Price and Income Elasticity of Demand for Services in India: A Macro Analysis
Satyanarayan Kishan Kothe
Assistant Professor (Sr)
Department of Economics
University of Mumbai, Mumbai (INDIA)
kothesk@gmail.com
+91 9699200509
Abstract
India has experienced phenomenal growth in recent period which is attributed by services
revolution. India emerged as prominent service provider to the global consumers. Besides
exports of services, Indian services are also consumed by domestic consumers. Domestic
demand for services equally contributes in India’s services led growth paradigm. A
systematic analysis of domestic demand for services is needed to understand services
revolution in India. The paper estimates domestic demand for services. The attempt is made
to estimate price and income elasticity of demand for services for 1951 to 2010. The Study
endeavours to find structural changes in demand for services during pre and post liberalized
period (1951 to 1990 and 1991 to 2010) through the most important determinants of demand
for any product/service i.e. price and income.
Keywords: Demand for Services, Price Elasticity of Demand, Income Elasticity of Demand,
Consumption Expenditure and Elasticity of Demand for Services.
Introduction:
India’s services revolution and services led growth have raised various issues for discussion.
The studies that have comprehend the sources of recent services revolution include Kothe and
Kouthe (2009) entails role of exports of services in economic growth, the impact of FDI on
services growth in India is discussed by Kothe and Sawant (2010), Kothe (2012a) endeavored
to bring out the linkage of globalization and services revolution in India and Kothe (2012b)
connoted that rise in demand for services with rise in per capita income induced more
employment in services sector. Kothe (2013) also pronounced that increase in demand for
services promoted capital formation in services sector in India. The standard export demand
function for India’s services is estimated and theorized in Kothe (2014) that India’s services
exports are highly income elastic and less immune to the changes in domestic prices. The
discussion encourages the desire to find more insights of services revolution in the context of
demand for services.
Demand for services function can be as conventional as the demand for goods. Perhaps we
find enough discussion about demand for goods functions, but not on demand for services.
Engle’s Law (1857) is however more conventionalized by Clark (1951) that the employment
in services sector increase with increase in income which implies that demand for services
respond to the changes in income. Baumol (1967) explicitly modeled that the demand for
services increases with the increase in income. Summers (1985) estimated price and income
elasticities of demand for services classified under SNA for thirty four countries. And he also
discussed service specific elasticities and their nature of response to price and income. Falvey
and Gemmell (1991) stated that service income elasticities in aggregate tend to be statistically
greater than, but numerically close to, unity. Further Falvey and Gemmell (1996) have been
inclined to reject the hypothesis that income-elastic demand for overall services but found
income elasticity estimates above and below unity for different types of services. Mahadevan
and Kalirajan (2002) did find the income inelastic demand for services in Singapore. Hansda
(2001) also remarked about the empirically found income elasticity of demand for services
and also talked about the demand for services in India. Since there have not been attempts to
find structural changes in the demand for services in India, the present study tries to discover
the same if exist and compare the measures for pre and post liberalization period.
Methodology and Data:
As noted in Falvey and Gemmell (1996), Summers (1985) used following three equations for
the estimation of income and price elasticity of demand for services.
