A Test for Moral Hazard in the Labor Market: Contractual Arrangements, Effort, and Health
Author(s): Andrew D. Foster and Mark R. Rosenzweig
Reviewed work(s):
Source: The Review of Economics and Statistics, Vol. 76, No. 2 (May, 1994), pp. 213-227
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A TEST FOR MORAL HAZARD IN THE LABOR MARKET:
CONTRACTUAL ARRANGEMENTS, EFFORT, AND HEALTH
Andrew D. Foster and Mark R. Rosenzweig*
Abstract-Moral hazard plays a central role in many models
depicting contractual relationships involving worker effort.
We show how time-series information on worker health, consumption and work time can be used to measure the effort
effects of payment schemes. Estimates from longitudinal data
describing farming rural households indicate that time-wage
payment schemes and share-tenancy contracts reduce effort
compared to piece-rate payment schemes and on-farm employment. The evidence also indicates, consistent with moral
hazard, that the same workers consume more calories under a
piece-rate payment scheme or in on-farm employment than
when employed for time wages.
/[ORAL hazardplays a centralrole in many
models depicting contractualrelationships
or payment schemes involving the supply of
worker effort, particularlyin the context of lowincome countries.Among the most prominentof
these are sharecropping,efficiency wages, and
permanentworker contracts (e.g., Stiglitz, 1982;
Eswaranand Kotwal,1985aand 1985b).The existence of moral hazard also has been used to
M
Received for publication June 20, 1992. Revision accepted
for publication March 22, 1994.
* University of Pennsylvania.
This research was supported in part by NICHD Grant No.
HD-28687. We are grateful to two referees for helpful comments.
Copyright C)1994
justify the superiorityof family labor over hired
laborpaid on a time basis, and hence the relative
cost advantageof small-scalefarms. Despite the
theoretical importanceof moral hazard and the
distinctionbetween worker time and worker effort, however,there is little direct empiricalevidence on worker shirking.The reason for this is
clear, the hypothesizedfeature of these models
that deters workersfrom supplyingfull effort,the
lack of observabilityby the employer of worker
effort, also representsa barrierto the empirical
verificationof the importanceof moral hazard,
namely the unobservabilityto the researcherof
workerperformance.
While there have been credible studies of the
disincentiveeffects of input use associated with
share tenancy (Bell, 1977; Shaban, 1985), this
evidence is based on the withholdingof readily
measured inputs such as fertilizer, seeds and
worker time. There is no evidence on the withholdingof workereffort,givenworkerlabor time,
due to the lack of full incentives. In this paper,
we show how time-series informationon worker
health and the inputs to worker health can be
used to measure the effort effects of different
[ 213 1
214
THE REVIEW OF ECONOMICS AND STATISTICS
labor payment schemes. In particular,we make
use of the biological balance equation in which
calorie intake and energy expendituredetermine
weight(bodymass) change.The basic idea is that
the loss of worker body mass, for given calorie
intake, should vary directly with the degree to
which the workerappropriateshis/her contribution to output if there is moral hazard.Similarly,
calorie intake, which can be directly measured
but is not necessarilyinformationknown by the
employer,mayalso be positivelyrelatedto worker
effort incentives. A study of these relationships
between payment schemes, calorie intake and
health may also help illuminate the conflicting
results from studies of the effects of calories
and/or health statuson workerwages and contributions to output (e.g., Strauss(1986), Behrman
and Deolalikar (1989), Deolalikar (1988) and
Schultz(1992)).
In section I we set out a simple model in which
individualswork in different activities that differentlyrewardworkereffortand in whichworker
health is affected by effort and food intake. The
model is used to show how the relationshipbetween reward schemes and unmeasured effort
can be inferredfrom health and calorie information and to derive the relationshipbetween calorie intake and worker incentives. In section II
we describe the data used. These data, from
Bukidnon,Philippines,providelongitudinalbody
mass, individualcalorie intake and wage information on workers who work for time wages, on
their own plots of land, under share tenancy and
for piece-rate wages in a setting in which these
differentrewardschemesare offeredfor the same
agriculturaltasks. We also discuss estimation issues and specificationof the tests for moral hazard derived from the model. The empirical results are reported in section III. The estimates
indicatethat time-wagepaymentschemes as well
as share-tenancyare associated with moral hazard. In particular,workersevidentlysupplymore
effort under a piece-rate payment scheme or in
own-farmwork compared to time-wage employment as reflected in the fact that they deplete
their body mass by approximately10%more, net
of calorie consumption,when working under a
piece-rate scheme or on their own plots of land
comparedto workingas time-wageworkers and
13% more than when workingunder share tenancy or when outside of the labor force. The
evidence also indicates that calorie intake is rewarded when workers work for piece-rates but
not when workerswork for time wages and that
the same worker consumes 23% (16%) more
calories per day when employed under a piecerate paymentscheme (on-farmemployment)than
when employed for time wages, also consistent
with the energyexpenditureimplicationsof moral
hazard.
I. Theory
A.
WorkerEffort
The hypothesisof moral hazardapplied to the
labor market is that the amount of an input
supplied by a workerto a task will depend positively on the extent to which that input is rewarded.This will in turn depend on the degree to
which the employercan monitor or observe the
input. Workereffort is one exampleof an imperfectly perceivedinput from the perspectiveof the
employerwhose supply,for given work time, will
depend positively on the degree to which it is
rewarded.' Another is the consumption of the
worker, which may also influence productivity.
While it is possible for the researcherto measure
directlyworker consumptionto evaluate the effects of moral hazard, at some expense, worker
effort is not likely to be easily observedby either
employersor researchers.
The key feature of the analysisthat follows is
that worker effort, although unobservable, directly influences the health of a worker, which
has measurable components, net of caloric intake, time spent working,and illness. In particular, it is assumedthat there is a health production
function:
Hit =h( Hit- cit, zit Sfit)(l
where Hit is a measure of the body size of
individuali at time t, Cit is nutrientintake, zit iS
illness in that interval, and fit is total effort
expended between periods t - 1 and t, where
the total amountof effort expended can be written
fit
=
Ejtljt
(2)
j E=_
1 Note the distinctionbetween effectivework,which is the
amountof workdone (e.g., the numberof bushelsof cornthat
are picked), and effort, which may be thought of as the
amountof energyexpendedper day.
MORAL HAZARD IN THE LABOR MARKET
where j indexes the differentrewardschemes of
the activitiesundertakenby individuali, lIit is the
numberof days spent doing activityj, and ejit is
the effort expended per day under payment
scheme J.
