Unemployment and health in macro-social analysis

Sot. Sci. Med. Vol. 16, pp. 1903to 1917,1982 Printed in Great Britain

UNEMPLOYMENT

0277-95361821221903-15$03.~/0 Pergamon Press Ltd

AND HEALTH ANALYSIS

IN MACRO-SOCIAL

INOEB~RG P. SPRUIT Institute of Social Medicine, Leiden State University, P.O. Box 9605, 2300 RC Leiden, The Netherlands Abstract-The hypothesis that unemployment is related to bad health arises from a general acceptance of the relationship between living standards and longevity. TO confirm the hypothesis three analytical statements have to be true: (a) an increase of prosperity leads to a decrease of mortality; (b) a decrease of prosperity leads to an increase of mortality; (c) people with the relatively lowest prosperity, in this case the unemployed, have the relatively highest mortality and so are the least healthy. For this purpose important types of macro-social research are described and the results are interpreted, briefly evaluated and discussed. Research comparing differences in life-expectancy in rich and poor countries, shows rough differences; Research applying a new method, diffusionalist comparison, shows that increased prosperity does not infinitely lead to improvement of life expectancy; Research on economic fluctuations and mortality is the only type of research attempting to describe a process, not a state of affairs; Research on socio-economic categories within countries (classes. neighborhoods) shows differences in mortality and morbidity. but not why these differences exist; Research on the relationship between a population’s body height (as a health indicator) and poorness/ unemployment gives rise to more differentiated conclusions, but neither provides final answers; Some small retrospective researches with divergent methodologies and using small hospital samples give different results.

INTRODUCTION

(1) an increase in prosperity leads to a decrease in mortality, (2) a decrease in prosperity leads to an increase in mortality, and (3) those with the relatively least prosperity, in this case the unemployed, show. the relatively highest mortality and are, therefore, the least healthy.

There is general agreement in Europe and North America that an improved standard of living has contributed strongly to a decline in mortality, particulary due to advances in nutrition. sanitation, education, and medical care. However, it is not certain that economic decline or instability leads to an increase in mortality, although it has been assumed that the unemployed or those subjected to prolonged industrial instability (frequent job change for example) have a shorter life-expectancy than others. In recent years many countries have been increasingly confronted with economic instability and steadily rising unemployment and it cannot be expected that this trend will be arrested in the near future. This makes research on the implications of such conditions for health highly opportune. The question discussed in this article is how far macro-social analysis contributes to an understanding of the potential relation between unemployment and health. The first part will consist of a description of important recent macro-social approaches. Then. in the second part a comparison of the results of these approaches will be given, as well as an overview of important current criticism and finally some conclusions.

At the macro-social level, the association between prosperity and longevity has been investigated on the basis of comparisons of both national and international data. In many European countries mortality rates, which are related to a long-term improvement

THE MAIS MACRO-SOCIAL APPROACHES; A DESCRIPTION

of economic conditions. have decreased since the middle of the eighteenth century [2]. In the same period, there was a constant reverse relationship

Macro-social studies [l] would support the supposition that unemployment is related to ill-health if:

Separate analyses have produced results which can be used to test if these statements are valid although none of them was concerned with health as such, but with two indicators of health: life-expectancy and mortality. Moreover, interesting studies have been done on body-height as an indicator of health. These studies are so comparable to those on mortality and life-expectancy that they will also be considered.

RELATIONSHIP BETWEEN PROSPERITY LIFE-EXPECTANCY:

PERSISTING

AND

POSITIVE

EFFECTS?

between socio-economic status and mortality: the higher the income. occupational. and educational levels. the lower the mortality rate and the longer the 1903

ISGEBORG

1904

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hood. So Philipsen’s questions ;IK hi&l) rels\ imt. He began b) esaminmg the problem formulated b! Galton [S] as earl! as 1888. Galton pointed out that countries should not be automaticall!, considered as comparable entities. because it was not estubhshed that they were historicall> independent of each other. Following this theoretical argument. Philipsen argued that it was incorrect to interpret relations m a functional-causal wax. when a diffilsional one cannot be excluded. Cultural anthropolog! uses this approach to explain the occurrence of relations b! historical diffusionism. Diffuslonism IS the spread of cultural characteristics and the incorporation of these ‘borrowed’ features into another culture. According to this theory. the question needing research is whether two interrelated factors or cultural characteristics together are spreading or have spread o\‘er J gi1.e region. Thus. a theoretical aspect of Galton’s problem means that identified relations are explained unilaterally on a functional basis. leaving aside whether they can be explained in terms of historical diffusion. So the question becomes: .*, when societies diKer in level of economic development and in life-expectant). does lower prosperity functionally, cause shorter lifeexpectancy. or is it better explamed by historicaldiffusional covariance‘?” [9]. After all. societies do borrow cultural traits from each other incorporating them into their own. A phenomenon like present prosperity cannot have been invented in all countries independently, any more than the idea of calculating life-expectancy and of investi_pating the relations between prosperity and longevity. However. an increase in longevity is not somethine that countries can borrow. as a cultural characteristic. from another country. This brings Philipsen to an important methodological aspect of Galton’s problem. The hypothesis that prosperity and longevity are interrelated has been tested by comparisons internationally. But if it is not certain that countries, as cultural entities. are independent of each other. a reliable sample cannot be obtained. The absence of such independence between entities raises the statistical chance of confirmation.

life-expectancy [3]. A comparison of the morfor individual countries differing widely as to the level of prosperity. shows large and easily observable differences. But. although the existence of an association between prosperity and longevity is not disputed. questions have recently been raised. for instance by Hagen [4] who took the GNP per capita of the population as a measure of prosperity. In his opinion. growth of the GNP initially had a positive influence on health. but at a certain level of production this effect was nullified by a growing increase of the negative external effects of production. damage to the natural environment. to the quality of labour. and to lifestyle. which radically changed with respect to nutrition. housing. sesuality. and even in the realm of genetics. As an illustration. Fig. 1 shows the relation between GNP and life expectancy at birth for j 1 poor and rich countries. This shows a steep rise in lifeexpectancy in the poorer countries, climbing from SO to $1500 GNP. In poor countries infectious and nutritional diseases are the main causes of death whereas in the richer countries the main causes are accidents. degenerative diseases, and neoplasms. Despite this different mortality pattern, there are also differences in longevity between the modern industrialized countries, although in the graph these are not very impressive. This divergence cannot be entirely explained by differences in the use of health-care provisions. so that other factors must also be involved [S]. Many investigators believe that these too must be linked to prosperity but again questions have been raised. For example. Philipsen [6] has wondered whether increasing properity can produce positive effects indefinitely. This seems to him unlikely, and he has therefore queried whether an association still exists between prosperity and longevity in the prosperous Europe of 1980. Powles [7] has pointed out that during the last 20 years the decrease in mortality which is supposed to accompany industrialization has virtually ceased. The life-expectancy of women is still increasing, but that of men has remained the same in some countries or even decreased, especially after adultmean

