"Population issues have drawn attention since the times of Aristotle, Plato and Confucius, but quantitative techniques were applied to such scholarly speculation only in the 18^th century" (Samuel). This is an important issue because the rate of population growth is a major concern. Rapid population growth is normally unfavorable because of its "perceived negative impact on resources, the environment, capital accumulation and employment among other arguments" (Samuel). Many have studied solutions to safely and morally reduce the birth rate worldwide. Population growth is slowing down in most parts of the world. This slow down is due in part to declining birth rates. Some of the suggested reasons for falling birth rates are: (1) better education, (2) urbanization, (3) better health care/health education (4) higher income, (5) labor force status, (6) status of women and marrying later.

This study examines the theory that increased education and urbanization leads to reduced birth rates in less developed countries. When women are better educated they tend to have less children. "The variable with the greatest impact on fertility is education, especially of females. It widens horizons, sparks hope, changes status concepts, loosens tradition, and reduces infant mortality. Many studies show that education reduces fertility and raises child survival, income, marriage age, and family planning acceptance" (Samuel).

Education is used in a broad sense. It does not necessarily mean the traditional, formal education. Formal education is important and helpful, but not the only means of education. Non-formal education is learning that takes place outside of schools. These programs are usually "shorter and narrowly focused." They can teach a variety of subjects and skills from contraception to literacy. Finally, informal education is on the job training through experience (Gillis 255). All of these forms are helpful in reducing the birth rate. "The 1990 World Conference on Education for All focused global attention on the need to invest more resources in education, especially basic education. The consensus reached there emphasized that ‘learning begins at birth,’ and recognized that the formal primary education system must be the main vehicle, but insisted that non formal education and literacy programs are indispensable supplements" (Thornley & Williams). This further illustrates the importance and great need for improved education.

Urbanization is the second part of this study. Countries that have a higher percent of the population living in urban areas have lower birth rates. "Urbanization is in turn a prerequisite for declining birth rates" (Polese). The link between urbanization and education is that most city areas are more likely to have schools than do rural areas. Therefore, urbanized areas are more likely to have better access to education, thus contributing to a lower birth rate. Other factors such as health care and a greater income are also more likely in urban areas, making birth rates lower.

Decreasing birth rates are a function of education and urbanization. Several variables were used to test this hypothesis. Forty-one countries were chosen from a list of countries with low to medium levels of gross national products. (See list of countries used pages 1 & 2) For each country data was obtained in the following areas: secondary female school enrollment, adult female illiteracy rate (aged fifteen and above), fertility rate, and infant mortality rate (per one thousand live births). The sources used were: The Illustrated Book of World Rankings 1997, World Development Indicators, the World Development Indicators web-site and several articles on the web describing similar studies on education and urbanization. The year tested was 1997. Scatter plots, regression equations, and mixed regression equations were the means of statistical analysis used. The dependant variables were the birth rate and infant mortality rate. The independent variables were urbanization, illiteracy rate, and secondary education of females.

The hypothesis was proven correct according to the graphed data. The first graph shows infant morality rate as the independent variable and the birth rate as the dependent variable. (Please see page 3) As the confidence of children surviving goes up, the likelihood of having more children declines. In other words, as the infant mortality rate rises the birth rate should rise as well, having a positive relationship between the two variables. The regression equation proves the positive correlation: Birth Rate= 1.90 + 0.0380 (IMR). Using forty degrees of freedom this is significant at the .005 level where the critical value is 2.704 and t-value was 10.36. The R-squared value indicates that 72.7% of the variation in the birth rate can be "explained by" the infant mortality rate. A very significant amount of the birth rate has to do with the infant mortality rate. This indicates that only about 27% of the variation in the birth rate is caused by other factors besides the infant mortality rate.

Infant Mortality Rate (X) Birth Rate (Y): Expect a positive relationship (graph 1)

The second graph plots the female secondary education as the independent variable and the birth rate as the dependent variable. (Please see page 4) The expected result was, as education goes up the birth rate should go down, an inverse relationship between the two variables. The graph clearly proves this correct. The regression equation was in fact a negative relationship. Birth Rate= 6.46 – 0.0426 (secondary education of females). The critical value again indicates that this is statistically significant at the .005 level with a t-value of –5.55. The R-squared value indicates that 42.7% of the variation in the birth rate can be explained by the secondary education of females.

