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Neighbourhood inequality as a health risk: empirical evidence from

Swedish registers

Preliminary version

Authors:

Sören Edvinsson, Centre for Population Studies, Umeå University

Erling Häggström Lundevaller, Department of Statistics, Umeå University

Nawi Ng, Department of Public Health and Clinical Medicine, Umeå University

Gunnar Malmberg, Department of Geography and Economic History, Umeå University

Introduction

We live in a world where life expectancy is rapidly increasing, leading to an ageing

population. There are however still large differences between different parts of the world as

well as within countries. These differences represent a mix of spatial and social factors that

are intertwined which makes it difficult to sort out the exact mechanisms and pathways that

result in different health outcomes. Many individual traits, being risk factors for mortality or

disease, have been identified. Demographic characteristics such as age, sex and marital status

are obviously vital for understanding individual health risks, but research has documented the

important role of individual social traits such as social status, wealth and income, social

networks and education. During recent decades there is a growing interest in context variables

as well. Health is not determined only by the pure individual characteristics but also the

individual in relation to his or her context. This becomes apparent when considering the role

of physical geography where environmental conditions such as climate, sanitary conditions,

pollution, and population density have been identified as potential health risks from ancient

times. There are however other context variables related to the social environment where we

live that can be of importance. During the last decades the possible negative impact of

economic inequality on health has been studied and analysed, first and foremost by Wilkinson

and developed further by him and his colleague Pickett (Wilkinson 1996; Wilkinson and

Pickett 2007, 2008, 2009). They argue that economic inequality leads to stress that in its turn

causes bad health. Their studies have inspired into a lively academic debate where their

results have been widely discussed and disputed. A large number of studies have been

performed and we now know more about the issue, but there is however still no consensus

among researchers in this matter (Deaton 2003; De Mayo 2012; Lynch 2004; Mackenbach

2002; Subramanian and Kawachi 2004; Wagstaff and van Doerslaer 2000; Wilkinson and

Pickett 2007). The debate continues.

To resolve the issue, good data and a clear idea about possible pathways is required. Some of

the central questions concerns time and space. The time dimension is obviously very

important in epidemiological studies. In order to identify causality, cross-sectional studies are

problematic, in particular when it comes to disease and mortality. It takes days, months, years

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or decades before a disease develops or death occurs and health risks are consequently related

to conditions earlier in life. Data on previous exposure is therefore crucial. The disease can in

itself lead to economic problems, something that moreover may force people to move to

poorer neighbourhoods. Most studies have been unable to analyse the question from a life

course perspective where previous conditions are considered in the analysis. In those studies

where retrospective data have been included, it is very rare to have data both on individual

and context level.

Questions about space concerns the level or levels where we expect to find the negative

effects of inequality. Wilkinson and Pickett (2009) have mainly analysed national data. Most

studies focusing on geographical divisions within countries have less often found such an

association (Lynch 2004). According to Wilkinson and Pickett (2007), we ought not to find it

on more local levels because of the effects of social segregation. This also connects to studies

of neighbourhood effects, where living in poor environments have been found to be an

additional health risk in addition to individual economic conditions (Diez Roux 2010). It

could thus even be positive to live in more unequal neighbourhoods if the wealth in these

areas is connected to an improved infrastructure and a safer environment that can be to the

advantage also for the poor.

In this paper, we explore the impact on mortality of income inequality in municipalities

among elderly 65-84 years in the years 2004-2009 The study utilizes a unique Swedish

longitudinal micro-data covering the entire Swedish population for the period 1960 – 2009. A

previous cross-sectional multi-level analysis has demonstrated an association between income

inequality and all-cause mortality in municipalities after controlling for mean income level and

personal income. These analyses are now complemented by longitudinal analyses of long-

term residential histories with exposure to equal/unequal municipalities and neighbourhoods

and the long-term impact on mortality. Our aim is to investigate the association between

mortality and income inequality at place of residence at different time lags, controlling for

individual characteristics during the life course. We can thus estimate the effects of previous

living conditions, both individual and at society level and investigate if the connections are

stronger at certain time lags.

