The Effects of Vouchers on Academic Achievement: Evidence

from the Chilean Preferential Scholastic Subsidy∗

Juan A. CorreaMinistry of Finance (Chile)

David InostrozaMinistry of Education (Chile)

Francisco ParroMinistry of Finance (Chile)

Loreto ReyesMinistry of Finance (Chile)

Gabriel UgarteMinistry of Education (Chile)

Abstract

We take advantage of the implementation of the Preferential Scholastic Subsidy (PSS)in Chile to test the effects of vouchers on academic achievement. The PSS has deliveredextra resources to low-income, vulnerable students since 2008. Moreover, under this scheme,additional resources are contingent on the completion of specific scholastic goals. Using adifference-in-differences approach, we find a positive and significant effect of vouchers on stan-dardized test scores. Additionally, our results highlight the importance of conditioning thedelivery of resources to some specific academic goals when features of imperfect competitionexist in the market.

Keywords: Vouchers; School choice; Academic achievementJEL Classification: H4; I2

∗

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1 Introduction

A central question in education economics is how to increase the quality of schools that low-income students attend. Since Friedman wrote his 1955 essay on the role of goverment ineducation, many have considered the use of vouchers to be a promising way to increase theeducation quality supplied by schools (Friedman, 1955). However, at the empirical level, theliterature is far from reaching a consensus. This paper contributes to the literature by providingadditional empirical evidence on the effects of vouchers on academic achievement. Our resultsshow that vouchers have a positive and significant effect on standardized test scores. Addition-ally, we show that focusing resources on specific scholastic commitments enhances the efficacyof vouchers in promoting a higher quality of education. Therefore, some conditionality in thedelivery of resources seems to be an effective way of increasing students’ academic achievementin markets in which some features of imperfect competition may exist.

At a theoretical level, vouchers can raise the academic results of low-income studentsthrough three main channels. First, simply, they allow low-income students to migrate frombad public schools to good private schools (if private schools indeed offer a higher quality ofeducation than public schools). Second, vouchers introduce competition to public schools whenthey are inefficient local monopolies. When subsidies are allocated only to public schools, theyface a fixed demand and thus have no incentives to supply a high education quality. Vouchersallow low-income students to migrate to private schools; the demand for public schools is nolonger fixed. Therefore, if parents were allowed to choose freely among schools, vouchers shouldraise the education quality supplied by public schools in an environment where schools competethrough quality supplied to the market. Third, if the incidence of the demand subsidy (thevoucher) on the supply of education is nonzero, vouchers (or an increase in the resources deliveredby vouchers to schools) increase the margin per enrolled students (the difference between thetotal monetary payments and the cost of educating a student) and thus encourage schools toattract more students. If there is competition, education quality increases within both publicand private schools even if there is no migration of students from public to private schools (therewould be greater competition within school types).

In 1981, Chile’s government began to provide vouchers to any student wishing to attenda private school (called voucher private schools in this paper). The Chilean case provides aunique opportunity to analyze the transition from a centrally controlled public school system toone in which all families can choose freely between public and private schools. In order to testempirically the first channel mentioned above, we simply have to look at two pieces of data fromthe Chilean voucher experiment: first, the strength of the migration of students from public toprivate schools and, second, the difference in the academic performance in those types of schools.

Using data from the Chilean education market, Figure 1 shows a strong migration ofstudents from public schools to private schools. Additionally, Figure 2 shows significant uncon-ditional differences in the average results on a standardized test (SIMCE)1 between public andvoucher private schools, although those results could be simply explained by differences in thesocioeconomic background of students attending different public and private schools.

1The SIMCE (Sistema de Medicion de la Calidad de la Ensenanza) is a mandatory national standardized testdesigned to evaluate the quality of the content taught in primary and secondary education in math, language,geography, and science. It is administered annually to fourth, eighth, and tenth graders through a system inwhich grades are chosen to be evaluated by turns.

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Up to 2000, the literature had found that after properly controlling for students’ socioe-conomic backgrounds, public and voucher private schools saw a similar performance on achieve-ment tests. For instance, Elacqua and Fabrega (2004) find that students attending voucherprivate schools do not necessarily outperform public school students and that competition hasnot necessarily improved the test results of both types of schools. Elacqua and Fabrega (2004)argue that the only thing that can be concluded from the Chilean experience is that free choiceand competition have led to a further segmentation of the education system. However, thesestudies faced important limitations associated with the lack of control for selection bias, theestimation of homogeneous treatment effects across students with different characteristics, andthe assumption that all publicly subsidized schools operate with the same budget (Sapelli andVial, 2002). Subsequent studies show that after selection bias is controlled for, there is a signif-icant difference in the academic performance of public and voucher private schools (Contreras,2001; Tokman, 2001). Sapelli and Vial (2002) find that when they control for selection bias andstudent characteristics, there is a positive effect on standardized test scores in favor of voucherprivate schools. Some international evidence supports the results of (Sapelli and Vial, 2002).2

Therefore, the migration of students from public to private schools, where there is adifference in the education quality supplied by both types of schools, seems to be a valid channelthrough which the Chilean voucher system should have increased the academic achievement oflow-income students. The evidence regarding the other two channels is more controversial.

Hoxby (2003) analyzes the impact that different programs had on school productivity:specifically, the effect of vouchers on achievement in Milwaukee public schools and the effectof charter schools on achievement in Michigan and Arizona public schools. Hoxby concludesthat the regular public schools boosted their productivity when exposed to competition andthat they responded to competitive threats that were surprisingly small. Neal (2002) concludesthat while it is difficult to predict the outcome of any large-scale voucher experiment, vouchersystems targeted toward large cities with a history of public school failure may have the greatestpotential for yielding large benefits. Based on the work of Nechyba (1999) and Epple andRomano (2002), Neal conjectures that it is possible to design targeted voucher systems thatwould result in better outcomes for most if not all students in large urban school districts likethose in Chicago and New York.

In other studies, the results are even less conclusive. Hsieh and Urquiola (2006) use thedifferential impact on enrollment that the introduction of a nationwide voucher has in Chileancommunities to measure the effects of unrestricted choice on educational outcomes. Using paneldata for about 150 municipalities, they find no evidence that choice improved average educationaloutcomes in Chile. Additionally, they find evidence that the Chilean voucher program led toincreased sorting, as the best public school students left for the private sector.

Some empirical difficulties arise when we use the case of the Chilean voucher systemintroduced in 1981 to try to evaluate the effects of vouchers on the academic achievement ofstudents (specifically, focusing on the second and third channels described at the beginning of

2There are also studies on voucher programs that show how achievement growth among economically disad-vantaged African-American children in U.S. cities rises if students are able to attend private schools instead ofpublic schools. Howell and Peterson (2002) find evidence that African-American children benefit from access toprivate schools. Pooling African-American children at all grade levels, they find that private schooling enhancesone- and two-year total achievement gains by 3.9 and 6.3 percentiles, respectively. Further, in New York andWashington, D.C., they find that the comparable three-year gain estimate is 6.6 percentiles.

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this section). Since 1981, more than 90% of schools have received vouchers. Therefore, the lackof a control group is one of the main empirical problems that scholars face in trying to evaluatethe results of the Chilean voucher experiment.

We take advantage of the introduction of the Preferential Scholastic Subsidy (PSS) in Chilein 2008 to present additional empirical evidence on how vouchers affect academic achievementby (1) forcing public schools to compete with private schools and (2) increasing the profit marginper enrolled student in both public and private schools.

In 2008, the Chilean government introduced the PSS, which aims to improve the qual-ity of education by giving an additional per-student subsidy to schools that voluntarily enrollvulnerable students. Under this scheme, additional resources are contingent on the completionof specific academic goals (the so-called Educational Improvement Plan [EIP]). In exchange forthese additional resources, schools face two costs by joining the PSS scheme. First, they mustaccept vulnerable students, who are more costly to educate. Second, they are not allowed tocharge a co-payment. This trade-off (more resources in exchange for educating more costly stu-dents and the prohibition of charging a co-payment) implies that only a fraction of schools decideto receive the PSS, thus generating a control group. Therefore, as discussed below, this newsubsidy offers us a better opportunity to evaluate the empirical effects of vouchers on educationquality.

Before the PSS was implemented, low-income, vulnerable students attended free publicschools only. Public schools were forced by law to enroll students who wished to attend and werenot allowed to charge any co-payment to them. Therefore, public schools faced a fixed demandby vulnerable students because those students did not have any alternative. Thus, public schoolsrepresented the kind of local monopolies argued by Hoxby (2003).

When the PSS was implemented, public schools received an extra amount of resources foreach enrolled vulnerable student. In addition, a significant fraction of private schools becamePSS receptors and began to enroll vulnerable students. The appearance of PSS private schoolsbroke the local monopolies of public schools on vulnerable students. The demand by vulnerablestudents for public schools was no longer rigid and became responsive to changes in quality.Therefore, the PSS should have encouraged public schools to supply a higher education qualityto avoid a massive migration of vulnerable students toward private schools3

Additionally, in public schools, the extra resources delivered by the PSS increase themargin per enrolled vulnerable student and thus encourage schools to attract more of thosestudents by increasing the education quality supplied to the market. The same happens in privateschools that become PSS receptors (those that charge a low co-payment, as we both theoreticallyand empirically show in this paper). Therefore, an increase in the academic achievement ofstudents within public and private schools should be observed when the PSS is implemented,even if the migration of students from public to private schools would not exist.

Finally, some market imperfections (lack of information or geographic constraints) couldprevent students from moving across schools. In that case, the demand is fixed (changes inquality are irrelevant) and the extra resources delivered by the PSS only represent economicrents for the owners of the schools. The PSS introduces a simple contract in which resources arecontingent on the achievement of some minimum required quality under the. This conditionality

3Of course, this conclusion holds as long as the value of the PSS voucher received by public schools is greaterthan the cost of educating a vulnerable student.

