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NOT FOR CITATIONWITHOUT AUTHORS’ PERMISSION
“Social Cohesion” and the Dynamics of Income in Four Countries
Miles Corak, Wen-Hao Chen, Abdellatif Demanti, and Dennis Batten
Family and Labour StudiesStatistics Canada
24th Floor, R.H. Coats BuildingOttawa K1A 0T6
Paper Prepared for the
Fifth International German Socio-Economic Panel ConferenceBerlin, Germany
July 3 and 4, 2002
* Miles Corak is Director of the Family and Labour Studies Division at Statistics Canada and isalso affiliated with Carleton University as an adjunct professor of economics and with the Institutefor the Study of Labor (IZA) as a Research Fellow. Wen-Hao Chen is Research Economist withthe Family and Labour Studies Division and a Doctoral candidate at Michigan State University.Abdellatif Demnati and Dennis Batten are with the Social Surveys Methods Division at StatisticsCanada. The responsibility for the content of this paper rests solely with the authors and inparticular should not be attributed to Statistics Canada. Comments may be sent [email protected].
Abstract
Longitudinal data from the United Kingdom, Germany, the United States and Canada are used tooffer a comparative analysis of the dynamics of household income during the 1990s, withparticular attention to both low- and high- income dynamics. The analysis begins by offering abroad descriptive overview of the major characteristics and events (demographic versus labourmarket) that determine levels and changes in adjusted household incomes. This overview ismeant to offer a broad picture of the state of household income in each country and thechallenges faced by the welfare state. The paper then employs discrete time hazard methods tomodel the dynamics of entry to and exit from both low-income and high-income. Both observedand unobserved heterogeneity are recognized but traditional methods based upon fixed points ofsupport are extended to analyse mixtures of distributions. These latent classes are interpreted asthe basis for identifying potentially “excluded” groups that raise challenges for the degree ofsocial cohesion.
“SOCIAL COHESION” AND THE DYNAMICS OF INCOME IN FOUR COUNTRIES
1. Introduction
The importance of viewing the labour market from a dynamic perspective is now accepted
wisdom in both policy and academic circles. The availability of longitudinal data and the
appropriate analysis of it has helped to create a clearer picture of North American and European
labour markets that relates directly to policy concerns. The most obvious example is the fuller
understanding that has developed about the nature of low-income. Bane and Ellwood (1986)
using longitudinal data from the US are often cited as one example of a particularly cogent
portrait of the low-income population and the dynamic processes that determine entry into and
exit from poverty. The gradual availability of similar data in other countries has spawned a wide
literature on this topic, only the most recent examples being Bradbury, Jenkins and Micklewright
(2001), Jenkins (2000) and Stevens (1999).
While issues dealing with low-income remain pressing concerns in many countries,
broader—but perhaps less well defined—concerns have also recently come to the foreground. In
part, these relate to the impact of increasing integration of product and labour markets during the
1990s that have at once widened the scope of the market in determining family incomes and
raised concerns about the role of the welfare state. The extent to which national governments can
intervene to alter labour market outcomes is sometimes also related to the degree of “social
cohesion,” and fears are often expressed that inequality in labour market outcomes erodes this
important prerequisite for collective action. In an analytical sense it is not clear what exactly
social cohesion means or what its relationship is to other frequently used
2
concepts like “social capital,” and “social exclusion.” Implicit in some discussions is a reference
to an underlying sense of community in a society as well as the degree of support given to
collective projects like the welfare state as a scheme for social insurance. This discussion is most
developed in the European context and is reflected in the development of a host of indicators by
the European Union as well as a set of specific targets to reduce social exclusion (D’Ambrosio et
al 2002, Stewart 2002).
Income inequality is certainly one important aspect of what is inherently a multi-
dimensional concept, and more unequal societies are sometimes thought to be less cohesive. It is
not, however, immediately clear why this should be the case without there being at the same time
a clear understanding of the underlying income dynamics that determine the cross-sectional
income distribution and the degree of inequality. It is easy to imagine that the welfare state will
have more broad based support if the income distribution is very fluid with all individuals,
regardless of their current situation, facing similar risks of both entering and exiting low-income.
In contrast, if the income distribution is very rigid with little movement into or out of low-
income as well as into or out of high-income then for the same level of cross-sectional inequality
it might be reasonable to suggest that a universal scheme of social insurance will not have as
wide support. This might be all the more so if the process determining income dynamics is very
different at the two extremes: if the rich perceive themselves as not really facing a risk of low-
income but also of being fundamentally different from those who do.
The objective of our research is to inform these types of policy concerns by building
upon the literature examining the dynamics of low-income to also study high-income dynamics.
We do this in a comparative way by focusing on North America and two European countries in
order illustrate the way in which the underlying determinants of market income dynamics
3
condition the nature and scope of the tax-transfer system. We use longitudinal data from Canada,
the United States, Germany and the United Kingdom and focus on developments during the
1990s.
The analysis begins by first describing the static characteristics of the income distribution
in these countries during the 1990s. An overview of market incomes during this period reveals
that the degree of inequality is broadly similar over time and across place. But this similarity in
market incomes disappears when taxes and transfers are taken into account. Inequality based
upon net incomes varies a good deal across the four countries. This forms the backdrop for our
analysis: is it possible that the extent to which governments alter market outcomes is determined
in part by the nature of the underlying dynamics of market incomes?
There are two broad parts to our analysis: (1) an extensive descriptive overview of market
incomes in the four countries; (2) an econometric analysis of the determinants of low and high
income spell dynamics. The analysis is structured to answer the following questions:
(a) How is income earned?
(b) What elements are most variable?
(c) What is the extent of movement at the two extremes of the distribution?
(d) What are the causes of these movements?
(e) Do these causes suggest there are fundamentally different sub-populations facing
risks of entering and leaving both high and low income?
The descriptive overview follows Jenkins (2000) and examines the major characteristics
and events that determine levels and changes in adjusted household incomes, paying attention to
movements into and out of low income but also high income. The econometric analysis of these
movements extends the discrete time hazard models in the manner of Stevens (1999) and
4
Heckman and Singer (1984). Both observed and unobserved heterogeneity are controlled for
with the intention of informing discussions about the fundamental differences between groups at
the two poles of the income distribution. The analysis is based upon mixtures of distributions and
allows the influence of unobservables to be reflected not only in the number of fixed points of
support but also different parameter estimates across these points.
The analysis uses data from the Cross-National Equivalent Files of the British Household
Panel Survey (for the UK), the German Socio-Economic Panel (for Germany), the Panel Study
of Income Dynamics (for the US), and the Survey of Labour and Income Dynamics (for
Canada). With the exception of the Canadian data these are all relatively long panels that permit
in the least an assessment of income dynamics for the 1990s. All of these data are discussed in
much more detail in the following section. A descriptive overview of income dynamics is offered
in sections 3 and 4, while the econometric methodoology is described in section 5 along with the
results.
2. Data Sources and a Descriptive Overview
The data come from the Cross-National Equivalent Files (CNEF). The CNEF brings together
multiple waves of longitudinal data from Canada, United States, Great Britain, and Germany.
Variables across the surveys have been defined in a similar manner in order to encourage cross-
national research. Burkhauser et al (2000) offer a detailed description of the CNEF. The current
panels available in CNEF include the Canadian Survey of Income and Labour Dynamic (SLID,
1993 to1999), the United States Panel Survey of Income Dynamics (PSID, 1980 to 1997), the
British Household Panel Survey (BHPS, 1991 to 1999), and the German Socio-Economic Panel
(GSOEP, 1984 to 2000). A key advantage of the CNEF is that it provides reliable estimates of
5
annual income variables that are not directly available on the original data sets. It includes pre-
and post-government household income, estimates of annual labour income, assets, private and
public transfers, and taxes paid at household level. The availability of information on each
household income source allows a full picture to be developed of the relative roles of market,
family, and state in determining income levels (OECD, 2001).
The other benefit of the CNEF is that it uses the most mature of a number of longitudinal
surveys across the OECD countries and therefore provides relatively long panels of information.
The SLID is the one exception. This is a result of its rotating sample design. SLID consists of
two overlapping samples, each of which is followed for only six years with the last of three years
of the older panel overlapping with the first three years of the newer panel.1
Economic well-being for each household member depends on the total household income
level as well as household composition. We use both market and post-tax post-transfer
definitions of income, but emphasize the former.2 The sharing unit is the household (persons
living in the same household whether or not they are related to each other by blood or marriage).
The annual household adjusted income is defined as the ratio of total household income to the
square root of family size. This can be regarded as an estimate of potential income for each
household member under the assumption of equal sharing. One advantage of using annual
1 SLID has the advantage of offering a much larger sample size, in the neighbourhood of about 30,000 individuals.The sample sizes from the other countries vary from about 4,000 for the BHPS to about 8,000 for the PSID. Infuture work we intend to supplement the SLID information by also using the Longitudinal Administrative Data(LAD) from tax files. The LAD is a 10 percent sample of Canadian tax filers followed as individuals over time andmatched into family unites on an annual basis. It offeres demographic, income and other taxation data forindividuals as well as their families for 1982 to 1998.2 For the CNEF we do not use the post-government variable due to the inconsistent definition across nations.Instead, we define our own Total household net income variable equal to the sum of household labour income, assetincome, imputed rent, private and public transfers, social security pensions, private retirement pensions, and totalhousehold taxes for all countries.
6
income rather than income at the time of interview is that it avoids temporarily high or low
income and more accurately reflects the annual economic well-being of individuals.
There is no one definition of low-income in the four countries we are examining, the US
being the only one with an official “poverty” line. Our analysis uses relative measures of low and
high income based upon the income distribution in each country in a given year since “social
cohesion” is itself an inherently relative concept. Individuals are defined as being in low income
if their household adjusted income falls below 50% of the national median in the first year of the
analysis; they are defined as being in high income if their household adjusted income is above
150% of the national median.3
Though in large measure the CNEF data are based upon consistent definitions across all
four countries, there are still some differences that should be kept in mind. Households are not
defined in exactly the same way in most of CNEF countries, nor are household heads. We
modify some of the variables in order to use concepts that are as consistent as possible across all
of the data. A summary table outlining these and other differences between the country data sets
is appended as Appendix Table 1.
The samples are meant to be representative of all individuals in the population including
children and non-working people. Data from 1990s are drawn from CNEF for each country.
Tables 1a and 1b show summary statistics of the household adjusted market income distribution
and income after-taxes and transfers during the 1990s. The data are adjusted for inflation and
expressed in each country’s own 1997 currency.
With respect to market income summarized in Table 1a, real median income grew only
modestly during the 1990s in all four countries. The degree of inequality is indicated by the Gini
3 In actual fact we preclude small transitory fluctuations in income from causing movements across these thresholdsby requiring the income change to be 10 per cent beyond the high or low income threshold.
7
coefficients and ratios between the 90th and 10th percentiles, as well as the 90th and 50th for the
upper part of the income distribution and 10th and 50th for the lower part. The Gini coefficients
range between 0.42 and 0.44 in Canada and the UK, are slightly higher in Germany (0.44 to
0.47) and a bit higher still in the US (0.45 to 0.49). [ There are a number of peculiarities in the
90-10 ratios. In the US there is a sharp rise between 1993 and 1994 reflecting both a drop in the
10-50 ratio in 1994 and a rise in the 90-50 ratio in 1995. This may be due to quality problems in
these data. The 90-10 ratios in Germany are much higher than all the other countries ranging
from 55 to 64 between 1992 and 1994, but over 100 in 1995 and reaching 200 in 1999. If we
exclude those individuals over 60 years of age this ratio falls to the neighbourhood of 14 or 15.
As such the very high ratio seems to be related to the market situation of those over 60 years of
age, but we are not clear why this should have changed over the period. Once taxes and transfers
are include the anomaly disappears and this also leads us to believe that the high market income
ratios have to do with the absence of any market income among the elderly and the operation of
the pension system in Germany. ] Similarly low-income rates are about 25 to 27% in Canada and
the US, and a little higher in the two remaining countries, though they fall slightly in the UK
after about 1994 and rise in Germany. High income rates are roughly 30% or a little less in all of
the countries with a tendency to rise with time. Generally inequality in market incomes does not
vary dramatically across the countries. Perhaps on the basis of the Gini coefficients Canada’s
relationship with the US is roughly similar to that between the UK and Germany.
The picture is different with respect to net income, as illustrated in Table 1b. The Gini
Coefficients are significantly lower in Canada, the UK and Germany, but less so in the US. The
Gini is about 0.3 in Canada, a bit higher in the UK and a bit lower in Germany. In the US it is
0.36 to 0.41 [ though the higher figures after 1993 may again reflect a data issue.] This same
8
general pattern holds for the 90-10 ratio, which are always below 4.0 in Germany, but never
below 5.0 in the US. Canada is closer to the German standard than the UK. The pattern also
holds for low-income rates though the differences are more stark. Low-income rates are for the
most part about 10% in Germany, about 11 to 13% in Canada, higher in the UK (though falling
significantly in the later part of the decade), and higher still in the US where it approaches 20%.
Comparing these rates to those in Table 1a suggests that the tax-transfer systems reduce low-
income rates by more than 10 percentage points in Canada and the UK, but that Germany and the
US stand out. The tax-transfer system reduces low income rates by as much as 20 percentage
points in Germany, but in contrast only in the order of 5 to 6 percentage points in the US. The
pattern is similar but more muted with respect to high income rates: four to five percentage point
reductions in most countries, but up to eight in Germany. [ Caution is still very much in order in
making these cross-national comparisons as the treatment of taxes may differ, this is particularly
the case in the United States where the reporting and assignment of taxes is done differently,
being based upon a simulation model. This is one reason for the focus on market incomes in
much of what follows.]
At a very broad level the main message from this information is that market incomes are
not drastically different between the countries with Canada standing roughly in the same relation
to the US as the UK does to Germany. Germany is the only country to experience growing low-
income rates yet it does significantly more redistribution than any of the other countries. The US
does the least to redistribute incomes and reduce low-income. This message is still appropriate
when a dynamic view is taken of the data. Tables 2a and 2b offer a picture of the incidence of
low and high income using a number of different definitions in order to capture developments
over a six year period. This information reveals the degree to which the “risk” of experiencing
9
low or high income is equally shared across the entire population, or in another sense the degree
to which there is a potential empathy for the plight of low-income individuals.
The focus in Table 2a is on market incomes.4 The incidence of at least one year of low-
income is very high across the four countries. About 40% of individuals have touched low-
income at least once, the Canadian and UK rates being only slightly lower than the US and
German. However, this risk is much lower when longer term measures of the incidence of low-
income are used: low income in all six years, and low income based on the average income over
six years (referred to as “permanent income” in the tables). In both these cases the US has lower
rates than the other countries but Canada is not too different. The incidence of low-income using
“permanent income” is about one-fifth in North America but about one-quarter in Europe. A
considerable fraction of the population faces the risk of experiencing at least a transitory bout of
low-income; a somewhat smaller though still significant fraction faces the chance of a long-term
spell or series of spells. These data begin to develop the suggestion that there are different sub-
groups in the populations of these countries. On the one hand when the focus is on incidence
there is the potential for a great deal of empathy with low-income individuals because a
significant fraction of the population faces this risk; on the other hand when the focus is on
severity the degree of empathy is likely to be much less and depend very much on the
determinants of long spells and the extent to which other groups feel they face the same
situation.
The chances of attaining high-income are greater: in North America 50 to 55% of the
population had at least one year in high income; in Europe about 45%. The incidence of high
4 The results in Tables 2 and also in Tables 3 are based on persons who were present in the surveys for all yearsbetween 1993 and 1998 (1991-1997 for US).
10
income based on longer term measures is about the same across all countries: 14% for high
income in each of six years, and not quite 30% for permanent income.
Table 2b offers similar information but on the basis of after tax – after transfer income.
The incidence of at least one spell of low-income over six years varies considerably more under
this definition than it does under market income. In Germany the risk falls by more than half:
from 43% to less than 20%. The tax-transfer system also significantly reduces the risk in Canada
and the UK, by 15 and 12 percentage points respectively, but only by 7 percentage points in the
US. The longer-term measures of low income are much reduced in all the countries but most
significantly in Canada and Germany. With respect to high income, Canada, the UK and
Germany all display very similar patterns – about 37 to 38 % experience high income at least
once, about 9% in all six years and about 7 to 8% on the basis of permanent income. The US is
the clear outlier with the tax-transfer system doing little to alter market outcomes.
Tables 3a and b display the average annual transition rates over these same years across
the income distribution. Individuals are classified into income groups according to the size of
their adjusted income relative to fixed year 1 median income. The top panel in Table 3a shows
the annual outflow rates from year t-1 income group to year t income group for market income;
the bottom panel the inflow rates. Overall there is considerable movement throughout the income
distribution with inflow and outflow rates ranging from 20 to as high as 40% in some cases.
However, those in the two tails of distribution are likely to stay in the same income group over
time. For instance, in Canada over four-fifths of those in the lowest and highest income
categories (less than 50% of year 1 median income and greater than 1.5 of year 1 median
incomes) remain in the same category in the following year. The low-income exit rate is about
20% in North America and but 15% in Europe. Only in Canada are outflow rates greater than
11
inflow rates, but only slightly so. Generally market dynamics imply a constant or growing
proportion of low-income individuals. The outflow rate is only slightly greater than the inflow
rate in the UK, but more noticeably so in the other countries. This is particularly the case in
Germany. On an average annual basis 18% of the low income populations entered this state
during the past year, but only 14% left. This leads to the growing stock of low income
individuals illustrated in Table 1a. Table 3b offers the same information on an after-tax, after-
transfer basis, and illustrates in Germany a reversal of magnitudes with outflow rates greater than
inflow rates. In a sense the German welfare state has to work overtime. Market forces are
generating net inflows that are reversed by the tax-transfer system. In Canada the welfare state
has been giving some breathing room by market forces.
