Informal risk sharing arrangements and the crowding out of
formal insurance markets: A cross-sectional analysis from
Vietnam
by
Fiona Wainwright and Carol Newman*
Abstract
Using cross-sectional household survey data from Vietnam, this paper examines the
extent to which the use of informal insurance mechanisms “crowds out” the take-up of
formal insurance products. We analyse formal insurance demand within rural Vietnam
in order to determine the likely barriers to household participation and include
measures of reliance on informal information networks and social organisations to
determine the extent to which the formal insurance market is crowded out by the
informal market. A significant negative impact of informal insurance information
sources on formal insurance participation and demand decisions provides evidence of
a “crowding out” effect. In contrast, seeking information and assistance from formal
social organisations has a positive association with insurance demand suggesting that
these channels may have an important role to play in matching households to formal
insurance markets. Overall, results indicate that while there is significant
underexploited growth potential in the formal Vietnamese insurance market, the
presence of the informal market should also be taken into consideration when
formulating an appropriate policy response.
Key Words: formal insurance demand, crowding out, informal risk sharing, Vietnam
JEL Codes: O12, D53, D85,
* Carol Newman (cnewman@tcd.ie) and Fiona Wainwright (wainwrif@tcd.ie) are at
the Department of Economics, Trinity College Dublin. Our special thanks go to Prof.
Finn Tarp at the Department of Economics and the Development Economics Research
Group, University of Copenhagen for permitting the use of the data.
1
1. Introduction
Recent literature suggests that the lack of insurance, both in terms of market
availability and actual take-up, is one of the key drivers of persistent levels of poverty
in developing countries (Morduch, 2002). Across the developing world the majority
of low income households are engaged in farming-type activities, and are therefore
potentially subject to material losses as a consequence of fluctuations in agriculturally
derived income from external shocks such as drought, floods, pest infestation and
livestock disease.1 The purchase of a comprehensive insurance package, however, is a
complex process (Showers and Shotick, 1994). Households evaluate their financial
needs and determine various insurance schemes to insure against adverse income
shocks. According to modern financial theory, a household should hold a diversified
market portfolio that minimizes non-systematic risk. In practice, however, (and
especially in developing economies) many idiosyncratic and aggregate level risks are
not insured and households often remain exposed to movements in local weather,
regional house prices, prices of commodities like rice, gasoline and heating oil and
national income fluctuations.
In the absence of well developed financial markets in developing countries,
households tend to engage in costly informal risk mitigating strategies to reduce
income fluctuations.2 These income-smoothing activities can succeed in reducing the
volatility of income, but at a very high cost. As such, in many developing countries
community based informal risk-sharing and risk-coping arrangements are common
whereby fluctuations in household income or idiosyncratic shocks do not have any
effect on the consumption of individual households (Ray, 1998). The role of informal
risk-sharing arrangements and their interaction with formal insurance markets is an
important area within the development economics literature. For example, Townsend
(1994) using data from three high-risk villages in India, finds that household
consumption co-moves with village average consumption and that once village level
risk is controlled for, household consumption is not influenced by contemporaneous
own income, sickness, unemployment, or other idiosyncratic shocks.
Overall, the evidence suggests that informal risk-sharing arrangements are important.
However there is also much evidence to suggest that informal risk-sharing is typically
partial and inefficient and as a result expected utility is likely to increase under formal
insurance arrangements (Cox and Jimenez, 1992; Grimard, 1997; Ligon 2001).
Furthermore, shocks such as weather events tend to affect all households in a local
geographic area making community-based risk-sharing mechanisms, such as interhousehold transfers and local credit and asset markets, less effective at easing the
impact of an income shock. In addition, Morduch (2001) highlights the fact that
informal insurance is typically limited by the inability to write binding, enforceable
long term contracts (Morduch, 2001). Instead, the arrangements stay together only for
as long as the expected value of staying true to the arrangement exceeds the value of
defecting and facing the risk alone (i.e. self-insuring). The arrangements weaken as
the self-insurance option improves. Townsend and Lim (1998) support this finding
1
For example Morduch and Sicular (2001) found that while average incomes in villages in the
Shandong Province in northern China were growing at 8 per cent per year, one quarter of the
population in any given year was suffering losses of about 20 per cent.
2
For example, Morduch (1995) finds that Indian farmers near subsistence level spatially diversify their
plots, and devote a larger share of land to low-yield traditional varieties of rice and castor.
