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GEN - Governance and Economics research Network
GEN Working Paper A 2014 – 5
http://webs.uvigo.es/infogen
THE INFLUENCE OF DECENTRALIZED TAXES AND
INTERGOVERNMENTAL GRANTS ON LOCAL SPENDING
VOLATILITY
Agnese Sacchi**
Simone Salotti*
* Oxford Brookes University (UK)
** Universitas Mercatorum (Italy) and GEN
The influence of decentralized taxes and
intergovernmental grants on local spending volatility
Agnese Sacchi
Universitas Mercatorum (Italy)
and
Governance and Economics research Network (Spain)
E-mail: a.sacchi@unimercatorum.it
Simone Salotti
Oxford Brookes University (UK)
E-mail: ssalotti@brookes.ac.uk
Abstract
We study what affects the volatility of sub-central spending in 20 OECD countries. The evidence
based on data from 1972 to 2007 shows that the volatility of intergovernmental grants from upper
levels is positively associated with the volatility of local expenditure. On the contrary, the volatility of
local tax revenues - mainly that of property taxes - exerts the opposite effect. Thus, making local
governments rely more on grants than own taxes seems to adversely affect the stability of their
spending, while allowing them to autonomously levy taxes on responsive tax bases provides
incentives to smooth their expenditure.
Keywords: local spending volatility, local revenues, property taxes, intergovernmental grants.
JEL classification: E62, H71, H77, R50
1
1. Introduction
In most advanced economies the fiscal responsibility assigned to sub-national government
levels has recently been increased with the aim of improving the efficiency in the allocation
of public resources (Stigler 1957; Musgrave 1959; Oates 1972), and possibly boosting
economic growth (Martinez-Vazquez and McNab 2003). However, in most countries those
reforms have led to a widespread mismatch between expenditure and tax decentralization,
with the former being more accentuated than the latter (OECD 2012). As a result, most sub-
central governments have varying degrees of autonomy over the different types of revenues
used to finance their expenditures, that is own taxes, piggybacked and shared taxes, and
grants that represent money flowing from other tiers of government - mainly from the central
government (OECD 2006a, Charbit 2010). Such divergence goes against the theoretical
prescriptions of the fiscal federalism literature according to which expenditure responsibility
should be combined with a sufficient budgetary autonomy at each government level (Shah
1998; Mc-Lure and Martinez-Vazquez 2000), and can affect the economic implications of the
decentralization process.
First, decentralization is thought to be less effective when local governments
1
rely on
transfers and grants rather than own resources (Weingast 2009, 2014)
because the existence
of grants lowers the accountability of local governments and does not respect the ‘benefit
principle’ of taxation. According to that principle, sub-central governments should rely on
taxes perceived by households and firms to be clearly linked to the public services received
(Musgrave 1983; King 1984; Oates and Schwab 1988). More autonomy is likely to lead to
better local services (Hoffman and Gibson 2005) and possibly to a sounder development path
over time (Sokoloff and Zolt 2006). Second, as pointed out by Ashworth et al. (2013) and
Rodden (2003), tax revenues raised by sub-central governments lead to smaller aggregate
government sizes, while grants have the opposite effects. Cassette and Paty (2010) also show
that grants-financing leads to larger local governments (consistently with the common pool
theory), while smaller local governments result from local expenditure being financed by
own taxes (according to the Leviathan hypothesis). Finally, according to a study by the
OECD (2009a) grants tend to exacerbate sub-central revenue fluctuations over the cycle.
A related but unexplored issue is whether the volatility of local spending is affected by
the revenues used to finance such expenditure. Sub-central spending volatility is worth
studying in light of its potential effects on the economy, as suggested by the literature on
1
The terms “local”, “sub-national”, and “sub-central” are used interchangeably throughout the paper.
2
aggregate spending volatility. On one hand, the latter may have detrimental effects on
economic growth and welfare (Fatas and Mihov 2003, 2005; Furceri 2007; Loayza et al.
2007; Afonso and Furceri 2010); on the other hand, it may be beneficial to smooth out
business cycle fluctuations (Furceri and Ribeiro 2009). It is conceivable for local spending
volatility to have similar effects, as it constitutes a significant part of aggregate spending in
most advanced countries (OECD 2009b).
Although the volatility of local expenditure may well be related to the size of local
governments, the literature briefly mentioned above has concentrated on the latter and has yet
to explore the former. The existing contributions dealing with volatility only investigate that
of aggregate expenditure measures, such as government consumption (Furceri and Ribeiro
2009) or discretionary spending (Albuquerque 2011). The main existing findings point
towards spending volatility being negatively associated with the quality of institutions and
country size/population. In a recent paper, Furceri et al. (2014) find that the level of fiscal
decentralization is also negatively associated with the volatility of government consumption,
suggesting that redistributing spending and taxing powers to sub-central governments may
alleviate government consumption volatility.
We contribute to the existing literature along the following lines. We study the volatility
of local public spending concentrating on the role played by the financing sources used by
local governments, and distinguishing between intergovernmental grants and own tax
revenues. We further break down the latter into the following three main tax components:
taxes on property, on income, and on goods and services. This disaggregation is relevant as
property taxes are normally assigned to sub-national governments through tax separation
arrangements, meaning that only sub-national governments are entitled to collect and manage
such taxes, deciding over the tax rates, the tax bases, and other relevant issues (e.g.,
exemptions and reliefs)
2
; on the other hand, the other two types of taxes usually follow tax
base/revenue sharing schemes. As a result, the literature suggests that they may be used
differently by governments, with different consequences in terms of efficiency and
accountability (McKenzie and Staaf 1978; Zax 1988), economic performance (Karras and
Furceri 2009), and fiscal discipline (Presbitero et al. 2014). Although most studies conclude
favorably on the utilization of property taxes (e.g. Charbit 2010), increasing the weight of
2
Although local tax systems differ across countries, experts agree that property taxes are the easiest to assign to
sub-national governments (Lotz 2006). McCluskey et al. (2012) provide a thorough analysis of local taxation,
with a particular focus on property taxes.
3
property taxes in the revenue mix of sub-central governments usually meets with strong
resistance given the perceived salience of this tax (Cabral and Hoxby 2012).
Our empirical model analyzes the determinants of local spending volatility, and its main
explanatory variables deal with the revenue mix of the local governments. We also take into
account potential endogeneity issues that may arise given the interdependency of the revenue
and expenditure decisions. The main results, based on a sample of 20 OECD countries over
the 1972-2007 period, can be summarized as follows: the volatility of local public spending is
significantly affected by that of the revenues available at the sub-central level. The evidence
suggests that there are important differences between own taxes and revenues over which
sub-national governments cannot exert much control, such as intergovernmental grants and
shared taxes. In particular, the higher the volatility of intergovernmental grants, the higher the
volatility of public spending. This proves that having to rely on transfers from upper levels
make local expenditure more prone to instability. This result fits well with related evidence
suggesting that grants also reduce the sub-central governments’ tax effort and inflate their
spending, with adverse consequences on local deficits and debt (Stein 1999, OECD 2009a).
On the other hand, the volatility of property taxes is inversely related to the volatility of
spending. This suggests that local governments manipulate own taxes in order to smooth their
expenditure when they have sufficient autonomy to do so. This seems to be in line with the
findings of Glaeser (1996) who points out that property taxes provide incentives for adequate
and efficient local public goods provision (see also Borge and Rattso 2008; Fiva and Ronning
2008). The impact of the volatility of the other types of local taxes (on income and on goods
and services) on that of local expenditure is instead more similar to that of grants' volatility.
This may reflect the fact that local governments do not enjoy high degrees of autonomy over
such taxes, making their economic effects resemble those of intergovernmental grants.
