Why Has Income Inequality in Thailand Increased?

1
Why Has Income Inequality in Thailand Increased?

• An Analysis Using 1975- 1998 Surveys.

2
What is Income Inequality?
Income inequality measures the distribution
of income among members of a society.
3
Income inequality in Thailand

• The effects of agricultural factors
• Financial development
• Education level
• Poverty Incidence
• Play an important role in explaining Thailands
  Inequality changes.

4
Per Capita GNP (1975 -1998)
Thailand has reached the more developed country
status
625 --gt 1831
5
Changes in Income Distribution
Income inequality in Thailand has increased
significantly in these 24 years.
6
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.

7
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.

8
Decomposability of Inequality Indices

• The average household income in BANGKOK is 2.6
  times larger than the rural area (northeastern
  Region) in 1975-1976, and 3.4 times in 1998.

• The interregional inequality is the driving force
  behind the inequality of the whole country.

9
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.

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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.
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• Lambert and Aronson decomposed the Gini index
  into 3 parts

Gini Index of whole country when income
distribution are perfectly equalized.
N and Ni are population of the whole country.
Population weighted average of Gini Indices
Mixture of between group and within group
inequality and not decomposable.
Between Group
Within Group
12

• 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

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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 everyones
  income.

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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.

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Any inequality measure that satisfies these FOUR
axioms is a generalized entropy measure
16

• MLD is one of these generalized inequality
  measures

• So MLD is decomposable as follows

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• MLD is used to decompose Thailands income
  inequality into inter- and intra- regional
  inequalities for an overview, using data from
  Household Socio-Economic Survey.

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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.

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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.

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• 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.

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EMPIRICAL FRAMEWORK

• Five factors that are
• Needed to be considered-
• Relative variability of agricultural/nonagricultu
  ral sectors
• Income
• Financial services
• Education level disparity
• Aging
• Note Civil liberty or trade openness are not
  necessary to take into account country-specific
  factors of an existing cross-country inequality
  analysis.

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Agricultural and Nonagricultural Sectors

• 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.

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Table 1 shows the occupational classification

• 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.

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• 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).

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Figure 5 SHARE AND RELATIVE INCOME OF
AGRICULTURAL HOUSEHOLDS

• This figure shows the changes in share and
  relative income of agricultural households.

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Income

• 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.

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Financial Service

• 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.

28
Education Level Disparity

• 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.

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Aging
Figure 6 shows that Thailands 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.

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Regression Result

• Data gathered from year 1975-1998. no. of
  observation is 116

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by using MLD and Dual as a inequality
measurement
The p values is lt 1 Thus, reject the validity
32
The p value lt 1 . This rejected the validity
Using Gini coefficient instead of MLD
Insignificant due to the fact that Thailand still
a developing country
33
Stays significant, due to the stronger variables
Whereas ,Fin and Edu are insignificant
Negative sign Implied that financial
development Decreased income inequality
34
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
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Conclusion

• 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