New Evidences on the Effects of the 300 Baht Minimum Wage on Employment, Hours Worked, and Wage Inequality in Thailand

Transcription

1 New Evidences on the Effects of the 3 Baht Minimum Wage on Employment, Hours Worked, and Wage Inequality in Thailand Dilaka Lathapipat, World Bank July 21, 215 1

2 Objective of the Study To inform the debate with new data and analysis on the effects of the 3 Baht minimum wage policy 2

3 Thailand relies heavily on primary and secondary educated labor around 82% of its workforce in 213 completed secondary education or less % Workforce Educational Composition 8% 7% 6% 5% 4% 3% 2% Primary Secondary TVET College 1% % 3

4 Since 25, the tightening labor market for primary workers has put upward pressure on their hourly wages Hourly Wage Rate Index - Constant Composition Primary Some secondary Upper secondary TVET College

5 The real hourly wage rates for primary workers kept rising despite declining real minimum wage rates Bangkok Nonthaburi Phra Nakhon Sri Ayuthaya Lop Buri Chai Nat Chon Buri Chanthaburi Chachoensao Nakhon Nayok Nakhon Ratchasima Surin Ubon Ratchathani Chaiyaphum Nong Bua Lam Phu Udon Thani Nong Khai Roi Et Sakon Nakhon Mukdahan Lamphun Uttaradit Nan Chiang Rai Nakhon Sawan Kam Phaeng Phet Sukhothai Phichit Ratchaburi Suphan Buri Samut Sakhon Phetchaburi Nakhon Si Thammarat Phangnga Surat Thani Chumporn Satun Phatthalung Yala Samut Prakarn Pathum Thani Ang Thong Sing Buri Saraburi Rayong Trat Prachin Buri Sa Kaeo Buri Ram Si Sa Ket Yasothon Am Nat Chareon Khon Kaen Loei Maha Sarakham Kalasin Nakhon Phanom Chiang Mai Lampang Phrae Phayao Mae Hong Son Uthai Thani Tak Phitsanulok Phetchabun Kanchanaburi Nakhon Pathom Samut Songkhram Prachuap Khiri Khan Krabi Phuket Ranong Songkhla Trang Pattani Narathiwat 5

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 % 35% 3% 25% 2% 15% 1% 5% NILF Unemployed Self employed Unpaid family Govt Private employee % 6

7 Evidences indicate that small private firms were most-affected with the rising low-skilled wages medium (1-1 workers) and large-sized firms (>1 workers) were able to pay their workers significant premiums over small firms 55% 5% 45% 4% 35% 3% 25% 2% Share of total hours worked below the minimum wage 5% 45% 4% 35% 3% 25% 2% 15% 1% 5% Hourly wage premiums over small firms (Primary and secondary workers) large medium 15% % 7

8 23.5% below min wage All Private employees in small firms (less than 1) % below min.9 wage Public sector and government enterprises.6 Private employees in med/large firms (more than 1) % below min wage

9 34% below min wage All Private employees in small firms (less than 1) % below min.9 wage Public sector and government enterprises.6 Private employees in med/large firms (more than 1) % below min wage

10 23% below min wage All Private employees in small firms (less than 1) % below min.9 wage Public sector and government enterprises.6 Private employees in med/large firms (more than 1) % 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 3 Baht minimum wage policy % Share of private employees by firm size category 35, Total capital of deregistered industrial firms (million Baht) 1,55 Total number of deregistered industrial firms 5% small medium large 3, 25, 1,5 1,45 4% 2, 1,4 1,35 3% 15, 1,3 1, 1,25 2% 5, 1,2 1% , Source: Department of Industrial Works, Thailand 11

12 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 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 (or 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

13 Modeling framework The basic specification uses provincial level panel data to control for unobserved provincial φ p and time τ t fixed effects as follows (quarterly data to 213) : y ipt = βmw pt + X ipt γ + φ p + τ t + ε ipt - MW pt is the log of real minimum wage for province p at time t - X ipt is a vector of individual characteristics (potential work experience, gender, schooling, province and area of residence) and provincial level timevarying control variables (annual change in (log) GPP per capita, shares of teenagers and old population (56-65 years old), and share of 15 to 65 yearold population with higher than secondary education Another specification uses a dynamic model which employs distributed leads and lags over a 1-quarter window to capture anticipation and long-run effects: 6 y ipt = βmw pt+k + X ipt γ + φ p + τ t + ε ipt k= 4 13

14 Estimation Results 14

15 Estimated impact (59% increase from 211 to 213) More than secondary education Secondary or lower - 25 to 65 years old Secondary or lower - 15 to 24 years old 15

16 Marginal effects of (log)minimum wage Dynamic model 16

17 Employment/population - All More than secondary education Secondary or lower - 25 to 65 years old Secondary or lower - 15 to 24 years old

18 Private employee/population - All More than secondary education Secondary or lower - 25 to 65 years old Secondary or lower - 15 to 24 years old

19 Agriculture/employment - All More than secondary education Secondary or lower - 25 to 65 years old Secondary or lower - 15 to 24 years old

20 Manufacturing/employment - All More than secondary education Secondary or lower - 25 to 65 years old Secondary or lower - 15 to 24 years old

