## _wp1551 - Section VI–VII model, evidence, and policy implications

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---

### Model purpose and core insights
- Introduces a simple two-sector model (agriculture vs manufacturing) to analyze how government policies affect factor distribution across sectors.
- Key equilibrium properties and assumptions:
  - Fixed uniform labor endowment; workers choose between agriculture (A) and manufacturing (M); free movement implies equal wage rate in equilibrium.
  - Competitive firms produce internationally traded goods; domestic prices equal international prices in absence of trade restrictions.
  - Equilibrium wage equals value marginal product of labor (VMPL = MPL × P) in each sector; VMPLs equalized across sectors.
  - Social planner optimum coincides with market outcome in an open economy: equalization of VMPLs maximizes value at world prices.
- Policy distortions illustrated:
  - Guaranteed above-world agricultural price (P”A > P’A) raises VMPLA, increases agricultural employment (L”A > L’A), lowers agricultural marginal and average productivity; welfare loss equals triangle CDE; taxpayer cost equals trapezoid ABCD.
  - Minimum wage applied only to formal sector (manufacturing) set above market wage limits manufacturing employment and enlarges agriculture.
- Dynamic externalities:
  - If manufacturing exhibits learning-by-doing externalities (sector-wide productivity depends on total manufacturing output), reduced manufacturing scale lowers both current average productivity and its growth rate.
- Caveats:
  - If manufacturing’s diminishing returns are much steeper than agriculture’s, allocation gains from reallocation could be small.
  - Uniform labor skill assumption is a simplification; imperfect substitutability and intrinsic sector preferences can alter allocations but price and wage interventions still tilt outcomes.

### Data, measurement, and datasets used
- Main datasets and vintage:
  - World Bank’s World Development Indicators (WDI): for most countries the last data point is 2012.
  - McMillan-Rodrik dataset: more detailed sectoral breakdown, more limited country coverage, ends in 2005.
  - Thailand national data: National Statistics Office (NSO) on employment and National Economic and Social Development Board (NESDB) on output (most up-to-date).
- Measurement choices and limitations:
  - Productivity examined both in constant prices (for growth) and current prices (for level comparisons).
  - Analysis largely limited to output per worker because working hours per sector are not available in cross-country datasets.
  - National vs cross-country series differ by a few percentage points; all series report similar agricultural shares in the last few years and show recent leveling.
- Proxy for hours worked:
  - Using NSO hours-range data and assumptions about distributions reduces agriculture’s employment share by about 3 percentage points and lowers the productivity gap between industry and agriculture by 10–15 percent.

### How Thailand compares with other economies — productivity gaps and employment shares
- Productivity patterns and cross-country comparisons:
  - Countries with higher overall productivity tend to have higher productivity in agriculture, industry, and services.
  - Thailand’s productivity gap with more advanced countries is particularly large in agriculture.
  - Value added per agricultural worker in Thailand is close to that in China, Indonesia and the Philippines; industry and services workers in Thailand are noticeably more productive than in India, Indonesia, the Philippines and Vietnam.
- Cross-sector dispersion:
  - Industrial workers are the most productive and agricultural workers the least productive in every economy considered.
  - Weighted coefficient of variation (WCV) across nine featured economies (Japan, Korea, Malaysia, China, Thailand, Indonesia, Philippines, India, Vietnam) — reported values (in the order Japan, Korea, Malaysia, Thailand, Indonesia, Philippines, China, India, Vietnam):
    - 0.14, 0.37, 0.28, 0.66, 0.65, 0.54, 0.54, 0.69, 0.58
- Employment shares and outlier status:
  - More advanced economies have lower fractions of workforce in agriculture.
  - Thailand appears to have a disproportionately large share of rural employment given its income level; agriculture is the largest employer in Thailand (WDI data for 2012).
  - Thailand’s agricultural employment share far exceeds what would be expected for its level of income and has moved broadly parallel to the typical trajectory since the late 1990s, with the distance increasing in recent years.
  - For broad middle-income country comparisons (WDI), Thailand is one of the extreme cases in agricultural employment share.

