January 2026
APRIL 2020
Working Paper (WP/26-01)
Thailand’s Automotive EV Transition:
Short-run Implications for Economic
Growth
Xianguo Huang
January 2026
Disclaimer: The findings, interpretations, and conclusions expressed in this material represent the views
of the author(s) and are not necessarily those of the ASEAN+3 Macroeconomic Research Office
(AMRO) or its member authorities. Neither AMRO nor its member authorities shall be held
responsible for any consequence from the use of the information contained therein.
[This page is intentionally left blank]
Thailand’s Automotive EV Transition: Short-
run Implications for Economic Growth
Prepared by Xianguo Huang1 2
January 2026
Abstract
Thailand, Southeast Asia’s leading automotive hub, is undergoing a structural transition from internal
combustion engine vehicles to electric vehicles (EVs), driven by domestic industrial up- grading
ambitions and global decarbonization pressures. While this shift is expected to support long-term growth
and sustain export competitiveness, its short-run macroeconomic implications remain underexamined.
This paper empirically assesses the automotive sector’s contribution to GDP growth, using a composite
activity index and interaction terms capturing the EV transition period. Results confirm that the sector
remains macro-critical, with real GDP exhibiting statisti- cally significant elasticity to automotive
activity. However, this elasticity has weakened over time, and the EV transition is associated with
additional short-run contractionary effects. These find- ings suggest that the accelerated pace of
transition under the EV incentive policies may have amplified adjustment costs, highlighting the need for
timely and inclusive policy responses to balance short-term dislocations with long-term structural
transformation. At the same time, the transition presents a strategic opportunity to upgrade domestic
capabilities and position Thailand as a competitive EV production hub in the region.
Keywords: Automotive, EV Transition, Industrial Policy, Thailand
JEL codes: L62, Q55, O25
1Author’s e-mail address: @. Xianguo Huang is a Senior Economist at AMRO. This paper was
authorized for distribution by Abdurohman, Deputy Director.
2I thank Benyaporn Chantana for her assistance. I would also like to extend my appreciation to Laura Britt-Fermo, Chenxu Fu,
Allen Ng, and Haobin Wang (all AMRO), as well as Patarapong Intarakumnerd (National Graduate Institute for Policy Studies) and
Farhad Taghizadeh-Hesary (Tokai University), for their constructive suggestions. I am grateful to the participants in AMRO’s
surveillance and Macro-Financial Research (MFR) meetings, as well as to seminar participants at the Bank of Thailand and the Thai
Ministry of Finance (TMOF), for their valuable feedback. Helpful written comments were received from the Fiscal Policy Office,
TMOF. All remaining errors or omissions are my own responsibility.
mailto:@
Contents
1 Introduction 3
2 Developments in the Automotive Sector 8
3 Macro-criticality and the Effect of EV Transition 9
Model Specifications ........................................................................................................................9
Econometric Considerations ...........................................................................................................10
Results.............................................................................................................................................14
Baseline .............................................................................................................................14
Robustness Checks ............................................................................................................16
Tests for Sub-sample Periods .............................................................................................18
PCA AutoIndex and Decomposition .................................................................................19
EV Transition ............................................................................................................................20
4 Discussions 22
5 Conclusion 23
A Appendix: Tables 24
List of Figures
1 Automotive Growth and EV Imports in Thailand ....................................................................3
2 New Car Registration and Real GDP Growth ..........................................................................5
List of Tables
1 EV and EV Incentive Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 Announcements in Thailand’s Automotive Sector (2024-2025) . . . . . . . . . . . . . . . 7
3 Variables in Use & Treatment for Stationarity . . . . . . . . . . . . . . . . . . . . . . . . 13
4 Regression Results (HAC SE, 2003Q1–2024Q4) . . . . . . . . . . . . . . . . . . . . . . 15
5 Robustness Checks of Baseline Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
6 Regression Results: Full Period and Sub-periods . . . . . . . . . . . . . . . . . . . . . . 18
7 PCA Details and Decomposition for Auto Sector Growth Rates . . . . . . . . . . . . . . 19
8 Regression Results with an EV Transition Interaction Term . . . . . . . . . . . . . . . . . 20
A1 Variance Inflation Factor (VIF) Diagnostics . . . . . . . . . . . . . . . . . . . . . . . . . 24
A2 Ramsey RESET Test for Model Specification . . . . . . . . . . . . . . . . . . . . . . . . 25
A3 Potential Channels of EV Transition Effects . . . . . . . . . . . . . . . . . . . . . . . . . 26
1 Introduction
Thailand has long been recognized as Southeast Asia’s automotive hub, dating back to the late 1980s to
early 1990s. In 2023, the country manufactured approximately million vehicles and ranked as the
world’s ninth-largest vehicle exporter (Thailand Board of Investment 2023). The auto- motive sector
contributes around 10–11 percent of Thailand’s GDP, provides direct employment to about 850,000 people,
and supports an additional million indirect jobs (ASEAN Briefing 2024). However, the industry’s
recovery following the COVID-19 pandemic was slow, and it experienced another sharp downturn in 2024
(Figure 1a).1
Figure 1: Automotive Growth and EV Imports in Thailand
Units, thousand, 3month moving average 250 percent 100
200 80
60
150
40
100
20
50
0
2004 2009 2014 2019 2024
Production Domestic sales Exports
(a) Automotive
0
2020 2021 2022 2023 2024 2025
EV as of total vehicle imports China
EV as of total EV imports
(b) EV imports
Thailand’s automotive sector is undergoing profound change amid a broader structural trans-
formation in the global automotive industry—namely, the accelerating shift from internal combus- tion
engine (ICE) vehicles to electric vehicles (EVs). Globally, momentum behind EV adoption has surged.
