c 3- amlan ghosh
TRANSCRIPT
Title:
Does the insurance reform promote the development of life insurance sector in India? An empirical analysis.
Dr. Amlan Ghosh*
Affiliation
Dr. Amlan Ghosh*University of North Bengal,[email protected]
1
Abstract
Reforms in the Indian life insurance sector began in 1999 and since then the growth of
the life business has been impressive despite some restriction. Whether the reforms in this
sector have helped the industry to grow or not, is an empirical matter. We, therefore,
studied the relationship between life insurance sector reforms in India and the growth of
life business in post reform period. At the empirical level, we first construct an index to
measure the reforms and then used VAR-VECM model to find out the long run
relationship. The Granger causality test suggests that life insurance sector reforms
improved the overall development of life insurance development in the recent years in
India.
Key Words
Life Insurance Reforms, VAR, VECM, Indian Life insurance, Granger Causality, India.
2
Introduction
Liberalization of the domestic financial market has been a common characteristic
of a number of economies since late 60’s. This was particularly true in case of
industrially advanced countries like Australia, Japan, UK, and France. However, this was
not been confined to these industrially developed countries only. In recent years, many
LDCs have taken macroeconomic reforms, which involve structural adjustment
programme. Main concentration was towards the financial system, especially banking
and insurance sectors, which typically either owned or controlled by the state itself. The
developing country like India along with other semi-industrialized countries has opened
up their financial sector1.
The New Economic Policy (NEP) introduced in India in June 1991 by the then
newly elected government and the process of liberalization of Indian financial sector is
part of that new policy. The main thrust of reforms in the financial sector was the creation
of efficient and stable financial institutions and markets. Reforms in the banking and non-
banking sectors focused on creating a deregulated environment, strengthening the
prudential norms and the supervisory system, changing the ownership pattern, and
increasing competition. The main idea is globalization, privatization, deregulation and
liberalization2.
With the paradigm shift in the development strategy, the economy is increasingly
opening up and there is a step forward towards market orientation. Consequently, some
financial markets such as capital market, for-ex market and banking sector have reformed
subject to various degrees of level. The insurance sector yet to receive the reform
initiatives to get the benefit out of the global changes that occurred in the recent past. The
3
Uruguay Round of GATT (now WTO) also advocated the removal of restrictions and
non-tariff trade barriers to free flow of international services across countries so that
domestic market of LDCs improve its efficiency and competitiveness and eventually
improve economic growth. It is against this backdrop that many countries have
deregulated its insurance sector and countries, which already allowed private insurance
business further deregulated their reinsurance business such as Brazil (1991) and Peru
(1991)3.
In India, the reforms in the insurance sector (Life and General) commenced with
the setting up of the Committee on Reforms on Insurance Sector under the chairman-
ship of Dr. R.N.Malhotra, the ex- governor of RBI, by the GOI in April 1993 for
examining the structure of insurance industry. The recommendations of the Committee
was submitted in 1994 which was accepted in principle by the government and started
implementing the recommendations since December 1999, thus heralding an era of
liberalization in the country’s insurance sector4. The setting up of Insurance Regulatory
and Development Authority (IRDA) and opening up of Insurance Business (life and
general) to foreign capital up to 26 per cent were the initial steps in this direction. It is
widely acknowledged that the opening up of the insurance sector has been aimed at
ushering in greater efficiency in the insurance business by maximising productivity and
minimising transaction cost. Competition is believed to bring a wider choice of products
at lower prices to the consumers, larger coverage of population, better customer service,
superior information technology, higher returns to the policyholders, and so on.
4
Fig: 1
Indian Life Insurance Market Share (2008-09)
70.92
6.92
4.79
3.25
2.22
2.5
2.06
5.06
1.051.23
LICI
ICICI
Bajaj
SBI
Reliance
HDFC
Birla
Tata
Kotak
Others
At present there are 21 private life insurers are operating in the Indian life
insurance market along with the only state own life insurer Life Insurance Corporation of
India (LICI) and at the end of the financial year 2008-09. The total volume of premium
reached to Rs. 221,791 crore in 2008-2009 from Rs. 24,630 crore in the year 1999-2000
which is little more than 800% increase by 22 numbers of insurers (including LICI) in
India. In India, private life insurers are slowly gaining the momentum to penetrate the
market with their new products, services and the global knowledge of expertise in doing
life business. This can be witnessed from their growing market share statistics which
shows (Fig: 1) nearly 30 percent of the Market are in their hands at the end of 2008-09
financial years. Most important aspect is that their acceptability is on the rise though it is
an urban phenomenon. The prominent private players operating actively are ICICI
Prudential Life (6.92%), Bajaj Allianz Life (4.79%), SBI Life (3.25%), HDFC Standard
5
Life (2.50%), Birla Sun Life (2.06%), Reliance Life (2.22%), Max New York Life
(1.73%), and TATA AIG Life Insurance Company (1.23%)5.
Literature Review
The role of financial development and economic growth has been well established
by the researchers and economic analysts in their empirical studies [Levine and Zervos6
(1998), Levine7 (1990), King and Levine (1993(a)8 and (b)9 Levine et al.10 (2000), and
Beck et al.11 (2000)]. These studies established the role of financial institutions and
financial intermediaries in fostering the economic growth by improving the efficiency of
capital accumulation, encouraging savings and ultimately improving the productivity of
the economy. Now the research has shifted from established link between financial
development and economic growth to understand factors that affects the overall financial
services, thereby the underlying factors that lead to improve the financial development.
