DP2014-14

Buffer Stock Sav ings
by Portfolio Adjustment:
Evidence from Rural India*
Katsushi S. IMAI
Bilal MALAEB
Revised February 27, 2015

* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional character.
In some cases, a written consent of the author may be required.

Buffer Stock Savings by Portfolio Adjustment: Evidence from Rural India

Katsushi Imai
Economics, School of Social Sciences, University of Manchester, UK& RIEB, Kobe University, Japan&
Bilal Malaeb
Economics, School of Social Sciences, University of Manchester, UK
Revised on 27th February 2015

Abstract
The empirical literature on household savings tends to treat savings simply as the residual ofincome minus consumption. This paper takes a unique approach to reconstruct the cash andasset balances using detailed household transaction data on farm households in rural India andgenerates monthly and seasonal ICRISAT panel data for the period 1976-1983. We havefound that households - irrespective of their landholding status - cope with temporary shocks
quite well by using crop inventory, currency and capital assets, rather than livestock, as buffer
assets. The importance of portfolio adjustments in smoothing consumption is also confirmedby the use of a system of equations in which both portfolio and production decisions are madeendogenous. It is concluded that not only the level but also the diversification of household
assets are important for buffering consumption. As an extension, we have explored themonthly ICRISAT panel data for the period 2009-2012 in the same villages and have found asimilar pattern in household portfolio responses to income shocks.
The JEL codes: O16, D12, C33

Corresponding Author:
Katsushi Imai (Dr)Economics, School of Social Sciences, University of Manchester, Arthur Lewis Building, Oxford Road,
Manchester M13 9PL, UK; Phone: +44-(0)161-275-4827 ; Fax: +44-(0)161-275-4928 ; E-mail:Katsushi.Imai@manchester.ac.uk

1Buffer Stock Savings by Portfolio Adjustment: Evidence from Rural India

1. Introduction
The traditional literature on savings and consumption smoothing has focused on the aspect of‘buffer-stock’ savings in contrast to the traditional literature of life-cycle saving by modelling
either the liquidity constraints of households (Deaton, 1990, 1991, 1992, 1997, Zeldes, 1989) or
the precautionary nature of savings (e.g., Kimball, 1990, Carroll, 1997). Buffer-stock savings are
particularly important in investigating rural poverty in developing countries because of the salientfeatures of rural economy associated with its uncertainty or risk, e.g., due to the dependence on the
agricultural sector, poor health services, low level of sanitation, and lack of access to formal credit.
All of these factors combined lead to welfare deterioration among the poor and their economic
development (Carter and Lybbert, 2012). However, most of the previous studies, except a few (e.g.
Carter and Lybbert), treat savings simply as the residual of income minus consumption. The main
aim of the current study is to shed a light on the “black box” by disaggregating the savings into
various subcomponents and examine the extent to which households in rural India buffer theirconsumption by adjusting their assets.
Much of the empirical literature has focused on the role of precautionary or buffer-stocksavings for household risk-coping in the context of developing countries, in and outside India. For
instance, using the annual ICRISAT data, Rosenzweig and Wolpin (1993) emphasize the role ofbullocks for credit-constrained households in rural India as a buffer stock for consumption. One of
their main findings is that sales of bullocks increase when income streams decrease, and viceversa. However, Lim and Townsend (1998), through a close investigation of how rural farming
households financed their deficit based on the monthly ICRISAT data, conclude that livestock -2including bullocks and major capital assets - play little part in smoothing intertemporal shocks.
They insist that buffer stock of crop inventory and currency, together with credit or insurance, aremuch more important. Chaudhuri and Paxson (1994), also using the monthly ICRISAT data inIndia, investigate the impact of seasonality in income on seasonality in consumption. They
conclude that seasonal patterns in consumption are common across households within villages but
are not related to income seasonality. Based on the seasonal data of rainfall, Jacoby and Skoufias(1998) reach a similar conclusion by estimating the household response to anticipated andunanticipated income shocks.
Outside India, Carter and Lybbert (2012) device a technique to understand the coexistence ofconsumption and asset smoothing regimes based on the poverty trap model of Barrett et al. (2011),
assuming that assets are not merely buffer stocks, but contemporarily act as productive assets withpositively-diminishing returns. They employ a Hansen threshold estimation method for data fromrural Burkina Faso between 1981 and 1985, which is a period where households are faced withsevere drought. Carter and Lybbert find that while those who are richer in assets - proxied bytropical livestock units - managed to smooth their consumption well, the asset poor households
tend to preserve their assets and smooth their consumption limitedly. There exists a critical herdsize threshold that separates households with high versus low consumption smoothing, and thosewith such high smoothing levels who rely primarily on livestock to achieve it (Carter and Lybbert,2012). Lee and Sawada (2010) assess the precautionary savings motive, or “prudence”, inPakistan, based on 14 rounds of survey from 1986 to 1991. Their results confirm the theory of
precautionary savings behavior among Pakistani households, particularly among those facing
liquidity constraints. Using the same sample as Carter and Lybbert (2012), Kazianga and Udry
(2006) find little evidence of consumption smoothing behavior. They confirm that households3with subsistence income in Burkina Faso do not liquidate their assets in favor of current
consumption, and households who face land-income volatility to a greater extent save more given
their income shocks. With the same dataset, Fafchamps et al. (1998) show that livestock sales did
not adequately serve as precautionary savings, particularly against negative income shocks, suchas drought. Drawing upon a data set from Thailand, Paxson (1992) concludes that most of thetransitory income attributed to rainfall shock is saved, that is, the saving behavior of farmers
accords with the theoretical predictions of buffer-stock savings. The literature suggests thathousehold savings matter in risk-coping, but the role of livestock savings/dissavings is generally
limited. In other words, household assets other than livestock are likely to be important.
The contribution of this paper to the above empirical literature is twofold. Firstly, we look at
not just the change in stock of a single asset, such as bullocks, but also the total portfolio
adjustment of households that face various risks: the possibility exists that the sale of bullocks andthe purchase of other items, like consumer durables for instance, may take place simultaneously. In
this paper, we focus on dynamic changes in the portfolio of households, such as those pertaining tolivestock, production capital or consumer durables, which has been largely neglected in theempirical literature. Here, we empirically examine how households mitigate income risk by
portfolio adjustment. Secondly, we explicitly take account of household portfolio adjustment by
the system of equations in which 1) transitory income, changes in a variety of household assets,
and expenditure are simultaneously estimated and, 2) some forms of savings, namely, changes infinancial assets, agricultural inputs, and production capital are allowed to affect transitory income
shocks. Most of the past literature on household savings assumes that savings in themselves do notaffect income. However, in rural economies, this is not a realistic assumption, because 1) physicalassets have roles of production assets as well as savings or accumulation, and 2) transitory changes

4

in financial assets or credit availability are key factors to transitory income changes. The idea issimilar to Behrman et al.’s (1997) study which incorporates the sequential decision-making
process in agricultural production in estimating saving function.1
The rest of the paper is organized as follows. The next section discusses the ICRISAT Village
Level Studies. The specifications and empirical results in which transitory and permanent income
are decomposed appear in Section 3. Section 4 discusses the methodologies and results of thesystem of equations whereby portfolio adjustment effects are estimated. Section 5 summarizes theresults based on more recent ICRISAT panel data. The last section offers our concluding remarks.

2. DataIn this study, we construct monthly data on income, consumption, savings, and credit using the
ICRISAT data - both monthly and seasonal data - between 1975/76 and 1984/85.2 This dataset iswell-known for its high quality and influence in the emergence of several of development
economics’ core findings (Walker and Ryan, 1990; Dercon et al., 2013). The survey is structured
in such a way that households are stratified according to their landholding classes. 40 households
in each village consist of four classes: the landless, small farmers, middle farmers, and large
farmers. Our analysis is based on the household transaction module, the production modules, thehousehold member schedule and the general endowment schedule in the ICRISAT data set. One ofthe distinguished features of the ICRISAT data set is the unusually detailed information that the1

The main difference between our study and Behrman, Foster, and Rosenzweig’ s (1997) is that while theformer deals with the portfolio of the entire household savings, the latter uses only a component of the savings,
namely 1) net changes in financial assets, 2) net borrowing, and 3) transfers to friends and relatives.
2
The first year (1975/76) and the final two years (1983/84-1984/85) have been dropped from the final
estimations taking account of the consistency between the data recorded in the transaction modules and theincome or consumption data.