China leads the world, accounting for over 60% of global EV sales in 2023, followed by the EU and the
.. Several countries—including Norway, Germany, and the .—have also an- nounced plans to phase
out new ICE vehicle sales in the coming years, reinforcing the global pivot to electrification (International
Energy Agency 2024). Countries transitioning toward EV production have also been actively expanding
exports amid increasing domestic EV adoption rates.
As a major auto manufacturing hub in the ASEAN+3 region, Thailand is undergoing this tran- sition
on both the production and consumption fronts. Although still at an early stage, the transition has gained
momentum in recent years through targeted policy support. Thailand has embarked on a strategic shift to
position itself as a key global EV production base, as envisioned in the 30@30 strat- Policymakers have
introduced a range of measures to support this transformation and address
1. Total production dropped by 20 percent to a four-year low.
2. The 30@30 strategy aims for 30 percent of Thailand’s total vehicle production to be zero-emission vehicles (ZEVs) by 2030.
emerging challenges, including EV tax incentives, investment promotion in advanced technologies, and
trade facilitation, as elaborated in the EV (announced in 2021) and EV (introduced in late 2023)
government EV subsidy programs (Table 1). These measures have helped accelerate domestic EV adoption,
primarily through imports since 2022, and drawn foreign direct investment to establish production facilities
(Figure 1b, 2a). Domestic EV production is expected to accelerate further in 2025 (Table 2).
The EV transition also presents structural challenges that could weigh on sectoral growth and have
broader macroeconomic implications. Unlike ICE vehicles, EVs require significantly fewer parts, resulting
in simpler and shorter supply chains (Kohpaiboon and Durongkaveroj 2024). Con- sequently, the shift is
expected to reduce demand for traditional automotive components, posing transitional risks to Thailand’s
auto parts industry. The implications could be far-reaching, potentially leading to job losses, stranded capital,
and valuation pressures across upstream and downstream segments of the sector.
For instance, ICE-based production facilities may face difficulties in retooling for EV manufac- turing,
reduced demand for existing models, and the risk of stranded assets due to shorter-than- expected product
cycles. At the same time, although charging infrastructure is expanding and con- sumer adoption is
increasing, new EV manufacturers face challenges in meeting local production requirements amid soft
domestic demand and intensifying external competition.
With domestic demand falling short of expectations—particularly in 2024—an oversupply of EVs has
emerged, triggering sharp price competition. In 2025, with EV manufacturers that benefited from EV
incentive programs required to meet local production targets, and as some ICE incumbents ramp up EV
output in Thailand, market pressures are likely to persist (Table 2) although prices have stablized in recent
months.
Difficulties in upgrading from a production base to an innovation base in ICE, as discussed by
Intarakumnerd (2021), may continue to persist in an EV-dominant era. While Thailand has success- fully
attracted Foreign Direction Investment (FDI), developing indigenous EV-related technologies re- mains
challenging, partly due to the lock-in effects within traditional Japanese automotive production networks,
where the incentive to shift forward has been limited by sunk costs in older technologies (Intarakumnerd
and Charoenporn 2024). In addition, the presence of free trade agreements (FTAs) may influence local
capacity-building and business opportunities. Ongoing debates also highlight the need to balance
technological upgrading with alleviating pressures on the existing ICE Vehicles (ICEV) industry.
Kohpaiboon and Durongkaveroj (2024) argues that radical policy interventions at the expense of the ICEV
industry could do more harm than good, while Kohpaiboon (2023) calls for a balanced approach—one that
incentivizes EV adoption and supports industrial transformation, while accounting for technological
uncertainties and the timing of capacity scale-up.
Opportunities and challenges in this sector are multifaceted, particularly in the context of green
growth and transition policies. EV adoption can lower greenhouse gas (GHG) emissions by up to 70% in
Thailand and significantly improve air quality (Khumpraphan 2024). Meanwhile, a signif- icant share of
EV-related employment could facilitate the adoption of Vehicle-to-Everything (V2X) technology and the
integration of renewable energy into smart grids through V2X-enabled platforms (Wattana and Wattana
2022). Drawing on the case of India, Wattana and Wattana (2022) finds that decarbonization can reduce
government revenue from and employment in traditional sectors.
Figure 2: New Car Registration and Real GDP Growth
million
yoy
6
4
2
0
2
4
2020 2021 2022 2023 2024
Petro Diesel Eletric Others
Total (RHS)
(a) Newly registered
6
2018 2019 2020 2021 2022 2023 2024 2025
Thailand ASEAN-5
(b) Real GDP Growth (4-period moving average)
Motivated by the transformation of Thailand’s automotive sector—particularly since 2022—and its
importance to the broader economy, this paper aims to quantify the sector’s macro-criticality and assess the
extent to which the transition from ICEVs to EVs poses short-run downside risks to economic growth. This
inquiry is further motivated by the widening gap observed in GDP growth between Thailand and its peers
in ASEAN during this period (Figure 2b). While a successful EV transition has the potential to enhance
long-term competitiveness through technological upgrading and contribute to a greener economy, this study
focuses exclusively on the short-run macroeconomic implications, incorporating both domestic and external
factors into the analysis.
The remainder of the paper is structured as follows. Section 2 reviews recent trends in automo- tive
production, domestic sales, exports, and investment. Section 3 evaluates the macroeconomic importance of
the automotive sector and analyzes the short-run effects of the EV transition. Sec- tion 4 concludes with a
discussion of policy implications and directions for future research.
Table 1: EV and EV Incentive Programs
Not applicable to pickups or motorcycles.
