RISK-SHARING IN A TRANSITION ECONOMY:

EVIDENCE FROM MONTHLY DATA IN BULGARI A

Emmanuel Skoufia sInternational Food Policy Research Institut e

The National Council for Eurasian and East European Researc h910 17th Street, N .W .

Suite 300Washington, D .C. 20006

TITLE VIII PROGRAM

Project Information*

Sponsoring Institution :

The Economics Institute

Principal Investigator :

Emmanuel Skoufias

Council Contract Number :

814-20g

Date :

July 9, 200 1

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The work leading to fhis report was supported in part by contracf or grant funds provided by the National Council fo rEurasian and East European Research, funds which were made available by fhe U .S. Department of Stafe under Titl eVIII (The Soviet-Easf European Research and Training Acf of 1983, as amended) . The analysis and interpretation scontained herein are those of the author .

ii

Abstract

Monthly data from a panel of households in Bulgaria in 1994 are used to examine the extent t o

which households, through formal and or informal arrangements, are able to insure their consumptio n

from fluctuations in their monthly real income . The empirical analysis reveals that households are able t o

achieve partial but not complete insurance of consumption from idiosyncratic fluctuations in their income .

Consumption frpm month to month appears to be smoothed more effectively in rural or smalle r

communities where the problems of information asymmetry, enforcement and moral hazard are les s

severe . In general, households seem to insulate their food consumption from fluctuations in their incom e

by adjusting their nonfood expenditures and by borrowing through formal and infprmal credit markets .

Inter-household transfers play only a small role m insuring consumptipn .

iii

Introduction

Between 1989 and 1994 the Bulgarian economy experienced a massive contraction in output an d

employment, while at the same time the real benefits to social transfer recipients declined substantially .

Since 1989, for example, the gross domestic product fell by nearly 30 percent, while employment in th e

state sector fell from 4 .4 million in 1989 to 2 .3 million by the third quarter of 1993, a drop of 47 percen t

(Hassan and Peters, 1994) . These developments were accompanied by dramatic increases in the level o f

prices. Between January 1994 and December 1994, for example, the average inflation rate in Bulgari a

from month to mpnth was close to seven per cent . Such shocks to the real income of households ar e

common during the process of transition to a market-based system .

In the face of such adverse economic circumstances, and with an ineffective social safety ne t

system, households may resort to formal and alternative informal arrangements that stabilize thei r

consumption across time (Morduch, 1999) . Households may use savings (Paxson, 1992), sell their assets

(Rosenzweig and Wolpin, 1993), send their children to work instead of going to school as a means t o

supplement income (Jacoby and Skoufias, 1997), or take loans from the formal financial sector to carr y

them through the difficult times (Udry, 1994) . Additional strategies include the mobilization of thei r

informal social support networks for accessing goods and services (Sik, 1994), and other arrangement s

such as informal loans (Besley, I995) or nonmarket transfers frpm friends and relatives (Ravallion an d

Dearden (1988); Cox and Jimenez (1990)) . Privafe transfers between households in Russia, for example ,

have been large and persistent through the transition period (Cox, Eser, and Jimenez. 1997) .

Although there is a considerable literature pn risk-sharing in developing countries, there is ver y

little evidence on the extent to which risk-sharing arrangements are at all successful at protecting

household consumption in economies during the process of transition from a socialist to a market-base d

system. In this study I use household panel data at the monthly level from the 1994 Househpld Budge t

Survey (HBS) collected by the Central Statistical Office of Bulgaria tp examine the extent to whic h

households manage to insure their consumption from monthly fluctuations in their real income . Three

1

main questions are addressed : (1) To what extent do households smooth their food consumption and

nonfood consumption across time? (2) Hpw is this smoothing accomplished? In particular, do household s

rely on credit markets, nonmarket transfers . or on potentially more costly strategies? and (3) Do credit

transactions and nonmarket transfers play distinct roles in buffering intertemporal income fluctuations ?

Much of the literature on risk-sharing in developing and developed countries alike has relied o n

repeated observations on households that are at least one year apart (Cochrane, 1991 ; Mace, 1991 ;

Townsend, 1994, 1995 ; Deaton, 1997 ; Jalan and Ravallion 1997). While these studies are able to shed

light on the extent to which households reduce their vulnerability to risk from year to year, they can be of

only limited use in determining whether household consumptipn can or is smoothed over shorter tim e

intervals .' During periods of economic restructuring households are exposed to considerable risk at a

very high frequency . For example, in January 1994 the monthly inflation rate in Bulgaria was 3 .8% ,

increasing to 21 .7% in April, then decreasing down to 0 .6% in July and increasing again to 11 .01% in

September 1994 .

The monthly household panel data used in this study offer the unusual opportunity to examin e

whether households managed fo smooth their consumption in the face of such frequent fluctuations in thei r

real income. As a complement to my analysis of household consumption . I also investigate in more detai l

some of the ways in which households accomplish this smoothing in their welfare .

In particular, I examine whether households rely more on credit markets or inter-househol d

transfers as a means of smoothing their consumption from month to month . Understanding the specifi c

strategies that households adopt to buffer income fluctuations is critical in evaluating the costs and benefit s

of policy interventipns. If monetary and/or in-kind transfers play a central role, then temporary povert y

relief prpgrams may be counteracted by reductions in private transfers . Cross-sectional studies of povert y

' One exception is Jacoby and Skoufias (I998), who examine risk-sharing among rural households in India fro mquarter to quarter .

and even intertemporal studies of consumption variability from year to year provide only limited insight s

on these issues .

The structure of the paper is as follows . The next section discusses in detail the data set and th e

construction of the key variables used in the analysis . Following that, the paper presents the basic mode l

of consumption insurance and its main implications about the effects of idiosyncratic income shocks o n

cpnsumption . The last section contains the empirical analysis and a discussion of the results .