The challenges to the Canadian welfare state seem to come more from high income
dynamics. The high income population is clearly growing, most substantially in Canada: less
than 17% of high-income individuals fall out of high-income but 19% move in each year. No
other country displays such a difference between outflow and inflow rates, though there is a
tendency for the inflow rates to be greater than the outflow rates.
Table 3a also displays a moving up trend for the upper middle income groups. For those
just above the low income threshold (0.5 to 0.75 of the median) the inflow from and outflow to
low-income are roughly the same and there is a tendency for the outflow upward to be slightly
greater than the inflow from above. This pattern, however, is much more noticeable for the
movements to and from the other categories (0.75 to 1.5): inflows from below dominate outflows
downward, and outflows upward dominate inflows down. In other words, the middle groups are
doing better in terms of earnings, while the lower middle and low-income group remain roughly
12
the same. In all countries there has been a tendency for the middle income groups to progress
upwards while the lowest and highest groups show much less mobility.
3. The sources of Income and their Variability
We explore the dynamics of income in more detail by examining the variability of each of eight
constituent components of total annual income following Jenkins (2000). The information in
Table 4 sets the stage for this discussion by offering six year averages of total income and the
components by the individual’s 1993 household type. The sum of the shares of each income
component in total household income is 100%.5 (Taxes are considered an income source with
negative contribution.) Overall, head’s labour earnings account for the largest share of household
income component, ranging from 42% in UK to 73% in the US. The lower UK findings could
result from differences concerning the definition of a household head. Secondary labour earnings
in a household are also important, and they account for about a quarter of total household
income on average. The importance of income sources other than labour earnings varies across
different household types and also across nations. Generally, disadvantaged groups rely on
market sources of income to much greater extent in the US. In Canada as well as in Britain, more
than 20% of income in lone parent households come from public transfers. This is 15% in
Germany but only 7% in the US. In both the US and Germany about 70% of the income of Lone
Parent families is derived from the Head’s labour earnings, in Canada and notably in the UK this
is lower. Similarly, 30 to 40% of income in elderly households in most of the countries comes
from social security pension, but the figure is almost 70% in Germany. Elderly households in
many countries also rely heavily on assets and transfers from non-household members. In
5 The shares do quite sum to 100% in the US and UK due to inconsistent information on individual labour earningsin both country’s data.
13
Canada and the US about 50% of income in senior households comes from assets or private
transfers.6 Two parent households with children receive slightly over 7% of their income from
public transfers in Canada, the UK, and Germany, but less than 2% in the US. The public
transfer system also plays little role in supporting single US households.
To explore longitudinal variability in incomes, two methods followed by Jenkins (2000)
are used in Table 5. The first deals with the longitudinal variability of Personal Equivalent
Income, net income, household size, and the equivalence scale. This can be characterized using
the coefficients of variation (CV) for these variables. The CV is the spread of the distribution
relative to its mean and is calculated longitudinally over six year periods for each person as
follows:
n
nx
xCV σ
= , (2)
where nxσ and nx are the standard deviation and mean respectively for variable x over six-year
observations for person n. The second method investigates the contribution of each income
component to the total variability of household net income. Here, we use the variance as a
measure of total variability, and the so-called “β coefficient”. As suggested by Shorrocks
(1982), let kiY denote the income of individual i from source k, and let total net income
�=k
kYY . Then, for each individual, the variance of total net income over 6 years period is:
���≠
+=kj k
kjjkk
kY σσρσσ 22 (3)
6 Notice that the shares of each income components are not quite comparable across nations. For example, one mightwonder why Canada has a lower tax burden (24.7%) then the US (28.7%). One possible reason is that payroll taxesare not included in Canadian data but are in the US data. Besides, the different definitions of head, household, andincome components would possibly alter the actual shares and makes cross-country comparison more difficult.
14
where jkρ is the correlation coefficient between income component jY and kY . For each
component, the contribution to net income becomes:
�≠
+==kj
kjjkkk
kY YYCov σσρσσ 22 ),( (4)
It is also useful to standardize the variability measure. We define *ks as the proportion of total
variability contributed by component k, and the sum of these is equaled to 1.
)(),()( 2
22*
YVarYYCovs
K
Y
kYk ==
σσσ , � =
kks 1)( 2* σ . (5)
As indicated in equation (5), the measure of *ks is the same as the slope coefficient from a six-
observation regression of the given income component on total net income for each person.
The first and second rows of Table 5 provide information of longitudinal variability for
household adjusted income as well as for household net income as measured by the CV. For each
country the longitudinal variability in household adjusted and net income is larger for single as
well as for lone parent households, while is relatively stable for couples and elderly households.
Across countries income is most variable in the US and least variable in Germany. In the US
lone parent families have particularly volatile incomes with CVs of about 0.40, much higher than
any other groups in any other countries. Lone parents in the UK face a roughly similar situation
but have much more stable incomes in Canada and Germany. (It should also be noted that in the
US they make up over 17% of all persons, but less than half that in the other countries.)
The middle rows of Table 5 provide the β coefficients for each income component. The
patterns are consistent with those shown by income share in Table 4. On average, head’s labour
income contributes the largest proportion (0.32 to 0.58) to longitudinal variability in household
net income. However, it is worth noting that although the secondary labour earnings (spouse and
others) account for about 30% of income share, they contribute a significant proportion (0.25 to
15
0.35) of longitudinal variation in net income, with Germany being at the upper extreme. This
indicates that changes in secondary labour earnings also play an important role in determining
income transitions. This is especially the case for couples and children. In the households with an
elderly head, the changes in asset and private transfer are the major source of longitudinal
variation in household net income (except Germany). Asset and private transfers together make
up at least 41 to 77% of longitudinal variation in net income among the four countries. However,
in Germany, 59% of income changes in elderly households are due to the changes of income
from social security pensions, and only 21% are due to the changes in assets and private
transfers. The high contribution of social security pension to longitudinal income variation along
with its high income share implies that the income transitions for German seniors are greatly
dependent on their government pension and welfare status. For lone parent families changes in
other family labour earnings represent an important source of risk, consistent with the fact that
household dissolution and formation are the major causes of both entering and leaving low-
income among this group. This is particularly so in Germany, where 40% of the variability in
income for this group is accounted for by other family labour earnings. (The longitudinal
variability of “demographic” events are located at the bottom rows of the table. Households with
single and lone parent appear to have relatively greater longitudinal variations in both household
size and equivalence scales.) Public transfers are also an important dimension of the variability
of income for this group. In Canada, the contribution of public transfers to longitudinal income
variability (0.11) is a lot smaller than its share (0.21). This might indicate that the transfer
system contributes to buffering household income from changes due to other more volatile
components. This is also the case in Germany and less so in the UK.
16
In sum incomes are most volatile in the US and least volatile in Germany, but in all
countries the major source of variation—over 50%—is due to the heads’ labour earnings. The
exception to this is elderly households where market risks are reflected in asset income and
private transfers (with the exception of Germany), and lone parent households where
demographic events are important.
4. The Extent of Movement at the Two Extremes of the Income Distribution and their Causes
How do these average patterns in the variability of income translate into dynamics into and out
of low and high income, and what are the underlying causes? We approach this question in this
section by offering empirical hazard rates of the associated transitions and provide an overview
of exit and re-entry rates for both low and high income. In addition, income transitions and their
coincident events are examined to assess the importance of the roles of market conditions,
family, and the state. Data is transformed into a person-year format where one person has
potentially multiple records, each corresponding to a year in a particular state. The sample covers
every person with non-left censored spells. For the time being if one person has more than one
spell, both spells are included and treated independently. To avoid potential measurement errors,
small income changes between income cutoffs are not considered as a transition.7 Figures in the
following tables are based on unweighted statistics, however persons who have zero cross-
sectional weights are not included.
Table 6 shows Kaplan-Meier estimates of low income exit and re-entry rates. We assume
that all persons start a spell that will last for at least one year. As a result, there is no transition
rate for the first year. In all of the countries more than one-third of people (36 to 47%) are able to
7 Persons whose post-transition earnings rise (fall) by not more than 10% above (below) low income line are notconsidered as an exit (re-entry). A similar definition is used for high income transitions.
17
leave low income after one year, but the exit rate is lower in North America than Europe. The
exit rates then fall further as the time spent in low income increases and reach about 0.2 at the
fifth year of low income, the UK being the exception experiencing a rise in the exit rate between
the fourth and fifth years. In the UK and Germany less than one-fifth of low-income spells are
still in progress by the fifth year, but almost one-quarter in Canada and almost one-third in the
US. Low-income spells last longer in North America.
The bottom panel of Table 6 shows the low income re-entry rates for low-income persons
who start a non-low income spell. In the US, as many as 25% of people fall back into low
income after one year, while the figures are only 10 to 15% in the other three countries. In the
longer term, almost one-half of those leaving low income in the US will have experienced
another spell within five years, compared to about 30% in Canada and the UK and only 25% in
Germany. The chance of falling back into low income drop sharply as survival time in non-low
income increases. The re-entry rates at the end of the fifth year drop to 6% in Canada, below 5%
in UK and Germany, and to about 10% in the US.
Combining both exit- and re-entry rates gives a more comprehensive view of the low
income dynamics. Persistent low-income is more acute in the US and is the result of both lower
exit rates and higher re-entry rates. In the UK a great percentage (94%) of those entering low-
income are able to escape within eight years. However, as high as 41% fall back during the
subsequent eight years. Germany has both the highest exit rates and lowest re-entry rates after
five years. Although a larger fraction of the German population remain in low income after eight
years than in the UK (12% versus 6%), the rate of re-entry is much lower (71% of individuals
who left low-income still have not fallen back in after eight years versus 59% in the UK). In fact
18
the German re-entry rate after eight years is comparable to that after five years in Canada and
after only two years in the US.
There is much more persistence in high income. The exit and re-entry rates for high
income transitions are presented in Table 7. For each country about 45 to 50% of individuals
remain in high income by the fifth year after a start of a spell, Canada and Germany being at the
upper end of this range. Except for the US this is more than double the low-income exit rate for
each country. The US is unique in that it appears to combine a high degree of persistence for
both low and high income, there being much more variation in low-income persistence across the
countries than in high-income persistence. The re-entry rates back into high income after a spell
has ended are about the same as the low-income re-entry rates in North America, but higher in
Europe. In Canada and Germany only slightly more than 30% of spells moved back into high-
income after five years, but in the UK about 40% and in the US about 50%.
In order to assess, in a descriptive way, the underlying causes of these transitions we
assign each person who experiences an income transition to one of 15 mutually exclusive groups.
(Left-censored spells are excluded). The approach follows Jenkins (2000) and is summarized in
Figure 1. For each spell we first determine if there was a change in family structure, and refer to
situations in which there is a change in household head concurrent with the transition as a
“demographic event.” The type of demographic event is then determined. On the other hand, if
there is no change in family structure (same head, same size) the transition is classified as an
“income event,” and categorized according to which income source changed the most during the
transition. If the family head remained the same but the size changed two possibilities are
allowed. The transition is considered an income event if the percentage change in household net
19
income is proportionately greater than the percentage change in equivalence scale. Otherwise, it
is a demographic event.
Tables 8 and 9 display low income spell ending and beginning types respectively by
person’s household type. The strong majority of low income spells end because of an income
event but there is considerable variability across the countries. In Germany these events account
for almost 85% of all spell endings, in the US and UK the comparable figure is 75%, but in
Canada it is 65%. In the US and UK income events account for roughly the same fraction of
spell beginnings as they do endings, but in Canada and Germany demographic events play a
more prominent role in determining the start of low-income spells, though the majority continue
to be caused by income events. In Canada about 56% of spells start because of an income event
(versus 66% that end for this reason), and in Germany the comparable figure is 66% (versus 84%
for spell endings). The risk of low-income is in the first instance an income event, but it is much
more likely to be demographic in Canada and Germany than in the other two countries.
Amongst income events, changes in labour earnings (especially heads’) are the leading
causes of transitions across non-elderly/lone parent households in all countries. Changes in social
security pensions are the most common event associated with low income endings for
households with an elderly head in Canada and Germany (35 and 53% respectively). However,
seniors in the UK are likely to rely on public benefits (32%), while older households in the US
are likely depending on assets/private transfer (30%) to escape low income. In Europe public
benefits provide greater help for lone parent households to get out of low income; this is less
likely to be the case in the North America. For example, nearly 23% of all low income endings in
UK are coincident with an increase in public transfer. On the contrary, most lone parent
households (over 40%) in the US rely on labour income to escape low income.
20
Turning to demographic events, a greater percentage of lone parents in Canada (33%) and
in the UK (25%) left low income because of marriage or partnership, while the proportions are
only 17% in the US and 12% in Germany. Undoubtedly, separation and divorce are the major
reason for beginning a low income spell for current lone parents in all countries but this is more
so in North America. In Canada, 47% of low income spells for lone parents begin because of
separation or divorce, in the US 35%, but in the UK and Germany only 24 and 28% respectively.
Tables 10 and 11 show the events coincident with high income transitions. Income events
are the major reason for high income transitions. The labour income of the secondary earner is as
important as head’s labour income in determining high income endings and beginnings. High
income spells are even more likely to begin because of income events: 87% of such spells in
Germany do so, 84% in the US and 70 to 76% in Canada and the UK. Double earners in a
household are an important factor in attaining high income, but this is less likely to be the case
in the US. Changes in the head’s labour earnings are still the leading event associated with high
income spells beginning or ending in the US (for non-senior couples). High paying jobs propel
US families into high income.
Income events other than those associated with the labour market have a smaller impact
on high income transitions across most of the household types for all countries. In households
with elderly heads, changes in asset/private transfers play an important role in determining high
income transitions. For example, as much as 50% of all high income spell beginnings in the US
are because of an increase in this category.
Compared to income events, the number of demographic events coincident with high
income transitions are lower. (In fact many of these calculations are based on less than 30
observations.) In general, a newly established family as well as a rise in needs are the leading
21
reasons for the ending of high income spells. Notice that newly established family refers to
people who leave a household to start a new one, while a needs rise refers to the same household
with additional people joining.
5. The Existence of Different Sub-Populations
Our econometric analysis of these transitions is designed to uncover the extent to which these
overall patterns reflect the existence of fundamentally different subpopulations. To this end we
generalize the approach used in Huff-Stevens (1999) by casting the econometric analysis in
terms of mixtures of distributions. That is, we extend the treatment of unobservables in the
economics literature from an assessment of “fixed points of support” to a mixture of distributions
with not only the constant but all of the coefficient values varying across the subpopulations. The
objective of the exercise is to establish the degree to which the model determining the transition
process characterizes the entire population or if there are different models for different
subpopulations. This is done conditional on a set of co-variates. That is to say, conditional on a
set of individual circumstances are there still underlying differences in the processes determining
these transitions and in what manner is the population sub-divided across these processes? If the
model determining transitions into and out of low and high income is fundamentally different
across a number of different sub-groups it may be that some groups see themselves as different
than others in terms of the risks to their income status. This would suggest a less cohesive
population and the presence of excluded groups. This is the interpretation we give to the
presence of unobservables, or as we term them “latent classes”, in the estimation of the hazard
rates determining low and high income dynamics.
22
The influence of latent classes will depend upon the explanatory variables included in the
model. In the estimations that follow we actually offer two sets of estimates based on two
different sets of explanatory variables. The first set includes (in addition to elapsed duration and
year effects) indicators for age, family structure, and gender. All other influences are considered
latent. That is to say the differences across family structures outlined in the previous tables are
taken into account. As the discussion surrounding Table 5 suggests, volatility in incomes varies
by family structure. We control for this and examine income dynamics net of family influences.
High income transitions are dominated to a greater degree by income events than low-income
transitions. So it is certainly possible that high income individuals may see themselves as
different from others on the basis of demographic “decisions,” particularly with respect to issues
surrounding separation, divorce and child bearing. This has certainly been part of the debate over
the role of public transfers in the US. Even so income events are the major reason for transitions
for all groups and as such our analysis takes this focus and should be considered as an
assessment of the role of market forces net of demographic circumstances.8 Further, the sharp
distinction between these two types of causes may not be totally appropriate. To some extent
demographic events are endogenous and could be the result of income events. The second set
includes all of these variables with the addition of education and an indicator for belonging to
what at least in Europe are considered to be a potentially “excluded” group. This latter variable
varies across the countries. In the US it is an indicator of race (whether the individual is black or
not); in Canada it is a visible minority indicator; in the UK an indicator of race; and in Germany
three separate indicators of whether the individual is a guest worker, East German, or an
8 Also the previous analysis has uncovered very different approaches to the support of the elderly and children. Wealso net out these influences by controlling for age.
