How to write research papers on
Labor Economic Modelling
Research Methods in Labor Economics
and Human Resource Management
Faculty of Economics
Chulalongkorn University
Kampon Adireksombat, Ph.D.
EIC | Economic Intelligence Center
Siam Commercial Bank
Outline
y What should we put in a research paper?
y Example 1: The Effects of the 1993 Earned Income
Tax Credit Expansion on the Labor Supply of
Unmarried Women
y Example 2: The Effects of Decentralized Minimum
Wage Setting on Actual Wages in Thailand:
Unconditional Quantile Regression Approach
y Some research ideas using Thai data
1
1. What should we put in a research paper?
No definite rules, need to make sure that readers will understand what we write
Introduction
•
Research questions***
•
Motivation
•
Brief summary of what we are writing in this paper
Institutional details
•
Background of what we want to study
Literature review
•
Review what previous work has done
Data
Empirical approach
•
Summary statistics
•
Regression specification
Results
•
Show your results
•
Discuss what you find
Conclusion/policy implication
2
Example 1: The Effects of the 1993 Earned Income Tax Credit
Expansion on the Labor Supply of Unmarried Women
Author: Kampon Adireksombat
Published in Public Finance Review (January 2010)
y Introduction
y Earned Income Tax Credit (EITC):
{
{
{
{
{
a refundable income tax credit
targets low- and middle-income working families in the United States.
the tax credit is paid as a lump sum along with the annual tax return
working as an earnings subsidy
the biggest group of recipients are unmarried women with children (single mothers)
y EITC expansion in 1993
{
Substantially increased the credit available to unmarried women with two or more
children (2+group) relative to those with one child (1 child group) and those with no
children (no children group)
y Motivation and research question
{
Check whether the 2+ group increased their labor supply relative to 1 child and No
children groups
3
Example 1: Institutional details (1)
Maximum credits in 2005 dollars dollars
$
4750
4500
4250
4000
3750
3500
3250
3000
2750
2500
2250
2000
1750
1500
1250
1000
750
500
250
0
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
Year
No Children
One Child
Two Children
4
Example 1 Institutional details (2)
(current prices)
5
Example 1: Literature review
Discuss what previous works examine and find
y Examples of previous works
{
{
{
{
Dickert, Houser, and Scholz (1995)
Eissa and Liebman (1996)
Meyer and Rosenbaum (2001)
Hotz, Mullin, and Scholz (2006)
Contribution
y Check whether the 2+ group increased their labor supply relative to 1
child and No children groups (different identification strategy)
y Use national level data
6
Example 1: Data
y Data: US Current Population Survey
y Sample: Widowed, divorce, separated or never-married women, aged
25-55
y Period: 1991-1993 and 1995-2000
7
Example 1: Empirical Approach: simple check labor force
participation
8
Example 1: Empirical Approach: simple check total hours
worked
9
Example 1: Empirical Approach: simple check hours worked
by those already in the labor force
10
Example 1: Empirical Approach: regression equation for
labor force participation
11
Example 1: Empirical Approach: regression equation for
annual hours worked
12
Example 1: Result (1)
13
Example 1: Result (2)
14
Example 1: Result (3)
15
Example 1: Result (4)
16
Example 1: Conclusion/Policy implication
EITC expansion in 1993
Effective policy
- Increase labor supply of unmarried women with two or more children
- Well-targeted, lower education women increase more labor supply than
higher education women
In 2009, change the threshold to
- No children
(max credit = USD 457)
- One child
(max credit = USD 3,043)
- Two children
(max credit = USD 5,028)
- Three and more children
(max credit = USD 5,657)
17
Example 2
The Effects of Decentralized Minimum Wage
Setting on Actual Wages in Thailand:
Unconditional Quantile Regression Approach
[Work in progress]
Kampon Adireksombat
Economic Intelligence Unit
Siam Commercial Bank
John Giles
Development Research Group
World Bank
18
In Brief
y Research Question:
{
{
What is the effect of the minimum wage on actual wages?