Theoretical background

A multitude of studies demonstrate that socio-economic conditions have strong impact on

health, even in economically advantaged welfare societies. One aspect of this is the potential

health risk of living in regions and nations characterized by socio-economic inequality as

suggested by Wilkinson and Pickett (2009), a question that has led to a lively public and

academic debate. They argue that inequality in society has an independent effect on health

and adds to increasing health differentials. Previous research provides so far an ambiguous

picture, partly because the majority of studies are either based on cross-sectional data, fixed

geographical units, or small study population resulting in lack of statistical power to detect

any possible effects. One of the larger problems is how to distinguish between possible

pathways (Subramanian and Kawachi 2004; Wagstaff and van Doorslaer 2002). An identified

association can equally well be caused by a so called concavity effect due the marginal effect

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on health being stronger for the least wealthy (Rodgers 1979). Several studies have shown a

multilinear association between income and health which would be in line with that

explanation (Fritzell et al 2004; Mackenbach 2005). Cross-sectional analysis can neither

demonstrate this effect nor consider the selective effects of migration and bad health and the

consequences this has on residential segregation. Possible selection effects are especially

serious when it comes to lower geographical units. In the following, we discuss theoretical

considerations first related to aggregation level and then we continue with the possible

implications of time lags. Finally, we discuss the role of age on this issue, if we can expect

particular effects on people in old age.

Aggregation level

It is possible or even probable that the mechanisms from spatial context to health outcomes

differ between levels of aggregation. The association between income inequality and different

health outcomes have primarily been demonstrated on national or regional levels representing

large and diversified populations. Wilkinson and Pickett (2006) argue that the negative effects

of income inequality primarily become evident on national or possibly higher sub-national

levels, while social residential segregation leads to different associations at local levels.

Rostila et al (2012, p 1092) state “… that income inequalities within small areas like

neighbourhoods are likely to capture a conceptually different topic, a marker of heterogeneity

and a socially mixed area”.

Most studies of neighbourhood and health focus on the possible impact of living in certain

areas. Neighbourhood and community usually “… refer to a person’s immediate residential

environment, which is hypothesized to have both material and social characteristics related to

health” (Diez Roux 2001, p 1784). The concept has however often been used quite loose and

imprecise in the actual studies. One explanation is that researchers have been obliged to use

available administrative records. Many studies have found that living in deprived

neighbourhoods is associated with negative health outcomes, but the results have varied

between studies and the associations have usually been modest compared to those observed

from individual characteristics (Diez Roux 2010, 127). These associations can be related to

either “… (1) effects of aggregate outcomes at the group-level on individual-level outcomes

…; (2) contextual effects of group composition; and (3) environmental effects.” (Diez Roux

2010, 128). There are many mechanisms that can cause the negative effects of living in

deprived areas, such as less social coherence, lack of social capital, problems with

infrastructure, criminality and safe environments. The majority of the studies have however

not considered income inequality in itself.

The association between income inequality and health problems is thus more visible at higher

geographical levels such as countries and American states. When it comes to lower levels,

positive associations are rare (after controlling for economic level in the units and analysed

with multilevel models). Studies related to the Nordic welfare countries demonstrate the same

diversity. The Norwegian case (Dahl et al 2006) is interesting because the authors find

significant association on economic region level (corresponding to what we call local labour

market region), while this could not be demonstrated at municipality level. This has however

not been the case in most of the Swedish cases. At even lower levels, positive associations

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have been difficult to establish. In a Danish study of parishes in Copenhagen — a rather local

level — individual income was shown to have strong impact on mortality while income

inequality had no significant impact (Osler 2002). Rostila (2012) compared the association at

municipality level with that at neighbourhood level. In the latter case, no detrimental effect

was found. A moderate effect of large income inequality increasing self-rated health was

identified, but the association disappeared when spending on social goods was included.

