Testing the Demographic Transition Hypothesis: A Statistical

 

 

 

 

 

 

 

 

 

 

Testing the Demographic Transition Hypothesis:

A Statistical Exploration

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Shadi Abdelaziz

 

 

December 1, 2017

 

 

Part 1:
Introduction and Method

Introduction

            The
phenomenon known as the demographic transition refers to the tendency of developing countries to
experience both lower birth rates and lower death rates, eventually leading to
a condition in which birth rates and death rates are in equilibrium or
near-equilibrium The demographic transition is due to sub-phenomena associated
with development, including the improvement of medicine, the availability and
use of birth control, higher levels of female education, higher levels of
female participation in the workforce, and sociocultural shifts in the desired
number of children per family.

The demographic transition can be
understood in geographic terms. Countries that are in the Global South are more
likely to experience the demographic transition, and most of the countries in
the Global South lie outside North America and Europe. In North America and
Europe, the two continents that predominantly represent the Global North, the
demographic transition has already taken place. Therefore, for these two
continents, it can be associated that there has been no statistically
significant decline in either birth rate or death rate, at least as measured
over the past several years. By contrast, particularly in the continents of
Africa, Asia, and South America—two Continents that predominantly represent the
Global South—there should be evidence of a demographic transition-taking place.
The purposes of this statistical report were to utilize provided data to
determine whether, in fact, there is a demographic transition underway outside
the Global South; and whether the demographic transition in the Global North is
complete.  

 

Method

            The
method of the study was quantitative. The analyses were focused on the
independent variable of continent and the dependent variables of (a) birth rate
and (b) death rate. Paired-samples t-tests
were used to determine whether (a) Europe and North America experienced no
charge in birth rates between 2009 and 2015; (b) Europe and North America
experienced no charge in death rates between 2009 and 2015; (c) Asia, Africa,
and South America experienced no charge in birth rates between 2009 and 2015;
and (d) Asia, Africa, and South America experienced no charge in death rates
between 2009 and 2015. The continents of North America and Europe represented
the Global North, whereas the continents of Asia, Africa, and South America
represented the Global South.  

 

Formulation of Hypotheses

            The
hypotheses of the study were as follows:

H1: There was evidence
of a birth rate-based demographic transition between 2009 and 2015.

H2: There was evidence
of a death rate-based demographic transition between 2009 and 2015.

H3: There was evidence
of a fertility rate-based demographic transition between 2009 and 2015.

 

Part 2: Results
and Analysis

Table of Samples

            All
the data in the 2009 and 2015 birth, 2009 and 2015 death, and 2010 and 2015
fertility  columns of the health
spreadsheet can be considered to comprise the table of samples for the study.

Results and Analysis

            The
first step in the analysis was to generate a table of descriptive statistics
for the variables in the study. This table offered an overview of the data that
would then be subjected to a paired-samples t-test,
resulting in answers to the research questions of the study.

 

Table 1

Change in Birth and Death Rates for
Each Continent

 

Continent

2009 Birth Rate

2015 Birth Rate

Change in 2009-2015
Birth Rate

2009 Death Rate

2015 Death Rate

Change in 2009-2015
Death Rate

2010 Fertility Rate

2015 Fertility Rate

Change in 2010-2015
Fertility Rate

Europe

11.41

10.71

-0.70

9.62

10.18

0.56

1.51

1.53

0.02

North America

19.80

18.64

-1.16

9.44

9.70

0

2.49

2.42

-0.07

South America

22.51

19.80

-2.71

9.82

10.88

1.06

2.81

2.49

-0.32

Africa

38.17

34.34

-.3.83

9.68

10.39

0.71

4.95

4.61

-0.34

Asia

22.35

22.00

-0.35

8.71

8.35

-0.36

3.01

2.86

-0.15

 

 

Table 1 contained the raw data for the
paired-sample t-­tests of the study. Next,
in order to visualize the data, Figures 1, 2, and 3 were generated to compare
the relative changes by continent.

 

Figure 1

Changes in Birth Rate (2009 to 2015) By
Continent

 

Figure 1 suggested the possibility of declines
in the birth rates of Africa and South America, two of the continents in the
Global South.

