elite school designation and house prices: quasi-experimental evidence from beijing…/file/e... ·...

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Elite School Designation and House Prices: Quasi-experimental Evidence

from Beijing, China

Bin Huanga, Xiaoyan Hea and Yu Zhub, *

a: School of Public Administration, Nanjing University of Finance and Economics, China a: School of Business, University of Dundee, UK

Version 2.0 (June 18, 2018)

Abstract

We explore two recent comprehensive reforms which aim to equalize access to elite elementary

schools in Beijing, to identify the causal effect of access to quality education on house prices.

Whereas the multi-school dicing reform involves randomly assigning previously ineligible

pupils to key elementary schools through lotteries, the reform of school federation led by elite

schools consolidates low quality schools through alliance with elite schools. We allow for

systemic differences between the treated and non-treated school attendance (catchment) zones

using the Matching Difference-in-Differences (MDID) approach. Our estimates indicate that the

causal effect on house prices of being eligible to enroll in a municipal-level key primary school

is about 7.5-10.5%, while the premium for being eligible for a district-level key primary school

is statistically insignificant. On the other hand, the price premium for access to a federation of

schools led by an elite school is around 2.4-4.3% and statistically significant.

Keywords: quality school designation, house price premium, Matching DID, China.

JEL code: R21 (Urban/Regional Economics: Housing Demand); I28 (Education: Government

Policy); H44 (Publicly Provided Goods: Mixed Market)

* Corresponding Author: Yu Zhu, School of Business, University of Dundee, Dundee, DD1 4HN, UK. E-mail:

[email protected].

1

“In Beijing’s overheated housing market, where schools go, money follows.”

– Caixinglobal (2017)

1. Introduction

It has been almost half a century since Oates’ seminal paper on the capitalization of local

property taxes on house values (Oates (1969)). Since then, a growing number of studies have

contributed to the literature on school quality capitalization under different contexts in terms of

country of study, school quality measures and methodological innovations, see Ross and Yinger

(1999), Gibbons ad Machin (2008), Black and Machin (2011) and Nguyen-Hoang and Yinger

(2011) for reviews.

The phenomenon of steeply priced “school district houses (xuequfang)”, i.e. properties

giving access to prestigious publicly funded schools, has consistently been one of the hottest

topics in the Chinese media in recent years. According to one estate agent, in 2013 house prices

in Beijing's elite school districts were 30 percent higher than other districts on average (Xinhua

2016).

Using a panel data of residential quarters, or school attendance (catchment) zones, derived

from comprehensive data on real estate transactions in Beijing in 2013-2106, we investigate how

house prices react to the quality of education offered by neighbouring publicly-funded

elementary schools. To overcome the endogeneity of education quality, we exploit two recent

comprehensive reforms which aim to equalize access to quality educational resources in

compulsory education, to identify the causal effect of access to quality school on house prices.

Whereas the multi-school dicing reform involves randomly assigning previously ineligible

pupils to key elementary schools through lotteries, the school federation led by elite schools

reform consolidates low quality schools through alliance with elite schools.

We start by estimating the spillover effects of public education quality on house prices in

Beijing, using the hedonic price model. The results indicate that, after controlling for housing

and residential features, neighborhood and location characteristics, the mean house price in key

primary school catchment areas is 9% higher than that for ordinary primary school catchment

areas. Moreover, the average house price for district-level key primary school and municipal-

level key primary school catchment areas are 5.5% and 18.6% higher than their ordinary primary

school catchment area counterparts, respectively. Secondly, school attendance zone changes

based on school district adjustment and multi-school dicing have increased the premium of key

2

primary school catchment areas.

Furthermore, we allow for systemic differences between the treated and non-treated school

attendance (catchment) zones using Propensity Score Matching and account for the common

trend in house price inflation using the Difference-in-Difference (DID) approach. Our Matching

DID (MDID) estimates indicate that the causal effect on house prices of being eligible to enroll

in a municipal-level key primary school is about 7.5-10.5%, while the premium for being eligible

for a district-level key primary school is statistically insignificant. On the other hand, the price

premium for access to a federation of schools led by an elite school is around 2.4-4.3% and

statistically significant.

Moreover, we find that the number of private primary schools within ten kilometers, higher

service charges and more local amenities all have a significant positive impact on the average

house price of school attendance zones. In contrast, mean floor area ratio, mean number of floors,

mean floor area per flat, and the distances to the city center, to the nearest top-grade hospital,

and to the nearest subway station all have significant negative correlation with average house

price of school attendance zones.

The remainder of the paper is structured as follows. Section 2 presents the background

of the reforms in Beijing. Section 3 briefly reviews the relevant literature. Section 4 discusses

the MDID methodology. Section 5 presents the data and the descriptive statistics. In Section 6,

the empirical analyses are presented and discussed. Finally, Section 7 concludes.

