New Evidences on the Effects of the 300 Baht Minimum

Wageon Employment, Hours

Worked, andWage Inequality in Thailand

Dilaka Lathapipat, World BankJuly 21, 2015

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To inform the debate with new data and analysis on the effects of the 300 Baht minimum wage policy

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Objective of the Study

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Thailand relies heavily on primary and secondary educated labor – around 82% of its workforce in 2013 completed secondary education or less

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Since 2005, the tightening labor market for primary workers has put upward pressure on their hourly wages

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The real hourly wage rates for primary workers kept rising despite declining real minimum wage rates

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The change in the employment composition of 15 to 65 year-olds suggests that many private firms were struggling with the rising low-skilled wages

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Evidences indicate that small private firms were most-affected with the rising low-skilled wages – medium (10-100 workers) and large-sized firms (>100 workers) were able to pay their workers significant premiums over small firms

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23.5% 23.5% below min below min

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42.5% 42.5% below min below min

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18% 18% below min below min

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34% 34% below min below min

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63% 63% below min below min

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20% 20% below min below min

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23% 23% below min below min

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52% 52% below min below min

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15% 15% below min below min

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Substantial decline in employment in small private firms and sharp increase in the number of deregistered industrial firms observed after the 300 Baht minimum wage policy

Source: Department of Industrial Works, Thailand

Formal framework for estimating the effects of the minimum wage on employment, labor mobility between sectors, and weekly hours worked

In particular, we study the effects on:•Employment/population (overall and by employment status)•Share of employed workers across major industries•Share of workers in by employment status (small/medium/large private firms, self employment, and unpaid family workers)

The analysis will shed light on the patterns of labor mobility between sectors, and movement into and out of employment•We are interested in the effects on the overall population/workers between 15-65 years of age, as well as on the sub-populations of youth (15-24 years old) and adults (25-65 years old) with secondary education or less, and those with higher than secondary education (15-65 years old)

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Modeling fremework

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Estimation Results

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Summary of key findingskey policy change:

Estimated impacts and unintended consequences:

Aggregate:-Almost 2 percentage point (ppt) fall in aggregate employment-1.6 ppt decline in small firm employment share-0.4 ppt increase in large firm employment share-0.8 ppt increase in unpaid family worker employment share- Anticipation effects are observed and

long-run impacts are larger- Substitution effects in production,

where the young and less-educated are severely affected (3.3 ppt decline in employment and 4.4 ppt increase in unpaid family worker share

- Many small firms unable to cope- Sustained increase in weekly hours for

employed less-educated labor (firms squeeze worker productivity)

300 Baht min wage implemented over 2012 to 2013 (around 59% increase in real term)

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The effects of the minimum wage on Thai wage inequality

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- The basic estimation framework is first propose by Lee (1999) and is further developed by Autor, Manning, and Smith (2010) – details are omitted

Estimated wage elasticity across the wage percentile

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- Model 1: include only non-agricultural workers – IV (left) and OLS (right)

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- Model 2: include all workers – IV (left) and OLS (right)

Spillover effects all the way up to the 70th percentile - Raising the minimum wage clearly improves wage inequality

(i) We expect firms, particularly large ones, to invest more capital in labor substituting technology in response to higher wage rates(ii) Evidence in Thailand indicates that capital deepening tend to substitute for medium-skilled workers, complement high-skilled workers, and have little effect on the low-skilled

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Our anticipation for the foreseeable

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Framework for studying substitution between capital and labor and between skill groups

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Detailed education group

Age group

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Capital deepening clearly favor highly educated workers

Year-on-Year Growth in Real Capital Stock by Industry

Estimated Skill-Biased Technological Change

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Wage decomposition from 1986 to 1996Primary Secondary TVET College

Changes in relative supplies 2.2% 1.0% -4.9% -2.8%Skill-biased technical change 2.9% -18.0% -9.9% 19.4%Capital intensity 23.8% 23.8% 23.8% 23.8%Residual change 28.6% 10.5% 14.5% 5.6%Total wage change 57.5% 17.2% 23.5% 45.9%

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Wage decomposition from 2001 to 2011Primary Secondary TVET College

Changes in relative supplies 12.5% 1.4% -7.8% -7.4%Skil l-biased technical change 6.7% -5.1% 5.6% -2.7%Capital intensity -5.6% -5.6% -5.6% -5.6%Residual change 5.8% -2.2% -7.3% 0.7%Total wage change 19.4% -11.4% -15.0% -15.0%

For more information about For more information about the study:the study:

Please contact Dilaka Lathapipat dlathapipat@worldbank.org

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