Stories about Institutions and Patterns of Slow Economic Growthfrom 21st Century Thailand

Eric D. Ramstetter
Lecturer, Faculty of Economics, Thammasat University; andVisiting Professor and Professor Emeritus, Asian Growth Research Institute (AGI)

Working Paper Series Vol. 2024-15
July 2024

The view expressed in this publication are those of the author(s) and do
not necessarily reflect those of the Institute.

No part of this article may be used or reproduced in any mannerwhatsoever without written permission except in the case of brief
quotations embodied in articles and reviews. For information, pleasewrite to the Institute.

Asian Growth Research InstituteStories about Institutions and Patterns of Slow Economic Growth from 21st Century Thailand
Eric D. Ramstetter1, July 2024

Abstract (235 words)
This essay examines how institutions and economic trends have evolved during a period of slow economic
growth after 2006, focusing on comparisons to 2000-2006, when the economy recovered from the Asianfinancial crisis, and the economic boom during 1990-1996. Private fixed investment declined sharply andremained low after 1996, contributing to relatively low growth. Institutional instability, particularly inpolitical and financial markets, increased investor uncertainty and contributed to low growth. In contrast,
important improvements in infrastructure, labor productivity and wages, education, health, and poverty
reduction continued to boost growth after 2006. Income distribution among households and regions alsotended to improve, although changes were small. Thailand’s large international trade, policies that limit both
import and domestic competition, and the need to strengthen environmental protection present important
policy challenges.
The political alignments after the 2023 election offer hope that political parties and other politicalinstitutions can mature, stabilize, and help promote rebounds in private investment and growth, while helping

1

Lecturer, Faculty of Economics, Thammasat University Tha Prachan Campus; Bangkok, Thailand andVisiting Professor and Professor Emeritus, Asian Growth Research Institute (AGI), Kitakyushu, Japan.
Email: ramstandy “at mark” gmail.com. This research is part of the Senior Research Scholar Program in
Social Science 2022, which was awarded to Associate Professor Archanun Kohpaiboon. I thank Archanun
Kohpaiboon, Craig R. Parsons, Michael G. Plummer, and Fredrik Sjöholm for helpful comments on previous
versions. I also benefited from comments during an AGI Seminar in Sep 2022, a Thammasat University
seminar in May 2023, and a Yokohama National University seminar in Dec 2023. All remaining errors andall opinions and interpretations are the sole responsibility of the author.
1

promote further gains in education, health, and human capital formation. Literature on development andgrowth emphasizes the key roles of strengthening and stabilizing institutions that foster peace (the lack ofviolence, actual or threatened) and human capital formation (through healthcare, education, etc.) because
positive externalities are often large in related markets. Fostering continued wage increases driven bycorresponding increases in labor productivity (benefitting both producers and consumers) is anotherimportant challenge for high-income developing economies like Thailand.
JEL codes: O53, O11, O12, O15, O24, O25, O43, O44
Keywords: Thailand, Institutions, Economic Growth, Physical & Human Capital Formation, Income
Distribution, Industrial & International Trade Policies

1. Introduction
Thailand is a small, price-taker economy in almost all world markets with a medium-sized population of
about 70 million in 2019-2022. Thailand experienced substantial rises in per capita GDP and many othermeasures of economic welfare during most of the post-World War II era. Modern Thais usually much earnhigher incomes, consume more and higher-quality goods and services, get more education, are healthier, andhave a lower probability of living in poverty than previous generations. However, long-term growth rates ofper capita GDP in 2002 baht fell from 6.9 percent annually during the boom period in 1990-1996 to 4.5
percent during the post-Asian-Crisis recovery period in 2000-2006 and then stagnated at 2.9 percent during
the slow growth period in 2006-2019 (Table 1). The covid19 shock caused a sharp decline in 2020 andremained below the slow-growth average in 2021-2022. This purpose of this essay to paint a “big picture”
of the Thai economy, comparing trends and patterns of growth and other important indicators of economic
welfare and performance during the boom, recovery, and slow growth periods and analyzing implications
for the future.

2

The essay consists of analytical “stories” about the evolution of the Thai economy.2 Section 2 begins with“discouraging” stories about (a) slow growth, (b) the structure of aggregate demand and low levels of private
fixed investment, and (c) slowed accumulation of private fixed capital. These stories are “discouraging”
because they reflect slower increases in Thai incomes and economic welfare after 2006 than would be
expected at Thailand’s income level. Section 3 turns to “encouraging” stories about continued improvementsin (a) infrastructure, (b) employment, labor productivity, and wages, (c) poverty reduction and income
distribution, and (d) human capital formation. Section 4 discusses “challenging” stories about (a) exports
and imports, (b) import protection, and (c) domestic competition and regulation. Section 5 concludes.
The essay emphasizes how discrete disruptions in financial markets and/or political institutions in 19971998, 2001, 2006, and 2014 created persistent instability and contributed to the decline in growth and private
fixed investment. Large changes in financial markets and related institutions also contributed by reducing
willingness to both lend and borrow, especially after the Asian Financial Crisis. In contrast, other institutions
improved in education, health care, and infrastructure development, and boosted growth.
I focus on institutions because they play a key role determining the efficiency with which inputs are
combined to produce output. Economists have long identified improvements in this statistically ambiguous
definition of technology (estimates of which vary depending on production function form and the numbers
and/or types of inputs and outputs hypothesized) as the major source of long-term economic growth in mostdiversified economies like Thailand. These arguments are consistent with studies emphasizing how legal andpolitical transparency, for example, sometimes contribute positively to growth in cross-country samples(Barro 1996). Unfortunately, standard economic analyses that emphasize incremental adjustments toincremental changes are unlikely to depict the effects of large, discrete, institutional changes realistically.
Thus, this essay relies on descriptive analyses and references to existing literature.

2

A single paper such as this can never be comprehensive. Several important issues, including fiscal ormonetary policies or regulation of bank and financial institutions, are not analyzed.
3

2. Discouraging Stories: Slow Economic Growth and Reduced Capital Accumulation
The growth rate of per capita GDP in 2002 baht is a key indicator of changes in living standards as perceived
by Thais over time. Subsequent sections will examine trends in other important indicators of economic
welfare. This section focuses on trends in growth and accumulation of fixed capital, and includes a simple
analysis of aggregate demand structure suggesting that fiscal and monetary stimuli are relatively ineffectivein Thailand.

2a. Growth Trends
Low growth after 2006 concerns Thai policy makers because increasing incomes is still a high priority formany Thais. Per capita GDP was US$3,370 in 2006, US$7,813 in 2019, and US$7,070 in 2022 (Table 1).
One would normally expect middle-upper income developing economies to grow more rapidly, as they catch
up with richer economies. However, as of October 2023, real per capita GDP growth rates were projected toremain at 2.5-3.1 percent in 2023-2025 (International Monetary Fund [IMF] 2023). Similar long-term trends
were observed in neighboring Malaysia.3
Relatively low growth after the Asian Financial Crisis in Thailand and Malaysia often helped motivate
analyses of the so-called middle-income trap in country-level samples (Agenor 2017; Felipe et al. 2017).
However, most of the literature fails to identify economically meaningful “traps” which make policy
ineffective. Rather, most literature suggests that long-term, national growth performances are primarily
determined by domestic institutions, politicians, economic actors, and related policies, even in small, open
economies like Thailand and Malaysia.

3

IMF estimates of real per capita GDP growth for Malaysia were 6.9 percent in 1990-1996, 2.5 percent in

2000-2006, 3.3 percent in 2006-2019, -5.2 percent in 2020, and 2.7-3.2 percent in 2021 and in 2023-2025
projections. 2022 growth was much higher, 8.4 percent.
4

In Thailand, inflation rates were relatively low during the period studied, particularly after the Asian
Financial Crisis. The baht depreciated markedly in from 25-26/US$ in 1990 and 1996 to 40 in 2000, but thenfluctuated to 30-35 range in 2007-2022. The stability of prices and exchange rates also reflects generally
conservative monetary and fiscal policies throughout the period studied. On the other hand, the precipitous
decline in private capital formation after 1996 has been both a result and a cause of slow economic growth.

2b. GDP Expenditure Components and Fixed Investment Trends
Private consumption is the largest component of domestic aggregate demand, accounting for between about
half or slightly more of GDP (Table 1 sources).4 Relatively high shares of private consumption can explaina some of the relatively high growth in the recovery period. Reflecting effects of the baht’s depreciation,
ratios of goods’ exports and imports to GDP rose after the Asian Financial Crisis, from 26-30 percent forexports and 33-35 percent for imports in 1990 and 1996, to as high as 58 and 52 percent, respectively, in2006. Both export and import shares fell before the covid19 crisis to 45 and 40 percent respectively, in 2019,
but increased sharply after covid19, to 58 and 55 percent, respectively, in 2022.5 Services exports and imports
also grew rapidly during the 1990s and fluctuated afterwards. Services exports contracted conspicuously
during 2020 and remained low in 2021-2022, reflecting reduced tourism.
If exports are assumed to be exogenous, as is normal for a small country, fiscal or money multipliers are
relatively small in Thailand because the marginal propensity to import is probably similar to or larger thanthe marginal propensity to consume. Thus, a relatively large portion of fiscal or monetary stimulus leaks out
4

In 1996-2019, for example, household consumption shares of GDP were a mean of 51 percent in bothThailand and Malaysia, and similar to levels in Korea (49%), Taiwan (53%), but larger than in poorer
economies like Indonesia (57%), Vietnam (61%), and the Philippines (72%). Data from Asian Development
Bank (2023).
5

Exports or imports can exceed GDP or value added, which excludes intermediate consumption, because

they are measured as sales or output values, including intermediate consumption.
5

through imports. In short, policies like the proposed handout of 10,000 baht to many Thais or public works’
expenditures are unlikely to create large aggregate demand stimulus.6
The decline of private fixed investment from 20 percent of GDP in 1990 and 1996 to 8-9 percent from 2000
was one of the most conspicuous long-term changes in the Thai economy after the Asian Financial Crisis(Table 1). The largest decline was in private construction, which fell from 12-15 percent of GDP in 19901996 to 3-4 percent in 2000 and later years. Before the crisis, private construction investment was often
speculative and contributed to high asset prices in the early 1990s, but prices of land and stocks fell markedly
in the 2-3 years before the crises. Falling asset prices and government failure to properly supervise lending
practices of Thai financial institutions contributed to large debt accumulation and were a major cause of the
Asian Financial Crisis. After the crisis, private investors have apparently become more risk averse. Further
analyses of how Thailand’s largest lenders and borrowers changed after the Asian Financial Crisis and again
after the World Financial Crisis (e.g., Menkhoff and Suwanaporn 2011 for pre-crisis years) would help clarify
important aspects of these changes.
Previous studies estimate the effects of economic uncertainty on private investment. Jongwanich andKohpaiboon (2008) hypothesize uncertainty is related to the volatility of output growth, inflation, the realexchange rate, and the terms of trade. The negative effects of economic uncertainty on private investors arealso emphasized in studies of Malaysia (Ang 2010) and Thailand (Apaitan et al, 2020). Suwanprasert’s(2023) event study of the 2006’s coup’s discrete effects finds that consumption’s GDP share decreased, while
military spending and tourism rose afterwards. In contrast, effects on real GDP and many other indicators