### Aggregate implications and quantified illustration
- Stylized measures:
  - Scaled covariance (captures efficiency of labor allocation) shows Thailand improved before the Asian financial crisis, slowed in the subsequent decade, and had little change in the last five years; Thailand’s efficiency is on par with India, Indonesia, the Philippines and Vietnam and considerably below China, Japan, Korea, and Malaysia.
- Key summarized facts:
  - (i) The dispersion of labor productivity across sectors in Thailand is high compared with many Asian economies.
  - (ii) Labor productivity in agriculture relative to the rest of the Thai economy is very low.
  - (iii) Agriculture’s share of employment is considerably higher than usual for a country at Thailand’s income level.
  - (iv) Little movement toward more typical values in recent years.
- Numerical illustration of potential gains:
  - Reducing the agricultural share to 22 percent—the value consistent with Thailand’s level of income—by shifting some farm workers into industry and services (in proportion to the relative size of those two sectors) while maintaining Thailand’s productivity in each individual sector would raise Thailand’s economy-wide productivity by 20 percent.

### More detailed sectoral breakdown (McMillan-Rodrik dataset) — seven-sector findings
- Dataset sectorization (original nine sectors reduced to seven by removing mining and public utilities):
  - Ag (agriculture, forestry, hunting and fishing), Manuf (manufacturing), Constr (construction), Trade (wholesale & retail trade plus hotels & restaurants), Transp (transportation, storage, and communication), FIRE (finance, insurance, real estate and business services), Gov (community, social, personal, and government services).
- Findings:
  - On a seven-sector scaled covariance measure, Thailand shows a noticeable gap versus the bulk of advanced and emerging market economies; pace of efficiency gains slowed after the Asian financial crisis.
  - Visual decomposition (Thailand 2003 vs 2013 and Malaysia 2003) shows:
    - Wide dispersion of sectoral productivity in Thailand—much wider than in neighboring Malaysia.
    - A very large proportion of the population engaged in the lowest-productivity sector (agriculture).
    - Stagnation: agriculture share did not change between 2003 and 2013; construction expanded a bit; manufacturing shrank; finance grew marginally as a fraction of employment.
  - In nominal terms agriculture’s relative standing improved due to higher agricultural prices rather than productivity gains; in real terms the distribution widened with high-performing sectors improving and lagging ones deteriorating.

### Dynamics: within-sector growth vs reallocation (Thailand, 1995–2013)
- Sectoral trends:
  - Manufacturing is the key driver of economic growth; trends in agriculture and services look fairly flat.
  - Between 2000 and 2013:
    - Productivity grew more than twice as fast in manufacturing as in agriculture or services.
    - Manufacturing productivity grew almost ten times as fast as in other industry (mining, utilities, construction).
  - Employment shifts:
    - Manufacturing’s employment share remained roughly flat over the last 15 years.
    - After some shift from agriculture to services, employment shares have been quite stable since 2005.
- Decomposition (cumulative labor productivity growth since 2000) — three components:
  - Intrasectoral productivity growth (largest contributor).
  - Intersectoral labor reallocation (very small contribution since mid-2000s).
  - Dynamic interaction (small, turned negative in 2008−12 mainly due to manufacturing employment falling below base-year levels while manufacturing productivity remained higher).
- Conclusion: bulk of productivity increase since 2000—and almost all since the mid-2000s—comes from intrasectoral growth (primarily manufacturing), with little contribution from structural transformation.

