Discussion Paper No.90
Does disclosure of school quality information
increase disparity in academic achievement?
The effect through the housing market
Yuta Kuroda

December, 2018

Data Science and Service Research
Discussion Paper

Center for Data Science and Service Research
Graduate School of Economic and Management
Tohoku University27-1 Kawauchi, Aobaku
Sendai 980-8576, JAPANDoes disclosure of school quality information increase
disparity in academic achievement?
The effect through the housing market *Yuta Kuroda †Abstract
This study investigates the effect of disclosure of information regarding school quality on thestratification of elementary schools and the widening disparity in the academic ability of students
between schools. Using the event in which school-level test scores were first disclosed in October
2014 in Matsue City in Japan, I estimated the change of the effect of test scores on housing rents andpopulation in a school district before and after disclosure. I found that school-level test scores had asignificantly positive effect on housing rents of apartments intended for a family after the scores
were disclosed. I also found that disclosure significantly increased the population of elementary
school-aged individuals within the attendance district of school with high test scores. Additionally, I
found that the relative standard deviation of test scores after disclosure became larger than thatmeasured before disclosure. These results suggest that disparity in the academic ability of students
between schools may expand through increased housing rents after disclosure of school-level test
results.
Keywords: Housing rent, School quality, School district, Disparity in school, Information disclosure
JEL classifications: R31, R21, I20, I24*

I would like to thank my advisors, Kentaro Nakajima, Midori Wakabayashi and Akira Hibiki. I am
deeply grateful to Paul Thorsnes, Lixing Li, Iljoong Kim, Jenn-Jou Chen, and Wen-Chieh Wu forhelpful comments and suggestions. I would also like to express my gratitude to the participants at the
Joint Seminar at the University of Tsukuba, the 2018 Japanese Economic Association Spring
Meeting, the 21st Labor Economics Conference at Doshisha University, the Tokyo Labor Economics
Workshop and the Data Science Workshop at Tohoku University. All remaining errors are my own.
This study was supported by Joint Research Program No.762 at the Center for Spatial Information
Science (CSIS), University of Tokyo (Real Estate Database 1999-2016 by At Home Co., LTD).
†
Graduate School of Economics and Management, Tohoku University, Sendai 980-8576, Japan.
Email: kuroyu0725@gmail.com
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1.

Introduction
In recent years, accountability has been introduced by many public institutions for the purpose of

efficient operation and improving performance, and the disclosure of information regarind thequality of service is progressing. With disclosure of information on quality, suppliers will operate
more responsibly, and demanders will be able to make better decisions. In the field of education, thedisclosure of school quality information may change the educational behavior of parents or improvestudents’ academic performance. Koning and van der Wiel (2013) found that disclosure of negative
information related to quality of a school decreased the number of first-year students in the
Netherlands who chose that school after disclosure. Hastings and Weinstein (2008) demonstrated
that the fraction of parents who chose higher-performing schools significantly increased with receipt
of information on school quality. Burgess, Wilson, and Worth (2013) found significant and robust
evidence indicating that the abolition of school performance tables markedly reduced school
effectiveness in Wales relative to England.
In Japan, despite concerns over excessive competition and expansion of disparities among schools,
national academic achievement tests for elementary and junior high school (compulsory education)
students were not conducted until 2007. Furthermore, only the average scores of prefectures on thenational test were disclosed until 2014 (Kawaguchi, 2011). Under such circumstances, since 2000,
several educational problems such as a decline in children’s academic performance, inequality ofeducation, and expansion of child poverty have been discussed in Japan (Kariya et al., 2002; Abe,2008), and the introduction of a national test and disclosure of its results were strongly requested for
efficient operation and improved performance. As a result, the national test known as the National
Assessment of Academic Ability (NAAA) has been conducted since 2007, and since 2014 schools’
average test scores have been disclosed in several municipalities.
In this study, I used the unique event of disclosure of school quality information, which had notoccurred until 2014, and investigated how it affected students’ academic performance and parental
educational behavior. However, because there is no difference in fees associated with public
education among schools in Japan, parents’ educational demand is not reflected in school fees and it
is difficult to directly observe their educational investment behavior and willingness to pay for theirchildren’s schooling. Therefore, I focused on the housing market in areas where a school district
system existed to indirectly observe the effect of the disclosure of school quality information on the
behavior of parents.
There have been many studies investigating the effect of school quality on real estate prices, andconsistent results have been obtained indicating that "school quality has a significantly positiveeffect on housing or land prices" (Black & Machin, 2011; Nguyen-Hoang & Yinger, 2011). The
mechanism is as follows: If a school district system exists, in order to attend a specific school it isnecessary to live within its attendance district; thus, the demand for housing or land within the2attendance district of high quality schools rises, and as a result, the price of housing and land within
the attendance district also become high. Specifically, the value that parents place on education iscapitalized as housing or land prices. However, there have not been many studies investigating theeffect of the disclosure of school quality information on the housing market compared to studies
focusing on the relationship between school quality and the housing market. Particularly, few studies
have tried to determine whether competition and disparities among schools occurs as a result of
disclosure, and therefore the effect of disclosure on them must be examined. Additionally, from thepolicy evaluation perspective, because the educational system and environment greatly differ fromcountry to country, it is also important to investigate what happened after disclosure of school
quality information using Japanese data..
In this study, using the event that the school-level test scores were first disclosed in October 2014
in Matsue City in Japan, I estimated the effect of disclosure on housing rents and population in theschool district. To avoid the problem of unobserved confounding factors, I controlled the area fixed
effect and the various trends of the region. I also investigated how disparity in academic
achievement among schools changed after disclosure. As a result, I found that school-level test score
had a significantly positive effect on rents of apartments intended for a family after the school-level
test scores were disclosed. I also found that the disclosure effect was significant only in areas withmany children, and was insignificant in areas with few children. Additionally, my results suggest that
disclosure significantly increased the population of elementary school-aged individuals within theattendance district of schools with high test scores. These results indicate that parents choose
residential areas in response to new information regarding school quality, and that the housing
market is also affected. Finally, I also found that the value which indicated the degree of disparity(i.e., relative standard deviation of test scores) became large as a result of disclosure.
These results suggest that disparity in the academic ability of students between schools may
expand through increased housing rents after disclosure of school-level test results. The potential
mechanisms are as follows: The disclosure of information on school quality makes parents exercise
more selective behavior for their children, and as a result, the amount of money they need to pay toattend good schools increases, reflecting growing demand for good education. Additionally, parents'
school choice behavior is heterogeneous, and parents with a higher educational background andincome, or with more enthusiasm for their children's education, tend to choose higher quality schools.
In contrast, parents with a relatively low educational background and income, or with lesswillingness to pay for their children's education, are crowded out of good school districts throughincreased housing rentals, and sorting of students occurs. As a result, potentially talented students(who have parents with good educational backgrounds, higher incomes, or enthusiasm for education)gather at high quality schools, and therefore disparity in academic achievement between schools will
expand.
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Relative to previous studies, this study makes several contributions to the literature. First, by using
housing prices instead of land prices, I was able to estimate and interpret the effect of school quality
on the property market more clearly because the rental housing data included indices on whether theproperties were intended for families. Second, previous studies mainly imply the possibility of
selective disclosure by real estate agencies; however, by using not only housing data but also
population census data for small areas organized by age group, my results indicate that there isevidence that the residential choice of parents is also changing due to disclosure of school quality
information. Third, I was able to clearly measure the effect of disclosure of public school quality
information on a regional community by focusing on a city where a school district system clearly
existed and few private schools were present. Finally, although academic performance, school choice,and the property market were estimated in each previous study, in this investigation I consider theeffect of disclosure on all three categories.
The structure of this paper is as follows: Section 2 reviews the previous literature and explains thecontributions of this study. Section 3 describes the empirical strategies used. Section 4 discusses thedata and setting of the study. Section 5 presents the results and discussion. Finally, Section 6
provides concluding remarks.

2.

Literature review
Many studies have considered the effects of school quality on housing rents, and Black andMachin (2011) and Nguyen-Hoang and Yinger (2011) offer comprehensive reviews of this literature.
These studies have shown consistent results, indicating that school quality has a significant positiveeffect on the price of housing or land (e.g., Black, 1999; Figlio & Lucas, 2004). In Japan, Ushijima
and Yoshida (2009) and Kuroda (2018) also found that increasing school quality increased property
price significantly. Ushijima and Yoshida (2009) found that school quality affected land prices only
in areas with high-quality schools, and the results of Kuroda’s investigation (2018) indicated that
school quality had a greater effect on rents of apartments with larger occupied areas. These results
suggest that there is heterogeneity among families or properties affected by school quality.
Studies investigating the effect of disclosure of school quality information can be roughlyclassified as focusing on the following categories, depending on the type of outcome variable:
academic achievement, school choice, and property price. Koning and van der Wiel (2012)
investigated the effect of disclosure of school quality information on academic performance. They
found that disclosure of information on the relative quality of schools in the national newspaper
improved performance in a group of schools with relatively lower performance scores. Conversely,
Burgess, Wilson, and Worth (2013) investigated whether the abolition of disclosure of school
performance information negatively affected school effectiveness. They found significant and robust
4evidence suggesting that abolition of school performance tables markedly reduced school
effectiveness, but that it did not affect schools in the top quartile of the league tables. The results ofthese studies indicated that disclosure of school quality information had an effect on academic
performance, but that the effect might have heterogeneity among schools depending on quality.

