Presidential Address: Education and Poverty: Confronting the Evidence

Helen F. Ladd

Abstract

Current U.S. policy initiatives to improve the U.S. education system, including No Child Left Behind, test-based evaluation of teachers, and the promotion of competition are misguided because they either deny or set to the side a basic body of evidence docu- menting that students from disadvantaged households on average perform less well in school than those from more advantaged families. Because these policy initiatives do not directly address the educational challenges experienced by disadvantaged students, they have contributed little—and are not likely to contribute much in the future—to raising overall student achievement or to reducing achievement and educational attain- ment gaps between advantaged and disadvantaged students. Moreover, such policies have the potential to do serious harm. Addressing the educational challenges faced by children from disadvantaged families will require a broader and bolder approach to education policy than the recent efforts to reform schools. C© 2012 by the Association for Public Policy Analysis and Management.

INTRODUCTION

Evidence-based policymaking. That is the rallying cry for policy researchers like many of us and also for many policymakers, including the Obama administration itself. Providing a forum for researchers to present and discuss policy-relevant re- search that can provide the evidence needed for better policymaking is one of the major functions of the Association for Public Policy Analysis and Management (APPAM).

Policy-relevant evidence often comes from careful studies of specific policy in- terventions such as job training or negative income tax programs and is based on random control trials or other forms of rigorous quantitative and qualitative analysis. Many of you in the audience today have made major methodological and substantive contributions through research of this type in a range of policy areas.

I want to focus today on the policy importance of evidence of a broader type—a type that does not require any sophisticated modeling. And I will do so in the context of my main field of policy research, education policy.

Historically this country prided itself on its outstanding education system, which educated a higher proportion of its population to more advanced levels than most other countries. The Sputnik challenge from Russia in the late 1950s and the pub- lication of A Nation at Risk (1983) during the Reagan years, however, highlighted significant concerns about the quality of the U.S. education system. Concerns today are based on average test scores of U.S. students that are middling compared to Journal of Policy Analysis and Management, Vol. 31, No. 2, 203–227 (2012) C© 2012 by the Association for Public Policy Analysis and Management Published by Wiley Periodicals, Inc. View this article online at wileyonlinelibrary.com/journal/pam Supporting Information is available in the online issue at wileyonlinelibrary.com. DOI:10.1002/pam.21615

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those of other nations, on U.S. graduation rates that once were well above those of most other countries but now have been overtaken by rising rates in other countries, and on abysmal educational attainment and test score performance of many disad- vantaged students, especially those in urban centers. These patterns and trends, as well as recent widely publicized documentaries including for example, Waiting for Superman, have convinced many people that our education system is in crisis.1

During the decades following A Nation at Risk, U.S. education policymakers re- sponded to the perceived crisis in a variety of ways such as creating ambitious national goals and promoting standards-based reform. Of interest here are the pol- icy initiatives of the past decade, which include school accountability in the form of the federal No Child Left Behind (NCLB) Act, test-based approaches to eval- uate teachers, and promotion of expanded parental choice, charter schools, and competition.

I will argue today that these current policy initiatives are misguided because they either deny or set to the side a basic body of evidence documenting that students from disadvantaged households on average perform less well in school than those from more advantaged families. Because they do not directly address the educational challenges experienced by disadvantaged students, these policy strategies have con- tributed little—and are not likely to contribute much in the future—to raising overall student achievement or to reducing achievement and educational attainment gaps between advantaged and disadvantaged students. Moreover, such policies have the potential to do serious harm.

Addressing the educational challenges faced by children from disadvantaged fam- ilies will require a broader and bolder approach to education policy than the recent efforts to reform schools. It will also require a more ambitious research agenda, one that APPAM researchers—not just those of us who typically focus our research on education policy, but also researchers in a wide range of social policy issues—are in a good position to advance.

