Snakes and Ladders:

An analysis of life-course events and income

mobility in old age

By

Asghar ZaidiSAGE ESRC Research Group

Department of Social Policy

London School of Economics

(Work-in-progress)

Paper presented at the

German Institute for Economic Research

14 November 2001

2

Snakes and Ladders: An analysis of life-course

events and income mobility in old age

Abstract:

This paper carries out multivariate analyses on the income mobility of older people in Great

Britain. The objective is to capture the impact of life-course events on older people’s income,

and in the process identify events and attributes that enhance the odds of downward and upward

income mobility (metaphorised as snakes and ladder). These multivariate analyses are a natural

extension of the bivariate descriptive analyses undertaken in Zaidi et al. (2001). Using the

binomial and the multinomial probability models, we measure how changes in marital status and

living arrangements, and changes in employment status trigger income mobility during old age.

The impact of reliance on different sources of pension income is also examined.

All empirical results have been produced by using longitudinal data from the first seven waves

of the British Household Panel Survey, covering the period 1991 to 1997. Following the

analyses of Zaidi et al. (2001), older people are defined as all those who have reached the

statutory retirement age (60 for women, and 65 for men). All references to income stand for

‘equivalised’ net household income. The results show that for both older men and older women

the exit from the labour force significantly increases the chances of downward income mobility.

These results refer to the selective subgroup of older people who had delayed exit from the

labour force till after the retirement age. The death of the spouse makes it more likely for older

women to observe downward income mobility, whereas the same event when experienced by

older men makes it more likely for them to observe upward income mobility. The living

arrangements of older people also have a significant impact on income mobility: both men and

women are more likely to observe downward income mobility when they start living

independently. Moreover, as can be expected, a reliance on state benefits makes it less likely

that older people experience income mobility, and a reliance on labour income and investment

income has the opposite effect. One policy conclusion that can be drawn from these longitudinal

analyses of older peoples’ incomes is that the old age social benefit system in Great Britain

needs to be further strengthened to safeguard older women against downward income mobility

when they become widows.

3

1. Introduction

Zaidi et al. (2001) concludes by examining the incidence of income mobility across

different subgroups of older people. These bivariate analyses facilitate the identification

of two broad sets of factors contributing to income mobility amongst older people: (1)

life-course events that occur during old age, and (2) the composition of income that

people receive during old age. The empirical work of the current paper seeks to capture

the ceteris paribus effect of these two factors with the help of multivariate analyses on

incomes of older people. This study of covariates of income mobility is distinctive in

two ways from other studies on income dynamics:

(i) It focuses exclusively on changes in the income during old age. This focus on the

dynamics of post-retirement income provides essential information on the extent to

which pensioners experience falling or rising economic resources as they age and

what factors trigger these changes. This type of analyses offers a much-desired shift

away from mere forecasting of income at the time of retirement to a study of how

income changes in the years after the retirement age. The emphasis on the post-

retirement phase of life has become important since people spend a longer time in

retirement and rely progressively more on private sources of income which may

expose them to the risk of greater income fluctuations.

(ii) An emphasis is placed on income mobility per se, rather than the poverty dynamics

(as is common to other studies: see, e.g. Holden et al. (1988) and Hurd and Wise

(1989)). This focus reflects our view that volatility in income alone may affect the

well-being of older people, irrespective of whether these fluctuations alter their

poverty status or not. Individuals who observe changes in income may be oblivious

as to whether that change has taken them to the state of poverty (especially if

poverty is viewed as a discrete event). Moreover, while poverty dynamics research

is important in its own right, it may be strongly influenced by the conceptualisation

and measurement of what constitutes poverty.

More specifically, in this Paper we analyse how changes in marital status and living

arrangements, and changes in one’s own employment status as well as the employment

4

status of the spouse, have an impact on older peoples’ income. The impact of reliance

on different sources of income during this post-retirement phase of life is also

examined. From this empirical work, we identify ladders and snakes which are life-

course events as well as those economic and demographic attributes that expose older

people to higher downward and upward income mobility.

In quantifying the effect of these attributes and events, this paper makes use of the

binomial probability models as well as the multinomial logit model. The data is derived

from the first seven waves of the British Household Panel Survey, covering the period

1991 to 1997. Most empirical choices made are in line with the empirical work carried

out in Zaidi et al. (2001), and they are reiterated in this paper when it is necessary to

emphasise the choice made.

The remainder of this paper is organised in five sections. Section 2 outlines the

econometric models that are used in identifying factors associated with income

mobility. Section 3 provides a discussion on the empirical choices made in order to

operationalise these models. Section 4 describes the dependent and explanatory

variables used in the model, and presents the descriptive statistics as a precursor to the

results of the multivariate model. Section 5 presents the results of the multivariate

analysis using the binomial logit model as well as the multinomial logit model. Section

6 concludes with a summarising discussion.

2. The econometric models

The first deciding factor in our choice of econometric models is the form in which we

use our dependent variable: either as a continuous or as a discrete variable. This choice

rests on the objective of the analyses and, as mentioned above, the main objective of

this paper is to identify covariates of income mobility. As argued in detail in Zaidi et al.

(2001), the study of income mobility merits attention only when it captures a significant

change in income (e.g. where a threshold is crossed). In this way, we seek to make a

distinction between ‘transitory’ (or: insignificant) fluctuations around an individual’s

otherwise persistent characteristics and fluctuations which represent meaningful change.

Thus, income mobility is considered meaningful when changes in income exceeds a

5

certain threshold, although the choice of the threshold may be subject to debate. Taking

this perspective, we use our dependent variable (income) as a discrete variable which

may be dichotomous (that is, the change in income crosses a given threshold or not) or

polychotomous (that is, more than one threshold is used to define different levels of

income change). More discussion on our choice of the dependent variable is provided in

Section 4.

This empirical choice to work with income that is defined as a discrete variable restricts

us to a limited range of models. The simplest of these is the linear probability model,

which uses the dependent variable as the binary variable in the linear regression model.

Below we briefly discuss the linear probability model, followed by a discussion on its

extension to the logistic regression model. These two models are classified within the

generalised group of binomial probability models (Section 2.1). This is followed by a

description of the multinomial logistic regression model (Section 2.2), which makes use

of the dependent variable that takes more than two categories. A concluding discussion

about the merits of these models and the possible alternative models is given at the end

of this section (Section 2.3).

2.1 Binomial probability modelsThe binomial probability model is used when the dependent variable is a binary or

dichotomous variable. These models present a special case of the Generalised Linear

Model (GLM) and are commonly used to predict the probability of an event occurring

and to identify variables that are significant in determining this occurrence. Like any

other multivariate regression model, these models provide an improvement over

bivariate analyses since they show the effect attributed to a change in a single

characteristic of a household while keeping other characteristic unchanged.

The claim that these models measure a cause-and-effect relationship requires careful

examination. One needs a theoretical model of the relationship in question, and -

lacking a theory - the association of income mobility with any household characteristic

may be merely statistical in nature. The provision of economic theory to buttress the

choice of explanatory variables in the model will circumvent this problem. Thus, in

Section 4, we describe the explanatory variables and justify their theoretical relevance

6

in a model of income mobility. Moreover, in Section 5, we try out several specifications

of the binomial logit model in order to ensure that explanatory variables which are

relevant to explaining variations in the experience of income mobility are all

considered.

Below we provide a description of the linear probability model and a discussion of its

limitations. It is useful to start within the familiar terrain of the linear regression model,

as it helps us understand better the advantages that are offered by the logistic regression

models. Moving on from the linear regression model, we outline the logistic regression

model which overcomes the limitations of linear probability models by a specific

transformation of the dependent variable from mere probability of income mobility to

the (log of) odds ratio of income mobility.

2.1.1 Linear probability models

In estimating the probability that an individual with certain characteristics will

experience income mobility, one may simply use multivariate linear regression analysis

in which the dependent variable is binary. This procedure makes use of the model as

specified below:

iK

ikki uXbY +=�

where Yi is the binary dependent variable taking the value 1 or 0 depending upon

whether the individual has experienced income mobility or not, bk (k=1,2...K) are the

regression coefficients, Xik are explanatory variables and ui is a stochastic error term.

The expected value of Yi given Xik (i.e. the conditional mean) will give the probability

that an individual with characteristics Xik will experience income mobility. This can be

formulated as:

7

where P(Yi=1), hereafter referred to as Pi, is the probability that the individual will

experience income mobility, given the values of the independent variables. Since the

model assumes a linear relationship between the probability and the explanatory

variables, this model is commonly referred to as the linear probability model. One of

the strong attributes of this model is that it is simple and it is easy to estimate.

However, the use of the linear probability model brings with it serious problems.1 First,

in the case of a binary dependent variable the usual assumptions of regression analysis

are violated (e.g., the disturbance term mentioned in the first equation will be

heteroskedastic). The consequences are that this model will give inaccurate estimates of

bk and that the statistical inferences (e.g. hypothesis testing based on t- and F-statistics)

of this model would be misleading. Second, the estimated probabilities are not bound to

lie within the admissible range [0,1]. Third, a specification error is expected to occur by

assuming a linear relationship between the probability and the explanatory variables (as

reflected by the second equation given above). This will result in the specification bias,

such as a possible understatement of the estimates of bk and inaccurate statistical

inferences (see e.g. Aldrich and Nelson 1984: 30; Hosmer and Lemehow 2000: 4-7).

The linear probability model is still frequently used in empirical applications despite its

limitations. When fixed-effects are a concern, the linear probability appear to be the

easiest to implement. Moreover, when some of the explanatory variables are

endogenous, and an instrumental scheme is necessary, the linear probability model is

most convenient (Johnston and Dinardo 1997: 431). However, the above-mentioned

limitations present a strong case for a consideration of a non-linear model when

estimating the probability of income mobility, the topic to which we now turn.

2.1.2 Non-linear probability models

The requirement here is to find a specification which would express the probability, Pi,

as a non-linear function of Xik while also obtaining probability estimates within the

1 These are set out in detail by Hosmer and Lemehow (2000), Aldrich and Nelson (1984) and Maddala(1983).

E Y P Y b Xi i k ikK

( ) ( )= = = �1

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feasible limits [0,1]. It would also be desirable to obtain a specification which gives an

estimate of the probability whose change diminishes as it approaches one of the two

limits, 0 and 1.