There have also been studies on the effect of disclosure of school quality information on
choice of school. Koning and van der Wiel (2013) found that the number of enrollments forschools with lower scores decreased after disclosure of information about the quality of highschools in the Netherlands. They also found that these effects were large for the mostacademically rigorous path, such as the college-preparatory track. Nunes, Reis, and Seabra(2015) found that disclosure of school ranking based on the national academic exam decreased
the number of enrollments in schools that were rated poorly and increased the probability thatthey would be closed. They also observed that these effects were stronger for private schools.
Hastings and Weinstein (2008) examined both a natural experiment and a field experiment inwhich direct information on school test scores was provided to lower-income families in asetting involving a public school choice plan, and they found that receiving information
significantly increased the fraction of parents who chose higher-performing schools. Theirresults also indicated that attending a higher-scoring school increased student test scores, thus
implying that school choice would effectively increase academic achievement for disadvantaged
students when parents could access school quality information. According to these results,
parents and children referred to school quality information when choosing their school if they
could access the information; therefore, high-quality schools attracted a large number ofenrollments. Conversely, the number of enrollments at low-quality schools decreased.
Additionally, because of the heterogeneity among schools and parents, children and parents thatwere more interested in education cared more about school quality; thus, high-quality schools
might be attractive to students with high motivation or ability. Consequently, disparity inacademic achievement among schools might expand due to disclosure of school quality
information.
Recently, there have been several studies on the effect of disclosure of school quality informationon the price of housing or land. Imberman and Lovenheim (2016) studied the effect of disclosure ofvalue-added information regarding school quality. Their results suggest that parents and homeowners
do not value the ability of schools and teachers to increase test scores, nor are they unaware of theimportance of value-added information because the measures are derived from a complicatedstatistical model that is difficult for non-experts to understand. Carrillo, Cellini, and Green (2013)
found evidence indicating that real estate agents selectively disclosed information on school
assignments. They also found no significant effects of disclosure on home prices and observed point
estimates very close to zero by controlling for school quality with elementary school fixed effects.
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They reasoned that school quality information might be obtained by buyers before disclosure
because of the growth of the Internet and the increasing availability of data. Haisken-DeNew et al.’s(2018) study examined the effect of launching a website providing standardized information aboutschool quality to the Australian public. They found that favorable information on schools increased
real estate prices, but bad information did not have a significant effect, and even controlling forschool quality, this result is significant. They interpreted these results as indicative of buyers being
unaware of the importance of school quality information and real estate agents conducting strategic
and selective disclosure (i.e., disclosing information on good schools and not disclosing information
on bad ones) to raise selling price.
In short, previous studies have found consistent results suggesting that disclosure of school quality
information has a positive effect on academic performance in low-quality schools and increases thenumber of enrollments in schools with good quality. The former effect can reduce disparity between
schools, while the latter can expand it. Due to heterogeneity among schools and parents, children andparents that are more interested in education care more about school quality; therefore, high-quality
schools may be attractive to students with high motivation or ability. Thus, through disclosure, thescores of schools that originally had higher scores increased, while the scores of schools thatoriginally had lower ones decreased. As a result, disparity between schools might expand.
However, there is no consistent evidence as to whether the real estate market accurately reflects ofdisclosure of information on school quality. If property prices within good school districts increase
as a result of disclosure of information on school quality, parents have to pay more money to send
their children high-quality schools. Therefore, high quality schools attract parents that have highincome and high educational backgrounds, and disparity among schools may increase due to parents’income and educational background strongly correlating with their children's academic ability.
Relative to the previous research discussed above, this study makes several contributions to theliterature. First, by using housing prices instead of land prices, I was able to estimate and interpretthe effect of test scores on the property market more clearly because rental housing data included
indices regarding whether the properties were intended for families. I was also able to show that
there were no events besides disclosure of school quality information that affected the property
market by analyzing properties intended for single people and rental shops/offices that were thought
insignificant in relation to school quality.
Second, I was able to demonstrate that the population of school-aged children changed as a resultof disclosure, by using not only housing data but also population census data by age group of small
areas. Previous studies have mainly suggested that housing rents will increase in high quality school
districts due to selective disclosure by real estate agencies. However, in this study, I was able toshow that disclosure of school quality information affected not only housing rents but also
population. This suggests that the rise in housing rental reflects actual parents’ demands for their
6children's education.
Third, I was able to clearly measure the effect of disclosure of public school quality information
by focusing on a city where a school district system clearly existed and few private schools were
present. Schwartz, Voicu, and Horn (2014) and Fack and Grenet (2010) suggest that the relationshipbetween school quality and real estate price becomes weak when schools can be freely chosenirrespective of school district. Thus, if there are many ways to receive education besides publicschools, the effects of school quality or disclosure might not be measured accurately. However, in
Matsue City, which I analyzed, these problems were solved since most ways of receiving primaryeducation are limited to public schools. Additionally, there are no entrance exams in Japanese public
elementary and junior high schools; thus, the importance of residential choice is high.
Finally, I examined not only the effect of disclosure on the property market and population, butalso on disparity between schools. In previous research, academic performance, school choice, andthe property market have been evaluated, but in this study, I consider the effect of disclosure on all
three categories. By doing so, I can suggest the possibility of the existence of the following
mechanism: The disclosure of school quality information promotes parents’ residential choice
behavior, which is reflected in the real estate market, and as a result, disparity between schools will
expand. Although I cannot refer to each causal relation, it is important from a policy evaluation
perspective to suggest the possibility that disparity will expand due to disclosure.

3.

Empirical strategies

3.1. Housing rents
I estimated the effect of test scores on housing rents and the change in the effect due toinformation disclosure using hedonic regression. Since parents did not have a way of obtaining
official information regarding school quality before disclosure, and they were able to access that
information after disclosure for the first time, so the effects of the test score (which was an index ofschool quality) on housing rents would change before and after disclosure. Therefore, I regressed theinteraction term of the test score and “after-disclosure dummy" on housing rentals with various
control variables included. The basic estimation equation was as follows:
𝑙𝑙𝑙𝑙 𝑃𝑃𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽1 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑎𝑎 ∙ 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑡𝑡 + 𝛽𝛽2 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑎𝑎 + 𝛽𝛽3 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑡𝑡 + 𝛾𝛾𝛾𝛾𝑖𝑖 + 𝛿𝛿𝛿𝛿𝑡𝑡 + 𝜃𝜃𝜃𝜃𝑗𝑗 + 𝜇𝜇𝜇𝜇𝑗𝑗𝑗𝑗 + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖

where 𝑃𝑃𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 was the rent of apartment 𝑖𝑖 in area 𝑗𝑗 in attendance district 𝑎𝑎 in quarter 𝑡𝑡. 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑎𝑎

was the test score of the school located in attendance district 𝑎𝑎, and was to be used as a proxyvariable of the school's quality, which was converted into the deviation value and the rank of theschool within the area from raw data. 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑡𝑡 was the after-disclosure dummy taking one after7

disclosure of school-level test scores 1, or indicating the days elapsed from the date of disclosure. The
vector 𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖 included characteristics of the apartments in question, such as occupied area and the ageof the building. 𝑌𝑌𝑡𝑡 was a quarter dummy and 𝑍𝑍𝑗𝑗 was an area dummy to control time and area fixed

effect respectively 2 . 𝑊𝑊𝑗𝑗𝑗𝑗 contained the area characteristics that changed over time (such as
population and average age). 𝑊𝑊𝑗𝑗𝑗𝑗 also controlled for specific trends in the central urban andsuburban areas 3, as well as the north and south areas 4.

In the above estimation equation, 𝛽𝛽2 represented the effect of the test scores on housing rents

before disclosure of school-level test scores, and 𝛽𝛽1 represented the effect of test scores on housing
rents after disclosure. If parents considered the test scores as representative of school quality, and ifthey could not know that information before the disclosure of school-level test scores, there was no

significant relationship between the test scores and housing rents before disclosure, and there shouldhave been a significant relationship between the test scores and housing rents only after disclosure 5.
As Carrillo, Cellini, and Green (2013) suggest, we must note that parents could obtain school quality
information from not only official disclosure, but also other data source such as local communities
and networks. Therefore, school quality was capitalized to a certain extent by housing rent before
disclosure, but the impact of the first official disclosure of information was still large, and it might
have had the effect of raising the rent, as well.
Additionally, I used the intersection terms of each quarter dummy and school rank to analyzechanges in effect over time. I used the following estimation equation:ln 𝑃𝑃𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛼𝛼 + �

20

𝑡𝑡=2

𝛽𝛽𝑡𝑡 𝑌𝑌𝑡𝑡 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑎𝑎 + 𝛾𝛾𝛾𝛾𝑖𝑖 + 𝛿𝛿𝛿𝛿𝑡𝑡 + 𝜃𝜃𝜃𝜃𝑗𝑗 + 𝜇𝜇𝜇𝜇𝑗𝑗𝑗𝑗 + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖

where 𝛽𝛽𝑡𝑡 represented the impact of school quality on housing rents for each quarter 𝑡𝑡. In this study,

I performed my analysis using 20 quarterly (5 yearly) data, and information on school quality wasdisclosed between the 12th and 13th quarters. Therefore, with the first quarter (𝑡𝑡 = 1) as the baseline,

if school quality became capitalized related to housing rents due to disclosure, 𝛽𝛽𝑡𝑡 indicatedsignificance when 𝑡𝑡 ≥ 12. Conversely, if there was no significant relationship between school

quality and housing rents before disclosure, 𝛽𝛽𝑡𝑡 indicated insignificance when 𝑡𝑡 < 12. I also

analyzed whether the effect of disclosure on housing rents was short-term or long-term. Additionally,
Specifically, this dummy variable took one after October 23, 2014.
I used quarter dummies instead of year dummies because the real estate market experiences seasonality.
I also analyzed data using month dummies, and the results were consistent.
3
The definitions of central urban and suburban were based on the "Matsue City Central Area
Revitalization Basic Plan" published by Matsue City.
4
Matsue City is divided into northern and southern areas by a large river, and each has different
characteristics. The southern area is downtown and has a central station, and the northern area is a
residential one where a university and castle ruins exist.
5Since the planned disclosure of school-level test scores was announced in local news, etc. for several
months prior to the disclosure date, it was likely that many people knew about the disclosure at that time.
12

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by confirming the significance of 𝛽𝛽𝑡𝑡 before the 12th quarter, we could also verify whether theparallel trend assumption was satisfied.

3.2. Number of posted apartments
There was concern that the property data that I used in this study came from data posted on realestate information websites and did not indicate actual transactions. If cheap property was posted at a
specific time and area, or a high-class condominium with large capacity was built during the analysis
period, the average regarding property rentals might have been affected greatly. Additionally, if there
was bias in the number of properties between the high-quality and low-quality school districts, theresults might also have been biased accordingly. To deal with this concern, I estimated the number ofsamples in each area per month as the control variable. Additionally, I also made panel data whichindicated the number of properties used for each area and month and estimated the number ofproperties regressed by the school quality indicator. The estimation equation used is as follows:
𝑁𝑁𝑡𝑡𝑡𝑡𝑡𝑡 = 𝛼𝛼 + 𝛽𝛽1 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑎𝑎 ∙ 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑡𝑡 + 𝛽𝛽2 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑎𝑎 + 𝛽𝛽3 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑡𝑡 + 𝛿𝛿𝛿𝛿𝑡𝑡 + 𝜃𝜃𝜃𝜃𝑗𝑗 + 𝜇𝜇𝜇𝜇𝑗𝑗𝑗𝑗 + 𝜀𝜀𝑡𝑡𝑡𝑡𝑡𝑡

where 𝑁𝑁𝑡𝑡𝑡𝑡 indicated the number of posted properties in area 𝑗𝑗 in attendance district 𝑎𝑎 in quarter

𝑡𝑡. As in the case of estimating of housing rents, to analyze the change in the relationship between

school quality and the number of posted properties over time, I estimated by using the intersection
terms of the quarterly dummy and school quality. The estimation equation used is as follows:
20

𝑁𝑁𝑡𝑡𝑡𝑡𝑡𝑡 = 𝛼𝛼 + �

𝛽𝛽𝑡𝑡 𝑌𝑌𝑡𝑡 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑎𝑎 + 𝛿𝛿𝛿𝛿𝑡𝑡 + 𝜃𝜃𝜃𝜃𝑗𝑗 + 𝜇𝜇𝜇𝜇𝑗𝑗𝑗𝑗 + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖

𝑡𝑡=2

where 𝛽𝛽𝑡𝑡 represented the impact of school quality on the number of posted properties for eachquarter 𝑡𝑡. By these analyses, I was able to confirm that the number of posted properties had notchanged in areas with high or low test scores. Specifically, there was no significant relationshipbetween the number of posted properties and school quality.

3.3. Enrollment and population
Apart from analysis using housing rents, I also estimated the enrollments of each school andpopulation by specific age of each area and each month as an independent variable to analyze thechange in school and residential choice after disclosure. The estimation equation used is as follows:
𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑡𝑡𝑡𝑡 = 𝛼𝛼 + 𝛽𝛽1 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑎𝑎 ∙ 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑡𝑡 + 𝛽𝛽2 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑎𝑎 + 𝛽𝛽3 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑡𝑡 + 𝛿𝛿𝛿𝛿𝑡𝑡 + 𝜃𝜃𝜃𝜃𝑗𝑗 + 𝜇𝜇𝜇𝜇𝑗𝑗𝑗𝑗 + 𝜀𝜀𝑡𝑡𝑡𝑡𝑡𝑡
𝑃𝑃𝑃𝑃𝑃𝑃𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 = 𝛼𝛼 + 𝛽𝛽1 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑎𝑎 ∙ 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑡𝑡 + 𝛽𝛽2 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑎𝑎 + 𝛽𝛽3 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑡𝑡 + 𝛿𝛿𝛿𝛿𝑡𝑡 + 𝜃𝜃𝜃𝜃𝑗𝑗 + 𝜇𝜇𝜇𝜇𝑗𝑗𝑗𝑗 + 𝜀𝜀𝑡𝑡𝑡𝑡𝑡𝑡
9where 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑡𝑡𝑡𝑡 indicated the number of enrollment students of school 𝑎𝑎, in year 𝑡𝑡, and 𝑃𝑃𝑃𝑃𝑃𝑃𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜

indicated the number of people aged 𝑜𝑜 years old in area 𝑗𝑗 in attendance district 𝑎𝑎 in month 𝑡𝑡. For
analysis on enrollment of students, I used panel data at the annual and school level, and analysis on

population by age was estimated using panel data at the month and area level.

