Privatization Reform and Inequality of Educational Opportunity - The Case of Chile

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Privatization Reform and Inequality of Educational Opportunity: The Case of Chile Florencia Torche Queens College and Columbia University Chile has experienced considerable educational expansion over the past few decades, as well as a privatization reform in 1981 that introduced full parental choice through a voucher system, in the context of a market-oriented transformation of the country. Using a cohort analysis of the 2001 Chilean Mobility Survey, this article examines trends in educational stratification in Chile over the past 50 years, with a focus on the changes that followed the privatization reform. The analysis shows that, in line with international findings, there is “persistent inequality” of educational opportunity across cohorts in Chile. Persistent inequality is not total, however. There is a small but significant increase in inequality in the transition to secondary education, which is cotemporaneous with the market-oriented transformation. Furthermore, when school sector—a form of “qualitative inequality” expressed in the distinction among public, private-voucher, and private-paid schools—is considered, the analysis suggests an increase in the advantages that are associated with private-voucher schools after the privatization reform, as well as in the benefits of attending private-paid schools during and after the reform. The article concludes by discussing the relationship among economic context, privatization reform, and educational inequality.

I

nequality of educational opportunity (IEO)—the effect of parental resources and conditions when growing up on individual educational attainment—is a key component of the intergenerational reproduction of inequality in contemporary societies (Shavit and Muller 1998; Treiman and Yip 1989). To date, trends in IEO have been studied in about two dozen countries, including almost all Western and Eastern European nations, the United States, Japan, Hong Kong, Malaysia, the Philippines, Israel, Korea, and Brazil (Garnier and Raffalovic 1984; Mare 1980, 1981; Park 2004; Pong 1993; Post 1994; Raftery and Hout 1993; Shavit and Blossfeld 1993; Shavit and Westerbeek 1998; Silva 2004; Simkus and Andorka 1982; Szelenyi

1998). Most of these national studies have shown that the effect of social background on educational attainment has remained constant over the past few decades, in spite of massive educational expansion and a varied set of policy interventions to reduce inequality. This finding has been summarized as “persistent inequality.”

Researchers have used four theoretical approaches to explain trends in educational stratification: the industrialization theory, the reproduction theories, the “maximally maintained inequality” (MMI) hypothesis, and the “effectively maintained inequality” (EMI) hypothesis. These theoretical approaches have contributed immensely to the exploration of processes that drive temporal

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Privatization Reform in Chile change in educational stratification. However, with the exception of EMI, they share a relative disregard for the specific institutional arrangements and mechanisms—organization of educational systems and policy interventions—that lead to their predicted outcomes.

The finding of persistent inequality cutting homogeneously across countries as different as France and Israel, England and Korea, and the United States and Poland seems to support the disregard for nationally specific educational policies and reforms. After all, if there is no variation in the outcome, why would one need to introduce institutional arrangements and policies as explanatory factors?

Before one gives up on the exploration of the institutional and economic context of educational stratification, however, it is important to consider two factors. First, persistent inequality is not universal. There are at least three countries—Sweden, the Netherlands, and Germany—in which educational inequality has been found to have declined over time (De Graaf and Ganzeboom 1993; Erikson and Jonsson 1996; Jonsson, Mills, and Muller 1996), and a recent analysis suggested that it might have also declined in Britain, France, and Poland (Breen et al. 2005). There is also at least one case, Russia, in which inequality has been found to have increased at higher educational levels (Gerber 2000, 2003; Gerber and Hout 1995). Second, the pool of countries in which trends in educational stratification have been studied is still small and confined almost entirely to the industrialized world. The question of processes of educational stratification in countries with different levels of development and institutional arrangements is still open.

This article presents an analysis of trends in IEO in Chile and attempts to accomplish two objectives. The first is to introduce Chile to the comparative template of research on educational stratification, thereby adding variation to an enterprise that is focused mostly on industrialized nations. Chile provides an interesting case because it is both a late-industrializing country and one that experienced a radical privatization reform of its educational system in the early 1980s as part of a market-oriented transformation by the authoritarian

317 regime of General Augusto Pinochet. Perhaps the most salient component of the privatization reform was the introduction of full parental choice through a nationwide voucher system at the primary and secondary educational levels. Thus, the second objective of this article is to explore the educational stratification processes that followed the privatization reform of the early 1980s. To formulate this question, I used the notion of qualitative inequality (Ayalon and Shavit 2004; Breen and Jonsson 2000; Lucas 2001). While traditional studies of educational stratification have focused on quantitative inequality (“how much” education different socioeconomic groups obtain), qualitative inequality highlights the fact that educational systems are not one dimensional, but that at any given educational level, there may exist different subsectors (e.g., tracks and school sectors) that provide unequal opportunities for further attainment. In this article, I explore different kinds of schools that were formed after the privatization reform in Chile—public, private-voucher, and private-paid schools—as a potential vehicle of educational stratification.

This article is organized in six sections.

Following this introduction, the second section reviews the hypotheses about mechanisms that drive educational stratification.

The third section describes the Chilean educational system and its change over time, with a focus on the privatization reform of the 1980s. The fourth section introduces the data, variables, and methods. The fifth section presents the analysis, and the sixth section presents the discussion and conclusions.

THEORETICAL APPROACHES Analysts have used four theoretical approaches to explain trends in educational inequality: the theory of industrialization; reproduction theories; MMI; and, more recently, EMI. Flourishing in the self-confident post–World War II period, the theory of industrialization claimed that educational inequality would decline as countries industrialized (Parsons 1970; Treiman 1970). As a result of economic, institutional, and cultural modernization, larger segments of the population would gain

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318 access to education, and their educational attainment would be increasingly less dependent on background and more dependent on merit (Bell 1972). Empirical findings in the fields of social and educational mobility have cast doubts on these optimistic predictions and have highlighted the intergenerational resilience of inequality. In an attempt to explain the role of the educational system in the persistence of inequality, reproduction theories claim that educational systems are organized so as to reproduce the unequal social structure. Thus, even if education expands, stratification will persist because those who are in advantaged positions will successfully implement strategies to maintain privilege. The strategies highlighted by reproduction theories vary, but they tend to emphasize the cultural dimension of inequality in the form of socialization by the school into hierarchical social roles that are functional to capitalism (Bowles and Gintis 1976), the use of language to learn and express class differences (Bernstein 1971), or the rewarding of the cultural capital that upper-class students build naturally at home and less-privileged students lack (Bourdieu and Passeron 1973).

Even if industrialization and reproduction theories are opposed in their predictions, they share a claim of universal validity and relative little attention to the particularities of national institutional contexts. More recently, an alternative approach has emerged in the context of empirical research. MMI (Raftery and Hout 1993) was formulated as an explicit attempt to explain a question that was posed by findings in several industrialized countries: Why is it that educational expansion and egalitarian reforms do not reduce educational inequality among socioeconomic strata? MMI asserts that an expansion in the educational system that does not specifically focus on the less-advantaged classes provides new opportunities for all children. On average, children of advantaged classes have more economic and cultural resources, perform better in school, have higher aspirations, and are more acquainted with the educational system (Entwisle, Alexander, and Olson 1997; Lareau 1987, 2000, 2003; Sewell and Hauser 1975); in short, they are “better prepared than are others to take advantage of new educational opportunities” (Ayalon and Shavit

Torche 2004:106). Therefore, only when the advantaged classes have reached saturation at a particular level of education—transition rates at or close to 100 percent—will other sectors of the society benefit from educational expansion.

Only in these cases will educational expansion contribute to the reduction of socioeconomic inequality of educational opportunity (Raftery and Hout 1993).

According to MMI, a decline in inequality can be reversed. If, for example, an educational reform pushes expansion at the secondary level, but this expansion is not coupled with a growth of similar magnitude at the college level, the increasing number of high school graduates face a bottleneck, leading to competition for scarce college places.

The advantaged classes have an upper hand in that competition, which could lead to growing inequality at the college level.

Evidence supporting this kind of process was found for the Russian case during the lateSoviet and post-Soviet periods (Gerber 2003; Gerber and Hout 1995). As Hout (2003) indicated, MMI has found empirical support in a variety of settings, but it has been disconfirmed in others, thus transforming it from an empirical generalization into a useful concept to guide research.

