Group work with Deepika Gupta & Deepa Somdas

1. INCOME DISTRIBUTION
SUBMITTED BY:
DEEPA.S
DEEPIKA GUPTA
GARGI GHOSH

2. STRUCTURE OF PRESENTATION
MEASUREMENT OF INEQUALITY
Lorenz curve & Gini’s coefficient
IMPACT OF DEVELOPMENT ON INCOME DISTRIBUTION
A note on U hypothesis
Economic growth and income inequality
Economic development, Urban
Inequality, poverty and development
underemployment& Income inequality
IMPACT OF INCOME DISTRIBUTION ON DEVELOPMENT
Inequality, Political instability and
Distributive politics and economic growth
Investment
CASE STUDIES – Taiwan, Brazil

3. LORENZ CURVE AND GINI COEFFICIENT
Concentration area (area of inequality
•The % of households is
plotted on the x-axis, the
percentage of income on
the y-axis.
•Complete equality occurs
if a % of household
received a % of income.
•Perfect inequality represents
the case where one
household has 100% of the
country’s income.
•Gini coefficient-
•Ratio of concentration area to the total area
under the line of equality.
•Ranges from 0 to 1.
• larger the Gini’s coeff., greater the
inequality

5. ECONOMIC GROWTH AND INCOME INEQUALITY.
The central theme-
Character and causes of long term
changes in personal distribution of
income.
• Incomes are grouped should be family expenditure units-adjusted on the
basis of family members
• Distribution should be covered all units of a country-LIG,HIG,MIG
• Units should segregate ,Income earners are still in the learning or retired
stages.
• Income defined as national income in this country.
• Units should be grouped by secular levels of income, free of transient
disturbances.

6. TRENDS IN INEQUALITY : OBSERVATION 1
• Data before direct taxes show greater
inequality.
1
• Addition of Govt. reliefs and direct taxes
2
• Reduction in inequality.
• (Data after taxation should only be considered
3 for calculating inequality)

7. TRENDS IN INEQUALITY : OBSERVATION 2
Stability or reduction in the inequality of the % shares was
accompanied by significant rises in real income per capita
CONSTANT RATE OF HIGHER RATE OF
INCREASE INCREASE FOR LIG
•Rise in per capita •Rise in per capita
income income
•No change in •Decrease in
inequality. inequality.

8. TRENDS IN INEQUALITY : OBSERVATION 3
With technological advancements income is less prone to
transient disturbances.
DISTRIBUTION BY DISTRIBUTION BY LONG-
ANNUAL INCOME TERM AVERAGE
SHOWS MORE INCOME SHOWS LESS
INEQUALITY. INEQUALITY.

9. EXPLANATION OF TRENDS
• Lower average per capita income
in rural population(than urban)
1 • Lower inequality.
• Urbanization leads to rural urban
shift
2
• Increasing share of unequal
URBAN POPULATION ECONOMIC PEAK
component in economy.
3
1. Adaptation of the children of rural-
urban migrants to city’s economic IMMIGRANTS
life
2. Political power of urban lower LOW INCOME GROUP DOWNWARD TREND
income groups increase

10. OTHER TRENDS IN INCOME INEQUALITY
INITIAL PHASE NEW
WIDENING
OF ECONOMIC INDUSTRIAL
INEQUALITY U
GROWTH SETUP
C
U
R
STABILIZED V
LATER PHASE INDUSTRIAL E
GROWTH DECREASING
OF ECONOMIC
+PROGRESSIVE INEQUALITY
GROWTH
TAXES

11. Inequality,Poverty,and Development
The purpose is to explore the relationship between the distribution of
income and the process of development on the basis of cross country data
on income inequality.
SAMPLE
60 countries
40- developing
14-developed
6-socialist
Kuznets's Hypothesis: The U shaped curve.

12. Inferences
• Non linear relationship between income inequality &
development.
1
• Turning point is different for different income groups of an
economy
2
• Nature of U-curve is different for the entire sample and
for only developing countries.
3
• Dominancy of middle income group dictates the
nature of U-curve which makes its long term relevancy
3 questionable..

