The Impact of Municipal Mergers on Pollution Control:
Evidence of River Pollution in Japan
Kazuki Motohashi

Michiyoshi Toya

Hitotsubashi University

METI∗

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This version: January 5, 2024

Abstract
Municipal mergers, aimed at improving local public services, can not only internalize
pollution spillovers affecting neighboring areas, but also weaken pollution control due
to coordination costs and political power imbalances. We use the staggered implementation of Japan’s mergers, which reduced municipalities by half, to explore their impact
on environmental quality. We find that municipal mergers increase river pollution by
5.5%, persisting for 14 years. These effects are concentrated in equal-footing mergers
with high coordination costs and in incorporated municipalities with little politicalpower. We find no evidence supporting alternative mechanisms, including changes inpollution spillover patterns and land use.
JEL: D73, Q52, Q53, R11, R58
Keywords: Municipal Mergers, Water Pollution, Political Economy, Negative Externality∗

Motohashi:
Hitotsubashi Institute for Advanced Study, Hitotsubashi University (e-mail:kazuki.motohashi@r.hit-u.ac.jp). Toya: Ministry of Economy, Trade and Industry, Japan (e-mail: toyamichiyoshi@meti.go.jp). For helpful comments, we thank Shunichiro Bessho, Ujjayant Chakravorty, Molly Elder, Marc Jeuland, Cynthia Kinnan, Etienne Makdissi, Shinsuke Tanaka, Takanao Tanaka, Kensuke Teshima,
Duc Anh Tran, and Akio Yamazaki, as well as seminar participants at J-TREE/Aoyama Gakuin University,
Tufts University, Hitotsubashi University, Musashi University, and conference participants at 2020 Berkeley/Sloan Summer School in Environmental and Energy Economics, 2020 SEEPS Annual Conference, 2022
AAEA Annual Meeting, Fifth Annual Conference of the Japan Economy Network, LSE Environment Day,
2023 JEA Autumn Meeting. We also thank many local government officers for helpful discussions on theconsequences of municipal mergers in Japan. The views expressed in this paper are those of the authors andshould not be attributed to the Ministry of Economy, Trade and Industry, Japan.

11Introduction

The optimal level of decentralization, i.e., power distribution between local and central
governments, has long been debated in academics (Tiebout, 1956; Oates, 1972; Besley and
Coate, 2003). Under decentralized governance, competition among municipalities is expected
to ensure Pareto-efficient allocation of local public services. However, this allocation canbecome inefficient when local public services have spatial spillover effects on neighboringmunicipalities without inter-jurisdictional coordination (Tiebout, 1956; Oates, 1972; Jannin
and Sotura, 2020). One key example is the negative externality of weak pollution control that
affects neighboring municipalities’ environmental quality. In accordance with this argument,
an increase in local jurisdictions under decentralized governance has recently been shownto negatively affect environmental quality in developing countries (Burgess et al., 2012;
Lipscomb and Mobarak, 2016). However, there is little research on the effect of the opposite
scenario—a decrease in local jurisdictions, which is more prevalent in developed countries—
on environmental quality. Do fewer, larger municipalities facilitate the internalization ofnegative externalities, thus improving environmental quality?
We examine how municipal mergers, in which two or more municipalities combine toform a new municipality, affect environmental quality by changing pollution control efforts.
Municipal mergers have been widely adopted in approximately 20 developed countries (including Germany, France, Japan, and the United States) and are expected to increase in
the future due to declining birth rates and aging populations, especially in rural areas. The
aims of municipal mergers are to provide local public services with higher quality at a lower
cost based on economies of scale — a larger municipality can provide local public services atlower unit costs (OECD, 2014). Consistent with this aim, a negative externality theory suggests that municipal mergers can improve environmental quality by internalizing pollution
spillovers that exist across pre-merger municipalities. Conversely, coordination costs andunbalanced political power among pre-merger municipalities may hamper the pollution control efforts of merged municipalities, leading to the deterioration of environmental quality.
The relationship between municipal mergers and environmental quality seems ambiguous
and deserves careful empirical examination.
We test the relationship between municipal mergers and river water quality in the context
of Japan’s municipal mergers from the late 1990s to the 2000s during the “Heisei” era. The“Great Heisei Mergers” in Japan have unique characteristics that allow us to investigate
our research question effectively. First, municipal mergers are implemented over time. We
exploit this staggered implementation of municipal mergers as the setting of a quasi-naturalexperiment to examine the causal effects of mergers on river pollution. Second, these mergers

2drastically reduced the number of municipalities by approximately 50%, from 3,238 in 1998 to
1,725 in 2012 across Japan. The setting of these large-scale and nationwide municipal mergers
increases the external validity of our results. Third, the primary objective of these municipal
mergers was to strengthen the administrative and financial foundation of municipalities,
rather than being driven by specific policy agendas like pollution control. This context
allows us to examine the unintended consequences of mergers on water pollution with lessconcern about the endogenous timings of municipal mergers. We use 30 years of water
quality panel data from 3,000 monitoring stations to examine the impact on water quality.
Exploiting the staggered implementation of municipal mergers, we investigate the causal
effects of municipal mergers on river water quality. We adopt difference-in-differences (DID)
and event study designs, comparing the outcomes of municipalities that merged and nevermerged municipalities, as well as comparing the outcomes of municipalities that merged earlier and municipalities that merged later. In this staggered-adoption DID design, a two-way
fixed effects estimator may be subject to bias from the bad comparison between municipalities that merged early and late (Goodman-Bacon, 2021). Thus, we adopt recently developed
alternative estimators (Callaway and Sant’Anna, 2021; Sun and Abraham, 2021) that arerobust to this concern as a baseline specification.
Contrary to the negative externality theory narrative, we find that municipal mergers
increase river pollution by 5.5%. This negative effect persists for 14 years. The causality ofthis effect hinges on the assumption that treated and untreated municipalities would movein parallel in the absence of mergers. We find that the water quality levels of merged andnever-merged municipalities appear to move parallel in pre-merger periods. The event study
plots also do not show differential effects on water quality before the mergers. The baseline
results remain robust across alternative specifications that limit our sample to comparable
municipalities. This includes a specification that uses only the variation in merger timings among merged municipalities, and another using a subset of municipalities matched byindustry compositions and financial conditions.
Heterogeneity analyses suggest that the mechanisms driving the negative effect of municipal mergers are coordination costs and unbalanced political power among pre-merger
municipalities. We first test whether municipal mergers with higher coordination costs leadto larger river pollution because insufficient coordination of local public services after mergers
can weaken pollution control efforts (coordination costs mechanism). We find that pollution increases for “equal-footing” mergers that entail higher coordination costs, but not for“incorporating” mergers with lower coordination costs.1 Second, in the case of incorporat1“Equal-footing” mergers involve creating new cities between municipalities of similar size and electing
a new mayor. Conversely, “incorporating” mergers involves a larger municipality incorporating smaller3

ing mergers, we examine whether the smaller, incorporated municipalities with less politicalpower experience a greater increase in river pollution than the larger, incorporating municipalities with greater political power. The mayor of the larger, incorporating municipality
retains her position post-merger and may prioritize pollution control in her original area,
weakening pollution control in the areas of smaller, incorporated municipalities (political
economy mechanism). We find that incorporated municipalities experience increased riverpollution, while incorporating municipalities do not experience a change in river pollution.
As for the source of river pollution, we find suggestive evidence that a slowdown in sewer
investments increases untreated domestic wastewater from households. Municipalities areprimarily responsible for controlling sewage by constructing and operating sewage treatment
plants. By conducting the same difference-in-differences analysis on the municipality-level
panel of sewer coverage, we find that municipal mergers decelerate sewer investments for up to
five years following the mergers. This negative effect on sewer coverage is primarily observed
in “equal-footing” mergers, which is consistent with our baseline results. These findings
suggest that a greater volume of domestic wastewater remains untreated in municipalities
that have undergone mergers, consequently leading to heightened river pollution.
Consistent with the baseline results, we do not find spatial patterns supporting the negative externality theory, which would predict the internalization of pollution spillovers. The
negative externality theory predicts that pollution increases as a river approaches the downstream border of a municipality because there is less harm caused to people within thatmunicipality by polluting farther downstream (Lipscomb and Mobarak, 2016). We test thistheory by examining how changes in the distance from water quality monitoring stations totheir closest municipality borders affect the water quality at these stations. We find thatpollution levels do not change as a river flows downstream within a municipality, a patterninconsistent with the negative externality theory.
We do not find evidence supporting another alternative mechanism, namely changes inland use, which can lead to increased river pollution by generating new pollution sources
without altering pollution control efforts. Municipal mergers can cause land use changes, suchas expanding industrial and residential areas, by increasing economic activities. This shift
in land use could result in an increase in sources of water pollution, namely industrial anddomestic wastewater, thereby escalating river pollution. However, we do not find effects on
various land use classifications near water quality monitoring stations, including agricultureareas, built-up areas, and forests.
Our findings suggest that municipal mergers can have unintended negative effects onenvironmental quality due to coordination costs and political power imbalances. With themunicipalities, with the mayor of the larger, incorporating municipality retaining her position.

4

expectation of more mergers due to population decline in many countries, the potential forsuch negative effects should be carefully considered. Moreover, weakened policy implementation, resulting from coordination costs and political economy mechanisms, could extend
to other policies, highlighting the importance of careful consideration of these mechanisms
in future mergers.
Our paper makes three contributions. First, we contribute to the literature on decentralization and fiscal federalism by showing that consolidations of jurisdictions may not resolve
pollution spillovers across local jurisdictions, and coordination costs and balance of politicalpowers matter. Previous studies show that splits of local jurisdictions exacerbate pollution
spillovers along rivers in Brazil (Lipscomb and Mobarak, 2016), and conversely, mergers internalize spillovers of air pollution in China (Wang and Wang, 2021), which are consistent
with the negative externality theory. However, we find no evidence of the negative externality theory, and instead, mergers increase river pollution in Japan. We show alternative
underlying mechanisms, i.e., weaker pollution control due to coordination costs and politicalpower imbalances, which should be newly considered in the discussion of decentralization.
Second, we contribute to the literature on the impacts of municipal mergers by showing longer-run negative environmental impacts. Most previous studies investigate the fiscal
and macroeconomic impacts of municipal mergers on local public finance (Hinnerich, 2009;
Reingewertz, 2012; Moisio and Uusitalo, 2013; Blesse and Baskaran, 2016; Hirota and Yunoue, 2017; Miyazaki, 2018; Pickering et al., 2020), economic activity (Egger et al., 2018),
and infrastructure expenditure (Li and Takeuchi, 2023), as well as determinants of municipal
mergers (Weese, 2015). Instead, we focus on the environmental impacts of municipal mergers, where only a little evidence exists. A particularly relevant paper is Wang and Wang
(2021), which showed that township mergers in China internalized the negative externalitiesof air pollution by reducing firms’ emissions. We complement this paper in two ways: (i)we show that municipal mergers can instead increase pollution and that negative externality
theory may not hold in a developed country context; (ii) our 30-year panel data on water
quality enables us to examine the longer-run impacts of municipal mergers, revealing that
their negative impacts persist for up to 14 years.2
Third, we contribute to the literature on water pollution by providing the first causal
estimates of the effects of municipal mergers on water quality. Past studies show that waterpollution levels are affected by political boundaries (Sigman, 2002; Helland and Whitford,
2

Another relevant paper is Mizunoya et al. (2021), which examined the impacts of municipal mergers
on watershed management of the Lake Kasumigaura Basin in Japan using the simulation of a dynamic
expanded input-output model. This paper is a case study of a specific water body and relies on a simulation
based on the model. Instead, we examine the causal effects of municipal mergers across Japan adopting aquasi-experimental design.