This objective is achieved by transforming Pi as Pi/(1-Pi) and taking its natural

logarithm to define the dependent variable. The term log [Pi/(1-Pi)], unlike the binary

variable Yi, can take a value equal to a real number from - ∞ to + ∞ . A linear

relationship is then assumed between this transformed variable and the independent

variables. This model can be expressed as:

�=− K

ikki

i XbP

PLog1

Since Pi is the probability that an individual with given characteristics will experience

income mobility, 1-Pi would be the probability that the individual will not observe

income mobility. The ratio of these two terms will give us the odds that the individual

will experience income mobility. Thus, the left hand side of the last-mentioned equation

gives us the log of the odds that the individual will experience income mobility. This

interpretation of the model is useful in understanding the meaning of each estimated

coefficient presented in Section 5.

If, for notational ease, we say Z b Xi k kk

= � then the solution of this equation in terms

of mobility probability would be:

)exp(1)exp(

i

ii Z

ZP+

=

or:

)exp(11

ii Z

P−+

=

9

This expression is commonly referred to as the logistic function and is the basis of the

logit model estimated in Section 5. In this model, Pi is near zero when Zi is near

negative infinity, it increases monotonically with Zi and it approaches 1 as Zi goes to

positive infinity. This model is estimated using the Maximum-Likelihood-Method.

Among other alternatives, one may also assume that the relationship between the

probability and the independent variables has the form of a cumulative normal

distribution function. This defines the probit model. Since the difference between the

two curves defining the logit and probit models lies in the thickness of the tail (how

quickly the curve approaches 1 or 0 when Zi approaches positive or negative infinity),

they yield almost the same results. In practice, they yield estimated probabilities which

differ by less than .02, which can only be distinguished in the case of large samples

(Aldrich and Nelson, 1984: 34). Hence, the choice between the two models can rest on

such practical matters as the availability and usefulness of computer programmes,

personal experience and preference.

Next, we describe the multinomial logistic model which is useful for situations in which

we want to be able to classify our discrete dependent variable in more than two

categories. This type of regression is similar to the binomial logit model described

above, but it is more general because the dependent variable is not restricted to just two

categories.

2.2 Multinomial logistic regression modelIncome mobility outcomes can also be defined on the basis of different thresholds of

income changes and whether income is rising or falling. The outcome variable that we

have used in our multinomial analysis in this paper has five categories indicating

whether an individual has experienced:

1. Long-range downward income mobility (falling income by more than 15%),

2. Short-range downward income mobility (falling income by 5-15%),

3. No income mobility (change in income not exceeding 5%),

4. Long-range upward income mobility (rising income by 5-15%), and

5. Short-range upward income mobility (rising income by more than 15%)

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We refer to Section 4 for more discussion on this choice of the dependent variable. The

objective here is to model the odds of different income mobility outcomes as a function

of its covariates.2

2.2.1 Description of the model3

Consider the outcomes 1, 2, ..., m recorded in the dependent variable Yi, and the

explanatory variables are given by the matrix X. In the multinomial logit model, we

estimate a set of coefficients )1(β , )2(β , ..., )(mβ corresponding to each outcome

category:

)()2()1(

)1(

...)1Pr( mXXX

X

eeeeY

βββ

β

+++==

)()2()1(

)2(

...)2Pr( mXXX

X

eeeeY

βββ

β

+++==

.....

.....

)()2()1(

)5(

...)Pr( mXXX

X

eeeemY

βββ

β

+++==

The model, however, is unidentified since there are more than one solution to )1(β ,)2(β , ..., )(mβ that leads to the same probabilities for Y=1, Y=2, ...., Y=m. To identify

the model, one of )1(β , )2(β , ..., )(mβ is arbitrarily set to 0. Say, we arbitrarily set )1(β =

0, the remaining coefficients )2(β , ..., )(mβ would then measure the change relative to

the Y=1 outcome. This category is referred to as the ‘base outcome’ for the dependent

variable. It does not matter which of the m-categories becomes the base outcome, since

2 McFadden (1974) was the first to propose this extension of logistic regression and called it a discretechoice model. Therefore, the model is commonly referred to in econometric literature as the discretechoice model, whereas it is called the multinomial or polychotomous logistic regression model inhealth and life sciences. Following Hosmer and Lemeshow (2000), we also use the term multinomialin this paper.

3 For a detailed exposition of the multinomial logit model, see Hosmer and Lemeshow (2000), Greene(1997) and Aldrich and Nelson (1984).

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the predicted probabilities for Y=1, Y=2, ...., Y=m would remain the same (the

coefficients will be different only because they will have different interpretations in

terms of odds ratio). Thus, parameterisation of any choice of the base outcome will form

the solution to the same underlying model.

Choosing Y=1 outcome as the base outcome, and thus setting )1(β = 0, the equations

mentioned above will become:

)()2(

...11)1Pr( mXX ee

Yββ +++

==

)()2(

)2(

...1)2Pr( mXX

X

eeeY

ββ

β

+++==

.....

.....

)()2(

)5(

...1)Pr( mXX

X

eeemY

ββ

β

+++==

The relative probability of e.g. Y = 2 to the base category Y = 1 is

)2(

)1Pr()2Pr( βXe

YY =

==

This ratio is commonly referred to as the relative-risk-ratio. The results that we produce

using the multinomial logit model (Table 6 and 7) are presented in the form the relative-

risk-ratio.

2.3 Summarising choice of econometrics modelsAs the objective of the paper is to find covariates of income mobility, and income

mobility is defined in terms of changes in income that cross certain thresholds, it is

obvious that we work with econometric models that allow dependent variable to be a

discrete variable. We first discussed the implications of using the linear regression

model in such a situation, and outlined how the logistic regression model provides an

alternative that eliminates certain limitations of the linear regression model. This

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discussion is provided while discussing the generalised class of binomial probability

models. Next, we outline the multinomial logistic regression model. This model is used

in the situation when the dependent variable is discrete and takes up more than two

values. The choice is made to work with the logit transformation for these probability

models, however it is noted that the probit transformation is not much different and is

likely to produce results that do not diverge much from the logit transformation.

Before these models can be put to operational effect, we need to make a variety of other

empirical choices. In part, these choices exhibit the perspective taken by the researcher.

It is therefore essential that we provide an orderly description of choices that we have

made before presenting any empirical results. These choices are outlined in the next

section (Section 3).

3. The empirical choices

In this section, firstly we outline the choice of the dataset and the sample of older people

used for empirical results (see Subsection 3.1). Secondly, we refer briefly to the concept

of mobility used in this paper (see Subsection 3.2). Thirdly, we discuss the choice of the

income variable (see Subsection 3.3), and finally our choice of equivalence scale is

described (see Subsection 3.4). These choices are in line with those made in Zaidi et al.

(2001), but it is worth reiterating them here in order to better interpret the results of the

mutlivariate model. Two final model-specific choices refer to the choice of the

dependent variable and the choice of explanatory variables in the model. These choices

are discussed in the next section (Section 4) along with the necessary descriptive results.

3.1 Dataset and sample in useAt the outset, we mention the choice of the first seven waves of the British Household

Panel Survey as the dataset in use. The choice to use BHPS is justified on the grounds

that this is the only longitudinal dataset in Great Britain that provides data for a number

of continuous years. An important sub-choice is to restrict all our empirical analyses to

a balanced panel of individuals (i.e. the panel of people who were present in all seven

waves). This implies that all our results are conditional upon the survival of individuals

for at least seven years during old age. This choice may have consequences since the

13

sample may have been affected by ‘selective attrition’ (such as the selective mortality

for older people). An important extension of the empirical work can thus be identified

as to test whether, and how, the sample attrition has an impact on covariates of income

mobility.

3.2 Income vs. consumption mobilityWe measure income mobility, rather than consumption mobility, to measure changes in

economic welfare of older people. The study of consumption-mobility is not possible

because of the unavailability of consumption data in the BHPS.4 On theoretical grounds,

one may argue that consumption mobility provides better information about changes in

economic welfare. This is due to the fact that individuals smooth their consumption

stream even though their income stream may not be smooth. However, as discussed in

detail in Zaidi et al. (2001), by taking consumption as the indicator on the basis of these

arguments, we are allowing individuals to make their own judgements about future

income prospects and about their borrowing capacity in the capital market. The

implication is that households may consume on an ‘unsustainable basis’ or voluntarily

choose to have a low level of consumption. Another limitation is that liquidity

constraints may hamper the intertemporal smoothing of consumption. These theoretical

limitations reinforce our choice to prefer income as the measure of the economic well-

being of older people.

3.3 Absolute vs. relative income mobilityIn this paper, we have used annual changes in income to measure income mobility, and

thus restricted ourselves to the absolute concept of income mobility. As argued in Zaidi

et al. (2001), the choice between the relative and the absolute concept depends upon the

weight that one may assign to changes in one’s relative position within the reference

society in comparison to changes in one’s own income. Our choice to perform

multivariate analyses by using the measure of the absolute income mobility only rests

on our view that for mobility analysis involving shorter periods (e.g., annual change)

4 In fact, certain components of consumption expenditures are recorded in BHPS (such as expenses onfood items, on leisure) but the data on total consumption expenditures is missing, and in most instances,the expenses on individual components are recorded as a banded-variable.

14

older people are more likely to assign greater weights to absolute changes in income.

This is because it is difficult for anyone to realise how their relative position in the

society has changed within a short period.

However, over the longer period, it is likely that more weight is assigned to changes in

relative position than to absolute changes in income. The choice of different thresholds

to define significant changes in one’s income, and thus the dependent variable for our

regression analyses, is discussed in more detail in the next section (Section 4).

3.4 Choice of the income variableThe definition of income is extremely important in studies on income mobility. The

principal choice components are: the income unit (i.e. individual or household

income?), the time span within which income is measured (i.e. annual or current

income?), and the composition of income (i.e. gross or net income? before or after

housing costs?). The choices made to produce the empirical results reported in this

paper are discussed below.