As with analysis of housing rents, I also analyzed changes in the effect of disclosure on
population over time 6. The estimation equation used is as follows:
20

𝑃𝑃𝑃𝑃𝑃𝑃𝑜𝑜𝑜𝑜𝑜𝑜 = 𝛼𝛼 + �

𝛽𝛽𝑡𝑡 𝑌𝑌𝑡𝑡 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑎𝑎 + 𝛿𝛿𝛿𝛿𝑡𝑡 + 𝜃𝜃𝜃𝜃𝑗𝑗 + 𝜇𝜇𝜇𝜇𝑗𝑗𝑗𝑗 + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖

𝑡𝑡=2

By this estimation, I analyzed the change in population in the area with high (low) test scores.

4.

Data
In this study, I mainly used three kinds of data (e.g., housing, school, and district data). Thissection provides detailed information regarding the data used.

4.1. About Matsue City
Matsue City is a regional city in southwest Japan. Its population was approximately 200,000 andits population density was approximately 390 people per square meters in 2018. The average score
on national tests of elementary school students in Matsue City in 2014 was 66.613, which was
almost the same as the national average of 66.175; therefore, Matsue City was an average city interms of academic achievement.
In Matsue City, information on school quality was first disclosed in 2014. There was no official
announcement on school quality (such as the average test score) before 2014, so parents were able toaccess this information for the first time after disclosure in 2014. Whether to disclose school-level
results had been discussed since the beginning of 2014, and after disclosure had been decided upon,plans regarding it were announced on local news or websites a few months before the disclosure
occurred. Therefore, it was believed that many people who lived in Matsue City knew about thedisclosure and when it would take place.
There are several reasons why I focused on Matsue City, just like Kuroda (2018). The primaryreason is that there are very few private elementary and junior high schools in Matsue City. If many
private schools are present, the relationship between public school quality and housing rentals in theschool district may be weakened because anyone can go to a private school regardless of where they
live (Schwartz, Voicu, & Horn, 2014; Fack and Grenet, 2010). However, because there are few
6Since enrollment data had a small number of samples, only population by age was estimated.

10

private schools in Matsue City, we could clearly identify the effect of public school test scores on
housing rentals because it was difficult for students to receive primary education from somewhereother than public schools.
Furthermore, in Matsue City, a school choice system had not been introduced, and a school district
existed. Therefore, the school that a student can attend is decided according to his/her residential
address, and students cannot go to school outside the boundaries of their school district. In
exceptional situations such as when an address is changed due to moving during the school fiscal
year, students can go to school outside their school district; however, this situation is very rare.
Therefore, in Matsue City, one’s place of residence is closely related to the quality of education thathe or she can receive, and the importance of residential choice for parents with children is high.
Figure 1 presents a map of the school districts in Matsue City used for this study. The bold blacklines represent public elementary school district boundaries, and only one school exists in eachschool district. 7 There are 35 public elementary schools in Matsue City, but the number of schools
used in this analysis was 25 because I could not use data for schools in non-urban areas where there
were very few apartments in the district or where the district had a branch school that did not release
its test scores due to a limited number of students. In Matsue City, the residential choice of parents
could be influenced by school quality information due to identical school fees, lack of entrance
examination, and the presence of several public transport options within the narrow area.

～ Attendance Districts
・Elementary Schools

Figure 1. Elementary School Districts of Matsue CityAs an exception, one branch school exists. However, since its number of students during the analysis
period was only one or two and its test scores were not disclosed, this school would not affect theestimation results.

711

4.2. Property data
I used rental property data from 2012 to 2016 for Matsue City from the “Real Estate Database
1999-2016.” 8 This dataset includes nationwide real estate data from 1999 to 2016, and there are
22,767 samples from 2012 to 2016 for Matsue City. As I mentioned in the previous section, thisproperty data was obtained from data posted on real estate information websites and did not indicate
actual transactions. Property offered for sale by the real estate agency was included in this dataset,and it contained the date that properties were posted as for sale on the real estate websites.
In this study, I excluded land and sales property data from samples because I focused on rental
properties. Each property had data on the property number, category (such as owned house or rental
apartment, and newly or used), the rent or price, the occupied area (by square meter), the floor level,
the number of floors in the building, the age of the building, the building’s construction (such as
wooden or block), and the date of disclosure. As shown in Table 1, I set the dummy variables foreach category (e.g. rental apartment, rental townhouse, rental mansion, rental store, or office 9) andstructure (e.g. autoclaved lightweight aerated concrete, precast concrete, reinforced concrete, steel
reinforced concrete, block, light steel, steel, and wooden). Furthermore, I set the “after-disclosure
dummy” and “the number of elapsed days after disclosure.”
In this study, I defined apartments of over 40 square meters as "family-oriented apartments." InJapan, some real estate agencies have said that apartments with two or more rooms, or of 40 or moresquare meters are intended for families (e.g., Homemate, n.d.). 10 As shown in Figure 2, it seems that
40 square meters is the boundary between one room and two or more rooms, so this definition wasconsidered reasonable. I derived the main results using samples which included family-oriented
apartments. To confirm the robustness of this definition, I also made estimates that divided thesample according to the occupied area.
Additionally, because this dataset did not contain a strict address, there were properties that could
not be identified, such as to which school district a property belonged. In this study, since suchproperties were excluded from the sample, 16,065 properties (7,781 for families; 7,752 for singles;
and 532 for rental shops and offices) were used for analysis. 11
I must address another concern regarding whether parents with elementary school children lived
in the apartments, since many people live in their own houses rather than rental properties inThis dataset was provided by the Center for Spatial Information Science, the University of Tokyo.
In Japan, the terms apartment, town house, and mansion all refer to a general type of residential realestate, indicating a meaning similar to "apartments" in American English.
10
Of course, this definition changes depending on population density or economic conditions of a city,
but in Japanese local cities, it is ordinarily said that an apartment with 40 or 50 square meters or more isintended for families.
11
Analysis using all apartments and single-person apartments was also conducted, and the results wereused for confirming the robustness of the main results. Summary statistics of samples of single-person
apartments and rental shops/offices are shown in Appendix A.
8912Japanese cities. Therefore, I demonstrated the relationship between household type and housing
format in Shimane Prefecture 12 as follows. Table 2 indicates the number of households by type in
Shimane Prefecture in 2013. The number of households in Shimane Prefecture was 254,700, of
which 185,400 households (73%) lived in owned houses and 69,300 households (27%) lived inrented ones. However, households which had elementary school children (in this table, their age wasassumed to be under 9 years old) exhibited a different tendency because many elderly households
rather than younger ones tended to live in owned houses. Of all the three to five-person households
evaluated, 5,100 households lived in owned houses and 8,800 households lived in rented ones.
Specifically, over 60% of married couples with elementary school children lived in rented houses;
therefore, it was reasonable to use the rented house for this study.

Figure 2. Number of Properties by Occupied Area

12
Matsue City is the capital city of Shimane Prefecture, and holds approximately one-third of the
population and households of the prefecture. Due to data constraints, I used Shimane Prefecture’s
information instead of Matsue City’s.

13Table 1. Summary Statistics (Main Estimation)
House characteristics
Housing rent (YEN)
After-disclosure dummy
Elapsed days after disclosure
Number of floors
Located floor
Occupied area
Age of building
Rental apart dummy
Rental town house dummy
Rental mansion dummy
Structure ALC dummy
Structure PC dummy
Structure RC dummy
Structure SRC dummy
Structure block dummy
Structure light steel dummy
Structure steel dummy
Structure wooden dummyN
Area characteristics
Number of households
Population
Average household size
Number of children
Number of elementary students
Average age
Number of samples (family)
Number of samples (single)
Urban dummy
North dummyN
School characteristics
Number of enrollments
Test score 2014
N

Mean

S.D.

Min

Max

58971
0.447
168.600
2.479
1.796
53.13014.050
0.800
0.006
0.195

11189
0.497
238.209
1.309
1.079
9.130
7.942
0.400
0.074
0.396

35000
0
0
1
1
40
0
0
0
0

5500001801
14
10106.640
50
1
1
1

0
0
0
0
0
0
0

1
1
1
1
1
1
1

8.4
10.8001.286
0025.813
0
0
0
0

4460
9250.200
3.280
1073
559
61.16851421
1

859.525185
72.750

NA
0.002
0.136
0.008
0.001
0.120
0.455
0.278

0.045
0.343
0.092
0.025
0.325
0.498
0.448
7781

496.272
1151.646
2.269
141.891
77.13245.45113.96012.0300.244
0.370

588.424
1382.317
0.378
184.623
98.131
6.434
12.685
7.840
0.430
0.483
148

70.21066.61339.8166
3.202
25※ALC, PC, RC, and SRC stand for "Autoclaved Lightweight aerated Concrete", "Precast
Concrete", "Reinforced Concrete", and "Steel Reinforced Concrete", respectively.
14

Table 2. Number of Households Living in Owned Houses and Rented Houses
Type of household
Total
Single-person households
Two-person households
Three- to five-person households
married couple and children (under 9 years old)
married couple and children (from 10 to 17 years old)
married couple and children (over 18 years old)
othersOver six-person households

Owned houses
185,400
33,10060,800Rented houses
69,30035,00014,000Total
254,700
68,10074,8005,100
7,600
43,40015,00015,300

8,900
4,200
3,300
3,000
800

14,00011,80046,70018,000
16,100※Created by editing 2013 Housing and Land Survey (Statistics Bureau, M inistry of Internal Affairs and4.3. Area data
I obtained statistical data and information on school districts from Matsue City’s official website.
Population census data are reported on the website monthly, and populations by age and gender ineach area are described. 13 In the main estimation, I used the number of households, population,
average age, number of people per household, number of children (under 12 years old), number of
elementary school students, and total population by each area and each month to control for local
characteristics that change over time. The number of areas was 148, and details of each variable areshown in Table 1. In Matsue City, the residential environment is totally different in central urban
areas versus the suburbs 14, so I controlled for different time trends of housing rentals, respectively.
Additionally, because the characteristics and amenities of houses are different between areas northand south of the central river, I also controlled for different trends, respectively.

4.4. School data
In this study, I used two kinds of data on schools: the number of enrolled students and the resultsof academic achievement tests. The summary statistics are shown in Table 1. Test scores were
derived from two types of tests: 6th grade elementary school students took the National Assessmentof Academic Ability (NAAA), and 4th and 5th grade elementary school students took the Shimane
Academic Ability Survey (SAAS). I used the sum of the math and language test scores, averaged
over 3 years, as the test score for each school. The NAAA is the scholastic ability examination that
the Ministry of Education, Culture, Sports, Science and Technology (MEXT) has carried out once
In this study, the minimum unit of address was called "area." This is known as "cho-cho" in Japanese,
which describes a location that is more detailed than a city but less detailed than an exact street address.
Although not strict, it refers to a location such as a street name in the United States.
14
Central urban areas are commercial areas that are centered about Matsue Station, and there are many
residential areas and agricultural lands in the suburbs.
13

15

per year since 2007, and it is applied to sixth and ninth graders. The SAAS is the scholastic ability
examination that Shimane Prefecture originally conducted, and it is applied to 4th, 5th, and 6th
graders in public elementary schools and students in public junior high schools. In Matsue City,
however, the SAAS was applied only to 4th, 5th, 7th, and 8th grade students because 6th and 9th
graders took the NAAA instead of the SAAS.
Both academic ability tests did not disclose school-level results before October 2014, andschool-level results were first disclosed on October 22, 2014. The NAAA was administered on April
22, 2014, and the SAAS was given on April 24th and 25th. After 2015, these tests began being
conducted around May, and results are disclosed at the end of October every year. In this study, I
considered the test scores of 2014 as the scores for each school and used them, since school-level
test scores did not demonstrate extreme change over several years.
For analysis, the test scores were converted into a deviation value, and the school rankings of thetest scores were also used. 15 This was because parents might have considered the rank of a school in
the region, rather than how many points the school’s score had risen. In either case, results that were
almost consistent were obtained, but I show both results below to compare the coefficients with theprevious studies.