Secondary Education of Females (X) Birth Rate (Y): Expect a negative relationship (graph 2)

Since the infant mortality rate was such a large factor in determining the birth rate the third graph plots the secondary education of females against the infant mortality rate. (Please see page 5) The expected outcome was a negative relationship, as secondary education goes up the infant mortality rate should go down. This was again proven correct. The regression equation was: IMR= 109 - 0.902 (secondary education of females). This relationship was also statistically significant at the .005 level with a t-value of –5.01. The R-squared value showed that 37.6 % of the variation in the infant mortality rate is attributed to the secondary education of females.

Secondary Education of Females (X) Infant Mortality Rate (Y): Expect a negative relationship (graph 3)

The fourth graph plots the illiteracy rate of females as the independent variable and the birth rate as the dependent variable. (Please see page 6) The expected outcome was a positive relationship, as the illiteracy rate goes up the birth rate goes up as well. This was a correct assumption. The regression equation was: Birth Rate= 2.66 + 0.0466 (illiteracy). The data is statistically significant at the .005 level with a t-value of 7.06. The R-squared value shows that 55% of the variation in the birth rate can be explained by illiteracy of adult females. This further proves that as females are better educated, birth rates decline.

Illiteracy Rate (X) Birth Rate (Y): Expect a positive relationship (graph 4)

The fifth graph plots the illiteracy rate as the independent variable and the infant mortality rate as the dependent variable. (Please see page 7) The infant mortality rate was used again, due to the strong relationship it has with the birth rate. The expected outcome was a positive relationship, as the illiteracy rate goes up so does the infant mortality rate. This was a correct assumption. The regression equation was: IMR = 25.9 + 1.05 (illiteracy). The data is statistically significant at the .005 level with a t-value of 7.08. The R-squared value indicates that 55.1% of the variation in the infant mortality rate can be explained by the female illiteracy rate.

Illiteracy Rate (X) Infant Mortality Rate (Y): Expect a positive relationship (graph 5)

The sixth graph moves from education to the effects urbanization has on the birth rate and the infant mortality rate. (Please see page 8) This graph plots urbanization as the independent variable and the birth rate as the dependent variable. As urbanization levels increase the birth rate is expected to decrease according to the hypothesis. Thus, a negative relationship was expected. The regression equation was: Birth rate= 6.09 - 0.0475 (urbanization). This data was statistically significant and the .005 level with a t-value of –4.87. The R-squared value indicates that 36.2% of the variation in the birth rate is attributed to urbanization.

Urbanization (X) Birth Rate (Y): Expect a negative relationship (graph 6)

The seventh graph plots urbanization levels against illiteracy. (Please see page 9) The purpose of this graph was to show the link between urbanization and education. As urbanization levels rise the illiteracy levels should drop. Therefore the two variables should have and inverse relationship. The regression equation was: Illiteracy = 63.5 - 0.757 (urbanization). The data was again statistically significant and the .005 level with a t-value of –4.80. The R-squared value indicated that 35.6% of the variation in the illiteracy rate is explained by urbanization levels.

Urbanization (X) Illiteracy Rate (Y): Expect a negative relationship (graph 7)

The final graph has the urbanization level as the independent variable and the infant mortality rate as the dependent variable. (Please see page 10) The relationship was expected to be negative, as the urbanization level goes up the infant mortality rate should go down. This was a correct hypothesis. The regression equation was: IMR = 109 – 1.21 (urbanization). This again was statistically significant and the .005 level with a t-value of –6.06. The R-squared value indicates that urbanization levels explain 47.2% of the variation in the infant mortality rate.

Urbanization (X) Infant Mortality Rate (Y): Expect a negative relationship (graph 8)

After all the graphs were made a multiple regression line was calculated. The dependent variable was the birth rate and the independent variables were urbanization, infant mortality rate and secondary education of females. (Please see page 11) The regression equation is Birth rate = 3.03 - 0.00120 (urbanization) + 0.0317 (IMR) - 0.0134 (secondary education). The data for the infant mortality rate is statistically significant at the .005 level. The secondary education of females is significant at the .025 level and urbanization is significant at the .40 level. The R-squared value indicates that urbanization, infant mortality rate and secondary education of females can explain 74.1% of the variation in the birth rate. Only about 26% are due to other factors.

A second mixed regression was done excluding urbanization so the secondary education of women and the infant mortality rate could be tested versus the birth rate. The regression equation was Birth rate= 2.97 + 0.0322 (IMR) - 0.0135 (secondary education). Eliminating urbanization from the equation made the t-values of the infant mortality rate and secondary education of women increase slightly. The R-squared value shows that the infant mortality rate and the secondary education of females explain 74.8% of the birth rate.

