Intrahousehold Time Allocation in Rural Mexico: Evidence

from a Randomized Experiment

Marta Rubio-Codina�

June 1, 2009y

Abstract

This paper examines the e¤ects of Oportunidades, a conditional cash transfer program, on

the allocation of time of household members in rural Mexico. I exploit the random placement

of bene�ts across communities in the evaluation sample and the program�s eligibility crite-

ria and scheme of incentives to identify e¤ects. The majority of Oportunidades bene�ts are

linked to children�s school attendance, implying a reduction in the price of schooling. I argue

that changes in relative prices lead to substitution e¤ects whereas the (almost) unconditional

nutritional transfer translates into an income e¤ect. Findings show increases in schooling

and reductions in children�s participation in market and nonmarket work. Although the pro-

gram does not seem to substantially alter adults�time allocation, evidence suggests that adult

women substitute for children�s time in nonremunerated activities.

Keywords: Intrahousehold Time Allocation, Income and Substitution E¤ects, Condi-

tional Cash Transfer Programs, Mexico.

JEL Codes: D10, J22.

�The Institute for Fiscal Studies: marta_r@ifs.org.uk.yI am grateful to two anonymous referees, Orazio Attanasio, Tania Barham, Pierre Dubois, Paul Gertler and

Sylvie Lambert for useful comments and suggestions on earlier versions. The usual disclaimer applies.

1

1 Introduction

Time is both a fundamental and a scarce resource in the economy. The study of the role of

time in labor economics and the use of time budget data to model microeconomic behavior

have developed since Becker (1965) seminal piece. It is nowadays widely acknowledged that

time availability � or scarcity � and its allocation to various activities can ultimately deter-

mine the relative prices of goods and services, total production levels, and the distribution of

income (Juster and Sta¤ord 1991). However, the many methodological issues involved in the

measurement and treatment of time data have somehow limited the amount of applied research

in the area. Notable exceptions are the works by Gronau (1977), Evenson (1978), Mueller

(1984), Kooreman and Kapteyn (1987), Skou�as (1993) and Parker and Skou�as (2000). Ellis

(1994) provides a review of descriptive studies on time allocation in agricultural households in

developing countries.

Within a household, family members devote time to many di¤erent activities: participation in

the labor force, household chores, studying, or "pure" leisure activities, like hobbies or personal

needs and care (sleeping, eating, etc.).1 Moreover, the marginal rate of substitution of time

allocated to each of these activities between di¤erent household members is likely to be a¤ected

by certain government policies, such as work taxation, education stipends or child care subsidies.2

If available, detailed individual data on time use provides a unique research resource to examine

the impacts of public policies on individual behavior, both inside and outside the household.

This paper uses cross-sectional data on time allocation for all household members (older than

8 years old) to examine intrahousehold time responses to one such policy in rural Mexico. In

1997, the Mexican Federal Government launched the Education, Health and Nutrition Program,

Progresa, renamed Oportunidades under the Fox Administration. This antipoverty strategy pro-

vides monetary and in-kind bene�ts to targeted poor households conditional upon preventive

care and school attendance. The poor rural communities where the program operates are tra-

1Gronau (1977) �rst distinguished between leisure and work at home in theoretical models, while acknowledging

that the distinction is not straightforward. For example, eating and sleeping could be alternatively considered an

input in the household production function because they increase productivity.2The impact of a particular policy on intrahousehold time allocation depends on the household decision-making

model assumed. Behrman (1997) and Bergstrom (1997) provide excellent reviews of the existing theories.

2

ditionally characterized by very high labor force participation rates of men compared to those

of women. In addition, children �typically boys �tend to begin their labor force participation

at early ages in order to contribute to family income.

Oportunidades aims to increase basic education school enrollment and reduce child labor by

linking part of the bene�t to school attendance. This implies a reduction in the shadow wage

of children�s time allocated to activities other than schooling, and amongst other household

members �i.e. a change in relative prices. As a result, own-substitution and cross-substitution

e¤ects are likely to arise. For example, and assuming that school and work are substitutes,

one would expect increases in school participation and reductions in children�s time allocated

to market and nonmarket work. Moreover, a reallocation of adult household members�time to

market and nonmarket activities previously done by eligible children in the household are also

expected. The rest of the bene�t is an (almost) unconditional �xed amount that all treatment

households receive to spend on improved nutrition. This translates into an expansion of the

household�s budget set, and hence is likely to lead to a reduction in the labor supply of household

members through the income e¤ect.

The objective of this paper is to decompose any changes in the time allocation of house-

hold members as a result of the program into income, own-substitution, and cross-substitution

e¤ects. Identi�cation relies on the predictions of a very simple unitary household model and

exploits the random placement of bene�ts across communities in the evaluation sample, base-

line household composition, and the program�s scheme of incentives. Results show increases in

schooling and reductions in children�s labor participation in market and nonmarket activities,

primarily through the own-substitution e¤ect. Although the program does not seem to substan-

tially alter adults�time allocation, there is evidence that adult women in the house substitute

for children�s time in domestic and farm work. These �ndings indicate that Oportunidades is far

from providing disincentives to adult female labor supply or generating an increase in welfare

dependency amongst this population group. Rather, the program is motivating a reallocation of

time towards nonmarket work activities for adult female such that children can attend school.

This paper contributes to the existing literature on the e¤ects of Oportunidades on promoting

human capital accumulation. Overall, �ndings in the literature suggest positive impacts of the

3

program on school enrollment (Schultz 2004, Parker and Skou�as 2001) and on grade progression

(Behrman et al 2006, Dubois et al 2008). Concerning labor force participation, Parker and

Skou�as (2000, 2001) �nd reductions in child labor in paid activities for boys. For adults,

both Parker and Skou�as (2001) and Skou�as and DiMaro (2008) �nd very little impacts of the

program on adult labor supply and leisure. This piece builds on Parker and Skou�as (2000) in an

attempt to identify any changes in intrahousehold time allocation resulting from the intervention,

as well as the transmission channels of these e¤ects.

The remainder of the paper is organized as follows. Next section describes the Oportunidades

intervention in greater detail. In section 3, I discuss the expected e¤ects of the program on time

allocation within a unitary model of household behavior framework. Section 4 describes the

data. Section 5 and 6 develop the identi�cation strategy and present results on individual and

intrahousehold time allocation. Finally, section 7 concludes.

2 The Rural Oportunidades Program

Oportunidades is an antipoverty program that combines a traditional cash transfer approach

with �nancial incentives for positive behavior in health, education and nutrition. Over its �rst

three years, the program extended bene�ts to almost all eligible families living in rural areas.

Starting in 2001, it expanded to urban areas and currently covers around 5 million families all

over Mexico. It is one of the largest conditional cash transfers program in the world, distributing

approximately 3 billion US dollars to some 5 million bene�ciary households in 2008.3

2.1 Bene�t Structure

The cash transfers are disbursed bimonthly in two forms. The �rst is a �xed nutritional grant

intended for families to spend on more and better nutrition. It is complemented with nutritional

supplements and immunization directed to 0 to 2 year olds, and to pregnant and lactating women.

Children 2 to 5 also receive nutritional supplementation if signs of malnutrition are detected.

Families must complete a schedule of visits to health care centers and follow monthly talks on

disease prevention, nutrition and health �called "pláticas" �to receive the nutritional stipend.

3http://www.oportunidades.gob.mx/informacion_general/main.html

4

The second is an educational grant given for each child younger than 18 and enrolled in school

between the third grade of primary school and the third grade (last) of junior high.4 It rises

as children progress to higher grades in order to re�ect their monetary contribution to family

income if they were working. In particular, the transfer amount increases substantially after

graduation from primary school and is higher for girls than boys during junior high and high

school to try to reduce dropout during the transition year. Bene�ciary children also receive

money for school supplies once or twice a year. Students lose their bene�ciary status if they

miss school more than 15 percent of the time for unjusti�ed reasons or if they repeat a grade

twice.

There is an upper limit in the total transfer amount received per household. Whenever this

cap is hit, the educational monthly grants are proportionally reduced so that the total household

transfer amount equals the maximum. Table 1 contains the monetary values of the transfers

in October 1997 pesos (baseline). The cash transfer is directly given to the female head of the

household for two reasons: �rst, because Oportunidades seeks to promote gender equality; and

second, because it is believed that resources controlled by women are more likely to be translated

into greater improvements in children�s wellbeing than resources controlled by men. On average,

the total transfer received represents over 20 percent of total household income.

2.2 Bene�ciary Eligibility and Enrollment

When Oportunidades was �rst rolled out in rural areas in 1997, program eligibility was deter-

mined in two stages (Skou�as et al 2001). First, underserved communities were identi�ed based

on the proportion of households living in poverty as de�ned in the 1995 population census.

Second, low-income households in these communities were chosen using a proxy means test.

Pre-intervention (baseline) data to construct the index was collected in October 1997 on all

households in eligible communities through the Survey of Household Socioeconomic Characteris-

tics (Encuesta Socioeconómica de Hogares, or ENCASEH). This classi�cation scheme designated

52 percent of households in selected communities as eligible for bene�ts ("poor"). Subsequently,

the Government decided that a subset of the "non-poor" households had been unduly excluded.

They expanded the eligibility criteria to include a set of slightly wealthier households in a process

4Beginning in 2001, high school scholarships are granted to all bene�ciaries younger than 21 enrolled in school.

5

called "densi�cation". However, many of these households were not incorporated into the pro-

gram until later because of administrative delays and operational di¢ culties (Hoddinott and

Skou�as 2004).

All eligible households living in treatment localities were o¤ered Oportunidades and over 90

percent enrolled. Once enrolled, households received bene�ts for a three-year period with the

possibility of being recerti�ed if all household members obtained the prescribed preventive med-

ical care, children attended school and mothers participated in the "pláticas". About 1 percent of

the households were denied the cash transfer for non-compliance. New households were not able

to enroll until the next certi�cation period, which prevented migration into treatment communi-

ties for Oportunidades bene�ts. Households in rural areas were "recerti�ed" (reassessed with a

proxy means test) after three years on the program to determine future eligibility. If a household

was recerti�ed as eligible, it would continue receiving bene�ts. If not recerti�ed, the household

was guaranteed six more years of support before transiting o¤ the program. Thus, households

could expect a minimum of nine years of bene�ts upon enrollment (OPORTUNIDADES 2003).

2.3 Experimental Design and Data Sources

A remarkable aspect of Oportunidades was the commitment of the Government to conduct a

rigorous evaluation. Given budgetary and logistical constraints, the Government could not enroll

all eligible families in the country simultaneously and had to phase in enrollment over time. For

ease of implementation, the Government decided that whole communities would be enrolled at

a time and that they would be enrolled as fast as possible so that no eligible household would

be kept out of the program.