6

Because large portions of Thai exports conducted by large multinational enterprises, exports may not be

entirely exogenous as usually assumed. However, I know of no study that estimates the effects ofendogenizing exports on Thailand’s macroeconomic multipliers.
6were insignificant. It is difficult to measure how discrete changes in political leadership and related
institutions affect the economy, partially because effects often take a long time to be realized.7

2c. Capital Accumulation, Average Capital Productivity, and Private Shares of Fixed Capital
Table 2 shows data on the nominal value of net capital stocks at replacement value to compare with dataon nominal GDP (Table 1) and provide a long-term perspective on the causes and effects of prolonged, lowlevels of fixed capital formation. For example, the growth rate of nominal capital stocks fell from 15 percent
during the boom period to 6.8 percent during the recovery period and 4.2 percent during the slow growth
period (Table 2). If the capital stock deflator is used, real growth rates were similar, 15, 6.2, and 4.1 percent,respectively, but real growth rates were a bit lower if the GDP deflator is used (Table 1, Table 2 sources).8
The average product of capital has been relatively stable, decreasing from 0.45 in 1990 to 0.40 in 1996, and0.37 in 2006 before rebounding to 0.41-0.44 in 2019-2022 (Table 2). There was substantial variation of
average capital productivity among industries. It was higher than average in manufacturing and trade andvehicle repair, below the average in transportation and storage, and closer to the average in agriculture andconstruction. This variation reflects marked differences between industry distributions of the capital stockand GDP (Table 1). Transportation and storage and manufacturing had the two biggest capital stocks of thesix industries in Table 2 in many years. “Other” industries accounted for much larger shares of fixed capitalthan of production, particularly during the boom and recovery periods.9

7

For example, the 2019 constitution can be viewed as a delayed result of the 2006 and 2014 coups’ failures

to eliminate political opponents from power.
8

In order to calculate real capital stocks and related growth rates, one must account for changes in theeconomic value of pre-existing capital stocks, which is difficult when other asset (e.g., stock or land) prices
fluctuate widely, in addition to accounting for how prices of new additions to the capital stock change.
9

Finance and real estate (capital stock shares of 32, 27, 24, and 20-21 percent in 1990, 1996, 2006, and2019-2022, respectively) was a large component of this “other industries” category.
7

Reflecting slow growing private fixed investment, the private share of total net capital stocks fell from 77
percent in 1990 and 1996 to 69-72 percent in 2006 and later years (Table 2). Private capital dominated (95percent or more of the total) in manufacturing and in trade and vehicle repair, but private shares were only
two-thirds or less in agriculture, construction, and transportation and storage. Low private shares inagriculture, construction, and relatively low shares in accommodation and food services are surprisingbecause most production and employment in these industries probably originates in the private sector. If
most production is indeed private, these data suggest that private producers are allowed to use government
capital stocks in these sectors. Controversies related to the use of government-owned land by private
agricultural producers are common, for example.

3. Encouraging Stories: Infrastructure, Labor Markets, Human Capital, and Poverty Reduction
There are also important, encouraging stories relating to (a) infrastructure and innovative effort, (b)employment, labor productivity, and wages, (c) poverty reduction and income distribution, and (d) humancapital formation. They are reviewed below.

3a. Infrastructure and Innovative Effort
Reflecting rapid growth of capital stocks in related industries, use of transportation and communication
infrastructure increased (Table 2). These usage increases largely reflect improvements in related
infrastructure. Rail transport remains inefficient and a low priority for the Thai government (which does notreport data on passengers after 2017 and freight after 2011. Conversely, air and road transport infrastructure
are high priorities and grew rapidly during recent decades. Quantities of air freight and air passengers grew
13 and 9.4 percent annually, respectively, during the boom, but only 3.5 and 2.4 percent, respectively, during
the recovery. Air freight continued growing slowly in 2006-2019 (0.8 percent), but air passengers’ growth
skyrocketed to 11 percent. Container port traffic also increased substantially, 9.8 percent annually in 2000-

8

2006 and 4.7 percent annually in 2006-2019. After the covid19 pandemic, the number of air passengers was
only about 1/3 of 2019 levels in 2020 and 1/6 in 2021.
Communications infrastructure also improved rapidly and became highly sophisticated (Table 2).10 In theboom period, all subscription types grew rapidly, but after 2006 fixed line subscriptions fell, as mobile
subscriptions substituted for many of them. In 2019-2022, the average resident had 1.6-1.8 mobile
subscriptions, illustrating how most Thais have embraced the tech revolution, with heavy reliance on mobile
devices.
There are also some indicators of increased innovative effort by private firms. Thailand’s R&D expenditures
have been relatively low, at only 1.3 percent of GDP in 2020, but markedly higher than the 0.1-0.2 percent
in 1996 and 2006 (Table 5). Private firms were increasingly dominant R&D spenders, accounting for 68-78
percent of the total in 2019-2020, up from 45 percent in 2006. Conversely, the share of higher education fell
from 33 to 16-28 percent and the combined share of the government and public enterprises fell from 21 to
6-10 percent.

3b. Employment, Average Labor Productivity, and Wages
Slow growth of population, the labor force, and employment has been a conspicuous feature of the Thai
economy since 1990 (Tables 1, 3). Even during the boom period, employment grew only 0.6 percent annually.
On the other hand, employment growth remained positive (0.3 percent) during the crisis years in 1996-2000,
despite sharp declines in production. Employment growth increased to 2.2 percent annually during therecovery of 2000-2006, before falling back to 0.4 percent during the slow growth period. Growth was slow
(0.2 percent) in 2020-2021 but rebounded robustly (3.9 percent) in 2022 as the economy recovered from thecovid19 downturn.

10

Fixed-line subscriptions grew at 20, 3.2, and -2.2 percent, respectively, which mobile line subscriptions

grew 74, 52, and 9.0 percent, respectively.
9Thailand is aging rapidly. Thus, labor force participation rates declined from 85 percent in 1990 to 81
percent in 2006, and 76 percent in 2019 for men and from 74 to 64 and 59 percent, respectively, for women.
(Table 3). However, labor force participation rates changed little in 2020-2022. Despite relatively low growth
rates of GDP per capita and employment in recent decades, unemployment remained low.
Unemployment remained low, partially because many Thai workers are able to find employment in thelarge agriculture, forestry, and fishing industry during economic downturns. Agriculture’s share of
employment fell markedly during the boom, from 63 percent in 1990 to 45 percent in 1996, and more slowly
thereafter to 40 percent in 2006 and 30-32 percent in 2019-2022 (Table 3). Agriculture’s share of Thai
employment remains larger than in most developing economies with comparable per capita GDP and land
resources. 11 After 2000, manufacturing, trade and vehicle repair and other industries were the three
categories with double-digit employment shares.
The industrial structure of employment differs from the corresponding production structure. Despite being
the largest employment category, agriculture, fishery, and fishing accounted for only 10 percent of nominal
GDP as early as 1990, with shares falling to 8-9 percent in subsequent years (Table 1). Outside of theheterogeneous other industries’ group, GDP shares were largest in manufacturing (26-30 percent) and tradeand vehicle repair (14-19 percent). Construction’s share rose to 7.5 percent in 1996 but fell to 3.0 percent in2000 and lower in subsequent years, similar to trends in private fixed investment in construction. Reflecting
increases in tourism during the slow growth period, the share of accommodation and food services rose to
6.1 percent in 2019 from 3.0 percent in 2006. The share fell to 3.2 percent in 2021 and recovered to 4.3
percent in 2022, reflecting the effects of the covid19 crisis.12

11

For example, according to Asian Development Bank (2023), agriculture’s share of total employment in2019 was smaller in Indonesia (29 percent) and the Philippines (23 percent).
12

This paper focuses on the six main industries in Table 1-3 because industry definitions in data on

production, labor, and capital stocks are relatively consistent and because these industries combined toaccount for 81 percent of employment in 2019-2021, and larger shares in previous years (Table 3). Although
10Reflecting rapid increases in production compared to employment, nominal average labor productivity
increased 12 percent annually during the boom, 6.4 percent during the recovery, and 5.1 percent during theslow growth period (Table 3). As expected in competitive labor markets, growth rates of compensation per
employee (14, 5.7, and 5.3 percent, respectively) followed a similar trend. In agriculture, increases in averagelabor productivity and compensation per employee were even more rapid, but growth was slower inmanufacturing.
These data suggest that manufacturing workers had unusually high productivity and wages compared toagricultural workers in Thailand. Manufacturing workers were a staggering 18 times more productive andearned 64-times higher compensation per worker than agricultural workers in 1990 (Table 3). Ratios
subsequently fell to 8.3 and 19, respectively, in 2006 and 6.1 and 12, respectively, in 2019. In other words,
combining official national accounts estimates of GDP and employee compensation (available through 2020)
with labor force survey estimates implies that the average manufacturing worker continued to be 6 times
more productive and earn 12 times more compensation than the average agricultural worker as late as 2019.
These manufacturing-agriculture ratios all seem implausibly large. For example, ratios for wages are muchsmaller if calculated from samples of formal workers, 2.6 in 2000, 2.0 in 2006, and 2.2 in 2019 (National
Statistics Office 2022). Manufacturing-agriculture ratios are likely much higher for informal workers, but
aggregate manufacturing-agriculture ratios for average labor productivity, including large informal sectors
in all economies, are also higher in Thailand than in Indonesia and the Philippines, despite higher incomesthat should contribute to lower ratios in Thailand (Table 3). Thai labor force surveys probably overestimate

the heterogeneous other industries group accounted for under one-fifth of employment, about one-third of
Thai GDP is produced by other industries (Table 1). This category includes the important finance, insurance
and real estate industry, as well as public administration and defense, mining and utilities, and education, forexample.
11

agricultural employment, contributing to unusually high Thai ratios,13 but underestimation of GDP or and/or
employee compensation in agriculture relative to manufacturing may also contribute.