The remainder of the paper is organized as follows. In Section 2 there is a brief review of
the two strands of literature that this paper builds upon, and some hypotheses are drawn as a
basis for the empirical analysis. Section 3 illustrates the empirical strategy and the data, while
section 4 contains the results of the analysis. Finally, section 5 concludes and provides some
policy implications.
2. Review of related literature
Our contribution integrates two different strands of literature: 1) the studies on fiscal
decentralisation, both those analysing the expenditure/tax mismatch, and those focusing on
4
the implications of the various sources of revenues for the sub-central and aggregate
government size; 2) the literature on the determinants of government (aggregate) spending
volatility.
2.1 Fiscal decentralisation and the local revenue system
The investigation of the volatility of local expenditure needs to take into account not only the
relationship between expenditure decentralization and tax decentralization, but also the
structure and composition of the revenue side of local budgets. When certain expenditure
tasks are assigned to sub-national authorities, adequate revenues are likely to be needed to
finance them. These financing requirements are usually met with a mix of own and shared
taxes, and intergovernmental grants. Existing studies demonstrate that fiscal decentralization
funded by intergovernmental grants gives rise to common revenue pool issues and it is
associated with higher overall and local government spending (see, among others, Rodden
2003; Fiva 2006). On the contrary, fiscal decentralization based on own taxes seems less
likely to lead to soft budget constraints and it is associated with smaller local and overall
governments (Jin and Zou 2002, Cassette and Paty 2010; see Golem 2010 for a review).
Given those findings on the ‘common pool versus own resources’ issue, there is
surprisingly little research digging further into the role of local revenue composition. Liberati
and Sacchi (2013) constitute a notable exception studying the impact of different
disaggregated tax revenues on the size of local governments. According to their findings, not
all types of local taxes tend to constrain sub-central government spending over GDP:
property taxes are strongly associated with smaller local governments, but income and goods
and services taxes are not. The intuition behind this result lies in the latter taxes being usually
assigned to lower tiers of governments following revenue-sharing mechanisms and
piggybacked formulas (implying overlapping fiscal competences among government levels
and less taxing power and autonomy for sub-national authorities). On the other hand,
property taxes are more frequently based on tax-separation schemes, i.e. on tax bases used
solely by local governments (OECD 1999). Thus, local governments enjoy higher degrees of
autonomy and responsibility over property taxes (Bordignon and Minelli 2001), which
explains their different economic consequences with respect to the other types of taxes (Slack
and Bird 2014).
There are other reasons that may explain why property taxes seem to be more effective in
limiting the growth of local public spending. Contributions as early as Tiebout (1956)
recognized that property taxes are the ideal form of local taxation because they encourage
5
local policy makers to design efficient policies that attract capital and labor and reduce the
wastage of resources. Brennan and Buchanan (1978, 1980) develop theoretical arguments
suggesting that a responsive tax base, such as that of property taxes, may help limit the
growth of the public sector in the case of Leviathan governments. According to some
researchers, the reliance on property taxes can increase the incentives to control costs in the
public goods provided at the local level such as utility services and school districts (Borge
and Rattso 2008; Fiva and Ronning 2008; Crowley and Sobel 2011). When local property
taxes finance local services, public sector decisions are likely to be more efficient because
taxpayers would presumably support those activities whose perceived benefits exceed the
burden of taxes. Put differently, property values would increase to the extent that benefits and
taxes are capitalized into property values (Fischel 2001).
Moreover, property taxes are a more reliable and predictable source of revenues than
other forms of taxation (Norregaard 2013), since the property tax base is mostly immovable
and taxpayers can hardly relocate it to areas with lower tax rates. This reduces the tax
competition that may arise in the case of mobile tax bases (Eyraud, 2014). The reliability of
property tax revenues also lies in the legally defined value of properties (Brunori 2003; Giertz
2006; Alm et al. 2011; Doerner and Ihlanfeldt 2011; Lutz et al. 2011). There are other factors
reducing the cyclicality of local revenues arising from property taxes: for instance, Lutz et al.
(2011) find that policy makers tend to offset declines in the property tax base (e.g. following
housing prices’ declines) by raising the tax rates, despite the sensitivity of voters to changes
of this particular tax as documented by Cabral and Hoxby (2012).
3
Our paper is also related to the studies on the widespread divergence between sub-
national expenditures and tax revenues, leading to a well-documented fiscal imbalance
(OECD 2012, Sacchi and Salotti 2014). The lack of correspondence between local taxes and
local expenditures is made up for by transfers from upper government tiers. As argued by
Ashworth et al. (2013), grants and revenue sharing programs may de facto blur the
responsibility for spending decisions and make it easier for sub-central governments to shift
the political and economic costs of their spending decisions onto others. If local governments
do not finance services themselves, the connection between expenditures and revenues is lost
and the choice of services will not be based on an accurate perception of their cost (Bird and
Slack 2013).
3
One may think that housing prices booms and busts such as those experienced by a number of countries in the
recent decade may render property tax revenues highly volatile, however the empirical evidence does not
support this idea: both Lutz et al. (2011) and Doerner and Ihlanfeldt (2011) demonstrate that property tax
revenues do not tend to decrease following house price declines.
6
Thus, expenditure decentralization without corresponding local taxing powers is likely to
neither generate beneficial tax competition among government levels, nor solve principal-
agent problems between residents and local representatives (Rodden et al. 2003; Rodden
2003; Devarajan et al. 2009; Khemani 2010).
4
Given the findings illustrated above, we posit the following hypotheses:
H
1
: The volatility of the revenues utilized by local governments affects that of their
expenditure.
However, given the different nature of the different types of revenues used to finance
local expenditure, we further develop hypothesis H
1
as follows:
H
2
. The higher (lower) the volatility of grants, the higher (lower) the volatility of local
spending. This would be explained by the fact that local governments can only respond to the
volatility of a revenue stream over which they have no control by modifying their
expenditures, therefore increasing their volatility;
H
3
. The higher (lower) the volatility of local taxes, the lower (higher) the volatility of
local spending. This would signal the ability of local governments to manipulate their taxes in
order to smooth the volatility of their expenditures. However, the autonomy over own taxes
differ from that over shared taxes: property taxes usually pertain to the former group, while
income and consumption taxes to the latter. Thus:
H
3a
. H
3
should hold for property taxes more so than for taxes on income and on goods
and services, over which local governments have less autonomy.
Our empirical analysis is constructed so to be able to test the above hypotheses, therefore
it analyses how the volatility of intergovernmental grants and local taxes (further
disaggregated into property, income, and consumption taxes) influences the volatility of local
expenditure. The general framework used builds on the literature on spending volatility
reviewed in the following sub-section.
2.2 Government spending volatility
The existing literature offers some contributions on the determinants of the volatility of
government aggregate expenditure and on its economic effects. However, to the best of our
knowledge, no effort has been made to deal with the determinants of local spending volatility
4
The importance of the vertical imbalance is also proved by the many empirical findings differing depending on
the use of expenditure versus tax decentralization series (e.g., Afonso and Hauptmeier 2009; Escolano et al.
2012; Gemmell et al. 2013).
7
as we do in our paper. Therefore, we can only partially rely on, and take advantage from, the
existing studies on spending volatility for our analysis. Although local expenditure constitutes
a non-negligible part of aggregate expenditure, the volatility of the former is likely to be
driven by different factors (above all, those related to the revenue side of the local budget).
The literature offers a few studies on the determinants of aggregate government spending
volatility. Furceri and Ribeiro (2009) use data for 160 countries from 1960 to 2000 to prove
that country size, proxied by total population, is associated with lower government
consumption volatility. This suggests that smaller countries are characterized by more
volatile public spending. In addition to country size, the literature (Rodrik 1998, Fatás and
Mihov 2003, 2005) suggests that both demographic (e.g., the urbanization rate, and
population density) and macroeconomic (e.g., GDP per capita, openness, and inflation)
factors may potentially affect government consumption volatility. In general, the level of
development is thought to crucially affect the latter, with low-income economies
experiencing higher volatility than high-income ones (Furceri et al. 2014). Given that our
analysis deals with developed countries only (and that it concentrates on the volatility of
local, rather than aggregate, spending), we do not expect such variables to play particularly
crucial roles, but we still include them as controls in some of the estimates.