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Unemployment

and health in macro-social analysis

Aside from this objection, we can also point to the risk of contamination, because in this case the empirical and the theoretical relationships are not separated conceptually. Philipsen measured the diffusion sensitivity of variables on the basis of geographical gradients and neighbour scores (the mean score of neighbouring countries graded according to the length of their border) [lo]. This enabled him to demonstrate that the prosperity level. measured from the mean per capita income of the population, is indeed highly diffusion-sensitive. Longevity turned out not to be diffusion-sensitive. This means that the correlation between longevity and prosperity does not disappear when diffusion effects are tested, particularly for lifeexpectancy from birth, if we wish to interpret this correlation functionally. When, however: “. the correlation between a country’s prosperity level and mean life expectancy is less strong than the correlation between its prosperity level and the score of the neighbouring country. as well as the correlation between a country’s mean life-expectancy and the score of the neighbouring country, it cannot be concluded that there is a diffusional relation” [I 11. So, only when the correlation between a country’s mean life expectancy and the country’s prosperity level is the strongest of all possible correlations may there be said to be a functional relation. If these relations are tested for prosperous Europe of 1980. we find that the life-expectancy of men and women older than forty is sensitive to diffusion effects, and the same holds for males at birth. A significant correlation between life-expectancy and mean income is found only for girls (at birth). In Philipsen’s opinion. this at least shows that increasing prosperity cannot provide indefinitely extrapolated favourable effects and that for contemporary Europe differences in prosperity alone cannot explain these differences in longevity completely. If. however. it is thought that the relation may be contaminated. either because the logical and empirical relations overlap or. in terms of the diffusion theory, that the composition of the entities in question is too heterogeneous with respect to the hypothesis [12], even this should not be concluded because we do not know whether. or to what extent. prosperity determines differences in longevity. But Philipsen’s approach does not make it possible to answer the question of the extent. and gives only a partial answer to the question of whether there is such a relation. When we return to Statement 1. which says that an increase in prosperity leads to a decrease in mortality, we find no doubts expressed in recent literature about the functional relation between prosperity and longevity in the past. However. macro-social analysis based on the comparison cf countries raises. on this level, well-founded doubts as to whether such a correlation holds for the present. Furthermore. it is not known where the borderline should be taken. Hagen is rather vague on this point. The scatter in his graph shows. for instance. no pronounced differences between U.S. S2000 and SXOOOGPN and a rather broad cluster below S500 and between SSOO and $1500 GNP. Philipsen’s approach is new and the calculations have been done for only one year. It would be interesting to repeat them retrospectIveI> for several years.

1905

Next, let us see what can be said about Statement 2; a decrease in prosperity leads to an increase in mortality. ECONOMIC FLUCTUATIONS

AND MORTALITY

Even if it is accepted that an extended increase in prosperity leads to an extended decrease in mortality, it may not be concluded without investigation that each decrease in prosperity will lead, immediately or later, to an increase in mortality. The latter, however, has also been studied, usually by an investigation of the mortality rate in relation to economic fluctuations, as, for example, indicated by the unemployment rate. Eyer’s time-series analysis, but especially the models devised by Brenner, have been applied to various countries. In view of Brenner’s many publications and the attention given them by advisory bodies concerned with policy we shall deal with this work in some detail. Brenner’s multivariate time-series analysis led him to conclude that there is an appreciable degree of correlation between trends in national unemployment rates and mortality in various European countries and the United States of America. In one of his first reports on this subject [13] the main finding was that a 1% increase in unemployment in the United States persisting over a period of 6 years, “. . . has been associated . . with increases of approximately 36,887 total deaths”. In earlier and also later work Brenner found statistical correlations between unemployment rates and mortality for cardiovascular diseases [ 143, suicide, homicide, and liver cirrhosis [lS], as well as between unemployment rates and numbers of first admission to psychiatric institutions [16]. He also analyzed the correlation between mortality rates and economic instability for various age groups and both sexes [ 171. In all cases he found a significant correlation. Figure 3 shows the correlation between child mortality and unemployment rate. Fig. 2 that between the age group of 50-54 years and unemployment rate. To facilitate reading, the unemployment curve is inverted. In both figures a lag of 2 years was applied; in other words, the indicated increase of the mortaiity occurs 2 years after the increase in the unemployment rate. Figures 4 and 5 show the correlation between unemployment and mortality for men aged 45-49 years and women in the same age group, calculated with different timelags (2 and 3 years, respectively). The graphs show the agreement between the actual mortality figures and those predicted by Brenner on the basis of calculations [18]. In 1979. Brenner found the same trends for England and Wales as reported earlier for the United States [19]: a secular decline in mortality rate associated with the long-term trend of economic growth, whereas fluctuations in mortality rates can be largely explained by both short economic recessions and short periods of economic growth. For these calculations he took an explanatory model in which he used four main components: (i) the smooth and expogrowth, trend of long-term economic nential measured by the real per capita income of the population: (ii) the unemployment rate: (iii) ‘rapid economic

1906

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Fig. 2. Graphic analysis of the relation between the total mortality rate of the population aged SO-54* and the employment ratet, United States. 1907-1976. *Long term trends subtracted from the mortality series. tEmployment rate = Inverted Unemployment Rate. Employment rate is lagged 2 years. Scaled difference: both series are scaled for viewing such that the greatest amplitude from the arithmetic mean of each series, which is set equal to zero, has been normalized to within the range of +4 if positive, or -4 if negative. Total mortality rate; --- employment rate. (Source: Brenner [18].) growth’, measured by deviations from the long-term exponential trend of the real per capita income of the population and annual changes in the growth rate of this income; and (iv) government expenditure for welfare as a percentage of the total government expenditure. On the basis of these data, Brenner calculated the mortality rates to be expected if these factors do indeed predict mortality, and then compared them with the actual mortality rates. The results for England and Wales are shown in Fig. 6. Thus, Brenner considers both unemployment and unusually rapid economic growth rates as indicators of economic instability, and concludes that such instability leads to an increase in mortality. He also distinguished a pattern in this mortality: the lagged effect of unemployment varies according to the time span and the specific causes of death under analysis. Homicide and suicide, for instance, show increases