21 Construction/employment - All More than secondary education Secondary or lower - 25 to 65 years old Secondary or lower - 15 to 24 years old

22 Wholesale and retail/employment - All More than secondary education Secondary or lower - 25 to 65 years old Secondary or lower - 15 to 24 years old

23 Hotel and restaurant/employment - All More than secondary education Secondary or lower - 25 to 65 years old Secondary or lower - 15 to 24 years old

24 Small firm/employment - All More than secondary education Secondary or lower - 25 to 65 years old Secondary or lower - 15 to 24 years old

25 Medium firm/employment - All More than secondary education Secondary or lower - 25 to 65 years old Secondary or lower - 15 to 24 years old

26 Large firm/employment - All More than secondary education Secondary or lower - 25 to 65 years old Secondary or lower - 15 to 24 years old

27 Self employment/employment- All More than secondary education Secondary or lower - 25 to 65 years old Secondary or lower - 15 to 24 years old

28 Unpaid family workers/employment - All More than secondary education Secondary or lower - 25 to 65 years old Secondary or lower - 15 to 24 years old

29 (Log) weekly hours - All More than secondary education Secondary or lower - 25 to 65 years old Secondary or lower - 15 to 24 years old

30 (Log) hourly wage - All More than secondary education Secondary or lower - 25 to 65 years old Secondary or lower - 15 to 24 years old

31 key policy change: Summary of key findings Estimated impacts and unintended consequences: 3 Baht min wage implemented over 212 to 213 (around 59% increase in real term) percentage point (ppt) drop in aggregate employment ppt decline in small firm employment share ppt increase in large firm employment share -.9 ppt increase in self-employment share - Anticipation effects and larger long-run impacts are observed - Substitution effects across skill groups observed - long-run increase in private sector employment for high-skilled vs. decline for low-skilled (the young and less-educated are most adversely affected) - Decrease in weekly hours for employed and lesseducated labor (Reduction in overtime?) - Unintended adverse impacts mainly due to the large and sudden increase in the minimum wage 31

32 The effects of the minimum wage on Thai wage inequality - The basic estimation framework is first propose by Lee (1999) and is further developed by Autor, Manning, and Smith (21) details are omitted (w pt q w pt 7 ) = α t + φ p + ψ p. t + β 1 (w M pt w pt 7 ) + β 2 w M pt w pt ε pt - (w pt w pt 7 ) is the gap between the log of the hourly wage at percentile q and the log of real provincial wage at the 7 th percentile - (w pt M w pt 7 ) is the gap between the log of the hourly minimum wage and the log of real provincial wage at the 7 th percentile 32

33 Estimated wage elasticity across the wage percentile - Model 1: includes only non-agricultural workers IV (left) and OLS (right) Wage percentile Wage percentile 33

34 Spillover effects all the way up to the 7 th percentile - Raising the minimum wage clearly improves wage inequality - Model 2: includes all workers IV (left) and OLS (right) Wage percentile Wage percentile 34

35 Our anticipation for the foreseeable future (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 tends to substitute for medium-skilled workers, complement high-skilled workers, and has little effect on the low-skilled 35

36 Framework for studying substitution between capital and labor and between skill groups Quality-adjusted labor N N t = θ Ht N Ht Y t = A t N t α K t 1 α σ HL 1 σ HL + θlt N Lt σ HL 1 σ HL σ HL σ HL 1 Broad education group H L N Ht = θ COt N COt σ HH 1 σ HH + θ TVt N TVt σ HH 1 σ HH σ HH σ HH 1 Detailed education group Age group 1 2 CO 3 4 TVET SR PR N Lt = θ HSt N HSt N kt = σ LL 1 σ LL 4 j=1 σ LL 1 σ LL + θprt N PRt θ kj N kj σ EXP 1 σ EXP σ LL σ LL 1 σ EXP σ EXP 1 36

37 Capital deepening clearly favors highly Estimated Skill-Biased Technological Change educated workers I_L I_H I_HL ln θ COt θ TVETt Annual Growth in Real Gross Capital Stock by Industry.8.12 Agriculture.6.4 ln θ Ht θ Lt Industries Services Total.2 ln θ HSt θ PRt

38 Wage decomposition from 1986 to 1996 Primary Secondary TVET College Changes in relative supplies 2.2% 1.% -4.9% -2.8% Skill-biased technical change 2.9% -18.% -9.9% 19.4% Capital intensity 23.8% 23.8% 23.8% 23.8% Residual change 28.6% 1.5% 14.5% 5.6% Total wage change 57.5% 17.2% 23.5% 45.9% 6% 5% 4% 3% 2% 1% % -1% -2% -3% -4% Primary Secondary TVET College Schooling Group supply shift SBTC capital intensity actual 38

39 Wage decomposition from 21 to 211 Primary Secondary TVET College Changes in relative supplies 12.5% 1.4% -7.8% -7.4% Skill-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%.7% Total wage change 19.4% -11.4% -15.% -15.% 6% 5% 4% 3% 2% 1% % -1% -2% -3% -4% Primary Secondary TVET College Schooling Group supply shift SBTC capital intensity actual 39

40 For more information about the study: Please contact Dilaka Lathapipat 4