52003; Sigman, 2005; Kahn et al., 2015; Lipscomb and Mobarak, 2016), political incentives(Kahn et al., 2015; He et al., 2020), and sanitation investments (Motohashi, 2023). Anotherset of studies has examined the effectiveness of interventions in reducing pollution, including
water pollution regulations (Greenstone and Hanna, 2014; Keiser and Shapiro, 2019) andcourt rulings (Do et al., 2018). This paper shows that a decrease in political boundaries, i.e.,municipal mergers, can also cause water pollution due to weaker control efforts.
The rest of the paper is organized as follows. Section 2 provides background information
on municipal mergers and water quality in Japan. In Sections 3 and 4, we explain the dataand empirical strategy, respectively. Section 5 discusses the results. In Section 6, we analyze
the underlying mechanisms behind our results. Section 7 concludes.

2

Background

2.1

The “Great Heisei Mergers” in Japan

The “Great Heisei Mergers” took place in Japan predominantly in the late 1990s to 2000s
during the “Heisei” era. These were large-scale municipal mergers that occurred in all
prefectures across Japan (Figure 1). Consequently, the number of municipalities in Japandrastically decreased by approximately 50% from 3,238 in 1998 to 1,725 in 2012.
The primary objective of the “Great Heisei Mergers” was to strengthen the administrativeand financial foundation of municipalities and foster more efficient municipal administration.
Municipalities, especially those in rural areas, were grappling with challenges such as dwindling birth rates, aging and declining populations, and fiscal difficulties following the burstof the bubble economy in the early 1990s. Municipal mergers were envisaged as a means
to rejuvenate these struggling municipalities. Since these mergers were not implemented toaddress specific policy agendas like water pollution control, our study reveals an unintended
effect of municipal mergers on pollution.
The financial incentives under the revisions of the “Act on Special Provisions of the
Merger of Municipalities” in 1995 and 1999 played a key role in fostering municipal mergers.3
The 1995 revision announced that the purpose of the Act was to push municipal mergers
forward and introduced a five-year preferential treatment in local tax allocation for mergedmunicipalities. The 1999 revision further strengthened these financial incentives by extending
the period of preferential treatment in local tax allocation to 10 years. Furthermore, mergedmunicipalities were allowed to issue “Special Provision Bonds” for up to 10 years after themergers to fund public projects included in their merger proposals. These municipalities
3

The Act had been in place since 1965, originally focusing on facilitating merger procedures.

6

were required to pay back only 30 percent of the amount borrowed for these bonds, andthe central government paid the remaining 70 percent. Thus, the bonds effectively acted
as a 70 percent public project subsidy for merged municipalities. The government initiallyannounced that, to be eligible for these benefits, municipalities had to complete merges byMarch 2005. However, this was extended by one year to March 2006. providing additionaltime for municipalities struggling to complete mergers by the original deadline.
Thanks to the strong push from these financial incentives, the number of municipal
mergers in Japan increased over time starting in the late 1990s. As illustrated in Figure
2, the “Great Heisei Mergers” were implemented in a staggered manner. The first merger
occurred in 1999, coinciding with the strengthening of financial incentives. However, thenumber of mergers remained low for the next few years. The vast majority of mergers
took place between 2004 and 2006. This not only reflects the time required to complete
the merger process but also indicates a bunching behavior by municipalities to meet theinitial and final deadlines of 2005 and 2006, respectively. Municipal mergers then continued
until 2011. Moreover, the “Great Heisei Mergers” were not a forced policy by the central
government, leading to a variation between municipalities that underwent municipal mergers
and those that did not.4 We exploit the staggered implementation of municipal mergers inour difference-in-differences and event study analyses.
Municipal mergers are officially categorized into two types: “equal-footing” mergers and“incorporating” mergers. The former type involves mergers on an equal-footing, typicallybetween municipalities of similar size, with a new name assigned to the merged municipality. Following the completion of the merger, a mayor for the new municipality is newly
elected in the subsequent election. In our sample, 74% of the total mergers are classified as“equal-footing” mergers. Conversely, “incorporating” mergers involve a larger municipality
incorporating smaller ones, with the resulting municipality retaining the name of the larger,
incorporating municipality. The mayor of the incorporated municipality continues as themayor of the newly created municipality, while mayors of the incorporated municipalities
lose their positions post-merger. These “incorporating” mergers account for 26% of the total
mergers in our sample.
4

The autonomy of municipalities was respected. Municipalities had a choice to negotiate mergers andselect their negotiation partners. The merger process involved several steps. Firstly, interested municipalities
were to form a panel to discuss potential mergers. Secondly, the panel was to negotiate and formulate merger
proposals. Finally, a merger was formally announced and implemented following final voting by the involved
municipalities and administrative approval by the prefectural governor and the Minister of Internal Affairs
and Communications.

7

2.2

Water Quality and Pollution Control in Japan

Ambient water quality in Japan is monitored under the Environmental Quality Standards for
Water Pollution. These standards are non-mandatory policy targets.5 Among the multiple
water quality indicators monitored, we use Biochemical Oxygen Demand (BOD) as themain outcome, although we also adopt other indicators, including dissolved oxygen andsuspended solids, as robustness checks. BOD measures the amount of dissolved oxygen
needed by aerobic biological organisms to break down organic material present, capturing
the overall level of water contamination from various sources such as domestic, agricultural,and industrial wastewater. A higher level of BOD values indicates a higher level of waterpollution. The Environmental Quality Standards set the BOD limit values in rivers from 1
up to 10 mg/L, depending on the designated usage of water in a given location.
Under these environmental quality standards, water quality in Japan has generally improved over time. The average BOD in rivers declined from 3.6 mg/L to 1.4 mg/L from 1990
to 2018 in our sample. Our analysis examines whether municipal mergers alter the existing
positive trend of river water quality in merged municipalities.
The main sources of water pollution can be categorized into three types: (i) domestic wastewater (sewage), (ii) industrial wastewater, and (iii) agricultural wastewater. Ourconversations with local government officers in Japan indicate that municipalities primarily
control pollution from domestic wastewater. Domestic wastewater is typically treated in
sewage treatment plants, which are constructed and operated by the municipalities themselves. Conversely, municipalities have limited roles in controlling industrial and agricultural
wastewater. In the case of industrial wastewater, effluent standards for factories are enforcedat the prefectural level through reporting and inspections. While designated municipalities
under the Water Pollution Prevention Act are entitled to enforce these standards on behalfof prefectures, as of 2018, only 111 municipalities have this designation.6 Therefore, mostmunicipalities do not play a significant role in controlling industrial wastewater. Furthermore, agricultural wastewater is a non-point source, diffused over large areas due to factors
such as precipitation. This diffusion makes it more challenging for municipalities to control
agricultural wastewater through their policies.
5
The Environmental Quality Standards for Water Pollution differ from effluent standards, which aremandatory requirements for the water quality of effluents from factories and sewage treatment plants. The
non-mandatory nature of ambient water quality standards allows us to observe changes in ambient water
quality due to municipal mergers.
6
The designated municipalities tend to be large municipalities that have not undergone municipal
mergers.

8

2.3

How Do Municipal Mergers Affect Water Quality?

Municipal mergers, and more broadly, changes in the number of local jurisdictions, can affect water quality through three main mechanisms. First, past studies emphasize the roleof pollution spillovers across jurisdictions, which would predict an improvement in environmental quality post-merger (Lipscomb and Mobarak, 2016; Wang and Wang, 2021). Second,in contrast, coordination costs and unbalanced political power among pre-merger municipalities may hamper the pollution control efforts of merged municipalities, leading to thedeterioration of environmental quality. Third, changes in land use could lead to increased
water pollution by generating new pollution sources.
One mechanism often emphasized in the past literature is the role of pollution spillovers or
negative externalities across jurisdictions, which would predict a positive effect of municipal
mergers on water quality. Lipscomb and Mobarak (2016) developed a framework of negative
externality theory, where pollution within a municipality located higher upstream on a river
adversely affects other municipalities located downstream. Based on this framework, they
showed that an increase in the number of districts along a river path, due to district splits,
worsens water pollution. Based on their model, we may expect that municipal mergers,which conversely decrease the number of municipalities along a river path, would improvewater quality in rivers by internalizing pollution spillovers.7
One of the key predictions of the negative externality theory in Lipscomb and Mobarak(2016) is that pollution increases exponentially as the river flows downstream within a municipality. Following Lipscomb and Mobarak (2016), consider a municipality that spans anarea from 0 to 1 on the horizontal axis and is located along a river with a population uniformly distributed in this area (Appendix Figure A1). The river flows from 0 to 1; thus, 0
and 1 are the upstream and downstream municipality borders, respectively. A local governorchooses how much economic activity to pursue and, consequently, how many pollutants toemit at each point within her municipality. Because the local governor aims to minimize thenegative effect of emissions on her population and does not consider the impact on people
living in other municipalities, including those downstream, she would choose to focus mostof the economic activity and emissions near the downstream border, at point 1. Most of hermunicipality’s population, living upstream of this point, would not be adversely affected byemissions occurring near the downstream border. We examine the presence of such spatial
patterns to test the validity of the negative externality mechanism in Section 6.4.8
7Wang and Wang (2021) shows results consistent with this prediction in the case of air pollution,
demonstrating that township mergers reduce firm-level emissions in China.
8
Another associated prediction is that there is a structural break in the slope of the pollution function
at the municipality border. This prediction means that emissions are high just upstream of a municipality
border but are low just downstream of a municipality border.