ln(𝐸𝑠 ⁄𝑌) = 𝛼1 + 𝛽1 ln 𝑅𝑌 + 𝑢𝑖
(1)
ln(𝑅𝐸𝑠 ⁄𝑅𝑌) = 𝛼2 + 𝛽2 ln 𝑅𝑌 + 𝑢𝑖
(2)
ln 𝑅𝐸𝑠 = 𝛼3 + 𝛽3 ln 𝑅𝑌 + 𝛾3 ln(𝑃𝑠 ⁄𝑃𝑔𝑑𝑝 ) + 𝑢𝑖
(3)
Where 𝐸𝑠 is expenditure per capita on services and 𝑌 is GDP per capita, both converted to $
at nominal exchange rates; 𝑅𝐸𝑠 and 𝑅𝑌 are respectively “real” expenditure per capita on
services and “real” GDP per capita (i.e. converted to $ using category specific PPP exchange
rates). 𝑃𝑠 and 𝑃𝑔𝑑𝑝 are the (domestic) price of services and GDP respectively, and 𝑢𝑖 is a
random error term. The nominal and real share of services rise with GDP per capita if 𝛽1 > 0
and 𝛽2 > 0 respectively, and services may be deemed to be income-elastic in demand if 𝛽3 >
0. Real expenditure (𝑅𝐸) here are equivalent to quantities - in national accounting terms - and
thus equation (3) may be viewed as a simple demand function. It may attract objections that it
omits ‘taste’ variable. Therefore a more complete specification is:
ln 𝑅𝐸𝑠 = 𝛼4 + 𝛽4 ln 𝑅𝑌 + 𝛾4 ln 𝑃𝑠 + 𝛿4 ln 𝑃𝑐 + 𝜀4 𝑍 + 𝑢𝑖
(4)
Where, Z is a vector of ‘test’ variables. It is to be noted that in (4) service and commodity
prices (𝑃𝑠 and 𝑃𝑐 ) appear separately allowing testing of homogeneity condition 𝛿4 = −𝛾4 .
Equations (3) and (4) can be used to estimate the income elasticities of aggregate or
individual services with 𝑃𝑐 a ‘composite’ of commodity and other service prices in the later
case. Therefore it is very much agreeable that equation (4) after omitting Z could be of help
in estimation of price and income elasticity in our case. Z is a vector of ‘test’ variables that
can be omitted to compare the results with that of Summers (1985).
Though the above methodology does not accomplish the necessary data requirements are
accepted by economists as general form of simple demand for services function. Hence for
the computation of conventional demand function of goods/services one need to have the data
on quantities of services, prices of services and GDP. There are few issues in defining
quantities of services and prices of services. These issues have been sorted in the following
discussion. As the quantities/units of services cannot be defined as structured as it is required
and also are not readily available. Summers (1985) suggested that consumption expenditure
are equivalent to quantities in national accounting terms, perhaps, as discussed above have
estimated elasticity of demand for services with the help of consumption expenditure function
for services.
Prior to the estimation of demand for services, it is required to define the demand for
services. But discussion by Hill (1999) makes it easier task to define the demand for services.
The whole debate on conceptualization of services is carried out by Hill (1999) in which he
discussed the issues in conceptualization of services and also about the tangibility and
intangibility features of goods and services. In the deliberation he stated that services cannot
exist independent of their producer, they consist of change brought about in the condition of
one economic unit by the activities of another economic unit. Many services are capable of
bringing material changes in persons or products. Since services are not entities, they cannot
be stored at large. Therefore every unit of service is produced and consumed within a
stipulated time. Many times the production and consumption of services is simultaneous. A
very few services can be stored in physical nature. Having known this we can conclude the
discussion about the storability characteristic of services and assume that every unit of
service produced in the economy is consumed by either an individual as consumer service or
an institution as producer service. In that sense, therefore, services GDP is same as the
consumption expenditure on services.
However the consumer expenditure on services does not interpret the demand for services
truly. Though the consumer expenditure on services do not represent demand for services
truly, the studies by Summers (1985), Falvey and Gemmell (1991) (1996) and Mahadevan
and Kalirajan (2002) have considered the former as demand for services for the estimation of
elasticities of demand for services. And also in the present study, elasticities are measured for
demand for services function. Hence defining consumption expenditure on services in India
is not that difficult though no official data is published on consumption of services. Having
some arithmetic, we can get the data on consumption expenditure on services. The
consumption expenditure on services would be equal to the services GDP (+/-) net services
trade.
In India, services price index is not measured, therefore it would not be appropriate to
measure price elasticity using WPI or CPI both of which do not represent the changes in
prices of services of which weights are too small and also do not cover all the services. The
GDP deflator could be better measure in which prices of all the goods and services are
approximated with their respective shares in GDP. Perhaps in the present study we have
computed services output deflator i.e. the product of the ratio of nominal services output to
real services output and hundred and considered that to be Services Price Index (Ps).