Because effort is not directlyobservable,estimationof equation(1) requiresthat we derive an
expressionfor the effort that each individualwill
expend as a function of observable (to the researcher)characteristicsof the workeras well as
the paymentscheme. It is assumedthat payment
schemes can be characterizedby a function
wjit =wj( Hit-1, cit, ejit)
(3)
where wjit is the daily earnings of individual i
under contract j. The extent to which effort is
rewarded is determined by
Wje
= 9wl/de. Be-
215
and p,3,8 denote the Lagrangemultipliersat time
t associatedwith the health productionfunction
(1) and the budget constraint (4), respectively,
and assuming for simplicity that workers have
positive employmentin all schemes3 the first order conditionsare:
uct + At
E wjcliit) -Pt)IthcLh
= 0
Uf tljit + At(Wjejt)ljit + Iithf,tliit = 0
Ufi
ekit
+
AtWk
UHIt+ At+1 (
-thftekit
?
(6)
(7)
(8)
Ht+l jit+l
jeJ
+ ?t - At+lhHit8 = 0
At-At_1(1 + r)f3 = 0.
(9)
(10)
Equations(7) and (8) may be solved to obtain an
effort supplyfunctionby paymentregime.In particular, these two equations imply that effort in
sector k will be chosen to equate the marginal
and average products of effort in that regime
Because daily earningsin a given
(Wke = wk/ek).
regime depend only on effort, consumptionand
health, this latter equationimplies that the maximizing choice of effort under scheme k can be
writtenas a functionof health and caloriesalone.
Since the marginalproductsof effort in the different regimesmust also be equal (an implication
(4) of equation (7)), the effort under regimes other
Ait+1 = (1 + r)Ait + Ew1J.-PL ci.
jfEJ
than k may also be writtenas a functionof effort
Each individual has a felicity function that is under regime k, consumption and health and
increasingin body size and consumptionand de- thus on consumptionand health alone, that is,
creasingin total effort expenditureU(Hit, Cit,fit)
ejit = ej( Hit -1, cit).*(11)
and he/she chooses consumption,effort,and days
workedin order to maximizediscountedutility: Moreover, if effort is rewarded more under
scheme j than under some scheme j' (i.e., Wie >
T
max E f3sU(His,cis,fis)
(5) Wj,e) and the wage is concave in effort over the
=
relevant range, effort in regime j must exceed
ljls
e,lsX
s
O
c1s,
cause effectiveworkis fullyrewardedin piece-rate
work or in self-cultivation,it is expected that for
a given level of health and consumptionwie will
be higher under these schemes than in sharecroppingand time-wageemployment.
Effort by payment scheme, labor allocation,
and calorie consumption are determined as a
solution to a dynamicoptimizationproblem.For
simplicity,we assumethat individualsmayborrow
and lend as much as they want at a fixed interest
rate r so that net assets in period t + 1 are
subject to equations (1)-(4) and the health pro- that in j'.
Substitutingequation(11) into (1) and takinga
ductionfunction.We assumethat there is at least
linear
approximationaround H*, c*, and l* gives
one payment scheme, indexed by k, for which
labor demand is perfectly elastic.2 Letting At3t
Hit= ao + allHHitl + azzit
2Although labor supply in self-cultivationand share-crop-
ping maybe constrained,workersare likelyto have considerable libertywith regardto their choice of the numberof days
spent in off-farmemployment.If an individual'slabor supply
were to be constrainedin all work regimesthen the optimal
effortexpendedwill in generaldependon the marginalutility
of incomeand thus estimationof the modifiedhealthproduction function (equation (11) below) would be substantially
more complicated.
+ accit +
E
aIjljit +
UHit
jeJ
3 The requirementthat there is positive employmentin all
schemes is made primarilyfor notational convenience.As
long as the individualchooses positiveemploymentin scheme
k, the analysisis unchangedexceptfor the fact that the effort
functions will only be defined for those schemes in which
laboractivityis positive.
THE REVIEW OF ECONOMICS AND STATISTICS
216
where
alj = hf.,ej
aH= hHit-1 +
E dH
IJit
(12)
and
de.
ac =hclt
+ L dc iJ*t
jedCJ it
In order to use the above model to obtain a
tractableempiricaltest of whether calorie allocations are a function of the incentive schemes of
work activities,it is therefore necessaryto make
an importantsimplifyingassumption:the singleperiod utilityfunctionis assumedto depend only
on body size Hi, and consumption cit. Thus,
energy expenditureonly leads to a decrease in
utility to the extent that it lowers body size. This
assumption allows us to combine equations (6)
and (8) to obtain
Of particularinterest are the coefficientson the
aij terms, which provide measures of the effort
Ucit= Apt7kit
expendedin task j. Given an a priori rankingof
n paymentschemes by the degree to which they where
rewardeffort, from lowest to highest, the test of
~~'
7rkit
Pt
moral hazardis that a1l < a12 ... < ain < 0.
h
ekit
B.
Calorie Consumption
The above analysis indicates that to identify
the presence of moral hazard with respect to
work effort based on changes in worker health
requiresmeasurementof the calorie intakeof the
workeramongotherworkercharacteristics.Information on this variablealso permits another test
of the role of moral hazard. If caloric intake is
also not readilyknownby employersbut is associated with worker productivity,either because it
directly augments the amount of effective work
done by an individual(as suggestedby the wage
equation (2)) or because workers who expend
greatereffortare likelyto consumemore in order
to maintain their health, then the allocation of
consumptionacross periods may also be related
to payment schemes due to moral hazard. In
particular,in periodswhen individualsare working primarilyin piece-rate activities one should
expect to observeelevated levels of consumption.
On the other hand, in periods when individuals
are primarilyworkingfor time wages we should
expect to see low levels of consumption.
A standard approach to the analysis of the
intertemporalallocationof consumptionis to derive Euler equationsfor consumptionfrom a dynamic stochastic model. Unfortunately, implementationof this approachis greatlycomplicated
by the fact that body size at a given point in time
depends on previousbody size. As a result, calorie allocationsin one period will depend on future as well as present labor-marketreturns to
nutrition.
(13)
hfit
E wjcitljit
(14)
jE.J
and k indexesthe paymentregimein which labor
demandis assumedto be elastic. The interpretation of equations(13) and (14) is that the marginal
utility of calorie consumptionin any particular
period must equal the marginalutility of wealth
in that period times a shadow price that reflects
the net cost of increasing calorie consumption
and work in paymentregime k in such a way as
to keep health unchanged.Equation(13) for two
different periods may then be combined with
equation(10) to obtain a Euler equation
Ucit
8i
(1 +
7rkit
r) vkt(15)
ucit + 1
7rkit + 1
In order to derive an estimableform for equation (15) it is helpful to furthersimplifythe wage
and health technologiesas follows:
U(cit,
Hit)
=
1l
h(Hit_ 1, cit, fit)
wj( Hit_ 1, cit, ejit)
CtYCH[yH
h(Hit- 1, Ocit -fit)
=
=
mj( Hit
1) nj(6cjcit
+ ejit
(16)
We also assume that sector k is the time-wage
sector and that calories are not rewardedin that
sector (i.e., 8ck = 0), and thus that ekit does not
vary across individuals or over time.4 Thus a
log-linear approximationto equation (15) in cit,
Pto Wkit,
and 1jit around c*, p
,
wk, and 17,
4 Evidencethat caloriesare not rewardedin the time-wage
sector is presentedbelow.
