metadata: authors: 0: author: Cerdan-Infantes,Pedro
1: author: Vermeersch,Christel M. J.
count: Uruguay
docna: 0: docna: More time is better : an evaluation of the
            full time school program in Uruguay
docty: Policy Research Working Paper
subtopic: Primary Education,Tertiary Education,Educational Sciences,Educational Institutions & Facilities,Effective Schools and Teachers
teratopic: Education
abstracts: cdata!: This paper estimates the impact of the
            full-time school program in Uruguay on standardized test
            scores of 6th grade students. The program lengthened the
            school day from a half day to a full day, and provided
            additional inputs to schools to make this possible, such as
            additional teachers and construction of classrooms. The
            program was not randomly placed, but targeted poor urban
            schools. Using propensity score matching, the authors
            construct a comparable group of schools, and show that
            students in very disadvantaged schools improved in their
            test scores by 0.07 of a standard deviation per year of
            participation in the full-time program in mathematics, and
            0.04 in language. While the program is expensive, it may, if
            well targeted, help address inequalities in education in
            Uruguay, at an increase in cost per student not larger than
            the current deficit in spending between Uruguay and the rest
            of the region.
geo_reg_key: 119228,517191
repnme: repnme: More time is better : an evaluation of the
            full time school program in Uruguay
display_title: More time is better : an evaluation
            of the full time school program in Uruguay
listing_relative_url: /research/2007/03/7451171/more-time-better-evaluation-full-time-school-program-uruguay
topicv3: Teachers Management m1326119 1523,Primary Education m1326139 480,Tertiary Education m1326349 482,Teacher Training m1326786 1532,Education m1327048 470
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Education
no_of_pages: 25
author_keys: 0: author_key: 857582
1: author_key: 677541
model_extractions: WPS4167 <mark>I</mark> MPACT <mark>E</mark> VALUATION <mark>S</mark> ERIES <mark>N</mark> O <mark>. 13</mark>

# More Time Is Better:

# An Evaluation of the Full-Time School Program in Uruguay

## Pedro Cerdan-Infantes and Christel Vermeersch*

## Abstract

This paper estimates the impact of the full-time school program in Uruguay on standardized test scores of 6th grade students. The program lengthened the school day from a half day to a full day, and provided additional inputs to schools to make this possible, such as additional teachers and construction of classrooms. The program was not randomly placed, but targeted poor urban schools. Using propensity score matching, we construct a comparable group of schools, and show that students in very disadvantaged schools improved in their test scores by 0.07 of a standard deviation per year of participation in the full-time program in mathematics, and 0.04 in language. While the program is expensive, it may, if well targeted, help address inequalities in education in Uruguay, at an increase in cost per student not larger than the current deficit in spending between Uruguay and the rest of the region.

## World Bank Policy Research Working Paper 4167, March 2007

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at_ _<u>http://econ.worldbank.org.</u>_

> *Human Development Department, Latin America and the Caribbean Region; Email: pcerdaninfantes@worldbank.org and cvermeersch@worldbank.org. We are thankful to Emiliana Vegas for her comments and guidance. The paper benefitted from the comments of Andres Peri and seminar participants in Montevideo and Washington, DC.
# 1 Introduction

The length of the school day varies greatly among countries, as does the number of school days per academic year. Even though the cross-country correlation between hours spent in school and achievement is not clearly signi...cant, many countries, including the United States, Chile or Colombia in Latin America, have chosen to lengthen the school day as a way of improving student learning. With a longer school day, it is expected that students learn more because they spend more time with teachers and devote more time to school tasks. In addition, after-school programs in the United States have been used to prevent at-risk students from engaging in harmful activities, especially in high school.

Whether or not more time in school results in better learning outcomes depends broadly on two factors: what happens in the school in the extra time, and what would the bene...ciaries do were they not in school during the extra time. Since these two factors depend greatly on the characteristics of the existing education system, the implementation of the program and the bene...ciaries, the e¤ects of these programs will also depend on these characteristics. As a consequence, cross-country regressions will shed little light on the issue and careful impact evaluations would be better suited for estimating these e¤ects. Unfortunately, the existing impact evaluations are scarce and their results, though generally favorable, are not robust. A general concern with the existing literature is the identi...cation issues arising from the non-random assignment of students to schools or classes with longer school days. For example, Walston and West (2004) compare students who attended kindergarten full-time to students who attended part-time, within the same school, and show that full-time students had signi...cantly higher test scores in both math and reading than part-time students. However, the self-selection of children into full-time and part-time kindergarten makes it di¢ cult to attribute the di¤erences in achievement to di¤erences in kindergarten attendance. The authors cannot rule out that these di¤erences in achievement are driven by inherent di¤erences in the students that cannot be observed. In Latin America, two recent papers, Valenzuela (2005) and Bellei (2005), use exogenous variation in the expansion of the full-time school program in Chile to estimate the impact of longer school days on student achievement and show positive though small e¤ects, with larger e¤ects in language than in mathematics.

The Uruguay Full-Time School (FTS) Program is a prominent recent case of extension of school time in a middle-income country. The program lengthened the school day from a half day to a full day, and provided additional inputs to schools to make this possible, such as materials, teachers, teacher training, and construction or rehabilitation of classrooms. The program was a relatively intense intervention, targeting mid-sized, disadvantaged urban schools, and has been in place since the early 1990s. Since then, the Ministry of Education has commissioned two evaluations of the 1
program. The ...rst evaluation, a qualitative study of the performance and inner workings of Full Time Schools, was carried out by Equipos/Mori for MECAEP (Spanish acronym for Program for the Improvement of Quality in Primary Education, ...nanced by the World Bank) in 2001 (Estudio de la Evaluación Social de las Escuelas de Tiempo Completo) and showed that, while opinions about the program were generally positive, implementation was incomplete at the time of the study, especially regarding the provision of resources to complement the extension of the school day. This study, however, did not attempt to measure the impact of the program on learning outcomes. The second evaluation was carried out by the Administración Nacional de Educación Pública (ANEP) and showed that the average test score in full-time schools was higher than non-full time schools with similiarly unfavorable socioeconomic characteristics, using the National Assessment of Student Learning (Evaluación Nacional de Aprendizajes), a nation-wide standardized test. While the restriction of the comparison group to schools in similar contexts does control for some socioeconomic characteristics, the analysis did not use the richness of data available to minimize biases from unobservable di¤erences between schools that did and did not participate in the program.

In this paper, we evaluate the impact of the FTS program on student oucomes, trying to shed more light on the e¤ects of the program using student level data and controlling for characteristics of the school that may be driving these di¤erences. The paper is organized as follows: Section 2 describes the main characteristics of the Full-Time School program in Uruguay since its inception, emphasizing implementation issues that may a¤ect our estimation. Section 3 describes the data, Section 4 explains the estimation strategy, Section 5 presents the main results, and Section 6 shows some cost-bene...t considerations. Finally, Section 7 concludes.

# 2 Program Description<sup>1</sup>

The FTS program in Uruguay was introduced in the early 1990s as a means to increase student achievement in disadvantaged schools. The conversion of schools to the FTS was e¤ectively done in three phases, in which both the content of the program and the characteristics of the bene...ciary schools varied notably: before 1996, 1997 to 1998 and 1999 to 2005. The 52 schools that converted to FTS before 1996 served a very disadvantaged part of the population; 25 of them were "open-air" schools (that is, schools with no buildings or with buildings in very bad condition).