Rostila et al thus attributes the association to spending of the local government. Stjärne et al

(2006) investigated the effect of income inequality on myocardial infarcts in neighbourhoods

in Stockholm County. They found that living in a low-income neighbourhood increased the

risk in addition to the individual social characteristics. The association with socio-economic

heterogeneity had no or negligible effect on the risks of infarcts. The authors conclude that

their results have limited application on the theories of income inequality and population

health that mainly concerns higher aggregation levels. Most other Swedish studies have

neither found any effect at the municipality level (Gjerdtham et al 2004; Henriksson et al

2006) Edvinsson et al (2012) on the other hand found that income inequality was, although

moderately, associated with mortality among the elderly in 2006. Higher Gini levels increased

the mortality risks.

Hypothetically, the effects as well as the pathways are different at different levels. If we

believe that psycho-social conditions represent the main pathway, we would in principle

expect an association not only at higher geographical levels but also in neighbourhoods. The

closest environment is the most obvious place where social comparisons are made in every-

day life. This effect may however be diluted or counteracted by other effects, such as being a

part of the general status level in the neighbourhood or the quality of its physical environment

(neighbours with money attract service and investments). An environment with face-to-face

contacts may further social cohesion even though income inequality may be large. However,

neighbourhoods can also be an effect of inequality in the way that segregation often leads to

economically, socially and ethnically homogenous areas. This complicates the possibility to

isolate the effect of social comparison.

Political decisions can have implications on the material conditions affecting health,

something that relates to a neomaterial explanation (more equal societies being more prone to

invest in infrastructure and welfare benefitting all). The lowest political level is the

municipality. All municipalities are independent with different priorities leading to different

political decisions. This can be how much is spent on infrastructure, how much equal access

to amenities and public assistance is prioritized and how the public sector is organized

(privately or publicly organized). One important question is how much this differs between

municipalities. In the Norwegian case (Dahl et al 2006) argue that national laws make

differences between municipalities small, something that according to them explain why they

find no effect at that level. Rostila et al (2012) on the other hand find a positive association

between income inequality and low self-rated health at municipality level within the county of

Stockholm, something they attribute to differences in spending on social goods. At first sight,

Norway and Sweden ought not be that different, both being welfare states with a basically

common political culture, but there may have appeared decisive differences in the role of

private versus public service during the last decades. The Norwegian study took place in the

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1990’s while the Swedish by Rostila et al refers to the early 2000’s and the one by Edvinsson

et al investigates 2006. It is thus possible that there is a temporal effect reflecting political

changes over time and perhaps between countries, although we do not consider this to be that

probable.

Time lag

There is certainly a time lag between income inequality and health status, something the

majority of previous studies have been unable to consider. Only a few studies have

incorporated the possibility of a time lag. Lynch (2004) listed only four such studies. The

study by Blakely et al (2000) on self-rated health in American states indicate that income

inequality of a time lag of 15 years may be stronger associated than income inequality

measured contemporaneously. They were however unable to control for possible changes of

residence over time and own previous income. Subramanian and Kawachi (2004, p 86)

investigated the association between health and income inequality in American states with 5-,

15- and 25 lag periods and found the strongest effects of the 15 years lag. Their study has the

same restrictions as the one of Blakely et al. In our case we are able control for the individual

place of residence at different times and the experienced income inequalities at these times.

By including information on previous conditions in the life course we can thus better analyse

the associations between income inequality and subsequent mortality. A couple of studies

have used aggregate data with a short time lag. Rostila et al (2012) has included the Gini

levels with a two-year time lag. There is however no confirmation of their residences at the

year for Gini and neither is there any time lag for individual income. Henriksson et al (2004)

have not time lag, but they have a control that they resided in the same municipality five years

before.

There is in fact no study that has incorporated own previous income in the analysis,

something that ought to be crucial since possible effects of illness and disease may decrease

individual income levels. This is perhaps less serious for the retired population.

Old age

A possible assumption is that the effect of income inequality differs between age groups. The

effect of inequality is here studied on a population that mainly is retired, i.e. older than 65

years. This age group includes both those that still are quite healthy and being recently retired

and those that have reached ages when health problems become apparent. We can thus look at

a population being vulnerable in different degrees.