MORAL HAZARD IN THE LABOR MARKET
respectively,yields
Alnci
1
+ r))
t-ln(I3(1
YC
YH1
+ -A
In Hit --A
In pt
YC
YC
(0 + E SCj1j*)
1Wk*
W
+1
W*
YC
kek
*k
7*e8
E
ijeJ
6cjA lit
)
A
lnWkit
(17)
The key implicationof equation (17) is that the
parameter estimates associated with the labor
suppliedto each of the differentrewardschemes
reflect differencesin the return to calories icS
Thusin periodswhen an individualallocatesmore
work to payment schemes for which the returns
to calories are higher he/she will also, ceteris
paribus, consume a greater amount of calories.
This result is the dynamicanalogue to the result
in the static intrahouseholdallocation model of
Pitt et al. (1990), although that model did not
distinguishwork activities by their effort incentives.
II. Data
The above frameworksuggests that to test for
the existence and importance of moral hazard
associatedwith contractualarrangements,in the
absence of direct measuresof workereffort, it is
necessary to have longitudinal information on
health, calorie intakes, and activities, differentiated by incentives regimes, for individualworkers. The data used in this analysis are from a
stratified6random panel of 448 farming houseSNote that the fact that the marginal product of effort is
equated across the different schemes implies that wj,/8j, will
also be equated across schemes given the specification for wj
in equation (16).
6 The sampling frame for the survey stratified barrios (villages) into three classifications-those in which households
were mostly engaged in corn production, those in which
households were mostly engaged in sugar cane production,
and those barrios in which both corn and sugar were produced. These strata were formed on the basis of the distance
of the barrios from a sugar cane mill. Within these strata,
households and barrios were selected on a random basis.
Despite the stratification, the information on primary occupations provided by respondents indicates that 63% of the heads
of households in the sample were engaged in corn production,
8% were engaged in rice production, and only 6% were
engaged in sugar production, as either landowners, tenants or
laborers (Bouis, 1984).
217
holds in Bukidon in northernMindanao,Philippines where, as is typical of agriculturein the
Philippines, individualswork not only on their
own or rented (shared-in)plots of land but in the
labormarketunderboth piece-rateand time-wage
payment schemes.7 These households, whose
principal crops are corn, rice and sugar cane,
were interviewedin four rounds at four month
intervals in 1984-85 as part of an International
Food Policy Research Institute study by Bouis
and Haddad(1990). In additionto detailed information in each round on wages received, days
worked,paymentmethod, and activitytype (e.g.,
harvesting,planting,weeding), there is information for each householdmemberon weight,height
and calorie intake based on the 24-hour recall
method.
Table 1 provides descriptivestatistics for the
sample adults, aged 18 through 59, that we use
for analysis. Five types of contractualwork arrangements correspondinga priori to differing
effort incentivescan be distinguishedin the data
-piece-rate wages, under which workersreceive
all of the returnsfrom their work;on-farmwork,
where about half of workersreceive a full share
of their work and about half are workingunder a
share tenancy contractfrom which they typically
receive about 50% of the returns from their effort; agriculturaltime-wages, which are potentially subject to the most severe moral hazard,
and non-agriculturalwages, which are also paid
on a time basis. As can be seen, a substantial
number of sample individuals worked under
piece-rate and time-wage payment schemes; indeed, of adult workersin the sample households
who contribute at least two observationsto the
labor market data (i.e., at the very least they
performedthe same job in two different rounds
or differentjobs in the same or differentrounds),
70.7%of men and 67.7%of women workedboth
for piece-rate and time wages. Men evidently
spend more time in the wage labor market,but
are substantiallymore likely to work for time
wages than for piece-rates comparedto women.
7Althoughwe focus on the distinctionbetween piece-rate
and time-wageearnings,there is some heterogeneityin the
formof paymentwithineach type of work.Piece-ratesinclude
cash paymentson a unit basis as well as in-kindpaymentsthat
are a share of the harvest.It should also be noted that the
appropriateunit to be used in the piece-rate paymentfor
plowingor weedingmay,for example,be the plot of land.
218
THE REVIEW OF ECONOMICS AND STATISTICS
TABLE 1.-DESCRIPTIVE STATISTICS
Women
494)
Men
458)
(N=
(N=
Mean
Fraction of people working
during study period in:
Piece-rate
Time-wage
On-farm
Non-agric.
Fraction of total days
worked in:
Piece-rate
Time-wage
On-farm
Non-agric.
Calories
Body-mass index (kg/meter2)
Illness (fraction days)
Height (cm)
Education (years completed)
Land owned (hectares)
Std. Dev
Mean
Std. Dev.
0.423
0.581
0.530
0.366
0.495
0.494
0.500
0.482
0.240
0.194
0.367
0.251
0.428
0.396
0.482
0.434
0.052
0.125
0.128
0.157
2609.6
20.17
0.033
160.7
5.903
2.253
0.156
0.260
0.260
0.331
990.2
1.963
0.144
6.163
5.104
3.830
0.014
0.020
0.042
0.126
2160.0
20.64
0.045
150.1
6.581
1.953
0.065
0.091
0.151
0.313
861.5
2.815
0.175
5.491
6.760
3.458
This is due in part to the fact that piece-ratesare
predominantlypaid in harvesting,a labor-intensive activitythat employs a higher proportionof
women compared to other activities, although
piece rates are also paid in other activities-for
example, 23% of all plowing days are paid accordingto piece rates.8
As in other data sets from low-incomecountries reporting individual calorie consumption
(e.g., Pitt et al., 1990),men consume significantly
more calories on average than do women, a differential that may in part be explained by the
greater labor-forceactivityof men. Women also
report that they are ill more often than do the
men. We use a measureof health that is sensitive
to short-runconsumptionand work activity,the
body mass index (BMI),weight dividedby height
squared.This measure,as can be seen in table 1,
is approximatelythe same on average for men
and women,with men being on average7% taller
than women.
We estimate the health or BMI production
function over the four-monthperiod correspond-
8
Within tasks, payment form appears to be related to the
size of the farm operated by the employer due to evident scale
economies in hiring supervisors of piece-rate workers (Foster
and Rosenzweig, 1992).
ing to a surveyround using as determinantsthe
fractionof total days in that period spent by the
worker in each of the five contractualregimes,
calories consumedin the surveyday, the fraction
of daysthat the individualreportedthat he or she
was ill in the round (based on a two-weekreference period), sex, age and previous-periodBMI.
We also include in the specificationdummyvariables for the ten municipios, which will capture
differencesacross households in the local health
infrastructureand environment(e.g., water quality, sanitation).
