Household Expenditure Responds to Crop Loss and Lower Output Price
Case Study in Indonesian Farmers
Ni Made Sukartini 1
Email: nimade_sukartini@gmail.com
.
Abstract
This purpose of this paper is investigating any economic shocks expreienced by household, and focusing on agricultural household in Indonesia, to find out the impact of the economic shock to the household total expenditure. We distinguish the economic shock as idiosyncratic shock and common shock. Idiosyncratic shock is any shock that might influence individual or household but might not affect the other individu or household. The example of idiosyncratic shock areillness or death of one of the family member. Other example is individu or household head experience losing job. On the other hand, common shock is any shock that might influence a group of people or community, such as flooding, experiencing crop loss, or falling in the price of output. This paper utilize data from
Indonesia Family Life Survey (IFLS), especially wave 2 (1997) and wave 3 (2000) . Our
Ordinary Least Square (OLS) estimation result find that in terms of idiosyncratic shock, experiencing sickness, and the death of household member was found associate with higher spending for health and education. When a member of household loss job in the years of 1997, associated with larger food consumption. Meanwhile, affected by any type of disaster (flooding, fire, earth quake, etc) will affect higher expenditure for food, health and education. Experiencing crop loss, both in 1997 and 2000 cases, drive the household expenditure down, especially for educational matter. Buying formal insurance and getting income support from parent and relatives are taken by shock affected household, to mitigate the side effect of the shock and to smooth their consumption profile
Key words: economic shock, idiosyncratic shock, common shock, consumption smoothing, and Indonesia
JEL classification :D11, E21, E32, and Q12
I. Introduction
Since 1990s, Indonesia has been experiencing several economic shocks, not only due to economic crisis in 1997, but also due to various natural disasters related with the climate change fenomenom. The economic cost from the crisis in 1997, were reported that many people not only lossing their job but also their business. If the head of household being unemployed, it will affect the household income and expenditure. By the same time, weather anomalies such as long drought or persistent heavy rain are becoming frequent
1
Ni Made Sukartini, Department of Economics, Faculty of Economics and Business,
Airlangga University, Indonesia
1

recently. Long drought result less crop can be planted, while most farmers in Indonesia relly their farming on rainfall for watering. On the other hand, heavy and deviation of rainfall destroys most rice field, vegetable and fruit. Unless the farmers insuring their crops, it is imposibble for the farmer to cover their living cost, because drought and flooding usually happening during the cultivating and harvesting time. Low quality of product also frequently reported by farmers if their crops still producing during drought and flooding period. As a result, they can not bargain for a better price in the market.
While natural disasters are becoming more frequent, another unpredicted shock for majority of the poor is illness and the death of family member, especially head of household. When family member experience illness and tend to be persistent, it will associate with cost of the medical care used to diagnose and treat the illness, and the loss in income associated with reduced labor supply and productivity ( Gertler & Gruber , 1997). The unpredictability of these costs implies that families may not be able to smooth their consumption over periods of major illness. This is true, especially in developing countries, where few individuals are able to afford formal health and disability insurance ( World
Bank , 1993 and 1995a).
Many studies concern for the impact of weather anomalies or climate change on the household income and expenditure, especially the poor household in rural areas in developing countries. Variation in rainfall on year to year basis or between regions has significant impact on agricultural output and household income. Studies of Kochar (1995) in three villages of Central India, reported that due to lack of formal insurance, major illness experienced by household head, have negatif income effect, however, some househould able to cover it by participating in off-farm labor market. When the head of household experience long term illness, they only able to cope by borrowing money from families and relative. Kochar (1998) did further investigation on the effect of household
2

head illness or death for the household consumption smoothing. Kochar exploring the household in same village as her 1995’s study (Aurepalle, Shirapur, and Kanzara). She reported that long term variation in crops loss, wage differences between farm and offfarm market, and male or female household headed assosiated with difference risk coping behavior among the agricultural household. Kochar reported that some families planting multi crops for anticipating the impact of bad weather and harvesting risk, some reduce the risk through informal insurance such as agreement between land owner and contracting farmer, and some expand working hours or work overseas.
Earlier studies consider exogeneous rainfall variation as instrumental variable (IV) for economic shock experienced by rural agricultural household and it’s impact on household consumption patterns. Rosenzweig and Wolpin (1993) investigated both theoritical and emphirical agricultural household in the International Crops Research
Institute for the Semi-Arid-Tropics (ICRISAT) villages, India. To cope with high risk of crop loss from weather variation, their study reported that Indian farmers choose to invest in bullock, jewellery, and irrigation equipment. Bullock is typical animal in India that able to live in arid area, which is strong enough to carry agricultural output between the ricefield, village and the market. Like jewellery, if the families experiencing crop loss, they can use the bullock and the jewellery as collateral when borrowing money or simply sell it. Irrigation equipment on the other hand, this devise make their work easier and faster, therefore the families can save time for working off-farm.
Townsend (1994) proposed full insurance model by utilizing the same household sample in three villages of ICRISAT. Townsend reported that any idiosyncratic shock on poor individual or household member is predicted will have negative impact on household consumption and total expenditure, because poor household have lack of access on formal insurance. Idiosyncratic shock means any shock that will put risk only on specific
3

individual or household member, can not influence the community as whole.The opposite is common shock; any shock that has influence on several individual or household at the same time. Townsend consider sickness and being unemployed as idiosyncratic shock, and further evaluate it’s impact on labor income, profit from crops, trade plus handycrapt, and profit from investing on animal husbandry. If poor household have no formal insurance, risk sharing mechanism might apply in this community.
To investigate risk coping behavior between the household in the three village,
Townsend tests both statitically and exploring time series plot data. Townsend conclude there was statistically weak evidence that poor household’s consumption influenced by any idiosyncratic shock such as illness and lossing job. Qualitative data shows that household consumption are comove with the average village consumption, eventhough he found evidence that poor and landless household are insecured compared to the village neighbourhood. Townsend conclude this mechanism as informal risk coping behavior among the three ICRISAT villages in South India.
This paper will focus on the impact of both idiosyncratic and common shock on the household consumption expenditure in Indonesia. Due to economic fluctuation and weather anomalies, there is possibilities for Indonesian household in high risk for experiencing any of idiosyncratic and common shock all together. In this study, we classify losing job, illness, and the death of family member as example of idiosyncratic shock. Furthermore, the incidence of falling output price, crop loss, and other type of natural disaster (earth quake, fire, flooding, etc) as type of common shock. We aknowledge various studies that investigate economic shock in Indonesia. Our focus slightly different in terms of types of economic shock, the possible coping strategies, and the data coverage. The main questions that will be answered in this paper is: 1. Do
4