As part of this process, the Government randomly chose 506 experimental communities in

seven states in rural Mexico over which it randomly phased in treatment: 320 communities (60

percent) were then randomly allocated to the treatment group, and the remaining 186 commu-

nities (40 percent) to the control group. Eligible households in treatment communities began

receiving bene�ts in March/April of 1998; whereas eligible households in control communities

were not incorporated until November/December of 1999. In order to minimize anticipation

e¤ects, households in control communities were not informed that Oportunidades would provide

bene�ts to them until two months before incorporation. Behrman and Todd (1999) con�rm

6

that the randomization balanced control and treatment communities. Attanasio et al (2005)

explicitly test but �nd no evidence of anticipation e¤ects amongst control households.

Every six months, from October 1998 to November 2000, Oportunidades collected detailed

information on a host of topics in the Evaluation Surveys (Encuestas de Evaluación, or ENCEL).

The complete evaluation sample consists of a panel of the 24,077 eligible and ineligible households

in the 506 eligible experimental communities. In 2003, the evaluation added a new control group,

which was surveyed along with the original 506 communities. All these data can be merged at

the individual level to the ENCASEH census for a total of seven rounds of data between 1997

and 2003.

Skou�as (2005) provides a detailed description of the program and the evaluation design,

along with a summary of impacts over the �rst three years of implementation.

3 Oportunidades and Intrahousehold Time Allocation

This section develops a simple family labor supply model that characterizes the e¤ects of the

economic incentives o¤ered by Oportunidades in the allocation of time of all household members

under a unitary household framework. The model �based on Ashenfelter and Heckman (1974)

and Newman and Gertler (1994) �allows for choices between di¤erent work activities and breaks

down changes in individual time use due to the program into income, own- and cross-substitution

e¤ects.

Consider a household h consisting of i = 1; :::; I members (a = 1; :::; A � I adults, q =

1; :::; Q � I children and k = 1; :::;K � Q children eligible for bene�ts, such that A+Q = I and

K � Q) with a utility function of the form:5

U = U(L1; :::; LI ; C;X; ") (1)

where C is total (aggregate) household consumption and Li is the leisure time of individual i,

X represents observable heterogeneity (including individual, household and community char-

acteristics) and " represents unobservable household heterogeneity. Each household member is5A child is eligible for bene�ts if she: (i) lives in a poor family; (ii) has completed between second and eight

grade of basic education; and (iii) is younger than 18. Any eligible child e¤ectively receives bene�ts if she: (i)

lives in a treatment community; and (ii) is enrolled in grades 3 to 9 of basic education and attends school.

7

endowed with a total amount of time T that she can allocate between leisure and j = 1; :::; J

di¤erent activities: market work (m), farm work (f) and domestic work (d). Children can

additionally engage in schooling (s). The household faces the following budget constraint:

Pi

Pj 6=sW

ji H

ji + Y � PC +

Pi=qW

si H

si (2)

where Hji are the number of hours individual i spends on activity j and W

ji is i�s marginal

return to activity j 6= s.6 The price of schooling, W si , equals the real cost of schooling: fees,

books, transportation, etc. P is the price of the aggregate commodity good and Y is nonlabor

income. The I time constraints are:

Pj H

ji + Li = T 8i (3)

and the I � J non-negativity constraints are:

Hji � 0 8i; j (4)

After replacing the I time constraints in (1), the household optimization problem boils down to

choosing the hours each family member supplies to each of the J activities and total family con-

sumption C that maximize (1) under (2) and (4). Assuming that the second order conditions for

a maximum are satis�ed, the I � J �rst order conditions: Hji � 0 and H

ji

�@U(:)=@Li@U(:)=@C �

W jiP

�=

0;8i; j, plus the family budget constraint in (2) yield the resulting demands for aggregate con-

sumption and individual leisure:

C = C(W;P; Y ;X; ") (5)

Li = Li(W;P; Y ;X; ") 8i (6)

where W = (Wm1 ; :::;W

mI ;W

f1 ; :::;W

fI ;W

d1 ; :::;W

dI ;W

s1 ; :::;W

sQ). From the I time constraints in

(3), hours supplied by individual i to each activity j are:

Hji = H

ji (W;P; Y ;X; ") 8i; j (7)

6This amounts to assuming that household members have di¤erent productivities in the performance of a given

activity, and hence allows for specialization according to comparative advantages. This assumption is supported

by the data: indeed, (statistically signi�cant) di¤erent wages are reported at the community level for male, female

and children for di¤erent types of activities.

8

All standard consumer theory results hold under this framework. However, two substitution

e¤ects should be now considered: (i) the substitution e¤ect given the variation in a family

member�s (shadow) wage on its own labor supply, the own-substitution e¤ect ; and (ii) the

income-compensated e¤ect of such wage variation on any other family member�s labor supply,

the cross-substitution e¤ect.

In this stylized household, the Oportunidades intervention amounts to:

1. a reduction in the price of schooling given the educational grant, tk > 0; received by each

child k eligible for bene�ts and conditional on him attending school: dW sk = �tk < 0,

where d denotes variation.

2. an increase in nonlabor income given the (almost) unconditional nutritional grant n: dY =

n > 0.7

Therefore, for each household member i in a treatment household, the e¤ect of the Oportu-

nidades package (Pk dW

sk ; dY ) on her time supplied to activity j is:

dHji =

Pk@Hj

i@W s

kdW s

k +@Hj

i@Y dY 8i; j (8)

Using the Slutsky equation, the program�s total e¤ect on Hji can be re-written as:

dHji =

@ bHji

@W sidW s

i +Pk 6=i

@ bHji

@W skdW s

k +h�PkH

jkdW

sk + dY

i@Hj

i@Y 8i; j (9)

where bHji =

bHji (W;P;U ;X; ") is the Hicksian (utility compensated) labor supply function.

8

The �rst term in equation (9) is the own-substitution e¤ect on i = k�s time spent on activity

j from receiving the educational grant. By de�nition, this term will be nonzero for eligible7 It is not completely accurate to state that the nutritional supplement is unconditional. Mothers have to

perform a certain number of activities �namely, attend "pláticas" and take the children to health centers �which

are time consuming and therefore might change behavior through substitution e¤ects of a nature similar to those

generated by the educational grant. However, the time commitments for the mothers are much smaller than those

implied by the educational grant for the children and can arguably be ignored.8This expression assumes that Oportunidades has no e¤ect on local prices or wages (dP = dW j

i = 0;8j 6= s).

Hoddinot and Skou�as (2004) explicitly test for changes in food prices in treatment communities and �nd no

e¤ects. Similarly, Gertler et al (2008) �nd no signi�cant di¤erences in wages between treatment and control

communities. The theoretical model also ignores any community spillover e¤ects. This simplifying assumption

will be relaxed in the empirical part, which will allow for correlations across households in the same community.

9

children k living in treatment households and e¤ectively receiving the educational grant; and

zero otherwise. The second term is the sum of all cross-substitution e¤ects on Hji given the

educational grants received by the (other) k children in a treatment household �i.e. the (other)

children in a treatment household that receive the educational stipend. Hence, cross-substitution

e¤ects will arise for any individual i (or k) living in treatment households where there are (other)

bene�ciary children. The third term in equation (9) is the total income e¤ect coming from the

educational and the nutritional grants. All household members in treatment households are

subject to the income e¤ect given the (almost) unconditional nutritional grant, regardless of

whether there are eligible children enrolled in and attending school in the household.

It seems reasonable to assume that all j work activities are inferior goods�@Hj

i@Y < 0;8j 6= s

�,

and that schooling is a normal good�@Hs

i@Y > 0

�. This would imply a negative total income term

in equation (9) for the labor supply equations of any household member, and positive for the

schooling demand of school aged children.

For any bene�ciary child k, the own-substitution e¤ect will go in the same direction as the

income e¤ect under two further sensible assumptions: (i) schooling is an ordinary good, which

implies @ bHsk

@W sk< 0; and (ii) schooling and each of the j 6= s work activities are substitutes,

which implies�@ bHj

k@W s

k> 0

�.9 These e¤ects might however be o¤set by nonzero cross-substitution

e¤ects resulting from the reduction in the price of schooling of other bene�ciary children (likely

siblings) in the household. The sign of the cross-substitution e¤ect between any two individuals

will be positive or negative depending on whether schooling and work of the two individuals

are substitutes or complements. Thus, the total e¤ect on schooling demand and labor supply

depends on the magnitude and direction of the di¤erent e¤ects at play and remains an empirical

question.

For non-bene�ciary children in treatment households, an analogous reasoning predicts an

increase in schooling and a reduction in participation and hours spent in work related activities

9The existing empirical evidence suggests that market work and schooling are substitutes (see for example,

Ravallion and Wodon 2000). On the contrary, farm and domestic work are more likely to involve activities

compatible with schooling rather than perfect substitutes. Hence, as@ bHj

k@Ws

k! 0, smaller own-substitution e¤ects

on farm and domestic hours will be expected.

10

due to the income e¤ect. The own-substitution e¤ect for these children is zero as they are

not direct bene�ciaries of the educational grant. The total e¤ect of the program on their time

allocation will thus depend on the sign and size of the cross-substitution e¤ects, which will in

turn depend on whether schooling and work between non-bene�ciary and bene�ciary children

in the household are complementary or substitute activities.

Similarly, income and cross-substitution e¤ects are likely to a¤ect the labor participation of

adults in treatment households. For each activity j, a reduction in participation is expected as a

result of the income e¤ect, @Hji

@Y > 0. On the other hand, an increase in participation is expected

given the reduction in the price of schooling of k: @ bHjk

@W sk< 0 and dW s

k < 0. The underlying

assumption for positive cross-substitution e¤ects between adults and bene�ciary children is that

their work activities are substitutes. If so, the educational grant implies an increase in the

shadow value of adults�time to activities children would otherwise engage in if they were not in

school.10

Next, I turn to the description of the expected e¤ects of Oportunidades on the leisure time

of household members. Replacing equation (3) in equation (9), the total e¤ect of the program

on i�s leisure becomes:

dLji = ��@ bHs

i@W s

i+Pj 6=s

@ bHji

@W si

�dW s

i �Pk 6=i

�@ bHs

i@W s

k+Pj 6=s

@ bHji

@W sk

�dW s

k �

�hP

k

��T +Hs

k +Pj 6=sH

jk

�dW s

k + dYi�

@Hsi

@Y +Pj 6=s

@Hji

@Y

�8i; j (10)

As before, the �rst, second and third terms identify �in this order �the own-substitution, cross-

substitution and income e¤ects. For non-bene�ciary children and for adults, i 6= k, increases

in leisure time are expected if leisure is assumed a normal good. However, the expected total

program e¤ect on leisure is ambiguous as it depends on the sign of the cross-substitution e¤ects �

namely, whether the leisure times of household members i and k are complements or substitutes.

