I Escola de Verão em Economia do Desenvolvimento – FEA/USP

Transcrição

I Escola de Verão em Economia do Desenvolvimento
FEA/USP – 06 a 10 de Fevereiro de 2012
Minicurso: Eficiência do Gasto Público e Desenvolvimento Econômico
Docente: Fabiana Rocha
Este arquivo inclui os seguintes itens bibliográficos:
Adam, A., Delis, M.. Kammas, P. 2008. Fiscal Decentralization and Public Sector
Efficiency: Evidence from OECD Countries, CESIFO Working Paper No. 2364.
Afonso, A., Schuknecht, L., Tanzi, V. 2006. Public sector efficiency: an
international comparison, Public Choice 123, 321-347.
Angelopoulos, K., Philippopoulos,A., Tsionas, E. 2008. Does public sector
efficiency matter? Revisiting the relation between fiscal size and economic growth,
Public Choice 132, 245-278.
Souza, I., Nishijima, M., Rocha, F. 2010. Eficiência do setor hospitalar nos
municípios paulistas, Economia Aplicada, 14(1).
Fiscal Decentralization and Public Sector
Efficiency: Evidence from OECD Countries
ANTONIS ADAM
MANTHOS D. DELIS
PANTELIS KAMMAS
CESIFO WORKING PAPER NO. 2364
CATEGORY 1: PUBLIC FINANCE
AUGUST 2008
An electronic version of the paper may be downloaded
• from the SSRN website:
www.SSRN.com
• from the RePEc website:
www.RePEc.org
• from the CESifo website:
www.CESifo-group.org/wp
T
T
CESifo Working Paper No. 2364
Fiscal Decentralization and Public Sector
Efficiency: Evidence from OECD Countries
Abstract
This paper attempts to identify the effect of fiscal decentralization on public sector efficiency
(PSE). We employ data envelopment analysis on a panel of 21 OECD countries over the
period 1970-2000 to construct two alternative PSE indicators that reflect the governmental
goals of economic performance and stability. In turn, using a novel technique that merges the
methodologies of Simar and Wilson (2007) and Khan and Lewbel (2007), we regress the PSE
scores obtained on an extensive set of alternative fiscal decentralization measures. Backed by
strong empirical results, obtained from a number of different specifications, we contend that
PSE is increasing with fiscal decentralization.
JEL Code: C14, C24, H11, H50.
Keywords: public sector efficiency, fiscal decentralization, semi-parametric models.
Antonis Adam
Department of Economics
University of Cyprus
P.O. Box 20537
1678 Nicosia
Cyprus
[email protected]
Manthos D. Delis
Athens University of Economics and
Business
76 Patission Street
10434, Athens
Greece
[email protected]
Pantelis Kammas
Department of Economics
University of Cyprus
P.O. Box 20537
1678 Nicosia
Cyprus
[email protected]
23, February 2008
We are indebted to Jon Fiva and to Efthymios Tsionas for valuable suggestions and to Jakob
de Haan, Richard Jong-A-Pin, Jochen Mierau, Dan Stegarescu and Jose Tavares for
generously giving us access to their data. We also thank Konstantinos Angelopoulos,
Vangelis Dioikitopoulos, George Economides, Thomas Moutos, Apostolis Philippopoulos
and Nikos Tsakiris. Any remaining errors are ours.
1. Introduction
It has long been recognized that governments differ significantly in the efficiency of
delivering public services (see e.g. Tanzi and Schuknecht, 1998; Afonso et al., 2005). Some
are extremely wasteful and ineffective in performing basic activities, whereas others achieve
their objectives in a systematic and comprehensive way. The strive to increase government/
public sector efficiency (PSE hereafter) has spawned a vigorous theoretical literature on
channels that may affect it, with a quite prominent one being the design of fiscal relation
across the levels of government. A strand of the ongoing debate argues that fiscal
decentralization is positively associated with government efficiency and attributes this effect
either to increased electoral control – that comes as a result of increased decentralization (see
e.g. Seabright, 1996) – or to yardstick competition among local governments (see e.g. Besley
and Case, 1995; Besley and Smart, 2007).1 In contrast, other scholars note that local
politicians and bureaucrats are likely to face increased pressure from local interest groups
(see e.g. Bardhan and Mookherjee, 2000) and argue that fiscal decentralization, under these
or similar state of affairs, undermines government efficiency (Prud’homme, 1995).2
In the recent years, there is a small, albeit growing, body of empirical work that aims at
identifying the effect of fiscal decentralization on the quality of government (see e.g. Fisman
and Gatti, 2002a; Enikolopov and Zhuravskaya, 2007). In most of these studies, the
dependent variable is some internationally comparable outcome of government policy –
usually captured by socioeconomic indices like infant mortality, the literacy ratio,
immunization of population etc. – and the key explanatory variable is fiscal decentralization,
measured as the ratio of sub-national government expenditures (resp. tax revenues) to total
public spending (resp. tax revenues).3 Yet, the theoretical hypotheses postulated above are
1
Another branch of the literature argues that fiscal decentralization restricts the governments’ Leviathan
behavior and the consequent overspending by the politicians, through inter-jurisdictional fiscal competition (see
e.g. Brennan and Buchanan, 1980; Edwards and Keen, 1996).
2
This argument goes back to Alexander Hamilton, John Jay and James Madison who argued that the lower the
level of government, the greater is the extent of vulnerability to vested interest and the less protected the
minorities and poor tend to be [The Federalist Papers, 1787].
3
Fisman and Gatti (2002a) and Mello and Barenstein (2001) find that increased decentralization (measured as
the budgetary share of subnational governments) is associated with lower levels of corruption. In a similar vein,
Fisman and Gatti (2002b) and Henderson and Kuncoro (2004) using sub-national data for the US and Indonesia,
respectively, show that decentralization of public expenditure is effective in reducing corruption only if it is
accompanied by increased power to raise revenue (i.e. increased tax autonomy). Robalino et al. (2001) and
Khalegian (2003) in cross-country studies, also find support that fiscal decentralization is associated with lower
infant mortality rates and immunization rates (taken as measures of the quality of governance). Finally,
Enikolopov and Zhuravskaya (2007) examine the effect of decentralization on a set of four indicators of
governance quality (namely the three indicators used in studies reviewed above plus the illiteracy ratio) and
conclude that the effects of fiscal decentralization are beneficial only in countries that are also characterized by
a high degree of political centralization.
2
not comprehensively addressed simply by employing socioeconomic indicators as measures
of “good governance”. This is because these measures do not encompass the size of
government spending and thus fail to reflect the level of efficiency in delivering government
services. In the words of Barankay and Lockwood (2007) “[…] these regressions do not
estimate government “production functions” because they do not control for the inputs to the
output that is the dependent variable. […] In the absence of controls for these inputs, these
regressions cannot tell us much about the efficiency of government as any observed
correlation between decentralization and government output can be due to omitted variable
bias.”
In an effort to construct a plausible connection between theory and identification, the
purpose of this paper is to generate an empirical model that analyzes the relationship between
fiscal decentralization and PSE. Therefore, we opt for direct measures of PSE, derived nonparametrically at a first stage of analysis. In particular, we use data envelopment analysis
(DEA) on a panel of 21 OECD countries that covers the period 1970-2000 to construct two
alternative PSE indicators that reflect the goals of economic performance and stability. By
doing this, we implicitly assume that these indicators are derived from an underlying
government production relationship, where public spending serves as the input in the
production of public services.4 In the subsequent stage of analysis, we regress the PSE scores
obtained on a set of alternative fiscal decentralization measures following a technique that
merges the methodologies of Simar and Wilson (2007) and Khan and Lewbel (2007).5 Given
this methodological novelty, the main contribution of our study is that our dependent variable
allows for differences in the size of government spending and, therefore, does not give an
unfair credit to wasteful governments, even when the latter achieve better outcomes.
Our main finding is that government efficiency increases with the degree of fiscal
decentralization. This result appears to be robust to a number of different specifications and
fiscal decentralization measures. More precisely, we employ alternatively fiscal
decentralization measures as developed by Stegarescu (2005b), the measures reported in the
IMF’s Government Finance Statistics (2002), and measures of fiscal autonomy (reflecting
vertical fiscal imbalance and taxation autonomy) and we show that the positive relationship
between fiscal decentralization and PSE survives in all different specifications.
4
This is as in Tanzi and Schuknecht (1998) and Afonso et al. (2005).
We have resorted to this technique mainly because the second-stage analysis may not be robustly carried out
with conventional econometric methods. For details see Section 4.
5
3
The structure of the rest of the paper emerges along the following lines. Section 2
presents the theoretical considerations. In Section 3 we describe the data used in our
empirical analysis, as well as the DEA technique employed to obtain the government
efficiency estimates. In Section 4 we illustrate the econometric methodology used to regress
the government efficiency estimates on fiscal decentralization measures. In Section 5 we
present and discuss the empirical results and, finally, Section 6 concludes.