3.4.1 The choice of income unit

We have restricted ourselves to the analysis of the household income - as opposed to

individual or benefit-unit income5 - in measuring the economic resources of older

people. This choice of income unit depends in large part on the objective of the research

in question. Here, the choice is made to use household income, mainly because we are

examining the well-being aspect of older people. Since individuals share at least some

resources with other members of their families and households, economic well-being

will not be adequately measured by individual income or the benefit-unit income alone

(see Zaidi et al. (2001) for a more detailed discussion on this choice).

3.4.2 The choice of time unit

5 See Zaidi et al. (2001) for distinction between these different income units.

15

We have used current instead of annual household income.6 The current household

income is measured, in most instances, for the month prior to the interview (except for

earnings that are ‘usual earnings’ and investment income that is recorded for the whole

of the preceding year).7 This measure of income is the sum across all household

members of cash income from all sources (income from employment and self-

employment, investments and savings, private and occupational pensions, and other

market income, plus cash social security and social assistance receipts and private

transfers) minus direct taxes (income tax and employee’s National Insurance

Contributions) and occupational pension contributions.

The annual household income is similar to the variable for current net household

income, except that the recording period refers to the 12 months’ interval up to

September 1 of the survey year. We have, however, preferred current income to annual

income, since the former is more useful in associating a change in income to life-course

events. As widowhood can happen any time during the year and the use of current

income ensures that the income picture is captured more precisely than if the annual

income variable is used. This is mainly for the fact that the current income variable

records income either before or after the event of the death of the partner. The use of the

annual income variable, on the other hand, may include an income record of some

months that precede the event, with the rest following it. It is therefore difficult to

associate a specific event, such as widowhood, with changes in income using annual

income.

3.4.3 The composition of income

We have preferred the net household income over gross income. This choice also

reflects our emphasis on measuring the economic well-being of older people which is

6 The net income variables for the BHPS are provided by Jarvis and Jenkins (1996) and Baradasi et al.(1999).

7 Investment income totals the estimated income from savings and investments, and all receipts fromrent from property or boarders and lodgers, received in the months from September in the year prior tothe interview until August in the year in which interviewing begins. Income from investments is onlycollected as a banded variable, and the annual value is estimated as follows: income is £60 if in theband ‘less than £100’, income is £600 if in the band ‘between £100 and £1,000’, and income is £1,800if in the band ‘more than £1,000’. See Section 3.4 for a description of problems with respect to thelongitudinal comparability of this variable in the BHPS.

16

better given by the net economic resources that are available to older people. However,

we have restricted ourselves to the net household income variable before housing costs.

This choice enables us to provide a more meaningful comparison of income

entitlements of older people, rather than how their preferences towards housing affect

their means for other consumption items.

3.5 The choice of equivalence scalesThe existing literature on equivalence scales does provide any definite answers to the

choice of equivalence scale. Researchers choose a particular equivalence scale

depending upon the tradition of research in the country in question (e.g., the

McClements is popular among British researchers (see, inter alia, DSS 1999)), or they

may choose equivalence scales that are more often used in comparative research (e.g.,

the use of OECD equivalence scales in the cross country comparison of poverty and

inequality in EU countries (see Zaidi and De Vos 1997); or the use of a particular value

of equivalence elasticity as done by Buhmann et al. (1988). In this paper, all

adjustments for differences in family or household size are accounted for by the use of

the British McClements equivalence scale (McClements, 1978). This choice facilitates a

more useful comparison with other British studies (e.g., Jarvis and Jenkins (1998)).

As for the definition of the income variable, the choice of an equivalence scale is also

very crucial for the empirical analysis of this paper. This is mainly due to the fact that

changes in household income from one year to another might be triggered by changes in

family composition (say, owing to death of the partner). By examining equivalised

income, instead of the total household income, we allow for an automatic (albeit

imperfect) correction for changes in family composition.

An example can be used to highlight this choice. Consider a family that relies solely on

state benefits. The extent to which the equivalised income after the change in its

composition compares to the benefit entitlement of the newly-composed family will

determine whether the household experienced a fall, a rise or no significant change in

equivalised income. For instance, widows who rely solely on state benefits will receive

two-thirds of what they were receiving as a couple (approximately £85 in comparison to

£130 in 2000 prices, respectively), and their equivalised income using McClements

17

scales would be slightly higher (rising from £130 to £139). This comparison shows that

the McClements equivalence scales are very close to the official equivalence scales

implicit in the benefit system of Great Britain. This comparison also suggests that our

choice of a fall or rise in equivalised income in excess of 15% in real terms would

reflect a genuine change in income when family composition changes status to

widowhood.8

Two final choices are: which dependent variable to use and what explanatory variables

to consider in the specification of the econometric models. We discuss these choices

next, along with the descriptive results. The descriptive results will provide us the

justification of inclusion of specific variables in the model, and will serve as a useful

precursor to the results of the multivariate models.

4. The descriptive results

4.1 The dependent variableAs mentioned above, income mobility in this paper is defined on the basis of a

significant change in one’s own income from one year to the next. Therefore, the

dependent variable is discrete: either binary or polychotomous. The binary variable will

be the dependent variable in the binomial logit model, whereas the polychotomous

variable will be the dependent variable in the multinomial logit model.

For the purpose of the binary income variable, a significant change is taken as a 15%

change in income in any annual step. Thus, the dependent variable takes the value 1 if

an individual has experienced a 15% change in real income and 0 otherwise.9

The analyses of the binomial logit model are then supplemented by the analysis of the

multinomial logit model. For the multinomial model, we make use of five discrete

outcomes of income mobility, defined as follows:

8 In order to compare income in real terms, all income figures have been converted to 1997 prices usingthe yearly Retail Price Index.

9 See Zaidi et al. (2001) for a discussion on the choice of thresholds to define income mobility.

18

1 = “Fall, more than 15%” (long-range downward mobility),

2 = “Fall, 5 to 15%” (short-range downward mobility),

3 = “No significant change (i.e., less than 5% change)” (no mobility),

4 = “Rise 5 to 15%” (short-range upward mobility), and

5 = “Rise, more than 15%” (long-range upward mobility).

Table 1 and Figures 1 and 2 report the incidence of income mobility for men and

women during each of the one-year period between 1991 and 1997. The results make

use the sample of 950 older individuals: 300 of them are men and 650 are women.

These 950 individuals are observed for six annual changes in income during the period

1991 to 1997 (and therefore constitute 5,700 observations in the pooled sample). Table

1 shows that about one-third of these individuals observed no significant change in

income during a single year. As opposed to this, about 16% observed a fall in income

that exceeds 15%, and about 20% observed more than 15% rise in their income. This

trend holds true for almost all annual time periods.

Next, we outline the list of explanatory variables that are used in estimating the

binomial and the multinomial logit model. The results are presented in Section 5.

4.2 The explanatory variablesAs to the choice of the explanatory variables, we take the bivariate results of Zaidi et al.

(2001) as our starting point. Those results demonstrated that changes in marital status

and economic activity can be expected to be the strong predictors of income mobility

for older people. Moreover, the composition of pension income is also important in

explaining income mobility. Accordingly, this dictates the choice of the first three

principal sets of explanatory variables included in the final specification of our model:

(i) changes in marital status, (ii) changes in employment status, and (iii) the

composition of total pension income. More details on these variables are provided

below.

Another explanatory variable that is often associated with changes in marital status is

changes in living arrangements. For instance, the death of the partner for older people

may be accompanied by a move into households of their children. Two other control

19

variables that are used are age cohorts and income classes in the base year (1991). A

time dummy is also used to account for any changes that may have occurred during the

period in question but not accounted for by the above events. Below we discuss the

relevance of each of these explanatory variables in the income mobility model, and

provide descriptive results of income mobility using the five-fold categories of income

mobility (see Table 2 to 8).

4.2.1 Changes in marital status

This first set of variables is derived from changes in the marital status of older people

during a single year. This variable is essential in any model of income dynamics in old

age since the transition to widowhood is one of the most important demographic

transitions that affect older people (see e.g. Scott et al. (2001)). Recent studies for the

US and Germany also show that the death of the partner is associated with significant

changes in economic situation of older people (Burkhauser et al. (2001)).

The BHPS data provide us the following four categories to identify the marital status of

older people:

1. Married10

2. Widowed

3. Divorced or separated

4. Never married

This variable leads to six categories of changes (or no change) - as viewed in annual

steps - in marital status:

1. Remained married

2. Remained widowed

3. Remained divorced or separated

10 No distinction is made between de jure and de facto marriages for the purpose of the analysis carriedout in this paper. This practice is not problematic since a couple is identified only when both partnersreside in the same accommodation. Thus, the category ‘married’ also includes those who areunmarried but cohabit as a couple.

20

4. Remained as never married person

5. Changed from married to widowed

6. Other changes

Table 2 shows the number of observations that fall into each of these six different types

of annual transitions in marital status, as well as the incidence of the annual income

mobility for each of these categories. The results that are stratified by gender show that

in excess of 98% of all observations correspond to those who observed no change in

marital status (that is, they remained married, widowed, divorced, separated or never-

married). There are only 91 observations of changes to widowhood status from being

married in the preceding period (24 for men, and 67 for women). The incidence of

income mobility is found to be rather high amongst those who experienced widowhood.

One remarkable result is that widowhood results in downward income mobility (15% or

more fall) for 31.3% of women, and the same event results produces upward income

mobility (15% or more rise) for an equally large proportion (29.9% of all women). This

shows that this event will have differential impact on income mobility depending upon

the other attributes of people concerned. To some extent, the multivariate analysis will

control of other attributes, and therefore make it obvious the net effect of the death of

partner on income for older women.

As opposed to this, a large proportion of men (about two-third) experience upward

income mobility when they become widower. Since income mobility is defined on the

basis of ‘equivalised’ income, the rise in income for older men may be a result of

smaller denominator (due to reduction in household size) and no or only a little loss in

numerator (no loss in sources of income as older women rely more often on the pension

rights of their partners).