### Causes of Thailand’s large agricultural employment and productivity dispersion
- Rejection of simple comparative advantage explanation:
  - Thailand is inefficient in producing agricultural commodities relative to its other sectors; productivity in agriculture is far lower than elsewhere in the economy.
- Factors maintaining large agricultural employment:
  - Agriculture as residual employer: people who lose jobs in other sectors often move to farms rather than remain unemployed; some retirees take up farming; agriculture acts as an informal safety net.
  - Human capital barriers:
    - Agricultural workers have lower educational attainment than those in manufacturing.
    - Rural instruction quality tends to be lower; Thailand ranks number 90 in the world in terms of education quality (World Economic Forum, 2014).
    - Employer surveys indicate shortages of both basic and technical skills, but also a shortage of unskilled production workers—implying many farm workers could perform factory or service jobs, likely at higher value added than on farms.
  - Information, amenities and preferences:
    - Job information may be available (provincial information centers, online), but distance, access and ability to process information can limit utilization.
    - Urban deterrents: higher cost of living, traffic, pollution; cultural preferences and older age profile in agriculture may reduce mobility.
  - International migration:
    - Thailand hosts about one and a half million registered migrant workers and reportedly an even larger number of unregistered migrants.
    - IOM study reports migrants’ sectoral distribution: 15 percent fishing/seafood processing, 17 percent agriculture, 17 percent construction, 8 percent domestic employment, 43 percent other businesses.
    - Migrants generally have lower education, skill level, and productivity than average Thai workers.
  - Price dynamics and government policies:
    - Global food price increases in the 2000s raised returns to farming and likely drew more workers into agriculture.
    - Policy interventions:
      - Rice pledging scheme launched October 2011 guaranteed rice farmers a price considerably higher than world market price and incentivized expansion among four million rice farmer households; predecessor programs (including pledge values below 2011 but above market) operated in the 2000s; 2009–2011 income guarantee program paid people for staying on farms without incentivizing expansion as strongly.
      - Price support policies also operated for rubber and sugar; rubber support introduced after rubber farmers demanded equal treatment with rice farmers; sugar producers have been insured against price declines for many years.
    - Minimum wage:
      - Minimum wage increases in 2012−13 (a sharp two-step increase) may have contributed to recent uptick in agricultural employment, as the wage is enforced more strictly in the formal sector.
      - World Economic Forum competitiveness ranking on labor market flexibility: 44 in 2011 → 120 in 2013 → 113 in 2014.
  - Measurement issues:
    - Paavo and Poapongsakorn (2012) argue agricultural employment is overstated due to irregular/seasonal work, misreporting, and limited hours worked by many classified as farmers; after adjustments they estimate actual active full-time farmers at most 27−32 percent of total workforce vs official 38−39 percent.
    - Authors believe Paavo and Poapongsakorn may overstate the bias: Labor Force Survey is monthly and should capture seasonal patterns; even the low end of the adjusted range is about 5 percentage points higher than typical for a middle-income Asian economy.
    - Migrant worker recording uncertainty: if migrant output is recorded better than input, measured productivity could be biased upward; migrants appear fairly widely spread across sectors.

### Policy implications and recommendations
- Core recommendation:
  - Facilitate worker shift from agriculture to more modern sectors to realize sizable productivity gains.
- Remove agricultural price supports:
  - Abolish guaranteed above-market price schemes (e.g., rice pledging scheme) which reward low-productivity activity and draw workers into agriculture.
  - Recent actions: rice pledging scheme abolished; price support for rubber ended.
- Replace price support with better-targeted safety nets:
  - Facilitate development of price insurance markets rather than above-market price guarantees.
  - Provide broad social safety nets (including conditional cash transfer programs) not linked to residence or occupation—protect workers, not jobs.
- Foster transition to modern employment:
  - Improve education and training, especially in rural areas: modernize curriculum and teaching techniques, emphasize math, sciences, foreign language, creative thinking; align vocational training with employer needs.
  - Improve information dissemination about urban job opportunities and reduce frictions to mobility.
  - Facilitate hiring of newcomers in the formal sector:
    - Consider waiving or reducing minimum wage for apprentices.
    - Offer temporary tax breaks or other incentives for firms hiring and training newcomers.
    - Encourage private schemes (business associations) to subsidize on-the-job training.
- Increase productivity and opportunities in rural areas:
  - Improve agricultural productivity via efficient, incentive-based water management; improved agricultural research and knowledge transfer; reform land sales and tenancy laws to facilitate exit and consolidation of land holdings.
  - Promote agro-manufacturing and food processing to move up the value chain and allow agriculture to develop intensively rather than extensively.
  - Invest in infrastructure (power, transportation, communication) to connect all parts of Thailand to the modern economy and reduce regional disparities.
- Implementation caveats:
  - Support for agricultural workers should be through productivity-enhancing measures and safety nets rather than occupation- or location-tied subsidies.
  - Policies combining incentives, information, training, accommodative urban conditions, and broad safety nets can realize gains without social strain.