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within a year after a rise of unemployment. Cardiovascular mortality does not start to rise until 2-3 years after an increase in unemployment, and this

effect persists for 10-15 years. However, Brenner uses the term unemployment in two ways in his theory: on the one hand, unemployment rate is an indicator of economic conditions and on the other hand he is of the opinion that the higher mortality seen after economic recession occurs in the population of the unemployed or re-employed. The findings that under these conditions both sexes and all age categories show increased mortality he explains by family involvement in unemployment. For his explanation he distinguishes two classic series of economic loss accompanied by stress, and therefore by morbidity and mortality. According to one of these, the population is exposed to prolonged economic instability and long-lasting insecurity which reach crisis levels during recessions. Under these conditions the groups at risk are found among industrial workers whose employment is highly dependent on cyclic changes in the economy. Semi-skilled and unskilled workers are also at risk because they are the

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and health in macro-social analysis

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Fig. 6. Age-adjusted total mortality rates based per 1000 home population. Actual (-) and expected (-----) values based on fit of composite economic change model including long term and rapid economic growth variables, unemployment rates over O-IO years and ‘A Government welfare expenditures, England and Wales, 1936-1976. (Source: Brenner [19].)

first to lose their jobs and the last to get them back.

The population thus exposed to cyclic unemployment shows a higher mortality rate which appears 2-3 years after the recession that induces the process of morbidity. The second series is related to changes in the structure of a national economy caused by technological innovations in certain sectors (e.g. automation, microprocessors). The risk of becoming redundant rises for a number of categories of employees, and specialization makes it difficult to find other jobs with the same status. Furthermore, a new job can mean a radical change in an individual’s social environment. Brenner believes that under downward social mobility. morbidity begins with recession or unemployment with the likelihood of mortality increasing sharply within 2-3 years. He argues that for this category the next source of severe stress develops when they are becoming reintegrated into the economy, that is, in the period of rapid economic growth following a recession. For these individuals the probability of mortality increases during periods of rapid economic growth. These marked economic changes influence mortality trends interactively and additively. The effect of one of these changes cannot be estimated without statistical controls for the other two. When unemployment and rapid economic growth lead to an ‘increase’ in mortality, they do so because they inhibit the long-term decline of the mortality rates. The recent high level of unemployment in Great Britain, for example, is reflected not by an observable increase in mortality rate but by a retardation of the decline in that rate ordinarily seen. At this point the question raised by Philipsen again becomes relevant: can increased prosperity be expected to have positive effects indefinitely? Brenner of course speaks of statistical expectation. and perhaps Philipsen’s question could only be answered if the secular trend were to rise or drop more slowly and/or the steady inhibition of the decline associated with peaks of economic instability, as found by Brenner, showed a different pattern or none at all.

1907

According to Brenner, the mechanism by which unemployment and rapid economic growth retard the secular trend of the decline of mortality is the widening of socio-economic differentials in mortality. He interprets his results as meaning that high mortality occurs among the unemployed, that is, the least prosperous. In the United States and to some degree in Britain, it is those with poorer health who lose their job first and are re-hired last. In countries such as the Netherlands, which have extensive disability insurance, the less healthy individuals are likely to be kept out of the unemployed category (as defined by the Netherlands Bureau of Statistics). This raises the question of the extent to which one may generalize from U.S. and British data. If one does so, Brenner says, and if his theory is valid, countries with the most stable economic growth rates and the highest per capita income in their poorest groups should have the lowest mortality rates in all age categories. And, according to Brenner, they do. Eyer [20] comes to quite different conclusions. In his calculations for the United States for the period between 1870 and 1975, the total mortality rate rises with prosperity and falls during economic decline. He considers the dramatic decline in the mortality rate in the 1922-1932 period especially striking: “. . . when the unemployment rate rose to its historical peak of 25% in the Great Depression”. Of the 24 mortality peaks he shows for this period (Fig. 7), 12 coincide with the lowest unemployment in the industrial cycle, 5 occur in the year after that point had been reached, 5 occur just before the decline in unemployment, and only 2 occur during a peak in unemployment. The causes of death responsible for this fluctuating mortality range from infectious disease to accidents, cardiovascular diseases, cancer, and cirrhosis of the liver. Less than 2% of the mortality rate, the share taken by suicide and homicide, varies directly with unemployment. On the basis of earlier studies by Thomas [21], Eyer thinks that for the oldest data (nineteenth century) deterioration of housing and increased consumption of alcohol during ihese business booms can be held responsible for some of the variation. In the twentieth century, social stress is thought to play a dominant role. He considers overwork and the disruption of communities by migration to be two important sources of stress which increase with boom and are demonstrably related to the causes of death showing this variance. Despite the marked reduction of infectious diseases by, for instance, improved nutrition, sanitation, housing, and health care, the rise of mortality rates continues to be related to prosperity and not to economic crises. Since the Second World War the cyclic industrial pattern of infectious diseases has persisted but the greater part of the mortality-rate variance is now ascribable to non-infectious diseases. In his article Eyer does not discuss the long-term improvements due to prosperity and the secular decline of mortality, nor does he define the term business boom. In his graph a boom coincides with the lowest level of unemployment. Brenner concluded that both rapid economic growth and unemployment lead to increased mortality. These aspects cannot be distinguished seParately in Eyer’s studies, which makes it more difficult

1908

ISGEB~RG

Fig. 7. Total death rate and unemployment

P.

SPRUIT

rate in the United States, 187Ck1975.(Source: Eyer [20]).