> 1 Sections 2 and 3 draw on three documents: Evaluacion Nacional de Aprendizajes, Resultados en Escuelas de Tiempo Completo y Areas Integradas, and the Propuesta Metodologica para Escuelas de Tiempo Completo. These documents and more information are available MECAEP’s website (www.mecaep.edu.uy).

2
Between 1996 and 1999, the program only incorporated 4 new schools, while the Ministry was working on a new pedagogical model for the full-time schools. From 1999 onwards, the National Public Education Administration (ANEP) implemented this new pedagogical plan and 48 more schools were converted to FTS.

The new pedagogical model came amid concern about the e¤ectiveness of public education. In 1996, ANEP applied the National Census of Student Learning, which showed a number of alarming trends. First, overall student achievement was low: 43 percent of sixth grade pupils did not reach the “su¢ cient”score in language, and 65 percent failed to do so in math. Second, pupil performance was highly unequal and strongly correlated with the socioeconomic characteristics of the parents and the school. This evidence, combined with qualitative indications that the existing full-time schools had performed better than schools with similar characteristics, prompted the government to turn to the FTS program for solutions to low achievement in disadvantaged schools. The plans for expansion resulted in a special and comprehensive pedagogical plan (Propuesta Pedagógica), which was implemented in both existing and new full-time schools starting in 1999, and was supported by the World Bank through MECAEP.

The FTS program as implemented starting in 1996 encompassed not only an expansion of the school day, but also a combination of other interventions targeting students and teachers, additional classrooms and equipment, and community participation. The program includes the following interventions:

1. Construction of new classrooms;

2. A reduction of the recommended number of pupils per classroom to 25 in grades 1-3, and 28 in grades 4 to 6.

3. Lengthening of class time from 3.5 to 7 hours per day, 5 days per week, and an additional of 3 hours per week of complementary attention to students with special needs and/or community service activities, and 2 hours of teacher meetings; 4. Introduction of collective, complementary and classroom activities; 5. Constitution of a teacher committee; 6. Provision of nutritional and health care support for students; 7. Increased participation of parents, accountability/ community involvement; 8. From 1999 onwards: Teacher training in the FTS pedagogical model (“Curso I”) and follow-on subject related courses (“Curso II”, math, Spanish, science); 9. Provision of a set of teaching materials, such as maps, books or dictionaries.

Even though a qualitative evaluation of program implementation in 2001 indicated that provision of the di¤erent sub-components was unequal across participating schools, the implementation 3
of the extension of the school day, school building and teacher training was practically universal in participating schools. Other components, like additional teacher training and increased community participation were voluntary. These two characteristics of the sub-components of the program will be relevant when trying to assess their impacts, as they prevent the robust causal identi...cation of the e¤ects of separate interventions.

# 3 Data

In the analysis, we use three sources of data: the National Evaluation of Learning Achievements for the Sixth Grade of Primary School, the National Census of Schools and speci...c program information gathered from ANEP.

## 3.1 National Evaluation of Learning Achievements for the Sixth Grade

The National Evaluation of Learning Achievements for the Sixth Grade of Primary School<sup>2</sup> (hereafter "tests") was carried out in 1996, 1999 and 2002 in the context of the Program for the Improvement of Quality in Primary Education (MECAEP hereafter), which was ...nanced by the World Bank. It consisted of Math and Spanish assessments, and it was supposedly applied to a census of primary schools in 1996 and nationally representative samples in 1999, 2002 and 2005 (the 2005 data are not yet available). The 1996, 1999 and 2002 tests are directly comparable between years. The 2005 used item-response theory for the ...st time, which may limit the comparison in the future. While the 1996 test was meant to be a census, it excluded very small schools with fewer than 6 students in 6th grade. As a result, about 1,200 schools (approximately half of the total number of schools) were not included in the sample, but they likely represent a minority of students. (Cf. Table 1) The achievement tests consist of two sets of 24 questions each in mathematics and language. The mathematics test comprises 7 questions on comprehension of mathematical concepts, 12 questions that involve the resolution of a problem, and 5 questions involving an algorithm. The language test comprises 8 questions about language, 8 argumentative questions and 8 narrative questions. All questions are multiple choice with 4 possible options. The overall score is the number of correctly answered questions out of a possible 24. There is no penalty for giving the wrong answer, so that a child who guesses on every single question of the test is expected to obtain 6 points out of 24 on average.

Information about FTS was gathered both from administrative data and data collected

> 2 Evaluación Nacional de Aprendizajes, 6to año Enseñanza Primaria, Gerencia de Investigación y Evaluación ANEP-CODICEN

4
Table 1: National Evaluation of Learning Achievements for Sixth Grade: Sampling of Schools

|Year of Conversion<br>to FTS|N<br>Total|umber of Public <br>Tested in 1996|Primary Schools <br>Tested in 1999|(excluding special<br>Tested in 2002|schools)<br>Never Tested|
|---|---|---|---|---|---|
|1996 or before|52|49|39|52|0|
|1997|2|2|2|2|0|
|1998|2|2|2|2|0|
|1999|10|8|8|10|0|
|2000|7|3|1|7|0|
|2001|15|12|2|15|0|
|2002|3|1|0|2|1|
|2003|5|4|0|1|1|
|2004|8|7|0|2|1|
|Never a FTS|2382|1196|189|205|1162|
|Total|2486|1284|243|298|1165|

from ANEP. Table 1 shows the sample of schools tested in each year broken down by year of conversion to the FTS program (including those not converted). It was ANEP’s policy to apply the achievement tests in all full-time schools. However, schools that converted to full time status after the year of the test were only tested if the random selection of a nationally representative sample included those schools. For example, there were 24 schools that converted between 1999 and 2002, but only 2 of them were included in the sample of the 1999 achievement tests. For this reason, it is not possible to construct a panel of schools for 1996, 1999 and 2002. Instead, we use the 1996 test data as baseline data, and use the 2002 data as follow-up testing. Since very few schools converted between 1996 and 1999, the 1999 data are of little use for evaluating the impact of the full-time schools program.

For the purpose of the analysis, we restrict the sample of schools in the following way: we exclude those schools that were not tested in 1996 (i.e. the schools for which we do not have a baseline), the schools that converted to the full-time model by 1996, and the schools that did not participate in either of the post-baseline measurement in 2002. These restrictions lead to the sample presented in Table 2. There are 28 schools that converted between 1997 and 2002, and which were tested both in 1996 and 2002. In addition, there are 190 schools that were also tested in 2002, but that either did not convert to full time status or converted after 2002.

The achievement tests were complemented with questionnaires addressed to each child, the head of household of each child, the class teacher and the school director. While the additional questionnaires are slightly di¤erent in the three years, it is possible to extract a common set of questions, which cover: (1) in the pupil questionnaire: classroom activities and motivation for 5
Table 2: Test sample of FTS

|Year of conversion to FTS|Tested in 1996 and 2002|
|---|---|
|1997|2|
|1998|2|
|1999|8|
|2000|3|
|2001|12|
|2002|1|
|2003|1|
|2004|2|
|Never a FTS|187|
|Total|218|

school and learning; (2) in the head of household questionnaire: household demographics, family assets, parental education and occupation, preschool attendance; (3) in the teacher questionnaire: training, experience, opinions on pedagogy, and interactions with parents; (4) in the director’s questionnaire: training and experience of the school director, school infrastructure and equipment, school problems, interactions with parents, director’s opinion on pedagogy.