13. GROWTH AND INCOME DISTRIBUTION
RAISED INEQUALITY IN REDUCED INEQUALITY
LOW INCOME IN HIGH INCOME
COUNTRIES GROWTH
COUNTRIES
KUZNETS CURVE HAS BEEN
SUPPORTED BY
PAUKERT(1973), CLINE(1975),
AHLUWALIA(1976), AND
KYN(1987)
EMERGES
KUZNETS The study of high and low
GREATER CURVE income countries to
EQUALITY IN determine the effect of
EARLY STAGES
OF growth on inequality showed
LDC
DEVELOPMENT that inequality increases with
growth as frequently in low
income countries as in high
income countries

14. U HYPOTHESIS RELATING INCOME INEQUALITY AND ECONOMIC DEVELOPMENT:
DIFFERENT INCOME
DISTRIBUTIONS
SECTOR 1 SECTOR 2
ECONOMY
KUZNTETS U CURVE
Suppose W1 , W2 - population shares of two sectors
Y1 , Y2 - log mean
σ12 , σ22- log variances of income in the two sectors.
W1 + W 2 = 1 Overall log variance :
Overall log mean income : σ2= W1σ12 + W2 σ22 + W1(Y1-Y)2 + W2(Y2-Y)2
Y= W1Y1 + W2Y2
Log variance is a measure of income inequality.

15. Assuming that population share in sector 1 is increasing then from above
Overall log mean income :
equations :
Since A<0, the parabolic curve
σ2 = AW1 2+ BW1+ C will open downward. With
increase in W1, inequality first
Where A= -(Y1-Y2)2 increases, reaches a maximum
B=(σ12-σ22) + (Y1-Y2)2 and then decreases. Thus U
C=σ22 hypothesis is derived.
Since W1 ranges from 0 to 1, σ2 is maximum
when
W=(σ12-σ22) /2 (Y1-Y2)2 +1/2
Thus when log variances are more equal and log
mean incomes are more different, σ2 is maximum
when w is ½.
INEQUALITY CURVE IN US

16. ECONOMIC DEVELOPMENT, URBAN UNDEREMPOYMENT AND INCOME INEQUALITY
ECONOMIC
DEVELOPMENT
SHIFT OF LABOR FROM
AGRICULTURE TO NON
AGRICULTURE
INCOME INEQUALITY
INCREASES
RKF MODEL-
RURAL URBAN
INCOME
ROBINSON, DIFFERENCE
KNIGHT,AND CONSTANT
ASSUMPTIONS
FIELDS GAVE • CHANGE IN
BASED ON LESS
EXPLANATION SHARE OF
DEVELOPED
FOR INCOME AGRICULTURAL
COUNTRIES
INEQUALITY IN POPULATION
1976 • THUS
DEFINING
INEQUALITY BY
U CURVE

17. ECONOMIC DEVELOPMENT, URBAN UNDEREMPOYMENT AND INCOME INEQUALITY
SCENARIO OF LDC IN 1960
HIGHER WAGES
•RURAL SECTOR •UNDER
DEVELOPMENT
•URBAN SECTOR •UNEMPLOYMENT
HARRIS –TODARO
LOWER WAGES
MODEL (1970)
The Harris-Todaro model (HT) demonstrates that, in certain parametric
ranges, an increase in urban employment may actually result in higher levels
of urban unemployment and even reduced national product .
In LDC upon migration some rural migrants immediately obtain jobs in formal
sector while others get employed in small businesses and self employment i.e.
informal sector.

18. ECONOMIC DEVELOPMENT, URBAN UNDEREMPOYMENT AND INCOME INEQUALITY
The workers in formal sector earns more than informal sector, while there is
mobility over time from informal to formal sector.
 This share of urban labour force in LDC cities engaged in informal sector
ranges from 19% to 69% with a mean of 41%.
Log variance which is a measure
URBAN of inequality will form an inverted U
FORMAL
SECTOR curve.
WORKERS
RURAL URBAN
(AGRICULTU INFORMAL  When urbanization is low , and the
RAL)
WORKERS
SECTOR
WORKERS
pressure of land keeps rural
incomes are low , inequality will be
more and after urbanization it will
CLASSES
OF WAGE decrease.
EARNERS
INEQUALTY DEFINED BY THESE GROUPS