2. Background

A private housing market was only introduced in China in the early 1990s. Before that,

most urban residents lived in housing units built and owned by their employers. After the reform,

employees no longer received allocated housing and had to buy from a private housing market

which had grown from strength to strength (Sato (2006), and Zhang and Yi (2017)). According

to Fang et al. (2015), the residential housing market as measured by residential house sales

volume grew by about 15% per annum on average between 2002-2013.

Beijing offers an excellent case study on the education reform and housing market of

China. As the capital since the founding of the People’s Republic in 1949 and nation’s political,

cultural and educational centre, Beijing has not only the most developed housing market but also

arguably the best resources of education, in particular higher education, in China. However,

competition for access to the elite schools which traditionally has excellent track records of

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graduate enrolment into the country’s best-known universities, is exceptionally fierce and starts

well before the formal entry to the public education system.

The public schools dominate all stages of education in Beijing. In theory, access to 9-

years of compulsory education is free and non-selective, and based on the principle of “attending

nearby schools”, according to parental household registration (hukou) and house ownership

(Feng and Lu (2013)). This implies that securing an address in the catchment of the school district

is a necessary if not sufficient condition to enroll one’s kids into a so-called key school.

3. Literature

A large literature has been devoted to the effect of school quality on house prices, in

general finding support to the Tiebout model which predicts residential sorting (Tiebout (1956)).

Ross and Yinger (1999), Gibbons ad Machin (2008), Black and Machin (2011) and Nguyen-

Hoang and Yinger (2011) offer excellent reviews. While earlier studies are largely descriptive,

recent ones tend to use quasi-experimental framework in an attempt to uncover the causal

relationship, which is extremely important for policy designs.

Traditional hedonic pricing model estimates of the school quality effect are likely to

suffer from omitted variable bias or endogeneity problems. Black (1999) first applies the

regression discontinuity design (RDD) using administrative boundaries, also known as the

boundary discontinuity design (BDD) approach, in an attempt to remove time-invariant

unobserved neighbourhood fixed-effects which are correlated with school quality. Fack and

Grenet (2010) and Gibbons et al. (2013) further develop the RDD approach using matching.

Compared to the OLS baselines, they all find a smaller capitalization effect, at below 4% for a

one standard deviation increase in test scores.

To the best of our knowledge, Feng and Hu (2013) is the only causal study of the effect

of school quality on house prices in China. Using a difference-in-difference approach, they find

that the re-designation of a previously ordinary high school to a specific high-quality school

status increases the house price in its residential area by 6.9% in Shanghai. However, to the extent

that school designation policy by the municipal government is not entirely exogenous, e.g. due

to concerns for equal access across geographical areas (districts), one cannot rule out the

possibility of endogeneity bias in the DID estimates.

4. Methodology

4

This study employs a quasi-experimental research design to examine two recent

educational policy reforms in Beijing which aim to widen access to quality education for all.

Conventional multivariate regression analysis is unlikely to uncover the true causal effect of the

treatment due to omitted variable bias and endogeneity or self-selection in the treatment (see e.g.

Rubin (1974) and Blundell and Diaz (2009)).

To the extent that the treatment status is randomly assigned, a conventional Difference-

in-difference (DID) would suffice to uncover the true causal effect with the help of a well-defined

control group which is assumed to share the common trend. However, there are good reasons to

believe that the assignment of the treatment status by policy makers is non-random in our case.

In other words, the non-ignorable treatment assignment required for unbiased DID estimates is

not satisfied.

To deal with this issue, we will use propensity score matching to achieve data balance

such that DID can yield unbiased estimates on the post-matching data. In practice, we will use

4 alternative matching strategies to ensure that there are no systemic differences between the

treatment and control groups (Guo and Fraser (2010)). The strategies are defined by propensity

scores estimation using either logistic regressions method or Generalized Boosted Modelling

method, with either Mahalanobis distance or nearest neighbour within caliper.

Mahalanobis Metric

Nearest neighbor

within caliper

Logistic Regression Strategy 1 Strategy 2

Generalized Boosted Modeling (GBM) Strategy 3 Strategy 4

5. Data

This paper is based on a balanced panel of residential complexes (xiaoqu) in the 12 urban

districts in Beijing in 2013 and 2016.1 A residential complex is the urban equivalent of a village

and serves as the most fundamental organization unit for the urban population in China. Each

residential complex has its own neighbourhood or residents’ committee. In Chinese megacities

like Beijing, a residential complex usually contains hundreds of condominiums in medium or

1 The remaining 4 districts where data is unavailable are all rural suburbs, and far away from the Central Business

District (CBD).

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high-rise buildings within well-defined boundaries and one or several designated publicly funded

primary schools where the kids can enrol (Zhang and Yi (2017)).