Therefore the income and price elasticity of demand for services is measured by employing
log linear regression of demand for services (CONSERi) on GDP (Yi) as income and Services
Price Index (Ps) as price proxy for services. We have not only measured the price and income
elasticity but also attempted to find structural changes, if any, in the aggregate demand for
services function in pre and post liberalization period i.e. 1951 to 1990 and 1991 to 2010.
Therefore unlike equation (3) equation (5) estimates price and income elasticity of demand
for services for the entire sample period.
In the present study relatively simple demand for services function, unlike Summers (1985),
Falvey and Gemmell (1996) is estimated with the help of following equation.
ln 𝐶𝑂𝑁𝑆𝐸𝑅𝑡 = 𝛼1 + 𝛽1 ln 𝑌𝑡 + 𝛽2 ln (
𝑃𝑆𝑡
𝑃𝑅𝑆𝑡
) + 𝑢𝑡
(5)
Where 𝐶𝑂𝑁𝑆𝐸𝑅 is the demand for services, 𝑌 represents the GDP at constant prices, 𝑃𝑠 is the
Services Price Index and 𝑃𝑅𝑆 is the Price Index of Rest of the Services GDP, further and t
represent time. And t =1951 to 2010.
Perhaps, to discover the structural changes in demand for services, existing methods suggest
the use of Chow test (1960) on priory basis. Two different regressions could have been run
and applying Chow test (1960), useful to examine the structural stability of a regression
model, which would help in deciding whether the two periods give two different demand
functions or there is no need to do so. And also is possible to find structural change if any in
two different periods. But Chow test has its own limitations, as it is powerless to provide
reasons for the structural change if any. Such a structural change is due to difference in slopes
or intercept or both from each of the regressions of a model. But instead of that we have used
dummy variable approach by Gujarati (1970) that is more capable of providing specification
of any such difference. However after reviewing the available methodologies the final call is
taken on to use the following function form of the model. Therefore equation (6) attempts to
find structural changes in demand for services during pre and post liberalized period through
the most important determinants of demand for any product/service i.e. price and income.
𝑃𝑆𝑡
ln 𝐶𝑂𝑁𝑆𝐸𝑅𝑡 = 𝛼1 + 𝛼1 𝐷1𝑡 + 𝛽1 ln 𝑌𝑡 + 𝐷1𝑡 ln 𝑌𝑡 + 𝛽2 ln (
𝑃𝑅𝑆𝑡
) + 𝐷1𝑡 ln (
𝑃𝑆𝑡
𝑃𝑅𝑆𝑡
) + 𝑢𝑡
(6)
And t =1951 to 2010.
𝐷1 = 1 𝑤ℎ𝑒𝑟𝑒 𝑡 = 1951 𝑡𝑜 1990 𝑎𝑛𝑑 𝐷1 = 0 𝑤ℎ𝑒𝑟𝑒 𝑡 = 1991 𝑡𝑜 2010
Where 𝐶𝑂𝑁𝑆𝐸𝑅 is the demand for services, 𝑌 represents the GDP at constant, 𝑃𝑠 is the
Services Price Index and 𝑃𝑅𝑆 is the Price Index of Rest of the Services GDP, further t
𝑃
represents time. 𝐷1 is dummy variable associated with 𝑌 and (𝑃 𝑆𝑡 ) which define 𝐷1 = 1 for
𝑅𝑆𝑡
pre and 𝐷1 = 0 for post liberalization period respectively.
Similar way for the sub-sectors in services, demand functions are estimated and price and
income elasticities are measured. India’s national accounting system facilitate the
disaggregation of output of services into four categories, that are 1) Construction, (CNSTN)
2) Trade, Hotel, Transportation and Communication, (THTC) 3) Finance, Insurance, Real
Estate and Business Services (FIRB) and 4) Community, Social and Personal Services
(CSPS). For this purpose equation (5) and (6) are formed as base equations and the following
generalized form of equations are used to estimate the price and income elasticities of
demand for group of services stated above.
𝑃
ln 𝐶𝑖𝑡 = 𝛼1 + 𝛽1 ln 𝑌𝑡 + 𝛽2 ln (𝑃 𝑖𝑡 ) + 𝑢𝑡
(7)
𝑅𝑖𝑡
where 𝐶𝑖 is consumption expenditure in i sub service, 𝑌 is the GDP, and 𝑃𝑖 is the Services
Price Index of ith sub service and 𝑃𝑅𝑖 is the Price index of rest of the sub services GDP and
lastly t represents time. And t = 1951 to 2010.