10Note that perfect substitutability between adults�and children�s work is not required for these implications to

hold. As a matter of fact, the size of the cross-substitution e¤ect �i.e., whether adults partially or fully substitute

for children�s time in activity j �will depend on the degree of substitutability. Because the time allocated to

nonmarket activities is likely to be more elastic � this is, more of a substitute � to changes in other household

member�s wages than the time allocated to market work, larger cross-substitution e¤ects regarding domestic and

farm work activities are expected.

11

For bene�ciary children, i = k, there is an added degree of uncertainty because of the nonzero

own-substitution e¤ect. This e¤ect will have a positive or a negative sign depending on whether

the reductions in the total number of hours working implied by the program are larger than the

increases in total schooling time, or vice versa.

4 The Data

4.1 Analysis Sample and Balance

The dependent variables in this paper are constructed using information on a time use module

associated to the May 1999 evaluation survey. This module contains information on the amount

of time each household member older than 8 allocated to the 19 di¤erent activities �listed in

Table 2 �during the day before the interview. For the purpose of analysis, these activities have

been classi�ed into four di¤erent work categories �market (paid) work, farm work, domestic

work and the sum of the three (all work) �schooling and leisure.11 Leisure is de�ned as a residual

(i.e. 24 hours minus total time devoted to other activities) and includes time spent sleeping,

eating and socializing, which is estimated around 9 daily hours for Mexico (INEGI 2000).12

The sample of analysis is restricted to individuals that were not interviewed on a Sunday

or a Monday. Weekday and weekend time use patters are likely to be di¤erent, specially for

children in school. Basic descriptive statistics evidence that children spend less time in school

and increase their participation in household chores and farm related activities over the weekend.

Similarly, adults tend to spend less time working for a wage and more time working in the house

or enjoying leisure during weekends. As the main interest here is the e¤ect of the Oportunidades

educational incentives on the time use of all household members, I focus on the study of weekday

time use exclusively.

11Other activities, such as community work and transportation, have also been considered. However, estimates

(available upon request) are highly unreliable given the overall low participation rates in these activities, which

is likely due to the short reference period �i.e. the day before the interview.12One of the main challenges in time use surveys is the adequate treatment of activities performed simultaneously

in order to avoid double accounting. The Oportunidades questionnaire addresses this issue with a complementary

question that asks individuals to rank activities executed at the same time by order of importance. However,

answers to these questions are di¢ cult to interpret as they often refer to consecutive �rather than simultaneous

�activities, such as transportation and schooling or paid work.

12

Moreover, the analysis is also limited to eligible ("poor") households as classi�ed under

the original classi�cation scheme. In rural Mexico, children enroll in school for the following

academic year as early as in May. Hence, time allocation decisions in May 1999 are a¤ected by

choices made in May 1998. In order to observe behavioral household responses (children school

enrollment) to the program incentives, households should have received or at least be aware of

their inclusion in the program by May 1998. Administrative data on transfer payment records

shows that no household re-classi�ed as eligible under the "densi�cation" process had received

payments by then (Hoddinott and Skou�as 2004). In order to not attribute treatment e¤ects to

untreated households, I exclude "densi�ed" households from the analysis.

The �nal sample consists of the 35,148 individuals in 7,967 eligible households with weekday

time use information in May 1999. These represent almost two thirds (64 percent) of all eligible

households originally classi�ed as "poor". The treatment group includes 2,911 (37 percent)

"poor" households in treatment communities, and the control group the remaining 5,056 (63

percent) "poor" households in control communities. Note that the proportion of households

with existing time use data in the treatment and control groups is very similar to the original

(randomized) distribution �60 percent treatment and 40 percent control. At the time of the

time use survey, households in the treatment group had enjoyed bene�ts for over a year (since

May/April 1998), whereas no household in control communities had yet received any program

bene�ts. Recall that randomized out communities were not phased in until November/December

1999.13 It is important to clarify at this point that, treatment is de�ned as "intention to treat"

rather than "actual treatment" in the current setting. This implies that all households that

were o¤ered treatment are included in the sample, regardless of their �nal decision to take up

the intervention.14

Table 3 presents pre-intervention characteristics for households in the treatment and control

communities in the estimation sample. The head of the household is often the man and the

principal source of family income. Both he and his spouse have, on average, less than primary

school completed, conditional on having some education. The average household size is six13Time use data were collected again in November 2000. By then, however, all eligible households in both

treatment and control communities were receiving bene�ts.14Previous studies report high compliance rates � at around 90 percent � and low non-compliance with the

program rules �of about 1 percent (see Skou�as 2005, Gertler et al 2008).

13

members and around 65 percent of the households have at least one child eligible to receive

primary school bene�ts. The closest secondary school, an indicator of the distance to the largest

locality in the area and a proxy of the availability of labor markets, is on average about 3 km

far. The last column tests the di¤erence in means for pre-intervention characteristics between

the treatment and control groups. Out of the 32 characteristics tested, only the number of hours

the head�s spouse works for a wage is statistically signi�cant � at the 10 percent level. This

well-balanced sample suggests that the randomization process was e¤ective in generating truly

exogenous variation in treatment, and that the two groups are comparable on observed (and

unobserved) characteristics.

4.2 Time Use Allocation Before the Intervention

This subsection discusses the pre-intervention time allocation patterns of children (Table 4 ) and

adults (Table 5 ) in the poor rural communities Oportunidades operates. Because no data on

time use was collected at baseline, Tables 4 and 5 report participation rates and daily hours

devoted to the di¤erent activities in May 1999 by household members in control communities.

Arguably, the time use of the control group in May 1999 is a good proxy of the time allocation of

the treatment group before the intervention for two reasons: (i) the treatment and control groups

share similar characteristics and are hence comparable, as seen in the previous subsection; and

(ii) the control group had not received bene�ts by May 1999.

A common trend in Table 4 is the negative correlation between children�s schooling and work.

While school enrollment falls with age, participation in any of the work activities considered �

market, farm or domestic work � increases. However, there are some di¤erences by gender.

Younger girls, ages 8 to 11, show higher school enrolment than boys in the same age group. But

this pattern is reversed during adolescence, suggesting that girls drop out of school earlier than

boys and especially during the transition from primary to junior high school (at ages 12 or 13,

on average).15 Moreover, while teenage girls �ages 14 and older �engage in household chores

more than in schooling, teenage boys�participation in any type of work is higher than schooling

15School attendance rates are underreported in this survey round compared to other ENCEL rounds. Parker

and Skou�as (2000) point out the fact that school may have already �nished in rural isolated communities or may

be close to its end at the time the time use module was carried out as a possible explanation.

14

only at later ages (16 and over). On average, boys�market and farm work labor supplies are

higher than girls�at all ages.

The amount of time boys and girls devote to work increases with age, although some types

of work are more time demanding �and hence interfere with schooling more �than others. For

instance, domestic tasks require no more than one and a half hours on average for boys and

between 2 and 4 hours for girls; market activities demand over 7 daily hours; and farm work

takes up between 3 to 4 hours per day for boys and less than 2 hours for girls. This suggests

that while domestic and farm work are compatible with schooling, participation in market work

is a full time occupation.

The time use patterns for adult men and female in Table 5 re�ect a traditional gender division

of labor in these communities. While less than 7 percent of women report working for a wage,

over 90 percent participate in domestic work almost full time �over 5 and a half hours per day,

on average. On the other hand, men show higher participation rates in market work (60 percent)

than in domestic work (42 percent). As for children, market work is a full-time activity for both

men and women, requiring 7 to 8 hours per day on average. One �fth of women and one third of

men participate in farm activities. Conditional on participation, however, men devote three and

a half hours more to working the farm land and taking care of animals than women. Finally,

men enjoy less leisure time than women, which is at odds with previous evidence from time use

studies in urban Mexico (INEGI 2002). One possibility is that female leisure is overestimated

in the data if, for example, not all of the relevant housework activities women engage in on a

typical day in rural areas were included in the questionnaire.

5 Program Impact on Individual Time Use

5.1 Empirical Strategy and Identi�cation

This section presents intent to treat estimates of Oportunidades on the time allocation of children

(ages 8 to 17) and adults (ages 18 and over) in May 1999. This accounts for 18 months of program

exposure for eligible households in treatment communities. Because randomized out (control)

communities had not yet been incorporated in the program at the time, the average treatment

15

e¤ect is unbiasedly identi�ed with a treatment dummy variable as follows:

Yih = �o + �1Th +Pr�rXihr + "ih 8i = 1; ::; N (11)

where Yih can either be leisure time of individual i in household h; participation of i in activity

j; or hours i spends on activity j. The following j activities are considered: market work, farm

work, domestic work and schooling (only for children). Th is a dummy that takes on the value

of one if i lives in a treatment community and 0 otherwise. Xihr represents a vector of r char-

acteristics including: current individual characteristics (age, age squared, education, ethnicity,

and rank amongst siblings and parental characteristics �for children only); baseline household

demographics (sex of the head, and household size and composition), baseline household assets

(bathroom, electricity, land ownership and draft animal ownership); community distance to the

closest municipality and state dummies. The error term "ih includes individual preferences for

schooling and work and other unobservables, which are uncorrelated with the treatment sta-

tus by construction. Indeed, randomization guarantees that Th ? "ih. Because Oportunidades

sampled (and randomized) entire communities, I cluster standard errors at the community level.

For each activity type, equation (11) is separately estimated for boys, girls, men and women.

The leisure equation is estimated using OLS. The impact on participation in each activity j is

estimated using a probit model (variation at the extensive margin). Conditional on participation

changes in the amount of daily hours spent on each activity (variation at the intensive margin)

are estimated using a left-censored tobit model with censor value at zero. The mean of the

observation-by-observation marginal e¤ects have been computed for the probit and tobit models

as discussed in section 8.1 in the Appendix. For the tobit models, marginal e¤ects are reported

for the mean of all positive outcomes �i.e. conditional on participation.

In a second set of estimates, the household treatment status is interacted with several dum-

mies, Dihg that equal one if individual i belongs to age group g. The estimation equation

becomes:

Yih = �o +Pg�gDihg +

Pg gThDihg +

Pr�rXihr + "ih 8i = 1; ::; N (12)

The coe¢ cient on the interaction term ThDihg is thus an estimate of the average program impact

for those individuals in age group g. I consider the following age groups for children: 8 to 11

and 12 to 17; and 18 to 24, 25 to 34, 35 to 44, 45 to 54 and more than 55 years, for adults.

