Estimating Elasticity and Projecting Demand
To incorporate the three levels of GDP growth rate that IESS version-II introduces, it was
necessary to estimate how demand of the output from one sector would vary as rate of growth of
India’s GDP varies. Such an exercise was carried out for each of the sector using regression
curves. The choice of such a curve for a particular sector was based on:
1. Literature study of that sector that rationalizes the possible relationship between demand
activity and rising income levels of a country
2. Studying past trends of GDP v/s demand activity of other countries.
To elaborate on the methodology of calculating demand elasticity vis-à-vis GDP and projecting
future demand, we explain here for Steel and Telecom. Similar methodology was adopted for
other sectors.
TELECOM
Rationale for Gompertz curve: Studies show per capita Teledensity v/s GDP curve to be Scurve where:



In phase 1 the telecom demand is fairly inelastic to rise in GDP. This could be owing to
preferences where at low levels of income, telecom doesn’t account as priority
consumption.
In phase 2 there is rapid rise in telecom subscription with incomes high enough.
In phase 3 economy starts approaching saturation levels. Higher income would translate
to better telecom services, higher usage per subscription etc.
Past Trends: Countries studied were Brazil, Chine, Germany, Mexico, Russia, Singapore,
United Stated of America. All of them appear to follow S-curve for the past 18 years
(Data source: UNdata)
Indian Regression: Following is the India’s 18 year data Gompertz curve with Teledensity
(wireless subscriptions per 100 persons) on the Y axis and GDP/capita (PPP) on the X axis.
Source for data on Teledensity was taken from UNdata while data on GDP and Population was
taken from World Bank Databank for the years: 1995-2013.
Teledensity = b1*exp(-exp(-b2*(GDP/capita - b3)))
b1= 87.92556 (Expected saturation level)
b2= 0.001519
b3= 4016.781
Projecting Demand: Varying CAGR levels of GDP have been assumed for five year intervals
starting 2012 till 2047 such that they correspond to 7.4%, 6.7% and 5.8% (the three GDP
scenarios). Using population growth rate assumption, we calculated GDP per capita for the five
year intervals. These GDP per capita figures were then fed into the regression curve for each
sector which gave us the demand for the output of that sector. Growth rate of the demand was
estimated for the periods 2012-2020, 2020-2035, 2035-2047. These demand figures were then
translated to energy demand under various technological assumptions.
Scenario-1
Scenario-2
Scenario-3
Observed BTS Growth-Telecom
2012-2020 2020-2035
8.0%
6.3%
7.3%
5.5%
6.4%
4.8%
2035-2047
4.0%
3.8%
3.2%
STEEL
Literature Study: Multiple studies show that the relation between income and demand for steel
is hump shaped. In a first stage, steel demand will increase relative to economic activity with
rising living standards, but it will decline when income surpasses a level at which consumers
preferences shift towards services.
Estimation: In order to estimate the relationship between GDP and steel, we first used data for
GDP per capita (current US $) and apparent steel use per capita (kg crude steel), from 1968 to
2013. We took this data for 15 countries and divided them into 3 groups. The GDP data was
taken from the World Bank website and the steel consumption data was compiled from annual
steel yearbooks released by the world steel org.



Group 1: High Income Countries: France, Germany, Japan, United States of America,
and United Kingdom.
Group 2: Middle Income Countries: Argentina, Colombia, Iran, Romania, and
Venezuela.
Group 3: Developing Countries: Brazil, China, India, Russia, and South Africa.
There was an evident pattern in Group 1 and 3. While the per capita steel consumption of the
developed nations was falling as GDP increased, the steel consumption of the developing nations
was increasing with GDP.
Indian Regression: Using a nonlinear Gompertz Function to regress GDP per capita on
apparent steel consumption per capita, we estimated an equation for the demand elasticity of
India’s apparent steel consumption per capita:
Y: Steel demand (kg/capita)
T: GDP/capita
A (Expected saturation) =600
B = 3.50
C = 0.00
2
1.8
1.6
1.4
1.2
ln per capital steel
consumption
BRAZIL
1
INDIA
0.8
CHINA
0.6
US
0.4
0.2
0
0
0.5
1
1.5
ln GDP/capita
2
2.5
3
According to our analysis, elasticity of steel demand for India with respect to GDP does not
stabilise until 2047. Comparing with other countries, we find that elasticity of steel consumption
with respect to GDP doesn’t appear to stabilise for Brazil, China and US as well.
Projecting Demand: Varying CAGR levels of GDP have been assumed for five year intervals
starting 2012 till 2047 such that they correspond to 7.4%, 6.7% and 5.8% (the three GDP
scenarios). Using population growth rate assumption, we calculated GDP per capita for the five
year intervals. These GDP per capita figures were then fed into the regression curve for each
sector which gave us the demand for the output of that sector.
Scenario-1
Scenario-2
Scenario-3
2012
65.67
65.67
65.67
Iron and Steel kg/capita
2017
2022
2027
2032
99.19
143.49
195.94
253.10
88.58
117.22
155.13
202.74
84.56
107.29
136.61
171.96
2037
308.69
248.34
204.89
2042
350.48
282.70
228.32
2047
384.03
307.59
244.84
GDP energy demand relation: Relevance in the Indian context
While decoupling of GDP and energy consumption or greenhouse emissions has been theorized and has
been witnessed in some parts of the world, there isn’t substantial evidence of such a decoupling in the
Indian scenario. A plethora of empirical studies have established significant relationship between GDP
and energy consumption for India. The nature of this link has been described differently in different
studies. Theoretically, there exists a rationale for the existence of a bi-directional relationship between
energy consumption and economic growth. Higher energy consumption can stem from increased use of
energy as an input in production process resulting into higher output. On the other hand, higher per
capita income translates to increased aggregate demand for manufactured goods, services, and energy
consuming appliances and automobiles. In the Indian context, some of the empirical studies have shown
causality running from energy consumption to income; others have established the causality running
from GDP to energy consumption.
Along with the above relationship, energy intensity has also been observed to change along with
structural changes in the economy. National Manufacturing Policy announced by Government of India in
2011 aims to increase manufacturing sector’s share to 25% by 2022. Manufacturing is known to be a
highly energy consuming sector and such a structural change is also bound to affect the growth rate of
the economy. With such potential structural changes in the Indian economy, IESS Version 2 allows the
user to select the GDP growth rate as he expects. Also, policy makers can see what would be the
implication on energy demand and supply by targeting different growth rates for the economy and
energy implications of other policies via changes in GDP growth rate.
In the Version 2 of IESS, choosing a different level of CAGR of GDP would result in a change in demand
activity of all the sectors (except Agriculture). Within a particular CAGR of GDP, for each sector growth
trajectory of demand activity is determined for each efficiency level based on a number of assumptions.
Energy requirements are then calculated to meet that demand using the technology as assumed under
the specified level of efficiency. To illustrate, following is the Passenger Transport Demand trajectory
under 7.4% CAGR of GDP:Scenario-1
Level 1
Level 2
Level 3
Level 4
CAGR of GDP
7.4% CAGR
Units CAGR(2012-47)
BPKM
4.30%
BPKM
4.00%
BPKM
3.70%
BPKM
3.50%
2012
7286
7286
7286
7286
2022
13180
12826
12473
12296
2032
20693
19633
18573
18043
2042
27979
25864
23748
22691
2047
31392
28635
25879
24501