## REINVIGORATING THAILAND’S STRUCTURAL TRANSFORMATION

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### A. Background
- Structural transformation: reallocation of labor from lower- to higher-productivity sectors; typical pattern—agriculture share declines, industry rises then stabilizes, services expand.
- Thailand’s observed pattern:
  - Approximately one-third of Thai workers are still employed in agriculture.
  - Employment in industry and services is relatively low for Thailand’s income level.
  - Within services, a disproportionately large share of workers are in low-productivity services (trade services and other services); employment in high-productivity services (finance and business services) remains relatively low.
- Imbalance between sectoral labor and output shares:
  - Industry and high-productivity services account for the majority of value-added.
  - Low-productivity services and agriculture contribute substantially less to value-added but employ larger shares of labor.
  - Result: a wide and growing labor productivity gap across sectors.
- Implication: scope for productivity gains by reallocating labor toward higher-productivity sectors to achieve stronger growth.

### B. Assessing Thailand’s Structural Transformation
- Measurement approach:
  - Structural transformation quantified using covariance between sectoral employment share (s_i) and sectoral labor productivity (p_i), scaled by aggregate labor productivity.
  - Covariance equals aggregate labor productivity (P) minus simple average of sectoral productivity (P̄).
  - Covariance computed for both 3-sector classification (agriculture, industry, services) and 4-sector classification (agriculture, industry, low-productivity services, high-productivity services).
- Data:
  - UNU-Wider Economic Transformation Database (ETD): annual data on 12 disaggregated sectors across 51 economies from 1990 to 2018.
  - Sectoral labor productivity measured as value-added per worker; employment and value-added shares computed as each sector’s share of totals.
- Evolution and cross-country context (findings):
  - Most Asian countries exhibit negative covariance values historically (less productive sectors employ disproportionate labor), but values have improved over time.
  - Korea and Malaysia: covariance close to or above zero.
  - Chinese mainland: substantial improvement, approaching Korea and Malaysia levels.
  - Vietnam: most dramatic gains.
  - Thailand: progress in early 1990s, but momentum slowed since mid-2010s; covariance plateaued and declined slightly in recent years.
- Sectoral disaggregation sensitivity:
  - Using 3-sector classification, Thailand’s covariance appears relatively high—comparable to Malaysia.
  - Using 4-sector classification, Thailand’s labor allocation efficiency is significantly lower—below the Philippines and Indonesia, and close to Vietnam—reflecting imbalance within services between high- and low-productivity segments.

### C. Drivers of Structural Transformation (Empirical Results)
- Empirical specification:
  - Dependent variable: scaled covariance term.
  - Estimator: fixed effects with year dummies.
  - Controls and regressors: log GDP per capita (constant PPP), (log GDP per capita)^2, log population, old-age dependency ratio, average years of schooling (UNDP), Human Capital Index (Penn World Tables), trade-to-GDP ratio, FDI flows (% of GDP), terms of trade index, real effective exchange rate (REER), agricultural raw materials exports as share of merchandise exports, vulnerable employment share, domestic credit to private sector (% of GDP), Political Corruption Index (rescaled by ×100).
  - Policy robustness checks: Labor Market Regulations index (2024 Fraser Economic Freedom of the World), Gini Index (WIID), internet access share, enrollment ratios (primary, secondary, tertiary), Rule of Law indicator (WBI).
- Key regression findings (across specifications):
  - Log GDP per capita: strongly and positively correlated with covariance.
  - (Log GDP per capita)^2: negatively correlated (improvement with income is diminishing).
  - Log population: strongly positive.
  - Old-age dependency ratio: positive correlation.
  - Trade openness and terms of trade: associated with higher covariance.
  - REER appreciation: associated with lower covariance.
  - FDI inflows: not statistically significant.
  - Average years of schooling: positive and statistically significant.
  - Human Capital Index: not statistically significant.
  - Vulnerable employment (informality): negatively correlated (statistical significance in some specifications).
  - Agricultural export share: negatively associated with covariance.
  - Private credit-to-GDP ratio: positively correlated.
  - Political Corruption Index (higher corruption): negatively associated with covariance.
  - Labor Market Regulations (alternative specification): improved regulation associated with higher covariance.
  - Education enrollment breakdown: primary and secondary enrollment more strongly correlated with covariance than tertiary enrollment.
  - Internet access: positive coefficient but not statistically significant.
  - Rule of Law (alternative governance measure): stronger rule of law positively associated with structural transformation.

