# Determinants of Crude Birth Rate in India

By ranjeetarini
Mar 18, 2011
3070 Words

NAME: RANJEETA BHATTACHARYA

SUBJECT: ADVANCED ECONOMETRICS TERM PAPER

COURSE INSTRUCTOR: PROF. K.L.KRISHNA

TOPIC: THE BIRTH RATE AND ECONOMIC DEVELOPMENT: AN EMPIRICAL STUDY OF DEVELOPING COUNTRIES SUBMISSION DATA: 17/11/2010

INTRODUCTION

The birth rate has long interested economists as well as demographers. In recent years the emphasis of research by the members of both professions has been on the facts and their consequences for economic welfare. It has been agreed almost by every observer that preventive checks are necessary for raising the standard of living above the subsistence level in the underdeveloped areas. However, there is little agreement on the validity of the proposition that economic development automatically will dampen the birth rate. Thus in my paper I try to test empirically the exact relationship between birth rates and certain basic measures of economic development like per capita income and infant mortality. Another issue that in recent times has become a subject of debate is how significant per capita income in explaining cross-country variations in birth rates. In this context majority of the studies and empirical tests have pointed out that compared to per capita income Infant Mortality Rate (IMR) is a more important determinant of birth rates. One more issue that has often been raised is the direction of the relationship between birth rate and per capita income. While the popular thought is that this relationship is negative, the existing empirical studies and the theoretical considerations cannot decide whether this relation is negative or positive. This paper thus tries to provide quantitative information regarding the main explanatory factors of birth rate in developing countries as well as to check amongst per capita income and IMR which is more significant as a determinant of birth rate. At the same time I will try to figure out the nature of relationship between per capita income and birth rate using my set of inter-country data. In many developing countries like Greece, birth rate in the long run has shown a declining trend, which in the recent times has been accompanied by mass emigration of workers from Greece to Western European countries. If allowed to continue, such decline in the rate of population growth would have adverse effects on the countries’ socioeconomic dimensions and viability. Hence the question of whether there exist inherent factors related to the process of economic development that cause such decline in birth rate is of considerable interest for Greece and might have important policy implications for all such countries so as to achieve a satisfactory rate of economic growth in the future. The statistical approach followed in this paper consists in using cross-section data for developing countries at the same point of time. LITERATURE REVIEW-EARLIER RELATED WORK ON THE THEME

As stated above relationship between birth rate and economic development has always been an issue for discussion amongst the economists as well as the demographers. Various statisticians and econometricians have time and again tried to find out the various socio, economic and demographic factors explaining variations in birth rate both across countries as well as within countries across regions. Constantine G. Drakatos in his famous paper “The Determinants of Birth Rate in Developing Countries: An Econometric Study of Greece” provided a quantitative information regarding the main explanatory variables of birth rate in Greece with a purpose to investigate whether economic development, per se, has a dampening effect on population growth. He used cross-section data for different geographic regions across Greece at the same point of time. Drakatos using the method of OLS regressed crude birth rate on percentage of population of both sexes in the age bracket 15-44 years, IMR (demographic factors), level of development in various regions reflected in per capita income (economic factor), ratio of rural to total population and level of education given by percentage of literates in above-ten-years age group for ten major geographic regions of Greece in the early 1960s. He found that the regression coefficient of per capita income is negative though the sign of this coefficient is conclusive. He also concluded from his study that variables like percentage literates and proportion of rural population are more useful than per capita income in explaining inter regional birth rate variations. His results were consistent with Adelman’s inter country results in ranking the importance of the above two variables who used logarithmic instead of liner regression. Ekanem also in his paper (1972) drew similar conclusions. He on the basis of regression of a group of 32 underdeveloped countries for two years (1950 and 1960) concluded that “a decrease in literacy and IMR are optimal conditions of low fertility”. His results however gave some indication that the regression coefficients are not invariant over time. This suggested that any of the equations to make predictions of the course of fertility in a given country may be inappropriate since the effects of socioeconomic variables on fertility change over time (Barbara S. Janowitz). Robert Weintraub in his paper “The Birth Rate and Economic Development: An Empirical Study” empirically tested the relationship between birth rate and three basic measures of economic development-per capita income, ratio of population in farming and IMR for a group of thirty nations in the early 1950s. He deliberately ignored the relationship between birth rate and demographic factors such as age structure of the populations. The results confirm to the Malthusian hypothesis that increases in income generate increases in birth rate as well as the more widely held hypothesis that birth rates decline with urbanization and decreases in IMR. Hence we can see that there is more or less a consensus regarding the impact of IMR on birth rate. All the studies confirmed to the fact that declining IMR results in decreases in birth rates. As far as the relationship between per capita income and birth rate is concerned there has been a quite a mixed result. THE MODEL AND THE VARIABLES