to determine whether their results are really in conflict and, if so, to what extent. Their interpretations are, however, highly conflicting. According to Brenner, increased unemployment is followed by increased mortality, whereas according to Eyer increased unemployment is usually accompanied by a lower level of mortality. But Eyer sought direct, non-lagged relations between unemployment, social stress, and mortality, whereas Brenner accepts lagged relations, i.e. that mortality peaks in a given period are not to be ascribed to stress or environmental factors present at that time (in the same year) but rather to the effects of earlier unemployment lasting one or more years. Brenner calculated correlations between unemployment and mortality both with and without the lagged effect (O-5 years) of unemployment on mortality. He found the strongest effects of. for instance, cardiovascular diseases and liver cirrhosis at a lag factor of 2-3 years. In Eyer’s opinion, this .leads not so much to strong differences between their findings but rather to differences in emphasis on possible explanatory factors. Because industrial cycles have an average duration of 4 years, the conclusion that increased mortality is ascribable to the delayed effect of unemployment is the same as saying that mortality due to these diseases coincides with the boom of the cycle. There are two possible reasons for a lag: (a) that the stressful social effects of unemployment do not develop until the peak of the next boom, which is when the psychological stress mechanisms are activated leading to cardiac disease; and (b) that the social effects and physiological arousal develop strongly during the period of unemployment but do not lead immediately to mortality because of lags inherent in the pathological process. Although no definitive research results are available on this point, Eyer thinks that studies on severe social stress provide more indications for the oppo-

site, and that although social stress is certainly very high under mass unemployment, it is even higher during booms. However, neither author mentions that little still is known about the natural history of neoplasms and degenerative diseases. The lack of any theoretical or epidemiological basis for the lag structure is a weakness in Brenner’s calculations, just as the assumption that mortality follows stress without any delay is a weak point in Eyer’s conclusions. Brenner even uses rather divergent lags in, for instance his age-specific calculations of total mortality, and presents the curve showing the strongest correlation. As a result, there is a chance that his correlations are spurious. Like Eyer, Brenner was inspired by Thomas, who was the first to detect certain irregularities in mortality figures for the end of the nineteenth century and the beginning of the twentieth which suggested that the early phase of an industrial cycle tended to coincide with a higher level of mortality. This led Brenner to incorporate both the unemployment rate and economic growth spurts into his model. As he himself wrote: “Using a graphic analysis. Eyer interpreted the Thomas anomaly differently” [22]. He used advanced statistical methods such as a polynominial distributed lag, and recalculated the data according to Eyer’s interpretation for the U.S.A. He found no consistent negative correlation between unemployment and mortality rates: “Usually the relation was positive even at zero lag, and at lags of two to ten years the relationship was consistently positive and significant” [22]. Controlled for long-term economic growth, the lagged relations between unemployment and mortality become stronger (for all causes of death or for chronic diseases) and the unlagged relations weaker. Returning to Statement 2. we may conclude that up to now little macro-social research has been done on \ .

Unemployment

and health in macro-social analysis

the question whether a decrease in prosperity leads to an increase in mortality. Neither agreement nor clarity has been reached in this respect. This is due partly to differences in analysis and interpretation, but especially to the uncertainty concerning many of the factors involved. Consequently, a larger number of assumptions must be made when time-series analyses are undertaken, and this usually evokes considerable criticism. This point will be dealt with when discussing the implications of these divergent findings. Before turning to Statement 3, however, there is one interesting field of research which covers both Statements 1 and 2, but which uses a different health indicator: body height.

CHANGES IK THE INCREASE OF BODY HEIGHT Health, and certainly the health of a population, cannot be expressed directly as a measure or number. Indeed, longevity. morbidity, and mortality, as well as their patterns, are the most commonly used indicators. The basis for using body height as an indicator was established in France in 1829 by Villerme [23] who showed that young men from poor areas were shorter and more often rejected for military service than those from prosperous regions. In their attempt to explain differences in height between population groups, investigators have assigned more value to prosperity than to genetics. The relationship between stature and health is implicit. Just as prosperity has an influence on mortality and morbidity, it also has an effect on growth. Van Wieringen [24] argued that like morbidity and mortality statistics, which as it were form a ‘negative print’ of the general health status, secular growth changes are among the few indicators with practical value. Positive secular changes indicate improvement in the health status of a population, negative changes reflect retrogression. For example, the measured heights of conscripts for military service reflected what Zeeman [25] in Holland called in 1861: “the disease status of the population”: poverty and unemployment. as well as such associated phenomena as slum housing, destitution, hunger, and epidemics. Changes in the stature of children working in Dutch factories were found to correspond with the rising and falling of the market price of cereals. In the United States. within a few generations. descendants of Japanese immigrants became appreciably taller than their poorer relatives who had remained in Japan. Between 1935 and 1937. Vermet [27] studied the height and weight of Rotterdam schoolchildren in relation to unemployment. The fathers of 45”, of these children were unemployed. and these children were shorter than children of the same age whose fathers were employed. However. Vermet also found that such differences in height had already been present at pre-school ages before the father had become unemployed. The obvious conclusion is that unemployment hit particularly those whose circumstances were already bad. Various studies done between the turn of the century and the 1960s have shown that the quality of nutrition. particularly with respect to animal protein. is lower for economicall! and socially weak groups and is in all likelihood influenced by. for example. the

1909

gap between wages and prices. Van Wieringen [24] is therefore of the opinion that changes in diet (quantitative as well as qualitative) and a reduction of morbidity are the main factors contributing to shifts in secular growth. In 1932 Roland Holst [28] found that in the last century the arresting or reduction of the positive secular growth change could usually be explained by crop failures and the severe agricultural crisis of 1887-1895. The Second World War had a stronger inhibitory effect than the First World War and the economic crisis of the 1930s which are also reflected in changes in growth. The most interesting finding was that the relatively short categories showed the strongest reduction and the tallest groups little or no reduction. Studies on correlation between body height and prosperity have been done at both the macro- and the micro-social levels and also in the form of time-series analyses, comparative epidemiological studies of regions, and cross-sectional investigations of risk groups in a population. The less prosperous show a lag with respect to the positive secular growth shift, as they do for mortality and for so ‘sensitive an indicator as child mortality. Post-war research in prosperous times showed that the mean height of children among the lower-level occupational classes is a few centimetres less than that of children from middle-class occupational groups [26]. Van Wieringen [24] performed a time-series analysis (starting after World War II) of the relationship between height and unemployment among Dutch conscripts. This is a macro-social study based on the relative labour reserve and is therefore comparable with studies on mortality and unemployment rates. The analysis covered two periods of high unemployment. The first, with a peak of 4.8%, occurred in 195&1953; the second, which peaked at 3.0x, was in 1958-1959. The latter recession was not followed by any clearer changes in the secular growth curve, but after the former. which lasted 3 years. an effect was seen which also lasted 3 years, that is, in the drafts of 1957, 1958, and 1959. Most of these conscripts had been adolescents during the recession. Boys between 11 and 17 years of age underwent an ‘adolescence growth spurt’, a period of enhanced growth lasting about 2 years. The growth spurt for most of the ‘retarded’ conscripts coincided with the years of unemployment. Van Wieringen thinks that the various results indicating age differences in this respect show that temporarily unfavourable living conditions have an irreversible effect on the growth spurt in adolescence. The correlation he found between unemployment and an inhibition of the positive secular growth change is consistent with this pattern. To support his conclusions he pointed out that it is possible for an economic recession (i.e. high level of unemployment) to affect such a large number of young people that the inhibitory effect can manifest itself in the entire age group. According to his calculations. the first unemployment peak also fully affected the age groups of workers whose children were adolescent. At the same time. on the basis of other studies. he considers it justifiable to assume that the reduction in income associated with unemployment must have led to a reduction in the amount