Insurance is one of the important financial services that can trigger the growth in
an economy by channelising the long-term savings for the productive purpose and
providing a shield before the risk associated with any activity related to productivity,
assets or life. Recent studies show that the insurance industry can improve the economic
growth through financial intermediation, risk aversion and generating employment. For
example, we can highlight the studies of Outreville12 (1990 b), Catalan et. al.13 (2000) and
Ward and Zurbruegg14 (2002).
By identifying, the macro-economic factors that promote the demand for life
insurance it would be possible to find out the factors actually work as a catalyst in
6
promoting financial development and thereby economic growth. For example, recent
empirical work on insurance market by Browne and Kim15 (1993), Browne et. al.16
(2000), Ward and Zurbruegg17 (2002), Beck and Webb18 (2003) and Esho et al.19 (2004)
have shown that the level of insurance demand can be influenced by the economic,
demographic and legal factors. Despite the findings of several influencing factors
affecting the life insurance demand and the promotion of life insurance development,
there is meek guidance for the policy makers to focus on specific factor/s to foster the life
insurance development. More importantly, the focus on demand side has neglected the
supply side of the life insurance market. The causal relationship between insurance
development and the economic development has well being studied by Arena20 (2008)
and found that the insurance activity does promote the economic development. Another
study by Vadlamannati21 (2008) shows that the insurance reforms have positive
affect on the economic development in India. But The objective of this study is to
evaluate the life insurance reform, which is one of the factors leading to the increase of
number of players in the market along with the array of product choices for the
consumers in India, and to investigate the effects of these reforms empirically on the total
development of life insurance business in India and not the economic development. No
such study has yet been published so far on Indian life insurance market after the
implementation of reforms in this sector in 1999 and to the best of author’s knowledge
this is first such an attempt to measure the effects of life insurance reforms empirically.
The prime objective of this paper is to find out the causal relationship between life
insurance reforms and the overall life insurance development in India and by doing so we
would be able to answer whether we need more reforms in this sector or not. This study is
7
also significant as the new Insurance Bill which will allow more reforms (e.g. increasing
the FDI cap of foreign insurers from present level of 26% to 49%) in the insurance sector
is pending with the parliament in India.
Measuring Life Insurance Reforms
In this section we will try to evaluate empirically the effect of life insurance
reforms on the overall development of the life insurance business in India to strengthen
our earlier findings in this section. As far as our knowledge is concern no such attempt
has been made so far, at least at the time of writing this section, by any researcher to
investigate empirically the effects of reforms in the life insurance sector and its
implications in the market in India. And to do so, the first problem we face is that how to
measure the reforms in the life insurance sector. There is no such accepted measure is
available to be used in our study. Therefore, we considered to construct a composite
index of life insurance reforms which can be used in our study to find out the existing
relationship between reforms and the development of the life market in India. To
construct the index which has been named as Life Insurance Reforms Index (LIRI), we
have considered the fundamentals which are post reform phenomenon, i.e., those
elements which manifest the reforms initiatives in this sector. We have considered major
policy reforms and regulatory reforms in constructing the LIRI. The following are the
main categories which has been measured to construct the LIRI,
(a) FDI (Foreign Direct Investment) in life insurance business, and
(b) Regulatory reforms in life insurance sector.
8
FDI in life insurance business
In the post reform period India witnessed joint ventures in the life insurance
industry with foreign companies bringing maximum of 26 % capital which is stipulated
by the regulator IRDA22. Since, there is a cap on the FDI in India, foreign companies
can’t operate individually in the insurance market in India. Due to this regulation foreign
companies need to collaborate with a domestic company to enter into the life market.
This FDI cap reduces the operational ability of foreign companies in India and therefore,
we believe that the volume of FDI in every year would not show the exact picture of the
life insurance industry in India as it would be always 26% of the total capital along with
the foreign assets in the new life insurance companies. Using market share of the new
private foreign life companies also not convincing as a measure of FDI in life insurance
sector in India as these companies are very new and whatever the market share they have
achieved are mostly concentrated in the urban areas which represents the only urban
market of India and not the huge untapped rural market. Thus we come up with a simple
measure which defines the FDI in the life insurance sector. We have presumed the
number of new entrant of foreign and domestic companies every year to proxy the FDI in
life insurance sector. The scores in the FDI in life insurance sector would calculated as
under,
FDI in life insurance sector = 1 for every one new entrant in this sector.
= 2 for every two new entrant in this sector, and so on.
That is, if in any given year there are six new foreign entries, then score would be 6 (six)
in that particular year.
9
Regulatory reforms in life insurance sector
It is very demanding and complicated task to quantify the regulatory reforms
process in life insurance sector and it is more difficult when these reforms are in nascent
stage. We, therefore, come up with a scoring system which will, eventually, define the
regulatory reforms initiated and taken by the government. To measure the regulatory
reforms the following scoring system is being applied in this study,
Life insurance Regulatory reforms = ‘0’ for no reforms initiatives and steps;
= 1 for setting up of any committee;
= 1 for report submitted by any committee;
= 1 for any report accepted by the government;
= 1 for passing any bill in the parliament; and
= 0.25 for every new regulations framed under the
IRDA Act, 1999, till date.
As far as regulations are concerned, forming a committee and subsequently
submitting the report of that committee in the parliament is quite long and a democratic
process which is very important in implementing laws and reforms in any sector in India.