9

Contrary to the negative externality mechanism, two additional mechanisms, namely
the coordination cost and political economy mechanisms, can increase water pollution by
weakening municipalities’ pollution control efforts.
First, municipal mergers with high coordination costs can weaken pollution control efforts, resulting in increased water pollution (coordination costs mechanism). This mechanism
is suggested by the post-merger survey indicating a lack of organization and solidarity among
municipality officials, including difficulties and delays in policy coordination, as negative consequences of the “Great Heisei Mergers” (NATV, 2008). According to a JMRC (2008) survey,
which surveyed 416 municipalities, 44% pointed out that the continuation and coordinationof projects among pre-merger municipalities remained an issue. If coordination costs among
pre-merger municipalities are high, a newly created municipality will face difficulties in reformulating public services, which were previously separated in each pre-merger municipality,
into coherent new public services. Therefore, the quality of pollution control in merged municipalities with higher coordination costs may deteriorate. The levels of these coordination
costs can differ by the types of municipal mergers. “Equal-footing” mergers, which involve
creating new cities typically between municipalities of similar size and electing a new mayor,
are expected to have high coordination costs. Conversely, “incorporating” mergers, where alarge municipality incorporates other small municipalities and the mayor of the larger, incorporating municipality retains her position, are expected to have limited coordination costs.
Therefore, the coordination costs mechanism suggests that water pollution increases more
substantially in the case of “equal-footing” mergers with higher coordination costs than in“incorporating” mergers.
Second, municipal mergers can have differential impacts on water pollution depending on
the political power of the involved municipalities, especially in the case of incorporating mergers (political economy mechanism). In such mergers, the mayor of the larger, incorporating
municipality retains her position, while the mayors of smaller, incorporated municipalities
lose their positions. Post-merger, the mayor of the incorporating municipality may prioritize
pollution control in her original area because she has an electoral base there. Similarly, thecouncil members of the new municipality are likely to consist mainly of members from theincorporating municipality. Indeed, the post-merger survey notes that the voices of people
living in incorporated municipalities were not adequately reflected after mergers (NATV,
2008). According to a JMRC (2008) survey, 54% of the 416 municipalities surveyed pointed
out that the widening disparity between central and peripheral areas is a concern resulting
from municipal mergers. Consequently, pollution control efforts in the areas of incorporated
municipalities may be weakened post-merger. Thus, the political economy channel suggeststhat incorporated municipalities can experience larger water pollution than incorporating10

municipalities due to weaker pollution control efforts.
Another mechanism could be changes in land use, which might lead to increased waterpollution by generating new pollution sources without altering pollution control efforts. Municipal mergers can increase economic activities, as shown by Egger et al. (2018), potentiallyleading to changes in land use. Such changes might involve converting forest and agriculturalareas near rivers into industrial and residential areas. Consequently, this shift in land use
could result in an increase in sources of water pollution, namely industrial and domestic
wastewater, thereby escalating river pollution.

3

Data

We combine administrative datasets on ambient water quality and municipal mergers to
examine the effects of municipal mergers on water quality.
3.1

Water Quality

The main outcome variable is water quality. We use data from water quality monitoring stations provided by the Ministry of Environment, Government of Japan.9 These data include
yearly average water quality indicators, measured at monitoring stations across Japan. As
explained in Section 2.2, we use the yearly average BOD as a representative water quality
outcome in the analysis. BOD is a standard water quality measure monitored under theEnvironmental Quality Standards for Water Pollution in Japan.
Our analysis uses balanced panel data from 3,309 water quality monitoring stations along
rivers in Japan from 1990 to 2018 (Figure 3). We drop stations whose water quality data are
only partially available during this period to address the concern of endogenous constructionof monitoring stations. Exploiting the long panel of thousands of stations over 30 years,
we are able to assess the dynamic effects of municipal mergers on water quality over a long
period.
We complement this dataset with geospatial data of water basins in Japan, provided by
the Ministry of Land, Infrastructure, Transport, and Tourism, to identify the basin where
each monitoring station is located.
9

These water quality data are originally collected by each prefecture in Japan under Article 15 of theWater Pollution Prevention Act and reported to the Minister of the Environment. Then, the Ministry ofEnvironment compiles and publicizes these reported data.

11

3.2

Municipal Merger

The key treatment variable in our analysis is an indicator of whether a municipal merger
occurred each year in the municipality where each monitoring station is located. We obtaindata on the timing of municipal mergers and on the involved municipalities from the Ministryof Internal Affairs and Communications.
This dataset also includes information on the types of municipal mergers (“equal-footing”
mergers versus “incorporating” mergers) and whether the municipality is incorporating municipality or incorporated municipality in the case of “incorporating” mergers. We use thisinformation to examine the heterogeneous effects of municipal mergers in the analysis of
coordination costs and political economy mechanisms.
3.3

Other Municipality Characteristics

We supplement the above information with further data to account for municipality characteristics that might affect both water quality and the likelihood of municipal mergers.
Specifically, we use an economic indicator and population.
As an economic indicator, we use “Product shipment values” in the manufacturing sector
from 1990 to 2018 from the “Census of Manufacture” provided by the Ministry of Economy,
Trade, and Industry.
We also use population data from the Census. Because the Census is conducted every five
years, we compute the yearly population from 1990 to 2015 based on the linear interpolationof the reported population in 1990, 1995, 2000, 2005, 2010, and 2015.
For a balance check of municipality characteristics in the pre-merger period, we use (i)agricultural output values from the “Statistics of Agricultural Income Produced” provided
by the Ministry of Agriculture, Forestry and Fisheries and (ii) financial capability index fromthe “Annual Statistics on Local Public Finance” provided by the Ministry of Internal Affairs
and Communications.10
3.4

Data Matching and Sample Construction

We match water quality and municipal merger datasets by using the post-merger municipality
boundary data in 2020 provided by the Ministry of Land, Infrastructure, Transport, andTourism. We first use this boundary data alongside the GPS coordinates of monitoring
stations to identify the names of the post-merger prefectures and cities where these stations
10

The financial capability index is computed by dividing basic financial revenues by basic financial needs
and averaging these values over the past three years. A higher financial capability index means better
financial conditions for a local government.

12are located. Subsequently, we match water quality and municipal merger data based on thenames of these prefectures and cities. Similarly, all other data, including information onwater basins and various municipality characteristics, are merged based on the post-merger
names of prefectures and cities.
After data matching, we construct a balanced panel of approximately 3,000 water quality
monitoring stations in approximately 1,000 municipalities from 1990 to 2018. The baseline
specification uses panel data starting in 1996, five years before the first municipal merger
was observed in our dataset in 2001. However, we also conduct robustness checks by using
alternative sample periods, (i) 1990-2018 and (ii) 1999-2018, as discussed in Section 5.2.
3.5

Summary Statistics and Water Quality Trends

Summary statistics of all variables in the pre-merger period (before 2001 in our sample) are
shown in Table 1. In this period, merged municipalities (treatment group) differed fromnever-merged municipalities (control group) in BOD levels, product shipment values, agricultural output values, financial capability index, sewer coverage, and land use. Specifically,
these differences show that merged municipalities were more focused on the agricultural sector than the manufacturing sector, and had weaker financial conditions and better water
quality before the mergers.
Although the baseline level differences are observed, we rely on the assumption of parallel
trends in our difference-in-differences design to derive causal estimates. A comparison of thetrends of BOD values between the merged and never-merged municipalities encouragingly
shows signs of parallel pre-trends (Figure 4).11 These parallel pre-trends are formally tested
in the event study design in Sections 4.1 and 5.1.
Moreover, as shown in Figure 4, BOD levels decreased in both the merged and nevermerged municipalities in the post-merger period. However, the observed smaller decrease in
BOD levels in the merged municipalities suggests a negative impact of municipal mergers
on water pollution. This differential decrease in BOD levels is examined in our differencein-differences analysis in subsequent sections.12
11
The same pattern is also observed when plotting the trends of logarithms of BOD values, which areused as outcomes in the analysis (Appendix Figure A2).
12
The water quality trends appear to be converging towards a value of 1. This convergence might
introduce contamination in the effects, especially in later periods. Nonetheless, in the short term, whereconvergence poses less of an issue, we find significant effects, as demonstrated by the event study results in
Section 5.

13

4

Empirical Strategy

We examine the causal impact of municipal mergers on water quality by adopting a differencein-differences design that exploits variations in merger timings. Simple ordinary least squares(OLS) estimates are subject to bias due to potential endogeneity that comes from reverse
causality and omitted variables. Municipal mergers could be implemented to reduce water pollution in these municipalities.13 Additionally, spurious correlations may arise fromomitted unobservables, such as different priorities on water pollution control across municipalities. To address this potential endogeneity, we adopt a difference-in-differences design,
as explained in the next section.
4.1

Difference-in-Differences Design

We adopt the following difference-in-differences regression with two-way fixed effects:
Log(BODi,t ) = α + βDID M ergeri,t + λXi,t + δi + θb,t + εi,t(1)where the dependent variable, Log(BODi,t ), is a logarithm of BOD at monitoring station iin year t. M ergeri,t is an indicator variable that switches on and stays on for all subsequent
years when a merger takes place in the municipality where station i is located. Xi,t is a vector of control variables, including a municipality-level economic indicator (product shipment
values) and population, that can affect both water quality and the likelihood of municipal
mergers. Given the “bad control” concerns of these variables, which may be affected by municipal mergers, we control for their baseline values by interacting them with year dummies
as a robustness check.14 We include monitoring station fixed effects (δi ), which control fortime-invariant characteristics of each monitoring station, including positions (downstreamness) along rivers. To account for any secular trends in water quality across years, which
may vary across river basins, we include basin-by-year fixed effects (θb,t ). Lastly, standard
errors are clustered at the municipality level because the variation in municipal mergers isobserved at the municipality level.
The coefficient of interest is βDID . βDID could be negative if municipal mergers decrease
river pollution by internalizing pollution spillovers, as the negative externality theory would
predict. Conversely, βDID could be positive if municipal mergers increase river pollution due
to coordination costs and unbalanced political power among pre-merger municipalities.
To examine pre-trends and the dynamic evolution of the treatment effects, we also adopt
13

However, this is highly implausible, as municipal mergers are not implemented to address specific policy
agendas, including water pollution control, as discussed in Section 2.1.
14
Specifically, we use the average of these values in the pre-merger periods (from 1996 to 2000).