ISGEBORG P. SPRLIT

1910

of protein in the diet by at least lOO;,. He also points that other categories of manual out. however, labourers must also have been confronted with a slight decline in income, since in 1951 and 1952 expenditures for food tended to decline rather than remain stationary for the lower occupational groups (roughly 66% of the population). From this Van Wieringen concluded that during a slump the registered labour reserve represents only the top of the iceberg, and that a slump leads to a deterioration of the nutritional pattern of a very large part of the population. He does not discuss the absence of an inhibitory effect after the second unemployment peak. perhaps because he accepts a similarity with the results of earlier studies which explained the majority, but not all, of the observed growth fluctuations.. The results of studies in the Netherlands on height (as health indicator) in relation to prosperity aI1 show the same tendency: lower height is correlated with lower prosperity. Even short-term fluctuations in prosperity are in many, but not all, cases reflected by growth. So studies using a different health indicator point in the same direction as studies using mortality or longevity as an indicator. However, definite conclusions cannot be drawn as to causal relationships, and the question of whether continued prosperity would lead to continuously increasing height has not been raised. The conclusions are also somewhat more refined and not as straightforward as those drawn from the time-series analysis of economic fluctuations and mortality. It is important to note that although poor populations remain shorter, it is difficult to conclude without qualification that unemployment precedes the inhibition of growth. Some results suggest that the weakest groups show the unfavourable effect of relative poverty and unemployment on growth most strongly. Thus, it is clear that it is not easy to separate cause and effect. It is conceivable that unemployment only enhances the effects of existing relative poverty, but also that unemployment itself has an inhibitory effect on all income groups hit by unemployment. Data on unemployment indicate, however, that the weakest groups are oftenthe most affected. When the two coincide to a high degree, investigation of the effect of unemployment in this situation (double deprivation) is at least as relevant as an academically interesting study on the effect of unemployment as such. Another important aspect emerging from this kind of research is embodied in the indication provided by Van Wieringen’s study: that a slump may affect not only the families of the unemployed but also a much larger category. Differences in body height are also found to exist between socio-economic classes. So. Statement 3 (relatively poor groups show relatively poor health) appears to be true with respect to body height. In the next part findings on morbidity and mortality with reference to socio-economic status will be examined. SOCIO-ECONOMIC

ment (regarding the relationship between the lowering of prosperity and increase of mortality) and studies using body height as a health indicator have actually been analyzed as a process, in this case by time-series analysis. Hagen compared states: rich and poor countries and drew conclusions about the process implicitly assumed to be operative. Philipsen also started with a state (prosperity scores for individual countries, neighbouring countries, and a geographical gradient), but explicitly with a state resulting from an historical process. known as diffusion. In essence. this reflects the crux of the methodological dilemma: (1) if one describes differences between population categories. how does one know with certainty which processes are responsible for those differences. i.e. the problems of causality and the interpretation of correlation and (2) if one describes processes in terms of a number of factors extracted from population data. how does one know whether these factors are correctly operationalized according to social reality? On the macro- and meso-social levels. studies have been done on socio-economic categories, morbidity, and mortality. These concerned differences between social. classes, between economic-geographic regions. between neighbourhoods and districts. Invariably. relationships were found between higher mortality and morbidity on the one hand and reduced prosperity on the other. If we search for studies on this level concerning the unemployed. we find only a few dating from the 1930s. In the Netherlands. no comparable research had been done on differences in health between social classes, but there is much recent research in Great Britain [29]. An example of British research findings is given in Fig. 8 and Table 1. These show not only that the higher the mortality the lower the social class, but also the higher the morbidity the ‘poorer’ the lifestyle. particularly eating habits and smoking. In the Netherlands. research has been done on the relations between population density and housing density on the one hand and morbidity on the other, on the basis of a division of the country into 129 economic-geographical regions. Levy [32] found that the higher the population density. the higher the scores for mortality and for admission to hospitals and psychiatric institutions. For housing density he Social

z

200

r

class and heallh

Age 20- 24

mequolitles

55-64

35-44

CATEGORIES. ,MORRIDITY,

AND MORTALITY

Whereas the first two statements concerned processes. the third, concerns a state. Do less prosperous Populations have a higher death rate and/or marbidity? It should be noted that only the second state-

100 = Mortality rate

of all men of

that

age-group

Fig. 8. Mortality rates by social class. Men. 1959-1963. three age-groups. England and Wales (Registrar General. 1971). (Source: Carter and Peel C303.j

Unemployment

and health in macro-social analysis

1911

Table 1. Mortality of men by social class, ages 15-64, 1970-1972 Standardized ratios Social class I(4.8) Leading professions and business II(20) Lesser professions and business III NM( 16) Skilled workers, non-manual III M(33) Skilled workers, manual IV M( 19) Semi-skilled workers V(7.6) Unskilled

mortality

All causes

Coronary heart disease

77 81 99 106 114 137

88 91 114 107 108 111

All such men in England & Wales = 100. (% ‘heads of households’ in

census of

1971 in brackets.) Source: Morris [31].

found the reverse, which he sought to explain by viewing the family as both a source of diverse interactions and a protection against stress. At the level of urban ethnography, Habbema er al. [33] found that in Amsterdam (1981) the average socio-economic status of a neighbourhood was very closely related to health (see Table 2). Although the conclusions to be drawn from such studies appear tantalizingly simple, there are similar problems to those mentioned above. With respect to the last two types of study just referred to, Verdonk [34] observed that such research provides no insight into social causes or processes, is non-historical and has little problem-sensitivity. Above all, it yields little information about why characteristic traits of areas lead to morbidity or mortality. First of all migration data are unknown, but even if the communities were

Table 2. Six groups of neighborhood

characteristics

stable over a long period, we should not interpret observed relations functionally without asking whether they could be diffusional. It is not known whether urban neighbourhoods and other geographic entities are independent of each other historically any more than this is known for countries. In anthropology diffusion is usually referred to in terms of culturally defined populations in geographical areas, but this theory can also be applied to the concept of social classes. Unlike, for example, castes, social classes are relatively ‘open’. Mobility is possible, and culture traits can be spread from one class to another. Furthermore, the borderlines between classes are diffuse and variable, which is one of the reasons why there is so little such research outside Great Britain. A consensus has not been reached concerning the exact limits and divisions of social classes.

and their relations with various indices of ill health

Mortality 1. Demograpkic rariahles: e.g. migration. percentage of persons living alone. percentage aliens, age composition of population 3. Socio-economic characteristics: e.g. socio-economic status; political. religious, and occupational diversity; mean rental value of living quarterssocio-economic status separately 3. Housing characteristics: e.g. equipment available. age of building 4. Population densit!, (a) inhabitants per hectare (b) occupation per housing unit (c) rooms per occupant 5. Physico-chemical enrironmrntal characteristics (a) air pollution (b) traffic noise (c) aircraft noise (d) hardness of drinking water 6. Densit! qf general practirioners

Source: Habbema rt

al.