All in all, the relevance of studying the volatility of public spending mostly lies in its
potentially important economic effects. Afonso and Furceri (2010) argue that the volatility of
government consumption is detrimental to growth in advanced economies (this confirms
earlier evidence offered by, among others, Brunetti 1998; Gong and Zou 2002; Furceri 2007).
On the other hand, some authors argue that restrictions on government spending, and
therefore lower spending volatility, may result in a slower adjustment of the economy to
unexpected shocks (Roubini and Sachs 1989, Poterba 1995, Lane 2003), and therefore more
macroeconomic instability.
3. The empirical strategy
We use data for 20 OECD countries over the period 1972-2007 organized in three different
multi-year frequencies, i.e. 3-year, 4-year, and 5-year non-overlapping periods, used
alternatively for robustness purposes.
5
The use of multi-year periods is necessary in order to
5
The countries are the following: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany,
Greece, Iceland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, the United
Kingdom, and the United States. The sample starts in 1972 and ends in 2007, therefore it includes 36 years. This
means that there are twelve 3-year periods, nine 4-year periods, and seven 5-year periods (in the latter case, the
first period spans from 1972 to 1977, and the rest of the periods are regular 5-year periods).
8
be able to measure our object of interest, i.e. the volatility of local public spending, as well as
the main explanatory variables of the model, i.e. the volatility of the various types of local
revenues (see below for details on how we measure the volatility of those variables). We use
two specifications of the empirical model: the most parsimonious one (model A) includes the
local revenue side variables and country and period fixed effects as the sole explanatory
variables. The second specification (model B) also includes some macroeconomic and
demographic control variables taken from the literature investigating the volatility of
aggregate public spending. Thus, the model is the following:
_ '
,[ , ] ,0 ,1 ,[ , ] ,2 ,[ , ] ,3 ,[ , ] ,1 ,[ , ] , , ,
,
local G IT GST PT GR
i t t x i i i t t x i i t t x i i t t x i i t t x i j i t t i t
u
σ α α σ α σ α σ α σ τ
+ + + + +
= + + + + + + +β controls
(1)
where
_
,[ , ]
local G
i t t x
σ
+
stands for local spending volatility, which is defined as the standard
deviation of the annual growth rate of real local government expenditure (excluding
intergovernmental grants received from upper levels) over the multi-year periods described
above. The main explanatory variables are similarly defined, being the volatility of the
following: sub-central income taxes (
,[ , ]
IT
i t t x
σ
+
), sub-central taxes on goods and services
(
,[ , ]
GST
i t t x
σ
+
), sub-central property taxes (
,[ , ]
PT
i t t x
σ
+
), and intergovernmental grants (
,[ , ]
GR
i t t x
σ
+
). All
fiscal variables are converted into real terms using the GDP deflator.
6
Country fixed effects (α
i,0
) are included to control for time-invariant country-specific
characteristics (such as the countries being federal); period dummies (τ
t
) are included in order
to control for period-specific events that may potentially affect more than one country at the
same time; u
i,t
is the disturbance term
The volatility of the various revenues of sub-central governments is our main object of
interest. The inclusion of the four variables controlling for that permits us to test the
hypotheses laid out in sub-section 2.1 on the relationships between the volatility of local
expenditure and the (in)stability of the main revenue streams used to finance it.
7
Table 1
contains the simple pairwise correlations among the series measuring the volatility of local
6
We follow Afonso and Furceri (2010) on this, who use the GDP deflator in order not to eliminate any growth
in government spending that takes the form of an increase in the relative price of public sector outputs. Also,
there are no well-defined deflators for the series that we are using here.
7
Local non-tax revenues and capital revenues have not been considered as they are recorded irregularly. Also
note that sub-central levels of government include local, regional, provincial, and state (when existing)
governments, as opposed to the central government (for similar types of analysis see, among others, Dziobek et
al. 2011). The fact that we treat several tiers of government as equal by aggregating all sub-central units into a
single group may seem over-simplistic, but a further horizontal disaggregation would pose cross-country
comparability issues and would damage the actual data coverage.
9
spending and of the various sources of local revenues. The correlations obtained from the
data at the three different period frequencies used in the analysis suggest that measuring the
series over different periods does not fundamentally alter the relationships among the
variables. Values in Table 1 demonstrate that there is a strong positive correlation between
the volatility of local spending and that of intergovernmental grants. The correlation is always
very high (between 0.67 and 0.73) and statistically significant. This seems to indicate that
having to rely on grants (mainly from the central government, OECD 2006a) may adversely
affect the stability of sub-central spending. As for the other sources of revenues, i.e. different
local taxes, the correlations with spending volatility are in all cases much lower (being at
most equal to 0.22), and in most cases they are not statistically significant at standard levels.
The econometric analysis will yield more sophisticated results to better understand the
type of relationships characterizing the variables we are interested in. The shaded area of
Table 1 illustrates how the revenue side variables are correlated with each other. At all data
frequencies, correlations are positive but small, suggesting that the different revenue sources
indeed behave differently and that it is meaningful to analyze them separately. Also, the small
correlation coefficients prove that the inclusion of the revenue variables on the right-hand-
side of equation (1) does not pose multicollinearity problems.
INSERT TABLE 1 ABOUT HERE
In addition to estimating the parsimonious specification A of model (1), we also
estimate specification B of the model which includes the controls vector containing the
following: a) pop
i,t
stands for the logarithm of total population, commonly used as a measure
of country size (e.g., Alesina and Wacziarg 1998); b) urb
i,t
stands for urbanization measured
by the percentage of urban population over the total population; c) pop_dens
i,t
is population
density; d) gdp
i,t
is the logarithm of real GDP per capita; e) inflation
i,t
is inflation calculated
from the GDP deflator; f) open
i,t
is trade openness measured by the sum of imports and
exports divided by GDP. All these controls are included because the literature suggests that
they can potentially affect the volatility of aggregate public spending; therefore it seems
natural to include them in a model investigating the volatility of local public spending.
However, given that we focus on a panel of advanced economies (differently from most of
the literature on aggregate spending volatility that employs large panels of both developing
and developed countries), and that the dependent variable of our model only captures local
spending volatility, it is unclear which expected effects could be associated with those
10
controls, if any. All the control variables are taken at time t, i.e. at the beginning of the multi-
year periods, in order to deal with potential reverse causality issues (for similar applications
see, among others, Furceri and Zdzienicka 2012).
We estimate the two specifications of model (1) using the following estimators: OLS
with clustered standard errors, Fixed Effects (FE)
8
with heteroskedasticity-robust standard
errors, and FE with the standard errors robust to heteroskedasticity, autocorrelation, and
cross-sectional dependence (Driscoll and Kraay 1998).
9
In all cases, we use data at the three
different multi-year frequencies introduced above. We finally check the robustness of the
results by estimating a dynamic version of model (1) with the system-GMM (Blundell and
Bond 1998) to account for potential endogeneity issues concerning the revenue-side variables
in relation to the volatility of local expenditure.
4. Results
This section is organized in two parts. Sub-section 4.1 illustrates the OLS and FE estimates of
the two specifications of model (1). Sub-section 4.2 deals with the additional estimates
carried out to take care of potential endogeneity issues.
4.1 Benchmark estimates
Table 2 contains the estimated coefficients of the variables included in specification A of the
empirical model.
INSERT TABLE 2 ABOUT HERE
The first thing to notice is that results are remarkably consistent across the various
batteries of estimates, which differ in terms of estimators and data frequency. We interpret
this fact as a sign of robustness and consistency of the results. Turning to the hypotheses that
we formulated in Section 2, H
1
seems to be confirmed: there are significant linkages between
the volatility of sub-central spending and that of the various financing sources locally
available.
