BLS WORKING PAPERS

U.S. DEPARTMENT OF LABOR

Bureau of Labor Statistics

 

OFFICE OF PRICES AND LIVING

CONDITIONS

Some Explanations for Changes in the Distribution of

Household Income in Slovakia: 1988 and 1996

Thesia I. Garner, U.S. Bureau of Labor Statistics

Katherine Terrell, University of Michigan Business School

Working Paper 345

July 2001 (revised December 2001)

 

The views expressed are those of the authors and do not necessarily reflect the policies of the U.S. Bureau of Labor

Statistics or the views of other staff members. Terrell’s research was supported by the World Bank.
SOME EXPLANATIONS FOR CHANGES IN

THE DISTRIBUTION OF HOUSEHOLD INCOME

IN SLOVAKIA: 1988 AND 1996

Thesia I. Garner

PSB, Room 3105, 2 Mass. Ave. N.E.

Bureau of Labor Statistics

Washington, D. C. 20212

U.S.A.

Garner_T@BLS.gov

Telephone: (202) 691-6576

Fax: (202) 691-6583

Katherine Terrell

William Davidson Institute and

University of Michigan Business School

Ann Arbor, MI 48109

U.S.A.

terrell@umich.edu

Telephone: (734) 615-4558

Fax: (734) 936-8715

Paper version: December 12, 2001

JEL classifications:

O5 Income Distribution

P2 Socialist and Transition Economies

P3 Socialist Institutions and Their Transitions

________________________________________________________________________

An earlier version of this paper was presented at the International Atlantic Economic Conference in

Philadelphia, Pennsylvania, in October 2001. We appreciate the comments of Lisa Wilder, our conference

paper discussant, and those of others attending the session. We thank the Slovak Statistical Office and

CERGE-EI for making the Slovak Microcensus data available to us. Thanks are also extended to Stephan

Jurajda, Renata Kosova and Petr Sedlak for research assistance. Terrell’s work for this project was

supported by The World Bank.

This paper is also available as Working Paper No. 345, Bureau of Labor Statistics, and Working Paper No.

377, and The William Davidson Institute, University of Michigan Business School.

The views expressed are those of the author and do not reflect the policies of the Bureau of Labor Statistics

(BLS) or the views of other BLS staff members.
1

Abstract

This paper measures the change in overall net monetary income inequality during

the first seven years of transition and considers the relative importance of two possible

explanations for the increase in inequality. These are a) changes in the sources of

household income, and b) changes in household composition. Changes in the sources of

household income reflect the role of the government and market during the transition

period, while changes in household composition reflect social reactions to the changing

economic environment. We find that the increase in inequality in labor income drove the

large increase in overall inequality (i.e., the Gini index of household per capita income

rose from 0.195 in 1988 to 0.263 in 1996). Changes in the distribution of pensions and

other social payments mitigated the rise in earnings inequality, with the latter playing a

more important role in reducing changes in overall income inequality over time. We

show large shifts in the demographic composition of households over this period

including far fewer households with children, far more households headed by pensioners,

increases in the number of one-person households and decreases in large (five person)

households. Although our results suggest that these shifts in the demographic

composition of households contribute to increasing overall inequality, by increasing

between group inequality, relatively more of the change in inequality over time is

accounted for by increases in within group inequality. We conclude that over the first

seven years of the transition labor market forces drove changes in overall inequality in

Slovakia to a much greater extent than changes in the government’s social safety net or in

personal decisions about household formation.
2

1. Introduction

Under the Soviet system, the Central and East European (CEE) countries

maintained the most equal distributions of income in the world. Hence greater income

inequality was an expected outcome of the transition from a command to a market

economy. Indeed, as prices were liberalized and market forces unleashed, workers with

scarce skills saw their earnings rise, while others suffered severe declines in their

earnings and even unemployment (see e.g., Terrell, 1999 for a description of winners and

losers in the emerging labor market of transition economies).

As expected, we find in our earlier study (Garner and Terrell, 1998) that Slovakia

experienced a substantial increase in the inequality of labor earnings during the first four

years of transition (1989-1993), however the surprizing result was the very small increase

in overall income inequality.1

 Using Family Budget Survey (FBS) data, we found the

Gini coefficient for total household income per capita rose from 0.157 to 0.168 over this

period, whereas the Gini for per capita earnings from labor rose from 0.281 to 0.344

(with much of the rise resulting from self-employment income). The increase in total

income inequality arising from this earnings component was almost completely mitigated

by changes in the incidence of taxes and the distribution of transfers, with the former

playing a slightly more important role than the latter.

In this paper we build on our earlier work to learn about the extent to which

inequality increased as the transition progressed to 1996 and to examine some potential

explanations for the increase in inequality over this period. We compare inequality

before the transition began, in 1988, to the level of inequality eight years later using

Microcensus data. This is a larger database than the FBS, which we used earlier, and it is
3

designed to be representative of the total population with its own set of weights.2

 As in

our previous work, we decompose changes in total inequality by changes in sources of

income (e.g., earnings from labor, transfer income). In this way we examine the extent to

which the labor market affected the distribution of income and the role that the

government played in providing a social safety net in 1996 compared to 1988 (and to

1993). Moreover, in this paper we explore an additional factor: the extent to which

changes in the demographic composition of households may help explain changes in

income inequality over these eight years. The transition process that Slovakia has been

undertaking since 1989 has impacted both of these channels of income inequality.

2. Transition in Slovakia

The Slovak economy experienced an enormous transformation during the 1988-

1996 period. The macroeconomic statistics in Table 1 indicate the tremendous growth of

the private sector as its share of GDP rose from about 5 percent in 1990 to 70 percent in

1996. As in all the Central and East European economies, GDP fell for the first four

years of transition (by an average of almost 7 percent a year) but rebounded in 1994 with

strong growth through 1996, the end of our period of analysis. Inflation rose by 58

percent during the year that the government liberalized all prices (1991), fell to single

digits in 1992 and then rose to 25.7 percent in 1993, the year of the "Velvet Divorce"

with the Czech Republic. The decline in output impacted the level of employment, which

in 1996 was still only 84.5 percent of the level in 1989. This was accompanied by large

 1

 We refer to after-tax income, including in-kind payments.

2

 For our earlier study, we created population weights using the Microcensus and FBS data to make the

FBS data as representative as possible. The Central Statistical Office does not produce population weights

for the FBS.
4

sectoral shifts in the structure of employment away from agriculture and industry

(including manufacturing and utilities), which declined by 44.2 percent and 26.8 percent,

respectively. Employment in the service sector absorbed some but not all of the outflows

as it grew by 12.1. (Slovak Statistical Yearbook, 1997.) Hence unemployment rates were

fairly high -- ranging from 10 percent to 14 percent -- throughout the period under

analysis.