Therefore we have given importance to the committee formations and reports and passing
out of those reforms bill in the Parliament. Vadlamannati, (2008) also used similar kind
of parameters in his study. In calculating the life insurance regulatory reforms, we have
started from 1993 when the first step towards opening of the insurance sector was taken
by the formation of the Malhotra Committee23. Thus we have given score of 1(one) in
that year until any other major steps are taken. In 1994, the committee submitted its
report which fetches another one point in the total score. Again, in 1995, another
10
committee, named Mukherjee Committee was formed in the insurance sector24. This also
adds up one more to the score of the life insurance regulatory reforms. This is how we
have continued and come up with a score to quantify LIRI. For the total development of
the life insurance industry see the chronology of reforms in the life insurance sector and
regulations framed under the IRDA Act 1999, in the appendix.
Data and methodology
We have used two variables in our study to analyze the reforms initiative in India.
We used the total life insurance premium volume (LIP) as a measure of development of
life insurance business in India and a composite index (LIRI) to measure the life
insurance reforms in India. To eliminate the heteroscedasticity we have used the natural
logarithm of life insurance premium in our study25. The specified variables denoted as
Lt = log LIP and Rt = LIRI.
In this study we first check the stationary properties of the variables since the non
stationary time series variable might give spurious results26. We will use Augmented
Dickey Fuller (ADF) test27 and Philips Perron (PP) test28 to verify the stationary time
series variable. Non stationary variables may be used in our model provided the series are
co-integrated. Therefore co-integration study also been done to verify this property. We
will use Engle-Granger29 (1987) co-integration test. We also check the short run dynamics
of our model by using the VAR-VECM technique30.
11
Stationarity tests
Standard regression with non-stationary data leads to spurious relationship with
erroneous conclusion. It is, therefore, becomes pertinent to study the nature of the time
series data involved in our study. In our study one macro economic data series (total life
insurance premiums) are used which generally follow the random walk. The stationarity
of both the series has been checked by the unit root test which involves Augmented
Dickey Fuller (ADF) tests and Philips Perron (PP) tests.
The results of the unit root tests are very sensitive to the assumptions about the
time series under test, e.g. trend, intercept or both trend and intercept. To understand the
importance of the nature of the series under the unit root test, we plot them graphically at
their level values and after differencing.
Fig: 2Graphical Presentation of Life Premium and Life Insurance Reforms Index at Levels
0
10
20
30
40
50
88 90 92 94 96 98 00 02 04 06 08
LIP LIRI
12
Fig: 3Graphical Presentation of Life Premium and Life Insurance Reforms Index at First Difference
-2
0
2
4
6
8
10
12
88 90 92 94 96 98 00 02 04 06 08
DLIP DLIRI
From Fig.2, we can see that both the time series have some trend and intercept at
their levels. Considering the particular nature of trend in both the series, we have
differenced the data series once and the trends have been removed but the intercept
remained which can be seen in the (Fig: 3). Based on these characteristics the ADF test
and PP test are performed. The results of both the tests are summarised below in the table
1 and 2.
Table: 1ADF UNIT ROOT TEST
Lag Length: 1 (Automatic based on Modified AIC, Maximum Lag =4)
Variables Null Hypothesis ADF test Stat. Prob* DW stat
Critical Values1% 5% 10%
L L has a unit root(intercept & trend)
-1.6384 0.7381 1.8596 -4.5325 -3.6736 -3.2773
ΔL L has a unit root(intercept)
-3.1677 0.0391 2.0366 -3.8573 -3.0403 -2.6605
R R has a unit root(intercept & trend)
-2.2708 0.4281 1.7873 -4.5325 -3.6736 -3.2773
ΔR R has a unit root(intercept)
-2.8234 0.0747 1.8772 -3.8573 -3.0403 -2.6605
* Mac Kinnon (1996) one-sided p-values.
13
It is clear from the ADF test (Table: 1) that both the series (life insurance
premiums and life insurance reforms index) have unit root at their level values at 10%,
5% and 1% significance level. That is, the series are non-stationary. The same properties
of both the series are confirmed by the PP test which showed in (Table: 2).
Table: 2 PHILIPS-PERRON UNIT ROOT TEST
Bandwidth: 2 (Newey-West using Bartlett Kernel)
Variables Null Hypothesis ADF test Stat. Prob* DW stat
Critical Values1% 5% 10%
L L has a unit root(intercept & trend)
-1.0116 0.9949 1.5461 -3.8085 -3.0206 -2.6504
ΔL L has a unit root(intercept)
-3.0325 0.0498 1.8324 -3.8315 -3.0299 -2.6551
R R has a unit root(intercept & trend)
-1.9603 0.5865 1.3819 -4.4983 -3.6584 -3.2689
ΔR R has a unit root(intercept)
-2.4972 0.1316 1.7182 -3.8315 -3.0299 -2.6551
Mac Kinnon (1996) one-sided p-values.
After the first differencing, the hypothesis of unit root is rejected in both series (see
ADF test in Table: 2). That is, both the series become stationary after first differencing.
So, they are integrated of order one, i.e., I(1). These findings also confirmed by the PP
test except in case of R. but the correlogram, which shows Autocorrelation Functions
(ACF) and Partial Autocorrelation Function (PACF) at different lags (fig: 4 to fig: 7)
confirms our findings.
Co-integration
Co-integration tests are conducted to ascertain any long run equilibrium
relationship between these two series. The basic purpose of the co-integration test is to
determine whether a group of non- stationary variables are co-integrated or not. Engel
14
and Granger (1987) points out that the two non-stationary variables can be used in
regression if the linear combination of the two non-stationary variables are stationary. In
such cases, the variables are said to be co-integrated. For two series to be co-integrated,
both need to be integrated in the same order. Since the two variables in our study are
non- stationary and integrated of order I(1), we have used the Engel-Granger co-
integration test for the co-integration study. In order to test the co-integration of the
series Lt and Rt, we have estimated the following two equations [equation (1) and (2)] and
the residual series Ut and Vt of each estimated equation.