14

the event study design. The difference-in-differences analysis hinges on the parallel trends
assumption between treated and untreated municipalities. We empirically test the parallel
trends assumption in the following event study regression. This regression also allows us to
examine the long-run dynamic impacts of municipal mergers.
Log(BODi,t ) = α +17Xβτ M ergerτ,i + λXi,t + δi + θb,t + εi,t(2)

τ =−14

where M ergerτ,i is an indicator for each year τ relative to the timings of municipal mergers,
where τ is zero in the year that the municipal merger was implemented. τ ranges from -14 to
17 in our baseline sample. For monitoring stations located in never-merged municipalities,
all M ergerτ,i ’s are set equal to zero. τ = −1 is set as a reference year.
The coefficients of interest are βτ ’s. βτ ’s from τ = −14 to τ = −2 are examined to test
the parallel pre-trends. From τ = 0 to τ = 17, βτ ’s capture the dynamic evolution of thetreatment effects in the short and long runs for up to 17 years.
In our analysis, we exploit the staggered implementation of municipal mergers. Thus,
the estimates of βDID and βτ ’s in the regressions 1 and 2 are weighted averages of all possible two-group/two-period difference-in-differences estimates (Goodman-Bacon, 2021). In
other words, the estimate reflects all possible cases with different definitions of treatment
and control groups. One case could be comparing monitoring stations in municipalities thatexperienced mergers (treatment group) with monitoring stations in municipalities that neverexperienced mergers (control group). Another case could be comparing monitoring stations
in municipalities that experienced mergers in the early years (treatment group) with monitoring stations in municipalities that experienced mergers in the late years (control group).
Recent literature indicates that standard two-way fixed effect estimates can be subject tobias in the case of the staggered difference-in-differences design. The comparison between theearly and late merger municipalities becomes problematic in the presence of heterogeneous
treatment effects across treatment cohorts and time. This comparison leads to negative
weights, thus causing bias (Callaway and Sant’Anna, 2021; Goodman-Bacon, 2021; Sun and
Abraham, 2021).
To obtain unbiased estimates, we apply an alternative estimator of Callaway and Sant’Anna(2021) that is robust to negative weights as the baseline specification throughout this paper.
In this Callaway and Sant’Anna (2021) estimator, we use year fixed effects instead of basinby-year fixed effects. We present the results of Callaway and Sant’Anna (2021) estimator
together with the results of the two-way fixed effects specification.15 Furthermore, we adopt
15

The event study results of Callaway and Sant’Anna (2021) from Section 5 include the coefficientsfor event time -1. This is because a varying base period is used for estimating the pseudo-effects in pre-15

the Sun and Abraham (2021) estimator, which is another alternative estimator robust to
negative weights, for a robustness check in Section 5.2, and to analyze the effects on land
use in Section 6.5.

5

Results

5.1

Baseline Results

We find that municipal mergers increase river pollution, contradicting the negative externality narrative that mergers can internalize pollution spillovers. Table 2 shows that municipal
mergers increase water pollution by 5.5% when adopting the Callaway & Sant’Anna estimator (Column 1 of Panel A). This effect size is larger than the 3.6% increase estimated using
a two-way fixed effects specification, which suggests the presence of negative weights in thislatter specification when applied in a staggered difference-in-differences design. Additionally, our results are robust when clustering standard errors at the more conservative basin
level, accounting for the possibility of spatial dependence in water pollution that extends
beyond the municipality level (Column 2). Furthermore, the negative effect of municipal
mergers on water quality is robust to controlling for a municipality-level economic indicatorand population (Column 3).
We also find that the negative effects of municipal mergers on water quality persist
for 14 years. Figure 5 shows these results in the event study design of the Callaway andSant’Anna (2021) estimator. First, we find no differential pre-trends for most pre-merger
periods, reinforcing the validity of the parallel trends assumption. Second, the negative
effects on water quality intensify over time and remain statistically significant for up to 14
years, indicating the sustained, adverse impact of municipal mergers in the short to mediumrun. However, beyond 15 years post-merger, the effects diminish and become statisticallyinsignificant, suggesting that issues with weaker pollution control were eventually addressedin the long run. It is important to note, though, that the estimates become imprecise overtime, accompanied by larger standard errors, due to the smaller sample sizes consisting of
only those municipalities that merged very early.
5.2

Robustness Checks

The results remain robust across various analyses, including analysis comparing early versus late mergers, analysis on matched municipalities, consideration of spillover effects fromtreatment periods, aligning with the parallel trends assumptions outlined in Callaway and Sant’Anna (2021).
Specifically, the base (reference) period is the immediately preceding period. For example, for event time -1,
the base period is -2.

16upstream and border municipalities, the adoption of alternative estimators, sample periods,
and alternative water quality indicators.
Difference-in-Differences Analysis Comparing Early versus Late Mergers.—The baseline specification relies on the comparison between merged and never-merged municipalities, which
differ in terms of industry compositions and financial conditions, as discussed in Section
3.5. This difference may raise concern about selection bias, although the evidence of parallel
pre-trends supports a causal interpretation of the baseline results. However, as a robustness
check, we conduct a difference-in-differences analysis, restricting our sample to municipalities
that have undergone mergers. This robustness check compares municipalities that merged
earlier (treatment group) and those that merged later (control group), which are expected tobe more similar and balanced in characteristics. Appendix Figure A3 shows that, consistent
with our baseline findings, municipal mergers increase water pollution, especially six years
after municipal mergers.
Difference-in-Differences Analysis on Matched Municipalities.—To address the concern of
selection bias in comparing merged and never-merged municipalities, we also conduct a
difference-in-differences analysis only on municipalities that are matched based on their imbalanced municipality characteristics prior to the mergers. We specifically adopt Mahalanobis distance matching for product shipment values, agricultural output values, and thefinancial capability index.16 The matched samples exhibit balanced characteristics between
merged and never-merged municipalities (Appendix Table B1). Appendix Figure A4 showsthat the result for these matched municipalities is similar to that of the baseline specification,although the coefficients are imprecise beginning six years after the merger.
Spillovers from Upstream and Border Municipalities.— Our baseline analysis relies on thestable unit treatment value assumption (SUTVA). However, there is a potential for spillovers
from upstream merged municipalities to downstream never-merger municipalities along rivers.
To test this, we compare never-merged municipalities located within 25, 50, or 100 kilometersof upstream merged municipalities (control group subject to spillovers) to those located further away from upstream merged municipalities (pure control group).17 However, Appendix
Figure A5 shows that these spillover effects are statistically insignificant in most event years,
regardless of the chosen distance cutoffs.
16

In the matching process, we use the average of these values in the pre-merger periods (from 1996 to
2000).
17
We identify upstream municipalities for each station by using elevation raster data and river line data,
which are explained in Section 6.4. Specifically, we select upstream municipalities that intersect with riversegments at elevations higher than that of a given station, following the approach in Motohashi (2023).

17

As a robustness check to enhance the validity of the SUTVA, we run an analysis bydirectly removing the spillovers from upstream merged municipalities. Here, we only use
monitoring stations that do not have upstream merged municipalities located within 25,
50, or 100 kilometers. Appendix Figure A6 shows that the results in this specification are
similar to those in the baseline specification, where the average treatment effects on thetreated (ATT) are 0.047-0.055, with p-values of 0.009-0.024.
We also consider another type of spillover effect, originating from border municipalities.
Our baseline analysis assumes that water quality at a given monitoring station is affected
only by the merger of the municipality where it is located. However, monitoring stations
can be situated on rivers that flow along the border of multiple municipalities. In thiscase, water quality at these stations is likely to be affected by the mergers of multiple
border municipalities. To account for this spillover effect, we conduct an analysis where thetreatment indicator is set to one once at least one border municipality begins to undergo
mergers, specifically for stations located on municipality borders.18 As shown in Appendix
Figure A7, this analysis yields results consistent with the baseline results, where ATT is
0.053, with a p-value of 0.003.
Alternative Estimators and Sample Periods.—We find a similar negative effect of municipal
mergers on water quality using alternative estimators, such as the one proposed by Sun and
Abraham (2021), although the effect is slightly less pronounced (Appendix Figure A8).
Moreover, our findings demonstrate robustness when adopting alternative sample periods.
This includes both a longer period from 1990 to 2018 and a shorter period from 1999 to 2018,as shown in Appendix Figure A9. Across these different sample periods, the negative effectof municipal mergers on water quality is consistently observed, with the magnitudes of theseeffects being similar to those found in the baseline specification.
Alternative Water Quality Indicators.—We find a similar negative effect of municipal mergers
on water quality when focusing on dissolved oxygen (DO) levels, which, like BOD, captures
the overall level of water contamination from various pollution sources (Panel B of Appendix
Figure A10).19 The magnitude of this effect on DO is smaller compared to that on BOD,
where ATT is -0.013, with a p-value of 0.008. Conversely, we find no effects of municipal
mergers on suspended solids (SS), as shown in Panel C of Appendix Figure A10. SS measuressoil erosion, which is closely related to agricultural wastewater, as discussed in Lipscomb andMobarak (2016). Therefore, this result suggests that agricultural wastewater is unlikely to
18

We identify stations situated on municipality borders by selecting those located within 2 kilometers ofmore than one municipality.
19
A lower level of DO values indicates a higher level of water pollution.

18

be the primary source of pollution.