23
Immigrant (after 1984).9 We are interested in determining the extent to which the results change
by adding a choice variable important in the income determination (education), and direct
indictors of characteristics often considered to be the basis of social exclusion. If the results are
not sensitive to these choices this may suggest that “exclusion” may be a more difficult issue to
address than by the standard policy decisions directed to human capital choices and
discrimination.
a. Econometric Method
Let tiy , i = 1 ... n be the outcome for individual i at time t, t = 1 ... T , where 1=t
iy if
the ith individual makes a transition at time t and 0=tiy otherwise. We assume that the
conditional probability of success at time t is modeled as a logistic distribution with
)(log βti
ti xitp = where ))exp(1/()exp()(log aaait += , t
ix is a p×1 vector of independent
variables and β is 1×p vector of parameters. We are interested in making inferences about β. If
each individual is observed iT times until a transition or right censoring occurs the likelihood
function for individual i under-independence is ∏=
=iT
t
tii fL
1, where t
iti yt
iyt
it
i ppf )()1( 1−−= in
case of a Bernoulli distribution with tip as parameter. The estimation of β can be made by
standard statistical packages and obtained iteratively using Newton-Raphson or Fisher scoring
method. The estimator β̂ is the solution of an estimating equation of the form
)()( ββ ii
uU �= , where ti
ti
titi xpyu )()( −=�β in the case of independent Bernoulli
9 The models for the UK do not include an indicator for education. In the other countries this is controlled for bycreating indicator variables for less than and more than 12 years of education.
24
distributions. When interest is in two events or more the above formulation continues to hold.
For example when considering simultaneously the conditional probability of exiting low income
and the probability of re-entering low income,
t
0 1
0 λ−1 λt-1
1 q q−1
all that is needed is to redefine the vector of independent variables. Let tiz be the new vector of
independent variables of order )21( p× with )0,( ti
ti xz = for exiting low income and ),0( t
iti xz =
for re-entering low income, where 0 is )1( p× vector 0’s. Let ),( 21TTT βββ = be the new vector
of parameters of order )12( ×p . Each probability can be written in term of tiz and β ,
ti
ti
ti pxit =+= )0(log 21 ββλ and t
iti
ti pxitq =+= )0(log 21 ββ .
We seek to identify independent variables that predict the probability of a transition.
However, conditional on this choice the probability of a transition may also be influenced by a
latent class and as discussed above this is also an important part of the story we wish to address.
An unobservable discrete variable indicates the latent class of the i th individual. The variable is
assumed to take G distinct values, each of which corresponds to a distinct transition probability.
The conditional probability of success given that an individual is in group g can be modeled, as
is traditionally done, with common slopes as )(log βα tig
tig xitp += , where gα represents the
effect of latent group g . The identification of the latent class effect is statistically equivalent to
25
determining whether gα differs across latent groups. Mixture of distributions framework ,which
covers this special case, is used when the data can be viewed as arising from two or more
populations mixed in varying proportions. Each observed iy is drawn from a super-population
P which is a mixture of a finite number, say G , of populations GPP ,...,1 in some proportions
Gππ ,...,1 , with 1=�g gπ and 0≥gπ , Gg ,...,1= . Given the data, and a known form of the
distributions, we wish to estimate the model parameters and the mixing distribution. Mixture of
distributions can be handled by using the Expectation Maximization (EM) algorithm (Dempster,
Laird, and Rubin 1977). The maximization can be implemented using standard software. This
can be achieved by essentially creating G copies of the data and then using the conditional
probabilities as weights. The calculation of the weights and the estimation of the parameters are
repeated until convergence. Define the latent groups membership indicator variables as 1=igz if
gPi ∈ and 0=igz if gPi ∉ . The EM algorithm is applied to the mixture of distributions by
treating the variable igz as missing data. The log-likelihood for the complete data is given by
)log(log iggiggic fzl += �� π , where ),|,...,( 1 gyyff Tiiigig β= . Using some initial value for
)','(' πβφ = , say )(mφ , the E step requires the calculation of the pseudo complete log-likelihood
based on the incomplete data ),|(),( )()( mc
m ylEQ φφφ = . That is, each indicator variable igz is
replaced by its expectation igτ , conditional on
),( )(my φ ,where )(/)( )()()()( mjij
mjj
mig
mgig ff βπβπτ �= .
The M step intent is to choose the value of φ , say )1( +mφ , that maximizes ),( )(mQ φφ subject to
1=�g gπ . To accomplish the maximization step for each EM iteration, it suffuses to solve the
26
following estimating equation βτ ∂∂�� /log igiggif , and get the subsequent estimate of
gπ as follow, iggiigig ττπ ���= / . A convergence criteria is εφφ <− + ||max )1()( mm based on
the parameters because the likelihood is may be flat in some directions.
One limitation with the analysis of non-Gaussian longitudinal data is the lack of a rich
class of models such as the multivariate Normal for the joint distribution of )',...,( 1 Tiii yyy = .
The generalized estimating equations (GEE) proposed by Liang and Zeger (1986) allows
regression modeling of longitudinal data by specifying only the mean and the covariance of the
outcome variables. The GEE approach covers independent distributions and can be fitted using a
number of statistical software. These software now provide a large variety of common structures
for the correlation matrix. In this approach, we assume that the marginal expectation of tiy ,
ti
ti pyE =)( , is modeled as before by βt
iti xpg =)( , where (.)g is a known link function such as
the logit function and the estimating equation for β is also as before �=i iuU )()( ββ with
)()'/()( 1iiiii pyVpu −∂∂= −ββ where )',...,( 1 T
iii ppp = and iV is the working covariance
matrix of iy . Note that for the EM algorithm, the conditional expectation of the latent class
indicators require full specification of the distribution but the β parameter can be obtained using
the GEE approach.
Longitudinal surveys lead to dependent observations within and between individuals. The
latter is a result of complex sampling design. Under panel survey data, parameters can be
estimated by solving the corresponding design-consistent estimating equations,
�=i iiudU )()( ββ , where id ’s are the design weights. These are available for the SLID but
not for the other surveys. [As a result we estimate two sets of results for the SLID, one
27
incorporating design based information and other ignoring it, in order to assess at least in this
case the robustness of the results.]
b. Results
Table 12 offers a summary of the samples sizes used in each of the estimations. Table 13
summarizes the results from two separate models for each of four transitions and for each
country. These results assume independence across multiple spells for the same individual.10 Up
to three latent classes are estimated in each case, and the table reports the unconditional
probability associated with each grouping. These are ordered according to the magnitude of the
estimated probability of making a transition after the first year in a particular state. For example,
the first line of the table, for the transition probability of leaving low-income in Canada (under
the limited set of co-variates, those excluding the visible minority indicator), suggests that there
are three latent classes: 56% of the population are in the first group and experience a 60% chance
of leaving low-income after one year; 8% in the second with a 25% chance of leaving; and 36%
in the last group with 20%. (The exact transition probabilities are 61.9, 25.2 and 21.6%.)
With respect to the probability of leaving low-income it is the case in all four countries
that three latent classes can be identified. In all of the countries there exists a significant fraction
of low-income individuals, indeed the majority in Canada, the US and Germany and a close
majority in the UK, who face a very high chance of leaving low-income within a year. The
transition out of low-income after one year is 62% in Canada, 75% in the US, 55% in the UK,
10 An appendix with the detailed descriptive statistics for all the co-variates and results including parameterestimates is available upon request. In actual fact we estimate a series of models but report only the results for whichconvergence was obtained with the most number of latent classes, to a maximum of threee. In future analysis weintend to recognize the joint determination of entering and leaving a particular state, as well as incorporate thedesign effects inherent in complex surveys of the kind being used here.
28
and 56% in Germany. The two remaining latent classes tend to have much lower exit
probabilities that are not too dissimilar. Germany may be a bit of an exception. In Canada the
remaining groups face a 25 and 22% transition rate; in the US 35 and 30; in the UK 30 and 25;
but in Germany the rates at 16 and 10% are lower and at least proportionately more dissimilar.
This is to say that while low-income may be a state touched by many, the low-come population
consists of distinct sub-groups. In fact for the majority it is a very temporary experience. In this
sense the multi-variate analysis supports the inference made in this regard on the basis of
information in Table 2 but is able to identify the number of sub-groups and associate weights to
them.
The most interesting difference that emerges when the more complete set of co-variates is
used in the estimation concerns Germany. For the most part pulling information on the
potentially excluded groups from the unobservables to the observables does not change the
overall patterns: the data continue to support three latent classes, but there are some changes in
the actual transition probabilities they face with the differences between the two groups facing
the lower probabilities becoming more distinct. This, however, is not the case in Germany. There
remain three latent classes, but they are much more alike. The highest transition probability is
28%, then 12% then only 3%, and it is the strong majority—almost 60%—who face the
intermediate rate. By contrasting these results with those in panel 1a we are led to the suggestion
that social exclusion in Germany is associated very directly with the identifiable characteristics
in the data: that is with being East German, a guest worker, or an immigrant. Net of these
influences the dynamics governing the exit from low-income are much more similar across the
latent classes than in the other countries.
29
There is a broadly similar pattern with respect to the chances of re-entering low-income.
Those having left low-income in Germany fall into two distinct groups in terms of the chances of
returning. Almost 70% face a moderate chance of re-entering low income (18%), while the
remaining third face virtually a zero chance of this happening. But once the potentially excluded
groups are pulled out of the unobservable component the data support only one grouping with a
6% chance of re-entry. Interestingly, in Canada the pattern works in the opposite direction. The
data support only one group in the first instance, but three in the second. However, the
differences between two of these latent classes is rather small (both experiencing a transition
probability of less than one percent). In both panels 2a and 2b the US is distinguished by rather
high re-entry rates for significant fractions of the low-income population. Almost 80% of the
population face a 20 to 30% chance of re-entry low-income after only one year.
Panels 3 and 4 of Table 13 offer similar results for the exit and re-entry rates associated
with high income. Generally the high income population divides into two groups: one of which
has little likelihood of leaving, but if it has faces a reasonable chance of returning; the other
group has the opposite characteristics. In Canada the data support three latent classes with
respect to the exit probability, but two of these face very similar and small probabilities of
leaving. Essentially 45% of the high income population faces a less than 1% chance of exiting,
while 55% have over a 10% chance of exiting after one year. Introducing the full set of control
variables does not change things in a substantive way. The patterns are similar for the probability
of re-entering high income and in the other countries, though the magnitudes vary. (The only
exception is the re-entry transition rate for the UK, which collapses to one group after the race
indictor is added.)
30
6. Conclusion
The objective of this paper is to examine the main features of income dynamics in four countries
in order to shed light on the nature of social cohesion and the challenges faced by policy makers.
We outline the basic static features of the income distributions in Canada, the US, the UK and
Germany during the 1990s and stress the relationships between these characteristics and the
underlying dynamics. In a very broad sense market income inequality is similar in all four
countries. The low-income population is growing in the US, but most notably in Germany. The
high income population is growing in all countries. The risk of facing low-income is equally high
in all countries with about 40% of the population experiencing at least one bout of low income
over a six year period. However, the severity of low-income is not equally shared as some
significant fraction of individuals are able to leave low-income quickly. This heterogeneity in the
low-income population represents one important challenge to broad based income redistribution.
Another has to do with the fact that there is a moving up trend for middle income groups and
steady growth in the high income population. More and more middle income groups aspire to
bettering their position and likely share less and less in common with lower income groups.
After tax – after transfer incomes show much more variation across the four countries
than market incomes, with Canada and particularly Germany doing much more to reduce low-
income rates than the UK and particularly the US. Germany stands out by performing the most
redistribution but in the face of market forces that are generating growing inequality and low-
income. In this sense the German welfare state has had to sail upstream. The pattern is different
in Canada where market forces are reducing the low-income rate.
We attempt to understand these developments and give them more precision by
examining a series of related questions. First, how is income earned and what components are
31
most volatile? In other words what are the sources of risks that households face with regard to
their income status? The nature of these risks do not vary substantively across countries, though
their magnitude does. In all four countries labour earnings of the household head are the most
important component of income and the major source of variability. Contributions from
secondary earners are also important. The significance of other income sources depends very
much on family structure. Market sources continue to be important for disadvantaged groups (the
elderly and lone parent households) in the US. In Germany this is also the case for lone parents
but the incomes of the elderly have been taken entirely out of the market. Lone parents are best
treated in the UK and Canada. The variability of incomes is greatest in the US, least in Germany.
The major source of variation comes from the head’s labour earnings. Elderly households are the
exception to this, with asset and private retirement incomes being important (except in
Germany). Demographic events play an important role in the variability of the incomes of lone
parent households.
The second question we address asks how this volatility is reflected at the two extremes
of the income distribution? How much movement is there into and out of low and high income,
and what are the proximate causes? Low income spells last longer in North America. The US is
characterized by not only low exit rates but also high re-entry rates. Germany sits at the other
extreme with high exit rates and low re-entry. In the UK the exit rate is relatively high; in
Canada relatively low. Both of these countries have middling rates of re-entry. Indeed, the
German re-entry rate after eight years is comparable to that after five years in Canada, and after
only two years in the US. Low income spells end and begin because of income events, though
demographic events are an important source of spell beginnings in Canada and Germany. The
importance of demographic events also varies by family structure, being particularly salient for
32
lone parent households. Public transfers are an important reason for spell endings in Europe,
particularly for lone parent households. Exit rates from high income are lower than for low
income, and they are particularly low in Canada and Germany. Income events are the major
reason for high income transitions, but income from secondary earners is as important as the
head’s earnings.
Third, and finally, do these patterns in low and high income transitions reflect the
existence of different subpopulations? If it is the case that the transition process is very different
across distinct sub groups of the population then it is likely that the relatively advantaged groups
will not see themselves as having much in common with others. This may be the basis for a less
cohesive society. Our econometric analysis adds more precision to the descriptive finding that
low income is experienced by many but that its severity is concentrated. We extend traditional
methods of duration analysis in the presence of unobservables by using mixtures of distributions
and uncover the unconditional probabilities of belonging to unobserved groups. The models
control for the major differences between households determining the reasons for transitions into
and out of low and high income: family structure and age. Net of these influences we find that
the low income population separates into distinct groups. From 50 to 60% of those who
experience some time in low-income do so only temporarily, having a very high exit rate even
after just one year. Two other groups have much lower exit rates. Germany is a bit different.
When information about race and immigrant status are recognized the subgroups are much more
alike, and the majority of individuals have relatively low transition rates. This is not the case in
the other countries. In Germany social exclusion is associated very directly with identifiable
characteristics: either being East German, a Guest Worker, or an immigrant. Net of these
influences the subgroups identified in the data have much more in common in terms of income
33
dynamics than in the other countries. In Canada, the US, and the UK social exclusion is
associated with other less well defined characteristics. Similar patterns occur with respect to re-
entry rates. In Germany there are two distinct groups: about one-third of the population has
virtually no risk of re-entering low income, while the strong majority has a moderate risk of
doing so. Once status as an East German, Guest Worker, or Immigrant is recognized there is only
one group in the population with a moderately low chance of falling back into low income. The
US stands out as having a very high re-entry rate for 80% of the low income population.
In all of the countries high income dynamics are governed by two distinct groups in the
population: one with a low exit rate and a high re-entry rate; the other with a high exit rates and
low re-entry. There is a group of people permanently in high income, and a group that can aspire
to this state but with little chance of experiencing it on a long term basis. If this group is more
conscious of its chances of being high income than of its risk of becoming low income then the
processes governing high income dynamics are a force eroding social cohesion.
In sum, while many aspects of income dynamics are common across these countries there
are also many differences. These differences raise different challenges in developing policies
geared to addressing concerns about social exclusion. The German situation stands out as being
different qualitatively from the other countries. The sources of exclusion are directly identifiable
having to do with residency in East Germany or status as a Guest Worker or Immigrant. Aside
from these differences the low income dynamics process is on the whole common to a large
segment of the low income population. This raises both opportunities and challenges: it may be
one reason why the German welfare state is the most aggressive in reducing low income rates
over all, but there remains a need to focus on particular groups outside of the mainstream. In the
other countries the underlying characteristics that generate a different transition process for sub
34
groups in the population are not simply associated with traditional measures of race or immigrant
status and there is a stronger tendency for some groups to be very different than others. This is
particularly clear in the US with one group having a very high chance of leaving low-income
should it touch that state and a low chance of falling back in, while another group faces very low
chances of getting out and very high chances of returning. This would seem to resemble the pre
conditions for a less coherent basis for the conduct of redistributive policies.
35
BIBLIOGRAPHY
BANE, Mary Jo and David Ellwood (1986). “Slipping Into and Out of Poverty: The Dynamicsof Spells.” Journal of Human Resources. Vol. 21 No. 1, pp. 1-23.
BURKHAUSER et al (2000).
BRADBURY, Bruce, Stephen P. Jenkins and John Micklewright (2001). The Dynamics of ChildPoverty in Industrialized Countries. Cambridge: Cambridge University Press.
D’AMBROSIO, Conchita, Fotis Papadopoulos and Panos Tsakloglou (2002). “Social Exclusionin EU Member States: A Comparison of Two Alternative Approaches.” Paper presented tothe XVI Annual Conference of the European Society for Population Economics, Bilbao.
DEMPSTER, A.P., N. M. Laird and D.B. Rubin (1977). “Maximum Likelihood Estimation fromIncomplete Data via the EM Algorithm (with discussion).” Journal of the Royal StatisticalSociety. Series B. Vol. 39, pp. 1-38.
HECKMAN, James and Burton Singer (1984). “A Method for Minimizing the Impact ofDistributional Assumptions in Econometric Models for Duration Data.” Econometrica. Vol.52 No. 2, pp. 271-320.