As independent variables explaining variations in birth rates across developing countries I have incorporated demographic, economic and socio-economic factors.

Among the demographic factors which are assumed to be important to determine birth rates, is the ratio of reproductive population to total population. This is justified by the fact that emigration brings about changes in the age structure of the population. Thus by including in the relationship a variable reflecting the percentage of population of both sexes in the age bracket of 15-64 years, it is possible to estimate its impact on birth rate and compare it with the effect of other socioeconomic factors. Infant Mortality Rate( IMR) is also regarded as a demographic factor affecting birth rate, as better survival prospects amongst new-born may make parents limit the number of their offspring. The level of development in different countries is also assumed to be an important determinant of variations in birth rate. This factor is considered to be reflected in per capita income. Another set of factors responsible for birth rate differentiations among individual countries is associated with the economic and social changes that take place as a result of development process. In the first place, past experience with other countries shows that the ratio of rural to total population exerts a strong positive influence on family size. This can be justified on economic as well as social grounds. In fact, rural families tend to be larger than urban families, because farm children take part in the production process, and, at any rate, the cost of upbringing children in rural areas is far less than in the cities. Another factor affecting the birth rate is the level of education. Cultural improvements make possible some kind of family planning, thereby limiting the number of births--especially among low-income groups-to the point corresponding to existing economic conditions. The percentage of literates in the over-fifteen-years age group is thus taken as an indicator of this variable. On the basis of the above considerations, the explanatory variables used in the birth rate (Br) function are the percentage of reproductive population (Pr), per capita income (Y,), the percentage of rural population (A,), and the percentage of literates in the over-fifteen-years age group (E) and IMR (I). The model then to be estimated is given as follows:

Br= β0 + β1Pir + β2Yic + β3Aip + β4Ei1 + β5Ii +ui ………… (1)

where β0 is the constant or the intercept term and β1,β2,β3,β4 are the slope coefficients. ui is the error or the disturbance term, i=1,2,…..,n.

METHODOLOGY

To find out the determinants significant in explaining cross-country variations in crude birth rates we here apply the method of OLS. Using Stata we regress crude birth rate on all the explanatory variables mentioned above. We are able to apply the method of OLS as the model does not pose any problem of multicollinearity (which will be shown in the section results). After the estimation process is completed we check the significance of the explanatory variables at a given level of significance and derive our results. Framing of Hypothesis

The hypothesis to be tested here is whether per capita income is significant in explaining variations in birth rates across the developing nations. At the same time we test how significant is IMR in explaining the same. Thus the hypotheses to be tested are as follows: Ho: β2 = 0 against H1: β2≠ 0 ……… (a)

H0: β5 = 0 against H1: β5≠ 0………. (b)

These hypotheses are tested using t-test at α level of significance. We reject the null hypotheses H0 by using the usual rejection rule i.e. if the calculated value of the t statistic exceeds the critical value we fail to accept the null hypothesis and instead accept the alternative hypothesis H1. If we accept the null hypothesis then it implies the variables under consideration is not significant at the given level of significance and so can be dropped from the regression equation, otherwise remains significant in explaining the dependent variable. DATA SOURCE

For the purpose of regression I have taken data on birth rates and all the explanatory variables for a group of 63 developing countries for the year 2000. The data on crude birth rate have been collected from US CENSUS BUREAU, INTERNATIONAL DATA BASE. The data on all the independent variables have been taken from WORLD DEVELOPMENT INDICATORS, WORLD BANK. As the explanatory variables used are quite straightforward they were readily available with the sources mentioned above and hence no need was felt to construct the variables from the basic data base. The 63 countries chosen have been taken from IMF’s ranking of the developing nations of the World for the year 2010. Crude Birth Rate is being by number of births per thousand population, IMR as number of deaths per 1000 live births, per capita GDP in PPP per US $ and the remaining in percentages.

