Child poverty, labor markets and public policies across
industrialized countries
Bruce Bradbury
Social Policy Research Centre
University of New South Wales
Sydney
Australia
Markus Jäntti
Åbo Akademi University
Department of Economics and Statistics
Turku
Finland
March 2005
Draft
Not for citation
Abstract
This paper examine trends in relative and “real” child poverty across selected industrialized countries
using data from the Luxembourg Income Study. The relative importance of markets and the public
sector in determining the living conditions of the worst off children is examined by relating average
income of the poorest fifth of children to the overall median and the decomposing this into the part
that is rewarded by markets and the part that stems from net social transfers. The results suggest
that the composition of relative average income in the lowest quintile group of children appears to
vary quite substantially but at a broadly similar relative living standard within a country across time.
Regressing these components on unemployment rates, working-age social expenditures and a businesscycle indicator suggests this variation is at least in part accounted for by these variables.
1
Introduction
To what extent are the living standards of the worst off children in modern industrialised nations determined by markets and the public sector? The traditional view, based on a comparison of poverty
measured before benefits are received and taxes paid with that after the welfare state has intervened,
suggests that poverty rates are low in the countries that devote more public transfers to low income
families. Our research, based on comparing average market income and average net public transfers
across countries for the poorest fifth of children in a number of OECD countries, however, suggests that
countries devote surprisingly similar amounts of cash to the worst-off children. Variation in the market incomes is an important factor in accounting for differences in child well-being, at least measured
in terms of the average family income of the poorest fifth of children, whether in relative terms or in
Purchasing Power Parity (PPP) adjusted international dollars. This finding suggests that, if we want to
understand why children in some countries are better off than in others, we should examine both social
transfers and why the market income of the parents of the worst-off children varies across countries
(Bradbury and Jäntti, 1999).
The United Nations Children’s Fund (UNICEF) has published several research reports on child
poverty in rich countries.1 The latest UNICEF Report Card (UNICEF, 2000), based in large part on
Chen and Corak (2005) and Corak et al. (2005), extensively review the trends in relative and “backdrop”
child poverty in advanced countries. Among the countries they study, Norway, the United Kingdom and
the United States experienced declines in child poverty, Canada and Finland had little change and most
others, including Luxembourg, Mexico, Germany, Italy, Hungary and the Netherlands saw increases in
child poverty.
In studying factors that underlie the observed changes, Chen and Corak (2005) separate between
demographic factors, labour markets and public sector effects. Demographic factors turn out to be less
important than markets and the public sector. Both UNICEF and Chen and Corak (2005) are careful to
stress interactions between between labour markets and the public sector. This is particularly true of the
1 These
include Cornia and Danziger (1997), Bradbury et al. (2001), Bradbury and Jäntti (1999), UNICEF (2000), and,
most recently, UNICEF (2005) which is based on several papers, including Corak et al. (2005) and Chen and Corak (2005).
See http://www.unicef-icdc.org/research/ESP/CIIC1.html for their report card series and links to the
underlying research reports.
1
United States, where child poverty rates dropped substantially over the 1990s while also government
transfers declined. Indeed, a detailed decomposition analysis suggest that the effect of the change in
government transfers in the US would have been to increase poverty. It seems that the US in the 1990s
the composition of government transfer changed in such a way as to at least facilitate a greater reliance
on labour markets for children’s economic well-being.
The purpose of this paper is to re-examine trends in children’s living standard using data from
the Luxembourg Income Study (LIS), a collection of internationally comparable micro-data on income
distribution and selected labour market data from 29 countries spanning the early 1970s to currently the
year 2000. We offer a perspective that complements that used in UNICEF (2000), based on earlier work
for UNICEF (Bradbury and Jäntti, 1999, 2001). In particular, we study the average relative disposable
income of the poorest fifth of children in each country, a measure that is easily decomposable into a part
that stems from markets and a part that is the difference between direct taxes paid and income transfers
received. This measure allows us to examine simultaneously the well-being of the worst-off children
and the composition of their income packages, as well as how these change over time.
The paper is structures as follows. In Section 2 we present the definitions and methods we use for
measuring children’s living standards. Section 3 shows the trends in both poverty and our alternative
measure of children’s well-being, the average income of the poorest fifth relative to the overall median.
We turn in section 4 how the composition of this measure of child well-being varies across countries,
time and different household types. This section also presents some descriptive regressions that relate
the composition of children’s average income to labour market conditions, the business cycle and social
expenditures. A final section offers some concluding comments.
2
Data and methods
We measure children’s living standards using data from the Luxembourg Income Study (LIS).2 . LIS
covers 29 industrialised countries, most of which with information for several years, containing altogether 133 datasets. The objective is look at the general patterns of variation in child poverty outcomes
2 The
Luxembourg Income Study comprises a database of household income survey information, adjusted to be as
comparable as possible. For more information see http://www.lisproject.org/.