8
The Sargan-Hansen statistics (not reported but available upon request) support the choice of the FE rather than
the random effects estimator.
9
The Pesaran (2004) CD test indicates that residuals are cross-sectionally correlated and the error term is likely
to be serially correlated as well (results not reported but available upon request)
.
11
First of all, there is a positive and highly statistically significant relationship between the
volatility of local expenditure and that of intergovernmental grants, concurring with
hypothesis H
2
. The magnitude of the coefficients associated with the latter is consistent both
across the different estimators and across the different data frequencies, and lies within the
0.41/0.46 range depending on both the estimator and the frequency of the data used. Since
model (1) is linear, this implies an elasticity of 0.79/0.85 for average values of the variables
(
_
,[ , ]
local G
i t t x
σ
+
and
,[ , ]
GR
i t t x
σ
+
), which proves the economic importance of the relationship. This means
that when the volatility of grants increases by one percentage point, sub-central expenditure
becomes more volatile by between 0.79 and 0.85 percentage points. That is, local public
spending cannot be expected to be stable when local governments have to rely on revenues
over which they have no control to finance it. Central governments should be aware that
making local public finances mostly based on grants can result in a highly volatile local
expenditure.
This result is strengthened by the findings related to the variable accounting for the most
autonomous among the local taxes, i.e. the volatility of property taxes. The coefficients
associated with this explanatory variable are in all cases negative, and range between -0.14
and -0.16. Given the average values of the involved variables, the elasticity is smaller (in
absolute value) than that of grants’ volatility, but still economically important: it ranges
between -0.28 and -0.34. This suggests that local governments tend to utilize property taxes
in order to counteract the volatility of spending. The fact that local property taxes are
characterized by a reliable tax base (as they mostly refer to land, building, and other
immovable property) facilitates the activity of administration and collection by local policy
makers (Alm et al. 2011; Doerner and Ihlanfeldt 2011; Lutz et al. 2011; Norregaard 2013).
10
On the other hand, the coefficient associated with the volatility of local income and sales'
taxes are only rarely significant and positive, like those associated with grants, although
smaller in magnitude. Keeping in mind that those taxes are usually organized according to tax
sharing and piggybacking schemes, this result supports the intuition that not all local taxes
are equal in favoring government spending stability. In terms of our expectations, hypothesis
H
3a
- related to property taxes - is fully confirmed.
10
In most OECD countries some components of the property tax base pertain to the central government, e.g.
taxes on inheritances and gifts, and financial and capital transaction taxes. However, property taxation does not
occupy a central position in the overall revenue systems of such countries, while it contributes significantly to
the financing of sub-national governments (see Presbitero et al. 2014).
12
Of all our hypotheses, H
3
is only partially confirmed by the estimates, given the lack of a
clear relationship between the volatility of local spending and that of income and goods and
services’ taxes. This is consistent with the findings of Liberati and Sacchi (2013) regarding
government size and the different effects of property taxes versus those of income and
consumption taxes.
Finally, in many of the specifications the period dummies coefficients are statistically
significant, and positive in all cases. Since the omitted period dummy is the first one in all
cases, local spending has consistently been more volatile in the more recent part of the
sample period than in the first half of the Seventies.
A natural question is whether those results hold when controlling for additional
macroeconomic and demographic variables. Table 3 reports the estimates based on
specification B of model (1) in order to address this issue.
INSERT TABLE 3 ABOUT HERE
The evidence provided by Table 3 confirms all the above findings. In addition to that,
there seems to be weak evidence of a negative relationship between local spending volatility
and trade openness, and a negative one of the former with population. When the coefficients
of those controls are statistically significant, they are negative. However, in many cases they
are not statistically different from zero at standard confidence levels. The fact that more open
economies seem to experience lower local spending volatility seems consistent with the
existing evidence on aggregate government size (which obviously includes local spending):
Rodrik (1998) states that more open economies have bigger governments, and Furceri and
Ribeiro (2009) find a negative relationship between government size and spending volatility.
On the other hand, the positive relationship between population and local spending volatility
goes against the evidence of Furceri and Ribeiro (2009) regarding aggregate spending
volatility. The fact that negative coefficients are associated to population density (although
seldom statistically significant) seems to partially reconcile those contrasting findings.
Thus, the results in Table 3 prove the positive relationship between local spending
volatility and grants volatility, as well as the negative relationship between the former and the
volatility of local property taxes. The lack of strong linkages between the dependent variable
and local income taxes and those on goods and services is also confirmed: coefficients are
mostly positive, like those of grants, but they are not statistically significant at standard
levels. Thus, when local governments finance their spending with revenues over which they
13
do not exert much control (and for which they are not held responsible for), the volatility of
local expenditure increases, especially in the case of grants. On the other hand, when local
governments finance their spending with own taxes, particularly with property taxes, the
result is a lower spending volatility.
This result seems to be consistent with the common-pool hypothesis and with some
moral hazard on the part of local politicians when facing soft budget constraints, as in the
case of grants financing. Money transfers from other levels of government (especially those
not earmarked, that is not to be used for specific purposes) are likely to be spent with more
discretion and fickleness. As Bird and Slack (2013, 9) put it: "it is always easier and more
pleasant to spend (…) ‘other people's money’ in an unaccountable (and hence inevitably
somewhat irresponsible) fashion."
This can give rise to resource waste and inefficiency, as well as to higher spending
volatility as proved by our analysis. On the other hand, money from local taxpayers collected
on taxes more respondent to the benefit principle of taxation (like that stemming from
property taxes) is more likely to be spent constructively, with a closer link with local
spending, resulting in its lower volatility.
4.2 Robustness checks
This sub-section deals with potential endogeneity issues. Indeed, it is possible to conceive
that the volatility of the four revenue sources for local governments may not only influence
spending volatility, but also be influenced by it. In particular, local governments may respond
to the assignment of new spending responsibility by maneuvering the taxes that they control.
Also, central governments may assign new tasks to local governments (requiring changes in
spending) and at the same time manipulate the intergovernmental grants to ensure adequate
financing.
The estimates presented in Table 4 take into account all those possibilities. Dynamic
versions of specifications A and B of model (1) obtained by adding the lagged dependent
variable to the sets of regressors are re-estimated with the system-GMM estimator. The
lagged dependent variable and the revenue-side explanatory variables are instrumented with
their own second lags (in the case of specification B we treat as predetermined the rest of the
explanatory variables of the model by using them as instruments in the level equation) as
valid external instruments are unavailable.
11
Given the requisites in terms of number of
11
Results do not change when changing the number of lags used as instruments.
14
observations of the chosen estimator, Table 4 only contains the results obtained using the 3-
year periods. We report the results obtained with the one-step GMM estimator which is more
reliable for finite sample inference as the asymptotic standard errors of the two-step GMM
estimator can be biased downwards (Bond et al. 2001).
INSERT TABLE 4 ABOUT HERE
The estimates of the dynamic models dealing with potential endogeneity confirm once
again the main results relative to property taxes and intergovernmental grants. The
coefficients estimated with the system-GMM estimator are in all cases in line those of the
estimates of the previous sub-section. The AR(1) and AR(2) tests do not indicate any issue
with the estimates, and the Hansen J statistic never rejects the validity of the instruments,
suggesting that these new estimates are sound. Thus, the comments based on the benchmark
estimates presented in sub-section 4.1 remain valid even when controlling for potential
endogeneity issues, and even the magnitudes of the coefficients involved are robust to our
taking into account potential endogeneity.