The classical work of Rowntree on the poverty cycle demonstrated how conditions influence

families differently through the life course. Families with young couples usually do not have

good economy, but when children grow up and become independent, the parents get

wealthier. When they reach old age, the conditions usually become poorer again. This has in

history made the elderly a vulnerable group, even though many elderly in present-day high-

income countries have built up much wealth even though their income is lower than those in

working age. The “cumulative advantage theory” implies that advantages and disadvantages

will be accumulated during life, thus leading to increasing differences in old age. (Dannefer,

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Merton). The effect of income inequality along the life course on health is probably not the

same as the one for economic standard in itself, although the basic biological results of aging

of course have impact on both. When we age we become more and more vulnerable. A

possible effect could be that inequality becomes more discernible when vulnerability is high.

An alternative interpretation states that economic and social conditions become less and less

important as we age and will thus influence health to a lesser extent when we retire in

particular. Our health in old age is more dependent on our general biological conditions.

There can also be a selection effect in the oldest ages, where frail people have survived to a

larger extent among wealthy groups (Vaupel and others). If it is the latter case, we would

expect the association between income inequality and health to be strongest in middle age

(during working life) to diminish as we get older. In the highest age groups, the association

can be expected to be the smallest.

A further argument is that, if status and psycho-social conditions are important, then these

aspects become less important in old age. If low income inequality leads to social cohesion

and strong social capital in a society, this ought to lead to better health. If the association

between income inequality and health is explained by social comparison, then the effect may

be weaker in the higher age groups. Social comparison is important due to that we define our

status from it. Status is important throughout all life but it is possible that it is stronger during

the work-active years of life, while status becomes less important when people are retired.

This is an empirical question that is difficult to have an opinion of. It is possible that we,

when we retire, can feel more relaxed when it comes to our status positions.

Most previous studies have not considered income inequality in old age. Most studies have

focused on the working-active population and when elderly have been included they are

usually either as part of a general measure such as life expectancy or not analysed separately.

In a cross-country comparison with aggregate data, Torre and Myrskylä have however

analysed different age groups. They find the strongest connection between income inequality

and mortality in childhood and up to age 50 for men. The association was however missing in

old age. They declare that the different results or the non-positive results can be a

consequence of mixing different age groups with different associations. In a previous study by

Edvinsson et al, an association between income inequalities at municipality level for the age

group 65-74 could be established.

Data

To test the research questions, we utilize the Linnaeus database, an anonymized longitudinal

database maintained at the Centre for Population Studies at Umeå University. The Linnaeus

database consists of register data from the Statistics Sweden’s LISA database (a longitudinal

database for health insurance and labour market studies) for the period 1986-2009 and

population censuses 1960, 1970 and 1980. LISA provides us with the basic demographic and

economic information. The Linnaeus database also includes Cause of Death Registry and

Patient Registry from National Board of Health and Welfare for the period 1990-2006 that

however is not utilized in this study. It contains rich information about socio-economic

conditions for each individual as well as the location of place of residence in one-hundred

meter squares. The different registers have been linked to each other, enabling us to combine

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information from the different sources. The unique data available at Umeå University allows

us to follow individuals’ exposure to residential areas by income inequality, control for

various confounders on individual and neighbourhood level and further to follow up the long-

term health outcome. Using digitalized information of place of residence for all residents in

Sweden for the period 1970 – 2009, we are able to aggregate data to any spatial unit and

characterize any such unit by for instance level of income or education.

The outcome in this study is all-cause mortality. The explanatory variables are either

communal or individual and are identified at different points in time. Two economic

communal variables are included. All individuals are attributed to the communal variables in

their municipality of residence. For each municipality we have calculated mean income as a

measure of the general economic standard in the place of residence for the studied population.

The other communal variable is the Gini index that represents the level of economic

inequality in the municipality. Gini is a commonly used measure for inequality. It is based on

the distribution of income or wealth in a society. If there is no difference in income levels, i.e.

total equality, the Gini index would be 0. If the complete opposite distribution was at hand,

that one person had all the income, then Gini would be 1. In our case we have based our

economic measures on disposable household income, equalized for family size. This variable

is however not available for the 1970 census. Instead we have used individual total net

income. This variable is thus not completely comparable with those of a later situation, but it

is can still be used as a measure for the basic economic level and levels of income inequality

at that point in time.

