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«Does the Market Value Value-Added? Evidence from Housing Prices After a Public Release of School and Teacher Value-Added Scott A. Imberman∗ Michigan ...»

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value-added scores were generated separately for math and reading, the LA Times based their categorization on the mean of the two scores. The figure also shows the location of the school’s API percentile. Although the API information was publicly available prior to August 2010, it was more difficult to find and was not accompanied by the heightened media attention that accompanied the value-added release. Thus, for many people, this API information could have been new. The value-added rank was not available in any form prior to August 2010. Finally, the web page provided passing rates on the math and English exams for each school, which was also publicly available prior to the value-added release. To keep our estimating equation simple, in our analyses we will assume that any response to the LA Times reprinting the passing rates will be reflected in responses to API.7 A critical question underlying our analysis is whether LA residents knew about the release of this information and how to access these data. There is substantial evidence to indicate that residents were well-informed about the LA Times database. First, the Los Angeles Times is the largest newspaper in Southern California and the fourth largest in the country by daily weekday circulation, with 616,575 copies according to the Audit Bureau of Circulations. The existence of the database was widely reported in the newspaper: from August 2010 to May 2011, a total of 37 articles or editorials were written about the database, public response to the database, or value-added issues more generally. Given the high level of circulation of the paper, the attention paid to this issue by the LA Times likely reached many residents. Further, the release of the value-added data was mentioned in other outlets, such as the New York Times, National Public Radio, the Washington Post, ABC News, CBS News, CNN and Fox News. It also received much radio and television attention in the LA area in both English and Spanish, which is of particular importance for the Spanish-speaking population that is less likely to read the LA Times but for whom radio and television are dominant sources of news.8 Second, the LAUSD teachers’ union and the national American Federation of Teachers were This assumption is sensible because API scores are calculated almost entirely through using these test pass rates.

Some examples of Spanish language coverage include a story on Channel 22 on Nov. 8, 2010 covering a protest of the value-added after a teacher committed suicide (http://www.youtube.com/watch?v=RWKR8Ch06wY), a story covering an earlier protest on Channel 62 (http://www.youtube.com/watch?v=n1iNXtyPlRk), and a story on Univision 34 discussing LAUSD’s own value-added measures (http://www.youtube.com/watch?v=05dE0xLdpu8).

highly vocal in their opposition to the public release of the data. This culminated in a series of highly publicized and widely covered protests of the LA Times by teachers.9 Furthermore, US Secretary of Education Arne Duncan spoke about the value-added measures expressing his support. This indicates that news-makers were discussing the issue and gave it substantial media exposure. According to the LA Times, by late afternoon on the initial date of the release there were over 230,000 page views of the website for the database (Song, 2010). The article points out that this is an unusually large volume of views given that traffic tends to be higher during the week and provides prima facia evidence that the value-added release was well-publicized and known to a large number of residents.10 The initial August 2010 data release was followed up with two more information releases. In April 2010, LAUSD provided its own school-level (but not teacher-level) value-added measure called Achievement Growth over Time (AGT). Then, in May 2011, the LA Times updated the value-added results on its webpage to include another year of data, more teachers and some minor changes in the value-added methodology.11 While we estimate the effect of all three releases, our focus is on the information provided by the LA Times for a few reasons. One is that the LA Times uses a more econometrically sound value-added model. As discussed above, their model controls for lagged achievement and includes multiple years of data. Guarino, Reckase and Wooldridge (2012) argue that models like this (which they call “dynamic ordinary least squares”) are the most accurate. The LAUSD model, on the other hand, predicts student achievement growth from observable characteristics using one year of data after which the differences between predicted and actual achievement are averaged together across all students in a school.12 One of these protests occurred after an incidence in which a teacher with a low value-added score committed suicide. This incidence was also widely covered by local media.

Due to the prevalence of the Internet in 2010, the penetration of this information in Los Angeles likely was at least as large as in Florida when they first released school report card information in the late 1990s.

Figlio and Lucas (2004) show that the Florida information release, which was less contentious, had less publicity surrounding it, and occurred in a period in which information was more difficult to obtain, had large effects on property values.