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encourages the supply of a high education quality in markets where some features of imperfectcompetition exit.

Therefore, theoretically we expect the PSS to have positive effects on academic achieve-ment. We test those effects empirically in this paper. However, some empirical difficulties arise.The migration of students from public to private schools could, by itself, affect the average aca-demic achievement. For instance, assume that some vulnerable students with very low humancapital migrate to private schools. If public/private schools are more/less effective in educatingstudents with lower human capital, the average academic results could change even if nothingmore happens. Additionally, the increase in education attainment for vulnerable students couldalso affect the average academic results of schools. To solve those difficulties, we control forstudents’ socioeconomic backgrounds, which allows us to put aside the effect of changes in thecomposition of students attending public and private schools. Second , we include school-levelfixed effects to control for potential self-selection of schools into the PSS program. Third, weuse a difference in-differences (DD) approach. It allows us to remove the biases that result fromthe comparison of both groups in the second period and that can be explained by permanentdifferences between them and differences across time due to group-specific trends.4

We first use a sample of public and private schools. Controlling for students’ socioeco-nomic backgrounds and for national trends, we find a positive and significant effect of the PSSon standardized test scores for math. For language, we find no significant effect under somespecifications. These positive effects of the PSS on academic achievement (mainly in math testscores) are explained by a mix of higher competition for public schools and a higher marginper enrolled vulnerable student for both public and private schools. Moreover, after includingschool progress in the EIP, we find that a higher degree of accomplishment further raises testscores, in addition to the direct effect of the PSS. Those results show that focusing resourceson specific scholastic commitments (i.e., the EIP in the Chilean system) enhances the efficacyof vouchers in promoting a higher quality of education in markets in which some features ofimperfect competition may exist.

Next, we use a sample of only private schools. Even though two channels (more com-petition and a greater margin per enrolled student) operate for public schools and just one (agreater margin per enrolled student) operates for private schools, the magnitude of the effectsis slightly greater for private schools. Our interpretation of these results is that public schoolscould face incentives different from the textbook case in which schools simply maximize profits.Sapelli (2003) indicates that a key element to evaluating the results of the Chilean vouchersystem is its design, which is far from the ideal case in which both public and voucher privateschools operate under the same internal and external rules (i.e., under the same budgets andregulatory frameworks). In the Chilean case, public schools are regulated by a Teaching Statuteand receive additional funds from municipalities when necessary. Together, these differenceswould determine the budget constraints of public and voucher private schools and, hence, theeffect of competition on their academic achievement. Along the same line as Sapelli (2003),Hsieh and Urquiola (2006) show that despite extensive private entry and sustained declines inpublic enrollments, the aggregate number of municipal schools has barely fallen. Hsieh andUrquiola (2006) argue that municipal officials seem to have been unable or unwilling to close

4Therefore, treatment and control schools are identical except by the fact that, after the PSS, the treatmentschools face more competition (public schools) and receive a higher margin per enrolled student (public andvoucher private schools).

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public schools and, thus, public schools seem to not face strong incentives to compete. This is re-inforced by the fact that, for these schools, revenue losses are mediated by municipal educationalbudgets, which makes it possible for them to lose students without automatic consequences ontheir resources. Thus, “soft budget constraints” for public schools could explain why the PSShas increased academic achievement in public schools but not by the magnitude suggested byour theoretical analysis. However, overall, our results confirm the positive effects of voucherson academic achievement and the importance of conditioning the delivery of resources to somespecific academic goals when features of imperfect competition exist in the market.

The rest of the paper is organized as follows. Section 2 reviews the institutional details ofthe PSS in Chile. Section 3 develops a theoretical model that analyzes the effects of voucherson the quality of education supplied by schools in different competition environments. Section4 discusses the empirical strategy used to test the main predictions of the theoretical model,and section 5 describes the data. Section 6 presents and discusses the results. Finally, section 7concludes.

2 The Preferential Scholastic Subsidy (PSS)

2.1 The Educational System before the PSS

In 1980, Chile implemented several educational reforms seeking to improve education quality andthe efficient use of resources by fostering competition between schools. Before these reforms,Chile had a centralized education system whereby the vast majority of schools were publiclyfinanced and the Ministry of Education was directly responsible for designing and overseeingthe implementation of all education policies, both substantive and administrative.5 Three mainchanges were particularly transformative. First, administrative responsibility over public schoolswas transferred from the Ministry of Education to each municipal government, resulting in amore decentralized configuration. Second, a new scholastic subsidy system was introduced asthe principal financing mechanism. Subsidized private schools began to receive exactly the sameper-student payment as the public (municipal) schools. To distinguish these institutions fromthe subsidized private schools that existed before the reforms (mainly religious), we will callthem voucher private schools. The subsidy was calculated as a function of total student enroll-ment and average student attendance. Third, the government moved to actively encourage theparticipation of private entities in the financing and administration of educational institutions(Larranaga, 1995; Vial, 1998; Gonzalez et al., 2002; Hsieh and Urquiola, 2003).

By making public funding directly contingent on student enrollment and retainment,schools should compete for a larger share of the student body by offering a better educa-tion. However, in their efforts to minimize educational costs and maximize funding, privateand voucher private schools employed competitive admission processes to admit only the mostpromising applicants (Hsieh and Urquiola, 2003; Elacqua and Fabrega, 2004). In contrast, publicschools with vacancies were legally obliged to admit all applicants regardless of their academicpotential or socioeconomic backgrounds. As a result, those students who displayed more aca-demic potential became increasingly enrolled in private or voucher private schools, while those

5At the beginning of 1980, more than 90% of all Chilean students were enrolled in institutions directly depen-dent on the Ministry of Education, and only the remaining 10% were enrolled in completely private institutions.

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who displayed less academic promise were forced to enroll in municipal schools that, paradox-ically, also received less funding. Because academic potential tends to be positively correlatedwith socioeconomic background, this segmentation resulted in the concentration of wealthierstudents in wealthier private or voucher private schools and more vulnerable students in poorermunicipal schools.

In 1993, voucher private schools were allowed to charge students an additional tuitionfee to complement the state subsidy (Larranaga, 1995; Gonzalez et al., 2002).6 This system ofshared financing encouraged schools with a better academic record to charge higher fees thantheir less successful counterparts, leading to increased socioeconomic segmentation and greaterfunding inequality.

Therefore, since 1993, the Chilean primary and secondary education system has beencomposed of three types of schools: (i) public schools financed by government subsidies basedon students’ attendance and by additional funds from local governments (municipalities); (ii)private schools that are also financed by the government and by co-payments made by parents(voucher private schools ); and (iii) private schools financed exclusively by parents (privateschools).7

2.2 The PSS

Created in 2008, the PSS aims to improve the quality of education of the most vulnerable stu-dents by giving an additional per-student subsidy to schools that voluntarily enter the PSSprogram. By providing additional resources to less advantaged students, named priority stu-dents, the additional subsidy aims to both improve the quality of education received by prioritystudents and decrease socioeconomic inequality in the academic performance of students fromdifferent socioeconomic backgrounds, through a combination of individually allocated additionalfunds and an incentive-based school reform program. Moreover, for the first time, under thisscheme additional resources are contingent on the completion of specific scholastic reforms andimprovements in school academic performance on a standardized test (SIMCE).

2.2.1 Who Are Priority Students?

The fundamental basis of the PSS is the targeted support of a specific group of underprivilegedand vulnerable students through additional subsidies. To automatically qualify as priority, astudent must meet one of the following criteria in order, proceeding to further criteria if andonly if the preceding one is not applicable: (i) participation in the Chile Solidario program (asocial welfare program protecting those in extreme poverty), (ii) being in the most vulnerablethird of the population according to the latest measurement instrument, or (iii) belonging tothe most vulnerable group in the National Health Fund (FONASA). If none of the precedingcriteria are available, the student’s position can also be temporarily established according totheir family socioeconomic background.

6Subsidy law limited the family contribution to no more than US$153 per month (in 2012 values).7Public, voucher private schools, and private schools represented 40.71%, 50.73%, and 8.56%, respectively, of

the total enrollment in primary and secondary education in 2010.

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2.2.2 School Designations and Incentives under the Educational Improvement Plan

To receive PSS funds, schools are classified into three categories according to their academicperformance on the SIMCE and the socioeconomic characteristics of their students.8 After aschool is classified, it has to present an Educational Improvement Plan (EIP) to the Ministry ofEducation, which details educational reforms that the school will undertake to improve SIMCEresults and how PSS funds will be spent to improve the academic performance of priority stu-dents. Although the amount of resources distributed to each priority student is the same for allschools and varies only by educational level, the autonomy of schools to decide how to use thoseresources and under what level of supervision will depend on the school’s classification.9

While the EIPs form the central aspect of the incentives system inherent in the PSS law,in practice, they are only a part of the Equality of Opportunity and Educational ExcellenceAgreement (Convenio de Igualdad de Oportunidades y Excelencia Educativa) that each institu-tion must sign. In addition to a commitment to produce and complete an EIP, the agreementalso mandates that schools report the use of all resources received under the program and thestatus of specific projects, that all priority students must be allowed to attend free of tuitionand costs (cannot be required to pay any co-payment), that all admissions must be open toany prospective student,10 and that schools must retain all students, even those with poor aca-demic performance. These limitations on PSS funding and requirements for participation in theprogram are designed primarily to ensure that high-performing schools are accessible to all low-income students and that schools are not allowed to give preference to highly qualified prioritystudents.

2.2.3 Funds

PSS funding is allotted per priority student and delivered directly to the school instead ofto any municipal funding system. It is calculated based on the average attendance rate ofpriority students over the previous three months, the school’s classification as autonomous oremergent, the grade level of the priority student (younger students receive more resources), andthe concentration of priority students in the school. Table 1 shows the monthly subsidy deliveredper priority student in 2012.

