Discussion Paper Series
2014-01
Can Urban Planning Instruments Solve Externalities?
Empirical Analysis of Regulations and Taxes
Kazuyuki Iwata and Shunsuke Managi
April 1, 2014

Discussion Papers can be downloaded:
http://www1.tcue.ac.jp/home1/c-gakkai/dp/dp14-01

Can Urban Planning Instruments Solve Externalities?
Empirical Analysis of Regulations and Taxes

Kazuyuki Iwata 1* and Shunsuke Managi 2

1

Takasaki City University of Economics, Faculty of Regional Policy

1300 Kaminamie, Takasaki, Gunma, 370-0801, JAPAN. Tel.: +81-27-343-5417
iwata.kazuyu@gmail.com(* Corresponding author)

2

Graduate School of Environmental Studies, Tohoku University

6-6-20, Aza-aoba, Aramaki, Aoba, Sendai, 980-8579, JAPAN. Tel.:+81-22-795-3216.
managi@mail.kankyo.tohoku.ac.jp

Abstract

Urban success depends on the external effects of urbanization, for example, carbon dioxide (CO2)emissions and crime levels. In addition, there is a connection between urbanization policy andpopulation density. This study contributes to our understanding of indirect linkages from urban
policy to external effects. Using Japanese city level data from 1990 to 2007, we find that existingland use regulations such as taxes and command-and-control regulations can alter city density.
Increases in population density decrease CO2 emissions and the costs of waste cleanup but worsen
crime, traffic accidents and public finances. Thus, density-increasing urban planning simultaneously
generates both positive and negative effects on society.

Keywords: urbanization, urban planning instruments, land use regulations, command-and-control
regulations, carbon dioxide emissions, externalities

1. Introduction

Urban success surely depends on economic development (Glaeser, 2012), and the literature on
agglomeration tells us that growth of a city has many merits in terms of economies of scale andnetwork effects (Fujita and Thisse, 2013). Recent studies have analyzed external effects ofurbanization such as carbon dioxide (CO2) emissions and changes in crime rates (Glaeser andSacerdote, 1999; Glaeser and Kahn, 2010; Gaigné et al., 2012). Additionally, urban policy affects
urbanization through changes in population or population density (Song and Zenou, 2006).
Half of the world's population currently lives in urban areas (International Herald Tribune,2008). In the present study, we seek to contribute to our understanding of the indirect linkages fromurban policy to external effects through urbanization. External effects include common externalities
such as CO2 emissions and crime. Mitigation of these externalities can be important policy goals ofgovernments.
Environmental and urban economists are closely observing how environmentally friendly
urban policies can help counteract CO2 emissions. The notion of a compact urban city has attracted
the attention of international organizations (OECD, 2012). As an urban planning and urban designconcept, the compact city is a place of short distances, high residential density (spatial distribution of
the population) and mixed land use. Advocates of the compact city argue that such a design
encourages low energy consumption and reduced pollution. We therefore expect that CO2 emissions
from vehicles will decrease as cities become increasingly compact and city functions are relocated. 1
In this study, population density (or population per given land area) is used to measure the degree ofurbanization.
Glaeser (2012) discusses the need for urban policies, especially land use regulation and

1

The transportation sector has greatly contributed to increases in CO2 emissions around the world. Effective

environmental policies with respect to this sector are required to reduce CO2 emissions from vehicles. We therefore
apply emissions data for vehicle use.

other economic measures, to address negative externalities such as CO2 emissions, pollution, crimeand congestion. Previous studies have emphasized the effect of urban policy on density (Newman
and Kenworthy, 1989; Cervero and Murakami, 2010; Gleaser and Kahn, 2010; Gaigné et al., 2012).
CO2 emissions may decrease in dense cities, and indeed, empirically, a negative
relationship between CO2 emissions from vehicles and population density is found. Residents inhighly urbanized environments may have less need of vehicles, given well-established transportation
systems, than people in low density environments. Total vehicle miles traveled in a city may also be
low in high density cities. These factors lead to reduced CO2 emissions from vehicles.
However, city density/agglomeration generates economic growth (Cuberes, 2012) because
opportunity costs such as travelling time in densely packed areas are smaller than in sparse areas. If
the marginal utility of vehicle ownership is positive, economic growth might provide residents
without vehicles incentives to purchase vehicles. This is an income effect of agglomeration. If thepositive effects on CO2 emissions exceed the negative effects, city density leads to increased
emissions.
Suppose increases in population/population density indeed led to a decrease in CO2emissions. How could we then increase city density (Glaeser, 2012)? Glaeser and Kahn (2010)
suggest that the effects of land-use restrictions on people’s decisions to move to different locations
merit further research. One possible solution is to effectively utilize existing land use regulations,such as taxes and command-and-control mechanisms (CAC). We examine the following researchquestions: 1) Can land use regulations affect population density? 2) Are CO2 emissions fromvehicles and other external factors such as congestion and crime lower in high density areas than in
low density areas? We study Japan, using city level data for the 1970-2007 period. We use data for
all Japanese cities because there is wide variation in the population densities of regions in Japan (seeAppendix Figure A1). For example, the data include the world’s largest super-city, Tokyo-Yokohama,
which is larger than New York City, Mexico City, and Delhi (Demographia, 2013).
Using city-level data, this paper examines whether CO2 emissions from vehicles in citieswith high population densities are likely to be small relative to cities with low population densities.
Previous studies have investigated the US (see Glaeser and Kahn, 2010; Cervero and Murakami,2010). Building on this literature, we analyze the effects on population density of several urban
policies such as property taxes and urbanization control areas.
Furthermore, we examine other effects of city density, in addition to CO2 emissions. These
include traffic accidents, annual cleaning expenditures, the facility condition index 2 and crime.
These other outcomes are important in evaluating urban policy, as they represent social issues that,similarly to CO2 emissions, must be solved (Glaeser, 2012). If city governments succeed in raising
population density, traffic congestion may increase (Glaeser and Gottlieb, 2009), and an increase incongestion will result in an increase in traffic accidents (Wang et al., 2009). In addition, crime will
also most likely increase because crime in big cities is higher than in small cities (Glaeser andSacerdote, 1999). Therefore, the more dense cities become, the higher traffic accidents and crime are
expected to be. These are negative impacts on social welfare associated with increased city density.
Not only negative outcomes but also positive outcomes may be generated by raising city
density. Due to increased density, areas that city governments must clean and collect waste from may
shrink, as development and urban sprawl are reinforced by increased city density and residential
neighborhoods concentrate into city centers. City governments can thus save expenditures that may
have gone for cleaning and use them to improve their facility condition index. Similarly to
reductions of CO2 emissions, these effects improve social welfare.
The remainder of this paper is organized as follows: Section 2 describes the background of
our research and Japanese urban planning. Sections 3 and 4 present our empirical model used tomeasure the effects of population density and our estimation results, respectively. Section 5
concludes the paper.