5. Summary and conclusions
The recent economic literature has investigated the determinants of aggregate spending
volatility, given its relevant effects on the economic performance and welfare (Fatas and
Mihov 2003 and 2005, Furceri 2007, Afonso and Furceri 2008, Loayza et al. 2007). In this
paper we contribute to this line of research by focusing on the sub-central levels of
government whose importance has constantly increased in the recent decades due to
widespread decentralization reforms. In particular, we analyze the volatility of local public
spending in 20 OECD countries over the period 1972-2007.
Our focus is on the role of the revenues used by sub-central governments, that is
intergovernmental grants and the three main types of taxes they normally levy on their
residents, i.e. on income, on goods and services, and on property. This disaggregation is
relevant as local governments exert different degrees of autonomy over those different
revenues, highest for property taxes, and lower for shared and piggybacked taxes, and grants
(Blӧchliger and Petzold 2009). Overall, this suggests that such instruments can be used
differently by governments, with different consequences in terms of efficiency and
accountability.
15
The estimates presented in the paper, including those tackling potential endogeneity
issues, lead to the following results: there are significant linkages between the volatility of
sub-central public spending and that of the various local revenue sources. There are important
differences among revenues, in particular between own taxes and revenues over which sub-
national governments cannot exert much control, i.e. shared taxes and, above all,
intergovernmental grants. In particular, while volatile intergovernmental grants lead to
volatile local public spending, the opposite is true in the case of property taxes.
These results suggest that local expenditure turns out to be less stable when it is financed
with transfers from other government tiers. The underlying reason is that local decision-
makers have more incentives to better spend their own tax resources than those of the
common pool of national funds (Shah 1998; Weingast 2009; Boetti et al. 2012). This result
fits well with the public choice theory on fiscal federalism (Brennan and Buchanan 1980;
Salmon 1987; Breton 1987) suggesting that local politicians – normally not benevolent – may
misbehave by competing according to their own objective functions and have an
“irresponsible” spending behavior when there is not enough accountability of their financing
mechanisms to local voters. As a matter of fact, property taxes are envisaged to work in favor
of accountability and this is confirmed by the fact that they can be relied upon to attenuate the
volatility of local expenditure and induce responsible spending patterns.
Our evidence on intergovernmental grants and on property taxes suggests that future
local property taxation reforms in OECD countries should not be independent of changes in
the transfer system. The two issues are inevitably linked, so that supporting and encouraging
local accountability necessarily accompanies with some realignment of functions and
finances between levels of government (see, recently, Slack and Bird 2014).
More generally, our results have relevant policy implications within the realm of
intergovernmental relationships. It is not uncommon for central governments to decide over
decentralized tax and grant policies taking into account the existence and strength of different
regional factions. For example, when sub-national
identity differences emerge (many political
movements are demanding more local and regional empowerment in many developed countries,
Keating and Loughlin 1996), central governments are reluctant to give tax autonomy to sub-national
tiers and grants seem an appealing way to limit the growth of the within-country differences.
However, this strategy may
adversely affect local spending stability.
The power relations between central and sub-central levels of government are crucial in
determining the tax-grant balance and, consequently, the degree of local autonomy. For
example, according to Oates (2001), limitations on property taxes in the US have weakened
16
the role of such revenues in encouraging efficient budgetary decisions resulting in an increase
of intergovernmental transfers. This suggests that local autonomy over tax rates is
particularly important in countries where upper levels of government determine the tax base.
More in general, it seems that the possibility of having negotiations about fiscal tasks and
competences between central and sub-central governments without stringent legal
requirements may allow the latter to become more autonomous and more accountable to
citizens, with positive effects on the stability of sub-central expenditure.
17
References
Afonso, A., Hauptmeier, S. (2009). Fiscal behaviour in the European Union - Rules, fiscal
decentralization and government indebtedness. ECB Working Paper No. 1054.
Afonso, A., Furceri, D. (2010). Government size, composition, volatility and economic
growth. European Journal of Political Economy 26(4), 517-532.
Albuquerque, B. (2011). Fiscal institutions and public spending volatility in Europe.
Economic Modelling 28(6), 2544-2559.
Alesina, A., Wacziarg, R. (1998). Openness, country size and government. Journal of Public
Economics 69(3), 305-321.
Alm, J., Buschman, R.D., Sjoquist, D.L. (2011). Rethinking local government reliance on the
property tax. Regional Science and Urban Economics 41, 320-331.
Ashworth, J., Galli, E., Padovano, F. (2013). Decentralization as a constraint to Leviathan: a
panel cointegration analysis. Public Choice 156, 491-516.
Bird, R.M., Slack, E. (2013). Local taxes and local expenditures: strengthening the
Wicksellian connection. International Center for Public Policy Working Paper Series at
AYSPS GSU, paper 1323.
Blӧchliger, H., Petzold, O. (2009). Finding the dividing line between tax sharing and grants: a
statistical investigation. OECD working papers on fiscal federalism No. 10, OECD
Publishing.
Blundell, R., Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel
data models. Journal of Econometrics 87, 115–143.
Boetti, L., Piacenza, M., Turati, G. (2012). Decentralization and local governments'
performance: how does fiscal autonomy affect spending efficiency? FinanzArchiv: Public
Finance Analysis 68(3), 269-302.
Bond, S., Hoeffler, A., Temple, J. (2001). GMM estimation of empirical growth models.
Economics Papers 2001-W21, Nuffield College, University of Oxford.
Bordignon, M., Minelli, E. (2001). Rules transparency and political accountability. Journal of
Public Economics 80(1), 73-98.
Borge, L.E., Rattsø, J. (2008). Property taxation as incentive for cost control: empirical
evidence for utility services in Norway. European Economic Review 52, 1035-1054.
Brunetti, A. (1998). Policy volatility and economic growth: a comparative, empirical
analysis. European Journal of Political Economy 14, 35-52.
18
Brunori, D. (2003). Local Tax Policy: A Federalist Perspective. Urban Institute Press,
Washington D.C.
Cabral, M., Hoxby, C. (2012). The hated property tax: salience, tax rates, and tax revolts.
NBER Working Paper No. 18514.
Cassette, A., Paty, S. (2010). Fiscal decentralization and the size of government: a European
country empirical analysis. Public Choice 143, 173-189.
Charbit, C. (2010). Explaining the sub-national tax-grants balance in OECD countries. OECD
Working Papers on Fiscal Federalism No. 11, OECD Publishing.
Crowley, G.R., Sobel, R.S. (2011). Does fiscal decentralization constrain Leviathan? New
evidence from local property tax competition. Public Choice 149(1-2), 5-30.
Devarajan, S., Khemani, S., Shah, S. (2009). The politics of partial decentralization. In E.
Ahmad and G. Brosio (Eds.) Does Decentralization Enhance Service Delivery and
Poverty Reduction?, Edward Elgar Publishing, 79-101.
Doerner, W.M., Ihlanfeldt, K.R. (2011). House prices and city revenues. Regional Science
and Urban Economics 41, 332-342.
Driscoll, J.C., Kraay, A.C. (1998). Consistent covariance matrix estimation with spatially
dependent panel data. Review of economics and statistics 80(4), 549-560.
Dziobek, C., Gutierrez Mangas, C., Kufa, P. (2011). Measuring fiscal decentralization -
exploring the IMF’s databases. IMF Working Paper 126.
Escolano, J., Eyraud, L., Badia, M., Sarnes, J., Tuladhar, A. (2012). Fiscal performance,
institutional design and decentralization in European Union countries. IMF working paper
No. 12/45.
Eyraud, L. (2014). Reforming Capital Taxation in Italy. IMF Working Papers No. 14/6.
Fatás, A., Mihov, I. (2003). The case for restricting fiscal policy discretion. Quarterly
Journal of Economics 118, 1419-1447.
Fatás, A., Mihov, I. (2005). Policy volatility, institutions and economic growth. CEPR
Discussion Paper No. 5388.
Fischel, W.A. (2001). Homevoters, Municipal Corporate Governance, and the Benefit View
of the Property Tax. National Tax Journal 54(1), 157-173.
