Socioeconomic and health effects on mortality declines in developing countries

Soc. Sci. Med. Vol. 20, No. 5. pp. 453 460, 1985 Printed in Great Britain

0277-9536/85 $3.00 + 0.00 Pergamon Press Ltd

SOCIOECONOMIC AND HEALTH EFFECTS ON M O R T A L I T Y D E C L I N E S IN D E V E L O P I N G C O U N T R I E S BRIAN F. P E N D L E T O N I and SHu-O. W. YANG2 ~Department of Sociology, The University of Akron, Akron, OH 44325 and 2Marketing Research Department, General Electric. Cleveland, OH, U.S.A. Abstract--lt has been argued that mortality declines in developing countries have not been associated with

social and economic factors because of the diffusion from the developed countries to the developing countries of health and medical techniques. This paper examines the relationship between socioeconomic development and health and mortality declines in developing countries which are in two different stages of the demographic transition. A path model linking socioeconomic and health variables and mortality is developed and tested for early and late transition nations. The empirical findings indicated that the network of socioconomic variables and their effects on mortality were much more significant in late transition countries than in early transition countries. While the impact of health services on crude death rates is large in the early transition nations, its impact declines considerably as countries reach a more transitionally 'mature" stage.

INTRODUCTION

Ever since the demographic transition was first formulated, mortality declines have been described in terms of there being better, and more advanced, health practices and medical and agricultural technology available to populations who desire, as is the major cultural universal, to lower mortality rates and to increase life expectancy. It often is acknowledged that a major problem with demographic transition theory is its formulation being based upon the experience of Western countries, especially the experience of the European nations. Demographic transition theory postulates, in general, a relationship between birth and death rates where, at the earliest stage, birth and death rates both are high and population growth is very low. Death rates then decline at a moderate rate while birth rates remain very high; the result is rapid population growth. At a later point in time birth rates also decline, slowing the rate of population growth, and eventually coincide with already low death rates. The result of low birth and low death rates is minimal population growth. Arguments have been formed to show why transition theory is not applicable to the developing world but there seems to be growing consensus that the transition model, in general, reflects the demographic experiences of developing nations. The major exception to transition theory based on the European experience is the much more rapid decline in mortality that characterizes developing countries. This more rapid decline has been attributed to the sharing of advanced medical and health technology by the developed nations; a sharing usually accomplished within the socio-political dimensions of "humanitarian efforts'. The accepted argument is that iraPresented earlier at the annual meeting of the Population Association of America, Washington, DC, March, 1981. The authors would like to thank three anonymous reviewers for their helpful comments. 453

ported, low-cost, mass medical and public health" projects have accelerated the mortality declines in developing countries since the 1930s [1 7]. It has been argued that during the last four to six decades these developing-country mortality declines have been dissociated with social and economic phenomena because of the nearly absolute reliance on the transfer of health and medical technology from the developing nations [1, 2, 8-11; see also 12]. This is an important area to research because of the vast amounts of aid directed to the developing nations that are designed to stimulate social and economic development and the implied (from the experience of the developed nations) mortality reductions associated with such development [cf. 3, 7]. These linkages often are expressed in terms of the demographic transition being strongly correlated with increasing modernization or industrialization. We know that important shifts in the socioeconomic and political arena occur during the transition process, but there have been few studies that have attempted to look at the transition and differing stage-specific causes of mortality reduction. Are there different sets of reasons for mortality decline near the beginning of the transition than nearer the final stage of the transition? Are health and medical technologies more important for developing nations at the beginning of the transition than near the end of the transition? Are health and medical technologies more dissociated from socioeconomic phenomena at certain stages of the transition than others? The purpose of this paper is to explore these three questions using a concise multivariate model of mortality applied to developing nations that are at the beginning of the transition and those that are at a relatively late stage of the transition. A MODEL OF MORTALITY

Illustrated in Fig. 1 is a path model of mortality displaying relationships between socioeconomic and

