Shocks, household choices, and children’s

education

Francesca ModenaPh.D. in Economics and Management - CIFREM

OUTLINE Preliminary analysis of the data Model the household choices to cope with

shocks Shocks and implications for household

vulnerability Shocks and implications for children’s

education

PRELIMINARY ANALYSIS OF THE DATA

INTRODUCTION

Microeconomic shocks may push households into poverty

or exacerbate their existing poverty status. Income shocks

reduce household wealth not only directly, but also

indirectly as a consequence of the measures used by the

household to overcome or insure against them. Hence it is

important to understand how households cope with actual

shocks and the possibility of future shocks, and to evaluate

which responses are costlier for the household.

Some issues to be considered

1) Idiosyncratic versus common shocks

- Idiosyncratic shocks are household specific, for example the death or sickness of a householder.

- Aggregate shocks are common across any given area: this may be the case of crop loss or price falls.

Different types of shocks

2) Demographic versus non-demographic shocks

Demographic shocks are for example death and sicknessof a householder

Different risk coping strategies:

1) Risk-sharing strategiesformal institutions (insurance), and informal mechanisms such as transfers between friends and neighbours, and in general family/friend assistance

2) Intertemporal consumption smoothinghouseholds smooth consumption through savings, borrowing, accumulating and selling assets

DATA Indonesia Family Life Survey Data (IFLS), 1993 Data on household economic shocks

Type of shocksDeath of a householder

Sickness of a householder

Crop loss

Price falls

Business loss

Unemployment

Type of measuresExtra job

Loan

Asset sales

Family assistance

Use savings

Cut down on household expenses

DESCRIPTIVE ANALYSIS OF DATASample: 6280 households, 3454 rural (after dropping income outliers)

Nr shocks Nr households Perc

0 4319 68.77

At least one

shock

1

1961

1509

31.22

24.03

2 368 5.86

3 72 1.15

4 7 0.11

5 5 0.08

6280 100

Type of shock No. of households % tot

No. of rural households % tot rural

death 430 7 271 8

sickness 744 12 356 10

crop loss 663 11 580 17

price falls 323 5 249 7

business loss 186 3 64 2

unemployment 168 3 59 2

2514 40 1579 46

The most frequent shocks are sickness of a householder and crop loss

Business loss and unemployment affect only a few households

COSTS OF DIFFERENT SHOCKS (Rural Sample)

Type of shock Cost (thousand rupiah)Cost (% HH

income)

Cost %(tot village cost/tot village

income)

  Obs mean median median median

death 247 471.37 230 41.82 1.32

sickness 336 547.18 200 25.86 2.07

crop loss 507 311.60 135 31.75 2.91

price falls 208 248.04 100 20.40 0.13

• Demographic shocks are costlier than non demographic shocks

• Looking at the percentage on household income, crop loss becomes

costlier than sickness

• Shocks have a great impact on household income

• At the aggregate level, crop loss has the largest impact, even if the

aggregate effects of all these shocks are rather small

Focusing on rural sample, this table shows the

percentage of households that used each measure

Type of shock

Measure Taken death sicknesscrop

loss price fallsbusiness

loss* Unemployment*

Extra job 12.92 12.36 45.86 38.96 23.44 42.37

loan 26.2 31.18 21.03 18.88 23.44 28.81

Asset sales 28.41 33.43 18.97 15.66 31.25 11.86

Family assistance 33.58 21.63 7.07 4.02 7.81 25.42

Used savings 14.76 16.57 4.66 4.42 6.25 3.39

Cut down on household expenses 5.54 7.02 20.52 31.33 21.88 13.56

121.41 122.19 118.11 113.27 114.07 125.41

* Business loss and unemployment affect only a few households

Our data confirm what suggested in the literature (see Alderman and Paxson, 1992):

- informal insurance mechanisms, such as family and community assistance, may protect households against idiosyncratic shocks thereby smoothing consumption across households through risk-sharing

- the community may not provide an insurance if all households in the same area face the same shock (this could be the case of crop loss); therefore households would be obliged to use other means. In these cases, the consumption reduction is more severe than in the case of an idiosyncratic shock in response to which households seem more able to smooth consumption (see the percentage of households that cut down on expenditures)

Type of shock

Measure Taken death sickness crop loss price falls

Family assistance 33.58 21.63 7.07 4.02

Cut down on expenses 5.54 7.02 20.52 31.33

INFORMAL INSURANCE MECHANISMS

Type of shock

Measure Taken death sickness crop loss price falls

Asset sales 28.41 33.43 18.97 15.66

Another measure that may be more useful in coping with idiosyncratic shocks than with common shocks is asset sale: it gives less protection against common shocks because when the majority of households try to sell assets, their prices fall (Morduch, 1994; Frankenberg, Smith and Thomas, 2002).