EVIDENCE ON THE RELATIONSHIP BETWEEN FAMILY BACKGROUND AND EDUCATIONAL OUTCOMES

Study after study has demonstrated that children from disadvantaged households perform less well in school on average than those from more advantaged households. This empirical relationship shows up in studies using observations at the levels of the individual student, the school, the district, the state, the country. The studies use different measures of family socioeconomic status (SES): income-related measures such as family income or poverty; education level of the parents, particularly of the mother; and in some contexts occupation type of the parents or employment status. Studies based on U.S. administrative data often measure SES quite crudely, using eligibility for free and reduced price lunch, for example, as a proxy for low family income, and using student race as a proxy for a variety of hard to measure charac- teristics. Studies based on longitudinal surveys often include far richer measures of family background. Regardless of the measures used and the sophistication of the methods, similar patterns emerge.

I start with differences in test scores between U.S. students whose families have high and low SES as measured by family income. The best research on income-based achievement gaps appears in a recent study by Sean Reardon for which he compiled test scores for school-aged children and family income from a large number of U.S.- based nationally representative surveys over a 55-year period. By standardizing

1 Not everyone agrees that the system is in crisis. See, for example, the critique of this view by Berliner and Biddle (1995).

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Figure 1. Trends in Income and Black-White Gaps in Reading, 1943 to 2001 Cohorts (Simplified Version of Graph in Reardon, 2011, ch. 5).

income differentials and achievement levels to make them comparable over time, he was able to estimate the trend in reading and math test scores gaps between the children in the 90th and the 10th income percentiles. As shown by the rising line in Figure 1 for reading gaps, the results are striking. The figure shows that, when first measured in the early 1940s, the gap in reading achievement between children from high- and low-income families was about 0.60 standard deviations. It subsequently more than doubled to 1.25 standard deviations by 2000.2

These income-based achievement gaps are large. To put them in perspective, consider the black-white test score gap as measured by the National Assessment of Education Progress (NAEP) for 13-year olds, depicted by the dashed line in Figure 1.3 That gap was about one standard deviation in the 1970s, then fell to about 0.50 during the 1980s where it has remained relatively constant. As a result, the achievement gap between children from high- and low-income families is now far larger than the gap between black and white children.

People can disagree about whether the relationship between family income, or broader measures of SES, on the one hand and educational outcomes on the other is correlational or causal. For example, it may be that factors correlated with low income such as poor child health or single-parent family structures account for

2 Figure 1 is a simplified version of graph 5.3 in Reardon (2011). The trend line is estimated based on the income-based achievement gaps calculated from the 12 nationally representative studies that include data on reading scores for school-age children and information on family income. The fitted regressions line is weighted by the inverse of the sampling variance of each estimate. The figure for math is similar (see Figure 5.4 in Reardon, 2011). 3 The estimated black-white gap trend line is based on all the available black-white gap information that is available in NAEP long-term trends for 13-year olds and main NAEP for eighth graders, with the latter adjusted for age differences. The line can be interpreted at the trend in the gap for 13-year olds. See footnote 6 in Reardon (2011).

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the relationship rather than income itself. Further, people may disagree about the extent to which schools and school policies contribute to the low achievement of children from low-SES households. At this point, I simply want to draw attention to the correlation. Later I will say more about the mechanisms through which low SES may translate into low academic performance.

Suffice it to say at this point that research documents a variety of symptoms of low SES that are relevant for children’s subsequent educational outcomes. These include, for example, poor health, limited access to home environments with rich language and experiences, low birth weight, limited access to high-quality preschool opportunities, less participation in many activities in the summer and after school that middle-class families take for granted, and more movement in and out of schools because of the way the housing market operates for low-income families. Differences in outcomes between high- and low-SES families may also reflect the preferences and behaviors of families and teachers. Compared to low-SES families, for example, middle- and upper-class families are better positioned to work the education system to their advantage by assuring that their children attend the best schools and get the best teachers, and they are more likely to invest in out-of-school activities that improve school outcomes such as tutoring programs, camps, and traveling.4 The preferences and behaviors of teachers are also a contributing factor in that many teachers with strong credentials tend to be reluctant to teach in schools with large concentrations of disadvantaged students than in schools with more advantaged students (Clotfelter, Ladd, & Vigdor, 2011; Jackson, 2009).