2
across the industrialised world. We here only examine OECD countries (although the LIS does not
include some OECD countries, such as Greece, Japan, New Zealand and Portugal, which are therefore
not included in our analysis).
We examine child poverty as measured by the low-income status of their households. This does
not capture all aspects of child poverty or more broadly child deprivation, nor is it intended to do so.3
While all areas of the deprivation of children are highly relevant, there are good reasons to study the
income position of children in particular, including the fact that money income is a central vehicle for
generating economic well-being in modern industrialised countries and that income data are readily
available.4
Three major decisions that must be made in any poverty study concern the measure of resources,
the choice of sharing unit (e.g. within nuclear families or within households) and the equivalence scale
(the needs of different types of sharing units). There is a very large literature that addresses these issues
(see e.g. Gottschalk and Smeeding, 1997; Jenkins and Lambert, 1993; Jäntti and Danziger, 2000) . Our
choices on these matters are fairly standard, limited in part by the structure of the data available to us.
Our measure of resources is annual5 disposable income. This includes market incomes and government cash transfers, and deducts income taxes and compulsory social insurance contributions. Whilst
this is not a comprehensive indicator of the resources available to the families of children (e.g. it
excludes non-cash services) it remains the best available indicator of cross-national variations in living standards. These issues of the appropriate resource measure (and the role of non-cash benefits in
particular) are discussed in more detail in Bradbury and Jäntti (1999). For a recent treatise on the measurement of income, see Expert Group on Household Income Statistics (The Canberra Group) (2001).
We assume resources are shared within households and define every person in the household to
have the same poverty status. This definition is the one that is most commonly available across our
countries. The exception to this is Sweden, where the source data before the last wave of LIS is limited
to tax units, corresponding to nuclear families of parents and their dependent children. Prior to 2000,
3 The
approach of this paper rests on work done for the UNICEF (Bradbury and Jäntti, 1999, 2000, 2001). See also
UNICEF (2000), available at http://www.unicef-icdc.org/research/ESP/CIIC1.html.
4 See Chen and Corak (2005) for a recent discussion on measuring living standards and poverty.
5 Except in the UK, where current income is used.
3
Swedish adult children and lone parents living with their parents are treated as separate units.6
Children are defined to be persons who are 17 years old or younger. Their economic resources are
measured by allocating to each child the income per equivalent adult, calculated by dividing household
cash disposable income by a scale that is close to the so-called “old” OECD scale (see Bradbury and
Jäntti, 1999). Our scale, used by Jenkins and Cowell (1994) and also recommended for use by the
US National Science Foundation Poverty Commission National Research Council (1995), associates
children with lower needs than adults and then uses a power of the number of adult equivalents to
standardise for economies of scale. Bradbury and Jäntti (1999) examine this issue at some length. This
difference is not very important for ranking countries by level of child poverty.
The literature on poverty measurement has typically used two types of poverty threshold: absolute
and relative poverty lines. Absolute , or more properly, fixed real price poverty lines, are thresholds
which permit people living in specified family types to purchase the same bundle of goods and services in different countries or times. Families that fall below the common consumption threshold are
therefore considered to be poor. Relative poverty lines, on the other hand, are more closely related to
concepts of social exclusion. These poverty lines are typically defined with reference to a measure of
typical consumption levels (e.g. half median income).
Arguably, a focus on child poverty also calls for a somewhat different relative poverty line. If
children are excluded from social participation, the most important form of this may be exclusion from
the lifestyle typically enjoyed by other children. Similarly if the exclusion of children arises via the
exclusion of their parents, it will most often be other parents that they compare themselves with rather
than, say, the elderly. This suggests the use of a poverty line defined with reference to the average living
standard of children in the society.
The use of the median as anchor-point can be loosely justified in terms of a social exclusion, but
has also a practical basis. In household surveys, because data collection errors at the two extremes of
the income distribution are likely to be more frequent, the median is a more robust measure of central
tendency than the mean.
6 There are other differences within countries across time in LIS. The original datasets that underlie the “lissified” compa-
rable data may have changed and in one case (Germany, see below) the area to which it refers has changed. We do not make
allowances for changes in data sources within countries, but essentially treat all country datasets as being representative for
the countries’ population.
4
Though the comparison of real living standards across countries requires the use of strong assumptions, many would argue that it is a more important concept than that of relative poverty. To focus only
on the relative measures would be, for example, to discount entirely the poverty alleviation benefits of
income increases that were spread (proportionately) evenly across the population.