ASSETS SALE

LABOUR AS INSURANCE

Type of shock

Measure Taken death sickness crop loss price falls

Extra job 12.92 12.36 45.86 38.96

Labour supply response plays an important role in the face of crop loss and price falls (see Cameron and Worswick, 2003)

This is not the case for those shocks (demographic shocks) that affect the household’s labour force and may induce the household to use alternative, possibly costlier, methods of insurance (Kochar, 1995)

Sickness may increase the need for domestic labour

SAVINGS

Type of shock

Measure Taken death sickness crop loss price falls

Used savings 14.76 16.57 4.66 4.42

Separating households into groups according to the level on income:

Type of shock

death sickness crop loss price falls

Use savings

TOP 20% 23.81 35.71 10.98 14.29

BOTTOM 20% 6.67 7.35 1.54 2.13

savings is one of the most used measures by the richest 20% to cope with demographic shocks.

the percentage of households that use savings to overcome shocks is, in general, low, in particular for non-demographic shocks

Model the household choices to cope with shocks

EMPIRICAL STRATEGYThe aim of this work is to model the probability that a

household chooses a given measure to overcome a given shock

I would like to control for some income indicators household income and household

expenditures are endogenous

estimate the permanent and transitory income components allow us to solve the endogeneity problem and to examine the different roles of different income components

MEASUREMENT OF PERMANENT AND TRANSITORY INCOME: Cameron and

Worswick’s approach (2003)

Cameron and Worswick (2003), following Paxon (1992), propose a different approach that treats transitory income not as a residual but as a function of a set of variables.

Where is household income, and are vectors of variables that may affect permanent and transitory components of income.

hY

hTh

Phh XXY 210

PhX T

hX

Ph

Ph XY 10 ˆˆˆ

Th

Th XY 2ˆˆ

EXTENSION OF THE CAMERON AND WORSWICK’S APPROACH

Our approach extends the Cameron and Worswick (2003) method including other shocks (not only crop loss) in the estimation of the transitory component

What Cameron and Worswick call transitory component captures only certain negative shocks, and the residual includes important income information. Therefore, I prefer not to distinguish between permanent and transitory income, but to decompose income in three components: y1, y2 and y3, where y2 captures negative hardships and y3 is the residual of the income regression, i.e. the non-identified income component.

is the weighted average of households that experienced the shock in a given area, this variable controls for the commonality of shock in the same village

are demographic variables (i.e. sex and education of the household head)

, and are estimates of household income components: permanent component, a component that reflect transitory negative shocks, and the residual component

Logit equation: probability that household h uses the mode m to overcome shock s

1hY 2

hY

hmshvshhhhms eXDYYYR 543

32

21

10ˆˆˆ

vsD

hX

3hY

Estimates of income components Variables used to identify permanent income (X1): - demographic (the number of household members in each age categories, the number of adult members in a number of education/gender categories)- wealth (occupation of the household head, the number of householders that earn a wage, a dummy that identifies if there is a householder who has a non-farm business, the value of land- location dummy (Java-Sumatra or the rest of Indonesia).

Variable used to identify negative transitory component (X2): dummy variables that indicate whether a household experienced a shock in 1992-93

Some shocks may affect income in a permanent way death of a householder is included in the estimation of permanent income (y1)

INCOME EQUATION RESULTS• Death and unemployment of a householder have a significant effect on income

• The dummy if a householder works in a non-farm business is very relevant and significant (nt01), such as the number of household members that earn a wage/salary (N_empl)

• Land value is significantly and positively correlated to income

• Secondary and higher education play an important role in the income equation, both for male and for female

• The coefficients of household head’s employment type strengthen the positive correlation between wages/salaries and income

Variable Coef. tintercept 170.44 0.96death5yr -260.34 -3.5sickness 42.04 0.34sckn_emp -65.49 -0.45crop loss -98 -1.45business loss 171.73 0.62bns_nt01 -207.97 -0.51unemployment -395.54 -2.17price falls -47.58 -0.45N_empl 367.81 7.95nt01 585.72 10.72land value 0.04 4.3land value sq -1.41E-07 -1.72No. males over 18/sec shool 577.83 6.11No. females over 18/sec shool 572.68 4.37No. males over 18/hgh shool 1637.16 5.74No. females over 18/hgh shool 1846.86 4.7employed 393.65 2.45

Number of obs = 3352 F( 31, 3320) = 29.43 R-squared = 0.3277

The data we are examining have two important features:

1) Households have a number of possible ways of responding to shocks (we aggregate to 6 responses).

2) Multiple responses are possible – responses are non-exclusive

Multivariate logit allows modelling of multiple responses, but does not accommodate non- exclusivity.

Theoretical model

Households respond in such a way as to minimize the cost of shock adjustment

By analogy with the McFadden random utility model, adjustment shocks chms (for household h adjustment mode m to shock-type s) have a deterministic component fhms and a random component hms

where xh is a vector of household h characteristics. mshms hms hms h hmsc f f x

Choice of adjustment mode

We first consider the standard multinomial logit case in which choices are exclusive.