Using the half million or so actual transaction records of second-hand properties from the

two leading property websites Fang.com (http://www.fang.com/) and Lianjia.com

(https://www.lianjia.com/),2 we derive the mean transaction prices and key characteristics for the

3,167 residential complexes for 2013 and 2016 respectively. Using Google Maps, we can also

construct the distance of each SD to the city centre, the nearest subway station and the nearest

top-grade hospital and the number of independent schools within a 10 kilometre radius. The

designated schools are identified from the school’s admission policies available online for the

relevant years. The grade of the school and the policy regime it belongs to are derived from the

websites of the school itself and relevant District Education Authorities.

We also exclude residential complexes with too few transactions in either of the two years.

To ensure our results are not driven by outliers in the outcome measure of mean price per square

metre (in RMB yuan), we also drop the top and bottom 5% of the mean price distribution.

< Table 1 about here >

Table 1A presents the descriptive statistics for the overall sample before and after the policy

change year. The house prices are converted to constant 2013 prices using the Consumer Price

Index (CPI) for Beijing. The mean house price in Beijing grows from 38520 RMB yuan (USD

6105) in 2013, to 50917 yuan (USD 8069) in 2016, an increase of 32.2% in real terms over 3

years.3 The extent of house price appreciation is consistent with Zhang and Yi (2017), who show

that prices of newly-built houses increase by 15–24% for different quantiles in Beijing between

January 2013 and December 2013 alone. While no residential complex experienced a change in

the School District (SD) in 2013, 9.8% did in 2016. Only 1.6% of residential complexes in 2013

are subject to multi-school dicing, meaning that children are randomly assigned to a consortium

of schools rather than a single school. Three years later, this proportion was increased to 5.9%.

On the other hand, the proportion of SDs that were part of a school federation increased from

2.4% to 14.2% over the sample period. While 35.9% of all residential complexes are in the SD

of a Key primary school, the share of elite SDs grows to 45.7% in 2016, with increases in both

the district-level and municipal-level key schools. All control variables except for years since

2 Jointly they cover virtually all “used (second-hand)” transactions in Beijing.

3 The year-end exchange rates between USD and CNY are 6.152, 6.158, 6.284 and 6.643 for 2013, 2014, 2015 and

2016 respectively. We use the mean of 6.31 over the period to derive the USD equivalents.

http://www.fang.com/

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construction are time-invariant. The mean greening rate of 0.325 indicates that the green areas

account for almost one-third of the land surface of the residential quarter. The floor area ratio is

the ratio of total construction area to the land area. The average service charge is 1.704 RMB

yuan (0.27 USD) per month/m2. The mean number of floors is 13, reflecting the fact that is

Beijing is very densely populated metropolis. The mean floor area per flat is 83.5m2, while the

average years since construction is 15.5 in 2013. The average number of amenities including

stores, post-offices, banks and leisure facilities is 4. The straight-line distances to the city centre

and the nearest top-grade hospital are 10.9 and 2.2 km’s respectively, while the distance to the

nearest subway station is only 0.9 km.

Table 1B presents the descriptive statistics by the treatment status and year. The treatment

group includes all residential complexes whose designated school(s) changed status between

2013 and 2016, from ordinary to either district-level or municipal level key schools, whereas the

control group consists of residential complexes in the catchment of ordinary schools throughout

the sample period. This implies that we exclude residential complexes which are already in the

catchment of elite schools in 2013 from the main analysis. In 2013, the treatment group of

residential complexes enjoyed a price advantage of 3590 yuan over the control group even before

the treatment taking place, suggesting systematic difference between the two groups. After the

treatment taking place, the gap widened to 5660 yuan. Whereas there was no significant

difference in multi-school dicing or school federation between the treatment and the control

groups in 2013, by 2016 the treatment group has an advantage of 11.6 and 13.2 percentage points,

respectively. Three complexes of the SDs for the treated residential complexes were re-designated

as district-level key primary schools with the remaining one quarter as the more prestigious

municipal-level key primary schools. The treatment group is also surrounded by more

independent (or private) schools, compared to the control group.

It turns out that the treatment and the control groups have statistically significantly

different means in half of all the control variables, in the years since construction and the various

distance measures. This highlights the need to control for these systematic differences in the

formal analysis.

6. Empirical Results

6.1. Elite School Designation

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Table 2 presents the OLS estimates as well as the corresponding DID estimate, without

and with the breakdown of the elite schools into district or municipal-level. These will form the

benchmark against which the MDID results are compared. We choose the semi-log specification:

itiiiit Xschkeyprice +++= _ln 10

where lnpriceit is the logarithm of mean house price of residential complex i in year t, key_sch

is the grade of the designated primary school, Xi’s are control variables, εit is the error term, and

β0, β1, and βi’s are coefficients.