Both the relative and real measures provide important insights into the way the living conditions
of the most disadvantaged children vary across countries. Relative poverty is measured by estimating
the proportion of children whose economic resources are less than one half of the median of adjusted
disposable income in their country in the year of the survey. We also measure poverty defined in an
internationally comparable metric, Purchasing Power Parity-adjusted (PPP) international 2000 dollars,
relative to the US official poverty line for a family of four in 2000.7
The key method that is used in theis paper is based on the simple decomposition of the equivalised
disposable income of a household that
disposable income ≡ market income + net public transfers.
Since this holds for all households it also holds for the average income of the poorest fifth. Average
disposable income in the poorest fifth of children is closely related to the relative poverty rate if we
relate that average to median income, and to a internationally fixed real poverty line if we measure
disposable income in terms of PPP-adjusted international dollars. Using this decomposition, we strive
to account for how the private/public mix, measured in terms of this decomposition for the poorest fifth
of children, lines up across countries and changes within countries across time.
We have somewhat modified the standard LIS income variables for the analyses where we decompose disposable income in the poorest fifth into market income and net social transfers. In particular, we
depart from the usual practise of either truncating or censoring disposable income at zero and leaving
its constituent parts intact. Rather, we censor each of the following components at zero: earnings (head,
spouse, other members), self-employment income, property income, other private income, social insurance income, means-tested transfers. We then add all market-based income components together to
7 Chen
and Corak (2005) discuss an intermediate possibility, that of a “backstop” poverty line, which keeps the poverty
line within a country fixed a few years at a time. We have not implemented such a line in this paper.
5
form market income, sum transfers from which we subtract taxes to form net social transfers and then
define disposable income to be the sum of those. This is, obviously, coherent only for those datasets for
which gross incomes are available. In our analysis we therefore focus on those countries and datasets
that make available not only net incomes (i.e., income components from which taxes paid have been
subtracted) but the actual gross values along with direct taxes.
We often refer to LIS waves, which range from “Historical data” (year prior to 1978), to waves 1
through 5 (ending in 1982, 1987, 1992, 1997 and 2002, respectively). Wave 1 is thus roughly equivalent
to “around 1980”, wave 2 around 1985, wave 3 around 1990, wave 4 around 1995 and the final wave
5 around 2000. Information on the datasets that underlie the LIS data can be obtained from the LIS
website at http://www.lisproject.org/techdoc.htm.
3
Trends in children’s living standards
[Figure 1 about here.]
We start by examining long-run trends in relative child poverty, shown in Figure 1. These numbers,
derived using the LIS definition of disposable income, show a fairly familiar ordering of countries.
By the end of the 20th century the US has the highest poverty rate, followed by the United Kingdom,
Canada, Ireland. Poland and Hungary have the highest poverty rates among transition countries. Belgium, Germany, the Netherlands and France all have poverty rates close to 10 percent, while the Nordic
countries have rates around 5 percent.
The sharp rise in US child poverty turned into a small and later accelerating decline in the 1990s.
The UK trends seem more uncertain, but at least the sharp increase in the 1970s and 1980s appears
not to have continued. Canadian child poverty rates have hovered above 15 percent for most of the
time period covered by LIS. The Northern European countries, on the other hand, have experienced
increasing child poverty rates during the years covered.8
[Figure 2 about here.]
8 For
Germany, however, the year prior to 1994 include only former West Germany, while the former East Germany is
included from 1994 onwards, which should account for a substantial part of the increase in poverty in the early 1990s.
6
We show for selected countries the proportion of children with income less than the US poverty line,
as measured in international PPP adjusted dollars, along with the relative child poverty rate. These two
rates appear to converge in most cases by the end of the 1990s (except, ironically, in the US where they
diverge!). By the end of the 1990s, those countries that had low relative poverty rates also had low
“real” poverty rates.
[Figure 3 about here.]
Before we proceed any further, we wish to address a couple of questions regarding the focus on
relative child poverty. First, is a focus on children rather than all persons motivated? Second, does
examining relative poverty tell something that general inequality does not? Third, is the focus on
relative poverty misleading, or at least different from focusing on real living standards?
To examine the first of these questions, we have plotted in Figure 3 on the horizontal axis overall
relative poverty and on the vertical relative child poverty in each of the five LIS waves (along with
the least squares regression line in each wave). While the agreement between these two indicators of
poverty is quite good, there are some variations across the two measures. Moreover, the relationship
appears to vary from year to year. For instance, Sweden had in the historical wave of LIS a low level of
child poverty, but a relatively high level of overall poverty. Soon (by wave 1) Sweden’s overall poverty
rate became, along with its child poverty rates, quite low. Thus, while closely related, child and overall
poverty can and do change in distinct patterns across countries and time.
[Figure 4 about here.]