5

household transaction module records.3 As the contribution of the analysis in this paper is closely
associated with the use and adjustment of data in the transaction module, we first briefly describe
its features.
The main purposes of the transaction module are to assess the income position of households, tocompute consumption quantities and expenditures, and to record production expenditures andchanges in the debt or credit positions of the household (Singh et al., 1985). In principle, thetransaction module records all market transactions of households, including purchases, sales, gifts,
credit, and other market transactions with recall of about four-week intervals (Lim and Townsend,1998).4 The interview on this schedule was continued every month in the first week during theperiod 1975/76 (crop year from July 1975 to June 1976) to 1984/85 in three Indian villages,
namely Aurepalle, Shirapur, and Kanzara.5 All the cash and kind transactions after the previous
interview were recorded in cash value either as cash inflow or as cash outflow, which make itpossible to calculate monthly income, consumption, and changes in different components of thehousehold asset.6 Appendix 1 provides detailed information on how variables on monthly assetchanges have been created using the transaction module.
As an extension, we have also explored the ICRISAT Village Dynamics in South Asia datafrom July 2009 to June 2012 with focus on the same three villages for comparative purposes. The

3

Although the ICRISAT data set itself has been widely used in the literature, few studies have used the original
information found in the transaction module.
4
Lim and Townsend (1998) describe in detail the structure of the transaction module and the way of constructing
the monthly data on income, consumption, and asset change. We closely follow their methods and aggregate
them to the seasonal data.
5
In the other seven villages where the survey was carried out, transaction data were collected for only three orfour years for the selected timeframe.
6
There have been some discussions as to whether the data on consumption (own consumption of home
production in particular) and grain stock are correctly recorded. Ravallion and Chaudhuri (1997) – based on thetechnical details given by Gautam (1991) - note a systematic underreporting problem in the ICRISAT data on
own consumption of crop outputs produced at home. They argue that Townsend (1994) overestimates the degreeof risk sharing in the village mainly due to the measurement-error problem. We have corrected the transaction
data following Gautam in retrieving the cash and asset balances using the transaction data.

6

survey design of the new waves is very similar to the older one. We also match monthly rainfall
data obtained from the Indian Meteorological Department to the survey data at the district level.
The new dataset includes 90 households from Aurepalle, 89 households from Shirapur, and 62
households from Kanzara. The dataset is fairly balanced across different months.7
Due to minor differences in survey questionnaires and the difficulty in tracking households
between old and new datasets, we opt not to pool all the data. The apparent drawback of using thisdataset is that the crop inventory is recorded on an annual basis only and cultivation output data arecollected on a seasonal basis. So it is not feasible to accurately recover monthly information on
grain-stocks.

3. The Specification and the Empirical Results
Firstly, we compare the coefficient of variation (CV) of monthly consumption with the CV ofmonthly income in each year. Table 1 shows the results in four different landholding classes: thelandless, small farmers, medium-sized farmers, and large farmers. For all the landholding classes,
the CV of monthly consumption is significantly lower than that of monthly income at a 1 percent
level, which implies that households smooth consumption during a single crop year. However,Table 1 also suggests that the extent to which households stabilize their consumption varies acrossdifferent landholding classes. Although the average CVs of income of large and medium farmers
are relatively higher (about 170 percent) and those of small farmers and the landless are lower(about 100 percent), the average CVs of consumption are almost the same across different
landholding classes (about 50 percent). We also construct the smoothing ratios (SR) defined as
SR=1 – CVConsumption / CV Income where SR of 1 (or 0) corresponds to complete consumption
smoothing (or no consumption smoothing) (Carter and Lybbert, 2012). The results which are7

For this analysis based on the new data, we focus only on monthly changes.

7

shown in the last column indicate that SR varies across different landholding classes – with SR thehighest for medium-sized farmers (0.71) and the lowest for small farmers (0.52), although due towide confidence intervals, the differences across different landholding classes are not statistically
identified. Overall, our result is consistent with that of Townsend (1994) who shows that variationin consumption is surprisingly lower than variation in income based on the annual data of the
Indian ICRISAT survey.
(Table 1 to be inserted)
Then, an empirical question arises: how well did households smooth consumption acrossmonths within a single crop year? Following Paxson (1992) and Fafchamps et al. (1998), we try tocapture savings as a function of both permanent and transitory component of income.
(1)
where

is savings in various forms,

constant over time, andis permanent income, i.e., the portion of income that is

is transitory income. i and t denote household and time (or year-month,

t=1 for July 1976, t=2 for August 1976, … , t=84 for June 1983) respectively.8
income) and(variance of(household characteristics) are assumed to be factors which affect the level ofsavings. If household savings behavior can be described appropriately by the life-cycle/ permanent
income hypothesis, thenwould be 0; that is, permanent income does not affect the level ofsavings.
A crucial empirical question would be to identify the permanent and transitory components ofhousehold income. The studies on Indian households, such as those of Bhalla (1979, 1980) and
Wolpin (1982), identify permanent income by the instrumental variables which are correlated onlywith the permanent component and compute transitory income as the rest of household income.

8

A subscript denoting village, v, is omitted for simplicity (except for the rainfall variables).

8One problem with this approach is that it is difficult to distinguish transitory component frommeasurement error. Paxson’s (1992) study of rice farmers in Thailand isolates the transitory
components of income which are exogenous by directly estimating the effects of transitory rainfall
variation on crop income. We closely follow Paxson’s estimation strategy by using the rainfall
data to identify the transitory component.
The permanent component is determined by household characteristics and regional dummies,
both of which affect long-term income-earning abilities of households. Permanent income ischaracterized as;(2)
is a village fixed effect andis a set of household characteristics.

is the error component.

Transitory income is;
(3)
where

is seasonal dummy variables,

is a vector of village specific shocks, namely, rainfall

shocks.

is the household landholding, which is interacted with a set of rainfall variables to takeaccount of the fact that the rainfall shock affects households differently according to the size of
their land. Combining the equations (2) and (3), we can describe the income equation as:(4)
Through the estimation of income equation (4) as in Paxson, we can decompose total householdincome into permanent and transitory components.

is household fixed effects, that is, theunobserved characteristics which may be added to the permanent component. The predicted
permanent and transitory incomes are then denoted by:
̂

̂
̂

̂

̂

(2)’

̂

9

̂

̂

̂

̂

(3)’

Empirically, we first draw upon the two-step procedure in which income equation is estimated in
the first step and savings equation for the change of each asset in the second.
In the present study, we use lagged deviations from the mean of village-level monthly rainfall
in the ICRISAT data following the specification of Paxson (1992) and Fafchamps et al. (1998)
based on the rainfall data to identify the transitory component. More specifically, we have defined
rainfall variables in such a way that the seasonal pattern of rainfalls and their temporary shocks arecaptured at the same time. We have grouped lags of rainfall variables into three groups: the sum of
the current rainfall and the first, the second and the third lags (R1), that of the fourth to the seventhlags (R2) and that of the eighth to the eleventh lags (R3).9 Monthly dummy variables, which
express the deterministic seasonal patterns within a single crop year, are also included in thetransitory factors.
The factors which determine permanent income include village dummies, sex/age/education
variables, and the dummy variables on caste. To capture the combined effects of sex, age, andeducation on the permanent component of income, we generate count variables for the wholesample to capture the effects of the fifteen groups by sex, age group, and educational status (e.g.
number of people aged 0 to 5, or number of males with primary education aged between 18 and
64). Owned land as well as a share of the irrigated area in owned land is added as permanent
factors.