5.

Results and discussions

5.1. Main results for the housing market
Table 3 shows the results of estimating the housing samples by restricting them intofamily-oriented apartments. 16 Column (1) indicates the results of using deviation values while
controlling for house and area characteristics. This result shows that school quality had a significant
positive effect on housing rents before disclosure of school quality information, and the effect wassignificantly increased additionally after disclosure. Column (2) shows the result of controlling time
fixed effects in addition to column (1), but the result was almost the same as in the first column.
Column (3) shows the result of adding area fixed effects to the control variables, and the coefficientof the interaction term of school quality indicator and the after-disclosure dummy still had
significant positive effect, although the impact was weaker than in columns (1) and (2). Column (4)The deviation value was obtained by normalizing the test score so that the mean was 50 and thestandard deviation was 10, and this value is ordinarily used in Japan as the index of students’ academic
achievement. Specifically, it is derived by dividing the difference between each test score and the mean oftest scores by the standard deviation, multiplying by 10, and adding 50.
16
I analyzed data using all the samples without dividing, but the results suggested that main variables
had no significant effect on housing rents if I controlled for all the variables (see Appendix B and C). I
interpreted these results as suggesting that the impact of family-oriented and single-person apartments
was canceled out.
15

16

shows the result of adding the different trends in the central urban and suburban areas, and column(5) shows the result of adding the different trends in the northern and southern portions of the city,
but the coefficients of the intersection term were still positive and significant. These results indicatedthat the rents of family-oriented apartments located within a high-quality school district significantly
increased additionally after the disclosure of school quality information, and these results wererobust even if various variables were controlled. The effect of the disclosure of school quality
information on single-person apartments will be analyzed in a later subsection. The bottom part ofTable 3 shows the results of estimation using school rank instead of deviation value as a proxyvariable of school quality. Since the level of school ranking decreased as school quality increased,
the main coefficients indicated the opposite of the results using deviation value. Column (6) shows
the results of controlling the house and area characteristics similarly to column (1), and the results
indicated that school quality had a significant positive effect on housing rents before disclosure, but
the effect was strengthened by disclosure. The results of columns (7)-(10) are also similar to those ofcolumn (2)-(5). Summarizing the main result of this study, after disclosure of information on school
quality, rents of family-oriented apartments located in a district with a high-quality school increased
compared to those located in a school district with a low-quality school. 17
Table 4 shows the result using the number of elapsed days from disclosure instead of theafter-disclosure dummy, were other specifications the same as in Table 3. Columns (1) and (2) show
the results of using deviation value without controlling the area fixed effects. Both results showedthat the interaction terms were positive and significant coefficients, suggesting the possibility thattest score would affect housing rents gradually as time passed after disclosure. Column (3) shows theresults of adding the area fixed effects, Column (4) shows the result of adding the different trends inthe central urban and suburban areas, and column (5) shows the result of adding the different trends
in the northern and southern parts of the city. All the coefficients of intersection terms were still
positive and significant, but the number of elapsed days had no significant effect on housing rents.
The results from column (6) to column (10) showed the same results as column (1) to column (5),and there were no large differences between deviation value and school rank in this estimation.
To analyze the effect of changing over time, I estimated using the intersection terms of eachquarter dummies and school quality indicator, and the results are shown in Table 5 and Figure 3. 18
Column (1) and column (2) show the results obtained using deviation value and school rank
respectively. In Figure 3, the horizontal axis represents time, the vertical axis represents theI also analyzed data using housing rents per occupied area used in previous studies instead of raw
housing rents; however, the results were almost the same as those obtained using raw housing rents data.
The results are shown in Appendix D.
18
The result of estimating the effect of change over time using single-person apartments is shown inAppendix E. According to the results, rentals of single-person apartments showed a decreasing trend
before disclosure, and the assumption of a parallel trend was not satisfied. Results using single-person
apartments will be interpreted in detail in Section 5.2.1.
17

17

coefficient, the black dot represents the coefficients of interaction terms of each quarter dummy andschool quality indicator, and each width represents the 95% confidence interval. According to theresults of Table 5, school quality had a nearly significant effect on housing rents before disclosure ofschool quality information. Specifically, there was no significant difference in housing rents between
high-quality school districts and low-quality school districts before disclosure, which also suggeststhat the probability of satisfying the parallel trend assumptions was high. However, after disclosureof school quality information, school quality had a significant positive effect on housing rents. These
results emphasized that the rise in housing rents for family-oriented apartments was not due to priortrends or other shocks, but was certainly due to disclosure of school quality information. Although it
was not statistically significant, it was important to note that even when controlling various variables
and fixed effects, the rents of apartments in a high-quality school district were higher than those in a
low-quality school district. This suggests that even before the official disclosure occurred, residents
might have accessed school quality information from informal sources such as reputations or rumors.
In fact, in areas where school quality information was not disclosed, unofficial information on school
quality was available through parents’ interactions or local communities, so this is a plausible
explanation.
Regarding the impact of the main regression results, the results for column (5) in Table 3 suggestthat a 10-point increase in deviation value led to an approximately 0.05 percent additional increase in
housing rents after disclosure. This implied that parents were willing to pay JPY294 more inmonthly rent for housing, and this impact was very small (the mean associated with housing rents
was approximately JPY59,000). This impact was about two-thirds of the result obtained by Kuroda(2018) that analyzed using cross-sectional data after disclosure in Matsue City. There were several
reasons why the impact was small compared to previous studies on school quality and the housing
market. First, school quality might have already been capitalized in relation to housing rentals tosome extent before disclosure. As mentioned above, there were several ways to obtain information
on school quality besides through the official disclosure, although accuracy was not guaranteed, so
real estate agents and parents might have made decisions based on that information before thedisclosure. In column (2) of Table 3, the sum of the coefficients of the deviation value and theinteraction term suggests that housing rents increased by 3.7%, with the increase of one standard
deviation, which was consistent with the results of several previous studies indicating that one
standard deviation was related to a 2 - 5% higher property price ((Black, 1999; Bayer, Ferreira, &McMillan, 2007). Second, there might be strong heterogeneity among parents’ behavior with respect
to school quality, as previous studies suggested (Koning and van der Wiel, 2013; Ries andSomerville, 2010). This might lead to a small impact and large significance indicating that a certain
minority of parents are very concerned about school quality, but the majority do not care about it.
Third, since there were not many parents interested in education compared to those in large cities18

that were used in previous studies, the impact on the real estate market might be relatively small.
Parents who are interested in education enough to care about elementary school quality tend to live
in a metropolitan city where many schools are available that provide advanced elementary education.
In fact, there are many high-quality private elementary schools, kindergartens, and cram schools foryoung children in large cities such as Tokyo, and supply and demand for advanced primaryeducation are also high. Therefore, there may be relatively fewer parents who are interested inprimary education for their children in smaller areas like Matsue City compared to large cities.

19

Table 3. Baseline Results
Deviation value
After-disclosure dummy

(1)

(2)

.0026***.0025***

(3)

(4)

(5)

(.0001)(.0001)***-.0710-.0642***-.0281**-.0358***-.0245**(.0086)(.0105)(.0088)(.0093)(.0090)

***

***

***

***

.0012

.0012

.0006

.0007

.0005**(.0002)(.0002)(.0001)(.0002)(.0002)

House characteristics

YES

YES

YES

YES

YES

Area characteristics

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

Deviation value × After-disclosure dummyControl variables

Time fixed effects
Area fixed effects
Urban/suburban trends
North/south trends

YES

N

7617

7598

7513

7494

7475

Adjusted R2

0.66380.66680.77540.77820.7795

(6)

(7)

(8)

(9)

(10)

.0106

.0147*.0096

School rank

***

***

-.0033.-0033(.0002)(.0002)*.0186*

After-disclosure dummy.0095(.0039)(.0076)(.0058)(.0059)(.0058)School rank × After-disclosure dummy-.0018***-.0018***-.0008***-.0010***-.0007***(.0002)(.0002)(.0002)(.0002)(.0002)Control variables
House characteristics

YES

YES

YES

YES

YES

Area characteristics

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

Time fixed effects
Area fixed effects
Urban/suburban trends

YES

North/south trends

YES
YES

N

7617

7598

7513

7494

7475

Adjusted R2

0.66230.66550.77540.77830.7795※*, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively.
※Standard errors are adjusted for clustering at the area level.

20

Table 4. Elapsed Days
Deviation value
Number of elapsed days
Deviation value × Elapsed days

(1)

(2)

.0026***.0026***(.0001)(.0001)

(3)

(4)

(5)

-1.584×10-4*** -.8402×10-4-.2082×10-4-.3701×10-4-.2163×10-4(.1828×10-4 )(.4599×10-4 )(.3571×10-4 )(.3599×10-4 )(.3559×10-4 )

-6***

-6***

-6***

-6***

1.134×10-6

2.678×10-6

2.627×10-6

1.234×10-61.444×10-6***(.3664×10 )(.3630×10 )(.3159×10 )(.3421×10 )(.3248×10-6 )House characteristics

YES

YES

YES

YES

YES

Area characteristics

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

Control variables

Time fixed effects
Area fixed effects
Urban/suburban trends
North/south trends

YES

N

7617

7598

7513

7494

7475

Adjusted R2

0.6636

0.66710.77550.77820.7796

(6)

(7)

(8)

(9)

(10)

6.316×10-5

6.092×10-5

5.416×10-5(3.342×10-5 )(3.325×10 )School rank
Number of elapsed days
School rank × Elapsed days-.0034***-.0034***(.0002)(.0002)2.802×10-5***9.980×10-5*(.8120×10-5 )(4.378×10 )-3.916×10

-6***

-5

-3.861×10

-6***

-5

(3.338×10 )-1.632×10-6***-1.891×10

-6***

-5

-1.406×10-6***(.4931×10-6 )(.4900×10-6 )(.4252×10-6 )(.4530×10-6 )(.4255×10-6 )House characteristics

YES

YES

YES

YES

YES

Area characteristics

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

Control variables

Time fixed effects
Area fixed effects
Urban/suburban trends
North/south trends

YES

N

7617

7598

7513

7494

7475

Adjusted R2

0.66180.66570.77550.77820.7795※*, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively.
※Standard errors are adjusted for clustering at the area level.

21

Table 5. Changes in Effect Over TimeDeviation value / School rank
×2012 Q2
×2012 Q3
×2012 Q4
×2013 Q1
×2013 Q2
×2013 Q3
×2013 Q4
×2014 Q1
×2014 Q2
×2014 Q3
×2014 Q4 (disclosure)×2015 Q1
×2015 Q2
×2015 Q3
×2015 Q4
×2016 Q1
×2016 Q2
×2016 Q3
×2016 Q4
N
Adjusted R2

Deviation value(1)

School rank
(2)

.0008
(.0006)*.0015(.0006).0004(.0007).0005(.0006).0006(.0006).0007(.0006).0004(.0006)*.0013(.0006).0011(.0006).0009(.0006).0007(.0006).0010(.0006).0018**(.0006).0015*(.0006).0019***(.0006).0017**(.0006).0012(.0006).0014*(.0007).0017**(.0006)7492
0.7770-.0016(.0009)**-.0025(.0009)-.0009(.0010)-.0011(.0009)-.0012(.0009)-.0013(.0009)-.0014(.0009)-.0027*(.0009)*-.0022(.0009)-.0017(.0009)-.0016(.0008)*-.0017(.0009)-.0028**(.0009)-.0022**(.0008)-.0028***(.0008)-.0027**(.0008)*-.0020(.0009)*-.0023(.0009)-.0024*(.0009)7492
0.7771※*, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively.
※Standard errors are adjusted for clustering at the area level.
※All control variables (house characteristics, area characteristics, year fixed effects,
area fixed effects, urban/suburban trends, and north/south trends) were controlled.