16

Identi�cation relies on the exogeneity of the treatment status conditional on observables:

Th ? "ih p (Xihr; Dihg). This assumption, which is guaranteed by the randomized allocation

of treatment, implies that E ("ih j Xihr; Dihg) = 0. Randomization also implies that treatment

is independent of individual characteristics, (Th ? Dihg), and hence the treatment e¤ect for

di¤erent age groups is orthogonal to the unobservables: ThDihg ? "ih p (Xihr; Dihg). Therefore,

E ("ih j ThDihg; Xihr; Dihg) = 0 follows and the di¤erent b g�s are identi�ed.5.2 Results on Children�s Time Use

Table 6 presents estimates of the overall program impact on the time allocation to the di¤erent

activities considered (in columns) for boys (Panel I) and girls (Panel II). Estimates on the pooled

sample (Models A) and by age (Models B) are reported in rows.16

Results show a signi�cant reduction in leisure time for girls ages 12 to 17 (column 1, Model

IIB). Indeed, as a result of the program, teenage girls increase the amount of time they spend in

school (column 3, Model IIB) by more than they reduce the number of hours they work (column

5, Model IIB). For boys, the increase in school hours (column 3, Model IB) and reduction in

work time (column 5, Model IB) are quantitatively similar. As a result, there is no signi�cant

impact on their leisure time. Increases in school participation are also larger for teenage girls

(8.7 percentage points) than for teenage boys (4.3 percentage points). This �nding is consistent

with previous research on Oportunidades (Schultz 2004; Parker and Skou�as 2000; 2001) and is

likely to respond to the program structure of incentives �i.e. larger transfer amounts for girls

during junior high school. Note also that impacts are larger at the intensive margin, which is

not too surprising considering the relatively high primary school enrollment rates.

The reduction in work hours for girls (column 5, Model IIA) � and especially amongst

teenage girls (column 5, Model IIB) �is primarily driven by a reduction in the number of hours

they devote to household chores (column 11, Models IIA and IIB). This �nding is of particular

importance given that 60 percent of teenage girls spend, on average, more than 3 hours per day

performing domestic tasks (see Table 4 ). For boys, the program reduces their time in market,

farm and domestic activities, although these reductions are not statistically signi�cant. The sum

16All regressions include the covariates listed above � point estimates on the covariates are available upon

request. Results (upon request) are qualitatively robust to the exclusion of these covariates.

17

of e¤ects is, however, signi�cant (column 5, Models IA and IB). Further disaggregation of the

e¤ects by ages shows a dramatic reduction �of 3.3 percentage points or a 65 percent decrease!

�in market work participation for boys ages 12 to 13 (results available upon request).

Figures A1 to A3 plot the distribution of the estimated observation-by-observation mar-

ginal e¤ects for those activities where the program has a larger impact: boys�schooling, girls�

schooling, and girls�domestic work. For each �gure, the �rst column plots the unconditional

distribution of the marginal e¤ects; the second plots marginal e¤ects by age; and the third by

the number of children eligible for educational bene�ts in the household. E¤ects on participation

and hours are plotted on the �rst and second rows, respectively.

As the �rst graph in Figure A1 shows, around 20 percent of boys increase attendance by the

maximum value of 3.7 percentage points. On the other hand, the distribution of schooling hours

follows a normal, with a majority of boys experiencing increases similar to the average increase

of 0.17 hours. The distribution of e¤ects by age shows that the marginal impact on participation

is higher for teenage boys (ages 12 to 17). Nonetheless, e¤ects are rather constant by number of

eligible children in the household, suggesting little cross-substitution e¤ects in schooling. The

plots in Figure A2 show very similar distributions of marginal e¤ects on girls�schooling.

Concerning girls� domestic work, around 25 percent of girls experience the maximum re-

duction of 2.1 percentage points (�rst graph in Figure A3 ). By age, the distribution of e¤ects

suggests stronger and less spread e¤ects for girls 12 to 15. More children eligible for bene�ts in

the household is associated with larger reductions in domestic work participation, which suggests

that siblings substitute each other in the performance of household chores.

5.3 Results on Adults�Time Use

Table 7 presents estimates of impact on time allocation for men (Panel I) and women (Panel

II). The structure of this table is analogous to that of Table 6.

Overall, results suggest very little impact of the program on adult time use, which is con-

sistent with the �ndings in Parker and Skou�as (2000) and in Skou�as and DiMaro (2008).

For men, there is a reduction in the amount of total hours worked (column 3, Model IA) that

translates in an increase in leisure time, signi�cant at the 10 percent (column 1, Model IA).

18

E¤ects are stronger for men ages 25 to 34 (columns 1 and 3, Model IB). Concerning women�s

time allocation, there is a 0.2 hours increase in household chores by women 25 to 34. Results

also show an almost signi�cant increase �signi�cant at the 10 percent �of 4.1 percentage points

in market work participation for women 45 to 54.

Figure A4 plots the estimated marginal e¤ects on women�s participation and hours to do-

mestic work. In both cases, the distribution of marginal e¤ects is skewed to the right. This

implies that, while there is very little variation at the extensive margin, over 70 percent of the

women in the treatment group increase the number of hours to domestic work. By age, the

distribution of marginal e¤ects suggests small but positive program impacts for middle aged

women; and negative �and less precisely estimated (more disperse) �e¤ects for younger and

elder women.

6 Program Impact on Intrahousehold Time Allocation

The simple theoretical framework developed in section 3 predicts a reduction in children�s par-

ticipation in work activities and an increase in adults� participation in those same activities

given the reduction in the price of schooling. Indeed, the estimates presented in the previous

section suggest that Oportunidades has reduced teenage girls�time and participation in domestic

work, and boys�participation in market work. Moreover, there is evidence of an increase in the

number of hours prime-age women spend in household chores and working for a wage. Together

these �ndings are indicative of potential cross-substitution e¤ects in the household, channeled

through a reallocation of domestic (market) activities between mothers and daughters (sons).

This section aims to formalize this preliminary evidence. Based on equation (9), I use the

randomized treatment status and household demographic structure at baseline �this is to say,

the presence of eligible children in the household �to decompose the e¤ects of Oportunidades

on the labor supply and schooling demand of each household member into own-substitution,

cross-substitution and income e¤ects.

19

6.1 Identi�cation of Income and Substitution E¤ects

The reduced form proposed to disaggregate the total program impact for individual i in house-

hold h is:

Yi = �o + �1Th + �2Bi + �3ThBih + �4PBh + �5ThPBh + (13)

+4P

k 6=i;k=1 1ksexhk +

4Pk 6=i;k=1

2kgradehk + 3 ln(transfer)h +Pr�rXihr + "ih

where Th is a dummy equal to 1 if household h is in a treatment community and Bih takes on

the value of 1 if individual i in household h is eligible to receive the educational grant. Hence,

the coe¢ cient on the interaction term ThBih, b�3, identi�es the own-substitution e¤ect � i.e.the term

�@ bHj

i@W s

i

�in equation (9). Indeed, ThBih = 1 if i is a child eligible to receive bene�ts

(Bih = 1) and lives in a treatment household (Th = 1). As argued in section 3, for these children,

the own-substitution e¤ect arises as a result of the increase in the opportunity cost of time in

school that the educational grant they receive entails. Household members that are not directly

entitled to the educational stipend (Bih = 0), have no own-substitution e¤ect.

Similarly, PBh is a dummy variable that equals 1 if there are children eligible to receive the

educational grant in household h (other than i, if i herself is eligible for bene�ts). Arguably,

the coe¢ cient on the interaction term ThPBh, b�5, identi�es the sum of cross-substitution e¤ectsgiven the educational stipends that (other) eligible children living in the household receive �i.e.�P

k 6=i@ bHj

i@W s

k

�in equation (9). As seen, cross-substitution e¤ects may alter the time allocation

patterns of individuals living in treatment households (Th = 1) where there are children directly

bene�ting from the educational stipends (PBh = 1).17

In this setting, the coe¢ cient on the treatment variable, b�1, identi�es the (residual) incomee¤ect. In treatment households with no children eligible to receive the educational grant, both

Bih = 0 and PBh = 0, and the total program impact equals the income e¤ect coming from

the nutritional grant. However, in households with children eligible to receive the educational

17The speci�cation in (13) implicitely assumes that cross-substitution e¤ects are equal across households with a

di¤erent number of children receiving the educational stipend. I estimated a modi�ed version of (13) where PBh

is replaced with dummies accounting for the presence of di¤erent numbers of eligible children in the household

and tested the validity of this assumption.

20

grant, the income e¤ect is the remaining impact of the program, once the e¤ect of all educational

grants has been accounted for.

As noted, identi�cation strongly relies on both the randomization of the treatment status

and the use of pre-intervention data to construct the regressors of interest. The variables Bih

and PBh re�ect children eligible for bene�ts �i.e. potentially bene�ciary children �rather than

children e¤ectively receiving bene�ts � i.e. actual bene�ciaries � in order to correct for the

endogeneity linked to being a bene�ciary. Recall that eligible children receive the educational

grant conditional on attending school, which implies that being an actual bene�ciary re�ects in

part the decision of going to school. I determine potentially bene�ciary children in May 1999

taking the grade the child is enrolled in at baseline and moving it forward assuming no repetition

or dropout. The number of potential bene�ciaries is thus an overestimate of the number of actual

bene�ciaries. Moreover, because treatment status is independent of baseline characteristics and

school enrollment, Th ? "ih p (Xihr; Bih; PBh) and so ThBih ? "ih p (Xihr; Bih; PBh) and

ThPBh ? "ih p (Xihr; Bih; PBh) follow.

Besides the control variables in Xihr de�ned above, the speci�cation in (13) additionally

controls for the gender and the potential grade of the �rst four bene�ciary children (excluding i

if i is a bene�ciary child). These variables induce variation in the amount of transfers treatment

households receive.18 In order to obtain an estimate of the income e¤ect across households re-

ceiving the same monetary incentive, I also include the log of the potential total transfer amount

a household would receive in May 1999 as a regressor. As for potential children, this amount is

computed taking household composition at baseline and applying the program bene�t allocation

rules assuming no school dropout, no grade repetition and universal enrollment. Finally, note

that equation (9) implies an additional assumption to identify the income e¤ect: eligible children

in households receiving the same transfer amount (Pk dW

sk + dY ) must have similar time use

patterns �i.e. supply the same number of hours to activity j,�P

kHjk

�.

18Although a household can have up to eight potential bene�ciary children, 99 percent of the households in the

sample have no more than four. Moreover, the cap on the maximum amount e¤ectively implies that a household

does not receive transfers for more than three or four children.

21

6.2 Results on Intrahousehold Time Allocation

Tables 8 and 9 report coe¢ cient estimates of the income, own-substitution and cross-substitution

e¤ects resulting from the estimation of (13) for boys, girls, adult men and adult women.

