L Reunión Anual Noviembre De 2015

ANALES | ASOCIACION ARGENTINA DE ECONOMIA POLITICA L Reunión Anual Noviembre de 2015

ISSN 1852-0022 ISBN 978-987-28590-3-9

Monetary Incentives to Complete School in the province of San Luis.

Accursi, Federico Navarro, Ana Thailinger, Agustin Monetary Incentives to Complete School in the province of San Luis Federico Accursi1 Universidad Austral Ana Inés Navarro2 Universidad Austral Agustina Thailinger3 Universidad Nacional de Rosario August 31, 2015

Abstract The paper empirically explores the effects of the monetary incentive involved by the program Stamps School for my Future on the educational performance of students in the province of San Luis. Specifically it comes to measuring the impact of the program on the education gap, repetition and dropping out of school.

Using difference-in-difference methodology, the study finds that the effect is negative, i.e. reducing school delay and repetition rate in general, but for the dropout the effect is detected only for older students who are potentially closer to finishing high school.

Resumen

El trabajo explora empíricamente los efectos del programa de incentivos monetarios Estampillas Escolares Para mi Futuro sobre la performance educativa de los estudiantes de la provincia de San Luis. Específicamente se trata de medir el impacto del programa sobre la brecha educativa, la repitencia y el abandono de la escuela.

Usando metodología de diferencias en diferencias, el estudio encuentra que el efecto es negativo, es decir reduce el atraso escolar y la tasa de repitencia, en general y el dropout sólo para los estudiantes mayores quienes potencialmente están más cerca de terminar la escuela secundaria.

Keywords: monetary incentives, education. JEL Classification: I21, I38, D01.

1 [email protected] 2 [email protected] 3 [email protected] I. Introduction From the new National Education Act of 2006 on, the Federal Government and the Argentine provinces’ governments have implemented several aid programs and educational incentives for young and adult learners, in order to stimulate compulsory education framed in the aforementioned law. These programs are targeted to specific social groups and there are mostly non-monetary, but there are exceptions. At the provincial level, the program "Estampillas Escolares de Ahorro para mi Futuro" ("School Savings Stamps for my Future") better known as “Estampillas Escolares” (School Stamps) (SSSL from now on) was launched by the province of San Luis in 2011. This program annually gives every student that pass grade or school year, school postages valued in dollars redeemable once the student completed his studies. At the national level, the Universal Child Allowance (UCA), launched at the end of 2009, is a cash transfer aimed at children under 18 years conditional they had attended school during the year and had meet certain vaccination requirements and health controls. Although the main purpose of this benefit is improving the economic situation of minors living in a social vulnerability context, by conditioning on school attendance, contributes the children receive more quantity of education. The use of monetary incentives to improve student performance is becoming a common practice in the educational systems of several countries. These programs provides students financial incentives for increased school attendance, for reading, or for better grades. There is a growing empirical literature that evaluates the results obtained with these programs. The empirical literature analyses different kind of programs showing mixed results (De Paola, Scoppa and Nisticó, 2012; Slavin, 2009) probably because there are many different kinds of them. In general, conditional cash transfer programs are effective in improving assistance to secondary school, but not to primary school where attendance is quite full in developing countries. Other effects such as graduation rates or referred to real learning have been poorly documented. According to D’Elía and Navarro (2013), the UCA, a conditional transfer program with a broader purpose than strictly educational objectives, may have produced an increase of school enrollment but did not improve the schooling gap of the children receiving the assignment. This result is not surprising since the UCA did not include any mechanism to achieve more ambitious educational goals. Notwithstanding, Behrman, Sengupta, and Todd (2001) find less grade repetition and better grade-to-grade progression, as well as fewer dropouts, among the students benefited by the well known Mexican program called PROGRESA. Beyond the effectiveness of those programs, it has to be take in account that monetary incentives, not only in education but in other areas raise divergent views among specialists. On the one hand, monetary incentives can be helpful in getting people to study more or reduce smoking, but on the other hand the use of incentives in those areas could be defeating because extrinsic incentives could displace intrinsic motivation that is essential to achieve the desired behavior (Gneezy, Meier and Rey-Biel, 2011). These applications of The purpose of this paper is to analyze the effects of monetary incentives on student performance in the Province of San Luis. More precisely, we attempt to evaluate the impact of the SSSL program on the school gap, on the dropout and repetition rates of the students. However, due to data restrictions and the fact that the effects of the program are difficult to capture in the short run, this study is quite preliminary. Since the program was launched without any impact evaluation mechanism, the methodology used in this study is the difference-in-differences (DID) estimator. Our data comes from the quarterly household survey collected in Argentina by its National Statistical Institute. The structure of these survey is one of short panels, but considering that San Luis

is a small Province, the estimations uses independent cross-section due to the low number of observations we would have by using those short panels for the Province of San Luis. The paper is organized as follows. In section 2 it is described the different incentive programs to education recently implemented in Argentina. Section 3 discuss the methodology and section 4 presents the data. Section 5 shows the estimation results and section 6 concludes.

II. Educational Incentive Programs in Argentina. The SSSL

Argentina has a far-reaching experience on educational incentives, with a wide variety of programs carried out over the last past decade. After the huge economic and financial crisis of 2001-2002, the government implemented for public schools attended by poor children, school meal plans, where the main objective was to keep children and adolescents at school. The main purpose was to avoid a massive dropout due to the crisis, yet not focusing that much on increasing the enrollment or the education level. In this regard, the students would at least remain attending school. However, although the main macroeconomic variables in the country were quite recovered by the years 2003-2004 (i.e. poverty and unemployment rates), the plans were not completely eliminated. In exchange, programs that started recently concentrates not only on keeping students inside the system, but also reinserting those who had abandoned it. Accordingly, the new National Education Act approved in 2006, established secondary education levels as compulsorily, encouraging therefore the institution of new programs more focused on those outside the system. The majority of these plans are nowadays intended for children, adolescents, young adults and adults who have not finished primary or secondary school, and have consequently not received their diploma yet, which quite disqualifies them from having better employment opportunities.

Most of these programs are exclusively destined to the inhabitants of the specific Argentinean Province that finances them, with only just a few having a national reach. Some examples in the first category are the Plan de Inclusión Educativa (Educational Inclusion Plan) established by San Luis Province in 2011, the Plan de Finalización de Estudios y Vuelta a la Escuela (Completion of Studies and School Reinsertion Plan), in force exclusively at Buenos Aires Province from the year 2008 on, the Programa 14-17 (Program 14-17), implemented by the Ministry of Education of Córdoba Province by 2010, and the Vuelvo a Estudiar program (Back to School), set by the Ministry of Education of Santa Fe Province from the year 2013, among others. On a national basis, the Government of the City of Buenos Aires established the program Adultos 2000 (Adults 2000) on the year 1999, which later experienced a variety of modifications, being the last one set on the year 2013 (enforced from February 2014 onwards), which created the online version, Terminá la Secundaria (Complete the Secondary Level). In addition, the Federal Government implemented in 2008 the Plan de Finalización de Estudios Primarios y Secundarios – FinEs (Completion of Primary and Secondary Studies Plan), which presents a national reach as well. Table 1 and Table 2 show the main characteristics of the most relevant of them, concentrating the latter on the ones established on the studied Province and its surroundings.

Table 1 Monetary Incentives in Argentina Universal Child Allowance (UCA) School Savings Stamps for my Future (SSSL) National Social Security Administration (Administración Nacional de la Responsible Ministry of Education of San Luis Province. Seguridad Social - ANSES).

Reach National. San Luis Province.

Type Focused. Universal.

Starting year 2009 2011

Aim Break the intergenerational vicious cycle. Educational + Saving.

Increase the household’s purchase power in order to encourage Specific objectives of Encourage students’ academic performance and school attendance (↓ children’s access to health and education services → enforce their the program repetition and dropout rates). rights. Stimulate the registration of births and identity.