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BRIAN F. PENDLETON and SHt2-O. W. YANG

health/medical variables, and three measures of mortality. Five predictor variables are used: education, economic development, living standards, health/ medical and diet. The measurement and the expected sign of linkages among variables are described below. Linkages similar to those found here are explained in greater detail by Goldscheider [2] and Chang et al. [13] and have been used by Yang and Pendleton [14]. Economic' development (ECON) Overall economic development is a major contributing factor in lowering crude death and infant mortality rates in developing nations [15]. While more recent work tends to concentrate on the distribution of income within a particular country [4, 16, 17], for an aggregate of countries it seems an index of overall economic development will serve the purpose of identifying development trends. Gross National Product (GNP) is rejected because "it is incapable of telling us much about the quality of life or health of the people" [18, p. 2004]. In an attempt to tap into a different dimension of 'economic development', it will be measured in this study as an index of energy and newsprint consumption. This index should reflect both primary and secondary industrial activity. Economic development is expected to show lower crude death rates and infant mortality, and higher life expectancy. Based on work by Berg [19], McIntosh [20] and Turnham [21], expectations are for development to show better health services, higher living standards, and better diet and nutrition [see also 22]. Education (EDUC) As a major indicator of social development, education has been described as one of the most powerful predictors of mortality [9, 6]. A better educated population seems to be more aware of medical, sanitary, and nutritional information that would prolong life [1, 23-25, 4]. Education, like economic development, is expected to show lower crude death and infant mortality rates, and higher levels of life expectancy. As suggested by Palloni and Wyrick [6], education is measured unidimensionally in this study as the percent of the country's adult population which is literate. Education is expected to lead to higher living standards, better health services [13, 24, 26-29] and better diet and nutrition [20,23]. Living standard (LIVI) This composite includes of the number of radio receivers per 1000 members of the population and the number of telephones per 1000 members of the population. Living standards are indicative of the population's interest in broadened sources of communications and access to a variety of information sources. The composite is a result of economic development and education [13, 30-32], and is expected to show better quality health/medical services and diet and nutrition [23]. Living standards would be expected to show lower general and infant mortality and higher life expectancy. Health services (HEAL) A variety of authors have examined the relationship between various measures of health and

medical services and mortality. Haines and Avery [4], in their study of non-adult mortality in Costa Rica, find that the provision of medical services is a powerful inducement for lower child mortality. Caldwell. et al. [23] explore in depth the cultural conditions necessary to accept developed-nation medical and health practices and beliefs with the bottom line being decreased mortality (especially at the adult level) once acceptance is secured. According to a number of scholars, health and medical services should be of much greater importance than measures of social and economic development (economic development, education, living standard and diet and nutrition) in determining measures of mortality. In general, better and more available health services should reduce infant and crude mortality and increase life expectancy [23, 33 35]. A few recent authors, while not discounting the influence of health and medicine on measures of mortality, are not displaying the enthusiasm expressed in earlier research. Gwatkin [3], in a comprehensive survey of global mortality, notes a significant decrease in the capability of modern medical and health services to combat emerging developing nation diseases. While great strides were made in the control a parasitic and infectious diseases, mortality from diarrheic disease has become particularly problematic for modern medical and health technology in less developed countries (see also McDermott [36]). Caldwell et al. [23], Leigh [28] and Anderson [26] all note the importance education has in influencing a population's use of available health and medical services. Health services are measured in this study by the combination of hospital beds per 1000 population and the number of physicians available per 1000 members of the population. The importance of "number of physicians" has been discussed by Flegg [25]. Health services are expected to show lower general and infant mortality and higher life expectancy. Diet and nutrition (DIET) The link between diet and nutrition, and mortality has been investigated by a number of researchers [18, 37]. The World Health Organization [38] determined that malnutrition was the associated or underlying cause of 57'~, of the child mortality in 13 Latin American countries. Baertl [39] found that improved diet supplementation programs significantly reduced Peruvian child mortality. Caldwell et al. [23] note that unless considerably better nutrition is achieved in South India, it is doubtful that life expectancy will be raised much beyond 60. Diet and nutrition also have been found to play an intervening role between education and mortality [15, 16, 40]. The diet and nutrition composite consists of average calorie intake, percent animal origin from calories, and per capita intake of protein and is expected to reduce crude death rates and infant mortality rates, and increase life expectancy [37]. Mortality (CDR, IMR and LE) Three separate measures of mortality are used: crude death rates (CDR), infant mortality rates (IMR) and life expectancy at birth (LE).