2

The facility condition index is used to assess the financial resources of local governments. It is calculated as

standardized annual revenue divided by standardized annual demand. Therefore, the larger the index is for a given
city government, the greater its financial resources.

2. Urban Planning Instruments for Urbanization
2.1. Background

Several empirical studies have directly examined the relationship between population density andvehicle usage (CO2 emissions from vehicles). The first of these, Newman and Kenworthy (1989),
using 1980 data from large cities throughout the world, found that gasoline consumption per capita
in cities with high density was lower than that in cities with low density. In addition, Gleaser andKahn (2010) provided a detailed analysis based on individual data for large cities in the UnitedStates (US), finding a negative relationship between population density and automobile emissions.
Using data on 370 US regions for 2003, Cervero and Murakami (2010) showed that vehicle mile
traveled per capita is negatively correlated with population density, while Brownstone and Golob(2009), employing US household survey data, showed that people in low residential density areas
tend to use vehicles more than people in high residential density areas. 3 It is therefore generally
expected that policies that increase city density can be effective in achieving emissions reductions.
Figure 1 shows the relationship between population density and per capita CO2 emissions
from automobiles in all cities in Japan. Unlike Newman and Kenworthy (1989), Figure 1 includes
small cities and towns. As seen in the figure, even including small scale city data, the relationship isfound to be negative, which is consistent with previous studies. In addition, the plot shows a larger
variance of per capita CO2 emissions from automobiles in lower population density areas than inhigher population density areas. Therefore, it is crucial to include all city data to gain an overall
picture of the relationship between population density and CO2 emissions.

3

Several studies also indirectly support the view that population density is negatively correlated with CO2 emissions

from vehicles. Yamamoto (2009) reveals that population density has a negative effect on vehicle ownership in Japanand Malaysia. Liu et al. (2014) show that there is positive correlation between urban compactness and CO2 emissions
per GDP in China. Additionally, Fang (2008) provides evidence that people in high density areas tend to own small
vehicles, which have better fuel economy than large vehicles. These studies imply that CO2 emissions from vehicles
may decrease with increasing population density.

per capita CO2 emssions from vehicles (ton/person)
2
4
6
8
0
0

50
100
150
population density (person/hectare)

200

Figure 1. CO2 and Urbanization: The relationship between population density and per capita CO2
emissions from 1990 to 2007 in Japan
Note: The number of observations is 6,866. The figure is based on data from 1990, 1999, 2003 and2007. There are 12 cities in which per capita CO2 emissions are above 8.

If the negative relationship between population density and the CO2 emissions holds, asGleaser and Kahn (2010) show, governments can decrease CO2 emissions by increasing population
density. We hypothesize that several land use regulations can potentially increase population density.
These include property taxes (Brueckner and Kim, 2003; Song and Zenou, 2006) and urbanization
control areas (Sorensen et al., 2004). There is a connection between land use regulations andpopulation growth at the regional level (Glaeser and Ward, 2009). Our study is the first to assess
whether and to what extent various land use regulations affect CO2 emissions and other external
factors indirectly through changes in population density. Therefore, using datasets that cover all
Japanese cities, we extend previous studies by examining the indirect effects of land use regulations.
Land use regulations such as property taxes and urbanization control areas are
implemented to control the scale of cities and constrain urban sprawl. For example, Song and Zenou

(2006) show theoretically that property taxes affect population density, which in turn affects urban
sprawl. It is clear that the urbanization control areas influence population density because the policy
prohibits residents from living in designated areas. This paper is the first to examine the effects ofmultiple land use regulations on CO2 emissions and other external factors via population density. We
also note that direct policies, although they are not the focus of our study, affect emissions. For
example, Millard-Ball (2012) shows that cities that implement climate plans succeed in reducing
greenhouse gas emissions.