In addition to the regular PSS funds, schools qualify for a subsidy if priority students makeup more than 15% of the student body. Table 2 presents the concentration subsidy amount.

In relation to the current flat voucher based on student attendance, the PSS increasesthe resources given to schools for priority students by 70%. Table 3 presents the amount ofadditional resources given by the PSS.

8Depending on their performance on the SIMCE, schools could be classified as autonomous (if their performanceis at or above the median for their SIMCE group), emergent (if their performance is below the median fortheir SIMCE group), or in recuperation (applied to emergent institutions that fail, after four years, to meet thequantitative goals required by the program).

9For instance, emergent schools receive half of their funding as a subsidy and the other half as a contributionof additional resources to design and execute their EIP, and schools that are in recuperation receive all of theirfunding as a contribution of additional resources to their EIP.

10In cases where selectivity is requisite, schools may not take into account past academic performance, currentacademic ability, or socioeconomic status.

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3 The Model

In this section we build a model to analyze how the PSS affects the quality of education suppliedby schools. We first assume an environment of perfect competition in which schools must offera higher level of quality in order to attract more students. After doing so, we introduce frictionsthat break the assumption of perfect competition; for example, parents’ lack of informationand/or some geographical constraints may prevent students from moving across schools andthus produce a school-specific demand that is fixed. In this second model, we study the rolethat incentives attached to the delivery of PSS resources have on promoting a higher quality ofeducation.

3.1 Perfect Competition

We assume that two types of schools exist: public and voucher private schools. Public schoolsare not allowed to charge any fee. On the contrary, voucher private schools charge a privateco-payment that complements the resources delivered by a flat voucher. Voucher private schoolsare heterogeneous in the amount of the co-payment they charge to parents. Denote by pi theco-payment required by school i. We assume that pi has a continuous uniform distribution witha support pi ∈ (0, p]. Therefore, for public schools pi = 0, and for voucher private schools pi > 0.

We assume the following demand function of education:

ni (qi) = βqi, (1)

where qi denotes the quality supplied by school i. Therefore, the higher the quality supplied bya school, the greater the demand for that school. Assume that qi ≥ 0; that is, schools cannotsupply a “negative quality.”

On the demand side, there are two groups of students. The first group is composed ofstudents from families who have zero income . Because their families have zero income, thosestudents must attend free public schools. The second group is made up of students from high-income families. Those families have the resources to make co-payments and, therefore, canchoose among any type of school. We assume the following cost function of providing a qualityqi to high-income (h) and low-income (l) students:

ch (q) = αqi, (2)

cl (q) = αqi; (3)

∀ α ≥ α. We assume a segregated education market in which low-income students attendfree public schools and high-income students attend voucher private schools that require co-payments. Figure 3 shows the average family income by school dependence. We can observethat segregation indeed exists in the Chilean education market.

Because low-income students do not have another alternative, the demand for publiceducation by those students is fixed. Denote by np the fixed demand for public education.Public schools choose the quality level, qp, that maximizes their profits:

maxqp{(v − cl (qp))np} (4)

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where v is the flat voucher received by both public and voucher private schools. It is clear thatpublic schools have incentives to offer qp = 0.

Voucher private schools charge a co-payment and thus receive only students from high-income families. The maximization problem, which a voucher private school i must solve, is thefollowing:

maxqi{(v + pi − ch (qi))ni (qi)} . (5)

Therefore, the quality supplied by school type i is:

qi =v + pi

2α(6)

Notice that ∂qi/∂pi > 0. Moreover, it is straightforward to conclude that qi > qp for alli. Therefore, voucher private schools offer a higher quality than public schools; and the higherthe co-payment charged, the higher the quality supplied. Two main reasons explain why thequality of education in voucher private schools is higher than in public schools. First, positiveco-payments in voucher private schools bring extra resources, so it becomes profitable to attractmore students by increasing quality. Second, given that low-income students cannot afford aprivate education, public schools are local monopolies for that type of student . That is, thedemand for those schools by low-income students is fixed. Therefore, total demand for publicschools is nonresponsive to changes in quality, and, as a consequence, public schools do not haveincentives to increase quality. The reason why voucher private schools with higher co-paymentsoffer a higher quality is that higher co-payments increase the resources per student and thusencourage schools to attract more students by increasing quality.

Figure 2 shows that the average SIMCE score is higher in voucher private schools than inpublic schools. Additionally, Figure 4 plots the correlation between the co-payment level andthe SIMCE score in voucher private schools. We observe a positive correlation, as predicted bythe model. Therefore, the previous predictions of the model are indeed observed in the data.

3.1.1 The PSS System

We now assume that the PSS is implemented. As described in section 2, PSS schools havean additional amount of resources. Denote the total amount of resources (the flat voucherplus the extra resources) that a PSS school receives by vPSS . Only schools that accept low-income students receive the voucher vPSS . Additionally, PSS private schools are not allowedto charge co-payments to vulnerable students. Therefore, the maximization problem for PSSprivate schools can be expressed as follows:

maxqPSS

{(vPSS − cl (qPSS))nPSS (qPSS)} (7)

Therefore, the optimal choice of quality is:

qPSS =vPSS

2α(8)

Schools that do not receive the PSS maximize the profits function (5); thus, they offer a qualitylevel given by equation (6).

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Voucher private schools that choose to receive the PSS are those ones for which benefits(the greater voucher) overcome costs (the loss of the co-payment and the higher cost of educatinglow-income students). Therefore, we can have a co-payment level p∗, such that schools thatcharge a co-payment p∗ are indifferent between becoming or not becoming PSS schools. Schoolscharging a co-payment lower than p∗ become PSS schools, whereas schools with a higher co-payment do not choose to receive the PSS. The co-payment level p∗ is given by the followingexpression:

p∗ = vPSS (α/α)12 − v. (9)

We now impose the following constraint on vPSS , such that a positive number of PSS andnon-PSS schools exist:

0 < p∗ < p. [A.1]

Therefore, we have that

v

(α

α

) 12

< vPSS < (p+ v)

(α

α

) 12

. (10)

As long as the value of vPSS satisfies inequality (10), some voucher private schools becomePSS receptors and compete with public schools for low-income students. Additionally, all publicschools have incentives to become PSS receptors and get the additional resources delivered bythe PSS. Table 4 shows that this prediction is indeed observed in the data. Almost all publicschools subscribed to the PSS, whereas around half of voucher private schools became PSSreceptors.

Under the PSS system, the maximization problem of public schools is given by equation(7) and the education quality supplied to the market is given by equation (8).

3.1.2 Supply of Quality under a PSS and a non-PSS System

Now we analyze the supply of education quality of public schools in a PSS and a non-PSS system.Before and after the introduction of the PSS, public schools receive only low-income students.However, when the PSS is implemented, low-income students have the chance to attend thosevoucher private schools that become PSS receptors. Therefore, the demand for public schoolsby low-income students is no longer fixed but is responsive to changes in quality. Additionally,the PSS delivers extra resources to public schools, and, thus, the margin per enrolled studentis higher under the PSS system. Both facts imply that public schools have incentives to supplya higher education quality under a PSS than under a non-PSS system. That conclusion isstraightforward when we compare equation (8) with our previous conclusion that public schoolssupply a quality level qp = 0 when the PSS does not exist.

On the other hand, by comparing equations (6) and (8), we can establish that voucherprivate schools that charge a co-payment lower than some level p∗∗ supply a higher educationquality under a PSS than under a non-PSS system. p∗∗ is the co-payment level that equalsequations (6) and (8):

p∗∗ = vPSS (α/α)− v (11)

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Figure 5 shows the relationships between p∗∗, p∗, qi and qPSS .11 From this figure, we canextract four main conclusions.

First, voucher private schools that choose to be PSS receptors are those that charge aco-payment lower than p∗. Voucher private schools must agree to receive private co-paymentsfrom parents. That cost is higher for schools that charge a higher co-payment. For this reason,the lower the co-payment, the greater the profitability of becoming a PSS receptor.

Second, voucher private schools that charge a low co-payment increase the educationquality supplied under a PSS system more than under a non-PSS system. As discussed in theintroduction, the receipt of more resources per enrolled student means it is profitable to attractmore students, which encourages schools to supply a higher education quality. Under a PSSsystem, the margin per enrolled student increases more for schools that initially charged a lowco-payment. That explains why those schools experience greater increases in their educationquality.

Third, the unconditional effect of the PSS on the education quality supplied by voucherprivate schools is, in principle, ambiguous. As shown by Figure 5, there are some schools(those that charge a co-payment between p∗∗ and p∗) that decrease their education qualitycompared with their counterfactual scenario (the non-PSS system). The optimal choice ofeducation quality corresponds to the level at which the marginal cost of further increases inquality equals the marginal benefit. For schools with a middle-level co-payment (between p∗∗

and p∗), the PSS system increases marginal benefits by a relatively small amount because co-payments were relatively high under the non-PSS system. However, those schools could stillhave incentives to become PSS receptors.12.

Fourth, notice that as the difference between educating low-income and high-income stu-dents decreases, it is more likely to find a positive effect of the PSS on the average educationquality supplied by voucher private schools. In terms of Figure 5, the difference between p∗∗

and p∗ decreases when α/α tends to one. For instance, if the difference in the cost of educatinglow-income and high-income students is null (α/α = 1), schools that choose to be PSS receptorsare exactly those that would increase their education quality under a PSS system (with respectto the non-PSS scenario). A direct implication of the latter result is that, controlling for thesocioeconomic background of students, we should expect an unambiguous positive effect of thePSS on the education quality of voucher private schools (keeping the composition of studentsconstant is similar to imposing α = α in terms of the model). In section 6, we test all thoseempirical predictions of the model.