Data

The data set used in this study contains monthly observations on households from the 199 4

Household Budget Survey (HBS) from Bulgaria . The HBS is a representative sample of household s

constructed as a two-tier random sample . It is based on sample frame developed from the 198 5

Population Census . In the first stage the enumeration districts (clusters) are selected in rural and urban

areas of each province . In the second stage six households are sampled among the 90 pr so household s

contained in each cluster. Between January and December 1994, the HBS contains monthly obseryation s

for approximately 2500 households from 418 clusters, (271 towns and 147 villages) . In July 1994, th e

total number of clusters and households surveyed increases to 1018 clusters (685 towns and 333 yillages )

and 6,000 households, respectively . The empirical analysis uses the original 2,500 households observe d

for 12 months as well as the new households added in the sample later in July 1994 .

The HBS suryey contains information on the geographical location of the household (region, siz e

of town, yillage), the age, sex, and education level of each household member, as well as monthly detail s

on their employment status . Other variables include a measure of the income earned by each househol d

3

member and detailed information on househpld income by source (e .g. salary, pension, unemploymen t

benefits, child allowances, sale of food produced at home, monetary transfers received or given out, etc) . 2

A rather unique feature of the food module of the HBS is that for each food item it contain s

information on the quantity of stocks in the beginning of each month, as well as detailed information o f

the flows of quantities entering and leaving the household each month either from production taking plac e

at home pr given away as gifts . Specifically, the HBS food module contains monthly informatipn on th e

value and quantity purchased from the market for each individual item, as well as information on th e

quantities produced, prpcessed or donated to the household and the quantities used as seeds or donated b y

the househpld .

The availability of the food stocks and other flows of food items into and out pf the househol d

offers the opportunity of constructing a more reliable measure of food consumption of households in a

given month . Thus some of the pitfalls associated with the use of food expenditure data (e .g. see Mace

(1991); Cochrane (1991), and Nelson (1994)) can be avoided . In general, household fpod purchases d o

not directly correspond to household consumption, especially if households practice hoarding behavior i n

anticipation of future price increases or deregulation . To the extent that households store some of th e

commodities for later consumption, market purchases may overestimate household consumption . In

contrast, if households consume significant quantities out of their own stocks or own-production, the n

market purchases are bound fo underestimate household consumption .

For the purposes of the empirical analysis, for each food item, the value of consumption of foo d

item i in month t, denoted by Ci (t) was constructed using the formula :

c1(t) = VOP,(t)+ P,()*Q,( )

2 I have also checked whether the person-specific income information in the suryey aggregates to the income variabl ecollected at the household level . On average, the sum of the person-specific incomes was 88% of household incom ein urban areas and 87% of household income jn rural areas .

4

where VOPi (t) is the value of purchases of food item i in month t from the prlyate and state sectors and

co-ops. P, (t) is the national median unit value of all household-specific unit values of food item i i n

month t, and Q, (t) is a variable denoting nef inflow of food item i in month t that is not stored . Q, (t) is

constructed based on the following formula : Q, (t) = (quantity in stock at beginning of month + quantit y

obtained from reprocessing + quantity obtained from business organizations + quantity produced at hom e

+ quantity obtained from other sources - quantity used for reprocessing and feeding animals - quantit y

given out for presents or loans + quantity sold - quantity lost or wasted or used as seed - quantity in stoc k

at the beginning of the next month.' Food consumption includes cereals, meat, (including fish, milk, egg s

and dairy products), fruits and vegetables, sugar and fats (including other sweets), other food product s

(including non-alcoholic beverages and expenditures on eating out), and alcohol .

The survey did not collect information on the stocks of nonfood items . Although there was very

little domestic production of nonfood items, I did include the value of own production in my measure o f

nonfood consumption using the national median unit value for the specific food item in that month. As

with food, the value of total nonfood consumption (purchased and home-produced) is expressed in Jun e

1994 prices by dividing by the value of the national CPI with base in June 1994 . The major items

included in nonfood consumption include : electricity ; central heating ; other energy (including other

utilities, gas for heating, liquid fuels, coal, and firewood) ; trash and other fees ; water; telecommunications ;

education ; gasoline; transportation (including other expenses for vehicle maintenance and publi c

transportation) ; health ; clothing; entertainment and leisure ; rent and house maintenance ; insurance;

cleaning ; personal expenses ; other nonfood (including domestic services, small appliances and other fee s

and taxes) ; and fobacco. Furniture and the purchases of other durable items (such as car, house) were no t

For some food items, the variable Q, (t) had a negative yalue indicating that some of the commodities are stored .

Closer inspection confirmed that storage was taking place in food items such as cereals, sugar, and cooking oil, all o fwhich are easily storable

5

included in the nonfood consumption aggregate . All consumption measures were expressed in per capita

terms by dividing by family size .

The household gross income (cash plus value of in-kind) is constructed as the sum of salary, rent ,

interest income, pension income, child allowances, maternal benefits, family and other benefits, ne t

income from sales of farm products (i .e . subtracting farm related expenses) and other income . Incom e

from unemployment insurance or transfers received from friends and relatives and money borrowed were

excluded from total income . Net transfers received by the household are constructed as the differenc e

between money received from friends and relatives and money given to friends, relatives and students .

Finally, net debt is constructed as the sum of funds borrowed from banks and friends including

withdrawals from savings (including foreign accounts) and money received as repayment of past loans an d

advances minus funds paid out for loans, loans given and funds deposited in own accounts .

Households observed for only one month and households with missing or negative observations in

total consumption. or food or non-food consumption or total income, were dropped excluded from th e

analysis . These procedures led to a sample of 47,772 household/month observations from a total of 5,76 6

households . Table 1 (at the end of this paper) provides the number of observations in each of the sample s

and the mean values of the key variables used in the analysis .