3c. Education, Health, Human Capital Formation, Poverty, and Income Distribution
There were substantial improvements in human capital formation, many of them funded by the government.
For example, among people 25 and older, lower secondary completion rates rose from 29 to 48 percent in
2006-2019 for females and 36 to 52 percent for males (Table 4). Upper secondary completion rates rose from21 to 35 percent for females and 24 to 35 percent for males. Tertiary completion rates remained lower forboth males and females in 2019, 17 and 14 percent, respectively. There have also been important
improvements in education quality, but they are difficult to measure and sciences’ education remains
relatively weak.
Data on workers by education level also suggest improvements. Growth rates of workers finishing upper
secondary and tertiary education were highest in 2001-2006, 6.3 percent each, and relatively slow in 20062019, at 3.2 and 3.9 percent, respectively (Table 4). On the other hand, the number of workers not completing
primary education declined precipitously from 13-14 million in 2001 and 2006 to 7.8 million in 2019 andworkers with lower secondary and primary education completed grew relatively slowly. Increases in theshares of managers, etc., and clerks, etc., in total employment also reflect important aspects of improved
human capital formation. About half of the workers with tertiary education were occupied as managers, etc.,with clerks, etc., being the second largest occupation group. There were also large increases in R&D workers
per million Thais, from 101 in 1996 to 321 in 2007, 1,750 in 2019, and 2,024 in 2020. On the other hand,

13

In the 1990s, several officials and academics told me Thailand’s labor force surveys overestimatedagricultural employment, partially because they failed to account for the many hours that farmers are engaged
in manufacturing or services’ work. Labor force survey compilation methods were changed in 2014, partiallyto address this issue, contributing to lower agriculture shares in subsequent years.
12many Thai workers remain in non-wage occupations, often earning relatively low incomes in the informal
sector.14
Health care improved rapidly for most Thais and improvements accelerated after 2006 (Table 5). For
example, life expectancy increased 5 years in 1990-2006 and 4 years in 2006-2019.15 The number of nurses
per person also increased slightly more rapidly in the latter period. The number of doctors per person wasconsistently higher from 2017, suggesting a change in statistical definitions or methodology in that year.
Infant and under 5 mortality rates continued relatively rapid declines of -4.5 percent annually each in 20062019, following more rapid annual declines during the boom and recovery periods. Data on HIV and malaria
incidence also suggest large improvements with declines averaging -8.1 and -15 percent respectively, in
2006-2019. The decline in HIV was even larger during the boom and recovery periods (-11 percent annually
in both periods). Grueber et al. (2014) find that health policy reforms by the Thai Rak Thai (TRT)
government in 2001 led to increased healthcare utilization and reduced infant mortality, while Yu and Yang(2017) find they reduced out-of-pocket health expenditures. Modern Thais are much healthier, on average,
than 30 years ago and improvements in government policies have contributed to improved health.
Poverty reduction was also rapid in all periods. In rural areas, the decline of poverty rates accelerated from
-7.6 percent annually during the boom to -9.5 and -9.9 percent during the recovery and slow growth periods,
respectively (Table 5). In urban areas, declines were largest in the boom and recovery periods, -12 and -10
percent, respectively, but remained rapid during the slow growth period at -7.5 percent. Consequently, only
3-4 percent of urban households and 6-7 percent of rural households were impoverished in 2019-2022.

14

As late as 2008, only 44 percent of Thai workers had formal wage employment (Charoenloet 2015, 133).

According to the Labor Force Survey for the fourth quarter of 2019, only 49 of workers reported wage
income(https://www.nso.go.th/sites/2014en/Survey/social/labour/LaborForce/2019/Full%20Report_Q4_2019.pdf).
15

In 2006-2019, life expectancy also increased 4-5 years in India, Myanmar, and Korea but increase only 3

years in China, Hong Kong, Indonesia, and Singapore, for example (Asian Development Bank 2023).
13

Assertions that Thailand’s income distribution is unequal and has increased are common in the media, but
official data suggest weak trends toward greater equality since 1990 (Table 5). For example, the labor shareof GDP rose from 26 to 30 percent during the boom period, remained constant at 30-31 percent during therecovery period and varied between 30 and 33 percent during the slow growth period. In the large agriculture
sector, labor shares fell during the recovery, but rose during the boom and slow growth periods., In
manufacturing, a large increase during the boom was followed by a large decline during the recovery and athen slower decline.
Estimates of income shares from the World Bank suggest the share of the poorest decile increased 0.3
percentage points during the boom, declined 0.2 points during the recovery, and then increased 1.7 points
during the slow growth period (Table 5). Shares of the second poorest decile also increased during all periods
and the share of the poorest 40 percent rose from 15 percent in 1990 to 19 percent in 2019-2021. In contrast,
the share of the richest decile fell from 36 to 27 percent.
Alternative estimates use tax data to supplement household survey data because household data
underestimate shares of high-income earners (Jenmana 2018, 25). The alternative estimates reveal asubstantially smaller share for the poorest 50%, but faster growth of this share than suggested by official
data, from 9.2 percent in 2001 to 12 percent in 2006, and 15 percent in 2016, for example. Similarly, theshare of the highest decile fell from 56 to 53 and then 51 percent, respectively. Adjusted data suggest stronger
trends toward equality than official data.
Trends in Gini coefficients also suggest that consumption expenditures became more equally distributed inurban and rural areas (Table 5). When consumption Ginis are calculated for five of the seven major regions,the data suggest relatively large declines in inequality during the slow growth period and much smallerchanges during the boom and recovery periods. However, despite important poverty reductions, “lowincome households remain highly exposed to income shock” and “have much higher shares of essential
spending, which are harder to adjust” (Lekfuangfu, et al. 2020, p. 1).

14

3d. Regional Disparities and Politics
Regional disparities in per capita gross regional product (GRP) appear to have dissipated during therecovery and slow growth periods (Table 6). For example, growth rates of per capita GRP at 2002 prices
were relatively high in five low-income clusters of the Northeast (4.1-5.8 percent during the recovery period
and 3.6-5.0 percent during the slow growth period) and four in the North (3.2-5.9 and 2.5-3.0 percent,respectively) compared to high-income clusters in Greater Bangkok (-0.8 to 3.2 and -0.1-2.5 percent,respectively). Patterns contrasted in the South’s border cluster, where slow growth and continued politicalviolence resulted in one of the country’s lowest-income clusters by 2019-2021. The East was anotherexception where relatively high-income clusters grew relatively quickly. However, high-income clusters
generally grew relatively slowly, as illustrated by relatively strong, negative correlation coefficients ofgrowth rates to initial GRP per capita among the 20 clusters identified in Table 6, -0.53 and -0.56 in therecovery and slow-growth periods, respectively. Correlations such as these are more meaningful over longterm (decade+) periods such as the slow-growth period.
Although there were trends toward greater regional equality, regional disparities remained pronounced in
2020-2021 (Table 6). Per capita GDP ranged from 417,000 baht (at 2002 prices) in Bangkok metropolis and
342,000-355,000 baht in the western cluster of the East region to lows of 41,000-45,000 baht in the Northeast
upper 2, Northeast lower 2 and Southern border clusters. Differentials between high- and low-income
clusters were even larger in 1996, for example 317,000 baht in Greater Bangkok and 269,000 baht in the
East’s west cluster, compared to only 19,000-21,000 baht in the poorest two Northeast clusters. This pattern
again reflects relatively rapid growth in low-income clusters.
Regional growth patterns during 2020-2021 illustrate regional variation in the effects of the covid19 shock.
Specialization in tourism services made the West coast cluster (Phuket, Krabi, and Phang-Nga) particularly
vulnerable. Per capita GRP fell 26 percent in 2020 and another 12 percent in 2021 or 35 percent during 20192021 (Table 6). The western cluster of the East, where Pattaya is located, was also affected, but large declines
in this cluster and in Samut Prakan involved manufacturing and trade to a much greater extent than in theSouth’s west coast.
15

In 2000, the TRT won a single-party majority in parliament for the first time in modern Thai history,
implemented significant populist policy changes, and greatly changed Thai politics. The TRT won partiallybecause it successfully exploited discontent over unequal regional distribution in the North and Northeast,
where 30 million Thais live, many with relatively low incomes (Table 6). The TRT was banned after the2006 coup but soon reorganized as Puea Thai and won the 2011 general election. Especially after the 2014
coup, Puea Thai appears to have strengthened its internal infrastructure. It remains popular upcountry andits candidates also won the 2022 Bangkok mayoral election and the Prime Minister’s post after the 2023
election.
The Move Forward Party (MFP) secured the largest number of parliament seats in the May 2023 election.
It was unable to secure the Prime Minister’s position because of opposition from the military-dominatedSenate, particularly to MFP’s goal of amending the lese majeste law. Subsequently, Puea Thai negotiated adeal to get support from the military-related, conservative parties, even though removal of the TRT/Puea
Thai from Thai politics was the primary goal of the 2006 and 2014 coups, and Puea Thai Prime Minister,
Srettha Thavisin previously stated that Puea Thai would never form a coalition with military-related parties.
Nonetheless, this may be a first step in progress toward stronger political parties, greater political stability,
and smaller military involvement in politics and the economy. One important, remaining challenge will beto promulgate a constitution that most can support for governing subsequent civilian political successions.
Reform to facilitate more transparent discussion of policies and political institutions will also be required if
Thailand is to become a vibrant democracy. However, legal decisions in January 2024 suggest Thailand willcontinue to restrict open discussion of key political institutions. It also remains to be seen whether the Puea
Thai and MFP can realize their potential to become policy-based political parties, representing important
constituencies and helping promote the compromises needed to maintain stability, as well as increasetransparency and accountability in public policy making.

4. Challenging Stories: International Trade, Import Protection, and Domestic Regulation
16

The evolution of Thai politics described above is both encouraging and challenging. This section turns toother challenges related to international trade and regulation of competition in import and domestic markets.

4a. Exports and Imports
Exports and imports are large relative to GDP in Thailand and diversified among many industries.
Manufacturing accounts for much larger shares of exports and imports, three-fifths to three quarters of thetotals, respectively, than of production or employment. Manufacturing shares were largest during therecovery period and 2014-2018, but remained well over 70 percent for most of the recovery period and in
2020-2022 (Table 7). The combined export share of food and other agricultural goods (including many
products of food and beverage manufacturers) fell from 34 percent in 1990 to 25 percent in 1996, before
stabilizing at 17-18 percent in subsequent years.16 In recent years, manufacturing exports were largest in
three machinery sectors (office and telecoms, automotive, other transportation), chemicals, and othermanufactures, but textiles was the largest export category in 1990. Many of the machinery exports are partsand components used in product chains in neighboring economies.
Trends in revealed comparative advantage indices (RCAs or ratios of shares of commodity groups in Thai
exports to corresponding shares in world exports, Table 7) illustrate the ability of Thai firms to sell in export
markets relative to the world average. These indices always exceeded 1 in food and other agricultural goods.
This partially reflects cost advantages resulting from Thailand’s relatively large land endowment. Indicesalso exceeded 1 in office and telecom machinery in all years, automotive products and other transportationequipment in 2019-2022, and textiles and clothing through 2006. However, these indices need to beinterpreted cautiously. For example, increases of exports and RCAs for automotive and other transportationequipment are often cited as successful examples of Thai industrialization. High import protection in these16

In commodity classifications of trade data, the categories of food, other agricultural goods, and fuels andmining products include many products of manufacturing processors. In industrial data, the firms and plants
producing these goods are usually classified as manufacturers.
17

industries creates losses of consumer surplus that exceeded possible gains in the sum of producer surplus
and import tax revenue, and contributes to high RCA indices.
Similar to studies of the United States (Bernard 2018) and Japan (Kiyota and Urata 2008), Apiatan et al(2019) recently found that few firms in Thailand export and a few, large firms account for the vast majorityof Thai exports. The majority of these large-firm exports probably come from foreign multinationals(Ramstetter and Umemoto 2006), who are important sources of exports in in the three large machinery
sectors. Large Thai multinationals also export substantial amounts, especially in food and other agricultural
goods. Aptian et al. (2019, 20) emphasize the “fragility” of relying on large firms for export, but existing
evidence suggests a few large firms account for most exports in market economies.
Importing is especially important in developing economies because it provides access to advanced
technologies and cost-reducing inputs. The import-export ratios in Table 7 illustrate the product categories
in which Thai imports (both competing final goods and intermediate goods) are large relative to export
production, As might be expected, import-ratios were relatively high in categories where RCA’s and Thai
exports were relatively low. The import-export ratio in office and telecom was 0.7 to 1.0, illustrating how
exported commodities often require large imports of intermediate goods in this broadly defined category.17
Low ratios in automotive and other transportation equipment illustrate the effectiveness (i.e., high costs for
Thai consumers) of Thailand’s import restrictions in these industries.