Two sets of data have been however a little modified which are percentage of males and females in the total population in the age bracket 15-64 years and percentage of literates. Keeping in mind the fact that primary education is essentially as important as secondary I initially wanted to regress percentage of literates in the age bracket 10 years and above. However due to lack of the relevant data I changed it into age bracket fifteen years and above. Similarly the variable proportion of both sexes in the age group 15-64 years has been modified due to the same reason. The reason to incorporate this variable is to measure the level of fertility in the countries which plays an important role in explaining birth rate variations. The actual or the most relevant age group would have 15-44 years however due to data unavailability it could not be included. RESULTS

The results obtained from regressing Crude Birth Rate (Br) on percentage of reproductive population (Pr), per capita income (Y,), the percentage of rural population (A,), and the percentage of literates in the over-fifteen-years age group (E) and IMR (I) are as follows: TABLE 1: RESULTS FROM REGRESSION OF Br ON Pr, Yc, Ap, I AND E1

As can be seen from the above regression table for n=63 variables- IMR, proportion of both sexes to total population in the age group 15-64 years are statistically significant. The constant or the intercept term has also been found significant. Per Capita Income (PCI) however has been found relatively insignificant in my statistical analysis with the p-value being greater than 0 for PCI. Explanatory factors like proportion of population in rural areas has been found statistically insignificant and thus can be dropped from the regression equation. Surprisingly percentage of literates too has been found relatively insignificant in explaining cross country variation in birth rates. Another observation from the above table that can be drawn is that among the five indicators chosen two of them have been found to share a negative relation with crude birth rate. These are proportion of both sexes in the age bracket of 15-64 years and percentage of literates in the fifteen and above age group. Per Capita Income has been found to exhibit a positive relation with birth rate however. The value of R2 which measures the extent to which all the explanatory variables explain the variations in the dependent variable is 0.9177 which implies that the five independent variables taken together explain 91.77 % variation in crude birth rate. This is a very significant observation drawn from the regression process. In the section where I discussed the methodology I mentioned the problem of multi-collinearity that might crop up in some regressions. However in my paper this problem has not be found and the following table actually substantiates the point. We detect multi-collinearity by comparing the R2 obtained from an auxiliary regression with the R2 of the original regression. We now thus regress birth rate on all the four variables and drop out the variable percentage of rural to total population (which has the least significant or rather insignificant statistically) we get R2 = 0.9177. As the R2 value remains the same we can infer that there is no problem of multi-collinearity in our model (using Klein’s rule of thumb). TABLE 2: REGRESSION TABLE TO DETECT MULTI-COLLINEARITY

DISCUSSION OF RESULTS

From the regression tables aforementioned it is evident that among the five explanatory variables IMR, proportion of both sexes in the total population in the age group of 15-64 years are absolutely significant in explaining the variations in birth rates across the developing countries. Per Capita Income is relatively insignificant in explaining such variations and fluctuations. This actually fails to prove that per capita income increases have a dampening effect on birth rate. On the contrary for the developing nations IMR is found to be a better indicator in explaining trends in birth rates. The positive relationship between Per Capita Income and Birth Rate is also worth discussing. While it has been thought for a long time that increases in income will eventually lead to decline in birth rate and dampen the negative impacts of population growth, the given regression result says just the opposite. Though there has been a considerable debate over this relationship both in theories as well in the empirics, this result can be supported by the fact that birth rate will increase as income rises as income opportunities would provide families to raise additional children. The value of R2 for the original regression appears to be a quite impressive value. As has been already stated the five independent variables taken together explain about 91.77 % variations in birth rates. However, from the table it is visible that percentage of rural to total population is insignificant statistically. Thus we can easily drop this variable and run the regress of birth rate on the rest of four variables. The result obtained is given in Table 2 above. What we see is after this variable has been dropped the value of R2 remains the same which also proves the same idea that percentage of rural population is not able to explain fluctuations in birth rates. The reason that can be identified for this is that proportion of rural population is more efficient in explaining variations across regions within a country rather than across countries. This observation was found in Drakatos’ paper where he tried to explain the determinants for variations in birth rates across regions of Greece. However, after dropping this variable Per Capita Income becomes a little more significant than before, though no specific explanation cannot be provided for this. As far as the hypothesis testing is concerned we can conclude that for IMR we cannot accept the null hypothesis saying that β5=0 and so do we cannot for Per Capita Income at 95% confidence level. However when compared with IMR turns out to be relatively less significant in explaining variations in birth rates.