Are there any differential effects on the actual wages of workers in
formal and informal sectors?
y Sample Group: Female and male workers aged 15-60 years old
y Sample Period: 1999-2007
y Data: Thai Labor Force Survey
y Empirical Method: Kernel Density Function and Unconditional
Quantile Regression
19
Motivation: Dual Sector Model
y In the classical dual sector model: an increase in the
minimum wage in the formal sector results in a decrease in
wages in informal sector
{
Negative relationship between a minimum wage and the actual wages in
informal sectors.
y The Todaro ( AER 1969) model predicts that increases in
minimum wages in the urban area can indirectly raise rural
wages.
{
{
Positive relationship between a minimum wage and the actual wages in
informal sectors.
More recently, McIntyre(2004)’s model predicts that an increase in
minimum wages can increase wages in both sectors.
y Ambiguous theoretical prediction
20
Minimum Wage in Thailand
y 1973-first applied
y 1998-decentralized to provincial levels
{ following a recommendation by the International Labour
Organization (ILO)
{ As a result, there is substantial cross-provincial and cross-time
variation in minimum wages
21
Definition
A minimum wage rate was defined as a wage rate
which an employee deserves and is sufficient for an
employee’s living (Office of National Wage
Committee 1996).
{
{
Unlike other minimum wages in other developing countries,
where minimum wages are defined as the payment sufficient
for the worker and his family members to dwell in the society
(for example, Brazil (Starr 1982)), a Thai minimum wage rate
is defined for an employee only.
makes the minimum wage and individually actual wage more
comparable due to consistent definitions.
22
Bangkok
Central
Northeast
South
North
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
100 150 200
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
50
Thai Bahts
50
100 150 200
Minimum Wage (THB) by Region
Year
Graphs by Region
23
Infaltion Rate by Region
Bangkok
Central
North
Northeast
South
0
6
0
2
4
Inflation Rate
2
4
6
Overall
2000
2005
2010
2000
2005
2010
2000
2005
2010
Year
Graphs by Region
24
Real Minimum Wage (in 2007 THB) by Region
Central
Northeast
South
North
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
140 160 180 200
Thai Bahts
140 160 180 200
Bangkok
Year
Graphs by Region
25
Decentralized Minimum Wage Setting
y Under the 1998 Labour Protection Act, minimum
wage policy is decentralized into 2 levels
{
{
the basic level, set by the National Minimum Wage Committee
the provincial level, set by the Provincial Minimum Wage Sub
–committees,
y The provincial minimum wage must not be below the
basic level.
y In case that no minimum wage is set in a province,
the basic level is mandatory enforced.
y Increased variation in the nominal minimum wage
rates from 3 levels in 2001 to 21 levels in 2007
26
Three institutions of the Thai minimum wagefixing machinery
y National Wage Committee (NWC) : a tripartite
committee
y Provincial Subcommittee on Minimum Wage
(PSMW) : a tripartite committee
y Subcommittee on Technical Affairs and Review
(STAR): NWC appoints 11 members to form the
STAR
27
The Minimum wage setting procedure
y Each PSMW makes a recommendation on its
provincial minimum wage to NWC
y NWC sends a recommendation to STAR for a
technical review
y STAR submits a review back to NWC for a final
consideration and approval
28
Labor Force Survey Data
y LFS includes detailed information on demographic
variables (age, gender, region, marital status,
education) and employment and unemployment
characteristics (work status, hours worked, salary
per month, occupation, industry).
{
{
{
1984-1997, carried out three rounds annually (on February,
May and August)
1998-2000, the fourth round (on November) was added
From 2001, conducted monthly
29
Sample
y Male and female workers at working age (15-60 years
old)
{
{
With no earning information in LBF, the self-employed workers were
excluded from the sample.
The resulting sample is 103,418 observations.
y 9-year data from 1999 (when the LPA fully went into
effect) to 2007
{
{
To capture the full effects of minimum wage changes given the time
that is needed to adjust production process to economize on lowskilled labor.