Sources: Thailand Board of Investment (BOI), various sources
tees. Stricter production and tech criteria. No dual par- ticipation.
6
Program Component EV (2022–2023) EV (2024–2027)
Duration Subsidies for BEVs imported or produced in 2022 and 2023.
Local production required by 2024 and 2025.
Subsidies from 2024 to 2027. Imports eligible in 2024 and
2025. Local production mandatory from 2026 on- ward.
Covered Vehicle Types BEV passenger cars, pickups, and 2–3 wheelers (., e-
motorcycles, tuk-tuks).
Same types, plus commercial trucks and buses. Luxury cars
priced at over THB2 million excluded.
Subsidy Amounts THB 70k–150k per car (battery >30 kWh). Motorcycles:
approx. THB18k.
THB50k–100k (2024, 10–50 kWh), THB35k–75k (2025),
THB25k–50k (2026–2027). Motorcycles: approx. THB 10k.
Local assembly required after 2025.
Import Duty Reduction Up to 40% for EVs priced ≤ THB2M, 20% for THB2–7M. 40% for EVs ≤THB2M (2024–2025). No reduction post-
2025 or for vehicles > THB2M, pickups, or motorcycles.
Excise Tax Reduction Cars: from 8% to 2%. Pickups: from 10% to 0. Motorcy-
cles: from 5–10% to 1%. Effective through 2023.
Cars ≤ THB7M: 8% to 2% (2024–2027). Pickups: 10%
to 0% (2024–2025), 2% (2026–2027). Motorcycles:
5–10% to 1% (2024–2027).
Local Production Require- ment 1:1 ratio of local BEVs per imported unit by 2025. 2:1 in 2026 and 3:1 in 2027. EV quotas can be carried
forward.
Battery & Product Stan- dards Minimum battery size of 10 kWh for subsidy eligibility. No
requirement for local battery.
Battery size of 10–50 kWh for base subsidy; >50 kWh for
full subsidy.
Manufacturer Eligibility Open to importers via MOU. 14 participants by 2024. Open to new and existing applicants with bank guaran-
Table 2: Announcements in Thailand’s Automotive Sector (2024-2025)
Date Company Investment Amount Purpose
March 2024 SVOLT (not specified) Partnership with Banpu Next to produce
EV battery packs
March 2024 BMW Over billion
(EUR42 million)
May 2024 Toyota Part of billion
(THB50 billion)
July 2024 Honda Part of billion
(THB50 billion)
August 2024 Hyundai THB 1 billion (USD28
million)
Construct high-voltage battery assembly
plant in Rayong for EV production, start-
ing 2H 2025
Plan to manufacture electric Hilux by end of
2025
Plan to consolidate production in Prachin-
buri for hybrid vehicles by 2025
Set up facility for EV and battery assem- bly
(production starts in 2026)
January 2025 Great Wall THB 25-30 billion Expand to 150,000 units/year; focus on
EVs and hybrids by 2025
January 2025 SAIC - CP THB 60 billion by 2027-
28
Target 300,000 units/year; include
150,000 battery EVs
January 2025 BYD THB18 billion Produce 150,000 EVs annually by 2024-
25
January 2025 Neta THB8 billion Annual capacity of 50,000 units
January 2025 Hozon THB8 billion 100% EV production
January 2025 GAC Aion THB12 billion Produce 50,000 EVs per year
January 2025 MG THB5 billion 100% EV production
February 2025 Mazda THB5 billion (USD150
million)
Produce electric compact SUVs; target
100,000 units/year for exports
March 2025 Changan Exceeding THB10 billion Start production of EVs in Thailand
March 2025 PPG New plant, 2,000 tons
capacity
Waterborne coatings manufacturing; sup-
port sustainable demand
March 2025 Sunwoda More than USD1 billion Invest in EV battery cell plants
Source: Various news sources and company announcements
2 Developments in the Automotive Sector
Thailand’s automotive sector performance reflects a combination of cyclical fluctuations and deeper
structural transformations. In the short term, weakening domestic sales, softening exports, and subdued
investment signal mounting pressures on long-established manufacturers and the broader industrial base.
These developments are unfolding alongside a more fundamental transition: the shift from ICEV to EVs.
While EV models have begun to gain market share, the pace and structure of this transition carry significant
implications for Thailand’s competitiveness, supply chain integration, and macroeconomic resilience. This
section reviews recent market dynamics and examines the ongoing transition from ICEV to EVs.
From a longer-term perspective, the Thai automotive industry transformed from an assembly- focused
sector in the 1980s into a globally competitive production hub by the 2000s. On the con- sumption front,
domestic automobile sales grew steadily over several decades, peaking in 2014. After a modest recovery in
2019, the downward trend resumed and has intensified since 2024. Pressures have been particularly evident
among legacy players—primarily Japanese and American brands—that have long dominated the Thai market.
In contrast, new Chinese entrants such as BYD have expanded their presence since 2023. According to
newly registered vehicle data, the struc- tural decline in conventional vehicles—despite a temporary
cyclical rebound in 2022–2023—was reinforced in 2024. This recent contraction also marked a notable
compositional shift: registrations of petrol and diesel-powered vehicles declined, while EVs—including
battery electric, hybrid, and plug-in hybrid models—continued to grow, albeit from a low base.
Automotive exports have also faced increasing headwinds. While exports rebounded following the
COVID-19 slump, momentum has weakened markedly since 2024. The stagnation has been most
pronounced in assembled vehicle exports, with passenger car export values remaining flat over the past
decade. However, growth in parts and accessories exports has helped cushion the overall decline.