CONCLUDING REMARKS

The above inter-country analysis gives evidence for the long run negative impacts of country’s socioeconomic development on birth rate. This study however fails to prove that increases in income, per se, have a dampening effect on population growth. An interesting finding of this study is that per capita income is not so useful in explaining the variance in the birth rate. Instead demographic factors like proportion of both sexes in the age group of 15-64 years and particularly IMR gives a better fir and significant coefficients. In short, my results tally with the existing literature on this theme. The estimated equations, incorporating these socioeconomic and demographic factors, can be used for broad population and manpower projections. More elaborate econometric analysis in this field is, of course, required for more accurate and detailed forecasts.

BIBLIOGRAPHY

* Barbara S. Janowitz, “Cross-section Studies as Predictors of Trends in Birth Rates: A Note on Ekanem’s Results”, Demography, Vol. 10, No.3 (Aug 1973), pp.479-481. * Constantine G. Drakatos, “The Determinants of Birth Rates in Developing Countries: An Econometric Study of Greece”, Economic Development and Cultural Change, Vol.17, No.4 (July 1969), pp.596-603. * David R. Kamerschen, “The Determinants of Birth Rates in Developing Countries: A Comment”, Economic Development and Cultural Change, Vol.20, No. 2 (Jan 1972) ,pp. 310-315. * Robert Weintraub “The Birth Rate and Economic Development: An Empirical Study”, Vol.30, No.4 (Oct 1962), pp.812-817.

SUBJECT: ADVANCED ECONOMETRICS TERM PAPER

COURSE INSTRUCTOR: PROF. K.L.KRISHNA

TOPIC: THE BIRTH RATE AND ECONOMIC DEVELOPMENT: AN EMPIRICAL STUDY OF DEVELOPING COUNTRIES SUBMISSION DATA: 17/11/2010

INTRODUCTION

The birth rate has long interested economists as well as demographers. In recent years the emphasis of research by the members of both professions has been on the facts and their consequences for economic welfare. It has been agreed almost by every observer that preventive checks are necessary for raising the standard of living above the subsistence level in the underdeveloped areas. However, there is little agreement on the validity of the proposition that economic development automatically will dampen the birth rate. Thus in my paper I try to test empirically the exact relationship between birth rates and certain basic measures of economic development like per capita income and infant mortality. Another issue that in recent times has become a subject of debate is how significant per capita income in explaining cross-country variations in birth rates. In this context majority of the studies and empirical tests have pointed out that compared to per capita income Infant Mortality Rate (IMR) is a more important determinant of birth rates. One more issue that has often been raised is the direction of the relationship between birth rate and per capita income. While the popular thought is that this relationship is negative, the existing empirical studies and the theoretical considerations cannot decide whether this relation is negative or positive. This paper thus tries to provide quantitative information regarding the main explanatory factors of birth rate in developing countries as well as to check amongst per capita income and IMR which is more significant as a determinant of birth rate. At the same time I will try to figure out the nature of relationship between per capita income and birth rate using my set of inter-country data. In many developing countries like Greece, birth rate in the long run has shown a declining trend, which in the recent times has been accompanied by mass emigration of workers from Greece to Western European countries. If allowed to continue, such decline in the rate of population growth would have adverse effects on the countries’ socioeconomic dimensions and viability. Hence the question of whether there exist inherent factors related to the process of economic development that cause such decline in birth rate is of considerable interest for Greece and might have important policy implications for all such countries so as to achieve a satisfactory rate of economic growth in the future. The statistical approach followed in this paper consists in using cross-section data for developing countries at the same point of time. LITERATURE REVIEW-EARLIER RELATED WORK ON THE THEME