transitory income shock influence the agricultural household in Indonesia?. 2. How do the household smooth their consumption in respond with transitory income shock?
Limited to our concern, studies that investiagate the impact of economic shock on the household consumption expenditure still have space to be elaborated, especially in the case of Indonesia. Due to our limitation, we only aware for the abundant previous studies, such as: Cameron & Worswiwk (2001, 2003), Kevane & Levine (2003), Block et. al.
(2004), Chetty & Looney (2005), Newhouse (2005), Jayachandran (2006), Levine & Ames
(2006), , Levine & Yang (2006), Modena (2007), Berloffa & Modena (2009), Korkeala,
Newhouse & Duarte (2009), Machini and Yang (2009), and Noble (2012).
In general, these studies put concern on difference source of economic shock and it’s consequences. Block et al.
(2004) and Levine & Ames (2006) considered the impact of financial crisis in 1997 on the health and educational outcome of Indonesian children.
These studies also discuss gender bias during the economic hardship. Block et al.
utilize data of IFLS 1993, while Levine & Ames utilize 1996 and 1999 Survey Sosial Ekonomi
Nasional (SUSENA) data. Jayachandran (2006) and Noble (2012) investigated health shock experienced by children and evaluated it impact on infant mortality and parental labor supply. Jayachandran utilize susenas data in 1998, while Noble utilize data of IFLS
2000 and 2007.
Other studies investigate the effect of rainfall variation on the agricultural sector in
Indonesia. Cameron & Worswick (1999, and 2001) investigated the impact of weather anomalies on reported crop loss based on the IFLS 1993 data. These authors reported that the poorest households or farmers have to work longer in off-farm market and reducing their total hour in farm activity. In 2001 paper, Cameron and Worswick investigated the educational impact for families those experiencing crop loss. Levine & Yang (2006) explored the effect of individual who experienced bad weather or rainfall during his/her
5

prenatal and postnatal period on his/her adult education and health performance. Modena
(2007) and Berloffa & Modena (2009), both utilize IFLS 1993 data to investigate the coping strategies of Indonesian farmers when they experiencing crop loss. Korkeala,
Newhouse & Duarte (2009) described the effects of rainfall variation and difference landscape on the specific crop planted among the regions in Indonesia. In our concern, only the study of Chetty & Looney (2005) investigated the impact of crisis and lossing job on household consumption smoothing and further elaborated social insurance as coping strategies. Putting aware and aknowledge on these previous studies, this paper will concern mainly for various economic shock happened in Indonesia based on IFLS 1997 and 2000, and investigate the association on household different spending classification.
The organization of this paper as follows. Introduction in the first part is follow with related previous study and literatur review as the second section. The third section is about data and estimation strategy, which is followed by result and discussion. The last section is conclussion.
II. Previous Studies and Related Literature
Economic Shock in Indonesia
It is recognized that community lives in rural areas especially in developing countries vulnerable with many economic shock both idiosyncratic and common shock.
Following the previous studies, that refers idiosyncratic shock as any shock that has effect on individual or household only, while common shock is any shock that might influence some families alltogether ( Townsend , 1994 and Dercon , 2004:10).
The case off ilness, death or loss job experienced by a family member or head of the household might have economic consequences of the specific families; therefore are considered as example of idiosyncratic shock. Other case such as crop loss, natural disaster
6

(earth quake, fire, flooding, etc), and lower output price might have consequences on several household in the same time, therefore are classified as example of common shock.
Both idiosyncratic and common shock are randoomly happening. These economic shocks, if do not well anticipated, is predicted has negatif consequences on household expenditure. Living in rural areas of developing countries like Indonesia, can face with not only credit constraint but also hardly afford formal insurance. Kochar (1995, 1999) in her studies about agricultural household in India reported that typical idiosyncratic economic shock experienced by a family can be responded by the support from other family members and relatives, or among families in the neighborhood. However, the case of common shock such as crop loss, natural disaster, and lower output price are better responded by formal insurance and government intervention, such as designing pricing policies for agricultural output price or agricultural subsidy. Townsend (1994) argued that due to imperfect insurance market in the rual village of India, as the economic shock become more prevalent, he found that the community become more cooperative and solid.
This imply that the community do substitution from formal insurance for informal insurance, in terms of financial support among the relatives.
The economic crisis that hit Indonesia at the end of 1997, has consequence on the manufacturing industry in Indonesia. Many of small and enterprice company shut down and thousand of individu loss job. Block et al.
(2004) investigate the impact of 1997’s crisis, specifically on price fluctuation, it’s consequence on the Indonesian household expenditure. Focusing their study in the area of Central Java, Block et al.
reported that by the same time of the crisis, Central Java also experienced long period of drought. Gilligan et al .
(2000) claimed that the lowest rainfall period in Central Java during their investigation was between February 1997 to January 1998. This situation causes very low
7

output in the subsequent harvesting time. The authors claim that the rice production shortfall generated food shortage, and furthermore drove up the food prices.
On the other hand, deviation of natural cyclus is reported becoming more frequent.
Earth quake, forest fire and fire in the city can influence the sustainability of family business. As the weather getting warms, it can induce higher heat volume in electricity device. Altogether warmer climate, dense population and human error might cause the frequent city fire, such as recently happen in Jakarta. Event the economic cost can be borne by individual affected, but the more frequent this case happened the higher aggregate cost will be.
Rainfall Variation and Crop Loss
Many studies have been done regarding economic shocks and household consumption smoothing in Indonesia, especially those utilizing IFLS or Survey Sosial
Ekonomi Nasional (Susenas) data. Several studies concerns on crop losses due to weather anomalies and it’s various coping behavior, among other are: Cameron and Worswick
(1999 and 2001); Levine and Yang (2006); Modena (2007); Korkeala, Newhouse, and
Duarte (2009); and Berloffa & Modena (2009).
Cameron & Worswick (1999) investigated coping strategies chosen by Indonesian household when they experienced crop loss in 1993 or a year before. The authors estimated sample size of 3.073 household, and about 5% of the sample reported they experienced crop loss. Regarding the mechanism of coping strategies, the conclussion of this study support the result of Paxon (1992) for the case of Thailand, and Maitra (1997) for case of India. Poorer household tend to constantly cope with supplying more labor time, especially off-farm works and cutting down their household spending.; while better-off household can cope with acquiring loan or selling their asset. Studiy of Cameron and
Worswick (2001) investigated household respond on the educational expenditure, when
8