2. Theoretical considerations
The theoretical literature on fiscal federalism identifies two benchmark channels through
which fiscal decentralization is expected to affect positively the efficiency of governments.
These are (i) increased electoral control and (ii) yardstick competition among local
governments that comes as a result of decentralization.6 On the other hand, it has been also
pointed out that fiscal decentralization may be negatively associated with government
efficiency. In the presence of economies of scale (see e.g. Stein, 1997) or differences in the
quality of human capital between national and sub-national bureaucracies (Prud’homme,
1995), decentralization may lead to higher costs and thus increased inefficiency in the
delivery of public services. In the present section, we briefly review these mechanisms and
we set out the main testable hypotheses of our paper.
According to the electoral control mechanism, decentralization reduces the incentives for
officials to divert rents and increases the probability of “bad” incumbents to be voted out of
office, therefore affecting the overall efficiency of the government positively (Hindriks and
Lockwood, 2005). More precisely, Seabright (1996) shows that rent-seeking politicians,
when contesting in decentralized elections, face incentives to please the voters in each (local)
constituency, whereas in national elections they should please the voters only in a majority of
localities to get re-elected. Similar results are obtained by Persson and Tabellini (2000),
Hindriks and Lockwood (2005) and Myerson (2006).
The second path through which fiscal decentralization can alter the incentives and the
selection effects of elections is via yardstick competition. According to this theory (see e.g.
Shleifer, 1985; Salmon, 1987; Besley and Case, 1995), citizens have an advantage in
evaluating the performance of their policy makers when they are able to compare the policy
6
Barankay and Lockwood (2007) suggest an additional mechanism through which fiscal decentralization may
lead to increased efficiency, namely the decrease in lobbying by interest groups. However, since the theoretical
literature (see e.g. Bardhan and Mookherjee, 2000; Bordignon et al., 2003; Redoano, 2003) appears to be rather
inconclusive on this issue (mainly because under certain conditions there may be more lobbying with
decentralization), we prefer not to include this mechanism in the ones we refer to as benchmark.
4
choices of their own political representatives with the corresponding choices of neighbor
regions’ policy makers.7 Therefore, fiscal decentralization may raise PSE, since it provides
citizens the chance to compare public services and taxes across jurisdictions and helps them
to judge whether their government wastes resources through low human capital capacity or
rent-seeking (Besley and Smart, 2007).
However, fiscal decentralization may also exert a negative impact on the efficiency of
government. This impact may be attributed to a number of potential advantages of the
provision of public goods by central governments. First, in the presence of economies of
scale, decentralization may lead to higher costs (see e.g. Stein, 1997). Second, national
government bureaucracies are more likely to offer talented people better careers and
possibilities of promotion, which may in turn attract higher quality individuals (Prud’homme,
1995). Finally, other scholars underline the potential danger that local politicians and
bureaucrats are likely to face increased pressure from local interest groups (see e.g. Bardhan
and Mookherjee, 2000; Prud’homme, 1995). In view of these contradictory theoretical
underpinnings, we provide below an empirical framework to analyze the relationship
between fiscal decentralization and PSE.
3. The data
3.1. Public sector efficiency estimates using DEA
The measurement of PSE and the resulting comparison of individual countries in terms of
the efficient functioning of their public sectors, present a number of difficulties related to the
scarcity of publicly available data and the complicated problems that may emerge in the
estimation procedure. In the present study, we opt for a direct estimation of productive PSE
using Data Envelopment Analysis (DEA).8
DEA is a non-parametric programming technique that provides a linear piecewise
frontier, by enveloping the observed data points, and yields a convex production possibilities
set.9 As such, it does not require the explicit specification of a functional form of the
7
The theory of yardstick competition is also studied by Bordignon et al. (2004), Belleflamme and Hindriks
(2003), Besley and Smart (2007) and Bodenstein and Ursprung (2001).
8
Only recently a number of studies cultivated an effort towards the computation of PSE indicators. Concerning
OECD economies, Afonso et al. (2005) employed a nonparametric method to estimate relative efficiency scores
for several parts of the public sector during the 1980s and the 1990s, while Afonso and St. Aubyn (2005)
focused on the efficiency of government spending on education and health. Using similar techniques, Gupta and
Verhoeven (2001), Sijpe and Rayp (2007) and Afonso et al. (2006) focused on developing countries. Finally,
Balaguer-Coll et al. (2007) considered using DEA to analyze the efficiency of local governments in Spain.
9
For an excellent account on DEA, see Coelli et al. (2005).
5
underlying production relationship. To introduce some notation, let us assume that for N
observations there exist M inputs in the production of public goods, yielding S outputs.
Hence,
each
observation
x n = ( x1n , x2n ,..., xmn ) ∈ R+M
to
n
uses
a
produce
a
nonnegative
nonnegative
vector
vector
of
of
inputs
denoted
outputs,
denoted
y n = ( y1n , y2n ,..., ySn ) ∈ R+S . Production technology, F = {( y, x) : x can produce y} , describes
the set of feasible input-output vectors, and the input sets of production technology,
L( y ) = {x : ( y, x) ∈ F } describe the sets of input vectors that are feasible for each output
vector (Kumbhakar and Lovell, 2000).
To measure productive efficiency we use the following input-oriented DEA model,10
where the inputs are minimized and the outputs are held at their current levels:
θ * = min θ , s.t.
n
∑λ x
j =1
ji
≤ θ xi 0 i = 1, 2,..., m;
j
rj
≥ yr 0
j
=1
j
n
∑λ y
j =1
n
∑λ
j =1
λj ≥ 0
r = 1, 2,..., s;
(1)
j = 1, 2,..., n;
where public sector 0 represents one of the N public sectors under evaluation, and xi0 and yr0
are the ith input and rth output for public sector 0, respectively. If θ* = 1, then the current
input levels cannot be proportionally reduced, indicating that public sector 0 is on the
frontier. Otherwise, if θ* < 1, then public sector 0 is inefficient and θ* represents its inputoriented efficiency score. Finally, λ is the activity vector denoting the intensity levels at
which the N observations are conducted. Note that this approach, through the convexity
constraint Σλ = 1 (which accounts for variable returns to scale) forms a convex hull of
intersecting planes, since the frontier production plane is defined by combining some actual
production planes.
10
DEA may be computed either as input or output oriented. Input-oriented DEA shows by how much input
quantities can be reduced without varying the output quantities produced. Output-oriented DEA assesses by
how much output quantities can be proportionally increased without changing the input quantities used. The two
measures provide the same results under constant returns to scale but give slightly different values under
variable returns to scale. Nevertheless, both output and input oriented models will identify the same set of
efficient/inefficient public sectors (see Coelli et al., 2005). Also, a constant returns to scale assumption is only
appropriate when all public sectors are operating in an optimal scale (imperfections, asymmetries, etc. are not
present), and therefore we opt for a variable returns to scale specification.
6
Obviously, to measure PSE some “performance measures” are required that could be
interpreted as outputs of total public spending (which naturally serves as the input of the
production of public services) and should reflect the objectives (or alternatively the tasks) of
government. Following the rationale of the relevant literature (see e.g. Afonso et al., 2005;
Angelopoulos and Philippopoulos, 2005), we utilize two well-established performance
indicators that reflect benchmark tasks of government. These are: (i) the economic
performance indicator (EcPerf) and (ii) the economic stability indicator (EcStab).11
The EcPerf measure assumes that the government output is composed by the
unemployment rate, GDP per capita and the annual GDP growth rate. More precisely, lower
scores in the unemployment rate and higher scores in GDP per capita and GDP growth
reflect better economic performance. Data for the unemployment rate are taken from
OECD’s Economic Outlook (2005), whereas data for GDP per capita and GDP growth are
obtained from World Bank’s Development Indicators (2004). On the other hand, the EcStab
indicator consists of the standard deviation of the GDP growth rate, which is interpreted as a
measure of economic fluctuations, and of the inflation rate. In this case, lower scores in both
measures reflect higher economic stability. Data for standard deviation of the GDP growth
rate are taken from OECD Economic Outlook: Annual and Quarterly Data (2007)12 and data
for the inflation rate are obtained from World Bank’s Development Indicators (2004).
Finally, data for total public spending (the input) are also obtained from the World Bank’s
Development Indicators (2004). Our dataset consists of 21 OECD countries and spans the
period 1970-2000.13
Space constraints prevent reporting the yearly results of estimation of Program 1 and,
therefore, we present 10-year averages for each country on the Figures in Appendix B.14 The
first set of four graphs presents the relative efficiency scores when the government target is
economic performance and the second set the equivalent when the government target is
economic stability. Scores where data for any of the input or output of the production process
are missing are not derived for the particular country. Sensitivity analyses performed on
11
As Tanzi and Schuknecht (2000, pp. 75) state: “It is difficult or even impossible to consider all the social and
economic objectives (and thus all the socioeconomic indicators) that the governments might want to influence
with this spending. By necessity, the analysis will include fewer indicators than might have been desirable to
include”.