One important question is the correct age group to be used when measuring inequality. We

have used the income in working age. This is justified by the idea that it is a characteristic of

the local community. It can however be discussed what measure is best depending on

assumed pathway. If it is related to social cohesion or/and matters of local spending on

infrastructure, than income among the working-active population is probably the most

suitable. But if it is social comparison, then maybe the conditions in one’s own age group is

the relevant one. We compare ourselves with those in the same age. The individual disposable

income is however taken from the age studied, although previous conditions may be of

importance (see above on time lags). Furthermore, there is a strong correlation between

income in working age and in old age as pensions are based on previous income.

At the individual level and at the different points of time for observation, we include basic

demographic variables, such as sex, marital status and birth year. Birth year controls for the

age of observed persons. Individual economic condition is measured by disposable equalized

household income, i.e. the same variable that is used for the calculation of the communal

variables. In this way, we can better consider the possible pathways between health and

income, if the economic conditions determine the health or if it works in the opposite

direction. We can perhaps not conclusively identify causality, but we can get a bit on the road.

Finally, we have also constructed variables on changes in residence, in order to control for the

effects of residential segregation.

Here follows a list of variables included in the full model.

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Persons income (used for construction of other variables) For 1986 - 2004= disposable

income individualized from family. For 1970 = persons net income.

Relink = persons income divided by the mean persons income for a person borne the same

year, with the same sex in the same municipality.

Partner = if the person has a partner or not (married or cohabitants with children)

Kon = gender

Gini”year” = gini first calculated from persons income at municipality level. Then divided in

“low”, “medium”, “high” with low denoting the 33 % of the municipalities with lowest gini

and high the 33 % highest gini and medium in-between.

Mean = mean personal income in municipality ordered in categories as gini.

Municipalities

Municipality borders can change over time so to construct pseudo-municipalities that are

consistent over time municipalities that that have a large percentage of people in common

between 2 consecutive time period are merged. Years used are 1970, 1980, 1986, 1992, 1998,

2004 and the percentage used are 25% except 1970-1980 where 35% is used. The result is 268

municipalities.

Methods

The study focuses on mortality during the years 2005-2009 for all Swedish citizens resident in

the country at the last of December 2004 and aged 65-84 years, thus being born 1920-1939.

They are followed from that date until the first of the events death, emigration or end of

registration (i.e. last of December 2009). The time for follow-up is thus five years. In our

analysis, we investigate associations between the degree of equality (measured by Gini

coefficient) in the residential areas where the person has lived and the health outcome, in this

case all-cause mortality but with the future possibility to analyse cause-specific mortality. The

explanatory variables are defined at the year of baseline — 2004 — but also at time lags of

six (1998), 12 (1992) and 18 (1986) years. We also check for individual and communal

economic conditions in the census of 1970. In the analyses we have controlled for the effect

of various potential confounders at individual level including income, education, sex, marital

status, etc. We have also used aggregated contextual data, which allows us to identify and

separate contextual effects from those stemming from individual characteristics, such as mean

income in the municipality in order to control for the local economic level. The nested

structure of the data, with variables ranging from individual level to regional level, calls for

multilevel analysis. We apply multilevel survival analysis to model the association between

income inequalities in subsequent all-cause mortality (Yang et al.).

The Gini coefficient and mean income is likely to have a complicated relationship with

mortality and they are also highly related to each other. The approach of dividing each of

them into three groups and also using the interaction effects between them as we do here is a

robust way of handling a situation like this.

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We first define a basic model with no time lag, and then add the associations with different

time lags. Then we apply a special study on movers.

Results

Income and income inequality in Swedish municipalities 1970-2004

Sweden has experienced an increased economic inequality during the last decades as shown in

Figure 1. While the Gini coefficient was very low in the early 1980’s — probably among the

lowest in the world — there has been a continuous increase in the levels of inequality from

the late 1980’s and onwards. Inequality levels increased considerably the years after the

economic crisis in the early 1990’s. The increase slowed down during the early 2000 to once

again increase rapidly from about 2005. This is not the right place to discuss the causes for the

increasing inequality, but it is obviously strongly connected to economic development,

employment rates and political decisions. The economic crisis led to large reforms in

economic politics.