Using the pooled sample from both years which contains key school SDs in 2013,

Column 1 shows that the regression adjusted of price premium of access to a key primary school

is 9.0%. When we distinguish between district and municipal-level key primary schools in

column 2, we find that the price premium for the more prestigious municipal-level key school is

more than 3 times as high as its district-level counterpart, at 18.6%. All these estimates are

statistically significant at the 1% level. Moreover, there is a modest but significant effect of

school federation at around 3%. However, there is no significant effect of multi-school dicing

per se. Columns 3 and 4 exclude the residential complexes with access to elite schools in the

base year and only use the 2016 subsample. While the magnitude and the level of significance

have changed, the overall pattern remains the same. The effect of the re-designation as an elite

school is driven by the change to a municipal-level key school. On the other hand, the access to

school federation is now significant with the same magnitude as that of a municipal-level key

school.

The last two columns of Table 2 present the DID estimates. Again, the results are

consistent with the OLS. The re-designation as an elite school is only significant for change to a

municipal-level key school while the school federation is still significant.

Table 3 shows the post-matching balancing test results, for each of the 4 matching

strategies employed. Due to the common support restriction, the matched sample is smaller than

the unmatched sample used in Table 2. For the first 3 matching strategies, none of the variance

ratios are statistically significant at the 5% level post-matching. For Strategy 4 (GBM using

nearest neighbor within caliper) which has by far the largest sample size, there are some

remaining differences in the number of amenities and the distance to subway station squared.


8

Figures 1-4 compares the kernel densities of propensity score before and after matching,

for each of the 4 matching strategies used. They show that the matching has been successful,

especially for strategies 2 and 1.

< Figures 1-4 about here >

Table 4 shows the MDID estimates. The single indicator of SD change is only significant

at 10% level under Strategy 2 and at 1% level under Strategy 4, with a price premium of 2.4%

and 5% respectively. When we distinguish between district and municipal-level key schools,

only the latter is statistically at 1% consistently, with a price premium between 7.5% and 10.5%.


6.2. School Federation

The analysis so far is concerned with the effect of the re-designation of an ordinary

primary to a key primary school on the mean house prices of the affected residential quarter.

Over the same sample period, Beijing has undertaken an alternative reform which we term

“school federation” as a shorthand. This usually takes the form of the designated primary school

becoming part of a federation of schools by (another) existing elite school.

Table 5 presents the descriptive statistics by this alternatively defined treatment status

and year. In 2013, the treatment group of residential complexes enjoyed a price advantage of

2906 yuan over the control group even before the treatment taking place, suggesting systematic

difference between the two groups. After the treatment taking place, the gap widened to 5688

yuan. Whereas treatment group are more likely to be under the multi-school dicing regime, in

2013, this gap becomes statistically insignificant in 2016 due to the higher growth in the control

group. On the other hand, the treatment group is 3.3 percentage points less likely to be in the SD

of an elite school in 2013 compared to the control group, even though this gap is not significant

at the conventional level. However, three years on, the residential complexes in school

federations are 7.0 percentage points more likely to have access to elite schools (note that we do

not distinguish between school federations led by district or municipal-level key primary

schools).

While there appears to be no difference in the number of independent schools within

10km between the treatment and the control groups, the means of many of the control variables

are significantly different.

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In the interest of space, we will only focus on the post-matching balancing test results

using logistic regressions method, with either Mahalanobis distance or nearest neighbour within

caliper (i.e. Strategies 1 and 2, respectively) in Table 6. It is clear that no variance ratio is

statistically significant at the 5% level post-matching,


Figures 5 and 6 compare the kernel densities of propensity score before and after

matching, for matching strategies 1 and 2, respectively. They suggest that the matching has been

very successful, especially for strategy 2.

< Figures 5-6 about here >

Finally, Table 7 shows the OLS, DID and MDID estimates using the two alternative

matching strategies. While the key primary school on its own is significant at 9.3% for both OLS

and DID, the coefficient on school federation is only marginally positive and statistically

insignificant in both specifications. In contrast, school federation yields a price premium of 4.3%

which is significant at 5% under Strategy 1, and a more modest 2.4% which is also only

significant at 10% under Strategy 2.


6.3. Allowing for Interaction with Independent Schools

For both treatments, it turns that having more independent schools in the surrounding

areas (with 10km radius) have a statistically significant positive effect. We are concerned that

the effect might be endogenous, and importantly, could affect the estimates of the effect of the

reforms. Therefore, we rerun the estimation, allowing for the interaction of number of

independent schools with the key variables of interest. Our results in Tables A1 and A2 in the

Appendix suggest that none of these interaction terms are statistically significant in OLS, DID

and MDID specifications. This is reassuring.