4.2.2 Changes in living arrangements

Table 3 reports on income mobility differentiated on the basis of changes in the living

arrangements of older people.11 The variable derived here refers to situations in which

11 The variable used here is analogous to the one used in Scott et al. (2001).

21

older people may live with younger persons in the same household. This happens when

older people move to their children’s households for the purpose of providing and/or

receiving informal support. A comparable change is experienced when children rejoin

the household of older parents, mostly when they experience loss of employment or

break-up in marriage (referred to as the phenomenon of boomerang children in Scott et

al. (2001)). Thus, the category ‘independent living’ of older people is defined as when

they live alone as a single person household or live with their partner as a two-person

household. The category ‘living with others’ is defined as when they live with family

members other than their own partner. The results included in Table 3 show that - for

both men and women - changes in living arrangements are associated with greater

income mobility, whereas those who observed no changes in their living arrangements

are less likely to observe income mobility.

4.2.3 Changes in employment status

The next set of explanatory variables is derived from changes in the employment status

of older people. Most dynamic analysis of older people’s incomes concentrate on this

transition, and show that the transition to retirement is associated with fall in income

leading to poverty (see, e.g. Bardasi et al. (2001) and Gruber and Wise (2000)). Since

we define older people all those who have reached the statutory retirement age, our

work can be distinct from these other studies. We analyse the economic consequences

of the transition to retirement for those who had postponed retirement until after the

statutory retirement age, and therefore our analysis corresponds to a much smaller

number of observations of change in employment status.

The variable used here to record employment status is subjective, in that it is based on

the individuals’ own perception of their labour market status. The income mobility

results presented in Table 4 show that this variable is an important explanatory variable

for downward income mobility and therefore can serve as a good predictor of the

transition into retirement (see Bardasi et al. (2001) for other ways to operationalise the

exit from the labour market for older people).

22

The self-perceived labour force status given in the BHPS categorises older people into

four different types. Those who are:

1. Economically active12

2. Retired

3. In family care

4. Others (including long term sick)

Using this variable, the following seven categories of change the employment status,

viewed annually, are defined:

1. Remained economically active

2. Remained in retirement or in family care

3. Changed from economically active to retired or in family care

4. Changed from retired or in family care to economically active

5. Changed: other

We use these changes in employment status (5 categories) as the next set of explanatory

variables in our model, using individuals who remained in retirement or in family care

as the reference category. Table 4 shows the number of observations in each of these

employment transitions for older men and women. The results show that those who

made transitions from working life to retirement are considerably more likely to observe

a significant fall in income than others.

Since our income variable corresponds to household income, a likely source of income

mobility is the change in the partner’s employment status. Table 5 reports on income

mobility for those individuals whose partner may or may not have experienced a change

in the employment status. It is clear that there is a greater income mobility (change in

income in excess of 5%) for those individuals whose partner lost or quit employment.

12 This category includes not only those who consider themselves self-employed or employees but alsothose who categorise themselves as unemployed. The term ‘working’ is used interchangeably with theterm ‘economically active’ in the paper.

23

4.2.4 Components of income

Next, we introduce explanatory variables that show whether the composition of pension

income plays can be used as a predictor for income mobility experienced by older

people. Unlike events-based variables discussed above, income-based explanatory

variables are continuous and need to be contrived. For this purpose, we first calculate

the mean shares of different components of income to the total income of older people.

These shares for each component of income are then calculated on the basis of the

longitudinally averaged income for each person during the seven-year period. Benefit

income accounts for the major proportion of total income, representing about half of the

total income. Non-state pensions represent a little less than 30% of total income, while

labour income constitute slightly more than 12.5% and investment income constitute

about 9% of the total income.

Using this information, we define dummy variables that take the value 1 for an

individual when a component of income exceeds 1.5-times the share in the

longitudinally averaged income for the whole population. For instance, the dummy

variable that accounts for a larger-than-average share of benefit income takes the value

1 for all those individuals who derive more than 75% (1.5*50) of their income from

state benefits, and zero for all those individuals whose benefit income constitutes less

than 75% of their total income. Likewise, we define dummy variables for other

components of income (for non-state pensions, labour income and investment income).

Table 6 shows that a higher-than-average reliance on labour and investment income

makes it more likely that an individual experiences downward income mobility. This

result is consistent with the result that a less-than-average share from benefit income

and non-state pensions expose people to the risk of downward income mobility. A

somewhat unexpected result is that individuals who have more-than-average share from

benefit income observed a greater upward income mobility. This may be attributed to

the fact that people become entitled to additional disability and social care benefits as

they get older. The multivariate analysis of Section 5 will help us understand whether

other attributes associated with this subgroup contribute to higher upward income

mobility.

24

4.2.5 Age cohorts

Six age-cohorts are defined on the basis of individuals’ age in 1991. Table 7 shows how

the balanced sample of 950 individuals falls into these age-cohorts, as well as the

distinction in the incidence of income mobility between them. The results show that

there is hardly any difference amongst the age groups, except that the oldest group

(aged 85 or more) experience more often upward income mobility. This result can also

be explained by changes in benefit entitlement, and will be better understood when we

control for other attributes of this subgroup in the multivariate analysis of Section 5.

4.2.6 Income classes

The next set of explanatory variables are based on the income class in which the

individuals of the balanced sample ended up in 1991. Income classes are defined on the

basis of income quintiles (that is, by subdividing the population into five equal groups

depending on their income). It is clear from Table 8 that individuals who were in the

higher income quintiles were more likely to observe falling income than those in the

bottom income quintiles. It is not clear to what extent these results are affected by

measurement errors: these results do indicate that the rising income amongst bottom

income classes - and falling income amongst top income classes - may be a reflection of

improvement in income recording as people are enumerated in subsequent waves. The

multivariate results presented in Section 5 will test whether this result holds true when

we control for other attributes of these individuals.

5. Results of multivariate models

In this section, we report results by using the binomial and the multinomial logistic

regression model. Table 9, 10 and 11 present the results of the binomial logit model

(using 15% fall in income as the dependent variable), whereas Tables 12 and 13 report

the results of the multinomial logit model (using the five-fold mobility variable of

Tables 1 to 8). As mentioned above, all these results are produced using the balanced

panel of 950 individuals (300 men, 650 women) who were present in all seven waves of

the BHPS.

25

Table 9 reports the random-effect estimates for the binomial logit model. These

estimates takes into account the longitudinal structure of sample in use. It is important

to first look at the goodness-of-fit statistics given at the bottom of Table 9. It includes

the panel-level variance component which is parameterised as the log of the standard

deviation (labelled as lnsigma2u in the output). The standard deviation (sigma_u) is also

reported in the output, along with ρ (labelled as rho):

12

2

+=

u

u

σσρ

which is the proportion of the total variance contributed by the panel-level variance

component.

When 0=ρ , the panel-level variance component is unimportant and the panel

estimator is not different from the pooled-data estimator. The likelihood ratio test of this

is also reported at the bottom of Table 9. This test formally compares the pooled

estimator with the panel estimator. The test strongly rejects that there is any contribution

from the panel-level variance, and therefore the pooled estimator is as good as the

panel-data estimator. It is for this reason that we have used only the pooled-data

estimator in all subsequent results (Tables 10 to 12).

For brevity sake, we also do not report results of the specification search, and include

results for the most parsimonious specification only. The results are stratified for men

and women, since it was necessary to reject the model for the pooled sample of men and

women on the basis of the standard likelihood-ratio-test. One important feature of the

specification-search has been to test whether interaction terms between various

explanatory variables are statistically significant. The likelihood-ratio-tests show that

the interaction terms do not significantly contribute to the model’s goodness-of-the-fit.

26

The results for the multinomial logit model are also reported using the same

specification as used for the binomial logit model.13

5.1 Results of the binomial logit modelTable 10 and 11 give the results for the binomial logit model (for women and men,

respectively). The results are reported in terms of the odds-ratios that are associated

with different events and characteristics of older people. The standard error associated

with the odds ratio and the corresponding confidence interval are also included. Below

we analyse these results by working through the various subsets of explanatory

variables, and discuss how results differ between men and women.

5.1.1 Changes in marital status

The results show that women who become widows are more than twice as likely to

observe downward income mobility than those women who remain in marriage. In

contrast, men’s chances of downward income mobility are not affected by the event of

death of their spouse: there is no evidence that widowers have a significantly greater

chance of downward mobility than men who remain in marriage. This result is in line

with the observation that women rely more often on the pension rights of their husband

and therefore the death of husband may result in a partial or full loss of that income

source. The highest odds-ratio for men is observed for those who become divorced or

separated: they are more likely to observe downward income mobility than those who

remain married.

5.1.2 Changes in living arrangements

The results show that both men and women are about two-times more likely to observe

downward income mobility when their living arrangements change (that is, they start

living independently of family members other than their own partner, or they start living

with these other members of their family). As is obvious from the discussion in Scott et

13 The only difference is with respect to the age cohort variable used in the multinomial logit model. Forthis variable, we have put together the last two age categories in order to avoid too small a cell-size fordifferent mobility outcomes.

27

al. (2001), in Great Britain this subgroup consists mainly of those older people whose

children live with them for a short period and in the process raise the household income

temporarily.

If older people remain living with others, they are less likely to observe downward

income mobility (in comparison to those who remain living independently), although

this result is not highly statistically significant (at 10%-level for men, at 20%-level for

women).

5.1.3 Changes in employment status

The exit from the labour force stands out as the most hazardous event in old age,

although possibly a more predictable hazard when compared to widowhood. Women

and men who retire are about three times more likely to experience downward income

mobility than those who remain in retirement. The same odds of downward income

mobility are associated with the loss of employment for the partner. Women are more

than five times more likely to observe downward income mobility when their husband

exits the labour market, while men’s odds ratio is somewhat smaller: they are about

three times more likely to observe downward income mobility when their wife lost or

quit employment.

A note of caution is in order here. One must also take into account the standard errors

associated with these coefficients. As is obvious from Tables 10 and 11, the coefficient

for the transitions to retirement has a rather high standard error for both men and

women, and therefore they are subject to a rather wide confidence interval. For instance,

the odds-ratio of 5.5 for the loss of employment for husband has the confidence interval

ranging from 2.9 to 10.6. This coefficient is, therefore, not significantly different from

the coefficient associated with the transition to widowhood for women (equal to 2.3)

whose confidence interval ranges from 1.3 to 4.1. By analysing the point-estimates only,

we run the risk of portraying that some events or attributes of older people are more

hazardous than other events.