17

Comparisons of data on the domestic value added and gross values of Thai exports provide another usefulperspective on the importance of intermediate inputs for export production (OECD 2022). For example, in
2005-2015, only 61-66 percent of total gross export (sales) value (including intermediate input) came fromthe domestic value added embodied in those exports. In manufacturing, where processing activities
predominate, reliance on inputs is higher and ratios of domestic value added to gross exports were only 5458 percent. These discrepancies illustrate the role of global value chains involving Thai exporters in a numberof important processing industries.
18

4b. Import Protection
Because imports are an important source of high-quality capital goods, parts, components, and other inputs,
import protection is particularly costly in a developing economy like Thailand. A good, recent example ofthe problem is a case where the Supreme Court found that a Toyota affiliate guilty of avoiding import duties
on parts used in the production of its Prius models in Thailand. The penalty was 11.6 billion baht or US$315
million. 18 Because most of this cost increase will be eventually paid by Toyota consumers (including Toyota
itself and other firms using Toyota parts) in Thailand, these tariffs cannot possibly make Thailand better off
unless the tariffs lead to lower costs for Toyota or other firms in the future. However, in many ways,
Thailand’s automotive industry remains an expensive infant because it still relies on high import protection
decades after import substitution began in the 1960s.
A small, open economy like Thailand could benefit substantially by learning from Singapore andeliminating reduces almost all tariffs and other trade barriers. Empirical studies suggest that importprotection is usually costly worldwide. For Thailand, Sangkaew and Jayanthakumaran (2013) found thattariffs were negatively correlated with labor productivity in their samples of Thai plants for 1991, 1994, 1997,
2000, 2003, and-2007.19 Microeconometric studies of Indonesia’s plants (Amiti and Konings 2007; Imbruno
and Ketterer 2018) and Vietnam’s firms (Newman et al. 2023) in manufacturing also suggest efficiency gains
from reduced tariffs. Other estimates suggest costs of import protection were substantial in large economies
like China (Zhang et al. 1998), Europe (Messerlin 2001), Japan (Sazanami et al., 1994), and the United States(Hufbauer and Elliot 1994). Flaaen et al (2020) estimate the consumer cost of 2018 safeguard tariffs on U.S.

18

See Bangkok Post, 16 Sep 2022, https://www.bangkokpost.com/business/2392723/toyota-loses-315m-

tax-case.
19

Similarly, Karunaratne’s (1998) CGE simulations suggest a 25 percent tariff cut in 1990-2000 “more thantrebles international competitiveness … mainly because exports expanded due to gains in competitive
efficiency” (p. 534), in addition to increasing economic growth and employment, for example.
19

imports of washing machines at US$817,000 per job, or well over 10 times the annual earnings of mostworkers in the industry. They also cite cost estimates for Section 421 tariffs on tire imports from China
implemented in 2009 at about US$700,000 per job. Some argue that static losses can be offset by dynamic
gains if protection can encourage infant industry development. In contrast, Thailand’s experience illustrates
how many infant industries never grow up.
Thailand’s last, substantial, unilateral import liberalization occurred in the early 1990s. The Uruguay Roundnegotiations helped facilitate important steps toward liberalization during the politically tumultuous, 19911992 regimes of Anand Prachayaran, who was a technocrat and probably less beholden to protectionist
interests than previous or subsequent leaders. Commitments to lower trade barriers followed after the
Uruguay Round agreement. the World Trade Organization’s founding in 1995 and the Thai government’s
acceleration of related reforms after the Asian Financial Crisis in 1998-2000.
Creation of the ASEAN Free Trade Area (AFTA) in 1992 also increased the political appeal of importliberalization in the early 1990s. After the collapse of the Doha Round in July 2008, Thailand’s subsequent
trade negotiations have focused on regional trading arrangements such as AFTA and related agreements.
However, regional agreements often involve relatively little tariff reduction or trade liberalization in Thailand,
compared to the Uruguay Round, for example.
Reflecting trends in multilateral liberalization, World Bank estimates of the trade-weighted average ofmost-favored-nation (MFN) duties on imported manufactures fell from 35 percent in 1990 to 16 percent in
1995, 10 percent in 2000, and 6.5 percent in 2006 (Table 8).20 Trade-weighted averages are lower than simple
average tariff rates because imports of commodities with relatively low tariffs account for most Thai imports
in most categories.

20

Tables 8 and 9 include simple averages for reference, but trade-weighted averages are more meaningful.

Trade-weighted averages were lower than simple averages because relatively large imports are concentrated
in low-tariff commodity groups.
20In agriculture, both trade-weighted and simple averages remained over 25 percent in 2020-2022 (Table 9).
In non-agriculture, tariffs were much lower but largely unchanged in recent years. Simple averages were 7.18.4 percent in 2006-2022, while weighted averages rose from 3.2 percent in 2006 to 4.3 percent in 2010, 4.9
percent in 2014, and 4.4-5.4 percent in 2019-2022 (Table 9). In other words, relatively large shares of nonagriculture imports were subject to relatively high tariffs in recent years compared to 2006. For example,
non-agriculture imports facing duties of 10 percent or more increased from 3.8 to 6.5 percent of the total in
2006-2019 to 7.6-8.6 percent in 2020-2021, before declining afterwards. The share of non-agricultural
imports entering duty free peaked at 60 percent in 2019, but was lower before and after.
In non-agriculture, tariffs were high in transport equipment and the diverse wood group, but these categories
accounted for relatively small shares of Thailand’s non-agriculture imports. Non-agriculture accounted forthe vast majority of imports, making protection of non-agriculture relatively expensive for most Thaiconsumers. The most recent Trade Policy Review by the World Trade Organization (WTO) Secretariat
suggests recent increases in average MFN tariff rates were “mainly due to nomenclature changes” (World
Trade Organization 2021, 9).
Many small-economy governments impose import duties, even though it is well known that that tariffs
harm consumers more than they benefit protected producers and the government in small, open economies.
Why do Thailand and other small economies continue to tax consumers and reduce social welfare for the
benefit of protected producers? In Thai autos, many firms have benefitted from over six decades of importsubstitution, for example. History suggests this kind of distortion often results when firm lobbies convince
governments that import protection benefits the importing country, even though that assessment is almost
never accurate. Firm lobbies also effectively emphasize that that taxing imports is relatively easy politically,
because many consumers and voters fail to recognize the costs of import duties.
In Thailand and other developing economies, politicians and officials sometimes favor import protection
because they believe it promotes infant industries, independent of lobbying. However, I believe that lobbying
for import protection, which is often rent-seeking behavior and sometimes results in illegal corruption, is themost important part of this story. As is normal in market economies, large companies, both local and foreign,21

have always had substantial influence over many Thai government agencies and the top levels of the Thaigovernment.
On the other hand, Thai importing procedures may be improving, for example. Summaries of surveys and/orinterviews of hundreds of officials from foreign multinational firms in the early- to mid-1990s (Michener
and Ramstetter 1990; Ramstetter 1993, 1997) highlighted problems related to lack of transparency andcorruption, but subsequent informal communication with several previously interviewed officials in 20152019 indicates the seriousness of related problems has diminished. This is consistent with the World Trade
Organization’s (2021, 9) recent policy review, which says reform “helped simplify customs procedures andimproved transparency” but that Thailand’s “tariff structure remains relatively complex”. The WTO report
also detailed a myriad of import prohibitions, licensing requirements, and investment incentives, which
increase transactions’ costs for importing into Thailand.

4c. Domestic Competition and Regulation
Thai regulations related to business, health, safety, environmental, and other issues are often complex andambiguous, creating problems that are often relatively acute for small firms. In contrast, large firms often
use substantial resources to influence the formulation of policy and business regulations, for example. This
often involves lobbying for discretionary incentives and favorable regulation from the Board of Investment,
as well as other ministries and agencies. Lobbying is normal in a capitalist economy, but a fine line
distinguishes “normal lobbying” and “inappropriate or illegal influencing”. Unfortunately, domestic
regulation is often subject to “inappropriate or illegal influencing” in Thailand.
One well-known example was a previous regulation designed to benefit Thailand’s two largest beer
producers by requiring all commercial beer producers to have a minimum annual capacity (100,000 liters)
and capital investment (3 million baht). This capacity was larger than all but a few craft beer manufacturers
could utilize. Consequently, as late as 2021, I observed that most Thai brands of craft beer sold in Thai
supermarkets were imported, usually from neighboring Cambodia or Vietnam. I have heard arguments that22

the ban on small-scale production existed for health and safety reasons, but I believe the obvious intent of
this regulation was to restrict competition for benefit of Boon Rawd Brewery and Thai Beverage PCL, thebiggest Thai beer companies.
This regulation was changed in November 2021, allowing more small beer firms to produce and market in
Thailand. The new regulations have been criticized for continuing to impose unnecessary costs on small
producers.21 Nonetheless, throughout 2023 and into early 2024, I observed increases in the number of Thaimade craft beers on the shelves of Thai supermarkets and convenience stores, suggesting the 2021 reforms
were effective.
In Phuket, the provincial government continues to facilitate collusion among taxi and tuk-tuk drivers,
thereby limiting competition in what would normally be monopolistically competitive markets. Fares in
Phuket have usually been much higher (often 1.5 times or more per kilometer or per minute) than in Bangkok
over the last three decades (hundreds of personal observations). Two prominent examples of collusion areconcessions previously held by the main airport taxi company and various groups of taxi or tuk-tuk drivers
at key tourist pickup locations.
Recently, regulators have allowed increased competition from taxis and ride-share services. Prices to andfrom Phuket Airport were more variable, but not much lower, in January-February 2024 than a year previous.
Unfortunately, recent consultations among taxi groups and the local government have focused on protecting
the monopoly power of drivers, not on improvements of taxi services to benefit consumers or on how toincrease driver productivity and incomes.22

21"Embittered Thai

craft

beer

brewers

plead

for

justice",

Bangkok

4

Post,

Jan

2023,

https://www.bangkokpost.com/thailand/general/2474899/embittered-thai-craft-beer-brewers-plead-forjustice.
22"Officials

attempt

tounraveltaxi

driverwoes",

The

PhuketNews,

21

https://www.thephuketnews.com/officials-attempt-to-unravel-taxi-driver-woes-89634.php.
23

Sep

2023,

Thailand’s competition or anti-trust policies have usually been timid. For example, the Chairman of the
Office of the Trade Commission voted against the 2020 takeover of Tesco-Lotus supermarkets (sincerebranded as Lotus’s in Thailand) by Charoen Pokphand (CP), one of Thailand’s largest agribusinessconglomerates. He told the Financial Times “we are afraid that after the merger, they will have almost fullcontrol of the market” in most Thai provinces and “from the wholesale market they have Makro, they have
Tesco, and in the downstream they have 7-Eleven, they have Tesco Lotus Express.” 23 However, theCommission voted 4 to 3 in favor of the merger, suggesting that other members may have been convincedthat the merger would generate substantial efficiency gains and therefore be worthwhile, even if competition
was limited.
A more recent example is the True Corporation PCL’s 2022 takeover of DTAC or Total AccessCommunications PCL. 24 The National Broadcasting and Telecommunication Commission (NBTC)
approved this merger, which created the country’s largest mobile communications firm. Conditions for themerger include independent verification of service fees and cost structure, and separate billing for separate
services using average cost pricing principles. As with the CP purchase of Tesco-Lotus, this merger greatly
increases the ability of the merged firm to exercise market power if it chooses to do so. On the other hand,to repeat, mergers like this often occur because the merged firm can lower costs by increasing static
efficiency and investing in technological progress more than would have been possible before the merger.

