SCHOOLS AND PUBLIC BUILDINGS IN DECAY: THE ROLE OF POLITICAL
FRAGMENTATION
Lars-Erik Borgea & bArnt O. Hopland
a
Department of Economics, Norwegian University of Science and Technology
b
Department of Business and Management Science, Norwegian School of Economics
e-mails: [email protected], [email protected]
Abstract It is a widespread concern that schools and other public buildings are in poor
conditions. A popular explanation is that maintenance is given too little priority in the budgetary
process because politicians are shortsighted. In this paper we investigate this hypothesis using
two novel survey data sets on school and general building conditions in Norwegian local
governments. We use political fragmentation as a proxy for myopic behavior and provide strong
empirical evidence that a high degree of political fragmentation is associated with poor building
conditions, both for schools and for buildings in general. The finding is robust to handling of
controls, outliers, and estimation method. We also provide evidence that lack of maintenance
is the channel for poor building conditions.
Keywords
School building conditions • Political fragmentation • Shortsighted policy
JEL classification
H72 • H82
1
1 Introduction
It is a widespread concern that schools and other public buildings are in poor conditions due to
insufficient maintenance. Maintenance tends to lose the budgetary battle and may in particular be
an easy target when public budgets are to be cut. The popular explanations are many. Shortsighted
politicians mainly care about winning the next election and pay too little attention to maintenance
activity that may save costs in the long term. Principals and public managers are mainly focused
on the services their organizations provide and may not have sufficient competence on how to best
maintain buildings.
Poor building conditions are likely to have negative consequences for the users of public services.
The impact of school facilities on student test scores has received particular attention. As surveyed
by Hanushek (1997), the results from the early literature are mixed. A recent contribution by
Neilson and Zimmerman (2014) find that school construction projects increase student
achievement and house prices. Cellini et al. (2010) also find a positive effect of school construction
projects on house prices, while the estimated effect on test scores is generally insignificant. Beyond
the effects of larger school construction projects, Hopland and Nyhus (2015) provide evidence that
indicators of school facilities and the physical learning environment positively affect student
performance. Moreover, Taskinen et al. (1997) and Buckley et al. (2005) find that deferred
maintenance and poor building may lead to health issues, turnover, and sick leave that increases
labor costs.
The findings that school construction projects increase house prices suggest that homeowners value
good school facilities, even though the empirical literature is inconclusive on how good facilities
directly affect educational outcomes. It is thus a puzzle that school buildings are insufficiently
maintained and run down in the first place. This is a highly interesting issue and the research
question we address in this paper. More specifically, why do we observe that schools and other
public buildings are in good condition in some local governments and in poor condition in others?
While Cellini et al. (2010) and Neilson and Zimmerman (2014) investigate the consequences of
improved school facilities, we investigate why schools and other public buildings tend to end up
2
in a state where major improvements are needed. A proper understanding of this issue may shed
light on how to achieve long-term effects of facility improvements.
The conditions of schools and other public buildings are also of concern in Norway. A government
commission (NOU 2004) that was appointed to evaluate the facility management in the local public
sector concluded that buildings in decay or insufficient maintenance were a substantial problem in
two thirds of the local governments. A survey conducted by the commission indicates that political
factors are of importance. Nearly half of the local governments responded that low political priority
of maintenance was the main cause of buildings in decay. Each of the other alternatives were stated
by less than 15 percent of the respondents. None responded that lack of competence on facility
management was the main cause.
Why is maintenance given too little political priority in order to keep buildings in good condition?
In this paper we investigate whether political fragmentation is of importance.
The empirical
analysis utilizes two unique data sets on building conditions in Norwegian local governments. Both
are survey data sets where local governments are asked to rate the conditions of respectively
schools and the building mass in general. We are not aware of earlier studies using this type of
data. Both data sets document huge variations in building conditions. In a majority of the local
governments schools and other buildings are in poor condition, but there is also a sizeable minority
with well-maintained buildings.
It is a robust finding that a high degree of political fragmentation is associated with poor building
conditions, both for schools and the general building mass. We also provide evidence that lack of
maintenance is a channel for poor building conditions in local governments with a high degree of
political fragmentation. Taken together our interpretation is that local governments with a high
degree of political fragmentation are less able to take long run considerations into account, and as
a consequence maintenance is given too little priority in the annual budgetary decisions. This
finding adds to the empirical literature on political fragmentation that has paid most attention to
budgetary variables such as deficit, current expenditures, taxes, and investments.
3
The rest of the paper is organized as follows. We start out in Section 2 by discussing related
theoretical and empirical literature and our indicator of political fragmentation. Section 3 presents
Norwegian institutional details and the two survey data sets on building conditions. The
econometric specification is discussed in Section 4, while Section 5 presents the estimation results.
In Section 6 we investigate whether lack of maintenance is a mechanism underlying the estimated
effect of political fragmentation and whether the effect of political fragmentation is robust to the
handling of party effects. Finally, Section 7 offers some concluding remarks and a brief discussion
of policy implications.
2 Related literature
Schools and other public buildings like nursing homes and child care centers are best understood
as inputs of real capital in the production of public services. Buildings enter the production function
along with labor and equipment. Buildings can be further separated into amount and quality, and
building conditions say something about quality. Since buildings have a long lifetime, building
conditions are mainly affected by maintenance of older buildings. Investments in new buildings
will only have a minor impact on average building conditions as new buildings in most cases
constitute a small share of the existing building stock.
Given that building conditions are understood as the quality of the capital stock, the literature most
closely related to this paper is the literature on the political economy of public investments. Several
of the theoretical contributions (e.g. Besley and Coate 1998, Darby et al. 2004, Bohn 2007, and
Azzimonti 2012) analyze investments in public infrastructure that increases productivity in the
private sector. The typical prediction from these models is underinvestment in public infrastructure.