22

Table 5 - Column (1),Changes in the effect of deviation value on rents of family-oriented apartments

Table 5 - Column (2),Changes in the effect of school rank on rents of family-oriented apartments
Figure 3. Changes in Effect Over Time23

In summary, the main finding in this study showed that school quality indicators such as deviation
value and school rank had a significantly positive effect on rents of family-oriented apartments after
disclosure of school quality information. Even before disclosure, school quality might have had apositive effect on housing rents. In the following section, I also analyze information concerning theheterogeneity of properties and verify that the main results were certainly derived from disclosure of
information on school quality.

5.2. Various concerns
5.2.1. Placebo test
To ensure that change in rents of apartments was caused from nothing else but disclosure ofschool quality information, I conducted a placebo test using rents of units other than family-oriented
apartments. If events which could have affected housing rents occurred at the same time as
disclosure, the rent of properties such as shops and offices or single-person apartments that would
not have been affected intrinsically by school quality might also have been impacted. To address thisconcern, I obtained results using rental shops, offices, and single-person apartments by estimating
the same equation used in the main estimation. Table 6 shows the results obtained using rental shops
and offices, and these results suggest that all the main variables are statistically insignificant if
controlling for various variables. Of course, I must note that the rental shops and offices werelocated mainly in commercial areas and the number of samples was smaller than that of theresidential properties.
Therefore, I analyzed data using single-person apartments. These apartments are located widely inthe city, and in my dataset, the number of samples was almost the same as that of family-oriented
apartments (7,781 for families; 7,752 for singles). The results are shown in Table 7, and the basic
specification was the same as the main estimation. Columns (1), (2), (6), and (7) show results notcontrolling for regional fixed effects and coefficients of interaction terms that were indicatedconsistently negative, although significances were low. However, the results after controlling forarea fixed effects and urban/suburban trends are shown in columns (3), (4), (8), and (9). These
results suggest that school quality has a significantly negative effect on housing rents after disclosure.
However, controlling the northern/southern trends weakened the significance of coefficients ofinteraction terms, and in column (10), which shows the result obtained by using school rank, thecoefficient of the interaction term was insignificant. Additionally, as a result of analyzing thechanges of the effect over time using quarterly dummies, we could see that housing rents for
single-person apartments tended to decrease in areas with high test scores even before disclosure.
(The results are shown in Appendix E.) In summary, the results in this section indicated that24

disclosure of school quality information had no significant effect on housing rents of single-person
apartments, nor had any events occurred that affected the housing market at the same time as thedisclosure.
As for what actually happens regarding single-person apartments, this can be inferred byconsidering the demanders of these units. University students can be cited as major users of
single-person apartments in smaller cities in Japan. Shimane University, which has approximately
6,000 students, is located in Matsue City, and the residential area of the university’s students
overlaps with the attendance districts of two elementary schools which rank 1st and 2nd in testscores. 19 The number of enrolled students at Shimane University is decreasing every year, which
may be one reason why rents of single-person apartments are decreasing in an area that has
high-quality schools. 20 In fact, enrollments decreased by approximately 100 (from 1,602 to 1,507)
between 2008 and 2016. Specifically, enrollments decreased by 43 (from 1,576 to 1,530) between
2013 and 2014 when housing rents of single-person apartments decreased significantly. Therefore, it
seems that the downtrend of rents of single-person apartments might be due to the decreasing
number of university students, and not the effect of disclosure.

The definition of the residential area where the students of Shimane University live is based on thestandards published by the Shimane University Cooperative.
20
According to statistical information published by Matsue City, the populations of the "north" and"south" areas are almost the same (the north has 73,576 people, and the south has 72,595). However, thenorthern area where Shimane University is located has more than 800 people aged 18 to 22 than thesouthern area does. Therefore, the existence of Shimane University and the number of students can affectthe housing market.
19

25

Table 6. Results using Shops and Offices(1)
Deviation value
After-disclosure dummy

(2)

(3)

(4)

(5)

.4888

.2837

.4013

*

-.0124.-0216(.0094)(.0085)

.4320

.4751

(.6519)(.2917)(.3919)(.6089)(.9596)-.0076.-0080*-.0082-.0047.0063(.0099)(.0038)(.0055)(.0086)(.0139)House characteristics

YES

YES

YES

YES

YES

Area characteristics

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

Deviation value × After-disclosure dummyControl variables

Time fixed effects
Area fixed effects
Urban/suburban trends
North/south trends

YES

N

474

455

448

429

414

Adjusted R2

0.86210.87850.87900.88110.8805

(6)

(7)

(8)

(9)

(10)

*

School rank

.0169

.0390

(.0181)(.0169)After-disclosure dummy-.1069*-.0815-.0810-.0255.0593(.0416)(.1265)(.1182)(.0841)(.0619)

.0022

.0115

-.0111.0028-.0126(.0062)(.0061)(.0082)(.0139)(.0202)

House characteristics

YES

YES

YES

YES

YES

Area characteristics

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

School rank × After-disclosure dummyControl variables

Time fixed effects
Area fixed effects
Urban/suburban trends
North/south trends

YES

N

474

455

448

429

414

Adjusted R2

0.86190.87810.87880.88100.8806※*, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively.
※Standard errors are adjusted for clustering at the area level.

26

Table 7. Results using Single-Person Apartments
(3)

(4)

.0099

.0328*.0364*.0241(.0115)(.0166)(.0149)(.0148)(.0158)-.0004-.0004*-.0007***-.0008***-.0006*(.0002)(.0002)(.0002)(.0002)(.0002)

House characteristics

YES

YES

YES

YES

YES

Area characteristics

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

Deviation value
After-disclosure dummy
Deviation value × After-disclosure dummy

(1)

(2)

.0012***.0011***(.0002)(.0002)

***

.0629

(5)

Control variables

Time fixed effects
Area fixed effects
Urban/suburban trends
North/south trends

YES

N

7716

7697

7609

7590

7571

Adjusted R2

0.61610.62390.75370.75580.7560

(6)

(7)

(8)

(9)

(10)

-.0135.0098

School rank
After-disclosure dummy
School rank × After-disclosure dummy

***

***

-.0020-.0020(.0003)(.0003).0377-.0159-.0126(.0046)(.0123)(.0099)(.0098)(.0099)

.0003

.0003

.0007*.0008**.0004(.0003)(.0003)(.0003)(.0003)(.0003)Control variables
House characteristics

YES

YES

YES

YES

YES

Area characteristics

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

Time fixed effects
Area fixed effects
Urban/suburban trends

YES

North/south trends

YES
YES

N

7716

7697

7609

7590

7571

Adjusted R2

0.61750.62550.75350.75560.7558※*, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively.
※Standard errors are adjusted for clustering at the area level.

275.2.2. Estimation of divided samples
In this section, I analyzed data considering heterogeneity of effect regarding school quality anddisclosure. The degree of effect of school quality might differ depending on house characteristics or
location. As larger houses are better suited for families, people living in these dwellings are likely tohave children and may be more concerned about school quality. Additionally, parents with higher
education and income tend to live in larger houses, so rents of these units may be more affected bytest scores. Furthermore, since the definition of an area suitable for occupation by a family (over 40
square meters) is arbitrary, I must also analyze data using occupied areas other than those meeting
this definition. To address this concern, I estimated by dividing the sample according to occupied
area.
Table 8 shows the result of estimation performed by dividing all the samples by every 10 square
meters according to the occupied area. The upper part of the table shows the results obtained using
deviation value, and the lower part shows the results obtained using school rank. Most of theintersection terms of interest were not significant, but the interaction terms in columns (3), (6), and(12) were significant. The result of column (3) suggests that rents of apartments of 30-40 square
meters in size that were located in high-quality school districts decreased after disclosure of school
quality information. However, as mentioned in the previous section, this result might be led by theeffect of the decreasing number of university students, not disclosure. The results of columns (6) and(12) suggest that the rents of apartments of over 60 square meters in size that were located inhigh-quality school districts significantly increased after disclosure of school quality information.
The results showed that the more housing units were intended to be lived in by families, the morethey were affected by school quality. This tendency was consistent with the results of previous
studies (Kuroda, 2018; Carrillo, Cellini, and Green, 2013). Additionally, this result also suggests thathigh income or highly educated parents that tend to live in larger apartments may care more aboutschool quality.
The effect of school quality on housing rents might differ depending not only on the size of theapartment, but also on the area where the apartment is located. An apartment located in an area
where there are many children may be more affected by test scores. Conversely, even apartments
intended for families may not be affected by school quality if they are in areas with few children.
Specifically, if disclosure of school quality information has a significant effect on housing rents evenin areas with few children, this suggests the possibility that events affecting housing rents other thandisclosure were occurring at the same time. To address this concern, I estimated by dividing thesample according to the number of children in each area.
Table 9 shows the result of estimation performed by dividing all samples according to the number28

of children in the area where each sample was located. 2122 The upper part of the table shows theresults obtained using deviation value, and the lower part shows the results obtained using school
rank. Additionally, the results of the estimation obtained by using samples located in areas with arelatively high number of children are shown in columns on the table’s left side. Conversely, thecolumns on the table’s right side show the results of analysis performed using apartments located inareas with relatively few children. Columns (1) and (7) show the results estimated using apartments
in areas where there were over 100 children, indicating that school quality significantly increased
housing rents after disclosure. Columns (2) and (8) show the results estimated using apartments inareas where there were over 150 children, which indicated almost the same result as shown incolumns (1) and (7). Columns (3) and (9) show the results obtained using apartments located in areas
which had over 200 children, and these results also indicated that housing rents increased inhigh-quality school areas after disclosure, but the coefficients of the interaction terms became threetimes larger. This indicated that the apartments located in areas with especially high numbers ofchildren were more affected by school quality after disclosure. These results might be derived fromthe large ratio of families with children in the total demanders of larger apartments. However, theresults of using apartments located in areas with few children are shown in Columns (4)-(6) and(10)-(12), and all coefficients were not significant. This indicates that even family-oriented
apartments are not affected by school quality in areas with relatively few children. The results inTable 9 were consistent with the results and discussions presented so far and emphasized that school
quality information and its disclosure were certainly affecting families and children. Additionally,
the results of this section suggest that the degree of effect of school quality is different depending on
house characteristics and location.

In this analysis, people under 12 years old were defined as "children."I also analyzed data by dividing all samples according to the child population density in the area where
each sample was located; however, the results were almost the same as those obtained using the numberof children in the area. The results are shown in Appendix F.
21

22

29

Table 8. Sample Divided According to Occupied Area
under 20

20 to 30

30 to 40

40 to 50

50 to 60

(1)

(2)

(3)

(4)

(5)

(6)

.0589

-.0208.0799**.0130-.0111-.0469(.0547)(.0150)(.0275)(.0145)(.0143)(.0151)-.0006.0003.-0009

*

.0000

.0002

.0009

(.0009)(.0002)(.0004)(.0003)(.0002)(.0003)

House characteristics

YES

YES

YES

YES

YES

YES

Area characteristics

YES

YES

YES

YES

YES

YES

Time fixed effects

YES

YES

YES

YES

YES

YES

Area fixed effects

YES

YES

YES

YES

YES

YES

Urban/suburban trends

YES

YES

YES

YES

YES

YES

North/south trends

YES

YES

YES

YES

YES

YES

N

918

5159

1263

2898

2586

1728

Adjusted R2

0.92710.78710.84460.73850.57220.9132

under 20

20 to 30

30 to 40

40 to 50

50 to 60

over 60

(7)

(8)

(9)

(10)

(11)

(12)

.0209

-.0008

.0210

.0100

.0052

.0155

(.0244)(.0097)(.0115)(.0092)(.0095)(.0098).0003-.0005

.0007

.0001

-.0005-.0012***After-disclosure dummy
Deviation value × After-disclosure dummyover 60*****

Control variables

After-disclosure dummy
School rank × After-disclosure dummy(.0014)(.0003)(.0006)(.0003)(.0003)(.0003)Control variables
House characteristics

YES

YES

YES

YES

YES

YES

Area characteristics

YES

YES

YES

YES

YES

YES

Time fixed effects

YES

YES

YES

YES

YES

YES

Area fixed effects

YES

YES

YES

YES

YES

YES

Urban/suburban trends

YES

YES

YES

YES

YES

YES

North/south trends

YES

YES

YES

YES

YES

YES

N

918

5159

1263

2898

2586

1728

Adjusted R2

0.92710.78720.84410.73850.57220.9132※*, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively.
※Standard errors are adjusted for clustering at the area level.