12
Annual data on standard deviation of the growth rate is obtained by utilizing the quarterly data available in
the Economic Outlook.
13
The countries in our sample are: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany,
Greece, Ireland, Italy, Japan, Luxembourg, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, UK
and USA.
14
The full set of Stage 1 results is available upon request.
7
Program 1 showed that efficient public sectors remained efficient to any simultaneous data
changes in the respective inputs (for a detailed discussion of the sensitivity analysis on DEA
estimates see Zhu, 2003).
When the government goal is economic performance, the PSE frontier is mainly shaped
by Japan (if data is available) and Switzerland, with USA, Luxembourg, Canada and, most
importantly, Ireland gaining significant ground towards the end of the period. Besides these
countries, and although the ranking of public sectors appears to change through time, we note
that Australia and Norway are characterized by relatively efficient public sectors, whereas
Belgium, France, Greece, Italy and Ireland (in the beginning of the sample period) are the
poor performers. The results are strikingly similar when the government goal is assumed to
be economic stability. The frontier is shaped by exactly the same countries and the patterns
of change remain unaltered, with Ireland, Canada and USA substantially improving their PSE
scores by the end of the sample period. A noticeable development is that most countries tend
to achieve higher PSE scores towards the end of the period examined, even if they are among
the relatively poor performers. Overall, these results seem to be reasonable approximations of
prior academic belief and are aligned with findings of previous research (see e.g. Afonso et
al., 2005).
3.2. Fiscal decentralization measures
The best approximation of the degree of fiscal decentralization has been an issue of
considerable disagreement among empirical studies. Usually, it is proxied by the budgetary
share of sub-national units as recorded by the IMF’s Government Financial Statistics
(GFS).15 However, this widely employed measure bears major shortcomings, since it fails to
integrate vital aspects of intergovernmental relations. Most importantly, it fails to capture the
real degree of sub-national governments’ autonomy that is to reflect the extent to which
decisions regarding revenues and expenditures are truly assigned to lower levels of
government (see Ebel and Yilmaz, 2003; Stegarescu, 2005b; Barankay and Lockwood,
2007). In particular, it has been pointed out that the GFS measure tends to overestimate the
share of government expenditure and tax revenues that is controlled by sub-national
governments and that it does so in a way that varies widely across countries (Ebel and
Yilmaz, 2003). For example, consider the extreme case of a country where all taxes are set
nationally, but where the revenues are shared with local governments via a fixed formula.
15
Previous empirical studies based on the GFS measure of fiscal decentralization are Jin and Zou (2002),
Davoodi and Zou (1998), Fisman and Gatti (2002a) and Enikopolov and Zhuravskaya (2007).
8
The share of tax revenues going to sub-national governments is measured in the GFS
statistics as sub-national revenue, even though local governments have no control over the
tax rate and the tax base. Similar problems arise on the expenditure side from policies that
are controlled by central government, but implemented by sub-national governments
(Stegarescu, 2005b; Barankay and Lockwood, 2007).16
In view of these difficulties, Stegarescu (2005b) developed new measures of fiscal
decentralization and sub-national tax autonomy, based on the detailed data provided by
OECD (1999). The advantage of the OECD (1999) survey is that it classifies in a very
analytical way the taxes of sub-national governments according to the degree of decisionmaking autonomy.17 More precisely, it separates taxes that are set by sub-central
governments (i.e. sub-central governments determine the tax rate and the corresponding tax
base) from those that are determined by the central government at a national level and in turn
shared with sub-national units. To this end, Stegarescu’s measures of fiscal decentralization
reflect the “real” tax-raising autonomy of sub-national units, since they count as local tax
revenues only those strictly determined by sub-national governments.18
In this study, we employ both the decentralization measures developed by Stegarescu
(2005b) and the decentralization indicators of the public finance statistics reported in the
GFS database. More precisely, we employ (i) the tax revenue decentralization indicator,
(TaxRevDec) and (ii) the revenue decentralization indicator as constructed by Stegarescu
(RevDec);19 (iii) the GFS expenditure decentralization measure (DecGFS1) and (iv) the GFS
revenue decentralization measure (DecGFS2).20
Finally, following the paradigm of Jin and Zou (2002), we also use measures of revenue
and expenditure autonomy of the local governments. These measures are: (i) TaxAut, defined
as sub-central government own tax revenue as a share of sub-central government total tax
revenue (obtained from Stegarescu, 2005b); (ii) TaxAutGFS, defined as sub-national tax
revenue as a share of sub-national revenue and grants (taken from GFS, 2002); (iii)
VertImb1, defined as transfers from other levels of government as a share of sub-national
16
Stegarescu (2005b) finds that the GFS measure of tax revenue decentralization overestimates the extent of
fiscal decentralization. This is particularly the case for Austria (28.4% versus 3.5%), Belgium (44.4% versus
24.6%) and Germany (49.4% versus 7.3).The percentages refer to data from 1999 and 2000.
17
See Appendix A for details.
18
To our knowledge, empirical application of these measures is limited to Fiva (2006), Stegarescu (2005a) and
Lessmann (2006).
19
The RevDec indicator represents the vertical structure of all the sources of public revenue. Thus, compared
with the TaxRevDec index, it additionally accounts for the structure of non-tax revenue (e.g. user charges or
operational surplus of public enterprises).
20
See Appendix A for details and definitions and Appendix C for correlations between these measures.
9
expenditures; and (iv) VertImb2, defined as transfers from other levels of government as a
share of sub-national revenues and grants.21 The latter two indices (both taken from the GFS
database) are measures of vertical fiscal imbalance and are expected to affect PSE negatively.
This is because voters view intergovernmental grants and “own resources” through different
lenses and they are more likely to sanction overspending by politicians when local
governments are purely financed by intergovernmental grants (see e.g. Oates 1979, 1991).22
In contrast, TaxAut and TaxAutGFS are expected to affect PSE positively, as higher values
for both variables indicate higher degree of tax autonomy.
3.3. Other controls
To ensure correct econometric identification, we use a series of additional controls. First,
we employ a standard demographic variable, namely the dependency ratio of the population
(i.e. the share of population aged below 16 and above 65 to total population), denoted as
depend. Depend is expected to exert a negative impact on PSE. This is because a higher
proportion of economically dependent population generates fiscal needs for programs
targeted towards the dependant group. Note that these programs are mainly transfers that do
not directly affect (what is assumed here to be) government output, while at the same time
they increase total government spending.23 Data for depend are obtained from the WDI
(2004).
In order to control for the overall level of productivity in the economy, we employ a total
factor productivity growth index (denoted as TFP), which is estimated as the residual of the
regression of the growth rate of per capita capital on the growth rate of per capita output, for
each country in our sample (see Solow, 1957; Barro and Sala-i-Martin, 2004). Countries that
present higher productivity growth are expected to be characterized by higher productivity in
their public sectors as well. Note, that the causality between TFP and PSE may be reverse
(i.e. higher PSE may lead to increased overall productivity). Therefore, in the empirical
specification, we assume that these variables are endogenous.
21
See Appendix A for details and definitions and Appendix C for correlations between these measures.
As it is usually suggested, intergovernmental grants create the picture that local public spending is funded by
non-residents. This is because local voters within a central legislature receive benefits from grants without
internalizing their full cost (Weingast el al 1988, Rattsø 2000).
23
This effect holds only for the measures of government output we employ here. Certainly, as transfer programs
are expected to create additional effects in the economy (e.g. changes in inequality) they also affect other
government activities and, therefore, may represent forms of government output. Had we measured the
efficiency of government in providing such outputs, the relationship between Depend and government
efficiency may have been different from the one suggested in the text.
22
10
Moreover, we employ two measures of the constraints that the government faces, (i) a
simple openness indicator (i.e. total trade over GDP) corrected for country size24 (Open) and
(ii) an index of government regulation (EconFreedom) as measured by Gwartney and
Lawson (2004), in which higher values reflect less interventionist governments. For three
main reasons we expect both variables to be positively associated with PSE. First, more trade
and domestic restrictions create rents and therefore higher waste through rent seeking
activities (Krueger, 1974; Gatti, 1999). Second, lower international openness and greater
government intervention imply lower product market competition within the country, which
is also associated with increased government waste (Ades and Di Tella, 1999). Finally,
within an international setting, the domestic government must be more efficient if it seeks to
attract foreign investors (Wei, 2000).
To measure the propensity of the state to employ redistributive policies, we use a
measure of population heterogeneity. According to Alesina et al. (2003), La Porta et al.
(1999) and Alesina and La Ferrara (2005), countries with high ethno-linguistic
fractionalization are expected to exhibit inferior government performance. Once again many
reasons have been put forward to justify this relationship. First, high ethnic fractionalization
results into pressures for redistribution between groups (Easterly and Levine, 1997).