2.2 Japanese Urban Planning

Urban policies affect all residential economic activities via land use relocations of city facilities suchas city halls, hospitals, roads, railways, and schools, among others. Hence, planning may affect CO2
emissions from vehicles and have other affects as well. This paper examines five outcome variables:
CO2 emissions, traffic accidents, annual cleaning expenditures, the facility condition index andcrime.
Previous studies have shown that CO2 emissions (gasoline consumption) in cities withrelatively high population densities tend to be low (Glaeser and Kahn, 2010). At the same time,
Brownstone and Golob (2009) note that it may be difficult for governments to dramatically increase
residential density. We contribute to the literature by examining how governments can raise
population density.
The purpose of this paper is to examine the effects of urban policy on CO2 emissions byassessing Japanese city planning instruments designed to raise population density. The types of
instruments examined are two CAC regulations and two taxes stipulated by Japanese Urban
Planning Law. The CAC instruments, known as urbanization control areas and urbanization
promoting areas, have been implemented in certain cities. Under the law, urbanization control areas
are defined as areas where urbanization is restricted. As a general rule, certain kinds of development,

for example, development of home sites by chopping down forests, are prohibited in such areas. 4 Inother words, urban sprawl is restricted, and these areas act as buffer zones in protecting theenvironment. Therefore, city policy planners can control urban sprawl and promote urbanization byimplementing urbanization control areas.
However, urbanization promoting areas are major urban areas where governments decideto preferentially urbanize over a ten-year period in a systematic manner. To this end, city
governments actively create infrastructure such as water and sewage, roads and utilities indesignated areas. The locations and scale of the infrastructure, including roads, are effectively
planned. Well planned roads could lead to reduced gasoline consumption by reducing congestion.
Hence, urbanization promoting areas could decrease CO2 emissions from vehicles inside a city.
As for taxes, residents face urban planning taxes and property taxes imposed by their city
governments. Local city governments can change these tax rates because, similarly to the regulations
discussed above, they are local policies. Importantly, the two taxes differ significantly. Urban
planning taxes are exclusively for constructing infrastructure in urbanization promoting areas, andcity governments cannot use these taxes for other purposes. Therefore, residents in certain areas, forexample, in the central business district, are subject to both urban planning taxes and property taxes,
while persons living in an urbanization control area are subject only to property taxes. All city
governments impose a property tax, whereas an urban planning tax is not imposed by every city
government. Also note that tax rates vary across city governments.
Using the characteristics of the two taxes, city governments can incentivize residents
living in outer areas to move to central areas, so that population density might rise. To increase city
density, it is preferable to increase property taxes and reduce urban planning taxes (or subsidies)
simultaneously. Then the cost of living in an urbanization promoting area is lower than the cost ofliving in an urbanization control area.
This projection pertains to internal movements or residents within a city. In addition,
4

Living in such areas is permitted if certain conditions stipulated in the Urban Planning Law are met.

however, residents move externally between cities. That is, people may choose to live in cities where
both urban planning tax rates and property tax rates are low. In this case, the population densities ofcities with low tax rates will exceed those of cities with high tax rates. It is thus unclear whether
internal movements of a city population will outweigh external movements, or vice versa.
Furthermore, these instruments have the important feature that city governments canimplement them without permission from the national government. Implementations can be carriedout by passing/revising ordinances. Hence, such rate changes are feasible instruments of urban
policy to achieve higher city density, as city governments have the freedom to utilize the four
instruments for their own purposes.
Figure 2 presents an intuitive example of the four urban policy instruments in a circular
city. The outer of urbanization promoting area is set as urbanization control area.

Figure 2. Example for Taxes and Regulations in a Circular City

As of March 2010, there were 1,750 city governments in Japan. 5 Cities can be classified

5

In Japan, local governments are divided into two types. One is the prefecture, which is nearly equivalent to a statein the United States. There are 47 prefectures in Japan. The other is the city government, defined in this paper.

into four types: special ward, city, town and village. Breaking down these classifications, there are
23 special wards (exclusively in the Tokyo prefecture), 786 cities, 757 towns and 184 villages.
Among them, 687 city governments implement an urban planning tax. Although the standard
property tax rate is 1.4 percent, 126 city governments set higher tax rates.
Urbanization promoting areas and urbanization control areas have been established by
649 and 626 city governments, respectively. Histograms for tax rates and regulation area rates(regulation area divided by habitable area) in 2007 are presented in Figure 3. Figure 3 shows that theuses of each of these instruments vary. Exploiting these variations in tax rates and regulation area
rates, we can empirically identify the effects of the four instruments on population density.

1500

687
1063

Implemented
Not Implemented

126
1624

0

0

500

Implemented
Not Implemented

1000

Frequency
200 400 600 800 1000

Descriptive statistics, such as the means and standard deviations, are presented in section 3.

648
1097
5

1.5

1.6
1.7
Property Tax Rate

Implemented
Not Implemented
Missing

1.8

625
1120
5

0

0

500

Frequency
500

1000

Implemented
Not Implemented
Missing

1.4

.3

1500

.2
.1
Urban Planning Tax Rate

1000

0

0

1
.6
.8
.2
.4
Urbanization Promoting Area Ratio

0

6
4
2
Urbanization Control Area Ratio

8

Figure 3. Distributions of Taxes Rates and Regulation Area Rates in 2007 (Obs.=1,750)
Note: The vertical and horizontal axes denote frequencies and rates, respectively. The urbanization
promoting area ratio is measured as the regulation area divided by habitable area. The urbanization
control area ratio is calculated by the same procedure.

