1

The Impact of Fiscal Revenues on Local Government Cooperation

Edoardo di Porto1

EQUIPPE (Universités de Lille) and Sapienza (Universita di Roma)

Vincent Merlin2

CREM and University of Caen Basse Normandie

Sonia Paty3

GATE-LSE and University of Lyon

Abstract:

Cooperation among local governments has long been advocated as means to aggregate resources

and to improve public sector efficiency. In this paper, we use a unique panel dataset of French

municipal to analyze the relationship between the cooperation decision-making and fiscal revenues.

Given the peculiarity of our empirical analysis, which leads to a non linear estimation with fixed

effect and endogenous covariate, we develop a series of empirical specifications: 1) IV fixed effect

estimators a la Paapke (2005), 2) Panel data methods for bivariate response variables a la Paapke-

Wooldridge (2008). First, we find that fiscal revenues of municipalities are relevant for the

cooperation decision-making. Higher expected fiscal revenues raised by a cooperating locality

significantly increase the probability to join an inter-municipal community. However, although

extra tax revenues seem to explain the cooperation decision making when municipalities are

relatively poor, we find that when revenues are high, the propensity to cooperate decreases. Second,

estimation results show that a municipality is more inclined to cooperate when her neighbours have

also decided to join an inter-municipal jurisdiction. Finally, raising grants in order to convince

municipality to cooperate (a policy used by the French central government in 2000) does not lead to

a greater propensity to cooperate.

Keywords: inter-municipal cooperation, fiscal revenues, bivariate response variable, two-stage

probit least squares, endogeneity

JEL codes: H2, H7, C3

1 Dipartimento di Economia Via del Castro Laurenziano 9, 00161 Roma, Italia E mail: edoardodiporto@yahoo.it

2 17 rue Bloch 14032 Caen, France E mail: Vincent.merlin@unicaen.fr

3 93 Chemin des mouilles 69130 Ecully, France E mail: paty@gate.cnrs.fr

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1. Introduction

Inter-municipal cooperation is a current phenomenon in Western European countries (Hulst et al.,

2009). The OECD (2006, p. 25) notes that “Inter-municipal cooperation has been and remains an

important element of most national programmes.” For example, Austria and Sweden in the 1950s,

Germany and Belgium in the 1970s and more recently, Switzerland, Greece and Denmark, have all

encouraged waves of cooperation among their sub-national jurisdictions. In France, the first

experiences of inter-governmental cooperation involving municipalities administering some local

public services date back to 1890. However, there is a great variety of institutional arrangements for

cooperation, which provide for the joint delivery of local public services, from simple cooperative

agreements between independent municipalities, to complete mergers. Based on a comparative

research in eight European countries, Hulst et al. (2009) argue that increased scale of production,

urbanization and increased market pressures tend to favor the development of cooperative

arrangements, a first clue of the fact that agglomeration economies favour cooperation.

Issues related to the consolidation of states and jurisdictions have been debated by economic

theorists (see e.g. Blume and Blume, 2007, which provides a review of the pros and cons of local

authority mergers). The joint provision of public goods by communities (i.e. groups of

municipalities) could create economies of scale and thus offers greater possibilities to achieve cut

costs. But there is a trade-off between the achievement of economies of scale which provide

incentives for consolidation, and respect for local heterogeneity in preferences (as pioneered by

Tiebout, 1956) which tends to favor separation. Among others, the papers by Miceli (1993) and

Spolaore (2004) analyze the optimal size of coalitions. Cooperation might also be justified to

internalize inter-jurisdictional spillovers (see e.g. Bradford and Oates, 1974): the benefits of public

expenditure (infrastructure, road building, cultural facilities…) often spread across the boundaries

of the supplying jurisdiction and affect the welfare of the citizens in neighboring localities. Finally,

Hoyt (1991) shows that consolidation may be an efficient policy corrective for the undersupply of

local public goods generated by tax competition among local jurisdictions. Hoyt demonstrates that

limiting competition by reducing the number of the localities in a metropolis increases tax rates and

welfare.

Despite frequent claims that cooperation among local governments is a potential solution to

inefficiencies, there are few studies on its determinants. However, to our knowledge, the related

empirical literature is quite recent and is concerned mainly with the characteristics of optimal

coalitions - such as school districts - in the US context (Brasington, 1999, 2003a, 2003b; Gordon

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and Knight, 2009). Weese (2011) uses political coalition formation games to explain mergers

between Japanese municipalities. Saarima and Tukiainen (2010) investigate the political decision

making process behind coalitions in Finland. Also for Finland, Sorensen (2006) shows the impact

of political transaction costs on voluntary local government mergers. He identifies the role of state

grants, revenue disparities and expected changes in party strength after unification, on the process

of merging. However, all these empirical papers investigate an extreme case of cooperation since

they analyze local mergers in which former members disappear. To the best of our knowledge, there

is no empirical paper investigating the determinants of inter-municipal cooperation arrangements

when these are aimed to provide for the joint delivery of public services.

Our aim is to contribute to this literature by analyzing the determinants of inter-municipal

cooperation using the French experience, where cooperation (but not merger) between

municipalities is decided voluntarily by municipal councils.