Moreover, it may lead to high demand for publicly provided private goods, especially those
that can be targeted towards specific groups (Alesina et. al., 2003). It is also possible that a
relationship between fractionalization and corruption is formed. Finally, in more extreme
circumstances, increased ethnic fractionalization may lead to ethnic hatred and ultimately to
violent civil wars that disrupt the workings of government (see Fearon, 2003). Following
Easterly and Levine (1997), we control for ethno-linguistic fractionalization using a
Herfindahl index (named Ethnolig), which is calculated on the basis of the share of each
24
There are several reasons why the uncorrected for size measure of openness does not correctly reflect the
constraints that the economy faces from the international environment. The first is a simple statistical bias:
when a large and a small country trade with each other, the volume of trade is the same for both, but trade
shares as a portion of GDP differ. Second, in the presence of increasing returns to scale in production, the
market size affects the overall level of productivity. This argument goes back to Adam Smith who argued that
the size of the market imposes a constraint on the division of labor. Therefore, small countries that are relatively
closed to international trade must experience a lower level of productivity. Ades and Glaeser (1999), Wacziarg
(2001) and Alesina et al. (2000) provide empirical evidence consistent with these ideas: large countries
experience smaller dynamic gains from trade. Finally, according to Frankel and Romer (1999, p.382), “smaller
countries may engage in more trade with other countries simply because they engage in less within-country
trade”. For further details on the relationship between openness and country size see also Alesina and Spolaore
(2003, chapter 6) and Alesina and Wacziarg (1998, pp.306-307). To correct for this bias we run a regression
with total trade over GDP as the dependent variable and the share of country i's GDP to the average GDP of our
sample (at the same time period) as the independent variable. Then, we use the residuals from this regression as
an indicator for openness (see Bretschger and Hettich, 2002).
11
separate ethno-linguistic group over total population (data are obtained from La Porta et al.,
1999).
The final set of controls we employ includes variables that refer to the structure of the
elected government. Therefore, we use the variable NSM, taken from Mierau et al. (2007),
which reports the number of ministers that directly use part of the government budget (i.e.
the total number of ministers excluding the minister of finance). Since we expect that these
ministers care about the size of the budget they control,25 the relationship between NSM and
PSE should be negative.26 Finally, the variable coalition, taken from Tavares (2004), is a
dummy variable taking the value 1 if a coalition cabinet that includes ministers from two or
more parties is in power. As the number of parties involved in the government increases, the
accountability of each of the parties usually diminishes, thus providing fewer incentives for
efficiency. In addition, coalition governments are typically associated with a shorter life span
(see Schofield, 1993; Müller and Strøm, 2000) and therefore are less concerned with superior
performance.
4. Empirical methodology
Based on the theoretical considerations of the previous section, we estimate the following
empirical model:
pit = α 0 + β k zit + uit
(2)
where pit are government efficiency estimates derived from Program (1) and zit are the set
of explanatory variables described above.
Unfortunately, it turns out that estimation of Eq. (2) is not a trivial econometric issue. In
particular, when non-parametrically derived measures (like the DEA efficiency scores) are
regressed against a number of determinants, conventional censored regressions (such as Tobit
regressions) yield biased results.27 Only very recently Simar and Wilson (2007) proposed a
robust procedure to overcome the associated difficulties. Specifically, they offer an
algorithm, comprised of subsequent steps, that begins with a truncated regression and ends
with the estimation of confidence intervals. Still, as discussed above, the total factor
productivity variable may be endogenous in the PSE measures. To account for this
25
Ministers care about the size of the budget they receive for many reasons, which may include participation in
rent-seeking activities, increase in the size of the bureau they control (Niskanen, 1973) and the ability to make
income transfers as a means for controlling a larger political clientele.
26
This effect is consistent with the idea that there may exist diseconomies of scale in the administration of
government (see Stein, 1997).
27
This is mainly due (but not limited) to the fact that DEA efficiency estimates are serially correlated (for a
proof and further details, see Simar and Wilson, 2007).
12
endogeneity we follow the methodology put forth by Khan and Lewbel (2007), who for the
first time suggested a truncated regression model with endogenous regressors.28 To this end,
we merge the algorithm suggested by Simar and Wilson (2007) with the two-stage least
squares truncated regression model put forth by Khan and Lewbel (2007). We consider all
observations as cross sections and therefore we drop subscript t in Eq. (2). Consequently, the
following procedure may be used to provide inference on the determinants of PSE:
)
1. Obtain maximum likelihood estimates αˆ k of α k and σ u of σ u in the endogenous
truncated regression of pˆ i on its k determinants (zi) in Eq. (2), where pˆ i ≤1. The
instrument used is the one period lag of the endogenous variables (i.e. lagged one period
TFP).29
2. Loop over the next three steps L=2000 times to obtain a set of bootstrap
L
estimates Βi = (αˆ * , σˆ u* )b 
b =1
For each i=1,…,m, draw ui from the N (0, σˆ u2 ) distribution with left-truncation at (1 − zi aˆ ) .
For details on how to draw from a left-truncated normal distribution see the Appendix of
Simar and Wilson (2007).
Again for each i=1,…,m, compute pi* = ziαˆ + ui .
Use the maximum likelihood method to estimate the endogenous truncated regression of
pi* on zi , yielding estimates µ µ* ,νν* .
3. Use the bootstrap values in B and the original estimates α , σ u to construct estimated
confidence intervals for each element of α and for σ u . This is done by using the jth
element
of
each
bootstrap
value
α̂ *
to
find
values
µπ* ,ν π*
such
that
Pr  −ν π ≤ (αˆ *j − αˆ j ) ≤ µπ*  ≈ 1 − π , for some small conventional value of π , π = 0.05 in
the present analysis. The approximation improves as L → ∞ . Substituting µπ* ,ν π* for
µπ ,ν π in Pr  −ν π ≤ (αˆ j − α j ) ≤ µπ  = 1 − π leads to an estimated confidence interval
(αˆ j + µπ* , αˆ j +ν π* ) .
28
Their simulation results show that their new estimator performs well, while they specifically state that their
method is applicable in general contexts involving two-stage analyses with a nonparametric first stage, such as
ours.
29
The results of the paper remain intact if more lags as instrumental variables.
13
5. Results
In this section we discuss the results obtained by estimating Eq. (2), using the data
described in Section 3 and the empirical methodology presented in the previous section. The
baseline results are presented in Tables 1 and 2, while the extensive sensitivity analyses
performed are presented in Tables 3 and 4.30
5.1. Basic results
Table 1 reports the results of the regressions of PSE on alternative measures of fiscal
decentralization. In column 1, the government efficiency estimates are regressed on
TaxRevDec, as well as on our set of control variables (i.e. Coalition, NSM, EconFreedom,
Depend, TFP, Open, Ethnolig). In all estimated equations, we include regional dummies (see
Appendix A) and a time trend. Evidently, the coefficient on TaxRevDec bears a positive sign
and is significant at the 1% level, suggesting a strong positive link between fiscal
decentralization and PSE. This result is aligned with the propositions of the theoretical debate
discussed in Section 2. Focusing on the rest of the explanatory variables, we observe that
coefficients on both Coalition and NSM present negative signs and are significant at
conventional levels, indicating that coalition governments and large cabinets exert a negative
impact on PSE. In contrast, EconFreedom is positive and highly significant, whereas the
coefficient on Open is positive and marginally significant. These results may be explained by
the beneficial effects of internal and external market constraints on the function of
governments. Finally, Depend and Ethnolig appear to be insignificant determinants of PSE.
In column 2, we re-estimate the model by using Revdec, instead of TaxRevDec, as a
proxy for fiscal decentralization. As we have already pointed out, the Revdec indicator is a
more general measure of fiscal decentralization, since it accounts for the vertical structure of
non-tax revenues (such as user charges and operational surpluses of public enterprises),
which is not encompassed in TaxRevDec.31 Markedly, the main result of this second
specification remains unaffected. The coefficient on Revdec is positive and highly
significant, thus confirming the positive effect of fiscal decentralization on government
efficiency. As regards the rest of the explanatory variables, we observe that only slight
changes in the results emerge (compared to those presented in column 1). Specifically, the
coefficients on Coalition and TFP appear to lose their statistical significance, whereas
30
Note that in all estimated equations presented in the first two tables, we include regional dummies (see
Appendix A), which are not reported to save space. The full set of results is available upon request.
31
For details on this see Appendix A.
14
Ethnolig enters with a positive and significant coefficient. It should be noted that this result is
not in line with our theoretical priors, since it implies that the relationship between ethnolinguistic fractionalization and PSE is positive.
In column 3 we employ as a proxy for fiscal decentralization the GFS expenditure
decentralization measure (DecGFS1), which is also found to be positively and significantly
related to government efficiency. As a final test, in column 4 we use DecGFS2, reaching
similar conclusions. In these last two specifications, the behavior of the control variables is
much similar to that observed in column 2.