Lt = α+ β Rt + Ut ---------------------- (1)
Rt = γ + δ Lt + Vt --------------------- (2)
The results of the estimated equations are as follows,
Lt = 8.5379 + 0.0846 Rt --------------------- (3) S.E. (0.120) (0.005) t (71.085) (15.244)
Rt = -92.0701 + 10.9240 Lt --------------------- (4) S.E. (7.142) (0.716) t (-12.890) (15.244)
After we obtain the residuals, we plot them graphically (Fig. 8) to see whether
they contain any trend or not and then we examined the same with the help of ADF test
(Table: 3) and PP test (Table: 4) to check the unit root property.
15
Fig: 8 Graphical Presentation of Residual Series Ut and Rt
-6
-4
-2
0
2
4
6
8
10
88 90 92 94 96 98 00 02 04 06 08
UT VT
The ADF test and the PP test on the residual series indicate that both the series are
stationary at 5 % and 10 % level. Therefore, both the life insurance premiums and the life
insurance reforms are co-integrated in the long run. The correlogram of the residual (Fig:
9 and Fig: 10) series also confirms that they are stationary, i.e., I(0). Now we can say that
there is a stable long run relationship between insurance reform and the development in
the life insurance sector.
Table: 3 ADF UNIT ROOT TEST Lag Length: 1 (Automatic based on Modified AIC, Maximum Lag =4)Variables Null Hypothesis ADF test Stat. Prob* Critical Values
1% 5% 10%
Ut Ut has a unit root(intercept)
-2.6488 0.0111 -2.6923 -1.9601 -1.6070
Vt Vt has a unit root(intercept)
-2.2807 0.0253 -2.6923 -1.9601 -1.6070
Table: 4
16
PHILIPS-PERRON UNIT ROOT TESTBandwidth: 2 (Newey-West using Bartlett Kernel)
Variables Null Hypothesis ADF test Stat. Prob* Critical Values1% 5% 10%
Ut Ut has a unit root(intercept)
-2.6247 0.0115 -2.6857 -1.9590 -1.6074
Vt Vt has a unit root(intercept)
-2.2874 0.0248 -2.6857 -1.9590 -1.6074
Vector Error Correction Model (VECM)
In this model, both the series become stationary after first differencing. But
differencing may result in loss of information in long run relationship among the
variables. Even if there exists a long run equilibrium relationship between the two series,
there may be disequilibrium in the short run. Engel–Granger identifies that the co-
integrated variables must have an ECM (Error Correction Model) representation and a
VAR model can be reformulated by the means of all level variables. The Vector Error
Correction specification restricts the long run behaviour of the endogenous variables to
converge to their co-integrated relationships while allowing a wide range of short run
dynamics, hence, one can treat the error terms (ET) as the “equilibrium error”31. Through
the co-integration term, the deviation from the long run equilibrium is corrected gradually
in the course of a series of short run adjustments. Therefore, VECM gives us important
information about the short run relationships between these two co-integrated variables.
The general form of this modified equation by employing variables of our study is
presented below,
ΔLt = α1 + β1 ET1t-i + δΔLt-i + γΔRt-i + εt --------------- (5)
17
ΔRt = α2 + β2 ET2t-i + θΔRt-i + λΔLt-i + ωt ---------------- (6)
Where, εt and ωt are white noise error terms, and ET is equal to ET = [ΔLt-i – (ΔRt-i)]
which is the long run effect and lagged independent variables are short run effect. That is,
changes in the dependent variables are effected by the ET, ΔLt-i, and ΔRt-i. .
Before estimating the VEC Model with the co-integrated vectors it is necessary to
identify and select the optimal lag length of initial VAR32. Therefore, different
information criteria’s were computed for different time lags33. Based on the results of
different information criteria (AIC, SIC, HQ, LR, FPE) we have selected optimal lag 4 in
our study.
RESULTS OF VECM COEFFICIENTS ESTIMATION:- Table: 5 Co-integrating Vector Coefficients
Variables Coefficients ‘t’ statistics Standard ErrorsLt-1 1.0000Rt-1 -0.1290 -32.3301* 0.0039
C -7.8592*Null hypothesis that estimated coefficient is equal to zero can be rejected at 1% level.
Table: 6 (a) VECM Coefficients Dependent variable Explanatory variable Coefficients ‘t’ Statistics Standard
ErrorsΔLt Constant 0.5715 5.3572* 0.1066
ET1t-1 -0.1145 -3.3304* 0.0343ΔLt-1 -1.1102 -2.4900** 0.4458ΔLt-2 1.5126 1.9861** 0.7616ΔLt-3 -0.0992 -0.2299 0.4315ΔLt-4 -1.6077 -2.5831** 0.6224ΔRt-1 0.0258 2.9012** 0.0089ΔRt-2 -0.0471 -3.7163* 0.0126ΔRt-3 -0.0362 -1.7153*** 0.0211ΔRt-4 0.0261 1.3413 0.0194
Table: 6 (b) VECM Coefficients
18
Dependent variable Explanatory variable Coefficients ‘t’ Statistics Standard Errors
ΔRt Constant 8.7015 1.9722 4.4119ET2t-1 2.6264 1.8466*** 1.4223ΔLt-1 -12.6635 -0.6867 18.4395ΔLt-2 84.0103 2.6671** 31.4979ΔLt-3 -36.2187 -2.0294*** 17.8466ΔLt-4 -70.2233 -2.7280** 25.7412ΔRt-1 0.7069 1.9212*** 0.3679ΔRt-2 -1.8771 -3.5797* 0.5243ΔRt-3 -0.4905 -0.5619 0.8728ΔRt-4 2.4960 3.0984* 0.8055
Note: *, **, *** indicates significant at 1%, 5% and 10% level.