To keep the sample consistent and avoid the issue of repetitive
sample, we use the first round data for 1999-2000 and February
data for 2001-2007
30
Previous Studies
y A large body of literature on the effect of minimum wage on
actual wages in emerging and developing countries focus on
Latin America and Caribbean countries
{
For example, Bell 1997; Fajnzylber 2001; Lemos 2002; Strobl and Walsh
2003; Maloney and Núñez 2004; Gindling and Terrell 2005.
y They find consistent results that increases in minimum wages
have a positive effects on the wages of formal sector workers
y A pioneer study on the effect of minimum wage on actual
wages in Asia is Rama (2001).
{
Using 1993 aggregate data at the provincial level from Indonesia, Rama
finds that doubling the real minimum wage results in an increase in
average wages by 5 to 15 percent
31
Contribution
y Decreasing real minimum wage
y Stable inflation rate
y Definition of minimum and actual wages are more
comparable
y Exogenous cross-provincial and cross-time variation
in minimum wages
y Unconditional Quantile Regression (Firpo, Fortin,
and Lemieux (Econometrica 2009))
32
How To Define Formal and Informal Sectors?
y I follow Ginding and Terrell (2005) by using firm size and
location.
y Under the 1998 Labor Protection Act (LPA), a firm with 10
employees or more must file a work regulation, including
working conditions with the Ministry of Labor and is required to
make a contribution to the Compensation Fund.
{
Therefore, I define workers who work for a firm with 10 or more workers as
those in the formal sector and workers who work in a smaller firm as those
in the informal sector.
y To define formality by urban/rural dichotomy, I categorize a
firm located in municipal area as an urban firm and a firm
located in non-municipal area as a rural firm.
{
This urban/rural definition is consistent with Thai local administration.
33
How To Define Formal and Informal Sectors?
y As a result there are four sectors in my study
{ Urban Large (Formal)
{ Urban Small (Informal)
{ Rural Large (Informal)
{ Rural Small (Informal)
34
Empirical Method
y Kernel density function of log real wage and analyze
whether there are spikes in a distribution of actual
wage around the minimum wage rates.
y Unconditional Quantile Regression (Firpo, Fortin,
and Lemieux (Econometrica 2009))
{
To test whether there are differential effects of minimum
wages across sectors, I estimate these equations separately for
each sector.
35
Kernel Density : Urban Large Enterprises 1999
0
0
Density
1
Density
1
2
2
Kernel Density : Rural Large Enterprises 1999
0
1
2
3
4
5
Log of Real Daily Wage
6
7
8
0
1
2
3
4
5
Log of Real Daily Wage
Kernel density estimate
6
7
8
Kernel density estimate
Source:
data
fromsurvey
National
Statistic Office
and Minitstry
of Labor
Source:
19991999
Labor
force
of National
Statistical
Office and
Ministry of Labor
Source:
1999
Labor
NationalOffice
Statistical
Office and
Ministry of Labor
Source:
1999
dataforce
from survey
NationalofStatistic
and Minitstry
of Labor
Kernel Density : Urban Small Enterprises 1999
0
0
Density
1
Density
1
2
2
Kernel Density : Rural Small Enterprises 1999
0
1
2
3
4
5
Log of Real Daily Wage
6
7
8
0
1
2
3
4
5
Log of Real Daily Wage
Kernel density estimate
6
7
Kernel density estimate
36
Source:
forceNational
survey of
National
Statistical
Office and
Ministry of Labor
Source:1999
1999Labor
data from
Statistic
Office
and Minitstry
of Labor