The policy becomes myopic or shortsighted because politicians risk losing office and therefore
discount future benefits more heavily than voters.
The study by Natvik (2013) is of particular relevance for the politics of building conditions. He
extends the model of Tabellini and Alesina (1990) by including capital in the production functions
for public services. The incumbent makes decisions under the risk of losing office and being
4
replaced by a policymaker with different preferences for the mix of public services. If capital and
labor are complements in production, electoral uncertainty leads to myopic policy with
underinvestment in public capital. The intuition is that the return to investment, which depends on
future wage expenditures, is reduced when there is a risk of losing office.
Myopic policymaking is also emphasized in the empirical literature. For example, a key issue in
the debate on the “infrastructure crisis” in US state and local governments was whether the decline
in investment spending was a sensible response to changing economic and demographic conditions
(Holtz-Eakin and Rosen 1989, 1993) or whether it reflected myopic behavior by state and local
politicians (Inman 1983). De Haan et al. (1996) and Sturm (1998 ch. 3) find that frequent
government changes leads to cuts in public investment spending in a panel data analysis of OECD
countries. Their interpretation is that myopic governments cut investment spending more than
governments that have a longer time horizon. Darby et al. (2004) and Azzimonti (2012) also
provide empirical evidence that political instability is associated with lower levels of public
investment.
The theoretical and empirical literatures on public investments point towards an underinvestment
bias because of myopic policymaking. In this paper we investigate whether the same holds for
building conditions. Maintenance may be a particularly vulnerable spending type because it takes
time before the adverse consequences of insufficient maintenance becomes visible. Do schools and
other public building end up in poor conditions because shortsighted politicians give maintenance
too little priority?
In the empirical analysis we use political fragmentation as a proxy for myopic policymaking.
Several aspects of political fragmentation are emphasized in the literature; see, e.g., Ricciuti (2004).
Our focus in this paper is on so-called size fragmentation captured by the number of parties and
their seat share in the local council. We use the familiar Herfindahl-Hirschman (HH) index as
measure of political fragmentation:
P
HH   SH p2
(1)
p 1
5
where SH p is the share of representatives from party p and P is the total number of parties in the
council. The HH-index captures the number of parties in the local council and the distribution of
seats among them. The index can be interpreted as the probability that two randomly drawn
members of the council belong to the same party. The value of the index is reduced (fragmentation
increases) when the number of parties increases and when the seats become more equally divided
among a given number of parties.
The HH-index of political fragmentation is widely used as a determinant in empirical analyses of
budget deficits. The summary by Eslava (2011) emphasizes that the findings seem to confirm that
political fragmentation is related to larger budget deficits. Earlier studies of Norwegian local
governments have found that political fragmentation as measured by the HH-index increases
budget deficits (Borge 2005) and leads to more shortsighted spending behavior (Borge and Tovmo
2009).
3 Institutional details and measures of building conditions
3.1 Institutional details
Norwegian local governments are important providers of welfare services like child care, primary
and lower secondary education, primary health care, and care for the elderly. Other important tasks
are culture and infrastructure. The local public sector accounts for nearly 50 percent of government
consumption and their revenues make up 18 percent of GDP. The local governments have
substantial discretion in the allocation of resources between services, but are heavily regulated on
the revenue side. The central government controls around 85 percent of the local budget through
the grant system and tax revenue sharing
The political system at the local government level is a representative democracy where the
members of the local council are elected every fourth year. The national parties are important
players, and the national struggle between the socialist and non-socialist camps is mirrored at the
6
local level. Compared to national politics, a main difference is that the majority coalition does not
form a cabinet. The typical organization is an alderman model with an executive board with
proportional representation from all major parties.1 The executive board is led by the mayor, and
the members of the executive board, including the mayor and the deputy mayor, are in most cases
elected among the members of the local council.2 The mayor does not have veto power.
Prior to each fiscal year, the local council makes decisions regarding current spending, revenue,
investment activity and borrowing. The executive board and the chief administrative officer
(rådmannen) are central players in the early stages of the budgetary process, and the executive
board presents a budget proposal for the local council. The groupings in the local council are free
to put forward own suggestions, either small or large changes to the proposal from the executive
board, or totally different budget proposals. Finally, the local council determines the budget either
by voting over alternative budget proposals or issue by issue.
3.2 Building conditions
Local government buildings make up as much as ¼ of all non-residential buildings. Schools make
up nearly half of the total building mass and is the most important building type, followed by
nursing homes (22 percent), office buildings (11 percent), and child care centers (7 percent). In
addition, the building mass includes local culture centers (kulturhus), warehouses, and sports
facilities. On average the building mass amounts to 50 m2 per employee.
We have information about building conditions from two separate sources. The first source is the
Auditor General of Norway (Riksrevisjonen), who in 2004 conducted a survey on school building
conditions in a sample of local governments (Riksrevisjonen 2004-2005). The questionnaire was
mailed to the agency responsible for school buildings in 129 (out of 435 at the time of the survey)
local governments. All large local governments (population size above 20,000) were included. For
the rest, a stratified random sample was drawn, with stratification based on population size and
local government revenue. The response rate was as high as 85 percent, so we have data for 106
local governments. By design, large local governments are overrepresented in the sample.
1
2
A few larger cities have adopted a parliamentary system.
In the local elections in 1999 and 2003 the mayor was directly elected in some local governments.