To estimate the health productionfunction, it
is necessary to take into account the possibility
that activities,differentiatedby contractualterms,
and thus by effort expended, and calories are
allocatedaccordingto unmeasuredhealth-related
endowments,as found in Pitt et al. (1990).Moreover, illness is not likely to be orthogonal to
unobservedhealthiness, nor is the initial-period
BMI, and caloriesconsumedin the 24-hourrecall
period measurewith error the averageconsumption of calories over the round,which is approximately three months. Accordingly,both to take
into account the measurementerror in the calorie variable and the endogeneity of the activity
and health variables, we estimate the health
(BMI) productionfunction using two-stageleastsquares, where we employ variables reflecting
householdbudget constraints-household wealth
MORAL HAZARD IN THE LABOR MARKET
and area-specificprices-as instruments,including the land owned by the household,the age and
sex compositionof the household (which affects
the allocation of household resources to each
individualin the household), the height of the
individual,the price of corn (the principalcommodity for which there is price informationfor
each area and round), and round and municipio
dummyvariables,which reflect locale-specificlabor marketconditionsand consumptionprices.9
To make use of as much of the sample information as possible, we use all observationson
individualsin which there is adjacent-periodinformationon BMI, as well as informationon the
other variables.For most individuals,therefore,
we have three observations(one round is lost
because of the use of lagged-BMIin the specification), although some rounds are missing for
some individuals,mainly due to lack of information on BMI for consecutiveperiodsor on calorie
consumption.The total number of observations
from which the production function could be
estimatedis 2274. To take into account the nonindependence of the observations,we employed
an ARl-error scheme. We report the estimated
error correlations,which differ by specification,
below.
III. Estimates
A.
Determinants of BMI.
Table 2 reports the estimatesof the BMI production function (equation (12)).1oThe first column of the table reportsthe OLS-AR1estimates
in which we do not distinguish between selfcultivationon own land or cultivationunder share
tenancy. These estimates, which are rejected by
the Hausman-Wutest, indicate that, net of calorie consumption,time-wagework depletes body
fat no less than do self-cultivationand wage work
under piece-rates. The OLS estimates also indicate that calories do not significantlyaugment
9 The instrumental-variables
methodwill resultin consistent
estimatesas long as the measurementerrorsin calorie consumptionare random.
10We omit from the table all of the municipio dummy
variablecoefficients.In all specifications,the set of municipio
variablesis statisticallysignificant.We also tested whetheror
not the set of productionfunctioncoefficientsdifferedby sex.
The (preferred)two-stageestimatesindicatedthat we could
not reject the hypothesisthat the health productionfunction
is the same for both men and women(F(17,2238)= 0.67).
219
body mass and that participationin non-agricultural work actually increases BMI net of calorie
consumptioncomparedto the left out non-laborforce activity! Indeed, the results are sensible
only when two-stage least squares,which is preferred statistically,is used, as reported in the
subsequent columns. In all specifications estimated by the two-stage procedure, increases in
calories, net of activities, increase BMI and illness depletes BMI. Moreover,in column2, where
we replicate the OLS specification,the ordering
of the deleterious effects of work on BMI conforms to the a priori ranking of the potential
moral hazard entailed by the differentwork arrangements,with piece-rate work depleting BMI
by substantiallymore than the next most effortintensive payment scheme, on-farm work (some
of which is on a share basis), and with time-wage
agriculturalor non-agriculturalwork not diminishing BMI more than the reference non-laborforce activity.
In column 3, we report the estimates obtained
when we decompose the self-cultivationactivity
into share-tenancyand own-cultivationby adding
a variablethat is self-cultivationmultipliedby the
share of the farmer'stotal cultivatedland that is
farmed under share tenancy. If share tenancy
reduces effort, then the coefficient on this variable should be positive. The estimates confirm
this, and indicatethat the negativeeffect on body
mass of self-cultivationon own or fixed-rentland
is no differentfrom that of piece-ratewage work
(F(1, 2255) = 1.63),while work effort is evidently
substantiallyless in time-wage activities and under share tenancycultivationcomparedto either
piece-rate or own cultivation. The significantly
highereffortobservedunderpiece-ratecompared
to self-cultivationobservedin the second-column
estimates is evidentlydue to the high proportion
of cultivators in the sample working as share
tenants and the reduced incentives of that contractualform.
In the fourth column we test whether the significantlygreaternegative effect of work on BMI
elicited by a piece-rate regime or in self-cultivation comparedto time-wagework is due to differencesin the type of workactivity.For example,
59.0% of the days spent in harvestwork by paid
laborers were paid under a piece-rate scheme,
while only 14.6% of paid labor days in weeding
were compensatedunder a piece-rate regime. If
THE REVIEW OF ECONOMICS AND STATISTICS
220
LEAST SQUARES ESTIMATES
SQUARES AND Two-STAGE
TABLE 2.-LEAST
WITH AR1 ERRORS OF BODY-MASS INDEX (BMI) PRODUCTION FUNCTION'
TSLS
OLS
Piece-rate workb
On-farm workb
On farm work x
fraction
sharecroppedb
Time-wage workb
Non-agric. workb
Calories (x
10-3)b
Illness
Lagged BMIb
2
3
4
-0.175
(0.95)c
- 0.206
(1.33)
- 3.36
(2.48)
-1.28
(2.86)
- 3.33
(2.47)
-1.57
(3.08)
1.12
(1.17)
-4.49
(2.36)
-2.00
(3.22)
0.900
(0.88)
-4.78d
- 0.155
(1.29)
0.144
(2.19)
0.0112
(0.44)
- 0.0327
(3.64)
0.912
(11.4)
- 0.0682
(0.10)
- 0.382
(1.40)
0.146
(1.39)
-0.149
(2.50)
0.948
(44.5)
- 0.464
(0.60)
- 0.406
(1.50)
0.135
(1.29)
-0.144
(2.42)
0.948
(44.7)
- 0.150d
(0.10)
-0.0527
(1.13)
0.0742
(0.84)
0.111
(1.18)
- 1.04
(1.13)
- 0.249
(0.83)
0.151
(1.30)
-0.116
(1.76)
0.936
(39.1)
-0.175
(0.48)
1.78
(0.89)
0.878
(1.22)
0.134
(1.39)
(7,2249)
3.67
0.0006
(8,2247)
3.41
0.0007
(11,2241)
2.73
0.0017
(14,2235)
4.72
0.0001
Harvestingb
Harvest sugarb
Weedingb
Male
Hausman test:
F-test
P-value
5
1
(2.52)
- 1.90d
(2.57)
1.49d
(1.00)
-0.622d
(1.35)
0.0613d
(0.39)
-0.108
(1.62)
0.820e
(10.3)
0.121
(0.84)
a All
specifications also include nine municipio dummy variables.