And to evaluate the structural changes in demand for sub services as earlier the following
equation is used.
𝑃
𝑃
ln 𝐶𝑖𝑡 = 𝛼1 + 𝛼1 𝐷1𝑡 + 𝛽1 ln 𝑌𝑡 + 𝐷1𝑡 ln 𝑌𝑡 + 𝛽2 ln (𝑃 𝑖𝑡 ) + 𝐷1𝑡 ln (𝑃 𝑖𝑡 ) + 𝑢𝑡
𝑅𝑖𝑡
𝑅𝑖𝑡
(8)
And t =1951 to 2010.
where 𝐶𝑖 is consumption expenditure in i sub service i.e. CNSTN, THTC, FIRB and CSPS,
𝑌 is the GDP, 𝑃𝑖 is the Services Price Index of i sub service i.e. 𝑃𝑐𝑛𝑠𝑡𝑛 , 𝑃𝑡ℎ𝑡𝑐 , 𝑃𝑓𝑖𝑟𝑏 and 𝑃𝑐𝑠𝑝𝑠
and 𝑃𝑅𝑖 is the Price index of output of rest of the i sub services, further t represents time. 𝐷1 is
dummy variable associated with 𝑌 and
𝑃𝑖𝑡
𝑃𝑅𝑖𝑡
which define 𝐷1 = 1 for pre and 𝐷1 = 0 for post
liberalization period respectively.
All the variables at level i.e. the demand for services (𝐶𝑂𝑁𝑆𝐸𝑅) demand for CNSTN,
demand for THTC, demand for FIRB and demand for CSPS, GDP (𝑌), ratio of the Price
𝑃
𝑃
Indices (𝑃 𝑆 ) and (𝑃 𝑖 ), dummy variable (D1) and its product with GDP (D1𝑌) and ratio of
𝑅𝑆
𝑅𝑖
𝑃
𝑃
Services Price Indices 𝐷1 ln (𝑃 𝑆 ) and all 𝐷1 ln (𝑃 𝑖 )s are tested for stationarity, as the
𝑅𝑆
𝑅𝑖
variables form time series, with the help of augmented Dickey-Fuller (ADF) (1979) and
Phillips-Perron (1988) test for unit root. And both the tests confirmed that all the variables
found to be non-stationary at level.
According to Granger (1986), if variables are individually non-stationary and are I(1), that is,
they have stochastic trends, their linear combination is I(0). The linear combination cancels
out the stochastic trends in the two series. As a result, a regression of such non-stationary
variables at level would be meaningful (i.e., not spurious). It is said that the two variables are
cointegrated. Economically speaking, two variables will be cointegrated if they have a longterm, or equilibrium, relationship between them. Here the meaning of equilibrium is not as
same as it is used in economic theories. Hence, equation (5) and (6) are tested with the help
of Engle-Granger test (1987) for a cointegrating regression and also observed that these
regressions are not spurious, even though individually the two variables are non-stationary.
Therefore we run log linear regression to find the demand for services function which
ultimately estimates the income and price elasticity of demand for services. Such a regression
may be called the static or long run demand for services function and interprets its parameters
as long run parameters. Therefore the parameters represent long run income and price
elasticity of demand for services. The present study also attempts to find the change in
elasticity are due to structural change, if any, between pre and post liberalized period by
including dummy variable to distinguish the period.
Results and Observations
We estimated the above equations (5) and (6) to measure the response in demand for services
due to changes in price and income. We obtain three estimates from above equation (5) and
(6) that are summarized in the table (1).