Children and adolescents under 18 years old (with no age limit for the physically handicapped) who do not receive another family allowance, have an official and valid ID, reside inside the territory and are single and registered on the ANSES data basis related with the incumbent of the Primary and secondary school students who promote to the next school transfer. The psychically handicapped must have as well authorization Beneficiaries level. Students enrolled on a Young and Adults School Method (3 year from the ANSES. The legal guardians have to be unemployed or not curriculum) or on a Night School Method (4 year curriculum). registered, enrolled only at certain other social plans, self-employed, employed at housekeeping services or for certain seasons, or deprived of liberty. They have to be Argentinians as well, possess a valid ID, reside inside the territory, and be registered on the ANSES data basis.

Tools 640 Argentinean pesos ($) per month per child. School Postages with a certain nominal value in American dollars (U$D).

The total amount of money will be of U$D 1200: from 1° to 6° level of primary school, U$S 50 per year; from 1° to 5° level of secondary school, U$S 100 per year; for 6° level of secondary school (last year of compulsory education), 400 U$D. For students enrolled on other 80% of the subsidy is paid directly. The remaining 20% accumulates methods of learning as Young and Adults Schools and Night Schools, until March next year, when the documents proving the fulfilment of the the total quantity is the same, but it is distributed differently (U$S 300, requirements beneficiaries have to meet is submitted → minors up to six U$S 300 y U$S 600 in the first case and 4 times U$S 300 in the second Mode of operation years old: vaccination and health controls and enrolment on the Plan one). Exchange: when the student finishes his/her last year of Nacer/Plan Sumar; → from 5 to 18 years old: vaccination and health secondary school, having sit for all the required subjects. Exception: controls and attendance to educational institutions as well (state or disabilities or sickness of someone in the student’s nuclear family. The public). payments will be in Argentinean pesos at the corresponding exchange rate at the moment of the exchange. If the student repeats, he /she will receive the school stamp once he/she finally promotes to the next school year.

It does not have a formal control and monitoring mechanism. It does not It does not have a formal control and monitoring mechanism. It does not Evaluation exist a control group. Surveys of statistical information. exist a control group. Survey of statistical information.

Maximum of five children per family, prioritizing the younger and those Partial withdrawals are allowed. Utilization: higher education, new Other characteristics with limited capacities. Granted only to one of the parents, always technologies, productive enterprises and school trips, among others. prioritizing the mother.

100% guaranteed with short term financial assets of the Provincial Funding Less than 1% of the GDP. Government. Source: Author's elaboration.

Table 2 Non Monetary Incentives in Argentina Mendoza Province San Luis Province Protected Experience Classrooms (Aulas de Educational Inclusion Plan ( Plan de Inclusión 20/30 Plan (Plan 20/30 ) Experiencia Protegida) Educativa)

Ministry of Education of San Luis Province Ministry of Education of San Luis Province Responsible Ministry of Education of Mendoza Province throughout the University of La Punta (ULP). throughout the University of La Punta.

Reach Mendoza Province. San Luis Province. San Luis Province. Type Focused. Focused. Focused. Starting year 2013 2014 2015 Aim Educational. Educational. Educational. Reinsertion in the educational system. Job and Specific Completion of primary and secondary studies. academic skills granting. Ensure the objectives of the Completion of secondary studies. Inclusion, innovative learning and social compliance of compulsory education framed in program containment. the National Education Act.

Primary level: students who are 15 years old or over (12/31/14 onwards), have not completed their primary studies and guarantee that will access to the secondary level once concluded San Luis Province residents (at least 2 years) Children and adolescents who have never the primary. Secondary: students who are 20 between 20 and 30 years old who have started school or who have, but they abandoned years old or over (12/31/14 onwards) and have Beneficiaries completed primary school. They must have as or repeated twice or more times → over-age not completed their secondary studies. Merit well the Cédula de Identificación Provincial students. Scholarship ($1500 Argentinean pesos Electrónica – CIPE (Provincial Electronic ID). monthly): be between 16 and 19 years old (12/31/14 onwards), not to perceive a formal income and have an over 2 years’ discontinuity regarding his/her secondary path.

Agreement between the Dirección de Centros para la Capacitación del Trabajo (Direction of Job Training Centres) and the Centros de Digital State Schools (under the authority of the Digital State Schools (under the authority of the Tools Actividades Juveniles – CAJ (Juvenile Activities ULP) equipped with modern technology. ULP) equipped with modern technology. Centres) for workshops development. Pedagogic Professors, tutors, personalized overseeing. coordinator: personalized overseeing of students.

First stage (2 years and a half): students who Primary level → first stage (2 years): students have completed the primary level and the 7th who cannot write and/or read; second stage grade of EGB3 (or 1st year of secondary level). (year and a half): students who can write and Second stage (year and a half): students who read. Secondary level: same modality that Plan have passed the 1st and 2nd year of secondary 20/30. School-based modality: Digital State level but still have to sit for up to 3 subjects. Mode of Schools; virtual tutoring: online platform. School-based modality. Third stage (6 months): students who completed operation Primary: 30 students per class → 15 to 25 the first 3 years of secondary level (1st, 2nd and years old: 16 school-based hours and 12 online 3rd/7th, 8th and 9th of EGB3) and do not have to hours per week; → 26 years old and over: 12 still sit for any subject. School-based modality: school-based hours y 12 online hours per week. Digital State Schools; virtual tutoring: online Secondary: 25 to 30 students per class → 12 platform. Degree: Bachelor on Economics and school-based hours y 12 online hours per week. Business Management.

It does not have a formal control and monitoring It does not have a formal control and monitoring It does not have a formal control and monitoring Evaluation mechanism. It does not exist a control group. mechanism. It does not exist a control group. mechanism. It does not exist a control group. Survey of statistical information. Survey of statistical information. Survey of statistical information.

Option: monthly scholarship of $800 At the beginning of 2014, and due to the Argentinean pesos if the student fulfils the excellent results obtained, the program Based on the successful experience of the Other requirements (70% attendance and passing en extended to the total number of Aulas de “20/30 Plan” . The beneficiaries will not receive characteristics exam per month as minimum). The beneficiaries Aceleración* (Aceleration Classrooms) of San the “School Savings Stamps for my Future”. of the 20/30 Plan will not receive the “School Luis Province. Savings Stamps for my Future”. Funding Mendoza Province Government. San Luis Province Government. San Luis Province Government. *The Accelerated learning is a flexible educational methodology destined for the over-age students of school-going-age who have never started school or who have, yet they abandoned or repeated twice or more times. They are too older to be on a regular classroom but too young to be treated as adult students. The modality allows them to pass various levels in one year, overcoming this way their age discrepancy.

Source: Author's elaboration.

On the other hand, monetary incentives are not the norm but overall the exception in Argentina, agreeing with the pattern followed by most of Latin American countries. They started to be implemented in the area with the generalization of the Conditional Cash Transfers (CCT), mechanism that involves making cash transfers to individuals or families if they meet certain requirements. The programs usually present more widespread objectives, which do not only focus on educational grounds, but also on the improvement of urgent social situations, yet contributing as well with the human capital formation in the long run. Some examples of monetary incentive programs in Latin America are the PROGRESA program, later renamed Oportunidades (acronym for Health, Nutrition and Education/Opportunities) established in Mexico in the year 1997, the Familias en Acción program (Families in Action) implemented in Colombia in 2001 and based directly on the Mexican program mentioned above, and the Bolsa Escola, later renamed Bolsa Familia (Scholar/Family Allowance) set in Brazil in 2001 as well.

In the particular case of Argentina, there are only two examples of monetary incentives. One of them, the Universal Child Allowance (UCA), can be considered within the before mentioned category, given the fact that its objectives are quite extensive, being the educational area simply one more of them.