M o r t a l i t y declines in d e v e l o p i n g countries

455

Table 1. Variable definitions and sources Sources

Variables ECON:

EDUC: LIVI:

HEAL:

DIET:

LIFE:

IMR: CDR:

Economic development Energy consumption (1976) Newsprint consumption (1976) Education !';~ Adult literacy (latest) Living standard Radio receivers per 1000 population (1975) Telephones per 1000 population (1976) Health/medical services Hospital beds per 1000 population (latest) Physicians per 1000 population (latest) Diet and nutrition Calorie intake (latest) ~, Animal origin from calories (latest) Per capita consumption of protein (latest) Life expectancy Male life expectancy (197(~1975) Female life expectancy (1970 1975) Infant Mortality Rate (1970-1975) Crude Death Rate (1970-1975)

Table 142, Statistical Yearbook [31] Table 180, Statistical Yearbook [31] Table 1.3, Statistical Yearbook [31] Table 217, Statistical Yearbook [311 Table 168. Statistical Yearbook [3q Table 209, Statistical Yearbook [311 Table 209, Statistical Yearbook [31] Table 162, Statistical Yearbook [31] Table 162, Statistical Yearbook [311 Table 162, Statistical Yearbook [311 Table Table Table Table

4, 4, 4, 4,

Demographic Demographic Demographic Demographic

Yearbook Yearbook Yearbook Yearbook

Table 2. Developing nations Life expectancy

Life expectancy Early transition nations (N = 44) Afganistan Angola Bangladesh Benin Bolivia Cameroon Cape Verde CenAfrica Chad Comoros Congo Ethiopia Gabon Gambia Ghana Guatemala Guinea Haiti Honduras India Indonesia Ivory Coast Jordan Kenya Liberia Madagascar Malawi Mali Mauritania Niger Nigeria Pakistan Saudi Arabia Senegal Sierra Leone Somalia South Africa Sudan Swaziland Togo Uganda Upper Volta Zaire Zambia

;.M. 20/5 B

Male

Female

39.9 37.0 35.8 39.4 45.7 39.4 48.3 33.0 29.0 40.9 41.9 36.5 25.0 38.5 41.9 48.3 39.4 49.0 42.1 41.9 47.5 41.9 52.6 46.9 45.8 37.5 40.9 36.5 37.0 37.0 37.2 53.8 44.2 38.5 41.9 39.4 49.8 47.3 41.9 31.6 48.3 32.1 41.9 42.9

40.7 40.1 35.8 42.6 47.9 42.6 51.7 36.0 35.0 44.1 45.1 39.6 45.0 41.6 45.1 49.7 42.0 51.0 55.0 40.6 47.5 45.1 52.0 51.2 44.0 38.3 44.2 39.6 40. I 40.1 36.7 48.8 46.5 41.6 45.1 42.6 53.3 49.9 45. I 38.5 51.7 3 I. I 45.1 46, I

Later transition nations (N = 48)

Male

Female

Algeria Antigua Argentina Bahamas Barbados Bermuda Brazil Burma Chile China Colombia Costa Rica Cuba Cyprus Dominican Republic Ecuador Egypt El Salvador Greenland Grenada Guadelupe Guyana Hong Kong lran Iraq Jamaica Korea Lebanon Libya Malasia Martinique Mexico Moroccos Nicaragua Panama Paraguay Peru Philippines Puerto Rico Singapore St Lucia St Vincent Syrian Republic Thailand Tunisia Turkey Uruguay Venezuela

51.7 60.5 65.2 64.0 62.7 65.6 57.6 48.6 60.5 59.9 59.2 61.9 68.5 70.0 57.1 54.9 51.6 56.6 60.7 60.1 52.5 59.0 67.4 57.6 51.2 62.7 63.0 61.4 51.4 65.4 63.3 62.8 51.4 51.2 64.3 60.3 52.6 56.9 68.9 65.1 55.1 58.5 54.5 53.6 53.5 53.7 65.5 57,0

54.8 64.3 71.4 67.3 67.4 72.4 61. I 51.5 66.0 63.3 62.7 64.8 71.8 72.9 58.6 58.1 53.8 60.4 66.2 65.6 67.3 63.0 75.0 57.4 54.3 66.6 67.0 65.1 54.5 70.8 67.4 66.6 54.5 54.6 67.5 63.6 55.5 60.0 76.1 70.0 58.5 59.7 58.6 58.7 55.7 53,7 71,6 59,2

[43] [43] [43] [43]

456

BRIAN F. PENDLETON a n d SHu-O. W. YANG

Economic development

Measure of mortality

Education

Fig. 1. M o d e l o f m o r t a l i t y .