they transitory income shock, in terms of crop loss. The authors reported that educational expenditures fall significantly, especially for household head with primary school.
Regarding the distributional impact, thid study found that reduction in spending for girl’seducation higher than the reduction for boys.
Levine and Yang (2006) utilize aggregate district level data and combine it with
IFLS 1993 data to investigate the effect of positive rainfall shock on rice production. On average, the study of Levine and Yang concluded that 10% higher rainfall in Indonesian district leads for 0.4% higher on rice output. These authors put stressing that rainfall shocks should be interpreted as contemporaneous shock; the opposite of lagged, to locality-level rice output. Modena (2007) and updated study in Berloffa & Modena (2009), exploring IFLS 1993 data to analyzed the impact of crop loss and different respond among household conditional on their wealth level. These studies conclude that relatif better-off household use their cummulative asset to smooth their consumption, while poorer households tend to diversified their labour supply.
Korkeala, Newhouse, and Duarte (2009) compared the estimation of agricultural productivity by utilizing IFLS and SUSENAS data, condition on past rainfall variation.
Refering to IFLS information for previous rainfallshock, the Geographic Information
System (GIS) mapping shows the most productive region for agricultural sector are West,
Central, and East Java, followed by South Sulawesi and South Kalimantan. Regarding the agricultural output, Korkeala et al.
repoted that the most productive agricultural output, not sensitive for previous raifall variation are cassava, maize, and sweet potato. Soybean, rice, and groundnut are classified as sensitive to weather variations, and considering the amount of production and the area harvested, these three output have very low productivity; -0.02,
-0.17 and -0.25 respectively (Irawan, 2002; in Korkeala, Newhouse, and Duarte (2009)). In the study of Falcon et al.
(2004) reported that West, Central and East Java are national
9

based rice production, counted for approximately 65% of national output. This imply, any weather anomalies or destroyed by any diseases in this region might influenced national stock of rice.
Illness or Death of Family Member
Pitt and Rosenzweig (1986) in Signgh, Squire, & Strauss (eds) explore the impact of illness experienced by agricultural household head on the farming profit. These authors utilize Indonesian household survey data and SUSENAS, the effect of household head and housewife illness on farm profit and farmer labor supply. Their study reported that: 1. the illness of household head would reduced male labor supply in farm significantly. When the household wife experienced illness both labor supply and farm profit decreased significantly. Higher price of food price (fish, meat, milk, fruit, vegetable, etc) could reduce food intake. Low quality of food intake will increase the probability of illness, which furthermore lowering farm profit.
Jayachandran (2006) exploring the impact of smoke from forest fire in Sumatra and
Kalimantan Island on air quality and the infant mortality rate based on Indonesian 2000 population sensus, SUSENAS and PODES data. Jayachandran study found that mother before and during her pregnacy life in smoking environment (anyfire, forest fire), dense population and cook using wood has higher probability having low fertility rate, controlling for number of doctor per 1000 population, the existence of materlity clinic, and mother educational level.
Income Fluctuation and Consumption Profile
Both idiosyncratic and common shock, when is not anticipated or well insured by household, this might influence the sustainability of the household consumption profile.
10

Theoretically, consumption spending will be co-movement with household income, based on linier consumption function. When income is high, consumption spending will high as well, and this is true for the opposite.
Theory of life cycle income hypothesis proposed by Modigliani argue that individual and household income fluctuate as high and low during his life. When the income is high enough, exceed the average income level, then part of this income can be saved. On the other hand, when the income level is low, individu or household can also withdraw his saving for smoothing his consumption smoothing. One of the direct application from the life cycle income analysis in Modigliani is the income reduction that is applied when individual starting his pension period Mankiw (2003). During active period of working, when wage income relatively higher then annual consumption spending, individual and household save and do asset or wealth accumulation. When the pension period coming, wage income fall below the average income, then individual and household start using their saving for smoothing their consumption.
Permanent income analysis on the other hand, propose that the patterns of consumption expenditure should be not only depend on the current income, but also should consider past income and expected future income. Both life cycle income and permanent income analysis are complement each other. While life cycle income hypothesis predicts that individual income has regular pattern, permanent income hypothesis on the other hand predicts that individual income is not regular, it fluctuate regularly, or follows randoom walk pattern.
According to Mankiw (2003:452), permanent income analysis of Friedman’s theory assume that individual current income (Y) as the summation of permanent income
(Y
P
) and transitory income (Y
T
); this can be written as Y = Y
P
+ Y
T
. Permanent income is defined as the expected income of individual, from current to the future income, while
11

transitory income is part of individual unexpected/unpredicted income in the future. A simple example from Mankiw : individual A who has tertiary educational level has higher probability getting good job and good salary; while those with low educational level might only able for clerical job, will be paid with subsisten income level. In case of farmers in
Indonesia, their annual income from harvesting, mostly far below subsisten level, because the majority of farmers in Indonesia is landless; they work for the land owner as paid labor.
It can be expected that individual with well education will have annual promotion conditional on his/her performance, while those in clerical job like farmers, higher income is probably happen when there is good weather and harvesting time is not similar for the whole regions. This expected higher income, however, can not continuing happen for the next period. The price of agricultural output will back to normal, because government usually control it by ceiling and floor price regulation. Friedman suggestes that the transitory income should not expected as determinant of consumption decision, because it is not permanent. If the transitory income is positive, it should be saved; when the transitory income is negative, individual normally take his/her saving to smooth their consumption profile.
Consumption Smoothing, Life Cycle, and Permanent Income Hypothesis
The validity of the life cycle income hypothesis has been tested for several cases. Deaton
(1992c) reported that he found this model overstate the situation in terms of the degree of comovement between variation of consumption and income. Deaton suggested that this model should be tested for the case of different industrial organizational style or pension policy and between developed and less develop countries.
In the case of less developed countries, where typically the local norm still exist, eldery or pensioners people do not life separately from their son or daughter, and supporting their life from pension. The majority of elderly still living together and still
12