2
that the most effective approach to risk-coping at the household level is not via
informally based risk-sharing but by self-insurance through in-kind saving (for
example, building up grain reserves and drawing them down as required). Rosenzweig
(1988) provides evidence that formal risk-sharing opportunities are crowded out by
informal networks. Using data from Karnataka State in India, the study highlights the
importance of private transfers within networks of families and friends, and shows
that such transfers frequently crowd out formal loans. On the other hand, research also
suggests that the crowding out of formal networks by the informal networks might not
be the case. Indeed, evidence from Taiwan demonstrates that informal and formal
networks co-exist (Levenson and Beasley, 1996). These authors highlight the
persistence of informal financial mechanisms despite the development of financial
markets, possibly for the reason that they may offer more cost-effective finance.
The ongoing debate regarding the persistence of informal risk-coping within rural
communities in developing economies presents an opportunity to examine how the
informal sector impacts upon the formal insurance market within the Vietnamese
context. This paper contributes to the literature by focussing specifically on the role of
formal insurance products in insuring against risk in rural Vietnam and identifying
causal relationships between informal risk sharing arrangements and the demand for
formal insurance. We use cross-sectional data from the Vietnamese Access to
Resources Household Survey 2006. We find that informal risk-sharing negatively
impacts on formal insurance demand while reliance on formal social organisations has
a positive effect.3
This paper is organised as follows. Section 2 details the methodological framework
used for the analysis while Section 3 describes the data. Section 4 discusses the
empirical findings and Section 5 concludes.
2. Methodological Framework
Ehrlich and Becker (1972) demonstrate that an individual’s need for insurance is no
different to their need for any other good or service. Our insurance demand model is
developed within a simple neo-classical framework under symmetric information. It is
assumed that the household chooses whether to purchase insurance (and the amount to
purchase) based upon expected utility values. Any factor that increases the expected
utility of purchasing formal insurance is therefore expected to increase the probability
of purchase.
An individual purchases an insurance contract so as to alter his/her pattern of income
across states of nature (Rothschild, Stiglitz, 1976). We begin by considering a riskaverse household with the following standard constant absolute risk aversion (CARA)
utility function:
U i (C ) = −
3
1
α
exp{− αC}
(1)
This latter result supports the findings of Newman et al. (2009) who find evidence of a role for
community governance, in the form of formal socio-political organisations, in correcting for both
infrastructural and information failures in financial markets at the commune level.
3
Where U (.) is increasing and strictly concave ( U ' (.) → 0 as C → 0 ), and where
α ≥ 0 measures risk aversion and C denotes household consumption. We assume
increasing relative risk aversion (IRRA) in that the coefficient of relative risk aversion,
R A = αC is increasing in consumption goods as measured by C :
dR A
=α
dC
(2)
Households can be exposed to many different states, s = 1,2,..., S , and the household
assigns a probability, π s , to each different state. Ys denotes the household level of
income associated with each state. I denotes the quantity of insurance demanded per
household while p (I ) denotes the price per unit coverage. p (I ) * I thus represents
the total insurance premium paid. L denotes the amount to be insured or the potential
loss incurred. Abstracting the analysis to two states of nature ( s = 1 representing a
state where a loss occurs and s = 2 representing a state where a loss does not occur)
so π 1 + π 2 = 1 , in any given time period the household is faced with the following
budget constraint if s = 1 :
Y1 = Y + I − L − p (I ) * I
(3)
and if s = 2 :
Y2 = Y − p (I ) * I
(4)
To keep the model simple we assume that there is no savings or borrowing and so
C1 = Y1 and C 2 = Y2 . Placing the budget constraints directly into the generalized form
of the utility function, the household seeks to maximise the following von-Neumann
Morgenstern Expected Utility function:
EU i (I ) = π 1U i (C1 ) + π 2U i (C 2 )
= π 1U i (Y + I − L − p(I ) * I ) + π 2U i (Y − p(I ) * I )
(5)
The insurance demand function is therefore:
I i * (π , p, L, Y , α ) = Arg max EU i ( I )
(6)
In a developing country context, the observed level of insurance purchased, I 0 , may
deviate from this optimum. This may be due to, for example, information failures or
the existence of informal risk-sharing arrangements that crowd out the demand for
formal insurance products. As such, the amount of insurance that each household
demands will not only depend on the outcome of this optimization problem, it may
also be influenced by any informal risk-sharing arrangements (measured by ε ) that
the household is a part of within the local commune.
I 0 ( π , p , L, Y , ε , α ) = I * ( π , p , L, Y , α ) − ε
4
(7)
Equations (6) and (7) form the basis for our empirical model. In the absence of price
information, the only observable factors in the insurance demand model are income
and the extent of informal risk sharing. We include, however, a number of variables to
control for the level of risk aversion (which will determine L and α ). The empirical
model for household insurance demand is:
I i = β 0 + β1Yi + β 2 N i + β 4 Zi + vi
(8)
where I i is measured as the insurance premium paid, Yi is income, N i is a measure
of informal risk-sharing arrangements that may crowd out the demand for insurance
and Z i is a vector of control variables including socio-economic characteristics of
households and regional indicators to control for supply-side variations.
