Poverty Causes Population Growth Causes Poverty - The Donella Meadows Project
Those who think poverty causes population growth favor direct economic aid, Take care of development, they say, and the birth rate will take care of itself. He arrived in , by which time Manupur's population had increased to Research predicts African children will account for 43% of global poverty by , although absolute number of poor will fall. See, for example, Female Literacy Rate is a Better Predictor of Birth Rate and Infant Mortality Rate in India for one characteristic What is the next cause of a high birth rate? Illiteracy is most common in poor countries with high birth rates.
Materials and Methods We did an ecological analysis of the data from the national surveys conducted by the Registrar General of India. State-wise literacy rates were taken from the Census of India. CBR was taken as a surrogate fertility indicator as other indicators such as generalized fertility rates, generalized marital fertility rates, and total fertility rates were available only for large states with more than 10 million population.
Each state or UT was taken as the unit for linear regression analysis.
The relationship between women’s education and fertility
CBR and IMR were taken as dependent variables while the overall, male, and female literacy rates were independent variables. The dataset used is given in Table 1. Table 1 Literacy rates, crude birth rates, and infant mortality rates of states and union territories of India Census and Sample Registration System SRS December Open in a separate window Best fitting lines on scatter plots were drawn and slope parameters and R2 value was calculated.
Initially, overall literacy rates and male literacy rates and female literacy rates were all analyzed individually with CBR and IMR, respectively.
Next, we combined male and female literacy rates as predictors of CBR and IMR, respectively in multiple regression analysis.
Results On linear regression, an inverse relationship was found between male literacy rates, female literacy rates [ Figure 1 ], and overall literacy rates with that of the CBR of the respective states and UTs [ Table 2 ]. The slope of A wide range of these products can be expected to influence mortality, and expenditures on all of them are represented, with varying weights, in national income. It is the indicator most comprehensive of these multiple factors.
Secondly, as the leading index of level of economic development, income per head is the focus of growth models from which policy measures are derived. There is no reason to expect a direct influence of national income per head on mortality; it measures simply the rate of entry of new goods and services into the household and business sectors.
The relationship between women’s education and fertility | World Economic Forum
Its influence is indirect; a higher income implies and facilitates, though it does not necessarily entail, larger real consumption of items affecting health, such as food, housing, medical and public health services, education, leisure, health-related research and, on the negative side, automobiles, cigarettes, animal fats and physical inertia. Levels of mortality and economic development can be related to one another conceptually and substantively in a variety of ways. It is useful at the outset to distinguish among at least three different types of relationships that have been proposed by various analysts, although the exact formulation is often only implicit in their work.
In order to simplify the task, we confine the review to international studies. Level of income influences level of mortality at a moment in time Attempts at empirical estimation have focused on the cross-sectional relationship between mortality and economic level. Most commonly, the relationship between national infant mortality rates and levels of income has been examined. The relationship is sufficiently strong for infant mortality rates on occasion to have been used as indicators of income levels when the requisite data for computing the latter are missing.
Frederiksen 2 provides partial support by showing that death rates at ages 1—4 are more closely correlated with gross national product per head in 15 countries than are death rates at ages 0—1, 20—24, or 65— One study, confined to less developed countries, has demonstrated a close cross-national relationship between an index of mortality at all ages and from all causes, life expectancy at birth and the level of national income.
We shall re-examine the type of relationship studied by Vallin in a subsequent section. On several occasions the United Nations Population Division has expressed the opinion that the cross-sectional relationship between mortality and level of economic development has become progressively weaker over time.
However, data to support the claim have not been presented, and the present analysis fails to support the contention.
Level of income influences rate of change in mortality Arriaga and Davis 13 suggest that the rate of improvement of mortality can be expected to be direct function of the existing level of mortality in a country.
They make it clear that they intend the existing level of mortality to be a proxy variable for a nation's level of income, so that the rate of change of mortality is considered to be a function of level of income.