At a national level, the UCA was launched at the end of the year 2009 by the Administración Nacional de la Seguridad Social - ANSES (National Social Security Administration). From the beginning, this particular program was set with a national approach, as a cash transfer aimed at all Argentinean children and adolescents under 18 years old (with no age limit for those with limited capacities) who accomplish certain requirements: not to receive another family allowance, to have an official and valid ID, to reside inside the territory, to be single and registered on the ANSES data basis related with the incumbent of the transfer (legal guardian). At the same time, the latter has to be unemployed or not registered, enrolled only at certain other social plans, self-employed, employed at housekeeping services or for certain seasons, or deprived of liberty. He or she as well has to be Argentinian, possess a valid ID, reside inside the territory, and be registered on the ANSES data basis.

As mentioned before, the UCA possesses wider objectives, which do not solely focus on educational grounds, yet their main purpose is to contribute to better the situation of minors living in a social vulnerable context, breaking in consequence the intergenerational vicious cycle of inequality. In this regard, the beneficiaries have to meet other certain requirements for their legal guardians to receive the subsidies offered, as vaccination and health controls, and attendance to public or private educational institutions as well. Once fulfilled, the Federal Government adjudged them with 640 Argentinean pesos per month, 80% of which is directly payed, being the remaining 20% accumulated until March next year, when the documents proving the fulfillment of the requisites are submitted. However, this total amount is regularly aligned given the significant inflation rate the country has been dealing with. The current 640 Argentinean pesos would represent almost seventy American dollars. Furthermore, the program is funded by less than 1% of the national GDP, and it can be given to a maximum of five children per family, always prioritizing the younger and those with limited capacities.

On the other hand, the School Savings Stamps for my Future (SSSL) is a specific plan of action implemented by San Luis Province in the year 2011. In this particular case,

the aim is more concrete, focusing on the improvement of enrollment and dropout rates, besides improving the educational output and not merely the inputs (as attendance).

School Savings Stamps for my Future (SSSL)

The SSSL program was fully implemented at the beginning of the scholar term of the year 2011 by the Ministries of Finance and Education of San Luis Province in Argentina - Law N° VIII-0752-2011. The purpose of the program is twofold. In first place, it aims to improve the educational achievement of students by reducing repetition and dropout and, in second place it also attempt to encourage savings habits on children. It also expressly pursues the promotion and or the dissemination of philatelic activity. From the commencement, its coverage has been universal for all students who live inside the territory and assist to public, private, self-managed, national or adult schools. If they promote to the next school year, each student receives a school postage with a nominal value in dollars, which depends on the school year they are currently attending. In this regard, from 1° to 6° level of primary school students will receive $50 per year, from 1° to 5° level of secondary school, $100 per year, and if they graduate from the last year of secondary education (6° level), $400, attaining a total amount of $1200 when finished secondary level. For students enrolled on other methods of education, as Young and Adults Schools and Night Schools, the total sum is equivalent, although it is distributed differently ($300, $300 and $600 in the first case and 4 times $300 in the second one). If the student repeats, he/she will receive the school stamp once he/she finally promotes to the next school year. The school postages will accumulate to be later exchanged for the equivalent in Argentinean pesos. When students are in the final year of secondary school and not owe any course of previous years, the school they attend must inform the Ministry of Education of the Province of the situation. The Ministry checks that information and starts the process for students to collect stamps accumulated value. However, the last stamp with a value of 400 dollars, can be charged only when the student passes all subjects for the last year. Furthermore, the value of the postage stamps is 100% guaranteed with short term financial assets of the Provincial Government. Table 1 on the Annex shows a brief summary with the main characteristics of the two monetary programs established in Argentina.

III. Data The data used in this study come from the quarterly household survey collected in Argentina by its National Statistical Institute (Insituto Nacional de Estadística y Censos, INDEC). The survey, named Encuesta Permanente de Hogares (EPH), covers 31 urban areas, all of which have more than 100,000 inhabitants representing 71% of the urban areas of the country and approximately 62% of the entire Argentinean population. It includes detailed socioeconomic information about individuals and households, such as employment, earnings, education level and primary demographic traits. From this data we build three different variables for measuring children’s achievement in school: the children’s schooling gap, the dropout rate and, the rate of repetition. We use a child’s schooling gap as defined by Andersen (2001) for children and teenagers. According to Andersen’s the schooling gap is the difference between the years of education a child should have completed if he had first entered school at the established age and had advanced one grade each year, on the one hand, and the years of education

actually completed, on the other hand. Applying this definition, to a 14-year-old student who has completed seven years of schooling, implies a schooling gap of two years (14-7-5=2) provided he or she lives in a country where children start school at age five. Hence, the schooling gap reveals the children’s school backwardness. This delay may reflect either a late start in school or that the child has repeated a grade. Because the data do not allow to differentiate between these situations, and it is common in Argentina for all children to start school at the normal starting age regardless of income, this study assumes that schooling gaps reflect a failure to advance to the next grade every year (Navarro, 2013). A word of caution about the data used for the definitions is that they do not allow an exact degree of accuracy as the survey does not provide information regarding the date of birth of individuals, so it is not possible to identify those whose birthday is in the second half of the year. Hence, they will appear as being a year behind schedule when they are not. However, data from previous surveys that included the respondents’ birth date show that as expected, student's birthday distributes fifty-fifty between the first and the second half of the year. Therefore you cannot correct this aspect of the data because, to do so, those born in the first half would be a year of school advancement. This limitation in the data covers all observations equally so it does not introduce a particular bias. The other two dependent variables used in this paper are the proportion of repeaters and the rate of dropouts. The first is defined as the proportion of the students whose schooling gap is more than a year4 and are still at school. The implied supposition is that the student started primary school at a normal age of six. The second one is the proportion of children out of school that should be attending because of their age. The time spans the period 2004-2014 but the data are taken for the second quarter each year to get a more accurate measure of the educational gap. Due to the Argentinian educational system, only at this moment the respondents are certain whether they pass grade or not. The sample covers children between six and eighteen years old. We did other estimations with a larger upper bound to reflect the backwardness phenomenon and to increase the number of observations. We do not show this results here, yet they are available upon request. Observations whose schooling gap was negative and larger than -2 are dropped since we assume they provide false information or incorrect one. For example, in the original data base, there is a twelve years old child that according to the Survey is in last year at Secondary school. The individual control variables are: age, gender, and attendance at a private or public school. The household variables are: real income, parents education, a dummy variable if there is an unemployed parent at home, and another dummy if the household receives or not any subsidy or social assistance (in cash) from the government, churches, etc. Since we used pooled data the sample is not random due to many households have been interviewed twice. This issue can be solved taking into consideration only odds or even years, either. However, that way we will lose many observations, so another possibility is to control them in the model.