For many years and especially since 1999, the French government has favored the creation of inter-

municipal jurisdictions through high state grants to solve the problem of “municipal fragmentation”

in France.4 Inter-municipalities group together several municipalities to enable collective financing

and management of some local public services. Nowadays, most French municipalities (more than

90% in 2010) are grouped within larger jurisdictions (known as „Etablissements Publics de

Coopération Intercommunale‟ or EPCI in French). Together with municipalities, these jurisdictions

have a large degree of autonomy to set their own local tax rate.

The main contribution of this paper is that it exploits an empirical setting that allows us to

test the impact of municipal fiscal revenues on the probability to cooperate. Econometric modelling

of the determinants of cooperation may thus be complicated for the reason that fiscal revenues and

cooperation decision-making are strongly interrelated. As well as we expect that municipalities will

take into consideration their potential fiscal revenues when joining an inter-municipal agreement,

fiscal revenues themselves within a locality depends on local intergovernmental agreement. To

address this problem of simultaneity (or reciprocal causation), we use different econometric

specification: an IV fixed effect estimator a la Paapke (2005) and a panel data methods for bivariate

and fractional response variables a la Paapke-Wooldridge (2008). First, we find that fiscal revenues

of municipalities are relevant for the cooperation decision-making. Higher expected fiscal revenues

4 In 2010 there were about 36,500 French municipalities, i.e. nearly half the total European municipalities (EU15).

Thus, 87% of French municipalities had less than 2,000 inhabitants, accounting for 25% of the metropolitan French

population (DGCL - DESL, 2010).

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raised by a cooperating locality significantly increase the probability to join an inter-municipal

community. However, although extra tax revenues seem to explain the cooperation decision making

when municipalities are relatively poor, we find that when revenues are high, the propensity to

cooperate decreases. Second, estimation results show that a municipality is more inclined to

cooperate when her neighbours have also decided to join an inter-municipal jurisdiction. Finally,

raising grants in order to convince municipality to cooperate (a policy used by the French central

government in 2000) does not seem to lead to a greater propensity to cooperate. This outcome

suggests that the delegation of some previous municipal competencies to the inter-municipal level,

the expected redistribution of new tax revenues and the supply of new public goods by this higher

level of government are sufficient incentives to join an inter-municipal community.

We believe that the issues raised by cooperation may contribute to the debate on the optimal

organization of the public sector. Reorganization of sub-national jurisdictions is still on the political

agenda in many countries. Our work seems to promote the idea that fiscal revenues are strong

incentives to cooperate. The role of state grants is more controversial.

The paper is organized as follows. The French inter-municipal cooperation wave is presented in

Section 2 in which we specifically explain the role of revenues in the fiscal cooperation decision

framework (2.2) and Section 3 presents the empirical model. Section 4 provides the results of our

estimations. Section 5 concludes.

2. The inter-municipal cooperation wave in France

In this section, we provide some useful information on the French local institutional context

and the development of local government cooperation (2.1) and on the fiscal revenues‟ formation

(2.2).

2.1. The French local institutional context

The French local institutional context is characterized by three tiers of overlapping local

governments. The lowest tier is made up of about 36,500 municipalities; the middle-tier consists of

96 counties (French “départements”); and at the highest level of local government are 22 regions.

Municipalities are responsible for local urban services, building, provision of nurseries and primary

schools, and sport facilities, and maintenance of municipal roads and urban public transport.

Counties administer social assistance, maintain departmental roads and middle schools. Regions are

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responsible for provision of vocational training, economic development and building and

maintenance of high schools.

Local revenues mainly come from taxation (54%) and grants (23%). The local business tax

(or "Taxe Professionnelle")5 is the major source of local governments tax revenue, accounting for

approximately 45% of the revenue derived from direct local taxes. The tax base consists mainly of

capital goods and is based on the rental values of buildings, and of equipment (assumed to be 16%

of the cost of the equipment). The remaining fiscal revenues are collected from households in the

form of residential tax (“taxe d‟habitation”), property tax (“taxe foncière sur le bâti”) and land tax

(“taxe foncière sur le non bâti”).

Since the beginning of the 1990s, several laws have been voted relating to local cooperation

in France.6 Based on the volunteer principle, neighboring municipalities that want to collectively

finance and manage some public services can create, or join, an inter-municipal community (EPCI

in French). This supra-municipal structure co-exists with the municipal structure and must respect

both “exclusivity” and “specialty” principles: the competences assigned to a community do not

apply to any other local government unit and delimit the boundaries of their decisional power. Since

1999, this form of local cooperation has been widely promoted by government based on financial

incentives, to solve the problem of “municipal fragmentation”. Communities were expected to

benefit from substantial economies of scale that will reduce public spending, and limit fiscal and

spending inequalities between member municipalities. This double objective is targeted by

transferring tax and spending abilities from the municipalities to the corresponding inter-municipal

group. In practice, municipalities democratically decide which services will be delegated to the

community among a total of 84 broken down in 14 categories (see Table 1 in Appendix 1). The

inter-municipal community is managed by a board of delegates elected by member municipalities

from their local councilors, at the absolute majority.7 Therefore, unlike municipalities,

“départements” or regions, inter-municipal jurisdictions operate under indirect democracy and

therefore, remains a decision making level and not a proper administrative level.

