Why Has Income Inequality in
Thailand Increased?
An Analysis Using 1975- 1998 Surveys.
What is Income Inequality?
Income inequality
measures
the distribution of
income among
members of a
society.
Income inequality in Thailand
• The effects of
agricultural factors
• Financial
development
• Education level
• Poverty Incidence
* Play an important role
in explaining
Thailand’s Inequality
changes.
Per Capita GNP (1975 -1998)
Changes in Income Distribution
Agricultural Sector
• While the share of agricultural sector in
total GDP decreased (27% to 12% from
1974 to 1998), the labor force still
accounted for 51% of total labor force.
• Income levels in this sector is lower than
other sectors.
• Farm prices and harvest affect
the value of agricultural output.
Poverty Incidence
• While poverty has decreased due to its economic
growth, the inequality increase can still be problematic
from the perspective of fairness.
• Inequality has a negative effect on poverty both
directly and through low growth rates.
Decomposability of Inequality Indices
• The average household income in
BANGKOK is 2.6 times larger than the
rural area (northeastern Region) in 19751976, and 3.4 times in 1998.
•The interregional
inequality is the driving
force behind the
inequality of the whole
country.
Gini Index
• It is impossible, however, to decompose
the Gini Index, which is the most popular
among many inequality indices.
• Mean Logarithmic Deviation (MLD):
as a decomposable inequality index in
addition to the Gini Index.
Changes in Income Inequality
Decomposability of Inequality Indices
•
The Gini index is denoted by
the following equation :
This can be expressed geometrically using the
Lorenz Curve by decomposed it into three parts.
• Lambert and Aronson decomposed the Gini
index into 3 parts :
Between Group
Within Group
• Bourguignon and Shorrocks proposed a
decomposable inequality index which is
defined axiomatically. (base on axiom)
• Let us set FOUR axioms that inequality
measures ought to satisfy:
1. The weak principle of transfers
2. Income scale independence
3. The principle of population
4. Decomposability
1. Weak Principle of transfers
• Means that the inequality measure
increases when the Lorenz curve
goes wholly outside.
2. Income scale independence
• Is satisfied when the inequality
measure is unaffected by
proportional changes of
everyone’s income.
3. The principle of population
• Implies that the inequality measure is
independent of population changes
under constant income shares.
4. Decomposability
• Means that inequality of the whole
population is a consistent function of the
inequality in its subgroups.
Any inequality measure
that satisfies these
FOUR axioms is a
generalized entropy
measure
•MLD is one of these generalized
inequality measures :
•So MLD is decomposable as follows :
• MLD is used to decompose
Thailand’s income inequality
into inter- and intra- regional
inequalities for an overview,
using data from Household
Socio-Economic Survey.
2. Household Socioeconomic Survey :
•Its objective is to collect data on
income, expenditure, and other
characteristics of households.
•Five regions : Greater Bangkok
Metropolitan Area, Central Region, Northern
Region, Northeastern Region, Southern
Region.
• The data of the late 1970s
and early 1980s shows that,
the income distribution
increased rapidly in this
period.
3. Inequality Decomposition
• The whole kingdom is divided into 13 sub
regions to obtain interregional and
intraregional inequalities.
•The whole kingdom MLD is
calculated using data from Ikemoto
and Uehara (2000) which provided
average household income by
deciles groups of households
ordered by household income.
• The bar chart shows regional decompositions
of the whole kingdom MLD, the interregional
inequalities are much smaller than the
intraregional inequality therefore; The shares of
intergroup inequalities increase as smaller
subdivisions are employed.
Five factors that are
Needed to be considered:• Relative variability of
agricultural/nonagricultural sectors
• Income
• Financial services
• Education level disparity
• Aging
Note: Civil liberty or trade openness are not
necessary to take into account countryspecific factors of an existing cross-country
inequality analysis.
• The share of agricultural in GDP are used as an
independent variable according to Ahluwalia (1976)
• Labor productivity are used as a more natural
variable according to Bourguignon and Morrisson
(1998)
• According to Kuznets (1955), these variables try to
capture directly the effect of the agricultural sector.
** However, the relative labor productivity is imperfect
because it does not take into account the population
share of the agricultural sector at all.
The HSES provides average household incomes for nine
occupational groups. Farm Operators, (mainly owning land and
renting land) and Farm Workers are classified as agricultural
households. Others are nonagricultural households.
• DUAL interpreted as an intersectoral
inequality measured
- This interpretation leads us to put MLD or the Gini coefficient
between the agricultural and nonagricultural sectors since
DUAL is large under relative household income disparity and
even household share of agricultural and nonagricultural
sectors.
- When income within the two sectors is equalized, MLDB and
GINIB are MLD and Gini coefficient of the regional population.
• Income distribution variation cannot be explained by other
determinants of income inequality; thus, the key point of a
regression analysis is to clarify if income distribution between
the agricultural and nonagricultural sectors can explain part of
the total income distribution variation. Also it is tautological to
explain total income distribution by the income distribution
between theses two agricultural and nonagricultural sectors
according to Bourguignon and Morrisson (1998).
This figure shows the changes in share and
relative income of agricultural households.
• Most empirical studies of income distribution include
income level or per capita GDP as an explanatory variable
• Agricultural variables are included in the regression
equation but may not be able to capture all the
distributional changes caused by sectoral factors of the
economy.
- For example, economic growth accompanied by a
sectoral shift from traditional industry to high-tech or
service industry can lead to increased income inequality.
Thus, household income, along with agricultural
variables, is included in the regression equation.
• The effect of financial service development on
income distribution is not straightforward.
• The development also locks in inequality.
• Developed financial services are often unavailable
for the poor.
• Education level
disparity is a
determinant of income
inequality on the
assumption that more
education leads to
more income
The appendix gives the
estimation method.
Figure 6 shows that
Thailand’s population is
aging by using the
average age of
household heads
denoted by AGE as an
explanatory variable to
take into account the
relationship between
aging and distribution.
•
•
Inequality should grow with age according to Deaton and
Paxson (1994).
In Japanese household survey data, half of the increase in
the economy wide consumption inequality during the 1980s
could be explained by population aging.
• Data gathered from year 1975-1998. no. of observation is 116
Using Gini
coefficient
instead of
MLD
Insignificant due
to the fact that
Thailand still a
developing
country
Stays significant,
due to the stronger
variables
Whereas ,Fin and Edu
are insignificant
Negative sign
Implied that
financial
development
Decreased income
inequality
Standardized coefficient of
Table 4a and 4b
The standardized coefficients show that the effects for variables capturing
sectoral factors dominate the effect of the other determinants
• The changed in sector of industry from the
agriculture to nonagricultural play very
effective role in determining the income
distribution in Thailand.
• The impact of the shift in sector of industry
seems to be larger than other variables.
• Thus, the increasing in income inequality in
Thailand are mainly come from the changed
in sectoral