### Conclusions — empirical findings and policy summary
- Empirical conclusions:
  - Thailand exhibits an exceptionally high dispersion of productivity across sectors and an exceptionally large share of population engaged in agriculture for its income level.
  - Agricultural sector serves as employer of last resort, cushioning shocks, but mostly signals sizable potential gains from reallocating labor into nonagricultural activities.
  - Structural transformation has slowed since the 2000s; broad food price increases and government policies (rice pledging and predecessor programs, some price supports) likely contributed to the slowdown and recent uptick in agricultural employment.
- Policy summary:
  - Facilitate structural transformation by removing agricultural price supports; modernizing and targeting education and vocational training (especially in rural areas); improving information dissemination and mobility; easing formal-sector hiring of newcomers (including apprenticeship wage policies or incentives); and increasing infrastructure investment.
  - Simultaneously pursue measures to raise agricultural productivity (water management, research/knowledge transfer, land reforms) and promote agro-processing to move up the value chain.
  - Provide broad social assistance targeted at workers rather than jobs or locations.
- Final assessment:
  - Thailand’s natural advantages suit it for food production, but agriculture should develop via the intensive margin and complement a larger shift of labor into higher-productivity industry and services to raise standards of living.

*Source: Excerpt from _wp1551 (PDF chapter Section VI–VII and surrounding sections) — author's calculations and cited datasets as presented in the source content.*

### Section VI introduces a simple model that

### _wp1551 - Section VI introduces a simple model that

### Model purpose and scope
- Introduces a simple model that helps analyze the impact of government policies on factor distribution across sectors.

*Source: _wp1551 - Section VI introduces a simple model that*

### Section VII we use the insights from the model and other considerations to shed light on

### _wp1551 - Section VII we use the insights from the model and other considerations to shed light on possible reasons for the large cross-sector productivity differences and excessive size of low-productivity agriculture

### Data and measurement
- Main datasets used:
  - World Bank’s World Development Indicators (WDI): for most countries the last data point is 2012.
  - McMillan-Rodrik dataset: more detailed sectoral breakdown, more limited country coverage, ends in 2005.
  - Thailand national data: National Statistics Office (NSO) on employment and National Economic and Social Development Board (NESDB) on output (most up-to-date).
- Measurement choices and limitations:
  - Productivity examined both in constant prices (for growth) and current prices (for level comparisons).
  - Analysis largely limited to output per worker because working hours per sector are not available in cross-country datasets.
  - National vs cross-country series differ by a few percentage points; all series report similar agricultural shares in the last few years and show recent leveling.
- Proxy for hours worked:
  - Using NSO hours-range data and assumptions about distributions reduces agriculture’s employment share by about 3 percentage points and lowers the productivity gap between industry and agriculture by 10–15 percent.

### How Thailand compares with other economies — Productivity differences and employment shares
- Productivity patterns:
  - Countries with higher overall productivity tend to have higher productivity in agriculture, industry, and services.
  - Thailand’s productivity gap with more advanced countries is particularly large in agriculture.
  - Value added per agricultural worker in Thailand is close to that in China, Indonesia and the Philippines; industry and services workers in Thailand are noticeably more productive than in India, Indonesia, the Philippines and Vietnam.
- Cross-sector dispersion:
  - Industrial workers are the most productive and agricultural workers the least productive in every economy considered.
  - Weighted coefficient of variation (WCV) across nine featured economies (Japan, Korea, Malaysia, China, Thailand, Indonesia, Philippines, India, Vietnam) — reported values (in the order Japan, Korea, Malaysia, Thailand, Indonesia, Philippines, China, India, Vietnam):
    - 0.14, 0.37, 0.28, 0.66, 0.65, 0.54, 0.54, 0.69, 0.58
- Employment shares and outlier status:
  - More advanced economies have lower fractions of workforce in agriculture.
  - Thailand appears to have a disproportionately large share of rural employment given its income level; agriculture is the largest employer in Thailand (WDI data for 2012).
  - Thailand’s agricultural employment share far exceeds what would be expected for its level of income and has moved broadly parallel to the typical trajectory since the late 1990s, with the distance increasing in recent years.
  - For broad middle-income country comparisons (WDI), Thailand is one of the extreme cases in agricultural employment share.

