Development I

�Risk, Financial Markets, and HumanCapital in a Developing Country�, by

Jacoby and Skou�asMark Klee

12/11/06

�Risk, Financial Markets, and Human Capital in a Developing Country�, by Jacoby and Skou�as 2

1 Introduction

Goals

� Examine response of human capital investment to �uctuations in family income�What are effects of �nancial market constraints in developing countries?� Estimate structural model of human capital investment to quantify cost of using childlabor as self-insurance

� Distinguish effects of anticipated, unanticipated income shocks

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Results

� Child labor plays signi�cant self-insurance role for poor families in rural India, thoughnot costly in the long-run

� Reject intra-village complete markets� Only small-farm households are inadequately insured ex ante

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1. Introduction2. Theoretical Framework3. Data4. Estimation Strategy5. Results6. Conclusion

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2 Theoretical Framework

Value of Education

� Average grade attainment in 6 ICRISAT villages rose steadily by cohort for bothsexes, suggests education valuable� Education as route to more lucrative employment outside agriculture� Education to enhance farm management ability� Estimate conditional farm pro�t function with household �xed effects, with educationof head shifting pro�t level and interacting with use of high-yielding seed varieties� Signi�cantly positive returns to education increasing with fraction of land underHYVs

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Model

� Child from household i is eligible for school for 0 � t � T consecutive agriculturalseasons

� Sit, school attendance given cumulative history of shocks at time t, augments begin-ning of period human capital stock Hit according to Hit = g(Sit; Hit; �it)

� Functional form of g determines cost of child labor as insurance (ex. git = Hitf (Sit; �it),f log-concave)

� @g@S ,

@g@H > 0

� �it is education productivity shifter, may re�ect illness or aggregate effects like schoolclosings

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Household0s Problem

� Consumers solve

maxfcit;Sitg

E0

"TXt=0

�tU(ct) + �(HiT+1; BiT+1)

#BiT+1 = end of schooling period bequest of �nancial assetscit = household consumption� Dynamics of school attendance governed by

Et�1

��it

�Wt

Wt�1

�zit

�= 1 (1)

Wt = child wage determined in child labor market, independent of human capital stockwhile still in schoolzit = gH(t)

gs(t�1)gs(t)

= Marginal Rate of Transformation between school attendance inadjacent periods�it = shadow price of date t consumption relative to date t-1 consumption, determinedby �nancial market structure

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Impact of Income Fluctuations on School Attendance

� Assume:

g(Sit; Hit; �it) = (1� �)Hitf (Sit; �it)

f (Sit; �it) = exp

� � exp

��1 [Sit � �it]

��, > 0

where f (0; 0) = 1, f 0 > 0; 8 and f is chosen so that log zit = (�Sit���it)

.� To focus on income uncertainty, if all changes in �it are anticipated, taking logs andrearranging equation (1) yields

�Sit = � [log �it +� logWit � log(1 + �it)] + ��it (2)

where �it is non-zero error in forecasting �it�Wt

Wt�1

�zit

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Role of Financial Markets: Complete Markets

� If households can trade in state-contingent consumption claims, can reallocate re-sources across time and states at �xed prices (lifetime budget constraint)

�With complete markets, �it constant across households in the same market at ratioof consumption claim in state "t to that in state "t�1, scaled by �

� Separation of household's human capital investment and consumption decisions� If markets are complete across villages, �it = �t for each village. In particular,�t =

1

1+rft� Income shocks (either aggregate or idiosyncratic) do not affect school attendance� If markets are only complete within villages, �it = �t in the village. In particular, �t isthe intertemporal MRS in consumption� Aggregate income shocks (to Fit) transmitted to school attendance by increasingshadow price of consumption

� Idiosyncratic income shocks have no effect on school attendance

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Role of Financial Markets : Incomplete Markets

� If consumption is not ex ante insurable, separation breaks down, extent to whichdepending on household's ability to transfer resources across time

� Relation between assets at beginning of t and end of t-1 given by A�it = Rt(Ait�1)� Assume MC of storage is increasing, so R00t < 0 for Ait�1 > 0, relevant for villageIndia

� Household budget constraint is now Cit + Ait = Rt(Ait) + Fit �WtSit

� �it is household speci�c intertemporal MRS in consumption (scaled by �), governedby Et�1 [�itR0t] = 1, or equivalently log �it = � logR0t(At�1) + log(1 + !it) where ! ismean 0 forecast error in �itR0t from unanticipated income shocks

� Presence of �it in equation 2 means that school attendance depends on unantici-pated idiosyncratic income shocks, not merely unlike complete markets

� Functional form of Rt determines how school attendance responds to anticipatedincome shocks

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�With increasing MC of storage, R0t is affected by anticipated income shocks, con-sequently school attendance

� If R000 < 0, magnitude of response of school attendance to increase in income islarger than response to decrease in income

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3 Data� ICRISAT Village Level Studies survey, data on time allocation of children 5-18 from40 households in 6 villages� Aurepalle and Dokur: red soil with limited water storage capacity, highly unpre-dictable rainfall

� Shirapur and Kalman: black soil with better water storage capacity, level and timingof rainfall erratic

� Kanzara and Kinkheda: more reliable rainfall� Data based on one day recall from June 1975 to the end of 1978� No appreciable summer cultivation, so two crop seasons: rainy (Kharif) and post-rainy (Rabi)

� Focus on response of school attendance to income �uctuations during season changessince full income only well de�ned over complete production cycle