### D. Understanding the Recent Slowdown
- Method:
  - Fitted values from estimated model compared to actual covariance to identify drivers of slowdown.
  - Changes in each driver between 2010-2014 and 2014-2018 multiplied by estimated coefficients to compute contributions.
- Observed shifts between 2010-2014 and 2014-2018:
  - Private sector credit growth slowed markedly in latter period.
  - Progress in trade openness reversed.
  - REER depreciated.
  - Share of vulnerable employment declined.
  - Terms of trade improved.
  - Acceleration in old-age-dependency ratio.
  - Reversal in FDI growth.
  - Slower growth in agricultural export share.
  - More moderate increase in average years of schooling.
  - Deterioration in governance (higher corruption).
  - Slower population growth.
- Net effects on Thailand’s covariance:
  - Factors that lowered covariance (slowed structural transformation):
    - Slower private sector credit expansion.
    - Weaker rise in average years of schooling.
    - Decelerating population growth.
    - Reduced trade openness.
    - Deteriorating governance (higher corruption).
    - Reversal in FDI growth.
    - Moderate agricultural export share effects.
  - Factors that mitigated slowdown (supported transformation):
    - Population aging.
    - Decline in vulnerable employment.
    - Improved terms of trade.
    - REER depreciation.
  - Overall conclusion: increasing downward pressures on the covariance term since mid-2010s, leading to plateauing and slight decline in recent years.

### E. Policy Implications and Priorities
- High-level policy directions derived from empirical drivers:
  - Strengthen human capital accumulation, emphasizing primary and secondary education (average years of schooling positively linked to covariance).
  - Improve financial access and expand private sector credit to facilitate reallocation and investment (private credit-to-GDP ratio positively linked).
  - Promote trade openness and favorable terms of trade to support expansion of higher-productivity tradable sectors.
  - Enhance governance and reduce corruption; strengthen rule of law (corruption negatively associated with covariance).
  - Address labor market informality to reduce vulnerable employment (vulnerable employment negatively associated).
  - Attract and channel FDI toward sectors that support higher-productivity employment, recognizing heterogeneity in FDI effects.
  - Monitor exchange rate competitiveness (REER appreciation linked to lower covariance).
  - Target within-services policies to raise productivity and labor absorption in high-productivity activities given low share of high-productivity services employment.
- Policy priorities listed:
  - Accelerate trade liberalization, including deeper integration into global and regional markets.
  - Invest in education, particularly at the primary and secondary levels.
  - Enhance financial inclusion and expand access to credit for productive investments and SMEs.
  - Reduce corruption and enhance the rule of law.
  - Reduce informality through expanded social insurance coverage.
  - Improve labor market flexibility by streamlining hiring and dismissal procedures and clarifying labor regulations.
  - Reinforce reforms with complementary measures to broaden financial access for firms and foster a more diversified financial structure to improve capital allocation efficiency.
- Lessons from regional peers (highlights):
  - Korea: human capital investment; average years of schooling increased from 5.4 in 1970 to 12.7 in 2023; STEM and vocational advances; Employment Insurance System support.
  - Malaysia: Bank Negara Malaysia’s Financial Inclusion Framework (2023–2026) with targets for subdistrict access, e-payments, SME financing, microinsurance/microtakaful, and insurance/takaful penetration; digital banks and DuitNow platform expanded access.
  - Vietnam: strong integration into global trade after Đổi Mới; Foreign Investment Law, WTO membership, 17 Free Trade Agreements in effect; ambitious restructuring and education improvements with persistent skill mismatches.
- Investment efficiency note:
  - Investment efficiency declined in Thailand after the global financial crisis, reflected in the high and rising incremental capital–output ratio (IMF, 2025a).
- Conclusions:
  - Thailand’s structural transformation has slowed but remains incomplete.
  - With targeted and well-coordinated policies that promote labor movement toward its most productive uses, Thailand can unlock substantial productivity gains, which would provide a much-needed boost to its economic growth.

