Analysis of birth rate across different countries


Section No. Title Pg. No.

1 Research Problem/Objective 2

2 Data Description/Introduction 3

3 Analysis 5

4 Conclusion/Recommendation 14

5 Appendix


Birth rate is the total number of live births per 1000 of a population per year. With great development and other changes, change in birth rates have also been is also observed that there are higher birth rates in developing countries as compared to developed ones due to various factors.

Tracking age specific and race/ethnicity specific trends in fertility and birth rates also provides information on the divergent needs of different population groups.

Demographers have attempted to explain the experience of these more developed countries as a demographic transition from high birth rates and death rates to the current low levels. This process tends to occur in three stages. First, birth and death rates are both high, so little growth occurs. Second, death rates fall due to improved living conditions, while birth rates remain high. During this period population grows rapidly. The third stage of the transition is reached when fertility falls and closes the gap between birth and death rates, resulting again in a slower pace of population growth. All the more developed countries have entered this third stage of the demographic transition. Some have gone on to a fourth stage in which death rates are higher than birth rates, and the population declines.

There is a concern about declining birth rates in both the developing and developed world. Fertility rates tend to be higher in poorly resourced countries but due to high maternal and perinatal mortality, there is a reduction in birth rates. In developing countries children are needed as a labour force and to provide care for their parents in old age. In these countries, fertility rates are higher due to the lack of access to contraceptives and generally lower levels of female education. The social structure, religious beliefs, economic prosperity and urbanisation within each country are likely to affect birth rates as well as abortion rates, developed countries tend to have a lower fertility rate due to lifestyle choices associated with economic affluence where mortality rates are low, birth control is easily accessible and children often can become an economic drain caused by housing, education cost and other cost involved in bringing up children. Higher education and professional careers often mean that women have children late in life. This can result in a demographic economic paradox.


The objective of this study is to analyse the birth rate of different countries

In order to fulfil the objective, we will be using simple statistical tools to analyse the data. More specifically, we will be using various descriptive statistics and graphs to come to a certain conclusion.


The subject of my study involves the analysis of the birth rate of various countries using statistical tools which helps in drawing a comparison between two and more variables(dependent or independent variables)

The data involves the comparison between countries over the years about the fluctuating birth rate.

The variables I have used are expressed in absolute value; different graphs like scatter diagram, bar diagram, pie charts are used in the analysis to bring out a conclusion.

The independent data in this set is 2006 and the dependent data is 2013.


2006 2007 2008

Mean 16.73333333 Mean 16.62176667 Mean 16.47933333

Standard Error 1.487057832 Standard Error 1.434890948 Standard Error 1.379703058

Median 13.5 Median 13.7625 Median 13.45

Mode 12 Mode 10 Mode 14

Standard Deviation 8.14495119 Standard Deviation 7.8592214 Standard Deviation 7.556944873

Sample Variance 66.34022989 Sample Variance 61.76736101 Sample Variance 57.10741582

Kurtosis 3.051975283 Kurtosis 2.785939036 Kurtosis 2.67560712

Skewness 1.633738288 Skewness 1.579468405 Skewness 1.57375349

Range 36 Range 34.061 Range 32.599

Minimum 8 Minimum 8.3 Minimum 8.3

Maximum 44 Maximum 42.361 Maximum 40.899

Sum 502 Sum 498.653 Sum 494.38

Count 30 Count 30 Count 30

2009 2010 2011

Mean 16.12763333 Mean 15.88726667 Mean 15.56563333

Standard Error 1.350795812 Standard Error 1.310129314 Standard Error 1.279641597

Median 13.15 Median 12.95 Median 12.75

Mode #N/A Mode 11.9 Mode 11

Standard Deviation 7.398613367 Standard Deviation 7.175873783 Standard Deviation 7.008885683

Sample Variance 54.73947976 Sample Variance 51.49316455 Sample Variance 49.12447852