enjoy flat consumption patterns, because they get support from the son or daughter. This is based on his investiagtion for poor household farmer in Cote d’Ivory, and in Thailand. The study of Deaton and Paxson (1992) reported that almost 99% of eldery still live together with their son and daughter’s family, and the same case approximately 80% in Thailand
( Deaton & Paxson , 1992). Those eldery still enjoy smooth consumption patterns because they have full support from their children.
Carroll and Summers (1991) investigated consumption pattern of household in
America. These authors found high correlation between consumption spending pattern with income and educational level. The association between consumption and income, educational level were robust, event the authors consider croos level of education and job’s type. Similar finding was reported by Attanasio and Davis (1993). These authors investigate consumption pattern of American for the periods of five years. They reported high correlation between consumption and income level, event the correlation was decreasing from year to year observation. Paxson (1996) compared the consumption and income pattern among developing countries (Cote d’Ivory and Thailand) and developed countries (US, UK and China;Taiwan). In general his conclussion is eventhough individual in those five countries have different income, educational, and type of jobs; the consumption paatern are similar, tends to high during the income level high or individual still in productive ages, and getting lower as individual move to preparation and pension period.
Deaton (1997) suggested adjustment or smoothing patterns could be follow as: low frequency smoothing consistent with the prediction of life cycle income hypotheis, and high frequency smoothing consistent with permanent income hypothesis. Deaton infered that short term consumption smoothing is adopted when individual or household predict they will experience negative income shock in the future. The determinant of consumption
13

smoothing among the poor household, especially those living in rural areas as element for policy making to determinet what policy to be delivered, instead of income transfer. If low frequency consumption smoothin is associated with the life cycle income hypothesis, could we directly make accosiation that high frequency consumption smoothing deal with permanent income hypothesis? ( Deaton , 1997:351)
Following the estimation strategy of Cameron and Worswick (2001), the impact of permanent and transitory income on consumption, in the equation below:
C i
 
0
 
1
Y i
P  
2
Y i
T   i
…(1) where C i
is the consumption spending of individual or household i, Y i
P
is Vector of variables that are predicted permanetly affect the income level, Y i
T is a vector of variables that are correlated with transitory income.

0
,

1
and

2
are parameters, and
 i is a mean zero error term. Cameron and Christopher (2001) the following permanent and transitory component of income as:
Y i
P 
^

0
 
^
1
X i
P
...(1a)
Y i
T  
^
2
X i
T

^ i
 
^
0
 
^
1
X i
P  
^
2
X i
T
...(1b)
…(1c) where
^

0
,

^
1
and
^

2
are the estimated of the parameters from equation (1). To assess what is the impact of transitory income shock on consumtion expenditure, Cameron and
Christopher estimate the following equation:
EXP i
 
0
 
1
Y
^ i
P
 
2
Y
^
T i
 
3

^ i
 
4
 i
 u i
…(2)
14

where X is a vector that reflects household characteristics such as age, sex, educational level, family size, etc.

0
,

1
,

2
,

3 and

4
are parameters, and u is a mean zero error i terms.
According to Cameron and Christopher (2001), perfect consumption smoothing that is implied by the permanent income hypothesis, requires that the marginal propensity to consume (MPC) in terms of permanent income, should be near to 1 (one), and the MPC of transitory income should close to zero. This is because all the transitory income is saced or dissaved. From equation (2), when the parameter

2

0 as indicator that families able to smooth their consumption profile. If families are unable to smooth consumption, then the coefficient of

2
should significantly greater than zero (

2

0 ). From equation (2), the component of residual income
 
^
 is expected to lie between zero and 1 (one), because it contains unobserved components of permanent and transitory income.
3. Data and Empirical Strategy
We utilize IFLS data, especially IFLS 1997 and 2000. We also present some information from IFLS 1993, and IFLS 2007. We do not include IFLS 1993, because all the previous studies already explore IFLS 1993. Regarding IFL 2007, most of the GE section data (economic shoack) are removed, so we can not access the data. Therefore, we only include data 1997 and 2000 in estimation.
In this studi we will do replication with little modification. Some studies did manipulation from the equation: Y = Y
P
+ Y
T
into Y
P
= Y - Y
T
, and estimate regression
 
Y
T with instrumental variable (IV) in the equation:
Assuming part of the transitory income are absorbed in the residual component, this equation can be estimated in cross sectional data, as long as no high correlation between
15

IV and component of permanent income (Y
P
). Some studies use the value of assets and educational level of household head as IV ( Musgrave, Bhalla and Wolpin in Deaton,
1997:352 ), and Deaton (1997) used rainfall variation as the IV. Deaton argue that rainfall variation as the most robust IV among the other IV.
In this study we will estimate the following equation. We start with the basic equation for consumption function :
C it
 
0
 
1
HHld charact
 
2
Y it
  it
...(3) where C it is household consumption expenditure, HHld charact
is head of household characteristics, inculding sex, age, and educational level. Y it
is the household income.
Index i refer to household i and index t refer to year 1997 and 2000. Next, we add the type of economic shocks experinced by household during the last 5 (five) years as indicator of transitory income shocks. The equation the can be written as :
C it
 
0
 
1
HHLd charact
 
2
Y it
 
3
Idiosyncra ticshocks
  it
...(3a) and
C it
 
0
 
1
HHLd charact
 
2
Y it
 
3
Commonshoc ks
  it
..(3b)
Finally, we add the coping mechanism variables, and the equation is
C it
 
0
 
1
HHLd charact
 
2
Y it
 
3
Economicsh ocks
 
4
CopingMech
  it
..(3c)
In the reprting result, we go directly to equation (3c), as the coeffeicent estimation of (2),
(3a) and (3b) are quite similar and robust in sign and significancy.
Result and Discussion
Before proceeding into the estimation result, first it will be presented the data description used in this paper. We include the sample size of all families that reported they experieced at least one type of economic shock in the past 5 (five) years. Table 1 in the appendix present the description of IFLS 1997 and 2000 data being analyzed in this paper.
There are 5291 household sample in 1997 and 7684 household who reported they
16

experienced at least one type of economic shock in the last 5 (five) years. These households are our sample in this paper. The average family size for household that reported experiencing economic shock are between 4 – 5 person per household in
Indonesia in 1997 and 2000. Family size in 2000 data relative smaller then the one in 1997, eventhough the standard deviation among families getting higher.
Adult household member are larger for the sampling families in 2000 compare to the one in 1997, however, the opposite is true for younger kid; those below 15 years old.
Regarding sex of household head, majority of household are male headed, with the average age between 40 – 50 years old. Educational level for household head slightly longer for familes in 2000 compare to those in 1997, which is in the context of Indonesia means, they were at least already in Junior high school level, eventhough they did not graduated for this level. The value of consumption level and total household income increase slightly from 1997 data to 2000 data.
Regarding the economic shock experienced by the household, both 1997 and 2000 still dominated by economic shock in terms of the death of household head, ilness and experienced crop loss. More families reported that one of their family member became jobless during the last five years, while experiencing natural disaster (fire, flooding, and earth quake), and lower output price getting smaller from sample in 1997 to sample in
2000. Regarding how to cope with income shock, more families are reported both getting cash transfer from other relatives or from parent, and having at least one type of insurance.
Figure 1a and 1b describe in more detail number of household respondents and the percentage of sample who experienced economic, both idiosyncratic and common shock. Despite slight differences in total sample size, figure 1a and 1b indicate that in terms of idiosyncratic shock, the number of household member experienced illness and losing job slightly increase from 1993, 1997, and 2000. In terms of common shock, while
17