Figure 2

Changes in Death Rate (2009 to 2015) By
Continent

 

Figure 2 suggested the possibility of
an increase in the death rate of Africa, one of the continents in the Global
South.

 

Figure 3

Changes in Fertility Rate (2010 to
2015) By Continent

 

Finally, Figure 3 suggested that the
greatest fertility declines were in the Global South continents of Africa,
South America, and Asia.

 

 

Results
Pertaining to Birth Rates (H1)

 

            A
paired-samples t-test revealed that
the mean change (-0.70) in the birth rate of Europe from 2009 to 2015
represented a statistically significant decrease, t(41) = -1.74, p =.0445.   

A paired-samples t-test revealed that the mean change
(-1.16) in the birth rate of North America from 2009 to 2015 represented a
statistically significant decrease, t(41)
= -2.29, p =.0144.

A paired-samples t-test revealed that the mean change
(-2.71) in the birth rate of South America from 2009 to 2015 represented a
statistically significant decrease, t(41)
= -2.74, p =.0090.   

A paired-samples t-test revealed that the mean change
(-3.83) in the birth rate of Africa from 2009 to 2015 represented a
statistically significant decrease, t(41)
= -5.86, p <.0001.  A paired-samples t-test revealed that the mean change (-0.35) in the birth rate of Asia from 2009 to 2015 did not represent a statistically significant decrease, t(41) = -0.57, p =.2844.  A paired-samples t-test revealed that the mean change (-0.90) in the birth rate of  the Global North as a whole from 2009 to 2015 represented a statistically significant decrease, t(41) = -2.86, p =.0028.  A paired-samples t-test revealed that the mean change (-2.01) in the birth rate of  the Global South as a whole from 2009 to 2015 represented a statistically significant decrease, t(41) = -4.92, p <.0001.    Results Pertaining to Death Rates (H2)               A paired-samples t-test revealed that the mean change (0.56) in the death rate of Europe from 2009 to 2015 did not represent a statistically significant increase, t(41) = 1.59, p =.0602.    A paired-samples t-test revealed that the mean change (0.26) in the death rate of North America from 2009 to 2015 did not represent a statistically significant increase, t(41) = 0.43, p =.3366. A paired-samples t-test revealed that the mean change (1.06) in the death rate of South America from 2009 to 2015 did not represent a statistically significant increase, t(41) = 1.31, p =.1074.    A paired-samples t-test revealed that the mean change (0.71) in the death rate of Africa from 2009 to 2015 represent a statistically significant increase, t(41) = 2.58, p =.0063.  A paired-samples t-test revealed that the mean change (-0.36) in the death rate of Asia from 2009 to 2015 did not represent a statistically significant decrease, t(41) = -1.56, p =.0621.  A paired-samples t-test revealed that the mean change (0.43) in the death rate of  the Global North as a whole from 2009 to 2015 did not represent a statistically significant increase, t(41) = 1.31, p =.0976.  A paired-samples t-test revealed that the mean change (0.29) in the death rate of  the Global South as a whole from 2009 to 2015 did not represent a statistically significant increase, t(41) = 1.54, p =.0630.   Results Pertaining to Fertility (H3)               A paired-samples t-test revealed that the mean change (0.02) in the fertility rate of Europe from 2010 to 2015 did not represent a statistically significant increase, t(41) = -0.43, p =.6680.    A paired-samples t-test revealed that the mean change (-0.07) in the fertility rate of North America from 2010 to 2015 did not represent a statistically significant increase, t(41) = 0.98, p =.3354. A paired-samples t-test revealed that the mean change (-0.32) in the fertility rate of South America from 2010 to 2015 represented a statistically significant decrease, t(41) = 3.34, p =.1074.    A paired-samples t-test revealed that the mean change (-0.34) in the fertility rate of Africa from 2010 to 2015 represented a statistically significant decrease, t(41) = 3.70, p =.0003.  A paired-samples t-test revealed that the mean change (-0.15) in the fertility rate of Asia from 2010 to 2015 did not represent a statistically significant decrease, t(41) = 2.00, p =.0253.  A paired-samples t-test revealed that the mean change (-0.12) in the fertility rate of  the Global North as a whole from 2010 to 2015 represented a statistically significant decrease, t(84) = 2.94, p =.09021.  A paired-samples t-test revealed that the mean change (-0.23) in the fertility rate of  the Global South as a whole from 2010 to 2015 represented a statistically significant decrease, t(115) = 4.70, p <.0001   Treatment of Missing Values             There were only 3 missing values in the dataset. These missing values were automatically excluded from the paired-samples t-tests carried out for the study.   