IV. Methodology

IV.1. Monetary Incentives for Students

4 Because of those born in the second half that have one year of school backwardness that does not correspond. 8

Basically, the role of economic incentives is to reward rational agents to get better performance from a greater effort. Based on this general idea, the use of monetary incentives to improve student performance has become common practice in many educational systems of several countries. The rationality to give a monetary reward to students according to their performance recognizes that there are a number of circumstances that prevent children from doing their best effort in studying. Students could invest little effort in their education, because they have a high discount rate of the future, have temporary inconsistencies in their preferences or underestimate the returns of education. Under these conditions, the extrinsic incentives that provide immediate benefits, would give an extra motivation to study (Gneezy, Meier, and Rey-Biel, 2011). Moreover, providing monetary help may reduce not only the financial constraint of the students from poor households but also the psychological stress they face every day that distracts them from their studies (Spencer, Noll & Cassidy, 2005). In the learning process, the student's personal effort is a key input to his success. For some authors, in the process of accumulation of human capital, motivation and attitude of the student are possibly more important than the quality of teaching or the resources schools have (Stinebrickner and Stinebrickner, 2008; Costrell, 1994; Bonesronning, 2004). However, studying is an expensive activity in terms of the disutility of effort and opportunity costs of time spent doing it. The cost of effort is evident to the student, while benefits of studying would not be seen with equal clarity. Moreover, in some contexts the student does not have an incentive to study hard in order to get a good job in the future. In some labor markets rather than personal achievement and merit, what defines a successful job placement is the family background. It is the family history and the social networks that carves in finding and securing a job (Pistaferri, 1991; Fabbri and Rossi, 1997). In addition, when public employment generates most of the jobs, pay and promotions would hardly be decided by meritocratic systems (see Alesina, Danninger and Rostagno, 2001). And, when the industrial system is specialized in traditional sectors, technologically poor that require low-skilled workers, low economic rewards on offer to skills may discourage students to study hard and may be at least partly responsible for the high dropout rates and the excessive length of the academic careers of students (De Paola and Scoppa, 2007). Extrinsic incentives have opponents, quite strong when it involves children and especially in the issue of education. Financial incentives could crowd out other underlying reasons for educational decisions and their inner motivation to perform the task without the additional incentive, can be reduced permanently. Furthermore, many educators believe that paying students is morally wrong and that one of the objectives of schools is to increase the importance of intrinsic motivation. Gneezy, Meier, and Rey-Biel (2011) show the result of a handful of studies that evaluate extrinsic incentives using field experiments at schools. The most salient results of these studies is that incentives work well in increasing attendance and enrollment but present mixed results on effort and achievements. In addition, monetary incentive do not work for every student.

IV.2. Empirical Strategy With the purpose of estimating the effect of receiving school stamps on a child’s educational achievement in the province of San Luis, this study uses differences-in- differences (DD) methods. This is a popular and convincing study design to evaluate treatment effects in the absence of truly experimental data, as in the case of this study. If the results are directly compared before and after applying a treatment to individuals

9 exposed to it, the results can be contaminated by temporal trends in the outcome variable or by the effect of events other than treatment that occurred between the two periods. However, in cases where a fraction of the population receives the treatment, the rest can be used as a control group to identify the temporal variation in the result that is not due to exposure to the treatment. The (DD) estimator is based on this basic idea (Abadie, 2005). In the simplest DD structure the results for two groups are observed at two points in time. One group, the treated group received the treatment in the second period, but not in the first. The other group, the control, received no treatment at any point in time. When panel data is available, DD removes biases in the second period comparisons between both groups that could arise from stable differences between them. It also removes biases from comparisons over time in the treatment group that could be the result of trends. In the simplest case we have two time periods and a binary program indicator, wit, which is unity if unit i participates in the program at time t. Following Imbens and Wooldridge (2007), a simple, effective model for panel data is

푦푖푡 = 훼 + 훾푑2푡 + 휏푤푖푡 + 푐푖 + 푢푖푡 , t=1,2, (1.1)

where d2t = 1 if t =2 and zero otherwise, ci is an observed effect, and uit are the idiosyncratic errors. The coefficient 휏 is the treatment effect. The data used in this study comes from the quarterly household survey collected in Argentina by its National Statistical Institute. The structure of the survey is one of short panels, so it would be possible to do the estimation using this short panels. However, in the scheme of rotating panels of this survey only 50% of the sample between a quarter and next year is preserved. In addition, San Luis is a small Province with less than 500,000 inhabitants and the survey collects information only for the capital of the State. On average, we have 400 observations for San Luis per year5. As we show above, the DD estimator can be extended from panel data to cross-sections data provided that it is possible to identify in the first period whether or not an individual is eligible for treatment. This is easy when the treatment applies only to individuals that reside in a particular state (Cameron and Trivedi, 2005). Hence, we decide to use independent cross-section data due to the low number of observations we would have by using those short panels for the Province of San Luis. In the particular case where a state implements a policy, this state is defined as the treatment group and the rest of the states that do not implement the same policy integrates the control group. With repeated cross-sections we can write the model for a generic member of any of state as

푦 = 훽0 + 훽1 푑푃 + 훿0 푑2 + 훿1푑2푑푃 + 푢 (1.2) where y is the outcome of interest, d2 is a dummy variable for the second time period. The dummy variable dP captures possible differences between the state that implements the policy (the treatment group) and the rest of states (the control group) prior to the policy change. The time period dummy, d2, captures aggregate factors that would cause changes in y even in the absence of a policy change. The coefficient of interest, 1, multiplies the interaction term, d2dP, which is the same as a dummy variable equal to one for those observations in the state (treatment group) in the second period. In this setting, the difference-in-differences estimate is

훿̂1 = (푌̅푇,2 − 푌̅푇,1) − (푌̅퐶,2 − 푌̅퐶,1) (1.3)

5 In tables all figures of observations are expanded by the factor pondera, which is given by the INDEC. In this way sample observation is transformed to the population one. 10

And controlling for individual characteristics, the model turns to

푦푖 = 훽0 + 훽1 푑푃 + 훿0 푑2 + 훿1푑2푑푃 + 훽2푋푖 + 푢푖 (1.4) According to Lee and Kang (2006), DD with independent cross-sections identifies the average effect at time two for those in the region where the treatment was implemented in time two. They established three assumptions for identify the effect. The first one is that observing an individual at the time t or t-1 do not depend on their results nor in its covariates, once defined the region where the individual lives. Since the policy was implemented for every child living in San Luis who does not finish school, and due the survey is collected randomly among households, this assumption seems reasonable in this study. The second one is that the composition of the treated area does not change, that is to say the group composition has to be stable; there has to be baseline uniformity across time for the same region. A priori, we discard the idea that many families from the rest of the country decide to move to San Luis to take advantage of the subsidy. Neither do we think possible that families have sent their children to study there without moving the whole family there. The third assumption is that trends in both regions are the same. In Figure 1, we show the trends for the three dependent variables we use in this study. We take as a control group, the rest of the country. A first look at the trends of the schooling gap in San Luis and the rest of the country highlights the sharp increase of school backwardness in the province of San Luis in 2009, which then falls sharply. However, if you look further, it is also noted a nationwide decline in the early years, as in San Luis and then an increasing trend reaching a peak in 2008. From 2008 to 2013 the nationwide schooling gap, declines also. Then in 2014 it raises again in the rest of the country but not in San Luis where it continues declining to a lower level than the rest of the country. Trends, although not identical have some similarity, but, as expected because of the larger size of the nationwide data, the measure on the schooling gap for San Luis presents much more volatility. Figure 1 also shows that the trends for repeaters in San Luis and the rest of the country are very similar from 2009 on. In the case of dropouts, from 2012 both jurisdictions have the same level. At a first glance the graphics of school backwardness, repetition or dropout in Figure 1 show an acceleration of the fall in their trend in San Luis for the years 2011 and 2012, but not for the rest of the country. For example, the average schooling gap falls 14% in San Luis in 2011 and 6% in the rest of the country and, in 2012 it fell 10% in San Luis while remaining stable without variations in the rest of the country.

Figure 1

Source: Author's elaboration from the Permanent Household Survey (EPH) second quarter of each year. National Institute of Statistics and Censuses. In order to get a better idea about the trends of the independent variables we will use further, we compare them with the ones based on data from the Ministry of Education. For a shorter period that spans between 2003 and 2012, we get data for San Luis and the rest of the country corresponding to the official figures at the provincial level. Since, the Ministry only publishes the rates split by educational level but not the corresponding raw data (the quantity of students registered in each level) we show only the rates for secondary students. This level of educational is the most representative of the phenomenon we are trying to describe. Though they are not identical we use the overage rate as a proxy of our schooling gap. The later, measures the intensity of the gap in years, so it is not the same if a student loss only one year or more than one, but the overage rate does not capture this difference. The repeaters and dropout rates are not defined equal to ours, also. In the Ministry’s definition someone who start later and never repeats is not a repeater but in our definition is. The dropout rate is quite similar to our definition. All in all, taking into account the largest volatility for San Luis data, it seems the pattern of the phenomena is not very different regarding the rest of the country.

Figure 2

Source: Author's elaboration from DiNIECE. Ministry of Education.