An important empirical consideration is that many households report a zero
willingness to buy formal insurance. The reasons could be that households do not
positively value this type of insurance or because they lack the financial resources to
pay any positive amount for it. Furthermore, it may be the case in rural areas that
households are not even aware that formal insurance products exist. Failure to take
this into account will result in biased and inconsistent estimates. We attempt to
control for this potential bias by modeling the demand for insurance using a censored
tobit model that conditions on the fact that households are aware of the existence of
formal insurance products. In addition, as a robustness check, we separately model the
insurance participation decision within a standard probit framework.
Denoting the decision to purchase insurance with a zero-one dummy variable d i and
the vector of explanatory variables determining this decision as Wi , the participation
decision can be modeled in a standard probit framework where d i * is an unobserved
underlying utility associated with the purchase of insurance:
d i * = δWi + u i
d i = 1 if d i * > 0
d i = 0 otherwise
(9)
Denoting the observed level of insurance purchased as insi and the vector of
explanatory variables determining this decision as X i , the expenditure decision can
be modeled in a standard tobit framework where insi * is an unobserved underlying
utility associated with the level of insurance purchased:
insi * = λX i + ei
insi = insi * if ins i * > 0
ins i = 0 otherwise
5
(10)
Assuming that the error terms are distributed as u i ~ N (0,1) for the probit and
(
)
ei ~ N 0, σ 2 for the tobit model, each can be estimated using Maximum Likelihood
Estimation.
The consistency of Maximum Likelihood Estimator for the censored tobit model
depends critically on the assumption of homoscedastic errors. To allow for the
possibility of heteroscedasticity we consider a multiplicative heteroscedasticity
structure where the error variance is assumed to be related to a sub-set of exogenous
variables, gathered in a vector H i (not including a constant) (Verbeek, 2004).4
V {ei | X i } = σ 2 exp{ωH i }
(11)
The tobit model is also heavily parametric in character, with error terms assumed
normally distributed. Such strong assumptions may be costly when the data do not fit
this distribution, resulting in inconsistent maximum likelihood estimators. A
conditional moments test against the null hypothesis of normal errors is conducted on
the demand equation. This test was derived by Pagan and Vella (1989). In the event of
non-normality the dependent variable is transformed by an Inverse Hyperbolic Sine
(IHS) transformation algorithm. This transformation accommodates zero, negative,
and positive values for the random variable and is known to better handle extreme
values than other transformations such as Box-Cox. The IHS transformation on the
insurance demand variable insi is:
T (θinsi ) =
sinh −1 (insi )
θ
[
= log θinsi + (θ 2 insi2 + 1)
12
]θ
(12)
The final tobit demand log likelihood equation that corrects for heteroscedasticity and
adjusts for non-normality with (IHS) transformation is given by:
⎡
⎛ βX
log L = ∑0 ⎢1 − Φ⎜⎜ i
⎝ σi
⎣
⎞⎤
⎟⎟⎥
⎠⎦
⎡
−1 2 ⎛ T (θins i ) − βX i
+ ∑1 ⎢ θ 2 ins i2 + 1 φ ⎜⎜
σi
⎝
⎣
(
)
⎞⎤
⎟⎟⎥
⎠⎦
(13)
where Φ (.) and φ (.) refer to the standard normal probability and density functions,
respectively.
A limitation of the probit participation and tobit demand models within the context of
this study is that they do not explicitly control for whether or not the household is
aware of the existence of insurance products. To accommodate this, all estimates are
conditional on awareness.
4
Likelihood ratio tests are performed to determine the subset of continuous explanatory variables that
are causing heteroscedasticity and appropriate corrections are made to the Log Likelihood function.
6
3. Data
The data are taken from the Vietnam Access to Resources Household Survey
(VARHS). This survey was carried out in rural areas of 12 provinces of Vietnam
between August and September 2006 thereby producing cross-sectional data on more
than 2,300 households. The survey was developed by the Development Economics
Research Group, Department of Economics, University of Copenhagen and the
Institute of Labour Studies and Social Affairs, Hanoi Vietnam. The VARHS explores
issues surrounding Vietnamese rural households’ access to resources and the
constraints that these households face in managing their livelihoods. The households
are spread over 161 districts and 466 communes. Along with detailed demographic
information on household members, the survey includes sections on household
savings, credit (both formal and informal), formal insurance, risk response, informal
safety nets and the structure of social capital.
In this paper we are interested in using the empirical model given in equation (8) to
test, in particular, the extent to which informal risk sharing arrangements crowd out
the demand of formal insurance products. The data indicate that 65 per cent of
households surveyed hold formal insurance. Among the 11 types of formal insurance
listed in the survey, health insurance schemes (including health insurance for children)
and vehicle insurance schemes have the highest participation rates. In contrast, there
are no households in our sample that have agricultural insurance. 5 Table 1
disaggregates the total insurance purchased by households into its constituent parts.