23

The quotes and information in this paragraph come from “Thailand’s competition chief criticises $10.6bn

CP-Tesco deal” in Financial Times, December 20, 2020, https://www.ft.com/content/1189fcac-df4c-41cdb7c3-4f01e1c02db0.
24

The information in this paragraph comes from “Thai telco regulator gives conditional clearance to True,DTAC merger”, Reuters, October 20, 2021, https://www.reuters.com/markets/deals/thai-telco-regulatorgreenlights-merger-true-dtac-with-conditions-2022-10-20/. AIS or Advanced Info Service PCL controlled
44 percent.
24

Environmental regulation has been weak in Thailand. This reflects a high priority on keeping firm costs
low and generating economic growth, and low priority on reducing the pollution costs. Environmental costs
are already high in urban Thailand and will explode as seas rise in the coming decades. Recent forecasts(Kahn 2019) suggest that most of almost all of greater Bangkok (like other low-lying, coastal areas) is likely
to be submerged by rising seas in the next 5-7 decades, or earlier. Environmental issues have been a low
priority for most Thais, but that may be changing. It remains to see if Thailand is ready for any serious
attempt to reduce pollution because substantially higher energy taxes and prices will be required andunpopular.
Thai regulators face at least four important tasks going forward. First, many regulations restrict competition
unnecessarily and illogically in markets that would otherwise be monopolistically competitive. Second,
many regulations and enforcement procedures are unnecessarily complex and lack transparency, but some
improvements were observed in recent years. Third, Thai regulators and policy makers appear to be heavily
influenced by large firms. Problems related to complexity and lack of transparency emerge if large firms use
this influence to obtain and/or exercise market power or convince policy makers to favor them withregulations, taxes, and tariffs. Fourth, environmental protection remains a relatively low priority for the Thaigovernment and the Thai public.
Most countries in the world face similar problems and it is unrealistic to expect any government to eliminate
all problems because new problems are always emerging. The focus of the proposed coalition between MFP
and Puea Thai after the May 2023 election included strong emphasis on greater accountability andtransparency in all areas of public policy making. The current Puea Thai government continues to emphasizethese points to some extent. One can thus hope for continued improvements in public policy making andimplementation.

5. Summary and Conclusion: Can Domestic Institution Building Help Revive Growth?

25Section 2 discussed three discouraging stories focused on slow growth after 2006, low levels of private
fixed investment after 1996, and the consequences for capital accumulation. If considered in isolation, thesetrends suggest a marked worsening of economic performance, especially after 2006. Section 3 tells
encouraging stories of important improvements that continued after 2006 in infrastructure, labor markets,human capital, and poverty reduction. In short, the living standards of many Thais continued to improve
rapidly in important respects not reflected in trends of GDP growth or investment rates. The section
concluded by discussing how perceptions of unfair distribution affected politics, emphasizing how bothofficial and adjusted estimates suggest that income distribution improved somewhat after 1990.
Section 3 also emphasized how Thailand’s military can contribute by yielding power to the political parties
and institutions, who are more representative of most Thais. There is a pressing need to draft a new
constitution that most Thais can agree is fair and has the potential to last for decades, if not for centuries. As
emphasized in the growth literature, political institutions have often been key contributors to relatively highlevels of investor confidence and growth.
Section 4 discussed challenging stories related to international trade, import protection, and domestic
regulation. The probability for serious war involving militarily-powerful, large, economic partners continues
to be high in 2024, but Thailand has little influence over related matters. On the other hand, Thailand still
has relatively high import protection in some industries like autos. As emphasized in studies of importliberalization, most Thai producers and consumers would benefit from eliminating this protection; thebiggest losers would be multinational automakers, who would likely adjust easily to lower tariffs. Four setsof domestic policy issues were discussed: (1) the need to reduce illogical intervention in monopolisticallycompetitive markets, (2) the need to make regulations less complex and more transparent, (3) the need tocarefully consider regulations of large firms, especially when they engage in anti-competitive behavior, and(4) the need control environmental externalities more vigorously.

References
26

Agenor, Pierre-Ricard (2017), "Caught in the Middle? The Economics of Middle-Income Traps", Journal of
Economic Surveys, 31(3), 771-791, doi: 10.1111/joes.12175.

Amiti, Mary, and Jozef Konings (2007), "Trade Liberalization, Intermediate Inputs, and Productivity:
Evidence from Indonesia." American Economic Review, 97(5). 1611-1638, DOI: 10.1257/aer.97.5.1611.

Amiti, Mary, Stephen J. Redding, and David E. Weinstein (2019), "The Impact of the 2018 Tariffs on Prices
and Welfare." Journal of Economic Perspectives, 33 (4): 187-210. DOI: 10.1257/jep.33.4.187

Ang, James B. (2010), "Determinants of Private Investment in Malaysia: What Causes the Postcrisis
Slumps?",Contemporary

Economic

Policy,28(3),

378-391,https://doi.org/10.1111/j.1465-

7287.2009.00155.x.

Apaitan, Tosapol, Piti Disyatat, and Krislert Samphantharak (2019), "Dissecting Thailand's International
Trade: Evidence from 88 Million Export and Import Entries", Asian Development Review, 36(1), 20-53.
https://doi.org/10.1162/adev_a_00122.

Apaitan, Tosapol, Pongsak Luangaram, and Pym Manopimoke (2020), "Uncertainty and Economic Activity:
Does it Matter for Thailand?", PIER Discussion Paper, Bangkok: Puey Ungphakorn Institute for Economic
Research, https://www.pier.or.th/dp/130/.

Asian Development Bank (2023), Key Indicators Database, https://kidb.adb.org/, accessed September.

Asian Development Bank (various years), Key Indicators for Asia and the Pacific, 2010 & 2022 issues,
https://www.adb.org/publications/key-indicators-asia-and-pacific-20__, where 20__ is the year of theissue, accessed September 2010 and September 2022.
27

Bank of Thailand (2023), Statistics and Dissemination, Real Sector, https://www.bot.or.th/en/statistics/realsector.html, accessed December.

Barro, Robert J. (1996), “Democracy and Growth”, Journal of Economic Growth, 1(1), 1-27,
https://doi.org/10.1007/BF00163340.

Bernard, Andrew B., J. Bradford Jensen, Stephen J. Redding, and Peter K. Schott (2018), “Global Firms”,
Journal of Economic Literature, 56(2), 565-619, https://doi.org/10.1257/jel.20160792.

Charoenloet, Voravidh (2015), "Industrialization, globalization and labour force participation in Thailand",
Journal of the Asia Pacific Economy, 20:1, 130-142, DOI: 10.1080/13547860.2014.974336

Felipe, Jesus, Utsav Kumar & Reynold Galope (2017), "Middle-income transitions: trap or myth?", Journalof the Asia Pacific Economy, 22:3, 429-453, DOI: 10.1080/13547860.2016.1270253.

Flaaen, Aaron, Ali Hortaçsu, and Felix Tintelnot (2020), "The Production Relocation and Price Effects of
US Trade Policy: The Case of Washing Machines," American Economic Review, 110 (7): 2103-2127, DOI:
10.1257/aer.20190611.

Gruber, Jonathan, Nathaniel Hendren, and Robert M. Townsend (2014), "The Great Equalizer: Health Care
Access and Infant Mortality in Thailand." American Economic Journal: Applied Economics, 6 (1): 91107. DOI: 10.1257/app.6.1.91.

Hufbauer, Gary Clyde and Kimberly Ann Elliot (1994), Measuring the Costs of Protection in the UnitedStates, Washington, D.C.: International Institute for Economics.
28

Imbruno, Michele and Tobias D. Ketterer (2018), "Energy efficiency gains from importing intermediate
inputs: Firm-level evidence from Indonesia", Journal of Development Economics, 135, 117-141,https://doi.org/10.1016/j.jdeveco.2018.06.014.

International

Monetary

Fund(2023),

World

Economic

Outlook

Database,

April,

https://www.imf.org/en/Publications/WEO/weo-database/2023/April.

Jenmana, Thanasak, (2018), "Democratisation and the Emergence of Class Conflicts: Income Inequality in
Thailand,2001-2016",

WID.worldworkingpaper

no2018/15,

https://wid.world/document/democratisation-and-the-emergence-of-class-conflicts-income-inequalityin-thailand-2001-2016-wid-world-working-paper-2018-15/.

Jongwanich, Juthathip and Archanun Kohpaiboon (2008), "Private Investment, Trends and Determinants in
Thailand", World Development, 36(10), 1709-1724, https://doi.org/10.1016/j.worlddev.2008.01.010.

Jongwanich, Juthathip and Archanun Kohpaiboon (2010), “The Determinants of Protection in Thailand”,
Economic Papers, 26(3), 276-294.

Karunaratne, Neil Dias (1998), "Trade Liberalization in Thailand: A Computable General Equilibrium(CGE) Analysis", The Journal of Developing Areas, 32(4), 515-540, https://www.jstor.org/stable/4192806

Kulp, Scott A. and Benjamin H. Strauss (2019), "New elevation data triple estimates of global vulnerability
to sea-level rise and coastal flooding", Nature Communications, 10:4844, https://doi.org/10.1038/s41467019-12808-z.
29

Lekfuangfu, Warn N., Suphanit Piyapromdee, Ponpoje Porapakkarm and Nada Wasi (2020), "Myths andFacts about Inequalities in Thailand", Discussion Paper No. 144, Bangkok: Puey Ungphakorn Institute for
Economic Research,https://www.pier.or.th/files/dp/pier_dp_144.pdf.

Mallikamas, Roong Poshyananda Mallikamas, Yunyong Thaicharoen and Daungporn Rodpengsangkaha(2003), "Investment Cycles, Economic Recovery and Monetary Policy", Discussion Paper 07/2003,
Bangkok: Bank of Thailand.

Menkhoff, Lukas and Chodechai Suwanaporn (2011), "On the rationale of bank lending in pre-crisis
Thailand", Applied Economics, 39(9), 1077-1089, https://doi.org/10.1080/00036840500447815.

Messerlin, Patrick A. (2001), Measuring the Costs of Protection in Europe: European Commercial Policy inthe 2000s, Washington, D.C.: Peterson Institute of International Economics.