19. EMPIRICAL ANALYSIS OF MODEL
Two predictions of informal sector made from the results of labor market behavior:
The informal sector share of the The INFORMAL sector share of total
labour force (1-Na –Nm)/(1-Na) labour force or underemployment rate
decreases with level of urbanization 1-Na –Nm follow an inverted U curve
with urbanization.
Let URB : 1-Na The data for URB and UNDER has been
UNDER : 1-Na –Nm . collected by PREALC covering 17 Latin
American countries for the years 1950,
SHARE : (1-Na –Nm)/(1-Na) 1960,1970 and 1980.
All these countries were lower middle
income or upper middle income. Variable Mean Std Minim Maxim
dev. um um
 Peak U in the underemployment rate URB 50.9 16.6 18.9 84.4
occurs when 61% of labour force has left
UNDER 11.3 3.4 4.5 20.4
the primary sector.
SHARE 23.8 8.4 10.9 44.0

20. CONCLUSIONS
According to FIELDS inequality during the growth process initially decreases and
then increases depicting a U curve in contrast to inverted U curve described by
inequality indices.
Fields (1987) offers the following explanation:
 at the initial stages of the growth process, inequality decreases because the
less “elitarian” position of the rich acts to reduce inequality;
in the last phases, inequality increases because of the increased “isolation”
of the poor.
Fields (1993) defines Elitism of the Rich (ER) and Isolation of the Poor (IP) as
functions of gap and numerical inequality.
But Robert Moore argued against Fields proposal.
University of Chicago, gave human capital based explanation where inequality
among identically endowed individuals is generated over time by differences in
market luck which parents pass to their children by investing in the children’s
human capital.
Thus market luck is the driving force behind inverted U.

21. THE INFORMAL SECTOR, INTRAURBAN INEQUALITY AND THE INVERTED U
INEQUALITY ALSO FOLLOWS
INVERTED U
labor market equilibrium
INFORMAL SECTOR SHARE OF condition :
URBAN LABOUR FORCE FALLS AND
INFORMAL SECTOR SHARE OF
TOTAL LABOUR FORCE FOLLOWS
AN INVERTED U expected utility
from working in the
agricultural sector =
RAUCH URBANIZATION INCREASES
MODEL expected utility
from working in the urban
sector.
As urbanization increases, land-labor ratio in agriculture rises, increasing
the marginal product of labor in agriculture and the agriculture wage.
This will be consistent with labor market equilibrium only if the average log
wage in urban sector increases. This is only possible if SHARE falls.

22. THE INFORMAL SECTOR, INTRAURBAN INEQUALITY AND THE INVERTED U
Inequality within agriculture is zero but inequality within urban sector is possible
because of the earnings of formal and informal sector.
The change in overall inequality will follow two trends:
In the early phase, SHARE will be more and the overall inequality will
increase.
Once SHARE shrinks below one-half , the further decline in SHARE as
urbanization increases reduces inequality with in urban sector , tending to
decrease overall inequality.
Thus the log variance measure of inequality can not decline in Rauch model
until after informal share of total labor force declines.

24. Distributive politics and Economic growth: Regression analysis
DISTRIBUTIONAL INITIAL PER
INDICATORS OF CAPITA
INCOME INCOME
AIM HERE IS TO VERIFY IF INITIAL
INEQUALITY IS A STATISTICALLY
SIGNIFICANT INDICATOR OF LONG
TERM GROWTH OF A COUNTRY
DISTRIBUTIONAL PRIMARY
INDICATORS OF SCHOOL
LAND ENROLLMENT
INDICTORS OF
WEALTH
DISTRIBUTION
REGRESSION EQUATIONS EMPLOYMENT
I=  + βE +є…….simple regression equation
I=  + βE + γY +є…multiple regression equation
EDUCATION