7
These data serve very well for our purpose, since the survey focused exclusively on school
buildings that are at least 20 years old, i.e., built prior to 1985. All school are relatively old and
conditions are not influenced by recent investments in new school buildings. Each local
government was instructed to evaluate up to 10 schools. In order to secure a random sample of
schools, local governments with more than 10 schools were instructed to pick schools in
alphabetical order. A total of 671 schools were evaluated as part of the survey.3
Another advantage by this data set is that it was emphasized that the respondents should have a
common reference for the evaluation. The chosen reference was the Norwegian Standard 3424
Building Condition Analysis, where buildings are evaluated according to the following 0-3 scale:4
3
A building in very good conditions with no defects and only minor wear and tear
compared to new buildings.
2
A building in good, satisfactory condition where all legal regulations are obeyed.
Some wear and tear compared to new buildings.
1
A building with some defects that need corrective maintenance and/or where legal
regulations are not obeyed.
0
A building with extensive damage and defects, considerable need for corrective
maintenance, and much wear and tear. Legal regulations are not obeyed.
Of the reported schools, 11 percent were given grade 3, 31 percent grade 2, 44 percent grade 1, and
14 percent grade 0. Moreover, in nearly 90 percent of the local governments at least one of the
investigated schools received grade 1 or 0. Since we are primarily interested in how political
fragmentation at the local government level affects building conditions and we do not have access
to control variables at the school level (like age), most analyses will be carried out at the local
government level with the average condition at the local government level as dependent variable.
However, we will also report results using school level data. Descriptive statistics for the average
condition are reported in Table 1. It appears that the average school building condition among the
3
This is a bit more than 20 percent of all schools.
In the original scale 0 is the best grade and 3 the worst. We have reversed the scale so that the indicator of school
building conditions has the same ordering as the indicator of general building conditions.
4
8
106 local governments in our sample is 1.4. This is somewhat better than “a building with some
defects that need corrective maintenance and/or where legal regulations are not obeyed”.
Table 1 about here
As an alternative dependent variable we will use the share of schools in good condition. Good
condition is defined as grade 2 or 3, i.e., school building where all legal regulations are obeyed and
only some wear and tear compared to new schools. As can be seen from Table 1, the average share
of schools in good condition is 43.5 %, but with huge variation across local governments. In nearly
25 percent of the local governments all schools are in poor condition, and in 11 percent all schools
are in good condition.
The second data source is from a government commission (NOU 2004) that was appointed to
evaluate the facility management in the local public sector. The commission conducted a survey
on building conditions, maintenance, and organization of the facility management. The survey was
mailed to all local governments and achieved a response rate of 55 percent. Small local
governments (population size below 5,000) are underrepresented in the sample. The questionnaire
was designed to be filled out by the top administrative management. In most cases it was done by
the head of the facilities management unit.
As part of the survey, the respondents were asked to state to which extent the building mass in
general is well maintained. An advantage with these data is that they cover all types of buildings,
rather than just schools. Moreover, the number of respondents was quite high, since 239 (out of
435) local governments answered this question.
This scale is more open to interpretation compared to the data from the Auditor General’s survey,
but given the central position of the respondents it should paint a good picture of the state of the
building mass. The answer was imposed to be on a 1-6 scale, where 1 is “to very little extent” and
6 “to very large extent”. The response was 1 in 5 percent of the cases, 2 in 25 percent of the cases,
3 in 35 percent of the cases, 4 in 25 percent of the cases, 5 in 9 percent of the cases, and 6 in 1
percent of the cases, yielding the descriptive statistics reported in Table 1.
9
Figure 1 about here
Since schools make up nearly half of the building mass, it is of interest to check whether the two
indicators of building conditions are correlated. A total of 66 local governments are included in
both surveys, and in Figure 1 we have plotted the two indicators in the same diagram. It appears
that the two indicators are positively and significantly correlated. The coefficient of correlation is
0.33. Given the very different design of the surveys, we think the positive correlation yields
credibility to both indicators.
Figure 2 about here
Political fragmentation is our key explanatory variable and Figure 2 provides scatterplots between
the HH-index and respectively school building conditions and general building conditions. In both
cases the corresponding regression lines indicate a positive relationship between the HH-index and
building conditions, i.e., a high degree of political fragmentation is associated with poor building
conditions. However, an econometric analysis is necessary to rule out that the relationship is due
to confounding factors that affect both political fragmentation and building conditions.
4 Econometric specification and data
The baseline empirical analyses are based on various versions of the following linear regression
model
yi   0   P HH i  x'i βC  ui ,
(2)
where the dependent variable yi is either average school building conditions, the share of schools
in good condition, or general building conditions, and 𝑢𝑖 is an error term. The data for school
building conditions are continuous, since we have averages for several schools in each
municipality, while the data for overall building conditions are categorical as each local
10
government report on a 0-3 scale. In addition, we also have access to the categorical response for
individual schools. Hence, we will report results using both OLS and ordered probit.
When it comes to operationalization of the explanatory variables, it is important to take account of
the fact that building conditions are affected by maintenance activity during several years and that
the explanatory variables must be measured over a longer period. In the empirical analyses we thus
use averages that cover the period 1998-2003 for all variables.5 In the following we discuss the
operationalization of the control variables (the vector x'i ). Descriptive statistics for the explanatory
variables are reported in Table 7 in the Appendix. Because of a few missing observations for some
explanatory variables, the number of observations is reduced from 106 to 103 for school building
conditions and from 239 to 235 for general building conditions.
The key explanatory variable is the HH-index. Since it is inversely related to political
fragmentation, we expect  P to be positive. An increase in the HH-index (a lower degree of
political fragmentation) is expected to be associated with better building conditions and a higher
share of schools in good condition.
