This study examines the relationship between demographic factors and GDP. Regression analysis showed that a country's GDP is best explained by two factors: industrial development and demographics. Demographic factors like birth rate, health spending, and median age 25 years prior had a significant positive relationship with GDP. The analysis identified three country clusters but they did not improve the regression model. In conclusion, demographic dividends from factors like a large workforce significantly contribute to a country's GDP.

1. qwertyuiopasdfghjklzxcvbnmq
wertyuiopasdfghjklzxcvbnmqw
ertyuiopasdfghjklzxcvbnmqwer
Dependence of Demographic Factors on GDP
tyuiopasdfghjklzxcvbnmqwerty
Anant Damle (PGP-11-102)
uiopasdfghjklzxcvbnmqwertyui
opasdfghjklzxcvbnmqwertyuiop
asdfghjklzxcvbnmqwertyuiopas
dfghjklzxcvbnmqwertyuiopasdf
ghjklzxcvbnmqwertyuiopasdfgh
jklzxcvbnmqwertyuiopasdfghjkl
zxcvbnmqwertyuiopasdfghjklzx
cvbnmqwertyuiopasdfghjklzxcv

2. Introduction Total Population and Labour Participation
This paper looks at the impact of birth rate on Rate for a particular year is a long term effect
long term GDP of countries throughout the of historical Birth Rate2.
world. The GDP is also affected by Health and
This study aims to establish the effect of birth Education which has actually created that
rate on GDP. It also tries to capture it due to Demographic dividend.
Health, Education, Urbanisation,
Infrastructure and Labour parameters. Literature
The media and academic circle is gung-ho
Background about the growth story of India and China and
Traditionally, GDP is expressed as the contribution of demographics. There was
no conclusive study as to how and what is the
exact impact of this to GDP of countries.
Where,
Various parameters have been chosen from a
GDP Gross Domestic Product list of 175 available indicators. The details of
C Private Consumption each indicator can be found in the Metadata
I Gross Investments sheet of the Excel file. Table 1 describes the
G Government Spending/Expenditure variable names and Labels in brief.
X Exports
Rationale of Variable Selection
M Imports
The indicators chosen are representative of
the sectors the effect of which the study aims
The effect of demographics, Infrastructure, to investigate.
Health, Education and Labour parameters is
Dependent Variable
captured in the equation indirectly. Like, due
In GDP in terms of constant 2000 USD. This
to better Labour, the production will be high,
has been done to remove the effect of
resulting in more Exports; or due to larger
Exchange rate fluctuations of country’s
number of working population, the Private
currency.
consumption and Gross Investments will be
higher and likewise. Health
Health Expenditure per capita, PPP (at
Demographic Dividend is a parameter which
constant 2005 USD) is an indicative of the
can be defined as a combination of Total
general health of the population. Good health
Population and Labour Participation Rate1.
1
Expressed as % of total population aged 15+
2
years Birth Rate (crude, births per 1000 persons)

3. of population is pre-requisite for high Other variables initially considered were
productivity and hence it is considered. Number of Physicians (per 1000), number of
beds available etc. but were later dropped as
Table 1 Variable Code and Indicator Names
they didn’t make sense.
Variable Code Year Indicator Name
NY.GDP.MKTP.KD.2008 2008
GDP (constant Manufacturing
2000 US$)
The manufacturing has a large impact on GDP
Birth rate, crude
SP.DYN.CBRT.IN.1983 1983 (per 1,000 and has already been explored. The indicators
people)
Urban of manufacturing are Goods transported by
population
SP.URB.GROW.2008 2008 rail, Overall Logistic Index etc.
growth (annual
%)
SL.TLF.TOTL.IN.2008 2008
Labour force, Consumption
total
Electricity Consumed (kWh) is a gross
Unemployment,
SL.UEM.TOTL.ZS.2008 2008 total (% of total indicator of private consumption.
labour force)
Labour Demographics
participation
SL.TLF.CACT.ZS.2008 2008 rate, total (% of A human being comes into the labour force
total population
ages 15+)
after being educated / trained around the age
Health between 15 – 25 years. Hence, Birth Rate with
expenditure per
SH.XPD.PCAP.PP.KD.2008 2008 capita, PPP a lag of 15 – 25 years is more relevant.
(constant 2005
international $)
Median Age of population etc.
Electric power
EG.USE.ELEC.KH.2008 2008 consumption The Birth-Rate lag of 25 years was finalised
(kWh)
Railways, goods after 6 iterations, of regression between GDP
IS.RRS.GOOD.MT.K6.2008 2008 transported
(million ton-km) and Birth-Rate with lags of 1, 5, 10, 15, 20 and
Logistics
25 Yrs. Highest explanatory power was found
performance
LP.LPI.OVRL.XQ.2009 2009
index: Overall in a lag of 25 Yrs., and hence it used in the
(1=low to 5=high)
study.
Median Age of
SP.DYN.MEDIAN.AGE 2010 the Population
(in Years)
Data Collection
Data was collected from Two (2) different
Health Sources
Health Expenditure per capita, PPP (at
constant 2005 USD) is an indicative of the 1. World Bank
general health of the population. Good health World Development Indicators and
of population is pre-requisite for high Global Development Finance
productivity and hence it is considered. Databank
2. The Fact Book by CIA

