Does Choice Increase Information? Evidence from Online School Search Behavior*

Transcription

1 Does Choice Increase Information? Evidence from Online School Search Behavior* Michael F. Lovenheim Cornell University and NBER Patrick Walsh St. Michael s College April 2014 Abstract We examine whether changes in the local school choice environment affect the amount of information parents collect about local school quality, using data on over 100 million searches from greatschools.org. We link monthly data on search frequency for over 800 geographic areas to information on changes in open enrollment policies, tuition vouchers, charitable scholarship tax credits, tuition tax credits, and local choice opportunities driven by No Child Left Behind sanctions. Our results indicate that expansions in school choice rules and opportunities in a given area have large, positive effects on the frequency of searches done for schools in that area. These estimates suggest that the market for local schools is characterized by incomplete information and that the information parents have about the quality of local schools is endogenous to the choice environment they face. * We gratefully acknowledge funding for this research from the Cornell Institute for Social Science and St. Michael s College. We also thank Leigh Wedenoja for excellent research assistance.

2 1. Introduction School choice policies, which allow students to attend local schools other than the ones to which they are zoned, have grown dramatically in popularity in the past several decades. For example, from 1993 to 2007, the percent of students attending their assigned public school dropped from 80 to 73 (Grady, Bielick and Auld 2010). This shift is driven by the increasing prevalence of charter schools, intra- and inter-district school choice programs, and private school attendance that is supported by tuition voucher programs. Furthermore, the 2001 No Child Left Behind Act includes provisions that students in failing schools that receive Title I funds should be allowed to attend a nearby non-failing school of their choosing. School choice thus has become a prevalent feature of American K-12 education. A central focus of prior school choice research has been on identifying its impacts on students who switch schools, most of whom move to another public or publicly-funded charter school. This research does not reach consistent conclusions, often with widely varying results across studies. What has received less attention is that in order for school choice to have positive impacts on student academic achievement, parents need to have accurate information about the quality of local schools. If some parents are unable to assess which local schools will be best for their children, school choice policies may not produce positive impacts on student achievement. Heterogeneity in parental knowledge about local schooling options thus may explain some of the variation in results from existing research on the effects of school choice. Consistent with this hypothesis, some prior work has found that providing choice-eligible families with simple and salient information about school quality leads them to select higherquality schools. Student test scores also rise as a result (Hastings and Weinstein 2008). This research suggests that many parents do not initially have comprehensive information about the 1

3 performance of local schools, but once given this information they use it to make school choices that benefit their children. The findings from this study underscore the importance of understanding how parents obtain such school quality information. This paper examines a novel question regarding parental information: does observable information search behavior respond to the local school choice environment faced by families? Parents with very limited choice sets may, quite rationally, choose not to engage in costly information searches. Likewise, parents with rich choice sets may find an investment in obtaining school information to be worthwhile. If so, then part of the benefits of school choice may be due to more accurate parental decision-making, driven by the better information on school quality parents collect in response choice policies. This may be particularly true for low- SES families, who face constraints in their ability to move to into areas with higher-quality schools and who thus face low returns to accumulating information absent school choice. Until now, the lack of data on parental information-gathering patterns has precluded empirical study of how parents acquire knowledge about the quality of local schools. To fill this gap, we use online searching behavior from GreatSchools Inc., a nonprofit organization whose website provides comparison information and reviews on all U.S. K-12 schools. The website provides simple and straightforward information on school test scores, user reviews, and school demographics in a manner that facilitates comparisons across schools. We obtained all search terms entered into their search engine between January 1, 2010 and October 31, 2013, comprising over 100 million individual searches. We use this information to measure the how frequently searches are performed for schools in each city and in counties outside of cities that we can identify in the data. We combine these data with state-level measures of school choice policies that relate to open enrollment, school vouchers, charitable 2

4 scholarship tax credits, and tuition tax credits. We also calculate city- and county-level proportions of the schools subject to choice under the NCLB provisions for Title I schools for the 24 states that constitute our analysis sample. Together, these variables provide comprehensive information about the choice environment people face in different areas of the country at any given time. We estimate difference-in-difference models that relate changes in choice policies at the state or local level to changes in online search behavior. Our findings suggest that parent knowledge about school quality is highly responsive to the choice environment they face. A one standard deviation increase in open enrollment regulations increases the number of searches by 2.7%, and for tuition vouchers a one standard deviation increase leads to 6.2% more searches. The effect of a one standard deviation increase in tuition tax credit regulations is large, at 25.5%, while a one standard deviation increase in the proportion of schools subject to open enrollment due to NCLB sanctions would increase search frequency by 8%. However, we find that charitable scholarship tax credits for private schools are negatively correlated with search. Using the individual search terms, we also examine how choice policies relate to searches for charter, private, high, middle, and elementary. We find that open enrollment policies substantially increase searches for all terms, while NCLB-induced choice mostly affects search for high, middle, and elementary terms. Overall, our results indicate that choice policies induce information search behavior and that this search behavior is greatly facilitated by the availability of online information. The type of information provided by greatschools.org has been shown by previous studies to lead parents to choose schools that increase student achievement. It is beyond the scope of this study to determine whether the information search behavior we observe leads to different school choices, 3

5 or to impacts on academic outcomes. However, that expanding school choice can lead parents to obtain more information is a novel finding in this literature and underscores the fact that there is not full information in this market. The rest of this paper is organized as follows: Section 2 describes the previous literature on school choice and the role of parental information and preferences, Section 3 describes the GreatSchools Inc. search data as well as the school choice information we use, Section 4 describes our empirical strategy and results, and Section 5 concludes. 2. Previous Literature As school choice policies become more prominent throughout the United States, a growing volume of research has emerged that examines what school features parents value, what factors influence which schools they choose, and the consequences of those choices for student academic performance. Existing estimates of the effect of choice on the outcomes of switching students are ambiguous. With respect to open enrollment policies, Hastings, Kane and Staiger (2006), Deming et al. (2014) and Hastings and Weinstein (2008) show large impacts of attending one s first-choice school because of winning an admissions lottery on academic performance and on college enrollment and completion. Conversely, Cullen, Jacob and Levitt (2006) show no positive, and perhaps even negative, impacts of winning a school choice lottery on student academic performance in Chicago. The literature on charter school effects is similarly inconsistent in terms of findings. Abdulkadiroğlu et al. (2011) find large effects of no excuses charter schools on academic achievement, with no effects evident for charter schools that more closely resemble traditional public schools. Dobbie and Fryer (2011) find similar-sized effects for the Harlem Children s Zone in New York City, while Angrist, Pathak and Walters (2013) show that positive impacts of 4