*Source: INTERNATIONAL MONETARY FUND, January 26, 2026 — "Reinvigorating Thailand’s Structural Transformation."*

### 1. Asian Countries: Income and Sectoral Employment Shares ___________________________ 2

### REINVIGORATING THAILAND’S STRUCTURAL TRANSFORMATION

### A. Background
- Structural transformation defined as reallocation of labor from lower- to higher-productivity sectors; typical pattern: agriculture share declines, industry rises then stabilizes, services expand.
- Thailand’s pattern:
  - Approximately one-third of Thai workers are still employed in agriculture.
  - Employment in industry and services is relatively low for Thailand’s income level.
  - Within services, a disproportionately large share of workers are in low-productivity services (trade services and other services); employment in high-productivity services (finance and business services) remains relatively low.
- Imbalance between sectoral labor and output shares:
  - Industry and high-productivity services account for the majority of value-added.
  - Low-productivity services and agriculture contribute substantially less to value-added but employ larger shares of labor.
  - Result: a wide and growing labor productivity gap across sectors.
- Implication: Scope for productivity gains by reallocating labor toward higher-productivity sectors to achieve stronger growth.

### B. Assessing Thailand’s Structural Transformation
- Measurement approach:
  - Structural transformation quantified using covariance between sectoral employment share (s_i) and sectoral labor productivity (p_i), scaled by aggregate labor productivity.
  - Covariance equals aggregate labor productivity (P) minus simple average of sectoral productivity (P̄).
  - Covariance computed for both 3-sector classification (agriculture, industry, services) and 4-sector classification (agriculture, industry, low-productivity services, high-productivity services).
- Data:
  - UNU-Wider Economic Transformation Database (ETD): annual data on 12 disaggregated sectors across 51 economies from 1990 to 2018.
  - Sectoral labor productivity measured as value-added per worker; employment and value-added shares computed as each sector’s share of totals.
- Evolution and cross-country context:
  - Most Asian countries exhibit negative covariance values historically (less productive sectors employ disproportionate labor), but values have improved over time.
  - Korea and Malaysia: covariance close to or above zero.
  - Chinese mainland: substantial improvement, approaching Korea and Malaysia levels.
  - Vietnam: most dramatic gains.
  - Thailand: progress in early 1990s, but momentum slowed since mid-2010s; covariance plateaued and declined slightly in recent years.
- Sectoral disaggregation sensitivity:
  - Using 3-sector classification, Thailand’s covariance appears relatively high—comparable to Malaysia.
  - Using 4-sector classification, Thailand’s labor allocation efficiency is significantly lower—below the Philippines and Indonesia, and close to Vietnam—reflecting imbalance within services between high- and low-productivity segments.

### C. Drivers of Structural Transformation (Empirical Results)
- Empirical specification:
  - Dependent variable: scaled covariance term.
  - Estimator: fixed effects with year dummies.
  - Country fundamentals: log GDP per capita (constant PPP), (log GDP per capita)^2, log population, old-age dependency ratio.
  - Human capital: average years of schooling (UNDP) and Human Capital Index (Penn World Tables).
  - Trade and investment: trade-to-GDP ratio, FDI flows (% of GDP), terms of trade index, real effective exchange rate (REER).
  - Other controls: agricultural raw materials exports as share of merchandise exports, vulnerable employment share, domestic credit to private sector (% of GDP), Political Corruption Index (rescaled by ×100).
  - Policy robustness checks: Labor Market Regulations index (2024 Fraser Economic Freedom of the World), Gini Index (WIID), internet access share, enrollment ratios (primary, secondary, tertiary), Rule of Law indicator (WBI).
- Key regression findings (across specifications):
  - Log GDP per capita: strongly and positively correlated with covariance.
  - (Log GDP per capita)^2: negatively correlated (improvement with income is diminishing).
  - Log population: strongly positive.
  - Old-age dependency ratio: positive correlation.
  - Trade openness and terms of trade: associated with higher covariance.
  - REER appreciation: associated with lower covariance.
  - FDI inflows: not statistically significant.
  - Average years of schooling: positive and statistically significant.
  - Human Capital Index: not statistically significant.
  - Vulnerable employment (informality): negatively correlated (statistical significance in some specifications).
  - Agricultural export share: negatively associated with covariance.
  - Private credit-to-GDP ratio: positively correlated.
  - Political Corruption Index (higher corruption): negatively associated with covariance.
  - Labor Market Regulations (alternative specification): improved regulation associated with higher covariance.
  - Education enrollment breakdown: primary and secondary enrollment more strongly correlated with covariance than tertiary enrollment.
  - Internet access: positive coefficient but not statistically significant.
  - Rule of Law (alternative governance measure): stronger rule of law positively associated with structural transformation.