A summary of the variables and their sources is presented in Table 1. METHODS

Ninety-two developing nations were selected for the analyses. They were divided into two groups, those that were early in the demographic transition, and those that were later into the transition. The countries and their status are listed in Table 2. The division into early and late transition countries was made on the basis of lower and higher than the average life expectancies calculated from these 92 countries. Palloni and Wyrick [6] note that life expectancy traditionally is the preferred measure for mortality. Thus it was felt that this dividing criterion would be useful in determining the relative impact of health and socioeconomic variables on mortality. Because direct comparisons among variables which consist of different mesurements would be misleading, z-scores are computed for all variables before conducting the path analysis. The simple z-score equation is:

where the mean of each variable is subtracted from each observation for that variable, then divided by the variable's standard deviation, and summed for all observations. The z-scores for each variable then are added together and divided by the number of variables used in constructing each composite to form a single value for each country. Scales were constructed for economic development, living standard, health services, diet and nutrition, and life expectancy. The

three unidimensional concepts are education, crude death rates and infant mortality rates followed where all paths delineated in Fig. 1 were tested for significance for each of the two groups of countries. Paths which were significant were retained in the model re-calculated. Significance was determined when the standardized regression coefficient was at least twice its standard error. Two re-tested models are the result; one model has the 44 low life expectancy countries (early into the transition), the other has the 48 high life expectancy countries (later into the transition). FINDINGS

The matrices of zero-order correlations for late and early transition nations are shown in Tables 3 and 4, respectively. Among late transition nations, five predictor variables, economic development, education, living standards, health and diet all are correlated in the hypothesized direction with three different mortality variables. Among early transition nations all relationships, except two with diet, have supported hypothesized links with mortality. The magnitude of the correlations between independent and dependent variables seem larger for late transition countries than for early transition countries. Knowledge of these individual correlations, however, cannot explain fully the effect of socioeconomic variables on mortality. The path analysis technique is used to assess the network of hypothesized relationships and to isolate the direct influence of socioeconomic and health/medical variables on mortality levels. The standardized path coefficients from the revised path model for late transition countries are shown in

Table 3. Zero-order correlations for late transition countries (N = 48) ECON

EDUC

EcoN EDUC LIVI HEAL DIET

1.00 0.39** 0.59*** 0.38** 0.40**

1.00 -0.04 0.34* 0.46**

LIFE IMR CDR

0.59*** 0.56*** 0,53***

0.54*** -0.54*** -0.52***

*P < 0.05: **P < 0.01: ***P < 0.001.

LIVI

0.39** 0.63*** 0.55*** -0.48*** -0.36**

HEAL

DIET

1.00 0.51"**

1.00

0.45** 0.42** 0.38**

0.55*** 0.26 0.24

LIFE

IMR

CDR

1.00 -0.?9*** 0.73***

1.00 0.81"**

1.00

Mortality declines in developing countries

457

Table 4. Zero order correlationsfor early transitioncountries(N = 44) ECON ECON EDUC LIVI HEAL DIET

EDUC

1.00 0.31 * 0.82"** 0.33* 0.53***

LIFE IMR CDR

1.00 0.31 * 0.35* -0.08

0.35* -0.18 -0.27

0.57*** -0.024 -0.57***

LIVI

HEAL

1.00 0.51 *** 0.46**

1.00 -0.007

0.30 -0.18 -0.21

0.43** -0.15 -0.52***

DIET

LIFE

IMR

CDR

1.00 -0.41"* 0.83***

1.00 0.42**

1.00

1.00 0.03 -0.11 0.05

*P <0.05; P <0.01; ***P <0.001.