6

Mechanisms

Heterogeneity analysis by merger types and involved municipalities suggests that the mechanisms driving the negative effects of municipal mergers are coordination costs and unbalanced political power among pre-merger municipalities. We also find suggestive evidence
that municipal mergers decelerate the expansion of sewer coverage, which increases the volume of untreated domestic wastewater from households. Conversely, we find no evidence ofalternative mechanisms, including the negative externality theory and land use changes.
6.1

Coordination Costs

We examine the coordination costs mechanism by investigating how the effects of municipal
mergers vary by merger types. Specifically, we compare the effect of equal-footing mergers,which entail higher coordination costs, with that of incorporating mergers, which entail lower
coordination costs.20 In this mechanism, we hypothesize that equal-footing mergers result inlarger increases in water pollution due to weaker pollution control under higher coordination
costs. To analyze the effect of equal-footing mergers, we conduct a difference-in-differences
analysis, restricting our sample to municipalities that have undergone equal-footing mergers(treatment group) and never-merged municipalities (control group). We repeat a similar
analysis to examine the effect of incorporating mergers.
We find that the negative effect of municipal mergers on water quality is concentrated
in equal-footing mergers, which entails high coordination costs. As shown in Table 3, equalfooting mergers increase water pollution by 6.7% (Column 1 of Panel A), whereas the effectis insignificant in the case of incorporating mergers (Column 2). These differential effects
between different types of mergers suggest that coordination costs can weaken pollution
control efforts. The event study results, presented in Figure 6, corroborate these findings.
Equal-footing mergers increase river pollution for up to 14 years, although this negative
effect is mitigated in the long run, possibly as municipalities eventually overcome initialcoordination challenges (Panel A). In contrast, incorporating mergers have limited effects onriver pollution except for a temporal negative effect around 3-5 years post-merger (Panel B).
20

Different levels of coordination costs between equal-footing and incorporating mergers are discussed in
Section 2.3.

19

6.2

Political Economy

We also examine the political economy mechanism by examining the differential effects on
incorporating and incorporated municipalities. In this mechanism, we hypothesize that thenegative effect on water quality is more pronounced in incorporated municipalities withlittle political power, compared to incorporating municipalities with larger political power.21
We analyze the effect of municipal mergers on incorporated (incorporating) municipalities
by conducting a difference-in-differences analysis, restricting our sample to incorporated(incorporating) and never-merged municipalities.
We find that the negative effect of municipal mergers on water quality is concentrated
in incorporated municipalities with little political power. As shown in Table 3, incorporated
municipalities experience a significant increase in water pollution post-merger by 8.6% (Column 3 of Panel A), while the effect is insignificant in incorporating municipalities (Column
4). This difference in results supports the argument of the political economy mechanism,
suggesting that incorporated municipalities with less political power incur weaker pollution
control relative to incorporating municipalities. The event study results, presented in Figure
7, show the same findings. Specifically, we find a rise in river pollution at an increasingrate in incorporated municipalities for up to 17 years post-merger (Panel A). Conversely, weobserve limited effects on river pollution in incorporating municipalities, with the exceptionof a temporary negative effect observed around 3-5 years post-merger (Panel B).
6.3

Pollution Sources Subject to Weaker Control

We investigate sources of pollution that increase following mergers due to weaker control.
Municipalities are primarily responsible for controlling domestic wastewater, but their rolein controlling industrial and agricultural wastewater is limited, as discussed in Section 2.2.22
The primary approach for controlling domestic wastewater is to construct and operate sewage
treatment plants.23 Therefore, we examine the effect of municipal mergers on sewer investment levels. Our analysis shows suggestive evidence that municipal mergers decelerate theexpansion of sewer coverage, especially in the case of equal-footing mergers. This finding
suggests that, post-mergers, a larger volume of domestic wastewater remains untreated in
21

Different levels of political power between incorporated and incorporating municipalities are discussed
in Section 2.3.
22
The insignificant effect of municipal mergers on suspended solids, predominantly associated with agricultural wastewater, further indicates that agricultural wastewater is unlikely to be the pollution source(shown in Section 5.2).
23
Another approach could be to subsidize the construction of “Johkasou”, a decentralized wastewater
treatment system installed at the household level. However, we could not test the effect of mergers on Johkso
coverage, as the municipality-level coverage data is publicly accessible only from 2013 onwards.

20

merged municipalities.
To investigate the effect of mergers on domestic wastewater control, we conduct a differencein-differences analysis using municipality-level panel data on sewer coverage. This analysis
uses sewer coverage data from the Sewage Statistics, spanning from 1996 to 2018, provided
by the Japan Sewage Works Association. We calculate the proportion of sewer coverage,
which ranges from 0 to 1, by dividing the population served by the sewer system by the total
population of each municipality in each year. To accommodate the changes in municipalities
post-mergers, we aggregate the data at the post-merger municipality level.24 Moreover, thisanalysis focuses on municipalities that maintained sewer infrastructure up to 2018 to ensure abalanced panel. Using the constructed panel data, we adopt the same difference-in-differences
design as outlined in our baseline specification in Section 4.1, employing municipality-fixed
effects instead of monitoring station-fixed effects.
As shown in Panel A of Figure 8, we find suggestive evidence that municipal mergers
decelerate sewer investments for up to 5 years following the mergers. However, it is important
to note that the anticipation effect is observed one year prior to the mergers, despite thepresence of parallel pre-trends in other periods. This anticipation effect could be attributed
to the possibility of sewer projects being suspended or canceled just before the mergers,
during the negotiation phase. The negative effect on sewer coverage is primarily observed inequal-footing mergers, which is consistent with the baseline heterogeneity results (Appendix
Figure A11).25
Conversely, we do not observe a negative effect on the operation of sewage treatment
plants. Specifically, we investigate whether the water quality of the effluent from theseplants changes following mergers, potentially due to deteriorating operation. We utilize
BOD values of sewage treatment plant effluent from 1996 to 2017, provided as plant-level
data in the Sewage Statistics. After aggregating this plant-level data into municipality-level
panel data on average BOD values of effluent, we conduct the same difference-in-differences
analysis. As demonstrated in Panel B of Figure 8, we do not find a statistically significant
effect of municipal mergers on effluent water quality. This suggests that pollution control
efforts are not weakened through changes in operation. This result is plausible given theexistence of mandatory effluent regulations for sewage treatment plants, which municipalities
must adhere to.
In summary, while municipal mergers did not alter the operation of sewage treatment
plants, they slowed the expansion of sewer coverage. This suggests that a greater volume
24

For example, if municipalities A and B merged into a new municipality C in year X, the sewer coverage
for C before X is derived from the total populations served by sewers in A and B.
25
We are unable to conduct a heterogeneity analysis comparing the impacts on incorporated and incorporating municipalities, as the panel data has been constructed at the level of post-merger municipalities.

21

of domestic wastewater remains untreated in municipalities that have undergone mergers,
consequently leading to heightened pollution in rivers following the mergers.
6.4

Alternative Mechanism 1: Negative Externality Theory

We test the negative externality theory as an alternative mechanism, which predicts that municipal mergers improve environmental quality by internalizing pollution spillovers. Consistent with the difference-in-differences analysis showing a negative effect of municipal mergers
on water quality, we do not observe the spatial patterns predicted by the negative externality
theory.
To investigate the negative externality theory, we conduct a river distance analysis, similar to that of Lipscomb and Mobarak (2016). Specifically, we examine how river distancesfrom monitoring stations to their closest upstream and downstream municipality borders
affect water quality, exploiting changes in these distances following municipal mergers. For
this analysis, we construct two distance variables, U and D. U refers to the distance along
the river from a monitoring station to its closest upstream municipality border, while D indicates the distance to the closest downstream border. These distances are calculated along
rivers, taking into account the flow direction. To calculate U and D, we use the river nodedata with elevation information, along with river line data provided by the Ministry of Land,
Infrastructure, Transport, and Tourism. This data allowed us to construct fine-resolution
elevation raster data, from which we determined the upstream-downstream relationships
among monitoring stations and municipality borders.26 Then, we calculate the two distancevariables along the river.27
We adopt the following regression to test the negative externality theory:28
Log(BODi,t ) = α + η1 Downstreami,t + η2 Downstream2i,t + λXi,t + δi + θb,t + εi,t(3)where Downstreami,t is a relative “downstreamness” indicator of a monitoring station within
its municipality, calculated as Ui,t /(Ui,t + Di,t ). Here, Ui,t and Di,t represent distances along
the river from monitoring station i to its closest upstream municipality border and its closest
26

We use pre-merger (1984) and post-merger (2018) municipality boundary data, as provided by Kirimura
et al. (2011).
27
Detailed steps are described in Appendix C.1.
28
In this river distance analysis, we use water quality data from monitoring stations located along theriver lines used in our data construction. We exclude stations located in either the uppermost or furthestdownstream municipalities, as one of the distance measures (U or D) cannot be calculated for these locations.
Our observations extend until 2015, as we include population as a control variable, and population data isavailable only up to that year. With these criteria, our final sample for the river distance analysis comprises
700 monitoring stations in 382 municipalities, spanning from 1996 to 2015.

22

downstream municipality border, respectively, in year t. Downstreami,t ranges from 0 to
1, with values closer to 1 indicating a station’s more downstream position within its municipality. Xi,t is a vector of time-varying control variables consisting of a municipality-level
economic indicator (product shipment values) and population. δi and θb,t are monitoring station fixed effects and basin-by-year fixed effects, respectively. Standard errors are clusteredat the monitoring station level to address serial correlation.
The coefficients of primary interest are η1 and η2 . According to the negative externality
theory, as detailed in Section 2.3, pollution levels are expected to rise at an increasing rate as
one moves downstream within a municipality. This predicts a convex relationship, with bothη1 and η2 (representing the first and second derivatives with respect to “downstreamness”)
expected to be positive.
A more flexible specification, following Lipscomb and Mobarak (2016), is also considered:Log(BODi,t ) = α + γ1 U i,t + γ2 U 2i,t + γ3 Di,t + γ4 D2i,t + λXi,t + δi + θb,t + εi,t(4)where the variables are as previously explained. According to the negative externality theory
in Lipscomb and Mobarak (2016), pollution is expected to increase exponentially as the
distance from the downstream border decreases (i.e., the further downstream within themunicipality), so we expect γ3 < 0 and γ4 > 0. Also, the presence of a structural break in
the pollution function at the municipality border implies that γ1 should differ from γ3 .
Table 4 presents the results of the river distance analysis, where we do not find spatial
patterns consistent with the negative externality theory. First, the results of the specification
using relative downstreamness (regression 3) are provided in Columns 1 and 2. We do not findthe effects of either the downstreamness indicator or its squared term on water quality. Also,
the coefficient of the downstreamness indicator (η1 ) is negative, contradicting the predictionsof the negative externality theory. Second, the results of the Lipscomb and Mobarak (2016)
specification (regression 4) in Columns 3 and 4 also fail to support the negative externality
theory. We do not find significant effects of D and D2 on water quality. Furthermore, theequality of the coefficients of U and D cannot be rejected when testing for a structural breakin the pollution function at the municipality border.
In summary, the negative externality theory of Lipscomb and Mobarak (2016) does nothold true in the case of municipal mergers in Japan. This discrepancy may be partly attributed to the differences between the developing countries studied in Lipscomb and Mobarak (2016) and developed countries like Japan. In the context of developing countries, as
discussed in Lipscomb and Mobarak (2016), the mechanism focuses on how local politicianspermit slum areas with inadequate water and sanitation infrastructure. In such a case, it
23could be easier to restrict or relocate these informal settlements, which often have weakerproperty rights. Additionally, the relocation of slums might be more feasible when nearby
cities are expanding. However, in developed countries, local governments are likely to face
the challenge of relocating established formal polluting sources such as residences and factories to internalize negative externalities following municipal mergers. This can be moredifficult or costly due to more stringent property rights and limited available land for relocation in developed countries. The null effects of municipal mergers on land use in Section
6.5 are consistent with this explanation.
6.5