Panels a, and c in Figure I provide graphs of the monthly average per capita consumption (th e

sum of food and non-food consumption) and income in the urban and rural areas of Bulgaria . Al l

variables have been divided by the national monthly CPI using June 1994 as the base month . There is a

noticeable increase in average per capita income (and consumption) during the month of March .

Discussions with World Bank staff working on Bulgaria revealed that the income increase in March o f

1994 reflects salary and pension adjustments to nominal income for inflation . ' The strong downwar d

trend in both income and consumption following these adjustments for inflation suggests that they were

4 It was not possible to determine whether this income adjustment was anticipated or not (or announced well i nadvance)

6

not sufficient to keep up with inflation . Clearly, households experienced a significant decline in their rea l

income that translated to reduced consumption and welfare over time across urban and rural areas .

In urban areas, mean consumption per capita is more or less equal to mean income per capita i n

almost every month of the survey, thus tracking the path of income very closely . In rural areas total

consumption is higher than total income. This suggests that the survey may be missing or underestimatin g

income from farm production or production at home .

Average Net Transfers & Net Debt p.c .FIGURE 2

Figure 2 also displays the monthly averages of the net debt and net transfers incurred b y

households from month to month . In both urban and rural areas, in the second half of the year average ne t

debt seems to have an upward trend in contrast to the downward trend of income and consumption . Thi s

suggests that households make use of credit markets and increase their indebtedness so as to reduce th e

variability in their welfare .

Model

The theoretical model guiding the empirical analysis is based on the consumer's optimizatio n

problem in the context of a complete market for state contingent commodities . The assumption of a

8

complete market for state contingent commodities may be considered as a simple approximation to all th e

formal and informal arrangements across space and over time that households can enter into in order t o

protect themselves from risk . With this in mind, households are assumed to purchase state contingent

commodities so as to maximize

T

Uh = E7r Ur(C')

0 ).s=1 r= t

where the h superscripts refer to a household, and where it is assumed that the probabilities of the state s

are the same for all households . Since a unit of consumption in state s at time t can be bought in period 1

for pst (1 + r) ` , the lifetime budget constraint of household h i s

ipstc,hto+r) =,4i

p.stY.st(l+ry t

(2 )s=1 t=l

s=l t= 1

where v , is labor income in period t and state s, the contingent claim to which has a value in the firs t

period of psty ;(1 + r) -` . Thus the existence of the market in contingent claims allows the problem to be

written as the maximization of expected utility subject to an expected value budget constraint . The firs t

order optimization condition for (I) subject to (2) i s

2,kh, )=v1 (csr)=B''1 +v` Pr1 _ e iut (3 )

(1+8\ `u`

P, twhere

=

(ch) is the marginal utility of consumption in period t, ois the rate o f1+ri rt

time preference and B is the lagrange multiplier for household h . Thus the main implication is that the

marginal utility of consumption has a two-factor structure, consisting of a household-specific componen t

eh and a time-specific component u, .

9

Given a specific functional form for the utility function, such as an isoelastic utility functio n

U(c,) = 1 1 c; f (_,) where f (z,) is a function allowing for the influence of time-varying tast eP

factors, equation (3) may be expressed, after logarithmic transformation, a s

ln c`' = –p-'(ln 0" – In f (z,)+ ln AO .

By taking first-differences over time, the above equation yield s

AIn c~ =–p (–Af(z,)+Aln,u,),

(4 )

which implies that the growth rate in household consumption between time t-1 and t is only a function o f

the growth rate in aggregate shocks as captured by the term –p ' (– Af (z,) + A Inf .[,) . A more common

interpretation of this equation is that the null hypothesis of a complete set of markets state contingen t

commodities implies a set of over-identifying restrictions . Once aggregate or uninsured shocks are

accounted for, idiosyncratic changes to household income or other idiosyncratic shocks should have n o

predictive power in explaining the household-specific consumption growth rate (Cochrane (1991) ; Mace

(1991); Townsend (1994, 1995)' .

Empirical analysis

The empirical analysis is based on estimating various forms of equation (4) . The extreme version

of the insurance model outlined above implies that changes in households-specific income and othe r

idiosyncratic shocks should not affect the growth rate of the consumption of the household as long a s

aggregate consumption within the insurance group is controlled . For the purposes of the empirical

analysis, an insurance group is defined to consist of the set of households within a given cluster . The

' As Alderman and Paxson (1992) argue, empirical evidence that idiosyncratic income changes do not haye asignificant effect on household consumption growth may also be consistent with the permanent income hypothesis ,especially if aggregate shocks to income are largely permanent and idiosyncratic shocks are transitory . The

1 0

maximum number of households within a cluster is 6, and on average there are 5 .7 households withi n

clusters in the data set . Generally, insurance arrangements are easier to organize and implement in smal l

or closely-knit communities than in larger groups, where the moral hazard, incentive and informatio n

difficulties are mor e severe.6

As a first step in the analysis of risk-sharing among households I examine the extent to which

there is covariability in consumption and income within clusters and over time . One of the key

implications of the consumption insurance model outlined above is that at any point in time the change i n

aggregate consumption within an insurance group, in this case a cluster, is one of the main determinants o f

the consumption growth of households . Evidence that there are significant cluster/month components i n

the consumption growth rate of households would be consistent with the presence of an insurance schem e

within clusters .

While the model above does not yield any predictions about the growth rate of household income ,

it is still of interest to investigate whether there is any significant covariability within clusters and months .

Households in the same cluster are subject to the same weather and may be subject to the same incom e

shocks if the plant in town or nearby closes down . High covariability of income growth within cluster s

may act as an obstacle to insurance arrangements, since all households being affected by the same shoc k

may be unable to help each other .