JENKINS, Stephen (2000). “Modelling Household income dynamics.” Journal of PopulationEconomics. Vol. 13 No. 4, pp.529-67.
LIANG, K.Y. and S.L. Zeger (1986). “Longitudinal Data Analysis Using Generalized LinearModels.” Biometrika. Vol. 73, pp. 13-22.
STEVENS, Ann Huff (1999). “Climbing Out of Poverty, Falling Back In: Measuring thePersistence of Poverty Over Multiple Spells.” Journal of Human Resources. Vol. 34 No. 3,pp. 557-588.
STEWART, Kitty (2002). “Measuring Well-Being and Exclusion in Europe’s Regions.” Paperpresented to the XVI Annual Conference of the European Society for Population Economics,Bilbao.
OECD (2001). Employment Outlook. Paris: OECD.
SHORROCKS, A. F. (1982). “Inequality Decomposition by Factor Components. Econometrica.Vol. 50, pp. 193-212.
36
Table 1aSummary Statistics for the Household Adjusted Income (market income) in the 1990s*
(Currencies are expressed in 1997 value for each nation)
NationYear Median Mean Gini
90-10Ratio
90-50Ratio
10-50Ratio
%1
Low income%2
Childpoverty
%3
High income
Canada1993 25,111 28,398 0.42 20.4 2.18 0.11 25.6 (25.6) 25.7 (25.7) 27.1 (27.1)1994 25,202 28,305 0.42 23.3 2.15 0.09 26.2 (26.3) 24.7 (24.8) 27.3 (27.0)1995 25,302 28,656 0.42 23.7 2.17 0.09 25.6 (25.8) 25.4 (25.6) 27.7 (27.4)1996 24,703 28,267 0.43 22.0 2.20 0.10 27.0 (26.6) 26.9 (26.6) 27.2 (28.0)1997 25,136 28,965 0.43 21.9 2.21 0.10 26.2 (26.2) 25.8 (25.8) 28.1 (28.1)1998 26,389 30,310 0.44 22.5 2.20 0.10 25.0 (26.2) 24.8 (26.0) 30.2 (27.7)1999 26,974 31,017 0.43 18.3 2.19 0.12 24.1 (25.5) 23.7 (25.4) 31.2 (27.2)
United States1990 25,083 31,360 0.45 15.6 2.46 0.16 24.8 (24.8) 27.5 (27.5) 27.8 (27.8)1991 24,149 30,176 0.45 15.2 2.45 0.16 25.3 (24.2) 27.0 (25.7) 26.8 (29.0)1992 24,017 30,400 0.46 16.9 2.48 0.15 25.6 (24.6) 27.6 (26.6) 27.1 (29.0)1993 24,359 30,991 0.46 18.6 2.54 0.14 25.4 (24.9) 28.1 (27.6) 28.9 (30.3)1994 25,532 33,477 0.49 25.7 2.51 0.10 26.3 (26.7) 29.0 (29.6) 31.1 (30.2)1995 24,790 32,637 0.48 22.4 2.60 0.12 26.2 (25.9) 29.3 (28.9) 29.4 (30.0)1996 24,935 32,493 0.48 19.8 2.61 0.13 26.2 (26.0) 29.6 (29.3) 29.6 (29.8)
Britain1991 11,591 13,236 0.42 27.1 2.25 0.08 26.0 (26.0) 20.3 (20.3) 28.7 (28.7)1992 11,421 13,205 0.44 39.6 2.37 0.06 27.6 (27.2) 21.3 (21.3) 28.3 (29.0)1993 11,702 13,091 0.44 44.1 2.28 0.05 28.0 (28.2) 25.5 (25.8) 28.8 (28.4)1994 11,375 13,109 0.45 46.6 2.37 0.05 29.0 (28.6) 24.4 (24.2) 27.9 (28.9)1995 11,431 13,284 0.45 41.4 2.34 0.06 28.3 (27.9) 21.4 (21.0) 29.1 (29.7)1996 11,988 13,675 0.44 31.8 2.31 0.07 26.9 (27.7) 19.4 (20.8) 30.8 (29.2)1997 12,237 13,948 0.44 28.2 2.27 0.08 26.7 (27.7) 22.8 (23.8) 31.3 (28.2)1998 12,401 14,197 0.44 27.9 2.18 0.08 25.5 (27.1) 22.8 (25.5) 32.3 (28.4)1999 12,551 14,576 0.44 26.5 2.26 0.09 25.3 (26.6) 23.5 (24.7) 33.2 (28.9)
Germany4
1992 32,452 36,461 0.44 55.7 2.30 0.04 26.8 (26.8) 16.6 (16.6) 27.5 (27.5)1993 33,580 37,283 0.44 64.3 2.22 0.03 27.4 (27.9) 16.9 (17.7) 28.8 (27.2)1994 33,332 37,180 0.45 58.7 2.23 0.04 28.0 (28.5) 17.3 (17.9) 29.1 (27.5)1995 32,843 36,819 0.45 89.4 2.24 0.03 29.4 (29.8) 19.2 (19.6) 28.9 (28.3)1996 33,918 38,110 0.46 107.0 2.30 0.02 29.5 (30.2) 18.7 (19.8) 30.3 (28.4)1997 33,440 37,498 0.46 134.5 2.28 0.02 29.9 (30.4) 19.0 (19.5) 30.1 (28.6)1998 33,284 37,336 0.47 138.5 2.31 0.03 30.5 (30.9) 19.7 (19.8) 29.9 (28.7)1999 33,011 37,457 0.47 172.2 2.37 0.01 31.0 (31.2) 20.2 (20.3) 30.1 (29.5)* Data source: CNEF. Incomes are household adjusted income based on market income (pre-taxes and pre-transfers), adjusted forhousehold size using the “square root of household size” equivalence scale.1. A person is in poverty if his/her adjusted income is less than half of the national median in 1991 (1993 for Canada). % belowhalf contemporary median is in parentheses.2. Child is defined as people who under age 18. % below half contemporary median is in parentheses3. A Person is in high income if his/her adjusted income is greater than 1.5 time of the national median in 1991 (1993 forCanada). % above 1.5 times contemporary median is in parentheses4. Germany refers to re-united Germany includes sample from West German, foreigner and E. German cohorts throughout thisperiod. Two new sub-samples, Immigrant (added in 1995) and Refreshed sample (added in 1998), are not included.
37
Table 1bSummary Statistics for the Household Adjusted Income (net income) in the 1990s*
(Currencies are expressed in 1997 value for each nation)
NationYear Median Mean Gini
90-10Ratio
90-50Ratio
10-50Ratio
%1
Low income%2
Childpoverty
%3
High income
Canada1993 23,703 26,361 0.29 3.86 1.84 0.48 11.3 (11.3) 15.9 (15.9) 20.2 (20.2)1994 23,534 26,138 0.30 4.01 1.83 0.46 12.3 (12.1) 15.1 (14.7) 20.1 (20.7)1995 23,738 26,202 0.29 3.95 1.83 0.46 12.0 (12.0) 15.8 (15.8) 20.2 (20.2)1996 23,573 26,060 0.30 4.07 1.83 0.45 12.9 (12.8) 17.4 (17.3) 20.3 (20.7)1997 23,824 26,533 0.30 4.17 1.88 0.45 12.4 (12.6) 16.2 (16.5) 21.3 (21.1)1998 24,560 27,485 0.31 4.13 1.88 0.45 11.6 (12.6) 15.1 (16.2) 23.2 (20.8)1999 25,196 28,148 0.30 4.11 1.87 0.46 10.7 (12.5) 13.5 (15.7) 24.8 (21.0)
United States1990 22,133 26,396 0.37 5.83 2.12 0.36 17.9 (17.9) 24.3 (24.3) 24.2 (24.2)1991 21,479 25,443 0.36 5.60 2.07 0.37 18.5 (17.6) 24.5 (23.2) 22.9 (24.6)1992 21,617 25,656 0.37 5.84 2.10 0.36 18.2 (17.3) 24.1 (22.9) 23.0 (24.7)1993 21,900 25,957 0.37 5.98 2.11 0.35 18.2 (17.9) 24.1 (23.8) 24.7 (25.2)1994 22,733 27,686 0.41 7.86 2.16 0.27 19.6 (20.3) 26.8 (27.7) 26.9 (25.6)1995 22,441 27,144 0.40 6.91 2.18 0.31 19.4 (19.7) 26.6 (26.9) 26.3 (25.4)1996 22,198 27,035 0.39 6.58 2.23 0.34 18.9 (18.9) 26.2 (26.3) 25.6 (25.5)
Britain1991 11,140 12,563 0.31 4.71 1.97 0.42 15.5 (15.5) 14.8 (14.8) 22.4 (22.4)1992 11,229 12,731 0.32 4.84 1.98 0.41 15.6 (15.7) 15.9 (16.0) 23.0 (22.5)1993 11,452 12,788 0.32 4.90 1.96 0.40 14.9 (15.8) 15.4 (16.2) 23.8 (22.3)1994 11,303 12,700 0.32 4.92 1.99 0.40 15.1 (15.6) 15.9 (15.9) 22.9 (22.0)1995 11,145 12,800 0.33 4.79 2.01 0.42 14.4 (14.4) 14.8 (14.8) 23.8 (23.8)1996 11,727 13,243 0.31 4.64 1.98 0.43 12.7 (14.5) 10.8 (12.0) 25.3 (22.2)1997 11,931 13,514 0.31 4.51 1.98 0.44 11.6 (13.8) 12.6 (15.8) 26.1 (21.9)1998 12,048 13,692 0.32 4.60 1.94 0.42 12.1 (14.7) 14.2 (17.3) 26.9 (21.3)1999 12,519 14,286 0.32 4.31 1.97 0.46 9.4 (13.0) 12.4 (17.1) 29.5 (22.4)
Germany4
1992 28,667 31,999 0.29 3.81 1.88 0.49 10.2 (10.2) 9.9 (9.9) 20.6 (20.6)1993 29,554 32,725 0.28 3.60 1.83 0.51 8.7 (9.6) 8.9 (10.0) 20.5 (19.0)1994 29,536 32,920 0.29 3.57 1.82 0.51 9.2 (9.6) 10.6 (11.1) 21.6 (19.7)1995 29,165 32,243 0.29 3.76 1.83 0.49 10.2 (10.8) 13.3 (14.2) 19.4 (18.7)1996 29,436 32,595 0.29 3.87 1.82 0.47 10.4 (10.9) 13.0 (13.4) 20.6 (19.3)1997 29,548 32,844 0.28 3.70 1.82 0.49 9.7 (10.2) 11.9 (12.4) 20.8 (18.7)1998 29,205 32,531 0.29 3.94 1.87 0.47 10.8 (11.1) 12.9 (13.3) 21.0 (20.0)1999 29,942 33,382 0.28 3.82 1.83 0.48 10.0 (10.8) 12.3 (13.3) 22.7 (19.8)* Data source: CNEF. Incomes are household adjusted income (after taxes and transfers), adjusted for household size using the“square root of household size” equivalence scale.1. A person is in poverty if his/her adjusted income is less than half of the national median in 1991 (1993 for Canada). % belowhalf contemporary median is in parentheses.2. Child is defined as people who under age 18. % below half contemporary median is in parentheses3. A Person is in high income if his/her adjusted income is greater than 1.5 time of the national median in 1991 (1993 forCanada). % above 1.5 times contemporary median is in parentheses4. Re-united Germany.
38
Table 2aIncidence of Low and High income: Market Incomes 1993-1998 (1990-1996 for United States)*
CountryLow Incomeat least once
(%)
LowIncomes inall years1
(%)
Low Incomein all years
usingPermanent-income 1,2
High Incomeat least once
(%)
High Incomein all years1
(%)
High Incomein all years
usingPermanent-income 1,2
Canada 39.1 13.2(0.34)
21.1(0.54)
55.8 14.4(0.26)
28.1(0.50)
United States 42.8 10.1(0.24)
20.4(0.48)
49.1 14.4(0.29)
29.9(0.61)
Britain 42.0 17.6(0.42)
26.4(0.63)
45.4 13.4(0.30)
27.1(0.60)
Germany 43.0 15.9(0.37)
24.6(0.57)
46.2 13.6(0.29)
28.3(0.61)
* Data source: CNEF. The last year of longitudinal weight is used for all countries.1. Incidence of low (high) income is shown in brackets. It is defined as the ratio of always Low Income (High Income) to everLow Income (High Income).2. Permanent income is used here to measure the always Low Income (High Income) and low (high) income incidence. Underthis definition, an individual is considered an always Low Income (High Income) if the sum of income (adjusted) across all yearsis less (great) than the sum of the low (high) income threshold across all years.
Table 2bIncidence for Low and High income: Net Incomes 1993-1998 (1990-1996 for United States)*
CountryLow Incomeat least once
(%)
LowIncomes inall years1
(%)
Low Incomein all years
usingPermanent-income 1,2
High Incomeat least once
(%)
High Incomein all years1
(%)
High Incomein all years
usingPermanent-income 1,2
Canada 24.1 2.88(0.12)
8.2(0.34)
37.5 9.4(0.25)
7.9(0.21)
United States 35.1 5.39(0.15)
13.8(0.39)
45.6 11.1(0.24)
25.7(0.56)
Britain 29.7 4.36(0.15)
11.8(0.40)
38.4 9.4(0.25)
8.1(0.21)
Germany 19.5 1.90(0.10)
5.2(0.19)
36.7 9.1(0.26)
7.0(0.19)
* Data source: CNEF. The last year of longitudinal weight is used for all countries.1. Incidence of low (high) income is shown in brackets. It is defined as the ratio of always poor (rich) to ever poor (rich).2. Permanent income is used here to measure the always poor (rich) and low (high) income incidence. Under this definition, anindividual is considered an always poor (rich) if the sum of income (adjusted) across all years is less (great) than the sum of thelow (high) income threshold across all years.
39
Table 3aLongitudinal Perspective on the Income Distributions (market income) 1993 ~ 1998*
Average Annual Transition rates (weighted) **
Outflow rates (%) from year t-1 income group origins to year t income group< 0.5 0.5 ~ 0.75 0.75 ~ 1.0 1.0 ~ 1.25 1.25 ~ 1.5 > 1.5
Country
= + - = + - = + - = + - = + - =Canada 82.0 18.0 19.7 46.6 33.7 21.7 45.9 32.4 23.6 45.0 31.4 28.8 41.0 30.2 16.5 83.5USA 78.3 21.7 23.4 43.3 33.3 26.2 42.1 31.7 31.5 38.2 30.3 33.1 35.4 31.5 17.8 82.2Britain 86.1 13.9 20.2 47.6 32.2 22.9 45.5 31.6 27.9 40.4 31.7 31.1 37.0 31.9 19.4 80.6Germany 85.6 14.4 24.6 40.6 34.8 22.5 45.0 32.5 27.3 42.5 30.2 29.3 40.2 30.5 18.5 81.5
Inflow rates (%) in year t income group origins from year t-1 income group< 0.5 0.5 ~ 0.75 0.75 ~ 1.0 1.0 ~ 1.25 1.25 ~ 1.5 > 1.5
Country
= + - = + - = + - = + - = + - =Canada 82.4 17.6 22.9 48.0 29.1 27.3 47.1 25.6 31.5 45.7 22.8 38.2 41.0 20.8 19.0 81.0USA 76.3 23.7 24.5 44.1 31.4 28.4 42.6 29.0 34.1 39.7 26.2 39.3 35.7 25.0 18.7 81.3Britain 85.8 14.2 20.1 47.9 32.0 25.9 45.7 28.4 29.7 42.1 28.2 38.0 36.5 25.5 20.5 79.5Germany 82.0 18.0 22.8 43.7 33.5 26.7 46.2 27.1 30.9 43.0 26.1 33.8 41.1 25.1 18.9 81.1
* Data source: CNEF sub-sample for individuals presented in all years between 1993-1998 (1990-1996 for USA). Sample sizes:Canada (29,772), USA (7,849), Britain (6,126), and Germany (11,733)** Income is family-adjusted net income (after taxes and transfers, and adjusted for equivalence scale) in 1997 currency. Personsclassified into income groups according to the size of their income relative to fixed real income cut-offs equal to 0.5, 0.75, 1.0,1.25, and 1.5 times median year 1 income. The last year of longitudinal weight is used for all countries.
