New Evidences on the Effects of the 300 Baht Minimum Wage on Employment, Hours Worked, and Wage Inequality in Thailand Dilaka Lathapipat, World Bank July 21,

To inform the debate with new data and analysis on the effects of the 300 Baht minimum wage policy 2 Objective of the Study

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

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

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

6 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

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

8 23.5% below min wage 42.5% below min wage 18% below min wage

9 34% below min wage 63% below min wage 20% below min wage

10 23% below min wage 52% below min wage 15% below min wage

11 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 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) 12

Modeling fremework 13

Estimation Results 14

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Summary of key findings key 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) 29

The effects of the minimum wage on Thai wage inequality 30 -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 31 -Model 1: include only non-agricultural workers – IV (left) and OLS (right)

32 -Model 2: include all workers – IV (left) and OLS (right) Spillover effects all the way up to the 70 th 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 33 Our anticipation for the foreseeable

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

35 Capital deepening clearly favor highly educated workers Year-on-Year Growth in Real Capital Stock by Industry Estimated Skill-Biased Technological Change

36 Wage decomposition from 1986 to 1996

37 Wage decomposition from 2001 to 2011

For more information about the study:  Please contact Dilaka Lathapipat 38