### D. Understanding the Recent Slowdown
- Method:
  - Fitted values from estimated model compared to actual covariance to identify drivers of slowdown.
  - Changes in each driver between 2010-2014 and 2014-2018 multiplied by estimated coefficients to compute contributions.
- Observed shifts between 2010-2014 and 2014-2018:
  - Private sector credit growth slowed markedly in latter period.
  - Progress in trade openness reversed.
  - REER depreciated.
  - Share of vulnerable employment declined.
  - Terms of trade improved.
  - Acceleration in old-age-dependency ratio.
  - Reversal in FDI growth.
  - Slower growth in agricultural export share.
  - More moderate increase in average years of schooling.
  - Deterioration in governance (higher corruption).
  - Slower population growth.
- Net effects on Thailand’s covariance:
  - Factors that lowered covariance (slowed structural transformation): slower private sector credit expansion; weaker rise in average years of schooling; decelerating population growth; reduced trade openness; deteriorating governance (higher corruption); reversal in FDI growth; moderate agricultural export share effects.
  - Factors that mitigated slowdown (supported transformation): population aging; decline in vulnerable employment; improved terms of trade; REER depreciation.
  - Overall: increasing downward pressures on the covariance term since mid-2010s, leading to plateauing and slight decline in recent years.

### E. Policy Implications (high-level syntheses from analysis)
- Policies to reinvigorate structural transformation should target the empirically identified drivers:
  - Strengthen human capital accumulation, emphasizing primary and secondary education (average years of schooling positively linked to covariance).
  - Improve financial access and expand private sector credit to facilitate reallocation and investment (private credit-to-GDP ratio positively linked).
  - Promote trade openness and favorable terms of trade to support expansion of higher-productivity tradable sectors.
  - Enhance governance and reduce corruption (corruption negatively associated with covariance); strengthen rule of law.
  - Address labor market informality to reduce vulnerable employment (vulnerable employment negatively associated).
  - Consider policies to attract and channel FDI toward sectors that support higher-productivity employment, recognizing heterogeneity in FDI effects.
  - Monitor exchange rate competitiveness (REER appreciation linked to lower covariance).
- Note: Sectoral and within-services imbalances (low share of high-productivity services employment) imply targeted policies within services to raise productivity and labor absorption in high-productivity activities.

*Source: INTERNATIONAL MONETARY FUND, January 26, 2026 — "Reinvigorating Thailand’s Structural Transformation."*

### 18.      The analysis highlights several policy priorities to promote structural transformation.

### sipea2026012 - 18.      The analysis highlights several policy priorities to promote structural transformation.

### Policy priorities to promote structural transformation
- Accelerate trade liberalization, including deeper integration into global and regional markets, to facilitate reallocation of labor toward more productive sectors.
- Invest in education, particularly at the primary and secondary levels, to build a workforce capable of transitioning to higher-productivity employment.
- Enhance financial inclusion and expand access to credit for productive investments to broaden opportunities and improve labor allocation efficiency.
- Reduce corruption and enhance the rule of law to create a predictable environment conducive to efficient labor reallocation.
- Reduce informality through expanded social insurance coverage to support the integration of informal workers into formal, higher-productivity sectors.
- Improve labor market flexibility by streamlining hiring and dismissal procedures and clarifying labor regulations to lower barriers to formal employment growth.
- Reinforce reforms with complementary measures to broaden financial access for firms and foster a more diversified financial structure to improve capital allocation efficiency, thereby further boosting productivity gains.
- Policy emphasis: advancing trade liberalization, investing in education and skills development, enhancing financial inclusion, and improving access to credit are key priorities to reinvigorate structural transformation.