Findings from VECM
From VECM, the estimated equation functions has the following forms
ΔLt = - 0.1145 (Lt-1 - 0.1290Rt-1- 7.8592) - 1.1102ΔLt-1 + 1.5126ΔLt-2 - 0.0992 ΔLt-3 - 1.6077 ΔLt-4+ 0.0258ΔRt-1-0.0471 ΔRt-2- 0.0362 ΔRt-3+ 0.0261 ΔRt-4+ 0.5715238422 ----- (7)
ΔRt = 2.6264 (Lt-1 - 0.1290Rt-1- 7.8592) - 12.6635ΔLt-1 + 84.01039 ΔLt-2 - 36.2187ΔLt-3 - 70.2233ΔLt-4+ 0.7069 ΔRt-1- 1.8771ΔRt-2- 0.49051ΔRt-3+ 2.4960 ΔRt-4+ 8.7015 ----- (8)
From the above results we can observed that the co-integrating vector coefficients
in the long run of in both the equations are significant at 5 % level. This indicates that the
system is in the state of short term dynamics. In the short run, in case of equation (5), the
lagged values of Rt variable of consecutive three years has significant influence on Lt (life
insurance premium volume) along with the lagged values of first, second and fourth year
of Lt . On the other side, in equation (6), the dependant variable Rt significantly
dependent on second, third and fourth year lagged values of Lt and first, second and
fourth year lagged values of Rt itself while other variables do not affect the life insurance
19
reforms in short run. The positive sign of ET2t-1 shows that the change in the value of Rt
(insurance reforms) positively depends on past errors.
The causal relationship
A long run relationship implies that there must be at least one causal relationship
exists among the variables. Therefore, the next step is to find out whether reforms in the
life insurance sector promotes the development of life insurance business in India or the
overall development in the life insurance sector helps to increase the reform process in
life insurance sector. Since the series in our study are I(1) and co-integrated, the proper
statistical inference can be obtained by analyzing the causality relationship on the basis of
error correction model (ECM) as the simple F statistic in the traditional Granger causality
test does not have a standard distribution. The result of the VEC Granger causality test
{in Table. 7(a) and (b)} shows that the relationship between the two variables in India is
bi-directional which means life insurance reforms in India improves the total
development in the insurance sector and the development in the insurance sector also
promote the overall reforms in India.
VEC Granger Causality
Table: 7(a)
Dependent variable: ΔLExcluded Chi-sq df Prob.
ΔR 19.44915* 4 0.0006All 19.44915 4 0.0006
Table: 7(b)
20
Dependent variable: ΔRExcluded Chi-sq df Prob.
ΔL 18.14406* 4 0.0012All 18.14406 4 0.0012
* Significant at 1% level.
Conclusion
The effect of the insurance sector reforms on the development of life insurance sector is
very important and highly debatable in India. In an attempt to shed light on this issue the
present study investigates the relation between life insurance sector reforms and the
overall development of life insurance business in the recent years in India by applying
VAR-VECM econometric methodology. The ADF test and the PP test statistics were
used to test the unit root properties of the variables. It is clear from the above empirical
study that the life insurance sector reforms improved the overall development of life
insurance development in the recent years in India. The VEC Granger causality test
shows that the relationship between the insurance sector reforms and development of life
insurance sector in India is bi-directional. This is probably due to the huge potentiality of
the life insurance market which is still under served and the untapped market itself works
as a catalyst in improving the reforms in this sector.
In summary, the results of this study supports the preposition that the reforms in
the insurance sector improve the overall development of this sector and if we could
improve upon the reform process in the life insurance sector, we will be able to see more
development in this segment. It would be interesting to know further whether the
development of the insurance sector has any impact on the economic development in the
post reforms period in India. If we could improve upon the reform process in the life
21
insurance sector, we will be able to see more development in this segment and ultimately
an improvement in the economy. Therefore, policy makers should improve upon the
reforms/reform process in life insurance sector for the development of life insurance
sector itself and for the development of the Indian economy due to the important role
played by the insurance industry.
References
22
(1) Rao, D. T, (2000), “Privatisation and Foreign Participation in (Life) Insurance
Sector”, Economic and political Weekly, March 25, pp. 1107-1120.
(2) Bhole, L.M., (1999), “Financial Institutions and Markets: Structure, Growth and
Innovations” (Third Edition), Tata McGraw-Hill Publishing Company Limited,
New Delhi, India.
(3) Palande, P.S, Shah, R.S and Lunawat, M.L, (2003), “Insurance in India: Changing
Policies and Emerging Opportunities”, Response Books, Sage Publications, New
Delhi, India, p.62.
(4) Government of India, (1994), “Report of the Committee on Reforms in the
Insurance Sector”, Ministry of Finance, New Delhi, India.
(5) Annual Report of IRDA, (2000-2001) and (2007-2008), IRDA, Hyderabad, India;
(www.irdaindia.gov.in).