Michener, Peter and Eric D. Ramstetter (1990), “Motives for U.S. Direct Investment in Thailand: A Surveyof Investors”, Asia-Pacific TNC Review, ESCAP/UNCTC Publication Series A No. 7, pp. 1-21.

National Economic and Social Development Council (2021), National Income of Thailand 2020, Chain
VolumeMeasures,

Bangkok:

NESDC,

https://www.nesdc.go.th/nesdb_en/

ewt_dl_link.php?nid=4487&filename=national_account, accessed September 2022;

National Economic and Social Development Council (2023a), Historical Data: Capital Stock of Thailand
2022,

Bangkok:

NESDC,

https://www.nesdc.go.th/nesdb_en/

filename=national_account, accessed December 2023.

30

ewt_news.php?nid=4503&

National Economic and Social Development Council (2023b), Gross Regional and Provincial Product 2021
(Excel), https://www.nesdc.go.th/nesdb_en/ewt_dl_link.php?nid=4316 &filename= national_account,accessed December 2023.

National Economic and Social Development Council (2023c), National Income of Thailand 2022, Chain
VolumeMeasures,

Bangkok:

NESDC,

https://www.nesdc.go.th/nesdb_en/

ewt_dl_link.php?nid=4508&filename=national_account, accessed December 2023.

National Statistics Office (2024), Key statistical data, Statistics of science, technology, & patents, accessed
15 February, http://statbbi.nso.go.th/staticreport/page/sector/en/20.aspx.

National Statistics Office (various years), Labor Force Survey, Quarter 4 issues, 2002, 2006, 2010, 2014,
2019,

2020,

http://www.nso.go.th/sites/2014en/Pages/Statistical%20Themes/

Population-

Society/Labour/Labour-Force.aspx, accessed September 2022.

Newman, Carol, John Rand, and Finn Tarp (2023), "Imports, supply chains and firm productivity", World
Development, 172, 106371, ISSN 0305-750X, https://doi.org/10.1016/j.worlddev.2023.106371.

OECD (2022), "Trade in value added", OECD Statistics on Trade in Value Added (database),https://doi.org/10.1787/data-00648-en (accessed on 18 September 2022).

Ramstetter, Eric D. (1994), "Technology-Related Activities in Japanese and U.S. Multinationals:
Implications for Thai Manufacturing Industries," in Tran Van Troh, ed., Japanese Management Style andTechnology Transfer in Thailand, Tokyo: Japan Center for Economic Research, pp. 11-57.

31

Ramstetter, Eric D. (1997), "Thailand: International Trade, Multinational Firms, and Regional Integration",in Wendy Dobson and Chia Siow Yue, eds., Multinationals and East Asian Integration, Singapore: ISEAS,
pp. 107-130.

Ramstetter, Eric D. and Masaru Umemoto (2006), “Exports and Foreign Ownership in Thai Manufacturing”
in Eric D. Ramstetter and Fredrik Sjöholm, eds., Multinationals in Indonesia and Thailand: Wages,
Productivity and Exports. Hampshire, UK: Palgrave-Macmillan, pp. 194-219.

Sangkaew, Piyapong and Jayanthakumaran, Kankesu (2013), Trade Liberalisation, Labour Productivity
Growth and Skilled Labour Complement: Evidence from the Thai Manufacturing Sector. In: Huynh, VN.,
Kreinovich, V., Sriboonchitta, S., Suriya, K. (eds) Uncertainty Analysis in Econometrics with Applications.
Advances in Intelligent Systems and Computing, vol 200. Springer, Berlin, Heidelberg.
https://doi.org/10.1007/978-3-642-35443-4_14.

Sazanami, Yoko, Shujiro Urata, and Hiroki Kawai (1994), Measuring the Costs of Protection in Japan,
Washington, D.C.: International Institute for Economics.

Suwanprasert, Wisarut (2023), "Consequences of Thailand’s 2006 military coup: Evidence from thesynthetic

controlmethod",European

Journalof

Political

Economy,80,

https://doi.org/10.1016/j.ejpoleco.2023.102475.

World Bank (2023), World Development Indicators, online database, accessed December,
https://databank.worldbank.org/source/world-development-indicators.

World Trade Organization (2024), WTO Stats Portal, downloaded January, https://stats.wto.org/.

32

102475,

World Trade Organization (2021). Trade Policy Review: Thailand, Geneva: WTO, WT/TPR/S/400/Rev.1,
https://docs.wto.org/dol2fe/Pages/FE_Search/FE_S_S006.aspx?
Query=((+%40Title%3d+thailand)+or+(%40CountryConcerned%3d+thailand))+and+(+(+%40Symbol%3d+wt%2ftpr%2fs%2f*+))&Language=ENGLISH&Context=FomerScriptedSearch&languageUIChan
ged=true#

Yu-Tung Hsu and Chih-Hai Yang (2017), Universal Coverage Scheme and out-of-pocket healthcare
expenditure: evidence from Thailand, Journal of the Asia Pacific Economy, 22:2, 309-329, DOI:
10.1080/13547860.2017.1291163.

Zhang Yansheng, Wan Zhongxin and Zhang Shuguang (1998), Measuring the Costs of Protection in China,
Washington, D.C.: Peterson Institute of International Economics.

33

Table 1: GDP, GDP per capita, Inflation, Exchange Rates, Population, and GDP Structure
Variable
1990
1996
2000
2006
2019

2020

2021

2022

BASIC INDICATORS
GDP, current baht trillions

2.26

4.64

5.07

8.40

16.89

15.66

16.19

17.38

GDP per capita, current 1000 baht

40.0

77.1

80.5

127.6

242.6

224.4

231.4

248.0

GDP, chained prices, 2002=100

60.1

95.5

91.6

125.1

190.8

179.2

181.9

186.6

compound annual growth rate, %

-

8.0

-1.0

5.3

3.3

-6.1

1.5

2.6

GDP per capita, 2002=100

68.1

101.7

93.3

121.8

175.6

164.5

166.6

170.6

compound annual growth rate, %

-

6.9

-2.1

4.5

2.9

-6.3

1.3

2.4

GDP per capita, current US$

1,564

3,044

2,004

3,370

7,813

7,170

7,227

7,070

GDP per capita, current Intl$

4,251

7,296

7,295

11,037

19,214

18,23819,295

21,154GDP per capita, 2017 Intl$

7,010

10,467

9,810

12,83818,432

17,27017,486

17,916GDP deflator, inflation rate, %

-

4.3

2.7

3.2

2.2

-1.3

1.7

4.7

CPI, inflation rate, %

-

5.0

3.8

2.7

1.7

-0.8

1.2

6.1

Baht/US$, annual average

25.6

25.3

40.2

37.9

31.0

31.3

32.0

35.1

Mid-year population, millions

56.6

60.1

63.0

65.8

69.6

69.8

70.0

70.1

EXPENDITURE SHARES OF GDP, percent at current prices
Private final consumption

53.4

51.7

54.1

54.5

49.8

53.0

52.2

54.6

General govt final consumption

10.0

11.6

13.6

13.5

16.2

17.8

18.2

17.7

Fixed investment, private construction

15.3

11.9

2.9

4.2

3.4

3.5

3.5

3.3

Fixed investment, private equipment

4.5

8.1

5.6

4.7

4.3

4.8

5.0

4.6

Fixed investment, public construction

19.2

19.3

10.6

15.8

13.5

13.2

13.5

14.0

Fixed investment, public equipment

1.5

2.3

2.5

2.2

1.4

1.6

1.6

1.5

Change in inventories

1.1

0.9

0.7

0.2

1.2

0.5

5.1

4.5

Exports goods

25.8

29.7

53.9

57.6

44.6

45.3

53.5

57.5

Exports services

7.2

9.3

11.0

11.1

14.9

6.2

5.0

7.9

Imports goods

33.3

34.9

44.7

51.5

39.7

37.2

47.1

54.8

Imports services

7.2

10.3

11.8

13.9

10.5

9.1

11.5

12.7

PRODUCTION SHARES OF GDP, percent at current prices
Agriculture, forestry, & fishing

10.0

9.1

8.5

9.4

8.1

8.7

8.7

8.7

Manufacturing

27.1

25.7

28.4

30.2

25.6

25.6

27.2

27.1

Construction

6.0

7.5

3.0

2.9

2.5

2.7

2.7

2.5

Trade & vehicle repair

18.6

17.4

16.7

14.0

16.2

16.9

16.2

15.4

Transportation & storage

5.5

5.6

6.5

6.0

5.8

4.8

4.6

4.7

Accommodation & food services

4.5

3.7

3.9

3.0

6.1

3.9

3.2

4.3

Other industries

28.3

31.1

33.0

34.4

35.6

37.5

37.4

37.3

Mining & utilities

3.8

3.9

5.3

6.1

5.5

5.0

5.2

5.6

Information & communication

1.4

1.8

2.2

2.3

2.6

2.8

2.8

2.8

Finance, insurance, real estate-a

8.4

10.8

8.1

8.7

10.0

11.0

11.0

11.0

Public administration & defence

3.7

4.5

5.9

5.7

5.9

6.6

6.5

6.2

Education

3.1

3.4

4.0

3.8

4.0

4.4

4.4

4.2

Human health & social work

1.8

1.7

2.0

1.8

2.2

2.5

2.5

2.6

Miscellaneous services

6.0

4.9

5.5

6.0

5.5

5.2

5.0

5.0

Sources: National Economic and Social Development Council (2023), International Monetary Fund (2023).