Table 1: Income and Price elasticity of Demand for Services
Income Elasticity of Demand
Price Elasticity of Demand
𝜷𝟏 = 𝜺𝑬𝑿𝑷/𝒀
𝜷𝟐 = 𝜺𝑬𝑿𝑷/𝑷𝒔
1951 to 2010
1.169
-0.29
1951 to 1990
1.218
-0.219
1991 to 2010
1.125
0.683
Summers (1985)**
0.977
-0.06
Falvey and Gemmell (1996)**
0.979
-0.32
Period
* All the measures are significant at 5 per cent level of significance
Source: Author’s estimation and ** are from Summers (1985) and Falvey and Gemmell
(1996)
Income Elasticity of Demand for Services (Total)
The income elasticity of demand for services (Total) is obtained from regression equations
(5) and (6). Results reported in Table (1) were obtained using ordinary least squares (OLS)
method.
Our long run estimate of income elasticity of demand for the period of 1951 to 2010 found to
be 1.169 which suggests that demand for services is elastic in response to change in income.
Therefore the empirical hypothesis that services are always supposed to be income-elastic in
demand is accepted. The results also suggest that services were more income elastic in pre
liberalized period in comparison to post liberalization. Decline in income elasticity of
demand for services in the post liberalized period suggests that service that were once
luxurious now tend to become comparatively less luxurious. Therefore that is the change
occurred in the behavior of the consumers in post liberalized period that services once were
luxuries have tend to become relatively normal good but it remained luxurious by and large.
Perhaps, it would be more appealing if we disaggregate the services and see how the behavior
of income elasticity of demand is found to be for the group of services for the entire sample
period and in also pre and post liberalization period. The reason for that is services are
heterogeneous and the need for services also differs from group to group. Though not
comparable with that of Summers (1985) and Falvey and Gemmell (1996) our estimated
results in long run are greater than that of from these two studies imply that services in India
are luxurious in total and found to income elastic in nature.
The national database does not classify each service separately rather it groups the services.
Therefore eventually our analysis limits ourselves to the group of services rather than each
service. But still with the given limitations our estimates provide enough substance to
elaborate broader perspective about income elasticity of demand for group of services.
We estimated the equations (7) and (8) to measure the response in demand for services, for
each group separately, due to changes in price and income. We obtain one estimate from
above equation (7) and two from equation (8) that are summarized in the table (2).
The Income Elasticity of Demand for Construction Services (CNSTN)
The general definition of demand for construction services include demand for housing,
demand for commercial and industrial buildings, demand for infrastructure such as roads,
hospitals, schools, bridges, ports, airports, dams, etc., demand for repair and maintenance.
With increase in income the need for all such demand for construction services also rise. That
indicates that income elasticity of demand for construction always is greater than 1.
Table 2: Group wise Price and Income Elasticity of Demand for Services
Price and Income Elasticity of Demand for group of Sub Services
Period
Construction
(CNSTN)
Trade, Hotel,
Transportation
and
Communication
(THTC)
Finance,
Insurance, Real
Estate and
Business
Services (FIRB)
Community,
Social and
Personal
Services (CSPS)
Income
Price
Income
Price
Income
Price
Income
Price
1951 to 2010
1.378
-1.108
1.325
-0.279
1.042
-0.604
1.093
-0.735
1951 to 1990
1.478
-1.207
1.398
-0.388
0.962
-0.499
1.174
-0.497
1991 to 2010
0.956
0.660
1.402
0.141
0.958
-0.136
0.922
1.193
* All the measures are significant at 5 per cent level of significance
Source: Author’s estimation
The long run income elasticity of demand for construction services has been found to be
1.378 which is significantly greater than 1. It seems that construction services in India have
been more income elastic and classify themselves as luxury services. Its importance in the
economy is narrated in the Report of Working Group on Construction (2011) that
construction industry is the second largest employer after agriculture, and an economic entity
causing and generating large multiplier effect, value added employment potential,
construction can add substantially to the growth and substance of the national economy.
Construction sector has two key segments: (i) Residential and Non-Residential Buildings
(Residential, commercial, institutional, industrial); and (ii) Infrastructure. Infrastructure
contributes roughly 50 per cent to the construction sector and the remaining is through
residential and non-residential building industry.
We may find bi-directional causal relationship between infrastructure and growth. However,
post liberalized two decades have recorded income elasticity of demand for construction
around 0.96 though it is greater than zero and approaches to 1 but compare to pre
liberalization period (1.48) it is declined by 50 per cent. The reason for that is surely the steep
rise in the prices in the industry in the post reforms period that are related to the prices of
inputs and also the rising demand for construction products in the later period.