6 charter schools appear localized to urban environments. Other studies examining charter school effectiveness using methods other than randomization from the admissions process have found charter schools either do not increase student achievement (Bettinger 2005; Bifulco and Ladd 2006; Hanushek et al. 2007) or do so only after a substantial lag (Sass 2006). 1 There also is some evidence that charter schools increase students behavioral outcomes even when they do not increase test scores (Imberman 2011), which can have high returns in the labor market. A final type of school choice studied by researchers is tuition vouchers for private school attendance. Rouse (1998) studies the Milwaukee voucher program and finds that receiving a voucher increased math test scores. Howell, Wolf and Campbell (2002) examine randomized voucher experiments in three cities and find large impacts on math scores, but only for African Americans. Krueger and Zhu (2004), however, show the estimates for New York City weaken substantially once one accounts for non-random attrition. Thus, the literature has not reached a consensus about the effects of choice on the achievement of switching students. Much of the variability in findings across studies could plausibly be due to heterogeneity in how families respond to school choice policies, driven in part by differences in the information parents have about local schools. While some recent studies have begun to manipulate the school quality information parents have to examine its effect on school choice, currently there is a paucity of information about how much parents know about the attributes of schools in their area and how they acquire school quality information. Our study is designed to address these questions more directly than has been possible in prior work. 1 One hypothesis for the differences in findings across studies is that the randomized lottery studies, which tend to find large, positive effects, are more credibly identified than the non-lottery studies that usually employ student fixed effects. However, as shown by Abdulkadiroğlu et al. (2011), randomized lottery estimates and models that control for lagged student test scores yield similar results. Thus, the methodological differences across these studies are unlikely to be the central reason for differences in results. 5

7 The effects of school choice on student achievement also are influenced by differences in the characteristics of schools that parents value. In survey-based studies, parents across the socio-economic spectrum state that academic quality is a high priority (Armor and Peiser 1998; Schneider et al. 1998; Vanourek et al. 1998; Kleitz et al. 2000). A substantial volume of research also shows that higher school test score levels are capitalized into housing prices, with a one standard deviation increase in test scores across schools raising home prices by between 1 to 2 percent (Black 1999; Figlio and Lucas 2004; Bayer Ferreira and McMillan 2007; Black and Machin 2011; Nguyen-Hoang and Yinger 2011). However, value-added information does not appear to be valued by homeowners at the margin (Imberman and Lovenheim 2013). The GreatSchools website contains the type of test score information that prior research suggests parents value, while it does not include value-added measures that they appear to value less. Studies examining parents actual school selection have found much more heterogeneity by socioeconomic status in revealed preference for academic quality. For example, Hastings, Kane, and Staiger (2010) estimate a model of school choice based on parents revealed preferences for school attributes in Charlotte, NC. They find that higher-income families prefer higher-performing schools, while minority families trade off preferences for school performance with preferences for schools that look demographically similar to them. Further evidence on heterogeneity in parental preferences comes from Schneider and Buckley (2002), who examine parental search behavior on a website that provided information on choice schools in Washington D.C. They find that while parents of all backgrounds demonstrated interest in the composition of student bodies, college-educated parents were much more likely to click through to information on school test scores, while non-college-educated 6

8 parents were slightly more likely to investigate school location and facilities. 2 One implication of this study is that providing simple and salient information to families in a manner that reduces the need to search for specific information may reduce disparities across the income distribution in knowledge about school quality. Results from Hastings and Weinstein (2008) are consistent with this hypothesis. They perform two experiments in which they manipulate the information environment of families who are allowed to choose their public school. Most relevant for our study, they send simplified test score sheets to a randomly selected group of parents in the Charlotte-Mecklenburg school district in North Carolina whose children attend schools that are mandated to allow choice under No Child Left Behind. These test score sheets allow parents to easily compare schools on this outcome in their local area, and thus this information treatment is very similar to the type of information and the ease of comparison across schools allowed by the greatschools.org website. They find that parents are more likely to exercise their choice options when there are highquality schools nearby but that responsiveness is sensitive to how close the alternative schools are. Furthermore, they present evidence that students who switch to high test score schools experience large increases in academic performance. These findings suggest that parents do not have full information about the quality of schools around them and that providing them with simple information about local schooling options can have large effects on their children s academic achievement. This research highlights the value of further understanding how parents 2 International evidence has not shown as clear a relationship between socioeconomic status and interest in schools quality. In the U.K., Burgess et al. (2009) find that the relationship between socioeconomic status and preference for academic quality is attenuated by controls for each family s realistic choice set. In Chile, Mizala and Urquiola (2007) use a regression discontinuity approach to show that receiving a widely-publicized award designation does not affect a school s enrollment, tuition, or composition. They argue that this result undermines the value of information in school markets. 7

9 obtain such information and whether the information itself is endogenous to the choice environment they face. This paper asks a novel question in this literature, namely whether parents respond to school choice policies by collecting the type of information that has shown to generate positive school choice effects on student outcomes. Even parents who genuinely value academic quality may rationally choose not to obtain information on schools that are outside their choice set, while a costly investment in information may be worthwhile for parents with many options. Howell (2006) finds suggestive evidence for this hypothesis in Massachussetts, where parents in NCLB Title I schools were much more likely to be able to name a preferred alternative public, private, or charter school than parents in non-title I schools. However, supporting the imperfect information hypothesis, he finds that around half of the preferred alternatives named were themselves underperforming Title I schools. The present study broadens this investigation to a nation-wide sample, asking whether a higher degree of choice whether because of open enrollment policies, private school choice or Title I-based school choice increases informationseeking behavior among parents. 3. Data 3.1. Great Schools Search Data Our school search data come from monthly search terms entered into the GreatSchools Inc. website ( by users. This website, which is free to use and does not require registration, provides detailed information on the universe of U.S. public and private schools. The website provides information along a number of dimensions that may be of interest to parents and that can be difficult to locate from other sources. Specifically, the website provides information from the most recent year available on school enrollment, grade levels 8

10 served and the racial/ethnic distribution of the student body. Featured prominently are two pieces of information that may be of particular interest to families with children. The first is a score from 1-10 based on recent standardized test results. Test scores by grade and demographic subgroup also are provided. These mean test scores have been shown to be valued by parents in the US as measured by capitalization into housing prices (Black and Machin 2011), and this website makes it very easy to observe this information and compare this metric across local schools. The second piece of information that parents may value is community ratings that ostensibly come from current and former students and their families. Schools are rated from one to five stars on three factors: teacher quality, principal leadership and parent involvement. There also are direct testimonials from reviewers, similar to the comments one might observe on Amazon.com for various products. This kind of rating information is unique to greatschools.org. Our data come from the universe of specific search terms entered by users from January through October 31, The data contain, for each month in our data, the specific text string entered into the search bar and frequency of times such a term was entered that month. Users have the option of entering the name of a city (e.g., Rochester), a school district (e.g., Brighton), a school (e.g., Twelve Corners Middle School), or a zip code (e.g., 14850). Users also can enter addresses and find schools nearby those addresses. Beginning in August 2011, we also have the state associated with each search from a state dropdown menu on the website. In prior months, we only have the state if it is entered into the search bar directly. Our data do not reveal the origin of the search (such as a user s IP address); only the target of the search is known. The data are comprised of 102,616,862 individual searches that contain over 3 million individual search terms over the almost four year study period. However, the distribution is 9