Alternative Mechanism 2: Land Use

We test another alternative mechanism, namely changes in land use, which can lead toincreased water pollution by generating new pollution sources without altering pollution
control efforts. However, we do not find the effects of municipal mergers on various land use
types, including agriculture, built-up areas, and forests.
As discussed in Section 2.3, municipal mergers can cause changes in land use, such as
expanding industrial and residential areas, by increasing economic activities, as demonstratedby Egger et al. (2018). This shift in land use could result in an increase in sources of waterpollution, namely industrial and domestic wastewater, thereby escalating river pollution.
To investigate this alternative mechanism, we examine the effect of municipal mergers
on land use patterns. Our analysis uses 100-meter raster data of land use for six periods(1991, 1997, 2006, 2009, 2014, and 2016), provided by the Ministry of Land, Infrastructure,
Transport, and Tourism. We specifically focus on land use patterns within 150 meters, 1
kilometer, or 5 kilometers from water quality monitoring stations. The analysis categorizes
land use into three main classifications: agriculture, built-up areas, and forests.29 Subsequently, we construct a binary indicator that identifies the major land use classification foreach monitoring station.30
Based on the panel of land use patterns over six periods, we apply the same differencein-differences approach as in our baseline specification in Section 4.1, using binary indicatorsof the three land use classifications as outcome variables. Due to the non-consecutive natureof our panel data, we are unable to employ the Callaway and Sant’Anna (2021) estimator,
which requires a balanced panel with consecutive years. Therefore, we instead use the Sun
and Abraham (2021) estimator in this analysis.
As shown in Figure 9, we do not find any statistically significant effects of municipal
29

Built-up areas include residential or urban areas where buildings are densely built up, and athletic
fields, airports, racetracks, baseball fields, schools, and harbor areas.
30
Detailed steps are described in the Appendix C.2.

24

mergers on land use classifications near monitoring stations, including agriculture areas,
built-up areas, and forests, across various distance cutoffs. This finding suggests that riverpollution resulting from municipal mergers cannot be explained by the changes in land use.

7

Conclusion

We document that municipal mergers, in which two or more municipalities combine, increase
environmental pollution. This result runs counter to the prediction of the negative externality
theory emphasized in past studies, i.e., municipal mergers can facilitate the internalizationof pollution spillovers across pre-merger municipalities.
Specifically, we investigate the case of Japan’s nationwide municipal mergers in the late
1990s to 2000s, which drastically reduced the number of municipalities by approximately
50%. I exploit the staggered implementation of mergers across Japan to estimate the causal
effects of municipal mergers on river water quality in the difference-and-differences analysis.
Contrary to the prediction of the negative externality theory, we find that municipal
mergers increase river pollution by 5.5%, and this negative effect persists for 14 years. Consistent with these results, we do not find evidence supporting the negative externality theory
in the river distance analysis that examines how river distances from monitoring stations totheir closest upstream and downstream municipality borders affect the water quality.
As for mechanisms, heterogeneity analyses suggest that municipal mergers weaken pollution control due to coordination costs and unbalanced political power among pre-merger
municipalities. We find that pollution increases in “equal-footing” mergers with higher coordination costs, but not in “incorporating” mergers with lower coordination costs. In thecase of “incorporating” mergers, we further show that smaller, incorporated municipalities
experience a larger increase in river pollution than larger, incorporating municipalities. Thisis likely due to the higher political power of incorporating municipalities, where the mayor retains her position post-merger. Moreover, we find suggestive evidence that municipal mergers
decelerate sewage investments for up to five years post-merger, especially in “equal-footing”
mergers. This suggests that the increase in water pollution is driven by domestic wastewater
not treated in sewage treatment plants.
Our results have several implications for policy and research directions on municipal
mergers. First, while proponents of mergers emphasize positive impacts on fiscal outcomessuch as local public finance, municipal mergers can have unintended negative effects onenvironmental outcomes. These negative effects should be carefully considered in future
municipal mergers.
Second, the negative externality narrative of pollution may not be as relevant in a de25

veloped country’s case of municipal mergers as in a developing country’s case of splits inmunicipalities. Our results point to the relative significance of coordination costs and political economy mechanisms in the case of Japan’s municipal mergers. Conducting careful
pre-analysis of likely relevant mechanisms on a country-by-country basis would be importantin addressing the unintended negative effects of municipal mergers.
Finally, the negative effects of municipal mergers we observed in the case of pollution
control can extend to other policies implemented at the municipality level, including thoserelated to education and healthcare. The coordination cost and political economy channels
may similarly weaken the implementation of these policies. Investigating these potential
negative effects in other policies may be a fruitful area for future research.

26

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Oates, Wallace E. 1972. Fiscal federalism.: Edward Elgar Publishing.
OECD. 2014. OECD Regional Outlook 2014: Regions and Cities: Where Policies and People
Meet.: OECD Publishing.
Pickering, Steve, Seiki Tanaka, and Kyohei Yamada. 2020. “The Impact of Municipal
Mergers on Local Public Spending: Evidence from Remote-Sensing Data.” Journal of East
Asian Studies, 20(2): 243–266.
Reingewertz, Yaniv. 2012. “Do municipal amalgamations work? Evidence from municipalities in Israel.” Journal of Urban Economics, 72(2-3): 240–251.
Sigman, Hilary. 2002. “International spillovers and water quality in rivers: do countries
free ride?” American Economic Review, 92(4): 1152–1159.
28

Sigman, Hilary. 2005. “Transboundary spillovers and decentralization of environmental
policies.” Journal of Environmental Economics and Management, 50(1): 82–101.
Sun, Liyang, and Sarah Abraham. 2021. “Estimating dynamic treatment effects in event
studies with heterogeneous treatment effects.” Journal of Econometrics, 225(2): 175–199.
Tiebout, Charles M. 1956. “A pure theory of local expenditures.” Journal of Political
Economy, 64(5): 416–424.
Wang, Shaoda, and Zenan Wang. 2021. “The environmental and economic consequencesof internalizing border spillovers.” Mimeo.
Weese, Eric. 2015. “Political mergers as coalition formation: An analysis of the Heisei
municipal amalgamations.” Quantitative Economics, 6(2): 257–307.

29

Figure 1: Locations of Municipal Mergers in Japan

300

Notes: This figure shows the boundaries of municipalities in Japan that underwent municipal mergers,
marked by red lines surrounding grey areas. Additionally, it shows the boundaries of prefectures in black.

Number of Mergers
100
200

295

151

82

22
5

6

3

7

4

12

2

0

1

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Year of Municipal Merger

Figure 2: Timing of Municipal Mergers in Japan
Notes: This figure shows the number of municipal mergers per year in Japan based on the merger data from
the Ministry of Internal Affairs and Communications. For our analysis, we specifically focus on the variation
in merger years from 2001 to 2011 based on our sample of municipalities located along rivers.

30

Figure 3: Locations of Water Quality Monitoring Stations in Japan0

1

BOD - Average (mg/L)
2
3
4

5

Notes: This figure shows the locations of water quality monitoring stations, marked in red, which areincluded in our sample along rivers. This is based on data from the Ministry of Environment. Additionally,
the boundaries of prefectures in Japan are also shown in black.

1990

2000

2010

2020

Year
MergedNever-Merged

Figure 4: Trends of BOD Values in Merged and Never-Merged Municipalities
Notes: This figure compares the changes in average BOD values from 1990 to 2018 between municipalities
that experienced municipal mergers (labeled “Merged”) and municipalities that did not (labeled “NeverMerged”). The vertical line in 2001 marks the year when the first municipal mergers took place in our
sample.

31.4.2Log(BOD)
0-.2-.4-14-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Years since Municipal Merger

Figure 5: The Dynamic Effect of Municipal Mergers on Water Pollution
Notes: This figure shows the coefficients of the Callaway and Sant’Anna (2021) estimator. The 95% confidence intervals are shown with dashed lines. Standard errors are clustered at the municipality level. This
specification includes monitoring station fixed effects and year fixed effects.

32

-.5

-.3

Log(BOD)
-.1
.1

.3

.5

A. Equal-footing Merger-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Years since Municipal Merger

-.5

-.3

Log(BOD)
-.1
.1

.3

.5

B. Incorporating Merger-14-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Years since Municipal Merger

Figure 6: Event Study Results: Mechanism of Coordination Costs
Notes: This figure shows the coefficients of the Callaway and Sant’Anna (2021) estimator. Panel A showsthe effect of equal-footing mergers by using data from municipalities that underwent such mergers and nevermerged municipalities. Panel B, in a similar manner, shows the effect of incorporating mergers. The 95%
confidence intervals are shown with dashed lines. Standard errors are clustered at the municipality level.
Both panels include monitoring station fixed effects and year fixed effects.

33

-.5

-.3

Log(BOD)
-.1
.1

.3

.5

A. Incorporated Municipalities-14-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Years since Municipal Merger

-.5

-.3

Log(BOD)
-.1
.1

.3

.5

B. Incorporating Municipalities-14-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Years since Municipal Merger

Figure 7: Event Study Results: Mechanism of Political Economy
Notes: This figure shows the coefficients of the Callaway and Sant’Anna (2021) estimator. Panel A shows theeffect of mergers in incorporated municipalities by using the data of these municipalities and never-mergedmunicipalities. Panel B, in a similar manner, shows the effect of mergers in incorporating municipalities.
The 95% confidence intervals are shown with dashed lines. Standard errors are clustered at the municipality
level. Both panels include monitoring station fixed effects and year fixed effects.