34

Table 2: Capital Stocks, Capital Productivity, Infrastructure, & R&D
1990 1996 2000 2006 2019 2020 2021 2022
Variable
NET CAPITAL STOCKS AT CURRENT REPLACEMENT COST, units as noted
5.06 11.71 15.23 22.61 38.82 38.46 38.98 40.02
All industries, current baht trillions
Agriculture, forestry, & fishing % share
8.5
6.4
6.9
7.1
7.8
7.7
7.7
7.8
Manufacturing % share
14.1 16.8 17.1 17.3 17.3 17.3 17.7 17.5
Construction % share
2.2
3.6
3.5
3.3
3.4
3.4
3.5
3.4
8.9
9.3
8.4
7.2
7.0
6.8
6.8
6.8
Trade & vehicle repair % share
Transportation & storage % share
13.0 14.8 16.1 18.8 18.7 18.5 18.1 18.6
Accommodation & food services % share
3.9
4.1
3.9
3.3
3.6
3.3
3.2
3.3
Other industries % share
49.5 45.1 44.0 42.9 42.2 42.9 43.0 42.6
Private/Total Ratios %, all industries
77
77
74
72
71
70
69
70
Agriculture, forestry, & fishing
61
54
52
50
54
50
49
50
Manufacturing
95
96
95
95
97
97
97
97
Construction
52
67
60
55
57
50
48
49
99
99
99
99
99
99
99
99
Trade & vehicle repair
Transportation & storage
47
52
47
49
45
45
45
46
Accommodation & food services
76
76
73
70
68
65
64
67
Other industries
80
77
76
73
71
70
69
70
VALUE ADDED (GDP)-NET CAPITAL STOCK RATIOS
0.45 0.40 0.33 0.37 0.44 0.41 0.42 0.43
All industries
Agriculture, forestry, & fishing
0.53 0.56 0.41 0.49 0.45 0.46 0.47 0.49
Manufacturing0.86 0.61 0.55 0.65 0.65 0.60 0.64 0.67
Construction
1.21 0.84 0.29 0.33 0.32 0.32 0.32 0.32
0.93 0.74 0.66 0.72 1.01 1.01 0.99 0.98
Trade & vehicle repair
Transportation & storage
0.19 0.15 0.13 0.12 0.14 0.11 0.11 0.11
Accommodation & food services
0.52 0.36 0.33 0.34 0.73 0.48 0.41 0.56
TRANSPORTATION & COMMUNICATION INFRASTRUCTURE; R&D-GDP ratios
Air freight, billion ton-km
0.66 1.35 1.71 2.11 2.33 0.68 0.60
Air passengers carried, millions
8.20 14.08 17.39 20.10 76.34 28.18 12.73
2007 2017
Rail transport, million ton-km
3,286 2,247 3,161
Rail passengers, billion passenger-km)
12.2
9.9
8.0
6.0
Container port traffic, TEU: 20 ft equiv.
3.2
5.6
10.1
9.6
10.4
Mobile line subscriptions/100 people
0.1
3.1
4.8
61
182
163
169
176
Fixed line subscriptions/100 people
2.4
6.9
8.9
11
7.6
7.0
6.5
6.1
Fixed broadband subscriptions, mil.
0.002 0.89 10.11 11.48 12.42 13.23
R&D-GDP ratios, %0.12 0.24 0.23 1.14 1.33
SHARES OF TOTAL R&D EXPENDITURES BY OWNER
2007
Government & public enterprise21
6
10
Higher education
33
16
18
Private enterprise
45
78
68
Sources: National Economic and Social Development Council (2023a, 2023c), National Statistics Office (2024),
World Bank (2023).

35

Table 3: Labor Force Indicators, Labor Productivity, and Employee Compensation
1990 1996 2000 2006
Variable

2019

2020

2021

2022

LABOR FORCE INDICATORS & INDUSTRY SHARES OF EMPLOYMENT in %30.82 32.12 33.22 36.43
Labor force, millions

38.18

38.54

38.70

39.90

Employment, all industries, millions
Agriculture, forestry, & fishing share

29.96

30.98

31.29

35.69

37.61

37.68

37.75

39.22

63.3

45.2

44.2

39.7

31.4

31.3

31.9

30.4

Manufacturing share

9.7

14.0

14.9

15.4

16.3

15.9

15.7

16.0

Construction share

-

-

4.8

6.0

5.8

5.9

5.9

5.6

Trade & vehicle repair share
Transportation & storage share

-

-

14.0

15.5

16.6

16.7

16.6

17.2

-

-

3.1

3.0

3.5

3.5

3.6

3.7

Accommodation & food services share

-

-

5.8

6.4

7.6

7.6

7.4

7.4

Other industries; defintions change 2011

-

-

13.3

14.0

18.8

19.1

19.1

19.6

Mining & utilities share

-

-

0.5

0.5

0.7

0.8

0.7

0.6

Information & communication, share

-

-

-

-

0.5

0.6

0.5

0.6

Finance, insurance, real estate, share

-

-

2.4

2.8

1.9

2.0

2.0

2.3

Public administration & defence, share

-

-

3.5

3.2

4.3

4.4

4.4

4.7

Education, share

-

-

3.0

2.9

3.1

3.2

3.0

2.8

Human health & social work, share

-

-

1.4

1.6

1.7

1.8

2.0

2.1

Miscellaneous services

-

-

2.5

2.9

6.6

6.3

6.4

6.6

Unemployment rate, %

2.2

1.5

3.6

1.5

1.0

1.7

1.9

1.3

Labor force participation rate, male %

84.5

78.7

77.1

80.9

76.2

76.4

76.3

76.6

Labor force participation rate, female %

74.0

62.7

60.9

64.0

59.4

59.8

59.9

60.3

All industries
Agriculture, forestry, & fishing

75.6

150

162

235

449

416

429

443

11.9

30.1

31.2

55.7

116

115

117

127

Manufacturing

210

275

309

460

707

670

743

749

Construction

-

-

101

114

191

189

198

199

Trade & vehicle repair
Transportation & storage

-

-

193

214

439

422

420

397

-

-

344

470

756

567

546

560

Accommodation & food services

-

-

110

112

362

211

185

255

GDP PER EMPLOYEE, 1000 current baht

MANUFACTURING/AGRICULTURE RATIOS OF AVERAGE LABOR PRODUCTIVITY
Value added per worker, Thailand

17.64

9.15

9.93

8.26

6.08

5.80

6.35

5.89

Indonesia

5.86

5.38

6.21

7.16

2.86

3.17

2.88

2.99

Malaysia

2.08

2.03

2.56

2.31

1.70

1.71

1.52

-

Philippines

5.06

4.78

6.73

6.82

5.36

5.31

5.41

5.21

All industries
Agriculture, forestry, & fishing

20.0

44.2

50.4

70.3

137

133

-

-

1.05

3.25

5.16

7.56

17.6

17.6

-

-

Manufacturing

67

106

115

146

218

213

-

-

Mining & utilities

-

-

302

505

599

491

-

-

Construction

-

-

56

61

94

93

-

-

Trade & vehicle repair
Transportation & storage

-

-

19

38

84

81

-

-

-

-

67

103

158

128

-

-

Accommodation & food services

-

-

13

19

70

46

-

-

COMPENSATION PER EMPLOYEE, 1000 current baht

Sources: Asian Development Bank (2023, various years), Bank of Thailand (2023), National Economic and Social
Development Council (2021, 2023c).

36

Table 4: School Completion Ratios and Employment by Education and Occupation
1990 1996 2000 2006 2019 2020
Variable
SCHOOL COMPLETION RATIOS FOR ADULTS 25+ BY SEX & LEVEL% Female Completed Lower Secondary
29
48% Male Completed Lower Secondary
36
52% Female Completed Upper Secondary
21
35% Male Completed Upper Secondary
24
35% Female Completed Bachelor's +17% Male Completed Bachelor's +14
EMPLOYEES BY EDUCATION COMPLETED, mil.
2001
16.6 13.9 13.4
7.8
7.5
No schooling or incomplete primaryPrimary
7.0
7.2
7.8
8.4
8.2
Lower secondary
3.1
4.1
5.1
6.4
6.3
Upper secondary
1.9
3.1
4.3
6.4
6.6
Tertiary
2.4
3.7
5.0
8.2
8.6
Others, unknown
0.0
0.1
0.2
0.4
0.4
EMPLOYEES BY OCCUPATION, millions
2001
Managers, professionals, technicians, etc.
2.4
4.7
5.4
5.2
5.7
Clerks, sales&service workers, etc.
5.2
5.3
6.3
9.3
9.1
Skilled agricultural/fishery workers
13.7 11.9 12.5 10.8 10.8
Craft, plant & machinery workers
1.3
3.7
4.0
4.2
4.2
Elementary & other workers
6.9
6.6
7.4
8.0
7.8
Others, unknown
0.0
0.0
0.1
0.1
0.0
TERTIARY GRADUATES BY OCCUPATION, mil. Q4
2002
Managers, professionals, technicians, etc.
2.3
2.9
4.0
4.5
Clerks, sales&service workers, etc.
1.0
1.3
2.6
2.6
Other occupations
0.5
0.7
1.6
1.7
R&D researchers & technicians per million people
2001 2007
Researchers in R&D
101 280 321 1,750 2,024
Technicians in R&D (2019=2017)38.3 112 143
297
-

2021

2022

-

-

7.1
8.3
6.4
6.8
8.7
0.3

6.5
8.3
6.7
7.6
9.7
0.4

5.2
9.4
11.0
4.2
8.0
0.0

5.6
10.0
10.9
4.2
8.5
0.0

-

-

-

-

Sources: Asian Development Bank (various years), Bank of Thailand (2023), National Statistics Office (various
years), World Bank (2023).

37

Table 5: Health, Poverty, and Distribution Indicators
Variable1990 1996 2000 2006 2019 2020
HEALTH INDICATORS, World Bank Estimates, for italics 1990=1991,1996=1997,2000=2001
Mortality rate, infant, per 1,000 live births
30
23
19
14
7.7
7.4
Mortality rate, <5, per 1,000 live births
37
27
22
16
9.0
8.6
Life expectancy at birth, total, years
70
72
72
75
79
79
Physicians per 1,000 people
0.23 0.27 0.30
0.29
0.90
0.93
Nurses and midwives per 1,000 people
0.73 0.92 1.23
1.53
3.08
HIV incidence per 1,000 uninfected
2.30 1.11 0.66
0.33
0.11
0.10
Malaria Incidence per 1,000 at risk
6.56
2.41
0.27
0.22
Tuberculosis incidence per 1,000 people
2.41
2.15
1.50
1.46
Population aged 65+, % of total
4.25 5.16 6.10
7.85
13.2
13.9
POOR HOUSEHOLDS (% of households with per capita income below the poverty line)
Urban households
37
17
19
10
3.7
4.3
Rural households
64
40
48
27
6.8
7.2
SHARES OF INCOME BY INCOME EARNER GROUP & GINI, World Bank Estimates
Lowest 20% of Earners
5.9
6.2
6.2
6.0
7.7
7.5
Second Lowest 20% of Earners
9.1
9.8
9.6
9.9
11.5
11.5
Second Highest 20% of Earners
19.8 20.4 20.9
21.4
22.3
22.4
Highest 20% of Earners
52.2 49.9 49.7
48.5
42.8
42.7
Highest 10% of Earners
36.4 34.3 33.7
32.5
27.2
27.0
GINI COEFFICIENTS
Consumption income, all households
0.51 0.51 0.52
0.51
0.43
Urban households
0.48 0.48 0.47
0.48
0.41
Rural households
0.45 0.44 0.47
0.48
0.41
Consumption expenditure, all households
0.44 0.43 0.43
0.42
0.35
0.35
Greater Bangkok
0.38 0.35 0.33
0.36
0.29
0.29
Central Region
0.39 0.36 0.37
0.36
0.32
0.31
Northern Region
0.41 0.39 0.37
0.41
0.31
0.32
Northeastern Region
0.38 0.38 0.35
0.40
0.32
0.33
Southern Region
0.36 0.37 0.37
0.37
0.33
0.33
EMPLOYEE COMPENSATION SHARES OF GDP BY INDUSTY (percent)All industries
26
30
31
30
31
32
Agriculture, forestry, & fishing
9
11
17
14
15
15
Manufacturing
32
38
37
32
31
32
Construction
53
48
55
53
49
49
Trade & vehicle repair
6
7
10
18
19
19
20
20
19
22
21
23
Transportation & storage
Accommodation & food services
8
9
12
17
19
22

2021

2022

7.1
8.3
79
0.09
0.16
1.46
14.5

15.2

3.9
6.1

3.1
5.8

7.6
11.5
22.4
42.9
27.3

-

0.43
0.42
0.41
0.35
0.30
0.31
0.31
0.32
0.34

0.34
0.29
0.30
0.34
0.33
0.33

-

-

Sources: National Economic and Social Development Council (2021), National Statistics Office (2023), WorldBank (2023).