The logical implication of the low achievement of poor children relative to their better-off counterparts is that average test scores are likely to be lower in schools, districts, or states with high proportions of poor children, all else held constant, than in those with fewer poor children. Figure 2 illustrates this negative relationship be- tween child poverty and test scores across U.S. states in 2009, with eighth-grade reading scores in Figure 2a and eighth-grade math scores in Figure 2b. The achieve- ment scores in these graphs are from the NAEP and are based on random samples of students in each state while state poverty rates are from the American Community Survey.

Of course, not all else is constant. Among other things that differ across states is the quality of the states’ education systems. Test scores in Massachusetts, for exam- ple, far exceed their predicted levels given the state’s 12 percent child poverty rate, presumably in part because the state implemented an aggressive and comprehen- sive education reform strategy in 1998 that included support for young children. In contrast, test scores in California, are well below those predicted for its 20 percent poverty rate, presumably in part because of its long history of limiting spending on education. Moreover, other factors may also contribute to the patterns. Mas- sachusetts, for example, has a highly educated parental population, and California has a large immigrant population. Nonetheless, the overall negative relationship between the child poverty rate and student performance in both graphs is clear.

Consistent with the graphs, a simple bivariate regression of state test scores and state poverty rates indicates that a full 40 percent of the variation in reading scores and 46 percent of the variation in math scores is associated with variation across states in child poverty rates. The addition of one other explanatory variable related to family background, the percent of children who are members of minority groups, increases the explanatory power of the relationship to about 50 percent in reading

4 See Duncan and Murnane (2011) and the articles therein for detailed empirical analysis of many of these mechanisms.

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Figure 2. (a) State National Assessment of Education Progress (NAEP) Eighth- Grade Reading Scores and Child Poverty Rate 2009. (b) State NAEP Eighth-Grade Math Scores and Child Poverty Rate 2009.

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Table 1. Within-state changes in National Assessment of Education Progress (NAEP) test scores (standardized) as a function of within-state changes in the child poverty rate.

4th-Grade 8th-Grade

Reading Math Reading Math

Child poverty rate (%) −0.023* (0.012) −0.030*** (0.011) −0.030** (0.012) −0.030*** (0.010) Constant 0.402* (0.209) 0.514 (0.194) 0.523 (0.205) 0.518 (0.0177) State fixed effects? Yes Yes Yes Yes Observations 282 240 277 239 R2 0.908 0.932 (0.917) (0.944)

Notes: Sample is NAEP test scores (standardized across states) for years 1998, 2002, 2003, 2005, 2007, and 2009 for reading and for years 2000, 2003, 2005, 2007, and 2009 for math. Calculations are by the author. *indicator significance at the 10 percent level, **at the 5 percent level, and ***at the 1 percent level.

and 51 percent in math. Clearly, the mix of family backgrounds is highly correlated with patterns of student achievement across states.

Stronger evidence that child poverty itself may be causally linked to educational outcomes, especially for math, is shown in Table 1. The estimates reported here are based on data from six administrations of the NAEP during the past 10 to 12 years and are based on panel regression models with state fixed effects.5 The outcome variables are fourth- and eighth-grade NAEP reading and math scores standardized across states. The state fixed effects control for time-invariant characteristics of a state such as its population mix and historical commitment to education that could well affect educational outcomes and that might be correlated with state poverty rates. Consistent with the view that child poverty adversely affects student achievement, the negative coefficients on the poverty rate variables demonstrate that increases in child poverty rates during the last 10 years translated into reductions in average test scores.

A strong correlation between student achievement and family background shows up as well in the international data for developed countries. The pattern emerges for comparisons both within and across countries. I focus here on test scores from the Programme for International Student Assessment (PISA) managed by the OECD (Organization for Economic Co-Operation and Development, 2010). To facilitate comparisons across developed countries of children from similar backgrounds, the OECD has constructed a measure of the economic, social, and cultural status (ESCS) of the families of all children tested. This measure incorporates information on the household’s occupational status, the parents’ education level, and, as a proxy for the family’s income or wealth, household possessions.6 This measure is comparable to what we in the United States would call SES and is an absolute scale that allows one to compare students with similar family backgrounds across countries.