The monetary stimulus in the form of savings has different characteristics to what would be getting the money at the moment the child pass to next grade. It is the expectation to obtain the full amount at the end of the school what encourages students currently in school to do their best effort in passing grade. That way they will accumulate faster the total amount especially when it is not possible to do partial withdrawals as in the school stamps from San Luis. It could also discourage dropout intentions by increasing the opportunity cost of leaving school. An equally important effect is that the accumulated savings make more visible for the young the advantage of ending the studies. Because stamps are obtained at the time of passing to the next grade regardless of whether the student pass in the corresponding year or later, the stimulus is on the completion of secondary beyond the time this will take to the student. In this sense the school backwardness of students currently enrolled should not be affected by this stimulus, at best it will deter some future backlog, mainly in the final stretch of secondary school.

Since school stamps were established in the province of San Luis at the beginning of 2011 some results could not be seen until next year. However, considering that until July secondary school students have the opportunity to pass the course she failed the previous year, it is possible that the monetary stimulus encouraged some to try to make it at the beginning of this year. In this case, the effect could be seen in the same year that the law was implemented. Moreover, the volatility of public finances adds some uncertainty that can act as an incentive to speed up the study to be more certain of getting their money sooner. In addition, for those who due to continual repetition decide to change the common school to the adult system, since in the latter the first four years of secondary school reduces to a half, the school delay will fall. In the case of a child who decides to return to school, it may be that the average grade gap increases since the reenter would have a higher than average school gap. Taking into account these different cases, it is not possible to say a priori the sign of the effect of monetary stimulus on average grade gap. At least in the short run. In the long run, is reasonable to expect a drop in the schooling gap but it will depend partly on the intellectual capacity of children and on the support the school provides for those who acquire knowledge is harder or do it more slowly. Since the repetition rate shows the proportion of students currently in school who have repeated one or more years, the monetary incentive would affect the rate of repetition in a similar way to the schooling gap explained above. For the dropout rate it is expected a decline of it in response to the monetary stimulus. For those who had already left school, the stamps are an incentive to return to it especially in cases where the young has no employment or lacks little to complete the full secondary education. However, for the student that left school for working, the stamp is not going to cover the income lost because they are redeemed in the future. This benefit the student already in school but, those who had abandoned school for work will not coming back. Only marginal student will do. The only way the student can get back if there is a secondary market for stamps. That is, I'm working, I go back to school, I lose my salary, but earn a stamp recoverable in t years. I sell this to someone collecting the money now. This way there I have not to wait t years. Taking into account the above analysis it seems that the sign and the moment of time when the impact of school stamps is registered will depend on several factors such as the type of student, whether they are currently enrolled or dropped out of school, if he left because he needed work, and in this case if there is secondary market where they can sell the stamps. In this regard, the study presented is only exploratory, and it does not pretend to result on conclusive outcomes, but to identify the existence of any short run effect and its sign. Applying DD it is assumed that, in the absence of treatment, the average outcome of the treated group and the control group have the same variation (Abadie, 2005). This assumption is very strict in cases where the treated and untreated individuals are very unbalanced in covariates that affect the dynamics of the outcome. Table 1 shows the mean values for age, sex, and other covariates (prior to the implementation of the SSSL) and their mean tests to check if before the application of the incentive program, treated and controls are different in the variables that affect student performance. Since the independent variables used in this study are measured for the second quarter of each year, the effect of school stamps must be sought in 2012. Although the law was in effect a year earlier no student could have improved their educational performance passing the school year until the end of the year.

Table 3 Mean Tests. Sample (2011)

Variable Rest of the country San Luis Difference Age 12.11 12.06 0.04 ln Real Income 2.49 2.45 0.05 Boys 0.51 0.538 -0.03 Private School 0.2 0.148 0.053*** Household allowance 0.354 0.2876 0.07*** Parents education 3.48 3.55 -0.06 Unemployed 0.1979 0.086 0.11*** Source: Author´s elaboration from the Permanent Household Survey (EPH) Notes: *** p<0.01, ** p<0.05, * p<0.1.

Table 3 shows that, on average, children attending public school are larger in San Luis than at the national average without San Luis. In addition, a smaller proportion of the families of these children receive state aid or other type of help. The other favorable difference for San Luis’s children respect to the rest of the country is that unemployment affects fewer families. But, the latter difference may respond to the activity rate in the province is lower than the national average. On the other variables, no significant differences were found. V. Estimation Results The School Stamps Act was approved in May 2011, so since the respondents would have certainty about having passed grade the next year, we take 2012 as the year of the treatment. The estimations were done for the variables in absolute values. An alternative may have been, instead of taking the variables of interest this way, taking the differences on performance variables over the national average. Thus, effects can be isolated from general trend and concentrate on the specifics of San Luis. We discard this approach because the results were not statistically significant and the signs were odd. The first approach to measure its effect is to use a sample for the years 2011-2012. Table 4 displays the preliminary results of estimating the three dependant variables using this sample. As it was mentioned before, since there are households that were interviewed twice and there are few observations for San Luis6-, we decided do not drop them. Instead, we added the covariate “Repeaters in the Database” to control them. All standard errors are clustered by household in order to allow correlation between brothers and sisters. The unconditional models show that San Luis has worse educational indicators than the average of the country, a fact that was already seen in Figure 1 and Figure 2. This is captured in the dummy for San Luis (DPSL). Either in the unconditional model or when covariates are introduced, the coefficient for the School Stamps effects is not statistically significant. The drawbacks of using the reduced sample specification are straightforward. Firstly, as we have few observations, the effect could be so low that we do not have enough power to reject the null hypothesis of no effect. Secondly, using this short span we are not taking into consideration the time trends, which means that a possible effect could be a fake because of a mean reversion process.

6 Only 3% of the observations are from San Luis. 15

Table 4 Sample (2011-2012) VARIABLES Average Schooling Gap Repeaters Rate Dropout Rate

DPSL 0.223*** 0.223*** 0.117 0.104*** 0.104*** 0.0630** 0.00880 0.0105 0.00306 (0.0733) (0.0734) (0.0756) (0.0302) (0.0302) (0.0307) (0.0120) (0.0121) (0.0127) d2 -0.0118 -0.0121 0.00401 -0.00289 -0.00288 0.00484 0.00461 0.00516 0.00615 (0.0245) (0.0245) (0.0233) (0.00941) (0.00942) (0.00884) (0.00423) (0.00425) (0.00403) School Stamps -0.0214 -0.0214 -0.0554 -0.0115 -0.0115 -0.0247 -0.00945 -0.00935 -0.0132 (0.0788) (0.0788) (0.0731) (0.0357) (0.0357) (0.0327) (0.0134) (0.0134) (0.0127) Repeaters in Database 0.00852 -0.0286 -0.000355 -0.0170 -0.0198*** -0.0208*** (0.0376) (0.0355) (0.0147) (0.0140) (0.00670) (0.00643) Age 0.0854*** 0.0401*** 0.0129*** (0.00344) (0.00130) (0.000763) log Real Income -0.0297 -0.0212** -0.000622 (0.0203) (0.00877) (0.00346) Boys 0.137*** 0.0659*** 0.0176*** (0.0233) (0.00962) (0.00457) Private School -0.140*** -0.0739*** -0.0253*** (0.0292) (0.0114) (0.00327) Household allowance 0.143*** 0.0594*** 0.00396 (0.0348) (0.0146) (0.00651) Parents education -0.0631*** -0.0278*** -0.0118*** (0.00982) (0.00384) (0.00159) Unemployed 0.134*** 0.0494*** 0.0232*** (0.0381) (0.0164) (0.00736) Constant 0.291*** 0.284*** -0.384*** 0.245*** 0.246*** -0.0648* 0.0399*** 0.0556*** -0.0804*** (0.0185) (0.0350) (0.0835) (0.00772) (0.0137) (0.0345) (0.00330) (0.00627) (0.0147) Region of residence No No Yes No No Yes No No Yes Number of clusters 10,064 10,064 10,018 10,043 10,043 9,997 10,990 10,990 10,937 Observations 21,011 21,011 20,924 20,939 20,939 20,853 24,426 24,426 24,328 R-squared 0.000 0.000 0.131 0.001 0.001 0.172 0.000 0.002 0.092 Source: Author´s elaboration from the Permanent Household Survey (EPH), National Institute of Statistics and Censuses Notes: *** p<0.01, ** p<0.05, * p<0.1. The number in parenthesis is the robust cluster standard error.