As a result of all these structural changes in the economy, individuals were faced

with much uncertainty about both their job security and the purchasing power of their

income during this period. This uncertainty had an impact on the family formation and

household structure of the Slovakian people. As seen in Table 2, marriage rates and

birth rates declined tremendously from 1989 to 1996, while the divorce rate rose only

slightly over the time period. Not surprisingly, the rate of natural increase (the rate at

which the population grows based on birth and death rates) fell from 5.0 to 1.6 over this

period. Unlike the dramatic case of Russia, where the male mortality rate rose during the

transition, the death rate and the infant mortality rate fell over the period.

In this paper we examine how this changing environment affected the distribution

of income over time. After measuring the change in overall inequality, we consider the

relative importance of two possible explanations for the increase in inequality: a) changes

in the sources of household income, and b) changes in the household composition.

Changes in the sources of household income reflect the role of the government and

market during the transition period, while changes in household composition reflect

social reactions to the changing environment. We note that these changes affect the
5

distribution of total income by changing both the numbers of people in different

demographic groups as well as the distribution of incomes per se.

3. Methods and Data

3.1 Data

The data for this analysis are from the Microcensuses taken in 1989 and 1997.

Data for each survey refer to income in each previous year. The sample for the first

survey represents approximately 5 percent of the households who were living in Slovakia

in 1988 (a subsample of the one used for the Czechoslovak Microcensus). The unit of

sample selection is the house or apartment. Data are available by common budget

households, defined as a set of persons in the same dwelling who share the main

household expenditures. People living in the dwelling declared their status according to

their sharing of expenditures (Atkinson and Micklewright, 1992). The sample for the

1997 Microcensus was created by selecting one percent of all households living in

Slovakia in 1997 following a similar procedure as was used for the earlier surveys

(Slovak Central Statistical Office website 2001). The 1988 data set includes information

on 31,600 households and the 1996 data set includes data on 16,336 households.

3.2 Distributional and Inequality Measurement

Our analysis of overall inequality uses deciles, Lorenz curves (L), concentration

curves, and inequality indices based on the ranking of population weighted persons by

their household adult equivalent total income. The indices include the standard Gini

coefficient (G) and three generalized entropy measures: one half the square of the

coefficient of variation (CV), the Theil coefficient (T), and the mean logarithmic
6

deviation (D).3

 The Lorenz curve for discrete distributions, in our case deciles, can be

defined as (Lambert 1993):

 = ∑ ∑ ≤ ≤ = 

 





= =

10

1

10

1

1 10

10 i i

i

i where j and Y Y

Y

j Y L . (1)

The concentration curve in our case simply keeps the population deciles ordered

according to total income and plots the cumulative shares of one of its components on the

vertical axis (see Lambert 1993).4

The indices of inequality can be defined as:

G = −

∑∑ Y Y

n Y

i j

i j 2 2 (2)

2

2 (CV)

 = 

1

2

1

2

n 1

Y

Y

i

i

n 



 



 − 











 =  ∑ (3)

 T= ( ) 



 



 ∑ 



 





= Y

Y

Y

Y

n i n

i

i / ln

1

1 (4)

 D = ( )∑ 



 





=

n

i Yi

n Y

1

1/ ln (5)

where Yi = the rank weighted income, Y = the mean income, and n = the number of

income units (persons in the population in our case). Each of the overall measures differs

in its sensitivity to income variations at different levels of the distribution. For equidistant transfers, the Gini index is considered to be more sensitive to transfers around the

 

3

 For definitions of these measures of inequality see Coulter et al., 1992

4

 Forster and Pillizzari (2000) refer to the concentration curve as the pseudo-Lorenz curve in their report on

income distribution and poverty in the OECD area.
7

mode, while the Theil measure and one-half the square of the coefficient of variation are

more sensitive to transfers at the top of the distribution. The mean logarithmic deviation

is relatively more responsive to transfers at the lower end of the distribution.

If the values of all the indices are higher in year t than they are in year t-1, then it

can be said that the distribution of income is more unequal in year t. When one Lorenz

curve lies above another at one or more points, and does not lie below it at any point, then

there is clear Lorenz dominance. However, if one Lorenz curve crosses the other, no

conclusions can be drawn regarding relative inequality.

Household data from the Microcensus are the basis of our analysis. However,

since the focus of this research is the inequality of income across individuals, we allocate

adjusted household income to each household member. This weighting results in the

individual distribution rather than household distribution of income. The amount of

adjusted (or “equivalent”) income per person in each household unit is calculated by

dividing total household income by the number of equivalent adults in the household.

We examine the robustness of our results using four different equivalence scales:

• the OECD used equivalence scale5

first adult receives a weight of 1, each additional adult receives a weight of

0.7, and each child a weight of 0.5

• the Luxembourg Income scale (LIS)

the square root of household size

• per capita (PC) adjustment

each person receives a weight of one
8

3.3 Decomposition Analysis

We undertake two types of decompositions in order to understand which factors

are important in contributing to the levels of inequality in each year and changes in

inequality over time. The first decomposition is by sources of income and the second is

by demographic composition of the household.

Total household income is defined as the sum of monetary income net of taxes

(wage taxes, other taxes, and fees) plus the cash value of in-kind income.6

 We analyze the

following six sources of income for 1988 and 1996:

• Earned Income

1. Earnings from any non-agricultural employment (“wage income”)7

2. Earnings from agricultural employment

• Social Payments

3. Pensions

4. Other social payments which include:8

• Sickness Related Benefits (which include income from health

insurance and financial support while taking care of a family

member);

• Unemployment Benefits (in 1996 only)

• Child Allowances

• Social Assistance and Other Family Benefits (including

maternity leave, and parental allowances)

• Other Income

5. In-kind income

6. Other monetary income, which includes income from property,

institutions or private persons and income from abroad.

 To analyze the share of inequality due to each of these sources of income, we use

the Lerman and Yitzhaki (1985, 1989, 1994) decomposition of the Gini.9

 The Lerman

 5

 The OECD did not create the scale but it was used in several of its earlier publications.

6

 We were unable to analyze taxes since this was available separately only in 1988.

7

 This includes income from the self-employed. We would have liked to analyze self-employment income

separately but this was not possible given the construction of the data set in 1988.

8

 We are unable to separate out the distributional impact of the subgroups of social payments over time

since the categories in 1988 are defined differently than they are in 1996.
9

and Yitzhaki method decomposes the Gini into three terms: the Gini of the factor

component (Gg), the correlation of the factor component with the cumulative distribution

of overall income (Rg), and the share of the factor component in overall income, (Sg):

G GRS g gg

g

G

= =

∑ .

1

(6)

where:

[ ]

g

g g

g

y

y F G 2 cov , = , (6a)

[ ] [ ] g g

g

g

y F

y F

R

cov ,

cov = (6b)

Y

y S

g

g = (6c)

where y1... yg represent the income levels of factor components g, Fg represents the

cumulative distribution of yg and gY represents the mean. F is the cumulative

distribution of Y and Y is the mean of overall income.