In the Norwegian context the socialist camp is dominated by the Labour Party, while the
nonsocialist camp comprises more equally sized parties. Consequently, there is strong correlation
between political fragmentation and political ideology. It is important to control for ideology in
order to rule out that our measure of fragmentation picks up the effect of ideology. In most
specifications we use the share of socialist in the local council (SOC) as indicator of ideology. The
correlation between the HH-index and SOC is around 0.40. As a robustness check we will also
conduct a finer investigation of party effects.
The typical assumption regarding ideological disagreement in the literature is that socialists prefer
a larger public sector than do nonsocialists. It is not clear what the implication of this assumption
is in the Norwegian context where local governments have little discretion to affect their revenues.
The main local decision is how to allocate the budget between different service sector and spending
5
The political variables are measured as averages over the three elections covering this period, i.e. the elections held
in 1995, 1999, and 2003.
11
categories (current expenditures, maintenance, and investment). Building conditions are affected
by maintenance and investments in new buildings, and a few recent studies have analyzed whether
ideology influences maintenance and investments. Borge and Hopland (2015) find no effect of the
share of socialists on neither maintenance nor investment spending. On the other hand, Fiva and
Natvik (2013) find that local governments with a nonsocialist incumbent increase investment
spending in response to increased re-election probability, while no effect is found for local
governments with a socialist incumbent.
Beyond ideology, the controls include variables that are common to include in empirical studies of
local government spending behavior as well as variables that are specifically relevant for building
conditions. We first control for local government revenue (REV) and relative population growth
(POPGROWTH). It may be easier for local governments with high revenues to allocate sufficient
resources to maintenance, to rehabilitate older buildings, and to invest in new buildings. Local
governments experiencing population decline may find it optimal to let some buildings fall into
decay.
The revenue measure requires some explanation. Compared to most other countries, the Norwegian
system of financing is quite centralized. Most local taxes are of the revenue sharing type where
effective tax limits have been in place since the late 1970s. We use a measure of revenue published
annually by the Ministry of Local Government. The starting point is the sum of local taxes and
general purpose grants, both measured per capita. But since high per capita revenue to some extent
is compensation for unfavorable cost conditions, the revenues must be “deflated” in order to
capture the real differences across local governments. The cost index from the spending needs
equalization system is used as deflator.6 It captures unfavorable cost conditions related to
population size, settlement pattern, the age composition of the population, and social factors. Since
the local taxes are of the revenue sharing type (where all local governments use the same tax rate
set by the central government), the real revenue measure can be interpreted as an indicator of fiscal
capacity. Differences in fiscal capacity reflect differences in tax bases and the design of the grant
system.
6
The calculation of the cost index is documented by the Ministry of Local Government (2006).
12
Since population size, settlement pattern, and age composition are included in the cost index, it
could be argued that they are sufficiently controlled for. However, we also include these variables
separately. Possible arguments are that the cost index is not ideal and that these variables may
affect local government priorities in other ways than through “real” revenues. Population size
(POPSIZE) is measured in thousands, the settlement pattern as percentage of the population living
in rural areas (RURAL), and the age composition of the population as the percentage of the
population 0-5 years (CH), 6-15 years (YO), and 80 years and above (EL). The three age groups
capture demand for respectively child care, primary and lower secondary education, and care for
the elderly. To account for other indicators in the cost index we also include the cost index (COST)
as a control variable.
Finally, we control for variables that may be of particular importance for building conditions. These
are climate measured by winter temperature (WTEMP) and average annual precipitation (PREC),
a dummy variable indicating whether or not the local government is located on the coast (COAST),
and county dummies.7 The survey conducted by the government commission provides information
on whether the responsibility for facility management is decentralized to individual institutions
(school, nursing homes, etc) or is handled at the local government level (centralized). We control
for organization of the facility management by a dummy variable (CENTR) that equals 1 if the
facility management is handled at the local government level.
5 Estimation results I: Building conditions
The results with school building conditions as dependent variable are reported in Table 2. The
starting point, Column (A), is a specification with the HH-index as the only explanatory variable.
The HH-index comes out as significant and with the expected positive sign as indicated by the
regression line in Figure 2a. This finding is consistent with the hypothesis that a high degree of
political fragmentation leads to shortsighted behavior and poor school building conditions.
Table 2 about here
7
There are 19 counties in Norway with an average of 23 local governments.
13
In column (B) we control for political ideology measured by the share of socialists in the local
council. The motivation is that the estimate of the HH-index in Column (A) may pick up an effect
of political ideology because of the substantial correlation between the two variables. It turns out
that the share of socialists comes out as insignificant and that the estimate of the HH-index
increases somewhat compared to Column (A).8 In Column (C) we also control for local government
revenue and population growth. Of these two variables population growth comes out as significant
and with expected positive sign, while local government revenue is insignificant. The inclusion of
additional controls increases the estimate and the significance of the HH-index.
In column (D) we include the full set of control variables, i.e. population size, the age composition
of the population, settlement pattern, the coast dummy, winter temperature, precipitation, and the
cost index. Of these additional variables only the share of children (0-5 years) and the share of the
population in rural areas come out as significant. Both variables are highly correlated with
population growth,9 which now becomes insignificant. The estimate of the HH-index increases
somewhat compared to column (C). Inclusion of county dummies in column (E) increases the
estimate of the HH-index further, but has little effect on the estimates of the other variables.
A visual inspection of the scatterplots in Figure 2 may indicate that the positive and significant
estimate of the HH-index is driven by a few observations with high values of the HH-index (above
0.5) and good building conditions. We investigate this in column (F) where the two local
government with HH-index above 0.5 are left out from the estimation.10 The estimate of the HHindex is very robust to this modification of the sample. The significance is somewhat reduced, but
the estimate is still significant at conventional levels.