For boys and girls, the estimation sample has been restricted to teenagers (children ages 12 to

17) since, as seen in section 5.2, it is within this age group that larger program impacts on time

allocation are observed. As expected, results show a positive and signi�cant own-substitution

e¤ect on participation and hours in school for boys (second and third columns in Table 8, Panel

I). Moreover, the presence of other bene�ciary siblings signi�cantly reduces the amount of time

they devote to school related activities (negative cross-substitution e¤ects). This suggests that

these households continue to face the so-called quantity/quality trade o¤ in educational choices,

even after extra resources are poured into the household. For girls, the own-substitution and

cross-substitution e¤ects go in the same direction as for boys but are not statistically signi�cant.

Consistent with the theory, there is strong evidence of a negative own-substitution e¤ect

(5.9 percentage points reduction) on market work participation for bene�ciary teenage boys.

For girls, there is weak evidence �signi�cant at the 10 percent �of a negative cross-substitution

e¤ect on market work hours. The income and own-substitution e¤ects are, however, very im-

precisely estimated and not signi�cantly di¤erent from zero. Estimates also suggest a 14.8

percentage point reduction in farm work participation for teenage girls through an income ef-

fect. Nonetheless, conditional on participation, the number of hours teenage girls devote to

farm activities increases with the number of (other) eligible children in treatment households.

In other words, (older) teenage girls in treatment households spend more time performing farm

related tasks to substitute for the time previously devoted by (other) bene�ciary children in the

household. Finally, own-substitution e¤ects result in a strong reduction in girls�leisure, which

is larger than the increase given the income e¤ect. This is in line with the overall reduction in

leisure time for teenage girls reported in Table 6. The e¤ects on leisure for teenage boys are

qualitatively similar but not statistically signi�cant.

Results in Table 9 show a common pattern regarding the e¤ects of Oportunidades on women�s

time allocated to the di¤erent work activities (with the exception of market work). On the one

hand, the negative income e¤ects imply lower adult female participation and less hours devoted

22

to farm and domestic activities, and to overall work. On the other hand, the positive cross-

substitution e¤ects suggest that adult female increase their participation and time in these

activities so as to substitute for the work that eligible children in the household were performing

before the intervention. These �ndings � in line with the evidence in sections 5.2 and 5.3 �

support the theoretically predicted positive intrahousehold interactions between adult women

and eligible teenagers to free up their time from domestic and farm tasks to go to school.

Not surprisingly, while the income e¤ect results in a positive and signi�cant increase of

leisure for adult women in the household, the cross-substitution e¤ect implies a reduction in

their leisure time. The total impact �reported in Table 7 �is not signi�cantly di¤erent from

zero. Indeed, further disaggregating the results by age (results available upon request) shows

that for prime age women 25 to 44, the income and substitution e¤ects cancel each other out.

For men, however, the positive income e¤ect is larger in size than the negative cross-substitution

e¤ect (column 1, Table 9 ), resulting in the almost signi�cant �signi�cant at the 10 percent �

overall increase in men�s leisure time reported in Table 7. These �ndings suggest that women

receive the burden of labor responsibility due to the program, while men do not substitute for

their children�s time to the same extent and increase leisure time instead.19

The qualitative evidence in Adato et al (2000) endorses these results. During the summer

of 1999, the authors conducted focus groups with 230 bene�ciary and non-bene�ciary women

to learn about their perceptions on the program. The authors report: "Another reason that

women�s time burden increases is because of the need to do work that was previously done by

children who are now attending school, particularly secundaria. However, their mothers see this

as worthwhile in order for their children to study. (...). Although some women said that the

father also does some of this work, more often it was the mother.", (Adato et al 2000, p. xiii).

19While the evidence of overall increase in men�s leisure is weak, it raises some concerns of whether the program

is promoting disincentives to adult men labor supply, in particular when combined with the increases in overall

work hours reported in Table 7. Parker and Skou�as (2000) and Skou�as and DiMaro (2008) use labor supply data

from the evaluation surveys dismiss this argument. The evidence in Gertler et al (2008) on investment of part of

the transfer in productive activities would also suggest that the program is not generating welfare dependency.

23

7 Conclusions

This paper examines the e¤ect of the economic incentives provided by the Oportunidades inter-

vention on individual and intrahousehold time allocations.

Overall, results on individual time use are consistent with previous evidence on the e¤ects

of the program on labor supply and school enrollment (see Skou�as 2005 for a review). For

teenage boys, impact estimates show reductions in market work and increases in schooling,

giving further evidence in support of the hypothesis that working for a wage and attending

school are substitutes. Market work also appears as a more important deterrent to school for

boys than for girls. The program has larger positive impacts on girls�school attendance than

on boys�, and reduces the amount of time teenage girls devote to household chores. On the

other hand, there is little evidence of time reallocations amongst adults, expect for increases in

domestic work hours and market participation for middle aged women.

Subsidizing school enrollment has repeatedly been acknowledged as an e¤ective mechanism

to reduce child paid labor (Ravallion and Wodon 2000, Parker and Skou�as 2001, for instance).

This paper shows that this remains true even when nonremunerated activities are included in the

de�nition of work. More generally, conditional cash transfer programs appear as a potentially

useful tool to reduce child labor in its various forms. Moreover, the �exibility in their design

make them particularly attractive to address speci�c (context�dependent) problems. As shown

above, the particular structure of incentives of the program �increasing transfers as the child

enrolls in higher grades and o¤ering larger transfers to girls �seems to operate in the desired

direction. Indeed, children older than 12 � in general � and girls � in particular � are the

demographic groups with larger impacts on time allocation.

The second part of the paper investigates the transmission channels of these e¤ects. The

randomized allocation of treatment and the program structure of incentives are exploited to

disaggregate the total program impact on individual time use into income, own-substitution

and cross-substitution e¤ects. Reduced form estimates suggested that there are positive cross-

substitution e¤ects between adult women and children concerning the two forms of unpaid work

considered: farm work and domestic work. This suggests that rather than promoting disin-

centives to adult female labor supply and create welfare dependency, conditional cash transfer

24

programs may indeed generate positive responses amongst adult household members �in par-

ticular women �to facilitate the investment in human capital of younger members.

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holds in Mexico�, International Food Policy Research Institute Research Report 139, Wash-

ington D.C.

[27] Skou�as, Emmanuel, Benjamin Davis and Sergio de la Vega (2001), �Targeting the Poor in

Mexico: An Evaluation of the Selection of Households into PROGRESA�, World Develop-

ment, 29(10):1768-1784.

[28] Skou�as, Emmanuel and Vincenzo Di Maro (2008), �Conditional Cash Transfers, Adult

Work Incentives, and Poverty�, The Journal of Development Studies, 44(7): 935-960.

27

8 Appendix

8.1 Computation of Marginal E¤ects

For probit models the estimated e¤ect of switching the j-th binary variable, xj , from 0 to 1 on

the probability that yi equals one is:

MPij =

4E[yijxij ]4xj = 4�(xi�̂)

4xj = �(x1i �̂)� �(x0i �̂)

where x1i is the vector of regressors with the j-th binary variable set to 1, and similarly for x0i .

�(:) and �(:) are the density and cumulative distribution functions of a standard normal. If xj

is a continuous variable, the e¤ect is:

MPij =

@E[yijxij ]@xj

= �̂j�(xi�̂)

For the tobit model, the estimated e¤ect of a binary variable on the mean of all positive yi�s,

i.e. conditional on participation is:

MTij =

4E[yijxij ;yi>0]4xj = 4[xi�̂+�̂�(zi)=�(zi)]

4xj =�x1i �̂ + �̂

�(z1i )

�(z1i )� x0i �̂ � �̂

�(z0i )

�(z0i )

�where z1i =

x1i �̂�̂ and z0i =

x0i �̂�̂ . If xj is a continuous variable, the e¤ect on the mean of all positive

yi�s becomes:

MTij =

@E[yijxij ;yi>0]@xj

= @[xi�̂+�̂�(zi)=�(zi)]@xj

=�1� zi �(zi)�(zi)

� �2(zi)�2(zi)

�For each observation i, the MP

ij and MTij depend on the values of all coe¢ cients and regressors

j. Mean observation-by-observation marginal e¤ects are computed by evaluating the marginal

e¤ect of variable j for each observation and taking the mean over the sample of these marginal

e¤ects:20

�MKj = 1

N

PIi=1M

Kij ;8K = P; T

The variance-covariance matrix of the observation-by-observation marginal e¤ects is calculated

using the Delta Method, in order to account for the existing covariance between the estimated

marginal e¤ects of any two observations. Given the gradient is a linear map, the gradient of

20One common alternative consists in evaluating the e¤ect of x at the mean value, �x, of the estimation subsample.

However, �x may not be meaningful, especially if x is binary.

28

the mean marginal e¤ect is the mean of the observation-by-observation gradients. Thus, the

variance-covariance matrix is calculated as:

V ( �MKj ) = V

�1N

PIi=1M

Kij

�= @

@�̂0

�1N

PIi=1M

Kij

�V (�̂) @

@�̂

�1N

PIi=1M

Kij

�0=

=

�1N

PIi=1

@MKij

@�̂0

�V (�̂)

�1N

PIi=1

@MKij

@�̂

�0;8K = P; T

where V (�̂) is the chosen covariance matrix for �̂.

8.2 Figures

Frac

tion

Boys' PpschObs-by-Obs Marginal Effect

.005463 .036876

0

.1

.2

Obs

-by-

Obs

Mar

gina

l Effe

ct

Boys' PpschAge

Obs-by-Obs Marginal Effect Mean Marginal Effect

8 9 10 11 12 13 14 15 16 17

0

.01

.02

.03

.04

Obs

-by-

Obs

Mar

gina

l Effe

ct

Boys' Ppsch(hh) Number of potential benefic

Obs-by-Obs Marginal Effect Mean Marginal Effect

1 2 3 4 5 6 7 8

0

.01

.02

.03

.04

Frac

tion

Boys' schObs-by-Obs Marginal Effect

.046302 .247168

0

.02

.04

.06

Obs

-by-

Obs

Mar

gina

l Effe

ct

Boys' schAge

Obs-by-Obs Marginal Effect Mean Marginal Effect

8 9 10 11 12 13 14 15 16 17

.05

.1

.15

.2

.25

Obs

-by-

Obs

Mar

gina

l Effe

ct

Boys' sch(hh) Number of potential benefic

Obs-by-Obs Marginal Effect Mean Marginal Effect

1 2 3 4 5 6 7 8

.05

.1

.15

.2

.25

Figure A1: Obs-by-Obs Marginal E¤ects of Oportunidades on Boys�Schooling: Participation

(top graphs) and Hours (bottom graphs). Unconditional Distribution, Conditional on Age and

Conditional on Number of Eligible Children in the Household.