3. Model and Data
3.1. Empirical Model

This section discusses the empirical model and data used to examine the relationship between cityurbanization/population density, urban planning instruments and CO2 emissions.
First, the following linear equation model (1) is used to identify the relationship between
population density and CO2 emissions. The dependent variable,
vehicles in city i in year t. The vector, denotes CO2 emissions fromon the right-hand side is a vector of explanatory variables

capturing city characteristics such as income per capita, road length and so on that affect CO2
emissions.
andis population density in city i in year t.

andare parameters to be estimated.

are city and time specific effects, respectively. An unobserved idiosyncratic error term isexpressed as.

ln Ei ,t = ln X i ,t β + γ ⋅ ln POPi ,t + ηi + µ t + ε i ,t(1)

If CO2 emissions from vehicles in a city in which population density is high are lower thanthose in a sprawled city, the estimated coefficientis negative. Therefore, the sign ofisimportant to our analysis.
In addition to CO2 emissions, we analyze traffic accidents, crime, annual cleaning
expenditures and the facility condition index as dependent variables. As city functions are compacted(city density increases), we expect traffic accidents, crime and the facility condition index to increase
and cleaning expenditures to decrease.
City governments may be able to raise their population densities through policy
instruments. Four urban planning instruments are considered, namely, urbanization promoting areas,urbanization control areas, urban planning taxes and property taxes. Hence, population density inequation (1) must be treated as an endogenous variable. We therefore estimate the following
equation (2).

ln POPi ,t = ln X i ,t δ + UPi ,t θ + n i + lt + ϖ i ,twhere the vector

represents the four urban planning instruments that affect population density.

The other control variables are contained in vector
expressed as

(2)

and

. City and time specific fixed effects are, respectively, unobserved independent and identically distributed error term.

Treatment of population density as an endogenous variable, as in equation (1), is consistent
with previous studies of urbanization (Brownstone and Golob, 2009). Therefore, a two-stage
estimation method is used to avoid endogeneity. That is, we estimate equation (2) and then estimate
equation (1), using estimated values from equation (2). The instrumental variables in equation (2),which only affect population density, are required to ensure consistency of the estimated coefficientsin equation (1). The estimated coefficients are biased in equation (1) if population density is treatedas an exogenous variable. In our model,

works as an instrumental variable vector because

urban planning taxes and regulations are not introduced to reduce CO2 emissions but to control land
use and design city characteristics.

3.2. Data

The study employs data for all 1,750 Japanese cities from 1990 to 2007. Table 1 presents descriptive
statistics for the variables used in this paper. Data sources for each variable are indicated in the table.
If there are no missing values, the number of observations is 31,500 (=1,750×18). Due to a lack ofavailable data on CO2 emissions from vehicles, the four years of 1990, 1999, 2003 and 2007 are usedin our analysis. There are also many missing values for the number of police stations.
City government data on CO2 emissions are provided in Statistics for Local Environment,
published by the Coalition of Local Governments for Environment Initiative. The Basic Resident
Register, which contains population data, is published by the Ministry of Internal Affairs andCommunications (MIAC). To convert population into population density, we use habitable area,
which is reported by the Geological Information Authority of Japan. The Annual Report on Fire

Defense, Taxation Status of Local Government and the Annual Report on Local Public Finance arealso published by MIAC. Both the Annual Survey on Urban Planning and the Annual Report on
Road Statistics are obtained from the Ministry of Land, Infrastructure, Transport and Tourism.
Property tax rates for each city were obtained through telephone surveys conducted incities with property tax rates above 1.4 percent. A list of these cities (126 city governments) was
obtained from MIAC. The list is presented in Figure 3.

Table 1. Descriptive Statistics
Variables
Population Density
CO2 (Total)

Unit
Obs.
Mean
S.D.
Min
Max
Sourceperson/ha
30923
13.8
23.0
0.12
195.7 Basic Resident Register
ton-CO26914 110115 226004
0 3758081 Statistics for local environment

CO2 (Passenger)
CO2 (Truck)
Traffic Accident
Crime
Annual Expenditures for Cleaning
Facility Condition Index
IncomeRoad LengthSinguler
Police Station
Urban Planning Tax Rate
Property Tax Rate
Urbanization Promoting Area Ratio
Urbanization Control Area Ratio

ton-CO2
ton-CO2
case
case
1,000 yen
index
1,000 yenkilometer
place
place
percent
percent
percent
percent

6921
60746 126105
6916
49295 103562
30860
467
1275
29618
1179
4233
29064 1273080 3674104
28792
0.74
0.64
31188 1.06E+08 2.96E+08
30553
806
5903
27961
93.3
233.4
16823
9.01
15.44
31500
0.10
0.13
31500
1.41
0.04
30720
0.14
0.33
30720
0.32
0.56

0 2121385 Statistics for local environment
0 1675453 Statistics for local environment
025712 Annual report on fire defence
0 136454 Annual report on fire defence
0 9.46E+07 Annual report on local public finance07.34 Annual report on local public finance239855 7.18E+09 Taxation status of local government
8 550300 Annual report on road statistics
04226 Annual report on fire defence
0259 Annual report on fire defence
00.31 Annual survey on urban planning
1.4
1.85 Telephone survey
037.91 Annual survey on urban planning07.73 Annual survey on urban planning

In Japan, significant consolidation of city governments began around 2002 (see Figure 4).
While there were more than 3,000 city governments before 2002, the total number fell dramaticallyas a result of these consolidations. To account these consolidations, we created a variable for city
government specific effects. Suppose, for example, that city A is consolidated with city B and thatthe new city is named city A. In this case, we cannot distinguish between the old city A and the newcity A because the two cities’ names are the same. Therefore, we cannot precisely account for city
A’s fixed effects in the estimations.