< Tables A1-A2 about here >

7. Concluding Remarks

This paper examines the effect access to quality education on house prices, by exploring

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two recent comprehensive educational reforms which aim to equalize access to elite elementary

schools in Beijing, China. While the multi-school dicing reform involves randomly assigning

previously ineligible pupils to key elementary schools through lotteries, the reform of school

federation led by elite schools consolidates low quality schools through alliance with elite

schools. Using the Matching Difference-in-Differences (MDID) approach, we identify the causal

effect of being eligible to enroll in elite primary schools on house prices while allowing for

underlying systemic differences between the treated and non-treated school districts. Our

estimates suggest that the price premium of being eligible to enroll in a municipal-level key

primary school is about 7.5-10.5%, while the premium for being eligible for a district-level key

primary school is statistically insignificant. On the other hand, the price premium for access to a

federation of schools led by an elite school is around 2.4-4.3% and statistically significant. Our

findings are robust to the use of alternative matching strategies and to possible interaction effects

of the reforms with the number of independent school in surrounding areas.

One limitation of our study is that we do not have measures of the probability of getting

into a key school under multi-school dicing or the exact formation of the school federation led

by an elite school. Having such variation would allow us to discriminate between treatments of

various intensity. Therefore, our estimates should be interpreted as a lower bound effect.

Nevertheless, our findings have important policy implications. Although both reforms

aim to equalize education opportunities for all, they are shown to have the unintended

consequences of pushing up the house prices that are already out of reach for people on average

earnings in this metropolis. Future educational policy changes would benefit from careful

evaluations of similar programmes implemented in different contexts and possibly randomized

controlled pilot studies.

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Appendix

Table A1: Effect of independent schools on price premium (OLS, DID)

OLS DID

(1) (2) (1) (2)

School characteristics

School District (SD) change -0.028

(0.039)

- 0.011

(0.011)

-

# independent schools (within 10km) 0.026***

(0.002)

0.025***

(0.002)

0.028***

(0.002)

0.028***

(0.002)

SD change X # independent schools 0.006

(0.005)

- 0.0002

(0.004)

-

SD change to district-level key school - -0.071*

(0.037)

- -0.004

(0.012)

SD change to district-level key school

X # independent schools

- 0.009**

(0.005)

- 0.002

(0.004)

SD change to municipal-level key

school

- 0.327**

(0.131)

- 0.063***

(0.019)

SD change to municipal -level key

school X # independent schools

- -0.026

(0.016)

- -0.021

(0.013)

Federation and consolidation of SD 0.092***

(0.017)

0.096***

(0.016)

0.079***

(0.015)

0.082***

(0.015)

Control Variables

Greening rate 0.021

(0.091)

0.018

(0.090)

0.041

(0.085)

0.039

(0.084)

Mean floor area ratio -0.016***

(0.004)

-0.016***

(0.004)

-0.016***

(0.003)

-0.016***

(0.003)

Service charges 0.034***

(0.008)

0.034***

(0.008)

0.031***

(0.007)

0.031***

(0.007)

# floors -0.003***

(0.001)

-0.003***

(0.001)

-0.003***

(0.001)

-0.003***

(0.001)

Mean floor area per flat -0.0002

(0.0002)

-0.0002

(0.0003)

-0.0003

(0.0003)

-0.0002

(0.0002)

Years since construction -0.002*

(0.001)

-0.002*

(0.001)

-0.001

(0.001)

-0.001

(0.001)

# Local amenities (Banks, stores etc.） 0.091***

(0.019)

0.108***

(0.177)

0.091***

(0.014)

0.109***

(0.015)

Distance to City Centre -0.016***

(0.001)

-0.016***

(0.001)

-0.018***

(0.001)

-0.018***

(0.001)

Distance to nearest top-grade hospital -0.026***

(0.004)

-0.028***

(0.004)

-0.027***

(0.004)

-0.028***

(0.004)

Distance to nearest subway station -0.049***

(0.014)

-0.049***

(0.014)

-0.039***

(0.012)

-0.039***

(0.012)

Distance to nearest subway station

squared

0.007***

(0.002)

0.007***

(0.002)

0.007***

(0.002)

0.008***

(0.002)

F-stats 83.19*** 77.67*** 434.44*** 380.58***

R2 0.525 0.531 0.668 0.671

Observations 1275 1275 2550 2550

Note: Robust standard errors in parentheses. ***, ** and * indicate statistical significance at the 1%, 5% and 10%

respectively.