case of experienced disaster decrease from period to period, the case of crop loss and falling output price always increase. This might as indicator of weather anomalies or environment becoming less fertile for agricultural production.
Quoting similar information and data of Kim and Prskawetz (2006), regarding the mitigation strategy taken by household in the IFLS survey 1993 and compared it with data in 2000, the information are presented in table 2.
The information in table 2 indicate that receive income transfer, withdraw saving, selling assets, and seek for loan were the kinds of mitigation steps taken by the Indonesian household who experienced family member or household head death in 1993 or before, and the case of illness in 2000 or the years before.
In general reducing household expenditure and increasing labor supply become the common consequences when Indonesian household exeperienced common shock, including disaster, falling the output price and crop loss. The mitigation step “ other ” include: praying, do nothing, closed the old business, starting new business, delay for buying goods, or eat less food.
Eventhough the sampling size is not proportional for each provinces, for the purpose of locating where the case of crop loss mostly pointed by the respondents, and to find out wheter the respondents has any insurance to mitigate the economic shock, table 3, shows the figure list of provinces and the case of crop loss in 2007. This very crowd figure presents the message that Indonesian are well insure again economic shock, eventhough it is not clear whether the insurance they bought exclusively for protecting them from crop loss.
Do Indonesian Follow Permanent or Life Cycle Income Hypothesis?
Estimation for the association of regular (permanent) income and ir-regular
(following life cycle) income hypothesis are tabulated in table 4a, 4b, 5a and 5b . Based
18

on the estimation in the table 4a and 4b, Indonesian are rely their consumption and other spending more on regular or permanent income. They do rely on ir-regular income, especially from side job as indicated by sample data of 1997, and rely on cash transfer in sample of 2000. In general, however, Indonesian household follows the prediction of permanent income hypothesis, as their consumption patterns associate more on regular income.
In all the estimation, (table 5a and 5b), we define regular income as any income from the respondent’s main job, plus any income from asset they accumulated. On the other hand, ir-regular income are income from doing side job or receive any transfer. Very simple estimation shows that ir-regular income transfer for sampling in 1997 are associated with educational spending, and partially as part of total expenditure in 2000.
In terms of household composition, our estimation shows household with more kids will spend more, especially spending for food and education, while household with more adult and eldery member will allocate more money for food, education and health.
To draw association between head of household characteristics and type of household spending, our data estimation that male household headed’s, tend to spend less in health and education, event in the data estimation these association is not robust and significant for the the specification. Furthermore, as the household head getting older, spending for household also increase significantly in 2000. The longer the educational attaintment of household head, seems to be associated with higher spending for all type of expenditure.
Considering the idiosyncratic shock experienced by household, the case of death in a family member or the household head, we find no association were significant in 1997 sampling, however, the case of death assosiated with increase in the spending for health in
2000. Illness experienced in the previous years by one of family member was found put consequent on higher spending for health and education. On the other hand, lossing job
19

associated with higher value for total spending and spending for food. Any common shock for the community, such as disaster, was estimated resulting total household expenditure increase significantly. On the other hand, falling of output produced by the household has different consequent on spending for food, health and education. Experincing crop loss in the previous year clearly put consequent on lower expenditure for food, health, education and total expenditure.
Regarding the coping strategies was taken by the shock affected household, data in
1997 shows that in general household not only rely on cash transfer from family and parents, these household also buy at least one type of insuranse to protect them from any economic shock. In general, Indonesian household has access on formal insurance and informal insurance, in terms of income support from their relatives and parent. While in
1997, shock affected household reported they get support from relative for buying food and health spending, this case in no longer in 2000. Whether this information implies more independent families or simply limited income to share, it is not clear, and we do not investigate further for this matter . The estimation indicate that buying insurance for the purpose of protecting education are taken by the majority of household both in 1997 and
2000. These household still receipt income support from their parents, and this support mainly for buying food, spending for health and education as well.
4. Conclusssion
To draw conclussion from our simple estimation model, we conclude that most of household in Indonesia live in vulnerable of idiosyncratic and common shock. Our data estimation find that Indonesian household associated their expenditure mainly from their regular income. Regarding the household size, household with more adult or eldery member tend to spend more on heath and educational spending, compare to those families
20

who have mode kids, who need to spend more for food expenditure. In terms of household head characteristics, the estimated data shows that if household head has better educational level, the household will afford to spend more.
Idiosyncratic shock in the forms household member experienced illness and being unemployeed, the affected household will pay for higher expenditure on food and health.
Experiencing crop loss on the other hand, will result household cutting expenditure for almost all items, icluding food, health and education. In 1997 sampling, more household smoothing their spending from buying insurance and getting support from parents, in 2000 however, most families have to relly on transfer from their parent.
21

References.
Attanasio, O. & Davis, S. (1993). Relative Wage Movements and the Distribution of
Consumption. Working Paper Department of Economic, Stanford University .
Berloffa, G. and Modena, F. (2009). Income Shocks, Coping Strategies, and Consumption
Smoothing. An Application of Indonesian Data. Working Paper in Economics, University of Trento, Italia.
BPS (2011) Data Sosial Ekonomi Indonesia, Booklet Oktober 2011
Block, S.A. et al.
(2004). Macro Shock and Micro(scopic) Outcomes: Child Nutrition
During Indonesia’s Crisis. Economics and Human Biology Vol 2.
750 – 80
Bauer, P. & Paish, F. (1952). The reduction of Fluctuation in the Incomes of Primary
Producers. Economic Journal Vol. 62, page
Cameron, L. and Worwick, C. (1999). The Labor Market as a Smoothing Device: labor
Supply Responses to Crop Loss in Indonesia. The University of Melbourne Working Paper in Economics.
________________________ (2001) Education Expenditure Responses to Crop Loss in
Indonesia: A Gender Bias. The University of Chicago. Paper available online in JSTOR code 175.111.91.19
Carroll, C.D. & Summers, L.J. (1991). Consumption Growth Parrarels Income Growth: some new evidence. Chicago University Press
Chianu, J.N., & Tsuiji, H. (2004). Determinants of Farmers’ Decision to Adopt or Not to
Adopt Inorganic Fertilizer in the Savannas of Northern Nigeria. Nutrient Cycling in
Agroecosystems No. 70: 293 – 301
Daryanto, A. (1999). Indonesia’s Crisis and the Agricultural Sector: the Relevance of
Agricultural Demand-Led Industrialisation. UNEAC Asia Paper No. 2
David, S,. (1998). Intra-Household Processes and the Adoption of Hedgerow Intercroping.
Journal of Agriculture and Human Values Vol. 15: 31 - 42
Deaton, Angus. (1997). The Analysis of Household Surveys, A Microeconometric
Approach to Development Policy. The World Bank Publisher
Deaton, A. & Paxson, C.P. (1992). Patterns of Aging in Thailand an dCote d’Ivory .
Chicago University Press
Dercon, S. (editor). (2004). Insurance Against Poverty. Oxford
Duflo, E,. & Udry, C,. (2004). Intrahousehold Resource Allocation in Cote D’ Ivoire:
Social Norms, Separate Accounts and Consumption Choices. NBER WPr No. 10497
22