As stated above relationship between birth rate and economic development has always been an issue for discussion amongst the economists as well as the demographers. Various statisticians and econometricians have time and again tried to find out the various socio, economic and demographic factors explaining variations in birth rate both across countries as well as within countries across regions. Constantine G. Drakatos in his famous paper “The Determinants of Birth Rate in Developing Countries: An Econometric Study of Greece” provided a quantitative information regarding the main explanatory variables of birth rate in Greece with a purpose to investigate whether economic development, per se, has a dampening effect on population growth. He used cross-section data for different geographic regions across Greece at the same point of time. Drakatos using the method of OLS regressed crude birth rate on percentage of population of both sexes in the age bracket 15-44 years, IMR (demographic factors), level of development in various regions reflected in per capita income (economic factor), ratio of rural to total population and level of education given by percentage of literates in above-ten-years age group for ten major geographic regions of Greece in the early 1960s. He found that the regression coefficient of per capita income is negative though the sign of this coefficient is conclusive. He also concluded from his study that variables like percentage literates and proportion of rural population are more useful than per capita income in explaining inter regional birth rate variations. His results were consistent with Adelman’s inter country results in ranking the importance of the above two variables who used logarithmic instead of liner regression. Ekanem also in his paper (1972) drew similar conclusions. He on the basis of regression of a group of 32 underdeveloped countries for two years (1950 and 1960) concluded that “a decrease in literacy and IMR are optimal conditions of low fertility”. His results however gave some indication that the regression coefficients are not invariant over time. This suggested that any of the equations to make predictions of the course of fertility in a given country may be inappropriate since the effects of socioeconomic variables on fertility change over time (Barbara S. Janowitz). Robert Weintraub in his paper “The Birth Rate and Economic Development: An Empirical Study” empirically tested the relationship between birth rate and three basic measures of economic development-per capita income, ratio of population in farming and IMR for a group of thirty nations in the early 1950s. He deliberately ignored the relationship between birth rate and demographic factors such as age structure of the populations. The results confirm to the Malthusian hypothesis that increases in income generate increases in birth rate as well as the more widely held hypothesis that birth rates decline with urbanization and decreases in IMR. Hence we can see that there is more or less a consensus regarding the impact of IMR on birth rate. All the studies confirmed to the fact that declining IMR results in decreases in birth rates. As far as the relationship between per capita income and birth rate is concerned there has been a quite a mixed result. THE MODEL AND THE VARIABLES

As independent variables explaining variations in birth rates across developing countries I have incorporated demographic, economic and socio-economic factors.

Among the demographic factors which are assumed to be important to determine birth rates, is the ratio of reproductive population to total population. This is justified by the fact that emigration brings about changes in the age structure of the population. Thus by including in the relationship a variable reflecting the percentage of population of both sexes in the age bracket of 15-64 years, it is possible to estimate its impact on birth rate and compare it with the effect of other socioeconomic factors. Infant Mortality Rate( IMR) is also regarded as a demographic factor affecting birth rate, as better survival prospects amongst new-born may make parents limit the number of their offspring. The level of development in different countries is also assumed to be an important determinant of variations in birth rate. This factor is considered to be reflected in per capita income. Another set of factors responsible for birth rate differentiations among individual countries is associated with the economic and social changes that take place as a result of development process. In the first place, past experience with other countries shows that the ratio of rural to total population exerts a strong positive influence on family size. This can be justified on economic as well as social grounds. In fact, rural families tend to be larger than urban families, because farm children take part in the production process, and, at any rate, the cost of upbringing children in rural areas is far less than in the cities. Another factor affecting the birth rate is the level of education. Cultural improvements make possible some kind of family planning, thereby limiting the number of births--especially among low-income groups-to the point corresponding to existing economic conditions. The percentage of literates in the over-fifteen-years age group is thus taken as an indicator of this variable. On the basis of the above considerations, the explanatory variables used in the birth rate (Br) function are the percentage of reproductive population (Pr), per capita income (Y,), the percentage of rural population (A,), and the percentage of literates in the over-fifteen-years age group (E) and IMR (I). The model then to be estimated is given as follows:

Br= β0 + β1Pir + β2Yic + β3Aip + β4Ei1 + β5Ii +ui ………… (1)

where β0 is the constant or the intercept term and β1,β2,β3,β4 are the slope coefficients. ui is the error or the disturbance term, i=1,2,…..,n.