Figure 1. Income inequality in Sweden 1976-2009, Gini index.

Source: Statistics Sweden

Is the process of increasing inequality at the national level a result of economic segregation

between municipalities, i.e. are the differences between municipalities increasing while the

income inequalities within municipalities are stable? The following figures present the Gini

coefficients and the mean incomes at the different years. Mean income has been converted to

0,000

0,050

0,100

0,150

0,200

0,250

0,300

0,350

Year

19

78

19

81

19

83

19

85

19

87

19

89

19

91

19

93

19

95

19

97

19

99

20

01

20

03

20

05

20

07

20

09

Gini

Gini

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the consumer price index, based on the figures of 2004. Please notice that the income data for

1970 is not comparable to those of the other years. We should thus not compare the levels

presented in 1970 to the later ones. It is however of interest to compare the income pattern for

which the 1970 data can be used.

Figure 2a-e. Income inequality (Gini) and mean income (1,000 SEK, adjusted to price level in

2004) in Swedish municipalities 1970-2004. Based on equalized disposable income for

working-age population except for 1970.

a. 1970

b. 1986

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c. 1992

d. 1998

12

e. 2004

When we split up the development within Sweden into the 268 constructed municipalities, we

can first establish that mean disposable income has increased substantially during the period

1986-2009 and that the increase is apparent in all municipalities. Although the dispersion of

income levels has not changed that much, some outliers with very high incomes have

appeared. These are mainly situated around Stockholm. The figures indicate that the

inequality is not only caused by segregation between municipalities, although the curve is not

as steep as the one for mean disposable income.

There has also been a large change in Gini levels in the municipalities. If we disregard 1970,

where we lack comparable figures, the levels have increased considerably. In 1986 and 1992,

the Gini levels in the municipalities were fairly compressed and in most cases very low. In

1986 it stretched between 0.17 and 0.26 with most municipalities below 0.23, i.e. very low

levels, and in 1992 it stretched between 0.18 and 0.32 with the majority still below 0.23.

Swedish municipalities were consequently characterized by high income equality. During the

more recent periods following the economic crisis in the early 1990’s, Gini levels increased.

No longer do we find any municipality having below 0.2 and in 2004 almost all municipalities

had levels higher than 0.25. Several municipalities now have fairly high inequality levels.

The transformed economy has furthermore led to a radical change in the relation between

Gini levels and mean income. In the early periods (1970 and 1986), the association was

basically negative but with a tendency towards being u-shaped. Poorer municipalities had

larger inequality while Gini decreased with higher mean incomes. The exception is a couple

of municipalities with high income and high Gini. In the later periods, first observed in 1992

and becoming more apparent for each period, the association is completely positive. The

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richer municipalities are the most unequal ones and the equal municipalities are those with

low mean income.

Thus, the pattern has changed from a society with income equality within municipalities and

fairly small differences in income levels and Gini levels between municipalities to one with

large(r) income inequality within and between municipalities. During recent years, Swedes

are now living in environments characterized by substantially higher levels of inequality.

There is a large consistency over time in the relative position of mean income (see Table 1).

Thus, there have been no large transpositions in the geographical patterns in these variables.

Gini levels have however been more sensitive to the economic changes and a larger

proportion of municipalities have changed position (Table 2).

Table 1a. Mean disposable income in Swedish municipalities 1986 and 2004 in three categories.

2004

High Medium Low

1986

High 65 21 5

Medium 23 44 24

Low 3 26 59

Table 1b. Mean disposable income in Swedish municipalities 1998 and 2004 in three categories.

2004

High Medium Low

1998

High 73 16 0

Medium 16 69 19

Low 0 19 69

Table 2a. Income inequality (Gini index) in Swedish municipalities 1986 and 2004 in three categories.

2004

High Medium Low

1986

High 47 29 13

Medium 29 33 29

Low 13 29 46

Table 2b. Income inequality (Gini index) in Swedish municipalities 1996 and 2004 in three categories.