In order to analyze the effect of household demographic composition on income

inequality, we decompose two indices that are members of the Generalized Entropy (GE)

measures of inequality measures, the Theil and the mean log deviation indices. Both are

additively decomposable by population subgroups (Shorrocks, 1984). To define these

indices, let the population be partitioned into k mutually exclusive sub-groups, for

 9

 Lerman (1999) wrote in a recent survey article, “It is now well understood that the seemingly simple

question ‘what is the role of an income source in overall income inequality’ is complex.” Surely, part of the

difficulty is that “a source’s contribution to inequality depends not only on aspects of the source itself but

also on how it interacts with other sources.” However, this does not invalidate the source decomposition.
10

example, household composition. The additive decomposability of T and D can be

illustrated by re-writing equations (4) and (5) as follows:

[ ] k k k k k k k k T = ∑ v λ T + ∑ v λ ln λ (7)

and

( ) k k k k k k D = ∑ v D + ∑ v ln 1/ λ (8)

where k v = nk / n is the proportion of the population in group k, λk = Y k /Y is group k’s

mean income relative to that of the whole population, and k k v λ is the income share of

group k in overall income. For each index presented in (7) and (8), total inequality can be

expressed as the sum of two contributions: the first term being the "within-group"

component (the weighted sum of the inequalities within each sub-group) and the second

term is the "between group" component (the inequality remaining if each person’s income

were equal to his/her sub-group’s mean income).

We decompose inequality changes and focus on the mean log deviation measure

(D) since is provides a useful decompositional formulation.10 The

change in inequality over the two years, t and t+1 can be written as

(9)

term A term B term C term D

The change operator is ∆ , and a bar over a variable represents the arithmetic mean of the

base and current period values.11 The overall change in inequality can be decomposed

 

10 According to Jenkins (1995).

11 Mookherjee and Shorrocks (1992) are credited with creating the decomposition. See Jenkins (1995) for

a further application.

∆ ≈ ∑k v k∆I 0k + ∑k I 0k∆v k + ∑k (λk − ln(λk ))∆vk + ∑k (θk − v k )∆ ln(Y k ) ) 0I
11

into four parts: term A represents the impact of ‘pure’ within-group inequality changes

over time; terms B and C represent the effect on overall inequality of changes in the

population shares on the 'within group' and 'between group' components, respectively.

Term D represents relative changes in the subgroup means with k k k θ = v λ .

4. Findings

The startling finding in this paper is how much income inequality increased over

the 1988-1996 period compared to the 1988/9-93 period. Whereas we (Garner and

Terrell, 1998) found total household per capita income inequality did not increase

appreciably from 1989 to 1993, we now find that over the 1988-1996 period it has grown

by a relatively large amount. As indicated in Table 3, the Gini indices rise by at least 33

percent between 1988 and 1996 when the per capita Gini rose by only 7 percent between

1989 and 1993. The other measures of inequality (Theil, half the coefficient of variation

squared, and the mean log deviation) essentially double in size between 1988 and 1966.12

We also note that the 1996 Lorenz curve, plotted in Chart 1, shifts out to the right of the

1988 Lorenz curve at each point. In looking for explanations for this rise in inequality,

we begin by examining changes in the sources of income.

4.1 Sources of Income

As noted above, we examine six sources of after-tax income: income from labor

(subdivided into agricultural and non-agricultural income), social transfers (subdivided

into pensions and other social payments) and other income (in-kind income and other

monetary income). We describe in this section the distributions of each of these sources

 

12 We note that in almost all cases the per capita income measures are higher than are those using the

OECD and LIS adult equivalent adjustments.
12

of income in 1988 and 1996 using various methods. Results are presented in Tables 4 and

5 and Charts 1 through 5.13 Table 4 contains information on the share of income from

each of the six sources across all persons within each decile of the income distribution.

For example, 23.5 percent of all income is from non-agricultural sources for persons in

the first decile in 1988. (Remember, the assumption is that all persons in a household are

allocated the income of the household on a per adult equivalent basis. The ranking is by

total income while the shares are person-based shares of non-agricultural income.14)

Table 5 presents results from the decomposition described in equations (6a) to (6c).

Charts 1, 2 and 4 present the Lorenz and concentration curves of each source of income.

Finally, Charts 3 and 5 provide information on the percentage of households in each

decile that have a positive value for a particular source of income.

Clearly the first place to look for an explanation of the significant increase in total

income inequality is in the change in the distribution of earnings from labor. A

comparison of the top with the bottom half of Chart 1, indicates that the distance between

the 1996 and 1988 concentration curves for earned income is greater than the distance

between the Lorenz curves for total income. This indicates a greater increase in the share

of earned income going to persons in the upper end of the distribution relative to the

distributional change in overall income.15

Decomposing earned income into two sources -- agricultural and non-agricultural

-- and plotting their concentrations curves for both years, we learn that the distribution of

earnings from non-agricultural activity has become far more concentrated among

 

13 The results in this section (in Charts 1-5 and in Tables 4 and 5) are based on person-weighted, adultequivalent (LIS) incomes.

14 If someone in the household received the income, a share was allocated to each person therein equally.
13

those in the upper end of the distribution. This is an indication that those at the top are

gaining even more relative to their position in 1988. Earnings from agricultural income

have become more equally distributed across the income distribution over this time

period, although those with higher incomes still have a greater percentage of agricultural

income compared to those at the lower end (Chart 2).

The numbers in Table 5 indicate that earned non-agricultural income contributes

more to total income inequality than any other source in both years and its contribution

rose over time. In fact the rise in its contribution is the largest increase from any source of

income. Its relative contribution to the Gini based on total income is 78.2 percent in 1988

and then rises to 101.9 percent in 1996. Values greater than one are possible given that

some of the income components contribute to reductions in overall income inequality.

Although non-agricultural income is more unequally distributed when compared to total

household income, its share of the total is quite small, thus reducing its overall impact on

inequality.

We next ask, what factors might be driving the changes in the contributions of

non-agricultural and agricultural income to overall household income inequality --

changes in the inequality of the source per se, or changes in the shares of the population

with the particular income factor (or sub-component)? In examining the relationship

between the population and distribution of income, we first focus on households who

have the income factor as a source. Again, the deciles are based on the person distribution

of total income. As seen in Chart 3, in 1988 the percentage of households with earnings

from non-agricultural activity was about the same in all deciles with the exception of the

 15 Hence, the relative change in inequality between earned ant total income over time is qualitatively

similar using the 1988-1996 the Microcensus data and the 1989-1993 Family Budget Survey data.
14

bottom two (approximately 90 percent of the households in each of the top seven deciles)

But in 1996 the percentages of households with non-agricultural earnings are not as equal

across the deciles. In that year over 90 percent of the households in the top four deciles

had non-agricultural income as a part of their household incomes, the proportion fell to

80 percent, 70 percent, and 60 percent for the next three deciles, respectively. A smaller

share of households in the second to the sixth deciles had non-agricultural income in

1996 compared with 1988 (Table 4).