(6) Levine, R. and S. Zervos, (1998), “Stock Markets, Banks and Economic Growth”,
American Economic Review, Vol. 88, No.3, pp. 537-558.
(7) Levine, R., (1997), “Financial Development and Economic Growth: Views and
Agenda”, Journal of Economic Literature, June, Vol. 35, No. 2, pp 688-726.
(8) King, R. G., and R. Levine., (1993a), “Finance and Growth: Schumpeter Might be
Right”, Quarterly Journal of Economics, Vol. 108, No.3, pp. 717-737.
(9) King, R. G. and Levine, R., (1993b), “Finance, Entrepreneurship and Growth:
Theory and Evidence”, Journal of Monetary Economics, Vol. 32, pp. 513-542.
(10) Levine, R., N. Loayza, and T. Beck, (2000), “Financial Intermediation and Growth:
Causality and Causes”, Journal of Monetary Economics, Vol. 46, No.1, pp. 31-77.
(11) Beck, T., Levine, R. and Loayza, N., (2000), “Finance and the Sources of Growth”,
Journal of Financial Economics, Vol. 58, No.1-2, pp. 261-300.
23
(12) Outreville, J. F., (1990b), “The Economic Significance of Insurance Markets in
Developing Countries”, Journal of Risk and Insurance, Vol.18, No.3, pp. 487-498.
(13) Catalan, M., Impavido, G and Musalem, A.R., (August, 2000), “Contractual
Savings or Stock Markets: Which leads?” Financial Sector Development
Department, the World Bank.
(14) Ward, D., and R. Zurbruegg., (2000), “Does Insurance Promote Economic Growth?
Evidence from OECD Countries”, Journal of Risk and Insurance, Vol. 67, No.4,
pp. 489-507.
(15) Browne, M. J. and Kim, K., (1993), “An International Analysis of Life Insurance
Demand”, Journal of Risk and Insurance, Vol. 60, No. 4, pp. 616-634.
(16) Browne, M.J., Chung J., and Frees E. W. (2000) International Property-Liability
Insurance Consumption Journal of Risk and Insurance, Vol.67, No.1, pp. 73-90.
(17) Ward, D., and R. Zurbruegg., (2002), “Law, Politics and Life Insurance
Consumption in Asia” The Geneva Papers on Risk and Insurance, Vol. 27, pp. 395-
412.
(18) Beck, T. and Webb, I., (October 2002), “Economic, Demographic and Institutional
Determinants of Life Insurance Consumption across Countries”, Working Paper:
World Bank and International Insurance Foundation.
(19) Esho, N., Kirievsky, A., Ward, D. and Zurbruegg, R., (2004), “Law and the
Determinants of Property Casualty Insurance”, ‘Journal of Risk and Insurance’.
Vol. 71, No.2, pp. 265-283.
(20) Arena, M, (2008), “Does Insurance Market Activity Promote Economic Growth? A
Cross Country Study of Industrialized and Developing Countries”, Journal of Risk
and Insurance’. Vol. 75, No.4, pp. 921-946.
24
(21) Vadlamannati, K., (2008), “Do Insurance Sector Growth and Reforms Affect
Economic Development? Empirical Evidence from India”, Margin: The Journal of
Applied Economic Research; Vol. 2; pp.43-86.
(22) Gazette of India Extraordinary Part III Section 4; Insurance Regulatory and
Development Authority (Investment) Regulations, 2000; (www.irda.org.in.).
(23) Government of India, (1994), “Report of the Committee on Reforms in the
Insurance Sector”, Ministry of Finance, New Delhi, India.
(24) Sinha, T., (2004), “The Indian Insurance Industry: Challenges and Prospects”, Swiss
Re Visiting Professor, Institute of Insurance and Risk Management, Hyderabad,
India, p.24.
(25) Gujarati, D.N and Sangeetha., (2007), “Basic Econometrics” (Fourth Edition), Tata
McGraw-Hill Publishing Company Limited, New Delhi, pp. 396-450.
(26) Gujarati, D.N and Sangeetha, (2007), op. cit, pp. 821-825.
(27) Dickey, D. A. and Fuller, W.A., (1979), “Distribution of the Estimators for
Autoregressive Time Series with a Unit Root.” Journal of the American Statistical
Association, Vol. 74, pp. 427-431.
(28) Phillips, P. C. B. and Perron, P., (1987), “Testing for a Unit Root in Time Series
Regression.” Biometrica, Vol. 75, No. 2, pp. 335-346.
(29) Engle, R and Granger, C. W. J., (1987), “Co-integration and Error correction:
Representation, Estimation and Testing”, Econometrica, vol. 55, pp.251-276.
(30) Enders, W., (2004), “Applied Econometric Time Series” (Second Edition), John
Wiley & Sons, Wiley India (P.) Ltd., pp. 320-362.
(31) Gujarati, D.N and Sangeetha, (2007), op. cit., p. 843.
(32) Enders, W., (2004), op. cit., pp. 335-339.
25
(33) Skrabic, B. and Tomic-Plazibat, N., (2009), “Evidence of the Long-run Equilibrium
between Money Demand Determinants in Croatia”, Proceedings of World Academy
of Science, Engineering and Technology, Vol. 37, January, ISSN 2070-3740, pp.
578-581.
Annexure:
Chronology of Reforms in the Life Insurance Sector
1993 Setting up of The Malhotra Committee
1994 Recommendations of the Committee released
1995 Setting up of the Mukherjee Committee
1996 Setting up of (interim) Insurance Regulatory Authority (IRA).
1997 Mukherjee Committee report submitted but not made public.
A Standing Committee on reforms created.