25. Distributive politics and Economic growth: Regression analysis
Table : Regression data
HIGHEST QUALITY SAMPLE HIGHEST QUALITY LARGEST POSSIBLE
1960 - 85 SAMPLE SAMPLE
1970 -85 1970 - 85
OLS TLS OLS OLS
CONSTANT 3.60 8.66 4.56 2.80
(2.66) (3.33)
GDP -0.44 -0.52 -0.29 -0.27
(-3.28) (-3.72)
PRIM 3.26 2.85 3.28 3.79
(3.38) (2.43)
GINI -5.70 -15.98 -9.71 -7.95
(-5.70) (-3.21)
GINI LAND - - - -
R2 0.28 0.27 .28 0.23
HERE, DEPENDENT VARIALBLE IS PER CAPITA GROWTH RATE, WHILE INDEPENDENT VARIABLES ARE GDP, PRIM, GINI & GINI LAND
RESULTS SHOW THAT
 GROWTH IS NEGETIVELY CORRELATED WITH INCOME INEQUALITY AND LAND DISTRIBUTION
 POSITIVELY CORRELATED WITH PRIMARY SCHOOL ENROLLMENT

26. Income Distribution, Political Instability and Investment
SOCIAL
DISCONTENT
DOES INCOME INEQULITY INCREASE
POLITICAL INSTABILITY?
INCOME POLICY
INEQUALITY UNCERTAINITY
DOES POLITICAL INSTABILITY REDUCE
INVESTMENT?
LOW POLITICAL
INVESTMENT IN STABILITY
CHANNEL OF EFFECT?
MEASURE OF POLITICAL INSTABILITY
LOW PRODUCTIVITY
REGRESSION MODEL
HIGH UNCERTAINITY
HIGH TAXATION
ANALYSIS
POLITICAL INSTABILITY

27. Income Distribution, Political Instability and Investment
MEASURE OF POLITICAL STABILITY
INV (1) SPI (1) INV(2) SPI (2)
SPI = 1.39ASSASS + 1.21DEATH CONST 27.36 37.43 27.85 32.44
+7.58SCOUP + 7.23UCOUP –
5.45DEM GDP .07 0.06
SPI -0.05 -0.57
PPPI -0.14 -0.15
REGRESSION MODEL
PPPIDE .04 0.05
INV = α0 + α1 SPI + α2 GDP + α3 PRIM -0.23 -0.32
PPPIDE + α4 PPPI +ε1 MIDCLA -1.01 -0.68
SPI = β0 + β1 PRIM + β2 INV + β3 SS
MIDCLASS +ε2
INV 0.72 0.66
LAAM 9.89
GDP – GROSS DOMESTIC PRODUCT
DEATH-NO OF PEOPLE KILLED IN MASS VIOLENCE ASIA 2.59
SCOUP-NO OF SUCCESFUL COUP
UCOPU- NO OF UNSUCCESSFUL COUP AFRICA -3.17
DEM-DUMMY VARIABLE
SPI-SOCIO –POLITICAL INSTABILITY INDEX
PPPIDE-DEVIATION OF PPP VALUE FOR
INVESTMENT DEFLATOR Table : Regression data
PPPI – PPP VALUE FOR INVESTMENT DEFLATOR

29. CASE STUDY : TAIWAN
• SUPERIOR PHYSICAL & DEVELOPING INDUSTRY
INSTITUTIONAL THROUGH AGRICULTURE
•LABOUR ALLOCATION TRANSITION INTO
INFRASTURCTURE – DEVELOPING
TO INDUSTRY AN INDUSTRIAL
PRIMARILY • JAPANESE OWNERSHIP AGRICULTURE THROUGH
• SAVINGS BASE FORM ECONOMY
AGRAGARIAN OF MANUFACTURING INDUSTRY
CAPITAL TO INDUSTRY
ECONOMY UNITS
120
100
96 91
80
68 79
60
40
20
32
21
9
0 4
1920 AD 1940AD 1960AD 1980AD
• LAND REFORMS
RETROCESSION •PROCESSING INDUSTRIES
FROM JAPAN - • HIGH INTEREST RATES FOR AGRICULTURE
1945 SAVINGS
•IMPORT RESTRICTIONS INDUSTRY
• RURAL AGRO