For this purpose, the growth rate in consumption and income is regressed on a set of binar y

variables signifying the cluster and the specific month in which the household is observed (cluster/mont h

interaction dummies) . The regressions estimated are of the for m

Alnch, = L (5N(Dv, )+sjrv~

(5 )N

information collected by the HBS survey prevented me from distinguishing empirically between the implications o fcomplete risk-sharing and the permanent income hypotheses (e .g. see Jacoby and Skoufias, 1998) .6 This, however, did not prevent some of the authors from using the whole country as an insurance group (e .g. Mace ,1991 ; Cochrane, I991)

1 1

where A In c <<, denotes the first difference in log consumption or the growth rate in total consumption (or

income) per capita of household h, in village or cluster v, in period t (i .e . between month t and month t-l) ,

U ,. , is a binary variable for each the cluster and month of the survey, and

is a household-specifi c

error term . Inclusion of the month-specific cluster dummies captures the presence of aggregate (o r

covariate) shocks common to all households in the cluster within any given month .

Table 2, at the end of the paper, presents the F-tests for the significance of these dummy variable s

for the full sample, as well as for the sub-samples of households in urban and rural areas and each regio n

of the country . In general the F-statistics for consumption and income growth are generally low bu t

statistically significant at the 5 percent level of significance . ' Thus common cluster/month components

have a significant role in explaining variations in the consumption growth rate of households as predicte d

by the social insurance hypothesis . Moreover, the F-statistics for consumption are generally higher than

those for income, which is not necessarily smoothed over households .

Consumption and Income Growt h

Next, I investigate whether the extreme form of insurance as predicted by the model of complete

insurance is supported by the data . For this purpose I estimate regressions of the for m

Alnc,,n, = Ety b, t.(1),,)+/3AlnYn V , +YX1,v, +Asp,,,

( 6 )

where A In_yl,,,, is the growth rate in household income between month t and t-l, Xis a vector of variable s

controlling for household taste shifters, d, /3, and 2/ are parameters to be estimated, and Os 111,. is a

household-specific error term, capturing changes in the unobservable components of househol d

preferences . The elements of the vector X are the number of members in the household in period t and t -

1, and the age (and its square) of the household head .

1 2

In the context of this regression, the month-specific cluster dummies control for the role o f

aggregate (or covariate) shocks common to all households in the cluster within any given month an d

A In y,, is a measure of the income shock idiosyncratic to household h. It should be noted that in thi s

regression the inclusion of these binary variables is equivalent to deviating consumption and incom e

changes from their respective month-specific cluster mean . Under the null hypothesis of complet e

insurance, /3=0, since idiosyncratic changes in household income should have no role in explainin g

household specific consumption growth rates .

Given that formal and informal insurance arrangements may be more or less effective in differen t

regions of the country, equation (3) is also estimated separately for the sample of households in urba n

areas (towns with more than 2000 residents) and for the sample of households in rural areas (villages wit h

less than 2000 residents), as well by region of the country .

Columns l, 2 and 3 in Table 3 present the estimated coefficients of the idiosyncratic change i n

household income obtained from regressions (6) . I estimated separate regressions for total consumption ,

and food and non-food consumption per capita . Since both income and consumption variables are in log s

these coefficients may also be interpreted as elasticity estimates of the response of consumption t o

idiosyncratic changes in household income .

Clearly, the growth of total consumption per capita is significantly affected by changes i n

household income (see coefficients in column 1) . This suggests that the extreme version of th e

consumption insurance hypothesis is not supported by the data . The size of the coefficients, however ,

indicates that the elasticity of consumption and income changes is quite low . Depending on the sampl e

used the elasticity of consumption to income changes ranges between 0 .038 (in the region of Russe) an d

0.127 (in Sofia city) . This suggests that consumption is at least partially protected from income changes .

The lower coefficient for rural settlements also suggests that the protection of consumption from income

changes is better in these areas . This result is consistent with the observation made earlier that insuranc e

However, it should be noted that all of the F-statistics are less than the log(sample size) critical value of th e13

arrangements may be easier to implement in smaller communities where the problems arising form mora l

hazard may be smaller .

The estimates for food and non-food consumption (columns 2 and 3) yield a similar picture .

Neither food nor non-food consumption appears to be completely insured from income shocks . Yet a

comparison of the estimates in columns 2 and 3 reveals that the coefficients of income changes in the foo d

consumption equation are much lower than those for non-food . This suggests that the consumption of

food is relatively better protected from income changes than non-food . In fact, one plausibl e

interpretation of these estimates is that adjustment in non-food consumption expenditures may act as a

means of partially insuring ex-post the consumption of food from the effects of income changes . In the

regions of Russe and Haskovo, for example, food consumption appears to be completely protected fro m

income changes as predicted by the model of complete insurance . In the same regions, however, non-foo d

consumption is significantly affected by changes in household income .

The estimates presented so far may be biased due to measurement error in the income variable an d

imputation errors in the calculation of the food consumption of households . By itself, measurement erro r

in the income variable gives rise to "attenuation bias" that biases coefficients towards zero . Given that the

income coefficients are significantly different from zero in the majority of cases one can be reasonably

confident that the hypothesis of complete insurance is justifiably rejected and that the significant incom e

coefficients in columns 1-3 provide a lower bound estimate of the true elasticity of consumptive t o

idiosyncratic income .

However, it is possible that imputation errors in the construction of the food consumption variabl e

may bias the income coefficients upwards (Deaton, 1997) . This is especially the case for households in

rural areas. For many of these households a significant share of income and consumption is accounted fo r

by food that is produced and consumed by the household and neither sold nor bought in the market . As

mentioned earlier, for the food produced at home a value is imputed using the national median price fo r

Schwartz test discussed in Deaton (1997) .1 4

the specific food item produced and the figure is included in consumption . Errors in this imputatio n

procedure may be positively correlated with measurement errors in the income variable, and for positiv e

coefficients, this upward bias may work in the opposite direction to the standard downward attenuatio n

bias produced by the measurement errors in the income variable alone . Given that the net effect cannot b e

signed in advance it is prudent to make an effort to control for these sources of bias in the estimates .

