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Jørgen LauridsenInstitute of Public Health

Unit for Health EconomicsUniversity of Southern Denmark

jtl@sam.sdu.dk This presentation and the articles quoted can be found on:www.sam.sdu.dk/~jtl/SeminarOecon/Seminar2007.htm

Income-related inequality in health in Denmark

- why do retirement contribute so heavy?

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Background• Social inequality in health has been extensively

discussed in literature• For a recent contribution and overview of literature, see

van Doorslaer and Koolman (2004)• Comparative studies across European countries show

that social inequality in health is relatively high in Denmark

• They further reveal an extraordinary high concentration of social inequality in health among retired people

• It was suggested by van Doorslaer and Koolman (2004) that it might be due to ill health among early retired (as the early retirement scedule is fairly generous in Denmark and thus attract disabled people

• We (Christiansen and Lauridsen, 2007) wish to examine this suggestion

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Purpose of the investigation• To do a replication of the study of van Doorslaer

and Koolman (2004) using a different data set• To look deeper into the high concentration of

social inequality of health among retired• Specifically, to address whether it is related to

early retired people?• Some secondary gains from the study

– Address differences caused by different versions of survey question on self-assessed health

– Include Schwitzerland (never studied before)

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Outline of the presentation• Social inequality in health

– How to define• Define health?• Define social inequality?

– How to measure– How to relate to determinants

• The data– About SHARE data set– About the selected data

• Results• Concluding remarks

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Social inequality in health- how to define health?

• It is very easy to define health and to ask people about their health– But it is difficult to obtain reliable answers!

• Necessary to apply a definition which is simple and yet adequate for one’s purpose

• Self-assessed health (SAH) is a good compromise. However, two versions are in circulation:

Compared to another person of your own age, would you then characterize your health as:

Very good Excellent

Good Very good

Fair Good

Bad Fair

Very bad Poor

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Social inequality in health- how to quantify health?

• Frequently, a continuous measure for health is requested (an index for health)

• SAH is not suitable to this end – it is an ordered categorical variable

• A solution is to use external information– i.e. information from another study, where SAH as well as data for

construction of an index were collected for the same individuals so that an index value could be connected to the borderline between each categori of the SAH question

– Then these index values can be imported to the present study– van Doorslaer and Jones (2003) established correspondence

between SAH and a Health Utility Index (HUI) using Canadian data

• This correspondence is used by most subsequent studies

– Lauridsen et al. (2004) established correspondence between SAH and a fifteen dimensional health index (15D) using Finnish data

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HUI mapping of SAH• HUI was constructed as a sum of the respondent’s rating of

self-assessed health on eight dimensions:– Vision, hearing, speech, ambulation, dexterity (behændighed),

emotion, cognition, pain– See Humphries and van Doorslaer (2000) on HUI

• For each level of SAH, the average of the HUI scores for respondents selecting this SAH level can be calculated– For some purposes, these averages can simply replace the SAH

levels

• Alternatively, the borderlines between SAH categories can be calculated– First, calculate the percentiles of sample in each SAH categori– Next, read off the corresponding percentiles in the HUI

distribution• For some purposes, these borderlines are necessary

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HUI mapping of SAH (2)

SAH percentiles mapped to HUI percentiles

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HUI mapping of SAH (3)Mapping of SAH to HUI (Doorslaer and Jones, 2003)

SAH level HUI average HUI lower bound HUI upper bound

Poor 0.557 0 0.428

Fair 0.758 0.428 0.756

Good 0.876 0.756 0.897

Very good 0.923 0.897 0.947

Excellent 0.945 0.947 1

Notice that – per construction - the averages of observed scores need not be restricted to lie within percentile bounds!This is violated for Poor and Fair health categories – shows problems of lacking correspondence between HUI and SAH

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So, we know how to define and measure health

• (with some reservation, okay!) • Now we need do define social inequality in health• Social inequality is related to many thing

– Social heritage– Education– Professional status– Income– Etc…..

• Studies traditionally equalizes income and social status (can be discussed!)