Source:
from
National
Statistic Statistical
Office andOffice
Minitstry
Labor of Labor
Source:
19991999
Labordata
force
survey
of National
andofMinistry
Kernel Density : Rural Large Enterprises 2007
0
0
1
Density
1
Density
2
2
Kernel Density : Urban Large Enterprises 2007
8
0
1
2
3
4
5
Log of Real Daily Wage
6
7
8
0
1
2
Kernel density estimate
3
4
5
Log of Real Daily Wage
6
7
8
Kernel density estimate
Source:
2007
dataforce
fromsurvey
National
and Minitstry
of Labor
Source:
2007
Labor
of Statistic
NationalOffice
Statistical
Office and
Ministry of Labor
Source:
data
fromsurvey
National
Statistic Office
and Minitstry
of Labor
Source:
20072007
Labor
force
of National
Statistical
Office and
Ministry of Labor
Kernel Density : Rural Small Enterprises 2007
0
0
Density
1
Density
1
2
2
Kernel Density : Urban Small Enterprises 2007
0
1
2
3
4
5
Log of Real Daily Wage
6
Kernel density estimate
Source:
from
National
StatisticStatistical
Office andOffice
Minitstry
Labor of Labor
Source:
2007 2007
Labordata
force
survey
of National
andofMinistry
7
8
0
1
2
3
4
5
Log of Real Daily Wage
6
Kernel density estimate
Source:
2007
data force
from National
Statistic
Office
and Minitstry
Labor
Source:
2007
Labor
survey of
National
Statistical
Office of
and
Ministry of Labor
7
8
37
Kernel Density : Less than Elementary 2007
0
0
.5
.5
Density
Density
1
1
1.5
1.5
Kernel Density : No Education 2007
3
4
5
Log of Real Daily Wage
6
7
3
4
5
6
Log of Real Daily Wage
Kernel density estimate
7
8
Kernel density estimate
Source:2007
2007Labor
data force
from National
and Office
Minitstry
Labor of Labor
Source:
survey ofStatistic
NationalOffice
Statistical
andofMinistry
Source:
from
National
OfficeStatistical
and Minitstry
of and
Labor
Source: 2007
2007 data
Labor
force
surveyStatistic
of National
Office
Ministry of Labor
Kernel Density : Lower Secondary 2007
0
0
.5
.5
Density
1
Density
1
1.5
1.5
2
2
2.5
Kernel Density : Elementary 2007
3
4
5
6
Log of Real Daily Wage
7
3
8
4
5
6
Log of Real Daily Wage
7
Kernel density estimate
Kernel density estimate
Source:
from
National
Office
and Minitstry
of and
Labor
Source: 2007
2007 data
Labor
force
surveyStatistic
of National
Statistical
Office
Ministry of Labor
Source:2007
2007Labor
data force
from National
and Office
Minitstry
Labor of Labor
Source:
survey ofStatistic
NationalOffice
Statistical
andofMinistry
Kernel Density : Diploma 2007
0
0
.5
.5
Density
1
1.5
Density
1
1.5
2
2
2.5
2.5
Kernel Density : Upper Secondary 2007
38
4
4.5
5
5.5
Log of Real Daily Wage
6
6.5
4
4.5
Kernel density estimate
5
Log of Real Daily Wage
5.5
6
Kernel density estimate
Source:2007
2007 Labor
data from
National
Office
and Minitstry
of and
LaborMinistry of Labor
Source:
force
surveyStatistic
of National
Statistical
Office
Source:
20072007
Labor
force
survey
of National
Office
and Ministry
Source:
data
from
National
StatisticStatistical
Office and
Minitstry
of Laborof Labor
0
1
Density
2
3
4
Kernel Density : College 2007
5
5.2
5.4
Log of Real Daily Wage
Kernel density estimate
Source:
2007
data
from
National
Statistic
OfficeOffice
and Minitstry
of Labor
Source:
2007
Labor
force
survey
of National
Statistical
and Ministry
of Labor
5.6
5.8
39
Average Proportion of Workers Earning at and below the Minimum Wage
P roportion Daily Wage
0.95-1.05 of Minimum wage
0.15
0.15
0.24
0.10
0.10
All
Urban large firms
Urban small firms
Rural large firms
Rural small firms
P roportion Daily Wage