EMI is the most recent approach to educational stratification (see Lucas 2001 for the original formulation, see also Ayalon and Shavit 2004; Breen and Jonsson 2000). EMI criticizes MMI for ignoring the simple fact that educational systems are not one dimensional, but include several branches at each particular level—for instance, academic and vocational education or college preparatory and non-college preparatory tracks. EMI argues that when saturation is reached at a particular level and inequality in attainment declines, “quantitative inequality” may be replaced by “qualitative inequality,” that is, the advantaged classes will be able to obtain educational credentials that provide them with enhanced opportunities for further attainment. By focusing on tracking, EMI explicitly considers the institutional organization of different educational systems, thereby emphasizing the relevance of including the institutional dimension in the study of trends in educational stratification.

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Qualitative Inequality and Privatization EMI highlights an increasingly important dimension of inequality. However, it presents some limitations when it is used for international comparisons. First, EMI treats qualitative inequality as an outcome whose determinants are to be analyzed. But qualitative inequality can also be thought of as a predictor, a variable that adds to or mediates the effect of social background and contributes to inequality of attainment. Second, to date, EMI has focused on tracking as an instance of qualitative inequality—for instance, the distinction among no-math, non-college preparatory, and college preparatory tracks in U.S. high schools (Lucas 2001) or the distinction between vocational and academic tracks in Sweden (Breen and Jonsson 2000).

However, tracking is not the only form of qualitative differentiation or even the most important in some educational systems. As the Chilean case highlights, the distinction among school sectors—public and different kinds of private schools—may be another important instance of qualitative or horizontal educational differentiation. Similar to the organization of tracking, the kinds of schools that are available to students at different educational levels vary widely across countries. In some nations, such as most of Western and Eastern Europe, Canada, and Israel, the distinction across school sector is of little relevance because the large majority of primary and secondary schools are public. This is not always the case, however. Because of historical reasons, other advanced industrial nations have a large proportion of private schools. For example, in Belgium and the Netherlands, the enrollment in government-sponsored religious private schools is more than 60 percent.

In nations like Australia and Spain, government-sponsored, mostly secular, private schools serve about one third of pupils, and in the United States, independent private schools represent about 11 percent of the total enrollment (OECD 2001).

The impact of attending a particular kind of school on educational attainment and achievement is not a new topic in the study of educational inequality. The relative efficien-

319 cy of private, particularly Catholic, schools has been a widely researched issue in the United States over the past few decades (see, e.g., Alexander and Pallas 1985; Coleman, Hoffer, and Kilgore 1982a, 1982b; Hoffer, Greeley, and Coleman 1985; Jencks 1985).

With the recent push for educational privatization, the role of school sector in educational achievement and attainment has regained relevance and has included new modalities of “school choice,” such as magnet, charter, and voucher schools1 (Altonji, Elder, and Taber 2002; Bryk, Lee, and Holland 1997; Gamoran 1996; Greene, Peterson, and Du 1998; Howell and Peterson 2002; Krueger and Zhu 2002; McEwan 2000; Rouse 1998; Witte 2000). In the past few years, most empirical research has focused on voucher programs, partly because of ideological reasons and partly because of the design of some of these programs, which randomly assigns vouchers to families. This design provides a convenient experimental framework that allows for the control of omitted family and individual characteristics (Howell and Peterson 2002; Krueger and Zhou 2002).

Research on vouchers and other forms of school choice has focused almost entirely on individual outcomes: How does Student A, who moved to a voucher school, fare in comparison with an “equivalent” Student B who remained in the public system? There has been little research on macrolevel consequences of school-choice programs on the inequality of the educational system as a whole.2 The lack of attention to aggregate outcomes of “school-choice” initiatives is understandable. Owing to the small scale of these initiatives, it is simply impossible to evaluate their consequences at a systemic level. As McEwan (2000, 2004) argued, we have to be extremely careful when extrapolating findings about short-term changes from small-scale voucher (or other schoolchoice) programs to long-term outcomes of large-scale programs.

An additional difficulty in studying the effect of changes in educational policy on inequality at the aggregate level is the fact that policy interventions are usually endogenous, in the sense that they are a response to popular demands for more or better educa-

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320 tion. For instance, a policy that is oriented to expanding secondary education is as much a cause of expansion as it is a consequence of a popular demand for it. Therefore attributing any observed change in educational stratification to the policy change, as if it was an exogenous force, disregards the fact that the very processes that lead to increased demand affect the outcomes that are being analyzed.3 In sum, the important question about the consequences of large-scale school-choice programs on educational stratification is still open, and its study faces important challenges. The Chilean case provides an opportunity to explore this issue. First, the Chilean voucher system is nationwide, and it has been in place almost unaltered for more than two decades. This fact permits researchers to evaluate the potential impacts of a national-level reform. Second, the characteristics of the Chilean privatization render it closer to an “exogenous shock” than other instances of policy intervention. On the one hand, the Chilean educational reform was part of a comprehensive neoliberal transformation of the welfare system that was based on the idea that the market should replace the government as the main allocation mechanism.

The neoliberal transformation included not only the privatization of education, but the restructuring of the labor market and the creation of quasi-markets in housing, health, and pensions. On the other hand, the educational reform took place in the context of an authoritarian regime that was unbound by democratic rule and was backed up by repression and violence. The transformation was carried out by a group of ideologically homogeneous and highly motivated young technocrats, who were known as the “Chicago Boys” because they had obtained their graduate education in the University of Chicago’s Department of Economics (Martinez and Diaz 1999). This group was completely isolated from the social demands of citizens, including the until-then-powerful Chilean Teacher’s Association (Gauri 1998).

As a result of these factors, the privatization of education took place in record time. As Gauri (1998:22, quoting Infante and Schiefelbein 1992) put it, “The most profound transformation ever experienced in Chilean public

Torche education was an idea conceived, designed and implemented in 18 months.” Even if no instance of a change in social policy is ever fully exogenous, the Chilean educational reform is as close as one may get to an external shock. Therefore, the Chilean case provides a useful setting not only for the analysis of trends in educational inequality in a developing country, but for the assessment of changes following the privatization reform, including the potential role of the voucher system.

The analysis of the Chilean case is not free of limitations, however. Because the data that are presented in this article came from a single cross-sectional study, I assess temporal change by using cohort analysis, as is standard in studies of educational stratification (Garnier and Raffalovic 1984; Gerber and Hout 1995; Mare 1980, 1981; Park 2004; Pong 1993; Raftery and Hout 1993; Shavit and Blossfeld 1993; Shavit and Westerbeek 1998; Silva 2004; Simkus and Andorka 1982; Smith and Cheung 1986; Szelenyi 1998).

Cohort analysis is useful for inferring trends in the effect of family background on school attainment, but it is less able to capture the specific causal effects of policy (Post 1994).

Therefore, it is important to state at the outset that this article does not claim to offer a definitive account of the mechanisms that led to the observed outcomes or conclusive causal interpretations of the effects of the voucher system on educational stratification.

The results of this study are valuable, however, in that they shed light on the institutional dimension of educational stratification and on the potential role of school choice in inequality.

THE CHILEAN EDUCATIONAL SYSTEM The Chilean educational system is comprised of eight years of compulsory primary education; four years of secondary schooling; and three types of tertiary education: colleges, professional institutes, and technical institutes. There is no academic-ability grouping at any level, and tracking begins at the sec-

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Privatization Reform in Chile ondary level, where students have to choose between vocational and academic high schools. Following a universal trend, Chile experienced significant educational expansion in the second half of the 20th century.

According to the Chilean census, from 1952 to 1992, the proportion of the population aged 25 and older with no education dropped from 23.5 percent to 5.8 percent, while the proportion with a secondary education grew from 19.1 percent to 29.6 percent and the proportion with a postsecondary education increased from 2.4 percent to 7.2 percent.

Expansion has unequally benefited different socioeconomic strata. Historical information on educational attainment by social background is scattered in Chile, with only three large studies available. A study conducted in 1955 in the city of Santiago found that the survival rate at the sixth grade was 80 for the upper class, 48 for the middle class, and only 28 for the lower class (Hamuy 1961:104). According to a study that was conducted in the mid-1960s (Soto 2000:55), access to tertiary education was extremely stratified, with only 3 percent of the college students coming from working-class families.