34

-.1

Sewer Coverage (0-1)
-.05
0

.05

A. Sewer Coverage-14-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Years since Municipal Merger-.5

Log(BOD)
0.5

B. Effluent Water Quality-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Years since Municipal Merger

Figure 8: Pollution Source: The Effect on Sewer Coverage and Effluent Water Quality from
Sewage Treatment Plants
Notes: This figure shows the coefficients of the Callaway and Sant’Anna (2021) estimator. The 95% confidence intervals are shown with dashed lines. Standard errors are clustered at the municipality level. This
specification includes municipality-fixed effects and year-fixed effects.

35

-3

-2

-1

0

1

2

3

4

5

6

7

8

9

10 11 12 13 14 15.4
Forest Land (=1)
0
.2
-.4

-.4

-.4
-4

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

7

8

9

10 11 12 13 14 15

-3

-2

-1

0

1

2

3

4

5

6

7

8

9

10 11 12 13 14 15

B. Built-up (within 1 kilometer from stations)

C. Forest (within 1 kilometer from stations)

-4

-3

-2

-1

0

1

2

3

4

5

6

7

8

9

10 11 12 13 14 15

Forest Land (=1)
0
.2
-.4

-.2

Built-up Land (=1)
0
.2
-.4

-.4

-.2

Agriculture Land (=1)
-.2
0
.2

.4

A. Agriculture (within 1 kilometer from stations).4Years since Municipal Merger

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

7

8

9

10 11 12 13 14 15

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

7

8

9

10 11 12 13 14 15

A. Agriculture (within 5 kilometers from stations)

B. Built-up (within 5 kilometers from stations)

C. Forest (within 5 kilometers from stations)

-4

-3

-2

-1

0

1

2

3

4

5

6

7

8

9

Years since Municipal Merger

10 11 12 13 14 15

Forest Land (=1)
0
.2
-.2
-.4

-.4

-.2

Built-up Land (=1)
0
.2

.4

Years since Municipal Merger.4Years since Municipal Merger.4Years since Municipal Merger

Agriculture Land (=1)
-.2
0
.2
-5

-4

Years since Municipal Merger

-.4

36

-5

-5

Years since Municipal Merger.4-5

C. Forest (within 150 meters from stations)-.2

Built-up Land (=1)
0
.2

.4

B. Built-up (within 150 meters from stations)-.2

Agriculture Land (=1)
-.2
0
.2

.4

A. Agriculture (within 150 meters from stations)

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

7

8

9

Years since Municipal Merger

10 11 12 13 14 15

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

7

8

9

10 11 12 13 14 15

Years since Municipal Merger

Figure 9: Alternative Mechanism 2: Effect on Land Use
Notes: This figure shows the coefficients of the Sun and Abraham (2021) estimator. The panels in each row show results fordifferent dependent variables, specifically indicators that take the value of one if the majority of land use within 150 meters, 1
kilometer, or 5 kilometers from monitoring stations is of the corresponding type. The 95% confidence intervals are shown withdashed lines. Standard errors are clustered at the municipality level. All specifications include monitoring station fixed effects andbasin-by-year fixed effects. The coefficients for event time before -5 are not shown because most are omitted in the regressions.

Table 1: Summary Statistics in Pre-Merger Period
Means
Variable

Difference

Obs.

Never-Merged

Merged3.227(3.716)

2.252(2.672)-0.975***(0.206)

3,309

2.272
(5.668)97.349(266.419)
4.128(4.978)0.601(0.315)2.954(5.674)111.842(157.791)
11.665(10.496)0.488(0.226)0.682*(0.367)14.494(13.983)
7.536***(0.541)-0.113***(0.018)958

0.528(0.275)
5.420(2.683)0.381(0.222)
7.545(24.293)-0.146***(0.018)2.125(1.863)748

4.402(4.746)3.518(4.179)0.554(0.278)4.161(4.906)
3.914(4.293)0.533(0.272)-0.241(0.461)0.396(0.430)-0.021(0.022)700

0.342(0.475)0.137(0.344)0.498(0.500)0.412(0.492)0.213(0.410)0.364(0.481)0.070***(0.026)
0.076***(0.020)-0.134***(0.031)

2,770

Panel A. Water Quality (Station-level)
BOD - Yearly average (mg/l)
Panel B. Municipality Characteristics (Municipality-level)
Product shipment values (100 billion JPY)
Population (thousand)
Agricultural output values (billion JPY)
Financial capability index

958
958
958

Panel C. Sewage Treatment Plants (Municipality-level)
Proportion of population connected to sewage treatment plants
BOD - Effluent from a sewage treatment plant (mg/l)

273

Panel D. River Distance (Station-level)
Distance from upstream border to station (km)Distance from station to downstream border (km)Downstream indicator (0-1)

700
700

Panel E. Land Use (Station-level)
Major land use within 150 meters from stations: Agriculture (0/1)
Major land use within 150 meters from stations: Forest (0/1)
Major land use within 150 meters from stations: Built-up (0/1)

2,770
2,770

Notes: This table compares summary statistics between merged and never-merged municipalities before the commencement of municipal mergers in our sample. Specifically, it focuses on the period before 2001, when the firstmergers were observed, and after 1996, following the baseline specification. The means are calculated by averaging
the values for all years in that period. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
The standard errors of the differences in means are clustered at the municipality level when variables are observed at
the station level, as in Panels A, D, and E.

37

Table 2: DID Results: The Effect on Water Quality
Log(BOD)
(1)

(2)

(3)

Panel A: Callaway and Sant’Anna (2021) Estimator
Merger (= 1)

0.055∗∗∗(0.018)0.055∗∗∗(0.018)0.049∗∗∗(0.017)0.036(0.025)0.036∗∗(0.017)

Panel B: Two-way Fixed Effects
Merger (= 1)
Observations
R2
Number of Stations
Number of Municipalities
Controls
Cluster SE at
Mean of Dep. Variable

0.036∗(0.018)75,555
0.881
3,285
971
NO
Municipality level
2.654

75,55573,738
0.881
0.881
3,285
3,206
971
946
NO
YES
Basin level Municipality level
2.654
2.602

Notes: The regression coefficients are reported. Standard errors are shown inparentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels,
respectively. Panel A includes year fixed effects and monitoring station fixed effects, while Panel B includes basin-by-year fixed effects and monitoring station
fixed effects. In both Panels A and B, Column 3 controls for pre-merger productshipment values and population after being interacted with year dummies. The
mean of the dependent variable represents the average of BOD values before thecommencement of municipal mergers in our sample.

38

Table 3: DID Results: Mechanisms of Coordination Costs and Political Economy (Dependent Variable: Log(BOD))
Coordination Costs(1)
Equal-footing(2)
Incorporating

Political Economy(3)(4)
Incorporated Incorporating

Panel A: Callaway and Sant’Anna (2021) Estimator
Merger (= 1)

0.067∗∗∗(0.019)0.039(0.026)0.086∗∗(0.034)0.027(0.027)

Panel B: Two-way Fixed Effects
Merger (= 1)

0.064∗∗∗(0.022)0.004(0.027)

0.098∗∗∗(0.029)-0.018(0.029)Observations
R2
Number of Stations
Number of Municipalities
Mean of Dep. Variable

56,120
0.885
2,440
849
2.664

49,565
0.886
2,155
629
3.018

33,994
0.891
1,478
572
3.104

46,138
0.887
2,006
625
3.129

Notes: The regression coefficients are reported. Standard errors, which are clustered at themunicipality level, are shown in parentheses. ***, **, and * indicate significance at the 1%,5%, and 10% levels, respectively. Panel A includes year fixed effects and monitoring station
fixed effects, while Panel B includes basin-by-year fixed effects and monitoring station fixed
effects. The mean of the dependent variable represents the average of BOD values before
the commencement of municipal mergers in our sample.

39

Table 4: Alternative Mechanism 1: Negative Externality Theory
Log(BOD)
(1)

(2)

(3)

(4)

Distance from upstream border to station (U )0.010∗∗∗(0.003)0.004(0.003)

Squared distance from upstream bortder to station (U 2 )-0.000∗∗(0.000)-0.000(0.000)Distance from station to downstream border (D)

0.002(0.004)-0.003(0.005)

Squared distance from station to downstream border (D2 )0.000(0.000)0.000(0.000)14,000
0.876
700
382
YES
NO
0.202
2.365

13,360
0.915
668
354
NO
YES
0.209
2.401

Downstreamness indicator (U/(U + D))-0.191 -0.215(0.230) (0.225)

Squared downstreamness indicator ((U/(U + D))2 )0.273(0.212)

Observations
R2
Number of Stations
Number of Municipalities
Year FE
Basin-Year FE
Equality Test: p-value for U = D
Mean of Dep. Variable

14,000
0.875
700
382
YES
NO
2.365

0.334
(0.224)

13,360
0.915
668
354
NO
YES
2.401

Notes: The regression coefficients are reported. Standard errors, which are clustered at stationlevel, are shown in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels,
respectively. This specification includes basin-by-year fixed effects and monitoring station fixed
effects, as well as product shipment values and population as controls. The mean of the dependent
variable represents the average of BOD values before the commencement of municipal mergers inour sample.

40

Appendix
The Impact of Municipal Mergers on Pollution Control:
Evidence of River Pollution in Japan
Kazuki Motohashi

Michiyoshi Toya

Contents
A Additional Figures

42

B Additional Tables

53

C Data Appendix

54

List of Figures
A1
A2

Prediction of Negative Externality Theory . . . . . . . . . . . . . . . . . . . 42
Trends of Logarithms of BOD Values in Merged and Never-Merged Municipalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
A3 Robustness Check: Difference-in-Differences Analysis Comparing Early versus
Late Mergers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
A4 Robustness Check: Difference-in-Differences Analysis on Matched Municipalities 45
A5 Robustness Check: Spillovers onto Control Municipalities . . . . . . . . . . . 46
A6 Robustness Check: Removing Spillovers from Upstream Merged Municipalities 47
A7 Robustness Check: Spillovers from Border Municipalities . . . . . . . . . . . 48
A8 Robustness Check: Alternative Estimators . . . . . . . . . . . . . . . . . . . 49
A9 Robustness Check: Alternative Sample Periods . . . . . . . . . . . . . . . . . 50
A10 Robustness Check: Alternative Water Quality Indicators . . . . . . . . . . . 51
A11 Pollution Source: Heterogeneous Effects on Sewer Coverage by Merger Types 52

List of Tables
B1

Balance Checks of Matched Municipalities (Pre-Merger Period) . . . . . . .