The survey also reveals the reasons why households do not demand each type of
formal insurance. Seven per cent of households are not aware of the existence of
formal insurance products, 58 per cent state that they have no need for insurance
while 18 per cent of households have no information regarding insurance products.
The data also reveal that of the 39 per cent of households that experienced an income
shock between 2002 and 2005 only 2.9 per cent of these losses were covered by
formal insurance. Eighteen per cent of households claim that they rely on informal
mechanisms such as borrowing from a friend or relative. While these observations
from the raw data do not provide evidence of crowding out they do suggest that
despite the fact that 65 per cent of households hold formal insurance, informal risk
coping measures remain important.
INSERT TABLE 1 HERE
To measure the extent of informal risk coping among households in our sample we
follow a similar approach to Jowett (2004). We consider two proxy measures of social
capital: one that captures informal arrangements and the other formal networks. The
informal proxy measures the importance to households of informal networks
(Friends/Neighbours/Family) as sources of information regarding credit and insurance.
The measure is binary, recording 1 if informal sources of information are important
and 0 if informal sources of information are not important. The formal proxy measure
of social capital is a binary measure reflecting whether the household expects to
receive future help from formal groups/organisations, recording 1 if the household
expects to receive future help and 0 if not. These groups exist at a more
5
This supports the informal statistic that only 1 per cent of farming areas in Vietnam hold insurance
(see Vietnamnet (www.vnn.vn), “Agricultural Insurance: Where is the State?”, 16/07/2004).
7
structured/organised level and consist of the Communist Party, Women’s Union,
Farmers Union and Veteran’s Union. We explicitly exclude more informal groups
(Neighbourhood Committee, Sports Club) from this classification.
The inclusion of these measures of informal risk sharing into the model could
potentially introduce endogeneity yielding inconsistent estimates. It could be argued,
for example, that the extent to which households rely on informal risk sharing
networks will depend upon unobserved household characteristics such as risk aversion
which will be included in the error term of the formal insurance demand model ( ei in
equation (10)). As the theoretical model (equation (7)) predicts, formal insurance
demand is increasing in risk aversion. We propose by extension that reliance on
informal risk sharing networks is also increasing in risk aversion. If this is the case
then the crowding out effect of informal risk sharing may be understated since the
effect of this variable will be dampened by the fact that it also captures the positive
effect of unobservable risk aversion on the demand for formal insurance. In contrast,
if complementarities exist between informal risk sharing and formal insurance
demand, the correlation with unobserved risk aversion will lead to this effect being
overstated.
We address the endogeneity issue by using an instrumental variables approach. We
consider two instruments for the informal proxy. First, we use a measure of the
distance from the commune to the nearest state bank on the basis that the further a
commune is located from a formal financial institution the more likely households are
to rely on informal networks for information and risk sharing. Second, we construct
an ordinal measure of informal risk coping intensity using data on sources of credit
accessed between 2002 and 2005. Households borrow from a range of different
sources including formal banks (such as, the Social Policy bank, or the Bank for
Agriculture and Rural Development) and informal sources (including relative and
friends, informal credit schemes and rotating savings and credit schemes). Households
borrowing from relatives and friends are given a high score while those borrowing
from formal sources score lowly. This measure thus reflects the strength of informal
financial networks at the household level. We argue that this instrument is not
contemporaneously correlated with unobserved risk aversion on the basis that it
measures only from which source, not how much a household borrows. This reflects
an income smoothing decision that a rational household will make (regardless of their
risk aversion) without incorporating the magnitude of any borrowing undertaken
(which could be correlated with risk aversion).
We also consider two instruments for the formal measure of social capital. First, we
use the distance from the household to the local people’s committee office on the
basis that the shorter the distance the more active formal groups are likely to be in the
commune and the more likely households are to expect help from these organisations
in the future. Second, we use the number of formal organisations that the household is
an active member. The more organisations that they are ‘active’ members of the more
likely they are to expect to receive help. We argue that membership of such formal
social organisations represents a measure of how the state still maintains an important
function in the mobilization of resources with respect to rural organisational life
within Vietnam and that this is not connected to a household’s level of risk aversion.
8
Aside from the crowding out hypothesis our model also allows us to test a number of
other interesting predictions. The IRRA utility function, which underpins our model,
predicts that the amount of insurance demanded should be increasing in the level of
wealth. In the model we allow wealth to equate to consumption, which in the absence
of savings and borrowing could be proxied by income. However, since we also treat
insurance like any other good, standard income effects might also be present. If
insurance is a normal good, then we would expect a positive relationship between
income and the amount of insurance demanded (i.e. β1 > 0 ). We proxy income in the
model with total expenditure incurred by the household less the amount spent on
insurance. Having controlled for income, our model predicts that household wealth
should also have a positive effect on insurance demand. We measure wealth as a
wealth quintile indicator using per capita household consumption.