A pioneer longitudinal study conducted during the 1970s found pervasive inequality in educational attainment and achievement. For instance, the high school graduation rate of children who were starting the eighth grade in 1970 was 100 percent for children of university-educated parents, but 32 percent for children of parents with a secondary education, 12 percent for children of parents with a primary education, and only 3 percent for children of parents with no education (Schiefelbein and Farrell 1982:89).

Educational inequality is still substantial in Chile. Differences in attainment by income level are noticeable even at the primary completion level in Chilean society. Whereas 99.1 percent of children in the wealthiest income quintile completed the primary level in 2000, only 71.9 percent of children in the poorest quintile did so. Socioeconomic differences are wider at the secondary level, with 30 percent of the children in the poorest quintile completing secondary school, compared to 95 percent of the children in the wealthiest quin-

321 tile. Disparities magnify at the tertiary level, with only 3.1 of the poorest youngsters, but 48.2 percent of the wealthiest ones, completing tertiary education (Spilerman and Torche 2004:Table 8.3).

Institutional Change in the Chilean Educational System The Chilean government slowly expanded primary and secondary enrollment during the first half of the 20th century and started to provide free public education in the 1920s. In the mid-1960s, the expansion of the system was boosted by a set of policies that were oriented to increasing access to primary and secondary education, when a progressive administration (1964–70) established a mandate to guarantee universal access to primary and secondary schooling, regardless of social background (Aylwin et al. 1983; Cox and Lamaitre 1999). The government built 3,000 new public primary and secondary schools in poorer and rural areas, implemented a double-shift school day, rapidly trained new teachers to meet the increased educational demand, and extended compulsory primary education from six to eight years (Gazmuri 2000; Soto 2000). The state’s effort to expand education was successful: Enrollment reached more than 93 percent at the primary level in 1970, and secondary enrollment rose from 18 percent in the late 1950s to 49 percent in 1970.

In 1973, a military regime took power after a violent coup and retained it until 1990.

During these 17 years, the military government conducted a sweeping market-oriented transformation of the economy and social welfare system. A major reform of the Chilean educational system was launched in 1981 as part of this transformation (Cox and Lemaitre 1999; Graham 1998). Perhaps the most revolutionary component of the reform was the introduction of a universal educational voucher system, in which a subsidy was paid to public and private schools on the basis of students’ enrollment, and families were free to choose among schools. Private schools could receive the subsidy in exchange for not charging fees to students. It is important to note that the Chilean voucher system differs

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322 from the U.S. voucher experiments in that the government does not give a tuition certificate to the family but, rather, pays the subsidy directly to the school that the student chooses. Thus, the Chilean voucher model is known as a “funds follow the student” system (Mizala and Romaguera 2000; West 1996).

However, the basic principles that guide voucher systems, as paradigmatically defined

by Friedman (1962) and Coons and Sugarman (1978)—competition among schools and freedom of the family to choose among schools—are the bases of the Chilean system. Hence, analysts of the Chilean choice model have described it as a voucher system (see, e.g., Carnoy 1998; Carnoy and McEwan 2001; Contreras 2001; Hsieh and Urquiola 2003; McEwan 2000; McEwan and Carnoy 2000; Mizala and Romaguera 2000; Parry 1997).4 Another important component of the privatization reform was the decentralization of public schools (Cox and Lemaitre 1999).

Before the reform, the Ministry of Education centrally controlled public schools and was responsible for all aspects of their operation.

It hired and paid teachers, maintained facilities, and designed the curriculum. With the reform, schools were transferred to about 300 local (municipal) governments (Gauri 1998).

In addition, in the context of welfare state retrenchment, public spending on education dropped from 4.9 percent of the gross domestic product in 1982 to 2.5 percent in 1989, and the educational budget reallocated funds from the tertiary level to lower educational levels. Because of the budget reduction, insufficient resources to maintain expenditures plagued the new voucher system, and the value of the monthly subsidy per primary and secondary student dropped by 20 percent between 1982 and 1987 and did not regain its 1982 value until 1994 (Cox and Lemaitre 1999). This decline in the educational budget was especially consequential because the expansion had drawn in relatively poorer children, who were more dependent on the educational system’s inputs (Birdsall, Ross, and Sabot 1997).

The privatization reform rapidly created an educational market at the primary and secondary levels. To understand the depth of

Torche these changes, a before-and-after comparison is useful. Prior to the privatization reform, almost 80 percent of Chilean students attended public schools. Private-paid schools charged relatively high tuition and catered to high-income families. These private-paid schools did not opt to take the government voucher, which was low in comparison to their fees. The voucher system thus enabled a new private sector to enter the market as providers of publicly financed education: the so-called private-voucher schools.5 Although private schools that received governmental subsidies existed in Chile before the privatization reform, they received only about half the budget allocated to public schools, and the subsidies were usually delayed and eroded by inflation (Espinola 1992; Hsieh and Urquiola 2003). Therefore, they functioned as a form of charity, rather than a component of the educational market. Even if they will be called voucher schools as will the government-sponsored private schools that emerged after the privatization reform, the reader should keep in mind that they are a different institutional form.

Some of the voucher schools that emerged after the privatization reform were managed by religious and nonprofit organizations, but the majority of them were run by private agents that capitalized on education as a profitable business (Hsieh and Urquiola 2003). All these schools competed for students with public schools. Voucher schools prospered in urban, highly populated, areas, where they became an attractive alternative for middle-income families who were unable to afford the expensive private-paid schools.

Voucher schools were not profitable, however, in poor and rural areas (Mizala and Romaguera 2000; Parry 1996). As Figure 1 indicates, following the introduction of the voucher system, the voucher sector dramatically expanded from an enrollment of 15 percent in 1981 to 37 percent in 1999. This expansion was at the expense of public schools, whose market share dropped from 78 percent to 54 percent of the total enrollment during that period.

The implementation of a voucher system in Chile gave rise to creaming, since public school students of a higher socioeconomic

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Figure 1. Primary and Secondary Enrollment, by School Sector: Chile 1981–99 Source: Ministry of Education Chile (2000). status were more likely to migrate to the private-voucher system (Hsieh and Urquiola 2003). Figure 2 illustrates the point. It presents the distribution of the school sector attended by income decile in 1990, after most of the selective migration from the public to the private-voucher system had been completed. The figure indicates that after the privatization reform, public schools served mostly low-income groups, private-voucher schools concentrated on the lower-middle and middle-income sectors, and private-paid schools catered to the top income deciles.

Two characteristics of the Chilean voucher system favored creaming. First, voucher schools were established in more-affluent areas of the country, where business was more profitable. Second, they could select students according to their own criteria, in contrast to public schools, which were forced by law to accept all students who registered (Cox and Lemaitre 1999; Parry 1996).

The Chilean voucher system has been in place for more than 20 years. Since democratic rule was reestablished in 1990, the Center-

Figure 2. School Sector Attended, by Income Decile: Chile 1990 Source: Author's calculations based on 1990 CASEN survey (Chilean Ministry of Planning)

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324 Left coalition in power has focused on channeling additional resources to “vulnerable” schools, increasing real educational spending and teachers’ salaries, and financially rewarding schools with the highest test scores (Hsieh and Urquiola 2003; Mena and Bellei 2000).

Nevertheless, the core of the privatization system—the per-student voucher payment and the freedom to attend any school—has been left intact.

The privatization reform also affected the tertiary level. Since 1980, the government has promoted the expansion of the tertiary level via private, fully paid institutions (Brunner 1994; Nuñez 1997). Many of these institutions do not require scores on the national university admissions test (analogous to the SAT), which is obligatory in public universities. Expansion based on private institutions was considerable. Gross tertiary enrollment grew from 8 percent of the relevant age population in 1982 to 20 percent in 1997.

The number of institutions increased dramatically from 8 to almost 300, and the proportion of students who attended private tertiary education expanded from 30 percent to 55 percent (Ministry of Education Chile, several years). In addition, the privatization reform introduced fees to the previously free public universities, increasing the cost of tertiary education (in 2001, the average annual fee was approximately two thirds of the median Chilean household income). This combination of fees in public institutions, plus the expansion of the system based on expensive private institutions, may have reduced the chances of lower-class individuals gaining access to tertiary education.