14

Table A2: Effect of independent schools on price premium (MDID)

Strategy 1 Strategy 2 Strategy 3 Strategy 4

(1) (2) (1) (2) (1) (2) (1) (2)

School District (SD) change 0.027*

(0.015)

- 0.024*

(0.014)

- 0.021

(0.019)

- 0.043***

(0.014)

-

# independent schools (within

10km)

0.041***

(0.005)

0.041***

(0.005)

0.033***

(0.004)

0.033***

(0.004)

0.041***

(0.006)

0.041***

(0.006)

0.025***

(0.005)

0.025***

(0.005)

SD change X # independent schools -0.013*

(0.007)

- -0.002

(0.005)

- -0.013

(0.008)

- 0.007

(0.007)

-

SD change to district-level key

school

- 0.008

(0.016)

- 0.008

(0.014)

- 0.009

(0.020)

- 0.026*

(0.015)

SD change to district-level key


- -0.012*

(0.007)

- -0.002

(0.005)

- -0.013*

(0.007)

- 0.009

(0.006)


school

- 0.089***

(0.025)

- 0.079***

(0.020)

- 0.075**

(0.030)

- 0.097***

(0.021)



- -0.027

(0.024)

- -0.008

(0.014)

- -0.029

(0.042)

- -0.009

(0.019)

Federation and consolidation of SD -0.024

(0.031)

-0.018

(0.030)

F-stats 265.44*** 178.50*** 352.66*** 250.71*** 166.03*** 119.54*** 195.10*** 157.19***

R2 0.436 0.443 0.587 0.589 0.503 0.512 0.380 0.385

Observations 548 800 312 800

Note: Robust standard errors in parentheses. ***, ** and * indicate statistical significance at the 1%, 5% and 10% respectively.

15

Figures

Figure 1: Comparison of kernel density of propensity scores before and after matching,

multi-school dicing, Strategy 1 (Logit & Mahalanobis Metric)


multi-school dicing, Strategy 2 (Logit+Neighbour+DID)

16


multi-school dicing, Strategy 3: Boost+Mahal+DID


multi-school dicing, Strategy 4: Boost+Neighbour+DID

17


Federation & and Consolidation of schools, Strategy 1 (Logit & Mahalanobis Metric)


Federation & and Consolidation of schools, Strategy 2: Logit+Neighbour+DID

18

Tables

Table 1A: Descriptive Statistics, Overall Sample

2013 2016

Price per m2 (dependent variable) 38519.5 50917.2


School District (SD) Change 0 0.098

Multi-school dicing 0.016 0.059

School federation 0.024 0.142

Key Primary School 0.359 0.457

District-level Key Primary School 0.266 0.327

Municipal-level Key Primary School 0.093 0.130

# independent schools (within 10km) 7.282 7.282

Control variables

Greening rate 0.325

Mean floor area ratio 2.615

Service charges 1.704

# floors 12.415

Mean floor area per flat 83.453

Years since construction 15.453 18.453

# Local amenities (Banks, stores etc.） 3.989

Distance to City Centre 10.940

Distance to nearest top-grade hospital 2.196

Distance to nearest subway station 0.910

Observation 2249 2249


respectively

19

Table 1B: Descriptive Statistics and balancing tests Year 2013 Year 2016

Treatment Control Mean

Difference

Variance

Ratio


Difference

Price per m2 (dependent variable) 39037.5 35447.6 3589.9*** - 51754.1 46094.5 5659.6***


School District (SD) Change 0 0 0 - 1 0 1

Multi-school dicing 0.004 0.007 -0.003 0.69* 0.157 0.041 0.116***

School federation 0.031 0.024 0.007 1.31* 0.260 0.128 0.132***

Key Primary School 0 0 0 - 1 0 1

District-level Key Primary School 0 0 0 - 0.753 0 0.753***

Municipal-level Key Primary School 0 0 0 - 0.247 0 0.247***

# independent schools (within 10km) 7.924 6.366 1.558*** 0.95

Control variables

Greening rate 0.319 0.325 -0.006 0.80

Mean floor area ratio 2.658 2.541 0.117 0.88

Service charges 1.719 1.628 0.091 1.41*

# floors 13.012 12.164 0.848* 1.04

Mean floor area per flat 81.338 83.008 -1.670 1.22

Years since construction 16.384 14.911 1.473** 0.93

# Local amenities (Banks, stores etc.） 3.973 3.990 -0.017 2.92*

Distance to City Centre 9.672 11.584 -1.912*** 0.45*

Distance to nearest top-grade hospital 2.060 2.457 -0.397*** 0.74*

Distance to nearest subway station 0.723 1.029 -0.306*** 0.33*

Observation 223 1219 - -

Note: Robust standard errors in parentheses. ***, ** and * indicate statistical significance at the 1%, 5% and 10% respectively

20

Table 2: Effect of multi-school dicing on price premium (OLS, DID)

OLS DID

(1) (2) (3) (4) (1) (2)


Key Primary School 0.090***

(0.008) - - - - -

District-level Key

Primary School -

0.055***

(0.009) - - - -

Municipal-level Key

Primary School -

0.186***

(0.014) - - - -

SD Change (Multi-

school Dicing) - -

0.022

(0.014) -

0.011

(0.011) -

SD Change to District-

level Key - - -

0.0005

(0.015) -

-0.004

(0.012)

SD Change to

Municipal-level Key - - -

0.098***

(0.032) -

0.063***

(0.019)

School federation 0.033***

(0.012)

0.039***

(0.012)

0.090***

(0.017)