The increased contribution to total inequality from non-agricultural income over

time (from 0.147 to 0.255) can be examined with respect to the decompositional

components of the Gini (see equation 6): the factor income share, Gini correlation, and

factor Gini. From 1988 to 1996 we find an increase in each of these: the share of total

income from this source increased from 0.622 to 0.678, the correlation rose from 0.652

and 0.821, and factor Gini representing the inequality in the distribution of this source

rose from 0.362 to 0.458. The relative magnitude of the Gini correlation is an indication

that higher non-agricultural incomes are even more correlated with higher total household

incomes in 1996 than they were in 1988.

The decreased contribution of agricultural income to total income inequality (the

relative contribution of this income source on overall income inequality was 0.238 in

1988 as compared to 0.016 in 1996) appears to arise largely because the share of total

income from agriculture for households fell from 0.094 to 0.013 (Table 5). However, the

drop in the Gini correlation also contributed to this fall in influence. As seen in Chart 3,

the shares of households with some agricultural income declined from 1988 to 1996. We

also see that the shares of total income from agriculture within each decile (both based on
15

person weighting) fell over time (Table 4). The distribution of agricultural income seems

to have become slightly more unequal as the factor Gini rose by only 11 percent (as

compared to 27 percent for non-agricultural income) as seen in Table 5. Although

agricultural income became more unequal over the time period, it became less

concentrated across the entire income distribution as seen in the lower half of Chart 2.

The income shortfall created by the decline in the overall share of agricultural

earnings and the decline in the share of non-agricultural earnings for the lower half of the

distribution was perhaps filled by pension income. Pension income became a larger

share of total income, rising from 0.124 to 0.180 over the time period (Table 5). As seen

in Table 4, this is due to the rising share of pension income in each decile, except for the

top and bottom ones. The concentration curves for pension income (Chart 4) indicate

that it is becoming slightly more directed away from the lower end of the distribution but

marginally more directed beginning with the third decile. The curve above the equal line

indicates that pensions were clearly concentrated among those who were poorer in 1988.

By 1996, those at the lower end of the distribution were receiving a disproportionate

lower share of pension income up until about the 30th percentile. After that point,

pensions became a larger share relative to the population ranking based on overall

income. For example, in 1996, 80 percent of the population had access to about 85

percent of all pension income. In contrast, in 1988, pension incomes were equally

concentrated among the population at the 80 percentile.

There was a decrease in the factor Gini for pension income from 1988 to 1996

from 0.755 to .730 (Table 5). And the overall effect of pension income on total income
16

inequality and the difference over time is small: in 1988 this income source contributed to

lowering the overall Gini index by 0.016 and by 0.012 in 1996 (Table 5).

Although other social payments have fallen as a share of income, from 0.111 to

0.083 (Table 5), their contribution in reducing overall income inequality has increased.

The relative contribution to the overall Gini was a reduction of 0.006 percent in 1988 but

0.061 percent in 1996. Clearly these benefits are being more targeted to the lower end of

the distribution by 1996. As seen in Table 4, the share of other social payments as a

percent of person weighted household income has fallen in the top eight deciles and risen

dramatically (from 11 percent to 31 percent for the lowest decile). For the second decile,

the increase was only slight. Similarly for households, Chart 5 indicates the share of

households with any other social income payments fell in all categories except the lowest

decile, where it rose. As seen in Chart 4, in 1988 the poorest 20 percent of the population

received less than its share of other social payments, while the top half of the distribution

received more. However the concentration curve in 1988 hovers around the 45-degree

line.

Although other monetary income is only a small share of total income in both

years (0.011 and 0.024), its share for the top two deciles rose considerably in 1996 (see

Table 4). Similarly, the share of households with other monetary income fell in all but

the top decile where it rose (Chart 5). The 1996 concentration curve reveals that other

monetary income is more concentrated in the upper range of the income distribution than

such income for 1988 (Chart 4). This source of income is quite eclectic, including

earnings from abroad as well as income from property and from other people.
17

Finally, in-kind income became more equally concentrated over the total income

population (Chart 4). The shares of this income across the deciles fell from 1988 to 1996

but the shares fell in almost the same way (Table 4). The results in Chart 5 reveal a much

lower and similarly distributed percentage of households with in-kind income in 1996 as

in 1988, with the exception of the top decile. Those in the top decile were most likely to

be recipients of in-kind income in 1996.

In summary: overall inequality rose during the period, largely due to changes in

non-agricultural earned income and partially due to changes in other monetary income

(which includes foreign income). The rise in the contribution of non-agricultural earnings

to total income inequality is likely due to not only an increase in this source’s inequality

but also its other characteristics, it share and correlation with total income..

4.2 Demographic Characteristics of Households

Among other channels, changes in income inequality can be driven by changes in

the composition of the household. The demographic shifts we noted in Table 2,

regarding the noticeable decline in the marriage rates and live-births are reflected in the

structures of the households in the 1988 and 1996 Microcensus data. For example, we

show in Table 6 that the share of the households with one or more children fell to 33

percent in 1996 from 45 percent in 1988. Similarly, we noted above that the death rates

declined slightly, yielding higher life expectancy. We find in the Microcensus data that

the average age of the head of the household rose as the share of households with heads

over 70 years of age increased and the share with heads less than 39 years of age fell.

As a result of the tremendous structural changes in the Slovak economy , with labor

being reallocated from the inefficient old state sector to the new private sectors, many
18

people became unemployed or took early retirement. Hence, it is not surprising to note in

Table 6 that the head of the household in 1996 is much less likely to be working and

more likely to be a pensioner or unemployed compared to 1988. However, the rise in the

share of households headed by pensioners – from 26 percent to 35 percent – could also

reflect other factors, such as general aging of the population or a change in household

formation, in addition to the increase in the number of pensioners brought about by the

restructuring. The figures in Table 7 reveal that the number of pensioners rose by about

10 percent from 1989 to 1995 and that the rise was higher among old-age and disability

pensioners (14 percent) than among widows (8 percent). As seen in Table 7, the

government has maintained the value of pension income by allowing it to rise at the same

rate that wages are rising. Hence pensions have been consistently around 45 percent of

the average wage over this period. Given these statistics, it is likely that the

government’s decision to maintain the purchasing power of pensioners at a relatively

high level has enabled pensioners to live independently. Consistent with this hypothesis

is the finding in Table 6 that there is a decrease in the share of households with five or

more persons and an increase in the share with one person.

How do these changes in household composition impact the distribution of income

during the 1988 and 1996 periods? We begin to examine this issue by describing the

demographic characteristics of the households in each decile in Chart 6. For example, the

number of persons per household declined over the period in all but the lowest decile,

which in 1996 has more members (2.5) than in 1988 (1.6). This is most likely due to a

decline in the number of children per household in each decile, except for the lowest

where it rose. Households in the lowest decile are comprised partially of more children,
19

more economically active adults and more unemployed adults (not shown in Chart 6).

However, households in the first decile are not composed of more pensioners, The lower

right hand chart in Chart 6 indicates that the percentage of households in the first decile

with a pensioner head decreased in 1996, while the percentage increased in all other

deciles in 1996 relative to 1988.