11 heavily skewed: the top 1,000 search terms account for 36 percent of the total searches and the top 2,000 search terms account for 46%. Using these raw search data, we generated counts of searches for each month for all feasibly identifiable areas that potentially constitute a school choice zone for residents. These areas are either a Census Core Based Statistical Area (CBSA) code that defines a city or it is a county that is not in a CBSA. Searches matched to counties not in CBSAs typically are for smaller cities and towns that have multiple schools districts in the county but that are too small to be considered a macro or micro CBSA. Henceforth, we term these individual CBSAs and counties Search Units. We can match about 60% of the search terms, or about 80 percent of the total searches. We match schools to cities and counties using the terms entered into the search field. In the majority of searches, users enter a city, zip code, county or school district name. These are fairly straightforward to match with local areas. We use an address finder to match the approximately 1% of searches that contain address strings. There are four core reasons why we are unable to match the remaining 20 percent of searches to cities or counties in our data. First, many searches include typos. However, since the greatschools.org search tool does not use probabilistic matching, searches with typographic errors return no results. 3 Thus, they are proper to exclude. Second, some users enter schools names only into the search engine without any identifying information about the school district. While these searches will provide results, they may be very difficult to use because all schools in the US with that name will appear. Thus, we think it likely that people searching for Lincoln Elementary School or even just for high school, for example, will search again using a more refined search. We have no way of determining which search terms originate from the same 3 Such errors can either come about from misspelling a word or from including the wrong state in the search, for example by search for New York, LA. Such errors are very likely to lead to a new, corrected search by the user. 10

12 individual, so to the extent that such searches would lead to a subsequent search, excluding these vague search terms is appropriate to avoid double counting. As we have no way of determining where such searches are targeted, we exclude them. The prevalence of such searches is unlikely to be correlated with changes in the choice environment, however. Third, we cannot match some of the addresses in the data to a location, but this is a very small proportion of our sample given the infrequency of users entering addresses. Finally, we cannot match some of the search terms because they refer to very small towns or rural areas that are difficult to locate systematically. These are areas where school choice is not likely to be relevant due to the lack of local schooling alternatives, however, so excluding these searches is unlikely to affect our estimates. The lack of state identifiers in much of the data prior to August 2011 creates some difficulties in matching searches to locations. Some city names, such as Springfield and Portland are represented in multiple states, and the same problem exists for school district and county names. Our solution to this problem is to assign these searches in the same proportions pre-august 2011 as they are post-august 2011 when we observe state identifiers. To the extent that the post-august 2011 proportions are themselves influenced by school choice policies, this assignment algorithm will cause our estimates to be attenuated. This attenuation stems from the fact that this assignment mechanism misses some of the within city and county variation in search frequencies in these geographic regions. Figure 1 shows the frequency of total searches by month beginning in January 2010, which is month 1. There is a clear cyclicality of search frequency, which is high in January through April and again in July and August. Search frequencies are quite low during the fall. The number of searches also has grown over time, which partially reflects the growth of the greatschools.org website. Tabulations in Table 1 show that the mean number of searches is 468, 11

13 but the standard deviation is enormous, at almost five times the mean. The large standard deviation is driven by the fact that there are several large cities that generate a lot of searches. Below, we will use log total searches as our dependent variable in order to guard against our estimates being unduly influence by these large areas School Choice Policies In order to characterize the school choice environments in the US, we collect state and local information on policies that facilitate residents attending a school other than their zoned public school. We consider five different types of school choice policies: open enrollment, tuition vouchers, tax credits for donations to private scholarship charities, tuition tax credits, and open enrollment for Title I schools driven by No Child Left Behind (NCLB) sanctions. We discuss each of these policies in turn below. Data on open enrollment plans were collected at the state level. Although many school districts have intra-district open enrollment programs, and some metro areas have inter-district open enrollment plans, the main variation in these policies is driven by state laws allowing or mandating inter- or intra-district open enrollment. These data were generated by interacting a snapshot of state laws in 2011 from CCSSO (2013) with changes in state open-enrollment laws before and after this snapshot. We generate a State Open Enrollment Index that characterizes the number of regulations supporting open enrollment in a given state and month. This index can potentially vary from zero to eight and is the sum of state-level indicators, by month, for i) intradistrict mandatory open enrollment, ii) intra-district voluntary open enrollment, iii) intra-district open enrollment for failing schools, iv) intra-district open enrollment for low income schools, and an equivalent set of indicators for inter-district open enrollment. Table 1 shows that the mean 12

14 state in our sample has 1.47 of these laws, with a standard deviation of The index ranges from zero to four, as no state has open enrollment policies along all 8 dimensions we measure. We also measure state-level tuition voucher programs. Between January 2010 and December 2013, targeted and/or capped state voucher programs were introduced in Indiana and Louisiana and were expanded in Ohio. Basic information on the existence of these programs was obtained from Friedman Foundation (2013), with details obtained from state websites. We calculate a State Tuition Voucher Index, which is the sum of state-level indicators for i) whether a state-level voucher program has been announced and ii) whether a voucher program is active. On average, states have 0.28 of these regulations, with a standard deviation of A third source of variation in school choice is state-level tax incentives for donations to private scholarship charities. These charities, in turn, award tuition scholarships for local private schools to eligible students. During the four-year period considered, such programs were in place in six states and initiated in another five. Basic information on the existence of these programs was obtained from Friedman Foundation (2013), with details obtained from state websites. We characterize these laws using a State Charitable Scholarship Tax Credit Index, which is the sum of three indicators, by month, for i) whether a state-level charity scholarship tax credit has been announced, ii) whether the charity scholarship tax credit is active, and iii) whether the donations to the scholarship charity are fully deductible as well as two continues measures of the percentage of individual and corporate donations that are tax deductible. On average, states have just over one of these laws, but the standard deviation of 1.82 shows there is much variation across states and over time. Fourth, we collect state-level tuition tax credits for parents who pay to send their children to private schools. During the four-year period considered, such incentives were in place in four 13