below 0.95 of Minimum wage
0.26
0.11
0.23
0.46
0.50
No Education
Urban large firms
Urban small firms
Rural large firms
Rural small firms
0.11
0.19
0.14
0.07
0.09
0.66
0.52
0.65
0.71
0.72
Less than elementay school
Urban large firms
Urban small firms
Rural large firms
Rural small firms
0.14
0.19
0.20
0.09
0.09
0.40
0.26
0.34
0.47
0.52
Elementary school
Urban large firms
Urban small firms
Rural large firms
Rural small firms
0.23
0.33
0.33
0.11
0.11
0.35
0.22
0.25
0.50
0.48
Lower secondary school
Urban large firms
Urban small firms
Rural large firms
Rural small firms
0.22
0.06
0.51
0.37
0.24
0.21
0.11
0.16
0.43
0.41
Upper secondary school
Urban large firms
Urban small firms
Rural large firms
Rural small firms
0.22
0.04
0.53
0.44
0.30
0.15
0.07
0.11
0.37
0.39
Diploma
Urban large firms
Urban small firms
Rural large firms
Rural small firms
0.07
0.02
0.26
0.17
0.56
0.04
0.03
0.05
0.25
0.32
College
Urban large firms
Urban small firms
Rural large firms
Rural small firms
0.01
0.00
0.05
0.06
0.27
0.01
0.01
0.01
0.13
0.23
Note: Author's calculation from 1999-2007 Labor Force Survey data.
40
Summary statistics by e ducation le ve l and s e ctor
Real Daily Wage (in 2007 THB)
Years of work experience
Male
Average weekly hours worked
Urban Large
185.28
24.79
0.54
49.25
No Education
Urban Small
Rural Large
143.69
165.15
27.11
27.37
0.54
0.58
48.37
48.54
Rural Small
138.08
28.57
0.62
45.59
Real Daily Wage (in 2007 THB)
Years of work experience
Male
Average weekly hours worked
Less than Elementary School
Urban Large
Urban Small
Rural Large
254.61
186.82
196.68
29.52
29.37
29.76
0.54
0.54
0.58
49.25
48.37
48.54
Rural Small
163.27
30.50
0.62
45.59
Real Daily Wage (in 2007 THB)
Years of work experience
Male
Average weekly hours worked
Urban Large
223.24
16.15
0.54
50.19
Elementary School
Urban Small
Rural Large
181.46
190.62
15.40
15.17
0.56
0.67
48.97
49.91
Rural Small
160.81
15.15
0.73
46.40
Real Daily Wage (in 2007 THB)
Years of work experience
Male
Average weekly hours worked
Urban Large
223.24
15.79
0.57
48.87
Lower Secondary School
Urban Small
Rural Large
181.46
190.62
13.20
13.00
0.57
0.67
48.76
49.80
Rural Small
160.81
11.32
0.76
46.61
Real Daily Wage (in 2007 THB)
Years of work experience
Male
Average weekly hours worked
Urban Large
304.75
11.02
0.54
48.43
Upper Secondary School
Urban Small
Rural Large
216.25
205.81
8.78
10.12
0.53
0.64
49.18
48.74
Rural Small
169.18
8.47
0.74
46.46
Real Daily Wage (in 2007 THB)
Years of work experience
Male
Average weekly hours worked
Urban Large
459.49
14.34
0.49
41.39
Diploma
Urban Small
Rural Large
391.45
322.60
13.27
12.10
0.50
0.50
42.10
46.98
Rural Small
191.79
7.72
0.65
48.11
Real Daily Wage (in 2007 THB)
Years of work experience
Male
Average weekly hours worked
Urban Large
723.38
13.70
0.49
41.39
College
Urban Small
Rural Large
525.21
447.15
12.32
9.17
0.50
0.50
42.10
46.98
Rural Small
256.57
6.12
0.65
48.11
Note: Data are from 1999-2007 Labor Force Survey, National Statistical Office of Thialand. Means are weighted with sample weights.
Standard Deviations are in parenthesis. Sample includes female and male work ers aged from 15-60 years old.
41
Estimation Method
y In Thailand, the effect of minimum wage on actual wages
is likely to be different at different points of the wage
distribution
y We need estimation methods that “go beyond the mean”
y Conditional quantile regression
{ Limitation: do not average up to their unconditional population
counterparts.