METHODOLOGY

To find out the determinants significant in explaining cross-country variations in crude birth rates we here apply the method of OLS. Using Stata we regress crude birth rate on all the explanatory variables mentioned above. We are able to apply the method of OLS as the model does not pose any problem of multicollinearity (which will be shown in the section results). After the estimation process is completed we check the significance of the explanatory variables at a given level of significance and derive our results. Framing of Hypothesis

The hypothesis to be tested here is whether per capita income is significant in explaining variations in birth rates across the developing nations. At the same time we test how significant is IMR in explaining the same. Thus the hypotheses to be tested are as follows: Ho: β2 = 0 against H1: β2≠ 0 ……… (a)

H0: β5 = 0 against H1: β5≠ 0………. (b)

These hypotheses are tested using t-test at α level of significance. We reject the null hypotheses H0 by using the usual rejection rule i.e. if the calculated value of the t statistic exceeds the critical value we fail to accept the null hypothesis and instead accept the alternative hypothesis H1. If we accept the null hypothesis then it implies the variables under consideration is not significant at the given level of significance and so can be dropped from the regression equation, otherwise remains significant in explaining the dependent variable. DATA SOURCE

For the purpose of regression I have taken data on birth rates and all the explanatory variables for a group of 63 developing countries for the year 2000. The data on crude birth rate have been collected from US CENSUS BUREAU, INTERNATIONAL DATA BASE. The data on all the independent variables have been taken from WORLD DEVELOPMENT INDICATORS, WORLD BANK. As the explanatory variables used are quite straightforward they were readily available with the sources mentioned above and hence no need was felt to construct the variables from the basic data base. The 63 countries chosen have been taken from IMF’s ranking of the developing nations of the World for the year 2010. Crude Birth Rate is being by number of births per thousand population, IMR as number of deaths per 1000 live births, per capita GDP in PPP per US $ and the remaining in percentages.

Two sets of data have been however a little modified which are percentage of males and females in the total population in the age bracket 15-64 years and percentage of literates. Keeping in mind the fact that primary education is essentially as important as secondary I initially wanted to regress percentage of literates in the age bracket 10 years and above. However due to lack of the relevant data I changed it into age bracket fifteen years and above. Similarly the variable proportion of both sexes in the age group 15-64 years has been modified due to the same reason. The reason to incorporate this variable is to measure the level of fertility in the countries which plays an important role in explaining birth rate variations. The actual or the most relevant age group would have 15-44 years however due to data unavailability it could not be included. RESULTS

The results obtained from regressing Crude Birth Rate (Br) on percentage of reproductive population (Pr), per capita income (Y,), the percentage of rural population (A,), and the percentage of literates in the over-fifteen-years age group (E) and IMR (I) are as follows: TABLE 1: RESULTS FROM REGRESSION OF Br ON Pr, Yc, Ap, I AND E1

As can be seen from the above regression table for n=63 variables- IMR, proportion of both sexes to total population in the age group 15-64 years are statistically significant. The constant or the intercept term has also been found significant. Per Capita Income (PCI) however has been found relatively insignificant in my statistical analysis with the p-value being greater than 0 for PCI. Explanatory factors like proportion of population in rural areas has been found statistically insignificant and thus can be dropped from the regression equation. Surprisingly percentage of literates too has been found relatively insignificant in explaining cross country variation in birth rates. Another observation from the above table that can be drawn is that among the five indicators chosen two of them have been found to share a negative relation with crude birth rate. These are proportion of both sexes in the age bracket of 15-64 years and percentage of literates in the fifteen and above age group. Per Capita Income has been found to exhibit a positive relation with birth rate however. The value of R2 which measures the extent to which all the explanatory variables explain the variations in the dependent variable is 0.9177 which implies that the five independent variables taken together explain 91.77 % variation in crude birth rate. This is a very significant observation drawn from the regression process. In the section where I discussed the methodology I mentioned the problem of multi-collinearity that might crop up in some regressions. However in my paper this problem has not be found and the following table actually substantiates the point. We detect multi-collinearity by comparing the R2 obtained from an auxiliary regression with the R2 of the original regression. We now thus regress birth rate on all the four variables and drop out the variable percentage of rural to total population (which has the least significant or rather insignificant statistically) we get R2 = 0.9177. As the R2 value remains the same we can infer that there is no problem of multi-collinearity in our model (using Klein’s rule of thumb). TABLE 2: REGRESSION TABLE TO DETECT MULTI-COLLINEARITY