Nowadays, we can differentiate three categories of EPCI. The communauté urbaine (CU)

5This tax mainly based on private capital was removed in 2010 and was replaced by a territorial economic contribution

based on property and firms value added. 6There are three main laws on the development of inter-municipalities in France: the law of 6

th February 1992 lays the

basis for inter-municipal cooperation and was reinforced and simplified by the law of 12th

July 1999, and the law of

13th

August 2004 which rationalized the inter-municipal map. 7 On condition that each municipality must have at least 1 seat, and in order that no single municipality can hold

more than the half of the inter-municipal council‟s seats, the number of seats held will be proportional to the

municipal population, or the same for each municipality, or the result of a bargaining process among the

municipalities.

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forces municipalities to transfer at least six competences pertaining to the categories of

responsibilities identified in Table 1. The CU model also requires the community to exceed 500,000

inhabitants. The communauté d‟agglomération (CA) relies on four mandatory competences and

requires the community to exceed 50,000 inhabitants. The communauté de communes (CC) relies

two compulsory competences but does not necessitate a minimum population size to be created.

Municipalities that are governed by the CU and CA urban models of cooperation lose more

responsibilities than those governed by the CC model of cooperation.

To finance her supply of public services, the inter-municipal community can apply an

additional tax rate on the four local tax rates (above cited) or apply a single business tax rate (SBT

or “Taxe Professionnelle Unique” in French).8 In the SBT case, the municipalities lose the right to

set their own tax rates. Map 1 shows that inter-municipal cooperation increased greatly during the

period of study (1993-2003). This preference for the SBT regime is due to a state grant, which is of

a higher amount when opting for SBT than for additional tax regime. The French government

wanted to give higher financial incentives to opt for the most integrated form of cooperation.

Map 1: Distribution of inter-municipal jurisdictions and their tax regimes

in 1993 in 2003

Additional business tax rate (445)

Single business tax rate (21)

Additional business tax rate (1420)

Single business tax rate (925)

Source: Charlot et al. (2010)

8 On our period of study, there is a third marginal case – a mixed case- where the community raises a single business tax

rate and an additional tax rate on households. The community gets a double source of tax revenue on households and

business while the municipality i only sets a tax on households.

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2.2. Fiscal revenues

In this section, we try to explain to what extent the municipal fiscal revenues are modified once the

local authority has concluded a cooperative agreement. These revenues depend on the tax regime,

which has been chosen by the community (single business taxation or additional taxation). When a

municipality is isolated, she receives a state grant, which depends on many criteria (but mainly

population). When one municipality enters an inter-municipal community, she still receives the

same central grant (from the same amount as long as municipal population remains the same) but

the community also gets an extra state grant to supply joint local public services.9 In what follows,

for the sake of simplicity, we will assume that all municipalities, which have joined the same

community, receive the same proportion of this state grant per capita.

When a municipality is not part of a cooperative agreement as described above, we denote

tim, the tax revenues on households in municipality i, Ti

m the business tax revenues in municipality i,

gim the state grant for municipality i, pi

m the population in locality i. Then the municipal fiscal

revenue p.c. can be written:

Bim=(ti

m+Ti

m+gi

m)/pi

(1)

If the municipality i has joined an inter-municipal group I, which has chosen an additional

tax regime (both levels of government set a business tax rate on the same tax base), her potential

fiscal revenue p.c. becomes:

Bim’

= (tim’

+Tim’

+gim)/pi+ (ti

a +TI

a+GI

a)/pI (2)

where TIa is the local business tax revenue raised by the community I, GI

a the state grant attributed

to the community I and pI the population in community I. The community now gets a proper source

of tax revenue with (tia +TI

a+GI

a). With regards to the isolated cases, there might be a variation in

the business tax revenue and the household tax revenue raised by the municipality. We denoted

these new revenues by tim’

+Tim’

. Let us denote Rim‟

the amount of tax revenues raised within

municipality i for its own budget, and RI’ the amount of fiscal revenues raised within municipality i

for the community‟s budget. Assuming that the benefits of the cooperation are equally distributed

among community‟s members, the potential fiscal revenue p.c. can be rewritten as

Bim’

= Rim’

/pi +RI’/pI (3)

9 This extra central grant is also mainly based on population (within the community) and on the number of

competencies delegated by municipalities to the inter-municipal government and is attributed every year as long as

the inter-municipal community exists.

8

If the municipality i has joined a community with a single business tax rate (where the

municipality does not set a business tax rate anymore), her potential fiscal revenue can be written:

Bim”

= (tim”

+gim)/pi+ (TI

s+GI

s)/pI (4)

where TIs is the local business tax revenue raised by the community I, GI

a the state grant attributed

to the community I and pI the population in community I. The community now gets a proper source

of tax revenue with (TIs+GI

s). With regards to the isolated cases, the only possibility for the

municipality is to tax households only. We denoted these new revenues by tim”

. Let us denote Rim”

the amount of tax revenues raised within municipality i for its own budget, and RI”

the amount of

fiscal revenues raised within municipality i for the community‟s budget. Assuming that the benefits

of the cooperation are equally distributed among community‟s members, the potential tax revenue

p.c. can be rewritten as10

Bim”

= Rim”

/pi +RI”/pI (5)

From this description, we can observe that the level of fiscal revenues raised within a

locality depends on her choice of joining an inter-municipal community or not. Nevertheless, we

can hardly conclude that the potential fiscal revenue when concluding an agreement are always

higher than the revenue the locality had when she was outside any cooperation agreement.