Table 5 shows the results of building a larger sample by adding many years before the law was passed and after it was approved (Full Sample, or 2004-2014). The results are still not significant in all the specifications, at least when the dependent variable is the Schooling Gap. However, using a subsample compound by the years 2010 and 2012 -to avoid the repetition of individuals in the data base- the punctual estimation is negative but, conversely to the 2011-2012 reduced sample, now it is statistically significant at 5%. As the sample is random, here the covariate “Repeaters in Database” is removed. Although there are few observations, the negative coefficient agrees with the sharp decline in the San Luis's schooling gap in those years, as shown in Figure 1. On the contrary, taking a different subsample for the period 2011-2013, the coefficient is negative as well, but not statistically significant. The sign of the rest of the covariates are all as expected.

Table 5 Full Sample (2004-2014) Sample (2010- Sample (2011- Schooling Gap 2012) 2013) Model 1 Model 2 Model 3 Model 4

DPSL 0.167*** 0.170*** 0.167*** 0.0837** 0.283*** 0.0975 (0.0324) (0.0296) (0.0295) (0.0328) (0.0681) (0.0741) d2 -0.0437*** -0.0354*** 0.00250 -0.00223 -0.0343 -0.0360 (0.0132) (0.0136) (0.0250) (0.0251) (0.0267) (0.0251) School Stamps -0.0352 -0.0564 -0.0535 -0.0560 -0.183** -0.0724 (0.0488) (0.0443) (0.0442) (0.0442) (0.0877) (0.0902) Repeaters in database -0.0440*** -0.0505*** -0.0575*** (0.0126) (0.0142) (0.0142) Age 0.0919*** 0.0919*** 0.0924*** 0.0934*** 0.0808*** (0.00156) (0.00156) (0.00154) (0.00342) (0.00326) Boys 0.124*** 0.124*** 0.124*** 0.149*** 0.106*** (0.0100) (0.0100) (0.0100) (0.0231) (0.0226) Private School -0.182*** -0.181*** -0.163*** -0.186*** -0.193*** (0.0121) (0.0121) (0.0126) (0.0303) (0.0285) logReal Income -0.0585*** -0.0652*** -0.0607*** -0.0195 -0.0319 (0.00825) (0.00845) (0.00863) (0.0198) (0.0210) Household allowance 0.160*** 0.151*** 0.155*** 0.116*** 0.129*** (0.0162) (0.0167) (0.0167) (0.0345) (0.0332) Parents Education -0.0603*** -0.0603*** -0.0625*** -0.0761*** -0.0526*** (0.00443) (0.00442) (0.00443) (0.0100) (0.00917) Unemployed 0.0648*** 0.0683*** 0.0803*** 0.0958*** 0.0685* (0.0160) (0.0160) (0.0161) (0.0343) (0.0357) Constant 0.318*** -0.451*** -0.460*** -0.373*** -0.470*** -0.326*** (0.00764) (0.0276) (0.0302) (0.0348) (0.0792) (0.0815) Years No No Yes Yes Region of Residence No No No Yes Yes Yes Number of clusters 47,532 47,181 47,181 47,181 12,625 12,311 Observations 118,423 117,544 117,544 117,544 21,421 20,440 R-squared 0.001 0.136 0.137 0.139 0.140 0.126

Source: Author´s elaboration from the Permanent Household Survey (EPH), National Institute of Statistics and Censuses Notes: *** p<0.01, ** p<0.05, * p<0.1. The number in parenthesis is the robust cluster standard error.

Table 6 shows the results using the full model for the rest of the dependent variables. The results show that, implementing school stamps, would have reduced the rate of repetition in San Luis by 4 percentage points compared to the rest of the country and around 2.5 percentage points in the dropout rate. The coefficient of interest “School Stamps” is statistically significant at 1% and 5% depending on the model. Here, in the sub sample models for the shortest periods 2011-2013 and 2010-2012, the coefficient of interest is not statistically significant.

Table 6 Repeaters Dropout Full Sample (2004-2014) Full Sample (2004-2014) Sample Sample (2011- Sample Sample Model 1 Model 2 Model 3 Model 4 (2010-2012) 2013) Model 1 Model 2 Model 3 Model 4 (2010-2012) (2011-2013)

DPSL 0.0941*** 0.0936*** 0.0923*** 0.0567*** 0.155*** 0.0574* 0.0207*** 0.0255*** 0.0256*** 0.00891 0.00963 0.00298 (0.0138) (0.0125) (0.0124) (0.0138) (0.0282) (0.0301) (0.00759) (0.00706) (0.00707) (0.00765) (0.0142) (0.0125) d2 -0.0112** -0.00664 0.00204 0.000396 -0.00708 -0.00515 -0.00294 -0.00465* -0.00620 -0.00619 0.00273 0.000610 (0.00542) (0.00535) (0.00959) (0.00963) (0.0101) (0.0101) (0.00227) (0.00244) (0.00414) (0.00413) (0.00469) (0.00439) School Stamps -0.0342 -0.0438** -0.0429** -0.0437** -0.118*** -0.0604 -0.0208** -0.0256*** -0.0259*** -0.0258*** -0.0226 -0.0140 (0.0226) (0.0205) (0.0204) (0.0204) (0.0397) (0.0377) (0.00981) (0.00923) (0.00925) (0.00925) (0.0166) (0.0145) Repeaters in Database -0.0186*** -0.0246*** -0.0270*** -0.0145*** -0.0204*** -0.0206*** (0.00502) (0.00563) (0.00567) (0.00228) (0.00266) (0.00266) Age No Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes Boys No Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes PrivateSchool No Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes log Real Income No Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes Household allowance No Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes Parents education No Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes Unemployed No Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes Constant 0.251*** -0.0778*** -0.0799*** -0.0468*** -0.0911*** -0.0462 0.0433*** -0.0872*** -0.0913*** -0.103*** -0.106*** -0.0938*** (0.00311) (0.0111) (0.0118) (0.0135) (0.0296) (0.0315) (0.00126) (0.00481) (0.00505) (0.00600) (0.0149) (0.0126) Years No No Yes Yes No No Yes Yes Region of Residence No No No Yes Yes Yes No No No Yes Yes Yes Number of clusters 47,432 47,083 47,083 47,083 12,600 12,281 51,701 51,308 51,308 51,308 13,906 13,514 Observations 118,033 117,158 117,158 117,158 21,353 20,365 136,860 135,821 135,821 135,821 24,883 23,708 R-squared 0.001 0.170 0.172 0.173 0.186 0.166 0.000 0.089 0.090 0.092 0.097 0.082

The results obtained so far are not very strong. One explanation is that, by taking all students from 6 to 18 years old, the effect looks diluted by the potentially low effect of the program on children attending primary school. Clearly, for them the incentive is a bit far and moreover the most important problems of delay, repetition and dropout, are among the older students, specially in the case of older teenagers for whom to leave school to enter the labor market is either a necessity or is more attractive in the short term than continuing studying. To capture this effect we circumscribe the sample to children from 16 to 18 years old. The results are shown in Table 7. Using the Full Sample (2004-2014) and as independent variables Repeaters and Dropout, the coefficient of School Stamps are negative and statistically significant at 10%, but the coefficient here almost doubles the obtained for the sample that includes younger children. It appears that the $400 the students obtained by finishing secondary school without owing any subject else, is a strong stimulus for those who are near of achieving the milestone. It is reasonable taking in account that the nominal value of this last stamp represents almost 38.35% of the household median income in San Luis conglomerate by the year 2011.

