transportation among selected Association of Southeast Asian Nation (ASEAN) members, only Singapore consistently maintained a comparative advantage throughout the decade. This is related to the fact that the country has been a hub for both sea and air transportation. Hong Kong, China; Japan; and the Republic of Korea also maintained comparative advantages in transportation. 8 RCAs were estimated using the following steps. (i) Divide the value of the service exports under consideration by the value of total exports for the country. (ii) Calculate the portion of the total value of those service exports in the world divided by the value of total exports in the world. (iii) Divide (i) by (ii). A Turning Point for the Service Sector in Thailand І 17 Table 9: Total Factor Productivity in Construction, Retail Trade, Transportation, and Services and Other Activities Period Construction Retail Trade GDP Labo r Capital TFP GDP Labo r Capital TFP 1982–1986 6.2 3.0 4.1 –0.9 3.2 0.6 3.1 –0.5 1987–1991 17.3 8.8 10.3 –1.8 12.0 0.8 9.1 2.1 1992–1996 8.3 4.6 12.6 –8.9 6.7 0.8 9.4 –3.4 1997–1998 –31.9 –8.2 –1.8 –21.9 –8.1 0.0 0.2 –8.4 1999–2001 –5.3 1.2 0.9 –7.4 2.0 0.3 –0.6 2.3 2002–2006 5.1 2.7 1.9 0.5 3.6 0.5 1.2 2.0 2007 2.0 0.0 2.7 –0.5 3.2 2.1 2.1 0.9 Average 1982–2007 4.1 3.2 5.6 –4.7 4.7 0.4 4.4 –0.3 Period Transportation Services and Other Activities GDP Labo r Capital TFP GDP Labo r Capital TFP 1982–1986 8.9 2.2 2.8 3.9 6.1 8.9 2.4 –5.2 1987–1991 11.4 1.8 6.8 2.8 8.2 3.8 3.3 1.1 1992–1996 11.1 0.8 10.1 0.3 4.4 1.1 4.9 –1.6 1997–1998 –2.2 –0.3 4.4 –6.3 –5.2 1.5 1.3 –8.0 1999–2001 6.8 0.5 1.7 4.6 –0.8 4.5 0.0 –5.4 2002–2006 5.6 0.2 2.2 3.3 5.9 1.1 0.6 4.2 2007 6.0 –0.3 2.6 3.8 4.0 2.2 0.9 0.9 Average 1982–2007 8.0 0.1 4.9 2.2 4.4 5.1 2.3 –3.0 Note: GDP = gross domestic product, TFP= total factor productivity. Source: Author’s calculations. 24 І ADB Economics Working Paper Series No. 353 World Trade Organization (WTO). 1995. General Agreement on Trade and Services Service Sector Classifications. www.wto.org/english/tratop_e/serv_e/mtn_gns_w_120_e.doc ———. Various years. International Trade Statistics. http://www.wto.org/english/res_e/statis_e/ its_e.htm ———. Various years. Service Profiles. http://stat.wto.org/ServiceProfile/ WSDBServicePFHome.aspx?Language=E A Turning Point for the Service Sector in Thailand Pracha Koonnathamdee No. 353 | June 2013 ADB Economics Working Paper Series A Turning Point for the Service Sector in Thailand This paper tests the hypothesis that the service sector is a growth engine in the Thai economy. While many developed countries maintain a positive association between the shares of the sector in output and per capita income, the opposite is true in Thailand. Using estimates from a fixed-effects model, the study confirms two waves of growth. In addition, total factor productivity and revealed comparative advantages are also discussed to determine the significant role of services and some service activities. About the Asian Development Bank ADB’s vision is an Asia and Pacific region free of poverty. Its mission is to help its developing member countries reduce poverty and improve the quality of life of their people. Despite the region’s many successes, it remains home to two-thirds of the world’s poor: 1.7 billion people who live on less than $2 a day, with 828 million struggling on less than $1.25 a day. ADB is committed to reducing poverty through inclusive economic growth, environmentally sustainable growth, and regional integration. Based in Manila, ADB is owned by 67 members, including 48 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance. Asian Development Bank 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines www.adb.org/economics Printed on recycled paper Printed in the Philippines