Details on the May 2011 LA Times methodology can be found in Buddin (2011). For this release, the LA Times also gave people the option to see how value-added scores changed using variations in methodology through an interactive program on the website. Since it is likely that most people who accessed the database did not attempt to compare different methods, we only use the value-added scores directly published on the website by the LA Times in our data.





Details on the LAUSD methodology can be found at http://portal.battelleforkids.org/BFK/LAUSD/FAQ.html.

While the LA Times methodology may be more appealing to researchers, this does not indicate that parents believed it more. There are several reasons to suspect the LA Times data were more relevant to the public, though. First, the initial LA Times release was seven months prior to the LAUSD release. Thus, there was more time for that information to be absorbed by the public. If there is any substantial lag in the timing of when prices respond, our data – which end in September 2011 – will only be able to pick up effects of the initial release.

Second, there was substantial discussion of the LA Times release in the news and responses by education organizations. The subsequent LAUSD release, however, elicited much less press coverage. Third, LAUSD did not release teacher value-added. While this will not necessarily matter for housing prices if parents value only the mean value-added in a school (a possibility that we test for below), the availability of the teacher information may have led more parents to seek out the LA Times information than the LAUSD information. Finally, it is easier to access the LA Times information. While both are available on the web, to access the LAUSD data people need to navigate through a series of links on the LAUSD website.

Despite our focus on the LA Times releases, we will nonetheless include estimates of the impacts of the LAUSD release in our regressions. Hence it is interesting to note that the correlation between both of the LA Times releases and the LAUSD value-added scores are very low. Figure 2 presents comparisons of the three school-level value-added measures using scatter plots with each school as an observation. The top left panel shows that the percentiles of the 2010 LA Times value-added are highly correlated with the 2011 LA Times value-added, with a correlation coefficient of 0.74. However, each of the LA Times value-added measures are very weakly correlated with the LAUSD measure - the correlation coefficients are 0.15 and

0.39 for the August and May releases, respectively. This likely reflects the differences in the methodology described above and the amount of data used.

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To assess the impact of the value-added data release on property values, we combine data from several sources. First, we use home price sales data from the Los Angeles County Assessor’s Office (LACAO). The data contain the most recent sale price of most homes in LA County as of October, 2011, which in addition to LAUSD encompasses 75 other school districts. We restrict our data to include all residential sales in LAUSD that occurred between April 1, 2009 and September 30, 2011.13 From LACAO, we also obtained parcel-specific property maps, which we overlay with the school zone maps provided to us by LAUSD to link properties to school zones.14 The property sales data additionally contain information on the dates of the three most recent sales, the square footage of the house, the number of bedrooms and bathrooms, the number of units and the age of the house that we will use to control for any potential changes in the composition of sales that are correlated with value-added information.

We drop all properties with sale prices above $1.5 million (5% of households) and limit our sample to properties in elementary school zones in Los Angeles Unified School District that received value-added scores in the August 2010 release. About 25% of the residential properties in the data do not have a sale price listed. Usually, these are property transfers between relatives or inheritances.15 Hence, we limit our sample to those sales that have “document reason code” of “A,” which denotes that it is a “good transfer” of property. After making this restriction, only 7% of observations are missing sale prices. For these observations, we impute sales prices using the combined assessed land and improvement values of the property. For observations that have all three measures recorded, the correlation between actual sale price and the imputed sale price is 0.89, indicating that the imputation is a very close approximation to the actual market value. Furthermore, we know of no reason why the accuracy of the imputation procedure should be correlated with value-added information, which supports the validity of this method.

Nonetheless, in Section 5, we provide results without imputed values and show they are very similar. Our final analysis data set contains 62,977 sales.

We obtained the exact value-added score for each school directly from Richard Buddin, and the April 2011 LA Times school value-added data as well as the August 2010 teacher-level Given that the value-added information only varies across schools within LAUSD, the addition of school fixed effects leaves little to be gained from adding the rest of LA County. Indeed, specifications using home price sales from all of the county, setting value-added percentiles equal to zero outside of LAUSD and controlling for school district fixed effects, provides almost identical results.

The school zones are for the 2011-2012 school year.