Table 2 presents the results of the regressions of PSE on the two alternative measures of
tax autonomy and the two alternative measures of vertical fiscal imbalance. In column 1, the
DEA government efficiency estimates are regressed on the Stegarescu (2005b) tax autonomy
measure (denoted as TaxAut), as well as on the rest of the explanatory variables. The results
suggest that TaxAut bears a positive sign and appears to be highly significant, which is
consistent with our theoretical priors outlined in Section 2. Indeed, increased tax autonomy
(or alternatively decreased dependency of local governments on intergovernmental transfers)
explains, at least in OECD economies, higher levels of PSE. As suggested above, this
relationship is probably associated with the adverse effect of tax autonomy due to the
“common pool” problem. Turning to the rest of the explanatory variables, we observe that
our results are similar to those presented in Table 1. More precisely, the coefficients on
Coalition and NSM are negative and significant, whereas the coefficients on EconFreedom,
TFP and Open are positive and significant. Finally, Depend and Ethnolig appear to be
insignificant at conventional levels of statistical significance.
In column 2 we employ the GFS tax autonomy measure (denoted as TaxAutGFS) instead
of the TaxAut and we re-estimate Eq. (2). As expected, TaxAutGFS enters with a positive
sign and is significant at the 1% level, validating the positive relationship between tax
autonomy and PSE. Concerning the rest of the variables, our results remain practically
unaffected, with the exception of Coalition (which loses its significance) and Ethnolig
(which presents a positive and significant effect on PSE).
In column 3, we employ a vertical fiscal imbalance measure (denoted as VertImb1) in
order to capture the fiscal dependency of local governments on intergovernmental transfers.
We observe that the coefficient on VertImb1 bears a negative sign and is significant at the 1%
level. This result is in accordance with our previous findings on the relationship between tax
autonomy and PSE, as well as the dominant view of the theoretical literature (see e.g. Oates
1979, 1991). A similar result is reached by using an alternative fiscal dependency measure,
15
(i.e. VertImb2 in column 4). In both estimations presented in columns 3 and 4, the behavior
of the control variables is similar to that observed in column 2.
5.2. Sensitivity analysis
In this section we inquire into the robustness of our results. First, we examine the
sensitivity of our estimates with respect to individual outliers, as well as with respect to
regional characteristics. Next, we re-estimate our model using five year averages, in order to
ensure that our results are not driven by the noise generated by annual data. Finally, we reestimate our benchmark model using an alternative measure of government efficiency,
namely the economic stability (EcStab) indicator, as defined above.32
Seeing that our sample consists of 21 OECD countries, which are quite heterogeneous in
many aspects, we first examine the sensitivity of our estimates to individual outliers or to
regional characteristics. To account for the first issue, we re-estimate our benchmark model,
this time excluding all observations with an error term in the upper or lower 5th percentile
(i.e. we drop 10% of our sample). The results, presented in column 1 and 2 of Table 3,
indicate that the conclusions presented in Sections 5.1 are firm as regards the influence of
individual outliers. The second issue that relates to the potential effect of regional
characteristics has been (partially) addressed by including the three dummies Scandinavian,
Anglo-Saxon, Mediterranean in the empirical models. Here we further examine whether the
results change when we exclude each of these groups in turn. The results, presented in
columns 5 to 10, suggest that the effects of Taxrevdec and TaxAut on PSE remain positive
and significant at conventional levels.
Another potential drawback of the analysis of the previous section is the annual nature of
the dataset. This was the preferred choice with the aim of increasing the size of our sample,
which may however come at the expense of also increasing the noise in the data.33 This
would imply that observed changes in PSE may be due to random factors (such as the
business cycle), which are not necessarily related to changes in the explanatory variables. For
this reason, we re-examine our two propositions using simple five-year averages of our data.
The results, reported in column 3 and 4 of Table 3, suggest that even though for most of the
control variables the statistical and economic significance drops, the variables of main
32
For expositional brevity, in Table 3 we present the results from using only Taxrevdec as the measure of
decentralization. We have verified, however, that our results carry through to the rest of the measures used in
Table2 1 and 2.
33
Rodden (2003) underlines the importance of panel studies in examining fiscal decentralization and argues that
cross-national studies fail to capture important aspects of this issue, since they do not account for the fact that
the process of decentralization unfolds overtime.
16
interest remain practically unaffected. We attribute the increase in the standard errors to the
fact that our sample is now confined to about 100 observations and it is well-known that
maximum likelihood estimators usually produce a bias in small samples, with this bias
diminishing as the sample increases. Nonetheless, as the coefficients on TaxRevDec and
TaxAut remain significant, we are confident that the positive relationship in hand is robust to
the larger time span of our observations.
An important sensitivity analysis involves estimation of Eq. (2) using a different
dependent variable (namely EcStab) that looks into economic stability as the ultimate goal of
governments.34 Once again, we use a number of alternative specifications that capture the
relationship between fiscal decentralization and PSE and between fiscal dependency and
PSE. The results, reported in Table 4, suggest no discrepancy from previous findings:
Increased fiscal decentralization and tax autonomy exert a positive impact on PSE, with the
results in some cases being enhanced compared to their counterparts of Tables 1 and 2.35
Concerning the rest of the explanatory variables our results also remained analogous to our
previous findings.
6. Conclusions
In this paper we specified an empirical framework to investigate the effect of fiscal
decentralization on public sector efficiency. With this aim we (i) directly measured PSE
using DEA, thereby specifying an underlying production process of public goods; and (ii)
examined the impact of the variables of interest on PSE via the amalgamation of two
prominent semi-parametric techniques. Therefore, we proceeded in two stages. The first
involved estimation of PSE, in terms of assuming governments to aim for either economic
performance or stability, while the second entailed regressing the PSE scores derived in stage
1 on a number of well-established indicators for fiscal decentralization. The analysis was
carried out on a panel that included 21 OECD economies over the period 1970-2000. Backed
by strong empirical results, obtained from a number of different specifications and sensitivity
analyses, we contend that public sector efficiency is increasing with fiscal decentralization.
This relationship calls for a deeper understanding of the inter- and intra-country mechanisms
that shape it; however, before moving on to another issue, we have better bring this entry to a
close.
34
Note that until now we assumed that governments aim at improved economic performance.
The vertical fiscal imbalance indicators enter the estimated equations (columns 5 and 6) with negative and
significant coefficients, much like in Table 2.