# 3.2 National Census of Schools

The National Census of Schools<sup>3</sup> is a registry that contains yearly information on all public school in Uruguay, from 1996 to 2004. The registry contains data on pupil enrollment, repetition, grade promotion, repetition, drop-outs and attendance at each grade level. We use the 1996 data as controls for the initial conditions at the school. As these variables are potentially endogenous to participation in the full-time model, we use only the 1996 data. The Census of Schools also contains a classi...cation of the sociocultural context of the school on a scale of 1 through 5, which is available for 1996 and 2002. In 1996, the classi...cation was computed on the basis of data from the additional questionnaires for the National Evaluation of Learning Achievements for Sixth Grade. (Cf. 3.1) In 2002, it was computed on the basis of data reported by the school director in the newly introduced Educational Monitor instrument<sup>4</sup> .

> 3 Hiperbase Primaria, Gerencia de Investigación y Evaluación

> 4 Monitor Educativo, Gerencia de Investigación y Evaluacion ANEP-CODICEN

6
# 3.3 Speci...c Program Information

In addition to administrative data, we gathered information from the Ministry on implementation and characteristics of each school participating. The dataset includes information for each full-time school on the year of conversion to FTS (or creation for new schools), the department and school number, as well as school characteristics and the main reason why the school was chosen for conversion. This information was complemented with informal conversations with ANEP’s sta¤ involved in the implementation of the program.

# 3.4 Summary Statistics

As explained above, the full-time school program targeted disadvantaged schools, which implies that full-time and non-full time schools had di¤erent characteristics at baseline. Table 3 presents summary statistics for key variables in 1996, broken down for schools that did not convert to FTS (columns 1-3), and schools that converted to FTS after 1996 (columns 4-6). The variables include test scores, teacher experience, school characteristics and household characteristics including size and parental education. Indices were constructed for “school equipment”and “buildings”using factor components analysis.<sup>5</sup> Column 7 presents t-statistics for the null hypotheses that the mean of the variables for the full-time schools is the same as for non-full time schools.

The two groups of schools di¤er on a number of di¤erent dimensions. First, full-time schools scored signi...cantly lower than other schools on the 1996 achievement tests, and the di¤erence is substantial (about 0.3 of a standard deviation). Second, full-time schools have signi...cantly worse socio-economic indicators than non-full time schools, including parental education and household size. Third, there are notable di¤erences at the school level: full-time schools had worse infrastructure, but better school equipment and smaller class sizes at baseline. Teacher experience does not seem to di¤er signi...cantly. These di¤erences were expected given that the program was targeted towards worse-o¤ schools. These di¤erences persist if we restrict the sample to schools that were tested both in 1996 and 2002.

> 5 The underlying variables were extracted from the 1996 director’s questionnaire, and include the availability of teaching materials (television, video, overhead projector, music) for the factor “equipment” and auxiliary buildings (library, lab, audio-visual room and computer room) for the factor “buildings”. Finally, the variables household size, household number of rooms and parental education try to capture the socioeconomic characteristics of the household and were constructed directly from parental questionnaires.

7
Table 3: Summary Statistics, all tested schools, 1996

|||Non-FT|S||FTS|||
|---|---|---|---|---|---|---|---|
|Variable description|Obs|Mean|St.Dev.|Obs|Mean|St.Dev.|t-stat|
|Score in language, out of 24|42616|14.41|4.72|984|12.45|4.60|-4.25|
|Score in math, out of 24|43854|11.97|4.60|999|10.49|3.86|-3.91|
|Student is female =1|44633|0.50|0.50|1022|0.50|0.50|-0.15|
|Student repeated at least one grade =1|43667|0.29|0.45|999|0.42|0.49|6.06|
|Student repeated two or more grades =1|43667|0.08|0.28|999|0.14|0.35|3.51|
|Student’s nr of years of preschool|41558|1.31|1.15|933|1.55|0.93|3.90|
|Household size|43847|5.16|1.89|1003|5.64|2.36|3.73|
|Household’s number of rooms|43827|3.77|1.55|1004|3.46|1.37|-3.63|
|Mother has no education =1|41762|0.14|0.35|960|0.26|0.44|5.24|
|Mother has at least primary education =1|41762|0.86|0.35|960|0.74|0.44|-5.24|
|Mother has done at least some secondary =1|41762|0.62|0.48|960|0.43|0.50|-6.25|
|Mother has at least ...nished secondary =1|41762|0.30|0.46|960|0.14|0.35|-7.93|
|Mother has completed tertiary education =1|41762|0.19|0.39|960|0.07|0.26|-9.08|
|Father has no education =1|38892|0.17|0.37|895|0.29|0.46|4.87|
|Father has at least primary education =1|38892|0.83|0.37|895|0.71|0.46|-4.87|
|Father has done at least some secondary =1|38892|0.59|0.49|895|0.40|0.49|-5.89|
|Father has at least ...nished secondary =1|38892|0.29|0.45|895|0.14|0.35|-7.49|
|Father has completed tertiary education =1|38892|0.17|0.38|895|0.08|0.27|-7.06|
|Teacher has only one year of experience|1753|0.02|0.15|47|0.06|0.25|1.17|
|Teacher has 2-3 years of experience|1753|0.05|0.22|47|0.04|0.20|-0.22|
|Teacher has 4-5 years of experience|1753|0.05|0.22|47|0.17|0.38|1.80|
|Teacher has 6-9 years of experience|1753|0.15|0.35|47|0.13|0.34|-0.37|
|Teacher has at least 10 years of experience|1753|0.73|0.44|47|0.60|0.50|-1.82|
|Average Class Size|1196|22.89|10.17|39|20.61|6.98|-1.39|
|School equipement (PC factor 1)|1196|0.19|0.83|39|0.10|0.71|-0.65|
|School auxiliary buildings (PC factor 1)|1196|-0.26|0.67|39|-0.47|0.26|-1.89|
|School socio-economic quintile|1196|3.54|1.43|39|4.44|0.82|3.88|

Notes: The statistics are based on the test scores and additional information that were collected at the time of the 1996 achievement tests. The non-full-time schools (columns 1-3) are those schools which were tested in 1996 and who had not converted to full-time status as of 2002. The full-time schools (colums 4-6) are the schools that were tested in both 1996 and 2002, and had converted to full-time status as of 2002. The potential comparison schools (columns 8-10) are those schools that were tested in both 1996 and 2002, and had not converted to full-time status as of 2002. t-statistics in column (7) are for a test of the null hypothesis that full-time and non-full-time have identical values. t-statistics in column (11) are for a test of the null hypothesis that full-time and comparison schools have identical values. The school socio-economic quintile was calculated by ANEP in basis of the 1996 data, and used for assigning schools to the full-time program. Schools in lower quintiles have better socio-economic environments. The t-tests at the student level and at the teacher level are clustered at the school level. * signi...cant at 10 percent; ** signi...cant at 5 percent; *** signi...cant at 1 percent.