Basically, in the SBT case, the municipality loses her main tax revenue on business and in the

additional case, each level of local governments (municipality and community) set a tax rate on the

same tax base for all local taxes.11

Therefore, the relationship between fiscal revenues and

cooperation decision-making becomes an empirical issue. To address this issue, we need to model

these two endogenous variables: the cooperation decision making and the fiscal revenues p.c. These

empirical approaches are presented in the following section.

3. Empirical Models

Our aim is to determine whether the level of fiscal revenues have an impact on the cooperation

decision-making of local governments. This is not an easy task given the specificity of our

framework. First, since our dependent variable is a dichotomous variable, we need to test a non

linear specification that recognizes the bounded nature of the cooperation decision making variable.

10

As mentioned in footnote 5, there is a third marginal case – a mixed case- where the community raises a single

business tax rate TIs and an additional tax rate on households tI

s . The community gets a double source of tax revenue on

households and business (tIs +TI

s+GI

s) while the municipality i only sets a tax on households. The municipal tax

revenue can be written as Bim’”

= (tim’”

+gim)/pi+ (tI

s +TI

s+GI

s)/pI =Ri

m’”/pi +RI

”’/pI

11 Tax base sharing by multi level governments may lead to the tragedy of the commons (see e.g. Flowers, 1988)

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Second, as highlighted above, fiscal revenues are determined simultaneously with the decision of

cooperation, raising endogeneity issue. Third, spatial informational spillovers among municipalities

might arise when deciding to join an inter-municipal community. It is very likely that one

municipality is influenced by neighbouring municipalities‟ decisions to cooperate (or to remain

isolated). We can expect that a municipality that would be surrounded by cooperating localities may

have strong incentives to join them. Inversely, the probability to join an inter-municipal community

is lower if a municipality is surrounded by isolated jurisdictions. Moreover, we can expect that

information on cooperation (expected revenues, expected state grant etc) is easier to get when an

isolated locality is located next to cooperating municipalities. Mimicking behaviour in terms of

cooperation is likely to be observed in such a context.

Finally, a municipality may raise more revenues than the others due to the existence of a

natural advantage coming from natural resources, land, climate, historical importance, accumulated

human capital or just accumulated tax base. Any missing natural characteristic that influences the

cooperation choice implying a change in revenues will bias an estimate of the revenues effect on

cooperation decision making. Combes et al. (2011) argue that in an agglomeration framework (as

the one we study), any unobserved natural covariates if missing can bias estimates producing both a

reverse causation and a missing variable problem12

.

To sum up, we can conclude that our best estimation strategy should come from a non linear

model and should be robust to several problems: simultaneity with the decision making and

unobserved fixed municipal characteristics, therefore a panel structure of data is desirable.

We would like to estimate an average population effect (APE) (Wooldridge, 2002).

Following a very general notation, we can express a general mean model such as:

E(Y(i,t)| R(i,t), X(i,t), η(i), v(i,t)) = Φ (α R(i,t)+ β X(i,t) + η(i)+ v(i,t)) (1)

where Y(i,t) is our dichotomous dependent variable, i.e. Y(i,t)= 1 if municipality i decide to cooperate

at time t and 0 otherwise. R(i,t) indicates tax revenues obtained by municipality i at time t. X(i,t) is a

matrix of exogenous covariates, η(i) are municipal fixed effects and v(i,t) is an idiosyncratic error

term. Φ can indicate a probit function as in Paapke-Wooldridge (2008).

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An empirical proof of how much bias can occur in a spatial framework without fixed effect is given by Cassette et al.

(2012)

10

To address endogeneity in a non linear framework, we specified two different empirical

models:

1. A linear instrumental variable fixed effect regression la Paapke (2005), (see section

3.1.)

2. A bivariate response variables model with endogenous explanatory variables a la

Paapke-Wooldridge (2008), (see section 3.2)

Both specifications rely on instrumental variable approach. We used time lagged tax capacity of

municipality i as an instrument. Tax capacity is the ability to generate local revenues within a

municipality or more precisely the theoretical tax revenues a locality would raise if she applied the

four average tax rates set by all French municipalities on her own local tax base (firms and

households). We will describe instruments and identification strategy in a subsection 3.3. Finally, in

subsection 3.4, we will explain how we intend to measure the efficiency of the policy reform

launched in 1999.

3.1 Linear instrumental variable fixed effect regression

Following Paapke (2005) we combine fixed effect model with instrumental variable estimation:

Y(i,t) = c+αr(i,t)+ β X(i,t) + η(i)+ v(i,t) (2)

with r(i,t) the predicted revenues from a first stage linear regression on fiscal potential Z(i,t) and

exogenous covariates X(i,t). This first approach in (2) has to be seen as a linear probability model

which seems anyway plausible if we aim to obtain average marginal effect at a mean level.