In order to increase our understanding of the role of demographic characteristics on

income inequality, we first decompose overall inequality into the portion due to

inequality within each demographic group and the portion arising from the inequality that

remained if each person’s income was equal to her/his sub-group’s mean income (i.e.,

between group inequality). Following this results, we present the results from the

inequality decomposition which reflects changes over time.

The findings from the within demographic decompositions, described in equations

(9) and (10), are presented in Table 8. The decompositions are carried out for the six

groups previously described in Table 6 plus two new demographic classifications of

households based on a combination of the other characteristics.

Not surprisingly, the decompositions for the Theil and mean log deviation indices

show that the majority of overall inequality can be explained by inequality from within

the groups -- very little is driven by between group differences.16 Moreover, the increase

in inequality over time is being driven by changes in within group inequality since in

most cases (six out of eight categories) the between group inequality has fallen over time.

For example, in the third category, number of economically active members, the

inequality within the groups (i.e., none, one, two, three+ members) represented 56

 

16 This is the finding in almost all decompositions of this sort – within group inequality is more important

than between group inequality in explaining overall inequality.
20

percent of total inequality in 1988 (i.e., 0.0337/0.0600) and it rose to explain 83 percent

of inequality in 1996. The difference in the within group inequality over time was 1.14 of

the difference in the overall inequality over time. Hence the share explained by between

group inequality was –0.14 of the difference in overall inequality.

The three categories for which the between group inequality is relatively more

important are the third (mentioned above), seventh (age of adults with number of adults

and number of children) and eighth (if children present and number of economically

active members) categories in Table 8. Here the differences in the means (not shown) of

each group are relatively larger in explaining overall inequality than the dispersion of

income within each group, as compared to the other five categories. Nevertheless, the

pattern of greater increase in within group inequality than between group over time still

holds.

The analysis of the impact on income inequality of demographic changes in the

populations over the 1988 to 1996 period are presented in Table 9. The decomposition

in Table 9 yields information on the impact that changes in the following four factors

have on changes in overall inequality over time: i) changes in within group inequality

(Term A); ii) changes in population shares on the within group component of inequality

(Term B); iii) changes in population shares on the between group component of

inequality (Term C); and iv) changes in the subgroup mean (Term D). The values in

Table 9 are expressed as proportions (or shares) of the total difference in overall

inequality. As was learned from the analysis in Table 8, the vast majority of the change

in overall inequality in 1988 compared to 1996 was brought about the changes in

inequality within each sub-group (e.g., the households with no, one, two or three plus
21

children). In all but two groups -- number of children and economic activity of head of

household -- the within group inequality (Term A) grew by more than 100 percent of the

change in overall inequality. In all but one group (number of children) the change in the

relative subgroup means (Term D) actually lowered the change in overall inequality.

Hence, the dispersion within each group grew, but the differences in the relative means of

these subgroups fell.

What about the shift in population shares (Terms B and C)? In general they increase

inequality but their impact is small compared to the impact of Terms A and D.

Moreover, they tend to impact between-group inequality more than within-group

inequality. For example, the shift in the population shares of households headed by

economically active individuals, pensioners and "other " (e.g., unemployed) increased

between group inequality by a larger amount than it impacted within group inequality.

Similarly, shifts in the share of the population across categories of "age of pensioner

head" increased between group inequality but lowered within group inequality. In sum

shifts in the demographic composition of the households are increasing overall inequality

over time.

5. Conclusions

We have found a large increase in total income inequality in Slovakia eight years

after the beginning of transition. The Gini index of household per capita income (net

monetary income plus in-kind income) rose from 0.195 in 1988 to 0.263 in 1996. Using

the LIS equivalent household income, the Gini rose from 0.188 to 0.250. In this paper we

examine the impact of markets and countervailing government safety nets on this change
22

in inequality over time by analyzing shifts in sources of income, in their distributions,

means, shares, and in the percentage of persons with these incomes (based on person

allocations). We learned that the earned non-agricultural income contributes most to

overall income inequality in each year, due to its large income share and to how

unequally it is distributed (Factor Gini). Moreover, in 1996 it accounted for much more

of total inequality than it did in 1988 and hence is the single most important factor

contributing to the large increase in overall income inequality between 1988 and 1996.

The distribution of pension income mitigated overall inequality in both years, but less so

in 1996, whereas the distribution of other social payments played a much larger role in

reducing income inequality in 1996 than in 1988.

 We show there are large shifts in the demographic composition of households

over time: far fewer households with children, far more households headed by

pensioners, increases in the number of one-person households and decreases in large (five

person) households. We find that these shifts in the demographic composition of

households are increasing overall inequality, by increasing between group inequality.

Their impact is larger than that found for the U.K. by Jenkins (1995) and Mookherjee and

Shorrocks (1982). Nevertheless, most of the change in inequality over time is accounted

for by increase in within group inequality. And given our finding above, we are lead to

believe that this is due to an increase in the dispersion of labor earnings over this period.

Finally it is interesting to note that although dispersion of income within each group has

grown significantly over time, the differences in the means of each group have actually

decreased over time. Hence between group inequality has declined from 1988 to 1996.

We conclude that over the first seven years of the transition labor market forces are
23

driving changes in overall inequality in Slovakia to a much greater extent than changes in

the Government’s social safety net or in individual’s decisions about household formation.
24

REFERENCES

Atkinson, Anthony B. and John Micklewright (1992), Economic Transformation in

Eastern Europe and the Distribution of Income, Cambridge University Press.

Buhmann, Brigitte, Lee Rainwater, Guenther Schmaus, and Timothy M. Smeeding

(1992), "Equivalence Scales, Well-Being, Inequality, and Poverty: Sensitivity

Estimates Across Ten Countries Using the Luxembourg Income Study (LIS)

Database," Review of Income and Wealth, Vol. 34., No. 2, June: 115-142.

Chase, Robert (1998), "Baby Boom or Bust? Changing Fertility in Post-Communist

Czech Republic and Slovakia" William Davidson Institute Working Paper No.

157

Commission of the European Communities (CEC, 1992), Employment Observatory,

Central and Eastern Europe, Employment Trends and Developments, No. 1,

January.

Forster, Michael F. and Michele Pellizzari (2000), Trends and Driving Factors in Income

Distribution and Poverty in the OECD Area, Labour Market and Social PolicyOccasional Papers No. 42, Directorate for Education, Employment, Labour and

Social Affairs, OECD, Paris, France.

Garner, Thesia I. and Katherine Terrell (1998), “A Gini Decompositon Analysis of

Inequality in the Czech and Slovak Republics During the Transition,” The

Economics of Transition, 6(1): 23-46.

Jenkins, Stephen P. (1991), "The Measurement of Income Inequality" in Lars Osberg

(ed.) Economic Inequality and Poverty: International Perspectives. Sharp

Publishers: London: 3-38.

Jenkins, Stephen P. (1995), "Accounting for Inequality Trends: Decomposition Analyses

for the U.K., 1971-86," Economica. 62(245): pp. 29-63.