Fiva, J.H. (2006). New evidence on the effect of fiscal decentralization on the size and
composition of government spending. Finanzarchiv 62(2), 250-280.
Fiva, J. H., Rønning, M. (2008). The incentive effects of property taxation: evidence from
Norwegian school districts. Regional Science and Urban Economics 38(1), 49-62.
19
Furceri, D. (2007). Is government expenditure volatility harmful for growth? A cross-country
analysis. Fiscal Studies28(1), 103-120.
Furceri, D., Ribeiro, M.P. (2009). Government consumption volatility and the size of nations.
OECD Working Paper 28.
Furceri, D., Sacchi, A., Salotti, S. (2014). Can fiscal decentralization alleviate government
consumption volatility? MPRA Working Paper No. 54683.
Furceri, D., Zdzienicka, A. (2012). Financial integration and fiscal policy. Open Economies
Review 23(5), 805-822.
Gemmell, N., Kneller, R., Sanz, I. (2013). Fiscal decentralization and economic growth in
OECD countries: matching spending with revenue decentralization. Economic Inquiry
51(4), 1915-1931.
Giertz, J.F. (2006). The property tax bound. National Tax Journal 59(3), 695-705.
Golem, S. (2010). Fiscal decentralisation and the size of government: a review of the
empirical literature. Financial Theory and Practice 34(1), 53-69.
Gong, L., Zou, H.F. (2002). Effects of growth and volatility in public expenditures on
economic growth: theory and evidence. Annals of Economics and Finance 3(2), 379-406.
Hoffman, B.D., Gibson, C.C. (2005). Fiscal governance and public services: evidence from
Tanzania and Zambia. Department of Political Science, University of California San
Diego, August.
Jin, J., Zou, H. (2002). How does fiscal decentralization affect aggregate, national, and
subnational government size? Journal of Urban Economics52, 270-293.
Karras, G., Furceri, D. (2009). Taxes and growth in Europe. South-Eastern Europe Journal of
Economics 2, 181-204.
Keating, M., Loughlin, J. (1996). The Political Economy of Regionalism. Routledge, London.
Khemani, S. (2010). Decentralization by politicians: creation of grants-financed local
jurisdictions. In N. Bosch, M. Espasa and A. Sole-Ollé (Eds.), The Political Economy of
Regional Fiscal Flows. Measurement, Determinants and Effects on Country Stability.
Cheltenham: Edward Elgar, 227-248.
King, D. (1984). Fiscal Tiers - The Economics of Multi-Level Government. Allen and Unwin,
London.
Lane, P.R. (2003). The cyclical behaviour of fiscal policy: evidence from the OECD. Journal
of Public Economics 87(12), 2661-2675.
Liberati, P., Sacchi, A. (2013). Tax decentralization and local government size. Public
Choice 157(1) 183-205.
20
Loayza, N.V., Ranciere, R., Servén, L., Ventura, J. (2007). Macroeconomic volatility and
welfare in developing countries: an introduction. The World Bank Economic Review
21(3), 343-357.
Lotz, J. (2006). Local government organization and finance: Nordic countries. In A. Shah
(Ed.), Local Governance in Industrial Countries. The World Bank, Washington D.C.,
223-264.
Lutz, B., Molloy, R., Shan, H. (2011). The housing crisis and state and local government tax
revenue: five channels. Regional Science and Urban Economics 41, 306-319.
McCluskey, W.J., Cornia, GC., Walters, L. C. (2012). A Primer on Property Tax:
Administration and Policy. John Wiley & Sons, Chichester.
Martinez-Vazquez, J., McNab, R.M. (2003). Fiscal decentralization and economic growth.
World Development 31(9), 1597-1616.
McKenzie, R.B., Staaf, R.J. (1978). Revenue sharing and monopoly government. Public
Choice 33(3), 93-97.
McLure, C., Martinez-Vazquez, J. (2000). The assignment of revenues and expenditures in
intergovernmental fiscal relations. Paper prepared for the core course on
Intergovernmental Relations and Local Financial Management, World Bank Institute,
Washington, D.C.: World Bank.
Musgrave, R. (1959). The Theory of Public Finance: A Study in Public Economy. McGraw-
Hill, New York.
Musgrave, R. (1983). Who should tax, where and what. In C.E. McLure (Eds.) Tax
Assignment in Federal Countries. Centre for Research on Federal Financial Relations,
Australian National University, Canberra, 2-19.
Norregaard, M.J. (2013). Taxing Immovable Property Revenue Potential and Implementation
Challenges. IMF Working Papers No. 13/129.
Oates, W.E. (1972). Fiscal Federalism. Harcourt Brace, New York.
Oates, W.E. (2001). Property taxation and local government finance: an overview and some
reflections. in W.E. Oates (ed.) Property Taxation and Local Government Finance.
Cambridge, Lincoln Institute of Land Policy, 21-31.
Oates, W.E., Schwab, R. (1988). Economic competition among jurisdictions: efficiency-
enhancing or distortion-inducing? Journal of Public Economics 35(3), 333-354.
OECD (1999). Taxing powers of state and local government. OECD Tax Policy Studies 1,
Paris.
21
OECD (2006a). Intergovernmental transfers and decentralised public spending. Working
Paper no. 3, Network on Fiscal Relations Across Levels of Government, OECD, Paris.
OECD (2006b). Fiscal autonomy of sub-central governments. Working Paper no. 2, Network
on Fiscal Relations Across Levels of Government, OECD, Paris.
OECD (2009a). Taxes and grants: on the revenue mix of sub-central governments. Working
Paper 7, Network on Fiscal Relations Across Levels of Government, OECD, Paris.
OECD (2009b). The spending power of sub-central governments: a pilot study. OECD
Network on Fiscal Relations Across Levels of Government COM/CTPA/ECO/GOV/WP
no. 8.
OECD (2012). Decentralization and economic growth. Note by the Secretariat, OECD, Paris.
Poterba, J.M. (1995). Balanced budget rules and fiscal policy: evidence from the States.
National Tax Journal 48(3), 329-336.
Presbitero, A.F., Sacchi, A., Zazzaro, A. (2014). Property tax and fiscal discipline in OECD
countries. Economics Letters, forthcoming.
Rodden, J. (2003). Reviving Leviathan: fiscal federalism and the growth of government.
International Organization 57, 695–730.
Rodden, J.A., Eskeland, G.S., Litvack, J. (2003). Fiscal Decentralization and the Challenge
of Hard Budget Constraints. The MIT Press.
Rodrik, D. (1998). Why do more open economies have bigger governments? Journal of
Political Economy 106, 997-1032.
Roubini, N., Sachs, J. (1989). Government spending and budget deficits in the industrialized
countries. Economic Policy 8, 99-132.
Sacchi, A., Salotti, S. (2014). The asymmetric nature of fiscal decentralization: theory and
practice. MPRA working paper 54506.
Shah, A. (1998). Fiscal federalism and macroeconomic governance: for better or for worse?
World Bank Policy Research Working Paper 2005.
Slack, E., Bird, R. (2014). The Political Economy of Property Tax Reform”, OECD Working
Papers on Fiscal Federalism No. 18, OECD Publishing.
Sokoloff, K.L., Zolt, E.M. (2006). Inequality and Taxation: Evidence from the Americas on
how Inequality may Influence Tax Institutions. Tax Law Review 59 (2), 167.
Stein, E. (1999). Fiscal decentralization and government size in Latin America. Journal of
Applied Economics 2, 357-391.
22
Stigler, G.J. (1957). The tenable range of functions of local governmnent. In US Congress
Joint Economic Committee (Eds.), Federal Expenditure Policy for Economic Growth and
Stability, Washington D.C., 2013-219.
Weingast, B.R. (2009). Second generation fiscal federalism: the implications of fiscal
incentives. Journal of Urban Economics 65(3), 279-293.