29

Frac

tion

Girls' PpschObs-by-Obs Marginal Effect

.003827 .064191

0

.1

.2

Obs

-by-

Obs

Mar

gina

l Effe

ct

Girls' PpschAge

Obs-by-Obs Marginal Effect Mean Marginal Effect

8 9 10 11 12 13 14 15 16 17

0

.02

.04

.06

Obs

-by-

Obs

Mar

gina

l Effe

ct

Girls' Ppsch(hh) Number of potential benefic

Obs-by-Obs Marginal Effect Mean Marginal Effect

1 2 3 4 5 6 7 8

0

.02

.04

.06

Frac

tion

Girls' schObs-by-Obs Marginal Effect

.075517 .442707

0

.02

.04

.06

Obs

-by-

Obs

Mar

gina

l Effe

ct

Girls' schAge

Obs-by-Obs Marginal Effect Mean Marginal Effect

8 9 10 11 12 13 14 15 16 17

.1

.2

.3

.4

.5

Obs

-by-

Obs

Mar

gina

l Effe

ct

Girls' sch(hh) Number of potential benefic

Obs-by-Obs Marginal Effect Mean Marginal Effect

1 2 3 4 5 6 7 8

.1

.2

.3

.4

.5

Figure A2: Obs-by-Obs Marginal E¤ects of Oportunidades on Girls�Schooling: Participation

(top graphs) and Hours (bottom graphs). Unconditional Distribution, Conditional on Age and

Conditional on Number of Eligible Children in the Household.

30

Frac

tion

Girls' PpdomObs-by-Obs Marginal Effect

-.021169 -.009402

0

.1

.2

.3

Obs

-by-

Obs

Mar

gina

l Effe

ct

Girls' PpdomAge

Obs-by-Obs Marginal Eff ect Mean Marginal Effect

8 9 10 11 12 13 14 15 16 17

-.02

-.015

-.01

Obs

-by-

Obs

Mar

gina

l Effe

ct

Girls' Ppdom(hh) Number of potential benefic

Obs-by-Obs Marginal Eff ect Mean Marginal Effect

1 2 3 4 5 6 7 8

-.02

-.015

-.01

Frac

tion

Girls' domObs-by-Obs Marginal Effect

-.219454 -.045298

0

.02

.04

.06

.08

Obs

-by-

Obs

Mar

gina

l Effe

ct

Girls' domAge

Obs-by-Obs Marginal Eff ect Mean Marginal Effect

8 9 10 11 12 13 14 15 16 17

-.25

-.2

-.15

-.1

-.05

Obs

-by-

Obs

Mar

gina

l Effe

ct

Girls' dom(hh) Number of potential benefic

Obs-by-Obs Marginal Eff ect Mean Marginal Effect

1 2 3 4 5 6 7 8

-.25

-.2

-.15

-.1

-.05

Figure A3: Obs-by-Obs Marginal E¤ects of Oportunidades on Girls�Domestic Work:

Participation (top graphs) and Hours (bottom graphs). Unconditional Distribution,

Conditional on Age and Conditional on Number of Eligible Children in the Household.

31

Frac

tion

Women's PpdomObs-by-Obs Marginal Effect

-.021143 -.000355

0

.02

.04

.06

.08

Obs

-by-

Obs

Mar

gina

l Effe

ct

Women's PpdomAge

Obs-by-Obs Marginal Eff ect Mean Marginal Effect

18 25 35 45 55 65 75 85 95 98

-.02

-.015

-.01

-.005

0

Obs

-by-

Obs

Mar

gina

l Effe

ct

Women's Ppdom(hh) Number of potential benefic

Obs-by-Obs Marginal Eff ect Mean Marginal Effect

1 2 3 4 5 6 7 8

-.02

-.015

-.01

-.005

0

Frac

tion

Women's domObs-by-Obs Marginal Effect

.009452 .057856

0

.05

.1

Obs

-by-

Obs

Mar

gina

l Effe

ct

Women's domAge

Obs-by-Obs Marginal Eff ect Mean Marginal Effect

18 25 35 45 55 65 75 85 95 98

0

.02

.04

.06

Obs

-by-

Obs

Mar

gina

l Effe

ct

Women's dom(hh) Number of potential benefic

Obs-by-Obs Marginal Eff ect Mean Marginal Effect

1 2 3 4 5 6 7 8

0

.02

.04

.06

Figure A4: Obs-by-Obs Marginal E¤ects of Oportunidades on Women�s Domestic Work:

Participation (top graphs) and Hours (bottom graphs). Unconditional Distribution,

Conditional on Age and Conditional on Number of Eligible Children in the Household.

32

8.3 Tables

Table 1: Oportunidades Monthly Transfer Amounts at Baseline (October 1997)

Transfer Component Level Grade Boys GirlsEducational Stipend Primary School 3rd year 60 60

4th year 70 705th year 90 906th year 120 120

Junior High School 1st year 175 1852nd year 185 2053rd year 195 225

School Supplies Stipend Primary, 1rst payment 80 80Primary, 2nd payment 40 40Junior High School 150 150

Nutritional Stipend (per family)Transfer Cap (per family)

90550

Source: Oportunidades (www.oportunidades.gob.mx). Transfer amounts are adjusted for inflation every semester according to theConsumer Price Index published by the Bank of Mexico. Transfer Cap is the maximum transfer amount awarded for basic education(primary school and junior high) and nutrition.

Table 2: List of Activities in the May 1999 Time Use Module

Market Work 1. Working for a salary or wage2. Working in own business

Farm Work 3. Working the family land4. Taking care of animals

Domestic Work 5. Purchasing food or other products for the household6. Sewing and making clothes for household members7. Taking household members to school, clinic or work8. Cleaning the house9. Washing and ironing clothes for household members10. Preparing food11. Fetching water, firewood or throwing out trash12. Taking care of small children, elderly and sick13. Making household repairs

Schooling 14. Attending school15. Doing homework after school

Community Work 16. Community Work17. Voluntary work for neighbours or other relatives

Transportation 18. Transportation time to work, school, market, etc.Other 19. Other activities

33

Table 3: Test of Equality of Means in the Estimation Sample in October 1997 (Baseline)

N Mean SD N Mean SD t-statHead's CharacteristicsAge Household Head 5056 42.25 (14.331) 2911 42.80 (14.894) -1.078Female Head =1 5056 7.32 (0.260) 2911 8.55 (0.280) -1.597Indigenous Head =1 5047 39.45 (0.489) 2905 39.93 (0.490) -0.080Head's Education (Years)† 3516 4.07 (2.313) 1982 3.90 (2.260) 1.563Head has a Paid Occupation = 1 5054 85.42 (0.353) 2910 84.71 (0.360) 0.450Monthly Hours Head Works in Paid Occupation† 4294 171.83 (55.399) 2453 173.35 (51.794) -0.569

Spouse's CharacteristicsAge Spouse of Head 4501 36.76 (12.503) 2561 36.55 (12.528) 0.498Spouse's Education (Years)† 2912 4.14 (2.144) 1617 4.11 (2.191) 0.278Spouse has a Paid Occupation = 1 4963 11.93 (0.324) 2843 11.15 (0.315) 0.390Monthly Hours Spouse Works in Paid Occupation† 579 150.48 (65.274) 309 163.69 (67.189) -1.75+

Household CharacteristicsChildren 0 to 7 =1 5056 76.09 (0.427) 2911 77.02 (0.421) -0.606Children 8 to 17 =1 5056 70.63 (0.456) 2911 70.04 (0.458) 0.437Adult Men 18 to 54 =1 5056 87.28 (0.333) 2911 85.85 (0.349) 1.382Adult Female 18 to 54 =1 5056 91.53 (0.278) 2911 91.00 (0.286) 0.608Adults Older than 55 =1 5056 27.63 (0.447) 2911 27.96 (0.449) -0.246Household Size 5056 6.03 (2.433) 2911 6.00 (2.418) 0.367Children Eligible for Educational Grant =1 5056 65.17 (0.476) 2911 64.14 (0.480) 0.673Number of Children Eligible for Educational Grant† 3295 2.13 (1.100) 1867 2.11 (1.074) 0.606Home Ownership =1 5052 93.84 (0.240) 2911 92.44 (0.264) 1.406Dirt Floor =1 5043 73.19 (0.443) 2902 74.29 (0.437) -0.377Electricity =1 5052 62.55 (0.484) 2910 59.86 (0.490) 0.545Water in the House =1 5047 4.93 (0.217) 2899 3.41 (0.182) 1.605Bathroom =1 5046 54.36 (0.498) 2904 52.07 (0.500) 0.599Land Ownership =1 5043 59.94 (0.490) 2910 58.04 (0.494) 0.601Farm Animals Ownership =1 5050 34.89 (0.477) 2908 32.81 (0.470) 0.794

Community CharacteristicsPre-school =1 5056 91.42 (0.280) 2911 89.08 (0.312) 0.728Satellite TV Secondary School (Telesecundaria) =1 5056 23.60 (0.425) 2911 25.46 (0.436) -0.310Mobile Health Unit =1 5056 72.31 (0.448) 2911 71.90 (0.450) 0.070Distance to Secondary School (Km) 3979 2.98 (1.636) 2308 3.46 (2.604) -1.189Monthly Community Agricultural Male Wage (1997 Pesos) 4911 568.79 (168.711) 2764 561.29 (177.962) 0.363Monthly Community Agricultural Female Wage (1997 Pesos) 3483 488.81 (169.676) 1933 477.99 (136.345) 0.501Monthly Community Agricultural Child Wage (1997 Pesos) 3468 386.50 (161.363) 1704 379.38 (135.025) 0.351

Treatment Group Control Group

Notes: +significant at 10%. †Conditional on being positive. T-stat of differences in means computed clustering SE at the community level. Mean of dichotomous variablesexpressed in percentages.

34

Table 4: Time Use of Boys and Girls Before the Intervention - Control Households in May 1999

Participation (%) Daily Hours† Participation (%) Daily Hours†

Schooling 8 to 11 78.99 5.86 80.49 5.7512 to 13 70.59 5.98 68.62 6.0814 to 15 54.18 6.11 45.01 6.3516 to 17 26.91 6.22 21.34 6.1312 to 17 53.16 6.06 48.26 6.17All ages 64.52 5.95 62.74 5.93

Market Work 8 to 11 0.83 6.48 0.74 3.1112 to 13 5.02 6.22 1.01 6.8714 to 15 15.27 7.65 4.89 7.7316 to 17 29.38 7.53 10.28 7.5612 to 17 15.13 7.40 4.89 7.56All ages 8.84 7.37 2.95 7.06

Farm Work 8 to 11 11.95 2.24 6.89 1.6712 to 13 17.82 2.84 9.40 1.3314 to 15 22.36 4.10 11.74 1.3916 to 17 25.93 4.55 11.57 1.1412 to 17 21.59 3.85 10.76 1.30All ages 17.35 3.36 9.02 1.43

Domestic Work 8 to 11 30.95 1.26 34.26 1.7812 to 13 33.56 1.49 51.68 2.3914 to 15 39.45 1.40 64.97 3.2916 to 17 40.25 1.65 65.04 4.2112 to 17 37.44 1.50 59.69 3.24All ages 34.59 1.40 48.27 2.78

Leisure 8 to 11 - 18.09 - 18.0712 to 13 - 17.86 - 17.8414 to 15 - 17.46 - 18.0516 to 17 - 17.43 - 18.7312 to 17 - 17.60 - 18.14All ages - 17.81 - 18.11

BOYS GIRLS

† Number of daily hours conditional on positive participation.