5 The years included in the analysis differ somewhat between reading and math regressions because of slight differences in when the tests were administered. The child poverty rates from 2002 to the present are from the American Community Survey and those for 2000 are from the U.S. Census. The 1998 reading scores by state are matched with state child poverty rates for 2000. 6 The index is based on the following variables: the international socioeconomic index of occupational status of the father or mother, whichever is higher; the level of education of the father or mother, whichever is higher, converted into years of schooling; and an index of home possessions, which is based on student reports of access to education related possessions such as desks, computers and books, and availability of items such as such as televisions, cars, and cellular phones. The index is standardized to a mean of zero for the population of students in OECD countries, with each country given equal weight. A score of −1.0 on this index means that the student is more disadvantaged than five-sixths of the students in the average OECD country (OECD, 2010, p. 29).

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Figure 3. Programme for International Student Assessment (PISA) Reading Scores by Economic, Social, and Cultural Status (ESCS) Percentile, 14 Countries.

Figure 3 displays student performance of 15-year olds in reading by ESCS per- centile for the United States and each of the 13 countries whose students scored higher on average than U.S. students in 2009. The reported scores on the vertical axis are standardized as of 2000 to have a mean of 500 and a standard deviation of 100.

Figure 3 shows strong positive correlations between family ESCS and student performance in all 14 countries. Average test scores for students in the fifth per- centile across all the countries are about 350, far below the average of about 660 for students in the 95th percentile, and the test scores rise monotonically both overall and within each country. Even in countries such as Korea, Finland, and Canada that are typically viewed as having high-performing education systems, the patterns hold: achievement levels of the low-ESCS children fall far short of those of their more advantaged counterparts.

Compared to other countries, Finland and South Korea appear to have the most success with their very low-ESCS students. This relative success largely reflects each country’s strong commitment to education and to equal educational oppor- tunity. In Finland, this commitment is rooted in the country’s Lutheran heritage and the recognition that an educated population is the country’s most valuable resource (Sahlberg, 2011). In South Korea, the country’s historical ties to Confu- cianism and current efforts to expand the economy lead parents in all ESCS groups to put tremendous pressure on their children to succeed in school (Ahn, 2011).7 But

7 Moreover, to keep advantaged families from gaining an advantage by putting their children in “cram” schools for additional tutoring, the government requires most high school students to remain in school until 10:00 or 11:00 each weekday night and to attend school every second Saturday. These behaviors

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Table 2. Programme for International Student Assessment (PISA) test scores, and child context, selected countries.

Children Child Well- PISA PISA Students Living Being (UNICEF

Reading Math with Low in Poor Scale (1 to 6, 2009 2009 ESCS (%)a Homes (%)b High is Better)c

United States 500 487 10.4 20.6 2 Finland 536 541 3.9 4.2 5 Canada 524 527 3.7 15.1 3 Netherlands 508 526 6.5 11.5 6

Notes: aFrom Organization for Economic Co-operation and Development (2010). Absolute scale across countries, approximated as percent of students more than one standard deviation below the mean. bPercent of students with income less than 50 percent of median income within the country. cUNICEF scale 2010. Recalculated by the author to eliminate the education component (scale = 1 to 6).

even in those countries, large differences emerge between students from low- and high-ESCS families.

The performance of U.S. students (see the bars at the far right in each set) fol- lows the same pattern as the other 13 countries. Notably, however, U.S. students in families with ESCS below the median perform particularly badly relative to their low-ESCS peers in other countries, while U.S. students from more advantaged backgrounds perform reasonably well by international standards. That is, the largest shortfalls in performance among U.S. students are concentrated among those with relatively low ESCS. These shortfalls suggest there is room for the United States to do better by its disadvantaged students.