The change in the income elasticity can be inferred in the context of the change in the nature
of the services to consumers. In the pre liberalized period construction services were highly
luxurious now in the later period tend to become the normal luxury commodity.
The Income Elasticity of Demand for Trade, Hotel, Transportation and Communication
(THTC)
The income elasticity of demand for THTC services in the long run is observed to be 1.325
which implies that Trade, Hotel, Transportation and Communication services have been
income elastic and formed themselves as luxurious services. The study does not find
significant difference between the income elasticity of demand for services in pre and post
liberalized periods. They have maintained their luxury status intact since last sixty years.
Further disaggregation may lead to have significant differences in the elasticity estimate
during the same periods. It can be proved with the help of taking the growth indicators and
elasticity estimates form a dominantly growing sector among THTC.
DOT, India while highlighting the telecom sector necessitates a world
class
telecommunication infrastructure is the key to rapid economic growth and to bring social
change. Indian telecommunication sector has undergone a major process of transformation
through significant policy reforms, particularly beginning with the announcement of NTP
1994 and was subsequently re-emphasized and carried forward under NTP 1999. Driven by
various policy initiatives, the sector witnessed a complete transformation in the last decade. It
has achieved a phenomenal growth during the last few years and is poised to take a big leap
in the future also. The growth of the sector in the recent past averaged annually around 45 per
cent. Such a rapid growth in the communication sector has become necessary for further
modernization of Indian economy through rapid development in IT.
Hence growth indicators and income elasticity in THTC contradict in some way. Unless you
have approximately 32 per cent growth in the economy, you cannot have income elasticity of
1.4 per cent. Therefore, undoubtedly disaggregation of the THTC would lead to significant
differences in the estimates. The similar stories could be observed in trade, hotel and
transport sectors.
The Income Elasticity of Demand for Finance, Insurance, Real Estate and Business
Services (FIRB)
The income elasticity of demand for FIRB services in the long run is 1.04 which implies that
Finance, Insurance, Real Estate and Business Services have been income elastic and appeared
to be luxurious services. The study does not find significant difference between the income
elasticity of demand for services in pre and post liberalized periods, but has been less than
one in both periods. They have been considered to be luxurious since last sixty years. The
stability of the estimate in the long run is associated with the decline in the relative prices in
these sectors. The business services sector includes the ICT sector that has been key sector in
the recent services revolution occurred in the economy. But a separate estimate of income
elasticity of demand for services could provide the cushion for the arguments to discuss the
amicable estimates of elasticity of demand for business services.
The Income Elasticity of Demand for Community, Social and Personal Services (CSPS)
The income elasticity of demand for CSPS services in the long run is registered around 1.1
which indicates that the community, social and personal services are income elastic. The
luxury nature of the service is not found to be same in pre and post reform period. CSPS
services were relatively more luxurious in pre reforms period compare to the later. There has
been significant change in the elasticity of demand for CSPS in pre and post liberalized
period that have been 1.17 and 0.92 respectively. These differences are associated with the
role of the government as most of the services include have public good characteristic in pre
reform period and the services become later either private-public or government reduced
expenditure significantly that includes general administration and have privatized or private
players are allowed to provide services mainly in health and education sectors.
Hence it can be concluded that the long run estimate of income elasticity of demand for all
the group of services for the period of 1951 to 2010 found to be greater than one which
suggests that the demand for services are highly income elastic. Therefore the empirical
hypothesis that services are always supposed to be income-elastic is accepted. The income
elasticity estimates corresponding to CNSTN, FIRB and CSPS are found to be 0.96, 0.96 and
0.92 respectively for the period 1991-2010 which implies that during post reforms period
CNSTN, FIRB and CSPS were elastic in response to income. But THTC have maintained its
nature of income elasticity of demand even after reforms. Therefore, in real sense THTC have
been luxurious services since last sixty years.
Price Elasticity of Demand for Services (Total)
The price elasticity of demand for services (Total) is obtained from regression equations (5)
and (6). Results reported in Table (1) were obtained using ordinary least squares (OLS)
method.