Dwayne, B. (1992). Household Composition, Labor Markets, and Labor Demand: Testing for Separation in Agricultural Household Models. Econometrica Vol. 60. No. 2: 287 – 322
Falcon, W.P. – Naylor, R. L. – Smith, W.L. – Burke, M.B. – McCullough, E.B. (2004): Using
Climate Models to Improve Indonesian Food Security . Bulletin of Indonesian Economic Studies,
Vol. 40, No. 3, pp. 355-377.
Jayachandran, S. (2006). Air Quality and early Life Mortality. Evidence from Indonesia’s
Wild fires. Working paper in Economics, University of California, Loss Angelos
Kochar, A. (1995). Explaining Household Vulnerability to Idiosyncratic Income Shocks. The
American Economic Review Vol. 85 No. 2
________(1999). Smoothing Consumption By Smoothing Income: Hours-Of-Work
Responses to Idiosyncratic Agricultural Shocks in Rural India. The Review of Economics and Statistics Vol. 81, No. 1: 50 -61
Korkeala, Newhouse and Duarte (2009). Distributional Impact Analysis of Past climate
Change Variability in Rural Indonesia. The World bank Report for East Asian and Pacific
Region.
Lanjouw, J.O., & Lanjouw, P (1995). Rural Non Farm Employment, A Survey. World
Development Report , World Bank
Lassa, J. ( no date ). Politik Ketahanan Pangan Indonesia 1950-2005. Diakses online tgl. 10
April 2011
Levine, I.D. and Ames, M. (2006). Gender Bias and the Indonesian Financial Crisis. Were
Girls Hit Hardest? Working paper in Economics, University of California, Berkley, LA
Machine, S. and Yang, D. (2009). Under the Weather: Health, Schooling, and Economic
Consequences of Early-Life Rainfall. American Economic Review.
Vol. 99 (3)
Modena, F. (2007). Transitory Shocks, Coping Strategies, and Consumption Smoothing.
An Application of Indonesia Data. Preliminarry Draft of WP in University of TRENTO
Murdoch, J,. (1999). Between the State and the Market: Can Informal Insurance Patch the
Safety Net? The World Bank Observer Vol 14, No. 2
Nkamleu, G.B.,.(2008). Modelling Farmers’ Decisions on Integrated Soil Nutrient
Management in Sub Saharan Afrika: A Multinomial Logit Analysis in Cameroon.
International Institute of Tropical Agriculture
Noble, N. (2007). The Impact of Negative Health Shocks on Consumption. Working paper in Economics, University of Notre.
Paxson, Christina H., (1992). Using Weather Variability to Estimate the Response of
Saving to Transitory Income in Thailand. American Economic Review. Vol 82
23

Pitt and Rosenzweig, (1986). Agricultural Prices, Food Consumption, and the Health and
Productivity of Indonesian Farmers, in Singh, I., L. Squire, J. Strauss (eds) (1986).
Smyth, Ines (1988) "Differentiation Among Petty Commodity Producers: The Effects of a
Development Project on Handicrafts Production in a Sundanese Village (West Java -
Indonesia)," Institute of Social Studies Working Paper No. 40. The Hague.
Townsend, Robert, M., (1994). Risk and Insurance in Village India. Econometrica Vol. 62
White, B. (1991) 'Economic Diversification and Agrarian Change in Rural Java, 1900-
1990," in Alexander, P. et. at. (eds.) In the Shadow of Agriculture: Non-Farm Activities in the Javanese Economy, Past and Present. Amsterdam: Royal Tropical Institute
Yang, D. & Choi, H. (2007). Are Remittances Insurances? Evidence from Rainfall Shocks in the Phillipines. The World Bank Economic Review Vol. 21, No. 2: 219 – 248
Yang, D. (2006). International Migration, Remittances, and Household Investment:
Evidence from Phillipines Migrants’ Exchange Rate Schock.
NBER Working Paper No.
12325
24

APPENDIX
Tabble1: Data Description
No
1 Family Size
Data Description
Adult (15 years and older)
Kids (below 15 years old)
IFLS
1997 (N=5291) 2000 (N=7684)
4,54
(0,33)
1,53
(1,98)
1,52
4,15
(1,93)
2,86
(1,37)
1,28
(1,33)
0,87
(0,33)
(1,22)
0,87
(0,34)
2 Sex of head household;
(Male =1)
3
Head of household’ age
4
5
6
7
8
9
10
Length of education (years)
Log consumption expenditure
Log household income
Experienced Economic Shock, YES =1
1.
Death
2.
Illness
3.
Crop Loss
4.
Natural Disaster
5.
Losing Job
6.
Lower Output Price
Notation * or number in the bracket are in percentage
Transfer from relatives (Yes = 1)
Transfer from parent (Yes =1)
Insurance (Has minimum 1 type =1)
1259
(9,70)*
773
(14,60)
784
(18,82)
713
(13,10)
46,56
(22,36)
5,34
(3,90)
5,21
(0,87)
12,95
(1,28)
202
(3.12)
502
(9,49)
82.205,84
(407.409,5)
89.073,35
(524.764,5)
0.161
(0,74)
Note:
Figures in parenthesis are the Standaed Deviation, unles for point no (7), the number in parenthesis are the percentage number
Source Data: IFLS 1997 and 2000
336
(4,37)
381
(4,96)
367.890,9
(1.700.000)
221.010,0
(2.661.275)
0,224
(0,417)
43,90
(20,69)
6,71
(4,63)
5,97
(0,75)
13,40
(1,29)
468
(6,32)
793
(10,32)
914
(11,89)
124
(1,61)
25