Instrumental variable is described in details in subsection 3.4. As noticed in Paapke (2005), even if

our instrumental variable (fiscal capacity) and our treatment variable (revenues) are not binary, a

specification such as (2), if robust, produces coefficient for revenues that can be interpreted as local

average treatment effect (Angrist and Imbens, 1994). Equation (2) includes fixed effect to take into

account the municipal level heterogeneity. We provide cluster standard errors at departmental level

to account for the nature of some covariates, this is explained in details in subsection 3.3.

3.2 A bivariate response variables model with endogenous explanatory variables

To address the non linear nature of our dependent variable, we can estimate equation (1) using the

fractional model for panel data proposed in Paapke-Wooldridge (2008). In the extreme case in

which fractional data take their limit values 0 and 1, we have a bivariate response variables model.

This extreme case can be developed with the same procedure as its general specification. This is a

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two stage model in which we include fixed effect through Mundlak device. Therefore we include in

both stages the average value of our covariates computed over time. First stage of our model regress

revenues on Z(i,t) its average value, covariates and average covariates. As adviced in Paapke-

Wooldridge (2008), we interact our instrument with year dummies. From the first stage, we predict

residuals that we include in our second stage regression developed through pooled probit model.

Given the two stage procedure and the use of predicted residuals in the second stage, we perform

bootstrap standard errors as in Paapke-Wooldridge (2008), allowing for 500 replications. We

estimate average population effect APE and quantile population effect to predict the effect of

revenues on cooperation.

3.3 Covariates and instrument

Given the nature of our agglomeration framework, we need to rely on covariates that can explain

the decision of cooperating with neighboring municipalities.

Tax revenues - our main variable of interest - is a complicated linear combination of different tax

bases, different tax rates, municipal and inter-municipal grants as explained in subsection 2.2. To

avoid any collinearity, we have chosen to focus on the broad value of tax revenues. To instrument

this variable we have chosen fiscal capacity which is the theoretical tax revenues a municipality

would raise if she applied the national average tax rates to her local tax base for the four local tax

rates (Property tax, land tax, household tax and business tax). It is worth to notice that similarly to

Paapke (2005), the value of our instrument is partly determined at a state level (for the tax rates)

and it is therefore unrelated to idiosyncratic shocks that affect municipal fiscal decisions leading to

exogeneity.

We include another explanatory variable which is the percentage of localities that cooperate within

the same department (“département” in French, similar to the county level elsewhere). Since

cooperation decision-making is strongly dependent from what neighbours decide, we expect a

positive sign for the parameter associated to this variable. Basically, by law, inter-municipal

community should gather contiguous localities. If no neighbours cooperate, the probability to join

an inter-municipal community is lower than if close neighbours already cooperate. Moreover, we

can expect from the local officials some mimicking behavior in terms of cooperation, knowing that

it can be stigmatizing to remain isolated when a high number of neighbouring localities have

concluded an intergovernemental agreement. Citizens/voters may be aware of new public services

provided within a close community (such as public transport, cultural and sport facilities) and put

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some pressure on their incumbents to join a community.13

Finally, officials might also be aware that

staying outside a close existing community (which supplies on average a high level of public

services for firms and households) could lead to a capital flight from their territory. The mobility of

tax base can explain this propensity by officials to imitate the cooperation decisions of their

neighbors.14

It is also likely that municipalities, when deciding whether or not to cooperate, mimic

neighbors‟ behavior following a common trend suggested by Manski (1993). This possible spatial

spillovers in the decision making generates another source of possible bias. The literature on local

fiscal decision making has extensively spoken about this kind of spillover and their identification

(Brueckner, 2003). A spatial smoother is needed, therefore we compute the average number of

municipalities that have decided to join an inter-municipal community within the same department

and we lagged this variable by one period respect to our dependent. Most all the communities

belong to one department, therefore we do not incur in selection bias using this spatial smoother.

Moreover, in line with Mc Millen (2010), we can assert that when no structural form indicates the

way in which we need to construct our spatial parameter, there is no need to compute usual spatial

lag based on distance or contiguity matrices. Mc Millen shows that results are not necessarily

induced by the structure of the geographical smoother. Municipalities when deciding to join or not a

community have just information about the proportion of cooperating municipalities within a

department. The past decision to cooperate of neighboring municipalities is exogenous by

definition. However, Di Porto and Revelli (2011) show that time lagged spatial smoother can be

considered valid predictors of actual spatial smoother and using one for the other does not lead to

huge bias estimates of the spatial spillover.

Finally, following the existing literature in local public finance, we have also included some socio-

economic characteristics of the municipalities:

- the municipal population and population density: We could expect a positive sign since the

larger the population of the municipality, the greater will be local public needs. In this case,

the municipality would have incentives to share or to transfer some competences and the

associated supply of services to the inter-municipal group.

- the municipal unemployment rate and the share of people in the municipality i who are more

than 60 years old. The expected impact of these two variables on the probability to

cooperate is positive. If the existence of the community is seen as a solution to share or to

transfer the supply of specific services to these categories of population, we should observe

13

The argument is very close to yardstick competition models (Salmon, 1987, Besley and Case, 1995) where

incumbents imitate public decisions of their neighbours to be reelected. 14

This is a tax competition argument.