Columns 4, 5 and 6 present the income coefficient estimates using instrumental variables for th e

changes in household income . The identifying instrumental variables used consist of binary variable s

identifying changes in the health and employment status of household members between the current mont h

t and the previous month t- I, and between month t-1 and t-2 . Specifically the list of instruments use d

includes binary variables identifying whether any household member was ill, changed status from

employment to unemployment, employment to pension, employment to other status such as self-

employment, unemployment to employment, and self-employment to unemployment and employment . In

addition, the list of variables used in the first stage regressions included the set of binary variable s

summarizing cluster/month effects, the age and age squared of the household head, the number of famil y

members in month t and t-1, as well a complete set of interaction terms among the instrumental variable s

and the binaty variables identifying the region of the country . In all cases the F-statistics from the nul l

hypothesis that the identifying instrument does not jointly explain household income changes revealed tha t

these variables play a significant role in explaining household income changes .

The instrumental variable (IV) estimates presented in Table 3 reveal some substantial difference s

from the main results obtained from the OLS estimates suggesting that the concerns about measuremen t

and imputation errors may have some foundation . For example, the income coefficients in the food

consumption regressions in column 5 become insignificant after using instrumental variables suggesting

that the consumption of food is insured completely .

1 5

Transfers, credit and income growt h

The analysis so far has not addressed any of the ways in which households manage to buffer

intertemporal fluctuation in their income . In this section, I examine how intra-household transfers and

credit transactions correlate with income changes . Savings withdrawals and intra-household transfers are

probably the most common means of consumption insurance, although households may have othe r

alternatlyes (such as accumulating or selling assets, or withdrawing children from school (Jacoby an d

Skoufias, 1997)) .

In the face of strong region-specific income shocks, credit markets that are segmented acros s

regions may be unable to provide consumption insurance . For example, in regions where the only source s

of loanable funds are the savings of local workers, closings of large plants may result in a severe shortage

of loanable funds as many of the laid-off workers withdraw their savings . In contrast, financial market s

that are effectively diversified across regions may be a very effective means of consumption insurance . In

the event that credit markets are regionally segmented and thus unable to insure households agains t

aggregate risks, one would expect that gifts and transfers (especially among households residing i n

different regions) may be an alternative means of consumption insurance in the face of region-specifi c

risk .

The empirical approach used to analyze the impact of income changes on net transfers and ne t

debt of households is generally similar to that used for consumption. The main difference is that th e

dependent and independent variables are expressed in terms of changes in the level rather than changes i n

the logarithmic value of the variable . The reason for this is that net debt and net transfers can be negative .

Thus the equations estimated are of the form :

A

= Ervon•(D,,)+/JOv, ,, +rxh,,r +As,.

(7 )

where T denotes the level of net transfers into the household or the level of net debt in incurred by th e

household .

1 6

The estimated coefficients of the change in the level of income are reported in columns 1 and 2 of

Table 4. The coefficients obtained using instrumental variables for the income changes are presented i n

columns 3 and 4 . Inspection of the estimates reveals that credit transactions are by far the most importan t

means by which households try to protect themselves from fluctuations in their income . In general higher

income changes are negatively correlated with the net debt incurred by households irrespective of th e

sample used . A comparison of the OLS and IV estimates also reveals that there are no substantia l

qualitative changes in the results . In most regions of the country it is mainly credit markets that serve the

role of buffering households against income fluctuations . Moreover, it should be noted that the OL S

estimates suggest that in the regions where credit transactions are not significantly correlated with income

fluctuations, such as in Plovdly and Russe, inter-household transfers are . This suggests that in these

regions transfers may act as a substitute for credit markets as a means to achieve at least partia l

consumption insurance .

Closely related to the preceding is the question of whether risk-sharing is more or less prevalen t

among households with certain characteristics. Table 5 presents additional estimates, obtained by OLS, b y

classifying households according to the poverty status, education level, age of the household head, and th e

accessibility of households to land and animals . A household is classified as poor (rich) if its consumptio n

averaged over all the months observed is less (greater) than the 25`h (75`h) percentile of the monthl y

average consumption across all households in the sample . The estimates reveal that there is als o

considerable heterogeneity in the impact of income variability depending on the characteristics of th e

household. The higher income coefficients for poorer households suggest that they are less able to insur e

their food and non-food consumption from income variability than richer households . This result i s

similar to the one obtained by Jalan and Ravallion (1997) in rural China .

The similarity of the coefficients of income changes on net debt also suggests that the differenc e

in the extent of partial insurance obtained by poorer or richer households is not necessarily due t o

differential access to credit . Also, households headed by someone with a secondary level of education or

1 7

higher (working typically in white collar occupations) seem to be less well protected from income

variability than households headed by someone with lower education . Interestingly, older household s

seem to be better protected than younger households. One plausible explanation for this is the fact tha t

older households also have access to transfers from their adult children . as suggested by the significan t

coefficient of income shocks on transfers received . Lastly, households without access to land and animal s

appear to be less able to protect their consumption from income variability .

The estimates so far provide strong evidence against the extreme hypothesis of complete insuranc e

in household consumption from income risk . It is plausible, however, that households may partially insur e

each other . In order to investigate whether partial insurance is in fact taking place among household s

within the same cluster . I also estimated an alternative version of equations (6) and (7) . The equation

v In c,,, = a + QD In y,,,,, + yy(ln _y,,, )+ 'X,,, + Ash,,

( 8 )

allows the growth rate in household consumption to be determined by the growth rate in household

income as well as the growth rate in average cluster income denoted by A(ln y,.,) . In a purely autarkic

world, the growth rate in the average cluster income should have no impact on the growth rate o f

consumption of any one household . Evidence that the growth rate in average cluster income plays a

significant role in the growth rate of household consumption (i .e ., y?) ) is consistent with the hypothesi s

that some risk-sharing is taking place within clusters .