30

Table 9. Sample Divided According to the Number of Children in an Area
over 100over 150

over 200

under 100

under 150

(1)

(2)

(3)

(4)

(5)

(6)

After-disclosure dummy-.0291**-.0295**-.0898***-.0362-.0199-.0102(.0162)(.0254)(.0180)(.0141)

Deviation value × After-disclosure dummy

.0006

.0006

***

.0008

.0005

.0003

(.0002)(.0002)(.0003)(.0005)(.0003)(.0002)

House characteristics

YES

YES

YES

YES

YES

YES

Area characteristics

YES

YES

YES

YES

YES

YES

Time fixed effects

YES

YES

YES

YES

YES

YES

Area fixed effects

YES

YES

YES

YES

YES

YES

Urban/suburban trends

YES

YES

YES

YES

YES

YES

North/south trends

YES

YES

YES

YES

YES

YES

(.0106)**(.0114)**.0019

under 200

Control variables

N

6265

5394

4325

1128

2000

3068

Adjusted R2

0.76570.72940.71350.89000.86200.8612over 100over 150

over 200

under100

under 150

under 200

(7)

(8)

(9)

(10)

(11)

(12)

.0119

.0375***

.0121

.0135

.0120
(.0085)*After-disclosure dummy.0135(.0068)(.0073)(.0092)(.0171)(.0113)

School rank × After-disclosure dummy-.0010***-.0008**-.0024***-.0008-.0008-.0006(.0002)(.0003)(.0004)(.0006)(.0004)(.0003)Control variables
House characteristics

YES

YES

YES

YES

YES

YES

Area characteristics

YES

YES

YES

YES

YES

YES

Time fixed effects

YES

YES

YES

YES

YES

YES

Area fixed effects

YES

YES

YES

YES

YES

YES

Urban/suburban trends

YES

YES

YES

YES

YES

YES

North/south trends

YES

YES

YES

YES

YES

YES

N

6265

5394

4325

1128

2000

3068

Adjusted R2

0.76580.72930.71330.88990.86200.8613※*, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively.
※Standard errors are adjusted for clustering at the area level.

315.2.3. Considering the effect of the number of posted apartments
In this study, all the analysis employing property data used data that was posted on websites,
which meant they did not indicate actual transactions. Therefore, during the analysis period, if manycheap apartments were posted at once in a specific area, it might seem as though housing rents had
decreased in that area. Conversely, if a luxury condominium was built and many rooms were sold at
the same time, it would seem as though housing rents had increased in the area. Thus, if the numberof posted apartments in areas was biased, the result might also be biased.
To address this concern, I created panel data of the number of posted family-oriented apartmentsin each month and each area, and then estimated the effect of school quality and its disclosure on thenumber of samples in each month and each area. The results are shown in Table 10, and whencontrolling for several variables, all the coefficients of main variables were not significant. This
result suggests that there is no significant relationship between disclosure of school quality
information and the number of posted apartments and confirms that the main results of this study did
not derive from the number of samples in a specific area.
Additionally, I performed an estimation using the intersection terms of each quarter dummy andschool quality indicator to analyze the changes in effect of school quality on the number ofapartments over time, and the results are shown in Table 11 and Figure 4. 23 In Figure 4, thehorizontal axis represents time, the vertical axis represents the coefficient, the black dot represents
the coefficients of interaction terms of each quarter dummy and school quality indicator, and eachwidth represents a 95% confidence interval. This result shows that almost all the coefficients were
insignificant and there was no specific trend during the analysis period. Additionally, there was no
particular change observed before or after disclosure. Therefore, we confirmed that many apartments
were not posted in areas with high-quality schools, and that the main results were obtained from thechange in housing rents, not the number of posted samples.

23
I conducted the same analysis using single-person apartments, and the results are shown in Appendix
G. As a result, there was no significant relationship between school quality and the number of posted
single-person apartments, and no specific trends through time were observed.

32

Table 10. Number of Family-Oriented Apartments(1)
Deviation value(2)-.0309***(.0038)After-disclosure dummy
Deviation value × After-disclosure dummy

(3)

(4)

(5)

***

-.0309(.0038)

.1880

.1607

.1607

.0332

-.0869(.3955)(.6029)(.2262)(.2263)(.2300)-.0028-.0028-.0028-.0005.0018(.0061)(.0062)(.0029)(.0029)(.0030)

YES

YES

YES

YES

YES

YES

YES

YES

YES

Control variables
Time fixed effects
Area fixed effects
Urban/suburban trends
North/south trends

YES

N

6836

6817

6705

6686

6667

Adjusted R2

0.00950.01020.85690.85770.8585

(6)

(7)

(8)

(9)

(10)

School rank
After-disclosure dummy
School rank × After-disclosure dummy

.0521

***

.0521

***

(.0062)(.0062)-.0205-.0479-.0479-.0146-.0172(.0635)(.4365)(.1493)(.1488)(.1480)

.0054

.0054

.0054

.0022

.0008

(.0100)(.0100)(.0042)(.0042)(.0043)

YES

YES

YES

YES

YES

YES

YES

YES

YES

Control variables
Time fixed effects
Area fixed effects
Urban/suburban trends
North/south trends

YES

N

6836

6817

6705

6686

6667

Adjusted R2

0.01460.01520.85690.85770.8585※*, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively.
※Standard errors are adjusted for clustering at the school district level.

33

Table 11. Changes in Effect Over Time (Number of Samples)

Deviation value / School rank
×2012 Q2
×2012 Q3
×2012 Q4
×2013 Q1
×2013 Q2
×2013 Q3
×2013 Q4
×2014 Q1
×2014 Q2
×2014 Q3
×2014 Q4 (disclosure)×2015 Q1
×2015 Q2
×2015 Q3
×2015 Q4
×2016 Q1
×2016 Q2
×2016 Q3
×2016 Q4
N
Adjusted R2

Deviation value(1)

School rank
(2)

.0112
(.0078).0149(.0082)*.0219(.0089)**.0232(.0083)-.0098(.0091).0045(.0085).0008(.0081)-.0045(.0087)-.0041(.0079).0135(.0099).0049(.0088).0012(.0074)-.0003(.0098)-.0002(.0105).0089(.0090).0093(.0087).0194*(.0076).0226**(.0083).0074(.0081)6650
0.8588-.0141(.0118)-.0212(.0125)*-.0273(.0131)*.0306(.0124).0187(.0135)-.0033(.0125)-.0013(.0119).0076(.0130).0097(.0114)-.0212(.0141)-.0013(.0126).0001(.0108).0076(.0145)-.0011(.0156)-.0114(.0134)-.0110(.0131).0226*(.0111)-.0256*(.0120)-.0072(.0118)6650
0.8589※*, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively.
※Standard errors are adjusted for clustering at the school district level.
※All control variables (time fixed effects, area fixed effects, urban/suburban trends, andnorth/south trends) were controlled.

34

Table 11 - Column (1),Changes in the effect of deviation value on the number of family-oriented properties

Table 11 - Column (2),Changes in the effect of school rank on the number of family-oriented properties
Figure 4. Changes in Effect Over Time (Number of Samples)35

5.3. Results for enrollment and population
So far, it has been shown that rents of family-oriented apartments increased after disclosure ofschool quality information. However, as pointed out in previous studies, we should consider whether
the changes in housing rents actually reflect parents’ willingness to pay for their children's education,or whether they were simply caused from advertisement behavior of real estate agents (Carrillo,
Cellini, and Green 2013; Haisken-DeNew et.al, 2018). If population did not change despite thenumber of housing rents increasing in areas with high-quality schools after disclosure, this might
only indicate that real estate agencies advertised high-quality schools and set high rental prices.
However, if the number of children increased in high-quality school districts after disclosure, this
could suggest that not only real estate agencies’ but also parents’ behavior was changed by thedisclosure. Therefore, in this section, I discuss the results focusing on school and residential choice
behavior of parents and children. Here, I analyze changes in the number of enrolled students at eachschool and the population in each area. Analysis using the number of enrolled students can moreaccurately capture the change in parental behavior caused by disclosure; however, a problem exists
in that the sample is very small because it is based on yearly data. Therefore, I conducted a moredetailed analysis by using data that included population by each age in each area.
Parents have incentives to send their children to better schools, so disclosure of school quality
information may increase the number of students enrolled in high-quality schools (Hastings andWeinstein, 2008; Koning and van der Wiel, 2013; Nunes, Reis, and Seabra, 2015). To confirm thiseffect, I will discuss the results of analyzing the effect of school quality information and its
disclosure on the number of enrolled students, using the data of elementary school entrants. Table 12shows the results, with the upper part of the table representing the results obtained using deviation
value and the lower part representing the results obtained using school rank. Additionally, the table’s
left half shows the results obtained using data from 2012 to 2016, and the right half shows the results
obtained using data from 2011 to 2018. These results indicate that regardless of which control
variables were used, disclosure of school quality information would not significantly affect the
number of enrolled students. Although this effect was not statistically significant, the disclosure ofschool quality tended to have a consistently positive effect on the number of enrolled students. In
this analysis, even if the analysis period was expanded, we had to consider that the sample size wasstill small. Therefore, to address this concern, I analyzed the effect of school quality and its
disclosure on the population of each area by age.
Columns (1) and (6) of Table 13 show the results estimated by using the population of children
who were seven years old for each month in each area as explained variables. In Japan, sincechildren entering elementary school must be seven years of age, the population of seven-year-olds
represents the number of first-year elementary students in that area (i.e., the number of enrolled
students). According to the results of columns (1) and (6), the number of first graders in elementary36

school has increased significantly in areas with high-quality schools after disclosure of information.
Considering the error range of one year which was caused due to variations in school year andbirthdays, columns (3) and (8) show the results using the total population of six- to eight-year-old
children instead of only seven-year-olds, and the interaction terms indicated a significantly positive
result similar to those shown in columns (1) and (6). Additionally, to address the concern that not
only the first-grade but also the entire population was fluctuating, I conducted an analysis using thethree- to five-year-old population and the nine- to eleven-year-old population for comparison.
According to the results of columns (2) and (7), the number of kindergarten children might increasein areas with high-quality primary schools after disclosure, but the effect was not significant.
Columns (4) and (8) show the results obtained using nine- to eleven-year-old students who had
selected elementary schools before disclosure, and there was no significant relationship between thenumber of children and school quality after disclosure. This suggests reasonable results indicatingthat change in population can be seen only when residential choice is timed according to elementary
school entry, when it is easy to move. Furthermore, columns (5) and (10) show the results of using a
population aged 18 to 22, which is the age of university students. The coefficients of the interaction
terms were negative and significant, which was consistent with the information indicating that the
number of enrolled students at the university was decreasing, which I mentioned in the above section.
Additionally, the decrease of the population of university-aged students was considered to be one
reason for the declining trends of rentals of single-person apartments in high-quality school areas. 24

24
I also analyzed information regarding the ages of the parent generation, but consistent results were notobtained. Unlike young students, adults have various socioeconomic characteristics, so it is difficult tocapture specific effects.