4. Methodology Factor Analysis
The theory / objective of study is to identify On running the factor analysis, the KMO (high)
the relation between factors described and and Bartlett’s test (Sig .000) supported the
their effect on GDP of a country. notion that data is good for factor analysis.
Initially, Only Birth Rate was considered and All MSA values in the Anti-Image Correlation
regression was run for varying amount of lag were found to be > 0.5 and also the
periods. The lag period of 25 was best suited communalities for all variables was higher
for exploration. Hence birth rate data is for than 0.5.
year 1983 Variable were factored into Three (3) factors
Other indicators provided logical relation only as per Table 2. The factors seem logical and
to the current value and hence their current hence no variable was dropped. There is no
values for year 2008 was considered independent variable left after factor
formation. The factors were then constituted
based on the scaled (to 1) co-efficient matrix.
Model Building
A data table was constituted consisting of all Cluster Analysis
indicators in Excel. It was found that out of A hierarchical cluster analysis was performed
208 countries under observation; only 85 had to identify any groups in countries. The cluster
all the required data points. These 85 analysis has resulted in primarily 3 clusters,
observations were transferred to usable USA being an exception. The clusters were
sheet. logical and hence considered.
Table 2 Factor Components
Factor # Factor Name Variables
1 Demographic Birth rate (crude, per 1000)
Factor Urban population growth (annual %)
Health expenditure per capita, PPP (constant
2005 international $)
Overall Logistic Index
Median Age of the Population
2 Industrial Factor Labour force, total
Electric power consumption (kWh)
Railways, goods transported (million ton-km)
3 Labour Factor Unemployment, total (%)
Labour Participation Rate (%)

5. Table 3 Country Clusters from Cluster Analysis
Conclusion
Cluster-3 Underdeveloped but fast It is observed that Demographic Dividend
growing countries with an does play an important role in the GDP.
exception of CHINA.
The point of concern is absence of Labour
Cluster-2 Developed Nations mainly
Factor for GDP explanation.
Europe, including Australia and
Japan It seems to the author that some variables
Cluster -1 Developing countries including which should have been considered were
India.
𝐺𝐷𝑃 𝑈𝑆𝐷 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡 2000
1.8 × 1011
Regression Analysis 6.057 × 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑖𝑎𝑙 𝐹𝑎𝑐𝑡𝑜𝑟
Multiple step-wise regression was performed 2.7 × 108 × 𝐷𝑒𝑚𝑜𝑔𝑟𝑎𝑝ℎ𝑖𝑐 𝐹𝑎𝑐𝑡𝑜𝑟
absent and hence Labour Factor didn’t have
between GDP and Identified factors. The final
sufficient explanatory power.
regression model didn’t contain Labour
Factor. The explanatory power was
acceptable at 80.7% (adjusted R2) References
1. http://data.worldbank.org/data-
Findings catalog/world-development-
indicators
1) Model-2 incorporates two factors
2. http://web.ebscohost.com/ehost/pdf
Industrial and Demographic and has
viewer/pdfviewer?sid=da9ebe64-
an R2 of 0.80 which is extremely
eb95-40c1-825c-
good. As the model is able to explain
087fb2505a47%40sessionmgr4&vid=2
80.7% of variance of the dependent
&hid=24
variable.
3. http://economictimes.indiatimes.com
2) The p-value of the constant (0.049)
/articleshow/11918847.cms
which is marginal hence considering
4. http://en.wikipedia.org/wiki/List_of_c
significant. Using the Unstandardized
ountries_by_median_age
Coefficients (B), to form the relation.
5. https://www.cia.gov/library/publicati
3) Clusters did not have better
ons/the-world-
explanatory power and hence
factbook/fields/2177.html
individual regression models were not
constructed.