Write household h’s response to a shock of type s as rhs

and let phms be the probability rhs = m. Focusing on adjustment mode 1,

Under the standard logit assumption that the stochastic component hms follow an extreme value (Gnedenko)

distribution, the probabilities phms are logistic:

1 1Pr 1 Pr , 2,.., |h s hs h s hms hp r c c m M x

1

1,2,..,hms

hjs

f

hms Mf

j

ep m M

e

Non-exclusive responses We generalize the random adjustment cost

framework by introducing a household-specific threshold th = t(xh).

Suppose the cost minimizing adjustment mode is 1. The household reports an adjustment mode set

The probability of choosing mode 1 is now

Under the standard logit assumptions

1{ : }hs hms h s hR m c c t

1

112

1

1h s hms

h s hms hhms

f fM

h s M f f tf m

m

e ep

e ee

* *1 1 1

2

Pr 1 Pr Pr .PrM

h s hs h s hs hms hs h s hms hm

p R c c c c c c t

Shocks and implications forhousehold vulnerability

(Future research)

SHOCKS AND VULNERABILITY

Reducing children’s education or health expenditure affects children’s human capital and may worsen the effect of transitory shocks

Other measures, such as asset sale, may destroy the household physical capital. Gold, usually in the form of jewellery, is seen as an important way to save money (Frankenberg, Smith and Thomas, 2003) and using it to mitigate transitory shocks reduces the possibility for a household subsequently to overcome long term shocks such as the 1998 financial crisis.

Different measures have different implications for vulnerability, in the sense of increasing the risk of entering poverty when faced with future shocks. Some responses may destroy or reduce the physical, financial, human or social capital of the household more that others (as suggested by Dercon, 2005).

The effects of shocks on future vulnerability are relevant in particular for poor households.

e.g. the percentage of households that take a loan is higher for the poorest 20% than for the richest 20%, in particular for a death of a householder (this is not true for price falls, probably because in this case poor people do not have collaterals).

This may push poor households into a poverty trap.

There are three principal approaches to assessing vulnerability (Hoddinott and Quisumbing, 2003):

1) Vulnerability as expected poverty (VEP)

vulnerability=probability that a household will fallinto poverty in the future

where z is the consumption poverty line

)Pr( 1, zcV thht

2) Vulnerability as low expcted utility (VEU)

vulnerability= the difference between the utility derived from

some level of certainty-equivalent consumption, zCE, at

and above which the household would not be considered

vulnerable, and the expected utility of consumption

riskofMeasure

hhCh

povertyofMeasure

ChCEhh cEUEUEUzUVhh

)()()()(

3) Vulnerability as uninsured exposure to risk (VER)

This approach asses whether observed shocks causes a

household to deviate from expected welfare

Estimates the effects of common and idiosyncratic shocks on household consumption net of the mitigating role played by private coping strategies and public responses

Shocks and implications for children’s education

The effects of shocks on child labour/child education

Do households use child labour as an insurance (by taking child out of school and sending them to work)?

Link between shocks, uncertainty, credit constraints and child labour/child schooling (Beegle, Dehejia and Gatti, 2003; Duryea, Lam and Levison, 2003; Fitzsimons, 2002; Jacoby and Skoufias, 1997;)

Child labour as the outcome of an intra-household bargaining process (Basu, 1999)

collective models

Household allocation decisions are the outcome of a bargaining process between parents or parents and child; there is no income pooling, but householders agree on how to divide the household income according to the individual bargaining power. The only assumption is that outcomes are Pareto efficient.

I will outline a simple one period cooperative model to analyse the effect of shocks on household human capital decisions.

YTwTwTwswplwlwcts

sllcusllcu

ccccs

ccsllc c

212211

212211,,,

)(..

),,,()1(),,,(max21

DATA taking child out of school as response to a shock why child has never been to school / stop going to

school (help parents earn money, help at home,… ) child works during the school year / how many hours per

week hours per day/per week spent in school and for studying

outside the schoolFor older than 10: whether they receive a salary/income what primary work (professional, management, farm-

forestry, production line, transportation, blue collar, etc.) & work field

HOUSEHOLD DECISION-MAKING (1)

Male Female

Yes, all 43% 53,7%

Yes, some/daily

42% 42,5%

No 14,4% 3,6%

Are you free to spend your money for household expenses?

Apart from hh exp., can you spend your income without consulting your partner?

Male Female

Yes 16,3% 22,2%

No 83,5% 77,8%

From a preliminary analysis (probit):

the probability the mother keeps some income depends mainly on household’s wealth (household tot expend.)

the probability the father keeps some income depends on HH wealth, but also on his education (+), and wife’s education (-)

HOUSEHOLD DECISION-MAKING (2)

EDUCTION Perc.

Father 3,87

Mother 6,55

Both 51,68

Both, +mother 13,38

Both,+father 12,35

CLOTHES Perc.

Father 1,9

Mother 19

Both 24,12

Both, +mother 19

Both,+father 4,5

Child 26,3