Table 2 : Mitigation Steps Taken by Household during the Economic Hardship
Death
Coping Strategies
Ilness Unemployment Disaster Falling Output Price
Labor Supply
Loan
Selling Assets
Saving
Receive Transfer
Reduce Expenditure
Others
1993
0.11
0.26
0.26
0.19
0.34
0.06
2000 1993 2000
0.18
0.10
0.09
0.19
0.31
0.28
0.15
0.29
0.18
0.15
0.22
0.18
0.30
0.26
0.32
0.12
0.06
0.12
0.26
0.19
1993
0.45
0.17
0.16
0.09
0.19
0.18
2000 1993 2000
0.39
0.23
0.31
0.14
0.29
0.18
0.12
0.19
0.15
0.04
0.11
0.05
0.07
0.21
0.13
0.14
0.16
0.15
0.37
0.33
1993
0.35
0.20
0.17
0.06
0.06
0.33
2000
0.35
0.18
0.05
0.07
0.07
0.26
0.29
Source: data are from Kim & Prskawetz (2006), modified
Table 3 : Crop Loss and Representative number of Respondent Who Has Insurance in
Indonesia, 2007
Region Crop Loss
Has at least 1 type
Insurance
Total sample
% Crop
Loss
%
Insurance
Crop Loss
1993 2000
0.44
0.40
0.20
0.17
0.20
0.09
0.05
0.02
0.07
0.05
0.22
0.20
0.29
Sumatera
North Sumatera
West Sumatera
South Sumatera
Lampung
Riau
Java
West Java
Central Java
East Java
Yogyakarta
Jakarta
Banten
Kalimantan
West Kalimantan
Central Kalimantan
Kalimantan Timur
South Kalimantan
Sulawesi
North Sulawesi
West Sulawesi
South Sulawesi
Bali
NTT
Other Provinces
INDONESIA
Source : IFLS 2007
32
29
31
20
0
41
96
56
37
1
28
0
0
0
14
0
2
43
26
41
1
498
213
230
198
91
28
737 1905
501 1400
487 1641
290
309
165
635
769
438
707
535
459
454
83
1
6
10
180
1
10
22
543
0
7
1
24
174
150
292
32
467
553
746
78
4101 11471
4.53
5.42
6.75
4.41
0
2.15
6.86
3.41
5.83
0.13
6.39
0
0
0
2.58
0
8.33
9.21
4.7
5.5
1.28
4.34
30.13
42.99
43.14
20.04
33.73
38.69
35.79
29.68
45.67
40.18
37.67
100
60
45.45
33.15
0
29.17
37.26
27.12
39.14
41.03
35.75
26

Table 4a : The Association of Income and Expenditure in Indonesia, IFLS 1997
List of Independence Dependent Variable: Expenditure for____(1997)
Variable Total Expdt Food Education Health
Constanta 432.189***
(19.20)
221.224***
(61.31)
22.252***
(14.90)
5.768***
(10.52)
Regular Income
Wage from primary job 0.07***
(13.39)
0.02***
(23.59)
0.01***
(21.05)
0.001***
Assets 0.03*
(1.79)
0.01**
(2.36)
0.02***
(19.97)
(12.83)
0.003***
(7.57)
Irregular Income
Side Job or Business
Cash Transfer
0.08**
(2.07)
0.105
(0.14)
Adj. R 2 = 0.034
Prob. F Stat. =
48.06
0.03***
(5.76)
0.00
(0.12)
Adj. R 2 = 0.104
Prob. F Stat. =
154.49
0.01**
(5.75)
0.02*
(1.68)
Adj. R 2 = 0.144
Prob. F Stat. =
222.69
0.000
(0.83)
-0.001
(-0.52)
Adj. R 2 = 0.04
Prob. F Stat. =
55.91
Note: number in parenthesis are T-Statistics. Notation : *** = significant at 1%, ** = significant at 5%, and *=significant at 10%
Source : Author’s calculation
Table 4b: The Association of Income and Expenditure in Indonesia, IFLS 2000
List of Independence Dependent Variable: Expenditure for___(2000)
Variable Total Expdt Food Education Health
Constanta 736.044***
(50.47)
450.582***
(79.63)
40.887***
(22.55)
13.274***
(14.41)
Regular Income
Wage from primary job 0.063***
(31.25)
0.021***
(26.80)
0.006***
(22.73)
0.001***
(10.55)
Assets 0.0000
(1.29)
0.0000*
(1.81)
0.0000*
(1.71)
0.0000
(0.24)
Irregular Income
Side Job or Business 0.0006
(0.50)
0.0007
(1.23)
-0.0001
(-0.95)
0.0000
(0.58)
-0.0000
(-0.50)
Cash Transfer 0.003***
(3.67)
0.0009***
(3.22)
0.0000
(1.39)
Adj. R 2 = 0.115
Prob. F Stat. =
249.71
Adj. R 2 = 0.087
Prob. F Stat. =
184.96
Adj. R 2 = 0.063
Prob. F Stat. =
130.02.69
Adj. R 2 =0.014
Prob. F Stat.
=28.50
Note: number in parenthesis are T-Statistics. Notation : *** = significant at 1%, ** = significant at 5%, and *=significant at 10%
Source : Author’s calculation
27