3.2 Imperfect Competition

In this subsection we deviate from the perfect competition environment. Indeed, we assumethat both public and voucher private schools face a fixed demand n. Some factors that mayexplain this school-specific rigid demand could be some lack of information for parents aboutschool quality or students’ mobility constraints. Both factors imply that an increase in thequality supplied may not attract more students. We can immediately conclude that the qualitysupplied by all school types will be the minimum- in this case, zero. Moreover, because the

11Notice that p∗∗ < p∗.12This conclusion is straightforward when we observe the equilibrium profits and the optimal choice of quality

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quality supplied is zero, the PSS will be ineffective in promoting a higher quality of education.The extra resources would only be economic rents for the owners of PSS schools.

Schools will choose to receive the PSS if the following condition is satisfied:

pi < vPSS − v. (12)

Therefore, as long as vPSS > v, there will be some schools that choose to receive thePSS. In these schools, the delivery of extra resources will not be effective in promoting a higherquality of education. They would only increase profits. A different type of contract must bedesigned to promote education quality.

3.2.1 The PSS Contract

The PSS contract in an environment of imperfect competition may condition resources to theaccomplishment of some goals that ensure that PSS schools have incentives to offer higher qualitylevels of education.

Assume a very simple contract to emulate the EIP, described in section 2: in exchange fornew resources, PSS schools must warrant a quality equal to or greater than q. Assume that

q ≤ qp. [A.2]

Assumption [A.2] implies that the constraints imposed by the PSS are not binding in anenvironment of perfect competition. Schools that subscribe to this type of contract face thefollowing maximization problem:

maxqPSS

{(vPSS − αqPSS)n} , (13)

such that

qPSS ≥ q. (14)

Therefore, PSS schools choose to offer the minimum required quality q, which is greater thanzero.

As before, we can have a co-payment level p∗, such that schools charging a co-paymentlower than p∗ choose to receive the PSS:

p∗ = (vPSS − v)− αq. (15)

As in the previous section, we impose assumption [A.1], which grants that a positivenumber of PSS and non-PSS schools exist. Assumption [A.1] imposes a limit to the value ofvPSS , as the following inequality shows13:

v + αq < vPSS < p+ v + αq. (16)

13As in the previous analysis, inequality (16) implies that all public schools subscribe to the PSS contract.

13

The quality supplied by schools when the PSS does not exist is simply zero. However,as long as the amount of the voucher satisfies the lower limit of inequality (16), there will bea positive number of PSS schools, and the average quality supplied by schools will be highercompared with that when the PSS does not exist. Therefore, when some frictions limit competi-tion among schools, a contract conditioning resources to the accomplishment of some minimumquality level is effective in promoting a higher quality of education among schools.

4 Empirical Strategy

We use a difference in-differences (DD) approach to evaluate the effects of the PSS on academicachievement. According to this methodology, there are two groups observed in two time periods,where one is exposed to a treatment in the second period but not in the first (treatment group).The other group is not exposed to a treatment during either period (i.e., the control group). Ifthe same units of the treatment and the control groups are both in the first and second periods,the effect of the treatment could be obtained by subtracting the average gain of the control groupfrom the average gain of the treatment group. This approach allows us to remove the biasesthat result from the comparison of both groups in the second period and that can be explainedby permanent differences between them and differences across time due to group-specific trends.

For two periods and a treatment that occurs only in the second period, we have thefollowing expression:

yit = β0 + β1d2t + β2wit + uit, ∀t ∈ 1, 2; (17)

where yit is the outcome and d2t is a dummy variable that takes the value of 1 for the secondperiod and zero otherwise. Also, wit is a binary program indicator that is equal to 1 if unit ireceives the treatment in period t, and uit is an error term. Taking differences, we have:

∆yi = β1 + β2∆wi + ∆ui. (18)

In this framework, β2 is the treatment effect. Moreover, if the changes in the treatmentstatus are not correlated with the error term (i.e., E[∆wi∆ui]), the OLS estimation of equation18 will produce consistent parameters. In the case where there are no units treated in the firstperiod, the OLS estimation can be obtained as:

β2 = ∆ytreated −∆ycontrol, (19)

where ytreated and ycontrol are the sample averages of the outcome variable (y) for the treatmentand control groups, respectively.

We use a panel of schools in which we can identify those that signed the Equality ofOpportunity and Educational Excellence Agreement and received PSS funds from 2009 to 2011(i.e., the treatment group) and those that decided not to participate in this program during thisperiod (i.e., the control group). In our model, the pre-treatment period is defined as the timebefore the PSS was implemented (i.e., from 2006 to 2008), and the post-treatment period asthe time in which the PSS was in effect (i.e., from 2009 to 2011). We use the following baseline

14

specification to estimate the effects of the PSS on academic achievement, in a context of paneldata:

SIMCEit = β1λt + β2wit + β3Xit + ci + uit, ∀t ∈ [1, T ]; (20)

where SIMCEit corresponds to the average score that school i obtained on the SIMCE (mathand language, depending on the econometric specification) in the period t, λt is a time trend,and wit is a binary program indicator that equals one if the school i participates in the programat time t and zero otherwise. Additionally, we include a vector of school characteristics, Xit,which contains the average gross family income and the education level of the parents of thestudents attending school i at time t. Finally, we include a fixed individual effect ci and anidiosyncratic error term uit.

Equation (20) constitutes our baseline empirical model. It allows for aggregate time effectsand controls. We estimate equation (20) using fixed effects to obtain the policy effect estimatorassociated with wit. We interpret it as the average effect of the PSS on schools’ academicachievement.

The key identifying assumption is that the trend in test scores would be the same in bothPSS and non-PSS schools in the absence of treatment. Although the treatment and controlindividual schools can differ, this difference is meant to be captured by the school fixed effects.Figure 6 shows the SIMCE scores in the treatment and control groups. We observe that thetrends followed by those school groups are fairly similar until 2008.14 Treatment induces adeviation from this common trend. The treatment is composed by a higher margin per enrolledstudent and more competition (in case of public schools). Therefore, controlling by fixed effectsand observable characteristics of schools, treatment and control schools are identical except bythe fact that, after the PSS, the treatment schools face more competition (public schools) andreceive a higher margin per enrolled student (public and voucher private schools).

5 Data

Our sample is composed of public and voucher private schools that were and were not exposed tothe PSS from 2009 to 2011. This database contains information about students’ socioeconomicbackgrounds, including variables such as gross family income and the education level of thestudents’ parents. Our database also includes information about the proportion of the EIPaccomplished by schools that participated in the PSS in 2010. As we previously explained, theEIP is a plan that details educational reforms intended to improve SIMCE scores and establisheshow financial resources provided by the PSS will be spent to improve the academic perform ofpriority students. Because schools define their EIPs over a period of four years,15 the Ministryof Education decided to carry out an evaluation before the end of the agreement. In 2010, theministry evaluated the goals accomplished by schools in math, language, and management. Weuse the proportion of the EIP accomplished in math and language in 2010 as a measure of theeducational reform progress.

14Moreover, an important assumption behind specification (20) is that changes in the trend are exclusivelyexplained by (1) the treatment and/or (2) changes in the covariates that control for omitted school-specifictrends.

15At the end of the four-year period, schools are evaluated on the basis of their performance on the SIMCE.

15

We link this information with school academic performance. In particular, we include theaverage score that a school obtained on the SIMCE between 2006 and 2011. Considering thatthe PSS was progressively implemented in preschool and primary education (and only since 2012in secondary education), we use schools’ average scores obtained by fourth graders in math andlanguage, which are the only measures of school performance available for the levels under thePSS.

Using this information, we build a panel for public and voucher private schools that allowsa pre-PSS period and a post-PSS period. We define the pre-PSS period, or the pre-treatmentperiod, as the three years before the implementation of the PSS. The post-PSS period, orthe post-treatment period, corresponds to the three years in which schools could receive thePSS by signing an Equality of Opportunity and Educational Excellence Agreement (Conveniode Igualdad de Oportunidades y Excelencia Educativa). By signing the agreement, schoolsagreed to carry out specific measures oriented to improve the quality of the education providedand perform specific actions to encourage the school’s retention and academic performance ofpriority students. Therefore, our panel links not only the participation status of schools in thePSS but also their academic performance on the SIMCE, the socioeconomic backgrounds oftheir students, and a measure of relative efficiency of the goals proposed in their EIP. Tables 5and 6 show summary statistics of treated and untreated schools for each year.

6 Results

Table 7 presents the results of equation (20). Columns (1) and (7) show that the effect of thePSS on math and language is positive, statistically significant (at the 1% level), and higherfor math than for language, when only the time trend (λt) and the policy indicator (wit) areconsidered. Despite the fact that the point estimate associated with math is higher than that forlanguage, the trend coefficient associated with language is higher than that for math. This couldbe explained by specific reforms carried out to increase the quality of the language educationcontent. As a result, SIMCE scores for language showed an increasing tendency attributableto curriculum reforms that were not implemented in math. Columns (2) and (3) and (8) and(9) show the estimated coefficients for math and language when family background is controlledfor. After family income and the years of schooling of both parents are controlled for, the policyindicator is positive and statistically significant (1%) and remains higher for math than forlanguage.

The next three columns include the proportion of the EIP accomplished by schools in2010 in math and language. This variable gives us an idea of how EIP reforms affect schoolperformance. According to the model developed in section 3, in an environment of perfect com-petition, this variable should be statistically insignificant. Schools compete through quality, andthe equilibrium education quality supplied to the market should be higher than the minimumrequired by the EIP. However, when some features of imperfect competition exist in the mar-ket, a higher minimum education quality required by the PSS contract should produce higherimprovement in academic achievement in schools that join the PSS system. Therefore, if theChilean education market is competitive, we expect that the coefficient of the policy indicatorwit) is statistically different from zero but that the coefficient of the proportion of the EIPaccomplished is statistically insignificant. The opposite conclusion is expected if the Chileanmarket is not competitive. Of course, a mix of results is also possible. A positive and significant

16

coefficient of the policy indicator and the proportion of the EIP accomplished should be a signalof a not fully competitive market where the EIP is, in some degree, binding with respect to thefree-market equilibrium quality.