15 states and initiated in a further three. Basic information on the existence of these programs was obtained from Friedman Foundation (2013), with details obtained from state websites. The State Tuition Credit Index is the sum of indicators by state and month for i) whether a state-level tuition tax credit has been announced and ii) whether the tuition tax credit is active. On average, Table 1 shows this index is 0.49 with a standard deviation much larger than the mean. The final source of variation in the school choice environment we consider is the districtlevel share of schools that are eligible for public choice under NCLB Title I provisions. Under NCLB, a school that received Title I funds and fails to meet AYP for two consecutive years must offer its students the option of transferring out of the school to another local non-failing school. We calculate, in each Search Unit defined by the areas over which individuals search for schools, the average proportion of schools in each school year and each district that is subject to NCLBinduced choice. We calculated these percentages by collecting information from each state department of education website on the School Improvement status of each Title I school over our analysis period. All Title I schools in School Improvement status face NCLB sanctions and must offer open enrollment. Districts then were geographically matched to CBSAs and counties to establish the Search Unit proportion of schools eligible for public choice. This measure, which varies at the Search Unit-month level, is the District NCLB Choice Percentage. Currently, we have collected information on 24 states in our sample, and our analysis below focuses only on these states. 4 As demonstrated in Table 1, about 24% of schools in our sample have NCLBinduced choice, with considerable variation across areas and over time. Table 2 presents correlations among the school choice variables in our sample. These policies are not highly correlated with each other, and the sign of the correlations vary. This 4 These states are Alabama, Arkansas, Arizona, California, Colorado, Georgia, Iowa, Illinois, Indiana, Kentucky, Louisiana, Maryland, Michigan, Minnesota, Missouri, Mississippi, Montana, North Carolina, New Jersey, New York, Ohio, Oregon, Pennsylvania and Tennessee. 14

16 suggests that there is sufficient independent variation in each of these laws to examine their independent impacts. However, that they are even weakly correlated with each other suggests it is important to control for all of these variables together to accurately characterize the choice environment people in each local area face. In order to present the variation in these measures that we use for identification, Figure 2 plots the monthly means of each of the state-level school choice regulation indices along with a one standard deviation range above and below the mean. Figure 3 shows a similar plot for the NCLB choice percentage. In both figures, it is clear that the majority of the variation is crosssectional: the width of the standard deviation range is much larger than the variation over time. However, there is some within geographic area variation over our sample period, as evidenced by both shifts in the means and changes in the width of the standard deviation bands. Aside from state open enrollment policies that increase and then decrease, the other school choice policies show an expansion of the prevalence of these laws over our analysis period, both in terms of the mean and the variance across areas. 4. Methodology and Results 4.1. Methodology The goal of this analysis is to link the school choice environment to online search behavior. As discussed in Section 3.2., we characterize the school choice environment using five policy types: open enrollment, tuition vouchers, charitable scholarship tax credits, tuition tax credits, and open enrollment induced by NCLB sanctions. The central endogeneity concern underlying such an analysis is that the state regulations regarding school choice are driven by unobserved local demand for less constrained local schooling options. That is, states with laws that allow more school choice may have more search behavior because residents have higher 15

17 demand for choice options, rather than policy changes inducing more search. Despite the prevalence of cross-state variation in school choice laws, using such variation to identify the effect of these laws on online school search behavior relies on assumptions that are difficult to justify. Using cross-sectional variation in NCLB-induced choice also is problematic, as areas in which more Title I schools are in school improvement are likely to be less wealthy and have populations that are more location-constrained. This feature highlights the importance of examining how these individuals respond to changes in their choice environment, because the latent demand for switching schools may be particularly high in places with more lowperforming schools. However, the fact that higher NCLB-induced choice is likely to be negatively correlated with socioeconomic status suggests the cross-sectional relationship between the proportion of schools in a district subject to choice under NCLB and search frequency will be negative. This negative correlation is due to the fact that lower-ses households have less access to the Internet (Chaudhuri, Flamm and Horrigan, 2005) and the fact that constraints in the ability to move to a better district may preclude families from collecting school quality information. The concerns with using cross-sectional state and NCLB-induced choice variation argue for estimating a panel model with geographic fixed effects that can account for the fixed differences across areas that are correlated with latent demand for school choice as well as the demographic composition of the area. As Figures 2 and 3 suggest, these fixed effects soak up a lot of the variation in our explanatory variables of interest, but the loss of statistical power is necessary to support a more credible identification strategy. We estimate fixed effects models of the following form: 16

18 ln(search) imt = β 0 + β 1 OE smt + β 2 Voucher smt + β 3 Scholarship smt + β 4 TuitCred smt + β 5 NCLB Choice imt + β 6 Waiver smt + δ i + γ m + θ t + ε imt, (1) where ln(search) imt is the log of the total number of searches in Search Unit i (in state s) in month m and in calendar year t. As discussed in Section 3.1., a Search Unit is defined as a Census CBSA (i.e., a city) or a county for the cities we can identify in the search data that are too small to be considered CBSAs. Thus, a Search Unit constitutes a broad area over which families exposed to more school choice options might search. Our analysis is based on 880 Search Units and 24,098 Search Unit-month-year observations. The OE, Voucher, Scholarship, and TuitCred variables are the regulation indices described in section 3.2 and vary by state (s), month, and year. NCLB Choice varies at the statemonth-search Unit level and is the mean proportion of Title I schools in each school district in the Search Unit that is in school improvement under NCLB and thus whose students have the option of attending another local school that is not in school improvement. We also control for Waiver, which is an indicator equal to one for the school years in which a state has successfully applied for a waiver from NCLB restrictions. 5 These waivers exempt states from NCLB-based sanctions. The exact impact such exemptions have on student choice varies by state, but often states allow students who already have switched schools to continue their enrollment while eliminating the school choice options for other students in Title I schools previously labeled as being in school improvement. Equation (1) also contains month fixed effects (γ m ), year fixed effects (θ t ) and Search Unit fixed effects (δ i ). 6 The Search Unit fixed effects control for fixed differences across cities that may confound search behavior with unobserved heterogeneity in the composition of local residents. Furthermore, the year fixed effects control for the upward trend in search prevalence 5 This information is available at 6 We will use the terms Search Unit and city interchangeably throughout the remainder of the paper. 17