{ As a result, cannot be used to estimate the impact of an explanatory
variable on the corresponding unconditional quantile.
{ In other words, cannot be used to answer a question as simple as
“what is the effect on the actual wages of increasing minimum wage
by one dollar, holding everything else constant?”
42
Unconditional Quantile Regression
y Proposed by Firpo, Fortin, and Lemieux (Econometrica 2009)
to estimate the impact of changes in the explanatory variables
on the unconditional quantiles of the outcome variable.
y First, running a regression of the Recentered Influence
Function (RIF) of the unconditional quantile dependent
variable on the explanatory variables. Second, running OLS
regression of the dependent variable on covariates [Stata
command: Rifreg]
y As a result, we yield RIF-OLS estimate, allowing us to
estimate the marginal effects of changes in the distribution of
an independent variable on a given quantile of the
unconditional distribution of Y, all else equal.
43
Regression Equation
ln(w it) = β0 + β1ln(MWipt) + Xitβj + βj Occupation dummies + βk Industry dummies
+ βt Year dummies + βp Province dummies + βr Region dummies + εit
Note: Xit is a vector of demographic characteristics, including age, marital status,
education, and potential work experience
44
Elasticities of Real Daily Wage wrt. Real Minimum Wage
At each quantile
Quantile
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Urban Large
2.875***
(0.294)
1.115***
(0.154)
0.395***
(0.153)
0.181
(0.217)
0.205
(0.272)
-0.436*
(0.236)
-0.110
(0.258)
-0.030
(0.281)
-0.129
(0.317)
Observations 47,665
Baseline (General CPI)
Urban Small Rural Large
0.882**
0.293*
(0.437)
(0.172)
1.390***
0.463**
(0.215)
(0.184)
1.208***
0.512***
(0.213)
(0.145)
0.746***
0.734***
(0.286)
(0.179)
0.874***
0.578***
(0.208)
(0.142)
0.770***
0.187
(0.258)
(0.138)
0.245
-0.109
(0.239)
(0.223)
0.764**
-0.279
(0.307)
(0.393)
-0.463
0.321
(0.417)
(0.635)
Rural Small
0.400
(0.345)
0.877***
(0.116)
1.128***
(0.118)
0.767***
(0.217)
0.858***
(0.178)
0.916***
(0.220)
0.506***
(0.177)
0.802***
(0.232)
0.684***
(0.240)
21,087
14,617
20,049
Notes: Estimated from the 1999-2007 Labor Force Survey sample weighted data.
Robust standard errors are in parentheses. * (**, ***) signifies statistical significance at the 1 (5,10) percent level.
All regressions include experience, province, region, occupation, industry and year dummies.
45
Quantile
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
General CPI with Bootstrap Std. errors
Lag of real minimum wage and General CPI
Quantile
Urban Large Urban Small Rural Large Rural Small
Urban Large Urban Small Rural Large Rural Small
2.875***
0.882*
0.293
0.400
0.1 2.454***
1.094**
-0.103
0.065
(0.324)
(0.459)
(0.178)
(0.354)
(0.315)
(0.450)
(0.181)
(0.347)
1.115***
1.390***
0.463**
0.877***
0.2 1.015***
1.355***
0.516*** 0.518***
(0.165)
(0.194)
(0.217)
(0.153)
(0.160)
(0.232)
(0.200)
(0.117)
0.395***
1.208***
0.512*** 1.128***
0.3 0.378**
0.887***
0.195
0.752***
(0.147)
(0.237)
(0.129)
(0.142)
(0.160)
(0.227)
(0.157)
(0.117)
0.181
0.746**
0.734*** 0.767**
0.4 -0.126
0.421
0.589*** 1.209***
(0.232)
(0.332)
(0.189)
(0.305)
(0.225)
(0.298)
(0.189)
(0.225)
0.205
0.874***
0.578*** 0.858***
0.5 0.002
0.587***
0.554*** 0.856***
(0.253)
(0.211)
(0.157)
(0.200)
(0.282)
(0.218)
(0.151)
(0.185)
-0.436*
0.770***
0.187
0.916***
0.6 -0.571**
0.560**
0.215
0.835***
(0.252)
(0.266)
(0.148)
(0.240)
(0.250)
(0.267)
(0.144)
(0.226)
-0.110
0.245
-0.109
0.506**
0.7 -0.367
0.412*
-0.055
0.236
(0.282)
(0.245)
(0.231)
(0.197)
(0.270)
(0.240)
(0.234)
(0.187)
-0.030
0.764*
-0.279
0.802***
0.8 -0.016
0.861***
-0.385
0.523**
(0.290)
(0.423)
(0.404)
(0.246)
(0.295)
(0.307)
(0.425)
(0.235)
-0.129
-0.463
0.321
0.684***
0.9 -0.058
-0.632
0.168
0.352
(0.309)
(0.540)
(0.708)
(0.250)
(0.331)
(0.425)
(0.685)
(0.245)
46
Conclusion
y What is the effect of the minimum wage on actual
wages?