Lambert, Peter J. (1993), The Distribution and Redistribution of Income, A Mathematical

Analysis, Manchester, UK: Manchester University Press.

Lerman R. (1999), “How income sources affect income inequality?” in J.Silber (ed),

Handbook on Income Inequality Measurement, Dordrecht: Kluwer Academic

Publishers.

Lerman, Robert I. and Shlomo Yitzhaki (1985), "Income Inequality Effects by Income

Sources: A New Approach and Applications to the U.S.," Review of Economics

and Statistics, February, Vol. 67: 151-156.
25

Lerman, Robert I. and Shlomo Yitzhaki (1989), "Improving the Accuracy of Estimates of

Gini Coefficients," Journal of Econometrics, September, Vol. 42: 43-47.

Lerman, Robert I. and Shlomo Yitzhaki (1994), "The Effect of Marginal Changes in

Income Sources on U. S. Income Inequality," Public Finance Quarterly, October.

Milanovic, Branko (1992), "Income Distribution in Late Socialism: Poland, Hungary,

Czechoslovakia, Yugoslavia, and Bulgaria Compared," Socialist Economies

Reform Unit, Country Economics Department, World Bank, Research paper

series, paper number 1, March.

Milanovic, Branko (June 1992), "Distributional Impact of Cash and In-Kind Transfers in

Eastern Europe and Czechoslovakia," Socialist Economies Reform Unit, Country

Economics Department, World Bank, Research paper series, paper number 9.

Mookherjee, D. and A. F. Shorrocks (1982), “A decomposition analysis of the trend in

UK income inequality,” Economic Journal, 92, 886-992.

Podder, Nripesh (1993), "The Disaggregation of the Gini Coefficient by Factor

Components and its Applications to Australia," Review of Income and Wealth,

Series 39, No. 1, March: 51-61

Shorrocks, A. F. (1984), "Inequality Decomposition by Population Subgroups,"

Econometrica, Vol. 52, No. 6: 1369-1385.

Shorrocks, A. F. (1980), "The Class of Additively Decomposable Inequality Measures,"

Econometrica, Vol. 48, No. 3: 613-625.

Slovak Central Statistical Office (2001), website: http://www.statistics.sk/.

Smeeding, Timothy M. (1991), "Cross-National Comparisons of Inequality and Poverty

Position," in Lars Osberg (ed.), Economic Inequality and Poverty: International

Perspectives, New York: M. E. Sharpe, Inc., pp. 39-59.

Tsakloglou P. (1993) "Aspects of inequality in Greece: Measurement, decomposition and

inter-temporal change: 1974, 1982," Journal of Development Economics, 40: 53-

74.

Yitzhaki, Shlomo (1989), "Ginjack and Ginjackw," Fortran program, September 27.
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Economic Growth 

%GDP growth (real) -2.5 -14.6 -6.5 -3.7 4.9 6.7 6.2 6.2 4.1 1.9 

GDP per capita ($US) 2,710 2,052 2,213 2,258 2,571 3,240 3,495 3,679 3,802 3,970 

Prices

CPI Index (% change) 18.4 58.3 9.1 25.1 11.7 7.2 5.4 6.4 5.6 14.0 

Trade

Current Account Balance (%GDP) -6.1 3 1.6 -5 4.8 2.3 -11.2 -10 -10.1 -5.5 

Market Liberlization

Private Sector Share of GDP (%) 5 NA NA NA 55 60 70 75 75 75 

Labor Markets

Unemployment Rate (%) 1.5 11.8 10.3 12.2 13.7 13.1 11.1 11.6 11.9 19.2 % change in productivity NA NA NA NA 6.8 4.0 2.5 4.1 11.5 2.0 % change in wages NA NA NA NA 7.0 5.7 9.8 7.5 6.1 -3.9 

Index of Employment Levels (1989=1)* 0.982 0.859 0.868 0.846 0.837 0.857 0.845 0.826 0.818 0.780 *Employment in 1989 was 2,504,079

Macroeconomic Data for Slovakia

Table 1
Indicator 1989 1990 1991 1992 1993 1994 1995 1996

Marriages 6.90 7.60 6.20 6.40 5.80 5.30 5.10 5.10Divorces 1.57 1.67 1.49 1.52 1.53 1.62 1.67 1.75Live-Births 15.20 15.10 14.90 14.10 13.80 12.40 11.40 11.20Deaths 10.20 10.30 10.30 10.10 9.90 9.60 9.80 9.50Infant Mortality 13.50 12.00 13.20 12.60 10.60 11.20 11.00 11.00Natural Increase 5.00 4.80 4.60 4.00 3.90 2.80 1.60 1.70Source: Statistical Yearbook of the Slovak Republic , 1996 (p. 154)

Table 2

Slovakia: Population Changes in Rates per 1,000 Inhabitants
OECD LIS PC OECD LIS PC OECD LIS PC

Log Deviation 0.050 0.062 0.062 0.125 0.128 0.142 150.1 107.2 129.3Thiel 0.051 0.060 0.065 0.111 0.112 0.128 117.6 86.7 96.9(CV)2/2 0.060 0.067 0.079 0.134 0.131 0.157 123.8 96.1 99.2

Gini 0.172 0.188 0.195 0.243 0.25 0.263 41.3 33.7 34.9

Data: Slovak Republic Microcensus 1988 and 1996 1Household equivalent after tax monetary plus in-kind income, LIS equivalence scale; person weighted distribution

Table 3

% difference (1996 vs.1988)

Slovakia: Overall Income Inequality: 1988 and 19961

(n=16,336)

1988 1996

(n=31,606)
Decile Non-Agricultural Agricultural Pension Excluding pension Total

1 23.5% 1.7% 58.1% 11.3% 2.1% 3.4% 100.0%2 48.6% 3.6% 24.6% 18.3% 1.8% 3.1% 100.0%3 59.1% 4.6% 14.1% 18.0% 1.3% 2.7% 100.0%4 65.6% 4.8% 10.0% 16.1% 0.8% 2.6% 100.0%5 68.0% 6.0% 8.3% 14.0% 0.9% 2.8% 100.0%6 68.0% 7.5% 8.1% 12.1% 0.8% 3.4% 100.0%7 67.1% 9.3% 8.6% 10.3% 0.9% 3.7% 100.0%8 66.9% 10.3% 9.0% 8.5% 0.9% 4.4% 100.0%9 66.2% 12.2% 8.7% 7.2% 1.0% 4.6% 100.0%

10 61.5% 18.8% 7.6% 5.4% 1.4% 5.3% 100.0%

1Decile ranking based on household adult equivalent after tax monetary plus in-kind income with source income also equivalized.

 Both based on LIS equivalence scale (square root of family size). Person weighted distribution.