As was true across U.S. states, these within-country patterns imply that countries with high proportions of low-ESCS students are likely to have lower overall test scores than counties in which incomes are distributed more equally. The data in Table 2 illustrate some cross-country patterns by comparing the United States to three high-performing countries: Finland, Canada, and the Netherlands. The first two columns show that U.S. 15-year olds score at lower levels on average than their counterparts in the other countries on both reading and math tests. The following three columns show that this lower average performance is not surprising in light of the significantly greater disadvantage of children in the United States relative to the other three countries.

As shown in the third column, the percentage of students living in low-ESCS families (defined by the OECD as those more than one standard deviation below the mean) in the United States, is more than 2.5 times that in Finland and Canada and 50 percent more than in the Netherlands. In contrast to the ESCS measure, which is based on an absolute scale across countries, the poverty measure in the following column is country specific and refers to the percent of students who live in households with income less than 50 percent of the country’s median income. According to this measure, more than 1 in 5 children in the United States live in poverty, far more than the 1 in 25 in Finland, 1 in 7 in Canada, and 1 in 9 in the Netherlands. The final column denotes the material and health well-being of children as measured by UNICEF. The highest score of 6 for the Netherlands on this measure denotes that the country was above average among 24 countries in terms of both the material and health well-being of its children, and the lowest score of 2 for

impose large societal costs in that Korean children have little time to interact with their families and to pursue nonschool activities (based on visits to Korean schools by the author in June 2011).

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the United States means that it was below-average on both measures.8 The patterns are fully consistent with the view that the low average test scores of U.S. students largely reflect our extremely high poverty rate and our relative lack of attention to the overall well-being of our children.

This pattern emerges in a more systematic manner from a large number of em- pirical studies based on international test score data such as Trends in Math In- ternational Mathematics and Study (TIMMS) and earlier versions of PISA, as ably summarized by Hanushek and Woessman (2010). In particular, cross-country stud- ies estimated at both the country level and the student level find strong associations between students’ socioeconomic backgrounds and their educational achievement (Hanushek & Woessman, 2010, p. 16 and Table 6). Moreover, the studies document that these associations with educational outcomes are far stronger than those for school resources.

My reading of the patterns in Table 2, as buttressed by the evidence from the larger and more systematic empirical studies, is that it would be difficult, if not impossible, for the United States to replicate the success of higher scoring countries such as Finland, Canada, and the Netherlands by focusing on school reform alone, and that is especially true for school reform that pays little attention to meeting the social needs of disadvantaged children.

I find it useful to summarize the basic point that I am making here with the following simple functional relationship:

Educational outcomes = f (public school quality, context). Public school quality refers to the quality of a specific school or of a larger unit de- pending on whether the analysis refers to individual schools, school districts, states, or countries. Context refers here to the socioeconomic backgrounds of the students, as well as cultural considerations, including the commitment level of families to the education of their children (as I highlighted above with reference to Finland and Korea) and the success of the country in meeting the noneducation needs of children (as I highlighted with reference to the Netherlands). According to this formulation, low educational outcomes could well reflect the low quality of the public schools, or they could reflect an adverse educational context, or some combination of both. Within a single country, the SES background of the children’s families is likely to be the most important component of context.

Defining and measuring what I have labeled “public school quality” raises a com- plex set of conceptual and empirical issues that Susanna Loeb and I have addressed elsewhere (Ladd & Loeb, in press). Two points about the concept as I am using it in this formulation are worth noting. First, because context matters, educational out- comes alone—even far richer and more comprehensive measures than the student test scores now being used in the United States—cannot serve as an appropriate proxy for school quality. To serve that role, at a minimum they would need to be adjusted for the relevant educational context of the school, district, or state.

Second, it may be helpful to think of public school quality as the direct output of the education system, where the system includes the managerial input of state and local education policymakers, school-level inputs such as teachers and principals, and educational resources such as technology, facilities, and instructional materials. School quality may differ across schools or jurisdictions because of differences in

8 The UNICEF overall measure of child well-being also includes educational well-being. I deleted the educational well-being component for this analysis to focus on the non-education components of child well-being (UNICEF, 2010).