3500
3000
2500
2000
1500
1000
500

Town

2011

2010

2009

2008

2007

2006

City

2005

2003

Ward

2004

2002

2001

2000

1999

1998

0

village

Figure 4. Transition of Number of City Governments in Japan

We construct our database by the following procedure. To avoid incorporating city A’s
fixed effects, we transform our unbalanced city government data into balanced data. That is, we add
the value for city B to the value for the old city A before consolidation and then treat the created“B+old A” as a new city A in the estimation. Fixed effects without missing values can be obtained byaccounting for the decrease in the number of city governments due to consolidation.

4. Results
4.1. Relationship between outcomes and population density

This section discusses the estimation results, using the all-city government panel data from 1990 to
2007. Before estimating equations (1) and (2), we run bivariate regressions in which the fiveoutcomes are dependent variables, and the explanatory variable is logged population density. A
plotted graph is presented in Figure 5. Due to missing values, the number of observations for each
regression differ. For CO2 emissions, the number of observations is smaller than for the others
because the sample period is only four years.
All relationships between CO2 emissions (total = passengers + trucks), CO2 emissions

(passengers), CO2 emissions (trucks) and population density are negative. Hence, population density
may lead not only to decreased passenger vehicle usage but also to decreased truck usage. In highly
dense cities, accidents and crime increase, and cleaning expenditures and the facility condition index
improve. These regression results agree with our expectations.

Figure 5. Relationship between Outcomes and Logged Population Density from 1990 to 2007
Note: The vertical axes represent outcomes, and the horizontal axes represent logged population
density.

4.2. Effects of urban planning instruments on population density

Population density is associated with income and policy variables. Table 2 shows the results forequation (2). Three of the instruments significantly affect population density in all cases. The
exception is the urban planning tax, although the urban planning tax is significant in models 1 and 2.
Decreases in both the urban planning tax rate and the property tax rate increase population
density—a result that does not support the view that the urban planning tax and the property tax havenegative and positive effects on population density, respectively. Instead, our alternative hypothesisof residential external movements between cities appears to be supported, as the coefficient for theurban planning tax rate is insignificant in model 3. Therefore, city governments can attract people
living in other cities by reducing property taxes. As for the two CACs, it is robustly revealed that
expansion of both urbanization promoting areas and urbanization control areas leads to increased
population density, as expected.
Income is used as a proxy for the economic scale in the city. As a growing city is attractive
to people in various respects (Glaeser, 2011), the sign of the coefficient is strongly significant andpositive. Thus, property taxes and regulations can make a city more compact. This empirical findingis important for urban policy planners.

Table 2. Estimation Results of Population Density on Urban Planning Instruments

Variablesln(income)Urban Planning Tax Rate
Property Tax Rate
Urbanization Control Area Ratio
Urbanization Promoting Area Ratio
Constant
Time Fixed Effects
City Government Fixed Effects
Adj. R-squared
F-value (P-value)

Model 1Model 2Model 3
Coef.
S.E.
Coef.
S.E.
Coef.
S.E.
0.458 0.045 ***0.460 0.045 ***0.395 0.019 ***-0.324 0.088 *** -0.336 0.088 *** -0.004 0.011-0.587 0.163 *** -0.587 0.161 *** -0.213 0.031 ***0.204 0.044 ***0.203 0.044 ***0.009 0.004 **1.082 0.574 *
1.080 0.573 *0.005 0.001 ***-5.409 0.549 *** -5.341 0.549 *** -4.470 0.408 ***
No
Yes
Yes
No
Yes
No
0.62
0.62
0.99
9030.2 (0.00)
2060.8 (0.00)
44985.9 (0.00)

Notes: Obs.=30,713. ***, ** and * indicate significance levels of 1%, 5% and 10%, respectively.
Robust standard errors are used.

Next, we conduct a quantile regression rather than the usual fixed effects regression (seeKoenker (2005) for a review of this method). In the fixed effect model, coefficients are estimatedunder the condition of variable means. Hence, the model cannot verify a hypothesis that the effects
of urban planning instruments on population density in cities with high density are different fromthose in cities with low density. Quantile regression can clarify this hypothesis. Estimation results forthe four urban planning instruments using a quantile regression are presented in Figure 6. The
vertical and horizontal axes in each graph denote estimated coefficients and quantiles, respectively.
Ninety-five percent confidence intervals are added as dashed lines to each figure. To takecomputation feasibility into account, city government- and time-specific fixed effects are omitted
from the estimations.
We find greater effectiveness for the two CAC regulations in low density cities, with the
exception of the coefficient for the urbanization promoting area ratio for the quantile from 0.1 and
0.2. On the contrary, in areas with low population density, the effectiveness of urban planning andproperty taxes are higher and lower, respectively. In particular, in quantile 0.1 to 0.5, the property tax
is statistically insignificant.
There are two important implications. First, the two regulations are less effective inincreasing population density in cities that are already dense compared with cities that are less dense.
On the other hand, the two regulations are useful in increasing density in low density cities.
Second, the urban planning tax is effective in increasing population density in low density
cities. Property taxes, by contrast, are not found to be helpful in increasing density, as thecoefficients are insignificant. While the usefulness of urban planning taxes diminishes with higher
levels of city density, property taxes are valuable tools for increasing population density in highly
dense cities. Therefore, appropriate instruments must be chosen to obtain additional population
density.