35
17
Table 1
Public sector efficiency and fiscal decentralization
PSE(EcPerf)
Taxrevdec
(1)
(2)
(3)
0.005***
(5.99)
Revdec
0.004***
(4.19)
DecGFS1
0.002**
(2.35)
DecGFS2
Coalition
NSM
EconFreedom
Depend
TFP
Open
Ethnolig
Obs
Wald
Sigma
(4)
-0.068**
(-2.46)
-0.008***
(-2.94)
0.104***
(4.90)
0.227
(0.94)
1.802***
(2.98)
0.003**
(1.97)
-0.166
(-1.34)
495
216.72
0.237
-0.015
(-0.55)
-0.020***
(-8.32)
0.146***
(8.01)
0.311
(1.34)
0.769
(1.57)
-0.001
(-0.75)
0.373***
(2.99)
398
363.87
0.193
-0.042*
(-1.70)
-0.018***
(-7.84)
0.135***
(7.28)
0.191
(0.86)
0.447
(0.02)
0.001
(0.78)
0.561***
(5.23)
468
308.51
0.205
0.003***
(2.69)
-0.038
(-1.53)
-0.019***
(-7.90)
0.138***
(7.38)
0.239
(1.06)
0.446
(0.02)
0.001
(0.33)
0.511***
(4.60)
468
316.53
0.204
Note: **,*** denote statistical significance at 5% and 1% level of statistical significance respectively. Country
dummies are included in all estimated equations
18
Table 2
Public sector efficiency and fiscal dependency of local governments
PSE(EcPerf)
TaxAut
(1)
(2)
(3)
0.001**
(2.62)
TaxAutGFS
0.002***
(3.49)
VertImb1
-0.002***
(-2.77)
VertImb2
Coalition
NSM
EconFreedom
Depend
TFP
Open
Ethnolig
Obs
Wald
Sigma
(4)
-0.088***
(-3.55)
-0.013***
(-5.34)
0.119***
(5.91)
-0.267
(-1.17)
1.441***
(2.99)
0.006***
(3.43)
0.188*
(1.75)
581
214.60
0.245
-0.039
(-1.52)
-0.018***
(-7.67)
0.119***
(6.05)
-0.045
(-0.21)
1.000**
(2.16)
0.001*
(1.86)
0.361***
(3.16)
473
268.04
0.214
-0.011
(-0.45)
-0.019***
(-8.16)
0.128***
(7.10)
0.147
(0.71)
1.286***
(2.88)
0.000
(-0.15)
0.610***
(5.56)
469
331.05
0.203
-0.003***
(-3.46)
-0.012
(-0.45)
-0.017***
(-6.86)
0.115***
(5.96)
0.078
(0.36)
1.231***
(2.69)
0.001**
(2.41)
0.465***
(4.08)
469
289.10
0.210
Note: *, **, *** denote statistical significance at 10%, 5% and 1% level of statistical significance
respectively. Country dummies are included in all estimated equations
19
Table 3
Sensitivity analysis I: Accounting for outliers, short-run dynamics and regional effects
PSE(EcPerf)
Taxrevdec
(1)
0.005***
(5.95)
TaxAut
Coalition
NSM
EconFreedom
Depend
TFP
Open
Ethnolig
dscand
das
dmed
Obs
Wald
Sigma
(2)
-0.069**
(-2.51)
-0.008***
(-2.82)
0.118***
(5.01)
0.322
(1.21)
1.722***
(2.71)
0.003*
(1.90)
-0.141
(-1.22)
0.055*
(1.83)
0.046
(1.12)
0.171***
(3.29)
446
233.72
0.214
(3)
(4)
0.041***
(2.68)
0.001***
(2.77)
-0.075***
(-3.10)
-0.011***
(-4.40)
0.116***
(5.63)
-0.130
(-1.22)
1.426**
(2.39)
0.005**
(2.48)
0.139
(1.40)
0.101*
(1.80)
0.048
(1.31)
0.164***
(3.02)
523
213.75
0.234
-0.125**
(-2.17)
-0.011*
(-1.81)
0.071**
(2.21)
0.176
(0.43)
3.900*
(1.79)
0.007**
(2.24)
0.329*
(1.67)
0.103*
(1.93)
0.141*
(1.82)
0.254***
(3.92)
100
82.92
0.192
0.001**
(2.34)
-0.107*
(-1.82)
-0.007
(-1.28)
0.053*
(1.82)
0.398
(0.96)
6.199***
(3.39)
0.006
(1.58)
0.667***
(3.38)
0.046
(0.80)
0.121*
(1.85)
0.186***
(2.69)
117
82.18
0.198
(5)
(6)
(7)
0.008***
(7.52)
0.005***
(5.39)
0.003***
(3.22)
-0.029
(-0.76)
-0.004
(-1.26)
0.136***
(5.32)
0.652**
(2.26)
3.134***
(3.93)
0.002
(0.95)
-0.400***
(-3.01)
0.003
(0.07)
0.250***
(4.77)
356
242.67
0.238
-0.083***
(-2.79)
-0.009***
(-3.18)
0.111***
(4.96)
0.099
(0.39)
1.969***
(3.03)
0.003*
(1.74)
-0.092
(-0.66)
0.069**
(2.04)
0.030
(0.73)
450
220.15
0.240
-0.116***
(-3.45)
-0.015***
(-4.27)
0.116***
(4.10)
1.132***
(3.07)
0.732
(1.02)
0.018***
(3.98)
0.166
(1.05)
0.100**
(2.59)
0.159***
(3.19)
352
168.97
0.237
(8)
(9)
(10)
0.001**
(2.43)
-0.073**
(-2.27)
-0.012***
(-4.75)
0.144***
(6.03)
-0.270
(-1.05)
1.840***
(3.21)
0.005***
(3.16)
0.158
(1.41)
0.001***
(3.56)
-0.111***
(-3.74)
-0.013***
(-5.24)
0.129***
(5.97)
-0.428*
(-1.69)
1.391**
(2.64)
0.004***
(2.76)
0.266**
(2.23)
0.116***
(3.62)
-0.007
(-0.20)
0.001**
(2.17)
-0.088***
(-2.85)
-0.017***
(-6.12)
0.136***
(5.36)
0.606*
(1.88)
1.050**
(1.98)
0.019***
(4.88)
0.267*
(1.82)
0.127***
(3.59)
-0.002
(-0.05)
0.229***
(5.09)
440
223.73
485
219.52
0.259***
(5.66)
436
194.93
Note: *, **, *** denote statistical significance at 10%, 5% and 1% level of statistical significance respectively. Country dummies are included in all estimated equations
20
Table 4
Sensitivity analysis II: Using economic stability as a measure of government performance
PSE(EcSstab)
Taxrevdec
Revdec
DecGFS1
DecGFS2
VertImb1
VertImb2
TaxAut
(1)
(2)
(3)
(4)
(5)
(6)
(7)
0.007***
(7.26)
0.007***
(6.80)
0.004***
(3.23)
0.002**
(2.09)
-0.002**
(-2.18)
-0002**
(-2.59)
0.003***
(8.04)
TaxAutGFS
Coalition
NSM
EconFreedom
Depend
TFP
Open
Ethnolig
Obs
Wald
Sigma
(8)
-0.008
0.029
-0.016
-0.015
-0.006
-0.004
-0.044*
(-0.27)
(0.93)
(-0.56)
(-0.52)
(-0.19)
(-0.13)
(-1.68)
0.004
0.001
-0.001
-0.001
-0.001
-0.001
0.001
(1.49)
(0.18)
(-0.37)
(-0.46)
(-0.46)
(-0.40)
(0.24)
0.147*** 0.155*** 0.153*** 0.149*** 0.136*** 0.137*** 0.102***
(6.13)
(7.13)
(6.99)
(6.73)
(6.26)
(6.11)
(4.75)
0.803*** 0.728** 0.548**
0.457*
0.296
0.216
-0.099
(2.97)
(2.64)
(2.10)
(1.72)
(1.19)
(0.84)
(-0.41)
1.532** 1.247** 1.233** 1.206** 1.261** 1.265** 1.567***
(2.25)
(2.13)
(2.35)
(2.28)
(2.35)
(2.29)
(3.06)
0.002
0.000
0.004*
0.004*
0.004** 0.005** 0.007***
(1.10)
(0.00)
(1.90)
(1.71)
(2.13)
(2.39)
(3.73)
0.031
0.444*** 0.883*** 0.871*** 1.022*** 0.931*** 0.403***
(0.22)
(2.99)
(7.00)
(6.60)
(7.73)
(6.91)
(3.53)
495
398
468
468
469
469
581
245.16
287.78
259.97
254.68
258.75
215.93
281.69
0.264
0.233
0.243
0.244
0.244
0.259
0.003***
(2.87)
-0.012
(-0.40)
-0.001
(-0.31)
0.144***
(6.44)
0.212
(0.83)
1.135**
(2.06)
0.005**
(2.41)
0.803***
(6.03)
473
212.18
Note: *, **, *** denote statistical significance at 10%, 5% and 1% level of statistical significance respectively. Country dummies are
included in all estimated equations
21
Appendix A : Data Sources and Descriptive Statistics
Description
Obs.
Mean
Std.
min
max
Sources
Dev.
DEA efficiency scores when
the output is Economic
Performance
DEA efficiency scores when
the output is Economic
Stability
Sub-Central Government own
tax revenue as a share of
General Government total tax
revenue.
Sub-Central Government
own tax and non-tax revenue
as a share of General
Government total tax revenue
Sub-national Expenditures as
a share of total expenditures
Sub-national Revenues as a
share of total revenues
Sub-Central Government own
tax revenue as a share of
Sub-Central Government total
tax revenue
Sub-national tax revenues as
a share of sub-national
revenues and grants
Transfers from other levels of
government as a share of subnational expenditures.
Transfers from other levels of
government as a share of subnational revenues and grants
Dummy variable taking the
value 1 if a coalition cabinet
is in power
Number of Spending
Ministers
Index of Economic
Regulation
630
0.58
0.29
0.28
1
Own calculations based
on Afonso et al. (2005).
630
0.61
0.32
0.27
1
Own calculations based
on Afonso et al. (2005).