41

53

A

Additional Figures

Water
Pollution0

Location on River(Downstreamness Indicator)

1

Figure A1: Prediction of Negative Externality Theory
Notes: This figure shows a convex relationship between a downstreamness indicator and water pollution, as
suggested by the negative externality theory of Lipscomb and Mobarak (2016).

42

1
Log(BOD)
.5
0

1990

2000

2010

2020

Year
MergedNever-Merged

Figure A2: Trends of Logarithms of BOD Values in Merged and Never-Merged Municipalities
Notes: This figure compares the changes in the logarithms of average BOD values from 1990 to 2018 between municipalities that experienced municipal mergers (labeled “Merged”) and municipalities that did not(labeled “Never-Merged”). The vertical line in 2001 marks the year when the first municipal mergers took
place in our sample.

43

.6
.4
Log(BOD)
0
.2
-.2
-.4
-14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1

0

1

2

3

4

5

6

7

8

9

Years since Municipal Merger

Figure A3: Robustness Check: Difference-in-Differences Analysis Comparing Early versus
Late Mergers
Notes: This figure shows coefficients of the Callaway and Sant’Anna (2021) estimator. The sample isrestricted to monitoring stations in municipalities that have undergone mergers, excluding those in municipalities that have never merged. The 95% confidence intervals are shown with dashed lines. Standard errors
are clustered at the municipality level. This specification includes monitoring station fixed effects and year
fixed effects.

44

.4
.2
Log(BOD)
0-.2-.4-14-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Years since Municipal Merger

Figure A4: Robustness Check: Difference-in-Differences Analysis on Matched Municipalities
Notes: This figure shows coefficients of the Callaway and Sant’Anna (2021) estimator. The sample is limited
to monitoring stations in matched municipalities. The 95% confidence intervals are shown with dashed lines.
Standard errors are clustered at the municipality level. This specification includes monitoring station fixed
effects and year fixed effects.

45

-.4

-.2

Log(BOD)
0.2.4

A. Spillovers onto Control (within 25 kilometers)-9 -8 -7 -6 -5 -4 -3 -2 -1 0

1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17

Years since Municipal Merger.4

C. Spillovers onto Control (within 100 kilometers)

Log(BOD)
0.2.2
Log(BOD)
0

-.2

-.2

-.4

-.4

46

.4

B. Spillovers onto Control (within 50 kilometers)-9 -8 -7 -6 -5 -4 -3 -2 -1 0

1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17

Years since Municipal Merger-9 -8 -7 -6 -5 -4 -3 -2 -1 0

1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17

Years since Municipal Merger

Figure A5: Robustness Check: Spillovers onto Control Municipalities
Notes: This figure shows the coefficients of the Callaway and Sant’Anna (2021) estimator. Panel A shows the spillover effect of
upstream mergers on downstream, never-merged municipalities located within 25 kilometers of merged municipalities. Similarly,
Panels B and C show the spillover effect of upstream mergers using cutoff distances of 50 and 100 kilometers, respectively. The
95% confidence intervals are shown with dashed lines. Standard errors are clustered at the municipality level. All panels include
monitoring station fixed effects and year fixed effects.

-.6

-.4

-.2

Log(BOD)
0
.2

.4

.6

A. No Upstream Mergers within 25 kilometers-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Years since Municipal Merger.4.6

C. No Upstream Mergers within 100 kilometers

-.6

-.4

-.2

Log(BOD)
0.2.4
Log(BOD)
0
.2
-.2
-.4
-.6

47

.6

B. No Upstream Mergers within 50 kilometers-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Years since Municipal Merger

Years since Municipal Merger

Figure A6: Robustness Check: Removing Spillovers from Upstream Merged Municipalities
Notes: This figure shows the coefficients of the Callaway and Sant’Anna (2021) estimator. Panel A shows a result using monitoring
stations that do not have upstream merged municipalities within a 25-kilometer radius. Similarly, Panels B and C show results
when using cutoff distances of 50 and 100 kilometers, respectively. The 95% confidence intervals are shown with dashed lines.
Standard errors are clustered at the municipality level. All panels include monitoring station fixed effects and year fixed effects.

.4.2
Log(BOD)
0-.2-.4-14-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Years since Municipal Merger

Figure A7: Robustness Check: Spillovers from Border Municipalities
Notes: This figure shows coefficients of the Callaway and Sant’Anna (2021) estimator. In this specification,
the treatment indicator is set to one once at least one border municipality begins to undergo mergers forstations located at the borders of municipalities. The 95% confidence intervals are shown with dashed lines.
Standard errors are clustered at the municipality level. This specification includes monitoring station fixed
effects and year fixed effects.

48

-.4

-.2

Log(BOD)
0.2.4

A. Callaway and Sant'Anna (2021)-14-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Years since Municipal Merger.2.4

C. Sun and Abraham (2021)

-.4

-.2

Log(BOD)
0.2
Log(BOD)
0
-.2
-.4

49

.4

B. Two-way Fixed Effects-15-14-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17-15-14-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Years since Municipal Merger

Years since Municipal Merger

Figure A8: Robustness Check: Alternative Estimators
Notes: This figure compares the event study results among three alternative estimators. Panels A, B, and C show the coefficientsof the Callaway and Sant’Anna (2021) estimator, two-way fixed effects estimator, and the Sun and Abraham (2021) estimator,
respectively. The 95% confidence intervals are shown with dashed lines. Standard errors are clustered at the municipality level.
Panel A includes monitoring station fixed effects and year fixed effects, while Panels B and C include monitoring station fixed
effects and basin-by-year fixed effects.

-.4

-.2

Log(BOD)
0.2.4

A. Baseline: 1996-2018-14-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Years since Municipal Merger.2.4

C. Robustness: 1999-2018

-.4

-.2

Log(BOD)
0.2
Log(BOD)
0
-.2
-.4

50

.4

B. Robustness: 1990-2018-20-19-18-17-16-15-14-13-12-11-10-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 1011121314151617-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Years since Municipal Merger

Years since Municipal Merger

Figure A9: Robustness Check: Alternative Sample Periods
Notes: This figure shows the coefficients of the Callaway and Sant’Anna (2021) estimator. Panel A is the baseline specification
when the sample years are 1996-2018, while Panels B and C are shown for robustness checks when the sample years are 1990-2018
and 1999-2018, respectively. The 95% confidence intervals are shown with dashed lines. Standard errors are clustered at themunicipality level. All panels include monitoring station fixed effects and year fixed effects.

-.4

-.2

Log(BOD)
0
.2

.4

.6

A. BOD (Biochemical Oxygen Demand)-14-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Years since Municipal Merger.4.6

C. SS (Suspended Solids)

-.4

-.2

Log(SS)
0.2.4
Log(DO)
0
.2
-.2
-.4

51

.6

B. DO (Dissolved Oxygen)-14-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Years since Municipal Merger-14-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Years since Municipal Merger

Figure A10: Robustness Check: Alternative Water Quality Indicators
Notes: This figure shows coefficients of the Callaway and Sant’Anna (2021) estimator. The 95% confidence intervals are shownwith dashed lines. Standard errors are clustered at the municipality level. All panels include monitoring station fixed effects andyear fixed effects.

-.1

Sewer Coverage (0-1)
-.05
0

.05

A. Equal-footing Merger-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Years since Municipal Merger-.1

Sewer Coverage (0-1)
-.05
0
.05

.1

B. Incorporating Merger-14-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Years since Municipal Merger

Figure A11: Pollution Source: Heterogeneous Effects on Sewer Coverage by Merger Types
Notes: This figure shows the coefficients of the Callaway and Sant’Anna (2021) estimator. Panel A showsthe effect of equal-footing mergers by using data from municipalities that underwent such mergers and nevermerged municipalities. Panel B, in a similar manner, shows the effect of incorporating mergers. The 95%
confidence intervals are shown with dashed lines. Standard errors are clustered at the municipality level.
Both panels include municipality fixed effects and year fixed effects.

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B

Additional Tables

Table B1: Balance Checks of Matched Municipalities (Pre-Merger Period)
Means
Variable

Difference Obs.

Never-Merged

Merged2.391(4.822)101.865(224.588)
10.858(9.222)0.483(0.231)

2.954(5.674)111.842(157.791)
11.665(10.496)0.488(0.226)

Product shipment values (100 billion JPY)
Population (thousand)
Agricultural output values (billion JPY)
Financial capability index

0.563(0.459)9.977(16.906)
0.807(1.228)0.005(0.022)

643
643
643
643

Notes: This table compares summary statistics between matched merged and never-mergedmunicipalities before the commencement of municipal mergers in our sample. Specifically,
it focuses on the period before 2001, when the first mergers were observed, and after 1996,
following the baseline specification. The means are calculated by averaging the values for
all years in that period. ***, **, and * indicate significance at the 1%, 5%, and 10% levels,
respectively.

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C

Data Appendix

C.1

Construction of River Distance

We construct two distance variables, U and D, based on the following steps.
1. We apply inverse distance weighting (IDW) interpolation to the river node point layer
to generate a 300-meter raster elevation layer. This layer offers a finer resolution
compared to readily available Digital Elevation Models and provides high accuracy
along river paths.
2. We use the river polyline layer and the municipality boundaries layer and intersectthem by point to obtain the municipality entry/exit points of rivers.
3. We then append elevation information to both the “entry/exit point” layer and themonitoring station layer.
4. To minimize error, we only focus on monitoring stations that are located within 2
kilometers of the river polyline.
5. We build a network based on the river polyline layer and perform a “find the closest
facility” analysis in ArcGIS. This allows us to pick the six closest municipality border
points along the river for each monitoring station and calculate distances between eachstation and border points.
6. We use the elevation information to determine whether each selected municipality
boundary point is upstream or downstream of the monitoring station. Then, we identify
the closest upstream and downstream border points for each monitoring station andfinally obtain values for U and D.
C.2

Construction of Land Use Measures

We calculate the numbers of raster pixels corresponding to the following three land use
classifications within 150 meters, 1 kilometer, and 5 kilometers from water quality monitoring
stations.31 Following this, we construct a binary indicator of major land use by identifying
the classification that represents the majority of pixels in each specified area.
1. Agriculture: rice paddies and other agricultural land
2. Forest: areas with dense perennial vegetation
3. Built-up: residential or urban areas where buildings are densely built up, and athletic
fields, airports, racetracks, baseball fields, schools, and harbor areas

31

While the original data contained finer land use classifications, we consolidated them into three majorclassifications for our analysis. We also excluded pixels representing sea and river water, roads, railroads,
and golf courses.

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