Kurtosis 2.358119276 Kurtosis 2.1378749 Kurtosis 1.90013921

Skewness 1.519346864 Skewness 1.479509882 Skewness 1.434736645

Range 31.314 Range 29.652 Range 28.456

Minimum 8.1 Minimum 8.3 Minimum 8.1

Maximum 39.414 Maximum 37.952 Maximum 36.556

Sum 483.829 Sum 476.618 Sum 466.969

Count 30 Count 30 Count 30

2012 2013

Mean 15.32383333 Mean 14.9683

Standard Error 1.243475302 Standard Error 1.22247967

Median 12.7 Median 12.4

Mode 12.6 Mode 10

Standard Deviation 6.810794723 Standard Deviation 6.695796912

Sample Variance 46.38692476 Sample Variance 44.83369629

Kurtosis 1.737216365 Kurtosis 1.552489747

Skewness 1.401659686 Skewness 1.367594171

Range 27.054 Range 25.865

Minimum 8.2 Minimum 8.2

Maximum 35.254 Maximum 34.065

Sum 459.715 Sum 449.049

Count 30 Count 30

This descriptive analysis shows that the mean is greater than the median which shows that the data is positively skewed.

As shown in the graph the mean in 2006 was 16.73333 and in 2013 was 14.9683. The country which has lowest birth rate in 2006 is Germany that accounts to be 8 and the country which has the highest birth rate in 2006 is Afghanistan that accounts to be 44. The country which has the lowest in 2013 is Japan i.e 8.2 and the country which has the highest birth rate in 2013 is Afghanistan i.e 34.065. The reason for low birth rate can be welfare state, secularism and feminism whereas for high birth rate could be early marriage,poverty and illiteracy.


Mode is a set of a number that occurs most often.


The median of a data set is the middle number when the data items are arranged in ascending order. The median in the year 2006 was 13.5 and in the year 2013 was observer to be 12.4.


Interquartile of the data set is the difference between the third quartile (also known as the 75th percentile) and the first quartile (also known as the 25th percentile). The interquartile of 2006 in 10.5 and for 2013 is 9.366


Percentile provides us with the information about how a data is spread over the interval from the lowest to the highest value. The percentile of 2006 is 12 and of 2013 is 10.63.


Range is the difference between the smallest and the largest value.


Variance is based on the difference between the value of each observation and it’s mean. It is the average of the squared differences. The variance of 2006 is 66.34022989 and 2013 is 44.83369629

Standard Deviation

Standard Deviation of the data set is the square root of the variance. It is more easily interpreted than the variance. The standard deviation in 2006 is 8.14495119 and in the year 2013 is 6.695796912.

A Histogram diagram consists of rectangles whose area is proportional to the frequency of a variable and whose width is equal to the class interval. In the above histogram data of the year 2006 is shown.


Correlation coefficients defined as

Rxy = sxy /


Rxy = sample correlation coefficient

Sxy= sample covariance

Sx= sample standard deviation of x

Sy = sample standard deviation of y

The correlation of coefficients is 0.981481813. It is near to positive 1 which means it has a positive relation. This data shows that birth rate per 1000 people is good as it is near to positive 1 which means there are good health facilities which enhance towards betterment of number of births per 1000 people across the world.


It is the measure of much two variables change with respect to each other.

For a sample size of n with observations ( x1, y1) , (x2 , y2) covariance is,

Sxy = Σ(xi— x Ì…)(yi—y Ì…)/ n-1

The covariance sample in this case is 53.52701379 for the year 2006 and 2013.


Regression is used to observe how two variables for example x and y are related to each other and the equation for regression is

y = βo +β1x

where E(y) is the estimated value of y for a given value of x and βo is the y- intercept of the regression line and β1 is the value of the slope.

The Regression in this case is 0.963306549 which is near to positive 1. This means it has a strong positive relationship. The linear regression line in the above scatter plot slopes upwards which shows a positive relationship between “x” axis and “y” axis.


As seen above, it can be concluded that birth rate was more in the yester years than it is in the newer years .We could analyse this through the various measures which are shown above. In 2006 the birth rate was more than in 2013. If we try to compare the birth rate between the under developed, developing and developed countries there is downfall seen in the birth rate. This could be because of poverty and lack of education as in the poor families many think that children are a helping hand for the family. Another reason can be preference of a male child which is due to religious beliefs and social conditions. There should be some measures in order to increase birth rate. Some of them are:-

Outlawing abortions

Increase day care services

Bonuses for more than 1 child

Tax credits for larger families

Improved health facilities

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