8
# 4 Estimation Strategy

The goal of this analysis is to identify the e¤ect of being converted to a FTS on student achievement. In particular, and since exposure to the program varies notably between 0 and 6 years, we are interested in knowing the change in test scores, if any, associated with each year of exposure to the FTS model. A ...rst approach to identifying this e¤ect would be to compare the test scores of students in schools that were converted to FTS with those that were never converted: where Yijt is the test score of student i in school j at time t, Xijt is a set of student-level controls, Ejt is the length of exposure to the full-time school model in school j as of time t (in years), and "ijt is a stochastic error term. In this speci...cation, � would provide an unbiased estimate of the e¤ect of one additional year of exposure to FTS only if Ejt is not correlated with the error term, that is, only if there are no omitted variables that are correlated with both the exposure variable and the dependent variable. Since the selection of schools was non-random, and in fact targeted to schools with students from disadvantaged backgrounds, it is likely that the � coe¢ cient in 1 would be biased. Since more disadvantaged schools were targeted for participating in the program, school characteristics, both observable and unobservable, are surely correlated with program participation. In addition, these characteristics are also likely to a¤ect test scores, and therefore their omission from equation 1 results in a biased estimate of program e¤ect. This estimation would therefore result in negative bias, or an underestimation of the program e¤ect.

Controlling for omitted variables in this model is challenging. As we saw in section 2, schools that were selected for conversion to full-time schools had to meet certain requirements like size, having one shift and having physical space to build additional classrooms or rehabilitate them. While these characteristics were necessary for conversion, they were not su¢ cient: there are many schools that met those requirements that were not converted. Including these variables in the regression will not eliminate all the bias since the selection among schools that met the requirements of the program, used additional criteria that are not easily measureable. The speci...cation we use attempts to minimize the potential bias introduced by the selection of schools based on unobservable school characteristics. We use school ...xed e¤ects and di¤erent propensity score matching methodologies to control for time-invariant observable and unobservable school characteristics, and minimize the di¤erences in the distribution of participating schools and their comparisons. In addition, the targeting of the program does allow some re...ning of the sample. Using information that 9
was collected with the 1996 test, ANEP constructed a socio-economic index, and excluded schools with belonged to quintiles 1 and 2 (the most advantaged ones) from participation in the full-time program. This rule was adhered to in all cases, which means that the 66 schools from quintiles 1 and 2 can be excluded from the sample.

# 4.1 Fixed E¤ects Model

In the ...xed e¤ects model, we control for time invariant observable and unobservable school characteristics using school-level ...xed e¤ects. We include also time dummies, to control for unobservable changes common to all the observations in a particular year, student characteristics, and teacher characteristics. The e¤ect of the full-time program is captured by the parameter of a variable that measures the number of years a school was exposed to the program before the 2002 follow-up achievement test. Since the sample excludes schools that were already full time at baseline in 1996, the highest number of years of exposure to the program is six. Since primary school has six years, and the achievement tests were taken in sixth grade, this is also the maximum number of grades that any child would have been exposed to the full-time model. The only potential bias left in this speci...cation comes from time-variant school characteristics. The ...xed e¤ects model speci...cation is as follows: where all variables from 1 are included, Djk is a categorical variable that takes value 1 if j = k, and 0 otherwise, Tt is a categorical variable that takes value 1 if t = 2002 and 0 otherwise, and "ijt is a stochastic error term. t can take values 0 (for 1996) or 1 (for 2002). The length of exposure is calculated as follows:

# 4.2 Propensity Score Matching Model

Finding a comparison group for participating schools in the absence of a clear targeting mechanism is a challenge. As mentioned above, schools that converted to the full time system were quite di¤erent from other schools at baseline. Even controlling for observable characteristics separately in the regression, the distribution of the two groups will be di¤erent, and the results from a full-sample ...xed e¤ects model may be biased if there are di¤erential trends between the full-time schools and the other schools which are not related to the full-time program. To address 10
this potential bias, we construct control groups using di¤erent matching methods in an attempt to restrict the comparison group to schools that are as similar as possible to the full-time schools. Ideally, we would try to replicate the targeting mechanism of the program in the matching model, in order to choose schools with the same characteristics to those participating in the program (i.e. we would include the variables used in the targeting to ...nd suitable control schools). In the absence of explicit rules for the assignment of schools to the program, we use the observable characteristics of the program schools to identify those comparison schools that are similar. In particular, we will use a propensity score model to identify schools that have a similar range of probability of participating in the full-time model, given their observable characteristics. We use the following model of participation: where Pj is a dichotomous variable that takes value 1 if school j participates in the full-time model, and Wj;1996 is a vector of characteristics of school j in 1996. This vector includes the key variables described in the summary statistics above plus "director’s motivation" and the "number of shifts in the school". The latter was an explicit rule included in the election of the schools, while the former is an attempt to control for the unobservable "interest on the part of the school", which was mentioned by ANEP to be a factor in the decision to transfer schools.<sup>6</sup> Using this model of participation, we estimate the propensity score - i.e. the probability of participating in the program - for both full-time and non-full-time schools. We then use the predicted probability to form three alternative comparison groups. For each of the proposed comparison groups, we ...t a population-average model similar to the ...xed e¤ects model, using probability weights. The ...rst comparison group is the set of the “closest neighbor” to each program school: for each program school, we identify the non-participating school with the closest propensity score. The second comparison group is the set of the “closest 5 neighbors” to each program school, by identifying for each program school the 5 non-participant schools with closest propensity scores. Note that in both methods, a non-program school can potentially be the closest to several program schools. We will take this into account in the second stage of the estimation by introducing probability weights on the observations.

Finally, we form a third comparison group by identifying those schools for which we can predict participation in the program with a probability above a cuto¤ point. This is motivated by the following observation: using the propensity score model, we ...nd that a number of program schools have zero predicted probability of participating in the program. This means that

> 6 Note that the results from the matching are robust to the inclusion of di¤erent combinations of these variables, as long as the variables for socio-economic characteristics of the students are included.

11
their baseline characteristics are very di¤erent from the baseline characteristics of other program schools. In particular, these schools were better o¤ in socio-economic terms at baseline than their fellow program schools, in addition to being better equipped. It thus seems that some schools were included in the program even though they did not have disadvantaged socio-economic characteristics. Conversations with ANEP o¢ cials con...rmed that certain program rules related to geographic allocation resulted in conversion of a number of better-o¤ schools. For the purpose of the analysis, it is useful to exclude those “outliers” and re-calculate the e¤ect of the program among the real target group of the program. In practice, we selected all schools that had a predicted probability of participation above .15, and compared outcomes between program and non-program schools within that restricted sample. By applying the same cuto¤ point to both program and comparison schools, we thus exclude schools with a-typical FTS characteristics.