Property Tax

-1

-1.5

-.8

-1

-.6

-.5

-.4

0

-.2

.5

Urban Planning Tax

.2

.6

.4

.8

0

1

.2

.6

.4

.8

1

q

q

Urbanization Control Area Ratio

Urbanization Promoting Area Ratio

1.8

0

2

.1

2.2

.2

2.4

.3

2.6

0

0

.2

.6

.4

.8

1

0

.2

q

.6

.4

.8

1

q

Figure 6. Policy Effects on Population Density: Estimated coefficients for impact of each instrument
by quantile of population density
Note: The vertical axis denotes the estimated coefficients, and the horizontal axis denotes quantilesof population density. The coefficients for the property tax rate for quantile 0.1 to 05 are
insignificant. The other coefficients plotted are significant at the 1 percent level. Standard errors arecalculated through bootstrapping, with 20 repetitions.

4.3. Effects of population density on CO2 emissions

Population density on the right-hand side of equation (1) is treated as an endogenous variable,
determined by the economic scale of the city and the urban planning instruments. We employ
two-stage estimation methods, whereby we first estimate equation (2) and then calculate thepredicted population density using the estimated coefficients. Next, using the predicted population
density, we estimate equation (1). As a robustness check against previous studies, we also estimate
specifications in which population density is treated as exogenous.

Table 3 shows the estimation results. In the first step, population density is estimated using
model 3 in Table 2. Here we apply prefecture-specific fixed effects instead of city government fixed
effects (1,750 city governments belong to 47 prefectures). The adjusted R2 in Table 3 is quite high,with a value above 0.9.
We evaluate the effects of population density on CO2 emissions. The coefficients for
population density in each model are negative and significant at the 1 percent level. Therefore,
increases in population density lead to decreased CO2 emissions from vehicles. If governments canraise population density by 1 percent, CO2 emissions would decrease by approximately 0.21 to 0.24
percent, even if we do not consider the endogeneity of population density (model 2). The population
density elasticity per vehicle mile traveled (VMT), reported by Cervero and Murakami (2010), isapproximately -0.38, which is not far from our estimates.

Table 3. Estimation Results: Regression of logged CO2 emission on Population Density

Variablesln(population density)ln(population density)×Year1999
ln(population density)×Year2003
ln(population density)×Year2007
ln(income)road length/habitable area
Constant
Endogenous Problem
Adj. R-squares
F-value (P-value)

Model 1
Coef.
S.E.
-0.242 0.011 ***0.996-0.342-6.0840.006 ***
0.210
0.103 ***
Yes
0.92
1471.36 (0.00)

Model 2-0.2400.996-0.342-6.081Model 3
Coef.
S.E.
0.010 *** -0.209 0.011 ***-0.015 0.011

0.006 ***
0.210
0.103 ***
No
0.92
1378.83 (0.00)-0.028-0.0890.997-0.317-6.1600.011 **
0.011 ***
0.005 ***
0.128 **
0.088 ***
Yes
0.92
1408.04 (0.00)

Notes: Obs.=6,642. ***, ** and * indicate significance levels at the 1%, 5% and 10% levels,
respectively. Prefectural and time fixed effects are included. Robust standard errors are used.

The effects of population density might change over time. We therefore add cross-terms
for population density and year dummies to form model 3. In the base year of 1990, the effect of
population density on CO2 emissions is -0.209. The additional effect had no significant influence in
1999. However, the reduction effect increased by -0.028 and -0.089 in 2003 and 2007, respectively.

Therefore, the effect strengthened over time, especially after 1999.
Income (the proxy for the economic scales of cities) positively affects CO2 emissions. It isthrough the scale effect that economic scale is associated with CO2 emissions. Road length per
habitable area is significant and negative only in model 3. Expansion of road length may thus reduce
traffic congestion, resulting in reduced CO2 emissions. Therefore, governments may be able to use
road construction as a policy to mitigate climate change.

4.4. Effects of population density on the other four outcomes

In this section, we discuss the effects of population density on the other four outcome variables:
traffic accidents, crime, cleaning expenditures, and the facility condition index. Similarly to section
4.3, we treat population density as an endogenous variable and employ a two-stage estimation
method (see Table 4 for results).
All coefficients are found to be significant at the 1 percent level. An increase in population
density is found to generate 1) an increase in traffic accidents, 2) an increase in crime, 3) a decrease
in cleaning expenditures and 4) a decrease in the facility condition index. This implies that, while
governments can reduce cleaning expenditures by increasing their population density, such anincrease also results in negative externalities such as increased traffic accidents and crime. Thus, wefind that an increase in population density is not necessarily advisable as urban planning policy.
Among the outcome variables, only the coefficient for the facility condition index has anadverse sign. Although city governments can save on cleaning expenditures by increasing population
density, the governmental facility condition index decreases. This might be related to our findings
regarding the effects of population density on traffic accidents and crime in that the latter outcomesmay instigate increased expenditures on public safety. As a result, reduced cleaning expenditures
may be offset by increased expenditures on public safety, causing the facility condition index to rise.
The expected signs are obtained for most of the other variables. Police stations as a proxyfor policemen is effective in controlling for traffic accidents and crime. Road expansion is also a
useful measure for these outcomes. However, traffic signal installation is positively associated withtraffic accidents. Thus, more accidents may occur as more traffic signals are installed; alternatively,
the finding may reflect insufficient control for economic scale.

Table 4. Estimation Results: Regression of the other outcomesln(traffic accident)
Variablesln(population density)ln(income)road length/habitable area
signal/habitable area
police station/habitable area
Constant
Adj. R-squares
F-value (P-value)
Obs.