30. CASE STUDY : TAIWAN –overall FID
INCOME DISTRIBUTION 1953 INCOME DISTRIBUTION 1959
120 120
CUMULATIVE INCOME
100
100
MEAN INCOME PER
80 HOUSE HOLD -22681 80
60 GINI COEFF - .558
60
40
40
20
20
0
0
0 20 40 60 80 100
0 20 40 60 80 100
CUMULATIVE HOUSEHOLDS
MEAN INCOME PER
INCOME DISTRIBUTION 1964 HOUSE HOLD -31814
120 GINI COEFF - .44
CUMULATIVE INCOME
100
80
60
40 MEAN INCOME PER
HOUSE HOLD -32450
20
GINI COEFF - .328
0
0 20 40 60 80 100
Currency in N.T. Dollars
CUMULATIVE HOUSEHOLDS

31. CASE STUDY : TAIWAN –Land Reforms
PRE- REFORM REFORMS POST REFORM
UNEQUAL REDISTRIBUTION OF
LAND NATIONAL WEALTH
DISTRIBUTION
REDUCING SALE OF
LAND RENTS PUBLIC LAND
FIERCE
COMPETITION LOWER RENT
OF SCARCE LAND TO
LAND – LOW TILLER
LEASE PERIOD
LAND
OWNERSHIP TO
HIGH RENT FARMERS
VALUE
ITEM REDUCTION SALE OF LAND TO TILLER TOTAL
IN FARM PUBLIC LAND PROGRAMME REDISTRIBUTI
RENTS ON
AREA AFFECTED 256.9 71.7 193.6 215.2
FARM HOUSEHOLD AFFECTED 302.3 139.7 194.9 334.3
PERCENTAGE OF CULTIVATED 29.2 8.1 16.4 24.6
LAND AFFECTED
PERCENTAGE OF FARM HH 43.3 20 27.9 47.9
AFFECTED

32. CASE STUDY : TAIWAN –other developments
REORGANISATION OF INSTITUTIONAL INFRASTRUCTURE
AGRICULTURAL DEVELOPMENT DURING 1950s
DISTRIBUTION OF ASSETS AND INDUSTRIAL GROWTH

34. CASE STUDY : Brazil
INCOME DISTRIBUTION INCOME DISTRIBUTION
100 100
90 90
CUMULATIVE INCOME
CUMULATIV EINCOME
80 80
70 70
60 60
50 50
40 40
30 30
20 20
10 10
0 0
0 20 40 60 80 100 0 20 40 60 80 100
CUMULATIVE HOUSEHOLDS CUMULATIVE HOUSEHOLDS
MEAN PER CAPITA INCOME – 513 US$/YR MEAN PER CAPITA INCOME – 679 US$/YR
GINI COEFF - .59 GINI COEFF - .63
DATA ADJUSTMENTS :
• NORMALISED ON NO.OF FAMILY MEMBERS
• INCORPORATING NON-MONETORY INCOME

35. CASE STUDY : Brazil
• GREATER SCOPE TO STABILIZATION
MARKET FORCES • INCREASE IN GDP AFFECT ONLY
• FREER REIN TO PRIVATE HIGHER INCOME GROUP
SECTOR • LEADS TO INEQUALITY
0.7 800
0.59 0.63
0.6 700
0.6
0.51 679 600
0.5
•POVERTY A RESULT
OF LOW RURAL 500
0.4 530
513 PRODUCTIVITY
400
0.3 450
• LACK OF GOVT 300
POLICIES
0.2
200
0.1 100
0 0
MILITARY
1960AD 1963 AD GOVERNMENT 1967 AD 1970 AD
• GOVT FISCAL POLICIES
• POLICIES DECLINE • UNEQUAL ALLOCATION OF • TAX RELAXATIONS
NOMINAL WAGES INFRASTRUCTURE • REDUCED REVENUE
GINNI'S COEFF •DECLINE OF REAL • INEFFECTIVE POLICIES e.g.
MINIMUM WAGES BY 20% education, labour allocation
MEAN INCOME DUE TO INFLATION etc

36. THANK YOU