Table 3bLongitudinal Perspective on the Income Distributions (net income) 1993 ~ 1998*
Average Annual Transition rates (weighted) **
Outflow rates (%) from year t-1 income group origins to year t income group< 0.5 0.5 ~ 0.75 0.75 ~ 1.0 1.0 ~ 1.25 1.25 ~ 1.5 > 1.5
Country
= + - = + - = + - = + - = + - =Canada 65.6 34.4 12.1 61.8 26.1 18.2 56.2 25.6 21.9 52.7 25.4 26.1 48.4 25.5 19.6 80.4USA 70.9 29.1 18.7 49.8 31.5 24.8 46.0 29.2 28.1 44.2 27.7 30.8 39.9 29.3 21.2 78.8Britain 67.1 31.9 15.6 55.7 28.7 21.8 50.1 28.1 26.9 46.0 27.1 32.7 41.0 26.3 22.6 77.4Germany 58.4 41.6 10.8 54.5 34.7 17.1 58.5 24.4 24.2 53.1 22.7 31.9 46.0 22.1 22.4 77.6
Inflow rates (%) in year t income group origins from year t-1 income group< 0.5 0.5 ~ 0.75 0.75 ~ 1.0 1.0 ~ 1.25 1.25 ~ 1.5 > 1.5
Country
= + - = + - = + - = + - = + - =Canada 67.0 33.0 14.4 62.0 23.6 21.6 58.0 20.4 28.5 53.5 18.0 35.3 47.7 17.0 23.0 77.0USA 69.9 30.1 20.9 50.1 29.0 27.4 46.3 26.3 30.8 45.2 24.0 35.8 39.5 24.7 22.0 78.0Britain 68.8 31.2 18.2 55.6 26.2 23.7 51.0 25.3 31.2 46.5 22.3 38.6 39.9 21.5 23.8 76.2Germany 59.1 40.9 13.0 57.5 29.5 20.8 57.2 22.0 27.3 52.4 20.3 34.6 46.2 19.2 23.2 76.8
* Data source: CNEF sub-sample for individuals presented in all years between 1993-1998 (1990-1996 for USA). Sample sizes:Canada (29,772), USA (7,849), Britain (6,126), and Germany (11,733)** Income is family-adjusted net income (after taxes and transfers, and adjusted for equivalence scale) in 1997 currency. Personsclassified into income groups according to the size of their income relative to fixed real income cut-offs equal to 0.5, 0.75, 1.0,1.25, and 1.5 times median year 1 income. The last year of longitudinal weight is used for all countries.
40
Tabl
e 4
Six
wav
e A
vera
ge In
com
es a
nd th
eir
Com
posit
ion,
by
pers
on’s
wav
e 1
(199
3) h
ouse
hold
type
– C
anad
a (1
993~
1998
) and
Uni
ted
Stat
es(1
990~
1996
)
Can
ada
Uni
ted
Stat
esH
ead
Age
< 6
0H
ead
Age
< 6
0A
llpe
ople
Hea
dA
ge60
+Si
ngle
Cou
ple
no k
idC
oupl
ew
/kid
sLo
nepa
rent
All
peop
leH
ead
Age
60 +
Sing
leC
oupl
eno
kid
Cou
ple
w/k
ids
Lone
pare
ntA
djus
ted
Hou
seho
ld In
com
e, P
I(6
wav
e m
ean,
in 1
997
dolla
rs)
26,8
4824
,722
26,0
7533
,075
27,4
2119
,341
27,2
3626
,773
25,8
3337
,863
28,1
1915
,661
Hou
seho
ld N
et In
com
e (6
wav
em
ean,
in 1
997
dolla
rs)
42,3
8531
,205
33,3
5648
,606
54,3
8531
,691
45,0
2734
,557
31,2
4455
,283
55,3
0128
,575
Inco
me
Sour
ce a
s % o
f Hou
seho
ldN
et In
com
e:H
ead’
s lab
our e
arni
ngs
64.4
015
.48
82.2
171
.98
73.0
666
.20
72.3
521
.53
91.8
773
.51
81.1
273
.33
Spou
se’s
labo
ur e
arni
ngs
23.0
45.
209.
9237
.43
30.7
76.
8923
.70
8.18
14.6
237
.21
26.6
910
.12
Oth
er fa
mily
labo
ur e
arni
ngs
6.70
4.08
10.9
71.
447.
6311
.00
2.62
4.46
2.49
0.16
2.62
6.49
Ass
et in
com
e4.
8715
.44
2.46
2.93
2.86
2.42
14.0
133
.86
10.3
812
.39
10.8
27.
03Pr
ivat
e tra
nsfe
rs9.
4831
.84
4.67
6.94
4.08
7.75
5.70
18.3
94.
524.
672.
657.
72Pu
blic
tran
sfer
s7.
744.
588.
556.
007.
6520
.76
1.44
1.07
1.53
0.78
1.16
6.63
Soci
al se
curit
y pe
nsio
ns8.
4841
.31
4.66
1.78
0.98
2.83
4.79
26.5
81.
701.
270.
924.
13To
tal t
axes
-24.
72-1
7.93
-23.
63-2
8.49
-27.
03-1
7.86
-28.
08-1
4.94
-31.
23-3
3.82
-29.
87-1
8.86
Hou
seho
ld si
ze2.
641.
761.
712.
233.
882.
772.
761.
771.
542.
233.
852.
94N
umbe
r of c
hild
ren
in H
H:
0.67
0.04
0.12
0.23
1.49
1.12
0.88
0.06
0.20
0.27
1.65
1.40
Equi
vale
nce
Scal
e R
ate*
1.57
1.29
1.26
1.48
1.95
1.63
1.60
1.30
1.20
1.48
1.94
1.68
Unw
eigh
ted
num
ber o
f HH
12,0
352,
995
2,01
91,
669
4,50
384
95,
959
1,13
21,
062
781
2,20
677
8U
nwei
ghte
d nu
mbe
r of p
erso
ns29
,772
4,83
72,
801
3,07
117
,000
2,07
311
,765
1,77
01,
099
937
5,93
82,
021
As a
% o
f all
pers
ons
100
16.2
19.
4110
.32
57.1
06.
9610
015
.04
9.34
7.96
50.4
717
.18
Subs
ampl
e fo
r per
sons
pre
sent
ed in
all
6 w
aves
in S
LID
(7 w
ave
in P
SID
). Fi
gure
s are
pre
sent
ed u
sing
the
last
yea
r of 1
998
(199
6 fo
r PSI
D) l
ongi
tudi
nal w
eigh
t. A
ll in
com
eco
mpo
nent
s (ex
cept
PI)
, hou
seho
ld si
ze a
nd h
ouse
hold
equ
ival
ence
scal
e lo
ngitu
dina
lly a
vera
ged
for e
ach
hous
ehol
d, a
nd th
en a
vera
ged
acro
ss h
ouse
hold
s by
subg
roup
.*
Equi
vale
nce
scal
e is
def
ined
as a
squa
re ro
ot o
f hou
seho
ld si
ze.
41
Tabl
e 4
(Con
clud
ed)
Six
wav
e A
vera
ge In
com
es a
nd th
eir
Com
posit
ion,
by
pers
on’s
wav
e 1
(199
3) h
ouse
hold
type
– B
rita
in a
nd G
erm
any
(199
3~19
98)
Brit
ain
Ger
man
yH
ead
Age
< 6
0H
ead
Age
< 6
0A
llpe
ople
Hea
dA
ge60
+Si
ngle
Cou
ple
no k
idC
oupl
ew
/kid
sLo
nepa
rent
All
peop
leH
ead
Age
60 +
Sing
leC
oupl
eno
kid
Cou
ple
w/k
ids
Lone
pare
ntA
djus
ted
Hou
seho
ld In
com
e, P
I(6
wav
e m
ean,
in 1
997
dolla
rs)
12,8
5811
,027
13,1
1216
,098
13,5
629,
018
34,1
6330
,902
33,7
2939
,297
35,6
5923
,863
Hou
seho
ld N
et In
com
e (6
wav
em
ean,
in 1
997
dolla
rs)
19,0
7413
,689
16,3
7523
,213
25,1
9013
,843
49,3
9437
,566
39,1
5657
,896
67,8
9938
,372
Inco
me
Sour
ce a
s % o
f Hou
seho
ldN
et In
com
e:H
ead’
s lab
our e
arni
ngs
41.5
06.
1572
.65
52.7
151
.52
36.0
066
.71
12.0
910
5.63
78.3
377
.44
70.6
3Sp
ouse
’s la
bour
ear
ning
s23
.52
5.36
14.1
641
.02
30.5
010
.14
25.5
84.
7117
.07
46.5
833
.98
15.4
8O
ther
fam
ily la
bour
ear
ning
s8.
266.
859.
132.
1710
.54
17.3
47.
349.
371.
150.
5810
.94
13.5
9A
sset
inco
me
15.8
227
.11
11.6
512
.77
11.8
910
.68
8.94
13.1
011
.47
5.86
6.93
6.61
Priv
ate
trans
fers
7.33
19.6
03.
664.
552.
225.
112.
235.
891.
640.
940.
455.
43Pu
blic
tran
sfer
s7.
416.
247.
203.
727.
6425
.58
5.77
1.65
5.67
5.51
7.33
14.9
7So
cial
secu
rity
pens
ions
8.52
29.7
02.
521.
350.
813.
5318
.82
67.6
45.
106.
442.
205.
47To
tal t
axes
-16.
92-3
.72
-23.
25-2
2.45
-21.
52-1
2.89
-35.
39-1
4.46
-47.
72-4
4.23
-39.
27-3
2.19
Hou
seho
ld si
ze2.
501.
651.
652.
203.
812.
872.
301.
621.
412.
233.
652.
70N
umbe
r of c
hild
ren
in H
H:
0.58
0.03
0.11
0.19
1.40
1.21
0.47
0.03
0.12
0.23
1.20
1.03
Equi
vale
nce
Scal
e R
ate*
1.53
1.25
1.24
1.47
1.93
1.66
1.46
1.24
1.16
1.48
1.89
1.61
Unw
eigh
ted
num
ber o
f HH
3,82
71,
127
459
624
1,33
528
24,
805
1,11
563
181
21,
976
271
Unw
eigh
ted
num
ber o
f per
sons
6,12
61,
572
480
994
2,70
937
111
,733
1,.8
6566
41,
602
7,00
459
8A
s a %
of a
ll pe
rson
s10
025
.66
7.84
16.2
344
.22
6.06
100
15.8
95.
6613
.65
59.6
95.
10
Subs
ampl
e fo
r per
sons
pre
sent
ed in
all
6 w
aves
in B
HPS
, GSO
EP. F
igur
es a
re p
rese
nted
usi
ng th
e la
st y
ear o
f 199
8 lo
ngitu
dina
l wei
ght.
All
inco
me
com
pone
nts (
exce
pt P
I),
hous
ehol
d si
ze a
nd h
ouse
hold
equ
ival
ence
scal
e lo
ngitu
dina
lly a
vera
ged
for e
ach
hous
ehol
d, a
nd th
en a
vera
ged
acro
ss h
ouse
hold
s by
subg
roup
.*
Equi
vale
nce
scal
e is
def
ined
as a
squa
re ro
ot o
f hou
seho
ld si
ze.
42
Tabl
e 5
Lon
gitu
dina
l Var
iabi
lity
of In
com
e, H
ouse
hold
Com
pone
nts,
and
the
Prop
ortio
nate
Con
trib
utio
n of
inco
me
Com
pone
nts t
o lo
ngitu
dina
lin
com
e V
aria
bilit
y, b
y pe
rson
’s w
ave
1 ho
useh
old
type
– C
anad
a (1
993~
1998
) and
Uni
ted
Stat
es (1
990~
1996
) - w
eigh
ted
Can
ada
Uni
ted
Stat
esH
ead
Age
< 6
0H
ead
Age
< 6
0A
llpe
ople
Hea
dA
ge60
+Si
ngle
Cou
ple
no k
idC
oupl
ew
/kid
sLo
nepa
rent
All
peop
leH
ead
Age
60 +
Sing
leC
oupl
eno
kid
Cou
ple
w/k
ids
Lone
pare
ntC
V, H
ouse
hold
Adj
uste
d In
com
e0.
240.
170.
290.
230.
260.
270.
320.
300.
380.
280.
270.
38C
V, H
ouse
hold
Net
Inco
me
0.26
0.20
0.35
0.24
0.25
0.29
0.33
0.31
0.39
0.28
0.29
0.40
Prop
ortio
nate
con
tribu
tion
ofIn
com
e co
mpo
nent
to lo
ngitu
dina
lin
com
e va
riab
ility
(β
coe
ffici
ent):
Hea
d’s l
abou
r ear
ning
s0.
550.
270.
660.
720.
600.
550.
580.
260.
710.
630.
650.
57Sp
ouse
’s la
bour
ear
ning
s0.
250.
050.
180.
440.
350.
140.
270.
090.
230.
420.
320.
16O
ther
fam
ily la
bour
ear
ning
s0.
160.
060.
200.
050.
220.
260.
080.
110.
050.
010.
090.
17A
sset
inco
me
0.09
0.24
0.04
0.04
0.04
0.02
0.13
0.33
0.08
0.12
0.08
0.05
Priv
ate
trans
fers
0.08
0.20
0.03
0.04
0.06
0.08
0.08
0.20
0.05
0.06
0.05
0.09
Publ
ic tr
ansf
ers
0.04
0.09
0.09
-0.0
1-0
.01
0.11
0.02
0.03
0.02
0.01
0.01
0.04
Soci
al se
curit
y pe
nsio
ns0.
060.
220.
030.
000.
010.
010.
040.
150.
030.
010.
010.
04To
tal t
axes
-0.2
2-0
.12
-0.2
3-0
.28
-0.2
7-0
.16
-0.2
9-0
.18
-0.3
0-0
.38
-0.3
4-0
.19
CV
, Hou
seho
ld si
ze0.
140.
090.
220.
130.
120.
180.
150.
100.
210.
140.
130.
21C
V, E
quiv
alen
ce S
cale
Rat
e*0.
070.
050.
110.
070.
060.
090.
080.
050.
110.
070.
070.
11
Unw
eigh
ted
num
ber o
f HH
12,0
352,
995
2,01
91,
669
4,50
384
95,
959
1,13
21,
062
781
2,20
677
8U
nwei
ghte
d nu
mbe
r of p
erso
ns29
,772
4,83
72,
801
3,07
117
,000
2,07
311
,765
1,77
01,
099
937
5,93
82,
021
As a
% o
f all
pers
ons
100
16.2
19.
4110
.32
57.1
06.
9610
015
.04
9.34
7.96
50.4
717
.18
Not
e: S
ubsa
mpl
e fo
r per
sons
pre
sent
ed in
all
6 w
aves
in S
LID
(7 w
ave
in P
SID
). Fi
gure
s are
pre
sent
ed u
sing
the
last
yea
r of 1
998
(199
6 fo
r PSI
D) l
ongi
tudi
nal w
eigh
t.C
oeffi
cien
ts o
f var
iatio
n (C
V) a
re c
alcu
late
d lo
ngitu
dina
lly fo
r eac
h pe
rson
, and
then
ave
rage
d ac
ross
per
sons
by
hous
ehol
d ty
pes.
β c
oeffi
cien
ts a
re c
ompu
ted
from
a si
x-ob
serv
atio
n (s
even
for P
SID
), fo
r eac
h pe
rson
, of e
ach
inco
me
com
pone
nt o
n to
tal n
et in
com
e, a
vera
ge a
cros
s per
sons
by
hous
ehol
d ty
pes.
* Eq
uiva
lenc
e sc
ale
is d
efin
ed a
s a sq
uare
root
of h
ouse
hold
size
.
43
Tabl
e 5
(Con
clud
ed)
Lon
gitu
dina
l Var
iabi
lity
of In
com
e, H
ouse
hold
Com
pone
nts,
and
the
Prop
ortio
nate
Con
trib
utio
n of
inco
me
Com
pone
nts t
o lo
ngitu
dina
lin
com
e V
aria
bilit
y, b
y pe
rson
’s w
ave
1 ho
useh
old
type
– B
rita
in a
nd G
erm
any
(199
3~19
98) -
wei
ghte
d
Brit
ain
Ger
man
yH
ead
Age
< 6
0H
ead
Age
< 6
0A
llpe
ople
Hea
dA
ge60
+Si
ngle
Cou
ple
no k
idC
oupl
ew
/kid
sLo
nepa
rent
All
peop
leH
ead
Age
60 +
Sing
leC
oupl
eno
kid
Cou
ple
w/k
ids
Lone
pare
ntC
V, H
ouse
hold
Adj
uste
d In
com
e0.
240.
200.
300.
230.
240.
330.
190.
190.
240.
180.
180.
27C
V, H
ouse
hold
Net
Inco
me
0.26
0.22
0.35
0.24
0.25
0.37
0.21
0.21
0.27
0.19
0.19
0.30
Prop
ortio
nate
con
tribu
tion
ofIn
com
e co
mpo
nent
to lo
ngitu
dina
lin
com
e va
riab
ility
(β
coe
ffici
ent):
Hea
d’s l
abou
r ear
ning
s0.
320.
090.
510.
500.
430.
310.
560.
131.
050.
720.
570.
60Sp
ouse
’s la
bour
ear
ning
s0.
220.
040.
240.
420.
320.
180.
350.
050.
300.
610.
450.
22O
ther
fam
ily la
bour
ear
ning
s0.
150.
080.
130.
060.
240.
270.
200.
150.
040.
020.
330.
40A
sset
inco
me
0.15
0.25
0.13
0.13
0.08
0.05
0.10
0.18
0.09
0.07
0.08
0.06
Priv
ate
trans
fers
0.06
0.16
0.03
0.01
0.00
0.02
0.01
0.03
0.00
0.01
0.00
0.07
Publ
ic tr
ansf
ers
0.12
0.20
0.11
0.03
0.05
0.21
0.04
0.04
0.01
0.03
0.06
0.06
Soci
al se
curit
y pe
nsio
ns0.
070.
170.
030.
030.
010.
010.
170.
590.
070.
060.
020.
01To
tal t
axes
-0.1
7-0
.04
-0.2
2-0
.27
-0.2
6-0
.17
-0.4
4-0
.16
-0.5
7-0
.52
-0.5
2-0
.43
CV
, Hou
seho
ld si
ze0.
110.
060.
220.
120.
110.
180.
090.
080.
160.
090.
090.
13C
V, E
quiv
alen
ce S
cale
Rat
e*0.
060.
030.
110.
060.
060.
090.
050.
040.
080.
050.
050.