For school buildings we have access to data on individual schools on the ordinal 0-3 scale. An
alternative to OLS on the local government level is to estimate an ordered probit using data on the
8
The share of socialists also come out as insignificant (t-value of -0.19) when it is included as the only explanatory
variable.
9
The coefficient of correlation is respectively 0.55 (between population growth and the share of children) and -0.45
(between population growth and the share of the population in rural areas).
10
The two local governments with HH-index above 0.5 have identical values for both the HH-index and average
school building conditions and may therefore look like one observation in Figure 2.
14
school level. The results reported in column (G) show that the estimate of the HH-index is positive
and highly significant also with ordered probit.
Table 3 about here
In Table 3 we report the estimates of the HH-index where the share of schools in good condition
as an alternative dependent variable. The specifications in columns (A)-(F) are the same as in
columns (A)-(F) in Table 2. As in Table 2, the HH-index consistently comes out as positive and
significant independent of the set of controls. Compared to Table 2, we observe that the
significance of the HH-index is somewhat higher with the share of good schools as dependent
variable.
In Table 4 we report the results with general building conditions as dependent variable. Table 4
has the same structure as Table 2. The only exceptions are that the organization of the facility
management is included as an additional control in Columns (D)-(G) and that the ordered probit
analysis in Column (G) is performed on the local government level. As for school buildings, the
estimate of the HH-index comes out as positive and significant. The estimate is very robust with
respect to inclusion of controls (Columns (A)-(E)), exclusion of outliers (Column (F)), and
estimation method (Column (G)).
Table 4 about here
The estimates for the control variables for general building conditions differ somewhat compared
to school building conditions. The main differences are that the share of socialists and local
government revenue are significant in some of the specifications for general building conditions.
The estimate of the share of socialists is significantly negative in Columns (B) and (C), but become
insignificant with the full set of controls in Column (D). Local government revenue comes out as
significant and with the expected positive sign in Columns (C) and (D), but become insignificant
when county dummies are included. This may reflect that the county dummies pick up a substantial
part of the regional variation in revenues. We also observe that settlement pattern and winter
15
temperature come out as significant when county dummies are included. Building conditions seem
to be better in rural areas and areas where winters are cold.
Spatial patterns are often emphasized in the empirical literature on local public expenditures and
taxation. Since, our two data sets only cover a sample of local governments, it is difficult control
for (geographical) neighbor-effects in the usual way. The county dummies included in the most
general specifications in Tables 2, 3, and 4 will to some extent capture spatial effects. For general
building conditions we have in addition estimated a model specification including dummies for
Statistic Norway’s classification of 90 economic regions rather than the 19 counties. The estimate
of the HH-index comes out as positive and significant also in this case.
The analyses above provide strong evidence that a high degree of political fragmentation is
associated with poor building conditions. The quantitative effects are also quite substantial.11 An
increase in political fragmentation by one standard deviation (around 0.075) is predicted to reduce
average school building conditions by 42 percent of a standard deviation (0.25 on the 0-3 scale)
and general building conditions by 32 percent of a standard deviation (0.34 on the 1-6 scale). The
share of schools in good condition is predicted to be reduced by 20 percentage points.
6 Estimation results II: Party effects and mechanisms
In this section we offer a finer investigation of effects from party ideology and provide some
evidence that maintenance is a channel underlying the estimated effect of political fragmentation.
So far we have used the share of socialists in the local council as indicator of political ideology. It
included the Labour party and parties to its left. It could be objected that this is a crude indicator
of ideology that masks finer party effects and also may affect our estimates of political
fragmentation. As an alternative we estimate model specifications that allow for finer party effects
where six parties or party groups are separated out. The two first are the major parties in Norway,
the Labour Party (Arbeiderpartiet) and the Conservative Party (Høyre). The third group is the socalled center parties with ideological orientation between the two major parties. The center parties
11
The following illustrations are based on the estimates in Tables 2, 3, and 4.
16
include the Liberal Party (Venstre), the Chrisitan Democrats (Kristelig folkeparti), and the Centre
Party (Senterpartiet). Left-wing parties are defined as parties to the left of the Labour Party and
includes the Socialist Left Party (SV) and Red Electoral Alliance (RV), while the Progress Party
(Fremskrittspartiet) is the only right-wing party. The remaining minor parties are to a large extent
local lists that are difficult to place on a traditional right-left scale.
Table 5 about here
In Table 5 we look more closely at potential effects from party ideology using the center parties as
reference category. For each of the three dependent variables (average school building conditions,
the share of schools in good condition, and general building conditions) we present results with
and without additional controls. The specifications without controls are extensions of Column (B)
in Tables 2, 3, and 4, while the specifications with controls are extensions of Column (E) in the
same tables. There is some evidence of party effects. For school building conditions the estimate
for the seat share of the Labour Party is in most cases significant, indicating that a high share of
representatives from the Labour Party is associated with poor school building conditions.
Otherwise most party effects are insignificant. Most important for our purpose is that the sign and
significance of the HH-index do not change when we allow for finer party effects.
The analyses carried out in Section 5 provide strong empirical evidence that a high degree of
political fragmentation is associated with poor building conditions. We have argued that the
underlying mechanism is likely to be that maintenance loses the budgetary battle because
politicians are short-sighted. It is a difficult task to detect the exact mechanisms at work, but in the
following we provide some evidence that maintenance is the channel. In doing so we utilize that
the respondents to the survey conducted by the government commission (NOU 2004) were asked
to assess to what extent proven need for maintenance is followed up in practice. The answer was
imposed to be on a 1-6 scale, where 1 is “to very little extent” and 6 “to very large extent”. The
responses indicate that on average proven need for maintenance to little extent is followed up.