### Lessons from regional peers
- Korea
  - Human capital investment has underpinned Korea’s growth strategy.
  - Average years of schooling increased from 5.4 in 1970 to 12.7 in 2023.
  - STEM capabilities were enhanced through flagship research institutions and targeted R&D initiatives, such as the Brain Korea 21 program.
  - Vocational training strengthened through Meister high schools, co-designed with industry partners.
  - Support mechanisms include the Employment Insurance System for workers adapting to technological change and industrial restructuring.
- Malaysia
  - Bank Negara Malaysia’s multi-year strategic frameworks promote financial inclusion with clear objectives and measurable targets under the Financial Inclusion Framework (2023–2026).
  - Key goals include expanding financial access to all subdistricts, increasing e-payment transactions, enhancing SME financing ecosystem, doubling microinsurance/microtakaful subscriptions, and raising insurance/takaful penetration.
  - Nearly all subdistricts now have access to basic financial services via agent banking and mobile banks.
  - Operationalization of digital banks and launch of the DuitNow real-time retail payment platform have broadened financial access for low-income households and micro-enterprises.
  - Framework emphasizes financial literacy, digital onboarding, inclusive product innovation, vulnerable groups, and gender equity.
- Vietnam
  - Structural transformation propelled by strong integration into global trade following Đổi Mới reforms.
  - Government enacted the Foreign Investment Law, joined the WTO, and signed 17 Free Trade Agreements in effect, enabling strong participation in global value chains and sustained investment inflows.
  - Authorities launched an ambitious reform agenda including significant restructuring of government organizations to streamline operations and enhance efficiency (implementation remains key).
  - Continued efforts in basic education have built human capital, though persistent skill mismatches highlight the need for better vocational training and upskilling.

### Drivers and constraints of Thailand’s structural transformation (findings)
- Structural transformation is associated with structural factors such as income levels and demographic trends, and policy-related factors in trade, education, credit access, governance, and labor markets.
- Recent supportive factors:
  - A decline in informal employment.
  - More favorable terms of trade supporting labor reallocation toward more productive sectors.
- Constraining factors contributing to the slowdown:
  - Deceleration in credit provision.
  - Limited progress in educational attainment.
  - Reversal in trade liberalization.
  - Deterioration in governance.
- Investment efficiency note: Investment efficiency declined in Thailand after the global financial crisis, similar to other countries in the region, reflected in the high and rising incremental capital–output ratio (IMF, 2025a).

### Policy measures recommended
- Advance trade liberalization and deeper integration into global and regional markets.
- Invest in education and skills development at primary and secondary levels and strengthen vocational training and upskilling to address skill mismatches.
- Enhance financial inclusion and broaden access to credit, especially for productive investments and SMEs.
- Reduce labor informality via expanded social insurance coverage to ease transition to formal employment.
- Improve labor market flexibility by streamlining hiring and dismissal procedures and clarifying labor regulations.
- Strengthen governance and reduce corruption to create a predictable environment for investment and labor reallocation.
- Complement labor and governance reforms with measures to broaden financial access for firms and diversify the financial structure to improve capital allocation efficiency and productivity gains.

### Conclusions
- Thailand’s structural transformation has slowed but remains incomplete.
- With targeted and well-coordinated policies that promote labor movement toward its most productive uses, Thailand can unlock substantial productivity gains, which would provide a much-needed boost to its economic growth.

*Source: sipea2026012 - 18. The analysis highlights several policy priorities to promote structural transformation. (PDF chapter).*

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_Source: https://www.imf.org/-/media/files/publications/selected-issues-papers/2026/english/sipea2026012.pdf_