06
Unw
eigh
ted
num
ber o
f HH
3,82
71,
127
459
624
1,33
528
24,
805
1,11
563
181
21,
976
271
Unw
eigh
ted
num
ber o
f per
sons
6,12
61,
572
291
994
2,70
937
111
,733
1,.8
6566
41,
602
7,00
459
8A
s a %
of a
ll pe
rson
s10
025
.66
4.75
16.2
344
.22
6.06
100
15.8
95.
6613
.65
59.6
95.
10
Not
e: S
ubsa
mpl
e fo
r per
sons
pre
sent
ed in
all
6 w
aves
in B
HPS
, GSO
EP. F
igur
es a
re p
rese
nted
usin
g th
e la
st y
ear o
f 199
8 lo
ngitu
dina
l wei
ght.
Coe
ffici
ents
of v
aria
tion
(CV
) are
calc
ulat
ed lo
ngitu
dina
lly fo
r eac
h pe
rson
, and
then
ave
rage
d ac
ross
per
sons
by
hous
ehol
d ty
pes.
β c
oeffi
cien
ts a
re c
ompu
ted
from
a si
x-ob
serv
atio
n, fo
r eac
h pe
rson
, of e
ach
inco
me
com
pone
nt o
n to
tal n
et in
com
e, a
vera
ge a
cros
s per
sons
by
hous
ehol
d ty
pes.
* Eq
uiva
lenc
e sc
ale
is d
efin
ed a
s a sq
uare
root
of h
ouse
hold
size
.
45
Table 6Kaplan-Meier Product-limit Estimate of Proportion Remaining Poor, and the Exit Rates fromLow-Income, by duration, for all Persons beginning a Low-Income Spell*
Country(1) Cumulative percentage of remaining poor (%)
(2) Annual exit rate from Low-IncomeCanada Britain United States Germany
Number of years since startof Low-Income spell
(1) (2) (1) (2) (1) (2) (1) (2)1 100.0 . 100.0 . 100.0 . 100.0 .2 61.6
(0.6).384
(.006)58.9(1.3)
.411(.013)
63.6(0.8)
.364(.008)
53.4(1.1)
.466(.011)
3 43.2(0.7)
.299(.009)
40.5(1.4)
.313(.018)
47.9(0.9)
.246(.010)
34.5(1.2)
.353(.017)
4 31.1(0.8)
.280(.015)
28.6(1.3)
.294(.023)
37.3(1.0)
.222(.014)
23.1(1.1)
.330(.023)
5 24.4(1.0)
.216(.023)
18.1(1.2)
.365(.031)
31.1(1.1)
.166(.018)
17.8(1.1)
.232(.031)
6 13.6(1.2)
.252(.039)
26.2(1.3)
.157(.028)
13.7(1.2)
.231(.043)
7 10.2(1.1)
.246(.053)
11.9(1.2)
.125(.052)
8 6.1(1.2)
.407(.095)
11.9(1.2)
.
Covered years 1993-1999 1991-1999 1990-1996 1991-1999Qualified spells 17,265 4,732 12,336 5,881Individuals 7,867 1,742 4,983 2,434Disqualified person** 786 297 578 332
Kaplan-Meier Product-limit Estimate of Proportion Remaining Non-Poor, and Low-Income Re-entry Rates, by duration, for all Persons ending a Low-Income Spell*
Country(1) Cumulative percentage of remaining non-poor (%)
(2) Annual re-entry rate to Low-IncomeCanada Britain United States Germany
Number of years since startof Low-Income spell
(1) (2) (1) (2) (1) (2) (1) (2)1 100.0 . 100.0 . 100.0 . 100.0 .2 88.3
(0.4).117
(.004)84.9(0.9)
.151(.009)
75.9(0.7)
.241(.007)
90.0(0.6)
.100(.006)
3 80.6(0.6)
.088(.005)
74.8(1.2)
.119(.010)
63.0(0.8)
.170(.008)
83.7(0.8)
.070(.006)
4 74.8(0.7)
.072(.007)
70.2(1.3)
.061(.009)
53.3(1.0)
.154(.011)
79.5(0.9)
.051(.006)
5 70.2(1.0)
.061(.010)
67.0(1.4)
.046(.009)
48.1(1.1)
.099(.012)
76.2(1.0)
.042(.007)
6 63.9(1.5)
.046(.011)
43.9(1.4)
.086(.019)
73.9(1.1)
.030(.007)
7 61.4(1.6)
.038(.013)
72.2(1.2)
.027(.007)
8 58.9(1.9)
.041(.018)
71.3(1.5)
.012(.012)
Covered years 1993-1999 1991-1999 1990-1996 1991-1999Qualified spells 20,802 6,713 13,238 10,396Individuals 9,013 1,986 5,231 2,921Disqualified person** 507 230 531 249Note: Standard errors are in parentheses.* Kaplan-Meier estimates are based on all non-left censored Low-Income spells. Here I assume that all persons starting a Low-
Income spell are poor for at least one year.** Persons whose post-transition earnings rise (fall) to not more than 10% above (below) Low-Income line are not considered as an
exit (re-entrant).
46
Table 7Kaplan-Meier Product-limit Estimate of Proportion Remaining High-Income, and Exit Rates fromHigh-Income, by duration, for all Persons beginning a High-Income Spell*
Country(3) Cumulative percentage of remaining high-income (%)
(4) Annual exit rate from high-incomeCanada Britain United States Germany
Number of years since startof high-income spell
(1) (2) (1) (2) (1) (2) (1) (2)1 100.0 . 100.0 . 100.0 . 100.0 .2 76.7
(0.6).233
(.006)76.4(1.1)
.236(.011)
68.3(0.9)
.317(.009)
79.0(1.0)
.210(.009)
3 66.2(0.8)
.137(.008)
58.4(1.3)
.236(.014)
56.5(1.0)
.172(.010)
65.1(1.2)
.176(.011)
4 59.1(1.0)
.108(.011)
52.1(1.4)
.108(.013)
49.7(1.2)
.121(.012)
56.4(1.3)
.133(.012)
5 49.3(1.5)
.166(.022)
45.9(1.5)
.119(.016)
44.4(1.4)
.107(.017)
48.5(1.4)
.139(.015)
6 39.6(1.6)
.138(.022)
36.1(1.9)
.188(.036)
44.8(1.5)
.078(.015)
7 36.2(1.8)
.086(.024)
38.9(1.7)
.131(.027)
8 35.1(1.9)
.029(.020)
34.7(2.2)
.108(.038)
Covered years 1993-1999 1991-1999 1990-1996 1991-1999Qualified spells 15,296 5,926 8,174 7,169Individuals 7,362 2,014 3,191 2,221Disqualified person** 683 326 494 619
Kaplan-Meier Product-limit Estimate of Proportion Remaining Non-High-income, and High-Income Re-entry Rates, by duration, for all Persons ending a High-Income Spell*
Country(3) Cumulative percentage of remaining non-high income (%)
(4) Annual re-entry rate to high-incomeCanada Britain United States Germany
Number of years since startof high-income spell
(1) (2) (1) (2) (1) (2) (1) (2)1 100.0 . 100.0 . 100.0 . 100.0 .2 89.3
(0.5).107
(.005)86.4(0.9)
.136(.009)
79.9(0.9)
.201(.009)
88.1(0.8)
.119(.008)
3 82.0(0.6)
.082(.005)
76.0(1.2)
.120(.010)
68.4(1.1)
.144(.010)
78.6(1.0)
.108(.009)
4 74.5(0.9)
.092(.008)
66.9(1.3)
.120(.012)
57.0(1.3)
.167(.014)
74.2(1.2)
.057(.008)
5 68.2(1.2)
.085(.012)
60.5(1.5)
.095(.012)
51.5(1.5)
.097(.015)
68.7(1.3)
.073(.010)
6 55.1(1.6)
.090(.014)
48.6(1.6)
.055(.016)
66.0(1.4)
.041(.010)
7 51.2(1.7)
.070(.016)
62.7(1.6)
.050(.014)
8 46.6(2.0)
.089(.024)
57.6(2.4)
.081(.029)
Covered years 1993-1999 1991-1999 1990-1996 1991-1999Qualified spells 15,052 6,606 7,501 7,334Individuals 6,108 1,806 2,726 2,056Disqualified person** 739 368 381 443Note: Standard errors are in parentheses.* Kaplan-Meier estimates are based on all non-left censored Low-Income spells. Here I assume that all persons starting a Low-
Income spell are poor for at least one year.** Persons whose post-transition earnings fall (rise) to not more than 10% below (above) high-Income line are not considered as an
exit (re-entry).
47
Figure1Classification of Income and Demographic Events Associated with a Spell Transitionbetween year t-1 and t
Each Spell endingor beginning
8 types ofIncome Events
Which income Sourceincreased the most?
INCOME EVENT
Same head, same sizebetween t-1 and t
Same head, Different sizebetween t-1 and t
If income change >equiv. scale changeINCOME EVENT
If equiv. scale change >income change
DEMOGRAPHIC EVENT
7 types ofDemographic
Events
Which demographic eventis associated with low
income ending?
DEMOGRAPHIC EVENT
Different Household headbetween t-1 and t
48
Tabl
e 8
Low
Inco
me
Spel
l End
ing
Typ
es, b
y Pe
rson
’s H
ouse
hold
Typ
e in
the
Las
t yea
r of
Low
Inco
me
Spel
l, C
anad
a (1
993~
1999
) and
Uni
ted
Stat
ed(1
990~
1996
), (u
nwei
ghte
d)
Can
ada
Uni
ted
Stat
esA
ge <
60
Age
< 6
0M
ain
even
t ass
ocia
ted
with
spel
len
ding
All
Pers
onA
ge 6
0+
Sing
leC
oupl
eno
kid
sC
oupl
ew
/kid
sLo
nepa
rent
All
Pers
onA
ge 6
0+
Sing
leC
oupl
eno
kid
sC
oupl
ew
/kid
sLo
nepa
rent
Rise
of i
ncom
e fro
m:
(65.
7)(7
4.5)
Hea
d’s l
abou
r inc
ome
37.6
10.3
25.8
48.1
53.8
29.6
45.5
9.9
53.3
53.0
53.9
41.2
Partn
er’s
labo
ur in
com
e7.
92.
90.
2*16
.315
.40.
1*12
.84.
88.
126
.422
.30.
9*O
ther
labo
ur in
com
e4.
07.
12.
9-
4.1
5.0
3.5
9.3
1.3*
-2.
15.
2A
sset
inco
me
1.9
6.5
0.8*
0.8*
2.0
0.7*
3.0
12.7
2.1*
1.3*
2.4
0.8*
Priv
ate
trans
fers
2.9
9.3
1.7
3.7*
1.7
2.6
4.5
17.7
4.1
6.4*
1.3
4.1
Publ
ic tr
ansf
ers
5.5
4.6
2.6
9.5
6.8
4.8
1.6
2.5*
1.0*
2.2*
1.7
1.1*
Soci
al se
curit
y pe
nsio
n5.
035
.31.
72.
7*0.
7*0.
5*3.
415
.22.
0*3.
8*1.
6*2.
1Ta
xes f
all
0.7
1.4*
0.1*
1.9*
0.8
0.2*
0.2*
0.8*
0.3*
--
0.1*
Dem
ogra
phic
eve
nt:
(34.
3)(2
5.5)
New
ly e
stab
lishe
d fa
mily
3.0
4.1
7.2
0.1*
1.1
2.7
3.4
5.0
7.3
-0.
9*4.
9Se
para
tion/
divo
rce
0.5
0.9*
-0.
8*0.
6*0.
2*1.
12.
6*-
1.0*
1.7
0.2*
Partn
ersh
ip/m
arria
ge8.
00.
8*8.
7-
-32
.77.
2-
8.5
--
22.3
Hou
seho
ld m
erge
16.6
12.0
38.6
8.4
7.7
16.1
6.7
9.0
3.3*
1.9*
5.0
11.5
Add
ition
to fa
mily
(chi
ld)
(no
n-ch
ild)
1.0
2.2
0.9*
1.2*
- 7.7
3.7*
0.8*
1.5 -
- 1.8
1.2
1.7
0.6*
2.9*
- 5.3
1.3*
1.3*
2.4 -
- 2.0
Nee
ds fa
ll3.
02.
72.
03.
1*3.
62.
94.
27.
03.
4*1.
6*4.
63.
5O
ther
s0.
02*
--
0.1*
--
0.02
*-
--
0.04
*-
Num
ber o
f spe
ll en
ding
s9,
495
1,10
62,
085
738
3,81
81,
748
5,82
964
587
331
52,
444
1,55
2
* Es
timat
es b
ased
on
less
than
30
obse
rvat
ions
; - N
o ob
serv
atio
n.N
ote:
Ana
lysi
s bas
ed o
n al
l per
sons
with
Low
Inco
me
spel
l end
ing
obse
rved
in C
NEF
rega
rdle
ss o
f lef
t cen
sorin
g. P
erso
ns w
hose
pos
t-tra
nsiti
on e
arni
ngs r
ise
to n
ot m
ore
than
10%
abo
veLo
w In
com
e lin
e ar
e no
t con
side
red
as e
ndin
g.1.
Pr
ivat
e tra
nsfe
rs in
clud
e no
n-go
vern
men
t pen
sion
s, al
imon
y, a
nd c
hild
supp
ort a
nd in
com
e fro
m n
on-h
ouse
hold
mem
bers
.2.
Pu
blic
tran
sfer
s inc
lude
Chi
ld ta
x be
nefit
, UI,
WC
, and
wel
fare
(SA
, AFD
C).
3.
Soci
al se
curit
y pe
nsio
ns in
clud
e CP
P/Q
PP, O
ld A
ge S
ecur
ity, d
isab
ility
and
wid
owed
mot
her’
s allo
wan
ce.
4.
Exam
ples
are
chi
ld b
ecam
e he
ad/s
pous
e, o
r spl
it-of
f hou
seho
ld.
5.
Exam
ples
incl
ude
adul
t hea
d/sp
ouse
retu
rn to
par
ent h
ome,
or n
on-h
ead/
spou
se in
divi
dual
s mov
e to
oth
er h
ouse
hold
.6.
It
is d
ue to
the
redu
ctio
n of
the
hous
ehol
d si
ze.
49
Tabl
e 8
(Con
clud
ed)
Low
Inco
me
Spel
l End
ing
Typ
es, b
y Pe
rson
’s H
ouse
hold
Typ
e in
the
Las
t yea
r of
Low
Inco
me
Spel
l, B
rita
in a
nd G
erm
any
(199
1~19
99)
(unw
eigh
ted)
Brit
ain
Ger
man
yA
ge <
60
Age
< 6
0M
ain
even
t ass
ocia
ted
with
spel
len
ding
All
Pers
onA
ge 6
0+
Sing
leC
oupl
eno
kid
sC
oupl
ew
/kid
sLo
nepa
rent
All
Pers
onA
ge 6
0+
Sing
leC
oupl
eno
kid
sC
oupl
ew
/kid
sLo
nepa
rent
Rise
of i
ncom
e fro
m:
(73.
6)(8
4.2)
Hea
d’s l
abou
r inc
ome
26.7
6.2
22.7
34.0
41.0
14.5
31.9
3.1*
47.5
33.1
46.3
29.7
Partn
er’s
labo
ur in
com
e10
.22.
1*0.
4*17
.219
.40.
9*13
.33.
7*9.
224
.420
.69.
8O
ther
labo
ur in
com
e5.
56.
02.
5*1.
4*3.
312
.94.
72.
8*1.
8*0.
6*5.
88.
4A
sset
inco
me
4.1
13.6
3.6*
1.3*
1.4*
2.1*
1.6
3.6*
2.2*
1.2*
0.5*
1.0*
Priv
ate
trans
fers
3.5
9.5
1.1*
4.4*
0.9*
4.8
4.3
4.6
5.2*
1.3*
0.7*
10.2
Publ
ic tr
ansf
ers
19.0
32.0
14.3
15.2
13.6
22.6
12.4
6.0
6.5*
16.3
16.8
14.2
Soci
al se
curit
y pe
nsio
n4.
319
.22.
3*1.
3*0.
5*0.
6*15
.952
.77.
917
.50.
6*5.
3Ta
xes f
all
0.3*
0.6*
--
0.4*
-0.
2*-
0.2*
-0.
4*-
Dem
ogra
phic
eve
nt:
(26.
4)(1
5.8)
New
ly e
stab
lishe
d fa
mily
1.1
0.9*
3.6*
-0.
5*1.
5*0.
6*1.
2*-
-0.
4*1.
1*Se
para
tion/
divo
rce
3.8
2.8*
-8.
1*6.
2-
0.5*
0.8*
-0.
6*0.
9*-
Partn
ersh
ip/m
arria
ge4.
32.
0*3.
8*6.
7*5.
24.
00.
4*-
1.8*
--
0.7*
Hou
seho
ld m
erge
11.2
1.6*
35.6
3.7*
4.1
24.8
6.6
11.3
3.6*
0.3*
3.2
11.6
Add
ition
to fa
mily
(chi
ld)
(no
n-ch
ild)
1.4
3.3
1.2*
1.0*
- 8.6
4.7*
1.4*
1.9 -
-10
.31.
13.
00.
4*1.
1*-
10.8
2.5*
0.9*
2.3* -
- 6.0
Nee
ds fa
ll1.
41.
4*1.
5*0.
7*1.
61.
1*2.
23.
2*3.
4*1.
2*1.
4*2.