Table 7 Repeaters Dropout Full Sample (2004-2014) Model 4 Model 4 DPSL -0.024 0.012 (0.030) (0.024) d2 0.052 -0.049 (0.026) (0.016) School Stamps -0.074* -0.055* (0.044) (0.031) Age Yes Yes Boys Yes Yes PrivateSchool Yes Yes log Real Income Yes Yes Household allowance Yes Yes Parents education Yes Yes Unemployed Yes Yes Constant 0.185 -0.584 (0.107) (0.066) Years Yes Yes Region of Residence Yes Yes Number of clusters 15.199 15.199 Observations R-squared Source: Author´s elaboration from the Permanent Household Survey (EPH). National Institute of Statistics and Censuses Note: *** p<0.01. ** p<0.05. * p<0.1. The number in parenthesis is the robust cluster standard error.

VI. Concluding Remarks The use of monetary incentives to improve student performance is becoming a common practice in the educational systems of several countries. In Argentina they are a new practice. Since 2009 the Universal Child Allowance conditions the receipt of the child benefit received by low-income families, on school attendance of their children. However this program does not condition on school performance of them. In this context, the implementation of the School Savings Stamps for my Future in 2011 in the province of San Luis is a novelty in the framework of educational incentive programs. Unlike subsidies to improve school attendance, these are a reward for passing grade or year and finish secondary school. The stamps are also a saving mechanism as they are received when the student passes from grade or year, but are redeemable only when the student finishes secondary school. However, since it is likely that a secondary market for them will form, given the sale of these school posts the Government of San Luis makes to the general public, the restriction of finishing school is less binding. In this paper we try to analyze the effects of this monetary incentive on the student performance in the Province of San Luis. More precisely, we attempt to evaluate the impact of this program on the school gap, on the dropout and repetition rates of the students. The strongest results are on the schooling gap and the repeaters rate, which in some sense detect the same effect but the former shows a larger coefficient. Given that, as we explained above, the variable schooling gap measures the intensity of the gap, while the repeaters rate does not, it is apparent that the incentive works better for the students that have a larger educational delay. When it comes to the percentage of early school leavers, the results show that for those who are near of achieving the milestone, that is to say for the elders of the sample (16 to 18 years old) the last stamp of $400, is a strong stimulus. Conversely we do not find a significant effect for those dropouts far from ending school. All in all the results we obtained are preliminary. To get stronger results we have to try -if possible- to compare the school performance of the children living in San Luis with children living in other provinces with similar socioeconomic characteristics. Other strategy could be analysing the graduation rate in a provincial panel. Finally, it is quite evident that having a proper evaluation impact design prior to the implementation of this program, would have been very useful for measuring its results. However, this is not a specific critic for the School Savings Stamps for my Future, but for all the programs running in Argentina. On the contrary, in many other Latin-American countries, the scheme of the evaluation goes hand in hand with the design of the program.

VII. References Abadie, Alberto (2005). Semiparametric Difference-in-Differences Estimators. Review of Economic Studies 72, 1–19. Beale Spencer, M., Cassidy, E., and Noll, E. (2005). Monetary Incentives in Support of Academic Achievement. Evaluation Review, 29(3), 199-222. Bradley, M, A., y Roland, G, F. (2011). The Power and Pitfalls of Education Incentives. Washington, DC, Estados Unidos: The Hamilton Project. Cameron, Colin and Trivedi Pravin K. (2005) Microeconometrics: Methods and Applications. Cambridge University Press D’Elia Vanesa and Ana Inés Navarro (2013). Universal Child Allowance and School Delay of Children in Argentina, Revista de Análisis Económico- Economic Analysis Review, Vol. 28, Nº 2 Dirección Nacional de Información y Evaluación de la Calidad Educativa, http://portales.educacion.gov.ar/diniece/2014/05/24/anuarios-estadisticos De Paola Maria, Vincenzo Scoppa and Rosanna Nisticó. (2012). Monetary Incentives and Student Achievement in a Depressed Labor Market: Results from a Randomized Experiment, Journal of Human Capital, University of Chicago Press, vol. 6(1), pages 56 - 85. Gasparini, L, y Cruces, G. (2010). Las Asignaciones Universales por Hijo. Impacto, Discusión y Alternativas. CEDLAS. Universidad de la Plata. Gneezy, Uri, Stephan Meier, and Pedro Rey-Biel. (2011). When and Why Incentives (Don't) Work to Modify Behavior. Journal of Economic Perspectives 25 (4): 191–210. Gobierno de la Ciudad de Buenos Aires. Terminá la Secundaria. http://terminalasecundaria.buenosaires.gob.ar/. Gobierno de la Provincia de Buenos Aires. Instituto Provincial de Administración Pública (IPAP). (2014). Medio centenar de empleados públicos completarán este año sus estudios secundarios.http://www.ipap.sg.gba.gov.ar/noticias/medio_centenar_de_empleados_p%C 3%BAblicos_completar%C3%A1n_este_a%C3%B1o_sus_estudios_secundarios/. Gobierno de la Provincia de Buenos Aires. Subsecretaría de Educación. (2008). Plan Provincial de Finalización de Estudios y Vuelta a la Escuela. Gobierno de la Provincia de Córdoba. Ministerio de Educación. Programa 14-17 Otra Vez en la Escuela. http://www.cba.gov.ar/programa-14-17-otra-vez-en-la-escuela/. Gobierno de la Provincia de Santa Fe. Ministerio de Educación. Vuelvo a Estudiar. http://www.santafe.gov.ar/index.php/educacion/guia/get_tree_by_node?node_id=157681/. Gobierno de la Provincia de Santa Fe. Ministerio de Economía. Ministerio de Educación. Vuelvo a Estudiar, “Tiempo de Superación”. Trayecto para Trabajadores Públicos Provinciales que no finalizaron sus Estudios Secundarios. Instituto Interamericano de Cooperación para la Agricultura (IICA). (2014). Experiencias sobre Sistemas de Incentivos a la Comunidad de Investigadores para Favorecer la Innovación Tecnológica. Oficina en Colombia. Ley N° 26.206. Ley de Educación Nacional. Ministerio de Educación. Presidencia de la Nación, Argentina, 27 de diciembre de 2006. 30p.

Lee, Myoung-jae and Kang Changhui (2006). Identification for difference in differences with cross-section and panel data. Economics Letters 92 270–276 Ley N° LEY VIII-0752-2011. ARGENTINA. De estímulo educativo y concientización del ahorro: “Estampillas escolares de ahorro para mi futuro”. Ministerio de Educación, Provincia de San Luis, Argentina, 16 de mayo de 2011. 3p. Nicholson, W. (2004). Teoría Microeconómica: Principios Básicos y Ampliaciones, 8° edición. México DF, México: Thomson. Montoya, S., Llach, J. J, y Roldán, F. (2000). Educación para Todos. Buenos Aires, Argentina: Distal. Observatorio de la Seguridad Social. (2012). La Asignación Universal por Hijo para Protección Social en Perspectiva. La Política Pública como Restauradora de Derechos. Administración Nacional de la Seguridad Social. Polack, M., E. (2014). Adultos: sin excusas para no terminar la escuela secundaria. Diario La Nación. http://www.lanacion.com.ar/1702572-adultos-sin-excusas-para-no- terminar-la-escuela-secundariai/. Slavin, R. E. (University of York) y Jhons Hopkins University. (2010). Can Financial Incentives Enhance Educational Outcomes? Evidence from International Experiments. Educational Research Review, 5(1), 68–80. Spencer, M. B., Noll, E., & Cassidy, E. (2005). Monetary incentives in support of academic achievement : Results of a randomized field trial involving high-achieving, low- resource, ethnically diverse urban adolescents. Evaluation Review, 29 (3), pp. 199-222. Stiglitz, J. (1988). La economía del sector público. Buenos Aires, Argentina: Antoni Bosch editor. Vanella, L, et al. (2013). Programa de Inclusión y Terminalidad de la Educación Secundaria para Jóvenes de 14 a 17 años (PIT) – Córdoba. Córdoba, Argentina. Fondo de las Naciones Unidas para la Infancia (UNICEF).