522
22.40
17.09
0.27
61.50
Stegarescu (2005b)
403
25.23
15.89
4.13
64.69
Stegarescu (2005b)
481
31.70
13.76
1.45
59.18
481
23.20
14.21
1.61
54.60
Government Financial
Statistics. IMF (2002)
623
77.27
33.91
2.83
100
Stegarescu (2005b)
486
40.59
17.09
2.15
108.73
483
40.04
16.61
8.39
82.00
483
41.42
17.70
8.45
86.41
609
0.55
0.49
0.00
1.00
Tavares (2004)
633
15.30
4.79
5.00
33.00
Mierau et al. (2007)
651
5.89
0.81
4.30
8.30
Population 16- and 65+ as a
share of total population
651
0.52
0.05
0.44
0.74
TFP
Total Factor Productivity
630
0
0.02
-0.20
0.10
Open
Residuals from regression of
Size on
(Exports+Imports)/GDP
630
0
10.77
-43.35
78.83
Gwartney and Lawson
(2006)
World Bank
Development
Indicators(WBDI)
(2004)
Own Calculations as
described in Section
3.3. Data from Penn
World Tables (2006)
Own Calculations as
described in Section
3.3. Data from WBDI
(2004)
Index of ethno-linguistic
fractionalization
651
0.13
0.11
0.003
0.376
PSE (EcPerf)
PSE (EcStab)
Taxrevdec
Revdec
DecGFS1
DecGFS2
TaxAut
TaxAutGFS
VertImb1
VertImb2
Coalition
NSM
EconFreedom
Depend
Ethnolig
La Porta et al. (1999)
22
Appendix B : Graphs of PSE(EcPerf) and PSE(EcStab)
DEA efficiency scores for EcPerf
(10-years averages, period 1970-1979)
1.20
1.00
0.80
0.60
0.40
SWI
UK
USA
SWI
UK
USA
SWI
UK
USA
SWI
UK
USA
SWE
SWE
SWE
SWE
SPA
SPA
SPA
SPA
POR
POR
POR
POR
NLD
NOR
NOR
LUX
JPN
ITA
IRL
GRE
FRA
GER
FIN
DNK
CAN
BEL
AUT
0.00
AUS
0.20
1.20
1.00
0.80
0.60
0.40
LUX
NLD
JPN
ITA
IRL
GRE
FRA
GER
FIN
DNK
CAN
BEL
AUT
0.00
AUS
0.20
1.20
1.00
0.80
0.60
0.40
0.20
NLD
NOR
LUX
JPN
ITA
IRL
GRE
GER
FRA
FIN
DNK
CAN
BEL
AUT
AUS
0.00
DEA efficiency scores for EcPerf, year 2000
1.20
1.00
0.80
0.60
0.40
NLD
NOR
LUX
JPN
ITA
IRL
GRE
GER
FRA
FIN
DNK
CAN
BEL
AUT
0.00
AUS
0.20
Note: Estimates of PSE(EcPerf) for Gernany prior to 1991 and for Japan after 1994 are not available due to the unavailability of
the data for unemployment and public spending respectively.
23
DEA efficiency scores for EcStab
1.20
1.00
0.80
0.60
0.40
0.20
UK
USA
SWE
SWE
SWI
SPA
POR
SPA
POR
NOR
NLD
NOR
LUX
JPN
ITA
IRL
GRE
GER
FRA
FIN
DNK
CAN
BEL
AUT
AUS
0.00
DEA ef f iciency scores f or EcStab
(10-y ears av erages, period 1980-1989)
1.20
1.00
0.80
0.60
0.40
0.20
UK
SPA
SWE
SWI
UK
USA
SPA
SWE
SWI
UK
USA
USA
SWI
POR
POR
NLD
LUX
JPN
ITA
IRL
GRE
GER
FRA
FIN
DNK
CAN
BEL
AUT
AUS
0.00
DEA efficiency scores for EcStab
1.20
1.00
0.80
0.60
0.40
0.20
NLD
NOR
LUX
JPN
ITA
IRL
GRE
GER
FRA
FIN
DNK
CAN
BEL
AUT
AUS
0.00
DEA efficiency scores for EcStab, year 2000
1.20
1.00
0.80
0.60
0.40
0.20
NLD
NOR
LUX
JPN
ITA
IRL
GRE
GER
FRA
FIN
DNK
CAN
BEL
AUT
AUS
0.00
Note: Estimates of PSE(EcStab) for Austria, for Germany prior to 1991 and for Japan after 1994 are not available due to the
unavailability of the data for standard deviation of growth, inflation and standard deviation of growth and public spending
respectively.
24
Appendix C : Correlation Matrix
DecGFS1
DecGFS2
Taxrevdec
Revdec
VetrImb
VertImb2
TaxAut
TaxAut
GFS
Coalition
NSM
Depend
Econ
Freedom
TFP
Open
DecGFS1
1
DecGFS2
0.93
1
Taxrevdec
0.71
0.78
1
Revdec
0.81
0.87
0.97
1
VetrImb
-0.45
-0.70
-0.52
-0.57
1
VertImb2
-0.47
-0.73
-0.53
-0.59
0.97
1
TaxAut
0.19
0.033
0.45
0.35
0.29
0.29
1
TaxAutGFS
0.44
0.66
0.57
0.57
-0.92
-0.86
-0.21
1
Coalition
-0.02
-0.06
-0.13
-0.08
0.11
0.13
-0.13
-0.07
1
NSM
-0.14
-0.12
-0.08
-0.16
-0.06
0.01
0.11
0.17
-0.25
1
Depend
-0.30
-0.35
-0.22
-0.27
0.22
0.27
0.19
-0.27
-0.23
0.09
1
Econ
Freedom
0.32
0.26
0.30
0.32
0.09
0.04
0.42
-0.09
-0.21
0.07
-0.07
1
TFP
-0.02
-0.02
-0.05
-0.04
0.07
0.06
-0.05
-0.05
-0.03
-0.02
-0.03
0.04
1
Open
0.24
0.31
0.20
0.27
-0.18
-0.21
0.045
0.12
-0.16
-0.20
-0.01
0.29
0.02
1
Ethnolig
0.10
0.20
0.45
0.41
-0.09
-0.07
0.22
0.14
-0.11
-0.04
-0.20
0.19
-0.06
0.063
Ethnolig
1
25
References
Ades, A., di Tella, R., (1999). Rents, competition and corruption. American Economic
Review 89, 982-993.
Ades, A., Glaeser, E.L., (1999). Evidence on growth, increasing returns and the extent
of the market. Quarterly Journal of Economics 114, 1025-1046.
Alesina, A., Devleeschauwer, A., Easterly, W., Kurlat, S., Wacziarg, R., (2003).
Fractionalization. Journal of Economic Growth 8, 155-194.
Alesina, A., La Ferrara, E., (2005). Preferences for redistribution in the land of
opportunities. Journal of Public Economics 89, 897-931.
Alesina, A., Spolaore, E., (2003). The Size of Nations. Cambridge: MIT Press.
Alesina, A., Spolaore, E., Wacziarg, R., (2000). Economic integration and political
disintegration. American Economic Review 90, 1276-1296.
Alesina, A., Wacziarg, R., (1998). Openness, country size and government. Journal of
Public Economics 69, 305-321.
Afonso, A., St. Aubyn, M., (2005). Non-parametric approaches to education and
health: Expenditure efficiency in OECD countries. Journal of Applied Economics 8,
227-246.
Afonso, A., Schuknecht, L., Tanzi, V., (2005). Public sector efficiency: An
international comparison. Public Choice 123, 321-347.
Afonso, A., Schuknecht, L., Tanzi, V., (2006). Public sector efficiency: Evidence for
new EU member states and emerging markets. ECB Working Paper no. 581.
Angelopoulos, K., Philippopoulos, A., (2005). The role of government in anti-social
redistributive activities. CESifo Working Paper no. 1427.
Angelopoulos, K., Philippopoulos, A., Tsionas, E., (2007). Does public sector
efficiency matter? Revisiting the relation between fiscal size and economic growth in
a world sample. Mimeo.
Balaguer-Coll, M.T., Prior, D., Tortosa-Ausina, E., (2007). On the determinants of
local government performance: A two-stage nonparametric approach. European
Economic Review 51, 425-451.
Barankay, I., Lockwood, B., (2007). Decentralization and the productive efficiency of
government: Evidence from Swiss cantons. Journal of Public Economics 91, 11971218.
Bardhan, P., Mookerjee, D., (2000). Capture Governance at Local and National
Levels. American Economic Review 90, 135-139.
Barro, R., Sala-i-Martin, X., (2004). Economic Growth. Second edition. Cambridge:
MIT Press.
26
Belleflamme, P., Hindriks, J., (2005). Yardstick Competition and Political Agency
Problems, Social Choice and Welfare 24, 155-169.
Besley, T., Case, A., (1995). Incumbent Behavior: Vote-Seeking, Tax-Setting, and
Yardsick Competition. American Economic Review 85, 25-45.
Besley, T., Smart, M., (2007). Fiscal Restraints and Voter Welfare. Journal of Public
Economics 91, 755-773.
Bodenstein, M., Ursprung, H., (2001). Political yardstick competition, economic
integration, and constitutional choice in a federation. CESifo Working Paper no. 501.
Bordignon, C., Colombo, L., Galmarini, U., (2003). Fiscal Federalism and
Endogenous Lobbies' Formation, CESifo Working Paper no. 1017.
Brennan, G., Buchanan, J., (1980). The power to tax: analytical foundations of a fiscal
constitution. New York: Cambridge University Press.
Bretschger, L., Hettich, F., (2002). Globalization, capital mobility and tax
competition: Theory and evidence for OECD countries. European Journal of Political
Economy 18, 695-716.
Coelli, T., Rao, D.S.P., O'Donnell, C.C., Battese, G.E., (2005). An Introduction to
Efficiency and Productivity Analysis. New York: Springer.
Davoodi, H., Zou, H., (1998). Fiscal decentralization and economic growth: A crosscountry study. Journal of Urban Economics 43, 244–257.
Easterly, W., Levine, R., (1997). Africa’s growth tragedy: Policies and ethnic
divisions. Quarterly Journal of Economics 111, 1203-1250.