DISCUSSION OF RESULTS

From the regression tables aforementioned it is evident that among the five explanatory variables IMR, proportion of both sexes in the total population in the age group of 15-64 years are absolutely significant in explaining the variations in birth rates across the developing countries. Per Capita Income is relatively insignificant in explaining such variations and fluctuations. This actually fails to prove that per capita income increases have a dampening effect on birth rate. On the contrary for the developing nations IMR is found to be a better indicator in explaining trends in birth rates. The positive relationship between Per Capita Income and Birth Rate is also worth discussing. While it has been thought for a long time that increases in income will eventually lead to decline in birth rate and dampen the negative impacts of population growth, the given regression result says just the opposite. Though there has been a considerable debate over this relationship both in theories as well in the empirics, this result can be supported by the fact that birth rate will increase as income rises as income opportunities would provide families to raise additional children. The value of R2 for the original regression appears to be a quite impressive value. As has been already stated the five independent variables taken together explain about 91.77 % variations in birth rates. However, from the table it is visible that percentage of rural to total population is insignificant statistically. Thus we can easily drop this variable and run the regress of birth rate on the rest of four variables. The result obtained is given in Table 2 above. What we see is after this variable has been dropped the value of R2 remains the same which also proves the same idea that percentage of rural population is not able to explain fluctuations in birth rates. The reason that can be identified for this is that proportion of rural population is more efficient in explaining variations across regions within a country rather than across countries. This observation was found in Drakatos’ paper where he tried to explain the determinants for variations in birth rates across regions of Greece. However, after dropping this variable Per Capita Income becomes a little more significant than before, though no specific explanation cannot be provided for this. As far as the hypothesis testing is concerned we can conclude that for IMR we cannot accept the null hypothesis saying that β5=0 and so do we cannot for Per Capita Income at 95% confidence level. However when compared with IMR turns out to be relatively less significant in explaining variations in birth rates.

CONCLUDING REMARKS

The above inter-country analysis gives evidence for the long run negative impacts of country’s socioeconomic development on birth rate. This study however fails to prove that increases in income, per se, have a dampening effect on population growth. An interesting finding of this study is that per capita income is not so useful in explaining the variance in the birth rate. Instead demographic factors like proportion of both sexes in the age group of 15-64 years and particularly IMR gives a better fir and significant coefficients. In short, my results tally with the existing literature on this theme. The estimated equations, incorporating these socioeconomic and demographic factors, can be used for broad population and manpower projections. More elaborate econometric analysis in this field is, of course, required for more accurate and detailed forecasts.

BIBLIOGRAPHY

* Barbara S. Janowitz, “Cross-section Studies as Predictors of Trends in Birth Rates: A Note on Ekanem’s Results”, Demography, Vol. 10, No.3 (Aug 1973), pp.479-481. * Constantine G. Drakatos, “The Determinants of Birth Rates in Developing Countries: An Econometric Study of Greece”, Economic Development and Cultural Change, Vol.17, No.4 (July 1969), pp.596-603. * David R. Kamerschen, “The Determinants of Birth Rates in Developing Countries: A Comment”, Economic Development and Cultural Change, Vol.20, No. 2 (Jan 1972) ,pp. 310-315. * Robert Weintraub “The Birth Rate and Economic Development: An Empirical Study”, Vol.30, No.4 (Oct 1962), pp.812-817.