Weingast, B.R. (2014). Second generation fiscal federalism: political aspects of
decentralization and economic development. World Development 53, 14–25.
Zax, J.S. (1988). The effects of jurisdiction types and numbers on local public finance. In
H.S. Rosen (Ed.), Fiscal Federalism: Quantitative Studies. Chicago: University of
Chicago Press, 79-106.
23
Tables
Table 1. Correlations between local spending volatility and the volatility of local revenues
3-y
1.00
0.22*** 1.00
0.13* 0.33*** 1.00
0.08 0.34*** 0.07 1.00
0.69*** 0.19*** 0.03 0.27*** 1.00
4-y
1.00
0.16* 1.00
0.07 0.33*** 1.00
0.04 0.35*** 0.03 1.00
0.67*** 0.21** 0.06 0.33*** 1.00
5-y
1.00
0.22** 1.00
0.14 0.32*** 1.00
0.09 0.32*** 0.06 1.00
0.73*** 0.20** 0.05 0.25*** 1.00
,[ , ]
IT
i t t x
σ
+
,[ , ]
GST
i t t x
σ
+
,[ , ]
PT
i t t x
σ
+
,[ , ]
GR
i t t x
σ
+
_
,[ , ]
local G
i t t x
σ
+
_
,[ , ]
local G
i t t x
σ
+
,[ , ]
IT
i t t x
σ
+
,[ , ]
GST
i t t x
σ
+
,[ , ]
PT
i t t x
σ
+
,[ , ]
GR
i t t x
σ
+
,[ , ]
IT
i t t x
σ
+
,[ , ]
GST
i t t x
σ
+
,[ , ]
PT
i t t x
σ
+
,[ , ]
GR
i t t x
σ
+
_
,[ , ]
local G
i t t x
σ
+
_
,[ , ]
local G
i t t x
σ
+
,[ , ]
IT
i t t x
σ
+
,[ , ]
GST
i t t x
σ
+
,[ , ]
PT
i t t x
σ
+
,[ , ]
GR
i t t x
σ
+
,[ , ]
IT
i t t x
σ
+
,[ , ]
GST
i t t x
σ
+
,[ , ]
PT
i t t x
σ
+
,[ , ]
GR
i t t x
σ
+
_
,[ , ]
local G
i t t x
σ
+
_
,[ , ]
local G
i t t x
σ
+
,[ , ]
IT
i t t x
σ
+
,[ , ]
GST
i t t x
σ
+
,[ , ]
PT
i t t x
σ
+
,[ , ]
GR
i t t x
σ
+
Note: ***, **, and * indicate statistical significance at 1, 5, and 10%, respectively.
24
Table 2. Model A: OLS, FE, FE-DK estimates, three different period frequencies
OLS FE FE-DK OLS FE FE-DK OLS FE FE-DK
0.07** 0.02 0.02 0.04* 0.01 0.01 0.07* 0.01 0.01
(2.21) (0.48) (0.96) (1.77) (0.51) (0.29) (1.87) (0.13) (0.15)
-0.14*** -0.14*** -0.14*** -0.15** -0.14*** -0.14** -0.14*** -0.16*** -0.16***
(-2.98) (-3.12) (-2.72) (2.55) (-2.87) (-2.44) (-2.75) (-3.10) (-2.93)
0.02** 0.02** 0.02* 0.001 -0.003 -0.003 0.02** 0.02*** 0.02***
(2.03) (2.16) (1.73) (0.14) (-0.59) (-0.45) (2.11) (2.89) (3.46)
0.41*** 0.42*** 0.42*** 0.41*** 0.42*** 0.42*** 0.45*** 0.46*** 0.46***
(3.54) (3.70) (4.58) (3.31) (3.57) (3.82) (4.45) (5.00) (6.58)
period_2 0.04 0.04 0.04*** 0.04 0.05 0.05*** 0.07* 0.07** 0.07***
(0.79) (0.74) (5.44) (0.89) (1.04) (3.82) (1.74) (1.97) (11.38)
period_3 0.12* 0.11** 0.11*** 0.07 0.08 0.08*** 0.05 0.05* 0.05***
(1.95) (2.11) (11.68) (1.36) (1.49) (4.27) (1.56) (1.78) (4.76)
period_4 0.07 0.06 0.06*** 0.04 0.05 0.05** 0.05 0.05 0.05***
(1.30) (1.29) (5.46) (0.94) (1.08) (2.52) (1.39) (1.40) (9.95)
period_5 0.05 0.05 0.05*** 0.06 0.07 0.07*** 0.03 0.03 0.03***
(0.95) (0.96) (3.27) (1.20) (1.33) (9.69) (0.84) (0.96) (3.33)
period_6 0.07 0.07 0.07*** 0.05 0.06 0.06*** 0.02 0.02 0.02***
(1.45) (1.59) (14.61) (1.11) (1.27) (3.07) (0.72) (0.67) (3.03)
period_7 0.07 0.07 0.07*** 0.02 0.03 0.03* 0.04 0.04 0.04***
(1.29) (1.33) (2.94) (0.42) (0.52) (1.76) (1.14) (1.04) (3.32)
period_8 0.06 0.06 0.06*** 0.05 0.06 0.06***
(1.08) (1.19 (3.54) (0.98) (1.07) (3.07)
period_9 0.03 0.03 0.03*** 0.05 0.05 0.05**
(0.59) (0.59) (2.97) (0.93) (0.95) (2.37)
period_10 0.06 0.05 0.05***
(1.08) (1.09) (4.21)
period_11 0.05 0.05 0.05***
(1.06) (1.10) (3.91)
period_12 0.06 0.05 0.05***
(1.18) (1.12) (3.81)
Observations 162 162 162 121 121 121 105 105 105
R-squared 0.63 0.64 0.64 0.65 0.69 0.69 0.67 0.69 0.69
3-year periods 5-year periods4-year periods
Variables/
estimator
,[ , ]
IT
i t t x
σ
+
,[ , ]
PT
i t t x
σ
+
,[ , ]
GST
i t t x
σ
+
,[ , ]
GR
i t t x
σ
+
Note: ***, **, and * indicate statistical significance at 1, 5, and 10%, respectively. t-statistics in parentheses
based on clustered standard errors (OLS), robust standard errors (FE), and Driscoll-Kraay standard errors (FE-
DK). Country fixed effects included in the FE and FE-DK estimates, but not reported.