Coef.
S.E.
0.195
0.051 ***
1.233
0.024 ***
-5.252
1.097 ***
8.526
2.679 ***
-341.256 52.892 ***-16.262
0.402 ***
0.56
338.86 (0.00)
15,568ln(expenditure forfacility condition
cleaning)
index
Coef.
S.E.
Coef. S.E.
Coef. S.E.
0.041 0.010 *** -0.081 0.006 *** -0.189 0.005 ***1.067 0.004 ***0.918 0.004 ***0.329 0.004 ***-1.166 0.126 *** -0.005 0.003 ***0.003 0.002 ***6.448 0.442-42.498 6.104-12.891 0.077 *** -3.091 0.062 *** -4.918 0.071 ***
0.94
0.88
0.53
4184.86 (0.00)
3055.08 (0.00)
491.01 (0.00)
15,49928,25928,244ln(crime)

Notes: ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.
Robust standard errors are used. Time and prefectural fixed effects are included.

4.5. Overall Effects of Population Density on Externalities

Externalities present significant problems for urban policy planners (Glaeser, 2012). We evaluate thefull effects of increased population density on externalities (to the extent possible) and find thaturban policy has both negative and positive effects. First, we calculate the marginal effects of
population density on CO2 emissions, traffic accidents, and crime.
Using the results of model 1 in Table 2 (for CO2 emissions) and Table 3 (for the other) onoutcomes), we calculate the marginal effects. The marginal effect of population density ([CO2 emissions ( ), calculated as the response of the average city, is ∂E E |X , POP] ∂Pop |X , POP(see Cameron and Trivedi (2005) for details on the method used). 6 The marginal effects on

6

The subscripts

andare omitted. For all outcome variables other than the facility condition index, the marginal

population density on the other outcome variables are determined by the same formula.
The estimated marginal effects are presented in Table 5. The marginal effects on cleaning
expenditures and the facility condition index are also indicated as a reference. The sample means foreach outcome are shown in the third row. CO2 emissions decrease by approximately 1,762 tons whenpopulation density increases by one person/hectare. The reduction is equivalent to 1.6 percent of
CO2 emissions on average. The reduction generated by increased population density is not negligible
because the mean of population density is 13.8 persons/hectare (see Table 1). If average city density
were to double, CO2 emissions would decrease by approximately 22 percent, assuming constantmarginal effects.
Traffic accidents and crime, by contrast, would worsen on average by 0.7 and 0.2 percent
when population density increases by one unit. Relative to the reduction in CO2 emissions, thechanges in traffic accidents and crime are small.

Table 5. Marginal Effects of Population Density

Outcomes
CO2 emissions from vehicles
Traffic accident
Crime
Expenditure for cleaning
Facility condition index

Marginal effects Sample mean Change rate from mean
-1761.69
110115
-1.6%
3.48
467
0.7%
1.82
1179
0.2%
-5494.41
1273080
-0.4%
-0.03
0.74
-4.2%

Notes: Units for CO2 emissions, traffic accidents, crime and cleaning expenditures are CO2-ton,case, case and 1,000 yen, respectively.

The units in which the marginal effects for the different outcome variables are calculated
effects are calculated as γˆ ⋅ POPγˆ −1⋅ Xβˆ because the dependent variables are transformed into log variables.

POP and X are the sample means for population density and other control variables used in the estimations,
respectively. γˆ and β̂ are estimated coefficients. Because the facility condition index is not in log form, themarginal effect is computed as γˆ ⋅

1
.
POP

are not uniform. For the overall evaluation of the effects of increasing population density, therefore,
we convert the units of the effects into monetary values using externality costs (shadow prices)estimated in previous studies. Upper and lower bound estimates for sensitivity analysis are also
provided in view of the uncertainty of these externalities (e.g., Boardman, et al., 2010).
For the externality cost of CO2 emissions, we use US$25 and US$345 per CO2-ton as theupper and lower bounds, following Tol (2009). Converting to yen evaluated in 2007 prices, 2.1 and28.8 thousand yen represent the lower and upper bounds, respectively.
The National Police Agency reports that the numbers of deaths and injuries caused bytraffic accidents are 0.9 and 99.1 percent, respectively, of total victims from 1990 to 2007. The
economic damage from one death is approximately 355 million yen (Tsuge et al., 2005), while theeconomic damage from one injury is 46 to 86 million yen (Cabinet Office Japan, 2009), evaluated in2007 yen. Using these percentages as weights, we use 49 to 88 million yen per case as bounds forthe external costs of traffic accidents.
McCollister et al. (2010) estimates the social costs of 13 types of crime in the UnitedStates. Among these, we use the externality cost of larceny/theft because larceny/theft constitutes
approximately 77 percent of total crime in Japan. 7 These authors show that the minimum andmaximum externality costs of larceny/theft, as assessed in the literature, are US$344 (Cohen, 1988)
and US $3,532 (McCollister et al., 2010), respectively, evaluated in 2008 dollars. After converting
into 2007 Japanese yen, we employ 35.8 and 368.1 thousand yen as lower and upper bounds of theexternality costs of crime.
Using these lower and upper bounds for externality costs, we present the valuations of themarginal effects of population density on each type of externality in Table 6. The lower and upperbounds of the estimated marginal effect of population density on CO2 emissions are 3.7 to 50.8
million yen, respectively. The lower and upper bounds of the marginal effect of population density
7This value is the average from 2003 to 2007 reported by the National Police Agency. Although there are also

atrocious crimes, such as murder and arson, these constitute, on average, only 0.5 percent of total crime during thisperiod.

on traffic accidents and crime combined are 160.7 to 291.4 million yen, respectively. Thus, an urban
policy of increasing population density is not helpful in mitigating externalities on the whole, both atthe lower and upper bounds, whereas it does contribute to reducing CO2 emissions. In implementing
urban policies designed to increase city density, therefore, city planners must carefully consider the
linkage between the negative effects of such policies on traffic accidents and crime and the positiveeffects on CO2 emissions reduction. We note that there are significant uncertainties in the values ofthese externalities. For example, Stern (2010) suggests that CO2 emissions represent the mostsignificant market failure and that public authorities should therefore prioritize it.