19 shown in Figure 1 that is in part due to the growth of the GreatSchools website. The month fixed effects account for seasonal patterns in search behavior that may be correlated with the timing of law changes or with changes in NCLB school improvement status. Conditional on these fixed effects, parameters β 1 -β 6 are identified using within-state or within-search Unit variation over time in choice regulations and NCLB sanctions, relating these changes in the search environment to changes in city-level search prevalence. The assumptions underlying identification of the parameters in equation (1) are akin to any difference-indifference model: 1) changes in the choice environment are uncorrelated with prior trends in search prevalence and 2) the timing of changes in the choice environment are uncorrelated with unobserved local shocks that independently influence search behavior. The relatively short time frame of our panel makes it difficult to test these assumptions. However, the fact that we have collected a comprehensive and detailed set of state-level regulations regarding school choice makes it unlikely our estimates are confounded with unobserved changes in the legal environment as they relate to school choice. Although we do not control for local business cycle measures (such as unemployment and real income per capita), we do not find it very plausible that such variables would be independently correlated with search prevalence or with regulations that affect the choice environment. 7 Changes in state or local demographics also are of potential concern. For example, a locality that is attracting relatively low-ses families might see a decrease in search behavior as well as an increase in Title 1 choice schools, while a gentrifying area may see the opposite. However, note that this effect will bias the estimate on NCLB Choice towards zero. Furthermore, 7 If business cycle variation causes changes in the choice environment, then we would not want to include local macroeconomic controls in our models because they are endogenous to the choice environment. The identification concern in equation (1) thus is that search behavior independently varies over the business cycle and happens to be correlated with the timing of changes in the choice policies we examine. 18

20 if our NCLB-induced choice estimates were being driven by changes in local composition, then the effect of waivers and NCLB-induced choice should be of the same sign. We show below they are not, which is inconsistent with our estimates being seriously biased by migration. Our estimates will be biased upward if expanded choice policies attract higher-ses families, who are also more likely to search for information. However, since most non-nclb choice policies are implemented on the state level, this effect will be muted unless educated parents move to a different state because of its choice policies. Moreover, this effect would tend to be small in the 4-year window we examine. Finally, we show below that changes in city-level choice driven by NCLB generates higher search behavior in June and July, which is consistent with families responding to increased availability of school choice by collecting information through greatgchools.org. We estimate equation (1) for the 24 states for which we have collected NCLB-induced choice data. Due to the potential for serial correlation within cities over time in search behavior and because our choice measures vary at the city level or higher, we cluster all standard errors at the Search Unit level below Baseline Results The results from estimation of equation (1) are shown in Table 3. Panel A of the table presents estimate without city, month or year fixed effects, while in Panel B we show results from the full model. For each of our choice variables, we show estimates that only control for a given variable in columns (i)-(v), while in column (vi) we include all state-level choice variables together and in column (vii) we include all choice measures. Reading across columns thus yields some insight into the importance of controlling more comprehensively for confounding state 19

21 policies, and the estimates across panels show the effect of restricting the identifying variation to be within city over time. In Panel A of Table 3, there is a clear positive correlation between the state open enrollment index as well as the tuition voucher index and search frequency. In columns (i) and (ii), an increase of one in these indices is associated with 10.2 and 14.6 percent increases, respectively, in the number of searches, although neither estimate is statistically different from zero at even the 10% level. The open enrollment estimates also are not robust to controlling for other choice policies, while the tuition voucher estimates become larger and statistically significant at the 10% level once other policies are included in the model. Tuition tax credits are strongly negatively correlated with the number of searches, as is the NCLB choice percentage. The results shown in Panel B suggest that failure to account for cross-sectional differences across cities in search prevalence and demographics yields a misleading picture of how the choice environment impacts online search behavior. The estimates show consistent evidence that more generous open enrollment laws lead to more search: an increase in one in the open enrollment index increases search frequency by 3%, which is statistically significantly different from zero at the 5% level. This estimate also is robust to controlling for other state laws as well as NCLB-induced choice. In order to get a sense of the magnitude of this estimate, one can consider the effect of a 1 standard deviation change in the open enrollment index. Table 1 shows the standard deviation is 0.87, so a 1 standard deviation increase in open enrollment regulations increases search frequency by 2.67%. Alternatively, one can consider moving from a state with the fewest to the most laws, which is a change in this index of 4. Our estimates imply such a change would increase the search frequency by 12%. 20

22 Panel B of Table 3 also provides some evidence that allowing for more tuition vouchers and tuition tax credits increases search on the Great Schools website. For tuition vouchers, there is a 5.2% increase in search for every 1 unit increase in the voucher law index, which is not statistically significant at traditional levels. But, when we control for other components of the search environment, the estimate becomes larger in magnitude and is statistically significantly different from zero at the 10% level in column (vii). 8 The marginal effect of 9.2% in column (vii) implies a one standard deviation increase of 0.68 would increase search by 6.2%, and moving from the least to the most stringent state would increase search by 18.2% (=9.1*2). Tuition Tax credit law changes are positively and statistically significantly related to search, and this relationship grows substantially when other laws are accounted for in columns (vi) and (vii). Since the standard deviation of this index is 0.86, the marginal effect in column (vii) implies search would increase by 25.5% from such an increase. Moving from the state with the fewest laws to the state with the most implies an increase in search of 59%. Despite the large responsiveness of online search to tuition tax credits, charitable scholarship tax credits are negatively correlated with search frequency. Although the bivariate estimate is small and is not statistically different from zero, once other choice factors are included in the model a one point increase in the charitable scholarship tax credit index is predicted to reduce search frequency by about 10%. This implies a -18% change from a one standard deviation change in the index, or about a 50% decline due to moving from the state with the fewest to the most generous charitable scholarship tax credit laws. The opposite-signed effects for tuition credits versus charitable scholarship tax credits is rather unexpected. One hypothesis consistent with this pattern of results is that these charitable donations go towards 8 As shown in Table 2, the reason controlling for other laws increases the size of the tuition voucher estimate is that tuition vouchers are positively correlated with tax credits (which are negatively correlated with search) and are negatively correlated with NCLB-induced search proportions (which are positively correlated with search). 21

23 specific private schools that provide their own information to potential students. This might reduce the need to search online for such information. Unfortunately, we do not have the ability to test this hypothesis with our data. Table 3 also shows consistent evidence that increases in the proportion of schools that are eligible for school choice based on NCLB restrictions are strongly positively related to increases in search frequency. Our results indicate that a 10 percentage point increase in the proportion of Title I schools that are in school improvement status leads to between a 2.8% and 3.1% increase in the number of searches for local schools. A one standard deviation increase in the NCLB choice percentage of 25% thus would imply between a 7% and 8% increase in search frequency. Additionally, the results show that post-waiver, search declines by 15%, which is further evidence that online searches related to local school quality are related to the NCLB-induced choice environment. When NCLB restrictions are no longer relevant, online searches through greatschools.org drop considerably. This finding also is inconsistent with our NCLB choice estimates being driven by unobserved constant trends in the underlying population, as such constant trends would force these coefficients to have the same sign. The timing of changes to the school choice environment driven by NCLB sanctions suggest that any impacts on search frequency should be most pronounced during the summer. This prediction stems from the fact that school improvement and AYP status is released each spring towards the end of the school year, and parents in Title I schools in improvement status are notified by the school that they can switch schools in this period as well. It thus sensible that search frequency should rise over the summer months when parents are looking for new schools for their children. In order to examine the search patterns in response to NCLB-induced choice, we interact both the local choice percentage and the waiver status with indicators for each month. 22