# 5 Results

## 5.1 Full Sample Fixed E¤ects Estimates

Table 4 presents the results from the ...xed-e¤ects model on the full sample of schools, and the full sample of schools in the third, fourth and ...fth socio-economic context quintiles (Equation 2). The dependent variable is the score on the Mathematics or Spanish language test, which ranges from 0 to 24, and the model includes parental, class and school level controls, in addition to school ...xed e¤ects and a year dummy. Columns 1 and 3 present the results for Spanish language, and columns 2 and 4 present the results for Mathematics. In columns 3 and 4, we restrict the estimation to those schools that belonged to socio-economic quintiles 3, 4 and 5, as none of the schools in quintiles 1 and 2 ever participated in the program. The results are positive for Mathematics, but are not statistically signi...cantly di¤erent from 0 in Language. The full-sample estimates, which are subject to the largest potential bias, show that one year of exposure to the full-time program is associated with an increase of 0.21 points in Mathematics. The program is also associated with 0.12 increase in Language test scores, though the coe¢ cient is only marginally signi...cant. Restricting the sample to those schools in the third, fourth and ...fth quintiles reduces the coe¢ cient in Math (to 0.16), and the Language coe¢ cient is not statististically signi...cant. The size of the coe¢ cient is relatively small: one year of exposure represents an increase of 3.5 - 4.6 percent of a standard deviation of student test scores, which means a full 6 year exposure would lead to a increase in test scores of 0.21 - 0.28 of a standard deviation in Math.

The coe¢ cients on all controls have the expected sign and are signi...cantly di¤erent from 0 in most cases. As expected, maternal education plays a very important role: other things equal, on 12
Table 4: Table Fixed E¤ects, Full Sample

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Language<br>out of 24|Math<br>out of 24|Language<br>out of 24|Math<br>out of 24|
|Years of exposure to FTS|0.12<br>(0.06)*|0.21<br>(0.07)***|0.08<br>(0.07)|0.16<br>(0.07)**|
|Pupil is female =1|1.04|0.11|1.15|0.16|
||(0.06)***|(0.06)*|(0.08)***|(0.08)**|
|Nr of people living in pupil’s household|-0.13|-0.08|-0.13|-0.07|
||(0.02)***|(0.02)***|(0.02)***|(0.02)***|
|Nr of rooms in pupil’s home|0.11|0.10|0.06|0.04|
||(0.02)***|(0.02)***|(0.03)*|(0.03)|
|Home equipm. modern appliances PCF1|0.52|0.58|0.48|0.52|
||(0.04)***|(0.04)***|(0.05)***|(0.05)***|
|Mother completed primary education|0.33<br>(0.11)***|0.33<br>(0.11)***|0.35<br>(0.12)***|1.00<br>(0.11)***|
|Mother has some secondary education|0.47<br>(0.08)***|0.46<br>(0.08)***|0.45<br>(0.10)***|0.49<br>(0.10)***|
|Mother completed secondary education|0.63<br>(0.09)***|0.62<br>(0.09)***|0.60<br>(0.13)***|0.49<br>(0.13)***|
|Mother completed tertiary education|0.53<br>(0.12)***|0.63<br>(0.12)***|0.52<br>(0.21)**|0.59<br>(0.21)***|
|Teacher Experience 1 year or less =1|-0.83<br>(0.22)***|-1.33<br>(0.23)***|-0.89<br>(0.25)***|-1.13<br>(0.25)***|
|Teacher Experience in years|0.00|0.03|0.02|0.05|
||(0.01)|(0.01)***|(0.01)***|(0.01)***|
|Class size|0.01|0.02|0.01|0.00|
||(0.01)|(0.01)**|(0.01)|(0.01)|
|This is an observation in 2002 =1|0.78|0.68|0.74|0.82|
||(0.10)***|(0.10)***|(0.12)***|(0.12)***|
|Constant|13.12|10.59|12.46|10.38|
||(0.28)***|(0.29)***|(0.35)***|(0.35)***|
|Observations|17249|17360|11061|11115|
|Number of Unique school identi...ers|218|218|152|152|
|R-squared|0.08|0.08|0.08|0.08|
|Socioecon. context quintiles in sample|All|All|3, 4 and 5|3, 4 and 5|
|Number of full time schools|31|31|31|31|
|Number of comparison schools|187|187|121|121|

Notes: The dependent variable is the raw test score on a scale of 0 to 20. Columns 1 and 2 include all 218 schools for which information was available in both 1996 and 2002, and who were not full time at baseline. Columns 3 and 4 are restricted to those of the 218 schools that pertain to the 3 least favored socio-economic quintiles. Those quintiles were calculated by ANEP in basis of the information that was collected in 1996. None of the schools pertaining to socio-economic quintiles 1 and 2 ever participated in the full time program. Standard errors are reported in parentheses. * signi...cant at 10 percent; ** signi...cant at 5 percent; *** signi...cant at 1 percent.

13
average, a student whose mother has tertiary education scores 1.6 standard deviations higher than a student whose mother only completed primary. Increased teacher experience is associated with higher test scores, while pupils learning with very inexperienced teachers score signi...cantly lower. The e¤ect of class size is ambiguous.

# 5.2 Propensity Score Matching Estimates

## 5.2.1 First stage: matching

As described in the methodological section, we match the program schools with a set of comparison schools using propensity score matching on a set of observable school characteristics. First note that schools in socio-economic quintiles 1 and 2 do not belong to the support of the propensity score model, because none of them ever participated in the program. Of the 152 schools in quintiles 3, 4 and 5, 141 had su¢ cient information to estimate a propensity score model with the following explanatory variables: directors enthusisam and experience, average household size, number of rooms in homes and household equipment, mothers’ education level, teacher experience, economic quintile, school equipment and buildings class size and type of shift system. The propensity score regression results are reported in Table 5.

The goal is to construct a comparison group of schools whose characterisitcs resemble those of the program schools. We verify the appropriateness of the methodology by comparing the baseline observable characteristics between the program group and the various comparison groups of schools (Tables 6 and 7). As evidenced in Table 6, stronger restrictions lead to the selection of a comparison group whose characteristics get closer to those of the full-time schools, though even with the closest neighbor matching, comparison schools seem to have better educated mothers and less repetition than full time schools. However, the best match of characteristics occurs when we exclude both the treatment and comparison schools that have a p-score below 0.15. As evidenced in Table 7, none of the key variables is statistically di¤erent between program and comparison schools when we restrict the sample to those schools for which we can predict participation in the program.

## 5.2.2 Second stage: Results using matched/restricted samples

Table 8 presents the summary of the results from the two ...rst matching speci...cations. Table 1, Column 1 reports the estimated treatment e¤ect using the full sample of schools in the 3, 4 and 5th socio-economic context quintile.<sup>7</sup> Columns 2 and 3 report the results when the sample is restricted to program schools and their 5 nearest neighbors and their closest neighbor, respectively.

> 7 Note that this sample restriction is similar to the one in Table 4, Columns 3 and 4.