0*O
ther
s-
--
--
-1.
35.
7-
-0.
1*-
Num
ber o
f spe
ll en
ding
s4,
609
868
525
297
2,01
590
43,
353
755
446
320
1,11
871
4
* Es
timat
es b
ased
on
less
than
30
obse
rvat
ions
; - N
o ob
serv
atio
n.N
ote:
Ana
lysi
s bas
ed o
n al
l per
sons
with
Low
Inco
me
spel
l end
ing
obse
rved
in C
NEF
rega
rdle
ss o
f lef
t cen
sorin
g. P
erso
ns w
hose
pos
t-tra
nsiti
on e
arni
ngs r
ise
to n
ot m
ore
than
10%
abo
veLo
w In
com
e lin
e ar
e no
t con
side
red
as e
ndin
g.1.
Pr
ivat
e tra
nsfe
rs in
clud
e no
n-go
vern
men
t pen
sion
s, al
imon
y, a
nd c
hild
supp
ort a
nd in
com
e fro
m n
on-h
ouse
hold
mem
bers
.2.
Pu
blic
tran
sfer
s inc
lude
Chi
ld ta
x be
nefit
, UI,
WC
, and
wel
fare
(SA
, AFD
C).
3.
Soci
al se
curit
y pe
nsio
ns in
clud
e CP
P/Q
PP, O
ld A
ge S
ecur
ity, d
isab
ility
and
wid
owed
mot
her’
s allo
wan
ce.
4.
Exam
ples
are
chi
ld b
ecam
e he
ad/s
pous
e, o
r spl
it-of
f hou
seho
ld.
5.
Exam
ples
incl
ude
adul
t hea
d/sp
ouse
retu
rn to
par
ent h
ome,
or n
on-h
ead/
spou
se in
divi
dual
s mov
e to
oth
er h
ouse
hold
.6.
It
is d
ue to
the
redu
ctio
n of
the
hous
ehol
d si
ze.
50
Tabl
e 9
Low
Inco
me
Spel
l Beg
inni
ng T
ypes
, by
Pers
on’s
Hou
seho
ld T
ype
in th
e Fi
rst y
ear
of L
ow In
com
e Sp
ell,
Can
ada
(199
3~19
99) a
nd U
nite
d St
ated
(199
0~19
96),
(unw
eigh
ted)
Can
ada
Uni
ted
Stat
esA
ge <
60
Age
< 6
0M
ain
even
t ass
ocia
ted
with
spel
lB
egin
ning
All
Pers
onA
ge 6
0+
Sing
leC
oupl
eno
kid
sC
oupl
ew
/kid
sLo
nepa
rent
All
Pers
onA
ge 6
0+
Sing
leC
oupl
eno
kid
sC
oupl
ew
/kid
sLo
nepa
rent
Fall
of in
com
e fro
m:
(55.
7)(7
2.4)
Hea
d’s l
abou
r inc
ome
27.2
21.9
14.6
37.3
43.9
13.6
35.8
14.2
42.6
42.2
51.1
25.6
Partn
er’s
labo
ur in
com
e6.
56.
42.
215
.510
.31.
6*8.
63.
71.
4*24
.718
.70.
2*O
ther
labo
ur in
com
e2.
34.
51.
52.
0*2.
71.
7*9.
224
.15.
44.
7*4.
48.
3A
sset
inco
me
2.0
6.8
0.5*
2.4*
2.4
0.5*
4.7
13.1
2.3*
2.2*
4.8
0.9*
Priv
ate
trans
fers
3.2
6.2
1.3
5.0
3.8
2.4
5.5
14.5
4.5
5.1*
1.8
4.2
Publ
ic tr
ansf
ers
11.3
10.2
5.7
11.0
16.2
11.9
3.5
4.0
4.1
2.9*
3.6
3.0
Soci
al se
curit
y pe
nsio
n1.
88.
60.
6*0.
8*1.
40.
9*5.
017
.63.
1*0.
7*1.
82.
6Ta
xes r
ise
1.4
3.8
0.4*
1.5*
2.0
0.3*
0.1*
0.2*
0.2*
--
-
Dem
ogra
phic
eve
nt:
(44.
3)(2
7.6)
New
ly e
stab
lishe
d fa
mily
21.2
10.2
53.5
21.0
4.3
8.1
5.0
0.9*
16.9
5.4*
2.4
4.2
Sepa
ratio
n/di
vorc
e11
.412
.06.
0-
-47
.010
.51.
7*5.
1-
-34
.5Pa
rtner
ship
/mar
riage
0.4
0.4*
-0.
4*0.
6*0.
3*0.
60.
2*-
3.3*
1.0*
0.2*
Hou
seho
ld m
erge
7.8
7.4
12.2
0.8*
5.1
9.6
5.1
2.8*
3.3*
2.6*
4.1
9.4
Subt
ract
ion
to fa
mily
0.7
0.7*
1.0*
0.5*
0.6*
0.5*
1.0
0.3*
2.8*
4.0*
0.6*
0.5*
Nee
ds ri
se2.
80.
8*0.
4*1.
5*6.
51.
6*5.
41.
8*8.
22.
2*5.
76.
3O
ther
s0.
01*
--
0.1*
--
--
--
--
Num
ber o
f spe
ll be
ginn
ings
8,28
388
12,
378
737
2,81
11,
476
5,57
41,
001
850
275
1,93
41,
514
* Es
timat
es b
ased
on
less
than
30
obse
rvat
ions
; - N
o ob
serv
atio
n.N
ote:
Ana
lysi
s bas
ed o
n al
l per
sons
with
Low
Inco
me
spel
l beg
inni
ng o
bser
ved
in C
NEF
rega
rdle
ss o
f lef
t cen
sorin
g. P
erso
ns w
hose
pos
t-tra
nsiti
on e
arni
ngs f
all t
o no
t mor
e th
an 1
0%be
low
Low
Inco
me
line
are
not c
onsi
dere
d as
beg
inni
ng.
1.
Priv
ate
trans
fers
incl
ude
non-
gove
rnm
ent p
ensi
ons,
alim
ony,
and
chi
ld su
ppor
t and
inco
me
from
non
-hou
seho
ld m
embe
rs.
2.
Publ
ic tr
ansf
ers i
nclu
de C
hild
tax
bene
fit, U
I, W
C, a
nd w
elfa
re (S
A, A
FDC
).3.
So
cial
secu
rity
pens
ions
incl
ude
CPP/
QPP
, Old
Age
Sec
urity
, dis
abili
ty a
nd w
idow
ed m
othe
r’s a
llow
ance
.4.
Ex
ampl
es a
re c
hild
bec
ame
head
/spo
use,
or s
plit-
off h
ouse
hold
.5.
Ex
ampl
es in
clud
e ad
ult h
ead/
spou
se re
turn
to p
aren
t hom
e, o
r non
-hea
d/sp
ouse
indi
vidu
als m
ove
to o
ther
hou
seho
ld.
6.
It is
due
to th
e re
duct
ion
of th
e ho
useh
old
size
.
51
Tabl
e 9
(Con
clud
ed)
Low
Inco
me
Spel
l Beg
inni
ng T
ypes
, by
Pers
on’s
Hou
seho
ld T
ype
in th
e fir
st y
ear
of L
ow In
com
e Sp
ell,
Bri
tain
and
Ger
man
y (1
991~
1999
)(u
nwei
ghte
d)
Brit
ain
Ger
man
yA
ge <
60
Age
< 6
0M
ain
even
t ass
ocia
ted
with
spel
lbe
ginn
ing
All
Pers
onA
ge 6
0+
Sing
leC
oupl
eno
kid
sC
oupl
ew
/kid
sLo
nepa
rent
All
Pers
onA
ge 6
0+
Sing
leC
oupl
eno
kid
sC
oupl
ew
/kid
sLo
nepa
rent
Fall
of in
com
e fr
om:
(71.
3)(6
6.1)
Hea
d’s l
abou
r inc
ome
24.0
10.9
17.9
33.5
36.2
12.4
22.5
6.2
24.8
34.8
42.6
13.9
Partn
er’s
labo
ur in
com
e14
.43.
2*2.
9*13
.724
.212
.912
.34.
4*5.
2*13
.714
.821
.3O
ther
labo
ur in
com
e6.
510
.14.
4*4.
9*1.
3*10
.95.
06.
42.
0*9.
9*4.
84.
3A
sset
inco
me
3.2
10.9
1.4*
2.1*
1.7*
1.0*
1.8
3.4*
1.2*
0.9*
1.7*
1.1*
Priv
ate
trans
fers
4.1
10.8
3.2*
2.1*
0.8*
5.6
4.8
5.5
3.8*
3.0*
0.8*
9.0
Publ
ic tr
ansf
ers
15.8
27.0
8.9
11.3
11.6
20.5
11.4
5.6
8.6
13.3
20.1
10.1
Soci
al se
curit
y pe
nsio
n3.
916
.01.
2*2.
8*1.
3*0.
2*8.
427
.42.
35.
2*1.
92.
4*Ta
xes r
ise
0.4*
0.9*
--
0.5*
--
--
--
-
Dem
ogra
phic
eve
nt:
(28.
7)(3
3.9)
New
ly e
stab
lishe
d fa
mily
9.4
2.3*
46.3
11.3
1.8*
4.4
10.0
0.3*
40.9
15.0
3.2*
2.0*
Sepa
ratio
n/di
vorc
e9.
24.
74.
3*4.
6*6.
424
.09.
95.
26.
20.
4*0.
3*27
.8Pa
rtner
ship
/mar
riage
3.2
1.3*
-6.
7*6.
00.
2*0.
1*-
-0.
9*-
-H
ouse
hold
mer
ge4.
01.
8*7.
22.
1*3.
35.
97.
422
.50.
8*0.
43.
64.
0Su
btra
ctio
n to
fam
ily0.
6*-
2.0*
2.5*
0.1*
0.4*
0.6*
0.8*
0.6*
-0.
9*0.
3*N
eeds
rise
2.4
0.1*
0.3*
1.8*
4.6
1.6*
3.3
0.9*
3.4*
2.6*
5.4*
3.9*
Oth
ers
0.1*
--
0.7*
0.1*
-2.
711
.4-
--
-
Num
ber o
f spe
ll be
ginn
ings
4,17
077
158
728
41,
692
836
2,78
565
750
123
364
874
6
* Es
timat
es b
ased
on
less
than
30
obse
rvat
ions
; - N
o ob
serv
atio
n.N
ote:
Ana
lysi
s bas
ed o
n al
l per
sons
with
Low
Inco
me
spel
l beg
inni
ng o
bser
ved
in C
NEF
rega
rdle
ss o
f lef
t cen
sorin
g. P
erso
ns w
hose
pos
t-tra
nsiti
on e
arni
ngs f
all t
o no
t mor
e th
an 1
0%be
low
Low
Inco
me
line
are
not c
onsi
dere
d as
beg
inni
ng.
1.
Priv
ate
trans
fers
incl
ude
non-
gove
rnm
ent p
ensi
ons,
alim
ony,
and
chi
ld su
ppor
t and
inco
me
from
non
-hou
seho
ld m
embe
rs.
2.
Publ
ic tr
ansf
ers i
nclu
de C
hild
tax
bene
fit, U
I, W
C, a
nd w
elfa
re (S
A, A
FDC
).3.
So
cial
secu
rity
pens
ions
incl
ude
CPP/
QPP
, Old
Age
Sec
urity
, dis
abili
ty a
nd w
idow
ed m
othe
r’s a
llow
ance
.4.
Ex
ampl
es a
re c
hild
bec
ame
head
/spo
use,
or s
plit-
off h
ouse
hold
.5.
Ex
ampl
es in
clud
e ad
ult h
ead/
spou
se re
turn
to p
aren
t hom
e, o
r non
-hea
d/sp
ouse
indi
vidu
als m
ove
to o
ther
hou
seho
ld.
6.
It is
due
to th
e re
duct
ion
of th
e ho
useh
old
size
.
52
Tabl
e 10
Hig
h In
com
e Sp
ell E
ndin
g T
ypes
, by
Pers
on’s
Hou
seho
ld T
ype
in th
e L
ast y
ear
of H
igh
Inco
me
Spel
l, C
anad
a (1
993~
1999
) and
Uni
ted
Stat
ed(1
990~
1996
), (u
nwei
ghte
d)
Can
ada
Uni
ted
Stat
esA
ge <
60
Age
< 6
0M
ain
even
t ass
ocia
ted
with
spel
lEn
ding
All
Pers
onA
ge 6
0+
Sing
leC
oupl
eno
kid
sC
oupl
ew
/kid
sLo
nepa
rent
All
Pers
onA
ge 6
0+
Sing
leC
oupl
eno
kid
sC
oupl
ew
/kid
sLo
nepa
rent
Fall
of in
com
e fro
m:
(60.
3)(7
9.3)
Hea
d’s l
abou
r inc
ome
25.1
30.1
20.3
31.1
23.3
13.1
*36
.523
.145
.440
.041
.110
.6*
Partn
er’s
labo
ur in
com
e12
.28.
9-
20.5
13.8
-13
.57.
1-
22.2
16.1
-O
ther
labo
ur in
com
e6.
28.
411
.61.
1*5.
516
.98.
516
.211
.9*
0.4*
6.6
22.0
*A
sset
inco
me
3.1
7.9
1.4*
3.0
2.4
1.6*
10.1
21.5
7.5*
4.6*
8.1
7.3*
Priv
ate
trans
fers
9.5
16.5
3.4*
11.5
8.8
6.0*
8.5
20.1
8.4*
5.9*
4.9
10.6
Publ
ic tr
ansf
ers
1.8
2.7*
0.8*
2.1*
1.6
3.3*
0.8*
1.2*
1.3*
0.4*
0.5*
4.1*
Soci
al se
curit
y pe
nsio
n0.
2*0.
1*0.
3*-
0.4*
-1.
53.
6*2.
2*1.
3*0.
7*1.
6*Ta
xes r
ise
2.3
4.2
1.3*
1.4*
2.5
--
--
--
-
Dem
ogra
phic
eve
nt:
(39.
7)(2
0.7)
New
ly e
stab
lishe
d fa
mily
18.6
10.0
32.9
3.4
22.1
33.9
5.6
4.5*
3.1*
0.8*
6.7
18.7
*Se
para
tion/
divo
rce
4.6
2.6*
-5.
56.
12.
2*2.
81.
0*-
4.2*
3.6
0.8*
Partn
ersh
ip/m
arria
ge0.
2*-
1.0*
--
1.6*
0.2*
-1.
3*-
-3.
3*H
ouse
hold
mer
ge8.
25.
618
.42.
67.
719
.72.
40.
5*0.
9*0.
4*3.
48.
9*Su
btra
ctio
n to
fam
ily0.
70.
7*0.
9*0.
2*0.
9*0.
6*1.
0-
0.9*
0.4*
1.6*
0.8*
Nee
ds ri
se7.
32.
3*7.
717
.74.
81.
1*8.
61.
2*17
.219
.36.
711
.4*
Oth
ers
0.0*
--
--
--
--
--
-
Num
ber o
f spe
ll en
ding
s6,
280
841
866
1,24
63,
144
183
3,02
058
122
747
71,
612
123
* Es
timat
es b
ased
on
less
than
30
obse
rvat
ions
; - N
o ob
serv
atio
n.N
ote:
Ana
lysi
s bas
ed o
n al
l per
sons
with
Hig
h in
com
e sp
ell b
egin
ning
obs
erve
d in
CN
EF re
gard
less
of l
eft c
enso
ring.
Per
sons
who
se p
ost-t
rans
ition
ear
ning
s fal
l to
not m
ore
than
10%
belo
w H
igh
inco
me
line
are
not c
onsid
ered
as e
ndin
g.1.
Pr
ivat
e tra
nsfe
rs in
clud
e no
n-go
vern
men
t pen
sion
s, al
imon
y, a
nd c
hild
supp
ort a
nd in
com
e fro
m n
on-h
ouse
hold
mem
bers
.2.
Pu
blic
tran
sfer
s inc
lude
Chi
ld ta
x be
nefit
, UI,
WC
, and
wel
fare
(SA
, AFD
C).
3.
Soci
al se
curit
y pe
nsio
ns in
clud
e CP
P/Q
PP, O
ld A
ge S
ecur
ity, d
isab
ility
and
wid
owed
mot
her’
s allo
wan
ce.
4.
Exam
ples
are
chi
ld b
ecam
e he
ad/s
pous
e, o
r spl
it-of
f hou
seho
ld.
5.
Exam
ples
incl
ude
adul
t hea
d/sp
ouse
retu
rn to
par
ent h
ome,
or n
on-h
ead/
spou
se in
divi
dual
s mov
e to
oth
er h
ouse
hold
.6.
It
is d
ue to
the
redu
ctio
n of
the
hous
ehol
d si
ze.
53
Tabl
e 10
(Con
clud
ed)
Hig
h In
com
e Sp
ell E
ndin
g T
ypes
, by
Pers
on’s
Hou
seho
ld T
ype
in th
e L
ast y
ear
of H
igh
Inco
me
Spel
l, B
rita
in a
nd G
erm
any
(199
1~19
99)
(unw
eigh
ted)
Brit
ain
Ger
man
yA
ge <
60
Age
< 6
0M
ain
even
t ass
ocia
ted
with
spel
len
ding
All
Pers
onA
ge 6
0+
Sing
leC
oupl
eno
kid
sC
oupl
ew
/kid
sLo
nepa
rent
All
Pers
onA
ge 6
0+
Sing
leC
oupl
eno
kid
sC
oupl
ew
/kid
sLo
nepa
rent
Fall
of in
com
e fr
om:
(71.