Fig. 2. According to this revised model, economic development has a strong positive effect on living standards which means higher economic development would result in better living standards. Education, somewhat surprisingly, leads to lower living standards. This may be due to the way each is measured. The more literate the population, the less they are engaged by radio and telephones. A literate population may become more enthralled by television, books, newspapers, etc.; the latter are not included in this research. Higher education and better living standards will have an impact on more physicians and more hospital beds. Living standard shows a strong effect leading to better diet and nutrition. The three separate mortality measures, crude death rate, infant mortality rate and life expectancy, are influenced directly or indirectly by all predictor variables except health. Living standards strongly leads to lower crude death rates and infant mortality and it also increases life expectancy. Thus, better living standards appears to be an important factor in contributing to lower mortality. Diet and nutrition, however, leads to an increase in crude death rates and infant mortality. This positive relationship is opposite what was expected. This may indicate that diet and nutrition status in developing countries is not deter-

mined by the availability of calories and protein [41]. Other factors, such as infectious diseases, have to be taken into account as sources of malnourishment. It may be that better diet-nutrition allows infants to survive slightly longer, but only to fall victim to diseases characterizing infant and childhood ages. Number of physicians and number of hospital beds do not show any direct or indirect effects on any of these three mortality measures. Health is, however, strongly related to measures of mortality. While not showing a direct influence, health does provide the milieu within which crude and infant mortality may decline, and life expectancy may increase. Economic development, although it also does not have a direct effect on mortality, exerts its indirect influence on mortality through living standards and diet. As shown in Table 5, 54~o of the variance in crude death rates and eighty percent of the variance in infant mortality, respectively, are explained by living standard, education, and diet. Life expectancy is a function of living standards and education which together explain well over half the variation in life expectancy (R 2 = 0.60). The revised model for early transition nations is shown in Fig. 3. Living standards and diet, respectively, are directly correlated with economic de-

0.87

0"~'

"

9

,0

~Education~

o,

/

~

-09B

- ~"-~ 7at r t a l i t y

t

0.63 Fig. 2. Revised path model of late transition countries (N = 48).

458

BmAN F. PENDLETON and SHu-O. W. YANG Table 5. Standardized path coefficients for late transition nations* Dependent variables Independent variables ECON EDUC

LIVI 0.71 (0.1591 0.31 (0.124)

LIV1

DIET -0.34 (0.1531 0.62 (0.097) 0.85 (0.123)

DIET HEAL R'~ 0.40 0.66 NS = not significant. *Standard errors are in parentheses.

velopment. I m p r o v i n g economic d e v e l o p m e n t will lead to higher living standards a n d better diet. H e a l t h is influenced only by living standards. Higher living s t a n d a r d s will lead to a larger n u m b e r of physicians a n d hospital beds. As s h o w n in Fig. 3, none o f the hypothesized paths have any effect on infant mortality; it is therefore deleted from the model. Crude d e a t h rates are lowered by e d u c a t i o n a n d health as hypothesized. These two variables together explain 310/o of the variance in crude d e a t h rates (Table 6). Life expectancy is increased only by education. Twenty-four percent of the variance in life expectancy is explained by education. There are no indirect effects t h r o u g h these predictor variables on any measures of mortality in early transition countries. Diet is not correlated significantly with either crude d e a t h rate or life expectancy. C o m p a r i n g the revised path models shown in Figs 2 a n d 3, respectively, it is clear that the internal networks between exogenous a n d e n d o g e n o u s variables are m u c h more significant in late transition nations t h a n in early transition countries. This means that the p r o p o s e d socioeconomic model on mortality is supported in the former much more t h a n in the latter. In examining the R ~' values a m o n g the three

HEAL NS

LIFE NS

CDR NS

1MR NS

0.36 (0.117) 0.40 (0.1311 NS

0.56 (0.096) 0.57 (0.107) NS

0.24

NS 0.60

- 0.87 (0.143) 0.82 (0.1831 0.68 (0.215) NS 0.54

- 0.98 (0.096) 1.06 (0.1231 0.86 (0.144) NS (I.80

measures of mortality (Tables 5 a n d 6) m a n y more variations are explained by the selected socioeconomic variables in late transition nations t h a n in early transition nations. DISCUSSION AND CONCLUSION In late transition nations, crude death rates and infant mortality rates were influenced by living standards, education and diet. Life expectancy was a function of living s t a n d a r d s a n d education. A l t h o u g h economic development did not influence any of the three measures of mortality, it did indirectly lower mortality t h r o u g h living s t a n d a r d s and diet. In the early transition nations, education and health lowered the crude death rate. E d u c a t i o n also h a d a direct effect on life expectancy. These results supported the associaltion between socioeconomic and health conditions a n d mortality in developing countries. F u r t h e r m o r e , it illustrates that the degree of this association or linkage will vary in terms of the stage of a nation in the d e m o g r a p h i c transition. In the early transition nations, health shares with education an ability to reduce crude d e a t h rates. It has no effect on the other measures of mortality. This direct effect disappears in the late transition countries