28

5.1.4 Components of income

The coefficients associated with the components of pension income are in line with

what we had expected from the descriptive results of Table 6. The greater-than-average

share of labour income increases the likelihood of downward income mobility: the

odds-ratio shows that men are about two-and-half-times as likely to observe downward

mobility, and women are about two times as likely to observe downward mobility when

they have higher-than-average share from earnings. This result may be attributed to the

fact that the number of hours worked may differ from one year to another for older

people.

The coefficient associated with the greater-than-average share of investment income is

close to one (i.e., the same likelihood as for the less-than-average share) and statistically

significant for women only. As mentioned in footnote 7, investment income variable is

collected as a banded variable in the BHPS, and the annual value is estimated as

approximately the mid-point of the band-width. One additional problem has been that

the bands used in recording this variable are not comparable longitudinally. This

additional problem, however, has been repaired for all results reported in the current

paper.

In contrast to the labour income share, the greater-than-average share of benefit income

(including basic state pensions) makes it less likely that older people experience

downward income mobility. This is to be expected since the state-benefits are by

definition flat in real terms. Likewise, the greater-than-average share of non-state

pensions (including occupational and personal pensions) makes older people less

vulnerable to downward income mobility. This last result is particularly strong for men,

while for women the result is significant only at 10%-level.

5.1.5 Age cohorts

One result that stands out from the subdivision on the basis of age cohorts is that women

are about 70% more likely to observe downward mobility in the oldest age cohorts

(aged 80 to 84, and aged 85 or more, in 1991) in comparison to the reference category

(women aged 65 to 69 in 1991). The result is surprising since this result holds true

29

despite the fact that we have controlled for the commonly-known hazardous events that

may make older women more vulnerable to experiencing downward income mobility

(such as widowhood and changes in living arrangements). We suspect, however, that

the results for the oldest age cohort may be affected by measurement errors in reporting

income that thus reducing longitudinal comparability of income variable.

5.1.6 Income classes

One other pattern that emerges from our results is that older women who belong to the

middle and higher income groups in 1991 are more likely to observe downward income

mobility than those who belong to the bottom quintile. The most significant coefficient

for women is observed in the second quintile group, who are about one-and-half times

more likely to observe downward income mobility than the bottom quintile group. For

men, none of these coefficients for income classes is significant.

5.1.7 Specific years during the period 1991 to 1997

We also find that women are more likely to observe downward mobility in the year

1993 to 1994 and in the year between 1996 and 1997. As yet, we are not aware of any

structural change during these specific years that may have affected older women’s

income. For men, all these coefficients are statistically insignificant.

5.2 Results of the multinomial logit modelTables 12 and 13 present the results of the multinomial logit model for older men and

women, respectively. The dependent variable takes five different outcomes:

1 = “Fall, more than 15%”,

2 = “Fall, 5 to 15%”,

3 = “No significant change (i.e., less than 5% change)”,

4 = “Rise 5 to 15%”, and

5 = “Rise, more than 15%”.

For all results, the middle category (“No significant change”) is used as the base-

category. In the discussion of results, a distinction between short-range and long-range

30

income mobility is also made. Short-range income mobility refers to annual change in

one’s own income of the magnitude 5 to 15% (categories 2 and 4), whereas long-range

income mobility refers to the change that exceeds 15% (categories 1 and 5).

The specification of the model remains the same as used in the binomial logit model.

We first analyse the results for long-range downward income mobility, followed by the

discussion of results for short-range downward income mobility. Next, we analyse - in

the same order - the results of upward income mobility.

5.2.1 Results for long-range downward income mobility

As can be expected, the results for long-range downward income mobility are almost

the same (for both men and women) as those obtained for the binomial logit model.

Women are more likely to experience downward income mobility when ‘widowed’ and

when they or their partner exit from the labour market. In contrast, a greater reliance on

non-state pensions will have the reverse effect: all women who have higher-than-

average reliance on non-state pensions are significantly less likely to observe downward

income mobility. Women whose living arrangements change are also more likely to

observe downward income mobility. Once again, we find that the oldest age cohort

(aged 80 or more in 1991) of women are more likely to observe downward mobility

than women who are aged 65 to 69 in 1991.

In line with the results of the binomial logit model, men are also more likely to observe

downward income mobility when they or their partner exit from the labour market. The

reliance on labour income for men makes them more vulnerable to long-range

downward income mobility, and a reverse effect is observed if men rely more often on

state-benefits.

5.2.2 Results for short-range downward income mobility

We look at the results of short-range downward income mobility by comparing them

with the results of long-range downward income mobility. Comparing the results for

women, we find that most coefficients that were significant for long-range downward

mobility are statistically insignificant for short-range downward mobility. The most

31

notable is the result that the event of the death of husband is no longer significant for

short-range downward income mobility. The coefficients associated with ‘living

arrangements changed’, and the coefficients for the higher-than-average share from

earnings and non-state pensions, are also statistically insignificant. Some exceptions

with the results for long-range downward income mobility are also observed: a greater-

than-average reliance on investment income makes older women about 30% more likely

to observe short-range downward income mobility.

5.2.3 Results for long-range upward income mobility

Changes in marital status: One rather surprising result comes from this very first

classification: women and men are also more likely to observe long-range upward

income mobility when their partner dies (in comparison to those who remain married).

This result is in line with the descriptive results of Table 2 in which we noted that those

who experienced death of their partner may observe either long-range downward or

long-range upward income mobility. This result can be attributed to the fact that those

who rely more often on the pension income of their partners are more likely to observe

downward income mobility, whereas those whose household income is not much

affected by the loss of their partner are more likely to observe upward income mobility.

Changes in living arrangements: No coefficient is significant for men in this set of

explanatory variables. The significant results for women show that those who remained

living with others and those who changed living arrangements are significantly more

likely to observe long-range upward income mobility. This rise in income can be

attributed to rise in income of other (mainly younger) members of the household.

Changes in employment status: Both men and women are more likely to observe long-

range upward income mobility when they come out of retirement and start working

again. Men are about five-times more likely to observe long-range upward mobility

when they return to the labour market (as compared to those who remain retired),

whereas women are almost three-times more likely to observe this increase. From the

descriptive results of Table 4, we note that this is a rather small subgroup and also had a

disproportionate incidence of upward income mobility.

32

Components of income: Those men and women who derive greater-than-average shares

from non-state pensions are about half as likely to observe long-range upward income

mobility in comparison to those who have less-than-average shares from non-state

pensions. The reliance of state benefits results in a greater chance of experiencing long-

range upward income mobility, which may be a result of changing benefit entitlements

during old age. This result holds true for both men and women.

Age cohorts: In comparison to women aged 65 to 69, older women (aged 70 to 74, and

75 or more) are more likely to observe long-range upward income mobility. This may

again be a reflection of rising benefits as the need for social and nursing care increases

with age. It may also be due to measurement errors that this age group of women may

have observed in reporting their income.

Income classes: Men who belong to the second and the fourth quintile group are

significantly less likely to observe long-range upward income mobility than women

who belong to the bottom income group. All results for women for this classification are

statistically insignificant, except that women in the top income group is more likely to

observe upward income mobility.

Specific years: For both men and women there is significantly less likelihood of

observing long-range upward income mobility during the year 1992 to 1993, the year

1993 and 1994 and 1996 and 1997.

5.2.4 Results for short-range upward income mobility

Most coefficients for short-range upward income mobility are statistically insignificant.

One significant result is observed for the greater-than-average share of investment

income: for both men and women the reliance on investment income results in greater

risk of short-range upward income mobility. Notably, the coefficient for the greater-

than-average share of investment income is statistically significant only for short-range

income mobility, in both upward and downward directions.

33

6. Conclusions

This paper extended the descriptive analyses of Zaidi et al. (2001) by carrying out

multivariate analyses of income mobility. A focus has been placed on identifying the

demographic and economic life-course events that increase the likelihood of income

mobility during old age. Since income mobility is defined as a discrete change in

income during an annual step, the choice of econometric models was restricted to

binomial and multinomial probability models. The limitations of the linear probability

model lead us to use the logit transformation of the dependent probability variable, and

therefore we made use of the binomial and the multinomial logistic regression model.

The results of the binomial probability model provide strong evidence that, for women,

changes in marital status to widowhood, changes in living arrangements, and changes in

own employment status and the employment status of the partner significantly increase

the chances of the hazardous downward income mobility in old age. In contrast, a

greater reliance on non-state pensions makes it significantly less likely that women

observe downward income mobility. The oldest age cohort of women (aged 80 or more

in 1991) is observed to experience a greater downward income mobility than women in

the age group 65 to 69 in 1991. It is suspected that this result may partly be attributed to

measurement errors in income reporting.

For men, their partner’s death significantly increases the likelihood of upward income

mobility. This may partly be due to the fact that men rely less often on pension rights

earned by their partner, whereas a larger part of women’s income may be lost at the

death of their husband. As for women, men also have lesser chances of experiencing

income mobility when they rely more often on state benefits and non-state pensions. An

exit from the labour market as well as partner’s exit from the labour market increases

the likelihood of downward income mobility for men as well.

Policy conclusions that can be drawn from this longitudinal analysis of incomes of older

people are that the old age social benefit system should be further strengthened to

provide a safeguard against downward income mobility of older women after the death

34

of their husband. Moreover, more opportunities towards occupational pensions will also

reduce the chances of downward income mobility for women.

35

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37

Figure 3.1: Annual income mobility between 1991 and 1997 for men

0.0

10.020.030.040.0

50.0

1991-1992

1992-1993

1993-1994

1994-1995

1995-1996

1996-1997

Period

Inci

denc

e of

inco

me

mob

ility

LR, fallSR, fallno changeSR, riseLR, rise

Figure 3.2: Annual income mobility between 1991 and 1997 for women

0.0

10.020.030.040.0

50.0

1991-1992

1992-1993

1993-1994

1994-1995

1995-1996

1996-1997

Period

Inci

denc

e of

inco

me

mob

ility

LR, fallSR, fallno changeSR, riseLR, rise

38

Tabl

e 1:

Ann

ual i

ncom

e m

obili

ty, s

trat

ified

on

the

basi

s of g

ende

r

No.

of

Fall

Fall

Non

-sig

n.R

ise

Ris

eTo

tal

Ann

ual t

ime-

peri

odca

ses

15%

+5-

15%

chan

ge5-

15%

15%

+

Men

1. 1

991

to 1

992

300

16.3

11.0

32.3

12.3

28.0

100.