25
Table 3. Model B: OLS, FE, FE-DK estimates, three different period frequencies
OLS FE FE-DK OLS FE FE-DK OLS FE FE-DK
0.04 0.02 0.02 0.03 0.01 0.01 0.03 0.0002 0.0002
(0.75) (0.62) (1.40) (1.14) (0.22) (0.09) (0.48) (0.00) (0.00)
-0.13*** -0.14*** -0.14*** -0.14*** -0.14*** - 0.14*** -0.14** -0.17*** -0.17***
(-2.94) (-3.34) (-2.93) (-2.60) (-3.39) (-2.90) (2.55) (-3.37) (-3.13)
0.02** 0.02** 0.02 -0.001 0.002 0.002 0.02** 0.03** 0.03***
(2.45) (2.03) (1.56) (-0.10) (0.17) (0.17) (2.15) (2.51) (2.74)
0.40*** 0.42*** 0.42*** 0.40*** 0.43*** 0.43*** 0.44*** 0.49*** 0.49***
(3.47) (3.83) (4.76) (3.27) (3.91) (4.39) (4.15) (5.97) (8.04)
pop 0.001 0.37* 0.37*** -0.003 0.22* 0.22*** -0.001 0.30** 0.30***
(0.19) (1.72) (5.45) (-0.80) (1.75) (5.31) (-0.29) (2.01) (3.03)
urb -0.0002 -0.01 -0.01** 0.0001 0.002 0.002 -0.0002 -0.01** -0.01***
(-0.52) (-1.24) (-2.09) (0.22) (0.66) (1.52) (-0.33) (-1.99) (-3.35)
pop_dens -0.00001 0.00003 0.00003 0.00004 -0.003** -0.003*** 0.00001 0.001 0.001
(-0.20) (0.02) (0.03) (1.00) (-2.36) (-2.93) (0.36) (0.86) (1.06)
gdp -0.05 -0.08 -0.08 -0.01 0.10 0.10 -0.05 -0.04 -0.04
(-1.08) (-0.76) (-1.29) (-0.19) (1.35) (1.37) (-1.39) (-0.53) (-1.34)
inflation -0.001 0.001 0.001 -0.00001 0.0004 0.0004 0.0002 0.001 0.001**
(-0.37) (0.62) (0.54) (-0.02) (1.29) (1.02) (0.42) (1.50) (2.51)
open 0.004* -0.002** -0.002*** 0.0002 -0.001 -0.001* 0.0003** -0.003*** -0.003***
(1.94) (-2.09) (-2.76) (1.01) (-1.59) (-1.72) (2.11) (-3.13) (-3.07)
period_2 0.04 0.06 0.06*** 0.04 0.05 0.05*** 0.07* 0.10*** 0.10***
(0.84) (1.05) (3.92) (0.92) (1.09) (2.96) (1.95) (2.79) (21.30)
period_3 0.12** 0.13** 0.13*** 0.07 0.07 0.07*** 0.05* 0.12*** 0.12***
(2.05) (2.44) (23.42) (1.43) (1.40) (3.28) (1.83) (3.13) (5.85)
period_4 0.07 0.11* 0.11*** 0.04 0.04 0.04 0.06** 0.13*** 0.13***
(1.41) (1.82) (6.20) (1.04) (0.81) (1.46) (2.07) (3.13) (6.06)
period_5 0.05 0.12** 0.12*** 0.07 0.04 0.04* 0.05 0.11** 0.11***
(1.08) (2.15) (3.93) (1.51) (0.90) (1.66) (1.57) (2.26) (5.05)
period_6 0.09* 0.15*** 0.15*** 0.06 0.03 0.03 0.05 0.15** 0.15***
(1.82) (2.85) (4.28) (1.47) (0.50) (0.79) (1.46) (2.37) (4.51)
period_7 0.08* 0.14** 0.14*** 0.03 - 0.01 -0.01 0.06* 0.17** 0.17***
(1.69) (2.58) (4.29) (0.64) (-0.21) (-0.36) (1.93) (2.43) (4.69)
period_8 0.07 0.13** 0.13*** 0.05 0.02 0.02
(1.46) (2.42) (3.71) (1.26) (0.29) (0.46)
period_9 0.05 0.12** 0.12*** 0.05 0.01 0.01
(0.91) (2.07) (2.58) (1.20) (0.14) (0.22)
period_10 0.07 0.16*** 0.16***
(1.42) (2.64) (3.08)
period_11 0.07 0.17*** 0.17***
(1.43) (2.56) (2.98)
period_12 0.08 0.18*** 0.18***
(1.55) (2.67) (2.97)
Observations 162 162 162 121 121 121 105 105 105
R-squared 0.65 0.67 0.67 0.68 0.73 0.73 0.70 0.77 0.77
Variables/
estimator
3-year periods 4-year periods 5-year periods
,[ , ]
IT
i t t x
σ
+
,[ , ]
PT
i t t x
σ
+
,[ , ]
GST
i t t x
σ
+
,[ , ]
GR
i t t x
σ
+
Note: ***, **, and * indicate statistical significance at 1, 5, and 10%, respectively. t-statistics in parentheses
based on clustered standard errors (OLS), robust standard errors (FE), and Driscoll-Kraay standard errors (FE-
DK). Country fixed effects included in the FE and FE-DK estimates, but not reported.
26
Table 4. Dynamic models A and B: System-GMM estimates, 3-year periods data
Model A Model B
-0.07 -0.05
(-1.03) (-0.66)
0.05* 0.03
(1.73) (0.78)
-0.17*** -0.17***
(-4.25) (-4.76)
0.02** 0.02**
(2.53) (2.45)
0.46*** 0.45***
(5.10) (5.21)
pop 0.001
(0.30)
urb -0.0002
(-0.51)
pop_dens -0.000004
(-0.13)
gdp -0.06
(-1.25)
inflation -0.001
(-0.71)
open 0.0004*
(1.69)
period_2 -0.003 -0.02
(-0.13) (-0.84)
period_3 0.02 0.02
(1.18) (0.85)
period_4 -0.01 -0.02
(-0.39) (-0.58)
period_5 -0.02 -0.03
(-1.48) (-1.40)
period_6 0.001 -0.01
(0.04) (-0.40)
period_7 0.02 0.01
(1.19) (0.60)
period_8 -0.001 -0.004
(-0.06) (-0.31)
period_9 -0.04*** -0.04***
(-2.86) (-2.96)
period_10 -0.01 -0.01
(-0.36) (-0.52)
period_11 -0.01 -0.01
(-1.00) (-1.39)
Observations 152 152
Hansen J statistic 1.00 1.00
AR(1) 0.02 0.01
AR(2) 0.24 0.53
Variables/
specification
3-year periods
,[ , 3 ]
IT
i t t
σ
+
,[ , 3 ]
PT
i t t
σ
+
,[ , 3 ]
GST
i t t
σ
+
,[ , 3 ]
GR
i t t
σ
+
_
,[ 4, 1]
local G
i t t
σ
− −
Note: *** and ** indicate statistical significance at 1 and 5%, respectively. z-statistics in parentheses based on
robust standard errors. The p-value of the AR(1), AR(2), and Hansen J statistics are reported.
27
Data Appendix
Local spending volatility
_
,[ , ]
local G
i t t x
σ
+
:
standard deviation of the annual growth of real (calculated
using the GDP deflator) local government expenditure (excluding intergovernmental grants)
over the multi-year periods.
Sources
: IMF Government Finance Statistics (GFS from now
onwards), and OECD.
Local income taxes volatility
,[ , ]
IT
i t t x
σ
+
:
standard deviation of the annual growth of real
(calculated using the GDP deflator) local income taxes over the multi-year periods.
Sources
:
IMF GFS, and OECD.
Local goods and services’ taxes volatility
,[ , ]
GST
i t t x
σ
+
:
standard deviation of the annual growth of
real (calculated using the GDP deflator) local taxes on goods and services over the multi-year
periods.
Sources
: IMF GFS, and OECD.
Local property taxes volatility
,[ , ]
PT
i t t x
σ
+
:
standard deviation of the annual growth of real
(calculated using the GDP deflator) local property taxes over the multi-year periods.
Sources
:
IMF GFS, and OECD.
Intergovernmental grants volatility
,[ , ]
GR
i t t x
σ
+
:
standard deviation of the annual growth of real
(calculated using the GDP deflator) grants from the central government to local governments
over the multi-year periods.
Sources
: IMF GFS, and OECD.
Population (
pop
i,t
). Logarithm of population.
Source
: OECD.
Urbanisation (
urb
i,t
). Urban population, percentage of the total population.
Source: World
Development Indicators (WDI).
Population density (
pop_dens
i,t
). People for square km of land area.
Source
: OECD.
Inflation (
inflation
i,t
). Inflation calculated from the Consumer Price Index.
Source
: IMF.
Real GDP per capita (
gdp
i,t
). Logarithm of real GDP per capita, volume, at 2005 PPP USD.
Source
: A. Heston, R. Summers, B. Aten: Penn World Tables (PWT) 7.1, Center for
International Comparisons of Production, Income and Prices at the University of
Pennsylvania, Nov. 2012.
Trade openness (
open
i,t
). Share of imports plus share of exports over GDP.
Source
: PWT 7.1.