14
Table 5: Propensity score matching results

|Dependent variable:|Full tim|e school=1|
|---|---|---|
|Director’s enthusiasm level|-0.12|(0.30)|
|Director’s experience|-0.04|(0.03)|
|Avg. household size|0.41|(0.29)|
|Avg. nr. of rooms in homes|0.63|(0.47)|
|Avg. household equipment p.c.|-0.82|(0.88)|
|Perc. of mothers with at least secondary education|3.23|(4.77)|
|Perc. of mothers with tertiary education|-9.75|(6.96)|
|Total teacher experience|0.05|(0.03)|
|School in economic quintile 4|-0.02|(0.57)|
|School in economic quintile 5|-0.30|(0.73)|
|School equipment p.c.|0.03|(0.28)|
|Auxiliary buildings p.c.|-0.26|(0.35)|
|Class size|0.01|(0.03)|
|School has morning shift|-2.13|(0.71)**|
|School has afternoon shift|-2.25|(0.72)**|
|School has 2 shifts|-1.29|(0.49)**|
|Constant|-4.28|(2.43)|
|Observations|141||
|Pseudo R2|0.27||
|ChiSq|37.97||
|Number of full time schools|28||
|Number of comparison schools|113||

Notes: The dependent variable is a categorical variable that takes value 1 if the school became a full time school between 1996 and 2002. The explanatory variables are measured at baseline in 1996. The sample only includes schools that were identi...ed in 1996 as belonging to socioeconomic context quintiles 3, 4 and 5, excluding the most advantages quintiles 1 and 2. This is because none of the schools from quintiles 1 and 2 were transformed into full time schools, and hence the model has no statistical support for those quintiles. The omitted category is quintile 3. Household equipment, school equipment, and school auxiliary buildings are calculated using principal components estimation. (cf. section 3.4) The model is probit at the school level. Variables concerning mothers/homes/households were collected for students who took the 6th grade exam, and were aggregated at the school level. Standard errors are in parentheses. * signi...cant at 10 percent; ** signi...cant at 5 percent; *** signi...cant at 1 percent.

15
Table 6: Summary statistics by propensity score group

||Fu|ll-time s|chools||C|omparis|on school|s||
|---|---|---|---|---|---|---|---|---|---|
|||||Full|Sample|Clo|sest 5|C|losest|
|Variable description|Obs|Mean|St.Dev.|Obs|Mean|Obs|Mean|Obs|Mean|
|Score in language, out of 24|710|12.48|4.62|4641|13.26|2260|12.81|809|12.87|
|Score in math, out of 24|710|10.25|3.68|4641|10.95*|2260|10.59|809|10.55|
|Student is female|710|0.51|0.50|4641|0.51|2260|0.51|809|0.52|
|Student repeated >=1 grade|706|0.41|0.49|4618|0.35***|2246|0.38|804|0.36|
|Student repeated >=2 grades|706|0.13|0.34|4618|0.11|2246|0.11|804|0.08**|
|Student’s years of preschool|673|1.59|0.91|4495|1.57|2183|1.57|786|1.59|
|Household size|710|5.64|2.34|4641|5.37**|2260|5.59|809|5.53|
|Household’s nr of rooms|710|3.50|1.37|4641|3.47|2260|3.44|809|3.44|
|Mother has no education|710|0.27|0.45|4641|0.20***|2260|0.23*|809|0.20**|
|Mother completed primary|710|0.73|0.45|4641|0.80***|2260|0.77*|809|0.80**|
|Mother attended secondary|710|0.42|0.49|4641|0.52***|2260|0.48*|809|0.51**|
|Mother ...nished secondary|710|0.14|0.35|4641|0.17|2260|0.15|809|0.16|
|Mother completed tertiary|710|0.08|0.27|4641|0.09|2260|0.07|809|0.07|
|Father has no education|657|0.30|0.46|4244|0.22***|2072|0.26|749|0.27|
|Father completed primary|657|0.70|0.46|4244|0.78***|2072|0.74|749|0.73|
|Father attended secondary|657|0.37|0.48|4244|0.49**|2072|0.45**|749|0.44|
|Father ...nished secondary|657|0.13|0.34|4244|0.18|2072|0.15|749|0.14|
|Father completed tertiary|657|0.09|0.28|4244|0.09|2072|0.07|749|0.06|
|Teacher experience <=1 yr|35|0.09|0.28|183|0.03|93|0.04|32|0.03|
|Teacher experience = 2-3 yr|35|0.06|0.24|183|0.05|93|0.08|32|0.09|
|Teacher experience= 4-5 yr|35|0.20|0.41|183|0.04**|93|0.04*|32|0.06|
|Teacher experience = 6-9 yr|35|0.09|0.28|183|0.16|93|0.17|32|0.13|
|Teacher experience >= 10 yr|35|0.57|0.50|183|0.72|93|0.67|32|0.69|
|Average Class Size|27|21.57|6.50|110|27.19***|57|25.99**|20|26.31**|
|School equipement|27|0.18|0.61|110|0.37|57|0.38|20|0.39|
|School auxiliary Buildings|27|-0.49|0.25|110|-0.40|57|-0.43|20|-0.50|
|School socio-econ. quintile|27|4.48|0.70|110|4.17*|57|4.39|20|4.50|

Notes: This table reports summary statistics on key observables at the time of the baseline, for full-time schools and for three di¤erent sets of comparison schools: the full sample, the set of 5 closest schools in terms of propensity scores, and the set of closest schools. The table excludes schools that are missing any of the key variables used in the second stage analysis. School Equipment and School Auxiliary Buildings are estimated using Principal Component Factors. A t-test was performed for the equality of means between each comparison group and the group of full time schools. The t-test results are reported using star coding with * signi...cant at 10 percent; ** signi...cant at 5 percent; *** signi...cant at 1 percent.

16
Table 7: Summary statistics for schools with high p scores

|||Non-F|TS||FTS|||
|---|---|---|---|---|---|---|---|
|Variable description|Obs|Mean|Std. Dev.|Obs|Mean|Std. Dev.|t-statistic|
|Score in language, out of 24|1554|12.70|4.32|544|12.74|4.62|0.05|
|Score in math, out of 24|1554|10.56|3.89|544|10.38|3.83|-0.32|
|Student is female|1554|0.51|0.50|544|0.53|0.50|0.66|
|Student repeated >=1 grade|1544|0.38|0.49|542|0.39|0.49|0.38|
|Student repeated >=2 grades|1544|0.11|0.32|542|0.11|0.32|0.11|
|Student’s years of preschool|1503|1.62|0.92|513|1.58|0.92|-0.63|
|Household size|1554|5.65|2.15|544|5.63|2.33|-0.06|
|Household’s nr of rooms|1554|3.45|1.36|544|3.57|1.41|1.01|
|Mother has no education|1554|0.24|0.43|544|0.28|0.45|1.00|
|Mother completed primary|1554|0.76|0.43|544|0.72|0.45|-1.00|
|Mother attended secondary|1554|0.44|0.50|544|0.42|0.49|-0.59|
|Mother ...nished secondary|1554|0.12|0.33|544|0.15|0.35|0.80|
|Mother completed tertiary|1554|0.06|0.23|544|0.08|0.27|1.31|
|Father has no education|1436|0.28|0.45|505|0.31|0.46|0.92|
|Father completed primary|1436|0.72|0.45|505|0.69|0.46|-0.92|
|Father attended secondary|1436|0.41|0.49|505|0.36|0.48|-1.26|
|Father ...nished secondary|1436|0.15|0.35|505|0.15|0.36|0.31|
|Father completed tertiary|1436|0.07|0.25|505|0.10|0.30|1.77|
|Teacher experience <=1 yr|65|0.05|0.21|27|0.11|0.32|0.99|
|Teacher experience = 2-3 yr|65|0.11|0.31|27|0.07|0.27|-0.51|
|Teacher experience= 4-5 yr|65|0.06|0.24|27|0.19|0.40|1.35|
|Teacher experience = 6-9 yr|65|0.15|0.36|27|0.07|0.27|-1.12|
|Teacher experience >= 10 yr|65|0.63|0.49|27|0.56|0.51|-0.65|
|Average Class Size|39|25.04|8.89|22|21.78|6.01|-1.53|
|School equipement|39|0.31|0.63|22|0.14|0.66|-1.05|
|School auxiliary Buildings|39|-0.53|0.33|22|-0.47|0.25|0.77|
|School socio-econ. quintile|39|4.56|0.64|22|4.50|0.67|-0.37|

Notes: This table reports summary statistics on key observables at the time of the baseline, for full-time schools and comparison schools with a p-score above .15. The table excludes schools that are missing any of the key variables used in the second stage analysis. School Equipment and School Auxiliary Buildings are estimated using Principal Component Factors. A t-test was performed for the equality of means between the comparison group and the group of full time schools. The t-test results are reported using star coding with * signi...cant at 10 percent; ** signi...cant at 5 percent; *** signi...cant at 1 percent.

