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 tomiles.corak@statcan.ca.

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

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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

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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

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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

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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

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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

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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).

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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

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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

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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.

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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.

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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.

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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

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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

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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.