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a positive impact.

Although local politics was a very important point in both Finnish empirical papers on

consolidation (Sorensen, 2006; Saarima and Tukiainen, 2010), we did not include any political

variables for the following reason: since more than 80% of our municipalities are very small (less

than 2,000 inhabitants), French mayors do not wish to communicate any political colour. Moreover,

contrary to mergers in Finland, French municipalities still exist after joining an inter-municipal

body and there is no expected change in party strength after cooperation in the French case.

However, we must admit that the political colour of the president of the inter-municipal council who

is elected by the members of this council (composed with members from municipal councils) may

have an impact on the cooperation decision-making of any municipality. Unfortunately, we cannot

test this hypothesis.

Data and descriptive statistics are described in Appendix 2. Summary statistics are given in Table 2.

Statistics on fiscal regime per year are given in Table 3.

3.4 Using different sub sample to asses policy evaluation on grants

In what follows, we want to investigate the specific role of state grants attributed to the inter-

municipal community in the cooperation decision making of the municipalities. In particular we

would like to understand if the rise in grants carried out after the reform 1999 by French central

government has effectively increased the probability that a municipality cooperates.

This state grant has always existed even before 1999, but the amount distributed to each community

was lower. Moreover, the allowance conditions have changed after 1999. The state grant attributed

to the inter-municipal community is based on a fiscal integration coefficient. The higher the number

of competences delegated from the municipalities to the community, the higher the fiscal integration

coefficient and therefore the state grant. Since every community has a fixed number of compulsory

competencies but remains free to choose the number of optional competences, this coefficient

differs among inter-municipal communities and justifies the existence of different levels of state

grants among communities.

We then tried to assess policy evaluation using two subsamples of municipalities. We perform two

different estimations. First, we split the sample into two parts, before 1999 and after, and we

perform IV-2SLS estimation. The main idea is to compare revenues coefficients and to understand

if the two processes of decision making are effectively different before and after the policy shock.

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Second we perform both empirical models in 3.1 and 3.2, using municipalities that decide to

cooperate in respectively 1999 and 2000. We denoted these municipalities as “movers”, since they

have decided to join an inter-municipal community either in the beginning of 1999 (before the law)

or in 2000 (after the law). We perform two regressions using bivariate response model since our aim

is to check whether there are differences in the coefficients of the two regressions. The second

sample of “movers”, i.e. which have joined an inter-municipal community in 2000, is likely to show

a different propensity to cooperate in term of revenues if grants are an effective incentive for

cooperation. If not, then it would suggest that the delegation of some previous municipal

competencies to the inter-municipal level, the expected redistribution of new tax revenues and the

supply of new public goods by the inter-municipal level of government are sufficient incentives to

join an inter-municipal community.

4. Results

Table 4 shows result for the Logit and the IV-2SLS specifications in which we have included fixed

effect. For the sake of simplicity, we only present results for our variables of interest. Other

covariates, which were never significant, do not appear in the Tables.

Estimation results of model (1) in column (1) shows a logit model fixed effect that does not take

into account endogeneity. Parameters associated to variables of interest are non significant and

positive suggesting upward bias.

Estimation results of model (2) in column (2) exhibit a positive and significant parameter (0.05) for

tax revenues as well as a positive and significant coefficient (0.67) for the average number of

cooperating municipalities within the same department. Let us note that both coefficients identify

the marginal fixed effect computed at the mean value for a linear probability model. F-test for 1st

stage regression is greater than 10 assuring that the instrument is not weak.

Insert Table 4

Estimations results of model (1) in Table 5 (column 1) show our bivariate response model and

confirm that both coefficients of interest are positive and significant. Results do not change if we

take into account the instrument Z(i,t) alone or interacted with time dummies. Coefficient for first

stage predicted residual in model (1) is negative but strongly significant confirming endogeneity of

the revenues.

15

Insert Table 5

From these estimation results, we can conclude that fiscal revenues p.c. have a positive impact on

the decision to conclude an inter-municipal cooperation arrangement. This outcome confirms that

fiscal revenues of municipalities are relevant for the cooperation decision-making.

Result 1: Higher potential fiscal revenues p.c. raised within a locality, which cooperates,

significantly increase the probability to join an inter-municipal community.

Table 6 provides more information on the role of tax revenues on the probability to cooperate. In

model (1), the average population effect (APE) associated with revenues coefficient is 0.11 at the

25th

percentile and at the 50th percentile. Turning to the 75th

percentile, the APE for revenues p.c.

decreases to 0.089 and finally takes a lower value of 0.075 at the 95th

percentile. Summing up, this

outcome suggests that when revenues are high, the propensity to cooperate decreases. Although

extra tax revenues seem to explain the cooperation decision making when municipalities are

relatively poor, it is more difficult to give incentives to cooperate when municipalities are relatively

rich, suggesting the existence of “decreasing return to revenues”.

Insert Table 6

If we now turn to the interpretation of the parameter, which gives the impact of the neighbours‟

cooperation on the decision-making of local officials, we find a highly significant positive sign. As

expected, this outcome suggests that the cooperation choice of neighbouring municipalities is

strongly relevant for the cooperation decision-making. One municipality will be more inclined to

join an existing community if her neighbours have already joined an inter-municipal jurisdiction.