9

Here the issues are whether our rainfall variables are justifiable and whether they are robust to other definitions.
First, no single definition of rainfall variables can be considered ideal as it is difficult to match the past rainfall
trends with the income on monthly basis given the seasonality of agricultural production. Our definition isadmittedly arbitrary in terms of grouping of lags, but it would capture a part of the lagged effects of rainfalls on
household income. We have tried a few other definitions (e.g. different groupings or number of lags) as
robustness checks and have obtained broadly similar results. The final choice of lags or grouping has been
guided by statistical significance of rainfall variables.

10One of the problems with the above estimation based on the monthly data is that it does not take
explicit account of the seasonal nature of agriculture in formulating an income equation.
Therefore, we apply a slightly different specification to estimate the crop-income equation,
drawing upon Jacoby and Skoufias (1998) and Carter and Lybbert (2012).
In the estimation of seasonal income, we model crop income in the peak season as a function of1) the household characteristics (sex/age/education variables, castes) in the agricultural slack
season, 2) the variables on production capitals and inputs in the slack season, 3) village dummies,4) the rainfall in the slack season and its cross term with owned land in the slack season, and 5) therainfall in the peak season (October to December) and its cross term with owned land in the slack
season (June to September). In the first stage, the profit in the peak season is estimated.
(

)

(

)

(5)
where

is farm/household characteristics and information sets available at the slack season;is rainfall before planting (June-Sept) (capturing transitory shocks in the slack season); andis rainfall after planting prior to harvesting (Oct-Dec) (capturing shocks in the corresponding
period).10 t stands for crop year (t=2 for 1977/78; t=3 for 1978/79, … , t=7 for 1982/83).

standsfor the household land holding. Rainfall variables are interacted with the current owned land to
take into account the fact that rainfall affects the households differently according to the size ofland. Transitory and permanent crop income can be written as:
̂

̂

̂

̂

) ̂

(
̂

̂

) ̂

(

̂

10

In the case where we estimate seasonal income, rainfall variables are defined to capture the season-specifictransitory rainfall shocks. The results are robust to other definitions of rainfall variables.

11

̂

̂

In the second stage, the household savings response to transitory crop-income shocks andpermanent incomes is estimated.
̂

̂

(6)

Savings in this case are defined as the net increase in a variety of assets during the peak period.
In order to capture the seasonality in agriculture, we use the household crop income in the peakseason, rather than the total household income. If

is positive and significant, we can conclude

that households save when the transitory crop income (both expected and unexpected transitory
income) in the peak season is high, and dissave when transitory income is low.
Table 2 shows the GLS estimates of the reduced forms of monthly and seasonal income
estimations specified by the above equations. The estimation results associated with rainfall showthat 1) rainfall during the period from the eleventh lagged month to the eighth lagged month has apositive impact on monthly income and, 2) the cross terms of owned land and rainfall during theperiod from the seventh lagged month to the fourth lagged month (or from the third lagged monthto the current month) have positive and significant effects on monthly income. The latter implies
that the income of households with larger areas of land is more strongly affected by rainfalls. In
Case B where crop income in the peak season is applied, we find that 1) the interaction term of
owned land and rainfall during the slack season (June to September) has a positive and significant
effect on crop income in the peak season and, 2) rainfall during the peak season (October toDecember) has a positive impact on crop income.
(Table 2 to be inserted)

Panel A of Table 3 includes the summary results of two-step GLS estimates of monthly andseasonal savings in various forms. Each form of savings is estimated separately. Cases (a), (b), (c),12(d), and (e), corresponding to the identity (the equation (1)), show the net increase in capital assets(production capital assets plus consumer durables), crop inventory, input inventory, financial
assets (including credit), and cash holdings respectively. Saving or dissaving as a form of cropinventory is the most important device for households to buffer consumption. The second
important device of consumption smoothing is currency, as Case (d) shows. As expected, currency
is not saved from the increase in permanent income. In the case of capital assets (Case (a)) andfinancial assets (Case (d)), both transitory and permanent incomes have positive and significant
coefficients. They are important not only as a device of consumption smoothing but also as ameasure to save permanent income. Financial assets in Case (d) include financial savings, credit(in terms of lending minus borrowing), and gifts from others, although they consist mainly ofcredit. Consumption smoothing through village-level risk-sharing mechanism roughly
corresponds to ‘credit’ in Case (d), considering the dominant role of informal borrowing andlending in the rural credit market. The fact that the coefficient of transitory income in Case (d) is
not so large (0.10) implies that households smooth consumption through intertemporal savings,
rather than through risk sharing among different households within the village.
(Table 3 to be inserted)

Cases (f) and (g) show that consumption is considerably smoothed out by savings, physicalsavings in particular. These results correspond to those in Table 1. Case (h) suggests that livestock
is not used as a buffer stock, contrary to the results shown by Rosenzweig and Wolpin (1993). We
decompose the net change of capital assets (Case (a)) into the net change in production capital(Case (i)) and the net change of consumer durables (Case (j)). In monthly analysis, consumer
durables seem more important than production capital as buffer stocks.

13Panel B of Table 3 shows the case of GLS estimates of a savings equation in which the seasonal
data are used. Cash holdings (Case (e)) are the most important factor to buffer consumption
because transitory income affects positively and significantly the net change in cash holdings.
Crop inventory seems to be used as a buffer stock, though the coefficient associated with transitory
income is not significant. Financial assets and capital assets do not serve as buffer stock at all.
Rather do they increase consumption fluctuations, because transitory income has negative
coefficients. Case (f) implies that consumption is significantly smoothed out across different
seasons but the physical savings (Case (g)) are less important. The buffer-stock role of consumer
durables is not clearly observed.
If the results based on the monthly data are decomposed by the landholding classes, it is foundthat all the landholding classes smooth consumption well, relying upon physical assets. 11 For all
the landholding classes, crop inventory plays an important part for consumption smoothing, while
capital assets are used only for large farmers and the landless. Only for large farmers do cash
holdings and savings/dissavings of livestock serve as buffer stock. For the landless, on the other
hand, production capital is one of the main devises to smooth consumption.

4. Extensions
The methodology in the last section has the following two limitations. First, as the savings
equation in the second step is estimated for each form of household asset separately, the coefficientof transitory income does not reflect the relative importance of different household assets. To see
the household response of portfolio adjustment to income shocks more clearly, it is necessary toestimate savings equations simultaneously. Second, some categories of the savings in the second
step are likely to affect the income equation in the first step. In particular, the changes in
11

Details will be provided on request.

14

production capital, input inventory, and financial assets (credit in particular) might affect thetransitory income. In this section, therefore, we estimate the system of equations as an extension of
the methodology put forward by Paxson (1992).
The following system of equations is estimated by three-stage least squares estimation.
(7)
where

is monthly income,is a set of dummies to capture the seasonal fluctuations,vector of lagged rainfall shocks.

is the stock of household landholding at the beginning of thecrop year to be interacted with landholding.

,andare the net monthly changes in

production capital, input inventory, and financial assets respectively.
variable.

is a village-level dummyis the household fixed effects. Because we focus on the temporary shocks insubsume permanent factors under

is a

, we

.

(8)
where

is the net monthly change in capital asset.

is the household characteristics which are assumed to affect savings. Asset changes are
assumed to be influenced by an endogenous temporary income shock,, and rainfall shocks.

is the annual stock of production capital at the last crop year which identifies the equation.12
The other savings equations are specified similarly.
(9)
(10)
(11)
(12)
(13)
12

In the asset equations, household fixed effects are not included (while a number of household characteristics
are included) to make the conversion of estimations achievable.