### Aggregate implications and quantified illustration
- Stylized measures:
  - Scaled covariance (captures efficiency of labor allocation) shows Thailand improved before the Asian financial crisis, slowed in the subsequent decade, and had little change in the last five years; Thailand’s efficiency is on par with India, Indonesia, the Philippines and Vietnam and considerably below China, Japan, Korea, and Malaysia.
- Key summarized facts:
  - (i) The dispersion of labor productivity across sectors in Thailand is high compared with many Asian economies.
  - (ii) Labor productivity in agriculture relative to the rest of the Thai economy is very low.
  - (iii) Agriculture’s share of employment is considerably higher than usual for a country at Thailand’s income level.
  - (iv) Little movement toward more typical values in recent years.
- Numerical illustration of potential gains:
  - Reducing the agricultural share to 22 percent—the value consistent with Thailand’s level of income—by shifting some farm workers into industry and services (in proportion to the relative size of those two sectors) while maintaining Thailand’s productivity in each individual sector would raise Thailand’s economy-wide productivity by 20 percent.

### More detailed sectoral breakdown (McMillan-Rodrik dataset)
- Dataset sectorization (original nine sectors reduced to seven by removing mining and public utilities):
  - Ag (agriculture, forestry, hunting and fishing), Manuf (manufacturing), Constr (construction), Trade (wholesale & retail trade plus hotels & restaurants), Transp (transportation, storage, and communication), FIRE (finance, insurance, real estate and business services), Gov (community, social, personal, and government services).
- Findings from McMillan-Rodrik based analysis:
  - On a seven-sector scaled covariance measure, Thailand shows a noticeable gap versus the bulk of advanced and emerging market economies; pace of efficiency gains slowed after the Asian financial crisis.
  - Visual decomposition (Thailand 2003 vs 2013 and Malaysia 2003) shows:
    - Wide dispersion of sectoral productivity in Thailand—much wider than in neighboring Malaysia.
    - A very large proportion of the population engaged in the lowest-productivity sector (agriculture).
    - Stagnation: agriculture share did not change between 2003 and 2013; construction expanded a bit; manufacturing shrank; finance grew marginally as a fraction of employment.
  - In nominal terms agriculture’s relative standing improved due to higher agricultural prices rather than productivity gains; in real terms the distribution widened with high-performing sectors improving and lagging ones deteriorating.

### Dynamics: contribution of within-sector growth vs reallocation
- Sectoral trends (Thailand, 1995–2013):
  - Manufacturing is the key driver of economic growth; trends in agriculture and services look fairly flat.
  - Between 2000 and 2013:
    - Productivity grew more than twice as fast in manufacturing as in agriculture or services.
    - Manufacturing productivity grew almost ten times as fast as in other industry (mining, utilities, construction).
  - Employment shifts:
    - Manufacturing’s employment share remained roughly flat over the last 15 years.
    - After some shift from agriculture to services, employment shares have been quite stable since 2005.
- Decomposition (cumulative labor productivity growth since 2000) — three components:
  - Intrasectoral productivity growth (largest contributor).
  - Intersectoral labor reallocation (very small contribution since mid-2000s).
  - Dynamic interaction (small, turned negative in 2008−12 mainly due to manufacturing employment falling below base-year levels while manufacturing productivity remained higher).
- Conclusion: bulk of productivity increase since 2000—and almost all since the mid-2000s—comes from intrasectoral growth (primarily manufacturing), with little contribution from structural transformation.