Nearly half of the respondents answered 1 or 2, while only 5 percent answered 5 or 6. For our
purpose, however, the key question is whether proven need for maintenance to a less extent is
followed up in local governments with a high degree of political fragmentation.
17
Table 6 about here
To answer this question, we estimate similar regressions as in Tables 2, 3, and 4, but where the
dependent variable, instead of building conditions, is to what extent a proven need for maintenance
is followed up in practice. The estimation results are reported in Table 6. It appears that the HHindex comes out as positive and significant in all specifications. The estimate is very robust to the
inclusion of controls (Columns (A)-(C)) and estimation method (Column (D)). The interpretation
is that is less likely that the respondents answer that a proven need for maintenance is followed up
in practice when the degree of political fragmentation is high (the HH-index is low). We think this
finding supports the view that lack of maintenance is a channel for poor building conditions in local
governments with a high degree of political fragmentation.
7 Concluding remarks
In this paper we have investigated the relationship between political fragmentation and the
conditions of schools and other buildings using two novel survey data sets on Norwegian local
governments. We provide strong empirical evidence that a high degree of political fragmentation
is associated with poor building conditions. The finding is robust to handling of controls, outliers,
and choice of estimation method. Our interpretation is that maintenance lose the budgetary battle
because of shortsighted policymaking. A supplementary analysis indicate that lack of maintenance
is a channel for the effect of political fragmentation.
Compared to the recent studies by Cellini et al. (2010) and Neilson and Zimmerman (2014) who
analyze how school construction projects affect home prices and student test scores, our analysis
provides an understanding of why schools end up in a state where major improvements are needed.
The results indicate that facility improvements are more likely to have long term effects when the
degree of political fragmentation is low.
18
The political fragmentation of the local councils is affected by the electoral system and the diversity
of preferences in the electorate. Given the diversity of preferences, the degree of fragmentation in
the council may be reduced by making it more difficult for small parties to be represented. This
could be achieved by reducing the size of the council, by imposing a minimum percentage of votes
required for representation, by changing the rules for distribution of representatives among parties
in favor of larger parties (e.g. by using d'Hondts method instead of Sainte-Laguës method)12, or by
my moving from a single to multiple electoral districts. This study of building conditions, as well
as earlier Norwegian studies of administrative spending (Kalseth and Rattsø 1998), budget deficits
(Borge 2005), and efficiency (Borge et al. 2008), indicate that reduced degree of fragmentation
may lead to improved policy outcomes. However, the benefits of less fragmentation must be
balanced against the disadvantage of a having a council that is less representative.
Acknowledgements
We are grateful for comments from participants at the Meeting of the European Public
Choice Society (Rennes), the Congress of International Institute of Public Finance (Ann Arbor), the NorwegianGerman Seminar on Public Economics (Munich), a seminar at the University of Maryland College Park, and
workshops in Barcelona and Uppsala. We would also like to thank Matz Dahlberg, two referees, and editor Marko
Köthenbürger for helpful comments. Per T. Eikeland (leader of the NOU 2004: 22 government commission) and the
Office of the Auditor General of Norway have kindly provided survey data on building conditions. Pernille Parmer
assisted us in collecting and constructing the data on temperature and precipitation. Some of the data are obtained from
the Norwegian Social Science Data Services (NSD). This paper is part of a project funded by the Norwegian Ministry
of Local Government and Regional Development. The authors bear the responsibility for the analyses and the
conclusions that are drawn.
Appendix Descriptive statistics for the explanatory variables
Table 7 about here
12
Fiva and Folke (2016) analyze a switch from d'Hondts method to a modified Sainte-Laguës with the 2003 local
election in Norway. They document that this switch increased political fragmentation measured by the effective
number of parties in the local council (the inverse of the HH-index).
19
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22
Tables
Table 1
Descriptive statistics for the dependent variables
Variable
Mean p10
p25
Average building conditions, N=106
1.4
0.7
1.0
Share of good schools, N=106
0.435
0
0.100
General building conditions, N=239
3.1
2
2
p50
1.3
0.414
3
p75
1.8
0.700
4
p90
2.1
1
5
Note: School building conditions are measures on a 0-3 scale and general building conditions on a 1-6 scale. A good
school is defined as a school with grade 2 or 3.
23
Table 2
The determinants of school building conditions
(A)
(B)
(C)
Local government average
1.381
1.662
1.958
(2.21)
(2.35)
(2.80)
-0.427
-0.506
(-0.92)
(-1.28)
0.0061
(1.06)
0.0295
(2.04)
(D)
(E)
(F)
2.391
(2.68)
-1.237
(-1.78)
0.0070
(1.46)
0.0047
(0.22)
-0.0014
(-0.60)
24.95
(1.97)
-2.81
(-0.32)
16.80
(1.59)
-0.753
(-1.78)
-0.264
(-1.45)
-0.083
(-0.20)
-0.0012
(-0.56)
-0.770
(-1.31)
3.392
(2.85)
-1.228
(-1.29)
0.0053
(1.07)
-0.0149
(-0.59)
-0.0011
(-0.43)
27.47
(1.78)
-0.38
(-0.04)
17.99
(1.51)
-0.733
(-1.63)
-0.288
(-1.27)
-0.038
(-0.69)
-0.0008
(-0.29)
-1.272
(-1.99)
3.260
(2.10)
-1.342
(-1.41)
0.0087
(1.51)
-0.0173
(-0.69)
-0.0011
(-0.46)
24.50
(1.47)
0.22
(0.02)
21.56
(1.83)
-0.562
(-1.16)
-0.334
(-1.41)
-0.036
(-0.64)
-0.0005
(-0.19)
-2.304
(-1.82)
Method
OLS
OLS
OLS
OLS
OLS
OLS
County dummies
Number of observations
R2/Pseudo R2
No
103
0.030
No
103
0.037
No
103
0.097
No
103
0.201
Yes
103
0.351
Yes
101
0.347
Herfindahl-Hirschman index, HH
Share of socialists in the local council,
SOC
Local government revenue, REV
Population growth (%), POPGROWTH
Population size (in 1000), POPSIZE
Share of population 0-5 years, CH
Share of population 6-15 years, YO
Share of population 80 years and
above, EL
Share of population in rural areas,
RURAL
Coast dummy, COAST
Winter temperature, WTEMP
Precipitation, PREC
Cost index, COST
Note: The dependent variable in Column (A)-(F) is average school building conditions, while the dependent variable
in Column (F) is the condition of individual schools. The t-values in parentheses are based on robust standard errors.