Our second question is if relative child poverty only measures relative inequality. In a technical
sense this is not the case – the share of persons below on half of the median focuses on only part of the
distribution, while relative inequality is measured on the whole range of incomes – but there is, again,
a close but not perfect correspondence between the two. Perhaps most importantly, the increase in
inequality among the Nordic countries in the 1990s seems not to have been associated with an increase
in child poverty. Moreover, child poverty rates seem to have varied between 5 and 25 percent in all of
the countries and time periods covered, while the range covered by the Gini coefficient has varied much
more across time.
7
[Figure 5 about here.]
Our third question concerns the focus on relative rather than “real” poverty. Whether one should
study relative poverty or not is an open question, to say the least. In our view, the study of relative
poverty and the study of poverty with respect to the same real standard of living across countries both
have their merits. It is interesting, however, to examine how countries line up by these two criteria.
[Figure 6 about here.]
This paper is motivated by the mix of market incomes and (net) public transfers. We next turn to
examine this mix using our decomposition of disposable income into market income and net social
transfers. Before doing so, however, it seems prudent to examine the extent to which our analysis
captures the ordering of countries with respect to disposable income poverty.
We measure the living standards of the worst-off children using the average income of the lowest
fifth of children ordered by the level of adjusted household disposable income. Our measure of relative living standard further relates this average to the overall median of adjusted household disposable
income. The scatterplot of this measure of children’s living standards against relative child poverty is
shown in Figure 6.
It is useful to remember that an increase in the average income of the lowest fifth is associated
with higher living standards, whereas of course the reverse is true for poverty. The poorest fifth of
children have around 20 percent of the income of the median person in the US, a number that varies
surprisingly little across time. In the UK and Canada, this ratio is around 30 percent, around fifty in
the group of Northern European countries that come next and around 50 percent of the median in the
Nordic countries. The ordering of countries by these two measures of children’s relative disadvantage
are reasonably close, especially in the latest two waves of LIS.
[Figure 7 about here.]
On obvious determinant of children’s living standards is whether or not they live with both parents.
It follows that variations across countries in the share of children living in lone-mother households is
likely to affect the poverty ordering of countries. The impact of this is not very large (see Bradbury
8
and Jäntti, 1999), but it does suggest that examining living standards separately for different type of
families is likely a good idea.
In Figure 7 the scatterplot of poverty rates and lowest fifth average income relative to median disposable income in the latest wave of LIS for all households and for two-parent and lone-mother households,
respectively.9
Two-parent poverty rates vary a lot less than those for lone mothers. Almost one half of US lonemother children are in poverty. The poverty rates for lone-mother children in the Nordic countries
is also higher than for two-parent children, at around ten percent. With the exception of Germany,
however, two-parent and lone-mother families follow reasonably closely that of all children.
4
Child poverty, labour markets and public policy
[Figure 8 about here.]
We now turn to examine the relative importance of markets and the public sector in determining
children’s living standards. The standard way of examining the role of the public sector in reducing
poverty is to compare poverty rates using incomes “before” social transfers have been added and (direct)
taxes have been subtracted with poverty rates based on disposable income. Such a comparison is subject
to many well-known objections – including the fact that, if no taxes were collected and no transfers paid,
the distribution of market income would likely be quite different. Despite such shortcomings, such
“first-order incidence” comparisons are widely used. We show in Figure 8 the “pre-post” scatterplot
using data from wave 5 in LIS for different household types.
The graphs are drawn on a log scale and include three rays, which separate between different degrees of poverty reduction achieved by the public sector – disposable income poverty being reduced
by 25, 50 and 75 percent, respectively, counting clockwise from the vertical axis. For all and for twoparent children, the Nordic countries reduce market income poverty by around 75 percent. Most other
9 Recall that lone-mother families are defined as those with only a mother with children and no other adults present.
A two-parent household likewise consists of two parents and their children but no other adults. Children in lone-father
households and children in families with other adults – including possibly adult siblings – are all in the “other” category
(but are of course included among all children.
9
countries reduce all children’s poverty by between 50 and 75 percent. The UK is remarkably more generous to lone-mother than to two-parent children, in that lone-mother child poverty rates, while high,
are reduced with more than one half, while the reduction for two-parent children is around 30 percent.
[Figure 9 about here.]
We also show in Figure 9 the pre and post poverty rates in all available LIS datasets that contain
more than just net income information. Lone-mother children, denoted by a “1” in this figure, always
have a lot higher market income poverty rates than two-parent children. However, a clear pattern does
emerge, in that in the US and Canada, those higher poverty rates are reduced with around 25 percent
or less, which is around the same proportion by which two-parent children’s poverty is reduced. In the
Nordic countries, higher pre poverty rates do not mean post rates are substantially higher. In the other
European countries lone-mother rates are reduced by a greater proportion than two-parent children’s
poverty rates. The UK, interestingly, seems to move from a North American regime toward a more
Northern European one in this respect, as suggested by the fact that by the last wave of LIS, UK lonemother children’s poverty rates are reduced by more then 75 percent.
[Figure 10 about here.]