Finally, it is important to consider the context in which the privatization reform took place. This context was marked by a deep economic crisis—the deepest in the country since the Great Depression—in the late 1970s and early 1980s. The recession further weakened the social safety net, producing a significant increase in poverty and inequality throughout most of the 1980s (Edwards and Cox-Edwards 1991; Larranaga 1999; Marcel and Solimano 1994; Meller 1991). This difficult economic context may have intensified educational inequality by forcing less-advantaged families to withdraw children from

Torche school to supplement the family income—a phenomenon that is well known in the developing world (Behrman, Birdsall, and Szekely 2000; Moser 1998). The effect of the economic crisis on the poor should be more noticeable at the secondary and tertiary educational levels because primary education was compulsory throughout the period under study. On the basis of international findings, the null hypothesis for this research is that there has been persistent inequality over time in spite of the expansion and reform of the Chilean educational system. However, if the economic crisis of the 1970s and 1980s depressed the educational opportunities of the lower classes, an increase in inequality may be found. Furthermore, if the privatization reform provided relative benefits to those who migrated to voucher schools, an increase in qualitative inequality associated with school sector may be found. These hypotheses are addressed in the analysis. Before I move to the analytical section, I present the data and methods.

DATA AND METHODS Data The data that were used in this analysis came from the 2001 Chilean Social Mobility Survey (CMS). The CMS is a nationally representative, multistage, stratified sample of 3,544 male heads of households aged 24–69. The main objective of the survey was to study occupational and educational mobility in contemporary Chilean society.6 The sampling strategy included three stages. First, 87 primary sampling units (PSUs), called comunas (counties), were selected. Blocks within PSUs were then selected, and finally households within blocks were chosen. Counties were stratified by size (less than 20,000, 20,000–100,000, 100,000–200,000, and more then 200,000 inhabitants) and geographic zone (North, Center, South). Optimal allocation was used to increase efficiency by including all PSUs in the large stratum (more than 200,000 inhabitants) in the sample. The fieldwork was conducted between April and

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Privatization Reform in Chile June 2001 and consisted of face-to-face interviews in the respondents’ households by trained personnel. The CMS includes information about family structure; detailed information about the occupation, education, income, and living standards of the head of the household and his partner; and retrospective information about the parents of both members of the couple.

The sample was weighted to bring the proportions of various strata into agreement with their representation in the population.

The weighting scheme used population projections that were based on the 1992 Chilean census. Excluding the households that were not eligible for the survey, the response rate was 63 percent.7 Nonresponse can yield bias if those who were unreachable or refused to participate are significantly different from those who were included in the sample.

Nonresponse bias was not corrected owing to the lack of information on the cases that were not obtained. To estimate the magnitude of the bias, I compared descriptive statistics between the CMS and the CASEN 2000 Survey. CASEN is a large survey (N = 252,595) conducted by the Chilean Ministry of Planning, similar to the Current Population Survey in the United States, and has a refusal rate that is lower than 10 percent. The comparison (available from me on request) suggests that the CMS slightly underrepresents agricultural workers and individuals in the upper end of the status hierarchy. Overall, however, there is no indication of major nonresponse bias. Missing cases represent 4 percent to 12 percent for different variables. I imputed the missing cases using a multiple imputation algorithm developed by King et al. (2001). This method uses an estimationmaximization procedure to create five complete data sets that are analyzed using standard statistical procedures, and parameters and standard errors are then combined. This technique assumes that data are missing at random. If the assumption is met, the procedure results in valid statistical inferences, which reflect the uncertainty that is due to missing values, and provide consistent and asymptotically efficient estimates (Allison 2002).

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Methods The main objective of this article is to evaluate the change in the effect of social background on educational attainment over time in Chile, with a focus on the changes that followed the privatization reform of the early 1980s. One way to do so is to use standard linear regression models. However, as Mare (1980, 1981) stated, the ordinary leastsquares (OLS) coefficients reflect not only the level of association, but the variance in educational attainment. Because the variance changes over time, given the expansion of the educational system, comparison of OLS coefficients over time will not reveal whether the structural parameters that regulate the process of educational attainment have changed (Lucas 2001:1645). An alternative is to formulate educational attainment as a set of subsequent transitions and to measure social background effects using logit models.

In contrast to OLS, logit coefficients reflect the net association between background and educational continuation decisions, uncontaminated by the variance of education.8 I formulated educational attainment as a series of transitions. The data enabled me to model four transitions: T1 (completion of primary education), T2 (entry to secondary education), T3 (graduation from secondary school), and T4 (entry to tertiary education).

To analyze change in social background effects over time, I distinguished seven birth cohorts: Cohort 1 (C1): 1936–44, Cohort 2 (C2): 1945–49, Cohort 3 (C3): 1950–54, Cohort 4 (C4): 1955–59, Cohort 5 (C5): 1960–64, Cohort 6 (C6): 1965–69, and Cohort 7 (C7): 1970–76. Reducing the analysis to the cohorts who were born between 1936 and 1976 yielded a weighted sample of 3,244. The selection of cohorts was driven by substantive interest in assessing the impact of the changes in the Chilean educational system. Figure 3 shows the educational trajectory of each cohort. The seven horizontal blocks identify the seven cohorts, and the diagonal block represents their educational careers.

Figure 3 should be read horizontally. For instance, starting from the bottom (the oldest cohort), the figure indicates that the oldest members of the oldest cohort were born in

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326 1936, entered primary education in 1942, and graduated from college (if he attended college) in 1957. Note that there is a change in the length of primary schooling in 1965, reflecting the extension of compulsory primary education from six to eight years.

As Figure 3 indicates, C1 attended school in the 1940s and 1950s. The educational experience of C2 and C3 took place mostly in the 1960s. C4 and C5 attended primary school largely in the 1960s and secondary school in the 1970s and thus experienced a new compulsory primary education of eight years and may have benefited from the educational expansion of the 1960s. C5 were the first to experience part of their educational trajectory in the 1980s, after the privatization reform was launched, but only partially, at the college level. Members of C6 were more affected by the privatization reform, fully in their college education and partly in their secondary schooling. Only C7, the youngest cohort, experienced their entire educational trajectory under the new, privatized system of education.

Following the protocol for the comparative analysis of educational stratification introduced by Shavit and Blossfeld (1993), I used father’s education and father’s occupational status when the respondent was aged 14 as measures of parental resources. In addition, I included mother’s education, shown to have a significant impact on educational attain-

Figure 3. Educational Career, by Birth Cohort

Torche ment in the developing world (Filmer 1999; Montgomery and Lloyd 1998; Thomas, Schoeni, and Strauss 1996). Father’s and mother’s education were measured as the number of years of schooling completed.

Father’s status was measured by the international socioeconomic index (ISEI) (Ganzeboom, de Graaf, and Treiman 1992).

Because one of the objectives of this study was to explore the qualitative dimension of inequality, I included school sector attended by the student at the primary and secondary levels (depending on the transition being analyzed) as an explanatory variable. I distinguished private-paid school, voucher school, and public school. Arguably, other variables that are not included in the models affect educational attainment—rural/urban residence, number of siblings, and family structure, among others. However, given that the main objective of this research was not to estimate the net effect of each individual background factor, but to evaluate the overall dynamics of intergenerational inequality in educational attainment, the use of these three standard indicators of socioeconomic background is granted. Table 1 presents the descriptive statistics of the variables that were included in the analysis by birth cohort.

As is shown in Table 1, there was a significant upgrade in educational attainment and social background across cohorts. This upgrade supports the use of logit models of

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Table 1. Descriptive Statistics: Means and Standard Deviations of Variables Across Cohorts, Chilean Men Born 1936–76 Variable

C1 C2 (1936–44) (1945–49)

C3 C4 (1950–54) (1955–59)

C5 C6 C7 (1960–64) (1965–69) (1970–76)

Years of schooling (SD)

7.6 (4.5)

8.9 (4.6)

10.1 (4.4)

10.8 (3.9)

11.0 (3.9)

11.1 (3.7)

11.6 (3.5)

Father’s education (SD)

5.4 (4.6)

5.9 (4.5)

5.9 (4.6)

6.8 (4.7)

7.1 (4.7)

7.0 (4.6)

7.5 (4.5)

Mother’s education (SD)

4.7 (4.1)

5.1 (4.1)

5.4 (4.1)

6.4 (4.4)

6.8 (4.3)

6.5 (4.3)

7.2 (4.4)

29.7 (12.0)

31.1 (12.8)

31.9 (13.5)

32.6 (13.6)

33.1 (12.3)

32.3 (12.6)

31.9 (13.9)

Public schoola Private-voucher school Private-paid school

76.1 11.2 12.7

77.2 10.7 12.1

77.4 10.8 11.8

77.1 11.3 11.6

71.6 18.2 10.2

65.4 23.6 11.0

63.6 24.2 11.9

Number of cases

584

464

453

481

474

430

358

Father’s occupational status (SD)

aSchool

sector percentages refer to the secondary education level.

educational transitions, insensitive to the variance of dependent and independent variables.