The OLS estimated coefficients of the growth rate in average cluster income are reported in Tabl e

5 . It should be noted that the coefficients of the idiosyncratic changes in income obtained from equatio n

(5) are identical to those reported in tables 3 and 4, using dummy variables for cluster/month interactions .

The estimates in Table 6 provide strong evidence in favor of partial insurance in total consumption and its

two main components : food and nonfood consumption . Thus changes in the growth rate of average

cluster income seem to have a positive and significant role in the growth rate of consumption of individual

households .

1 8

Concluding remarks

In this paper I use monthly data from a panel of households in Bulgaria in 1994 to examine the

extent to which households, through formal and or informal arrangements . are able to insure thei r

consumption from fluctuations in their real income . The empirical analysis reveals that households ar e

able to achieve partial but not complete insurance of consumption from idiosyncratic fluctuations in thei r

income. Consumption appears to be smoothed more effectively in smaller communities where th e

problems of information asymmetry, enforcement and moral hazard are less severe . Also the extent of

partial insurance seems to vary across regions and household characteristics . In general, households seem

to insulate their food consumption from fluctuations in their income by adjusting their nonfoo d

expenditures and by borrowing through formal and informal credit markets . Overall, inter-household

transfers play only a small role in insuring consumption .

The results of this study suggest that there are considerable benefits from public actions tha t

improve the safety net system in Bulgaria . Credit markets and private informal mechanisms do appear t o

provide insurance from idiosyncratic fluctuations in household income but this insurance is inadequate .

However, informal insurance and risk-sharing arrangements among households can only offer complete o r

partial protection from shocks that are specific to a household and do not simultaneously affect thei r

neighbors or other members of an insurance group . Shocks that are common to all the members of an

insurance group can still have devastating effects . A well-designed and well-targeted safety net system

can be very effective at minimizing household exposure to risk, even though it might displace some of th e

informal insurance mechanisms currently in operation .

1 9

References

Alderman, Harold and Christina Paxson (1992) : "Do the Poor Insure? A Synthesis of th eLiterature on Risk and Consumption in Developing Countries," Policy Research Workin gPapers. Agricultural Policies, WPS 1008, The World Bank, (October) .

Besley, Timothy (1995) : "Nonmarket Institutions for Credit and Risk-sharing in Low-Incom eCountries," Journal of Economic Perspectives, Vol . 9, Summer, pp . 115-127 .

Cochrane. John H. (1991) : "A Simple Test of Consumption Insurance," Journal of PoliticalEconomv, Vol . 99, pp . 957-76 .

Cox , D., Z. Eser and E . Jimenez, (1997) "Family Safety Nets during Economic Transition," Cha p9 in Poverty in Russia : Public Policv and Private Responses, edited by Jeni Klugman ,The World Bank, EDI Development Studies, Washington DC :

Cox, Donald and Emmanuel Jimenez (1990) : "Achieving Social Objectives through PrivateTransfers : A Review ." World Bank Research Observer. Vol . 5, no . 2, pp. 205-218 .

Deaton, Angus (1997) The Analysis of Household Surveys : A Microeconometric Approach toDevelopment Policv, Baltimore : The Johns Hopkins University Press .

Hassan, Fareed and R . Kyle Peters Jr . (1994) "Social Safety Net and the Poor During th eTransition : The Case of Bulgaria," Europe and Central Asia Department I . The WorldBank, Washington DC .

Jacoby, Hanan and Emmanuel Skoufias (1998): "Testing Theories of Consumption Behaviorusing Information on Aggregate Shocks : Income Seasonality and Rainfall in Rural India, "American Journal of Agricultural Economics, Vol . 80, No . 1, (February), pp . l-14 .

Jacoby, Hanan and Emmanuel Skoufias (1997) : "Risk. Financial Markets, and Human Capital in aDeveloping Country," Review of Economic Studies, Vol . 64, No . 3, (July), pp . 311-335 .

Jalan, Jyotsna and Martin Ravallion (1997) "Are the Poor Less Well Insured? Evidence o nVulnerability to Income Risk in Rural China." Policy Research Working Paper 1863 .World Bank, Development Research Group, Washington, D .C. Processed .

Mace, Barbara (1991) : "Full Insurance in the Presence of Aggregate Uncertainty," Journal ofPolitical Economy, Vol. 99, pp .928-56 .

Morduch, Jonathan (1999) "Between the State and the Market : Can Infornal Insurance Patch th eSafety Net?" The World Bank Research Observer, Vol . 14, no. 2 (August). pp. 187-207 .

Nelson, Julie A . (1994) : "On Testing for Full Insurance Using Consumer Expenditure Surve yData," Journal of Political Economy, Vol . 102, pp. 384-394 .

Paxson, C. (1992) : "Using Weather Variability to Estimate the Response of Savings to TransitoryIncome in Thailand ." American Economic Review, Vol . 82, no . l, pp. 15-33 .

20

Rashid, M ., and R . M . Townsend (1994) : "Targeting Credit and Insurance : Efficiency ,Mechanism Design and Program Evaluation," ESP Discussion Paper Series No . 47, Th eWorld Bank .

Ravallion . Martin and Lorraine Dearden (1988) : "Social Security in a `Moral Economy' : AnEmpirical Analysis for Java," The Review of Economics and Statistics . Vol . 70, no . 1, pp .36-44 .

Rosenzweig, Mark R. (1988) : "Risk, Implicit Contracts and the Family in Rural Areas of Low -Income Countries," Economic Journal, Vol . 89, pp . 1148-1170 .

Sik, E . (1994) "Network Capital in Capitalist, Communist and Post-Communist Societies, "International Contributions to Labor Studies, Vol . 4, pp . 73-93 .