In all cases, the effect of the PSS on SIMCE scores, as well as the level of completion ofthe EIP, remains positive and significant (with exception of the policy indicator for language incolumn [12], which is not significant). According to results in column (6), if a school accomplished100% of its math-specific goals proposed for 2010, its average SIMCE score would rise 4.7 points,in addition to the 2.6-point increase associated with the policy indicator (wit). For language,a full completion of the reforms implies, on average, 4.1 more points on SIMCE, in addition tothe points attributable to the direct effect of the PSS on the school’s academic performance (seecolumn [12]).

An additional question is whether or not the PSS has a cumulative effect on school aca-demic achievement, considering that, in our sample, all schools signed the agreement in 2009and remained in the program until 2011. It is possible that the PSS has an additional effect foreach additional year in the program. That lag in the effect of the PSS on academic achievementcould exist for two reasons. First, it is possible that there are some adjustment costs. Forinstance, vouchers could increase competition for public schools and increase the margin perenrolled student for both public and voucher private schools. Both school types would haveincentives to increase their quality. However, because of some adjustment costs (for instance,the construction of new infrastructure), the increase in quality would not materialize until somefuture period. Second, it is possible that students must be exposed to more than one period ofinvestments in order to experience improvements in academic achievement.

Table 8 presents the results of equation (20), including the interaction of the policy indica-tor and the trend. By doing this, we capture the average effect of an additional year in the PSSon SIMCE scores. Columns (1) and (7) indicate that an additional year increases SIMCE by1.5 points in math and 0.7 points in language, values that are statistically significant at the 1%level. After including school socioeconomic background variables (family income and parents’schooling), results remain positive and significant and are higher in math than in language.Moreover, when we include the percentage of EIP completion specific to each subject (columns[4]-[6] for math and [10]-[12] for language), our results indicate that each additional year of thePSS increases SIMCE scores by approximately 1.2 points in math and 0.33 points in language.Therefore, a school that has received PSS funds for three consecutive years and has completedall of the reforms proposed in its EIP by 2010 would increase its SIMCE score by 10 points inmath and 5.6 points in language. To have an idea of the impact of the PSS, we can considera school in the 50th percentile of the SIMCE score distribution-254 points in math and 264 inlanguage. An increase of 10 points would place the school in the 66th percentile in the mathscore distribution, while an increase of 5.6 points would put the school in the 59th percentile inthe language score distribution.16

Tables 9 and 10 present the results of the same empirical models of tables 7 and 8 butonly using a sample of voucher private schools. In general, we can observe that the coefficienton the policy indicator is slightly greater than in the model with both public and voucher

16In additional regressions, we included dummy variables that equal 1 for PSS schools one and two years beforethe introduction of the PSS system. In this way, we test whether our results can be simply explained by schoolsthat choose to be PSS receptors changing their behavior before signing the PSS contract. The conclusions do notchange using this specification.

17

private schools. However, as discussed in section 3, a smaller coefficient was expected. That isbecause two channels (more competition and a greater margin per enrolled student) operate forpublic schools, but just one (a greater margin per enrolled student) operates for private schools.We interpret this result as preliminary evidence that public schools could face incentives andbudget constraints that cause those types of schools to deviate from the profit-maximizationgoal. Sapelli (2003) shows that public schools are regulated by a Teaching Statute and receiveadditional funds from municipalities when necessary. Additionally, Hsieh and Urquiola (2006)show that despite extensive private entry and sustained declines in public enrollments, theaggregate number of municipal schools has barely fallen. Hsieh and Urquiola (2006) argue thatmunicipal officials seem to have been unable or unwilling to close public schools, and, thus,public schools seem to not face strong incentives to compete. This is reinforced by the fact that,for these schools, revenue losses are mediated by municipal education budgets, which makesit possible for them to lose students without automatic consequences on their resources. Theanalysis in Sapelli (2003) and Hsieh and Urquiola (2006) could explain why the PSS has increasedacademic achievement in public schools, but not by the magnitude expected according to thetheoretical analysis of section 3. However, overall, our results confirm the positive effects ofvouchers on academic achievement and the importance of conditioning the delivery of resourcesto some specific academic goals when features of imperfect competition exist in the market.

Finally, notice that another testable implication of the model discussed in section 3 is thatthe effects of the PSS on academic achievement might be lower for schools receiving higher co-payments. Table 11 presents the estimated coefficients of equation (20), including the interactionof the policy indicator and the shared financing made by parents. We conduct this exercise onlyfor voucher private schools, considering that public schools are not allowed to charge any co-payment to parents. Columns (1) to (12) show that, as our theoretical model predicts, thecoefficient associated with the interaction is negative and statistically significant at the 1%level. This implies that the PSS increases academic achievement in voucher private schoolsthat charge a lower co-payment more than in schools that receive a higher co-payment. Ourtheoretical model also predicts that schools that received a lower co-payment before 2008 havemore incentive to enroll in the PSS program. Table 12 shows that, indeed, voucher privateschools that enrolled in the PSS program charged a lower co-payment than did non-PSS schoolsbefore the introduction of the subsidy.

7 Conclusions

This paper provides empirical evidence of the effects of vouchers on the academic achievementof students. We focus on the Preferential Scholastic Subsidy (PSS) implemented in Chile in2008. Different from the previous flat voucher system introduced in 1981, this new demandsubsidy allows us to have a control group to evaluate the effects of the program. Our empiricalstrategy considers a difference-in-differences approach that allows us to remove the biases thatresult from the comparison of both groups in the second period and that can be explained bypermanent differences between both groups and differences across time due to group-specifictrends.

We find a positive effect of the PSS on standardized test scores. In an environment ofperfect competition, the positive effect of vouchers on academic achievement operates mainlythrough two channels. First, the freedom of parents of low-income students to choose schools

18

introduces competition for public schools. Second, if the incidence of the demand subsidy (thevoucher) on the supply of education is nonzero, vouchers (or an increase in the resources deliveredby vouchers to schools) increase the margin per enrolled students (the difference between thetotal monetary payments and the cost of educating a student), which encourages schools toattract more students. If there is competition, education quality increases within both public andprivate schools through this second channel. Additionally, our results highlight the importance ofconditioning the delivery of resources to some specific academic goals when features of imperfectcompetition exist in the market. Finally, we find a greater effect of the PSS when we consideronly a sample of voucher private schools than when we include both public and voucher privateschools. This result can be explained by the existence of “soft budget constraints” for publicschools, which means that public schools do not face strong incentives to compete.

An interesting avenue for future research is related to the differential effects that the PSShad on math and language. That is, why did competition promote a higher supply of educationquality in math than in language? One possible explanation is that parents value a scientificeducation relatively more than a humanistic education. Therefore, the demand for schools ismore elastic to quality increases in scientific than in humanistic education. In this scenario,it is more effective for schools to increase the quality of their math education rather thantheir language education to attract more students. A formal empirical test of this hypothesisconstitutes an interesting and important avenue to explore in the future.

19

References

Contreras, D. (2001). Evaluating a Voucher System in Chile. Individual, Family and SchoolCharacteristics. Facultad de Ciencias Economicas y Administrativas, Universidad de Chile,Documento de trabajo 175.

Elacqua, G. and R. Fabrega (2004). El Consumidor de la Educacion: El Actor Olvidado de laLibre Eleccion de Escuelas en Chile. PREAL, Universidad Adolfo Ibanez .

Epple, D. and R. E. Romano (2002). Educational Vouchers and Cream Skimming. NationalBureau of Economic Research (9354).

Friedman, M. (1955). The Role of Government in Education. From Economics and the PublicInterest, ed. Robert A. Solo.

Gonzalez, P., A. Mizala, and P. Romaguera (2002). Recursos Diferenciados a la EducacionSubvencionada en Chile. Serie Economıa, Centro de Economıa Aplicada Universidad de Chile,Working paper 150.

Howell, W. G. and P. E. Peterson (2002). The Education Gap: Vouchers and Urban Schools(with Patrick J. Wolf and David E. Campell). Washington, DC: Brookings Institution Press.

Hoxby, C. M. (2003). School Choice and School Productivity. Could School Choice Be a Tidethat Lifts All Boats? In: Hoxby, C. (Ed.), The Economics of School Choice. Chicago, IL:University of Chicago Press for NBER.

Hsieh, C.-T. and M. Urquiola (2003). When Schools Compete, How do they Compete? AnAssessment of Chile’s Nationwide School Voucher Program. National Bureau of EconomicResearch, Working paper 10008.

Hsieh, C.-T. and M. Urquiola (2006). The Effects of Generalized School Choice on Achievementand Stratification: Evidence from Chile

′s Voucher Program. Journal of Public Economics 90,

1477–1503.

Larranaga, O. (1995). Descentralizacion de la Educacion en Chile: Una Evaluacion Economica.Estudios Publicos 60, 243–281.

Nechyba, T. J. (1999). School Finance Induced Migration Patterns: The Impact of PrivateSchool Vouchers. Journal of Public Economic Theory 1 (1), 5–50.

Sapelli, C. (2003). The Chilean Voucher System: Some New Results and Research Challenges.Cuadernos de Economıa 40 (121), 530–538.

Sapelli, C. and B. Vial (2002). The Performance of Private and Public Schools in the ChileanVoucher System. Cuadernos de Economıa 39 (118), 423–454.

Tokman, A. (2001). Is Private Education Better? Evidence from Chile. University of California,Berkeley, Mimeo.

Vial, B. (1998). Financiamiento Compartido de la Educacion. Cuadernos de Economıa 35 (106),325–342.