0.090***

(0.017)

0.079***

(0.015)

0.079***

(0.015)

Multi-school Dicing 0.006

(0.019)

-0.001

(0.018) - - - -

# independent schools

(within 10km)

0.026***

(0.001)

0.026***

(0.001)

0.027***

(0.002)

0.026***

(0.002)

0.028***

(0.002)

0.027***

(0.002)

Control variables

Greening rate 0.074

(0.066)

0.108*

(0.064)

0.018

(0.091)

0.016

(0.090)

0.041

(0.084)

0.040

(0.084)


(0.003)

-0.010***

(0.003)

-0.016***

(0.004)

-0.015***

(0.004)

-0.016***

(0.003)

-0.016***

(0.003)


(0.005)

0.029***

(0.005)

0.035***

(0.008)

0.034***

(0.008)

0.031***

(0.007)

0.031***

(0.007)

# floors -0.003***

(0.0008)

-0.003***

(0.0008)

-0.003***

(0.001)

-0.003***

(0.001)

-0.003***

(0.001)

-0.003***

(0.001)

Mean floor area per flat -0.0005**

(0.0002)

-0.0005**

(0.0002)

-0.0002

(0.0003)

-0.0002

(0.0003)

-0.0003

(0.0002)

-0.0002

(0.0003)

Years since construction -0.0001

(0.0007)

-0.0004

(0.0007)

-0.002*

(0.001)

-0.002*

(0.001)

-0.001

(0.001)

-0.001

(0.001)

# Local amenities

(Banks, stores etc.）

0.083***

(0.023)

0.088***

(0.027)

0.091***

(0.019)

0.102***

(0.014)

0.091***

(0.014)

0.102***

(0.014)


(0.001)

-0.022***

(0.001)

-0.016***

(0.001)

-0.018***

(0.001)

-0.018***

(0.001)

-0.018***

(0.001) Distance to nearest top-

grade hospital

-0.019***

(0.003)

-0.020***

(0.003)

-0.027***

(0.004)

-0.027***

(0.004)

-0.027***

(0.004)

-0.027***

(0.004)

Distance to nearest

subway station

-0.018***

(0.007)

-0.019***

(0.007)

-0.049***

(0.014)

-0.039***

(0.012)

-0.039***

(0.012)

-0.039***

(0.012)

Distance to nearest

subway station squared

0.004***

(0.0004)

0.004***

(0.0003)

0.007***

(0.002)

0.007***

(0.002)

0.007***

(0.002)

0.007***

(0.002)

F-stats 780.55*** 775.60*** 88.68*** 464.06*** 461.61*** 417.45***

R2 0.662 0.676 0.524 0.669 0.668 0.670

Observations 4006 4006 1275 1275 2550 2550


respectively.

21

Table 3: Post-matching balancing tests

Note: ***, ** and * indicate statistical significance at the 1%, 5% and 10% respectively.

Strategy 1 Strategy 2

Treatment

Mean

Control

Mean

Variance

Ratio

Treatment

Mean

Control

Mean

Variance

Ratio

Service charges 1.64 1.64 0.99 1.719 1.729 1.13

# Local amenities 4 4 - 3.98 3.98 0.99

Distance to City

Centre

8.84 8.79 0.95 9.61 9.50 0.76*

Distance to nearest

top-grade hospital

1.75 1.75 1.01 2.00 2.07 1.05

Distance to nearest

subway station

0.60 0.61 1.04 0.72 0.72 1.18

Distance to subway

station squared

0.43 0.44 0.92 0.80 0.76 1.07

School Federation 0 0 - 0.04 0.03 1.04

Observations 548 800

Strategy 3 Strategy 4 Treatment

Mean

Control

Mean

Variance

Ratio

Treatment

Mean

Control

Mean

Variance

Ratio

Service charges 1.35 1.35 0.99 1.72 2.05 1.04

# Local amenities 4 4 - 3.98 3.99 2.56**

Distance to City

Centre

7.91 7.92 0.95 9.62 9.63 1.14

Distance to nearest

top-grade hospital

1.49 1.51 0.97 2.00 1.63 1.20

Distance to nearest

subway station

0.58 0.58 0.97 0.72 0.61 1.30*

Distance to subway

station squared

0.39 0.39 0.94 0.80 0.50 3.05**

School Federation 0 0 - 0.04 0.02 1.64*


22

Table 4: Matching Difference-in-differences (MDID) Estimates

Strategy 1 Strategy 2 Strategy 3 Strategy 4

(1) (2) (1) (2) (1) (2) (1) (2)

SD Change (Multi-

school Dicing)

0.027

(0.015)

- 0.024*

(0.014)

- 0.021

(0.019)

- 0.050***

(0.014)

-

SD Change to District-

level Key

- 0.008

(0.016)

- 0.008

(0.014)

- 0.009

(0.020)

- 0.033**

(0.015)