Townsend, Robert (1994) "Risk and Insurance in Village India" Econometrica, Vol . 62, pp .539-91 .

Townsend, Robert (1995) : "Consumption Insurance : An Evaluation of Risk-Bearing Systems i nLow lncome Economies," Journal of Economic Perspectives, Vol . 2, Summer), pp. 83 -102 .

Udry, Christopher (1994): "Risk and Insurance in a Rural Credit Market : An EmpiricalInvestigation in Northern Nigeria," Review of Economic Studies, Vol . 61, pp . 495-52 6

2 1

Table 1

Means of Key variable s

NobsTotalCons .

FoodCons

Non-food

NetCons

TrasfersNetDebt

Tota lIncome

Full Sample 47,772 2,313 1,352 961 -39 118 2,094

Urban Settlements 16,625 2,343 1,315 1,027 -6 94 2,285

Rural Settlements 31,147 2,258 1,419 839 -100 164 11,746

Region :Sofia City 4,041 2,699 1,490 1,209 72 157 2,572

Burgas 5,075 2,223 1,248 975 -19 91 2,179

Varna 5,967 2,539 1,378 1,161 -57 102 2,345

Lovech 6,320 2,290 1,349 942 -47 54 2,053

Montana 4,096 2,215 1,371 844 -80 52 11,80 2

Plovdiv 6,794 2,209 1,366 844 -37 134 1,969

Russe 4,515 2,245 1,275 970 -84 234 11,87 5

Sofia Region 5,960 2,114 1,295 820 -54 117 11,97 5

Haskovo 5,004 2,371 1,424 947 -26 146 2,132

Notes :1-- All variables are in Leva per month and per capita terms and expressed in June 1994 prices .

22

Table 2

Testing for the Presence of Covariant Riskin Consumption and Income Growt h

Consumption

IncomeMean F-value Mean

F-Value(%) (%)

Full Sample 0.52 1 .776 -O.56 1 .383

Urban Settlements 0.35 1 .655 -0.82 1 .333Rural Settlements 0.84 1 .997 -O.07 1 .41 1

Region:Sofia City -1 .57 1 .450 -1 .11 1 .337Burgas -0.11 1 .838 -1 .14 1 .250Varna 1 .72 1 .963 0.19 1 .344Lovech 1 .19 1 .671 0.16 1 .654Montana 2.09 2 .237 1 .30 1 .236Plovdiv -O .66 1 .727 -1 .68 1 .41 6Russe 1 .50 1 .826 0 .12 1 .286Sofia Region -0.06 1 .698 -0 .81 1 .50 2Haskovo 0.71 1 .579 -1 .67 1 .367

Rural settlements have less than 2,000 inhabitants.

2 3

Table 3

The Impact of idiosyncratic Changes in the Log of Household Income on Consumptio n

Dependent Variable : Month to Month Change In :

OLS Estimates IV Estimates1

In(Consumption)3

In(Non-Food Cons)4

In(Consumptlon)5

In(Food Cons)

6

In(Non-Food Cons )2

In(Food Cons )Coeff. t-value Coeff. t-value Coeff. t-value Coeff . t-value Coeff . t-value Coeff. t-valu e

Full Sample 0 .068 16 .85 0 .021 7 .68 0 .134 16 .55 0 128 5 .14 0 .005 0 .30 0 .263 5 .3 0

Urban Settlements 0 .095 15 .95 0 .028 7 41 0 .180 15 .83 0.139 4 .71 -0 .007 -0.32 0 .308 5 .33Rural Settlements 0 .041 7 .48 0 .014 3 .46 0 .088 7 .57 0 .127 2 .79 0 .034 1 .22 0 .210 2 .28

Region :Sofia City 0 .127 6 .07 0 .034 3 .13 0 .225 6 .46 0 .124 1 .50 -0 .030 -0 59 0 .274 1 .78Burgas 0.095 7 .56 0 .035 3 94 0 .169 6 .53 0 .115 1 .85 0.045 0 .85 0 .155 1 .1 7Varna 0 .084 8 .11 0 .033 4 .50 0 .149 7 57 0 069 1 .13 0 .045 098 0 .082 0 .68

Lovech 0 .061 5.71 0.023 2 .89 0 .130 5 .90 0 .072 0 .94 -0.034 -0 .62 0 .133 0 .99Montana 0 .070 6.19 0.021 2 .48 0 .153 6.30 0 .318 3 .69 0 .107 1 .41 0 .722 3 .77

Plovdlv 0 .067 6.96 0.020 2 .86 0 .135 6.42 0 .101 1 .50 -0 .065 -1 .26 0 .316 2 .3 2

Russe 0 .035 3.00 0.009 1 .04 0 .089 3.86 0 .116 1 .75 -0 .070 -1 .44 0 .297 2 .1 7Sofia Region 0 .055 4 .18 0 .017 2 .01 0 .099 3.54 0 .146 1 .65 -0 .050 -0 .90 0 .397 1 .76

Haskovo 0 .055 3.98 0 .005 0 .72 0 .113 4 .43 0 .188 2 .06 0 .099 1 .97 0 .318 2.03

Notes :1-- Additional regressors included but not reported : A constant term, family size in month t and in month t-1, age and (age squared)/100, and a full set of cluster an d month

interaction dummy variables .2--The t-values reported in columns 1-6 are based on standard errors that are corrected for heteroskedasticity and sample design effects .3--See text for details on the variables used as instruments .