02.

199

2 to

199

330

017

.010

.734

.016

.022

.310

0.0

3. 1

993

to 1

994

300

18.0

16.3

37.0

12.0

16.7

100.

04.

199

4 to

199

530

014

.013

.738

.714

.719

.010

0.0

5. 1

995

to 1

996

300

16.3

12.3

32.7

20.0

18.7

100.

06.

199

6 to

199

730

015

.720

.032

.315

.017

.010

0.0

Tot

al18

0016

.214

.034

.515

.020

.310

0.0

Wom

en1.

199

1 to

199

265

015

.513

.131

.115

.524

.810

0.0

2. 1

992

to 1

993

650

16.9

13.2

32.2

15.4

22.3

100.

03.

199

3 to

199

465

018

.815

.135

.512

.817

.810

0.0

4. 1

994

to 1

995

650

14.0

13.8

35.7

14.9

21.5

100.

05.

199

5 to

199

665

014

.612

.834

.615

.422

.610

0.0

6. 1

996

to 1

997

650

17.5

17.5

35.7

12.5

16.8

100.

0

Tot

al39

0016

.214

.334

.114

.421

.010

0.0

39

Tabl

e 2:

Inco

me

mob

ility

link

ed w

ith c

hang

es in

mar

ital s

tatu

s of o

lder

peo

ple,

stra

tifie

d by

gen

der

No.

of

Fall

Fall

Non

-sig

n.R

ise

Ris

eTo

tal

Cha

nge

in m

arita

l sta

tus

case

s15

%+

5-15

%ch

ange

5-15

%15

%+

Mal

e1.

Rem

aine

d a

coup

le1,

228

16.0

14.9

34.8

15.6

18.8

100.

02.

Rem

aine

d w

idow

ed37

617

.013

.331

.114

.424

.210

0.0

3. R

emai

ned

divo

rce/

sepa

rate

d69

20.3

11.6

39.1

11.6

17.4

100.

04.

Rem

aine

d ne

ver m

arrie

d10

314

.68.

746

.614

.615

.510

0.0

5. C

hang

ed to

wid

owho

od a

2412

.58.

38.

38.

362

.510

0.0

Tota

l1,

800

16.2

14.0

34.5

15.0

20.3

100.

0

Fem

ale

1. R

emai

ned

a co

uple

1,55

717

.014

.834

.114

.819

.310

0.0

2. R

emai

ned

wid

owed

1,75

114

.414

.434

.614

.721

.910

0.0

3. R

emai

ned

divo

rce/

sepa

rate

d17

817

.415

.233

.113

.520

.810

0.0

4. R

emai

ned

neve

r mar

ried

347

18.7

11.0

35.4

13.0

21.9

100.

05.

Cha

nged

to w

idow

hood

a 67

31.3

10.4

20.9

7.5

29.9

100.

0

Tota

l3,

900

16.2

14.3

34.1

14.4

21.0

100.

0

a. O

ther

cha

nges

in m

arita

l sta

tus (

e.g.

div

orce

d or

sepa

rate

d) a

re fe

w a

nd sp

urio

us, c

ause

d m

ainl

y by

mis

codi

ngs.

Thes

e ch

ange

s are

put

into

the

'rem

aine

d un

chan

ged'

cat

egor

y co

rres

pond

ing

to th

eir s

tatu

s in

the

base

yea

r.

40

Tabl

e 3:

Inco

me

mob

ility

link

ed w

ith c

hang

es in

livi

ng a

rran

gem

ents

of o

lder

peo

ple,

stra

tifie

d by

gen

der

No.

of

Fall

Fall

Non

-sig

n.R

ise

Ris

eTo

tal

Cha

nge

in li

ving

arr

ange

men

ts

case

s15

%+

5-15

%ch

ange

5-15

%15

%+

Men

1. R

emai

ned

livin

g in

depe

nden

tly a

1,64

216

.013

.934

.915

.020

.210

0.0

2. R

emai

ned

livin

g w

ith o

ther

s 13

116

.816

.031

.316

.019

.810

0.0

3. L

ivin

g ar

rang

emen

ts c

hang

ed27

29.6

7.4

25.9

7.4

29.6

100.

0

Tota

l1,

800

16.2

14.0

34.5

15.0

20.3

100.

0

Wom

en1.

Rem

aine

d liv

ing

inde

pend

ently

a 3,

535

15.6

14.5

34.9

14.4

20.5

100.

02.

Rem

aine

d liv

ing

with

oth

ers

302

20.2

11.6

27.8

15.2

25.2

100.

03.

Liv

ing

arra

ngem

ents

cha

nged

6333

.311

.119

.011

.125

.410

0.0

Tota

l3,

900

16.2

14.3

34.1

14.4

21.0

100.

0

a. T

he c

ateg

ory

inde

pend

ent l

ivin

g re

fers

to th

e si

tuat

ion

in w

hich

old

er p

eopl

e liv

e al

one

as a

sing

le p

erso

n ho

useh

old,

or li

ve w

ith th

eir p

artn

er a

s a tw

o-pe

rson

hou

seho

ld.

41

Tabl

e 4:

Inco

me

mob

ility

link

ed w

ith c

hang

es in

ow

n em

ploy

men

t, st

ratif

ied

by g

ende

r

No.

of

Fall

Fall

Non

-sig

n.R

ise

Ris

eTo

tal

Cha

nge

in e

mpl

oym

ent s

tatu

sca

ses

15%

+5-

15%

chan

ge5-

15%

15%

+

Men

1. R

emai

ned

econ

omic

ally

act

ive

116

21.6

10.3

34.5

14.7

19.0

100.

02.

Rem

aine

d re

tired

/ in

fam

ily c

are

1,59

815

.014

.135

.315

.320

.310

0.0

3. C

hang

ed: w

ork

to re

tirem

ent

3548

.622

.911

.48.

68.

610

0.0

4. C

hang

ed: r

et./f

am. c

are

to w

orka

1822

.216

.711

.116

.733

.310

0.0

5. C

hang

ed: o

ther

b 33

18.2

9.1

33.3

9.1

30.3

100.

0

Tota

l1,

800

16.2

14.0

34.5

15.0

20.3

100.

0

Wom

en1.

Rem

aine

d ec

onom

ical

ly a

ctiv

e19

619

.414

.327

.012

.227

.010

0.0

2. R

emai

ned

retir

ed o

r in

fam

ily c

are

3,06

215

.014

.335

.414

.920

.310

0.0

3. C

hang

ed: w

ork

to re

tirem

ent

7149

.322

.511

.37.

09.

910

0.0

4. C

hang

ed: r

et./f

amily

car

e to

wor

kin

3622

.28.

325

.08.

336

.110

0.0

5. C

hang

ed: o

ther

535

17.4

13.1

33.1

13.6

22.8

100.

0

Tota

l3,

900

16.2

14.3

34.1

14.4

21.0

100.

0

a. T

he c

ateg

ory

'Cha

nged

: ret

irem

ent/f

amily

car

e to

wor

k' is

rath

er sm

all f

or m

en, b

ut it

is n

ot m

erge

d w

ith o

ther

cate

gorie

s bec

ause

of i

ts si

gnifi

canc

e in

term

s of c

aptu

ring

the

cons

eque

nce

of re

turn

to w

ork

by o

lder

peo

ple.

b.

The

cat

egor

y 'C

hang

ed: o

ther

' ref

er m

ainl

y to

thos

e w

ho c

hang

e st

atus

bet

wee

n 'fa

mily

car

e' an

d 're

tirem

ent'.

42

Tabl

e 5:

Inco

me

mob

ility

link

ed w

ith c

hang

es in

par

tner

's e

mpl

oym

ent s

tatu

s, s

trat

ified

by

gend

er

No.

of

Fall

Fall

Non

-sig

n.R

ise

Ris

eTo

tal

Cha

nge

in p

artn

er's

em

ploy

men

tca

ses

15%

+5-

15%

chan

ge5-

15%

15%

+

Men

1. E

mpl

oym

ent s

tatu

s un

chan

ged

1,086

15.5

15.0

35.5

15.7

18.3

100.

02.

Los

t or q

uit e

mpl

oym

ent

134

20.1

11.9

30.6

14.2

23.1

100.

0

Tota

l1,220

16.0

14.7

34.9

15.6

18.9

100.

0

Wom

en1.

Em

ploy

men

t sta

tus

unch

ange

d1,346

16.6

14.5

34.6

15.0

19.3

100.

02.

Los

t or q

uit e

mpl

oym

ent

200

18.5

17.0

31.0

13.5

20.0

100.

0

Tota

l1,546

16.8

14.8

34.2

14.8

19.4

100.

0

43

Tabl

e 6:

Inco

me

mob

ility

link

ed w

ith th

e co

mpo

sitio

n of

tota

l pen

sion

inco

me,

stra

tifie

d by

gen

der

Shar

es o

f inc

ome

com

pone

nts

No.

of

Fall

Fall

Non

-sig

n.R

ise

Ris

eTo

tal

case

s15

%+

5-15

%ch

ange

5-15

%15

%+

Lab

our

inco

me

Men

1. L

ess t

han

1.5*

aver

age

shar

e15

3813

.914

.035

.915

.520

.710

0.0

2. M

ore

than

1.5

*ave

rage

shar

e26

229

.814

.126

.311

.817

.910

0.0

Wom

en1.

Les

s tha

n 1.

5*av

erag

e sh

are

3383

14.2

14.3

35.3

14.8

21.3

100.

02.

Mor

e th

an 1

.5*a

vera

ge sh

are

517

29.8

13.7

26.3

11.6

18.6

100.

0

Inve

stm

ent i

ncom

eM

en1.

Les

s tha

n 1.

5*av

erag

e sh

are

130 7

15.5

13.0

34.4

17.0

20.0

100.

02.

Mor

e th

an 1

.5*a

vera

ge sh

are

493

18.1

16.6

34.7

9.7

20.9

100.