b Endogenous variable. Instruments include education, height, age, age squared, land, household composition
(age, sex groups), round and municipio dummy variables, and interactions of these variables with land. Work by
contractual arrangement and by task and illness measured as fraction of days in the previous four-month period.
c Absolute value of t-ratios in parentheses.
d Estimates of work activity, calorie and BMI effects and their t-ratios computed at the sample mean estimate of
BMI from specification in which all of these variables are interacted with lagged BMI.
e Estimate of lagged BMI effect and its t-ratio computed at the sample means of the work activity and calorie
variables.
harvestingis a more energy-intensiveactivitythan
weeding or other activities,then the differences
observed in BMI depletion by contractualterms
may merely reflect differences in the energyintensity of work tasks. Variables reflecting the
proportion of days in the round (whether as a
wage laboreror self-employed)spent by the individual in harvesting,in weeding and in harvesting
sugar,alleged to be a particularlyonerous activity
(Imminkand Viteri, 1981), are thus added to the
specificationin column three. Although harvest
workis evidentlya more energy-intensiveactivity,
the results are essentially unchanged when the
variablesthat also classifywork activitiesby agricultural task are added to the specification;indeed, the joint F-test indicates that the three
agriculturaltask variablesare not jointly statistically significant (F(3,2252) = 0.69).
All of the specificationsreported in columns
one through four assume that BMI depletion
reflects energy expendituresequally for all individuals. The nutrition literature (e.g., Sukhatme
1977) suggests, however, that exertion rates, for
given activities,and basal metabolicrates depend
inverselyon weight. This implies that those individuals with greater initial BMI will draw down
their BMI more in any given activity than will
those with initiallylower BMI, and the effect of
calories on BMI will also be less for those with
initially greater BMI. Indeed, as might be the
case if more rapid growthin one period resulted
in less efficient growth in a subsequent period,
MORAL HAZARD IN THE LABOR MARKET
the estimates of the intrapersonalcross-period
correlations in residuals from all of the linear
specificationsare negative and statisticallysignificant, although not large (-0.10). To the extent
that BMI sorts people among activitiesdifferentiated by payment schemes, as implied by our
results reported below, not taking into account
potential interactionsbetween BMI and activities
in estimatingthe effects of the paymentschemes
on BMI depletion could result in misleadinginferences.
In the last column of table 2 we report estimates from a specification of the production function in which all of the (endogenous)
payment-schemevariablesand calories are interacted with the lagged BMI variable.In this specification all but one of the six estimated interaction coefficients were negative, consistent with
the hypothesis of homeostatic metabolism, and
the set of interactionterms is jointly statistically
221
ancy are statisticallydifferent (F(1,2249) = 3.09
and 4.54, respectively).Work paid under share
tenancy, moreover, essentially depletes BMI no
differently than non-participationin the labor
force net of calorie intake."
B.
Payment Method and the Return to Calories
The productionfunctionestimatesindicatethat
there is substantialmoral hazardassociatedwith
time-wageemploymentrelativeto piece-rate and
own-employmentpayment schemes with respect
to worker effort, as reflected in differential
scheme-specificrates of BMI depletion. The fact
that effortis not directlyobservableprecludesthe
direct estimationof the wage equation(2). However, it is possible to estimate directly whether
calories, which are also unlikely to be wellobserved by employers, are differentially rewarded according to payment scheme. Because
effort under paymentregime j may be written as
significant (F(6,2249) = 3.48). We report in the
a functionof caloric intake and health alone, we
last columnof table 2 the estimatedeffects of the
may substituteequation (11) into equation (2) to
paymentschemes and of calories implied by the
obtain a wage equation that depends only on
interactivespecificationat the sample mean level
health and calories:
of BMI; the reported BMI effect is that at the
sample mean values for the work activities and
j(Hit_l, COt)
wjit=w(i_,c,
calories. Although the estimated error-correla= wj*(Hit_1, Cit).
(18)
tion is positive (0.096) for this specification,suggestingthat the negativeautocorrelationobtained
In order to assess whether calories and health
from the linear specifications arose from the
are
differentially rewarded under piece and
omission of the interactions between initial
time-rate
wages we estimate equation (18) by
body-massand the efficiencyof calorie consumpthe
differencebetween a worker'spiecerelating
tion and expenditure, the mean effects of the
and
time
rate
wageswithinthe same surveyround
payment schemes are not very different from
to
calorie
his/her
consumption,BMI, height, sex,
those obtained from the linear (instrumented)
specifications.In particular,consistent with the schooling,and age.12The coefficientsof this specexistenceof moral hazardin time-wagework and ification are thus the difference between the
in share tenancy,the estimates indicate that, net piece-ratewage and time-wagecoefficientsin inof calorie consumption, for a worker with the dividualwage equations.If caloriesare not known
samplemean BMI employmentundera piece-rate by employers then we would expect a positive
payment scheme or self-cultivatingentirely on coefficienton calories. Coefficientson other variowned land over the surveyperiod would dimin- ables will depend on the extent to which, on the
ish BMI by 23% and 9%, respectively,relativeto one hand, these variables are associated with
the omitted activity,non-laborforce time, while higher effort given caloric intake in piece-rate
BMI would only be reduced (relatively)by 1% if work, in which case they will be positive, and on
all work time was devoted to time-wage work. 11
Of course, the left out activityincludes in some cases
The difference between the depletion of BMI household
production.The resultsdo not thereforeimplythat
under the piece-rate and self-employment "leisure"and workunder time wages are equallyonerous to
schemes is not statisticallydifferent(F(1,2249) = workers.
12 The price of corn may also affectwage rates if employers
2.18), while that between piece-rate work and use price as a signalof caloricintake, and is includedin the
either time-wagework or work under share ten- empiricalspecificationsof (18).
222
THE REVIEW OF ECONOMICS AND STATISTICS
In the second column of table 3 we report
estimatesobtainedfroma specificationwhichalso
includes controls for crop and task. Controlling
for the within-rounddifferencesin tasks does not
affect the inferences about the effects of incentives, although the precision of the differential
calorie effect is reduced.However,differencesin
tasks also do not appearto significantlyaffect the
differentialin piece-rate and time wages, net of
workercharacteristicsand the differencein payment regimes, and so are just adding random
noise to the specificationand reducingestimation
efficiency.