20

Figure 1: Enrollment Share by Type of School

Source: Ministry of Education.

Source: Ministry of Education.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Public Voucher Private

21

Figure 2: SIMCE Scores by Type of School

(a) Math 4th grade, 2002-2008

(b) Language 4th grade, 2002-2008

Source: SIMCE 2002-2008.

210

220

230

240

250

260

270

280

2002 2005 2006 2007 2008

Public Voucher Private

210

220

230

240

250

260

270

280

290

2002 2005 2006 2007 2008

Public Voucher Private

22

Figure 3: Family Income by School Dependence

Source: SIMCE 2008.

Source: SIMCE 2008. Notes: Gross family income is express in US dollars.

23.31%

9.6%

59.17%

47.3%

14.06%

27.43%

1.62%

5.79%

1.22% 5.9%

0.63%

4.0%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Public Voucher Private

≤200 201-600 601-1200 1201-1600 1601-2400 ≥2400

23

Figure 4: Shared Financing and SIMCE Scores (2007)20

025

030

035

0

Lang

uage

SIM

CE

Sco

re (

4th

grad

e)

4000 24000 44000 64000 84000 104000

Shared Financing (2007)

Source: SIMCE 2007, 4th grade.

(a) Language

150

200

250

300

350

Mat

h S

IMC

E S

core

200

7(4t

h gr

ade)

4000 24000 44000 64000 84000 104000

Shared Financing (2007)

Source: SIMCE 2007, 4th grade.

(b) Math

24

Figure 5: Supply of Quality in a PSS and a non-PSS system

q

qPSS

qi

pi p** p*

25

Figure 6: SIMCE Scores in the Treatment and Control Groups

(a) Math 4th grade, 2006-2008

(b) Language 4th grade, 2006-2008

Source: SIMCE 2006-2008.

210

220

230

240

250

260

270

280

2006 2007 2008

PSS No PSS

210

220

230

240

250

260

270

280

2006 2007 2008

PSS No PSS

26

Table 1: Monthly Subsidy per Priority Student (US$)

Pre-Kindergarten 5th and 6th Grades 7th to 12th Gradesto 4th Grade

Monthly per-student subsidy 86.6 57.6 29.1

Source: Ministry of Education (Chile). Notes: Values are for 2012 and are adjusted by purchasing power parity.

Table 2: Monthly Concentration Subsidy per Priority Student (US$)

Concentration of Pre-Kindergarten 5th and 6th Grades 7th to 12th GradesPriority Students to 4th Grade

15%-30% 6 4 230%-45% 10.3 6.9 3.445%-60% 13.8 9.2 4.6≥60% 15.4 10.3 5.2

Source: Ministry of Education (Chile). Notes: Values are for 2012 and are adjusted by purchasing power parity.

Table 3: Flat Voucher and PSS for Pre-Kindergarten and Fourth Graders (US$)

Category Subsidy

Flat voucher 143PSS 86.6Concentration subsidy 13.8

Total 243.4

Source: Ministry of Education (Chile). Notes: Values are for 2012 and

are adjusted by purchasing power parity.

27

Table 4: Number of School Types

Year Funding Type With PSS Without PSS

2009 Public 4,387 (89.17%) 533 (10.83%)

Voucher private 1,766 (51.65%) 1,653 (48.35%)

2010 Public 4,366 (89.42%) 513 (10.58%)

Voucher private 1,907 (56.30%) 1,480 (43.70%)

2011 Public 4,309 (90.54%) 450 (9.46%)

Voucher private 2,064 (61.47%) 1,294 (38.53%)

Source: Ministry of Education.

28

Table 5: Summary Statistics: Public Schools

Year Variable With PSS Without PSS

2006 Math SIMCE 233.11 203.33Language SIMCE 240.13 216.67Gross family income (US$) 373.53 408.12Mother’s education 9.63 10.25Father’s education 9.73 10.48

2007 Math SIMCE 229.55 226Language SIMCE 240.12 229Gross family income (US$) 397.25 445.31Mother’s education 9.69 9.78Father’s education 9.78 10.2

2008 Math SIMCE 229.96 215Language SIMCE 245.78 223.33Gross family income (US$) 409.99 456.89Mother’s education 9.60 9.73Father’s education 9.65 10.02

2009 Math SIMCE 234.59 218.33Language SIMCE 246.37 235Gross family income (US$) 396 513.69Mother’s education 9.69 9.92Father’s education 9.75 10.38

2010 Math SIMCE 235.83 214Language SIMCE 256.37 252.67Gross family income (US$) 451.96 378.11Mother’s education 9.78 10.09Father’s education 9.80 10.32

2011 Math SIMCE 244.76 239Language SIMCE 254.07 253Gross family income (US$) 468.55 495.47Mother’s education 9.81 10.3Father’s education 9.83 10.11

Source: SIMCE 2006-2011 and Ministry of Education (Chile).

29

Table 6: Summary Statistics: Voucher Private Schools

Year Variable With PSS Without PSS

2006 Math SIMCE 246.64 268.27Language SIMCE 252.81 271.21Gross family income (US$) 503.72 926.68Mother’s education 11.13 13.13Father’s education 11.15 13.4

2007 Math SIMCE 245.76 266.56Language SIMCE 254.58 272.32Gross family income (US$) 535.58 968.39Mother’s education 11.16 13.15Father’s education 11.16 13.31

2008 Math SIMCE 245.82 266.39Language SIMCE 260.17 276.56Gross family income (US$) 545.47 1,002.87Mother’s education 11.07 13.09Father’s education 11.06 13.2

2009 Math SIMCE 251.09 270.31Language SIMCE 260.83 276.76Gross family income (US$) 522.93 980.79Mother’s education 11.1 13.11Father’s education 11.08 13.23

2010 Math SIMCE 251.07 267.98Language SIMCE 270.37 283.71Gross family income (US$) 600.26 1130.90Mother’s education 11.2 13.21Father’s education 11.15 13.29

2011 Math SIMCE 257.26 268.62Language SIMCE 266.29 276.78Gross family income (US$) 622.46 1173.91Mother’s education 11.21 13.18Father’s education 11.14 13.24

Source: SIMCE 2006-2011 and Ministry of Education (Chile).

30

Tab

le7:

Eff

ects

ofth

eP

SS

on

Sch

ool

Aca

dem

icA

chie

vem

ent

(1):

Pu

bli

can

dV

ouch

erP

riva

teS

chool

sV

aria

ble

SIM

CE

4th-G

rad

eM

ath

SIM

CE

4th

-Gra

de

Langu

age

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

λt

0.863

2**

*0.

7382*

**0.

827

4***

0.7

632*

**0.

8464*

**0.

942

9***

2.44

63*

**2.

287

6***

2.4

144***

2.3

084***

2.4

320***

2.4

389***

(0.1

00)

(0.1

07)

(0.0

99)

(0.1

08)

(0.1

00)

(0.1

08)

(0.0

90)

(0.0

96)

(0.0

90)

(0.0

98)

(0.0

91)

(0.0

97)

wit

4.763

4**

*4.

9283*

**4.

468

4***

3.1

235*

**2.

7505*

**2.

601

3***

2.82

04*

**3.

029

7***

2.5

897***

1.1

458**

0.7

415*

0.7

313

(0.3

94)

(0.3

97)

(0.3

88)

(0.4

89)

(0.4

79)

(0.4

83)

(0.3

58)

(0.3

61)

(0.3

54)

(0.4

57)

(0.4

49)

(0.4

53)

Gro

ssfa

mily

inco

me

0.01

00**

*0.

0091

***

-0.0

077*

**0.0

128*

**0.0

121***

-0.0

005

(0.0

03)

(0.0

03)

(0.0

03)

(0.0

02)

(0.0

02)

(0.0

02)

Moth

er’s

years

ofsc

hool

ing

3.28

19**

*3.2

697

***

3.37

27**

*2.7

572***

2.7

532**

*2.7

605***

(0.3

00)

(0.3

01)

(0.3

03)

(0.2

69)

(0.2

72)

(0.2

75)

Fath

er’s

years

ofsc

hooling

2.00

68**

*1.9

680

***

2.10

27**

*1.4

507***

1.3

863**

*1.3

958***

(0.2

57)

(0.2

61)

(0.2

68)

(0.2

39)

(0.2

43)

(0.2

47)

Com

ple

tion

of

EIP

(%)

0.048

7***

0.046

6***

0.04

67*

**0.0

422***

0.0

414***

0.0

414**

*(0

.007)

(0.0

07)

(0.0

07)

(0.0

06)

(0.0

06)

(0.0

06)

Const

ant

241.

634

9**

*23

8.89

51*

**18

4.13

78**

*238

.785

6***

184

.377

3**

*18

3.897

6***

248.

008

7***

244.5

302

***

202.2

803***

244.3

855***

202.7

510***

202.7

170***

(0.2

71)

(0.7

43)

(3.1

25)

(0.7

53)

(3.1

58)

(3.1

66)

(0.2

42)

(0.6

75)

(2.9

42)

(0.6

86)

(2.9

80)

(2.9

90)

Obse

rvati

ons

18,2

03

18,2

0318

,203

17,

651

17,6

5117

,651

18,2

0318,

203

18,2

03

17,6

51

17,6

5117,6

51

Sou

rce:

SIM

CE

2006-2

011

an

dM

inis

try

of

Ed

uca

tion

(Ch

ile)

.N

ote

s:(a

)G

ross

fam

ily

inco

me

corr

esp

on

ds

toth

eaver

age

fam

ily

inco

me

that

stu

den

tsre

port

at

the

mom

ent

of

takin

gS

IMC

E.

Itis

expre

ssed

inth

ou

san

ds

of

Ch

ilea

np

esos

(CL

P)

for

each

on

eof

the

yea

rsco

nsi

der

edin

the

sam

ple

(1U

S$

=520

CL

P).