3)(71.
8)H
ead’
s lab
our i
ncom
e29
.917
.740
.931
.032
.420
.3*
26.8
19.3
46.1
26.3
26.8
42.1
*Pa
rtner
’s la
bour
inco
me
17.6
11.8
-27
.019
.3-
17.0
7.1*
-30
.317
.0-
Oth
er la
bour
inco
me
10.4
19.6
14.4
0.3*
9.8
28.3
11.9
19.8
6.1*
0.8*
14.4
15.8
*A
sset
inco
me
7.1
18.4
6.3*
4.4*
5.3
2.9*
6.0
11.9
7.0*
2.8*
5.8
-Pr
ivat
e tra
nsfe
rs4.
216
.11.
4*3.
8*0.
7*10
.1*
1.2*
4.8*
1.7*
0.4*
-8.
8*Pu
blic
tran
sfer
s1.
21.
4*1.
4*1.
1*1.
0*2.
2*3.
94.
5*0.
9*4.
4*4.
0-
Soci
al se
curit
y pe
nsio
n0.
9*2.
5*0.
5*0.
8*0.
4*2.
2*3.
717
.4-
1.6*
0.7*
-Ta
xes r
ise
0.1*
--
-0.
2*-
1.2*
-4.
3*2.
0*1.
1*-
Dem
ogra
phic
eve
nt:
(28.
7)(3
8.2)
New
ly e
stab
lishe
d fa
mily
10.5
5.0*
16.4
1.7*
14.3
17.4
*10
.47.
4*6.
1*0.
8*15
.222
.8*
Sepa
ratio
n/di
vorc
e5.
32.
9*-
8.0
5.8
2.2*
4.5
3.2*
-8.
83.
91.
8*Pa
rtner
ship
/mar
riage
3.3
1.6*
2.9*
5.3
3.2
1.4*
--
-0.
2*-
-H
ouse
hold
mer
ge3.
31.
9*8.
2*1.
5*3.
57.
3*2.
41.
1*1.
7*0.
6*3.
71.
8*Su
btra
ctio
n to
fam
ily0.
9*0.
2*1.
9*0.
5*1.
0*3.
6*0.
8*0.
5*-
0.2*
1.2*
1.8*
Nee
ds ri
se5.
41.
0*5.
8*14
.43.
32.
2*8.
81.
8*26
.119
.55.
15.
3*O
ther
s0.
1*-
-0.
3*-
-1.
1*1.
3*-
1.4*
1.0*
-
Num
ber o
f spe
ll en
ding
s3,
139
485
208
659
1,64
913
82,
265
379
115
502
1,21
257
* Es
timat
es b
ased
on
less
than
30
obse
rvat
ions
; - N
o ob
serv
atio
n.N
ote:
Ana
lysi
s bas
ed o
n al
l per
sons
with
Hig
h in
com
e sp
ell e
ndin
g ob
serv
ed in
CN
EF re
gard
less
of l
eft c
enso
ring.
Per
sons
who
se p
ost-t
rans
ition
ear
ning
s fal
l to
not m
ore
than
10%
belo
w H
igh
Inco
me
line
are
not c
onsid
ered
as e
ndin
g.1.
Pr
ivat
e tra
nsfe
rs in
clud
e no
n-go
vern
men
t pen
sion
s, al
imon
y, a
nd c
hild
supp
ort a
nd in
com
e fro
m n
on-h
ouse
hold
mem
bers
.2.
Pu
blic
tran
sfer
s inc
lude
Chi
ld ta
x be
nefit
, UI,
WC
, and
wel
fare
(SA
, AFD
C).
3.
Soci
al se
curit
y pe
nsio
ns in
clud
e CP
P/Q
PP, O
ld A
ge S
ecur
ity, d
isab
ility
and
wid
owed
mot
her’
s allo
wan
ce.
4.
Exam
ples
are
chi
ld b
ecam
e he
ad/s
pous
e, o
r spl
it-of
f hou
seho
ld.
5.
Exam
ples
incl
ude
adul
t hea
d/sp
ouse
retu
rn to
par
ent h
ome,
or n
on-h
ead/
spou
se in
divi
dual
s mov
e to
oth
er h
ouse
hold
.6.
It
is d
ue to
the
redu
ctio
n of
the
hous
ehol
d si
ze.
54
Tabl
e 11
Hig
h In
com
e Sp
ell B
egin
ning
Typ
es, b
y Pe
rson
’s H
ouse
hold
Typ
e in
the
Firs
t yea
r of
Hig
h In
com
e Sp
ell,
Can
ada
(199
3~19
99) a
nd U
nite
dSt
ated
(199
0~19
96),
(unw
eigh
ted)
Can
ada
Uni
ted
Stat
esA
ge <
60
Age
< 6
0M
ain
even
t ass
ocia
ted
with
spel
lB
egin
ning
All
Pers
onA
ge 6
0+
Sing
leC
oupl
eno
kid
sC
oupl
ew
/kid
sLo
nepa
rent
All
Pers
onA
ge 6
0+
Sing
leC
oupl
eno
kid
sC
oupl
ew
/kid
sLo
nepa
rent
Rise
of i
ncom
e fr
om:
(71.
4)(8
3.9)
Hea
d’s l
abou
r inc
ome
31.8
17.5
25.9
35.7
35.1
19.4
46.0
19.9
54.7
34.0
54.1
52.3
Partn
er’s
labo
ur in
com
e13
.05.
4-
21.4
15.9
-16
.17.
8-
28.5
16.7
-O
ther
labo
ur in
com
e10
.57.
26.
40.
8*14
.918
.13.
06.
1*1.
5*0.
7*2.
712
.3*
Ass
et in
com
e3.
915
.51.
4*2.
83.
14.
4*8.
724
.05.
6*4.
47.
84.
6*Pr
ivat
e tra
nsfe
rs8.
023
.63.
95.
17.
410
.1*
8.3
23.0
9.7*
6.7
5.5
10.0
*Pu
blic
tran
sfer
s1.
53.
3*1.
0*0.
7*1.
71.
3*0.
6*0.
3*1.
9*0.
8*0.
3*2.
3*So
cial
secu
rity
pens
ion
0.3*
1.8*
0.3*
0.3*
0.1*
-1.
27.
6*-
1.0*
-2.
3*Ta
xes f
all
2.3
2.7*
1.7*
0.9*
3.0
1.8*
--
--
--
Dem
ogra
phic
eve
nt:
(28.
6)(1
6.1)
New
ly e
stab
lishe
d fa
mily
2.6
3.0*
7.2
3.1
1.1
2.2*
1.8
0.3*
2.6*
5.0
0.9*
2.3*
Sepa
ratio
n/di
vorc
e0.
4*2.
6*0.
3*0.
1*-
1.8*
0.6*
3.7*
0.4*
--
2.3*
Partn
ersh
ip/m
arria
ge3.
31.
2*-
10.0
2.7
0.4*
3.5
1.2*
-7.
13.
31.
5*H
ouse
hold
mer
ge12
.98.
138
.13.
68.
630
.01.
90.
5*0.
7*0.
4*2.
75.
4*A
dditi
on to
fam
ily (C
hild
)
(
Non
-chi
ld)
1.3
2.2
1.2*
0.9*
- 4.7
- 6.0
2.2
0.2*
-5.
3*0.
4* 1.0
0.5*
1.0*
-2.
6*-
2.1*
0.7*
0.5*
- -N
eeds
fall
6.0
5.9
9.2
9.4
4.1
5.3*
6.8
4.2
20.4
9.5
4.7
4.6*
Oth
ers
0.1*
--
0.1*
--
--
--
--
Num
ber o
f spe
ll be
ginn
ings
7,51
566
41,
162
1,32
14,
141
227
3,47
548
026
970
41,
964
130
* Es
timat
es b
ased
on
less
than
30
obse
rvat
ions
; - N
o ob
serv
atio
n.N
ote:
Ana
lysi
s bas
ed o
n al
l per
sons
with
Hig
h in
com
e sp
ell b
egin
ning
obs
erve
d in
CN
EF re
gard
less
of l
eft c
enso
ring.
Per
sons
who
se p
ost-t
rans
ition
ear
ning
s ris
e to
not
mor
e th
an 1
0%ab
ove
Hig
h in
com
e lin
e ar
e no
t con
sider
ed a
s beg
inni
ng.
1.
Priv
ate
trans
fers
incl
ude
non-
gove
rnm
ent p
ensi
ons,
alim
ony,
and
chi
ld su
ppor
t and
inco
me
from
non
-hou
seho
ld m
embe
rs.
2.
Publ
ic tr
ansf
ers i
nclu
de C
hild
tax
bene
fit, U
I, W
C, a
nd w
elfa
re (S
A, A
FDC
).3.
So
cial
secu
rity
pens
ions
incl
ude
CPP/
QPP
, Old
Age
Sec
urity
, dis
abili
ty a
nd w
idow
ed m
othe
r’s a
llow
ance
.4.
Ex
ampl
es a
re c
hild
bec
ame
head
/spo
use,
or s
plit-
off h
ouse
hold
.5.
Ex
ampl
es in
clud
e ad
ult h
ead/
spou
se re
turn
to p
aren
t hom
e, o
r non
-hea
d/sp
ouse
indi
vidu
als m
ove
to o
ther
hou
seho
ld.
6.
It is
due
to th
e re
duct
ion
of th
e ho
useh
old
size
.
55
Tabl
e 11
(Con
clud
ed)
Hig
h In
com
e Sp
ell B
egin
ning
Typ
es, b
y Pe
rson
’s H
ouse
hold
Typ
e in
the
Firs
t yea
r of
Hig
h In
com
e Sp
ell,
Bri
tain
and
Ger
man
y(1
991~
1999
) (un
wei
ghte
d)
Brit
ain
Ger
man
yA
ge <
60
Age
< 6
0M
ain
even
t ass
ocia
ted
with
spel
lbe
ginn
ing
All
Pers
onA
ge 6
0+
Sing
leC
oupl
eno
kid
sC
oupl
ew
/kid
sLo
nepa
rent
All
Pers
onA
ge 6
0+
Sing
leC
oupl
eno
kid
sC
oupl
ew
/kid
sLo
nepa
rent
Rise
of i
ncom
e fro
m:
(76.
4)(8
7.0)
Hea
d’s l
abou
r inc
ome
29.7
10.4
37.6
32.7
33.3
19.8
34.1
18.5
56.0
34.8
35.8
30.6
*Pa
rtner
’s la
bour
inco
me
15.1
6.6
-21
.818
.0-
18.3
3.3*
-32
.518
.6-
Oth
er la
bour
inco
me
13.1
15.4
11.6
-16
.925
.515
.016
.34.
0*0.
7*20
.916
.7*
Ass
et in
com
e11
.427
.98.
1*6.
99.
212
.7*
9.7
16.3
11.2
*5.
1*10
.0-
Priv
ate
trans
fers
4.2
19.0
1.9*
2.6*
1.0*
5.7*
1.6
7.7*
0.8*
1.1*
0.2*
5.6*
Publ
ic tr
ansf
ers
1.9
1.4*
0.8*
1.0*
2.5
1.9*
2.8
4.7*
-3.
4*2.
5-
Soci
al se
curit
y pe
nsio
n0.
6*3.
4*-
0.7*
--
4.7
26.7
1.6*
1.3*
1.0*
-Ta
xes f
all
0.5*
--
0.9*
0.5*
-0.
8*0.
5*2.
4*0.
7*0.
6*5.
6*
Dem
ogra
phic
eve
nt:
(23.
6)(1
3.0)
New
ly e
stab
lishe
d fa
mily
1.2
0.2*
5.4*
3.4*
0.1*
0.6*
0.6*
-2.
4*1.
8*0.
1*-
Sepa
ratio
n/di
vorc
e3.
73.
6*3.
9*5.
52.
85.
7*0.
4*0.
3*3.
2*0.
2*0.
1*5.
6*Pa
rtner
ship
/mar
riage
4.3
4.0*
-9.
73.
1-
0.1*
--
0.2*
0.1*
2.8*
Hou
seho
ld m
erge
6.5
3.4*
14.3
2.7*
7.1
14.6
*2.
41.
1-
0.5*
3.5
8.3*
Add
ition
to fa
mily
(Chi
ld)
(N
on-c
hild
)1.
22.
22.
8*1.
2*-
5.0*
- 6.0
1.6
0.5*
-3.
2*0.
9* 2.6
0.6*
0.3*
- -- 9.6
1.4*
0.7*
-2.
8*N
eeds
fall
4.4
0.8*
11.2
*6.
03.
310
.2*
5.8
3.3*
18.4
*7.
34.
422
.2*
Oth
ers
--
-0.
1*-
-0.
3*0.
5*-
0.7*
0.2*
-
Num
ber o
f spe
ll be
ginn
ings
3,58
350
125
876
71,
900
157
2,56
336
312
555
11,
488
36
56
Table 12Sample Sizes used in Econometric Analysis of Transition Rates
Low Income High IncomeExit Re-entry Exit Re-entry
1. CanadaNumber of Spells 10 426 12 606 8 271 8 496Number of Individuals 5 382 5 278 4 254 3 787
2. United StatesNumber of Spells 8 045 8 109 5 673 5 374Number of Individuals 4 076 3 914 2 796 2 354
3. United KingdomNumber of Spells 7 242 7 757 6 792 7 543Number of Individuals 2 713 2 597 2 583 2 396
4. GermanyNumber of Spells 7 191 6 522 7 594 7 105Number of Individuals 3 016 2 348 2 763 2 502
57
Table 13Unconditional Distribution of Latent Classes in Logistic Models of Transition Rates
Transition Probability1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
1. Probability of Leaving Low-income
a. limited co-variatesCanada 0.56 0.08 0.36US 0.57 0.22 0.21UK 0.47 0.18 0.35Germany 0.56 0.33 0.11
b. complete set of co-variatesCanada 0.56 0.08 0.36US 0.58 0.2 0.22UK 0.47 0.18 0.35Germany 0.27 0.57 0.16
Transition Probability1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
2. Probability of Re-entering Low-income
a. limited co-variatesCanada 1US 0.13 0.66 0.21UK 0.46 0.27 0.27Germany 0.67 0.32
b. complete set of co-variatesCanada 0.35 0.09 0.56US 0.13 0.66 0.21UK 0.37 0.28 0.35Germany 1
Table entries are the estimated weights associated with each latent class from a logistic model of the transition process as outlinedin the text. For each row these add to one. The estimations assume independence of spells across individuals. Subject to thisrestriction the weights may be interpreted as unconditional probabilities of belonging to a particular group, and are sorted alongeach row according to the associated estimated transition probability for a reference case individual. For the panels a – the limitedset of co-variates – this is defined as the transition rate for the first year of a spell, for a male 18 to 34 years of age, in a couplehousehold with children. For the panels b – the complete set of co-variates – the definition is the same with the addition that theindividual has 12 years of education (for countries other than the UK) and does not belong to a potentially excluded group, asdefined in the text.
(Continued)
58
Table 13 (concluded)Unconditional Distribution of Latent Classes in Logistic Models of Transition Rates
Transition Probability1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
3. Probability of Leaving High-income
a. limited co-variatesCanada 0.55 0.06 0.39US 0.52 0.14 0.34UK 0.61 0.39Germany 0.29 0.29 0.42
b. complete set of co-variatesCanada 0.54 0.06 0.40US 0.55 0.17 0.28UK 0.61 0.39Germany 0.37 0.32 0.31
Transition Probability1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
4. Probability of Re-entering High-income
a. limited co-variatesCanada 0.38 0.1 0.52US 0.5 0.27 0.23UK 0.35 0.65Germany 0.29 0.31 0.40
b. complete set of co-variatesCanada 0.38 0.1 0.52US 0.5 0.23 0.27UK 1Germany 0.35 0.32 0.33
59
Appendix Table 1
Differences of variable definitions across CNEF files:
Canada (SLID) United States (PSID) Britain (BHPS) Germany (GSOEP)1. Household A person or group of
people who co-residein a set of livingquarter includingpeople not related byblood or marriage.
A person or group ofpeople who co-residein a set of livingquarter includingpeople not related byblood or marriage.
Excluded people notrelated by blood ormarriage.
A person or group ofpeople who co-residein a set of livingquarter includingpeople not related byblood or marriage.
2. Head Person with thegreatest individualincome for the year. Ifmajor income earneris a female living witha spouse, the malepartner is head.
For married couple,husband is HH headregardless his incomelevel.
The principle owner(renter) of theproperty. If jointownership, the maletaking precedence,and the oldest takingprecedence.
Person who knowsbest about the generalconditions underwhich the householdacts and is supposedto answer thisquestionnaire in eachgiven year.(DTC 2000)
3. Referenceyear
The calendar yearprior to interview year(e.g. 01/01/96 -31/12/96)
12 months prior to thestart of interviewperiod (e.g. 01/09/96 -31/08/97)
The calendar yearprior to interview year(e.g. 01/01/96 -31/12/96)
4. Taxes Include income taxesfor all HH members,but no payroll taxes.
Include income taxesfor all HH members,payroll tax for headand partner.(TAXSIM)
Include income andpayroll taxes, butexcluded local taxes.(Bardasi et al. 1999)
Include income andpayroll taxes for allpersons in the HH 16years of age and older.