37

Table 12. Number of Enrolled Students
2012 ~ 20162011 ~ 2018

(1)

(2)

(3)

(4)

(5)

(6)

.1330

.1699

.1522

.1940

.2176

.1738

(.2331)(.2345)(.2342)(.2005)(.2033)(.1961)

School fixed effects

YES

YES

YES

YES

YES

YES

Time fixed effects

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

Deviation value × After-disclosure dummyControl variables

Urban/suburban trends
North/south trends
N
Adjusted R2

YES

YES

75

71

67

132

125

118

0.95290.95330.95390.9441

0.9436

0.9481

2011 ~ 20182012 ~ 2016
School rank × After-disclosure dummy

(7)

(8)

(9)

(10)

(11)

(12)

-.0516-.0938-.0850-.0686-.0944-.0748(.1720)(.1751)(.1742)(.1481)(.1521)(.1459)Control variables
School fixed effects

YES

YES

YES

YES

YES

YES

Time fixed effects

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

Urban/suburban trends
North/south trends
N
Adjusted R2

YES

YES

75

71

67

132

125

118

0.95280.9531

0.95380.94380.94330.9479※*, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively.

38

Table 13. Population in Area by Age
18-22 years old

7 years old

3-5 years old

6-8 years old

9-11 years old

(1)

(2)

(3)

(4)

(5)

-2.144

**

.7573

4.136

***

After-disclosure dummy-1.461**-.7565(.0106)(.6445)(.6685)(.6011)(.8005)

Deviation value × After-disclosure dummy

.0307

***

.0140

.0407

***

-.0159-.0791(.0062)(.0096)(.0097)(.0102)(.0131)

Area characteristics

YES

YES

YES

YES

YES

Time fixed effects

YES

YES

YES

YES

YES

Area fixed effects

YES

YES

YES

YES

YES

Urban/suburban trends

YES

YES

YES

YES

YES

North/south trends

YES

YES

YES

YES

YES

N

7373

7373

7373

7373

7373

Adjusted R2

0.9856

0.9856

0.98320.98000.9895

7 years old

3-5 years old

6-8 years old

9-11 years old

18-22 years old

(6)

(7)

(8)

(9)

(10)

.6118

.1268

.5585

-.2204-1.046***

Control variables

After-disclosure dummy
School rank × After-disclosure dummy*(.3285)(.3912)(.3259)(.3231)(.4353)-.0336***-.0107-.0411***.1042.0751***(.0070)(.0108)(.0108)(.0116)(.0148)Control variables
House characteristics

YES

YES

YES

YES

YES

Area characteristics

YES

YES

YES

YES

YES

Time fixed effects

YES

YES

YES

YES

YES

Area fixed effects

YES

YES

YES

YES

YES

Urban/suburban trends

YES

YES

YES

YES

YES

North/south trends

YES

YES

YES

YES

YES

N

7373

7373

7373

7373

7373

Adjusted R2

0.93770.9856

0.98320.98000.9895※*, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively.
※Standard errors are adjusted for clustering at the area level.

Figure 5 shows the results using the seven-year-old population of each month in each area as anindependent variable and using the intersection terms of the quarterly dummies and the school
quality indicators. This result showed that there was no significant difference in the seven-year-old
population between high-quality and low-quality school areas before disclosure, but theseven-year-old population was increasing significantly after disclosure. Furthermore, its impact waslarge for one year after disclosure, which was similar to the effect on housing rents (see Figure 3).
In summary, the population of first graders of elementary schools in areas with high-quality
schools increased after disclosure of information on school quality. This suggests that parents withchildren chose their residential area by referring to recently disclosed school quality information.

39

Number of seven-year-old children, Deviation Value

Number of seven-year-old children, School Rank
Figure 5. Changes in Effect Over Time (Seven-Year-Old Children)40

5.4. Disparity in academic achievement
Finally, I confirmed whether disparity of academic achievement among schools had increased as aresult of disclosure of school quality information. Through disclosure of this information, parents
will be able to choose good schools, and housing rents in areas with good school quality will
increase. Therefore, parents with more willingness to pay for their children's education will be able
to send their children to better schools. Additionally, such parents tend to have high educational
backgrounds and income, or enthusiasm for their children, so the academic ability of their children
also tends to be higher. Therefore, good (bad) students tend to gather at good (bad) schools, and thesorting of students may occur due to disclosure. As a result, good schools will be better, bad schools
will be worse, and disparity in academic performance between schools can expand.
Table 14 shows the results of average test scores and indicators of disparity by school from 2014
to 2017. 25 The test in 2014 was administered before disclosure, and the tests after 2015 wereconducted subsequent to disclosure. This table shows that the relative standard deviation was 0.048
before disclosure, but it increased consistently to about 0.06 after disclosure. Additionally, we cansee that the difference between better and worse schools has also expanded after disclosure of school
quality information. These results suggest that disparity between schools may be expanding throughthe sorting of students due to disclosure of information on school quality.
Although it has been confirmed that disparity has expanded, we must compare the results withother municipalities in which information on school quality has not been disclosed and confirm thatthe expansion is caused from disclosure. However, because the data of other municipalities regarding
school quality information have not been disclosed and cannot be used, this point indicates alimitation of this study.
Additionally, it is also important to analyze the performance of students enrolled after disclosure.
If high-quality schools gather high-quality students due to disclosure of school quality information, it
might cause a widening of disparities. Such data will be available in a few years, so this is a task for
future research.

25
Regarding examination in 2017, fourth graders were not administered the exam because of the changeof the institution, and the results show the average test scores of fifth and six graders. All the test scores
before 2016 show the average of fourth, fifth, and sixth grade students.

41

Table 14. Disparity in Academic Achievement
Before disclosure

After disclosure

2014

2015

2016

(2017)

Mean

66.61361.32464.60463.247

Standard deviation

3.202

3.914

4.027

3.839

Relative standard deviation

0.048

0.064

0.062

0.061

Max

72.75071.85070.66370.833

Min

59.52552.85055.15056.000(Max - Min) / Mean

0.199

0.310

0.240

0.235

Average of upper half

69.10264.43167.95766.244

Average of lower half
(Avg. of upper half - Avg. of lower half) / Mean

63.95758.21761.25260.036

0.077

0.101

0.104

0.098

Average of top 5

71.43066.94369.59068.433

Average of bottom 5

61.61855.60857.58057.467

0.147

0.185

0.186

0.173

(Avg. of top 5 - Avg. of bottom 5) / Mean

42

6.

Conclusions
This study investigated the effect of disclosure of school quality information on housing rents,

population by age, and disparity in the academic ability of students between schools. I found that
school quality indicators such as deviation value and school rank had a significantly positive effecton rents of family-oriented apartments after disclosure of school quality information. Additionally,
the more that units were intended to be lived in by families, the more they were affected by school
quality. However, even if an apartment was family-oriented, it was not affected by school quality inareas with relatively few children. These results were consistent with results obtained from previous
studies.
I also found that disclosure significantly increased the population of elementary school-aged
children within the attendance district of schools with high test scores. This suggests that parents
with children chose their residential area by referring to the school quality information that had justbeen disclosed. As in the case of rent, although the significance was high, the impact is not. Thismight be because a minority of parents are very concerned about school quality, but the majority do
not care about it. In this study, I could not identify whether parents were directly affected by thedisclosure of school quality information or indirectly affected by the advertisement of real estateagencies. Even if this result might be caused from real estate agency advertisements, as noted inprevious studies, it is still important to identify if parents are actually moving since there is apossibility that sorting of students may occur by the gathering of people in high-quality school
districts. Whether sorting has occurred can also be observed by examining changes in academic
performance by school.
Finally, I found that the relative standard deviation of test scores after disclosure became larger
than before disclosure. These results suggest that disparity between schools may be expanding due todisclosure, and this may have been caused by the sorting of students. Specifically, this result may
suggest that children with high abilities who have parents with more willingness to pay for theireducation are gathered at high-quality schools as a result of disclosure of school quality information.
To summarize the results in this study, the following findings were obtained. Due to disclosure ofschool quality information, rents of apartments located in high-quality school districts increased. At
the same time, the number of elementary school first graders increased in high-quality school
districts. As a result, student sorting appeared to occur, and therefore disparity of academic
achievement between schools increased. In conclusion, through disclosure of school quality
information, it is suggested that disparity of academic achievement between schools may increase
through changes in the property market and residential choice.
However, I have concern that the analysis in disparity is simply a comparison between data before
and after disclosure, and it is not possible to mention causality. Therefore, I must compare the resultswith other municipalities that have not been discussed and confirm that the expansion is caused from43

disclosure. Additionally, from the point of view concerning disparity among schools, I must
investigate whether high-quality schools accumulate high-quality students after disclosure of school
quality information using future data.

44

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46

Appendix A. Summary Statistics (Single-Person Apartment, Rental Shop/Office)Single-person apartment
Max

Mean

House characteristics
Housing rent (YEN)43374 6605
15000
After-disclosure dummy
0.412
0.492
0
Elapsed days after disclosure 161.400 239.370
0Number of floors
2.475
1.090
1
Located floor
2.000
0.934
1
Occupied area
25.090 4.868
11
Age of building
13.170 8.991
0
Rental apart dummy
0.796
0.403
0
NA
Rental town house dummy

66100
1
800
10
9
39.75069
1144164 111968 25000 486000
0.310
0.463
0
1
120.000 222.884
0
798
5.938
3.520
1
12
3.254
2.773
1
10
101.570 58.033 19.15 311.970
28.170 5.827
9
48
NA
NA
NA
NA
NA

Rental mansion dummy
Structure ALC dummy
Structure PC dummy
Structure RC dummy
Structure SRC dummy
Structure block dummy
Structure light steel dummy
Structure steel dummy
Structure wooden dummy
N

S.D.

Rental shop and office

Min

Mean

0.204
0.003

0.403
0.053

0.121

0.326

0
0

1
1

0

1

NA
NA
NA
0.158
0.191
0.527

0.364
0
0.393
0
0.499
0
7752

47

1
1
1

0.156
0.402

S.D.

Min

0.363
0.491

Max

0
0

1
1

0
0

1
1

NA
NA
0.318
0.124

0.466
0.330
532

Appendix B. Results Using All Apartments (Deviation Value)
Deviation value
After-disclosure dummy
Deviation value × After-disclosure dummyHouse characteristics
Mansion dummy
Number of floors
Located floor
Structure ALC dummy
Structure PC dummy
Structure RC dummy
Structure SRC dummy
Structure light steel dummy
Structure steel dummy
Occupied area
Occupied area squared
Age of building
Natural log of age of building
Area characteristics
Number of households
Population
Average household size
Number of children
Number of elementary students
Average age
Number of samples (family)
Number of samples (single)
Urban dummy
North dummyControl variables
Time fixed effects
Area fixed effects
Urban/suburban trends
North/south trends
N
Adjusted R2(1).0015***(.0001)-.0275***(.0080).0008***(.0002)(2).0014***(.0001)-.0470***(.0106).0008***(.0002)

(3)

(4)

(5)

-.0037(.0097).0001(.0001)-.0041(.0095).0001(.0001).0063(.0100)-.0001(.0002).0228***(.0032).0141***(.0016).0022(.0012)-.1408***(.0196).1089***(.0091).0136**(.0045)-.1125***(.0169).0402***(.0024)-.0111***(.0023).0208***(.0005)-.0001***(.0000)-.0121***(.0004).0347***(.0041).0234***(.0032).0138***(.0016).0022(.0012)-.1347***(.0191).1079***(.0101).0145**(.0045)-.1121***(.0164).0416***(.0024)-.0107***(.0023).0208***(.0005)-.0001***(.0000)-.0121***(.0004).0354***(.0042).0327***(.0033).0078***(.0020).0017(.0011)-.1363***(.0213).1149***(.0064).0072*(.0043).0274***(.0128).0342***(.0028)-.0137***(.0025).0238***(.0005)-.0001***(.0000)-.0119***(.0004).0362***(.0044).0322***(.0033).0083***(.0020).0018(.0011)-.1364***(.0213).1148***(.0065).0070(.0043).0273(.0130).0345***(.0028)-.0139***(.0025).0237***(.0005)-.0001***(.0000)-.0120***(.0004).0369***(.0044).0315***(.0033).0083***(.0020).0017(.0011)-.1380***(.0216).1153***(.0065).0073(.0043).0173(.0132).0350***(.0028)-.0137***(.0025).0238***(.0005)-.0001***(.0000)-.0121***(.0004).0380***(.0044).0003***(.0000)-.0002***(.0000).0216***(.0060).0004***(.0000)-.0001***(.0000).0025***(.0003).0003*(.0001).0005***(.0001).0633***(.0047)-.0318***(.0027).0003***(.0000)-.0002***(.0000).0228***(.0061).0004***(.0000)-.0001***(.0000).0026***(.0003).0003**(.0001).0008***(.0001).0628***(.0047)-.0304***(.0027)-.0004***(.0001).0001*(.0000)-.0691**(.0216)-.0001(.0000)-.0001***(.0000)-.0063***(.0016)-.0004*(.0002)-.0001(.0002)-.0003***(.0001).0001*(.0000)-.0620**(.0219)-.0001(.0000)-.0001***(.0000)-.0061***(.0016)-.0004*(.0002)-.0001(.0002)-.0003***(.0001).0001(.0000)-.0546*(.0214)-.0001(.0001)-.0001***(.0000)-.0059***(.0016)-.0005**(.0002)-.0001(.0002)

YES

YES
YES

YES
YES
YES

15338
0.800315228
0.852715209
0.8529

YES
YES
YES
YES
15190
0.853215357
0.7993※*, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively.
※Standard errors are adjusted for clustering at the area level.