### Model insights (two-sector model, agriculture vs manufacturing)
- Model assumptions and equilibrium:
  - Fixed uniform labor endowment; workers choose between agriculture (A) and manufacturing (M); free movement implies equal wage rate in equilibrium.
  - Competitive firms produce internationally traded goods; domestic prices equal international prices in absence of trade restrictions.
  - Equilibrium wage equals value marginal product of labor (VMPL = MPL × P) in each sector; VMPLs equalized across sectors.
  - Social planner optimum coincides with market outcome in open economy: equalization of VMPLs maximizes value at world prices.
- Policy distortions illustrated:
  - Guaranteed above-world agricultural price (P”A > P’A) shifts VMPLA up, increasing agricultural employment (L”A > L’A), lowering marginal and average labor productivity in agriculture and raising manufacturing average productivity; welfare loss equals triangle CDE; taxpayer cost equals trapezoid ABCD.
  - Minimum wage applied only to formal sector (manufacturing) set above market wage limits manufacturing employment and makes manufacturing too small relative to optimum, enlarging agriculture (figure depiction).
- Dynamic externalities:
  - If manufacturing exhibits learning-by-doing externalities (sector-wide productivity depends on total manufacturing output), a reduced manufacturing scale lowers both current average productivity and its growth rate.
- Caveats:
  - If manufacturing’s diminishing returns are much steeper than agriculture’s, the average productivity gap may not imply marginal productivity differentials—allocation gains from reallocation could be small; investigating technology (marginal vs average) is beyond the paper’s scope.
  - Uniform labor skill assumption is a simplification; imperfect substitutability and intrinsic sector preferences can alter allocations but price and wage interventions still tilt outcomes.

### What explains Thailand’s idiosyncrasies?
- Rejection of simple comparative advantage explanation:
  - Thailand is inefficient in producing agricultural commodities relative to its other sectors; productivity in agriculture is far lower than elsewhere in the economy—opposite of comparative advantage.
- Factors maintaining large agricultural employment:
  - Agriculture as residual employer: people who lose jobs in other sectors often move to farms rather than remain unemployed; some retirees take up farming; agriculture acts as an informal safety net.
  - Human capital barriers:
    - Agricultural workers have lower educational attainment than those in manufacturing.
    - Rural instruction quality tends to be lower; Thailand ranks number 90 in the world in terms of education quality (World Economic Forum, 2014).
    - Employer surveys indicate shortages of both basic and technical skills, but also a shortage of unskilled production workers—implying many farm workers could perform factory or service jobs, likely at higher value added than on farms.
  - Information, amenities and preferences:
    - Job information may be available (provincial information centers, online), but distance, access and ability to process information can limit utilization.
    - Urban deterrents: higher cost of living, traffic, pollution; cultural preferences and older age profile in agriculture may reduce mobility.
  - International migration:
    - Thailand hosts about one and a half million registered migrant workers and reportedly an even larger number of unregistered migrants.
    - IOM study (Huguet and Chamratrithirong, 2011) reports migrants’ sectoral distribution: 15 percent fishing/seafood processing, 17 percent agriculture, 17 percent construction, 8 percent domestic employment, 43 percent other businesses — suggesting migrants are not overwhelmingly concentrated in agriculture.
    - Migrants generally have lower education, skill level, and productivity than average Thai workers.
  - Price dynamics and government policies:
    - Global food price increases in the 2000s raised returns to farming and likely drew more workers into agriculture.
    - Policy interventions:
      - Rice pledging scheme launched October 2011 guaranteed rice farmers a price considerably higher than world market price and incentivized expansion among four million rice farmer households; predecessor programs (including pledge values below 2011 but above market) operated in the 2000s; 2009–2011 income guarantee program paid people for staying on farms without incentivizing expansion as strongly.
      - Price support policies also operated for rubber and sugar; rubber support introduced after rubber farmers demanded equal treatment with rice farmers; sugar producers have been insured against price declines for many years.
    - Minimum wage:
      - Minimum wage increases in 2012−13 (a sharp two-step increase) may have contributed to recent uptick in agricultural employment, as the wage is enforced more strictly in the formal sector.
      - World Economic Forum competitiveness ranking on labor market flexibility: 44 in 2011 → 120 in 2013 → 113 in 2014.
  - Measurement issues:
    - Paavo and Poapongsakorn (2012) argue agricultural employment is overstated due to irregular/seasonal work, misreporting, and limited hours worked by many classified as farmers; after adjustments they estimate actual active full-time farmers at most 27−32 percent of total workforce vs official 38−39 percent.
    - Authors believe Paavo and Poapongsakorn may overstate the bias: Labor Force Survey is monthly and should capture seasonal patterns; even the low end of the adjusted range is about 5 percentage points higher than typical for a middle-income Asian economy.
    - Migrant worker recording uncertainty: if migrant output is recorded better than input, measured productivity could be biased upward; migrants appear fairly widely spread across sectors.