The analysis of the impact of the public sector on poverty above suffers, as noted, from a number of
shortcomings. While it is not obvious this is, in fact, a shortcoming, these kinds of pre-post comparisons
can not easily be subjected to decomposition analysis. This is the reason that we chose, above, to
examine the average income of children in the lowest fifth in the income distribution among all children.
Namely, as disposable income is identically equal to the sum of market income and net social transfers,
this naturally also holds for average disposable income in the lowest quintile group. A scatterplot of
countries by average market income and average net social transfers in the lowest fifth, relative to the
overall median, neatly summarises several aspects of the living standards of the worst-off children (see
Figure 10).
First, since the sum of net transfers and market income is disposable income, the placement of a
country along the iso-quants running north-west to south-east reveal the living standard of the worst-off
children. The further to the north-east a country, the lower their poverty rate.
10
Second, we divide the figure by three rays, drawn from the origin, show points in the market incomenet transfer plane where 25, 50 and 75 percent, respectively, of the average disposable income of the
poorest fifth originate from markets (vs. 75, 50 and 25 percent from net social transfers). The rays thus
allow us to examine the “income package” of the worst-off children and, in particular, to what extent
their incomes are being generated on labour markets or by public transfers.
Figure 10 brings out the importance of both (labour) market income and of social transfers. The
parents of Canadian (in 2000) and US children earn roughly 40 percent of the overall disposable median
income from markets. Indeed, Finnish and Swedish children earn even more, around 45 percent of
median income. The relative living standard of US children is a lot lower than than of Canadian and
of Nordic children, however, because the US pays far less transfer incomes to these than these other
countries do. On the other hand, the Dutch and Norwegian public transfers relative to the median are
around the same magnitude as those in the US, but their market incomes are substantially higher.
Finally, of the countries included in Figure 10, in the US, the Netherlands and Norway more three
quarters of the income package of the lowest fifth of children consists of marker income. Poland,
Germany, Canada in 2000, Sweden and Finland all have between 50 and 74 percent of children’s
income stemming from markets whereas Canada in 1998 and the UK has most of these children’s
income from the public sector.
[Figure 11 about here.]
[Figure 12 about here.]
An analysis of the income package of the poorest fifth of children by household type reveals that the
composition of the income package tends to vary quite substantially from year to year (see Figures 11
and 12). For instance, for the poorest fifth of US lone-mother children, 75 percent of their disposable
income in Wave 1 stemmed from net social transfers. This proportion increased until in wave 5 (in
the year 2000), then declined substantially to less than 50 percent. This variation in the composition
of the income packages was achieved at a remarkably stable relative living standard. In other words,
the initial increase and later decrease in net social transfers almost exactly corresponded to an equal
but opposite change in market income. Put in yet another way, the increase in market income was for
11
these US children not associated with an increase in relative living standards, as it was matched by a
decline in income transfers. Moreover, the relative poverty rate declined quite substantially in the US,
while this measure suggests little change in the living standards of the worst-off US children. It may
therefore be that the childrne who were moved out of poverty did not move very far, leading to know
change in the average of the poorest fifth.
Patterns in other countries, and also for two-parent children in the US, tends to be more varied,
reflecting both changes in the relative well-being of the poorest fifth of children and the composition of
their income packages. We see, for instance, an increase in the relative well-being of UK lone-mother
children while the share of net scoail transfers also increased.
In order to gauge the effect of factors that affect the well-being of the poorest fifth of children
as well as the composition of their income package, we estimate simple regression equations for all
children, lone-mother and two-parent children, respectively. Our explanatory variables consist of the
overall unemployment rate, social expenditures on the working-age population as a percent of GDP, the
log deviation of real GDP per capita from its long-run trends and the share of lone-mother children.10
We have two dependent variables, average market income of the poorest fifth relative to the overall
median and average net social transfers relative to the overall median.
We thus have two “seemingly unrelated” regression equations, albeit with the same explanatory
variables. The error terms are allowed to be correlated, and we allow for random intercepts and time
trends that vary across countries and income components. Formally, we estimate




 

 mii jt  Xit 0   βmi, j   εmi,i jt 

=

+

0 Xit
nsti jt
βnst, j
εnst,i jt
10 The
(1)
unemployment rate is taken from World Development Indicators 2004. Social expenditure data are drawn from
OECD’s Social Expenditure Database, available on www.oecd.org. We experimented with distinguishing between workrelated transfers and non-work-related, but settled on using their sum instead. Work-related transfers consist of relative
expenditures on active labour market programmes and unemployment-related expenditures. Non-work related transfers
include survivors benefits, incapacity-related benefits, family-related benefits, housing benefits and “other” benefits. (These
two consist of all social expenditure except that for health and old age.). Trends in real GDP per capita were estimated by
regressing the log of real GDP per capita from the WDI on year. The residual of this regression in the LIS year measures
business cycle position and is included among the regressors. The share of lone-mother children is estimated using LIS data
using our definition of lone mothers (which includes only households with a mother and children but with not other adults
present.