(b)

As

we

des

crib

ein

the

text,

we

incl

ud

eth

ep

rop

ort

ion

of

the

Ed

uca

tion

al

Imp

rovem

ent

Pla

n(E

IP)

acc

om

plish

edby

sch

ools

in2010

inm

ath

an

dla

ngu

age.

In2010,

sch

ools

un

der

the

PS

Sw

ere

vis

ited

by

Min

istr

yof

Ed

uca

tion

per

son

nel

tod

eter

min

eth

ep

erce

nta

ge

of

acc

om

plish

men

tof

the

mea

sure

sd

efin

edin

thei

rE

IP.

Sta

nd

ard

erro

rsin

pare

nth

eses

.***

p<

0.0

1.

**

p<

0.0

5.

*p<

0.1

.

31

Tab

le8:

Cu

mu

lati

veE

ffec

tsof

the

PS

Son

Sch

ool

Aca

dem

icA

chie

vem

ent

(2):

Pu

bli

can

dV

ouch

erP

riva

teS

chool

sV

aria

ble

SIM

CE

4th-G

rad

eM

ath

SIM

CE

4th

-Gra

de

Langu

age

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

λt

0.193

4*

-0.0

010

0.1

991

*0.0

505

0.240

1**

0.2

976*

*2.

2269*

**2.

024

4***

2.2

284***

2.0

802***

2.2

748***

2.2

682***

(0.1

08)

(0.1

15)

(0.1

06)

(0.1

16)

(0.1

07)

(0.1

17)

(0.0

97)

(0.1

04)

(0.0

97)

(0.1

06)

(0.0

98)

(0.1

06)

λt·w

it1.

496

1**

*1.

5607*

**1.

405

1***

1.3

539*

**1.

2044*

**1.

181

2***

0.72

15*

**0.

788

8***

0.6

488***

0.4

705***

0.3

266***

0.3

293***

(0.0

86)

(0.0

87)

(0.0

85)

(0.1

03)

(0.1

01)

(0.1

03)

(0.0

78)

(0.0

79)

(0.0

78)

(0.0

96)

(0.0

94)

(0.0

96)

Gro

ssfa

mily

inco

me

0.01

36**

*0.

0126

***

-0.0

039

0.0

141*

**0.0

132***

0.0

005

(0.0

03)

(0.0

03)

(0.0

03)

(0.0

02)

(0.0

02)

(0.0

02)

Moth

er’s

years

ofsc

hool

ing

3.22

62**

*3.2

307

***

3.28

39**

*2.7

345***

2.7

407**

*2.7

345***

(0.2

99)

(0.3

01)

(0.3

03)

(0.2

69)

(0.2

72)

(0.2

75)

Fath

er’s

years

ofsc

hooling

1.93

33**

*1.8

991

***

1.96

88**

*1.4

273***

1.3

687**

*1.3

607***

(0.2

56)

(0.2

60)

(0.2

67)

(0.2

39)

(0.2

43)

(0.2

47)

Com

ple

tion

of

EIP

(%)

0.028

9***

0.028

5***

0.02

87*

**0.0

356***

0.0

364***

0.0

363**

*(0

.007)

(0.0

07)

(0.0

07)

(0.0

06)

(0.0

06)

(0.0

06)

Const

ant

242.

932

9**

*23

9.28

14*

**18

6.76

39**

*239

.110

6***

186

.699

1**

*18

6.418

7***

248.

473

4***

244.6

694

***

203.1

822***

244.4

969***

203.3

795***

203.4

121***

(0.2

78)

(0.7

44)

(3.1

14)

(0.7

55)

(3.1

51)

(3.1

63)

(0.2

49)

(0.6

73)

(2.9

43)

(0.6

84)

(2.9

84)

(2.9

98)

Obse

rvati

ons

18,2

03

18,2

0318

,203

17,

651

17,6

5117

,651

18,2

0318,

203

18,2

03

17,6

51

17,6

5117,6

51

Sou

rce:

SIM

CE

2006-2

011

an

dM

inis

try

of

Ed

uca

tion

(Ch

ile)

.N

ote

s:(a

)G

ross

fam

ily

inco

me

corr

esp

on

ds

toth

eaver

age

fam

ily

inco

me

that

stu

den

tsre

port

at

the

mom

ent

of

takin

gS

IMC

E.

Itis

expre

ssed

inth

ou

san

ds

of

Ch

ilea

np

esos

(CL

P)

for

each

on

eof

the

yea

rsco

nsi

der

edin

the

sam

ple

(1U

S$

=520

CL

P).

(b)

As

we

des

crib

ein

the

text,

we

incl

ud

eth

ep

rop

ort

ion

of

the

Ed

uca

tion

al

Imp

rovem

ent

Pla

n(E

IP)

acc

om

plish

edby

sch

ools

in2010

inm

ath

an

dla

ngu

age.

In2010,

sch

ools

un

der

the

PS

Sw

ere

vis

ited

by

Min

istr

yof

Ed

uca

tion

per

son

nel

tod

eter

min

eth

ep

erce

nta

ge

of

acc

om

plish

men

tof

the

mea

sure

sd

efin

edin

thei

rE

IP.

Sta

nd

ard

erro

rsin

pare

nth

eses

.***

p<

0.0

1.

**

p<

0.0

5.

*p<

0.1

.

32

Tab

le9:

Eff

ects

ofth

eP

SS

onS

chool

Aca

dem

icA

chie

vem

ent

(1):

Vou

cher

Pri

vate

Sch

ools

Var

iab

leSIM

CE

4th

-Gra

de

Mat

hS

IMC

E4th

-Gra

de

Language

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

λt

0.516

2**

*0.

3657*

**0.

494

5***

0.4

096*

**0.

5304*

**0.

581

6***

2.09

32*

**1.

948

3***

2.0

717***

1.9

823***

2.0

980***

2.1

027***

(0.1

15)

(0.1

24)

(0.1

14)

(0.1

26)

(0.1

15)

(0.1

28)

(0.1

03)

(0.1

12)

(0.1

03)

(0.1

14)

(0.1

04)

(0.1

15)

wit

5.515

4**

*5.

7414*

**5.

424

2***

4.2

095*

**3.

9893*

**3.

906

2***

3.69

35*

**3.

911

2***

3.6

261***

2.8

009***

2.5

601***

2.5

529***

(0.5

50)

(0.5

54)

(0.5

41)

(0.6

70)

(0.6

54)

(0.6

62)

(0.4

92)

(0.4

98)

(0.4

86)

(0.6

41)

(0.6

26)

(0.6

35)

Gro

ssfa

mily

inco

me

0.00

99**

*0.

0088

***

-0.0

033

0.0

095*

**0.0

087***

-0.0

003

(0.0

03)

(0.0

03)

(0.0

03)

(0.0

03)

(0.0

03)

(0.0

03)

Moth

er’s

years

ofsc

hool

ing

2.95

79**

*2.8

152

***

2.88

16**

*2.5

048***

2.4

612**

*2.4

672***

(0.4

06)

(0.4

11)

(0.4

16)

(0.3

70)

(0.3

77)

(0.3

82)

Fath

er’s

years

ofsc

hooling

1.63

74**

*1.6

506

***

1.74

05**

*1.0

854***

1.0

122**

*1.0

204***

(0.3

54)

(0.3

62)

(0.3

76)

(0.3

37)

(0.3

45)

(0.3

54)

Com

ple

tion

of

EIP

(%)

0.056

7***

0.053

9***

0.05

39*

**0.0

303***

0.0

294***

0.0

294**

*(0

.012)

(0.0

11)

(0.0

11)

(0.0

10)

(0.0

10)

(0.0

10)

Const

ant

254.

508

6**

*25

1.02

93*

**19

9.21

13**

*251

.199

2***

200

.491

4**

*19

9.778

0***

259.

375

4***

256.0

246

***

216.1

968***

256.1

487***

217.3

962***

217.3

312***

(0.3

42)

(1.0

35)

(4.9

13)

(1.0

58)

(4.9

87)

(5.0

74)

(0.3

06)

(0.9

31)

(4.6

15)

(0.9

57)

(4.7

15)

(4.7

80)

Obse

rvati

ons

9,3

12

9,3

12

9,312

8,91

08,

910

8,91

09,

312

9,31

29,3

12

8,9

10

8,910

8,9

10

Sou

rce:

SIM

CE

2006-2

011

an

dM

inis

try

of

Ed

uca

tion

(Ch

ile)

.N

ote

s:(a

)G

ross

fam

ily

inco

me

corr

esp

on

ds

toth

eaver

age

fam

ily

inco

me

that

stu

den

tsre

port

at

the

mom

ent

of

takin

gS

IMC

E.

Itis

expre

ssed

inth

ou

san

ds

of

Ch

ilea

np

esos

(CL

P)

for

each

on

eof

the

yea

rsco

nsi

der

edin

the

sam

ple

(1U

S$

=520

CL

P).

(b)

As

we

des

crib

ein

the

text,

we

incl

ud

eth

ep

rop

ort

ion

of

the

Ed

uca

tion

al

Imp

rovem

ent

Pla

n(E

IP)

acc

om

plish

edby

sch

ools

in2010

inm

ath

an

dla

ngu

age.

In2010,

sch

ools

un

der

the

PS

Sw

ere

vis

ited

by

Min

istr

yof

Ed

uca

tion

per

son

nel

tod

eter

min

eth

ep

erce

nta

ge

of

acc

om

plish

men

tof

the

mea

sure

sd

efin

edin

thei

rE

IP.

Sta

nd

ard

erro

rsin

pare

nth

eses

.***

p<

0.0

1.

**

p<

0.0

5.

*p<

0.1

.