24 The estimates on the choice percentage are shown in Figure 4. These estimates come from models with Search Unit, month and year fixed effects as well as controls for all other choice environment variables in equation (1). The results show that search grows more in areas that experience higher levels of NCLB-induced open enrollment over the summer. In particular, the effect of a 10 percentage point increase in the proportion of NCLB-induced choice schools is 2.1% in June and is 1.3% in July, both relative to the excluded month of January. These two estimates are the largest across all months, and the only other month that is close in magnitude is March. That NCLB-induced choice leads to more online school searches over the summer, and specifically in June when many schools require parents to apply under open enrollment, is evidence that our estimates reflect the causal impact of changes in the choice environment on search behavior rather than other confounding influences such as changing demographics. In Table 4, we assess the robustness of our main estimates to how we code the state-level choice regulation variables. Coding them as indices assumes that each law change has the same proportional effect on search frequency. However, after the first law passes, subsequent regulations may not have a large influence on search behavior. Thus, in Table 4, we code all state regulations as indicator variables, which equal one if a state has any law for the given choice measure (i.e., if the index is strictly greater than zero). The estimates are extremely similar in terms of sign, magnitude and statistical significance to those shown in Panel B of Table 3. 9 In addition to providing evidence that search frequency is highly responsive to whether states have any laws pertaining to these choice measures, they also show that our baseline results are not driven by the particular manner in which we code state regulations Results for Specific Search Terms 9 We omit the estimates examining only NCLB-based choice percentages in Table 4 as they would be identical to the estimates in Panel B of Table 3. 23

25 In addition to overall search frequency, our data allow us to examine what types of schools people are searching for on the greatschools website. Using the search strings people enter, we calculated the frequency in each Search Unit and month that individuals entered the following five search terms: charter, private, high, middle, elementary. Estimates using these search term counts as the dependent variable are informative about how each of the choice environment factors we analyze influences the types of schools people search for. However, these estimates also should be interpreted cautiously because some individuals may search for a given type of school without entering any of these search terms. 10 At the same time, we are confident that someone who uses one these terms is focused on this specific type of schools. Because of the prevalence of zeros (and the lack of large outliers), the dependent variables are for this part of the analysis are the raw search counts rather than the natural log. Means of each search term are shown in Table 1. Table 5 presents the results from estimation of equation (1) using counts of each of the five search terms as dependent variables. The most consistent finding in the table is that increases in open enrollment laws have a positive, sizable and statistically significant impact on search frequency for all school type search terms. The largest marginal effect is for high schools, where a one point increase in the open enrollment index increases the number of searches that use the word high by 2.4 on average. Relative to the baseline search likelihoods shown in Table 1, the effect of a one point change in the index ranges from 8.9% to 20.6%. The estimates for tuition vouchers are also positive and large, but they are imprecise. In no case is any estimate statistically distinguishable from zero at even the 10% level, but the point estimates are consistent with these policies increasing searches for all school types. 10 For example, they might enter Boston with the intent of examining charter schools in Boston, but our search counts would not count this search as being related to charter schools (or to any other particular type of school). 24

26 The estimates for the charitable scholarship and tuition credits are imprecisely estimated and vary in sign across school types. However, Table 5 also shows that there is a large, positive relationship between NCLB-induced school choice and searches for public schools. A 10% increase in the proportion of Title I schools falling under NCLB school improvement sanctions is predicted to increase the number of searches for charter by 14.9%, the number of searches for high by 7.2%, the number of searches for middle by 9.8%, and the number of searches for elementary by 11.9%, all relative to their baseline search frequencies. While only the middle search term estimate is statistically significant at the 10% level, 11 these coefficients are universally large in magnitude. Furthermore, there is no discernable effect of NCLB sanctions on searches for private. 12 The pattern of result is similar for the NCLB waiver dummy, with sizable and statistically significant estimates for high, middle and elementary and a large but not statistically significant estimate for charter searches. Finally, we repeat our analysis by school search type using indicator variables instead of indices for the different state policies, similar to the analysis shown in Table 4. The results, shown in Table 6, are almost identical to those in Table 5 and indicate that our results are not being driven by the particular manner in which we code state school choice regulations. 5. Conclusion This paper examines how observed searches for school quality information respond to changes in the local school choice environment. We construct a unique dataset linking the location being searched to the local school choice policies in a given area and month over a fouryear period from over 100 million unique searches on greatschools.org. The type of information 11 The estimates for high and elementary schools are significantly different from zero at the 12.6 and 11.1 percent levels, respectively. 12 Note that these are not mutually exclusive categories. If someone searches for private high school they will be counted as searching both for private and high. Additionally, if they search for high schools, they may be interested in private high schools without indicating so in their search string. 25

27 available on this website facilitates easy comparisons across local schools, and prior work has shown that providing parents with this type of information, when combined with school choice, leads parents to choose schools for their children that increase their measured academic achievement (Hastings and Weinstein 2008). We find evidence that changes in the local school choice policies and options have sizable positive effects on the frequency of online searches about that locality. In particular, expanding state-level open enrollment rules and providing tuition vouchers and tax credits are strongly positively associated with the number of searches about local schools in that state. Furthermore, expansions in choice from increases in the number of schools in a given area that are subject to choice-based sanctions under NCLB have a large impact on online search frequency. When the state becomes eligible for a waiver that exempts them from these sanctions, however, search frequency declines in that state. We also show evidence that open enrollment policies lead to more searches for all school terms we can identify in the data, while NCLBbased choice mostly affects searches for high, elementary and middle. These terms are most likely to be associated with other local public schools rather than with particular private schools. The finding that parents are induced to search for information about local school quality due to expansions in the school choice environment is a novel finding in this literature and has several important policy implications. First, it implies that a significant fraction of parents do not have full information about the quality of schools in their area. For families who face financial constraints that preclude them from moving and thus who have few school choice options, it is perhaps not surprising that they would not incur the costs of obtaining detailed information about 26

28 other schools in the area. Our estimates are consistent with a high prevalence of incomplete information among families in the areas we study. Second, our results suggest that online search tools such as greatschools.org can be powerful mechanisms through which to provide families with the information they need to take advantage of choice programs. That the information is being provided by an independent third party also might increase the credibility of the information from parents perspectives, although more research is needed to understand how parents interpret school quality information from different sources. It might be possible for policymakers to use the existence of such online school quality information tools to increase the effectiveness of school choice policies insofar as they can help overcome the information asymmetries that exist in this market. We view such a possibility as a fruitful area for future research. 27