Investment in the automotive sector has also weakened and the decline in capi- tal formation—particularly
in traditional vehicle production—has been a notable drag on aggregate investment and growth, although it
has been partially offset by rising EV-related projects.
Beyond macroeconomic and industrial indicators, data from publicly listed automotive compa- nies
offer additional perspective. On average, these firms have underperformed the broader stock market.
Although aggregate industrial sentiment improved modestly in the second half of 2024 and into early 2025,
subindices for the automotive and auto parts industries point to continued pes- simism—particularly among
ICE-focused producers facing structural headwinds.
t t t
t
t
t
3 Macro-criticality and the Effect of EV Transition
As discussed in the previous sections, the automotive sector is a major source of employment, stim- ulates
private consumption through durable goods purchases, anchors substantial fixed investment, and is a key
export industry. Given the sector’s prominence in Thailand, we expect it to be macro- critical and aim to
assess both its size and how its contribution has evolved over time. The ongoing EV transition—reflected in
changes in production composition and rising domestic EV sales—raises important questions about whether
the sector’s contribution to the economy has been enhanced or diminished in the short run.
This section explores these issues empirically. We begin by estimating the elasticity of real GDP with
respect to the automotive sector, using a baseline model that controls for key domestic and global
macroeconomic factors. The sample is divided into sub-periods to assess changes in the strength of this
relationship over time. We then extend the analysis by incorporating an EV transition dummy to evaluate
whether the post-2022 shift toward EVs has amplified or moderated the sector’s short-term impact on
economic growth. The long-run effects of the transition are beyond the scope of the paper, however.
Model Specifications
The empirical analysis begins by examining the role of the automotive sector in driving overall eco- nomic
activity. This is done using the baseline specification presented in Equation 1 below, which regresses real
GDP on a set of automotive sector variables, along with relevant domestic and global control variables.
where:
• Yt: Real GDP
Yt = α + βXauto
Varia
s
ble
˛
o
¸
f in
x
terest
+ Γ′Xd
Dome
s
st
˛
ic
¸
c
x
ntrols
+ ∆′Xo
Oth
s
er
˛
c
¸
on
x
trols
+εt (1)
• Xauto: automotive sector activity
• Xd: a vector of domestic macroeconomic control variables, including
– consumer confidence
– household debt-to-GDP ratio
– interest rate
• Xo: a vector of other control variables (global factors and COVID-19 dummy), which include
– World real GDP
t
t
t t t
t
t t
– GFC dummy
– COVID-19 dummy
• εt: Error term
This specification allows us to isolate the contribution of the automotive sector to overall out- put
growth while controlling for macroeconomic fluctuations at both the domestic and global levels. Candidate
variables capturing automotive sector activity include vehicle production, sales, and ex- ports—each
transformed into log-differences to address non-stationarity. Domestic controls account for household
consumption sentiment and financial vulnerability, while global factors—such as ex- ternal demand and oil
prices—reflect broader international economic conditions.
To capture the evolving role of EVs and the structural transformation underway in the automotive
industry, the baseline model is extended by introducing an interaction term between automotive sector
activity and a time dummy variable indicating the accelerated EV transition period under government
incentive programs. The extended model is specified as follows:
Yt = α + βXauto
+ θ(Xauto × Dev) + Γ′Xd
+ ∆′Xo
+εt (2)
Varia
s
ble
˛
o
¸
f in
x
terest
where additional variables:
EV intera
˛
c
¸
tion term
x
Dome
s
st
˛
ic
¸
c
x
ntrols Oth
s
er
˛
c
¸
on
x
trols
• Dev: a time dummy variable capture periods of EV adoption accelerated by EV and programs
• Xauto × Dev: interaction terms capturing differential impact
This augmented specification in Equation 2 enables us to investigate whether the relationship between
the automotive sector and economic activity differs during periods associated with acceler- ated EV adoption
or shifts in consumer and production behavior. The coefficient on the interaction term (θ) provides insight
into whether the economic contribution of the automotive sector is amplified or weakened in the context of
the EV transition period.
Econometric Considerations
The model is estimated using Ordinary Least Squares (OLS) regression. To avoid spurious results, all
variables are tested for stationarity. Non-stationary variables are treated using log-differences or first
differences. Structural break tests are conducted to avoid bias, with additional GFC and COVID-19
temporal dummies included in the model specification. The COVID-19 shock is cap- tured using the
standard window of 2020Q2–2021Q1, while the GFC dummy covers the period 2008Q3–2009Q2.
t
As modeling the timing of the EV transition is critical for interpretation, the specification ac- counts
for multiple dimensions of sectoral transformation. From a policy standpoint, the Thai Board of Investment
(BOI) shifted its focus to battery electric vehicle (BEV) technology in 2020, announc- ing higher incentives
for BEV project investments, which accelerated the development of domestic production While
government support for EVs began earlier, a key turning point came in 2022 with the rollout of the EV
subsidy scheme, marking the start of a structured incentive program targeting both consumers and
manufacturers. This was followed by EV in 2024, which further tightened local content and production
requirements. A notable policy feature allowed major Chinese EV makers to begin selling imported BEVs
while gradually ramping up local production, leading to a phased transformation in the structure of vehicle
production and trade. This paper adopts 2022 as the starting point of Thailand’s accelerated EV transition
period, aligning with the implementation of incentive programs that catalyzed the shift, and capturing both
policy-driven and market-led dynamics through 2024.