2 4

TABLE 4

The Impact of Idiosyncratic Changes in the Level of Household Income on Transfers and Deb t

Dependent Variable : Month to Month Change in :

OLS Estimates IV Estimates1

Net Transfers in2

Net Debt in3

Net Transfers in4

Net Debt i nCoeff. t-value Coeff. t-value Coeff. t-value Coeff . t-value

Full Sample -0 .010 -2 .95 -0 .087 -4 .90 -0 .021 -0 .76 -0 .161 -2 .26

Urban Settlements -0 .003 -2 .14 -0 .125 -6 .03 -0 .013 -1 .10 -0 .088 -3 .43Rural Settlements -0 .021 -2 .49 -0 .024 -0 .98 0 .029 0 .55 -0 .095 -1 .86

Region :Sofia City 0 .002 0 .70 -0 .234 -7 .51 -0 .074 -1 .24 -0 .038 -0.6 1Burgas -0 .002 -0 .62 -0 .041 -1 .69 -0 .021 -1 .27 -0 .122 -2 .06Varna -0 .024 -3 .00 -0 .145 -3 .13 -0 .021 -0 .76 -0 .161 -2 .2 6Lovech 0 .000 -0 .07 -0 .181 -5 .23 0 .012 0 .30 -0 .139 -2 .09Montana -0 .011 -1 .10 -0 .106 -3 .67 -0 .035 -0 .65 -0 .092 -1 .34Plovdiv -0 .009 -3 .77 0 .024 0.52 -0 .021 -1 .62 -0 .071 -1 .03Russe -0 .005 -1 .26 0 .046 0 .85 0 .005 0 .17 -0 .036 -0 .4 7Sofia Region -0 .008 -3 .01 -0 129 -9 .22 -0 .001 -0 .13 -0 .126 -2 .9 1Haskovo -0 .020 -1 .47 -0 .057 -3 .90 0 .029 0 .54 -0 .045 -0 .81

Notes :1-- Additional regressors included but not reported . A constant term, family size in month t and in month t-1, age and (age squared)/100, and a fullset of cluster and month interaction dummy variables .2--The t-values reported in columns 1-6 are based on standard errors that are corrected for heteroskedasticity and sample design effects .3--See text for details on the variables used as instruments .

2 5

Table 5The Effect of Idiosyncratic Income Shocks on Consumptio nBy Characteristics of the Household or the Household Hea d

Dependent Variable : Month to Month Change in :

1In(Consumption)

2In(Food Cons)

3In(Non-Food Cons)

4Net Transfers in

5Net Debt i n

Coeff. t-value Coeff . t-value Coeff. t-value Coeff . t-value Coeff. t-value0 .073 6 .94 0 .025 3 .04 0 .170 5 .98 -0 .004 -1 .48 -0 .102 -5 .8 20 .062 5 .38 0 .018 2 .73 0 .105 5 .20 -0 .015 -1 .87 -0 .106 -4 .2 5

0 .053 2 .89 0 .004 0 .29 0 .144 3 .10 -0 .011 -1 .74 -0 .067 -1 .740 .052 7 .00 0 .018 3 .29 0 .113 7 .29 -0 .011 -2 .96 -0 .096 -7 .260 .091 10 .80 0 .023 4 .52 0 .169 11 .16 0 .000 0 .14 -0 .118 -3 .490 .103 3 .97 0 .036 2 .25 0 .164 3 .83 -0 .003 -0 .80 -0 .154 -4 .24

0 .084 12 .92 0 .024 6 .04 0 .157 12 .85 -0 .001 -0 .6 -0 .121 -5 .220 .050 7 .81 0 .011 2 .20 0 .121 8 .18 -0 .029 -2 .40 -0 .099 -7 .88

0 .047 7 .98 0 .008 1 .93 0 .109 8 .79 -0 .017 -2 .73 -0 .105 -7 .76

0 .095 11 .64 0 .035 6 .8 0 .175 11 .92 0 .000 -0 .18 -0 .140 -5 .04

Notes :1-- Additional regressors included but not reported : A constant term, family size in month t and in month t-1, age and (age squared)/100, and a full set of cluster and mont hinteraction dummy variables .2--The t-values reported: in columns 1-6 are based on standard errors that are corrected for heteroskedasticity and sample design effects .3--The coefficients reported in columns 4 and 5 are obtained by regressing the change in the level (not log) of Net Transfers and Net Debt against the change in the level of Income pe r ca

Poor HouseholdsRich Households

Education of Head :Primary or les sBasic1

Secondary/Professiona lCollege/Universit y

Age of Hh Head is :less than 56 yrs56 yrs or olde r

Household cultivates lan dand has animal sHousehold does NOT cultivate lan dand has NO animals

26

Table 6

Evidence on Partial Risk InsuranceThe Effect of Mean Cluster/Month Incom e

Dependent Variable : Month to Month Change in :

1

2In(Consumption)

In(Food Cons)3

In(Non-Food Cons)Coeff. t-value Coeff. t-value Coeff. t-value

Full Sample 0.091 12.34 0.070 11 .24 0.110 7.05

Urban Settlements 0.125 11 .93 0 .104 12.42 0.144 6.77Rural Settlements 0.063 6.02 0 .039 4.32 0.084 3.65

Region :Sofia City 0.140 4.46 0.099 4.15 0.161 2.58Burgas 0.113 4.43 0.094 4.41 0.119 2.2 1Varna 0.100 4.69 0.078 4.40 0.116 2.8 1Lovech 0.054 2.77 0.023 1 .23 0.078 1 .9 1Montana 0.048 1 .90 0.031 1 .43 0.084 1 .67Plovdiv 0 .092 5.15 0.089 5.88 0.118 2.84Russe 0 .121 5.93 0.096 5.87 0.097 2.1 8Sofia Region 0.084 3.80 0.054 3.20 0.131 2 .66Haskovo 0 .092 4.08 0.066 3.84 0.126 2.61

Notes :1-- Additional regressors included but not reported : A constant term, family size in month tand in month t-1, age and (age squared)/100, and the month to mont hchange in the (log of) household income (level of hosuehold income in columns 4 and 5 )2--The t-values reported in columns 1-6 are based on standard errors corrected fo rheteroskedasticity and sample design effects .

27