Much of the empirical literature investigating insurance demand finds that household
characteristics should be included as important controls of heterogeneity in the degree
of risk aversion across households (Magrabi et al., 1991). Showers and Shotick (1994)
hypothesise that the specific characteristics related to household demand for insurance
include family size, age, number employed and income. They also consider how the
number of household earners interacts with income to influence insurance demand.
They propose that multi-earner households perceive less risk of loss of income than
single-income households, even at the same income level. In contrast, families with
many members may in fact be more risk averse as they are more exposed to shocks.
Lazear and Micheal (1988) suggest that there may also be economies of scale in
insurance purchase and so as a family size increases we would expect demand for
insurance to also increase, but at a decreasing rate. We use the age of the head of
household as a proxy for the overall lifecycle stage of a family unit. As families age,
the level of income and number of dependents tend to rise. This should result in an
increase in risk aversion and so an increase in household demand for insurance.
However, in the declining years of a family, we would expect the demand for
insurance to decrease as the family reaches the end of their lifecycle with less
insurable risk. This relationship may be curvilinear (Duker, 1960), a consideration
also made in our model. The full list of variables considered for this analysis is given
in Table 2.
INSERT TABLE 2 HERE
4. Empirical Results
A censored tobit demand model for formal insurance, conditional on awareness, with
adjustments for heterosedasticity and non-normality (IHS transformation) is estimated
using STATA.6 All continuous variables enter in log form to smooth extreme value
volatilities. The baseline model includes income, wealth plus other interaction and
control variables to control for the unobserved measure of risk aversion and initially
exclude the measures of informal risk sharing. We estimate a baseline tobit insurance
demand model and a standard probit decision model. The latter captures the extent to
6
The Stata code to perform this estimation is non-standard and was written using maximum likelihood
estimation procedures. Tests for heteroscedasticity and non-normality are performed with
homoscedastic and normal errors both rejected at the 1 per cent significance level.
9
which causal relationships are effective at the margin rather than in the level of
demand thus serving as a robustness check on the model. The results are presented in
Table 3.
INSERT TABLE 3 HERE
The theoretical model predicts that the extent of household risk aversion is increasing
in wealth. When explicitly controlling for risk aversion, household wealth should have
a positive effect on insurance demand assuming insurance is a normal good. This is
reflected in the positive coefficients on the upper wealth quintiles of the tobit demand
estimates. The associated probit model also returns a significant and positive
relationship between two upper wealth quintiles and the probability that a household
participates in the formal insurance market. We also hypothesise that a household is
more risk averse in savings therefore financial savings should also positively impact
on insurance demand. We find that the level of household financial savings does have
a positive and significant effect on the demand for formal insurance and on the
probability of purchase.7 We also find that, as hypothesised, the greater the size of the
family, the greater the demand for formal insurance as there tend to be more insurable
risks in larger families. This result is also reflected in the participation equation The
level of education of the head of household has a positive and significant effect on the
demand for formal insurance. This could be due to the fact that educated households
are more aware of risks or else simply that they have more information about formal
insurance products. Farming households have a negative and significant impact on the
amount of insurance demanded. This is surprising given that farmers are more
exposed to weather related risks. Finally, a prior adverse income shock has an
insignificant effect on both the insurance demand and the probability of purchase
decisions. We note that while gender has a negative and significant impact on
insurance demand, it is not significant in the probit participation decision. This
inconsistency may reflect the fact that certain factors while important to a household’s
insurance market participation decision, can become less significant when deciding on
the amount of insurance to purchase.
Focussing specifically on the insurance demand equation we note that while wealth
records positive and significant effects on demand, income records an insignificant
effect contrary to the theoretical model predictions. An insignificant relationship
between the number of earners and insurance demanded is also found, thereby
rejecting Showers and Shotick (1994) hypothesis. Family size has a positive and
significant impact on insurance demand as proposed by Lazear and Micheal (1988)
and economies of scale in insurance purchase are also evident from the negative
coefficient against the square term. The age and age squared coefficients are both
insignificant thereby rejecting the Duker (1969) proposition that age can be used as a
proxy for the lifecycle stage of a family unit. These inconsistencies might be partially
explained by the social and economic differences between a developed economy such
as the US (the focus of Duker’s research) and a developing economy such as Vietnam
where insurance markets are less well developed and families tend to be more
cohesive and supportive of older members.
7
It could also be argued that savings are a form of self-insurance and so could crowd out the demand
for formal insurance. This does not seem to be the case.