29 Works Cited Abdulkadiroğlu, Atila, Joshua D. Angrist, Susan M. Dynarski, Thomas J. Kane and Parag A. Pathak Accountability and Flexibility in Public Schools: Evidence from Boston's Charters And Pilots. Quarterly Journal of Economics 126(2): Angrist, Joshua D., Parag A. Pathak, and Christopher R. Walters Explaining Charter School Effectiveness. American Economic Journal: Economic Policy 5(4): Armor, David J., and Brett M. Peiser Interdistrict Choice in Massachusetts, in Learning from School Choice, Paul E. Peterson and Bryan C. Hassel, eds. Washington, DC: Brookings Institution Press. Bayer, Patrick, Fernando Ferreira, and Robert McMillan A Unified Framework for Measuring Preferences for Schools and Neighborhoods. Journal of Political Economy 115(4): Black, Sandra Do Better Schools Matter? Parental Valuation of Elementary Education. Quarterly Journal of Economics 114(2): Black, Sandra E. and Stephen Machin Housing Valuations of School Performance in Eric A. Hanushek, Stephen Machin and Ludger Woessmann (Eds.) Handbook of the Economics of Education, Volume 3. North-Holland: Amsterdam. Bettinger, Eric The effect of Charter Schools on Charter Students and Public Schools. Economics of Education Review 24(2): Bifulco, Robert and Helen F. Ladd The Impacts of Charter Schools on Student Achievement: Evidence from North Carolina. Education Finance and Policy 1(1): Burgess, Simon, Ellen Greaves, Anna Vignoles, and Deborah Wilson What Parents Want: School Preferences and School Choice. Centre for Market and Public Organisation (CMPO) Working Paper No. 09/22. Chaudhuri, Anindya, Kenneth S. Flamm, and John Horrigan An Analysis of the Determinants of Internet Access. Telecommunications Policy 29, no. 9 (2005): Council of Chief State School Officers (CCSSO) School Choice in the States: A Policy Landscape. Cullen, Julie Berry, Brian A. Jacob and Steven Levitt The Effect of School Choice on Participants: Evidence from Randomized Lotteries. Econometrica 74(5): Deming, David J., Justine S. Hastings, Thomas J. Kane and Douglas O. Staiger School Choice, School Quality, and Postsecondary Attainment. American Economic Review 104(3): Dobbie, Will and Roland G. Fryer, Jr Are High-Quality Schools Enough to Increase Achievement Among the Poor? Evidence from the Harlem Children's Zone. American Economic Journal: Applied Economics 3(3): Figlio, David N. and Maurice E. Lucas What's in a Grade? School Report Cards and the Housing Market. American Economic Review 94(3): Friedman Foundation For Educational Choice. Choice-Programs. Grady, Sarah, Stacey Bielick and Susan Aud Trends in the Use of School Choice: National Center for Education Statistics, Institute of Education Sciences Report No. NCES

30 Hanushek, Eric A., John F. Kain, Steven G. Rivkin, and Gregory F. Branch Charter School Quality and Parental Decision Making With School Choice. Journal of Public Economics 91(5-6): Hastings, Justine S., Thomas J. Kane and Douglas O. Staiger Gender and Performance: Evidence from School Assignment by Randomized Lottery. American Economic Review Papers and Proceedings 96(2): Hastings, Justine S., Thomas J. Kane and Douglas O. Staiger Heterogeneous Preferences and the Efficacy of Public School Choice. Mimeo. Hastings, Justine S. and Jeffrey M. Weinstein Information, School Choice, and Academic Achievement: Evidence from Two Experiments. Quarterly Journal of Economics 123(4): Howell, William Switching schools? A Closer Look at Parents' Initial Interest In and Knowledge about the Choice Provisions of No Child Left Behind. Peabody Journal of Education 81(1): Howell, William G., Patrick J. Wolf, David E. Campbell, and Paul E. Peterson School Vouchers and Academic Performance: Results from Three Randomized Field Trials. Journal of Policy Analysis and Management 21(2): Imberman, Scott A Achievement and Behavior in Charter Schools: Drawing a More Complete Picture. Review of Economics and Statistics 93(2): Imberman, Scott A. and Michael F. Lovenheim Does the Market Value Value-Added? Evidence from Housing Prices After a Public Release of School and Teacher Value-Added. NBER Working Paper No Kleitz, Bretten, Gregory R. Weiher, Kent Tedin, and Richard Matland Choice, Charter Schools, and Household Preferences. Social Science Quarterly 81(3): Krueger, Alan B. and Pei Zhu Another Look at the New York City School Voucher Experiment. American Behavioral Scientist 47(5): Mizala, Alejandra and Miguel Urquiola School Markets: The Impact of Information Approximating Schools Effectiveness. NBER Working Paper No Nguyen-Hoang, Phuong and John Yinger The Capitalization of School Quality into House Values: A Review. Journal of Housing Economics 20(1): Rouse, Cecilia Elena Private School Vouchers and Student Achievement: An Evaluation of the Milwaukee Parental Choice Program. Quarterly Journal of Economics 113(2): Sass, Tim R Charter Schools and Student Achievement in Florida. Education Finance and Policy 1(1): Schneider, Mark, Melissa Marschall, Paul Teske and Christine Roche School Choice and Culture Wars in the Classroom: What Different Parents Seek from Education. Social Science Quarterly 79(3): Schneider, Mark and Jack Buckley What Do Parents Want from Schools? Evidence from the Internet. Educational Evaluation and Policy Analysis 24(2): Vanourek, Greg, Bruno V. Manno, Chester E. Finn, and L. E. Bierlein Palmer "Charter Schools as Seen by Students, Teachers, and Parents." Learning from school choice:

31 Table 1. Descriptive Statistics of Analysis Variables Variable Mean SD Min Max Total Searches Charter Searches Private Searches High Searches Middle Searches Elementary Searches State Open Enrollment Laws State Tuition Voucher Laws State Charitable Scholarship Tax Credit Laws State Tuition Credit Laws District NLCB Choice Source: Authors tabulations of greatschools.org search data and state and local school choice policy variables as described in the text. All tabulations include only the 24 states for which we have NCLB choice information. Table 2. Correlations among Choice Variables State Open Enrollment Variable Laws State Tuition Voucher Laws State Scholarship Credit Laws State Tuition Credit Laws District NLCB Choice State Open Enrollment Laws State Tuition Voucher Laws State Scholarship Credit Laws State Tuition Credit Laws District NLCB Choice Source: Authors tabulations of state and local school choice policy variables as described in the text. All correlations include only the 24 states for which we have NCLB choice information. 30