A constant term (not reported) is included in all equations. In column (F) the two local governments with HH-index
above 0.5 are not included. The t-values in Column (G) are based on standard errors clustered at the local
government level.
24
(G)
School
4.066
(2.64)
-1.148
(-1.48)
0.0130
(1.72)
-0.0303
(-1.15)
-0.0022
(-0.86)
32.83
(1.77)
-4.23
(-0.43)
25.17
(1.79)
-0.654
(-1.39)
-0.420
(-1.66)
-0.0536
(-0.82)
-0.0025
(-0.61)
-2.854
(-2.70)
Ordered
probit
Yes
658
0.061
Table 3
The determinants of the share of schools in good condition
Herfindahl-Hirschman index, HH
(A)
1.249
(3.31)
(B)
1.434
(3.60)
(C)
1.704
(3.69)
(D)
1.842
(2.86)
(E)
2.691
(3.45)
(F)
2.259
(2.41)
Method
Number of observations
R2
OLS
103
0.079
OLS
103
0.090
OLS
103
0.109
OLS
103
0.189
OLS
103
0.384
OLS
101
0.369
Note: The dependent variable is the share of schools in good condition defined as a school with grade 2 or 3. The tvalues in parentheses are based on robust standard errors. The control variables are the same as in the corresponding
models in Table 2. Column (A): None. Column (B): SOC. Column (C): Controls in (B) and REV, POPGROWTH.
Column (D): Controls in (C) and POPSIZE, CH, YO, EL, RURAL, COAST, WTEMP, PREC, COST. Columns (E)
and (F): Controls in (D) and county dummies. In column (F) the two local governments with HH-index above 0.5 are
not included.
25
Table 4
The determinants of general building conditions
(A)
2.289
(2.55)
(B)
3.194
(3.50)
-1.182
(-2.23)
(C)
4.145
(4.09)
-1.244
(-2.38)
0.0072
(2.82)
0.0496
(2.84)
(D)
4.558
(3.09)
-1.062
(-1.43)
0.0064
(2.20)
0.0036
(1.40)
0.0003
(0.07)
10.73
(1.07)
-5.01
(-0.66)
-4.51
(-0.49)
0.661
(1.45)
-0.280
(-1.48)
-0.0024
(-0.13)
0.0036
(1.79)
-0.737
(-0.93)
0.180
(0.96)
(E)
4.580
(3.01)
0.314
(0.42)
0.0027
(0.79)
-0.0071
(-0.26)
0.0009
(0.20)
15.75
(1.35)
-1.20
(-0.16)
-11.31
(-1.14)
0.927
(1.93)
0.183
(0.66)
-0.0794
(-1.97)
0.0051
(1.53)
-0.436
(-0.49)
0.121
(0.63)
(F)
3.408
(2.13)
0.568
(0.76)
0.0016
(0.43)
-0.0045
(-0.16)
0.0012
(0.25)
16.74
(1.31)
-2.88
(-0.38)
-19.88
(-1.86)
0.930
(1.91)
0.216
(0.77)
-0.0929
(-2.26)
0.0057
(1.66)
0.683
(0.55)
0.090
(0.47)
(G)
5.116
(3.16)
0.335
(0.43)
0.0029
(0.82)
-0.0066
(-0.22)
0.0011
(0.24)
16.66
(1.37)
-0.78
(-0.10)
-12.24
(-1.18)
1.014
(1.99)
0.202
(0.69)
-0.0905
(-2.18)
0.0058
(1.69)
-0.487
(-0.52)
0.128
(0.63)
Method
OLS
OLS
OLS
OLS
OLS
OLS
County dummies
Number of observations
R2/Pseudo R2
No
235
0.025
No
235
0.043
No
235
0.085
No
235
0.124
Yes
235
0.244
Yes
232
0.246
Ordered
probit
Yes
235
0.092
Herfindahl-Hirschman index, HH
Share of socialists in the local council,
SOC
Local government revenue, REV
Population growth (%), POPGROWTH
Population size (in 1000), POPSIZE
Share of population 0-5 years, CH
Share of population 6-15 years, YO
Share of population 80 years and
above, EL
Share of population in rural areas,
RURAL
Coast dummy, COAST
Winter temperature, WTEMP
Precipitation, PREC
Cost index, COST
Centralized, CENTR
Note: The dependent variable is general building conditions measured on a 1-6 scale. The t-values in parentheses are
based on robust standard errors. A constant term (not reported) is included in all equations. In column (F) the three
local governments with HH-index above 0.5 are not included.