Decile Non-Agricultural Agricultural Pension Excluding pension Total

1 32.7% 0.7% 30.9% 31.4% 1.5% 2.9% 100.0%2 39.8% 0.9% 36.2% 20.1% 1.3% 1.7% 100.0%3 46.7% 1.1% 33.2% 16.1% 1.0% 1.9% 100.0%4 50.8% 1.2% 32.2% 12.5% 1.2% 2.0% 100.0%5 57.9% 0.8% 26.9% 11.1% 0.9% 2.4% 100.0%6 65.6% 1.8% 20.1% 8.7% 1.2% 2.6% 100.0%7 73.4% 1.5% 14.6% 6.9% 1.2% 2.3% 100.0%8 76.8% 1.5% 12.5% 5.0% 1.6% 2.6% 100.0%9 77.9% 1.8% 11.5% 3.6% 2.5% 2.6% 100.0%

10 85.5% 1.1% 4.5% 1.5% 6.0% 1.4% 100.0%

1Decile ranking based on household adult equivalent after tax monetary plus in-kind income with source income also equivalized.

 Both based on LIS equivalence scale (square root of family size). Person weighted distribution.

Slovakia: Income Distribution1 by Source within Each Decile

Table 4

1988

Other monetary 

income

In-Kind 

Income

Earned Income Social PaymentsEarned Income Social Payments Other monetary 

income

In-Kind 

Income
Contribution Relative

Share of Gini Factor to Overall Contribution

Total Income Correlation Gini Gini to Gini

1988

earned non-agricultural income 0.622 0.652 0.362 0.147 0.782earned agricultural income 0.094 0.537 0.881 0.045 0.238pension income 0.124 -0.172 0.755 -0.016 -0.085other social payments 0.111 -0.020 0.474 -0.001 -0.006other monetary incomes 0.011 0.145 0.927 0.002 0.008in-kind income 0.038 0.426 0.721 0.012 0.063 TOTAL 0.188 1.000

1996

earned non-agricultural income 0.678 0.821 0.458 0.255 1.019earned agricultural income 0.013 0.312 0.979 0.004 0.016pension income 0.180 -0.093 0.730 -0.012 -0.049other social payments 0.083 -0.281 0.653 -0.015 -0.061other monetary incomes 0.024 0.598 0.972 0.014 0.056in-kind income 0.022 0.262 0.853 0.005 0.020 TOTAL 0.250 1.000

1Decile ranking based on household adult equivalent after tax monetary plus in-kind income with source income also equivalized.

 Both based on LIS equivalence scale (square root of family size). Person weighted distribution.

Slovakia: Decomposition by Source of Income 

Table 5

N.B.: Household LIS equivalent after tax monetary income, including in-kind, in current CSK crowns for 1988 and SR crowns for 1996, based on 

person weights, whose mean is 39410 in 1988 and 76566 in 1996.
Charactersitic 1988 1996 Charactersitic 1988 1996

Children Present in Household Age of Head

no children 55.4 67.0 head <=29 years of age 10.1 5.9children present 44.6 33.0 head 30-34 years of age 11.9 7.0

head 35-39 years of age 12.5 9.4

Number of Children head 40-44 years of age 10.6 13.0

no children 55.4 67.0 head 45-49 years of age 9.2 13.4one child 16.8 16.4 head 50-54 years of age 8.6 10.3two children 19.7 12.9 head 55-59 years of age 9.2 8.7three or more children 8.1 3.7 head 60-64 years of age 8.6 8.3

head 65-69 years of age 8.0 8.1

Economic Activity of Head head 70+ years of age 11.3 14.8

economically active 73.3 60.4 missing 1.1unemployed none 3.5pensioner 26.4 34.7 Age of Pensioner Head

other 0.3 1.4 no pensioners 72.7 65.3

pensioner head <= 59 years of age 4.4 5.2

Household Size pensioner head 60-64 years of age 5.9 6.9

one person 17.8 21.2 pensioner head 65-69 years of age 6.7 7.9two persons 24.3 23.0 pensioner head 70+years of age 10.3 14.8three persons 17.6 17.7four persons 24.1 25.4 No. of Econ. Active Members

five persons 10.9 8.7 no econ active members 21.5 28.2six or more persons 5.3 4.0 one econ active member 25.0 26.6

two econ active members 42.0 33.93 or more econ active members 11.5 11.3

Table 6

Slovakia: Demographic Composition of the Household (Percentage Distribution of Households)
Number of Pensioners (in thousands)

1989 1990 1991 1992 1993 1994 1995

Total 1065 1087 1124 1156 1172 1178 1173

Old-Age 488 506 532 548 553 556 558

Disability2** 218 223 230 243 252 256 248

Widow 270 275 279 283 286 288 291

Average Monthly Pension (in Slovak crowns)

1989 1990 1991 1992 1993 1994 1995

Old-Age 1432 1550 1884 2058 2367 2852 3102

Disability2 1310 1413 1750 1940 2247 2714 2950

Widow 742 825 1007 1118 1255 1431 1594

Average Pension as a Share of the Wage

1989 1990 1991 1992 1993 1994 1995

Old-Age 46.3% 48.2% 50.3% 45.5% 45.0% 46.8% 43.8%

Disability2 42.4% 43.9% 46.7% 42.9% 42.7% 44.6% 41.7%

Widow 24.0% 25.6% 26.9% 24.7% 23.9% 23.5% 22.5%

1 Monthly level of pension paid out excluding child support bonus and disability benefits2 For the handicapped

Table7

Slovakia: Number of Pensioners and Average Monthly Pension,1 1989-

1995

Source: Statistical Yearbook of the Slovak Republic , 1994 (p.148, 406) and 1996 (p.180, 

476)
Category Sample Year

1988 0.0600 0.0585 0.0015 0.0618 0.06

1 1996 0.1124 0.1090 0.0034 0.1284 0.122 Household Size 1988 0.0600 0.0522 0.0077 0.0618 0.05

1996 0.1124 0.1091 0.0032 0.1284 0.12

3 1988 0.0600 0.0337 0.0263 0.0618 0.03

1996 0.1124 0.0935 0.0189 0.1284 0.101988 0.0600 0.0525 0.0075 0.0618 0.05

4 1996 0.1124 0.1039 0.0085 0.1284 0.15 Age of Head 1988 0.0600 0.0502 0.0098 0.0618 0.05

1996 0.1124 0.1071 0.0052 0.1284 0.121988 0.0600 0.0529 0.0071 0.0618 0.05

6 1996 0.1124 0.1088 0.0035 0.1284 0.127 1988 0.0600 0.0413 0.0187 0.0618 0.04

1996 0.1124 0.0995 0.0129 0.1284 0.1

8 1988 0.0600 0.0299 0.0301 0.0618 0.02

1996 0.1124 0.0878 0.0245 0.1284 0.10

1Decile ranking based on household adult equivalent after tax monetary plus in-kind income with source income also equivalized.

 Both based on LIS equivalence scale (square root of family size). Person weighted distribution.