35

Table 5: Time Use of Adult Men and Women Before the Intervention - Control Households in May 1999

Participation (%) Daily Hours† Participation (%) Daily Hours†Market Work 18 to 24 54.96 7.66 9.27 6.91

25 to 34 66.59 7.66 5.77 6.4535 to 44 67.25 7.48 5.16 6.4245 to 54 63.64 7.63 4.43 6.83More than 55 41.81 7.32 3.99 6.62All ages 59.87 7.57 5.97 6.65

Farm Work 18 to 24 26.37 5.03 15.34 1.5025 to 34 30.91 5.24 21.05 1.2835 to 44 33.10 4.99 22.63 1.3245 to 54 36.96 5.06 26.58 1.50More than 55 40.47 4.95 19.93 1.39All ages 33.07 5.06 20.67 1.38

Domestic Work 18 to 24 37.78 1.66 84.66 5.5225 to 34 45.45 1.74 95.03 6.2735 to 44 42.25 1.90 95.78 5.8745 to 54 43.87 1.80 92.83 5.21More than 55 38.13 1.76 79.55 4.14All ages 41.73 1.78 90.36 5.61

Leisure 18 to 24 - 16.81 - 18.2625 to 34 - 15.66 - 17.4435 to 44 - 15.74 - 17.6745 to 54 - 15.73 - 18.14More than 55 - 17.46 - 19.86All ages - 16.23 - 18.13

† Number of daily hours conditional on positive participation.

MEN WOMEN

36

Tab

le 6

: Opo

rtun

idad

es Im

pact

on

Boy

s' a

nd G

irls

' Tim

e A

lloca

tion

- Poo

led

Impa

ct a

nd b

y A

ge

Leisu

reH

ours

Part

icip

atio

nH

ours

Part

icip

atio

nH

ours

Part

icip

atio

nH

ours

Part

icip

atio

nH

ours

Part

icip

atio

nH

ours

I. B

OY

S(N

= 7

691)

(OL

S)(P

robi

t)(T

obit)

(Pro

bit)

(Tob

it)(P

robi

t)(T

obit)

(Pro

bit)

(Tob

it)(P

robi

t)(T

obit)

Mod

el IA

:Al

l Age

s0.

033

0.02

90.

166*

*-0

.014

-0.1

04+

-0.0

11-0

.150

-0.0

04-0

.035

-0.0

09-0

.020

(0.1

51)

(0.0

23)

(0.0

58)

(0.0

21)

(0.0

62)

(0.0

08)

(9.6

30)

(0.0

14)

(0.7

89)

(0.0

21)

(0.0

50)

Mod

el IB

:8

to 1

10.

048

0.01

00.

071

-0.0

12-0

.059

-0.0

03-0

.089

0.00

50.

025

-0.0

15-0

.030

(0.1

82)

(0.0

32)

(0.0

87)

(0.0

25)

(0.0

79)

(0.0

21)

(9.0

59)

(0.0

18)

(0.8

10)

(0.0

23)

(0.0

54)

12 to

17

0.02

40.

043+

0.26

3**

-0.0

18-0

.145

*-0

.012

-0.1

74-0

.010

-0.0

74-0

.005

-0.0

13(0

.167

)(0

.021

)(0

.081

)(0

.024

)(0

.069

)(0

.009

)(9

.147

)(0

.015

)(0

.774

)(0

.024

)(0

.054

)

II. G

IRLS

(N =

732

1)

Mod

el II

A:Al

l Age

s-0

.211

0.04

9*0.

290*

*-0

.015

-0.0

84+

-0.0

03-0

.097

0.00

40.

043

-0.0

19-0

.097

*(0

.147

)(0

.023

)(0

.058

)(0

.021

)(0

.045

)(0

.005

)(2

8.38

5)(0

.011

)(1

.422

)(0

.021

)(0

.044

)

Mod

el II

B:8

to 1

1-0

.001

-0.0

14-0

.001

0.00

10.

010

-0.0

000.

030

0.02

10.

085

-0.0

05-0

.028

(0.1

84)

(0.0

34)

(0.0

86)

(0.0

25)

(0.0

66)

(0.0

09)

(27.

771)

(0.0

15)

(1.4

19)

(0.0

25)

(0.0

61)

12 to

17

-0.3

87*

0.08

7**

0.58

2**

-0.0

28-0

.145

**-0

.004

-0.1

21-0

.007

0.01

4-0

.030

-0.1

39**

(0.1

63)

(0.0

20)

(0.0

86)

(0.0

22)

(0.0

54)

(0.0

05)

(27.

008)

(0.0

13)

(1.3

52)

(0.0

21)

(0.0

53)

Not

es:+

Sign

ifica

ntat

the

10pe

rcen

tlev

el.

*Sig

nific

anta

tthe

5pe

rcen

tlev

el.*

*Sig

nific

anta

tthe

1pe

rcen

tlev

el.S

Ecl

uste

red

atth

eco

mm

unity

leve

lin

para

nthe

ses.

Forp

robi

tand

tobi

tmod

els,

mea

nof

the

obse

rvat

ion-

by-o

bser

vatio

nm

argi

nal

effe

cts

repo

rted.

All

regr

essio

nsin

clud

eth

efo

llow

ing

cont

rols:

age,

age

squa

red,

educ

atio

ndu

mm

ies,

rank

amon

gsts

iblin

gs,m

othe

r'set

hnic

ity,s

exof

the

hous

ehol

d's

head

,bas

elin

eho

useh

old

dem

ogra

phic

san

dho

useh

old

size,

base

line

dwel

ling

char

acte

ristic

s (b

athr

oom

, ele

ctric

ity, l

and

owne

rshi

p an

d dr

aft a

nim

al o

wne

rshi

p), l

og m

ale's

agr

icul

tura

l w

age

(in 1

997

pric

es),

dist

ance

to th

e cl

oses

t mun

icip

ality

and

stat

e du

mm

ies.

Age

and

age

squa

red

are

not i

nclu

ded

in M

odel

s B.

Scho

olA

ll W

ork

Mar

ket W

ork

Farm

Wor

kD

omes

tic W

ork

37

Tab

le 7

: Opo

rtun

idad

es Im

pact

on

Men

s' a

nd W

omen

s' T

ime

Allo

catio

n - P

oole

d Im

pact

and

by

Age

Leisu

reH

ours

Part

icip

atio

nH

ours

Part

icip

atio

nH

ours

Part

icip

atio

nH

ours

Part

icip

atio

nH

ours

I. M

EN (N

= 9

519)

(OL

S)(P

robi

t)(T

obit)

(Pro

bit)

(Tob

it)(P

robi

t)(T

obit)

(Pro

bit)

(Tob

it)M

odel

IA:

All A

ges

0.20

9+-0

.007

-0.1

41*

-0.0

12-0

.060

-0.0

05-0

.025

-0.0

04-0

.012

(0.1

27)

(0.0

11)

(0.0

72)

(0.0

18)

(0.0

66)

(0.0

19)

(0.1

65)

(0.0

20)

(0.0

29)

Mod

el IB

:18

to 2

4-0

.133

0.02

10.

058

-0.0

000.

003

-0.0

040.

000

0.01

60.

037

(0.2

42)

(0.0

15)

(0.1

64)

(0.0

27)

(0.1

51)

(0.0

29)

(0.2

01)

(0.0

34)

(0.0

51)

25 to

34

0.40

7*-0

.046

*-0

.247

+0.

005

0.06

9-0

.037

-0.2

41-0

.020

-0.0

17(0

.169

)(0

.022

)(0

.138

)(0

.025

)(0

.128

)(0

.024

)(0

.179

)(0

.028

)(0

.043

)35

to 4

40.

255

-0.0

11-0

.174

-0.0

36-0

.162

0.02

00.

116

-0.0

15-0

.062

(0.1

88)

(0.0

19)

(0.1

41)

(0.0

26)

(0.1

27)

(0.0

26)

(0.1

93)

(0.0

24)

(0.0

43)

45 to

54

0.23

70.

009

-0.1

70-0

.041

-0.3

14+

0.04

30.

238

0.00

1-0

.019

(0.2

28)

(0.0

20)

(0.1

82)

(0.0

31)

(0.1

61)

(0.0

32)

(0.2

10)

(0.0

33)

(0.0

54)

Mor

e th

an 5

50.

321

-0.0

13-0

.215

-0.0

03-0

.003

-0.0

37-0

.165

0.00

50.

009

(0.2

40)

(0.0

18)

(0.1

72)

(0.0

24)

(0.1

69)

(0.0

25)

(0.1

94)

(0.0

32)

(0.0

53)

II. W

OM

EN

(N =

103

70)

Mod

el II

A:Al

l Age

s-0

.159

-0.0

080.

052

0.00

90.

104

0.01

00.

012

-0.0

070.

046

(0.1

08)

(0.0

07)

(0.0

59)

(0.0

07)

(6.6

93)

(0.0

17)

(0.2

14)

(0.0

07)

(0.0

53)

Mod

el II

B:18

to 2

40.

035

-0.0

14-0

.192

-0.0

07-0

.098

0.00

10.

006

-0.0

14-0

.158

(0.1

91)

(0.0

12)

(0.1

26)

(0.0

09)

(6.2

78)

(0.0

27)

(0.2

13)

(0.0

13)

(0.1

14)

25 to

34

-0.2

38-0

.007

0.16

90.

005

0.05

00.

016

0.02

1-0

.001

0.20

1*(0

.168

)(0

.013

)(0

.109

)(0

.010

)(6

.370

)(0

.022

)(0

.213

)(0

.014

)(0

.099

)35

to 4

4-0

.230

-0.0

030.

134

0.01

90.

214

0.01

60.

028

-0.0

120.

087

(0.1

77)

(0.0

14)

(0.1

24)

(0.0

12)

(6.4

77)

(0.0

24)

(0.2

14)

(0.0

17)

(0.1

12)

45 to

54

-0.2

230.

003

0.12

20.

041+

0.48

5-0

.003

-0.0

220.

011

0.04

4(0

.228

)(0

.015

)(0

.167

)(0

.024

)(6

.501

)(0

.026

)(0

.213

)(0

.015

)(0

.150

)M

ore

than

55

-0.1

65-0

.009

0.02

40.