Selecting an appropriate measure to represent the performance of the automotive sector is non- trivial,
given the multidimensional nature of its activities. To address this, we construct a composite AutoIndex
Xauto using Principal Component Analysis (PCA) applied to three key monthly indicators: vehicle production,
domestic vehicle sales, and vehicle exports. Each indicator reflects a distinct but interrelated dimension of the
sector—supply, domestic demand, and external demand, respectively. Before applying the transformation, all
series are converted into log-differences to capture short- run dynamics and ensure stationarity. We extract
the first principal component, which captures the largest proportion of shared variance, and use it as a
summary measure of overall sectoral activity. This approach reduces dimensionality while retaining the most
informative combination of underlying indicators, thereby enhancing interpretability and minimizing noise.
Given the contemporaneous specification of the model, reverse causality is unlikely to be a major
concern due to the timing of data construction and the use of transformed variables. All key regressors—
including automotive production, domestic sales, exports, consumer confidence, and policy interest rates—
are derived from monthly indicators available prior to the release of quarterly GDP data. As such, they
reflect current economic conditions and are unlikely to be influenced by the GDP figures they aim to explain.
Additionally, expressing regressors in log-differences or first dif- ferences helps eliminate mechanical
identity-based endogeneity, particularly for sectoral indicators that may otherwise be components of GDP in
level terms. Concerns about simultaneity—especially regarding interest rates and sentiment indicators—are
further mitigated by the institutional sequenc- ing of data: these variables are either policy-determined based
on lagged information or collected independently prior to GDP release.
Beyond addressing reverse causality, omitted variable bias is mitigated through the inclusion of
3. EV policies were originally announced in 2017. The 2020 EV promotion package emphasized support for local battery
production and included corporate income tax exemptions and customs duty reductions on imported machinery.
a comprehensive set of domestic and external covariates—including consumer sentiment, house- hold
indebtedness, policy interest rates, and global GDP growth—and statistical tests are conducted to confirm
specification robustness. To ensure valid inference in the presence of heteroskedasticity and serial
correlation, the model employs Heteroskedasticity- and Autocorrelation-Consistent (HAC) standard errors.
Moreover, external regressors such as global GDP and oil prices are viewed as exogenous to Thailand’s
economic activity, consistent with its characterization as a small open econ- omy. Collectively, these design
features enhance the robustness of the model and support a causal interpretation of the estimated
relationships between the regressors and GDP growth.
An overview of all variables used in the analysis—including their definitions, stationarity trans-
formations, data sources, and coverage periods—is presented in Table 3.
t
Table 3: Variables in Use & Treatment for Stationarity
Variable Description Treatment Data Source Available Since
Yt Real GDP Quarterly log-difference (∆ log Yt) National Accounts (NESDC) 1993Q1–2024Q4
auto,pd
t auto,ex
t auto,sa
t
Vehicle production (in units) Log-difference Federation of Thai Industries –
Vehicle exports (in units) Log-difference Thai Customs Department –
Domestic vehicle sales (in units) Log-difference Toyota Motor Thailand –
auto
t AutoIndex (PCA of production, First principal component of log- Author’s calculations –
sales, exports) differenced variables
ev Time dummy for accelerated EV
adoption periods
Binary (1 if EV transition period) Author’s classification –
d
1,t
d
2,t
Consumer confidence index Log-difference University of the Thai Chamber of
Commerce
Household debt-to-GDP ratio First difference (∆ ratio) Bank of Thailand (BoT), NESDC, Au-
thor’s calculations
–
2003Q1–2024Q4
d
3,t
g
1,t
Interest rate (BoT policy rate) First difference (∆ rate) BoT –
World real GDP (trade-weighted) Quarterly log-difference (∆ log) Federal Reserve Bank of Dallas 1980Q2–2024Q4
g
2,t
Oil price (Europe Brent spot price, Quarterly log-difference (∆ log) Haver –
FOB, offshore)
gfc
t
covid
t
GFC dummy variable Binary (1 if GFC period) Author’s classification –
COVID-19 dummy variable Binary (1 if COVID-19 period) Author’s classification –
Note: All variables are expressed at a quarterly frequency. Vehicle production, domestic sales, and exports—originally available at monthly intervals—are aggregated to quarterly
data by summation. Consumer confidence, the BoT policy rate, household debt-to-GDP, and oil prices are converted to quarterly frequency using simple averages. The common
sample period spans from Q1 2011 to Q4 2024. Stationarity is assessed using the Augmented Dickey-Fuller (ADF) test.
13
X
X
X
X
D
X
X
X
X
X
D
D
t
1,t
2,t
3,t
1,t
t
Results
Baseline
As shown in Table 4, the empirical results underscore the significant short-run contribution of Thai- land’s
automotive sector to economic growth. The coefficient on the AutoIndex variable Xauto, pc, constructed
using PCA from production, sales, and export indicators, is positive and statistically sig- nificant at . This
suggests that stronger automotive activity is associated with faster real GDP growth.
Among the domestic control variables, changes in consumer confidence (Xd ) are positively
signed but not statistically significant, while increases in household debt-to-GDP (Xd ) are nega-
tively signed and significant at the 1% level, indicating a dampening effect on output. The coefficient
on the Bank of Thailand’s policy rate (Xd ) is also negative, although not statistically significant.
External demand, captured by world GDP growth (Xg ), is positively and significantly associated
with Thai GDP growth. As expected, the COVID-19 dummy variable (Dcovid) is large, negative, and highly
significant, indicating severe output losses during pandemic quarters.
The model demonstrates a good fit, with an adjusted R2 of , indicating that the explanatory variables
account for a substantial share of the variation in quarterly GDP growth. The F-statistic of 104 confirms
joint significance. To ensure valid inference, the model is estimated using HAC standard errors. The Durbin–
Watson statistic of suggests there is no meaningful autocorrelation in the residuals.