[33].

Occupational disability

Hospital admissions

Combined mortality and morbidity score for men aged 15-65 years

13

15

9

13

14

64

31

72

20

83

42

19

44

26

88 51

8 10 19

33 28 41

29 24 8

47 42 28

12 13 9 8

24 20 18 5

19 24 26 5

24 34 26 7

4

17

13

18

1912

ISGEB~RG

A return to’the impact of the three original statements shows. at first glance. that the results of macrosocial investigations on all three analytical problems point in the same direction. However. many questions remain and important uncertainties over interpretation remain unresolved. Generally it appears that the investigation of processes leads to greater difference in interpretation than the investigation of states. Methodological difficulties are inherent in these problems, so before any conclusions can be reached it is necess-’ ary to consider interpretation.

INTERPRETATION

OF THE

.MACRO-SOCIAL

RESULTS

OF

ANALYSES

Although a tendency can be detected to the effect that less prosperity means shorter body height, more morbidity, less health, and/or a shorter life span-the empirical findings obtained from this method of analysis are rather divergent. According to Hagen. prosperity only has an inverse effect on health above a certain rather indistinct level, whereas Eyer thinks this is always the case because a high level of unemployment and lower mortality coincide more often than not. Philipsen does not discuss relations in earlier times, but attempts mainly to determine whether accepted relations exist at present. He believes that current fluctuations in longevity are not influenced solely by prosperity. Studies of body height offer slightly more certainty over possible conclusions, but the level at which body height indicates ill-health is a difficult problem. This also holds, if to a lesser degree, for mortality, which is used more often as an indicator, when the proportion due, for example, to homicide, suicide and possibly traffic accidents increases. Only Brenner believes that the relationship between prosperity measured against income. unemployment, percentage welfare expenditure, and mortality. was and remains very strong. Bunn [35] reached the same conlusion for cardiovascular diseases in Australia with the use of models similar to those applied by Brenner. But Brenner too has observed changes in the pattern. For instance, when he started his analysis at 1950 he found that the lagged mortality pattern started and ended later than when he took 1936 or 1940 as his starting-point, and this held for total mortality as well as for mortality due to chronic diseases. Analyses using other approaches have also not led to any simple. uniform conclusions. For example. Ahr and others found in the U.K. a positive relationship between public psychiatric admissions and unemployment [36]. Dooley and Catalan0 [37]. in the U.S.A. reported that rises and falls in the economy are associated with tendencies in mental disorders. mental hospitalization. suicide and psycho-physiological symptoms. The significant time-series associations they found in a metropolitan community were. however. not replicated in subsequent research in nonmetropolitan communities [3X]. They believe this is due to fewer life events and psychological symptoms in non-metropolitan communities. greater satisfaction with sources of social support. and a higher disposition to respond in a socially desirable direction.

P.

SPRU~

In Wales. Burr and Sweetnam [39] compared a group of heart-infarction patients with a control group of other patients to obtain more information comparable to the data of Forsdahl [40]. who found that in Norway and Finland poverty in youth followed by a higher standard of living was a major risk factor for cardiovascular diseases. In each of the three social classes into which Burr and Sweetnam divided their population patients came from larger families than the control group and a significantly higher percentage had a father who had been jobless for more than a year during their youth (just before World War II). However, in later life these patients had not risen to a higher social status than patients in the control group. Poverty in youth, as measured by paternal unemployment and a large family. would, according to these findings. give a higher risk of heart infarction about 40 years later. This is a very different ‘lag factor’ from Brenner’s. If it were permissible to generalize from these data. it would be necessary to ask what a (partial) effect of unemployment occurring after 40 years would have on calculations. made with lag factors in mortality up to at most five years. Although Burr and Sweetnam’s study concerned patients with a disease that is among the main causes of death, it was a hospital study and not a morbidity study. On this scale it was too small to permit farreaching conclusions. Brennan and Lancashire [41] also found an influence of unemployment on the health of children. This concerned not the influence of jobless parents on their children but a significant correlation between unemployment rate and infant mortality. without lagged effects. As had been done in earlier studies (carried out in the 1940s) in which. for example, an association was found between child mortality, low wages, and over-population. the authors re-analysed infant mortality (O-4 years) and the mortality of the 5-14 year age group in England and Wales in 1971, using the Kendall correlation technique. This method was used to determine whether any association remained if the effects of social class and unemployment were kept constant. For the O-4 year age group they found not only a significant positive association with unemployment but also that this association remained when the effect of social class was kept constant. This higher mortality occurred mainly in children living in crowded homes. In the 5-14 year age group the association between mortality and housing density disappeared when the variable socio-economic class was held constant. and under this condition 110 association was found between mortality and unemployment. Thus. we repeatedly see different and often noncomparable results obtained with different analytical methods. heterogeneous variables. and the use of populations ditferent in composition. The use of different methods of analysis is indispensable for the detection and definition of the complexity of the problems involved. and the data obtained in one study can complement or modify. those obtained in another. However, in this held at present the findings are more likely to be conflicting or unrelated to each other. The confusion was increased when Brenner thought it necessary to use a ditferent method of analysis to \

Unemployment

and health in macro-social analysis

obtain conformation of the results he reached with his time-series analysis. He performed a cross-sectional analysis of the specific mortality rates for each county in England and Wales without going into the problem of multiple co-linearity. He found that 900, of the variance could be explained by unemployment (positive), a high income (inverse), and the proportion of the population older than 54 years (positive). None of these variables is independent of the others: on the contrary. they overlap. Without analysing co-variance in aggregate measures, however, structural and individual effects cannot be separated and the additive effect can mask widely differing individual processes [42]. But the difficulty of disentangling the separate effects of correlated variables may be even more important with respect to bias because of the omission of variables strongly related to unemployment, for example educational levels, occupational structure, geographical mobility and housing. OBJECTIONS