### Policy implications
- Core recommendation: facilitate worker shift from agriculture to more modern sectors to realize sizable productivity gains.
- Specific policy actions:
  - Remove agricultural price supports:
    - Abolish guaranteed above-market price schemes (e.g., rice pledging scheme) which reward low-productivity activity and draw workers into agriculture.
    - Recent actions: rice pledging scheme abolished; price support for rubber ended.
  - Replace price support with better-targeted safety nets:
    - Facilitate development of price insurance markets rather than above-market price guarantees.
    - Provide broad social safety nets (including conditional cash transfer programs) not linked to residence or occupation—protect workers, not jobs.
  - Foster transition to modern employment:
    - Improve education and training, especially in rural areas: modernize curriculum and teaching techniques, emphasize math, sciences, foreign language, creative thinking; align vocational training with employer needs.
    - Improve information dissemination about urban job opportunities and reduce frictions to mobility.
    - Facilitate hiring of newcomers in the formal sector:
      - Consider waiving or reducing minimum wage for apprentices.
      - Offer temporary tax breaks or other incentives for firms hiring and training newcomers.
      - Encourage private schemes (business associations) to subsidize on-the-job training.
  - Increase productivity and opportunities in rural areas:
    - Improve agricultural productivity via efficient, incentive-based water management; improved agricultural research and knowledge transfer; reform land sales and tenancy laws to facilitate exit and consolidation of land holdings.
    - Promote agro-manufacturing and food processing to move up the value chain and allow agriculture to develop intensively rather than extensively.
    - Invest in infrastructure (power, transportation, communication) to connect all parts of Thailand to the modern economy and reduce regional disparities.
- Implementation caveats:
  - Support for agricultural workers should be through productivity-enhancing measures and safety nets rather than occupation- or location-tied subsidies.
  - Policies combining incentives, information, training, accommodative urban conditions, and broad safety nets can realize gains without social strain.

### Conclusions
- Empirical conclusions:
  - Thailand exhibits an exceptionally high dispersion of productivity across sectors and an exceptionally large share of population engaged in agriculture for its income level.
  - Agricultural sector serves as employer of last resort, cushioning shocks, but mostly signals sizable potential gains from reallocating labor into nonagricultural activities.
  - Structural transformation has slowed since the 2000s; broad food price increases and government policies (rice pledging and predecessor programs, some price supports) likely contributed to the slowdown and recent uptick in agricultural employment.
- Policy summary:
  - Facilitate structural transformation by removing agricultural price supports; modernizing and targeting education and vocational training (especially in rural areas); improving information dissemination and mobility; easing formal-sector hiring of newcomers (including apprenticeship wage policies or incentives); and increasing infrastructure investment.
  - Simultaneously pursue measures to raise agricultural productivity (water management, research/knowledge transfer, land reforms) and promote agro-processing to move up the value chain.
  - Provide broad social assistance targeted at workers rather than jobs or locations.
- Final assessment:
  - Thailand’s natural advantages suit it for food production, but agriculture should develop via the intensive margin and complement a larger shift of labor into higher-productivity industry and services to raise standards of living.

*Source: Excerpt from _wp1551 (PDF chapter Section VII and surrounding sections) — author's calculations and cited datasets as presented in the source content.*