The estimatesof the differentialeffects of calorie consumptionon wage earningsunder the two
paymentschemes are based on a sampleof workers who were employedunder both schemes in a
single four-monthperiod. Although almost half
(47.1% based on all four rounds) of all wage
earnersin a periodworkedunderboth regimes,it
is not likely that this is a random sample of all
workers, particularlybecause only 28% of all
sample individualswork for wages at all in a
round.Indeed, it wouldbe expected,for example,
that workerswith unobserved(by employers)productivity-enhancingcharacteristicsthat are not
rewardedunder a time-wageregime would tend
to specializein either self-cultivationor piece-rate
work, while those with low levels of skills would
seek time-wageemployment.16Selectionalso may
apply to the group of workers under both payment methods; such workers are likely to be
those for whom differentialsin rewards across
wage paymentregimes are minimized.If calories
do increase piece-rate wages more than they do
time wages and workers are heterogeneous in
characteristicsthat affect their payment-method
reward differentials,the estimated effects of increased calorie consumptionon the differencein
earningsunderpiece-rateand time wage schemes
13
be underestimatedif such selection is not
may
The specificationalso includes municipio dummyvaritaken into account.This is because amongworkables.
14 These results may explain the finding in Behrmanand
ers with high levels of calorie consumption,only
the other hand they are used by employersusing
a time-wagepaymentschemeas a signalof caloric
intake (in which case they will be negative). A
likely candidatefor the latter would seem to be
BMI which is responsive to recent nutritional
intake(as is evident in table 2).
An advantageof the within-roundestimation
procedure is that the existence of any worker
characteristicsthat are not measuredin the data
but which are observedby employers,correlated
with the measured worker characteristics,and
have equal effects on piece-ratesand time-wages
will not bias the estimatesof the differentialwage
effects of the measured characteristics.Because
calorie consumption and BMI may depend on
wages, however, two-stage least-squaresis again
used. The instrumentsare land owned by the
householdand round dummyvariables.The sample consists of all individualswho worked under
both types of wage-paymentregimes within a
single round,with one randomroundselected for
each worker among those with more than two
roundsin whichhe or she had workedfor the two
wage types.
The weighted (by the number.of days in wage
activities) within-round,two-stage least-squares
estimatesare reportedin the firstcolumnof table
3.13 The estimates indicate, consistent with the
differentialincentives associated with piece-rate
and time-wage payment schemes and with the
difficultyof monitoringcalorie consumptionand
effort, that calories are significantlymore rewarded when workers are paid on a piece-rate
basis than when they receive time-wages,while
observableBMI, whichmaybe used by employers
as a signalfor calorieconsumption,is significantly
more rewardedunder time wages than under a
piece-rateregime.14"5
Deolalikar(1989), based on Indian data, that calories were
significantlymore rewardedin the labor marketduringthe
"peak" season while the opposite was true for weight-forheight because in that setting piece-rate wages are almost
exclusivelypaid for harvest(peak-period)operations.
15The positive coefficientson height, male and schooling
level should not be interpretedin the same way as that on
caloric intake, because there is no reason to believe that
employerswill have difficultyin measuringthese variables.
Instead,as indicatedabove,these coefficientslikelyreflectthe
fact that in equation (18) effort has been solved out. For
example,if the returnsto additionaleffort in termsof effective work accomplishedare higherfor taller individualsthan
these individualswill exhibithigherwages in piece-ratethan
in time-wageemployment.
16 In Foster and Rosenzweig(1993), evidence is presented
based on these data that a largecomponentof workerskillsis
not knownby employersand that adverseselectionis present:
MORAL HAZARD IN THE LABOR MARKET
TABLE
3.-WEIGHTED
DIFFERENTIAL
EFFECTS
Two-STAGE
LEAST
OF CALORIC
CONSUMPTION
MINUS
Variable
Calories (X 10-3)b
BMIb
Height
Male
Schooling
Age
Age squared
(X 10-3)
Corn price
Basic
Specificationa
0.311
(2.40)c
-19.9
(1.80)
1.38
(1.68)
0.477
(3.14)
0.0492
(1.80)
-
.00392
(0.06)
0.104
(0.10)
- 0.0638
(0.66)
TIME
SQUARES
WITHIN-ROUND
SIZE
ON PIECE-RATE
Add Task and
Sugar Harvesting
Controls
SelectivityCorrection:
Probit
SelectivityCorrection:
Multinomial Logit
0.215
(1.66)
- 22.9
(2.28)
1.56
(1.82)
0.361
(2.18)
0.0294
(1.05)
.0332
(0.52)
- 0.441
(0.48)
- 0.0279
(0.30)
1.23
0.317
(2.55)
- 19.1
(1.61)
1.33
(1.55)
0.483
(3.19)
0.0510
(1.85)
- .00830
(0.11)
0.168
(0.16)
- 0.0680
(0.69)
-0.101
(0.24)
1.18
0.347
(2.66)
-17.2
(1.36)
1.24
(1.42)
0.506
(3.28)
0.0554
(1.98)
- .0232
(0.29)
0.393
(0.34)
- 0.0719
(0.73)
-0.415
(0.58)
1.14
191
191
191
191
'All specificationsinclude municipio
b Endogenous variable: Instruments
BODY
WAGES
A (X 10-4)
Test statistic, task
variables + sugar
harvesting F(4,170)
Number of
observations
ESTIMATES:
AND
223
dummyvariables.
include owned land, household sex and age-composition,
and land interacted with round variables.
cAbsolute values of asymptotic t-ratios in parentheses.
those who were penalizedleast undera time-wage
regime would work for both piece-rate and time
wages.
To assess if sample selection importantlyaffects the estimatespresented in columnsone and
two, we estimated two selectivity models. First,
we estimatedthe standardtwo-stageprobitselection model (Heckman (1979)) in which we estimated in the first stage probit equationsfor each
of the four surveyroundsdeterminingthe probability that a worker earned both piece-rate and
time wages in that round.In principle,it is desirable to estimate a model that incorporatesthe
complete choice set of workers,of whichworking
under both paymentregimesis one of four alternatives, the others being working for piece-rate
wages only, for time wages only, and not participating in the wage labor market. From such a
model, the probabilityof being in the categoryin
which paymentforms are used can be computed
for each worker and this can be used to correct
for the selection of the sample. To estimate this
model and allow for error correlations across
round dummies
categories, as in a multinomialprobit model, is
extremelycomputationallyburdensome.Instead,
we estimate as a second model, the multinomiallogit selectivity model of Lee (1983), which
provides estimates characterizingthe complete
choice-set, although it assumes independent errors, and use those estimates to correct the second-stage equation for the selectivity,if any, of
being in any one of the alternativesample categories.