I present the analysis in two steps. In the first step, I report the effect of social background on the probability of making each transition using logit models. To measure changes over time, I enter cohorts as dummy variables and an exhaustive set of interactions between the set of cohort dummy variables and each background variable—father’s education, mother’s education, and father’s occupational status. These interactions identify cohort-specific effects of each social background variable and produce a set of (C-1) interactions for each social background variable (where C is the total number of cohorts).

To simplify matters, I follow Gerber’s (2000) strategy of estimating separate models for each transition, rather than pooling the data across transitions.

The model selection strategy follows the approach described by Gerber and Hout (1995). I choose the highest or lowest coefficient in each set of the (cohort * social background variable) interactions as the reference category to evaluate the statistical significance of the remaining coefficients. If none of

the dummy interaction terms capturing the change in the effect of social background across cohorts is significantly different from the reference category, there is no significant change in the effect being tested across cohorts. For example, the potential change in mother’s education on the probability of making T1 is evaluated by creating a set of seven interactions: ME*C1 through ME*C7.

Then, the interaction term with the lowest coefficient is excluded and used as the reference category. If, as is the case, none of the remaining six interaction terms is significantly different from zero, it indicates that the effect of mother’s education does not significantly change across cohorts, so I can express it using the main effect of mother’s education only. After I remove nonsignificant interactions, I reformulate each significant set of interaction terms as a single ordinal multiplier for the relevant social background effect, to obtain a more parsimonious model. For example, as will be seen shortly, the effect of father’s education on T1 significantly changes across cohorts. Using the same seven-dummy method as with mother’s education, I found that the coefficients associated with the interaction terms FE*C5 and FE*C6 are the small-

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328 est and not significantly different from each other, the coefficients associated with FE*C1, FE*C2 and FE*C3 are larger and not significantly different from each other, and the coefficients associated with FE*C4 and FE*C7 are the largest and not significantly different from each other. The best-fitting specification of these differences is an ordinal one that interacts father’s education with an ordinal variable coded 0 if cohort = C5 or C6; 1 if cohort = C1, C2, or C3; and 3 if cohort = C4 or C7. These ordinal respecifications are compared with the original, less parsimonious, specification using the BIC statistic (Raftery 1995). If the fit of the more-parsimonious model is not significantly worse, I choose the more parsimonious ordinal specification.9 As Wong (1994) demonstrated on the basis of a Montecarlo study, differences in BIC of less than 5 points should be seen as indeterminate. Therefore, to be selected, the less-parsimonious model has to have a BIC value at least 5 points lower (lower values indicate a better fit in the BIC metric) than the more parsimonious model. The preferred model from this procedure is the one that captures only substantively significant changes in social background effects over time, and it is presented for each educational transition in Appendix Table A as Model 1.10 In the second step, I include the kind of school attended by the respondent, distinguishing private-paid, voucher, and public schools. School sector is not a background variable, but an institutional factor that may operate as a mediator of family resources or as an additional vehicle of educational stratification. Therefore, including school sector in a separate step allows me to evaluate whether this variable adds to or mediates the effect of social background on educational attainment. To evaluate changes that are associated with school sectors across cohorts, I use the same technique described for Step 1. The preferred model of background and schoolsector effects is presented in Appendix Table A as Model 2 for each educational transition.

A note on the use of cohort analysis to study trends over time is in order. As is well known, cohort analysis conflates life-cycle, period, and cohort effects (Ryder 1965). The confounding impact of life-cycle differences

Torche can be removed by tracing members of each cohort back to the time when they entered or completed each level of schooling (Szelenyi 1998:29). However, cohort analysis does not allow me to distinguish period from cohort effects.11 This is a standard limitation in research on trends in educational stratification, which recommends caution in the interpretation of results, especially in the evaluation of policy effects. Therefore, as I previously stated, the focus of this article is not on conclusively asserting causal mechanisms that lead to observed outcomes, but on presenting a descriptive analysis of trends in inequality and of the role played by school sector in educational stratification and formulating empirically grounded, historically informed hypotheses about causal processes.

ANALYSIS Changing Transition Rates The expansion of the Chilean educational system in the past few decades can be assessed in the CMS by tracing the educational attainment across cohorts. Figure 4 displays the educational attainment of each cohort and suggests a significant increase over time. The fastest improvement took place from C1 to C4; then there was a plateau for C5 and C6, whose educational experience took place mostly in the 1970s and 1980s; and a new growth for the youngest cohort (C7), whose schooling career took place in the 1980s and 1990s.

The cohort-specific transition rates display educational expansion in a new light. Figure 5 presents the rates for each transition, conditional on having made the previous one, and shows increasing educational attainment across cohorts at the primary and secondary levels (T1, T2, and T3). The picture is different at the tertiary level (T4), where there is no expansion across cohorts in college entry conditional on secondary graduation, with a relatively flat transition rate of about 48. This trend has also been found in the United States (Hout, Raftery, and Bell 1993) and does not indicate that the absolute rate of entry

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Figure 4. Educational Attainment, by Birth Cohort: Chilean Men Born 1936–76

Figure 5. Educational Transition Rates, by Birth Cohort: Chilean Men Born 1936–76 into tertiary education remained constant across cohorts. Rather, tertiary education significantly expands over time, pushed by expansions at lower levels in the system.

What this pattern suggests is that having completed secondary education in greater numbers than before, the increasingly larger pool of high school graduates faces a constant hurdle at the college level. In sum, Figure 5 suggests that the expansion of the Chilean educational system has been largely driven by growth at lower educational levels.

Change In Educational Stratification Over Time With this background information, I move to the core of the analysis. As a first step, I evaluate the change across cohorts in the effect of social background on the probability of making each of the subsequent transitions.

Models 1 in Table 2 display this change, obtained from the preferred logit model for each educational transition (presented in Models 1 in Appendix Table A).

Cohort 4

.063*** .052** .033***

.078*** .059*** .019***

.177*** .095*** .035***

.173*** .082*** .040***

.057** .045* .031*** .097 .763***

.075*** .056*** .018*** -.927** .586**

.173*** .093*** .032*** .486* .913**

.169*** .126*** .039*** .425* .872**

.063*** .052** .033***

.078*** .059*** .019***

.177*** .033 .035***

.173*** .082*** .040***

.057** .045* .031*** .097 .763***

.075*** .056*** .018*** .168 .087

.173*** .032 .032*** .486* .913**

.169*** .126*** .039*** .425* .872**

.063*** .052** .033***

.078*** .059*** .019***

.177*** .033 .035***

.173*** .082*** .040***

.057** .045* .031*** .097 .763***

.075*** .056*** .018*** .168 .087

.173*** .032 .032*** .486* .913**

.169*** .126*** .039*** .425* .872**

.063*** .052** .033***

.078*** .059*** .019***

.177*** .033 .035***

.173*** .117*** .008

.057** .045* .031*** .097 .763***

.075*** .056*** .018*** .168 .586**

.063*** .052** .033***

.078*** .059*** .019***

.177*** .033 .035***

.169*** .173*** .160*** -.013 .007 .072*** .425* .872**

.173*** .032 .032*** .486* .913**

Cohort 6

Cohort 7

.057** .045* .031*** .097 .763***

.075*** .056*** .018*** .168 1.085***

.173*** .032 .032*** .486* .913**

.063*** .052** .033***

.078*** .059*** .019***

.177*** .157*** .035***

.169*** .173*** .032 -.013 .071*** .072*** .425* .872**

.057** .045* .031*** .097 .763***

.075*** .056*** .018*** .168 1.085***

.173*** .154*** .032*** .486* .913**

.169*** .032 .071*** .425* .872**

.063*** .052** .033***

.078*** .059*** .019***

.177*** .157*** .035***

.173*** .117*** .008

.057** .045* .031*** .097 .763***

.075*** .056*** .018*** 1.263*** 1.085***

.173*** .154*** .032*** .486* .913**

.169*** .160*** .007 .425* .872**

Model 1 Model 2 Model 1 Model 2 Model 1 Model 2

Cohort 5

330

Note: FE = father’s education, ME = mother’s education, FS = father’s socioeconomic status, VS = voucher school, and PS = private-paid school. Coefficients for Model 1 were obtained from Models 1 in Appendix Table A, and coefficients for Model 2 were obtained from Models 2 in Appendix Table A (see the text for details). a Reference category: public school. * p < .05, ** p < .01, ***p < .001 (two-tailed tests).