15where

,

,

,

, and

are the net increases in consumer durables, crop inventory,

input inventory, financial assets, and cash holdings respectively.

nd

are the annual stock of consumer durables, grain stock, owned land, net borrowings, and net
worth (i.e., real assets minus liabilities) respectively. The system of equations of (7) - (13) is firstestimated for monthly data. The same specification is then applied to seasonal data.
Here, the important question is the extent to which the system of simultaneous equations
captures the simultaneity in household decision making of portfolio adjustment where households
take into account their holding of a particular asset, when making adjustment in another asset.
Ideally, the interactions between different household assets should be explicitly modeled, but thedata limitations do not allow us to disentangle the complex causal relationships among different
assets in terms of the households’ portfolio adjustment and their underlying decision-making.
First, while our system of simultaneous equations could capture interactions between the income
equation and the asset equations, the cross-interactions across asset-change equations are notexplicitly modeled as each asset-change is only identified by its lags, while they are indirectly
linked through income changes, as contemporaneous interactions among error terms are allowed.13
Second, our approach is inherently limited - as in most of other econometric approaches - in asense that the ex ante portfolio adjustment decision is captured only by the past data. Whilehouseholds decide in advance whether they want to save or dissave a certain asset component, theactual savings reflected in the data may be different because of a lot of constraints for savings (e.g.
the market of livestock, price changes). Given these limitations, our regression result is at best asummary of the household portfolio adjustment behavior based on the ex post data. To supplement
our approach of using the system of simultaneous equations, we carry out a cluster analysis to13

We could use, for example, lagged values of asset changes as instruments, but this would make the system toocomplex to be estimated. We do not have valid external instruments to identify each asset-change equations dueto the data limitations.

16

understand, albeit descriptively, the different risk coping responses and observe the households’
characteristics based on their asset dissaving patterns in the wake of income shocks.
Table 4 shows the results of the income equation for monthly data (Case A) and seasonal data
(Case B). The net increase in production capital and input inventory has positive and significant
impacts on both the monthly and the seasonal income. Monthly income is positively affected by
the net change in financial assets, including credit. The coefficient estimate of financial assets is
not significant for seasonal crop income (Case B).
(Table 4 to be inserted)

Table 5 shows the results of asset change estimations for monthly data (Panel A) and seasonal
data (Panel B). The overall results are not so different from those in Table 3. In Panel A, cropinventory is the most important device in smoothing consumption. The coefficients associatedwith financial assets have become larger than those shown in Table 3, while the relative
importance of cash holdings has decreased. Consumer durables are still important as buffer stock,
while production capital and input inventory are not.
(Table 5 to be inserted)

It is evident from panel B of Table 4 that crop inventory and cash holdings are used as buffer
stock for seasonal fluctuation in crop income. In addition, production capital, consumer durables,and financial assets play a minor role in buffering consumption. Transitory income has a positiveand significant impact on the input inventory, which suggests that farmers adjust the timing ofpurchasing and selling so that consumption smoothing can be achieved.

17Comparisons of Panel A and Panel B are insightful in inferring some features of household
portfolio-adjustment behavior. While financial assets (including credit) are one of the importantdevices for consumption smoothing in the case where monthly data are used, they are notimportant in the case of seasonal data. It is rather the case that currency plays a key role inmitigating the seasonal fluctuation. Whilst consumer durables are used as buffer stocks formonthly crop shocks, it is production capital that appears to mitigate seasonal crop shocks. Thisimplies that the relatively productive assets, which are closely associated with crop production,
tend to be used as buffer stocks to mitigate the seasonal crop shocks.
As we have discussed, our approach using the system of simultaneous equations does not fully
capture the interactions between different household assets. Accordingly, we have carried out acluster analysis to examine the household portfolio effects by closely following Kusunose andLybbert (2014) (see Appendix 2 for details). The results of this analysis are broadly consistent withthose of econometric analyses in Tables 3 and 5. However, cluster analysis does not provide any
clear evidence in support of interactive portfolio effects using more than one type of asset to copewith a monthly income shock. In other words, dissaving of multiple assets does not appear to be
more effective than that of a single asset for coping with an income shock that occurs in aparticular month. However, as Kusunose and Lybbert note, cluster analysis is descriptive in natureand our results based on this approach should be interpreted with caution.
Furthermore, we have re-estimated the results of Table 5 village-wise and based on the
different landholding classes. Figure 1 comparatively shows the coefficient estimates and theirconfidence intervals in error bars for the different landholding classes and villages using monthly
data. For all landholding classes, crop inventory is the most important device for buffering
consumption, as its coefficient estimate is the largest and statistically significant for all categories.

18

For large farmers, apart from crop inventory, cash holdings and financial assets - both of which arestatistically significant - are used as buffer stock. For medium farmers, while the role of the cropinventory is still prominent, financial assets are also important, having a positive and significant
estimate. Small farmers seem to have various forms of smoothing consumption, namely cropinventory, cash, and production capitals, all of which are statistically significant. For the landless,production capital supplements the buffer-stock role of the crop inventory. The role of cash
holdings as a buffer stock is not evident in the case of the landless households, suggesting that they
may not have enough cash that can be used to cope with income shocks.
(Figure 1 to be inserted)

We also disaggregate the results by villages. In Kanzara where the average household income ishigh, the importance of the crop inventory as buffer stock is lower - though it is statisticallysignificant - than in the other two villages. In addition, cash holdings and consumer durables also
serve as buffer stocks as both respond positively to income shocks. In Shirapur and Aurepalle, therole of the crop inventory is dominant, but financial assets are also important. Cash holdings play
no role in smoothing consumption in Shirapur and Aurepalle in Figure 1.
It is difficult to find any common pattern across different landholding classes or villages.
However, it is noteworthy that consumption smoothing is achieved through savings or dissavingsof several kinds of assets and not by a single asset over a long period. 14 Another important
implication derived from our results concerns the relative importance of the risk-sharing
mechanism among households and the autarky of intertemporal risk-coping mechanism. Among avariety of portfolio choices, it can be reasonably assumed that a majority of ‘financial assets’14This may not be evident in the results of the cluster analysis; because of this method’s inability to capture
sequential dissavings of multiple assets over time. It is likely that households sell on asset at one point in time,
but several assets over a longer period.

19

(which include informal borrowing and lending and gifts) are classified into the former and the rest
(i.e., sum of production capital, consumer durables, crop inventory, input inventory and cash
holdings, and a part of financial assets, such as financial savings) is classified into the latter. As thecoefficient of transitory income associated with financial assets is positive but not large, it isadequate to conclude that the intertemporal savings (which draw upon crop inventory, capital
assets, or cash holdings) are more fundamental to risk coping mechanisms than they are to risk
sharing (such as lending or borrowing between households in the village).

5. Results based on more recent ICRISAT data
Using a more recent ICRISAT dataset between 2009 and 2012, we investigate the behavioral
responses of households in the same regions of rural India (Aurepalle, Shirapur, and Kanzara). The
variables in this section are slightly altered according to data availability and survey design of the
new data. We first estimate the income equation given in equation (4) and then decompose income
into transitory and permanent components in the same fashion as equations (2)’ and (3)’. In thissection, we focus on the results based on monthly fluctuations. Table 6 shows the results of the
reduced form income equation based on monthly data. It is found that the coefficient estimate ofrainfall is positive and significant, while the interaction of rainfall and area of owned land isnegative and significant. The latter suggests that larger landholders tend to be more severely
affected by rainfall shocks. This result is consistent with the coefficient estimates of the third lag of
rainfall and its interaction with land based on old ICRISAT data (Case A of Table 2).
(Table 6 to be inserted)20Based on this decomposition of income into permanent and transitory components, we estimatehousehold response of the following assets to transitory shocks: savings (total), net loan balance,
livestock, consumer durables, and machinery as reported in Table 7. The selection of thesecategories was guided by the availability of the comprehensive asset data in the new ICRISAT
data. To facilitate comparisons between Table 3 and Table 7, the same letters are used in upper
case for the asset categories (e.g. Case (F) in Table 7 corresponds to Case (f) in Table 3).15 That is,Cases (A), (F), (H), (I), and (J) show the net increases of capital assets, saving (total), livestock,production capital, and consumer durables respectively. Case (D) shows the net decrease of loanbalance. As previously noted, the monthly fluctuations of crop inventory could not be retrieved
from the new dataset.
(Table 7 to be inserted)

Here we restrict our attention to the coefficient estimates of transitory income for each case,because they are likely to capture the households’ asset responses to income shocks. In Case (F) of
Table 7 in which total savings is a dependent variable, the coefficient estimate of transitory income
is 0.93 - which is close to 1 - and highly significant, which suggests that households smooth theirconsumption well (Paxson, 1992; Carter and Lybbert, 2012) 16. This is close to the coefficientestimate of transitory income for total savings (0.99) in Case (f) of Table 3 based on the old
ICRISAT data. Consistent with our findings from the 1976-1983 dataset in Table 3, livestock(Case (H)) does not have a vital role as buffer stock, nor do consumer durables (Case (J)),
production capital (Case (I)), or capital assets (Case (A)), have a vital role as buffer stock.
Coefficient estimate is close to 0 in both cases. Loan balance (Case (D)), however, appears to be15

It is noted that only part of the asset categories are available in the new ICRISAT data.