Summary Graphics             Although individual continents were included in the analysis, the overall analysis was focused on the difference between the Global North and Global South. Figure 4 was created to demonstrate the decrease in birth rates in both the Global North and the Global South in the period under consideration; Figure 5 was created to demonstrate the similarities in death rates from 2009 to 2015, in both the Global North and Global South; and Figure 6 indicated that the fertility decline in the Global South was greater than fertility decline in the Global North.     Figure 4 Changes in Birth Rate (2009 to 2015) By Global North / South     Figure 5 Changes in Death Rate (2009 to 2015) By Global North / South Figure 6 Changes in Fertility Rate (2010 to 2015) By Global North / South     Part 3: Conclusion and Evaluation Conclusion             The demographic transition hypothesis predicts that, over time, developing countries will experience declining birth rates, declining death rates, and declining fertility rates while developed countries will experience low birth rates and death rates (Murtin, 2013, p. 617). The data analyses carried out for this study resulted in mixed support for the demographic transition. The (-0.90)  decline in the birth rate of the Global North as a whole from 2009 to 2015 was statistically significant, suggesting that the demographic transition is ongoing in the developed world. However, the even greater decline (-2.01) in the birth rate of  the Global South as a whole from 2009 to 2015 represented an even greater decrease. These data suggest that the demographic transition, as related to birth rate, is proceeding in the Global South as well, and at a much faster pace (nearly 225% as rapidly) as in the Global North. However, from the death rate data, there is less evidence for the existence of a demographic transition in the Global South. In the Global North, death rates have stabilized at a low rate, which is, in fact, the prediction made by demographic transition theory (Murtin, 2013, p. 620). In the Global South, death rates did not alter significantly from 2009 to 2015, suggesting that this phase of the demographic transition has yet to influence the Global South—at least from the perspective of changes in the 2009-2015 time period. The evidence for a demographic transition was strongest in terms of fertility rates, for which it was observed that the decline of fertility is much greater in the Global South than in the Global North, which already has low fertility rates.       Evaluation             The project went well in terms of aligning the theoretical predictions made in demographic transition theory to the actual data provided for analysis. The paired-samples t-test method also appeared to be an appropriate means of testing the empirical hypotheses generated from demographic transition theory. One of the difficulties of the project was sorting the data by continent rather than by country. If the project could be performed again, it could be improved by choosing specific countries to represent the Global North and Global South; performing the analysis by continent meant that certain countries in Europe (such as Bulgaria) that were closer to developing countries were wrongly included in the Global North, whereas certain developed countries in the Global North (such as Japan) were wrongly included in the Global South. One of the limitations of sorting by continent was this kind of false categorization. In addition, a study of this kind would have been more informative had it been possible to measure changes over a longer period of time than 6 years. Insofar as the demographic transition is a long-term process, it is a phenomenon whose existence could best measured if working with birth and death rate that encompassed a longer period of time (such as 20 or 30 years, but, ideally, even 50 or 100 years). In addition, had more time been available, the demographic transition could have been considered in terms of possible declines in the number of people per household, a variable that is another plausible measure of the demographic transition hypothesis.        References   LOCKWOOD, M. 1997. Reproduction and poverty in sub-Saharan Africa. IDS Bulletin, 28, 91-100. MURTIN, F. 2013. Long-term determinants of the demographic transition, 1870–2000. Review of Economics and Statistics, 95, 617-631. PERELLI-HARRIS, B. & GERBER, T. P. 2011. Nonmarital childbearing in Russia: second demographic transition or pattern of disadvantage? Demography, 48, 317-342.