0W

omen

1. L

ess t

han

1.5*

aver

age

shar

e29

7515

.613

.233

.915

.821

.410

0.0

2. M

ore

than

1.5

*ave

rage

shar

e92

518

.317

.534

.710

.119

.510

0.0

Ben

efit

inco

me

Men

1. L

ess t

han

1.5*

aver

age

shar

e12

2 619

.014

.435

.014

.417

.110

0.0

2. M

ore

than

1.5

*ave

rage

shar

e57

410

.313

.133

.416

.227

.010

0.0

Wom

en1.

Les

s tha

n 1.

5*av

erag

e sh

are

2255

18.8

15.5

34.2

13.3

18.2

100.

02.

Mor

e th

an 1

.5*a

vera

ge sh

are

1645

12.7

12.6

34.0

16.0

24.7

100.

0

Non

-sta

te p

ensi

ons

Men

1. L

ess t

han

1.5*

aver

age

shar

e13

2316

.313

.833

.613

.722

.610

0.0

2. M

ore

than

1.5

*ave

rage

shar

e47

715

.914

.736

.918

.713

.810

0.0

Wom

en1.

Les

s tha

n 1.

5*av

erag

e sh

are

3144

16.7

13.8

33.2

14.2

22.1

100.

02.

Mor

e th

an 1

.5*a

vera

ge sh

are

756

14.4

16.0

38.0

15.5

16.1

100.

0

44

Tabl

e 7:

Inco

me

mob

ility

link

ed w

ith 1

991

age

coho

rt, s

trat

ified

by

sex

Age

coh

ort i

n 19

91N

o. o

f Fa

llFa

llN

on-s

ign.

Ris

eR

ise

Tota

lca

ses

15%

+5-

15%

chan

ge5-

15%

15%

+M

en2.

65

to 6

9 a

738

17.5

13.3

36.2

13.7

19.4

100.

03.

70

to 7

454

014

.814

.336

.315

.219

.410

0.0

4. 7

5 to

79

336

17.3

14.3

28.0

19.0

21.4

100.

05.

80

to 8

415

011

.315

.339

.310

.723

.310

0.0

6. 8

5+36

22.2

16.7

13.9

19.4

27.8

100.

0

Tota

l1,

800

16.2

14.0

34.5

15.0

20.3

100.

0

Wom

en1.

60

to 6

497

218

.314

.532

.813

.720

.710

0.0

2. 6

5 to

69

1,20

614

.914

.836

.913

.719

.710

0.0

3. 7

0 to

74

822

15.7

13.3

34.3

15.9

20.8

100.

04.

75

to 7

952

814

.616

.332

.615

.521

.010

0.0

5. 8

0 to

84

318

18.6

11.9

30.2

13.8

25.5

100.

06.

85+

5418

.57.

431

.513

.029

.610

0.0

Tota

l3,

900

16.2

14.3

34.1

14.4

21.0

100.

0

a. O

lder

peo

ple

are

all t

hose

who

hav

e re

ache

d th

e st

atut

ory

retir

emen

t age

in 1

991

(60

for w

omen

, 65

for m

en).

Usi

ng th

is d

efin

ition

of o

lder

peo

ple,

the

first

age

gro

up (6

0 to

64)

is d

efin

ed o

nly

for w

omen

.

45

Tabl

e 3.

8: Ii

ncom

e m

obili

ty li

nked

with

inco

me,

stra

tifie

d by

sex

Inco

me

grou

p in

199

1N

o. o

f Fa

llFa

llN

on-s

ign.

Ris

eR

ise

Tota

lca

ses

15%

+5-

15%

chan

ge5-

15%

15%

+M

en1.

Bot

tom

one

-fifth

306

14.1

13.7

29.7

13.7

28.8

100.

02.

2nd

one

-fifth

330

12.4

13.9

39.1

13.3

21.2

100.

03.

3rd

one

-fifth

354

16.9

14.1

31.9

15.5

21.5

100.

04.

4th

one

-fifth

402

16.9

13.7

40.0

15.4

13.9

100.

05.

Top

one

-fifth

408

19.6

14.5

31.1

16.4

18.4

100.

0

Tota

l18

0016

.214

.034

.515

.020

.310

0.0

Wom

en1.

Bot

tom

one

-fifth

834

12.2

11.6

36.2

14.3

25.7

100.

02.

2nd

one

-fifth

810

13.7

13.8

37.4

13.7

21.4

100.

03.

3rd

one

-fifth

786

17.8

15.1

32.4

14.2

20.4

100.

04.

4th

one

-fifth

738

17.8

15.0

33.6

15.7

17.9

100.

05.

Top

one

-fifth

732

20.4

16.0

30.5

14.2

19.0

100.

0

Tota

l39

0016

.214

.334

.114

.421

.010

0.0

46

47

Table 9: Random-effects estimates of the binomial logit modelDependent variable: 15% change in income or not

Explanatory variables Coeff. Std error z P>|z| [95% conf. interval]

Female a

Male -0.054 0.091 -0.590 0.553 -0.232 0.124

1. Remained a couple a

2. Remained widowed 0.038 0.093 0.410 0.683 -0.145 0.2213. Remained divorce/separated 0.278 0.181 1.530 0.125 -0.078 0.6344. Remained never married 0.243 0.142 1.720 0.086 -0.035 0.5205. Changed to widowhood 0.627 0.253 2.470 0.013 0.130 1.123

1. Remained living independently a

2. Remained living with others -0.357 0.160 -2.220 0.026 -0.671 -0.0423. Living arrangements changed 0.727 0.242 3.000 0.003 0.252 1.201

1. Remained retired / in family care a

2. Remained working -0.285 0.180 -1.590 0.112 -0.637 0.0673. Changed: work to retirement 1.176 0.220 5.340 0.000 0.744 1.6074. Changed: ret./fam. care to work 0.037 0.354 0.110 0.916 -0.656 0.7315. Changed: other 0.128 0.125 1.020 0.308 -0.118 0.374

1. Employment status unchanged a

2. Lost or quit employment 1.496 0.261 5.730 0.000 0.984 2.007

1. 65 to 69 (reference category) a

2. 60 to 64 (only for women) -0.058 0.117 -0.490 0.622 -0.288 0.1723. 70 to 74 0.112 0.101 1.110 0.269 -0.086 0.3104. 75 to 79 0.158 0.118 1.340 0.180 -0.073 0.3905. 80 to 84 0.285 0.150 1.910 0.056 -0.008 0.5796. 85+ 0.504 0.291 1.730 0.083 -0.066 1.074

1. Higher-than-average share of earnings 0.838 0.144 5.820 0.000 0.556 1.1202. ………………...of investment income 0.202 0.089 2.260 0.024 0.027 0.3783. …………………….. of state benefits -0.298 0.108 -2.750 0.006 -0.510 -0.0854. ……………….. of non-state pensions -0.137 0.121 -1.130 0.259 -0.373 0.100

1. Change from 1991 to 1992 a

2. Change from 1992 to 1993 0.105 0.129 0.810 0.416 -0.147 0.3573. Change from 1993 to 1994 0.288 0.126 2.280 0.023 0.040 0.5354. Change from 1994 to 1995 -0.027 0.134 -0.200 0.841 -0.289 0.2355. Change from 1995 to 1996 0.070 0.132 0.530 0.595 -0.188 0.3286. Change from 1996 to 1997 0.256 0.129 1.980 0.047 0.003 0.509

1. Belonged to 1st income quintile in 1991 a

2. Belonged to 2nd income quintile in 1991 0.018 0.130 0.140 0.893 -0.237 0.2723. Belonged to 3rd income quintile in 1991 0.256 0.128 2.000 0.046 0.005 0.5074. Belonged to 4th income quintile in 1991 0.092 0.139 0.660 0.508 -0.181 0.3655. Belonged to 5th income quintile in 1991 0.221 0.149 1.480 0.139 -0.072 0.513

Constant -2.107 0.177 -11.930 0.000 -2.454 -1.761

ln(sigma2u) -14.000 306.908 -615.529 587.529

Sigma_u 0.001 0.140 2.2E-134 3.8E+127rho 0.000 0.000 4.8E-268 1

a. The reference category

Likelihood ratio test of rho=0: chibar2(01) = 0 Prob >= chibar2 = 1.000Random-effects logit Number of obs = 5700Group variable (i) : pid Number of groups = 950Wald chi2(30) = 240.94Log likelihood = -2400.2529 Prob > chi2 = 0.000

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Table 10: Results of binomial logit model: odds of downward income mobility for older women Dependent variable: 15% change in income or not

Explanatory variables Odds ratio Std err. z P>|z| [95% conf. interval]

1. Remained a couple a

2. Remained widowed 0.975 0.109 -0.230 0.818 0.783 1.2133. Remained divorce/separated 1.185 0.260 0.770 0.440 0.770 1.8224. Remained never married 1.364 0.224 1.890 0.058 0.989 1.8825. Changed to widowhood 2.339 0.666 2.980 0.003 1.339 4.088

1. Remained living independently a

2. Remained living with others 0.780 0.150 -1.290 0.196 0.536 1.1363. Living arrangements changed 2.190 0.633 2.710 0.007 1.243 3.859

1. Remained retired / in family care a

2. Remained working 0.765 0.175 -1.170 0.242 0.489 1.1983. Changed: work to retirement 3.484 0.945 4.600 0.000 2.047 5.9304. Changed: ret./fam. care to work 1.037 0.453 0.080 0.935 0.440 2.4405. Changed: other 1.124 0.148 0.890 0.374 0.869 1.454

1. Partner's employment status unchanged a

2. Partner lost or quit employment 5.526 1.826 5.170 0.000 2.892 10.559

1. 65 to 69 (reference category) a

2. 60 to 64 (only for women) 0.999 0.128 -0.010 0.995 0.778 1.2843. 70 to 74 1.230 0.160 1.600 0.110 0.954 1.5874. 75 to 79 1.145 0.177 0.880 0.378 0.847 1.5495. 80 to 84 1.686 0.297 2.970 0.003 1.194 2.3806. 85+ 1.748 0.643 1.520 0.129 0.850 3.593