0.58

Economtc development

0.82

Living P standard

0.87

051

0.77

-036 ~. Health

~

~ Education

Crude ~, death rate

3B4 0.57

Life w, expectancy

1

O. 83 Fig. 3. Revised path model of early transition countries (N = 44).

Mortality declines in developing countries

459

Table 6. Standardized path coefficients for early transition nations* Dependent variables Independent variables ECON EDUC

LIVI 0.82 (0.087) NS

DIET 0.62 (0.114) --0.27 (0.117)

LIVI

HEAL NS

LIFE NS

CDR NS

IMR NS

NS

0.57 (0.120) NS

-0.44 (0.132) NS

NS

NS

NS NS

0.30

0.24

NS -0.36 (0.164) 0.31

0.51

NS

(0.147)

DIET HEAL 0.66 0.30 R~ NS = not significant. *Standard errors are in parentheses. while m a n y of the socioeconomic effects are strengthened. In the early stages of the transition, health appears to play a m u c h more i m p o r t a n t (but shared) role in lowering crude d e a t h rates. This influence, however, does not exist in late transition countries. This would s u p p o r t the c o n t e n t i o n by G w a t k i n [3] t h a t the direct impact of health a n d medical services, while great in the earlier stages of development, decline in the later stages. It also supports Sharpston's [42] c o n t e n t i o n t h a t medical science plays a minimal direct role in increasing life expectancy (see also Flegg [25]). Health does exist as a c o n t r i b u t i n g c o n d i t i o n to all dimensions of mortality, for nations at b o t h stages of the transition, as indicated by the bivariate relationships displayed in Tables 3 a n d 4. E d u c a t i o n plays a n i m p o r t a n t role in directly affecting all three measures o f mortality in hypothesized directions in the late transition nations, a n d life expectancy and the crude d e a t h rate in early transition nations. This supports the implication m a d e by Palloni a n d Wyrick [6] that such p r o p o r tionate effects would be found. The purpose of this p a p e r was to explore the following three questions: (1) are there differing sets of reasons for mortality decline near the beginning of the transition t h a n nearer the final stage of the transition? (2) are health and medical services more i m p o r t a n t for developing nations at the beginning of the transition or near the end of the transition? and (3) are health and medical technologies dissociated from socioeconomic p h e n o m e n a at certain stages o f the transition t h a n another? It is significant to note that the network between socioeconomic variables and mortality levels in late transition nations, when c o m p a r e d to early transition nations, indicates that socioeconomic i m p r o v e m e n t is necessary for lowering mortality levels once the nations have proceeded to a late transition stage. However, for nations which are still in the early transition stage, characterized by high fertility a n d high mortality, economic development, living s t a n d a r d and diet do not constitute relatively i m p o r t a n t factors in reducing mortality levels. O t h e r factors, such as education a n d health and medical measures i m p o r t e d from more developed nations, seem to be p r i m a r y reasons for overall mortality reductions a n d a n increase in life expectancy. W h e t h e r mortality decline in developing countries in the twentieth century is related to socioeconomic d e v e l o p m e n t or is only a result of i m p o r t e d health a n d medical technologies should not be a p o i n t of

NS NS 0.40

argument. The stages of transition which a n a t i o n withstands should be addressed in terms of determining mortality declines. A t the beginning stage of the transition, mortality declines h a d been achieved principally t h r o u g h health a n d medical measures which had been largely c o n t e r m i n o u s with socioeconomic development. W h e n developing nations reach a certain stage of transition characterized by lower mortality, i m p o r t a n t health a n d medical technologies have already been adopted. I m p r o v e m e n t s in economic development, education, living s t a n d a r d a n d diet are, therefore, the most viable routes t h r o u g h which crude a n d infant mortality declines can be achieved a n d life expectancy increases can be enjoyed. O f future interest would be the decomposition of mortality into infectious, degenerative, and social causes for nations at various stages of the transition. REFERENCES

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