SD Change to

Municipal-level Key

- 0.089***

(0.025)

- 0.079***

(0.020)

- 0.075**

(0.029)

- 0.105***

(0.021)

# Local amenities - - - - - - 0.152***

(0.025)

0.166***

(0.026)

Distance to City Centre - - -0.028***

(0.002)

-0.028***

(0.002)

- - - -

Distance to nearest

subway station

- - - - - - -0.065

(0.057)

-0.061

(0.057)

Distance to subway

station squared

- - - - - - -0.002

(0.017)

-0.005

(0.017)

School Federation - - - - - - -0.066**

(0.032)

-0.063**

(0.032)

F-stats 423.33*** 266.76*** 430.69*** 308.15*** 246.37*** 159.96*** 238.39*** 196.24***

R2 0.261 0.271 0.455 0.460 0.281 0.299 0.273 0.280



23

Table 5: Descriptive Statistics and balancing tests, School Federation

Year 2013 Year 2016


Difference

Variance ratio Treatment Control Mean

Difference

Price per m2 (dependent variable) 40990.0 38084.0 2906.0*** - 55721.3 50032.8 5688.5***

School Characteristics

Multi-school Dicing School District 0.041 0.013 0.028*** 2.99* 0.075 0.059 0.016

Key Primary School 0.331 0.364 -0.033 0.96 0.519 0.449 0.070**

# independent schools (within 10km) 7.398 7.282 0.116 0.50* 7.398 7.282 0.116

Control variables

Greening rate 0.319 0.326 -0.007* 0.89

Mean floor area ratio 2.834 2.579 0.255** 1.07

Service charges 2.089 1.658 0.431*** 2.75*

# floors 13.410 12.275 1.135*** 1.09

Mean floor area per flat 85.238 83.308 1.930 1.40*

Years since construction 16.932 15.261 1.671*** 0.95 19.932 18.261 1.671***

# Local amenities 3.996 3.990 0.006 0.20*

Distance to City Centre 8.679 11.315 -2.636*** 0.62*

Distance to nearest top-grade hospital 1.697 2.277 -0.580*** 0.42*

Distance to nearest subway station 0.800 0.931 -0.131** 0.27*

Distance to nearest subway station squared 0.906 1.840 -0.934 0.01*

Observations 266 1930 - -


24

Table 6: Post-matching balancing tests


Treatme

nt Mean

Treatme

nt Mean

Variance

Ratio

Treatme

nt Mean

Treatme

nt Mean

Variance

Ratio

Service charges 1.476 1.442 1.15 2.089 2.008 1.37*

Mean floor area per flat 75.376 75.037 1.07 88.793 86.2 1.32*

# Local amenities 4 4 - 3.996 4 -

Distance to City Centre 6.817 6.924 0.91 8.988 9.343 1.14

Distance to nearest top-grade hospital 1.440 1.449 0.99 1.756 1.643 1.14

Distance to nearest subway station 0.647 0.656 0.98 0.786 0.805 1.59*

Distance to subway station squared 0.501 0.508 0.93 0.891 0.822 1.80*

# independent schools (within 10km) 7.642 7.606 1.09 7.423 7.714 0.56*

School Federation 0.009 0.009 1.00 0.030 0.051 0.65*



25

Table 7: Matching Difference-in-differences (MDID) Estimates

OLS DID PSM_DID


School Federation 0.011

(0.012)

0.005

(0.010)

0.043**

(0.018)

0.024*

(0.013)

Greening rate 0.078

(0.067)

0.078

(0.067)

- -


(0.003)

-0.010***

(0.003)

- -


(0.005)

0.030***

(0.005)

- 0.021***

(0.008)

# floors -0.003***

(0.001)

-0.003***

(0.001)

- -

Mean floor area per flat -0.0004*

(0.0002)

-0.0004*

(0.0002)

- -0.0007*

(0.0004)

Years since construction 0.0001

(0.0007)

0.0001

(0.0007)

- -

# Local amenities 0.082***

(0.023)

0.082***

(0.023)

- -


(0.001)

-0.023***

(0.001)

- -

Distance to nearest top-grade

hospital

-0.019***

(0.003)

-0.019***

(0.003)

- -

Distance to nearest subway station -0.017**

(0.007)

-0.017**

(0.007)

- -0.122**

(0.061)

Distance to subway station squared 0.004***

(0.0004)

0.004***

(0.0004)

- 0.008

(0.023)

# independent schools (within 10km) 0.026***

(0.001)

0.026***

(0.001)

- 0.034***

(0.004)

Key Primary School 0.093***

(0.009)

0.093***

(0.009)

- -

Multi-school dicing 0.007

(0.019)

0.007

(0.019)

- 0.108**

(0.042)

F-stats 772.48*** 730.66*** 380.24*** 249.51***

R2 0.661 0.661 0.318 0.408



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