Diagnostic tests support the robustness of the model. While the Omnibus and Jarque–Bera statistics
indicate some deviation from normality, this is not uncommon in small-sample macro time series. HAC
inference is used given that residuals show modest skewness and slightly elevated kurtosis. The relatively
low condition number () indicates that multicollinearity is not a significant concern—an assessment
further supported by the variance inflation factor diagnostics (Table A1). The RAMsey RESET test suggests
that the model is correctly specified, with no major concerns regarding omitted variables (Table A2).
Table 4: Regression Results (HAC SE, 2003Q1–2024Q4)
Variable Coef. Std. Err. t P-value 95% CI
α [, ]
auto, pc
t
d
1,t
d
2,t
d
3,t
g
1,t
t
[, ]
[, ]
[, ]
[, ]
[, ]
X
X
X
X
X
Dcovid [, ]
Model Statistics
R-squared AIC
Adj. R-squared BIC
No. of Obs. 86 F-statistic
Durbin–Watson Cond. No.
Omnibus Prob(Omnibus)
Jarque–Bera (JB) Prob(JB)
Skew Kurtosis
t
t
2,t
Robustness Checks
To verify the stability and reliability of the baseline results, a set of robustness checks was conducted. These
included (i) augmenting the model with additional global variables and time dummies, and (ii) re-estimating
the baseline model using bootstrapped standard errors.
First, global oil prices (Xoil ) were measured as the quarterly log-difference in Brent crude prices and
added as an external control variable in alternative specifications (Alt. 1 and Alt. 3 in Table 5). The
inclusion of oil prices is motivated by their potential impact on both global demand and domestic inflation
expectations, which in turn may influence real GDP growth. The coefficient on the oil price variable is small
but statistically significant at the 10% level, indicating a mild procyclical effect. Im- portantly, the
coefficient on the AutoIndex variable (Xauto, pc) remains positive, statistically significant at the 1% level, and
similar in magnitude to the baseline, thereby confirming the robustness of the main finding. Other domestic
and external control variables maintain their expected signs and levels of
Second, the robustness of statistical inference is tested by replacing HAC standard errors with
nonparametric bootstrap standard errors based on 1,000 resampling replications. This approach provides a
distribution-free estimate of standard errors and inference. As shown in the last column of Table 5, the
AutoIndex coefficient remains highly significant under bootstrapping, reaffirming the core result. However,
the COVID-19 dummy (Dcovid), while still negative and large in magnitude, becomes statistically
insignificant at conventional levels. Nevertheless, the main structural finding on the automotive sector’s
contribution to GDP growth remains robust across specifications and estimation techniques.
4. The inclusion of oil prices marginally improves model fit (adjusted R2 remains stable, while AIC and BIC increase only
slightly), but also raises the condition number from to over 90, suggesting a potential rise in multicollinearity. This could be due
to shared variance between oil prices and global GDP. Given its relatively weak explanatory power and potential to compromise
model stability, global oil prices are excluded from the preferred specification in favor of parsimony.
Table 5: Robustness Checks of Baseline Model
t
1,t
2,t
3,t
1,t
t
2,t
t
Notes: Significance levels ̸ ̸̸p < , ̸ ̸p < , ̸ p < . “Bootstrap SE” uses bootstrapped standard errors for the baseline model
(1000 resamples). Dashes (–) indicate the variable was not included in that sub-period.
Variable Baseline Alt. 1 Alt. 2 Alt. 3 Bootstrap SE
α ̸̸̸ ̸̸̸ ̸̸̸ ̸̸̸ ̸̸̸
Xauto, pc ̸̸̸ ̸̸̸ ̸̸̸ ̸̸̸ ̸̸̸
Xd
Xd ̸̸̸ ̸̸ ̸̸̸ ̸̸ ̸̸̸
Xd
Xg ̸̸ ̸̸ ̸̸̸ ̸̸ ̸
Dcovid ̸̸̸ ̸̸̸ ̸̸̸ ̸̸̸
Xoil – ̸ – ̸ –
Dgfc – – –
Model Statistics
R-squared
Adj. R-squared
AIC
BIC
F-statistic
Durbin–Watson
Cond. No.
t
1,t
Tests for Sub-sample Periods
To assess the evolving macroeconomic contribution of Thailand’s automotive sector, the base- line
regression is re-estimated across three sub-periods: 2003Q1–2015Q4, 2007Q1–2019Q4, and 2012Q1–
2024Q4. These windows are chosen to capture dynamics before and after key policy and structural inflection
points—including the Global Financial Crisis, domestic policy shifts, and the on- set of the EV transition.
Table 6 presents the coefficient estimates for the full sample alongside those for each sub-period.
Table 6: Regression Results: Full Period and Sub-periods
t
1,t
2,t
3,t
1,t
t
Notes: significance levels ̸̸̸p < , ̸̸p < , ̸p < . Dashes (–) indicate the variable was not included in that sub-period.
The coefficient on the AutoIndex variable (Xauto, pc), which represents the elasticity of real GDP growth
to automotive sector activity, shows a clear downward trend over time. In the earliest sub- period (2003–
2015), the elasticity is estimated at and is significant at the 1% level. This declines modestly to
in the 2007–2019 window, and further to in the 2012–2024 period. All estimates remain significant at
the 1% level. This gradual reduction suggests that while the sector retains macroeconomic influence, its
short-run importance in driving growth has diminished over time.
Several structural explanations may account for this shift. First, Thailand’s ongoing economic
diversification, including growth in services and digital sectors, may have diluted the relative con- tribution
of legacy manufacturing industries to aggregate output. Second, in the latest period, the transition from
internal combustion engine (ICE) vehicles to electric vehicles (EVs) has likely intro- duced temporary
frictions in production, supply chains, and investment cycles. Third, the entry of new players—especially
foreign EV manufacturers—may have altered the sector’s domestic value- added structure.