38

Table 6: Per Capita GRP and Its Growth Rate in 2002 Baht by Cluster
1996 2000 2006 2019
Region, cluster(s), number of provinces
PER CAPITA GRP BY REGIONAL CLUSTER, 2002 baht, thousands
23
22
29
54
Northeast, upper 1, 5 provinces
21
20
25
40
Northeast, upper 2, 3 provinces
25
25
35
57
Northeast, central, 4 provinces
27
25
35
57
Northeast, lower 1, 4 provinces
19
18
25
42
Northeast, lower 2, 4 provinces
50
46
58
80
North, upper 1, 4 provinces
25
25
32
46
North, upper 2, 4 provinces
32
30
36
53
North, lower 1, 5 provinces
36
35
50
69
North, lower 2, 4 provinces
78
81
94
133
South, western, 6 provinces
61
61
74
89
South, eastern, 5 provinces
South, border, 3 provinces
44
39
45
43
East, western, 3 provinces
269 263 352 376
East, eastern, 5 provinces
63
67
107 172
108
107
142
186
Center, 6 provinces
59
59
76
96
West, 6 provinces
317 282 323 446
Bangkok-center, Metropolis
262 288 276 273
Bangkok-southeast, Samut Prakan
Bangkok-north, PatumThani & Nonthaburi
220 163 156 159
213 183 221 236
Bangkok-west, Nakhon Pathom & Samut Sakhon

2020

2021

53
41
57
57
43
77
45
50
64
99
84
42
342
165
179
90
417
223
152
231

55
43
58
60
45
78
46
51
64
87
84
43
355
160
187
92
417
221
149
233

COMPOUND ANNUAL GROWTH RATE OF PER CAPITA GRP, 2002 baht, percent
-0.6
4.6
5.0
-3.0
4.2
Northeast, upper 1, 5 provinces
-0.9
4.1
3.6
2.2
3.9
Northeast, upper 2, 3 provinces
0.0
5.8
3.8
-0.7
2.8
Northeast, central, 4 provinces
-1.5
5.5
3.7
1.0
4.6
Northeast, lower 1, 4 provinces
-1.1
5.6
4.1
3.8
3.5
Northeast, lower 2, 4 provinces
-2.0
3.9
2.5
-3.7
2.1
North, upper 1, 4 provinces
0.1
4.2
2.9
-2.3
2.2
North, upper 2, 4 provinces
-1.2
3.2
3.0
-5.4
1.4
North, lower 1, 5 provinces
-0.4
5.9
2.5
-6.8
0.6
North, lower 2, 4 provinces
0.9
2.5
2.7
-26.1
-12.0
South, western cluster, 6 provinces
-0.2
3.4
1.4
-5.0
0.1
South, eastern cluster 5 provinces
South, border cluster 3 provinces
-3.1
2.6
-0.4
-3.2
2.4
East, western cluster 3 provinces
-0.5
5.0
0.5
-9.1
4.0
East, eastern cluster 5 provinces
1.4
8.2
3.7
-4.1
-2.9
-0.2
4.8
2.1
-4.0
4.7
West region 6 provinces
0.2
4.4
1.8
-6.4
1.8
Center region 6 provinces
-2.8
2.3
2.5
-6.7
0.0
Bangkok-center, Metropolis
2.4-0.7 -0.1 -18.2-1.3
Bangkok-southeast, Samut Prakan
Bangkok-north, PatumThani & Nonthaburi-7.2 -0.8
0.1
-4.4
-1.9
-3.8
3.2
0.5
-2.3
0.9
Bangkok-west, Nakhon Pathom & Samut Sakhon-0.27 -0.53 -0.56
not meaningful
SIMPLE CORRLEATION W/INITIAL LEVEL
Source: National Economic and Social Development Council (2023b).

39

Table 7: Exports, Revealed Comparative Advantage Indices, and Import-Export Ratios
Variable1990 1996 2000 2006 2019 2020
MERCHANDISE EXPORTS, WTO classification, shares of exports unless noted
Total, US$ billions
23.1 55.7 69.0 130 246 232
Food
29
20
14
11
14
14
Other agricultural goods
5.1
5.4
3.3
5.3
3.7
3.6
Fuels & mining products
1.9
2.3
4.4
6.5
5.0
4.5
Manufactures
63
71
75
76
68
71
Textiles & clothing
16
10
8.3
5.5
3.0
3.3
Chemicals
2.0
3.4
5.9
8.0
9.8
9.3
Iron & steel
0.6
0.8
1.3
1.4
0.7
0.6
Office & telcom machinery
15
24
27
23
14
16
Automotive products
0.5
0.8
3.5
7.6
11
10
Other transportation equipment
1.2
3.2
4.4
10
14
13
General & precision machinery
5.1
10.0
8.5
4.4
1.1
3.6
Other manufactures
22
19
16
16
16
15
Miscellaneous goods
1.2
1.3
3.0
1.1
9.1
6.5
REVEALED COMPARATIVE ADVANTAGE INDICIES
Food
3.17 2.24 2.16 1.82 1.71 1.62
Other agricultural goods
1.79 2.31 1.79 3.41 2.72 2.71
Fuels & mining products
0.13 0.20 0.33 0.34 0.30 0.34
Manufactures
0.92 1.00 1.03 1.11 1.02 1.03
Textiles & clothing
2.67 1.71 1.51 1.26 0.72 0.75
Chemicals
0.24 0.38 0.65 0.78 0.85 0.74
Iron & steel
0.19 0.32 0.59 0.46 0.29 0.29
Office & telcom machinery
1.78 1.91 1.81 1.89 1.29 1.35
Automotive products
0.05 0.08 0.39 0.91 1.35 1.34
Other transportation equipment
0.34 0.86 1.19 1.28
General & precision machinery
2.13 1.21 0.26 0.67
Other manufactures
1.05 1.11 1.07 1.02
Miscellaneous goods
0.21 0.22 0.55 0.21 1.26 0.83
IMPORT-EXPORT RATIOS
Total
1.43 1.30 0.90 0.99 0.96 0.89
Food
0.25 0.27 0.27 0.34 0.41 0.47
Other agricultural goods
1.33 0.87 0.79 0.35 0.36 0.33
Fuels & mining products
9.89 6.47 3.14 3.79 3.53 3.54
Manufactures
1.70 1.42 0.91 0.87 0.87 0.88
Textiles & clothing
0.25 0.27 0.31 0.33 0.60 0.54
Chemicals
7.37 3.65 1.67 1.26 0.96 1.10
Iron & steel
19.65 9.75 3.09 4.28 7.31 7.14
Office & telcom machinery
0.97 0.81 0.75 0.71 0.83 0.83
24.55 11.49 0.86 0.43 0.38 0.37
Automotive products
Other transportation equipment
13.83 4.13 1.13 0.49 0.48 0.42
General & precision machinery
3.31 2.00 1.41 2.65 8.13 2.89
Other manufactures
0.79 0.84 0.70 0.77 0.81 0.87
Miscellaneous goods
2.61 2.80 0.50 2.46 1.28 0.37
Source: World Trade Organization (2024).

40

2021

2022

272
13
4.1
6.1
73
3.2
10.4
0.9
16
11
14
2.2
15
3.4

287
14
4.1
6.5
72
2.6
9.8
0.9
16
11
14
2.9
15
3.6

1.62
2.93
0.37
1.10
0.80
0.84
0.33
1.40
1.71
1.49
0.40
1.04
0.46

1.77
3.19
0.33
1.14
0.71
0.80
0.36
1.54
1.68
1.56
0.55
1.10
0.46

0.98
0.49
0.40
3.33
0.92
0.55
1.14
6.66
0.88
0.34
0.43
4.74
0.88
0.79

1.06
0.48
0.37
4.19
0.94
0.69
1.22
6.05
0.89
0.39
0.46
3.39
0.88
0.90

Table 8: MFN Tariff Rates: World Bank Estimates (percent)
Variable1991 1995
48.5 38.5
Primary products, simple average
28.9 12.1
Primary products, trade-weighted average
44.8 21.1
Manufactures, simple average
34.7 15.6
Manufactures, trade-weighted average

2000
30.5
8.0
16.2
10.1

2006
16.2
2.9
10.8
6.5

2010
14.3
2.7
8.9
6.3

2014
18.8
7.1
8.9
6.7

Source World Bank (2023).
Table 9: MFN Tariff Indicators: WTO Estimates (percent)
Variable2006 2010 2014 2019 2020
AGRICULTURE
Simple average duty
22.0 22.8 31.3 29.0 29.3
Trade-weighted average duty
14.1 13.3 41.2 16.5 29.4
NON-AGRICULTURE
Simple average duty
8.2
8.0
8.3
7.2
7.1
Trade-weighted average duty
3.2
4.3
4.9
4.4
5.4
% of imports duty free56.0 48.1 47.1 60.0 52.8
% of imports with duties 0% <= 10%
40.3 46.1 46.5 33.5 38.5
% of imports with duties 10% >= 100%
3.8
5.9
6.4
6.5
8.6
% of imports facing non ad valorem duties
8.2
3.4
3.7
1.2
1.8
SIMPLE AVERAGE DUTIES FOR WTO NON-AGRICULTURE PRODUCT GROUPS
Minerals & metals
5.9
6.0
6.2
5.1
4.9
Petroleum
9.4
6.0
6.1
5.6
6.1
Chemicals
3.8
3.1
3.3
2.6
2.6
Wood, paper, textiles, clothing, leather & footwear
12.3 12.9 14.4 16.4 15.2
Non-electrical machinery
4.7
4.1
4.3
3.0
3.0
Electrical machinery
8.3
7.5
8.1
6.8
6.7
Transport equipment (incl. automotive products)
20.7 20.3 20.7 22.8 22.8
Other manufactures
11.0 10.2 10.6
7.8
7.6
SHARES OF TOTAL IMPORTS FOR WTO NON-AGRICULTURE PRODUCT GROUPS
Non-agriculture groups
95.2 94.4 93.9 92.6 91.7
Minerals & metals
20.9 24.8 22.1 22.5 20.6
Petroleum
17.8 14.2 17.2 11.6 10.2
Chemicals
10.7 10.7 10.2 11.0 11.8
Wood, paper, textiles, clothing, leather & footwear
5.3
5.4
5.3
6.2
6.0
Non-electrical machinery
14.1 13.4 13.1 12.3 12.4
Electrical machinery
18.0 16.3 15.6 16.7 18.9
Transport equipment (incl. automotive products)
3.8
5.0
6.0
5.9
4.9
Other manufactures
4.6
4.6
4.4
6.4
6.9

2021

2022

31.2
37.5

26.6
30.4

8.4
5.0
55.3
37.1
7.6
1.2

7.1
4.6
55.9
37.9
6.2
0.9

5.0
4.4
2.6
19.0
3.0
6.7
22.8
7.5

4.8
5.6
2.9
2.9
6.7
20.7
8.1

92.7
23.5
11.5
12.2
5.9
11.3
17.9
5.0
5.4

-

Note: Average Duties for the wood & paper, textiles, etc., group are trade weighted averages of these 4 WTO
categories.
Source: World Trade Organization (2024).

41