### References

### References

### Bibliographic entries
- Ahn, JaeBin, 2015, “Structural Changes, Efficiency, and Growth: Cross-Country Analysis with a Case Study of Indonesia,” forthcoming IMF Working Paper.  
- Amarase, Nakarin, Tosarol Apaitan, and Kiatipong Ariyapruchya, 2013, “Thailand’s Quest for Economic Growth: From Factor Accumulation to Creative Disruption,” Bank of Thailand Discussion Paper 02/2013.  
- Asian Development Bank, 2013, “Asia’s Economic Transformation: Where to, How, and How Fast?” Special Chapter in Key Indicators for Asia and the Pacific 2013.  
- Bank of Thailand, 2013, Monetary Policy Report July 2013.  
- Chuenchoksan, Sra, and Don Nakornthab, 2008, “Past, Present, and Prospects for Thailand’s Growth: A Labor Market Perspective,” Bank of Thailand Discussion Paper 06/2008.  
- Dabla-Norris, Era, Giang Ho, Kalpana Kochnar, Annette Kyobe, and Robert Tchaidze, 2013, “Anchoring Growth: The Importance of Productivity-Enhancing Reforms in Emerging Market and Developing Economies,” IMF Staff Discussion Paper 13/08.  
- Huguet, Jerrold W., and Aphichat Chamratrithirong (editors), 2011, Thailand Migration Report 2011.  
- Isnawangsih, Agnes, Vladimir Klyuev, and Longmei Zhang, 2013, “The Big Split: Why Did Output Trajectories in the ASEAN-4 Diverge after the Global Financial Crisis?” IMF Working Paper 13/222.  
- Kaldor, Nicholas, 1967, Strategic Factors in Economic Development, New York, Ithaca.  
- Kuznets, Simon, 1966, Modern Economic Growth: Rate, Structure, and Spread, New Haven, Connecticut.  
- Lathapipat, Dilaka, and Thitima Chucherd, 2013, “Labor Market Functioning and Thailand’s Competitiveness,” Bank of Thailand Discussion Paper 03/2013.  
- Lewis, W. Arthur, 1954, "Economic Development with Unlimited Supplies of Labor," Manchester School of Economic and Social Studies, Vol. 22, pp. 139–91.  
- Matsuyama, Kiminori, 1992, “Agricultural Productivity, Comparative Advantage, and Economic Growth,” Journal of Economic Theory, Vol. 58, pp. 317−334.  
- McMillan Margaret S., and Dani Rodrik, 2011, “Globalization, Structural Change and Productivity Growth,” NBER Working Paper 17143.  
- Organization for Economic Cooperation and Development, 2013, Economic Outlook for Southeast Asia, China and India 2014: Beyond the Middle-Income Trap.  
- Paavo, Eliste, and Nipon Poapongsakorn, 2012, Sustaining Competitiveness of Thai Agriculture, Unpublished manuscript.  
- Trairatvorakul, Prasarn, 2013, Asian and Thai Economies in a Volatile World, Speech at The Nation’s 43rd Anniversary International Conference.  
- Trairatvorakul, Prasarn, 2014, Achieving Thailand’s True Growth Potential: The Role of the Central Bank, Speech at the Thailand Focus 2014: Reforming for Sustainable Growth Forum.  
- World Bank, 2012, Leading with Ideas: Skills for Growth and Equity in Thailand.  
- World Economic Forum, 2014, The Global Competitiveness Report 2014 – 2015.

---

### Appendix I — Productivity Growth Decomposition

### Key formulae and identities
- Average productivity (output per worker) equals the weighted average of sectoral productivities, with the weights given by sectoral labor shares:
  i
  iii
  t
  ii
  tttt
  ttt
  i
  ttt    tt
  Y
  YYLY
  PSP
  LLL  LL
      
  
   
- Change in economy-wide average productivity over a span of time can be decomposed into the sum of three terms:
  
   
  0000000000
  00000   0
  iiiiiiiiiii    iiii    i
  tttttttttt
  ii   ii  iii  i   i   i
  tttt
  P   PSPSPSPSPSPSPS  P    PS    S  P
  SP PS SPS S  P P
   
     
     
  

### Interpretation of decomposition terms
- The first term aggregates productivity growth within sectors, using beginning-of-period employment shares as weights.
- The second term captures intersectoral reallocation of labor. It will be positive if more people get employed in higher-productivity sectors and negative otherwise.
- The third term captures interaction between intrasectoral growth and intersectoral reallocation. It will be positive if workers on average move into sectors experiencing faster productivity increases.
- The second and third terms are associated with structural change in the economy.
- To convert level change to productivity growth rate in the above formula, divide the total and each term therein by the initial economy-wide productivity P0.

- Footnote 26: The interaction term, being the product of two changes, tends to be relatively small.

*Source: _wp1551 - References*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2015/_wp1551.pdf_