12
where i indexes countries, j is either all, lone-mother or two-parent children, X is the vector of explanatory variables and ε are error terms for which
εk,i jt = ak,i j + bk,i j t + uk,i jt ,
k = mi, nst.
(2)
That is, we allow the relative market income or net social transfers of the poorest to vary by country and
across time. This kind of regression equation is known by many names, such as “hierarchical model”,
random coefficient model and linear mixed effect model. We use the function lme by Pinheiro and
Bates (1999) in the statistical package R to estimate the β coefficients.
[Table 1 about here.]
The estimation results are reported in Table 1. The random time trends turn out to be statistically
insignificant and are dropped in the reported regression equations. The coefficients were estimated
in “interaction form”. The coefficients with no prefix for market income and the rows that start with
“Inc.ComponentNST” should be added to the corresponding coefficient for market income to yield the
coefficient on that variable for net social transfer. Thus, for instance, for all children (the first column),
0.532 is the intercept of market income whereas 0.016 = 0.532 − 0.516 is the intercept for net social
transfers. The standard errors should be used with some caution, as no correction as been made for the
fact that many of the variables consist of sample estimates with varying sampling variation. (Variation
across the income components and within-country correlation are taken into account, however.)
The coefficient estimates are of expected signs and are measured with reasonable precision (bearing
in mind the caveat that the standard error estimates are suggestive at best). Having more children in
lone-mother families decreases relative market income and increases relative net social transfers in all
three cases examined. An increase in the overall unemployment rate also leads to a decrease in market
income and an increase in net social transfers. Our measure of where in the business cycle LIS captures
a country, the log deviation of GDP PPP per capita from its trend, is associated with higher relative
market income and lower net social transfers.
The most interesting finding concerns social expenditures (denoted by Transfers) which for all and
two parent children is associated with higher market income and lower net social transfers, but has no
13
effect for lone-mother children. This may be driven by the fact that lone-mother children are relatively
scarce, but is interesting nonetheless.
5
Concluding comments
We have examined trends in relative poverty and an internationally comparable real income poverty
standard. Our results show large declines in US poverty, substantively unchanged poverty in many
countries and increases in some,mainly Northern European countries. Our alternative measure of children’s well-being, relating the average income of the poorest fifth to median disposable income, generates a similar ordering of countries but allows us to decompose their living standard by market income
and net social transfers.
Trends in this measure of children’s well-being suggest somewhat smaller changes in the living
conditions of the worst-off children. In particular, neither the UK nor the United States display large
declines in poverty according to this measure. Not unexpectedly, children in two-parent households are
more likely to rely to a greater extent on market income than are children in lone-mother households.
The estimates also reveal that the composition of poor children’s income package can vary substantially with little or not change in their living standards within a country. Our descriptive regressions