Subgroup defined as follows:Number of Children: no children, 1 child, two children, 3+ children

Number of HH Members: one, two three, four, five+

Number of economically active members: none, one, two, three+

Economic Activity of Head: economically active, pensioner, other

Age of Head: 15-39, 40-54, 55-64, 65+

Children and Econ. Active MembersAge of Adults, No. of Adults and No. of Children

Between-group 

inequality

Aggregate 

Inequality

Age of Pensioner HeadNumber of Children

Age and No. of adults and no. of children: 1) Age l.t. Age l.t. 65, no children,two adults; 3) Age l.t. 65, no l.t. 65, children, two adults; 5) Age l.t. 65, on child, tchildren, two adults; 7) Age l.t. 65, three+ children, twchildren, three+ adults; 9) Age g.t. 65, with and withoEcon. Activity and no. of children: 1) no children, no children, one econ active adult; 3) no children, two echildren, three+ econ active adults; 5) children, no eone econ active adult; 7) children, two econ active a

Within-Group and Between-Group Income Inequality1 in Slovakia: 1988 and 199

Table 8 

No. of Econ. Active MembersEconomic Activity of Head

Theil Mean Log 

Aggregate 

Inequality

Within-group 

inequality

Within

inequ
one econ active adult; 7) children, two econ active a

Age of pensioner: no pensioner head, pensioner l.t. 65, pensioner g.t. eq. 65 years. active adults.
Within Group 

inequality

 Group 

Mean 

Incomes

(Term A) (Term B) (Term C) (Term D)

No. Children 0.889 0.087 -0.022 0.046

Household Size 1.067 0.013 0.015 -0.095

No. Econ. Active Members 1.127 -0.007 0.099 -0.218

Econ. Activity of Head 0.928 0.046 0.128 -0.102

Age of Pensioner Head 1.061 -0.009 0.047 -0.100

No. Children, No. and Age of Adults 1.089 0.009 0.026 -0.125

Children-Econ Active Members 1.077 0.005 0.139 -0.221

 

Subgroup defined as follows:

Number of Children: no children, 1 child, two children, 3+ children

Number of HH Members: one, two three, four, five+

Number of economically active members: none, one, two, three+

Economic Activity of Head: economically active, pensioner, other

Econ. Activity and no. of children: 1) no children, no econ active adult; 2) no children, one econ active 

adult; 3) no children, two econ active adults; 4) no children, three+ econ active adults; 5) children, no 

econ active adult; 6) children, one 

Age of pensioner: no pensioner head, pensioner l.t. 65, pensioner g.t. eq. 65 years.

Contribution to change in overall inequality due 

to changes in:

Population Shares 

effect on 

within between

1

Household equivalent after tax monetary plus in-kind income, LIS equivalence scale; person weighted 

distribution; Based on the Mean Log Deviation Measure of Inequality.

 Table 9 

Age of Head: 15-39, 40-54, 55-64, 65+

Age and No. of adults and no. of children: 1) Age l.t. 65, no children,one adult; 2) Age l.t. 65, no 

children,two adults; 3) Age l.t. 65, no children, three+ adults; 4) Age l.t. 65, children, two adults; 5) Age l.t. 

65, on child, two adults; 6) Age l

Sub-group Decompositions of Changes in Aggregate Income 

Inequality: 1988 - 19961
shares of total

after tax household

income including

in-kind

Deciles of persons ranked by equivalent total after tax household income including in-kind -LIS scale

shares of

income from

working (earned)

Deciles of persons ranked by equivalent total after tax household income including in-kind -LIS scale

Lorenz Curve: Total Household Income Including In-kind

Concentration Curve: All Earned Income

Chart 1

Lorenz Curve and Concentration Curve for All Income

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0 1 2 3 4 5 6 7 8 9 10

1988 1996 equality

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0 1 2 3 4 5 6 7 8 9 10

1988 1996 equality
shares of

non-agr. income

Deciles of persons ranked by equivalent total after tax household income including in-kind -LIS scale

shares of

agr. income

Non-Agricultural Earned Income

Earned Agricultural Income

Chart 2

Concentration Curves: Non Agricultural vs. Agricultural Income

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0 1 2 3 4 5 6 7 8 9 10

1988 1996 equality

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0 1 2 3 4 5 6 7 8 9 10

1988 1996 equality
Chart 3

Percentage of Households with Earned Income: Agricultural vs. Non-Agricultural 

Non-Agricultural Income

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4 5 6 7 8 9 10

Income Decile

1988

1996

Agricultural Income

0

10

20

30

40

50

1 2 3 4 5 6 7 8 9 10

Income Decile

1988

1996
shares of shares of

pension social incomeincome not including 

pensions

Deciles of persons ranked by equivalent total after tax household income including in-kind -LIS scale Deciles of persons ranked by equivalent total after tax household income including in-kind 

shares of shares ofother net in-kind monetary incomeincome

Deciles of persons ranked by equivalent total after tax household income including in-kind -LIS scale Deciles of persons ranked by equivalent total after tax household income including in-kind 

Other Net Monetary Income In-kind Income

Chart 4

Concentration Curves of Other Source of Income

Pension Income Social Income not including Pensions

0.000.100.200.300.400.500.600.700.800.901.00

0 1 2 3 4 5 6 7 8 9 10

1988 1996 equality

0.000.100.200.300.400.500.600.700.800.901.00

0 1 2 3 4 5 6 7 8 9 10

1988 1996 equality

0.000.100.200.300.400.500.600.700.800.901.00

0 1 2 3 4 5 6 7 8 9 10

1988 1996 equality

0.000.100.200.300.400.500.600.700.800.901.00

0 1 2 3 4 5 6 7 8 9 10

1988 1996 equality
Deciles of persons ranked by equivalent total after tax household income including in-kind -LIS scale

Deciles of persons ranked by equivalent total after tax household income including in-kind -LIS scale

Chart 5

Percentage of Households with Unearned Income by Source

Pension Income

0

20406080

100

1 2 3 4 5 6 7 8 9 10

Income Decile

19881996

Social Income (excluding pensions)

0

20406080

100

1 2 3 4 5 6 7 8 9 10

Income Decile

Other Net Monetary Income

05

10152025

1 2 3 4 5 6 7 8 9 10

Income Decile

19881996

In-Kind Income

0

20406080

1 2 3 4 5 6 7 8 9 10

Income Decile
Deciles of persons ranked by equivalent total after tax household income including in-kind -LIS scale

Deciles of persons ranked by equivalent total after tax household income including in-kind -LIS scale

Demographic Characteristics by Income Decile

Chart 6

Average Number of Persons per 

Household

0

0.5

1

1.5

2

2.5

3

3.5

4

1 2 3 4 5 6 7 8 9 10

Income Decile

19881996

Average Number of Children p

Household

0

0.20.40.60.8

1

1.21.4

1 2 3 4 5 6 7 8 9 

Income Decile

Average Number of Economically Active 

Members per Household

0

0.5

1

1.5

2

2.5

3

1 2 3 4 5 6 7 8 9 10

Income Decile

19881996

Percentage of Households with Pensio

Head

0.0%

10.0%20.0%30.0%40.0%50.0%60.0%70.0%80.0%90.0%

1 2 3 4 5 6 7 8 9 

Income Decile