We discovered long run estimate of price elasticity of demand for the period of 1951 to 2010
i.e. -0.219 which suggests that demand for services is inelastic in response to change in price.
The price elasticity of demand for services is computed using equation (5), unlike summers
(1985) in which he used the ratio of services price to prices of GDP, where we have made
some rectification, as suggested by Falvey and Gemmell (1996) to use price of ‘composite’,
and used the ratio of prices of services to prices of rest of the services output in the economy.
Therefore our elasticity estimate of price is quite different than that of Summers (1985) and
Falvey and Gemmell (1996).
Therefore the empirical hypothesis that the estimate of price elasticity for goods/services for
the period of 1961-2010 is expected to be negative (less than zero) is accepted. The results
from equation (6) also suggest that services were price inelastic in pre liberalized period.
Perhaps the same is not true in post reforms period. The structure of demand for services in
response to price is changed in the post reform period. A one per cent increase in prices of
services compare to prices of other goods/services in the economy increases the demand for
services by 0.68 per cent. Perhaps, it would be more appealing if we disaggregate the services
and see how the behavior of price elasticity of demand is found to be for the group of services
for the entire sample period and also in pre and post liberalization period.
We estimated the equations (7) and (8) to measure the response in demand for services, for
each group separately, due to changes in price and income as stated above.
Price Elasticity of Demand for Construction (CNSTN)
The construction sector is highly price sensitive sector in the economy, though it is one of the
basic needs. It is not only housing but all sorts of construction either for individual household
or industry also fulfills the criteria of a basic need. As housing is necessity for households,
buildings are essential for industries, hospitals are necessary part of each individual, and
roads are required for all the stakeholders in the economy. Therefore output of construction
industry holds the property of basic needs. The empirical studies suggest that price elasticity
of demand for necessary goods/services as also for construction must be zero. Perhaps our
estimate contradicts the expected price elasticity of demand for construction. The long run
price elasticity of demand for construction services in the economy for the period 1961 to
2010 found to be -1.1. It implies that with 1 per cent increase in prices of construction there is
decrease in demand for construction services by 1.1 per cent. Therefore the demand for
construction services is more elastic. But in pre reforms period, demand for services found to
be more elastic (-) whereas the post reforms period records positive elasticity of demand for
construction services. Perhaps price elasticity of demand for construction services in post
reforms period suggests that prices of construction services rise by 1 per cent, the demand for
construction services rise by 0.66 per cent. It is evident from the prices of housing, and all
sorts of construction that rose three fold due to rise in the input prices in last two decades, but
the demand for consumption services have been persistently rising in post liberalized period.
The rapid growth of the Indian economy has had a cascading effect on demand for
commercial property to help meet the needs of business, such as modern offices, warehouses,
hotels and retail shopping centers.
Price Elasticity of Demand for Trade, Hotel, Transportation and Communication
(THTC)
The long run price elasticity of THTC services is registered to -0.28 for the period of 1951 to
2010. It shows that THTC have been price inelastic since 1951. But same is recorded around
-0.39 for the period of 1951 to 1990, indicates that degree of elasticity is higher during the
later period. Perhaps 1991 to 2010 is the period in which we get price elasticity around 0.14
that indicates that demand responded positively to the rise in price though less
proportionately. We find here structural change in the shape of demand curve of THTC
services. The degree of price elasticity proposes that THTC services have been comparatively
necessary services. More or less comparable results are ascribed by Deb and Filippini (2013)
that the price elasticity of demand for transport is around -0.35 and he also stated that the low
elasticity values indicate that the public transport in India is a necessity. Though the results in
the present study are not comparable truly with that of Deb and Filippini (2013) but depict the
macro scenario of the THTC which also includes transportation. Therefore it provokes to
state that the trade, hotel, transportation and communication have become necessary services
in the post liberalized period and the rising prices do not affect the demand for these services.
Moreover the price elasticity estimate suggests that increasing prices do not cause for decline
in the demand but increase the demand for these services.