� Two seasonal averages of school hours (including travel)� S1 averages all child's monthly observations in a season, including zeros (despite

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possible measurement error)� S2 averages only positive observations, capturing adjustments in school day lengthrather than attendance

� After deleting entries which don't allow study of school attendance in consecutiveseasons, 1256 observations on 258 children

� 84 cases of zero school attendance for an entire season coming between two sea-sons of positive attendance

� 20% child labor market participation based on original sample

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Measures of Income

F = (wmNm + wfNf + wcNc) + � + VY = wmLm + wfLf + wcLc + � + VF = full incomeY = conventional incomeNi = number of type i people residing in the household = 156 for each season� = seasonal pro�t from crop cultivationV = income from homemade handicrafts, sale of livestock products, land rental

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4 Estimation Strategy

�Sivt = �0 + �1�flogFivt + �2�logFvt � � logWvt + "ivt

�Sivt = change in school attendance of child i, in village v, between season-years tand t� 1�flogFivt = deviation of change in log full income from village-season-year mean change�logFvt = village-season-year mean log full income change"ivt captures school productivity shocks (��it) and is assumed orthogonal to changesin full income and child wages�1 = �2 = 0 implies complete markets both within and across villages�1 > 0 suggests incomplete markets within village, though no inference on cause ofmarket failure

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Separating In uence of Credit and Insurance Markets

�Fivt = �0vt +X0ivt�1�1 + (Xivt�1 DRvt)0�2 + uivt

�0vt is a season-village-year �xed effectXivt�1 is a vector of farm characteristics lagged one seasonDRvt is deviation of season-year t rainfall from long-run village mean�2 identi�ed by assumption that effect of rain shocks varies across farms

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�clogF aivt = X 0ivt�1

b�1�clogF uivt = (Xivt�1 DRvt)0b�2�Sivt = �0vt + �1a�clogF aivt + �1u�clogF uivt + �ivt

�0vt = village-season-year �xed effect to purge effect of aggregate shock, focus onidiosyncratic� Provides separate test of perfect intravillage credit markets (�1a = 0) and perfectinsurance markets (�1u = 0)

� Increasing MC of storage consumption testable by estimating

�Sivt = �0vt + �+1aD

+ivt�

clogF aivt + ��1a(1�D+ivt)�

clogF aivt + �1u�clogF uivt + �ivtwhere D+

ivt = 1 if �clogF aivt > 0

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5 Results� Both coef�cients signi�cant, have expected sign using both trimmed (removing zerosfrom beginning and end of each child's time series) and untrimmed data

�Weaker wage effect and insigni�cant income effect using S2, suggests meaningfulvariation comes in days of attendance rather than length of school day

� Replacing full income with conventional, income effect is much smaller due to expost adjustments

�Wage effect still strongly signi�cant, negative after including adult wage: child wagedoesn't re�ect other village shocks

� Smaller wage effect, insigni�cant income effect including village-season dummies� Larger wage effect including child effects (accounts for self-selectivity� IV estimates of � logWvt (due to correlation with �) similar to row 7� Instruments: lagged child wage, lagged rainfall, village-season dummies

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Financial Markets Structure

� Aggregate income changes affect school attendance more than do idiosyncratic (i.e.�2 > �1), though not after controlling for seasonal effects� Either aggregate income shocks are fully insured though idiosyncratic are not, oraggregate seasonal �uctuations are predictable (collinear w/ dummies)

� Insigni�cant effect of idiosyncratic shocks on school attendance in Kanzara may rep-resent highest, least erratic rainfall, relative abundance of formal credit institutions

� Responses to aggregate shocks differ across villages� Reject intra-village complete markets after adding village-season-year �xed effects� After instrumenting income, small farm households worse insured than large

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Anticipated v: Unanticipated

� Instrument income with lagged farm characteristics, interactions with current devia-tions of quarterly rainfall, deviations squared, onset day of monsoon

� Both anticipated and unanticipated idiosyncratic income shocks have signi�cant ef-fect on school attendance for small farms� not well insured ex ante, nor have perfect access to seasonal borrowing, lendingopportunities

� Only anticipated idiosyncratic income shocks matter for large farms, less effect thansmall farm

� �+1a > ��1a, as predicted by increasing MC of storage, though not signi�cantly differentat 5%

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Impact of Market Incompleteness

� Attendance of uninsured child B differs in a given period from that of insured child A(Svt) by �eBt�̂1.�With same initial HC, learning technology, expected percentage difference in HC at

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end of education is:

E log

�HAT+1

HBT+1

�= E

�exp

���1eBt

�� 1� TX

t=0

exp

��1 �Svt

�� If eBt~N(0; �2), difference in terminal HC increases with �1 and �2

� Small estimated Human Capital losses for uninsured children, regardless of how �1, determined

� Losses largest in village with most variable rainfall� Even though large farm households have larger variance of idiosyncratic incomeshocks, �̂1 estimate is half that of small farm households

� These gains represent gain from eliminating "excess" variability of attendance, ignorelikely increase in average level of school attendance

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6 Conclusion� Reject complete markets within village� Credit market constraints, perhaps combined with limited storage, affect human cap-ital investment decisions

� Costs of variable school attendance are relatively low� Prospective policies must understand relevant economic risks, constraints� Compulsory schooling laws may lower household welfare� Policies promoting short-term credit and insurance may affect economic growth viahuman capital investment decisions

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