The last two columns of table 3 report the
estimates of the differencedwage equationscorrected for the selectivityof the sample using the
probitand multinomialselectivitymodels, respectively. The first-stageprobit and the multinomial
logit estimates are presented in appendixtables
Al and A2. Both selectivitymodels yield similar
results.The signsof the coefficientsof the A-terms
in each column, which indicate the correlation
between the residuals of the selection equation
and the wage difference equation, are, as expected, negative,suggestingthat individualsmore
likely to work under both payment regimes also
224
THE REVIEW OF ECONOMICS AND STATISTICS
TABLE
4.-FIXED-EFFECTS
ESTIMATES
To
EULER
Piece-rate work
OF LINEAR
0.225
(2.24)a
On-farm work
0.225
(2.24)
0.158
0.161
(1.61)
(0.98)
On-farm work x
fraction sharecropped
Time-wage work
Non-agric. work
Log BMI
Illness
Log wage
Log corn price
Hypotheses
Piece-rate and on-farm coeffs.
equal
Piece-rate and non-agric. coeffs.
equal
Piece-rate and time-wage coeffs.
equal
Hausman testb
All coefficients zero
APPROXIMATION
EQUATION
- 0.00386
(0.02)
0.0457
(0.58)
0.00513
(0.07)
-0.151
(0.40)
-0.190
(1.96)
0.0456
(0.58)
0.00515
(0.07)
-0.151
(0.40)
-0.190
(1.97)
-
-
0.0276
- 0.0276
(0.64)
(0.63)
0.423
- 0.423
(5.31)
(5.30)
F(1,588)
p-value
F(1,588)
p-value
F(1,588)
p-value
F(7,581)
p-value
F(8,588)
p-value
0.27
0.606
4.82
0.029
4.23
0.040
0.32
0.945
5.68
0.000
F(1,587)
p-value
F(1,587)
p-value
F(1,587)
p-value
F(8,579)
p-value
F(9,587)
p-value
0.13
0.723
4.82
0.029
4.22
0.040
0.28
0.973
5.04
0.000
a Absolute values of t-ratios in parentheses.
bJoint test for endogeneity of all variables other than corn price. Instrumentsinclude municipio and round
dummyvariablesinteractedwith land, age, sex, education and height.
experience a smaller premium for piece-rate
wages relativeto time-wages.And, the estimated
differential effect of calorie consumption on
piece-rate wages is greater when selectivity is
taken into accountthan when it is not. However,
the A coefficientestimatedunder either model is
not statisticallysignificant,suggesting,conditional
on the selectivitymodels approximatingthe true
model relativelywell, that sample selectivity is
not important,given the specificationof the wage
differentialequation.
C. Payment Method and the Allocation
of Calories
If, as is evident in table 3, calorie consumption
is not well-observedby employers,then we should
expect that a worker'scalorie consumption,like
worker effort or energy expenditure,is withheld
when workingfor time-wagescomparedto piece
rates or self-employment.Table 4 reports estimates of equation (17), which relates the in-
tertemporalallocation of the log of calories for
individualsworkingin the wage labor marketto
their work activitiesclassifiedby paymentregime,
their (log) BMI, illness, their (log) wage rate in
piece-ratework and the log of the price of corn,
the dominant consumption good.'7 Consistent
with the unobservabilityof calorie allocationsto
the employer and with the existence of moral
hazard with respect to calories, the estimates
indicate that piece-rate work evokes a significantly greater calorie allocation compared to
time-wageand non-agricultural
workbut no more
so than on-farmwork.18
17
We estimatedthe Euler equation on a sample of wage
workersbecause it is necessaryto includethe period-specific
value of time as a regressor,and this is not availablefor
individualswho are employedexclusivelyin own-farmactivities. Because the Euler equation is estimated using fixedeffects, sampleselectivityshouldnot be a problemas long as
the determinantsof the probabilityof wage employmentare
not differentacrossperiods.
18 The Hausman-Wutest does not reject the orthogonality
of the regressorsto the errorterm.
MORAL HAZARD IN THE LABOR MARKET
The estimates in column one indicate that
workersdevotingall of their work time to piecerate activities are allocated 23% more calories
per day on average over the reference period
comparedto a situationin which they devoted all
of their time to the non-labor-marketactivity,
workers devoting all of their time to on-farm
work are allocated 16% more calories while
workersemployed exclusivelyunder a time-wage
paymentscheme receive only an insignificant5%
greater allocationcomparedto being engaged in
the non-labor-forceactivity. In the second column we again differentiatecultivationby whether
it is performedunder a share tenancy contract.
The addition of the share tenancy variable increases the own-cultivationcalorie effect so that
it is even closer in magnitudeto that for piecerates (the differenceis not statisticallysignificant),
and suggests, as expected, that share tenancy
reduces the calorie allocation,althoughthe effect
is not statisticallysignificant.These results, consistent with the a priorirankingof workerincentives associated with the payment regimes with
respect to calorie intake, thus conform to the
findings from the productionfunction estimates
with regardto effort, as measuredby BMI depletion.19
V. Conclusion
In this paperwe have establisheda method for
testing for the existence of moral hazardin labor
markets using information on worker health,
calorie consumptionand type of paymentregime.
19
225
The estimates, based on longitudinaldata from
the Philippines, indicate that contractualterms
significantly affect worker performance, with
workereffort, as reflectedby both calorie expenditure and intake, inverselyrelated to the degree
to which the worker has a claim on his or her
contributionto output. In particular,workersevidently supplymore effort under a piece-ratepayment scheme or in self-cultivationon own land
comparedto time-wageemploymentas reflected
in the fact that they deplete their body mass by
10% more, net of calorie consumption, when
workingunder a piece-rate scheme comparedto
working as time-wage workers and 13% more
than share tenants or those outside of the labor
force. The evidence also indicates that calorie
intake is rewardedwhen workerswork for piecerates but not when workerswork for time wages
and that the same worker consumes 23% (16%)
more calories per day when employed under a
piece-rate payment scheme (on-farm employment) than when employed for time wages, also
consistent with the energy expenditureimplications of moral hazard.
These results not only provide some empirical
foundationfor the vast theoreticalliteraturethat
focusses on the central problemof effort elicitation, but suggeststhat existingempiricalevidence
on moral hazard,based on directlyobserved input use, such as labor time, fertilizer,etc., understates the extent of moral hazard. The findings
also provide evidence that enterprises that rely
on wage labor, to the extent that such labor
cannot be paid on a piece-rate basis,20or that
engage in operations on a share contract basis
will be inefficientrelative to operationsthat rely
on familylaborutilizedon own resources.Finally,
the finding that calorie consumption augments
productivityand negativelyrespondsto the price
of the staple food crop implies that the seasonal
pattern of food price variationin part augments
productivity.This is because,typically,food prices
are lowest in the peak (harvest) work periods.
Thus, stabilizationschemes may reduce total output in such contexts, to the extent that workers
work under paymentschemes that rewardeffort.
We performed two other tests of the Euler-equation
specification.First,we tested whethercalorie allocationrules
differedbetweenmen and women.The test statisticbased on
the Chowtest, in which the samplewas split intarsubsamples
containingmen and women exclusively,was not statistically
significant(F(9,576) = 1.32). We also tested whether there
were significantdifferencesin the costs of transferringresources across periods among households. Individuals'intertemporalstrategiesof storingcaloriesin the form of food
stocksor in the body, throughaugmentingBMI, may depend
importantlyon direct storagecosts and rates of interest.To
assess the importanceof heterogeneityin these costs, which
may vary by season and by wealth, we added round dummy
variablesand interactionterms involvingland size (to proxy
20
For a discussion of the conditions that determine the
householdwealth) and the round variablesto the specification. However, the set of coefficientsassociatedwith these feasibilityof piece-rateworkin the contextof agriculture,see
termswas also not statisticallysignificant(F(6,582) = 0.60).