Annex 1

Incentivos no monetarios en Argentina Back to School Provincial Program of Secondary Completion of Studies and School Complete the Secondary Level → Adults Studies Completion for Workers Completion of Primary and Secondary Virtual Back to School: Virtual Back to School “Time for Overcoming” Reinsertion Plan (Plan de Finalización de 2000 (Terminá la Secundaria – Adultos Program 14-17 (Programa 14-17) (Programa Provincial de Finalización Studies Plan (Plan FinEs) Back to School (Vuelvo a Estudiar) Educational Paths (Vuelvo Virtual: (Vuelvo a Estudiar "Tiempo de Estudios y Vuelta a la Escuela) 2000) de Estudios Secundarios para Trayectorias Educativas Virtuales) Superación") Trabajadores)

Secretary of Human Resources and Undersecretary of Government Undersecretary of Education of Buenos Ministry of Education of Córdoba Ministry of Education of Santa Fe Responsible National Ministry of Education. Government of the City of Buenos Aires. Ministry of Education of Santa Fe Province. Secretary of Education of Santa Fe Modernization (Buenos Aires Aires Province. Province. Province. Province. Joint effort. Province). Reach National. Buenos Aires Province. National. Córdoba Province. Santa Fe Province. Santa Fe Province. Santa Fe Province. Buenos Aires Province. Type Focused. Focused. Focused. Focused. Focused. Focused. Focused. Focused. From de 1999. Last modification: 2013; Starting year 2008. 2008. 2010. 2013. June 2015. 2013. 2012 in force from February 2014. Aim Educational. Educational. Educational. Educational. Educational. Educational. Educational. Educational. Reinsertion in the educational system of children and adolescents Reinsertion in the educational system. Reinsertion in the educational system by Reinsertion in the educational system. of school-going age. Completion of Completion of secondary studies. Completion of secondary studies. Ensure creating alternative paths for children and Specific objectives of the programCompletion of primary and secondary Completion of secondary studies. secondary studies. Enforcement of Completion of secondary studies. Personal, professional and civic Completion of secondary studies. the compliance of compulsory education adolescents of school-going age. Completion studies. their rights to education, development of state workers. framed in the new National Education Act. of secondary studies. Educational inclusion. encouraging study, work and effort habits. Completion of Studies category: students 1st stage (2008): 25-18 years old; who have attended the secondary level of students who have attended the education at any Argentinean educational institution but have not passed all the secondary level but have not passed all Young/adults over 18 years old who have required subjects, not having therefore 14-17 year olds who have Youngsters/adults over 18 years old who the corresponding subjects. completed and passed the primary level School-going-age children and adolescents State provincial workers who have not State workers who have not finished Beneficiaries received the corresponding diploma. abandoned secondary school from have not finished the primary level of of education but have the secondary level (primary and secondary level). completed their secondary studies. secondary level. 2010 on, or who have not initiated it. education. School Reinsertion category: out-of-school complete or incomplete. 2nd stage (from 2009): over 18 years old; students who wish to complete any level students who have not initiated/finished of secondary school, consequently the primary/secondary level of education. returning to the system.

Activities that ease the reinsertion and the school continuity: cultural, sportive, participative spaces, scholarships, health Professors from the Plan FinEs . Tutoring: professors who will help the centres. Specific lines of action for particular Tutors: beneficiaries’ co-workers who Tutors, professors, video classes Professors, advisers, counsellors. Study Tutoring and pedagogic coordination Studying Strategies Workshop offered. students during classes and final exams. actors: youngsters in conflict with the law, Professors: content compilers, tutors, will guide and listen to them. Tools throughout Encuentro channel, virtual material throughout an online platform. for students to be able to organize Educational companionship (e.g. co- More flexible learning formats (adaptability unionized workers, physically or mentally evaluators. Professors from the Ministry of tutoring (educ.ar), books, library. Personalized overseeing of each student. their time, study material, etc. worker who have concluded his/her to the diversity of situations). impaired. Institutional referents of the plan in Education. studies). the schools: student reception, motivation and monitoring. Youth counsellor: out-of-school overseeing.

Basic and mandatory subjects, and 89% virtual tutoring throughout an complementary and optional ones educational online platform: reading → more flexible learning format. material, videos, collaborative activities, Evaluation for each particular forums, videoconferences, chats, slide School-based modality. Virtual tutoring Virtual tutoring: activities, practical work, subject, although it is as well Identification and visit of the dropout’s shows; 11% attending based schools near can be implemented to develop certain study guides, consulting. School-based important the global learning Training throughout the Provincial households: sensitizing and offering of the beneficiaries’ neighbourhoods. topics. Evaluation: → 18-25 years old: School-based modality. Less than 10 modality: consulting, library, workshop process, which will be taken into Combined: school-based modality on Institute of Public Administration alternatives. Determinate reasons for inclusion Schedule: two semesters → two subjects Mode of operation final exam in the school the beneficiary students per group. Final examination per work. If the student lives in Capital account to determine the final grade. Night Schools or union headquarters; (IPAP ). Two alternatives depending on and exclusion, continuity and discontinuity of each → minimum length of 3 years (12 attended the secondary level; → over 25 subject. Federal or GBA, he/she can replace the Those students who do not fulfil the virtual tutoring: online platform. the subjects the beneficiaries have to different sociocultural realities. Following and subjects in total). Elaboration of a Socio- years old: essays, reports, researches virtual tutoring for a school-based minimum requirements of sit for. guiding of beneficiaries. Community Action Project that relates the concerning the student’s working grounds. modality. Final examination per subject. attendance and qualifications, will student’s social context with his/her have to sit for an exam with a “non educational process. Evaluation: forum regular status” or re-attend the participation, mandatory activities and the subject. Degree: Bachelor on Social Action Project. Sciences.

Quantitative data survey at → based schools: to evaluate the general performance of the plan on each district; → all the schools regardless if they are Control and monitoring mechanisms It does not have a formal control and It does not have a formal control and It does not have a formal control and It does not have a formal control and It does not have a formal control and based or not: to determine the number of It does not have a formal control and performed and coordinated by the National monitoring mechanism. It does not exist monitoring mechanism. It does not monitoring mechanism. It does not exist a monitoring mechanism. It does not monitoring mechanism. It does not Evaluation graduates as a consequence of the plan. monitoring mechanism. It does not exist a Secretary of Education. It does not exist a a control group. Survey of statistical exist a control group. Survey of control group. Survey of statistical exist a control group. Survey of exist a control group. Survey of Information about the results on each control group. Survey of statistical information. control group. information. statistical information. information. statistical information. statistical information. stage. Qualitative data survey: determine obstacles and enablers. Systematic control and monitoring instances. It does not exist a control group.

Set in the framework of the Back to Articulation between the Ministry of Education School plan. Agreement with the Agreement between Province and Coordination between Plan FinEs y Adherence to the Back to School Other characteristics of the Province and other areas of the Province University of Granada (throughout the Federal Government, who offers the COA*. program. and Local Government. National University of Rosario) for the Plan FinEs. professors’ education on virtual tutoring.

Federal Government’s funds administrated National Ministry of Education’s funds Funding Government of the City of Buenos Aires. Córdoba Province Government. Santa Fe Province Government. Santa Fe Province Government. Santa Fe Province Government. Buenos Aires Province Government. by the Provinces Governments. distributed among based schools. *COA: Centros de Orientación y Apoyo or Orientation and Support Centres. Set up on the year 2003, with the aim of promoting the completion and accreditation of the secondary level of education. Source: Author's elaboration.