Result 2: A municipality is more inclined to cooperate when her neighbours have also decided

to join an inter-municipal jurisdiction.

Let us now examine the impact of the policy reform launched in 1999, which consists in raising

inter-municipal grants in order to give higher incentives for municipalities to cooperate.

Columns 3 and 4 in Table 4 show that when splitting the sample into two subsamples - before and

after 1999 - does not lead to significant different coefficients for our variables of interest. This

outcome suggests that raising inter-municipal grants did not cause the expected behavior:

16

cooperation seems to be similarly influenced by all the covariates. Columns 2 and 3 in Table 5

present the same picture. We can notice that the coefficient for the year dummy in column (3),

which is expected to identify all those changes derived by yearly specific factors, is very similar to

the same coefficient in column (2). This might be another proof that raising the level of state grants

did not increase the probability to cooperate.

This outcome suggests that the delegation of some previous municipal competencies to the inter-

municipal level, the expected redistribution of new tax revenues and the supply of new public goods

by this higher level of government are sufficient incentives to join an inter-municipal community.

5. Conclusion

Since the beginning of the 1990s, several laws have been voted relating to local cooperation in

France. The aim of this paper was to assess the role of fiscal revenues as a determinant of the fiscal

cooperation decision-making of French municipalities. To estimate the probability of joining an

inter-municipal jurisdiction, we used IV fixed effect estimators a la Paapke (2005) and panel data

methods for bivariate response variables a la Paapke-Wooldridge (2008). Our estimation results

confirm that fiscal revenues of municipalities are relevant for the cooperation decision-making.

Higher potential fiscal revenues p.c. raised within a locality, which cooperates significantly increase

the probability to join an inter-municipal community. We also find that one municipality will be

more inclined to join an existing community if her neighbours have already joined an inter-

municipal jurisdiction.

However, further research should be done to improve our understanding of the determinants

of fiscal cooperation. Cooperation is a complex process with different levels of cooperative

behaviour. In the French context, municipalities can opt for a single business tax regime (higher

degree of cooperation), an additional or a mixed regime. The determinants of each choice might not

be the same and should be investigated.

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APPENDIX 1: Competences and inter-municipalities

Table 1. List of the 14 categories of potential and compulsory competences under the jurisdiction of

the EPCI

Name of the competences category

CU CA CC

Energy production and supply

Environment and living environment a

Funeral services

Sanitary and social

Urban policy a a

Local plans of action of crime prevention

Economic development and planning a a a

Social and cultural development and planning

Space planning a a a

Road network

Tourist development

Accommodation and housing conditions a a

Infrastructures a

Other

Compulsory competences according

to the EPCI legal form

Source : Frere et al. (2012)

APPENDIX 2. Data

Our dataset is composed by 36203 municipalities observed on 8 years (1995-2003). Our

dataset is thus a balanced panel. For convenience we developed our regressions excluding the

municipality of Paris which result to be a clear outlier and the Corsican municipalities for which the

spatial matrix (W) is difficult to construct.

Fiscal data come from the Direction Générale des Collectivités Locales (DGCL, Ministère

de l‟Intérieur) and were kindly provided by the INRA-CESAER (Dijon). The remaining control

variables come from the Centre Maurice Halbwachs.

Table 2 below shows summary statistics, mean standard deviation and maximum of the main

covariates used in the regressions.