022

0.26

50.

013

0.00

9-0

.011

-0.0

05(0

.195

)(0

.011

)(0

.154

)(0

.019

)(6

.464

)(0

.025

)(0

.214

)(0

.013

)(0

.139

)N

otes

:+Si

gnifi

cant

atth

e10

perc

entl

evel

.*Si

gnifi

cant

atth

e5

perc

entl

evel

.SE

clus

tere

dat

the

com

mun

ityle

veli

npa

rant

hese

s.Fo

rpro

bita

ndto

bitm

odel

s,m

ean

ofth

eob

serv

atio

n-by

-obs

erva

tion

mar

gina

leffe

ctsr

epor

ted.

All

regr

essi

ons

incl

ude

the

follo

win

gco

ntro

ls:

age,

age

squa

red,

educ

atio

ndu

mm

ies,

mar

ital

stat

us,

head

ofth

eho

useh

old

stat

us,

ethn

icity

,ba

selin

eho

useh

old

dem

ogra

phic

san

dho

useh

old

size

,ba

selin

edw

ellin

gch

arac

teris

tics

(bat

hroo

m,e

lect

ricity

,lan

dow

ners

hip

and

draf

tani

mal

owne

rshi

p),l

ogm

ale

agric

ultu

ral

wag

e(in

1997

pric

es),

dist

ance

toth

ecl

oses

tmun

icip

ality

and

stat

edu

mm

ies.

Age

and

age

squa

red

are

noti

nclu

ded

inM

odel

s B.

All

Wor

kM

arke

t Wor

kFa

rm W

ork

Dom

estic

Wor

k

38

Tab

le 8

: Ow

n-Su

bstit

utio

n, C

ross

-sub

stitu

tion

and

Inco

me

Eff

ects

on

Tee

nage

rs' (

Age

s 12

to 1

7) T

ime

Allo

catio

n

Lei

sure

Hou

rsPa

rtic

ipat

ion

Hou

rsPa

rtic

ipat

ion

Hou

rsPa

rtic

ipat

ion

Hou

rsPa

rtic

ipat

ion

Hou

rsPa

rtic

ipat

ion

Hou

rsI.

BO

YS

(N =

428

7)(O

LS)

(Pro

bit)

(Tob

it)(P

robi

t)(T

obit)

(Pro

bit)

(Tob

it)(P

robi

t)(T

obit)

(Pro

bit)

(Tob

it)Tr

eatm

ent (

T) =

10.

260

-0.0

67-0

.268

-0.0

120.

086

0.06

30.

676

0.02

4-0

.026

-0.0

63-0

.097

(Inc

ome

Effe

ct)

(0.5

98)

(0.0

77)

(0.3

43)

(0.0

66)

(0.2

71)

(0.0

40)

(5.6

50)

(0.0

50)

(0.7

91)

(0.0

65)

(0.1

11)

T *

Ben

efic

iary

= 1

-0.3

440.

119*

*0.

608*

*0.

033

-0.1

17-0

.059

*-0

.583

-0.0

16-0

.094

0.12

7**

0.15

7(O

wn-

Subs

t Eff

ect)

(0.3

54)

(0.0

45)

(0.2

35)

(0.0

41)

(0.1

94)

(0.0

30)

(3.0

96)

(0.0

34)

(0.7

40)

(0.0

40)

(0.1

07)

T *

Nb

Oth

er B

enef

icia

ries

0.25

1-0

.085

+-0

.558

*0.

030

0.17

4-0

.016

-0.1

850.

027

0.24

9-0

.047

-0.0

20(C

ross

-Sub

st E

ffec

t)(0

.401

)(0

.045

)(0

.251

)(0

.047

)(0

.209

)(0

.034

)(0

.322

)(0

.035

)(0

.181

)(0

.050

)(0

.077

)

II. G

IRL

S(N

= 3

992)

Trea

tmen

t (T)

=1

0.41

4-0

.015

0.05

7-0

.011

-0.2

000.

016

0.25

9-0

.148

**-0

.547

-0.0

10-0

.055

(Inc

ome

Effe

ct)

(0.4

99)

(0.0

74)

(0.3

51)

(0.0

68)

(0.2

49)

(0.0

24)

(23.

256)

(0.0

55)

(0.9

04)

(0.0

69)

(0.2

09)

T *

Ben

efic

iary

= 1

-0.7

30*

0.02

20.

013

0.04

60.

289+

0.00

90.

238

0.02

90.

099

0.02

60.

133

(Ow

n-Su

bst E

ffec

t)(0

.364

)(0

.043

)(0

.246

)(0

.046

)(0

.169

)(0

.015

)(2

2.75

1)(0

.027

)(1

.844

)(0

.046

)(0

.144

)

T *

Nb

Oth

er B

enef

icia

ries

0.63

8-0

.025

-0.2

03-0

.037

-0.1

67-0

.038

-0.7

23+

0.05

20.

223+

-0.0

18-0

.067

(Cro

ss-S

ubst

Eff

ect)

(0.4

05)

(0.0

49)

(0.2

66)

(0.0

53)

(0.1

95)

(0.0

23)

(0.4

23)

(0.0

30)

(0.1

22)

(0.0

55)

(0.1

65)

Not

es:+

Sign

ifica

ntat

the

10pe

rcen

tlev

el.

*Sig

nific

anta

tthe

5pe

rcen

tlev

el.*

*Sig

nific

anta

tthe

1pe

rcen

tlev

el.S

Ecl

uste

red

atth

eco

mm

unity

leve

lin

para

nthe

ses.

For

prob

itan

dto

bitm

odel

s,m

ean

obse

rvat

ion-

by-o

bser

vatio

nm

argi

nal

effe

cts r

epor

ted.

All

regr

essi

ons i

nclu

de th

e fo

llow

ing

cont

rols

: age

, age

squa

red,

edu

catio

n du

mm

ies,

rank

am

ongs

t sib

lings

, mot

her's

eth

nici

ty, s

ex o

f the

hou

seho

ld's

head

, bas

elin

e ho

useh

old

dem

ogra

phic

s and

hou

seho

ld si

ze, b

asel

ine

dwel

ling

char

acte

ristic

s(ba

thro

om,e

lect

ricity

,lan

dow

ners

hip

and

draf

tani

mal

owne

rshi

p),s

exan

dgr

ade

ofth

efir

stfo

urbe

nefic

iary

child

ren

inth

eho

useh

old,

log

ofth

eto

talp

oten

tialh

ouse

hold

trans

fera

mou

nt(in

1997

pric

es),

log

mal

e'sag

ricul

tura

lw

age

(in 1

997

pric

es),

dist

ance

to th

e cl

oses

t mun

icip

ality

and

stat

e du

mm

ies.Sc

hool

All

Wor

kM

arke

t Wor

kFa

rm W

ork

Dom

estic

Wor

k

39

Tab

le 9

: Ow

n-Su

bstit

utio

n, C

ross

-sub

stitu

tion

and

Inco

me

Eff

ects

on

Adu

lts' T

ime

Allo

catio

n

Lei

sure

Hou

rsPa

rtic

ipat

ion

Hou

rsPa

rtic

ipat

ion

Hou

rsPa

rtic

ipat

ion

Hou

rsPa

rtic

ipat

ion

Hou

rsI.

ME

N (N

= 9

519)

(OL

S)(P

robi

t)(T

obit)

(Pro

bit)

(Tob

it)(P

robi

t)(T

obit)

(Pro

bit)

(Tob

it)

Trea

tmen

t (T)

=1

0.58

3*-0

.004

-0.2

620.

026

0.16

4-0

.047

-0.3

19-0

.053

-0.0

71(I

ncom

e Ef

fect

)(0

.280

)(0

.021

)(0

.190

)(0

.033

)(0

.173

)(0

.035

)(0

.186

)+(0

.036

)(0

.056

)

T *

Nb

Ben

efic

iarie

s-0

.119

-0.0

17-0

.035

-0.0

04-0

.051

0.03

70.

171

-0.0

14-0

.055

(Cro

ss-S

ubst

Eff

ect)

(0.2

04)

(0.0

17)

(0.1

58)

(0.0

23)

(0.1

46)

(0.0

27)

(0.1

13)

(0.0

25)

(0.0

43)

II. W

OM

EN

(N =

103

70)

Trea

tmen

t (T)

=1

0.44

3*-0

.027

+-0

.328

*0.

017

0.24

0-0

.051

+-0

.164

-0.0

26-0

.185

(Inc

ome

Effe

ct)

(0.2

23)

(0.0

14)

(0.1

54)

(0.0

14)

(7.2

06)

(0.0

30)

(0.1

62)

(0.0

16)

(0.1

39)

T *

Nb

Ben

efic

iarie

s-0

.245

0.01

00.

254+

-0.0

29*

-0.4

21**

0.04

3*0.

130*

*0.

011

0.26

2*(C

ross

-Sub

st E

ffec

t)(0

.194

)(0

.014

)(0

.131

)(0

.012

)(0

.165

)(0

.019

)(0

.040

)(0

.015

)(0

.118

)N

otes

:+Si

gnifi

cant

atth

e10

perc

entl

evel

.*S

igni

fican

tatt

he5

perc

entl

evel

.**S

igni

fican

tatt

he1

perc

entl

evel

.SE

clus

tere

dat

the

com

mun

ityle

veli

npa

rant

hese

s.Fo

rpro

bita

ndto

bitm

odel

s,m

ean

ofth

eob

serv

atio

n-by

-obs

erva

tion

mar

gina

leff

ects

repo

rted.

All

regr

essi

ons

incl

ude

the

follo

win

gco

ntro

ls:a

ge,a

gesq

uare

d,ed

ucat

ion

dum

mie

s,m

arita

lsta

tus,

hous

ehol

dhe

adst

atus

,eth

nici

ty,b

asel

ine

hous

ehol

dde

mog

raph

ics

and

hous

ehol

dsi

ze,b

asel

ine

dwel

ling

char

acte

ristic

s(b

athr

oom

,ele

ctric

ity,l

and

owne

rshi

pan

ddr

afta

nim

alow

ners

hip)

,sex

and

grad

eof

the

first

four

bene

ficia

rych

ildre

nin

the

hous

ehol

d,lo

gof

the

tota

lpot

entia

lhou

seho

ldtra

nsfe

ram

ount

(in19

97pr

ices

),lo

gm

ale

agric

ultu

ral

wag

e(in

1997

pric

es),

dist

ance

toth

ecl

oses

tmun

icip

ality

and

stat

edu

mm

ies.

Age

and

age

squa

red

are

noti

nclu

ded

inM

odel

sB

.

All

Wor

kM

arke

t Wor

kFa

rm W

ork

Dom

estic

Wor

k

40