show that unemployment, business cycles and lone parenthood affect the relative well-being of children
as one expected.
References
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Bradbury, Bruce and Markus Jäntti (2001), Child poverty across twenty-five countries, in B.Bradbury,
14
S. P.Jenkins and J.Micklewright, eds, ‘The Dynamics of Child Poverty in Industrialised Countries’,
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16
Figure 1 Relative child poverty trends in selected countries
1970
AEurope
1980
1990
2000
EEurope
0.25
UK
UK
0.20
IE
UK
0.15
0.10
UK
IEIEIE
UK
PL
HU
UK
UK
UK
SK
SI
SI
0.05
NAmerica
US
CA US
alpov
CA
NEurope
US US
US
CA
US
US
0.25
0.20
CACA
CA
CA CA CA
AT
0.15
GE
AT
GE
GE
Nordic
NL NL
NL
GE AT
GE
NL
GE
0.25
0.20
IT
IT
FR
FR
0.10
SW
0.05
1970
DK
NW
NW
FI
SW SW
1980
DK
SW
NW NW
NW
FI
SW
SW
FI FI
1990
2000
17
year
FR
0.10
0.05
SEurope
0.15
NL
GE
BE
FR
Figure 2 Relative and “absolute” child poverty trends in selected countries
HalfMedian
US.PovertyLine
1970
1980
1990
2000
Austria
Belgium
Canada
Denmark
Finland
France
0.6
0.5
0.4
0.3
0.2
0.1
0.6
0.5
0.4
0.3
0.2
HalfMedian + US.PovertyLine
0.1
Germany
Ireland
Netherlands
Norway
Sweden
United Kingdom
0.6
0.5
0.4
0.3
0.2
0.1
0.6
0.5
0.4
0.3
0.2
0.1
United States
0.6
0.5
0.4
0.3
0.2
0.1
1970
1980
1990
2000
18
year
Figure 3 Child poverty and overall poverty
0.05
0.10
Wave:0
0.15
Wave:1
0.25
US
US
CA
0.20
CA
CA
0.15
0.10
UK
UKFR
FR
UK
SW
GE
NW
SW GE
0.05
Wave:2
Wave:3
US
US
Relative poverty of children
0.25
UK
0.20
UK
IE
CA
IT
CA
GE
AT GENL DK
NL
NW
FI SW
FR
NL
0.10
DK
GE
NW SW
FI
Wave:4
0.05
Wave:5
US
US
0.25
US
0.20
CA
CAAT
UK
IE
IE
0.15
PL
HU
GE NL
GE
AT
SK
FRSI
BE
NL
0.10
0.05
0.15
IT
FI
SI
SW
FI
NW
NW
SW
0.05
0.10
0.15
19
Relative poverty of all persons
UK
CA
CA
Figure 4 Relative child poverty and overall inequality – share below half of median and Gini coefficient
0.2
0.3
Wave:0
Wave:1
0.25
US
US
CA
0.20
0.4
CA
CA
0.15
UK
UK
0.10
Relative poverty of children (percent below half of median)
GE
UK FR
FR
SW
NW
GE
SW
0.05
Wave:2
US
Wave:3
US
0.25
UK
0.20
CA IE
IT
UK
CA
0.15
IT
FR
NL
GE
GENL
DK
NL
NW
FI
SW
0.10
DK GE
NW
FISW
Wave:4
0.05
Wave:5
US
US
0.25
US
UK
0.20
CA
CA
0.15
HU
NL
GE
GE
AT
SK
BE
SINLFR
0.10
0.05
CA UK
CA
PL
UK
IE
IE
IE
SI
SW
FI
NW
NW
SW
FI
0.2
0.3
0.4
20
Relative inquality of all persons −− Gini coefficient
Figure 5 Relative and “real” poverty among children
0.05
0.10
Wave:0
0.15
0.20
0.25
Wave:1
1.0
0.8
0.6
UK
SW
0.4
UK
CA
0.2
CA
US
FR
FR
UK
NW
CA
SW
GE
GE
Child poverty relative to US poverty line
Wave:2
IT
US
Wave:3
IT
1.0
0.8
IE
NLNL
SW
FI
AT
NW
DK
GE
0.6
UK
CA
GE
FR
US
SW
GE
DK
FI
NW
1.0
US
CA
NL
Wave:4
0.4
UK
Wave:5
SK
HU PL
0.8
0.6
0.4
SI
0.2
SW
FI
NW
0.05
FR
GE
BEAT
NL
0.10
IE
IE
IE
UK
AT
CA
CA
0.15
SI
UK
US
US
FISW
NW
0.20
NL
GE
UK
CA
CA
0.25
Relative poverty of children (percent below half of
21
current median)
US
0.2
Figure 6 Child poverty and average income in lowest fifth of children by LIS wave
0.05
0.10
Wave:0
0.15
0.20
0.25
Wave:1
FR
1.0
0.8
0.6
0.4
SW
NW
GE
GE
SWUK
UK
FR
UK
CA
CA
CA
US
US
0.2
Lowest fifth average DPI / median DPI
Wave:2
Wave:3
FR
1.0
0.8
AT
SW
FI DK
NW
GE
NLNL GE
IT
FI
NW
SW
DK
GE
IT
NL
IE
UKCA
0.6
CA
0.4
UK
US
US
Wave:4
Wave:5
SI
SI
FR
SK
1.0
AT
0.8
IE
IE
IE
HU
AT
0.6
FI
SW
NW
0.4
BE
NL GE
NW
FISW
CA
CA
UK
UK
0.2
0.05
0.10
0.15
0.20
GE
NL
US
US
PLCA
CA
UK
0.25
22
Disposable income less then half of median DPI
US
0.2
Figure 7 Child poverty and average income in lowest fifth of children by household type in Wave 5
(around 2000)
HHtype:All
SI
1.0
HU
0.8
0.6
NW
FISW
GE
NL
0.4
PLCA
CA
UK
US
0.2
Lowest fifth average DPI / median DPI
HHtype:Lone mother
1.0
HU
0.8
SI
0.6
FI NWSW
NL
PL
UK
GE
CA
0.4
CA