T4 Enter Tertiary ME FE FS VS a PS a

T3 Graduate Secondary ME FE FS VS a PS a

Cohort 3

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T2 Enter Secondary ME FE FS VS a PS a

Cohort 2

Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2

Cohort 1

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T1 Complete Primary ME FE FS VS a PS a

Transition

Table 2. Social Background and School Sector Effects on Educational Transitions Based on the Preferred Logit Model: Chilean Men Born 1936–76

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Torche

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Privatization Reform in Chile Before I move to the interpretation, a word is necessary on how the coefficients in Table 2 were obtained. For example, with regard to the change in the effect of father’s education on the first educational transition (completion of primary school), this effect is not significantly different from zero in C5 and C6 (-.013), as indicated by the main effect of the variable in Model 1 for T1 in Appendix Table A. Appendix Table A also includes the ordinal multiplier FE*Cohort Trend T1 , which captures a linear increase in the effect of father’s education from its baseline value in C5 and C6; to .082 [-.013 + (1*.095)] for C1, C2, and C3; and to .117 [-.013 + (2*.095)] for C4 and C7. All coefficients in Table 2 were obtained by combining the baseline effect of the social background variable with the interaction terms capturing changes over time.

The first thing to notice in Models 1 in Table 2 is the minor change in social background effects across cohorts. Social background effects change only in the two earlier transitions, primary completion and secondary entry. In T1, there is a change over time in the influence of father’s education and father’s occupational status. The influence of father’s education is the largest in C4 and C7; smaller but still significant in C1, C2, and C3; and insignificant for C5 and C6. The effect of father’s occupational status follows the reverse trend: It is the smallest in C4 and C7; larger for C1, C2, and C3; and the largest for C5 and C6.

This finding suggests that the two effects may counteract each other to yield an overall outcome of no change across cohorts. The magnitude of these effects cannot be immediately compared because the variables have different measurement units, but I obtained a rough equivalence by standardizing the coefficients. The standardized effects are .442 for father’s education (SD FE = 4.65) and -.415 for father’s occupational status (SD FS = 12.96).

Therefore, the magnitude of the standardized effects is similar and opposite in sign, which suggests no change across cohorts in educational stratification at the primary completion level.

The analysis of trends in social background effects on T2 (entry to secondary

331 education) indicates that the only effect that varies is that of father’s education. The effect is the lowest for C2, C3, C4, and C5; larger for C1; and the largest for C6 and C7, suggesting an increase in educational stratification for the two youngest cohorts. No change in social background effects is found in the two higher transitions—completion of secondary education and entry to tertiary education.

In sum, the results of the educational transition model indicate little variation in the effect of social background across cohorts in Chile, in spite of the major educational expansion and reform of the educational system, with one exception. The only significant change across cohorts is that of the effect of father’s education on the probability of entering secondary school (T2). To show the magnitude of this increase in inequality, Figure 6 presents the predicted probability of entering secondary school for three hypothetical cases: children whose fathers have 1 year of schooling, 6 years of schooling, and 12 years of schooling, with all other social background variables held at their means.12 The figure indicates a decline in the socioeconomic gap from C1 to C2, then a constant gap across educational levels through C5, followed by a significant increase in inequality for C6 and C7.

Even if it is not major, the growth in the effect of father’s education is relevant because it suggests increasing inequality in the probability of entering secondary school—a finding that departs from the results of persistent or decreasing inequality in international comparative research. This finding speaks to the MMI hypothesis. If the advantaged classes are defined as parents with 12 years of education (which represent approximately the top 10 percent of the parental educational distribution), the Chilean trends indicate that rather than declining, inequality increased after the advantaged classes reached saturation (from C6 to C7).13 Therefore, the Chilean case does not support the MMI hypothesis and suggests that inequality may be driven by factors other than saturation of the advantaged classes.

What can these factors be? C6 and C7 transitioned to secondary education in the midst of the market-oriented reform in a context of a deep economic crisis (see Figure 3).

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Torche

Figure 6. Predicted Probability of Entering Secondary Education (T2) for Children with Fathers with 1, 6, and 12 Years of Schooling Across Cohorts: Chilean Men Born 1936–76 Note: Obtained from Model 1, Transition 2, in Appendix Table A. All social background variables with the exception of father’s education were held at their means.

The observed trend suggests that dire economic conditions, coupled with the retrenchment of the social safety net, may have caused growing educational stratification even as the educational system was expanding. Note also that according to Figure 6, the growth in inequality was driven by losses by the least-advantaged classes, not by gains by the most-advantaged ones. This finding is consistent with the hypothesis that the growing cost of education, in the form of foregone earnings, may have induced some children in the poorest families to drop out of school at an early stage of their educational careers to enter the labor market and supplement their households’ incomes.

If the analysis of the Chilean case considered only quantitative inequality, the conclusion would be that the Chilean results are in line with the finding of persistent inequality in most countries of the world, with the exception of a noteworthy increase in inequality in T2, evidenced in losses of the most disadvantaged classes, probably driven by growing poverty and inequality in the context of a retrenchment in the safety net.

However, as EMI suggests, there may have been changes in qualitative inequality in the period under study. Specifically, the shock that was introduced by the privatization

reform may have induced inequalities that were associated with different kinds of schools. For instance, the creaming that resulted from the voucher system in the 1980s may have resulted in growing benefits associated with switching from public to voucher schools or, alternatively, with losses for those who remained in the public system.

Also, the reduction of the educational budget by the military regime may have led to a widening of the gap in attainment between public and voucher schools that were financed by the government and private-paid schools that were funded by private tuition.

To explore these hypotheses, I add school sector to the educational transition models as a predictor of educational attainment. Several studies of the Chilean educational system after the privatization reform have attempted to evaluate the relative efficiency of different school sectors at an individual level, usually measured in terms of students’ test scores (e.g., Carnoy and McEwan 2001; Contreras 2001; McEwan and Carnoy 2000; Mizala and Romaguera 2000; Sapelli 2003). This is not the objective of this article. My objective is more basic: to explore if there were changes across cohorts in the benefits of attending different school sectors.

To explore this question, I add school sec-

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Privatization Reform in Chile tor in Models 2 in Table 2 to the social background variables presented in Models 1 and evaluate the changes over time in the advantages of attending public, private-voucher, and private-paid schools. I enter school sectors as a set of dummy variables, with public school as the reference category. Coefficients are obtained from the preferred logit model of social background and school-sector effects presented in Models 2 in Appendix Table A. This formulation permits the assessment of the attainment gap across different school sectors and of any change in this gap across cohorts. In addition, by comparing the coefficients of social background variables with the coefficients presented in the comparable Models 1, I am able to evaluate the extent to which the school sector attended mediates the effect of social background variables.

Models 2 in Table 2 indicate that the benefits that are associated with different types of schools are similar for the first two transitions (completing primary education and entering secondary school). In both transitions, students in private-voucher and private-paid schools do significantly better than do public school students, and in both cases the gains that are associated with private-paid school are larger, although the difference is not statistically significant (χ2 tests for the difference: p = .22 and p = .26, respectively). Moving to the focus of this analysis, the evaluation of trends, in both cases the effects of school sectors do not vary across cohorts.

The pattern is different for T3. When the probability of graduating secondary education is analyzed, the advantages that are associated with private-paid school and voucher school relative to public school change across cohorts. The benefit of attending private-paid school is the smallest in C2 and C3; greater in C1 and C4; and the greatest in C5, C6, and C7—the cohorts who were at risk of completing secondary education in the late 1970s,1980s, and early 1990s. This finding suggests that Chileans who were able to pay for private education got the most for their money in the cohorts who completed secondary schooling during and after the market-oriented transformation. Because the privatization reform did not directly alter pri-

333 vate-paid schools, this increase in stratification is likely to be associated with the growing poverty and inequality and the reduction of the educational budget in the 1980s, rather than with the institutional reorganization of the educational system brought about by the privatization reform. In other words, private school students experienced relative gains because they were not dependent on the underfunded public system.