The sub-period analysis also reveals notable variation in other macroeconomic drivers. Con-
sumer confidence (Xd ), while insignificant in the full sample and early periods, becomes positive
and statistically significant in the post-2012 period, indicating growing sensitivity of household be-
Full Period Sub-periods
Variable 2003–2024 2003–2015 2007–2019 2012–2024
α ̸̸̸ ̸̸ ̸̸̸
Xauto, pc ̸̸̸ ̸̸̸ ̸̸̸ ̸̸̸
Xd ̸ ̸̸̸
Xd ̸̸̸ ̸ ̸̸ ̸̸̸
Xd ̸ ̸ ̸̸̸
Xg ̸̸̸ ̸̸̸ ̸̸̸
Dcovid ̸̸̸ – – ̸̸̸
1,t
t
.
Σ
Σ
havior to sentiment amid economic and technological uncertainty. The household debt-to-GDP ratio
d
2,t ) exerts a consistently negative and increasingly significant influence across all sub-periods—
especially after 2012—highlighting the growing role of financial constraints in shaping near-term
growth. Meanwhile, global GDP growth (Xg ) maintains a positive and significant relationship with
Thai output in the earlier periods, but its influence fades while including more recent periods. Over- all, the
sub-period results underscore a structural moderation in the automotive sector’s short-run macroeconomic
impact.
PCA AutoIndex and Decomposition
The constructed variable of interest, Xauto, pc, is derived using PCA to capture the shared variation across
core growth indicators in Thailand’s automotive sector, as described in the earlier section. As detailed in
Table 7, the PCA loadings, combined with the standard deviations of the underlying indicators, yield
weighted contribution. Notably, the relatively higher weight on production suggests that fluctuations in
output volumes account for a larger portion of the sector’s short-term dynamics.
Table 7: PCA Details and Decomposition for Auto Sector Growth Rates
Component (i) PCA Loading (wi) Std. Dev. (σi) Contriubtion
wi ·σi β
j
wj ·σj
Production (pr)
(X
Sales (sa)
Exports (ex)
t
2,t
3,t
1,t t
t t
EV Transition
The baseline provides the analysis where the transition effects have been part of the changing elasticity of
economic growth to the auto sector acitivities. In this section, a regression based on Equation 2—taking
into account of EV transition seperately—is conducted and the result is shown in Table 8.
The regression results confirm the significant role of the automotive sector in supporting Thai- land’s
short-run economic growth, consistent with the baseline in terms of direction. In terms of value, the
coefficient on the AutoIndex Xauto, pc is bigger, taking the EV transition effect into consid- eration. The EV
interaction term Xauto, pc × Dev is negative and statistically significant, suggesting
that the GDP impact of auto sector activity was partially dampened during the examined transi- tion period,
potentially reflecting adjustment costs, structural reallocation, or transitional uncertainty during the industry
shift. (Appendix Table A3 provides a list of potentail channels).
The control variables behave in line with macroeconomic expectations. Consumer confidence
d
1,t ) is positively associated with growth, while increases in household debt-to-GDP (X
d ) and
monetary tightening (Xd ) reduce GDP growth. Global economic conditions, captured by world
GDP growth (Xg ), exert a significant positive effect. The COVID-19 dummy (Dcovid) is negative
and significant, reflecting the contractionary effects of the pandemic.
The model demonstrates relatively strong statistical performance, with high model explanatory power
and confirming the joint significance of regressors. Residual diagnostics suggest no serious issues: the
Durbin–Watson statistic rules out strong autocorrelation; the Omnibus and Jarque–Bera tests show no
significant deviation from normality; and the condition number of indicates low multicollinearity. The
use of HAC standard errors allowing up to two lags ensures robust inference in the presence of any
autocorrelation or heteroskedasticity.
Table 8: Regression Results with an EV Transition Interaction Term
Variable 2003–2024 2012–2024
t
t t
1,t
2,t
3,t
1,t
t
Notes: Significance levels ̸ ̸̸p < , ̸ ̸p < , ̸ p < . “Bootstrap SE” uses bootstrapped standard errors for the baseline model
(1000 resamples).
(X
HAC Bootstrap HAC Bootstrap
α ̸̸̸ ̸̸̸ ̸̸̸ ̸̸̸
Xauto, pc ̸̸̸ ̸̸̸ ̸̸̸ ̸̸̸
Xauto, pc × Dev ̸̸ ̸ ̸̸ ̸
Xd ̸̸̸ ̸̸̸
Xd ̸̸ ̸̸̸ ̸̸̸ ̸̸̸
Xd ̸̸̸ ̸
Xg ̸̸̸ ̸
Dcovid ̸̸̸ ̸̸̸ ̸̸̸
It is not uncommon for the automotive sector to account for a significant share of GDP. For instance,
Ballew and Schnorbus (1994) shows its impact on the . economy is far bigger than the auto industry’s
share of total GDP. There are also a wide range of estimates on multipliers: the average multiplier in
developed economies is around , and it is higher in automotive-intensive countries such as Germany,
Japan, and South Korea, where average multipliers are approximately 3 (Saberi 2018). In the ., estimates
suggest an upper bound of (Alliance for Automotive Innovation 2025). These variations in multiplier
effects are typically driven by structural factors such as supply chain complexity and domestic value
addition, export intensity, and R&D investment, in which Thailand may exhibit relatively lower levels of
sophistication compared to its peers.