Although education, urbanization, and decreases in the infant mortality rate account for much of the decline in birth rate, other factors do contribute. One is health care. The more health care given to women and more focus on the infant mortality rate definitely lowers the birth rate. If couples expect the children they have to survive they will be less likely to have more. Later marriage age also helps explain why birth rates are declining. Gaining status in the labor force for females is another factor. "Research has established that female employment, especially non-agricultural, reduces fertility, as well as dependence and increases women’s desire to marry later and limit births" (Samuel). The status of women as a whole is another contributor, not just in the work force. Finally, there is an inverse relationship between birth rates and gross domestic product per capita (Rhodd).

All of the variables tested pointed toward a relationship between education, urbanization and the birth rate. The hypothesis was proven correct from the tests. The birth rate is lower in countries with a higher percentage of people in urban areas, high education rates and low illiteracy rates. Education has more benefits than just contributing to a decreasing birth rate. The economic benefits of increased education include an increase in productivity, increased income, income equality and better income distribution. The social benefits are that people will have more skills for continued learning and are more aware of their rights ("Basic Education").

Other studies have also confirmed what this data has shown. "In developing countries, increased schooling for girls is the top reason why teen childbearing has dropped in the last 20 to 30 years. As more people migrate to urban areas, educational needs increase and some parents are seeing the benefits of keeping their daughters in school, researchers say" (Feldmann). The study also goes on to say that the more educated a woman is the more likely she will be to "delay marriage and parenthood" (Feldmann). "Around the world, delaying parenthood is seen as a key component in efforts to control global population" (Feldmann). The study further illustrates the importance of not only formal school education but family planning education as well. The United States funds an international family planning program that is serving people over the age of 20 but many also work to "help teens delay sexual activity or, if already active, to obtain birth control" (Feldmann). This, at times, becomes a problem in countries where it is customary and encouraged to marry and have children very young. So this problem is a complex one due to different cultures and customs.

Countries that want to decrease the birth rate need to implement policies that include education. Although urbanization is important, it is not as productive in lowering the birth rate as education. Offering government subsidies for education is one way to expand education. The government must also invest in schools and educational programs. Schools must have the appropriate curriculum and supplies in order to be effective. The placement of the schools must also be considered; rural areas need schools as well as urban. Any prejudice that might exist, denying females education must be done away with at once. The tests clearly show that as female education increases the birth rate decreases, making education a necessity. This is a difficult task but one that is much needed and highly beneficial in the long run.

Some interesting follow up research would be to look at other countries and different years to see if the same results would occur. Also, this study focused mainly on the secondary education of females. It would be interesting to test data using primary education of females to see if the outcome would differ. Another way to take the research would be to look at health care due to the strong relationship between the infant mortality rate and birth rate. This topic of lowering birth rates is one that is dynamic and could take several different angles.

Works Cited

"Basic Education: A Precondition for Sustainable Development." Canadian International

Development Agency Nov. 1993. www.acdi-cida.gc.ca/xpress/dex/dex9310.htm

Feldmann, Linda. "Education for Girls Credited for Drop in Teen Birth Rates in Third

World." Christian Science Monitor 13 Feb. 1997: 8.

Gillis, Malcom., et al. Economics of Development. New York: W.W. Norton &

Company, 1996.

Polese, Mario. "Urbanization and Development." Canadian International Development

Agency 4 Nov. 1997. www.acdi-cida.gc.ca/xpress/dex/dex9704.htm

Rhodd, Rupert. "Birth Rates, Economic Growth, and Structural Transformation in

LDC’s." Atlantic Economic Journal 21 (1993): 88.

Samuel, John. "World Population and Development: Retrospect and Prospects."

Thornley, Allan, and Jasmine Williams. "Investing in Society: Health, Education and

Development." Canadian International Development Agency 2 Nov. 1997.

www.acdi-cida.gc.ca/xpress/dex/dex9702.htm

The Illustrated Book of World Rankings, 1997.

World Development Indicators website: www.worldbank.org

1. List of Countries and Statistics
2. List of Countries and Statistics

 

3. Graph 1

 

4. Graph 2

 

5. Graph 3

 

6. Graph 4

 

7. Graph 5

 

8. Graph 6

 

9. Graph 7

 

10. Graph 8

 

11. Mixed Regression

 

12. Mixed Regression