Price Elasticity of Demand for Finance, Insurance, Real Estate and Business Services
(FIRB)
Cole, Sampson and Zia (2010) rightly stated that increased demand by households for
financial services may improve risk-sharing, reduce economic volatility, improve
intermediation, and speed overall financial development. This in turn could facilitate
competition in the financial services sector and, ultimately, more efficient allocation of
capital within society. The consumption of financial services facilitate benefits to individuals
i.e. individuals may save more, and better manage risk, by purchasing insurance contracts.
Therefore a competitive financial and insurance sector is always well demanded by the
society and same is equally true for demand for real estate and business services.
India’s FIRB sectors show less elastic demand in response to prices. In the long run, the
elasticity of demand for FIRB services found to be -0.6 which indicates that during 1951 to
2010 the FIRB services were less elastic. The aggregate behavior of the consumer for FIRB
services in pre liberalized period recorded -0.499 and that in post reforms period it is -0.136,
the difference in the estimate in these two period indicate the change in the behavior of the
individuals in relation to demand for FIRB services in response to the price variable. It also
suffices to confirm that the FIRB services tend to be necessary services in the post reforms
period.
Price Elasticity of Demand for Community, Social and Personal Services (CSPS)
Education and health constitute an important part of the community, social and personal
services. The long run price elasticity of demand for CSPS shows that CSPS have been less
elastic, recorded price elasticity of demand (-0.735). That indicates that during 1951 to 2010
the price elasticity of demand for CSPS services was less than one. But we found some
unusual estimates in pre and post reforms period as far as the price elasticity of demand for
CSPS are concerned. We found that the former period estimate is negative (-0.497) whereas
the late period recorded positive (1.19) elasticity of demand for CSPS services.
As it is accepted that demand for education and health is very high in developed countries as
also in developing country like India. The increase in price may not affect the demand for
these services, either government subsidizes or individuals spent from their valets. Papola and
Sahu (2012) have rightly described that an increase in per capita income raises the demand
for personal services at an unprecedented rate. Media and entertainment services and security
services have shown enormous growth. Services towards household management and
personal care are also emerging fast, particularly in urban areas. Many of these services at
present are provided by semi-skilled and low-paid workers on an unorganized basis. Their
increasing demand warrants that appropriate institutional and organizational mechanisms are
developed to ensure services of reasonable standard to the consumers, on the one hand, and
reasonable earnings to those rendering these services, on the other. Existence of such
mechanisms is likely to increase demand for these services.
The above explanation by Papola and Sahu (2012) subscribe to our estimates that even if the
price changes by 1 per cent there would be increase in demand for CSPS by more than 1 per
cent.
Conclusion:
Our long run estimate of income elasticity of demand for the period of 1951 to 2010 found to
be 1.169 which suggests that demand for services is elastic in response to change in income.
The results also suggest that services were more income elastic in pre liberalized period in
comparison to post liberalization. Decline in income elasticity of demand for some services in
the post liberalized period suggests that service that were once luxurious now tend to become
comparatively less luxurious. Therefore that is the change occurred in the behavior of the
consumers in post liberalized period that the services once were luxuries have tend to become
relatively normal luxury services. Therefore the services revolution observed in India in
recent period, that generated more per capita income, have impacted demand for services.
We discovered long run estimate of price elasticity of demand for the period of 1951 to 2010
i.e. -0.219 which suggests that demand for services is inelastic in response to change in price.
Estimates also suggest that services were price inelastic in pre liberalized period. Perhaps the
same is not true in post reforms period. If the prices of services increase by 1 per cent that
uncommonly increases the demand for services by 0.68 per cent. Therefore we found
structural change in demand for services in response to price in the post reform period
compare to the earlier. Hence the change in prices of services also have significant impact on
demand for services in the post liberalized period which is characterized by services
revolution in India. The rising demand for services is caused by the dissemination of
information and knowledge through latest communication and IT technology.
With the increase in income, any increase in demand for services like, health, education,
telecommunication, transportation and IT and ITES would enhance productivity of
households. Recognizing the role of communication and IT and ITES in boosting the
productivity, the Govt. of India and private firms in India have initiated the IT education and
training to enhance the skills of workforce in the country. That would further generate more
income from services and help the economy to sustain the services led growth.
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