• So we define social inequality in health to be income-related inequality in health

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Measuring income-related inequality in health

-Hvis de 20% rigeste (fattigste) besidder hver 20% af sundheden er den lige fordelt (den grønne linje)-Hvis de 20% rigeste besidder mere end 20% af sundheden og de fattigste 20% besidder mindre end 20% er fordelingen ulige (den røde kurve)-Samlet ulighed måles ved to gange arealet mellem kurven og linjen (= index fra 0 til 100%): Koncentrationsindexet (vi regner det normalt fra 0 til 1)-En del af arealet er deterministisk (=kontrollerbart); bestemt af sociodemografiske karakteristika; kan påvirkes med målrettede indsatser

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On interpretation of the concentration index (CI)

• The concentration index can be interpreted as the extent to which health concentrates among the wealthy

• Or – correspondingly – the extent to which wealth concentrates among the healthy

• This endogeneity is yet another problem studied in literature

• A concentration index can be calculated for any phenomenon – not just health

• Education (concentration of education among the wealthy)

• Retirement (concentration of wealth among the not-retired)

• ……..

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How to calculate the CI• Can be conveniently done by a regression

approach (regress standardised health on standardised income ranks):

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Which regression to apply??• Health is measured using SAH – this is a categorical

variable• One solution could be to replace SAH values with average

HUI score and use OLS– But this is inefficient – we replace individual health with average

health for those with same SAH value – we kill the individual variation

• An alternative approach could be ordered logit regression with SAH values– Use the restriction that thresholds between SAH levels are known

(=lower and upper HUI bounds) – this is more efficient than unrestricted ordered logit regression

• This special case of ordered logit regression is called interval regression– a standard routine in STATA

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Determinants of income-related health inequality

• A part of the concentration index is deterministic, i.e., systematically related to socio-demographic characteristics

• The basic device for quantifying these contributions is by a regression of health (y) on determinants (x1, …., xK):

The predicted CI is simply the sum, and the contribution from each variable can be expressed in percentage of this sum.

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The SHARE data• We used the SHARE data, which consists of

samples from 10 countries• Survey of Health, Aging and Retirement in Europe

• Austria, Germany, Sweden, Netherlands, Spain, Italy, France, Denmark, Greece, Switzerland

• A later release also include Belgium and Israel

• The data are freely available from

www.share-project.org– you just need to registrate to get a password

– and to aknowledge SHARE if your results are published

• The data cover respondents 50 years and elder

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The SHARE data (2)

Data are organised in modules (=data files in SPSS format)Each module contains a wealth of questions (the easiest overview is to use the SPSS files, where data definitions etc. are provided)The data can be used for many types of studies ….

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Preparation of data• SAH was present in SHARE, but in the two

versions (see slide 5), each allocated at random to half of the survey

• We included a dummy variable for those who got the second version

• We applied the HUI scaling of SAH

• The explanatory variables listed on next slide…

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Preparation of data (2)

• Notice that we divide Retired in – Young Retired (64 and less)– Old retired (65 and older)

• In order to investigate the proposition of van Doorslaer and Koolman (2004)– i.e. that early retirement might be the major

contributor for DK

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Results

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For the case of DK, 42.9% of predictable income-related health inequality comes from old retired.For DK, this is the largest contribution.The contribution from old retired is much larger in DK than other countries.Contribution from young retired almost ignorable for the case of DK.

Where do the heavy contribution for old retired stems from? – two sources: - Old retired may have worse health than population - Old retired may be economically disfavoured as compared to population

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Health of young retired worse than for old-retiredBut the difference is not extreme – actually it is worse in Sweden

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Conclusions• Similar to van Doorslaer and Koolman (2004) we find a an

extraordinarily high contribution from Retirement to income-related health inequality

• We can partly confirm that health is relatively poor among young retired– But the situation is not very much worse than for other countries– So this cannot explain the difference between DK and other

countries

• Rather, we refer to the extraordinary high CI for old-age retired– This is the major reason for the high contribution from retired to

income-related health inequality

• Thus, the reason for the high contribution is poor economic conditions of the old-age retired – rather than ill health among the early-age retired