1. Higher-than-average share of earnings 2.126 0.380 4.220 0.000 1.498 3.0182. ………………...of investment income 1.268 0.139 2.170 0.030 1.023 1.5723. …………………….. of state benefits 0.809 0.105 -1.630 0.103 0.627 1.0444. ……………….. of non-state pensions 0.819 0.126 -1.300 0.193 0.606 1.106

1. Change from 1991 to 1992 a

2. Change from 1992 to 1993 1.124 0.176 0.740 0.457 0.826 1.5273. Change from 1993 to 1994 1.398 0.215 2.180 0.029 1.035 1.8894. Change from 1994 to 1995 1.013 0.165 0.080 0.937 0.737 1.3935. Change from 1995 to 1996 1.062 0.171 0.370 0.710 0.774 1.4566. Change from 1996 to 1997 1.405 0.220 2.170 0.030 1.034 1.909

1. Belonged to 1st income quintile in 1991 a

2. Belonged to 2nd income quintile in 1991 1.097 0.167 0.600 0.546 0.813 1.4793. Belonged to 3rd income quintile in 1991 1.418 0.216 2.290 0.022 1.052 1.9124. Belonged to 4th income quintile in 1991 1.220 0.207 1.170 0.241 0.875 1.7025. Belonged to 5th income quintile in 1991 1.387 0.252 1.800 0.072 0.971 1.981

a. The reference categoryNumber of obs = 1800

Logit estimates Prob > chi2 = 0.0000Log likelihood = -751.97224 Psuedo R2 = 0.0525

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Table 11: Results of the binomial logit model: odds of downward income mobility for older men Dependent variable: 15% change in income or not

Explanatory variables Odds ratio Std err. z P>|z| [95% conf. interval]

1. Remained a couple a

2. Remained widowed 1.335 0.243 1.590 0.112 0.935 1.9063. Remained divorce/separated 1.765 0.589 1.700 0.089 0.918 3.3964. Remained never married 0.929 0.282 -0.240 0.810 0.512 1.6865. Changed to widowhood 0.843 0.543 -0.270 0.790 0.239 2.977

1. Remained living independently a

2. Remained living with others 0.609 0.184 -1.650 0.100 0.338 1.1003. Living arrangements changed 1.926 0.888 1.420 0.155 0.781 4.753

1. Remained retired / in family care a

2. Remained working 0.692 0.209 -1.220 0.222 0.383 1.2503. Changed: work to retirement 2.848 1.093 2.730 0.006 1.342 6.0434. Changed: ret./fam. care to work 1.062 0.649 0.100 0.921 0.321 3.5215. Changed: other 1.329 0.641 0.590 0.555 0.516 3.422

1. Partner's employment status unchanged a

2. Partner lost or quit employment 3.159 1.405 2.590 0.010 1.321 7.552

1. 65 to 69 (reference category) a

3. 70 to 74 0.975 0.161 -0.150 0.881 0.705 1.3494. 75 to 79 1.240 0.237 1.120 0.261 0.852 1.8035. 80 to 84 0.681 0.210 -1.250 0.213 0.373 1.2466. 85+ 1.206 0.593 0.380 0.703 0.460 3.160

1. Higher-than-average share of earnings 2.508 0.620 3.720 0.000 1.545 4.0712. ………………...of investment income 1.196 0.190 1.130 0.259 0.877 1.6323. …………………….. of state benefits 0.550 0.113 -2.910 0.004 0.368 0.8234. ……………….. of non-state pensions 0.976 0.195 -0.120 0.905 0.661 1.443

1. Change from 1991 to 1992 a

2. Change from 1992 to 1993 1.055 0.240 0.240 0.812 0.676 1.6473. Change from 1993 to 1994 1.172 0.264 0.700 0.481 0.754 1.8214. Change from 1994 to 1995 0.879 0.209 -0.540 0.588 0.552 1.4005. Change from 1995 to 1996 1.081 0.249 0.340 0.736 0.688 1.6976. Change from 1996 to 1997 1.080 0.250 0.330 0.741 0.685 1.701

1. Belonged to 1st income quintile in 1991 a

2. Belonged to 2nd income quintile in 1991 0.888 0.225 -0.470 0.639 0.541 1.4583. Belonged to 3rd income quintile in 1991 1.023 0.247 0.100 0.924 0.637 1.6434. Belonged to 4th income quintile in 1991 0.879 0.219 -0.520 0.606 0.539 1.4345. Belonged to 5th income quintile in 1991 0.949 0.255 -0.190 0.846 0.560 1.608

a. The reference category

Logit estimates (pooled estimater) No. of obs = 1800.000Log likelihood = -751.97 Prob > chi2 = 0.000

Psuedo R2 = 0.053

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Table 12: Results of multinomial logit model: relative-risk-ratio of income mobility forolder women

Dependent variable: five-categories income mobility variableFall Fall Rise Rise

Explanatory variables 15%+ 5-15% 5-15% 15%+

1. Remained a couple a

2. Remained widowed 0.93 1.02 0.86 0.943. Remained divorce/separated 1.18 1.17 0.85 1.004. Remained never married 1.26 0.70 * 0.79 1.095. Changed to widowhood 2.98 *** 1.16 0.73 2.22 **

1. Remained living independently a

2. Remained living with others 0.95 0.92 1.42 1.72 ***3. Living arrangements changed 3.15 *** 1.46 1.45 2.04 *

1. Remained retired / in family care a

2. Remained working 1.01 1.23 1.17 2.19 ***3. Changed: work to retirement 6.81 *** 4.90 *** 1.46 1.614. Changed: ret./fam. care to work 1.33 0.78 0.81 2.67 **5. Changed: other 1.11 0.91 0.88 1.09

1. Partner's employment status unchanged a

2. Partner lost or quit employment 5.91 *** 2.16 0.33 0.74

1. 65 to 69 (reference category) a

2. 60 to 69 1.03 0.97 1.09 1.093. 70 to 74 1.33 ** 1.02 1.25 1.154. 75 to 79 1.33 * 1.36 * 1.35 * 1.215. 80+ 2.02 *** 1.07 1.28 1.54 ***

1. Higher-than-average share of earnings 1.82 *** 1.01 0.75 0.67 *2. ………………...of investment income 1.22 1.32 ** 0.62 *** 0.913. …………………….. of state benefits 0.94 1.02 1.43 ** 1.36 **4. ……………….. of non-state pensions 0.71 ** 0.91 0.84 0.67 ***

1. Change from 1991 to 1992 a

2. Change from 1992 to 1993 1.07 0.99 0.94 0.873. Change from 1993 to 1994 1.17 1.04 0.68 ** 0.62 ***4. Change from 1994 to 1995 0.89 0.97 0.78 0.74 **5. Change from 1995 to 1996 0.96 0.90 0.84 0.846. Change from 1996 to 1997 1.19 1.23 0.64 *** 0.59 ***

1. Belonged to 1st income quintile in 1991 a

2. Belonged to 2nd income quintile in 1991 1.10 1.20 1.03 0.873. Belonged to 3rd income quintile in 1991 1.64 *** 1.57 *** 1.35 * 1.074. Belonged to 4th income quintile in 1991 1.46 ** 1.47 ** 1.74 *** 1.085. Belonged to 5th income quintile in 1991 1.86 *** 1.78 *** 1.93 *** 1.40 *

a. The reference categoryb. The reference outcome in the dependent variable is 'Non-significant change' in income.

Multinomial regression Number of obs. = 1800.00Log likelihood = -2645.576 LR chi2 (108) = 251.56

Prob > chi2 = 0.00Psuedo R2 = 0.0454

51

52

Table 13: Results of multinomial logit model: Relative-risk-ratio of income mobility forolder men

Dependent variable: five-categories income mobility variableFall Fall Rise Rise

Explanatory variables 15%+ 5-15% 5-15% 15%+

1. Remained a couple a

2. Remained widowed 1.40 * 0.92 0.94 1.283. Remained divorce/separated 1.41 0.72 0.59 0.594. Remained never married 0.70 0.39 ** 0.69 0.665. Changed to widowhood 3.27 2.21 2.04 11.25 ***

1. Remained living independently a

2. Remained living with others 0.67 1.38 1.26 0.983. Living arrangements changed 2.28 0.95 0.80 1.81

1. Remained retired / in family care a

2. Remained working 0.64 0.63 1.12 0.903. Changed: work to retirement 5.24 *** 4.51 ** 1.90 1.174. Changed: ret./fam. care to work 3.05 3.55 3.90 5.27 **5. Changed: other 1.40 0.85 0.70 1.46

1. Partner's employment status unchanged a

2. Partner lost or quit employment 3.32 ** 2.03 0.45 0.69

1. 65 to 69 (reference category) a

3. 70 to 74 0.99 1.07 1.12 0.944. 75 to 79 1.54 ** 1.39 1.78 *** 1.165. 80+ 0.77 1.13 0.95 0.93

1. Higher-than-average share of earnings 2.95 *** 1.63 1.05 1.342. ………………...of investment income 1.22 1.46 ** 0.62 ** 1.123. …………………….. of state benefits 0.63 ** 1.02 1.37 1.45 **4. ……………….. of non-state pensions 1.00 1.21 1.33 0.68 *

1. Change from 1991 to 1992 a

2. Change from 1992 to 1993 1.01 0.94 1.23 0.803. Change from 1993 to 1994 1.02 1.34 0.84 0.54 ***4. Change from 1994 to 1995 0.78 1.11 0.97 0.60 **5. Change from 1995 to 1996 1.10 1.17 1.54 * 0.736. Change from 1996 to 1997 1.12 1.96 *** 1.18 0.63 **

1. Belonged to 1st income quintile in 1991 a

2. Belonged to 2nd income quintile in 1991 0.77 0.85 0.87 0.64 **3. Belonged to 3rd income quintile in 1991 1.00 0.95 1.18 0.844. Belonged to 4th income quintile in 1991 0.71 0.71 0.97 0.51 ***5. Belonged to 5th income quintile in 1991 0.97 0.90 1.32 1.04

a. The reference categoryb. The reference outcome in the dependent variable is 'Non-significant change' in income.

Multinomial regression Number of obs. = 3900.00Log likelihood = -5843.305 LR chi2 (108) = 376.43

Prob > chi2 = 0.00Psuedo R2 = 0.0312

53