16

Given that Saving=Income – Consumption, a decrease in income by, say, 1000 Rupees is offset by a decreasein saving by as much, keeping consumption smooth.

21

the most responsive to transitory income with a positive and significant coefficient. The
coefficient estimate is 0.45, the largest among all the cases except Total Savings. That is, if income
increases by 1000 Rupees, net loan balance tends to decrease by 450 Rupees. This is similar toCase (d) of Table 3 based on the old dataset in which the coefficient of financial assets (including
credit) is positive and significant, though much smaller than in Case (D) of Table 7. This implies
that the relative importance of credit as a means of risk coping increased in more recent years.
Production capital or consumer durables is statistically insignificant (Cases (I) and (J)) as in Cases(i) and (j) of Table 3. It is difficult to carry out further extensions based on the new dataset, e.g., toestimate the system of equations, because the data for only a part of household assets are available.
However, the results based on the new ICRISAT panel data are broadly consistent with our main
findings based on the old ICRISAT data.

6. Conclusion
One of the most important implications derived from the panel data estimation is that not only thelevel but also the diversification of household assets is important for smoothing consumption. The
results of our analysis yield several crucial conclusions.
First, in the case where monthly data are used, savings as changes of major household assets
have a role in buffering consumption. In particular, change in crop inventory, currency capital
assets (consumer durables in particular), and financial assets (credit in particular) are important for
consumption smoothing. We confirm that when permanent income increases, a household saves
crops, production capital and financial assets, rather than currency or livestock. In general,
livestock plays little part in smoothing the fluctuation of household consumption within a single
year. These results derived from monthly data are not so different in the case where crop income in22the peak season is estimated, except that cash holdings play a more important role as buffer stockin the latter. These results are based on the ICRISAT panel data for the period 1976-1983, but wehave examined the robustness of our results using the new ICRISAT monthly panel data for theperiod 2009-2012. We have found using the new dataset that financial assets, rather than livestock,play a more important when households respond to transitory income shocks. This is consistent
with the main findings based on the old dataset.
Second, the importance of portfolio adjustment and the consumption-smoothing mechanism
are also confirmed by the system of equations in which portfolio adjustment and production
decisions are simultaneously estimated. This result is important, not just because the majority ofthe past studies on consumption smoothing or savings treat income as exogenous, but also because
the empirical studies on savings do not normally pay explicit attention to the aspect of portfolio
adjustment.
Third, decomposition by the landholding class or village suggests that consumption smoothingis achieved through savings or dissavings of several kinds of assets and not by a single asset. The
pattern of portfolio adjustment, however, differs among different landholding classes. While large
farmers rely on a number of assets, including crop inventory, currency, financial assets, and capital
assets in smoothing consumption, small and medium farmers use the crop inventory as a main
device for buffering their consumption. The landless households smooth consumption through an
adjustment of multiple assets, such as grain stock, financial assets, production capital andconsumer durables. However, our cluster analysis does not provide any clear evidence in supportof interactive portfolio effects (i.e. dissaving more than one type of asset simultaneously) to copewith a monthly income shock. The household is thus likely to use a single asset at one time, or in a23

particular month, to cope with a sudden income shock, but more than one type of assets over a long
period.
Fourth, it appears that intertemporal savings, which draw upon crop inventory, capital assets, orcurrency, are more important as a measure of risk coping than risk sharing, through lending or
borrowing across different households. On one hand, these results are in sharp contrast with theanalysis of Townsend (1994) which shows that consumption is smoothed out by the risk-sharing
arrangement within the villages on the basis of the annual ICRISAT data. On the other hand, ourdiscussion is in line with Ravallion and Chaudhuri (1997), a critique against Townsend’s seminal
paper. Our paper suggests that Townsend’s results, which support the ‘risk-sharing’ hypothesis,
can be largely affected by the autarkic ‘intertemporal savings’ of each household which can follow
a common trend among different households within the villages.
It is often argued that the poor are constrained by lack of access to credit or savings, but thepresent study suggests that once we track the record of all the household assets, even the landlesshouseholds cope with income shocks quite well by adjusting a variety of their assets over time.
Any policy interventions to address the vulnerability of the poor in rural areas should consider this
aspect. Future studies should investigate whether the pattern of the portfolio adjustment is similar,
or whether the portfolio adjustment (e.g. dissaving of production capital) has any implications forpoverty dynamics.

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Singh, R. P., H. P. Binswanger, and Jodha, N. S., 1985, Manual of Instructions for Economic
Investigators in ICRISAT’s Village Level Studies (Revised), Patancheru, India, ICRISAT.
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27

Figure 1 Household Reaction to Transitory Crop Income Shocks: Decomposition bylandholding class and village (based on 3SLS shown in Table 5): Monthly Data
0.8
0.6

△Production capital

0.4△Consumer Durables

0.2△Crop Inventroy0△Input Inventory△Financial Assets-0.2△Cash holdings-0.4
Large Medium Small
The
Shirapur Kanzara
Farmers Farmers Farmers Landless Aurepalle

Total

Table 1 The Comparison of CV (Coefficient of Variation) of Monthly Income and CV ofMonthly Consumption in rural India, 1976-84
CV for each year, 1976-84

Smoothing
Ratios1-(b)/(a)t testConfidenceInterval(95%)
For
Smoothing
RatiosAverage
CV ofIncome(a)

Average
CV ofConsumption (b)Averagereduction

Landless

100.8

43.8

57

205

2.4

**

0.57

0.104

1.027

Small farmer

103.1

49.3

53.9

243

8.16

**

0.52

0.397

0.648

Medium-Sized

169.8

49.4

120.3

240

5.32

**

0.71

0.447

0.969

Large farmer

167.4

58.6

108.8

243

15.22

**

0.65

0.566

0.734

136.6

50.5

86

931

10.4

**

0.63

0.511

0.748

Total

**= significant at 1% level. *= significant at 5% level.

No. ofObservations(a)-(b)+=significant at 10% level.

Table 2. Estimations of the Reduced Form Income Equations based on the ICRISAT datafrom 1976 to 1983 (summary results)
Case A(Monthly Income)28

Case B(Crop Income in Peak Season)

Variable
Transitory Factors
Rainfall variables:1)(R1- mean of R1) :R1 =r0 + r-1 + r-2 +r-3
where r-t is the t-th lagged monthly rainfall(R1- mean of R1)*(Owned Land)(R2- mean of R2):R2 =r-4 + r-5 +r-6 +r-7
(R2- mean of R2)*(Owned Land)(R3- mean of R3):R3 =r-8 + r-9 +r-10 +r-11(R3- mean of R3)*(Owned Land)(R4- mean of R4) :R4 =Rainfall in June-Sept(R4- mean of R4)2
(R4- mean of R4)*(Owned Land)(R5- mean of R5) :R5 =Rainfall in Oct-Dec(R5- mean of R5)2
(R5- mean of R5)*(Owned Land)

Parameter
Estimate t-ratio

Parameter
Estimate

t-ratio-2.22(-2.35)*-------------------

0.66
-3.37
0.81
2.35
-0.94

(5.56)**(-3.60)**(6.87)**(2.53)*(-8.16)**---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------10.76 (-2.11)*
0.04 (1.77)†
0.002 (2.15)*
23.80 (1.80)†-0.21 (-1.32)
0.003 (0.40)