TO MACRO-SOCIAL

ANALYSIS

The macro-social approach is open to considerable criticism. Philipsen’s article can be seen as a form of criticism. combined with an attempt to find an alternative to a premature functional-causal interpretation of correlation. Many objections have also been raised with respect to time-series analysis. Eyer and Brenner found themselves in conflict not only over their results but also in direct debate [43]. According to Cooper [44], Eyer confused primary and secondary causes and paid too little attention to fundamental material changes in industrial society responsible for the long-term trends of degenerative and neoplastic diseases. Kasl [45] deplores the inadequate discussion of the use of industrial cycles and related macro-social data to explain the behaviour of individuals. He objects particularly to the studies done by Bunn and Brenner. “Disturbing questions have been raised about the approach”, he writes. “However. the prospects for a systematic debate of these issues appear small and. instead, we are likely to see more publications.. which use this methodology uncritically and unquestioningly”. But he is even more disturbed by the prospect of *’ congressional committees accepting whole-’ heartedly the interpretations and conclusions from these analyses. without realizing that the scientific community has not yet engaged in a throughgoing examination of the underlying methodology” [46]. His objections are twofold. First. it is often not clear which effects must be ascribed to which variables, because the distinction between them is not adequately made. Statistical manipulation of the raw data makes it impossible to distinguish which ‘residual’ phenomenon is being studied. or to define its limits. As an example he mentions Brenner’s study of the relationship between ischaemic heart diseases and the industrial cycle. In this analysis. for instance. the industrial cycle can explain 60”, of the variance of the residual mortality due to this disease. This is because Brenner first adjusts the raw data by. for example. eliminating trends and some cycles or by standardization of the excess variance in other cycles. This leads Kasl to ask whether the residual ischaemic heart disease is then 10. 5. 1 or 0.1”” of the total morbidity in

1913

which we are interested. The &trending applied by Brenner means that it is no longer possible to deter_ mine which phenomena we are really dealing with after the various manipulations. For other. comparable. situations. Philipsen intro_ duced the highly appropriate term “mixed news” ~121 referring to results and correlations which cannot be interpreted and between which it is difficult to distinguish. This forms Kasl’s second point. He has another series of objections which concern the lack of dehnition or provision of any basis for the rationale underlying statistical manipulations; or for the seeking after and maintenance of relations in the absence of theoretical indications. Besides the risk of spurious and meaningless relations, these practices lead to a continually growing list of possible explanatory factors for one and the same phenomenon, as well as an endless generation of new hypotheses before older ones have been invalidated and without any further attempt to test the new ones. Criticism of Brenner’s data was also put forward at the meeting of the WHO Regional Committee for Europe on the influence of economic development on health [47]. Here. too, reference was made to the risk of spurious correlations and particularly to the fact that support for the use of different ‘lag years’ can be inadequate. Among the other main points which attracted criticism were: (1) Mortality rates were biased by altered disease patterns, definition/registration of causes of death, changed classification patterns for diseases, and the like. (2) The meaning of the indicators of economic growth was not clear. (3a) Unemployment was defined in different ways (analogous to 1). (3b) The unemployment rate and the population of unemployed were mistakenly used exchangeably and served as indicators for too many different and sometimes conflicting factors. (4) Since as indicators both mortality and unemployment were biased by so many factors not included in the analysis, there was good reason to ask whether the statistical correlations were not equally biased. Another important matter is, that up to now only Bunn has published findings similar to those of Brenner. Independent replication of these models seems to be highly opportune, but as yet insufficiently done. In a first trial to replicate the models. John in Germany, Sogaard in Denmark [48] and Tazelaar in the Netherlands [49] were unable to show the same results. Recently Gravelle and others [SO]: published their findings from an ihdependent replication of the model for England and Wales and they. too. did not obtain the same results. Their criticism concerns: (1) the choice and omission of variables leading to bias; (2) insufficient reliability of the data because of geographically inconsistent series: (3) the lag structure appears to be arbitrary and mis-specified; (4) spurious correlations cannot be excluded; (5) a test of the robustness shows considerable structural instability. So they argue that Brenner’s estimates are artifacts.

IsGEB~RG

f

Brrnnrr forecost

typo

9l.y I

I

1971 IS?2 1973

I

I

I

I

I

1974

1975

1978

1977

197s

Fig. 9. Actual and forecast Mortality rates 1971-1978. England and Wales (Source: Gravelle ef al. [W.) arising from his choice of period, and are no improvement on a naive model (see Fig. 9). This strong criticism should not be interpreted to indicate that it has been proved that unemployment has no adverse health effects. It does not rule out the possibility that such effects do exist, but merely that a method has not yet been found to estimate their magnitude and form. Although Gravelle and others were unable to ‘prove’ a relation between unemployment and mortality, neither was its non-existence proved. Much work will have to be done before the main objections to research on statistical correlations can be satisfied. Gravelle and others are looking for better specified models and superior statistical techniques; Philipsen seeks the solution to this problem in the testing of diffusion effects; Kasl in strict agreements on the methodological level. We cannot do without macrosocial analysis. Differences in mortality or morbidity between various categories have not been adequately explained, not even by the cumulative influence of established risk factors.

DISCUSSION It is important to relate to each other results obtained from different analytical approaches. In this respect, investigation of both the secular trend and the fluctuations within that trend using one approach would be important. For the present, time-series analyses cannot lead to conclusions about factors responsible for correlations. And the same holds for the application of the diffusion theory, because it attempts only to differentiate an accepted relationship. Specific research asks for collaboration between anthropologists and sociologists. The purpose of this summary was to determine which results of macro-social analyses yield information about the influence of unemployment on health. The time-series analyses are the most clearly applicable to this aspect, but do not lead to definite conclusions. Studies using body height have also shown the influence of poverty unequivocally, but here again too few studies have given similar results in relation to unemployment. Comparative research on the health status of populations differing as to prosperity (delimited geographically according to urban neighbourhood or socially according to class)

P.