10
Next, we extend our analysis to consider the extent to which informal risk sharing
arrangements crowd out the demand for formal insurance. We present the results for
the un-instrumented and the instrumented cases in Table 4. Only the informal risk
sharing measures are presented for ease of illustration. The results for the other
variables remain robust to their inclusion.
INSERT TABLE 4 HERE
The two measures of informal risk sharing included are: an informal proxy measuring
the importance to households of informal networks as sources of information
regarding credit and insurance; and a formal proxy measure of social capital
measuring whether the household expects to receive future help from formal
groups/organisations. In the un-instrumented case we find that informal information
sources negatively and significantly impact on the demand for insurance (and on the
participation decision). In contrast, the formal measure has a positive and significant
impact on both decisions. To correct for possible bias as a result of the endogeneity of
these variables we instrument using four different instruments as discussed above.8
We run first stage regressions on each of the potentially endogenous regressors, the
results of which are presented in the appendix, and find that all are significant at least
at the 5 per cent level. We then estimate the probit and tobit models using the fitted
values from this first stage regression in the second stage model. 9 Results are
presented in Table 5.
INSERT TABLE 5 HERE
Results confirm that the instrument for social capital measuring whether the
household expects to receive future assistance from formal groups/organisations has a
positive effect on both the probability of purchase and on the insurance amount
demanded. This result suggests that social groups may fill an important information
gap in the insurance market by directing households toward formal insurance products
in times of need. In contrast, we confirm that the reliance on informal information
networks has a significant negative effect on both the participation in formal
insurance markets and on the amount of formal insurance demanded. This suggests
that informal information sources ‘crowd out’ formal sources. Overall, these results
suggest two conflicting effects of informal networks and social capital on insurance
demand in rural Vietnam. On the one hand, as found in much of the other literature on
developing countries, there is some evidence to suggest that informal risk-sharing
arrangements crowd out the demand for formal insurance. On the other hand,
membership of formal social groups linked to the state may play an important role in
linking households to formal insurance markets.
8
We test for endogeneity using the ivreg GMM approach. Both informal risk sharing regressors were
found to be endogenous with GMM C statistics of 6.68843 and 9.71733 respectively. Applying our
instruments results in a test of overidentifying restrictions that fails to reject the null of exogenous
instruments with a Hansen’s J statistic of 0.277245. All other coefficients are robust to these tests.
9
We also implement as an additional robustness check the Stata module TOBITIV by Jonah B.
Gelbach that implements the method of Whitney Newey, 'Efficient Estimation of Limited Dependent
Variable Models with Endogenous Explanatory Variables', Journal of Econometrics (1987). The
standard errors require adjustment but the point estimates will be consistent. All coefficients remain
robust to this estimation.
11
While we have sought to eliminate the endogeneity present in the informal risk
measures through the use of suitable instrumental variables, we will continue to seek
additional instruments to verify the robustness of our existing estimates. In addition,
we propose to extend the analysis to include marginal effects in order to evaluate the
magnitude of the effects of our measures of informal risk-sharing on formal insurance
demand - both when instrumented and when not.
6. Conclusion
Formal insurance markets are underdeveloped in developing countries. In Vietnam,
there is reason to believe that the rural economy may benefit greatly from sustainable
insurance products. Targeting specific market segments with information about the
advantages of purchasing formal insurance and the specific types of insurance
products available could potentially stimulate formal insurance demand and thus
provide rural communities with a guaranteed safety net in the face of adverse income
shocks. As the composition of rural Vietnam evolves, changes in household
circumstances and characteristics will influence the demand for formal insurance. In
this study, the determinants of household formal insurance demand are explored
together with the extent to which informal information channels and formal social
organisations crowd out the formal insurance market.
Using data from the Vietnamese Access to resources household survey 2006, a probit
purchase decision and a tobit model of formal insurance demand is estimated
conditional upon awareness. We find that wealthier households with greater financial
savings, higher net incomes and better education, demand more formal insurance.
Farming households are less likely to purchase. We find conflicting evidence on the
role of social networks in insurance demand decisions. A significant negative impact
of informal insurance information sources on the decision to purchase and the amount
of insurance demanded provides evidence that informal arrangements ‘crowd out’
formal insurance demand. In contrast, associations with formal social organisations
have a positive effect on insurance demand suggesting that they may have an
important role to play in matching households to formal insurance markets.
A major consideration of this study is the endogeneity of the measures of informal
risk sharing used in this model. We attempt to control for risk aversion of the
household to as great an extent as possible in the model and select suitable
instruments to correct for this endogeneity bias. Future work will attempt to find
additional instruments in order to enrich our existing measures of social capital.