85.86 (0.99)
208.00 (2.38)*
340.37 (3.84)**
889.42 (10.06)**
831.07 (8.98)**764.12 (8.30)**
398.63 (4.38)**558.59 (6.37)**724.17 (8.23)**556.82 (6.42)**204.59 (2.38)*------------------------------------------------------------------------------------------------------------------------------------------------------------144.57 (-1.79)†-194.36 (-2.42)*-30.2.09 (-3.17)**-4010.96 (-4.55)**-7.30 (-0.32)
53.90 (1.65)†
25.48 (0.70)-59.23 (-1.63)
47.56 (1.33)-122.26 (0.48)
9.26 (0.03)
42.75 (0.11)
749.12 (1.94)*-352.16 (-0.90)

41.08 (1.00)
114.36 (2.19)*
116.68 (2.02)*
102.79 (2.00)*

395.84 (0.84)
72.55 (0.13)
84.82 (0.13)
458.13 (0.89)

Illiterate

84.63 (2.08)*-1010.68 (-2.15)*

Primary school or less23.61 (0.35)

777.29 (1.04)

Secondary school

116.68 (2.02)*-303.69 (-0.44)

Seasonal Dummies: 3)
Whether July or not
Whether Aug or not
Whether Sept or not
Whether Oct or not
Whether Nov or not
Whether Dec or not
Whether Jan or not
Whether Feb or not
Whether Mar or not
Whether Apr or not
Whether May or not
Permanent Factors
Village dummies: 3)
Whether Shirapur or not
Whether Aurepalle or not
Sex/ age/ education variables:
Number of people aged 0-5
Number of males aged 6-11
Number of females aged 6-11
Number of males aged 12-17
Number of females aged 12-17
Number of males aged 18-64
Illiterate
Primary school or less
Secondary school
Post secondary school
Number of females aged 18-64

29

Post-secondary school

102.79 (2.00)*-1299.04 (-1.21)

Number of males aged 65 or more
Number of females aged 65 or more-158.81 (-1.97)*-61.47 (-0.60)

82.12 (0.10)
465.16 (0.61)-59.85 (-0.63)
158.34 (1.80)†
4.17 (0.04)

460.50 (0.42)
1354.87 (1.51)
1514.69 (1.47)

23.93 (2.78)**773.79 (7.52)**0.08 (7.07)**
0.02 (6.44)**------------------322.61(-2.54)**362.53 (4.69)**5465.59 (4.41)**0.65 (5.73)**
0.04 (1.05)
1.98 (1.99)**
1679.3 (1.49)

Variable on the caste:3)
whether high caste or notwhether mid-high caste or notwhether mid-low caste or not
Owned Land (ha.)
Share of Owned Land which is Irrigated
Stock of Livestock (Rs.)
Stock of Production Capital (Rs.)
Input Spending in Slack Season (Rs.)
Constant

Number of Observations
7703
504
Note: 1) Square takes negative value when the deviation is negative. 2)Number in parentheses is t ratio. **= significant at 1% level. *=significant at 5% level. †= significant at 10% level. 3) Dummy variable.

30

Table 3 Two-step Random-effects GLS Estimates of Savings Equations
Panel A: Based on Monthly Data
Case(a)Dependent Variable:△Capital Assets(-Σ△ Kijt Pijtt)

Explanatory Variable:
Transitory Income
Permanent Income
Number of Observations
Dependent Variable:

Explanatory Variable:
Transitory Income
Permanent Income
Number of Observations

Case(b)△Crop Inventory(-Σ△ SijtPijt)

Case(c)Case(d)Case(e)△Input Inventory△Financial Assets (-Σ△BijtPijt)(including credit)
Parameter
Estimate t-ratio
0.10(2.52)**
0.09(1.92)†
7703
Case (i)△Cash holdings2)(–Σ△ IijtPijt)

Parameter
Parameter
Estimate t-ratio
Estimate t-ratio
0.13(4.09)**
0.54(11.42)**0.11(2.73)*
0.56(9.64)**
7703
7703
Case (f)Case (g)Savings Total△Physical Savings(Sum of Case a, b, c, d,(Sum of a & b)
& e)

Parameter
Estimate t-ratio-0.02(-4.70)**-0.002 (-0.42)7703
Case (h)Parameter
Estimate t-ratio
0.99(19.26)**0.82(10.94)**
7668

Parameter
Estimate t-ratio
0.01(1.38)-0.06(-4.95)**
7703

Parameter
Estimate t-ratio
0.67 (11.64)**0.65(8.45)**
7703△Livestock 4)△Production
Capital 4)
Parameter
Estimate t-ratio-0.06 (-1.60)
0.09(1.95)
7703(–△Mjt)Parameter
Estimate t-ratio
0.23(5.78)**-0.02(-0.37)7703
Case (j)△Consumer
Durables 5)
Parameter
Estimate t-ratio
0.19(7.20)
0.04(1.03)
7703

Panel B: Based on Seasonal Data
Case(a)Dependent Variable:△Capital Assets(-Σ△ Kijt Pijtt)

Explanatory Variable:
Transitory Income
Permanent Income
Number of Observations
Dependent Variable:

Case(b)△Crop Inventory(-Σ△ SijtPijt)

Parameter
Parameter
Estimate t-ratio
Estimate t-ratio-0.06(-0.46)0.42(1.37)
0.04(1.02)
0.55(7.66)**
504
504
Case (f)Case (g)Savings Total△Physical Savings(Sum of Case a, b, c, d,(Sum of a & b)
& e)

Explanatory Variable:

Case(c)Case(d)Case(e)△Input Inventory△Financial Assets (-Σ△BijtPijt)(including credit)
Parameter
Estimate t-ratio-0.33 (-1.48)-0.03 (-0.60)
504
Case (i)△Cash holdings2)(–Σ△ IijtPijt)
Parameter
Estimate t-ratio
0.03(2.01)*
0.02(4.27)**
504
Case (h)△Livestock 4)△Production
Capital 4)(–△Mjt)Parameter
Estimate t-ratio
0.88(2.62)**
0.28(3.12)**
504
Case (j)△Consumer
Durables 5)

Parameter
Parameter
Parameter
Parameter
Parameter
Estimate t-ratio
Estimate t-ratio
Estimate t-ratio
Estimate t-ratio
Estimate t-ratio
Transitory Income
1.02(2.94)**
0.35(1.01)
0.02 (0.37)-0.17 (-1.13)
0.11(1.30)
Permanent Income
0.79(7.60)**
0.57(6.65)**-0.04 (-2.74)**
0.02 (0.50)
0.02(0.98)
Number of Observations
504
504
504
504
504
Note: 1)Number in parentheses is t ratio. **= significant at 1% level. *= significant at 5% level. †= significant at 10% level. 2) Both Production capital (Case (h)) and Consumer Durables(Case (i.) ) are a part of Capital assets (Case (a)). 3) Livestock (Case (j)) is a part of production capital of financial assets (Case (d)).

31

Table 4 System of Equations (based on 3SLS for Monthly Data and Seasonal Data)
Income Equation
Variable
Rainfall variables:1)(R1- mean of R1) :R1 =r0 + r-1 + r-2 +r-3
where r-t is the t-th lagged monthly rainfall(R1- mean of R1)*(Owned Land)(R2- mean of R2):R2 =r-4 + r-5 +r-6 +r-7
(R2- mean of R2)*(Owned Land)(R3- mean of R3):R3 =r-8 + r-9 +r-10 +r-11(R3- mean of R3)*(Owned Land)(R4- mean of R4) :R4 =Rainfall in June-Sept(R4- mean of R4)2
(R4- mean of R4)*(Owned Land)(R5- mean of R5) :R5 =Rainfall in Oct-Dec(R5- mean of R5)2
(R5- mean of R5)*(Owned Land)
Seasonal Dummies: 3)
Whether July or not
Whether Aug or not
Whether Sep or not
Whether Oct or not
Whether Nov or not
Whether Dec or not
Whether Jan or not
Whether Feb or not
Whether Mar or not
Whether Apr or not
Whether May or not△ Production Capital△ Input Inventory

Case A (Monthly Income)Parameter
Estimate t-ratio

Case B (Seasonal Crop Income)Parameter
Estimate
t-ratio

2.53(2.21)*-------------------

0.23
-1.45
0.38
-1.75
-0.49

(1.77)†(-1.23)(3.98)**(-1.49)(-5.08)**----------------------------------------------------------------------------------------------------------------------------------------------------------------

9.49 (1.55)-0.016 (-0.73)-0.001 (-1.56)
9.01 (0.63)------------------------------------0.03 (-0.19)
0.006 (1.18)

242.75 (2.27)*
214.87 (2.36)*
260.10 (3.07)**384.10 (4.23)**320.72 (3.54)**254.76 (2.94)**
246.22 (2.54)**314.29 (3.67)**
507.11 (5.28)**364.00 (3.76)**251.96 (3.27)**

1.64 (8.41)**
6.82 (7.14)**3.08 (20.36)**-----------------------------------------------------------------------------------------------------------------------------------------------------------

1.80 (3.27)**54.22 (6.71)**-0.52 (-1.49)△Financial Assets(including Credit)
250.40 (2.72)**3389.43 (6.30)
Constant
Number of Observations
7703
504
2)
Note: 1) Square takes negative value when the deviation is negative.
Number in parentheses is t ratio. **= significant at 1% level.
3)*= significant at 5% level. †= significant at 10% level. Dummy variable.