SPRLTT

form a relevant supplement to time-series analyses; the results seem to support the plausibility of a relation between unemployment and health. especially when such a relationship occurs in countries with a good system of social security and relatively little poverty. But relatively little poverty does not mean equality, and so it does not solve all the problems of avoiding intervening variables. Despite an excellent system of welfare provisions in the Netherlands and a small minority of ‘abusers’. unemployment means a decline in income. Although severe poverty can be avoided as long as the benefits remain linked to the minimum loan, the financial situation is far from comfortable for those whose unemployment is prolonged. Research in the field of regional or class inequality of health does not exclude intervening and interrelated variables sufficiently to justify definite conclusions on the effect of unemployment. There are two other types of study which. although they do not provide direct support for a relation between health and unemployment. nevertheless strengthen its plausibility. The most direct support has been given by investigations on the psycho-social and health effects of involuntary unemployment. Indications for such effects have been found, as well as suggestions concerning the mechanisms underlying the sources of health hazards. However, the results of these studies are not directly generalisable from country to country, e.g. because of differences in social legislation. The more indirect studies concern the mental and physiological effects of stressful life events (e.g. bereavement, divorce, or change of job), which can have a negative effect on health. The most important function of macro-social analyses is the detection of trends and phenomena which should then be further analysed in different ways because, as already mentioned. this is not possible by macro-social analysis itself. Further research on the relation between unemployment and health of the unemployed themselves would. however, only deals with one interesting aspect of the described analyses. Even if unemployment, with or without a lagged effect, proved to be injurious to health, this would not explain all facets of the current findings. For instance, the hypothesized damage to health need not necessarily be ascribed only to reduced prosperity. Philipsen’s conclusion that prosperity is not the sole explanatory factor can be viewed in combination with results indicating that the stress factor plays an important role in unemployment. Our culturally formed ideas about the importance of work and the loss of work have a strong effect on our responses to job loss. The ranking of social positions and interactions which are at least partially centered around the work aspect, plays an equally large role in the side effects of the loss of employment. Nor can the hypothesized damage to health be ascribed only to the unemployed population. Even if we accept Brenner’s conclusion that a higher unemployment rate leads to higher mortality (actually: to a decrease of the decline in mortality), it may not be concluded therefrom that it is the unemployed population itself that shows this higher mortality. This is indeed indicated by his finding that the mortality of all age categories is influenced. Nor may it be concluded that it is the families or those close to the unemployed. Dimin-

Unemployment and health in macro-social analysis ishing employment can also mean an intensification of competition stress for those still working. Persistent shrinkage of the labour market can lead to permanent or increasing insecurity for the categories employed in unstable industrial sectors and can influence the workload and the socio-economic and socio-political climate, so affecting the employed population as well. Draper and others [Sl] have spoken of a ‘hostile economic environment’ analogous to environmental terminology. Even if a higher level of mortality were found among the unemployed and their families, the degree might not be high enough to explain the total variance of the actual findings. In this respect, the conclusions drawn from studies on body height are more detailed than those concerning mortality and unemployment. Lastly, it is of great importance to apply caution in extrapolating the results of studies done in one or more countries to other countries. In the United States and probably also in Great Britain, employees with relatively poor health are the first to lose their job. This can have a strong influence on findings. In countries like Sweden, Germany and the Netherlands, where the sick are eligible for disability insurance and are therefore not included in the unemployment figures, we should at least find another theory. In the Netherlands. research has shown that disability benefits have the effect of prolonging illness and are also sensitive to the labour-market situation. Only when a plant closes down completely is there no discrimination in firing ‘potential benefitters of disability insurance’ and healthy employed people. None of the analyses has given attention to the influence of purchasing power on the economic survival of the self-employed shopkeepers and the like. This population also increases when the unemployment rate rises. Furthermore, the altered quality of work unquestionably plays a role in health. Nothing is known, however, about possibly strong historical fluctuations among, for example, the number of individuals who perform very heavy labour. work very many hours (overwork), and do shift work or dangerous work (higher risk of accidents, distinctly unhealthy work, or work with injurious health effects with a long incubation period). Equally little is known about the effect of impending unemployment on the subjective evaluation of the quality of the work and thus perhaps on health or. for example, the tendency for the number of those who continue to work while sick to increase. For many years sickness/absenteeism has shown a distinct rise. but this is related just as much to the quality of work as to health care and the definition of ill-health and disease. to a number of demographic and social factors. and to the endurance of individuals [52]. The decline in sickness-absenteeism in Holland is so recent that the scientific debate on its impact is only beginning. It is impossible to make a general statement about relationships between unemployment and healthespecially where the unemployed population itself is concerned-on the basis of the macro-social research results available at present. even if one ignores criticism of the methodology of the macro-social analyses. The lack not only of conceptual refinement and a theoretical basis. but also the obligatory use of

1915

assumPtiOns, makes it unjustifiable to draw rapid and comprehensive conclusions. This does not mean, however, that valuable information has not been obtained. Besides conflicting and non-comparable results, there are results of analyses that supplement each other. On this basis, the three analytical questions raised at the beginning of this paper can be answered provisionally as follows: (1) The relationship between diminished prosperity and poorer health, given the indicators, has been indisputably established at various levels, but requires a number of specifications. (a) Divisions between levels of prosperity in relation to the influence on health are still rather vague. (b) The possibility that there is an upper limit to the health-improving effect of prosperity must be given serious consideration. But a detailed statement cannot be made about the population categories that are, or can be, involved. (2) Process-related statements on the influence of increases and decreases of prosperity can only be made in terms of tendencies. With respect to shortterm fluctuations, interpretation is uncertain. (3) It is probably insufficient to attribute changes in mortality solely to unemployment and poverty or prosperity. Related factors mentioned in the literature include stress, nutrition, and housing. Well-founded conclu&ons have not been reached about the nature or the degree of the relationship of these factors with prosperity, unemployment, and/or poverty, but there are apparently many implicit assumptions. (4) There are indications for the hypothesis that unemployment may influence health not only via an associated decline in income but also as an independent factor. In any case, the two should be clearly distinguished conceptually in research. (5) There are interesting indications that unemployment can have a prolonged negative influence on health, but this must not be taken to mean that: (a) unemployment always influences health. Too little is known about the ‘who, what, where, and how’ to permit this conclusion. This holds for the definition of unemployment, e.g. according to frequency, duration, intensity, and occurrence, as well as for the individuals affected by it and the processes associated with it; (b) possible negative effects of unemployment must only be sought or can only lie among the unemployed themselves. Virtually nothing is known about the relative impact of a possible influence on the employed as well as the unemployed. (6) There are sufficient indications to conclude that no conclusions may be drawn about the unemployed part of the population under study, nor about the operative processes. from studies in which the unemployment rate has been used as an analytical factor. Nevertheless. interpretations should apply to the entire population. (7) Little support has been obtained for Eyer’s findings indicating that increased unemployment reduces mortality and stress, but neither has it been proved that this is not true.

ISGEBORG

1916 REFERESCES

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