US
HHtype:Two parent
SI
1.0
HU
0.8
0.6
NW
FI
SW
0.4
GE
NL
CAPL
CA
UK
US
0.2
0.1
0.2
23
0.3
0.4
Disposable income less then half of median DPI
0.5
0.2
Figure 8 Relative poverty before and after taxes and transfers by household type
HHtype:All
0.5
0.4
0.3
US
0.2
Disposable income less than half median DPI (log scale)
0.1
CA CA
PL
UK
NL GE
SW
FI
NW
HHtype:Lone mother
US
CA
0.5
CA
GE
0.4
UK
NL
0.2
PL
SW
NW
FI
0.1
HHtype:Two parent
0.5
0.4
0.3
0.2
0.1
US
CA UKPL
CA
NL
GE
NW SWFI
0.2
0.4
0.3
0.6
24
Market income less than half median DPI (log scale)
0.8
Figure 9 Child poverty based on market income and disposable income across time in selected countries
(1 is lone-mother children, 2 is two-parent children)
0.2 0.4 0.6 0.8
BE
CA
0.2 0.4 0.6 0.8
DK
1
11
1
FI
0.6
1
11
11
0.4
2
2
22
1
FR
0.2
1
1
22
GE
11 11
22
2
IE
NL
0.6
11
1
22
0.4
1
0.2
1 1
NW
1
1
1
2
1
2
2
2
222 1
2
222
2
apov
1
1
PL
SW
11
UK
1
1
1
1
11 1
2222
2
1
11
1
1
1
2
2
22 2
US
11
111
11
0.6
0.4
0.2
22
222
0.2 0.4 0.6 0.8
1
1
25
ampov
2222
2
2
0.6
11
1
1
1
0.4
0.2
Figure 10 The relative living standard and income package of poorest fifth of children – net social
transfers and market income
Average net social transfers of lowest fifth / median
0.6
0.4
UK1999
CA1998
FI2000
SW2000
CA2000
GE2000
PL1999
0.2
NW2000
US2000
NL1999
0.2
260.4
0.6
Average market income of lowest fifth / median
Figure 11 The relative living standard and income package of lone mother households in the poorest
fifth of children in all LIS waves by household type – net social transfers and market income (the
numbers denote LIS waves)
0.2
Belgium
0.4
0.6
0.2
Canada
Denmark
0.4
0.6
Finland
0.6
4
45
55
344
2
1
0.4
3
3
2
0.2
2
Average net social transfers of lowest fifth / median
France
Germany
Ireland
Netherlands
4 2
3
0.6
0.4
5
1
25 2
4
0.2
Norway
2
3
1
Poland
Sweden
United Kingdom
4
45
32
43
4
5
5
2 1
0.4
53
14
2
0.6
3
432
4
1
0.2
5
27
0.2
0.6
1
United States
0.4
2
0.4
0.6
Average market income of lowest fifth / median
0.2
Figure 12 The relative living standard and income package of lone mother households in the poorest
fifth of children in all LIS waves by household type – net social transfers and market income (the
numbers denote LIS waves)
0.2
Belgium
0.4
0.6
0.2
Canada
Denmark
0.4
0.6
Finland
0.6
0.4
4
45
4
Average net social transfers of lowest fifth / median
France
3
5
12
3
Germany
Ireland
4
5
0.2
3
2
Netherlands
2
0.6
2
0.4
1
0.2
5
Norway
4
2
Poland
3
2
3
45
United Kingdom
Sweden
2
1
0.6
43
2
34
5
4
43
United States
51
5
2
21
1
0.4
3
0.2
44
0.4
35
21
4
0.2
5
0.6
0.2
0.4
28
0.6
Average market income of lowest fifth / median
2
Table 1 Regression results – dependent variables: relative market income and net social transfers
(Intercept)
All
0.591
(0.113)
(0.084)
(0.132)
Inc.ComponentNST
−0.592
−0.175
−0.690
(0.148)
(0.113)
(0.172)
lone.mother.share
−1.075
−0.377
−0.743
(0.616)
(0.552)
(0.747)
unemployment.rate
−0.025
−0.015
−0.023
(0.007)
(0.005)
(0.008)
Transfers
log.gdp.ppp.per.capita.resid
Inc.ComponentNST:lone.mother.share
Inc.ComponentNST:unemployment.rate
Inc.ComponentNST:Transfers
Lone mother
0.348
Two parent
0.663
0.015
0.004
0.010
(0.006)
(0.006)
(0.007)
0.377
0.270
0.382
(0.246)
(0.161)
(0.280)
2.027
1.129
1.045
(0.806)
(0.741)
(0.979)
0.044
0.030
0.044
(0.009)
(0.007)
(0.010)
−0.019
0.000
−0.011
(0.008)
(0.008)
Inc.ComponentNST:log.gdp.ppp.per.capita.resid
−0.781
n
σ
logLik
AIC
80
0.103
50
-71.9
(0.312)
−0.543
(0.212)
80
0.0605
68
-108
(0.010)
−0.849
(0.355)
80
0.115
39.7
-51.5
Note: The equations were estimated using linear mixed effects, allowing for random intercepts within
countries that differ across income components.
Source: Authors’ estimates based on LIS, OECD and WDI data.
29