26
Table 5
Investigating party effects
Herfindahl- Hirschman
index, HH
Seat share left-wing
parties
Seat share Labour party
Seat share Conservative
party
Seat share right-wing
party
Seat share other parties
Additional controls
Number of observations
R2
Average school
building conditions
3.345
3.898
(4.18)
(2.55)
0.513
0.313
(0.53)
(0.20)
-1.492
-1.748
(-2.99)
(-1.60)
-1.012
0.286
(-1.34)
(0.25)
2.748
-0.265
(2.19)
(-0.15)
-0.338
0.333
(-0.71)
(0.53)
Share of schools in
good condition
2.277
2.930
(4.57)
(3.23)
0.274
0.058
(0.39)
(0.07)
-0.813
-1.220
(-2.72)
(-2.33)
-0.282
0.352
(-0.61)
(0.65)
0.733
-1.508
(1.10)
(-1.68)
-0.297
0.127
(-1.22)
(0.34)
General building
conditions
3.811
4.544
(2.81)
(2.72)
-1.736
1.390
(-1.18)
(0.87)
-1.421
-0.046
(-1.72)
(-0.05)
-1.335
-0.655
(-1.38)
(-0.68)
1.530
-1.46
(1.22)
(-0.68)
-0.727
0.164
(-1.02)
(0.21)
No
103
0.104
No
103
0.124
No
235
0.055
Yes
103
0.377
Yes
103
0.438
Yes
235
0.250
Note: The t-values in parentheses are based on robust standard errors. A constant term (not reported) is included in
all equations. The center parties constitute the reference category. The additional controls are the same as in column
(E) in Tables 2, 3, and 4 and includes REV, POPGROWTH, POPSIZE, CH, YO, EL, RURAL, COAST, WTEMP,
PREC, COST, and county dummies.
27
Table 6
The determinants of follow up of proven need for maintenance
(A)
3.430
(4.00)
(B)
4.443
(4.54)
-1.314
(-2.33)
(C)
4.463
(3.03)
-0.300
(-0.37)
0.0060
(2.00)
-0.0069
(-0.24)
0.0023
(0.58)
15.06
(1.24)
-5.59
(-0.74)
-16.36
(-1.96)
0.694
(1.50)
0.056
(0.22)
-0.0656
(-1.47)
0.0048
(1.54)
0.699
(0.80)
-0.076
(-0.38)
(D)
4.872
(3.25)
-0.402
(-0.49)
0.0066
(2.18)
-0.0028
(-0.10)
0.0027
(0.69)
15.21
(1.21)
-5.98
(-0.77)
-17.20
(-2.00)
0.680
(1.43)
0.056
(0.22)
-0.0779
(-1.72)
0.0055
(1.72)
0.835
(0.92)
-0.110
(-0.55)
Method
OLS
OLS
OLS
County dummies
Number of observations
R2/Pseudo R2
No
234
0.053
No
234
0.073
Yes
234
0.247
Ordered
probit
Yes
234
0.094
Herfindahl-Hirschman index, HH
Share of socialists in the local council,
SOC
Local government revenue, REV
Population growth (%), POPGROWTH
Population size (in 1000), POPSIZE
Share of population 0-5 years, CH
Share of population 6-15 years, YO
Share of population 80 years and
above, EL
Share of population in rural areas,
RURAL
Coast dummy, COAST
Winter temperature, WTEMP
Precipitation, PREC
Cost index, COST
Centralized, CENTR
Note: The dependent variable is whether proven need for maintenance is followed up in practice on a 1-6 scale. The
t-values in parentheses are based on robust standard errors. A constant term (not reported) is included in all
equations.
28
Table 7
Descriptive statistics for the explanatory variables for each survey sample
Variable
Description
Herfindahl-Hirschman
index
Share of socialists in the
local council
Local government revenue
An indicator of party fragmentation in the local council.
Average for the elections held in 1995, 1999, and 2003.
The share of socialists in the local council. Average for the
elections held in 1995, 1999, and 2003.
The sum of local taxes and lump-sum grants from the
central government. Measured per capita and adjusted for
spending needs. Normalized such that the weighted average
(for all local governments) equals 100 each year. Average
1998-2003.
The percentage growth in population size from January 1,
1988 to January 1, 2003.
The number of inhabitants, in thousands. Average 19982003.
The share of the population 0-5 years. Average 1998-2003.
Population growth
Population size
Share of population 0-5
years
Share of population 6-15
years
Share of population 80 years
and above
Share of population in rural
areas
Coast
The share of the population 6-15 years. Average 1998-2003.
The share of the population 80 years. Average 1998-2003.
The share of the population living in rural areas. Average
1998-2003.
A dummy equal to 1 if the local government has a coastline.
Winter temperature
Average winter temperature 1998-2003.
Precipitation
Average annual precipitation in cm. Average 1998-2003
Cost index
The spending need index used to adjust the local
government revenues measure. Average 1998-2003
29
Mean
(st.dev)
School buildings
Buildings
in general
0.256
0.262
(0.076)
(0.075)
0.371
0.382
(0.135)
(0.134)
102.1
103.8
(15.9)
(23.3)
4.1
(12.6)
18.3
(31.1)
0.078
(0.010)
0.139
(0.014)
0.050
(0.0160)
0.424
(0.307)
0.66
(0.48)
-1.89
(3.20)
114.12
(42.40)
1.10
(0.21)
2.0
(12.1)
11.6
(2.2)
0.076
(0.993)
0.139
(0.014)
0.051
(0.016)
0.470
(0.269)
0.64
(0.48)
-2.46
(3.50)
112.42
(46.03)
1.11
(0.17)
Figures
Figure 1
Correlation between the building condition measures
30
Figure 2
Correlation between the HH-index and building conditions
31