48

Appendix C. Results Using All Apartments (School Rank)
School rank
After-disclosure dummy
School rank × After-disclosure dummyHouse characteristics
Mansion dummy
Number of floors
Located floor
Structure ALC dummy
Structure PC dummy
Structure RC dummy
Structure SRC dummy
Structure light steel dummy
Structure steel dummy
Occupied area
Occupied area squared
Age of building
Natural log of age of building
Area characteristics
Number of households
Population
Average household size
Number of children
Number of elementary students
Average age
Number of samples (family)
Number of samples (single)
Urban dummy
North dummyControl variables
Time fixed effects
Area fixed effects
Urban/suburban trends
North/south trends
N
Adjusted R2(1)-.0021***(.0002).0322***(.0033)-.0015***(.0002)(2)-.0021***(.0002).0110(.0077)-.0014***(.0002)

(3)

(4)

(5)

.0045
(.0067)-.0003(.0002).0044(.0067)-.0003(.0002).0014(.0067)-.0001(.0002).0217***(.0032).0141***(.0016).0023(.0012)-.1419***(.0198).1098***(.0092).0155**(.0046)-.1111***(.0170).0408***(.0024)-.0099***(.0023).0209***(.0004)-.0001***(.0000)-.0121***(.0004).0342***(.0041).0223***(.0032).0139***(.0016).0023(.0012)-.1357***(.0193).1088***(.0102).0165**(.0045)-.1106***(.0164).0421***(.0024)-.0095***(.0023).0208***(.0004)-.0001***(.0000)-.0122***(.0004).0349***(.0042).0328***(.0033).0078***(.0020).0017(.0011)-.1362***(.0213).1150***(.0065).0072(.0043).0270*(.0128).0342***(.0028)-.0137***(.0025).0238***(.0005)-.0001***(.0000)-.0120***(.0004).0363***(.0044).0322***(.0033).0083***(.0020).0018(.0011)-.1363***(.0212).1149***(.0065).0070(.0043).0239(.0130).0346***(.0028)-.0139***(.0025).0237***(.0005)-.0001***(.0000)-.0120***(.0004).0370***(.0044).0316***(.0033).0084***(.0020).0017(.0011)-.1375***(.0216).1153***(.0065).0073(.0043).0169(.0131).0351***(.0028)-.0137***(.0025).0237***(.0005)-.0001***(.0000)-.0121***(.0004).0380***(.0044).0004***(.0000)-.0002***(.0000).0177**(.0060).0004***(.0000)-.0001***(.0000).0023***(.0003).0003**(.0001).0005***(.0001).0659***(.0047)-.0326***(.0027).0004***(.0000)-.0002***(.0000).0187**(.0061).0004***(.0000)-.0001***(.0000).0024***(.0003).0004**(.0001).0008***(.0001).0655***(.0047)-.0312***(.0027)-.0003***(.0001).0001*(.0000)-.0712***(.0216)-.0001(.0000)-.0001***(.0000)-.0063***(.0016)-.0004*(.0002)-.0001(.0002)-.0003***(.0001).0001*(.0000)-.0640**(.0218)-.0001(.0000)-.0001***(.0000)-.0061***(.0016)-.0004*(.0002)-.0000(.0002)-.0003***(.0001).0001(.0000)-.0584**(.0215)-.0001(.0001)-.0001***(.0000)-.0058***(.0016)-.0005**(.0002)-.0001(.0002)

YES

YES
YES

YES
YES
YES

15338
0.801315228
0.852815209
0.8529

YES
YES
YES
YES
15190
0.853215357
0.8002※*, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively.
※Standard errors are adjusted for clustering at the area level.

49

Appendix D. Results Using Housing Rents per Square Meter
(1)

(2)
***

Deviation value.0026(.0001)(.0001)After-disclosure dummy-.0717***-.0647***

Deviation value × After-disclosure dummy

.0012

.0026

(.0086)(.0105)

***

.0012

(3)

(4)

(5)

-.0279**-.0354***-.0245********(.0088).0005***(.0093)(.0090)***.0005**.0007(.0002)(.0002)(.0001)(.0002)(.0002)

House characteristics

YES

YES

YES

YES

YES

Area characteristics

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

Control variables

Time fixed effects
Area fixed effects
Urban/suburban trends
North/south trends

YES

N

7617

7598

7513

7494

7475

Adjusted R2

0.65560.65860.77000.77300.7742

(6)

(7)

(8)

(9)

(10)

.0101

.0142*.0092

School rank.-0033***.-0033***(.0002)(.0002)After-disclosure dummy.0092*.0185*(.0039)(.0076)

School rank × After-disclosure dummy-.0018***-.0018(.0002)
House characteristics
Area characteristics(.0059)(.0059)(.0059)***-.0010***-.0007***(.0002)(.0002)(.0002)(.0002)

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

YES

***

-.0008Control variables

Time fixed effects
Area fixed effects
Urban/suburban trends
North/south trends

YES

N

7617

7598

7513

7494

7475

Adjusted R2

0.65390.65720.77010.77300.7742※*, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively.
※Standard errors are adjusted for clustering at the area level.

50Appendix E. Changes in Effect Over Time (Single-Person Apartments)

Deviation value / School rank
×2012 Q2
×2012 Q3
×2012 Q4
×2013 Q1
×2013 Q2
×2013 Q3
×2013 Q4
×2014 Q1
×2014 Q2
×2014 Q3
×2014 Q4 (disclosure)×2015 Q1
×2015 Q2
×2015 Q3
×2015 Q4
×2016 Q1
×2016 Q2
×2016 Q3
×2016 Q4
N
Adjusted R2

Deviation value(1)

School rank
(2)-.0010(.0009).0000(.0008).0005(.0007).0002(.0007)-.0009(.0007)**-.0019(.0007)-.0013(.0007)-.0010(.0007)***-.0027(.0007)**-.0023(.0008)-.0032***(.0007)**-.0021(.0007)-.0021**(.0007)-.0018**(.0007)-.0023**(.0008)-.0012(.0007)-.0005(.0007)-.0007(.0008)-.0017*(.0007)7588
0.7568.0017(.0011).0006(.0010)-.0005(.0009)-.0003(.0009).0014(.0009)**.0029(.0009)*.0018(.0009).0014(.0009).0035***(.0010)**.0031(.0010).0040***(.0009)**.0026(.0010).0026**(.0010).0021*(.0009).0029**(.0010).0016(.0010).0007(.0009).0010(.0010).0022*(.0010)7588
0.7561※*, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively.
※Standard errors are adjusted for clustering at the area level.
※All control variables (house characteristics, area characteristics, year fixed effects,
area fixed effects, urban/suburban trends, and north/south trends) were controlled.

51

Appendix E - Column (1),Changes in the effect of deviation value on rents of single-person apartments

Appendix E - Column (2),Changes in the effect of school rank on rents of single-person apartments
52

Appendix F. Sample Divided according to the Child Population Density in an Area
over 100over 300

over 500

under 100

under 300

(1)

(2)

(3)

(4)

(5)

(6)

After-disclosure dummy-.0261*-.0487***-.0709***-.0312-.0054-.0053(.0162)(.0201)(.0136)(.0131)

Deviation value × After-disclosure dummy

.0005

.0009

***

.0007

.0002

.0001

(.0002)(.0002)(.0003)(.0005)(.0003)(.0003)

House characteristics

YES

YES

YES

YES

YES

YES

Area characteristics

YES

YES

YES

YES

YES

YES

Time fixed effects

YES

YES

YES

YES

YES

YES

Area fixed effects

YES

YES

YES

YES

YES

YES

Urban/suburban trends

YES

YES

YES

YES

YES

YES

North/south trends

YES

YES

YES

YES

YES

YES

(.0101)**(.0136)***.0013

under 500

Control variables

N

6589

4374

1980

790

2999

5385

Adjusted R2

0.76490.75010.88580.83080.82540.7243over 100over 300

over 500

under 100

under 300

under 500

(7)

(8)

(9)

(10)

(11)

(12)

.0084

.0163*

.0202

.0131

.0100

.0014
(.0079)After-disclosure dummy
School rank × After-disclosure dummy(.0062)(.0079)(.0104)(.0174)(.0089)-.0007***-.0014***-.0020***-.0009-.0003-.0001(.0002)(.0003)(.0004)(.0007)(.0004)(.0003)Control variables
House characteristics

YES

YES

YES

YES

YES

YES

Area characteristics

YES

YES

YES

YES

YES

YES

Time fixed effects

YES

YES

YES

YES

YES

YES

Area fixed effects

YES

YES

YES

YES

YES

YES

Urban/suburban trends

YES

YES

YES

YES

YES

YES

North/south trends

YES

YES

YES

YES

YES

YES

N

6589

4374

1980

790

2999

5385

Adjusted R2

0.76500.75010.88600.83060.82540.7243※*, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively.
※Standard errors are adjusted for clustering at the area level.

53

Appendix G. Changes in Effect Over Time (Number of Samples, Single-Person)

Deviation value / School rank
×2012 Q2
×2012 Q3
×2012 Q4
×2013 Q1
×2013 Q2
×2013 Q3
×2013 Q4
×2014 Q1
×2014 Q2
×2014 Q3
×2014 Q4 (disclosure)×2015 Q1
×2015 Q2
×2015 Q3
×2015 Q4
×2016 Q1
×2016 Q2
×2016 Q3
×2016 Q4
N
Adjusted R2

Deviation value(1)

School rank
(2)

.0031
(.0077).0064(.0077)*.0181(.0089)***.0313(.0093)**.0302(.0092)***.0332(.0088).0122(.0090)-.0007(.0083)-.0054(.0084)-.0041(.0079)-.0008(.0071).0019(.0079).0148(.0097)*.0177(.0088)-.0034(.0070).0021(.0078).0034(.0074).0054(.0078).0099(.0078)6650
0.6918-.0029(.0100)-.0045(.0100)-.0110(.0114)**-.0312(.0114)*-.0291(.0120)**-.0370(.0122)-.0092(.0130).0083(.0114).0125(.0115).0171(.0104).0064(.0096).0048(.0102)-.0088(.0134)-.0150(.0121).0118(.0093).0072(.0105).0036(.0101).0031(.0106)-.0038(.0105)6650
0.6913※*, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively.
※Standard errors are adjusted for clustering at the school district level.
※All control variables (time fixed effects, area fixed effects, urban/suburban trends, andnorth/south trends) were controlled.

54Appendix G - Column (1),Changes in the effect of deviation value on the number of single-person properties

Appendix G - Column (2),Changes in the effect of school rank on the number of single-person properties55