Roumassetand Uy (1980)and Foster and Rosenzweig(1992). -
226
THE REVIEW OF ECONOMICS AND STATISTICS
REFERENCES
Bell, Clive, "Alternative Theories of Sharecropping: Some
Tests Using Evidence from Northeast India," Journal
of Development Studies 13 (1977), 317-346.
Behrman, Jere R., and Anil B. Deolalikar, "Agricultural
Wages in India: The Role of Health, Nutrition, and
Seasonality," in David E. Sahn (ed.), Seasonal Variability in Third WorldAgriculture (Baltimore: Johns Hopkins, 1989), 107-117.
Bouis, Howarth, "Progress Report on Philippine Cash Cropping Project," The International Food Policy Research
Institute, Sept. 1984, mimeo.
Bouis, Howarth E., and Lawrence J. Haddad, Agricultural
Commercialization, Nutrition, and the Rural Poor: A
Study of Philippine Farm Households (Boulder: Lynne
Rienner Publishers, 1990).
Deolalikar, Anil B., "Nutrition and Labor Productivity in
Agriculture: Estimates for Rural South India," this
REVIEW
70 (3) (1988), 406-413.
Eswaran, Mukesh, and Ashok Kotwal, "A Theory of Two-tier
Labor Markets in Agrarian Economies," American
Economic Review 75 (2) (1985a), 162-177.
, "A Theory of Contractual Structure in Agriculture,"
American Economic Review 75 (3) (1985b), 352-367.
Foster, Andrew D., and Mark R. Rosenzweig, "Unequal Pay
for Unequal Work: Asymmetric Information, Sex Discrimination, and the Efficiency of Casual Labor Markets," manuscript, University of Pennsylvania (1992).
, "Information, Learning and Wage Rates in LowIncome Rural Areas," Journal of Human Resources 28
(4) (1993), 759-790.
Heckman, James J., "Sample Bias as a Specification Error,"
TABLE
Al.-MULTINOMIAL
LOGIT
OF RECEIVING
OR BOTH
Variablea
Schooling
ONLY
TYPES
Econometrica 47 (Jan. 1979), 153-162.
Immink, M., and V. Viteri, "Energy Intake and Productivity
of Guatemalan Sugarcane Cutters: An Empirical Test
of the Efficiency Wages Hypothesis, Parts I and II,"
Journal of Development Economics 92 (1981), 251-287.
Lee, Lung-Fei, "Generalized Econometric Models with Selectivity," Econometrica 51 (Mar. 1983), 507-512.
Pitt, Mark M., Mark R. Rosenzweig, and M. N. Hassan,
"Productivity, Health and Inequality in the Intrahousehold Distribution of Food in Low-income Countries,"
American Economic Review 80 (5) (1990), 1139-1156.
Roumasset, James, and Marilou Uy, "Piece Rates, Time
Rates, and Teams: Explaining Patterns in the Employment Relation," Journal of Economic Behavior and
Organization 1 (1980), 343-360.
Schultz, T. Paul, "Measurement of Returns to Adult Health:
Morbidity Effects on Wage Rates in Cote D'Ivoire and
Ghana," mimeo (1992).
Shaban, Radwan A. "Testing between Competing Models of
Sharecropping," Journal of Political Economy 95 (5)
(1987), 893-920.
Stiglitz, Joseph E., "Alternative Theories of Wage Determination and Unemployment: The Efficiency Wage Model,"
in Mark Gersovitz, Carlos Diaz-Alejandro, Gustav
Ranis, and Mark R. Rosenzweig (eds.), The Theory and
Experience of Economic Development (London: George
Allen and Unwin, 1982),
Strauss, John, "Does Better Nutrition Raise Farm Productivity?" Journal of Political Economy 94 (2) (1986),
297-320.
Sukhatme, P. V., "Malnutrition and Poverty," Ninth Lal
Bhaduri Shastri Memorial Lecture, Indian Agricultural
Research Institute, New Delhi, 1977.
ESTIMATES:
A PIECE-RATE
OF PAYMENTS
IN A ROUND
Piece-Rate Only
-0.219
(8.87)b
Age
Age squared
Height
Land owned
Corn Price
Male
DETERMINANTS
WAGE,
0.421
(7.23)
- 0.00661
(7.47)
- 0.0194
(1.80)
- 0.410
(8.48)
- 0.490
(2.16)
2.26
(12.0)
N
x2(57)
a Specification also includes three round and nine municipio
bAbsolute value of asymptotic t-ratio in parentheses.
ONLY
OF THE LOG-ODDS
A TIME
RELATIVE
WAGE
TO NEITHER
Time Wage Only
Both Wages
-0.135
(5.72)
0.306
(5.74)
- 0.00454
(5.73)
- 0.0107
(1.01)
- 0.289
(8.25)
0.115
(0.51)
1.27
(7.39)
4896
1785.9
-0.167
(9.02)
0.200
(5.82)
- 0.00294
(5.92)
- 0.0248
(2.96)
0.241
(10.43)
-0.279
(1.62)
2.34
(15.8)
dummy variables.
227
MORAL HAZARD IN THE LABOR MARKET
TABLE
A2.-PROBIT
ESTIMATES,
PIECE-RATE
AND
BY ROUND:
TIME
DETERMINANTS
WAGE
RATES
OF RECEIVING
BOTH
IN ROUND
Round
Variablea
Schooling
Age
Age squared
Height
Land owned
Corn price
Male
N
X2(16)
a
1
- 0.106
(3.75)b
0.189
(2.87)
- 0.00303
(3.01)
-0.00955
(0.80)
-0.142
(3.27)
0.189
(0.41)
0.871
(4.36)
1224
149.0
2
3
4
- 0.0908
- 0.0614
- 0.0876
(3.70)
0.122
(2.57)
-0.00210
(2.97)
-0.0199
(1.84)
-0.174
(4.58)
0.196
(0.54)
0.147
(7.40)
1224
246.5
(2.34)
0.117
(2.18)
-0.00177
(2.24)
-0.00912
(0.76)
-0.150
(3.16)
-0.267
(0.81)
0.636
(3.27)
1152
91.3
(3.68)
0.310
(4.85)
- 0.00474
(4.87)
0.00650
(0.63)
-0.167
(4.15)
-0.590
(3.50)
0.561
(3.29)
1224
206.4
Specification also includes nine municipio dummy variables.
value of asymptotic t-ratio in parentheses.
bAbsolute