32 Table 3. OLS Estimate of the Relationship Between the Choice Environment and Log Total Searches Panel A: No Controls Independent Variable (i) (ii) (iii) (iv) (v) (vi) (vii) State Open Enrollment Index State Tuition Voucher Index State Charitable Scholarship Credit Index State Tuition Credit Index District NLCB Choice Percentage NCLB Waiver (0.079) (0.097) (0.034) ** (0.069) ** (0.218) (0.073) (0.082) 0.198* (0.101) (0.037) ** (0.078) (0.080) 0.171* (0.104) (0.036) ** (0.079) ** (0.223) ** (0.071) Panel B: Year, Month and Search Unit Fixed Effects Independent Variable (i) (ii) (iii) (iv) (v) (vi) (vii) State Open Enrollment Index 0.030** 0.032** 0.026** (0.013) (0.012) (0.012) State Tuition Voucher Index * (0.048) (0.051) (0.050) State Charitable Scholarship Credit Index (0.010) ** (0.052) * (0.052) State Tuition Credit Index 0.111** 0.307** 0.297** (0.054) (0.145) (0.145) District NLCB Choice Percentage 0.283** 0.306** (0.130) (0.130) NCLB Waiver ** ** (0.026) (0.026) Source: Authors estimation of equation (1) using greatschools.org search data and local school choice policies as described in the text. All results include only the 24 states for which we have NCLB choice information. The Search Unit is defined as the CBSA or the county if the area is not in a CBSA. Standard errors clustered at the CBSA level are in parentheses: * indicates significance at the 10% level and ** indicates significance at the 5% level. 31

33 Table 4. OLS Estimate of the Relationship Between the Choice Environment and Log Total Searches Using Binary State Choice Measures Independent Variable (i) (ii) (iii) (iv) (v) (vi) Any State Open Enrollment Any State Tuition Voucher Any State Charitable Scholarship Any State Tuition Credit District NLCB Choice Percentage NCLB Waiver 0.131** (0.038) (0.050) (0.040) 0.223** (0.109) 0.133** (0.038) 0.136** (0.066) ** (0.136) 0.467** (0.194) 0.110** (0.038) 0.165** (0.064) ** (0.136) 0.448** (0.195) 0.304** (0.130) ** (0.026) Source: Authors estimation of equation (1) using greatschools.org search data and local school choice policies as described in the text. All results include only the 24 states for which we have NCLB choice information. The Search Unit is defined as the CBSA or the county if the area is not in a CBSA. The estimates include Search Unit, month and year fixed effects. Standard errors clustered at the CBSA level are in parentheses: * indicates significance at the 10% level and ** indicates significance at the 5% level. 32

34 Table 5. OLS Estimate of the Relationship Between the Choice Environment and Different Search Term Counts Independent Variable Charter Private High Middle Elementary State Open Enrollment Index 0.122** 0.037** 2.402** 0.796** 1.451** (0.037) (0.010) (0.724) (0.273) (0.412) State Tuition Voucher Index (0.320) (0.067) (9.972) (2.192) (4.884) State Charitable Scholarship Credit Index (0.093) (0.069) (3.795) (1.435) (1.612) State Tuition Credit Index (0.265) (0.158) (9.592) (3.276) (4.246) District NLCB Choice Percentage * (1.326) (0.088) (11.164) (4.118) (12.206) NCLB Waiver ** * ** (0.373) (0.036) (3.228) (1.085) (3.143) Source: Authors estimation of equation (1) using greatschools.org search data and local school choice policies as described in the text. Dependent variables are counts of the number of times each search term is used. All results include only the 24 states for which we have NCLB choice information. The Search Unit is defined as the CBSA or the county if the area is not in a CBSA. The estimates include Search Unit, month and year fixed effects. Standard errors clustered at the CBSA level are in parentheses: * indicates significance at the 10% level and ** indicates significance at the 5% level. 33

35 Table 6. OLS Estimate of the Relationship Between the Choice Environment and Different Search Term Counts Using Binary State Choice Measures Independent Variable Charter Private High Middle Elementary Any State Open Enrollment 0.528** 0.102** 7.930** ** (0.148) (0.039) (3.036) (1.805) (3.005) Any State Tuition Voucher (0.664) (0.118) (12.724) (3.995) (7.983) Any State Charitable Scholarship (0.348) (0.187) (11.887) (5.093) (5.899) Any State Tuition Credit (0.454) (0.179) (14.015) (4.906) (6.739) District NLCB Choice Percentage * (1.326) (0.088) (11.154) (4.122) (12.237) NCLB Waiver ** * ** (0.375) (0.036) (3.215) (1.086) (3.154) Source: Authors estimation of equation (1) using greatschools.org search data and local school choice policies as described in the text. Dependent variables are counts of the number of times each search term is used. All results include only the 24 states for which we have NCLB choice information. The Search Unit is defined as the CBSA or the county if the area is not in a CBSA. The estimates include Search Unit, month and year fixed effects. Standard errors clustered at the CBSA level are in parentheses: * indicates significance at the 10% level and ** indicates significance at the 5% level. 34

36 Figure 1. Total Searches by Month Total Searches Month (1=January 2010) Source: Monthly search data from greatschools.org from January 2010 through October 2013.

37 Figure 2. Monthly Averages of State Choice Indices 2.5 Open Enrollment 1.5 State Tuition Vouchers Month (1=January 2010) Month (1=January 2010) State Charitable Scholarship Tax Credits Month (1=January 2010) State Tuition Credits Month (1=January 2010) Source: Authors calculations from state school choice regulations as described in the text. The solid line shows monthly means of the given choice index, and the dotted lines show one standard deviation above and below the mean. 36

38 Figure 3. Monthly Averages of District NLCB Choice Percentage District Choice Month (1=January 2010) Source: Authors calculations of the percentage of schools each school district in our analysis sample that are subject to choice under NCLB sanctions. The solid line shows monthly means of the given choice index, and the dotted lines show one standard deviation above and below the mean. 37

39 Figure 4. Estimates of District NLCB Choice Percentage Interacted with Month Indicators Source: Authors estimation of equation (1) but including interactions between month and District NCLB Choice Percentage as well as month and NCLB Waiver status using greatschools.org search data and local school choice policies as described in the text. The estimates include Search Unit, month and year fixed effects as well. Each point in the figure shows the estimate from the interaction between District NCLB Choice Percentage and month relative to January, which is the excluded month. The dotted lines show the bounds of the 95% confidence interval, which is calculated from standard errors that are clustered at the Search Unit level. 38