17
Table 8: Summary results

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Sample|All sch. in quintiles 3, 4, 5||Matched Sampl|e|
|Methodology|Fixed-E¤ects|Closest 5|Closest|p-score>15|
||Language||||
|Number of Years FTS|0.08<br>(0.07)|0.08<br>(0.07)|0.14<br>(0.09)*|0.21<br>(0.08)**|
|Constant|12.43<br>(0.35)***|11.56<br>(0.40)***|12.52<br>(0.56)***|11.89<br>(0.46)***|
|No obs.|11061|5910|3431|4313|
|No schools|152|83|49|63|
|R square|0.08||||
||Mathematic|s|||
|Number of Years FTS|0.16<br>(0.07)**|0.2<br>(0.07)***|0.2<br>(0.08)**|0.29<br>(0.08)***|
|Constant|10.35<br>(0.34)***|9.74<br>(0.40)***|10.73<br>(0.55)***|10.26<br>(0.45)***|
|No obs.|11115|5910|3431|4313|
|No schools|152|83|49|63|
|R square|0.08||||

Notes: This table reports the estimated program e¤ects under di¤erent sample restrictions. Column 1 reports the estimated treatment e¤ect using the full sample of schools in the 3, 4 and 5th socio-economic context quintile. Columns 2 and 3 report the results when the the sample is restricted to program schools and their 5 nearest neighbors and their closest neighbor, respectively. Finally, in Column 4 the sample is restricted to those schools with a propensity score higher than 0.15. All coe¢ cients and standard errors in columns 2, 3 and 4 are bootstrapped using 300 replications, and observations that do not belong to the support of the p-score function are excluded. Full results are reported in the next table. * signi...cant at 10 percent; ** signi...cant at 5 percent; *** signi...cant at 1 percent.

18
Finally, in Column 4 the sample is restricted to those schools with a propensity score higher than 0.15. The estimated impact of the program is positive, and more so in Math than in Language. The use of di¤erent sample restriction strategies points to an interesting fact: the more restricted the sample of control schools is, the larger is the e¤ect of the full-time school program. In Math, the full-sample estimate of the program impact is 0.16 points per year of participation in FTS. Limiting the sample to the 5 closest neighbors, or to the closest neighbor of treatment schools, we ...nd that the estimated program impact is 0.20 of a standard deviation. (Top panel, Columns 2 and 3) Program e¤ect estimates for Language are positive but smaller than the estimated e¤ects for Math, and they are only signi...cantly di¤erent from zero when we match using the closest neighbor.

Column 5 reports results when the sample is restricted to schools for which we have a predicted probability of participating higher than 0.15. In this approach, a number of better-o¤ schools which participated are excluded, ensuring more uniform characteristics among the schools included in the regression. Under that speci...cation, we ...nd large and signi...cant e¤ects of the program, for Math (0.29 points per year of participation) and Language (0.21 points per year of participation). The larger estimates in this speci...cation can be interpreted as follows. Assuming that the e¤ect of the program depends on the characteristics of the school, and that more disadvantaged schools bene...t more from the program, we expect that the estimated program e¤ect will be larger if we restrict the sample to more disadvantaged schools. Unfortunately, the limited sample size prevents us from testing this theory by estimating interaction e¤ects between the treatment variable and school characteristics. Nevertheless, the results from the di¤erent speci...cations and the characteristics of excluded schools do seem to support this explanation.

The e¤ect of the program is sizeable, especially when considering disadvantatged schools. Our largest estimated e¤ect in Math is 0.29 points per year, or about 0.063 standard deviations. Thus, a full 6 year cycle may result in an increase of close to 0.38 of a standard deviation in these schools. Since the average score in Math in disadvantaged schools is slightly above 12 (the minimum passing grade), and 65 percent of their students score below passing grade, the impact of the program in these schools is substantial: in an average disadvantaged school 6 years of participation in the program would bring close to 10 percent additional students to the minimum score for passing. Though the improvement in Language scores is more modest than the improvement in Math scores (0.21 points, or about 0.044 of a standard deviation per year of exposure to the program), it is still substantial. We would expect this improvement in learning outcomes to have an e¤ect on completion and transition to secondary in low achieving schools, increasing schooling and helping address the substantial inequalities in the Uruguayan education system.

A quick review of the other coe¢ cients in the matching regressions, Table 9, shows that 19
Table 9: Propensity score matching full results

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||closest 5|neighbors|closest n|eighbor|pscor|e<.15|
||Language|Math|Language|Math|Language|Math|
|Years of exposure to FTS|0.08<br>(0.07)|0.2<br>(0.07)***|0.14<br>(0.09)*|0.2<br>(0.08)**|0.21<br>(0.08)**|0.29<br>(0.08)***|
|Pupil is female =1|1.15|0.32|1.18|0.45|1.16|0.21|
||(0.10)***|(0.11)***|(0.14)***|(0.14)***|(0.13)***|(0.12)*|
|Nr of people living in pupil’s hh.|-0.14|-0.09|-0.15|-0.1|-0.12|-0.07|
||(0.02)***|(0.03)***|(0.03)***|(0.03)***|(0.03)***|(0.03)**|
|Nr of rooms in pupil’s home|0.03|0|-0.01|-0.02|0.03|-0.03|
||(0.04)|(0.04)|(0.06)|(0.05)|(0.05)|(0.05)|
|Home equipm. modern appliances|0.57|0.53|0.57|0.53|0.51|0.46|
||(0.08)***|(0.08)***|(0.10)***|(0.10)***|(0.08)***|(0.09)***|
|Mother completed primary educ.|0.33<br>(0.14)**|0.41<br>(0.14)***|0.14<br>(0.19)|0.45<br>(0.22)**|0.4<br>(0.16)**|0.49<br>(0.16)***|
|Mother has some secondary educ.|0.45<br>(0.13)***|0.52<br>(0.12)***|0.66<br>(0.17)***|0.61<br>(0.17)***|0.46<br>(0.15)***|0.59<br>(0.15)***|
|Mother completed secondary educ.|0.33|0.22|-0.01|0.06|0.38|0.34|
||(0.21)|(0.18)|(0.28)|(0.26)|(0.26)|(0.24)|
|Mother completed tertiary educ.|0.58|0.65|0.42|0.52|0.43|0.55|
||(0.29)**|(0.30)**|(0.38)|(0.35)|(0.38)|(0.36)|
|Teacher exper. 1 year or less=1|-0.89|-1.21|-0.97|-1.44|-0.73|-0.81|
||(0.35)**|(0.33)***|(0.43)**|(0.46)***|(0.37)*|(0.31)***|
|Teacher experience in years|0.05|0.08|0.08|0.1|0.03|0.07|
||(0.01)***|(0.01)***|(0.01)***|(0.01)***|(0.01)***|(0.01)***|
|Class size|0.03|0.02|0|-0.02|0.01|-0.01|
||(0.01)**|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|This is an observation in 2002 =1|0.38|0.31|-0.03|0.24|0.33|0.21|
||(0.15)**|(0.17)*|(0.25)|(0.25)|(0.19)*|(0.18)|
|P-score|0.64|-0.04|0.55|-0.47|0.7|0.37|
||(0.35)*|(0.31)|(0.40)|(0.36)|(0.42)*|(0.36)|
|Constant|11.56|9.74|12.52|10.73|11.89|10.26|
||(0.40)***|(0.40)***|(0.56)***|(0.55)***|(0.46)***|(0.45)***|
|Observations|5910|5910|3431|3431|4313|4313|
|Number of Unique school identi...ers|83|83|49|49|63|63|
|Number of full time schools|28|28|28|28|23|23|
|Number of comparison schools|55|55|21|21|40|40|