In extracting policy implications from this research, a deeper understanding of why
households elect to purchase different insurance types can provide useful insights
concerning future needs for both the private and public sector alike. Overall, our
results suggest that targeting rural farming households through local formal groups
could be an effective way to increase demand for formal insurance products. In
particular, social organisations may have the potential to act as a positive conduit for
disseminating product information. It should also be noted, however, that if
encouraging insurance purchase is desirable from a policy perspective, information
quality is important in seeking the desired market response.
12
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14
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15
Tables
Table 1: Explanatory Variables and descriptions
Explanatory Variable
Description
Formal Social Organisations Help
Measure
Informal Information Sharing
Measure
Gender
Debt
Net Income
Wealth
Financial Savings
Age
Age Squared
Family Size
Family Size Squared
Number of Earners
Number of Children
Children Supporting (1 Yes, 0 No)
Married (1 Yes, 0 No)
Education
Farmer (1 Yes, 0 No)
Prior Income Shock (from 2002 to
2005)
Net Income * Number of Earners
Provincial Dummies
Whether household expects to receive future help from formal social
organisations (Communist Party, Farmers Union, Veterans Union).
The importance to households of informal sources of information on
insurance and credit (1 important, 0 not important).
Gender of head of household (1 Male, 0 Female).
Household financial debt amount.
Household net income (less insurance premiums paid).
Wealth Quintile Measure based on per capita food expenditure.
Formal financial savings of household.
Age of household head.
Age squared measure for hypothesis testing.
Size of family.
Size of family squared for hypothesis testing.
Number of family members earning an income.
Number of children in the household.
Whether household receives financial support from children.
Whether household head is married.
A Measure from 1 to 5 where 1 = Cannot read and write; 2 = Can
read and write but did not finish primary school; 3= Finished
Primary School; 4 = Finished Lower Secondary School; 5 =
Finished Upper Secondary School; 6 = Third Level.
Whether household head of household is a farmer.
Has the household suffered from prior income shock between the
years 2002 to 2005 (1 Yes, 0 No).
Interaction term for hypothesis testing.
Dummy variable representing each of the 12 provinces surveyed.
Used for control purposes.
Table 2: Types of Insurance against percentage of participating households
Types of Insurance
Fire
Life
Agriculture
Social
Farmers Social
Health
Free Social
Free Health for Children
Vehicle
Education
Other
16
Percentage of households
0.1
7.0
0.0
15.4
1.0
53.3
3.0
36.2
29.9
7.2
3.9
Table 3: Baseline Tobit Demand Equation and Probit Participation equation
Tobit Demand Equation
Wealth Measure of Household – Quintile 2
Wealth Measure of Household – Quintile 3
Wealth Measure of Household – Quintile 4
Wealth Measure of Household – Quintile 5
Financial Savings of Household
Gender of Head of Household (1 Male, 0 Female)
Debt
Net Income
Net Income * Number of Earners
Age
Age Squared
Family Size
Family Size Squared
Number of Earners
Number of Children
Children Support (1 yes, 0 no)
Married (1 Yes, 0 No)
Education Measure
Farmer (1 Yes, 0 No)
Prior Income Shock (1 yes, 0 no)
Provincial Dummies
Coefficient
0.4070
0.8982***
1.8078***
2.6899***
0.0672***
-0.5872*
-0.0141
0.2844
1.1992
0.0873
-0.0008
1.2324***
-0.0678***
-0.3713
-0.2695*
-0.4070
0.2719
0.7738***
-0.5460***
0.0354
Yes
Standard Error
0.4568
0.4581
0.4541
0.4868
0.0319
0.4014
0.0480
1.3857
1.3651
0.0728
0.0007
0.3750
0.0316
0.4197
0.1879
0.3203
0.4608
0.1237
0.2621
0.2741
Heteroscedasticity equation
Coefficient
Standard Error
Financial Savings
-0.0249***
0.0071
Net Income
-0.1112***
0.0256
_Cons
2.5630
30.6278
*** denotes significance at 1% level; ** denotes significance at 5% level; *denotes significance at 10
level%
Log Likelihood = -3029.4335
Number of Observations = 1751
IHS estimate = 1.21409
Probit Participation Equation