Their path-breaking analysis of developments in Latin America shows that the expected relationship applied prior to or but that thereafter the rate of change in life expectancy became independent of levels of income.
A lack of relationship was also suggested by Stolnitz 14 in his review of post-war mortality trends in less developed regions. It is difficult to devise a plausible model in which the rate of change of mortality is a direct function of the level of income. One mechanism that conceivably could produce such a relationship occurs when a positive fraction of additions to current income is invested in various enterprises such as housing, hospitals and training programme for medical personnel that exert an effect on subsequent mortality.
When the assumption is made that these investments continue to cumulate at a given level of income rather than simply to replace depreciating facilities and withdrawing personnelit becomes plausible that higher incomes will produce larger gains in life expectancy. The assumption is treacherous, however, and lacks an empirical basis. It implies that a country at a constant level of income will experience continuous increments in its stock of health-related capital, which it can do in reality only if the proportion of income invested in such capital is constantly rising or if the rate of depreciation of such capital is constantly falling.
A second mechanism that could produce a relationship between the level of income and the rate of change of mortality is dependent upon an association between the level of income and the rate of change of income.
If low-income countries typically have slowly growing economies, and if the growth of income is positively associated with the gain in life expectancy, then one would observe larger gains in richer countries. This is probably the mechanism that Arriaga and Davis have in mind.
However, the resulting relationship between level of income and change in mortality is clearly dependent upon the more fundamental and logically separable relationship between changes in income and changes in mortality.
We could continue to list possible reasons for expecting a relationship between level of income and change in mortality: But these mechanisms become increasingly speculative and groundless.
There is no persuasive reason for expecting an association with other variables such as income change. Rate of change of income influences rate of change of mortality A cross-sectional relationship between income and mortality, firmly established in the references cited earlier, also implies a dynamic relationship between the two. If the relationship is indeed causal, then a certain change in income should be associated with a particular change in mortality, with relative magnitudes of change determined by coefficients of the relationship.
Additional elements may figure in the dynamic relationship, however. In particular, the cross-sectional relationship between mortality and income may itself be changing in response to new influences.
Malthus, of course, postulated a negative dynamic relationship between mortality and income level as a central tenet of his dismal theory. Those who have recently examined the relationship fail to uncover support for the postulated relationship. Although perhaps obvious, it may be worth emphasizing that such a pattern is not inconsistent with a tight cross-sectional relationship between mortality and economic level throughout the period under consideration, provided that the structure of the cross-sectional relationship is changing.
The relations re-examined It is a straightforward matter to indicate what has happened to the cross-sectional relationship between income and mortality during the 20th century. The accompanying figure presents a scatter diagram of the relationship between level of life expectancy average, male and female and national income per head US dollars in the s, s and s.
The criterion for inclusion was simply the availability of measures of the two variables; however, in the s countries with populations of less than 2 million were excluded in order to reduce sampling variability.
The data on which the figure is based are presented in Appendix 1. Life expectancy is computed by standard, direct methods with a few exceptions noted in the Appendix. This is only one possible way of proceeding, and it is well known that any choice of prices for weighting output in time or space is arbitrary.
The present procedure is the only one capable of yielding as much information as is utilized here. As noted further, it seems virtually certain that a different procedure would not change the fundamental conclusions.
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- Female Literacy Rate is a Better Predictor of Birth Rate and Infant Mortality Rate in India
Attention is focused on the relationships in the s and s, for which most data are available. A logistic curve, plotted on the figure, was fitted to each set of data. Placing all three sets of data on the same graph may obscure the fact that a curve fits the data for a particular period quite well throughout their range Figure 1.
The simple correlation between life expectancy and the logarithm of income per head is 0. Despite the simplicity of approach, the following points can be made with some degree of assurance. The relationship between life expectancy and national income per head has shifted upwards during the 20th century The point can be made with greatest certainty for the period from the s to s.