Notes: This table reports the estimated program e¤ects under di¤erent sample restrictions. Columns 2 and 3 report the results when the sample is restricted to program schools and their 5 nearest neighbors in terms of p score. Columns 4 and 5 report the results when the sample is restricted to program schools and their closest neighbor in terms of p score. Finally, in Column 5 and 6, the sample is restricted to those schools with a propensity score higher than 0.15. All coe¢ cients and standard errors are bootstrapped using 300 replications. In all columns, observations that do not belong to the support of the p-score function are excluded. * signi...cant at 10 percent; ** signi...cant at 5 percent; *** signi...cant at 1 percent.

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all estimates are stable, except for the coe¢ cient on mother’s tertiary education and the 2002 year dummy. Given that the percentage of mothers with tertiary education is much lower in more disadvantaged schools, it is not surprising that the coe¢ cient estimates on this variable are much less precise in the restricted sample. Regarding the year dummy, we interpret the change in coe¢ cients on the 2002 dummies and on years of exposure as follows: between 1996 and 2002, test scores increased signi...cantly for the country as a whole. However, the time e¤ect did not occur in poorer schools. These are likely to have su¤ered disproportionately from the crisis in the early 2000’s, which may have resulted in di¤erential trends between poor and better-o¤ schools. When we include better-o¤ schools in the comparison group for the full-time schools (column 1), we underestimate the e¤ect of the full time school program because we do not account for the di¤erential trend in learning achievement.

In summary, we ...nd that the full time schools program seems to have raised student achievement, expecially in the most disadvantaged schools. It is important to remember that the goal of restricting the sample is to create a control group of schools that resembles the treatment group as much as possible. Given that the program targeted particularly disadvantaged schools, this implies that we are restricting the control group to disadvantaged schools. It is in this comparison, when we reduce the potential bias arising from di¤erent characteristics in the treatment and control groups, that we ...nd the larger e¤ects. The size of the e¤ect is substantial: using our preferred estimates, we ...nd a 0.044 of a standard deviation improvement in language, and a 0.063 percent of a standard deviation improvement in Mathematics per year of exposure to the program. For a child who spends 6 years in the program, this would add up to a 0.26 of a standard deviation improvement in Language, and a 0.38 of a standard deviation in Mathematics.

# 6 A Note About Cost

Even though the program appears to have yielded positive e¤ects on learning outcomes of participating schools, the full-time school program is expensive. As currently designed, it requires the provision of new infrastructure, ranging from construction to teaching materials, and a substantial increase in recurrent costs, especially teacher’s salaries. Additional activities, teacher training and parallel nutritional and health interventions in these schools contribute to the higher cost of these schools. The question of whether an expansion of the program is worthwhile would ideally involve a careful exploration of the impacts of each of the components of the program and an exact estimation of the bene...ts that would go farther than learning outcomes. As we discussed above, the break-down of the impacts of the major components of the program (teacher training, 21
school time, community participation) is not possible since participation in those sub-components was either universal or voluntary, which causes identi...cation problems that we cannot solve with the data available. Nevertheless, in this section we use estimates of costing data from ANEP to try to shed some light on the cost and bene...t implications of an expansion of the program.

According to ANEP, FTS have on average 50 percent more recurrent costs (excluding teacher training) than regular urban schools,<sup>8</sup> mostly due to the increase in the cost of teacher salaries, which explains 71.2 percent of the di¤erence in costs. Nutrition interventions account for an additional 19 percent, while the remaining cost increase is accounted for by the extra-curricular activities that are associated with the implementation of the new padagogical approach in FTS schools. If we also include teacher training in the costing, FTS turn out to cost 60 percent more than the average regular urban schools. Assessing whether the increase in learning compensates this extra cost requires some degree of subjectivity, given the analysis we are able to perform. However, it is important to note the following: First, even though FTS are more expensive to run than regular urban schools, this does not necessarily imply that they are too expensive. On the contrary, it may indicate underspending in the rest of the education system. In fact, by international comparisons, Uruguay underspends in the education sector given its level of income. The average per-student expenditure in primary and secondary schools is 7.2 percent of GDP per capita, well below the regional average of 13.1 percent.<sup>9</sup> Thus, the additional cost of FTS, while substantial when compared the cost of regular urban schools, would only bring Uruguay closer to the regional average. This by no means means that the program should be scaled up universally, but provides a relative measure of the increase in costs from the program. The second important point is that the program has a large e¤ect in schools with students from very disadvantaged backgrounds. These are the students for whom the bene...t of a marginal increase in learning would be the largest, assuming that this increase in learning results in a higher probability of completing secondary and post-secondary education. Thus, e¢ ciency and equity arguments would recommend a targeting mechanism that ensures that the participating schools serve disadvantaged populations.

# 7 Conclusions

Our preferred estimates show that the full-time school program increased student test scores in third grade by 0.063 standard deviations per year in mathematics and 0.044 standard deviations

> 8 MCAEP ANEP, "Estimacion del gasto por alumno en Educación Primaria Pública y costos comparados de la educacion comun y de tiempo completo". These estimates control for the size of the school.

> 9 Source: The World Bank (SIMA) and EdStats, 2004.

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per year in Language in schools with students from disadvantaged backgrounds. We estimate that the program leads to an additional 10 percent of students reaching the passing score on the third grade test in these schools. The program is relatively expensive, increasing recurrent costs by approximately 60 percent. A careful cost-bene...t analysis could not be performed due to data limitations and the characteristics of the implementation of the program. Despite the substantial cost of the program, it may be worth considering expanding the program considering the fact that Uruguay substantially underspends in primary and secondary education when compared to the region and to countries with a similiar income level. An increase in 60 percent in expenditures would only bring Uruguay closer to the region’s average. The full-time school program, if well targeted, could help address inequalities in education in Uruguay, at an increase in cost per student not larger than the current de...cit in spending between Uruguay and the rest of the region. When considering scaling up the program, the targeting mechanism should ensure that the program focuses on schools serving very disadvantaged populations. In addition, it would be useful to evaluate cost and impact of the di¤erent components of the program in order to optimize the interventions. A careful roll-out plan for future expansion of the program would help evaluate the impact of these sub-components.

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