32

Table 5 System of Equations (based on 3SLS for Monthly Data and Seasonal Data)
Asset Equations
Panel B: Based on Monthly Data
Dependent Variables:

Explanatory Variable:
Monthly Income(Transitory Income)Net worth: Real assets –liabilities
Stock of Production Capital
Stock of Consumer
Durables
Stock of Grain Stock△Production
Capital△Consumer
Durables△Financial
Assets△Cash holdings2)

Parameter
Estimate(t-ratio)
0.02(0.92)------

Parameter
Estimate(t-ratio)
0.12(5.79)**------

Parameter
Estimate(t-ratio)
0.44(18.84)**------

Parameter
Estimate(t-ratio)
0.003(0.83)------

Parameter
Estimate(t-ratio)
0.23(8.42)**------------------------

Parameter
Estimate(t-ratio)
0.11(3.62)**
0.0003(3.84)**------

0.04(4.01)**-------------------0.002(-4.75)**-------------------------------------------------------0.07(-8.58)**------------------------------------------

0.63(2.22)*-------------51.93(-1.00)7703-5.44(-0.14)7703

77.26(1.78)
7703

2.07(0.31)
7703-0.0002(-0.47)-116.75(-2.33)
7703△Financial
Assets△Cash holdings2)

Parameter
Estimate(t-ratio)
0.06(1.59)------

Parameter
Estimate(t-ratio)
0.41(5.14)**-0.025(-2.23)*

Owned Land

△Crop
Inventory△InputInventory

Stock of Net Borrowings

Number of Observations-65.67(-1.19)7703

Panel B: Based on Seasonal Data
Dependent Variables:

Explanatory Variable:
Crop Income in peak season(Transitory income)Net worth: Real assets –liabilities△Production
Capital△Consumer
DurablesParameter
Estimate(t-ratio)
0.06(2.37)*------

Parameter
Estimate(t-ratio)
0.03(1.58)------△Crop
Inventory△InputInventory

Parameter
Estimate(t-ratio)
0.36(7.05)**------

Parameter
Estimate(t-ratio)
0.02(8.03)**------

33

Stock of Production Capital
Stock of Consumer
Durables
Stock of Grain Stock

0.04(4.08)**-------------------------------------------------------------------0.005(-0.37)------------------------------------

0.15(0.63)-------------------------------------1.48(-1.23)-------------271.22(-0.78)-10.16(-0.05)-0.43(-0.001)-61.21(-2.61)-0.02(1.01)-514.98(-0.96)

504

504

504

504

504

Owned Land
Stock of Net Borrowings
Constant

Number of Observations

Note: 1) Square takes negative value when the deviation is negative.
level.

2)504-287.40(-0.33)

Number in parentheses is t ratio. **= significant at 1% level. *= significant at 5% level.

34†= significant at 10%

Table 6 Estimations of the Reduced Form Income Equations based on the ICRISAT data from 2009-2012
Monthly Income
Variable
Transitory Factors
Rainfall variables:(R1- mean of R1) :R1 =r0 + r-1 + r-2 +r-3
where r-t is the t-th lagged monthly rainfall(R1- mean of R1)*(Owned Land)(R2- mean of R2):R2 =r-4 + r-5 +r-6 +r-7
(R2- mean of R2)*(Owned Land)(R3- mean of R3):R3 =r-8 + r-9 +r-10 +r-11(R3- mean of R3)*(Owned Land)

Parameter
Estimate

t-ratio

9.19(2.88)*

-0.86
12.87
-1.14
8.41
-0.78

(-3.05)*(3.48)**(-3.41)**(1.83)(-2.03) †-684.781304.321039.87
2199.241140.77-673.111167.83
957.931249.56
1594.663112.72(-1.08)(1.65)(1.31)(3.30)**(1.52)(-1.06)(1.55)(1.75)(1.90)(2.32) †(3.97)**

3352.023564.00(4.89)**(4.84)**

660.73315.03-910.40561.56-446.66(2.37) †(1.26)(-5.35)**(3.56)**(-2.29) †Seasonal Dummies:
Whether July or not
Whether Aug or not
Whether Sept or not
Whether Oct or not
Whether Nov or not
Whether Dec or not
Whether Jan or not
Whether Feb or not
Whether Mar or not
Whether Apr or not
Whether May or not
Permanent Factors
Village dummies: 1)
Whether Shirapur or not
Whether Aurepalle or not
Sex/ age/ education variables:
Number of people aged 0-5
Number of males aged 6-11
Number of females aged 6-11
Number of males aged 12-17
Number of females aged 12-17
Number of males aged 18-64

35

with primary education or lesswith middleschool education
with highschool education
with intermediate education
with higher education
Number of females aged 18-64

2690.79
2411.26
2881.85
1587.41
1856.04(9.44)**(7.74)**(16.99)**(5.42)**(3.96)**

with primary education or lesswith middleschool education
with highschool education
with intermediate education
with higher education
Number of males aged 65 or more
Number of females aged 65 or more

-329.74
1347.64961.892321.40
1374.18-276.17421.49(-1.03)(2.70)*(2.19) †(3.74)**(1.99) †(-1.12)(1.21)

Variables on the caste1)
Backward Caste
Forward Caste
Nomadic Tribe
Scheduled Caste
Other Caste-880.68-576.36
1918.43
1383.56-240.98(-1.04)(-1.14)(3.33)**(2.02) †(-0.34)-100.89-629.40-52.97-930.29(-2.87)*(-1.05)(-8.51)**(-1.18)

Owned Land (ac.)
Share of Owned Land which is Irrigated
Stock of Livestock (Rs.)
Constant

2902
Number of Observations
Note: Number in parentheses is t ratio. **= significant at 1% level. *= significant at 5% level.

36†= significant at 10% level. 1) Dummy variable.

Table 7 Two-step Random-effects GLS Estimates of Savings Equations using 2009-2012 ICRISAT data
Dependent Variable:

Explanatory Variable:
Transitory Income
Permanent Income
Number of Observations
Dependent Variable:

Explanatory Variable:
Transitory Income
Permanent Income
Number of Observations

Case (A)∆Capital
Assets(1)Parameter
Estimate
t-ratio0.01(0.30) (2)0.07(0.38)
548
Case (H)∆Livestock

Parameter
Estimate
t-ratio0.00(0.22)-0.43(-1.13)2902

Case (D)-∆Loan Balance

Parameter
Estimate
0.45*
-0.55
2260

t-ratio(2.48) †(-1.34)

Case (I)∆Production
CapitalParameter
Estimate
0.01
0.06

t-ratio(0.29)(0.27)
548

Case (F)Total Savings (3)Parameter
Estimate
t-ratio
0.93***(31.47)**
1.41***(6.72)**
2902
Case (J)∆Consumer Durables

Parameter
Estimate
0.00
-0.01

t-ratio(0.28)(-0.25)548

Note:The capital letter designation of the cases corresponds to their lower case designation in Table 3 for ease of comparison. (1) Capital assets include Consumer Durables andProduction Capital (2) Numbers in parentheses are t ratio. **= significant at 1% level. *= significant at 5% level. †= significant at 10% level. (3) Total Savings = Income- Consumption.