The most impressive trend is, however, the change across cohorts in the effect of attending private-voucher schools on T3, secondary school graduation. The effect of voucher school is slightly negative in C1 and is not different from public school for C2–C6. This finding is not surprising, given the different nature of private-voucher schools as charity institutions before the privatization reform.

The positive effect of attending voucher schools surged for the youngest cohort (C7), who were the most affected by the privatization reform. In other words, attending a private-voucher school, rather than a public school, paid off significantly more in terms of educational attainment after the Chilean educational reform that introduced a voucher system of financing.

To provide a more graphical picture of the differential attainment across school sectors before and after the privatization reform, I follow the same strategy as presented in Figure 6. Figure 7 presents the predicted probability of graduating secondary school across cohorts for children attending private-paid schools, voucher schools, and public schools, holding all social background variables constant at their means.

Figure 7 depicts the probability of graduating secondary school for an “average student” who attended public, private-voucher, and private-paid schools across cohorts. In the earliest cohort (C1), public school students performed better than did those in privatevoucher schools. For C2–C6, the statistically insignificant advantage that is associated with private-voucher over public school remained constant. A new development affected the youngest cohort: Students who attended the newly implemented voucher schools caught up to and drastically surpassed those attending public schools. According to Figure 7,

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Figure 7. Predicted Probability of Graduating Secondary School Across Cohorts, by School Sector Attended Across Cohorts: Chilean Men Born 1936â&#x20AC;&#x201C;76 Note: Obtained from Model 2, Transition 3, in Appendix Table A. All social background variables held at their means. members of C7 who attended a voucher school had a 90 percent chance of graduating from high school, compared with only 73 percent for those who attended a public school.

This jump in the attainment of voucher-school students occurred precisely for the cohort who was the most affected by the privatization reform (see Figure 3). Even if no causal inferences can be made from a cohort analysis of a cross-sectional study, the sharp increase in the advantages that are associated with voucher schools suggests that the segmentation of students across school sector that followed the privatization reform became a vehicle for the reproduction of educational inequality. Last, there was no change across cohorts in the benefits associated with privatepaid and voucher schools for T4 (entry to tertiary education).

The analysis also allows me to explore to what extent the beneficial effect of attending voucher schools mediates or adds to the effect of social background. As Figure 2 indicates, there is high segmentation of school sector by income in Chile: The lower- and middle-income sectors attend public schools, the middle- and upper middle-income sectors attend private-voucher schools, and the most-advantaged sector pays for private schools. Therefore, it may be that school sector mediates only the effect of social back-

ground on educational attainment. A comparison of the social background coefficients between Models 1 and 2 of Table 2 (before and after school sector was controlled) reveals that the coefficients diminished only marginally in value after school-sector variables were entered in the modelsâ&#x20AC;&#x201D;a decline of approximately 15 percent. This finding suggests that school sector adds to more than mediates the effects of social background in Chile.

DISCUSSION AND CONCLUSIONS I return now to the themes raised in the introduction. Has inequality of educational opportunity changed during the past few decades in Chile? If so, are these trends related to changes in the structure of the Chilean educational system, specifically to the privatization of education by the authoritarian regime in the 1980s? What are the implications of this case study for the comparative analysis of educational stratification?

To address these questions, I examined the probability of making four subsequent educational transitions for different socioeconomic groups across cohorts. The analysis revealed that the Chilean case is in line with findings in most countries of the world: There was little change in educational stratification across

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Privatization Reform in Chile cohorts, in spite of significant educational expansion and radical educational reform.

Persistent inequality does not fully apply to the Chilean case, however. Two departures are particularly important. First, the analysis detected a rise in the effect of father’s education on the probability of entering secondary schooling for the two youngest cohorts, signaling an increase in inequality. The increase in inequality questions MMI because it took place precisely as the advantaged classes were reaching saturation for this particular educational transition. Furthermore, the growth in inequality was driven by losses by the least-advantaged classes, rather than by gains by the most privileged. What can explain this increase in inequality? It affected the cohorts who experienced the transition to secondary education during the late 1970s and 1980s, a period in which the retrenchment of the safety net, coupled with an economic crisis, resulted in growing unemployment, poverty, and inequality. A plausible explanation is therefore the increasing cost of noncompulsory education for the leastadvantaged families, which may have pushed children out of the educational system and into the labor market.

Growing inequality is a noteworthy finding because it departs from trends of persistent or declining inequality that have been found in international comparative research and raises an important question: Is persistent inequality as universal as researchers currently believe? Or is it partly an artifact of the biased pool of countries—mostly industrialized— where studies of IEO trends have been conducted? Given the economic decline and market reform experienced by most Latin American and other developing countries during the 1980s and 1990s, it is reasonable to hypothesize that these trends may have led to a growth in educational inequality similar to or greater than that reported for Chile.

Only research using fresh data from countries beyond the industrialized core will address this question and shed light on potentially important mechanisms that drive educational stratification.

Second, this article has also explored school sector as a source of qualitative inequality, specifically the advantages that are

335 associated with private-voucher and privatepaid schools after the privatization reform of the 1980s. The analysis found that attainment was significantly different across school sectors. In general, students of private-paid schools fared better than did those who attended private-voucher schools, who, in turn, had higher attainment than those who attended public schools. More important, the analysis detected a significant change across cohorts, with an increase in inequality for the younger cohorts. On the one hand, the beneficial effect of attending private-paid schools seems to have increased for those cohorts whose educational careers occurred during and after the market transformation. This finding indicates that the payoff of a family’s financial investments in education in the form of private-school tuition has grown in recent decades in Chile. On the other hand, the analysis shows a large improvement in the chances of graduating from secondary school for voucher-school students relative to public school students for C7, the cohort who were the most affected by the privatization of education. Naturally, this does not mean that the voucher sector that emerged after the privatization reform created educational inequality, but it does mean that this sector became a relevant arena in which inequality was actualized and reproduced.

The analysis also shows that school sector adds to, rather than mediates, the effect of socioeconomic status on educational attainment. This finding raises important questions.

One of the most pressing inquiries that emerges from the growing gap between private-voucher and public school students in Chile is about the mechanisms that drive this breach. On the basis of the current literature, several nonexhaustive mechanisms may be suggested. One of them is a selection effect: unmeasured characteristics related to educational outcomes, such as motivation, ability, or social networks, that may be higher among those who migrate to the voucher system than among those who stay in public schools.

Another possible mechanism is peer effects resulting from school sorting (Cullen, Jacob, and Levitt 2005; Fiske and Ladd 2000; Hsieh and Urquiola 2003; Martinez, Godwin, and Kemerer 1996). Yet another is any type of orga-

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336 nizational feature of voucher schools that makes them function better, such as a morechallenging academic environment (Witte 1992, 1996), “communal organization” (Bryk et al. 1997), or more-autonomous management (Chubb and Moe 1998). Exploring the potential impact of these mechanisms—or any other force that drives the growing inequality across school sectors—is beyond the scope of this article. The evidence presented here, however, forcefully suggests that the institutional organization of the educational system is a relevant, distinct source of qualitative inequality and that much would be gained by its careful examination in other national contexts.

This study of trends in educational stratification in Chile has uncovered some similarities and some departures from the industrialized world—similarities, because in most transitions, persistent inequality fits the Chilean case well, and departures, because growing inequality, which is likely to be related to the economic crisis and privatization of education, was found. Only more time and fresh data will allow for the confirmation of these findings. I hope that this analysis has served as a baseline to which future studies of Chile and other developing countries can refer.

NOTES 1. Despite the significant attention given to charter schools, educational vouchers, and other forms of educational privatization, they currently enroll a small number of students in the United States. In 2000, charter schools represented 0.09 percent of total primary and secondary enrollment. With regard to voucher programs, only four states have operational voucher programs, and they serve a total of approximately 27,000 students (National Center for the Study of Privatization in Education 2005a, 2005b).

2. But see the studies that have explored the links between increasing competition from private schools and public school quality using school districts or other geographically bounded areas as units of analysis (e.g., Arum 1996; Dee 1998; Hoxby 1994).