Table 5a: Estimation for Household Expenditure and Economic Shock, IFLS 1997
Dependent variable: Expenditure for__in 1997
List of Independent Variables
Total Expdt Food Education Health
Constanta
Regular Income
Wage from main job
Assets
Ir-regular income
Side jobs or business
Any Transfer
Household Size
Number of Kids
Number of Adult
Head of Household Characteristics
Sex (male=1)
Age (years)
Length of Education (years)
Economic Shock
Idiosyncratic Shocks(YES =1):
Death
Illness
Unemployment
Common Shock (YES =1):
Disaster
Price of Output Fall
Crop Loss
Coping Strategy (YES = 1)
Buy at least 1 type Insurance
Get Cash Transfer from Relatives
Get Cash Transfer from parents
(1.04)
-26,235
(0.48)
-31,959
(-0.68)
5,320
(0.11)
0.08*
(1.87)
0.06
(1.43)
Adj. R 2 = 0.05
Prob. F Stat. =
14.59
(6.53)
-17,609
(-0.36)
842
(0.94)
28,918***
(6.22)
18,519
(0.42)
89,793**
(2.00)
118,394
(1.45)
102,830
-6,989
(-0.10)
0.04***
(5.62)
0.04***
(2.66)
0.01
(0.18)
0.11
(1.01)
12,202
(1.02)
76,960***
(2.99)
1,068
(0.10)
-13,819
(-1.47)
19,746**
(2.09)
0.03***
(3.30)
0.01**
(2.20)
Adj. R 2 = 0.16
Prob. F Stat. =
53.45
(12.87)
9,529
(0.970
21.9
(0.12)
9,539***
(10.30)
14,180
(1.59)
1,501
(0.17)
55,242***
(3.41)
59,129***
38,278***
(2.71)
0.01***
(9.52)
0.007***
(2.59)
0.02**
(2.31)
0.002
(0.10)
18,100***
(7.60)
30,195***
(2.63)
3,643
(0.91)
-6,127**
-1.79)
12,622***
(3.66)
0.01
(0.37)
0.009***
(3.44)
Adj. R 2 = 0.23
Prob. F Stat. =
80.35
(11.73)
-4,537
(-1.27)
93.51
(1.44)
3,880***
(11.49)
556
(0.17)
3,905
(1.20)
-815
(-0.14)
18,936***
-2,191
(-1.12)
0.001***
(8.02)
0.03***
(28.11)
-0.002
(-0.50)
0.01***
(2.03)
-214
(-0.65)
10,036***
(3.58)
-412
(0.30)
1.49
(0.06)
326***
(2.53)
270
(0.22)
19,788***
(15.96)
1,013
(0.45)
4,999*
-29,024***
(-5.64)
0.001***
(4.05)
0.004***
(10.15)
-0.001
(-1.05)
-0.001
(-0.55)
1.408
(1.62)
1,166***
(1.82)
-1,629
(1.07)
606
(0.47)
34
(0.03)
0.004***
(3.09)
0.002*
(1.80)
Adj. R 2 =0.11
Prob. F Stat.
=33.68
Note: number in parenthesis are T-Statistics. Notation : *** = significant at 1%, ** = significant at 5%, and *=significant at 10%
Source : Author’s calculation
28

Table 5b: Estimation for Household Expenditure and Economic Shock, IFLS 2000
Dependent variable: Expenditure for___ in 2000
List of Independent Variables
Total Expdt Food Education Health
Constanta
Regular Income
Wage from main job
Assets
Ir-regular income
Side jobs or business
Any Transfer
Household Size
Number of Kids
Number of Adult
Head of Household Characteristics
Sex (male=1)
Age (years)
Length of Education (years)
Economic Shock
Idiosyncratic Shocks(YES =1):
Death
Illness
Unemployment
Common Shock (YES =1):
Disaster
Price of Output Fall
Crop Loss
Coping Strategy (YES = 1)
Buy at least 1 type Insurance
Get Cash Transfer from Relatives
(14.26)
-37,471
(-0.95)
1,649**
(2.57)
56,881***
(17.47)
4,207
(0.08)
11,443
(0.28)
13,021
(0.21)
411,901***
-58,504
(-1.10)
0.04***
(17.64)
0.000
(1.38)
0.0000
(0.19)
0.002**
(2.30)
33,471***
(3.22)
138,455
(4.17)
130,699***
(2.25)
-80,746***
(-2.05)
-30,791
(-0.85)
-0.0000
(-0.11)
0.034***
(19.29)
20,941
(1.40)
542**
(2.21)
20,078***
(16.16)
22,931
(1.15)
4,848
((0.31)
6,284
(0.27)
233,555***
56,757***
(2.79)
0.012***
(13.34)
0.0000**
(2.05)
0.0000
(1.02)
0.000
(1.48)
27,503***
(6.94)
71,455***
(6.19)
26,780
(1.21)
-22,311
(-1.48)
-3,359
(-0.24)
-0.0000
(-0.42)
0.002***
(15.45)
-10,269**
(-2.92)
492***
(6.12)
5,321***
(13.06)
7,155
(1.10)
9,196*
(1.78)
7,289
(0.95)
22,976*
-60,922***
(-9.13)
0.003***
(10.99)
0.0000*
(1.82)
-0.0000
(-1.53)
-0.0000
(-0.05)
2.708**
(2.08)
18,766***
(1.86)
24,067
(3.31)
-9,026*
(-1.183)
9,078**
(2.00)
-0.000
(-0.39)
0.0000*
(3.62)
212
(0.08)
132***
(3.19)
1,750***
(8.34)
12,035***
(3.58)
34,035***
(12.80)
-3.613
(-0.91)
9,973
-12,600***
(-3.54)
0.0007***
(5.16)
0.0000
(0.17)
-0.0000
(-0.74)
0.0000
(0.64)
-618
(-0.92)
2,265***
(1.56)
-857
(-0.23)
-898
(-0.35)
-1,010
(-0.78)
-0.0000
(-0.63)
0.001*** Get Cash Transfer from parents
(7.20)
Adj. R 2 = 0.18
(9.25)
Adj. R 2 = 0.83
(1.70)
Adj. R 2 = 0.05
(3.51)
Adj. R 2 =0.12
Prob. F Stat. = 95 Prob. F Stat. = 96 Prob. F Stat. = 23 Prob. F Stat. =59
Note: number in parenthesis are T-Statistics. Notation : *** = significant at 1%, ** = significant at 5%, and *=significant at 10%
Source : Author’s Calculation
29

0.18
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
0.16
0.07
0.08
0.16
0.12
0.11
0.04
0.03
0.04
0.03
0.02
0.14
0.13
0.11
0.1
0.05
0.05
D ea th
Ill ne ss
Idiosyncratic Shock
U ne m pl oy m en t
D is as te r
C ro p
Lo ss
Common Shock
Fa lli ng
Q
P ric e
1993 1997 2000
Figure 1a: Percentage of Household Experience Economic Shock, data in 1993, 1997, and
2000
Source : Data 1993 from are adopted fromKim & Prskawetz (2006)
0.5
0.4
0.3
0.2
0.1
0
1993
Death
2000 1993
Ilness
2000 1993 2000 1993 2000
Unemployment Disaster
Coping Strategies
1993 2000
Falling Output Price
1993 2000
Crop Loss
Labor Supply Loan Selling Assets Saving Receive Transfer Reduce Expenditure Others
Figure 1b : Mitigation steps taken by affected Household for various Economic Shock, data in 1993, 2000
Source : Data 1993 from are adopted fromKim & Prskawetz (2006)
30