37

Appendix 1: Constructions of Monthly and Seasonal Asset Variables
Based on household transaction and crop-production modules, we have calculated the following
monthly variables. All of these are household variables. Seasonal variables are constructed byaggregating the monthly variables during the agricultural slack season from April to September
and the peak season from October to March.
Real Monthly Income is the sum of monthly income from agriculture, labour, trade, handicrafts
and net transfers
Y = Yagriculture + Ylabour + Ytrade + Yhandicrafts + NetTransfers(A)

Real Monthly Consumption is sum of monthly expenditures on all the food and non-food
expenditures
Consumption = ∑ Expenditurefood/non-food(B)

Financial Savings is the Net Real Monthly Increase of Financial Assets based on the difference
between financial assets and the withdrawal
FinancialSavings = Savings + Deposits + LifeInsurance + Others– Withdrawal(C)

Credit is the Net Real Monthly Decrease in Liabilities
Credit = Lending – Borrowings + Repayment(D)

Change in Financial Assets – denoted as -Σ△BijtPijt above – is the sum of (C) , (D) and income
from gift and others
The Net Real Monthly Increase of All the Livestock is based on bullocks, cows, young cattle,
buffalo, young buffalo, horses, donkeys, goats, sheep, pigs, poultry, and others∆Livestock = Purchase – Sale – LossLivestock17(E)
The Net Real Monthly Increase of Main Production Capital is based on dry land, wet land, wells,
tanks, cattle sheds, cattle yards, storage facilities, oil or electric pumps∆MainProdCapital = Purchase – Sales – LossProdCapital + ExpenditureOnProdCap(F)
Net Real Monthly Increase of All Consumer Durables which are not included in Consumption, e.g.
jewellery, cycles, furniture etc.
∆MainDurables= Purchase – Sales – LossDurables + ExpenditureOnDurables (G)Change in Capital Assets – referred to as -Σ△ Kijt Pijtt above - is the sume of (F) and (G)Savings is computed as the difference between Income and Consumption
Savings = Income – Consumption(H)

Monthly Change in Currency – referred to as -△Mjt above – is the difference between theacquisition of cash and the use thereof.

17

Loss of livestock due to death, theft etc.

38

Change in Crop Inventory – referred to as as -Σ△ SijtPijt above – is the sume of crop production
and purchase less crop sales and the consumption of self-produced crops∆CropInventory = CropProduction +CropPurchase – SaleCrops – ConsumptionCrops(I)
Change in Input Inventory – referred to as -Σ△ IijtPijt above – is the net change in fertilisers,
manure, pesticides, and insecticides .
∆InputInventory = ∆Fertilisers + ∆Manure + ∆Pesticides + ∆Insecticides (J)All of (A), (B), (C), (D), (E), (F), (G), (H), (I) and (J) are in monthly terms and deflated by thevillage-level monthly CPI – referred to as PctYit above.
Appendix 2: Cluster analysis on the effects of different household assets to cope with shocks
Drawing upon Kusunose and Lybbert (2014), we have carried out cluster analysis to further
investigate the effects of different household assets to cope with income shocks. We have usedk-means clustering method through which k clusters are created, each containing households ofsimilar characteristics or trends (in our case, dissaving of certain assets). The method initiallyallocates households randomly into the k clusters, then rearranges them such that it minimizes eachcluster’s within variation to keep similar households in each, and maximizes cross-cluster
variation.(ibid., 2014). This method is discussed in further detail in Brown et al. (2006).
We have clustered all the monthly observations according to whether a household reduced aparticular type of asset, namely, livestock, production assets, consumer durables and cropinventory, or any combination of these assets in case the household reduced more than one type of
assets. For cluster analysis, binary variables are defined as whether a household reduced more than
10% of the initial asset balance of each type of asset in a particular month. We do not include cash
balance as the initial balance of cash holdings is unavailable. Credit balance – or liability - is notconsidered either because the meaning of the balance is different from that of other assets withpositive values.
Although this is a descriptive analysis and subject to limitations (e.g. ignoring the panel
structure of the data), comparisons of means of total or food consumption per capita acrossdifferent clusters would provide an insight into whether the use of more than one type of asset
would facilitate household risk coping. Also, we are able to characterize different types ofhouseholds by comparing other variables, such as consumption or the initial stock of various
household assets.
We have identified eight mutually exclusive clusters in Appendix Table according to whether ahousehold sold, or reduced more than 10% of the stock of, one or more types of assets in aparticular month. Cluster 1 is the benchmark case where households did not sell any types of asset.
Clusters 2 to 5 correspond to the cases in which households sold only one type of asset, namely,
livestock, production capital, consumer durables and grain stock in a particular month. Cluster 6 isthe case where a household sold livestock and production capital at the same time, while consumer
durables and grain were dissaved simultaneously for Cluster 7. Those four types of asset
reductions appear in Cluster 8.
The results will have to be interpreted with caution as the figures are unconditional means ofobservations for clustered observations. However, it is found that (i) clusters of households selling
only livestock (Cluster 2) or only consumer durables (Cluster 4) have consumption lower than thebenchmark case (Cluster 1) and these clusters of households are characterized with low levels of39

initial assets which were sold; (ii) households selling only production capital (Cluster 3) had
consumption not much different from the benchmark case, which is consistent with our
econometric results (Case (a) of Table 3); (iii) selling grain stock appears to be the most effectiverisk-coping strategy (Cluster 5) resulting in the highest total or food consumption – which is in linewith our econometric result (Case (b) of Table 3), but these households tend to have higher levelsof not only grain stock, but also other assets; (iv) in general there is no clear evidence to show thatthe use of multiple assets facilitate keeping the consumption levels (e.g. Clusters 6 and 7); and (v)households selling all the four assets had consumption (Cluster 8) only slightly lower than thebenchmark case, but this is probably because such a drastic reduction of assets was possible forhouseholds with higher levels of initial assets.
In sum, the results of cluster analysis are broadly consistent with those of econometric analyses
in Tables 3 and 5, but they do not imply that the use of multiple assets is more effective thanrelying on only a single asset for coping with monthly income shocks.
Appendix Table
Cluster 1

No pattern

No. ofObservations

6674

Cluster 2

Cluster 3

Sold LiveStock 1

Sold
Production
Capital 1

2176

69

Cluster 4

Sold
Consumer
Durables 1

223

Cluster 5

Sold Grain
1

1059

Cluster 6

Cluster 7

Sold
LivestockandProduction
Capital 1

Sold
Consumer
Durablesand Grain

886

484

1

Cluster 8
Sold
Livestock,
Production
Capital,Durablesand Grain 1
2237

Means of Variables
Per Capita Food
21.5**
13.85**
18.59
12.32
23.72*
7.96**
8.11**
16.22
Consumption
Consumption
Total Per Capita
22.7**
14.89**
20.49
13.08
25.16*
8.58**
8.76**
17.26
Consumption
Initial stock ofConsumer
3701.97**1505.23** 4535
6.95**
4508.04**
1353.76**0**3182.95
DurablesInitial stock of
554.78**207.08**432.06
1273.63**653.94**217.8**
48.45**
517.66grain stocks
Initial Stock at
Net Borrowing at
the Beginning
the Beginning of
2123.42**1312.67** 2402.98
140.14**2208.45*1546.32
184.64*1805.6of the year
the year
Initial Stock of
2577.33**478.57**2676.04
1480.27**2912.93**866.362525.31*1977.45*
Livestock
Initial Stock ofProduction
1756.38**588.78**765.97750.36
1686.66
818.091134.43
1352.96
Capital
1. A clustering variable for “selling asset” is defined as a binary variable, taking 1 if a household reduced more than 10% of the initial stock of household asset, and0 otherwise. We used four clustering variables for livestock, production capital, consumer durables, and grain stock.
2. * shows that the mean differs from the mean of the rest of the sample at 5% significance, while ** is used for the cases significant at 1% level.

40