4 Discussions
Thailand’s automotive sector remains macro-critical, contributing significantly to aggregate GDP through
output, investment, and trade. Econometric analysis confirms a positive and statistically robust relationship
between activity in the sector and real GDP growth. However, this elasticity has declined over time—
notably during the EV transition period.
The inclusion of an interaction term in the regression confirms that the sector’s short-run growth
contribution is significantly lower during this transition phase. This result is consistent across spec-
ifications and signals a structural recalibration of the sector’s macroeconomic role. The weakening elasticity
likely reflects transitional frictions related to evolving technologies—such as production retooling, supply
chain downsizing and realignment, and the need to adapt infrastructure to new standards.
At the heart of this transformation is the shift from ICE to battery technologies, which are me-
chanically simpler, resulting in vehicles being less reliant on traditional tier-2 and tier-3 suppliers. These
structural characteristics have weakened domestic value-added linkages and contributed to production
dislocations—particularly among SMEs and workers with skills tied to legacy technolo- gies.
That said, Given the nature of the transition, this disruption should be temporary, however. The short-
run pain is also in part a function of the acceleration driven by the EV incentive packages. These programs
have been successful in drawing FDI at a faster pace, particularly from Chinese automakers, which has also
intensified near-term adjustment pressures on existing firms and work- ers. If managed poorly, these
pressures risk undermining the sector’s contribution to growth in the short run.
However, delaying the transition would be far costlier. In a rapidly evolving global market, the time
window to establish a competitive EV ecosystem is closing quickly. A failure to act decisively could lead to
the permanent loss of FDI and supply chain opportunities to more proactive regional peers, potentially
resulting in a missed opportunity to the opportunities to secure the industry’s long- term viability.
There are important implications for industrial and labor policy as well. While EV and have
been effective in initiating and accelerating the transition, a more balanced and inclusive policy framework is
needed to mitigate adverse side effects. Labor market disruptions and SME vulner- abilities must be
addressed. Reskilling programs should be rapidly scaled to support displaced workers, while tailored
financing and technical assistance can help SMEs adapt to new production technologies and standards.
To ensure long-term competitiveness, Thailand could pursue a strategic and coordinated pol- icy
agenda that links industrial upgrading with inclusive adjustment. This includes actively incen-
tivizing domestic value creation in high-value segments such as batteries, power electronics, and
automotive software—through joint ventures, upstream supplier development, and expanded R&D
investment. At the same time, broader policy alignment is essential. Priority actions include scaling
vocational and digital training, accelerating EV infrastructure deployment, and fostering innovation
ecosystems that can anchor long-term productivity growth. Complementary fiscal measures—such as
targeted green subsidies, public investment, and counter-cyclical support for affected workers and SMEs—
will be critical to cushion near-term adjustment costs and crowd in private capital.
The EV transition marks a strategic inflection point. With timely, coordinated, and inclusive policies,
Thailand can position itself as a competitive and sustainable EV hub in Asia. The challenge is not merely to
adapt, but to capture a rapidly closing window of opportunity. Decisive action today will determine whether
the sector regains momentum and evolves into a higher value-added growth engine—or risks stagnation amid
global realignment.
5 Conclusion
In sum, Thailand’s EV transition is not only a technological or industrial shift—it is a structural recon-
figuration with macroeconomic consequences. While near-term frictions are evident, the long-term gains
from securing investment, deepening value chains, and sustaining export competitiveness should not be
underestimated. The challenge lies in managing the transition holistically—minimizing dislocations while
enabling transformation. Policymakers should act decisively to anchor investor confidence, support labor
and SMEs, and position Thailand at the forefront of Asia’s green and sus- tainable industrial future. The
success of this transition will ultimately depend on how swiftly and strategically Thailand turns disruption
into opportunity.
A Appendix: Tables
Table A1: Variance Inflation Factor (VIF) Diagnostics
Variable VIF
α
auto, pc
t
d
1,t
d
2,t
d
3,t
g
1,t
covid
t
Notes: VIF values of above 5 are generally considered indicative of
multicollinearity issues.
X
X
X
X
X
D
Table A2: Ramsey RESET Test for Model Specification
Test Result
F-statistic
p-value
Degrees of Freedom dfnum = 2, dfdenom = 77
Regression
Yt = α+βXauto+Γ′Xd+∆′Xo+γ1Ŷ
2+γ2Ŷ
3+εt. (3)
t t t t t
where Ŷt are fitted values from the original model.
Hypotheses:
H0 : γ1 = γ2 = 0 (model correctly specified)
Ha : At least one γi ̸ = 0 (model misspecified)
Table A3: Potential Channels of EV Transition Effects
Category Effects
Cons + lower operating and maintenance costs
+ enhanced user experience
+ reduced emissions and environmental benefits
– increased strain on electricity grid and power infrastructure
Investment + greenfield investments in plants, batteries, and charging infrastructure
/Production + crowding-in of private capital
+ shift toward higher value-added production
– high transition and adjustment costs for firms
– financial vulnerability for smaller or leveraged firms
– short-term output disruption as ICE production declines
– stranded assets and underutilized legacy plants
– price war if EV production needs to meet government targets
Labor + new job creation in EV and battery manufacturing
+ opportunities for reskilling and upskilling
– job losses in ICE-related segments
– skills mismatch and regional displacement
BOP + increased export opportunities for EVs and components, replacing ICEVs
+ reduced oil imports dependence
– rising imports of batteries and/or critical materials
– exposure to critical mineral price volatility
Fiscal + potential revenue gains from EV-related industries
– declining fuel tax revenues and ICE-realated revenue
– high fiscal burden from subsidies and infrastructure investment
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