33

Tab

le10:

Cu

mu

lati

veE

ffec

tsof

the

PS

Son

Sch

ool

Aca

dem

icA

chie

vem

ent

(2):

Vou

cher

Pri

vate

Sch

ools

Var

iab

leSIM

CE

4th

-Gra

de

Mat

hS

IMC

E4th

-Gra

de

Language

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

λt

0.271

7**

0.08

420.

2655

**0.

1426

0.3

107

***

0.3

359

**2.

0113*

**1.

845

3***

2.0

019***

1.8

849***

2.0

300***

2.0

242***

(0.1

20)

(0.1

30)

(0.1

19)

(0.1

32)

(0.1

20)

(0.1

35)

(0.1

08)

(0.1

18)

(0.1

07)

(0.1

20)

(0.1

08)

(0.1

21)

λt·w

it1.

329

0**

*1.

3930*

**1.

292

8***

1.1

681*

**1.

0820*

**1.

072

3***

0.78

43*

**0.

841

0***

0.7

566***

0.6

507***

0.5

687***

0.5

709***

(0.1

16)

(0.1

17)

(0.1

14)

(0.1

38)

(0.1

35)

(0.1

37)

(0.1

03)

(0.1

05)

(0.1

02)

(0.1

30)

(0.1

26)

(0.1

29)

Gro

ssfa

mily

inco

me

0.01

15**

*0.

0104

***

-0.0

015

0.0

102*

**0.0

093***

0.0

004

(0.0

03)

(0.0

03)

(0.0

03)

(0.0

03)

(0.0

03)

(0.0

03)

Moth

er’s

years

ofsc

hool

ing

2.91

69**

*2.7

926

***

2.82

32**

*2.4

804***

2.4

438**

*2.4

367***

(0.4

06)

(0.4

11)

(0.4

17)

(0.3

70)

(0.3

77)

(0.3

83)

Fath

er’s

years

ofsc

hooling

1.60

15**

*1.6

188

***

1.66

02**

*1.0

701***

1.0

014**

*0.9

918***

(0.3

53)

(0.3

60)

(0.3

75)

(0.3

37)

(0.3

44)

(0.3

54)

Com

ple

tion

of

EIP

(%)

0.046

3***

0.044

8***

0.04

48*

**0.0

283***

0.0

284***

0.0

284**

*(0

.011)

(0.0

11)

(0.0

11)

(0.0

10)

(0.0

09)

(0.0

09)

Const

ant

255.

052

2**

*25

1.02

35*

**20

0.65

45**

*251

.156

4***

201

.618

3**

*20

1.282

1***

259.

599

3***

256.0

339

***

216.8

767***

256.1

452***

217.9

100***

217.9

877***

(0.3

47)

(1.0

37)

(4.9

01)

(1.0

61)

(4.9

79)

(5.0

71)

(0.3

10)

(0.9

31)

(4.6

16)

(0.9

56)

(4.7

17)

(4.7

86)

Obse

rvati

ons

9,3

12

9,3

12

9,312

8,91

08,

910

8,91

09,

312

9,31

29,3

12

8,9

10

8,910

8,9

10

Sou

rce:

SIM

CE

2006-2

011

an

dM

inis

try

of

Ed

uca

tion

(Ch

ile)

.N

ote

s:(a

)G

ross

fam

ily

inco

me

corr

esp

on

ds

toth

eaver

age

fam

ily

inco

me

that

stu

den

tsre

port

at

the

mom

ent

of

takin

gS

IMC

E.

Itis

expre

ssed

inth

ou

san

ds

of

Ch

ilea

np

esos

(CL

P)

for

each

on

eof

the

yea

rsco

nsi

der

edin

the

sam

ple

(1U

S$

=520

CL

P).

(b)

As

we

des

crib

ein

the

text,

we

incl

ud

eth

ep

rop

ort

ion

of

the

Ed

uca

tion

al

Imp

rovem

ent

Pla

n(E

IP)

acc

om

plish

edby

sch

ools

in2010

inm

ath

an

dla

ngu

age.

In2010,

sch

ools

un

der

the

PS

Sw

ere

vis

ited

by

Min

istr

yof

Ed

uca

tion

per

son

nel

tod

eter

min

eth

ep

erce

nta

ge

of

acc

om

plish

men

tof

the

mea

sure

sd

efin

edin

thei

rE

IP.

Sta

nd

ard

erro

rsin

pare

nth

eses

.***

p<

0.0

1.

**

p<

0.0

5.

*p<

0.1

.

34

Tab

le11

:E

ffec

tsof

the

PS

Son

Sch

ool

Aca

dem

icA

chie

vem

ent

(3):

Vou

cher

Pri

vate

Sch

ools

Var

iab

leSIM

CE

4th

-Gra

de

Mat

hS

IMC

E4th

-Gra

de

Language

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

λt

0.516

2**

*0.

3622*

**0.

495

6***

0.4

073*

**0.

5311*

**0.

573

6***

2.09

32*

**1.

944

8***

2.0

728***

1.9

802***

2.0

988***

2.0

943***

(0.1

15)

(0.1

24)

(0.1

14)

(0.1

26)

(0.1

15)

(0.1

28)

(0.1

03)

(0.1

12)

(0.1

03)

(0.1

14)

(0.1

04)

(0.1

15)

wit

9.395

4**

*9.

6528*

**8.

765

7***

8.0

127*

**7.

1983*

**7.

107

1***

7.49

31*

**7.

741

1***

7.0

049***

6.7

826***

6.0

597***

6.0

690***

(0.9

18)

(0.9

21)

(0.9

05)

(1.0

41)

(1.0

23)

(1.0

34)

(0.7

86)

(0.7

93)

(0.7

82)

(0.9

36)

(0.9

21)

(0.9

34)

Share

dfinan

cing·w

it-0

.2965

***

-0.2

985**

*-0

.2551

***

-0.2

808*

**-0

.237

2**

*-0

.235

5***

-0.2

903*

**-0

.292

2***

-0.2

579***

-0.2

894***

-0.2

545***

-0.2

546***

(0.0

55)

(0.0

55)

(0.0

54)

(0.0

60)

(0.0

59)

(0.0

59)

(0.0

44)

(0.0

44)

(0.0

43)

(0.0

49)

(0.0

48)

(0.0

48)

Gro

ssfa

mily

inco

me

0.01

01**

*0.

0089

***

-0.0

028

0.0

097*

**0.0

088***

0.0

003

(0.0

03)

(0.0

03)

(0.0

03)

(0.0

03)

(0.0

03)

(0.0

03)

Moth

er’s

years

ofsc

hool

ing

2.84

06**

*2.7

149

***

2.77

07**

*2.3

861***

2.3

487**

*2.3

428***

(0.4

04)

(0.4

09)

(0.4

14)

(0.3

68)

(0.3

75)

(0.3

81)

Fath

er’s

years

ofsc

hooling

1.59

14**

*1.6

088

***

1.68

37**

*1.0

389***

0.9

693**

*0.9

614***

(0.3

53)

(0.3

61)

(0.3

75)

(0.3

36)

(0.3

43)

(0.3

52)

Com

ple

tion

of

EIP

(%)

0.050

8***

0.049

0***

0.04

91*

**0.0

241*

*0.0

239**

0.0

239**

(0.0

12)

(0.0

11)

(0.0

11)

(0.0

10)

(0.0

10)

(0.0

10)

Const

ant

254.

508

6**

*25

0.94

77*

**20

1.17

57**

*251

.145

6***

202

.203

4**

*20

1.599

2***

259.

375

4***

255.9

447

***

218.1

831***

256.1

014***

219.2

678***

219.3

318***

(0.3

40)

(1.0

37)

(4.8

96)

(1.0

61)

(4.9

77)

(5.0

68)

(0.3

04)

(0.9

30)

(4.6

09)

(0.9

56)

(4.7

12)

(4.7

79)

Obse

rvati

ons

9,3

12

9,3

12

9,312

8,91

08,

910

8,91

09,

312

9,31

29,3

12

8,9

10

8,910

8,9

10

Sou

rce:

SIM

CE

2006-2

011

an

dM

inis

try

of

Ed

uca

tion

(Ch

ile)

.N

ote

s:(a

)G

ross

fam

ily

inco

me

corr

esp

on

ds

toth

eaver

age

fam

ily

inco

me

that

stu

den

tsre

port

at

the

mom

ent

of

takin

gS

IMC

E.

Itis

expre

ssed

inth

ou

san

ds

of

Ch

ilea

np

esos

(CL

P)

for

each

on

eof

the

yea

rsco

nsi

der

edin

the

sam

ple

(1U

S$

=520

CL

P).

(b)

As

we

des

crib

ein

the

text,

we

incl

ud

eth

ep

rop

ort

ion

of

the

Ed

uca

tion

al

Imp

rovem

ent

Pla

n(E

IP)

acc

om

plish

edby

sch

ools

in2010

inm

ath

an

dla

ngu

age.

In2010,

sch

ools

un

der

the

PS

Sw

ere

vis

ited

by

Min

istr

yof

Ed

uca

tion

per

son

nel

tod

eter

min

eth

ep

erce

nta

ge

of

acc

om

plish

men

tof

the

mea

sure

sd

efin

edin

thei

rE

IP.

(c)

Sh

are

dfi

nan

cin

gco

rres

pon

ds

toth

ein

tera

ctio

nb

etw

een

the

paym

ent

mad

eby

pare

nts

(in

thou

san

ds

of

Ch

ilea

np

esos)

an

dth

etr

eatm

ent

vari

ab

le(w

it).

Sta

nd

ard

erro

rsin

pare

nth

eses

.***

p<

0.0

1.

**

p<

0.0

5.

*p<

0.1

.

35

Table 12: Shared Financing Charged by PSS and Non-PSS Schools (US$)

School Category Mean SD

PSS Schools 26.2 41.1Non-PSS Schools 66.2 17.1

Source: SIMCE 2007.

36