California allows relatives to transfer property to each other without a reassessment of the home’s value for property tax purposes. Due to property tax caps, this rule creates large incentives for within-family property transfers in California, and hence there are a lot of such transactions in the data. Because these transfers do not reflect market prices, we do not include them in our analysis.

value-added data were provided to us by the LA Times. The LAUSD value-added information was collected directly from Battelle for Kids, with whom LAUSD partnered to generate the value-added measures.16 The value-added data were combined with school-by-academic-year data on API scores, school average racial composition, percent on free and reduced price lunch, percent disabled, percent gifted and talented, average parental education levels and enrollment.

These covariates, which are available through the California Department of Education, control for possible correlations between value-added information and underlying demographic trends in each school. To maintain consistency with the LA Times value-added data, we convert both the LAUSD value-added scores and API scores into percentile rankings within LAUSD.

We also link each property to its Census Tract characteristics from the 2005-2009 American Communities Survey (ACS). Given the strong correlation between test scores and demographic characteristics as well as the evidence of sorting by families in response to cross-sectional differences in test scores (Bayer, Ferreira and McMillan, 2007; Kane, Riegg and Staiger, 2006), it is important to control, to the extent possible, for differences in the demographic and socioeconomic makeup of neighborhoods as they relate to observed school quality. A full listing of variables used in our controls can be found in the notes to Table 5.

Summary statistics of some key analysis variables are shown in Table 1. The table presents means and standard deviations for the full sample as well as for the sample above and below the median value-added score for the 2010 LA Times release. On average, home sales in LAUSD are in Census Tracts that are about 50% black and Hispanic,17 but the schools these properties are zoned to are 73% black and Hispanic, with the difference ostensibly due to enrollments in private, charter and magnet schools. The schools in our data set also have a large proportion of free and reduced price lunch students. The second two columns of Table 1 show that value-added is not completely uncorrelated with school or census tract demographics, although housing characteristics are balanced across columns. The higher value-added areas have a lower minority share, higher property values, a more educated populace and have higher API scores. These correlations could be driven by the fact that better schools are indeed located The data is available at http://portal.battelleforkids.org/BFK/LAUSD/Home.html.

Note that since the ACS counts Hispanic as a separate category from race, some of the black and white populations are also counted as Hispanic.

in the higher socioeconomic areas, or they could be an indication that the value-added models used do not fully account for underlying differences across students.

Figure 3 shows that, despite the differences shown in Table 1, value-added is far less correlated with student demographic makeup than API scores. The figure presents the nonfree/reduced-price (FRP) lunch rate, API percentile (within LAUSD) and value-added percentile for each elementary school in LAUSD. The boundaries denote the attendance zone for each school. As expected, API percentiles, which are based on test score proficiency rates, map closely to poverty rates. High-poverty (low non-FRP lunch) schools tend to have lower API scores. While this relationship remains when replacing API with value-added, it is far less robust. There are many schools, particularly in the eastern and northern sections of the district, where API scores are low but value-added scores are high. Similarly, some schools with high API scores have low value-added scores. Figure 4 further illustrates this point. It provides scatter plots of API percentiles versus value-added percentiles for each of the three value-added measures. While there is a positive relationship between value-added and test score levels,18 it is quite weak - the average correlation between the measures of school quality is only 0.45.

As seen in Figure 3, there are a number of schools which, based on API, are at the top of the distribution but according to the value-added measure are at the bottom, and vice-versa.

For example, Wilbur Avenue Elementary had an API percentile of 91 in 2009 but an initial value-added percentile of 13. On the other end of the spectrum, Broadous Elementary had an API percentile of 5 but a value-added percentile of 97.

The fact the API rank and value-added rank are only weakly related to each other does not mean that the value-added information provided by the LA Times was new information.

It is possible that each of these measures could be predicted based on existing observable characteristics of the school. In Table 2, we examine this issue directly, by predicting API, the percentile from the first LA Times value-added release, and the LAUSD value-added percentile as a function of school observables in the pre-release period. Column (1) shows the results for API percentile, and as expected, with an R2 of 0.71, it is strongly related to school demoA linear regression of the August 2010 LA Times value-added percentile on API percentile provides an estimate of 0.43 (standard error 0.04) but an R-squared of only 0.19.



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