2004

High Medium Low

1998

High 70 18 1

Medium 17 21 53

Low 2 20 66

Income inequality and health from a life course perspective

After having described the development of the economic conditions on a general level, we

now continue with the analysis of the association between Gini and health outcomes. First of

all, we look at the association at baseline, i.e. 2004 with follow-up 2005-2009. Then we

investigate the possible effects at different time lags, still controlling for the conditions at

baseline. Finally we make a special study on the movers, to see how migration to

environments with different settings influenced survival chances.

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First we look at the association with the economic conditions of 2004, i.e. at baseline. The

analysis has been performed with a relevant set of control variables, but here we present only

the results for the different levels of mean income and income inequality. Table 3 shows that

mortality is negatively associated with higher incomes as expected. It also shows that at each

income level, mortality is higher in the more equal municipalities, thus not confirming our

previous findings of higher risks associated with higher inequality levels.

Table 3. Gini levels, mean disposable income for year 2004 and death risks at age 65+ for the

years 2005-2009. Swedish municipalities. Mean income 2004

Low Medium High

Gini 2004

Low 0.1369 0.0926 0.0878

Medium 0.1188 0.0702 0.0431

High 0.1292 0.0531 0

Table 4 presents the effect of adding the different time lags. The estimates have been

extracted from the full model. The different time lags are all interrelated which complicates

the interpretation of the results. All Swedish municipalities have been categorized according

to the levels of income inequality (Gini) and mean disposable income. For the longest time

lags, the associations are not conclusive but some interesting results can be noticed. For the

longest time lag, the one stretching back to 1970, the highest mortality is found for those

living in areas that were characterised both by high mean income and large inequality. A

rather surprising circumstance is that having lived in higher income settings in 1970 was not

clearly associated with improved survival in old age, rather the opposite. Within each mean

income category, the direction of the association between survival and income inequality

differs however. For the municipalities with median income, successively lower levels of

income inequality is protective, but this pattern is less clear in the other income categories

where median levels of inequality is associated with the best survival. For this long time lag,

there is an indication that lower income inequality had a positive effect on mortality later in

life.

The 1986 economic conditions do not present any coherent pattern. At this time lag (18

years), the advantage of more equality is found in the low-income municipalities, while large

income inequalities are good for those living in high-income places. There is however a

tendency towards lower municipal mean income being associated with better survival.

For the two shortest time lags, 6 and 12 years, the associations mainly point in one direction

in the different municipal income categories. Higher inequality levels improve the survival

chances. The influence of level of mean income is however less clear. The conditions at

baseline in 2004 are in line with that of the two closest time lags. Higher Gini level is

associated with lower mortality. At this point in time, there is also a clear tendency that higher

mean income in the municipality of residence is beneficial for the survival of the elderly.

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Table 4: Gini levels, mean disposable income at different time lags and death risks at age 65+

for the years 2005-2009. Swedish municipalities.

Mean income 1970

Low Medium High

Low -0,0013 -0,0625 -0,036

Gini index 1970 Medium -0,0426 -0,0335 -0,0609

High -0,0138 -0,0147 0

Mean disposable income 1986

Low Medium High

Low -0,0399 0,0361 0,0605

Gini index 1986 Medium -0,0134 0,0307 0,0626

High 0,0055 0,0291 0

Mean disposable income1992

Low Medium High

Low 0,0415 -0,0088 0,0127

Gini index 1992 Medium -0,0105 -0,0034 0,0251

High -0,0066 -0,0275 0

Mean disposable income 1998

Low Medium High

Low 0,0752 0,0524 0,0399

Gini index 1998 Medium 0,0058 0,0371 0,022

High -0,0043 -0,0303 0

Mean disposable income 2004

Low Medium High

Low 0,1153 0,0899 0,0753

Gini index 2004 Medium 0,1058 0,0657 0,0326

High 0,1112 0,0393 0

As mentioned in the introduction, we can be misled by identifying people to places by cross-

sections. Income and health can make people move to certain places, a process of segregation.