3. I thank one anonymous reviewer for highlighting this point.

Torche 4. Some analysts have described the Chilean system as a “charter school” model of educational provision (see, e.g., Welsh and McGinn 1999). However, the Chilean private schools that are supported by public funds do not fulfill the essential characteristic of charter schools, namely, that they negotiate a contract (charter) with an authorized public body that is designed to be unique in its focus or student clientele.

5. In Chile these schools are called private subsidized schools because of the per-student subsidy they receive from the government.

6. The survey includes only men under the assumption that the dynamics of mobility vary significantly across genders, and because of the moderate sample size, it would not be possible to conduct a separate analysis of women. The survey excludes men who are not heads of households, who represent 17 percent of the male population of the relevant age (Mideplan 2000), 86.5 percent of whom are the sons of heads of the households, 12.4 percent of whom are other relatives of the heads, and 1.1% of whom are other nonrelated men. Their occupational distribution, once age is controlled, is almost identical to that of heads of households. The small proportion represented by this group and their similar occupational distribution suggest that their inclusion would not significantly alter the findings presented here.

7. Nonresponse rates are usually not reported in Chilean surveys. Correspondence with Chilean experts indicated that nonresponse is usually about 20–25 percent for face-to-face household surveys of individuals aged 18 or older. The higher nonresponse rate of the CMS is due to the difficulty in contacting male heads of households.

8. See Cameron and Heckman (1998) for a critical evaluation of the “educational transition” approach and Lucas (2001) for a response to that critique.

9. Note that these ordinal specifications assume a linear change across cohorts in the effect of the social background variable. Even though I could not ascertain if the change is strictly linear, the fact that the specification yields the best-fit model supports their selection.

10. It should be emphasized that the term

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Privatization Reform in Chile effects is used here as shorthand for partial association and does not imply causal attribution.

11. Alternative ways to distinguish among cohort, period, and life-cycle interpretations of change require the use of panel data or repeated cross sections, which are unavailable for the Chilean case.

12. The predicted probability was obtained from the logit regression model.

The logit transformation can be interpreted as the natural logarithm of the odds of success and can be expressed as follows: yik = ln{pik/1pik}Σβjxij, where pik represents the conditional probability than individual i will complete transition k, given completion of the previous transition k-1; xij are the variables that linearly influence the log-odds; and βj are parameters to be estimated from the data. A feature of the logit model is that the rate of change

337 in pik with respect to a change in xij is given by: δpik/δxij = βj pik(1-pik) (Mare 1981). This implies that the partial effect of a change in x on the transition probability is maximal at p = .05, where it equals .25*β. As the baseline probability moves further from .05 in either direction, the effect of a one-unit change in x decreases (Gerber 2000).

13. The MMI hypothesis relies heavily on the notion of advantaged classes, but it does not clearly define who belongs to these advantaged groups. The lack of a strict definition introduces arbitrariness to the analysis and makes international comparison difficult.

To address this limitation, alternate definitions of advantaged classes—as children whose fathers have 14 and 16 years of education, as well children of parents with top-decile education and occupational scores—were used in this analysis. The findings are insensitive to the definition that was used.

.062** (.020)

(.209) (.211) (.213) (.215) (.228) (.248)

.061**

.486* .913**

(.020)

(.231) (.311)

.173*** (.021) .032 (.023) .032*** (.007)

.743*** 1.083*** 1.123*** .637** 1.465*** .886***

(.187) (.181) (.177) (.176) (.181) (.197)

.078*** (.016) .059*** (.016) .019*** (.005)

.358 .462* .526** .421* .624*** .973***

Model 1 (.193) (.187) (.182) (.182) (.187) (.214)

(.223) 1.095*** (.313)

.499*

-.927** (.339) .087 (.271)

.075*** (.016) .056*** (.016) .018*** (.005)

.276 .390* .423** .290 .459* .627**

Model 2

-1.411*** (.181) -1.407*** (.182) -1.724*** (.219) -1.693*** (.220) -1.367*** (.197) -1.238*** (.202)

-.032*** (.010)

(.010)

.177*** (.021) .033 (.022) .035*** (.007)

(.208) (.210) (.212) (.215) (.227) (.247)

Model 2 (.240) (.232) (.223) (.220) (.224) (.226)

-2.357*** (.247)

.063*** (.018) .052** (.018) .033*** (.005)

.213 .123 -.002 .082 .067 .082

Model 1

(.243) (.234) (.225) (.223) (.227) (.230)

continued

-2.316*** (.250)

.097 (.154) .763*** (.179)

.057** (.018) .045* (.019) .031*** (.005)

.184 .145 .011 .109 .077 .088

Model 2

T4: Attended Tertiary

338

Intercept

.094*** (.024)

(.024)

(.219) (.303)

.169*** (.020) -.012 (.028) .071*** (.012)

(.020) (.027) (.012)

.774*** 1.088*** 1.130*** .642** 1.446*** .864***

Model 1

T3: Graduated Secondary

11:31 AM

.425* .872**

.227 .619*** 1.388*** .304 .462 1.429***

(.153) (.164) (.298) (.299) (.298) (.315)

Model 2

(.153) (.163) (.296) (.297) (.296) (.314)

Model 1

C2 a .237 C3 .625*** C4 1.372*** C5 .324 C6 .454 C7 1.400*** Social Background Mother’s education (ME) .173*** Father’s education (FE) -.013 Father’s status (FS) .072*** School Type Voucher school (VS) b Private school (PS) b Change Across Cohorts T1. Primary Completion FE*C. trend (C5, C6 = 0 ; C1, C2, C3 = 1; C4, C7 = 2) .095*** FS*C. Trend (C5, C6 = 0; C1, C2, C3 = 1 C4, C7 = 2 ) -.032*** T2. Secondary Entry FE*C. trend (C2, C3, C4, C5 = 0; C1 = 1; C6, C7 = 2) T3. Secondary Graduation PS*C. trend (C2, C3 = 0; C1, C4 = 1; C5, C6, C7 = 2) VS*C. trend (C1 = 0; C2, C3, C4, C5, C6 = 1; C7 = 2)

Variable

T2: Entered Secondary

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T1: Completed Primary

PARAMETER ESTIMATES OF PREFERRED LOGIT MODELS OF BACKGROUND EFFECTS (MODEL 1) AND BACKGROUND AND SCHOOL SECTOR EFFECTS (MODEL 2) ON EDUCATIONAL TRANSITIONS

APPENDIX TABLE A

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1443 -888.85 218.18 11 .110 1443 -898.27 199.33 9 .100 2142 -1226.03 253.50 13 .094 2142 -1240.68 224.21 9 .083 Note: Reported coefficients are logits; standard errors in parentheses. * p < .05, ** p < .01, *** p < .001 (two-tailed tests). a Reference category = C1. b Reference category = public school.

2632 -1014.28 483.65 12 .193 2632 -1021.36 469.49 10 .187 3244 -1255.72 630.50 13 .201 3244 -1262.27 617.39 11 .197 Sample Size Log-likelihood Model LR χ2 Parameters Pseudo R2

Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Variable

T3: Graduated Secondary T2: Entered Secondary T1: Completed Primary

APPENDIX TABLE A (CONTINUED)

T4: Attended Tertiary

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Florencia Torche is an Assistant Professor of Sociology at Queens College, City University of New York, and a Research Associate at the Center for the Study of Wealth and Inequality, Columbia University. Her research focuses on the processes of the reproduction of inequality in the occupational, educational, and wealth spheres in different national contexts. She is currently completing a book manuscript entitled “Inconsequential Mobility: The Chilean Case in Comparative Perspective.”

This research was supported by Grant 1040-1239 from the Ford Foundation to the Center for the Study of Wealth and Inequality, Columbia University, and by Grant 1010474 from the Chilean National Center for Science and Technology FONDECYT. I would like to thank Peter Bearman, Theodore Gerber, Nicole Marwell, Carolina Milesi, Seymour Spilerman, Donald Treiman, Christopher Weiss, and participants in a variety of settings in which this work has been presented for their helpful comments and suggestions. Direct correspondence to Florencia Torche, CWI-ISERP, Columbia University 420 West 118th Street, 805B, New York, NY 10027; e-mail: fmt9@columbia.edu.