Table 6. Valuation of Marginal Effects
Outcomes
CO2 emissions from vehicles
Traffic accident
Crime
Total

lower

upper
-3.68
160.670.07
157.05-50.77290.710.67
240.61

Notes: all units are in millions of yen evaluated in 2007.

We note that there are limitations in our valuations. Our study does not consider otherimportant externalities, such as congestion, air pollution, disaster risk and agglomeration.
Agglomeration, for example, is a well-known positive externality associated with city growththrough economies of scale and network effects (Fujita and Thisse, 2013). On the other hand,congestion, air pollution and disaster risk are negative externalities. Congestion and air pollution are
typically positively correlated with city density via vehicle use. In addition, damage caused bynatural disasters may be greater in dense areas than in sparse areas (Kellenberg and Mobarak, 2009).
Therefore, further research is needed to assess these untreated externalities: the potentially positiveeffects of agglomeration and the potentially negative effects of the other three externalities cited.

5. Conclusion

Urbanization is not by itself a solution to the problem of designing an optimal living space.
Successful cities are highly sustainable, while unsuccessful cities are plagued by economic,
environmental, and social problems. This paper examines what is needed to implement suitableurbanization policies with respect to environmental and social problems.
We examine the relationship between urbanization and city environmental and social
problems using data for all Japanese cities from 1990 to 2007. Employing population density as ameasure of urbanization, we select CO2 emissions, traffic accidents, crime, and two measures of city
governmental expenditures (cleaning expenditures and the facility condition index) as relevant
externalities.
We further enhance our understanding of urbanization by examining how city governments
can increase population density. Specifically, the paper draws connections between four urban policy
instruments—urbanization promoting areas, urbanization control areas, urban planning taxes andproperty taxes. This is first paper to integrate policies designed to affect population density with theeffects of population density on environmental and social problems.
Ourresults

show

that

governments

canincrease

residential

densitybychanging/implementing the policy instruments identified. Setting and expanding both types of land
use regulation, i.e., urbanization control areas and urbanization promoting areas, increase
population density, while decreasing both types of taxes considered similarly generates increased
density.
The effects of these instruments vary across cities. Specifically, their effects in low density
cities differ from their effects in high density cities. We find that a decrease in the urban planning tax
rate is more effective in increasing density in sparse cities than in dense cities. By contrast, adecrease in the property tax rate is found to be more effective in high density cities. City
governments can also increase density through the command-and-control mechanism of anurbanization promoting area, whereas city governments with high population density are advised

not to utilize this instrument.
City governments in Japan are free to set both of the tax rates considered in this paper,
although the Japanese (central) government officially places some limits on city actions, such that itis desirable to set the property tax rate at 1.4% and the urban planning tax rate at less than 0.3%. Inview of such social issues as CO2 emissions and the other outcomes examined in this paper, theJapanese government would be well advised to be more flexible with respect to these limitations.
Next, we examine the influence of population density on CO2 emissions and the four otheroutcome variables. Population density is found to have a negative impact on CO2 emissions. Urban
planning policy designed to increase population density can thus play a role in climate change
mitigation. Higher density also leads to savings on public cleaning expenditures. On the other hand,such policies lead to increased traffic accidents, increased crime and a decrease in the facility
condition index. Therefore, the use of urban planning to raise population density simultaneously
generates both positive and negative outcomes for society.
Increases in population density give rise to multiple outcomes. To assess the effects ofurban policies designed to increase population density on the external outcomes considered (CO2emissions, traffic accidents and crime) as a whole, we calculate the marginal effects of raisingdensity on these outcomes in monetary terms. We find that increases in population density appear toworsen social outcomes on the whole because the negative effects on traffic accidents and crime arelarge. Therefore, when governments seek to increase population density, it is necessary to introduce
complementary policies to address traffic accidents and crime.
There are limitations to our analysis. First, we do not evaluate whether the policy improvessocial welfare. Second, our model assumes only inter-city population transfer, while inner-city
population mobility is not considered. If overall city density in one city is equivalent to that in
another city, then both cities are treated the same, even if the density in the center of one city is
substantially greater than that of the other city. This implies that our model assumes only inter-city
population transfer. Further research should also consider inner-city population mobility.

Acknowledgements
This paper was supported by Japan Society for the Promotion of Science (Grant Number 23730223).

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0.1

Density.2.3.4

Figure A1: Distribution of City Level Population Density from 1990 to 2007 in Japan-2

Notes: Obs.=30,923

02ln(population density)

4

6

The Society of Regional Policy, Takasaki City University of Economics
1300, Kaminamie-machi, Takasaki-city, Gunma 370-0801 Japan+81-27-344-6244
c-gakkai@tcue.ac.jp
http://www1.tcue.ac.jp/home1/c-gakkai/dp/dp14-01