Govt. gives greater autonomy to LICI
1998 Cabinet decides to allow 40% foreign equity in private insurance companies -26% to foreign companies and 14% to NRIs, OCBs and FIIs.
Setting up of the TAC1999 The Standing Committee headed by Mr. Murli Deora decides that the foreign
equity in private insurance companies should be limited to 26%. The IRA Act was renamed as The Insurance Regulatory and Development Authority (IRDA) Act.
Cabinet clears the IRDA Act
2000 The President gives assent to The IRDA Act.
Source: IRDA annual reports, various years.
26
Calculation of Life Insurance Reforms Index (LIRI) in India
Years FDI(Total No. of Comp.)
Regulatory Reforms
LIRI Index
1988-89 01 0 1.001989-90 01 0 1.001990-91 01 0 1.001991-92 01 0 1.001992-93 01 1 2.001993-94 01 2 3.001994-95 01 3 4.001995-96 01 4 5.001996-97 01 7 8.001997-98 01 9 10.001998-99 01 11 12.001999-00 01 14.5 15.502000-01 11 15 26.002001-02 14 17.5 31.502002-03 13 17.75 30.502003-04 14 18.50 32.502004-05 15 19.25 34.252005-06 16 19.25 35.252006-07 17 20 37.002007-08 21 21.25 42.25
27
Fig – 4 Correlogram of Lt (lag=20)Autocorrelation Partial Correlation AC PAC Q-Stat Prob
. |****** | . |****** | 1 0.843 0.843 17.145 0.000
. |***** | . *| . | 2 0.688 -0.074 29.194 0.000
. |**** | . | . | 3 0.553 -0.027 37.395 0.000
. |*** | . *| . | 4 0.425 -0.059 42.535 0.000
. |**. | . *| . | 5 0.305 -0.061 45.345 0.000
. |* . | . *| . | 6 0.187 -0.082 46.475 0.000
. |* . | . *| . | 7 0.069 -0.099 46.639 0.000
. | . | . | . | 8 -0.033 -0.050 46.678 0.000
. *| . | . | . | 9 -0.117 -0.049 47.232 0.000
.**| . | . *| . | 10 -0.192 -0.068 48.858 0.000
.**| . | . *| . | 11 -0.261 -0.076 52.138 0.000
***| . | . *| . | 12 -0.321 -0.078 57.662 0.000
***| . | . *| . | 13 -0.370 -0.071 65.917 0.000
***| . | . | . | 14 -0.398 -0.039 76.842 0.000
***| . | . | . | 15 -0.401 -0.012 89.805 0.000
***| . | . | . | 16 -0.393 -0.037 104.71 0.000
***| . | . | . | 17 -0.369 -0.013 121.14 0.000
***| . | . | . | 18 -0.323 0.020 137.94 0.000
.**| . | . | . | 19 -0.252 0.057 153.26 0.000
. *| . | . |* . | 20 -0.141 0.141 162.89 0.000
Fig – 5 Correlogram of Rt (lag=20)
Autocorrelation Partial Correlation AC PAC Q-Stat Prob
. |*******| . |*******| 1 0.875 0.875 18.499 0.000 . |****** | . | . | 2 0.753 -0.055 32.917 0.000 . |***** | . *| . | 3 0.626 -0.091 43.429 0.000 . |**** | . *| . | 4 0.485 -0.139 50.106 0.000 . |*** | . *| . | 5 0.334 -0.140 53.471 0.000 . |* . | . *| . | 6 0.185 -0.105 54.574 0.000 . | . | .**| . | 7 0.017 -0.211 54.584 0.000 . *| . | . *| . | 8 -0.136 -0.100 55.272 0.000 .**| . | . |* . | 9 -0.235 0.085 57.494 0.000 .**| . | . | . | 10 -0.309 0.004 61.690 0.000 ***| . | . | . | 11 -0.369 -0.052 68.279 0.000 ***| . | . *| . | 12 -0.416 -0.087 77.579 0.000 ***| . | . | . | 13 -0.436 -0.020 89.077 0.000 ***| . | . | . | 14 -0.418 0.056 101.13 0.000 ***| . | . | . | 15 -0.377 -0.012 112.57 0.000 ***| . | . *| . | 16 -0.335 -0.073 123.40 0.000 .**| . | . | . | 17 -0.280 0.011 132.88 0.000 .**| . | . | . | 18 -0.220 0.004 140.66 0.000 . *| . | . | . | 19 -0.156 -0.016 146.55 0.000 . *| . | . | . | 20 -0.087 -0.014 150.18 0.000
28
Fig – 6 Correlogram of ΔLt (lag=20)
Autocorrelation Partial Correlation AC PAC Q-Stat Prob
. |**. | . |**. | 1 0.280 0.280 1.8198 0.177
. *| . | .**| . | 2 -0.184 -0.285 2.6459 0.266
. *| . | . | . | 3 -0.176 -0.036 3.4503 0.327
. *| . | . *| . | 4 -0.175 -0.182 4.2952 0.368
. |* . | . |**. | 5 0.195 0.311 5.4070 0.368
. |* . | . *| . | 6 0.154 -0.135 6.1524 0.406
. | . | . |* . | 7 -0.026 0.068 6.1754 0.519