20

TABLE 2: Summary statistics

Summary statistics: mean, sd, min, max, sum

by categories of: year

year | coop p.c.revenues % unemp pop dens %old pop

---------+------------------------------------------------------------

1995 | .3007773 .2248108 .040195 1739.722 984.8364 .2314191

| .4586038 .2690384 .0167188 7999.751 4168.414 .0800157

| 0 0 2.05e-07 6.518261 2.771363 2.58e-06

| 1 11.03648 .1759135 431623.7 132775.9 .9007214

| 8823 6594.601 1157.697 5.10e+07 2.89e+07 6788.448

---------+------------------------------------------------------------

1996 | .3560374 .2366949 .040479 1746.292 1148.976 .2321162

| .4788346 .2848526 .0171255 8025.193 4863.149 .0798589

| 0 0 1.93e-07 6.621913 3.233256 2.64e-06

| 1 11.96648 .1737659 434851 154905.3 .8991827

| 10444 6943.208 1165.876 5.12e+07 3.37e+07 6808.896

---------+------------------------------------------------------------

1997 | .425854 .2470462 .0407844 1752.862 1313.115 .2328504

| .4944802 .2980595 .0176771 8050.907 5557.885 .0800421

| 0 0 8.25e-08 6.725566 3.69515 2.70e-06

| 1 11.56924 .1726452 438078.3 177034.6 .8970981

| 12492 7246.854 1174.672 5.14e+07 3.85e+07 6830.435

--------+------------------------------------------------------------

----------------------------------------------------------------------

1998 | .4628077 .2547872 .0411204 1759.432 1477.255 .2336328

| .4986233 .3080734 .0183324 8076.892 6252.62 .0805467

| 0 .0086012 9.13e-08 6.829218 4.157044 4.91e-07

| 1 11.75924 .181605 441305.7 199163.9 .8941146

| 13576 7473.929 1184.351 5.16e+07 4.33e+07 6853.384

---------+------------------------------------------------------------

1999 | .4951592 .2552579 .0449421 1773.073 170.2986 .2366791

| .4999851 .3151208 .0200942 8116.022 710.8644 .0807569

| 0 .0085318 0 7 .3641661 0

| 1 11.14636 .2097902 445452 23395.71 .9

| 14525 7487.736 1318.33 5.20e+07 4995541 6942.745

---------+------------------------------------------------------------

2000 | .5458512 .2639255 .0453821 1779.643 334.438 .2375152

| .4979017 .3272464 .0208429 8142.622 1404.787 .0817689

| 0 .0089185 2.00e-07 7.103652 1.00077 2.86e-06

| 1 11.60571 .2144276 448679.3 45357.14 .897821

| 16012 7741.99 1307.095 5.22e+07 9810406 6967.272

---------+------------------------------------------------------------

2001 | .597191 .2723015 .0456724 1786.213 498.5774 .2383963

| .4904713 .342506 .0219246 8169.485 2099.247 .0831555

| 0 .0087285 1.04e-07 6.676498 1.462664 4.71e-07

| 1 12.55288 .2271326 451906.7 67318.57 .9179431

| 17518 7987.694 1315.457 5.24e+07 1.46e+07 6993.116

---------+------------------------------------------------------------

2002 | .6813595 .2850743 .0460097 1792.783 662.7168 .2393394

| .4659572 .3606903 .0230711 8196.606 2793.844 .084941

| 0 .0089409 4.34e-08 5.014748 1.924557 4.65e-07

21

| 1 12.27067 .3330074 455134 89280 .9990222

| 19987 8362.37 1325.171 5.26e+07 1.94e+07 7020.782

---------+------------------------------------------------------------

Total | .4831296 .2549873 .0430774 1766.253 823.7766 .2352436

| .4997164 .3149683 .0197516 8097.353 3988.969 .0814533

| 0 0 0 5.014748 .3641661 0

| 1 12.55288 .3330074 455134 199163.9 .9990222

| 113377 59838.38 9948.649 4.14e+08 1.93e+08 55205.08

----------------------------------------------------------------------

TABLE 3: Statistics on the fiscal regime of the municipalities

| tax regime

year | ADD MIXTE TPU | Total

-----------+---------------------------------+----------

1995 | 7,691 0 1,132 | 8,823

1996 | 8,901 0 1,543 | 10,444

1997 | 10,273 0 2,219 | 12,492

1998 | 10,466 0 3,110 | 13,576

1999 | 10,513 0 4,012 | 14,525

2000 | 10,093 158 5,761 | 16,012

2001 | 9,350 338 7,830 | 17,518

2002 | 9,107 606 10,274 | 19,987

-----------+---------------------------------+----------

Total | 76,394 1,102 35,881 | 113,377

TABLE 4: Logit and IV regressions

(1) Logit (2) IV-2SLS

(3) IV-2SLS before

1999

(4) IV-2SLS after

1999

Revenues p.c. on 1000 hab. 0.345 0.054 0.114 0.133

(0.328) p 0.234 (0.029) p 0.065 (0.060)p 0.057 (0.038) p 0.00

Average # of coop. in depart. 1.957 0.673 0.546 0.490

(0.672) p 0.004 (0.040) p 0.000 (0.051) p 0.000 (0.107) p 0.00

% unemployment 16.254 0.280 0.153 -2.999

(14.283) p 0.244 (0.228) p 0.218 (0.293) p 0.601 (0.777) p 0.700

1.st stage F-test for Z(i,t)

17.87

Municipal fixed effect Yes Yes Yes Yes

year dummies Yes Yes Yes Yes

cluster standard errors Yes Yes Yes Yes

obs. 66983 183879 105075 78804

Note: Standard errors in parentheses. Dependent variable: cooperation dummy

22

TABLE 5: Bivariate model a la Paapke Wooldridge and policy evaluation

Note: Standard errors in parentheses. Dependent variable: cooperation dummy

TABLE 6: APE

25

th 50

th 75

th 90

th

Revenues p.c. 0.11 0.11 0.089 0.075

p-values 0.0004 0.0004 0.0004 0.0004

(1) Bivariate response

PW

(2) Bivariate response

movers 98-99

(3) Bivariate response

movers 99-00

Revenues p.c. on 1000 inhab. 0.283 -3.958 1.519

(0.057) p 0.000 ( 2.428) p 0.103 (3.334) p 0.659

Average # of coop. in depart. 1.842 1.092 0.051

(0.047) p 0.000 ( 1.591) p 0.493 (0.900) p 0.954

% unemployment 0.933 -10.396 -9.512

(0.669) p 0.163 (39.886) p 0.794 (2.926) p 0.001

1st stage predicted residual -1.347

(0.334) p 0.000

Year

5.055 4.682

(0.408) p 0.000 (0.389) p 0.000

fixed effect yes yes yes

year dummies yes yes yes

standard errors bootstrapped 500 rep clustered bootstrapped 500 rep

obs. 66983 183879 105075