{
A decline in real minimum wage are associated with a decline
in the actual wages, especially for workers the lower quantile
of wage distribution
y Are there any differential effects on the actual wages
of workers in formal and informal sectors?
{
Yes, the effects are more economically and statistically
significant in informal sectors, especially workers in small
firms
47
Limitation and Future Research
y Include self-employed and foreign workers into the
sample
y Compare results from pre- and post-decentralization
periods
y Effects on employment and income
48
Some research ideas using Thai data
Data sources
• Bank of Thailand
•http://www.bot.or.th/THAI/STATISTICS/Pages/index1.aspx
• National Economic and Social Development Board
•http://www.nesdb.go.th/Default.aspx?tabid=92
• National Statistical Office
•http://service.nso.go.th/nso/nso_center/project/search_center/23project-th.htm
49
Average actual wages in many provinces are lower than the
existing minimum wages
Actual daily wages and provincial minimum wages in 2010*
Unit: THB
Average actual daily wage
300
250
Existing provicial minimumwage
268
Proposed new minimum wage: country-wide THB250 259
206
205
200
151
150
152
111
120
Phayao
Sisaket
142 151
100
50
0
Note:
Source:
Maehongson
Nontaburi
Bangkok
Actual daily wages are calculated from 2010 Labor Force Survey (LFS) data, using only the first quarter data (most updated data available)
SCB EIC analysis based on data from Labor force survey (National Statistical Office) and Ministry of Labor
50
Sluggish labor force growth in the next decade
2000 - 2010
2010F – 2020F
Unit: % compound annual growth rate
Unit: % compound annual growth rate
Philippines
2.6%
Malaysia
2.3%
Vietnam
2.5%
Indonesia
1.6%
Singapore
1.6%
Thailand
Korea
1.0%
0.4%
2.1%
1.6%
0.9%
1.1%
- 0.4%
0.2%
- 0.4%
Source: SCB EIC analysis based on data from International Data Base (IDB) of US Census Bureau; National Economic and Social Development Board (NESDB)
51
Low formal workforce
Unit: Million persons
38
17
9
Note:
Source:
Monthly wage
Labor force
21
Wage
8
Non-wage
Daily wage
and others
Employees are those employed by private and public sector. Non-employees include own account workers, unpaid family workers, employers and
unemployed persons. Unemployed persons include seasonal inactive labor force as well.
SCB EIC analysis based on data from Labor Force Survey (National Statistical Office)
52
While GDP has grown, wages have not
Unit: Index 2001=100
148 Real GDP
100
2001
Note:
Source:
102 Real wage
2004
2007
2010F
2010 GDP is from SCB EIC forecast. Wage data are calculated from Labor force survey data, using full-year data, with the exception of 2010 data, using
only the first quarter data.
SCB EIC analysis based on data from Labor Force Survey (National Statistical Office); National Economic and Social Development Boards
53