Wealth Measure of Household – Quintile 2
Wealth Measure of Household – Quintile 3
Wealth Measure of Household – Quintile 4
Wealth Measure of Household – Quintile 5
Financial Savings of Household
Gender of Head of Household (1 Male, 0 Female)
Debt
Net Income
Net Income * Number of Earners
Age
Age Squared
Family Size
Family Size Squared
Number of Earners
Number of Children
Children Support (1 yes, 0 no)
Married (1 Yes, 0 No)
Education Measure
Farmer (1 Yes, 0 No)
Prior Income Shock (1 yes, 0 no)
Provincial Dummies
_cons
17
Coefficient
0.0259
0.1314
0.3074***
0.6105***
0.0155**
-0.0937
0.0027
0.1238
0.1730
0.0231
-0.0002
0.2867***
-0.0167***
-0.0658
-0.0677*
-0.0654
0.0787
0.1660***
-0.0857*
0.0016
YES
-5.064***
Standard Error
0.1051
0.1082
0.1099
0.1224
0.0099
0.1099
0.0130
0.0130
0.3526
0.3470
0.0187
0.0002
0.0949
0.0080
0.1074
0.0486
0.0827
0.1220
0.0328
0.0690
0.6691
Table 4: Informal Risk Sharing Effects
Tobit Demand Equation
Informal Information Sharing Measure
Formal Social Organisations Measure
Coefficient
-0.6689**
1.5154***
Standard Error
0.2576
0.3038
Probit Participation Equation
Informal Information Sharing Measure
Formal Social Organisations Measure
Coefficient
-0.2055***
0.3730***
Standard Error
0.0678
0.0789
Table 5: Instrumented Informal Risk Sharing Effects
Tobit Demand Equation
Informal Information Sharing Instrument
Formal Social Organisations Instrument
Coefficient
-10.5860 ***
3.8406***
Standard Error
3.0140
0.6234
Probit Participation Equation
Informal Information Sharing Measure
Formal Social Organisations Measure
Coefficient
-2.6688***
1.0612***
Standard Error
0.7800
0.1778
18
Appendix
Stage 1 Reduced form Estimates:
Coefficient
Standard Error
Informal risk sharing networks
Wealth Measure of Household – Quintile 2
0.0233
0.0375
Wealth Measure of Household – Quintile 3
0.0482
0.0390
Wealth Measure of Household – Quintile 4
0.0759**
0.0399
Wealth Measure of Household – Quintile 5
0.0218
0.0438
Financial Savings
0.0029
0.0035
Gender
0.0006
0.0392
Debt
0.0125***
0.0046
Net Income
-0.1720
0.1201
Net Income * Number of Earners
0.1377
0.1162
Age
-0.0069
0.0067
Age Squared
0.0000
0.0000
Family Size
0.0552
0.0336
Family Size Squared
-0.0053*
0.0028
Number of Earners
-0.0190
0.0361
Number of Children
0.0227
0.0174
Children Supporting (1 yes, 0 no)
0.0361
0.0302
Married (1 yes, 0 no)
-0.0448
0.0439
Education Measure
0.0012
0.0117
Farmer (1 yes, 0 no)
0.0084
0.0248
Prior Income Shock (1 yes, 0 no)
0.0450**
0.0257
Instruments for informal risk-sharing
networks
Distance from commune to local state bank
0.0782**
0.0480
Index of informal risk-coping intensity
0.0093***
0.0046
Instruments for formal risk-sharing networks
Distance of household to local peoples committee 0.0068***
0.0029
office
Formal group membership
0.0041
0.0109
_cons
0.5255***
0.2408
*** denotes significance at 1% level; ** denotes significance at 5% level; *denotes significance at 10
level%
19
Appendix (continued)
Stage 1 Reduced form Estimates:
Coefficient
Standard Error
Formal risk sharing networks
Wealth Measure of Household – Quintile 2
0.0177
0.0289
Wealth Measure of Household – Quintile 3
-0.0001
0.0301
Wealth Measure of Household – Quintile 4
0.0188
0.0308
Wealth Measure of Household – Quintile 5
0.0218
0.0337
Financial Savings
0.0033
0.0027
Gender
0.0309
0.0302
Debt
0.0048
0.0036
Net Income
-0.2488***
0.0926
Net Income*Number of Earners
0.2230***
0.0896
Age
-0.0039
0.0052
Age Squared
0.0000
0.0000
Family Size
0.0603***
0.0258
Family Size Squared
-0.0032
0.0022
Number of Earners
-0.0547**
0.0279
Number of Children
-0.0228**
0.0135
Children Supporting (1 yes, 0 no)
0.0228
0.0233
Married (1 yes, 0 no)
-0.0122
0.0339
Education Measure
-0.0052
0.0090
Farmer (1 yes, 0 no)
-0.0038
0.0191
Prior Income Shock (1 yes, 0 no)
0.0395**
0.0198
Instruments for informal risk-sharing
networks
Distance from commune to local state bank
0.0711***
0.0370
Index of informal risk-coping intensity
-0.0076***
0.0035
Instruments for formal risk-sharing networks
Distance of household to local peoples committee 0.0025*
0.0022
office
Formal group membership
0.1779***
0.0084
_cons
0.6126***
0.1856
*** denotes significance at 1% level; ** denotes significance at 5% level; *denotes significance at 10%
level
20