In our data we can analyse the effect of changes in the character of municipalities due to

migration. Table 5 and 6 present movers and the effect of migration to destinations of

different character. The results are extracted from the full model. For the time being, we

analyse only the age group 65-74 years and focus on the two longest time lags where we

found a possible positive effect of income equality. At this stage, we have not included

information on possible changes in relative income.

It is obvious that moving upwards when it comes to municipalities was beneficial for survival.

Those having both their origin and their residence in 2004 in high-income areas had the best

survival, but we also see that moving to wealthier positioned places (from low to medium or

high and from medium to high) improved the chances for a longer life. In correspondence

with this result, we also find that moving downwards (from high to low or medium and from

medium to low) increased mortality.

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The changes in inequality levels present a similar pattern. Moving from equal to more unequal

municipalities, (i.e from low to medium or high and from medium to high) resulted in lower

mortality and migrations from wealthier areas (high to low or medium and medium to low)

led to higher mortality.

Table 5: Migrations between 1970 and 2004 and death risks at age 65-74

Mean income 2004

Low Medium High

low 0,0568 -0,0375 0,016

Mean income 1970 med 0,1437 0,0936 0,0179

high 0,0703 0,0622 0

Gini index 2004

Low Medium High

low 0,09 -0,049 -0,024

Gini index 1970 med -0,0254 -0,0087 -0,0637

high 0,072 0,0571 0

Table 6: Migrations between 1986 and 2004 and survival at age 65-74

Mean income 2004

Low Medium High

low 0,0531 -0,0081 -0,331

Mean income 1986 med 0,0564 -0,0074 -0,0117

high 0,0842 0,0617 0

Gini index 2004

Low Medium High

low 0,1275 0,0872 0,0524

Gini index 1986 med 0,1053 0,0677 0,0420

high 0,0921 0,0501 0

Discussion

In this paper we have been able to analyse the possible effect on survival in old age of income

levels and income inequality at a fairly local level, Swedish municipalities. It is a complicated

undertaking to analyse this issue from a longitudinal perspective, something that require

access to data of high quality and correct modelling of the analysis. These are the first

preliminary results from the longitudinal approach and the analysis can be sharpened and

deepened. The results do however lead to some tentative suggestions and conclusions.

First of all, we do not find any strong support for negative effects of income inequality when

it comes to the shorter time lags. A high median income is however positively associated with

improved survival. Our results rather indicate that there are neighbourhood effects of richer

municipalities being beneficial for all. This is partly deviating to our previous results where

17

we found a moderate effect also at municipality level. The different methods have led to

somewhat different results, something we plan to investigate further.

When it comes to the longer time lags, there are some indications that income inequality may

have had some harmful effects. The results are however not that conclusive, but they does

however stimulate further studies on this. We can furthermore show that the direction of

migrations had a strong effect on survival, which illustrates the importance to analyse the

issue from a life course perspective.

One complication in our study is that the time lags we have used correspond to times when

income inequality was much smaller. Even at the latest time lag (1998), the Gini levels were

considerably smaller than is the case during the last decade. Wilkinson as well as others has

suggested that there is a threshold where no association would be expected below a certain

level. A Gini coefficient of 0.3 has been discussed. It is therefore possible that inequality at

the time lags were too small to have any substantial effect. Still, it is at the longer time lags

that we find indications of a negative effect of income inequality.

A further complication is that the income data is not completely perfect. In areas where

income levels could vary between different years depending on the sale of capital-intensive

goods, for example a house, both the mean income levels and the inequality measure would

be distorted. These effects may have increased during the last decades when a larger

proportion of the wealth comes from income of capital. Imperfect income data is however a

problem in most studies, but we believe that our data is stronger than those for most other

studies.

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Extra:

Gini levels, mean disposable income for year 2004 and risk of death 2005-2009. Swedish

municipalities. Example person: woman, borne 1920 + with partner + and with mean income.

Mean income 2004

Low Medium High

Gini 2004 Low 0.3509 0.3468 0.3492

Medium 0.3492 0.3359 0.3321

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High 0.3398 0.3299 0.3204