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both the quantity and quality of inputs as well as in the effectiveness with which they are used. Because of the complexity of the concept, it is difficult to measure public school quality in practice, and is probably best done through some combination of cost-adjusted resources and direct observation (Ladd & Loeb, in press).

The functional relationship highlights that while education policymakers have direct control over school quality, they have less control over educational outcomes because of the role that context—and particularly the family background of the students—plays in shaping educational outcomes.

POSSIBLE POLICY RESPONSES TO THIS EVIDENCE

I now turn to the potential policy responses to the empirical correlation between educational outcomes and educational context. Policy responses depend in part on the policy goals. Throughout the rest of my talk, I will assume there are two interrelated goals: one is to increase average educational outcomes, and the other is to reduce skills and attainment gaps between advantaged and disadvantaged students. Raising average achievement or performance levels is often justified in terms of the need to prepare graduates for a knowledge-based society and the desire to make sure U.S. workers remain competitive with their international counterparts for future jobs. Perhaps even more important, a well-educated populace is essential for a functioning democracy and for the nurturing of a culturally rich and innovative society. Reducing achievement gaps recognizes the importance of education to the life chances of individuals and the fact that the United States as a whole has a stake in assuring that all citizens can participate fully in the economic and political life of the country. Of course policies that closed gaps by raising the achievement of disadvantaged students with no decline in the achievement of advantaged students would also raise average achievement.

Reduce the Incidence of Poverty or Low SES

One logical policy response to the correlations I have been describing would be to pursue policies to reduce the incidence of poverty or other contributors to low SES. That might be done, for example, through macro-economic policies designed to reduce unemployment, cash assistance programs for poor families, tax credits for low-wage workers, or an all-out assault “war on poverty” as pursued by Lyndon Johnson in the 1960s. This approach would appear to be a particularly desirable policy response in the present period given the current high unemployment rates and also the dramatic increase in income inequality in this country since the early 1970s. In the three decades after 1970, the coefficient of variation in family income increased by 40 percent (Campbell et al., 2008, Table 3.1). Moreover by 2010 the poverty rate had risen to 15.1 percent, its highest level since 1993, and the child poverty rate had risen to 21 percent.

Inattention to these inequalities is likely to lead to even greater achievement gaps in the future. Moreover, many considerations that extend well beyond the realm of education policy make a compelling case for the country to take strong steps to reduce income inequality.9

Nonetheless, I do not dwell on this policy response here. The main reason is that such a policy thrust is not in the cards, at least in the near term. With the budget crises at the national and state levels, and the strong political power of conservative groups, no one with significant political power is actively pushing the strategy of

9 See, for example, the arguments for why greater equality makes societies stronger in Wilkinson and Pickett (2010).

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reducing poverty and income inequality at this time. Nor are they likely to do so in the immediate future, unless the current protests in New York City and elsewhere succeed in putting the issue of income inequality back on the policy agenda.10

A second reason for not dwelling on this policy response, regardless of how desir- able it may be, is that any serious effort to reduce poverty and to equalize incomes will take a long time, and the country cannot wait that long to address the educa- tional needs of the current generation of children.

I note, however, that past efforts to address poverty and socioeconomic inequali- ties appear to have played some role in reducing achievement gaps, especially those between black children who are disproportionately represented among low-income families and white children who tend to come from more affluent families. In combi- nation with other policies including civil rights initiatives, for example, antipoverty programs during the 1960s appear to have contributed to some of the significant reduction in the black-white test score gaps during the 1960s and early 1970s. But, as I said before, I am not optimistic that such policies will be revived in the current political environment.

Deny the Power of the Correlation and Expect Schools Alone to Offset Any Adverse Effects of the Educational Context

An alternative policy response is for education policymakers simply to deny the correlation between education outcomes and family background or other relevant elements of the context. Policymakers can deny the correlation by setting the same high achievement and attainment expectations for all students and requiring all schools to meet the proficiency standard, regardless of the mix of students in the school. In other words, schools serving large proportions of low-SES students that failed to fully offset the adverse family contexts of their students would be labeled as failing schools. That is, in fact, what our current federal policy, NCLB, does.