. *| . | . *| . | 8 -0.123 -0.171 6.7287 0.566
. *| . | . | . | 9 -0.161 0.048 7.7670 0.558
. *| . | . *| . | 10 -0.063 -0.166 7.9412 0.635
. *| . | . *| . | 11 -0.065 -0.068 8.1487 0.700
. | . | . *| . | 12 -0.048 -0.103 8.2732 0.763
. *| . | . *| . | 13 -0.100 -0.114 8.8988 0.781
. *| . | . | . | 14 -0.098 -0.053 9.5987 0.791
. | . | . *| . | 15 -0.021 -0.074 9.6379 0.842
. | . | . | . | 16 0.013 0.024 9.6571 0.884
. |* . | . | . | 17 0.072 -0.010 10.417 0.885
. | . | . | . | 18 0.023 0.004 10.538 0.913 . | . | . | . | 19 0.002 0.001 10.539 0.938
Fig – 7 Correlogram of ΔRt (lag=20)
Autocorrelation Partial Correlation AC PAC Q-Stat Prob
. |*** | . |*** | 1 0.371 0.371 3.1958 0.074 . *| . | .**| . | 2 -0.077 -0.249 3.3394 0.188 . | . | . |**. | 3 0.062 0.235 3.4392 0.329 . |* . | . *| . | 4 0.067 -0.105 3.5638 0.468 . *| . | . *| . | 5 -0.176 -0.168 4.4692 0.484 . *| . | . |* . | 6 -0.059 0.143 4.5781 0.599 . |* . | . | . | 7 0.125 0.014 5.1104 0.646 . *| . | . *| . | 8 -0.074 -0.157 5.3139 0.724 . *| . | . | . | 9 -0.166 0.009 6.4138 0.698 . *| . | .**| . | 10 -0.179 -0.275 7.8313 0.645 . *| . | . | . | 11 -0.127 0.059 8.6190 0.657 . *| . | . *| . | 12 -0.153 -0.132 9.9067 0.624 . | . | . |* . | 13 -0.015 0.093 9.9209 0.700 . | . | . | . | 14 0.049 -0.046 10.093 0.755 . | . | . | . | 15 0.002 -0.018 10.094 0.814 . | . | . | . | 16 -0.024 -0.028 10.158 0.858 . | . | . | . | 17 -0.029 -0.042 10.282 0.891 . | . | . *| . | 18 -0.046 -0.076 10.751 0.905 . | . | . | . | 19 -0.051 0.011 11.906 0.890
29
Fig – 9 Correlogram of Ut (lag=20)
Autocorrelation Partial Correlation AC PAC Q-Stat Prob . |****** | . |****** | 1 0.718 0.718 12.437 0.000 . |*** | .**| . | 2 0.384 -0.269 16.193 0.000 . |* . | . *| . | 3 0.118 -0.086 16.567 0.001 . *| . | . *| . | 4 -0.113 -0.185 16.931 0.002 .**| . | . *| . | 5 -0.309 -0.186 19.807 0.001 ***| . | . *| . | 6 -0.416 -0.105 25.389 0.000 ***| . | . | . | 7 -0.408 -0.025 31.127 0.000 ***| . | . *| . | 8 -0.375 -0.154 36.365 0.000 .**| . | . *| . | 9 -0.313 -0.082 40.298 0.000 .**| . | . *| . | 10 -0.225 -0.098 42.519 0.000 . *| . | . | . | 11 -0.082 0.033 42.845 0.000 . |* . | . |* . | 12 0.096 0.087 43.342 0.000 . |**. | . |* . | 13 0.265 0.104 47.591 0.000 . |**. | .**| . | 14 0.233 -0.313 51.342 0.000 . |* . | . *| . | 15 0.133 -0.090 52.767 0.000 . |* . | . | . | 16 0.066 -0.013 53.184 0.000 . | . | . *| . | 17 -0.022 -0.078 53.241 0.000 . *| . | . | . | 18 -0.100 -0.019 54.838 0.000 . *| . | . | . | 19 -0.118 0.006 58.227 0.000 . | . | . |* . | 20 -0.033 0.087 58.739 0.000
Fig – 10 Correlogram of Vt (lag=20)
Autocorrelation Partial Correlation AC PAC Q-Stat Prob . |****** | . |****** | 1 0.750 0.750 13.594 0.000 . |*** | .**| . | 2 0.449 -0.260 18.720 0.000 . |* . | . *| . | 3 0.191 -0.094 19.699 0.000 . | . | .**| . | 4 -0.054 -0.204 19.782 0.001 .**| . | .**| . | 5 -0.280 -0.210 22.148 0.000 ***| . | . *| . | 6 -0.419 -0.091 27.789 0.000 ****| . | . *| . | 7 -0.460 -0.066 35.083 0.000 ****| . | .**| . | 8 -0.479 -0.195 43.607 0.000 ***| . | . | . | 9 -0.430 -0.056 51.060 0.000 ***| . | . *| . | 10 -0.342 -0.127 56.181 0.000 .**| . | . | . | 11 -0.191 0.025 57.943 0.000 . | . | . |* . | 12 0.001 0.067 57.943 0.000 . |**. | . |* . | 13 0.199 0.085 60.326 0.000 . |**. | ***| . | 14 0.213 -0.358 63.461 0.000 . |* . | . *| . | 15 0.157 -0.128 65.453 0.000 . |* . | . | . | 16 0.124 -0.045 66.932 0.000 . |* . | . *| . | 17 0.067 -0.077 67.474 0.000 . | . | . | . | 18 0.004 -0.032 67.476 0.000 . | . | . | . | 19 -0.023 -0.020 67.600 0.000 . | . | . | . | 20 0.022 0.039 67.834 0.000
30
31