The starting point under NCLB is similar achievement standards for all children. Specifically, it requires that all children meet grade-specific proficiency standards, as measured by test scores, by the school year 2013/2014, with the proviso that the proficiency standards can differ by state. Because many children, and especially those from disadvantaged backgrounds, started out well below the achievement standards, the legislation required states to set year-by-year goals for the schools that would move all students to proficiency by the deadline. Of course, even if we set-aside the role of family background, the goal of 100 percent proficiency is absurd unless the proficiency levels are set so low as to be meaningless. The reason is that it ignores the normal distribution of talent among individual students. But my focus here is on how the legislation in practice denies the power of the correlation between family background and student achievement for groups of students.

Under NCLB, each school must meet the same standard, regardless of whether it serves low- or high-SES students and must do so for all relevant subgroups within the school defined by income, minority status, and Limited English Proficient status. Interestingly, NCLB policy explicitly acknowledges that some groups of students are likely to perform at lower levels than others, which is fully consistent with the correlations I have been talking about. But NCLB acknowledges those differences only to make sure that the schools do not ignore the disadvantaged students. In fact, the policy is clearly based on the presumption that the schools themselves can and should offset any educational disadvantages those children bring to the classroom. In this sense, NCLB denies the correlation between family background and student achievement.

10 This is a reference to the Occupy Wall Street protests that were occurring in New York City and that were spreading to other cities at the time this talk was written.

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Possible Rationales for Denial

Why might policymakers have chosen to deny the correlation? I can think of at least four reasons.

One possibility is that policymakers believe that schools themselves should off- set the effects of low SES. This normative view might reflect in part the historical observation that schooling has often served as the route to prosperity and social mo- bility. This normative view may also reflect the increasing importance of education to an individual’s life chances. Data clearly show, for example, that the earnings of workers with low levels of education have been level or even falling in recent years for a combination of demographic, technological, and institutional reasons, while the earnings of those with a college degree have risen, which implies a significant increase in the returns to education (Goldin & Katz, 2008).

This normative perspective suggests that it would be inappropriate—and even immoral—to let schools off the hook simply because they serve large concentrations of children who face greater educational challenges than other children. It does not, however, confront the difference between what might be desirable from a normative perspective and what is feasible in practice.

A second possible rationale for policymakers to deny the correlation between low SES and educational outcomes is that they simply do not want to set lower expec- tations for some groups of children than for others, or to engage in what President George W. Bush referred to as the “soft bigotry of low expectations”(quoted in Noe, 2004). The fear here is that if they set lower outcome goals for some schools than for other schools, it will become a self-fulfilling prophecy.

Sending a signal that some children are less able to learn than others would be inconsistent with the basic tenet of the standards-based reform movement. As articulated by O’Day and Smith in their well-known 1993 paper, the standards movement starts from the premise that, while it may take some children longer than others, all children can learn to high and ambitious standards. Of course, for that learning to occur, the conditions must be right. In the effort to translate their views into policy, supporters of standards-based reform paid attention to part of the required conditions by calling for “opportunity to learn” (OTL) standards (Ravitch, 1995). These OTL standards were intended to make sure that all children would have access to the quality teaching necessary for them to learn, but still implicitly assumed that schools alone could address the challenges of low-SES children. In any case, the high resource costs of implementing OTL standards made them a political nonstarter.11

Though understandable and also commendable in some ways, this reluctance even to suggest that some children face educational challenges that schools alone may not be able to address signifies a denial of the basic correlations between family background and student achievement. Simply wanting something to be true does not make it so.

A third possible rationale for denying the correlation is the evidence that some schools appear to have successfully achieved high academic results for large con- centrations of children from disadvantaged family contexts. The argument is that if some schools can “beat the odds,” it is reasonable to expect all schools to do so. Included among the “successful” schools are various charter schools, including the highly touted Knowledge is Power Program (KIPP) schools, as well as specific schools operated by charismatic leaders.

One must be careful about this argument for a number of reasons. One is that a close look at the data shows that many of the schools cited as being successful in

11 Discussion with Diane Ravitch, October 4, 2011.

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