Ebel, R. D., Yilmaz, S., (2003). On the measurement and impact of fiscal
decentralization, in J. Martinez-Vazquez and J. Alm (eds), Public Finance in
Developing and Transitional Countries: Essays in Honor of Richard Bird.
Cheltenham: Elgar.
Edwards, J., Keen, M., (1996).Tax competition and Leviathan. European Economic
Review 40, 113-134.
Enikolopov, R., Zhuravskaya, E., (2007). Decentralization and political institutions.
Journal of Public Economics 91, 2261-2290.
Fearon, J., (2003). Ethnic and cultural diversity by country. Journal of Economic
Growth 8, 195-222.
Frankel, J.A., Romer, D., (1999). Does trade cause growth? American Economic
Review 89, 379-399.
Fisman, R., Gatti, R., (2002a). Decentralization and corruption: Evidence across
countries. Journal of Public Economics 83, 325–346.
27
Fisman, R., Gatti, R., (2002b). Decentralization and corruption: Evidence from US
transfer programs. Public Choice 113, 25-35.
Fiva, J., (2006). New evidence on the effects of fiscal decentralization on the size and
the composition of government spending. FinanzArchiv 62, 250-280.
Gatti, R., (1999). Explaining corruption: Are open countries less corrupt? The World
Bank, mimeo.
Gupta, S., Verhoeven, M., (2001). The efficiency of government expenditure:
Experiences from Africa. Journal of Policy Modelling 23, 433-467.
Gwartney, J., Lawson, R., (2004). Economic Freedom of the World: 2004 Annual
Report. Vancouver: The Fraser Institute.
Hamilton, A., Jay, J., Madison, J., [1787] (1982). The Federalist Papers. New York:
Bantam Classic Books.
Henderson, J.V, Kuncoro, A., (2004). Corruption in Indonesia. NBER Working
Paper, no. 10674.
Herrera, S., Pang, G., (2005). Efficiency of public spending in developing countries:
An efficiency frontier approach. World Bank Policy Research Working Papers, no.
3645.
Hindriks, J., Lockwood, B., (2005). Decentralization and electoral accountability:
Incentives, separation, and voter welfare. Warwick Working Papers in Economics, no.
717.
IMF, (2002). Government Finance Statistics, CD-ROM. Washington D.C., USA.
Jin, J., Zou, H., (2002). How does fiscal decentralization affect aggregate, national
and subnational government size? Journal of Urban Economics 52, 270-293.
Khalegian, P., (2003). Decentralization and public services: The case of
immunization. World Bank Policy Research Working Paper, no. 2989.
Khan, S., Lewbel, A., (2007). Weighted and two stage least squares estimation of
semiparametric truncated regression models. Econometric Theory 23, 309-347.
Krueger, A., (1974). The political economy of the rent-seeking society. American
Kumbhakar, S.C., Lovell, C.A.K., (2000). Stochastic Frontier Analysis. Cambridge:
Cambridge University Press.
La Porta, R., Lopez-De-Silanes, F., Shleifer, A., Vishny, R., (1999). The quality of
government. Journal of Law, Economics and Organization 15, 222-279.
Lessmann, C., (2006). Fiscal decentralization and regional disparity: A panel data
approach for OECD countries. Ifo Working Paper No.25.
28
Mello, L., Barenstein, M., (2001). Fiscal decentralization and governance: A crosscountry approach. IMF Working Paper, no. 01/71.
Mierau, J., Jong-A-Pin, R., de Haan, J., (2007). Do political variables affect fiscal
policy adjustment decisions? New empirical evidence. Public Choice 133, 297-320.
Müller, W., Strøm, K., (2000). Coalition Governments in Western Europe. Oxford:
Oxford University Press.
Myerson, R., (2006). Federalism and incentives for success of democracy. Quarterly
Journal of Political Science 1, 3-23.
Niskanen, W. A. (1973). Bureaucracy: Servant or master? London, UK: The Institute
of Economic Affairs.
Oates, W., (1979). Lump-Sum Intergovernmental Grants Have Price Effects. In Fiscal
Federalism and Grants-in-Aid, ed. P. Mieszkowski and W. Oakland. Washington,
D.C.: The Urban Institute.
Oates, W., (1991). On the Nature and Measurement of Fiscal Illusion: A Survey. In
Studies in Fiscal Federalism, ed.Wallace Oates. Brookfield: Edward Elgar.
OECD (1999). Taxing Powers of State and Local Government. OECD Tax Policy
Studies no. 1, Paris.
OECD (2002). Fiscal Design Surveys across Levels of Government. OECD Tax
Policy Studies no. 7, Paris.
OECD (2005). Economic Outlook.
OECD (2007). Economic Outlook: Annual and Quarterly Data no.81.
Persson, T., Tabellini, G., (2000). Political Economics: Explaining Economic Policy.
Cambridge, MA: MIT Press.
Prud’homme, R., (1995). On the dangers of decentralization. World Bank Research
Observer 10, 201-220.
Rattsø, J., (2000). Spending growth with vertical fiscal imbalance: Decentralized
government spending in Norway: 1880-1990. Typescript. Norwegian University of
Science and Technology.
Redoano, M., (2003). Does centralization affect the number and the size of lobbies?
Warwick Economic Research Paper, no. 674.
Robalino, D.A., Picazo, O.F., Voetberg, V.A., (2001). Does fiscal decentralization
improve health outcomes? Evidence from a cross-country analysis. World Bank
Policy Research Working Paper, no. 2565.
Rodden, J., (2002). The dilemma of fiscal federalism: Grants and fiscal performance
around the world. American Journal of Political Science 46, 670-687.
29
Rodden, J., (2003). Revisiting Leviathan: Fiscal federalism and the growth of
government. International Organization 57, 695-729.
Salmon, P., (1987). Decentralization as an incentive scheme. Oxford Review of
Economic Policy 3, 24–43.
Schofield, N., (1993). Political competition and multiparty coalition governments.
European Journal of Political Research 23, 1-33.
Seabright, P., (1996). Accountability and decentralization in government: An
incomplete contracts model. European Economic Review 40, 61-91.
Shleifer, A., (1985). A theory of yardstick competition. Rand Journal of Economics
16, 319-327.
Sijpe, N., Rayp, G., (2007). Measuring and explaining government efficiency in
developing countries. Journal of Development Studies 43, 360-381.
Simar, L., Wilson, P., (2007). Estimation and inference in two-stage, semi-parametric
models of production processes. Journal of Econometrics 136, 31–64.
Solow, R., (1957). Technical change and the aggregate production function. Review of
Economics and Statistics 39, 312-320.
Stegarescu, D. (2005a). Costs, Preferences, and Institutions: An Empirical Analysis of
the Determinants of Government Decentralization, ZEW Discussion Paper No. 05-39,
Mannheim
Stegarescu, D. (2005b). Public Sector decentralization: Measurement concepts and
recent international trends. Fiscal Studies 26, 301-333.
Stein, E., (1997). Fiscal decentralization and government size in Latin America. In:
Fukasaku, K., Hausmann, R., (Eds.), Democracy, Decentralization and Deficits in
Latin America. IDB-OECD.
Tanzi, V., Schuknecht, L., (1998). Can small governments secure economic and social
well-being? In: Grubel, H. (Ed.), How to Spend the Fiscal Dividend: What is the
Optimal Size of Government? Vancouver: Fraser Institute.
Tanzi, V., Schuknecht, L., (2000). Public Spending in the 20th Century: A Global
Perspective. Cambridge: Cambridge University Press.
Tavares, J., (2004). Does right or left matter? Cabinets, credibility and fiscal
adjustments. Journal of Public Economics 88, 2447-2468.
Treisman, D., (2000). The causes of corruption: A cross-national study. Journal of
Public Economics 76, 399-457.
Treisman, D., (2002). Decentralization and the quality of government. Mimeo,
University of California.
30
Tulkens, H., (1993). On FDH efficiency analysis: Some methodological issues and
applications to retail banking, courts, and urban transit. Journal of Productivity
Analysis 4, 183-210.
Wacziarg, R., (2001). Measuring the dynamic gains from trade. World Bank
Wei, S.J, (2000). Local corruption and global capital flows. Brookings Papers on
Economic Activity 2, 303-354.
Weingast, B., Kenneth, S., Christopher, J., (1988). The political economy of benefits
and costs: A neoclassical approach to distributive politics. Journal of Political
Economy 89, 642-664.
World Bank, (2004). World Bank Development Indicators, CD-ROM. Washington
D.C., USA.
Zhu, J., (2003). Quantitative Models for Performance Evaluation and Benchmarking.
New York: Springer.
31
CESifo Working Paper Series
for full list see www.cesifo-group.org/wp
(address: Poschingerstr. 5, 81679 Munich, Germany, [email protected])
___________________________________________________________________________
T
T
2301 Harald Badinger and Peter Egger, GM Estimation of Higher Order Spatial
Autoregressive Processes in Panel Data Error Component Models, May 2008
2302 Jan K. Brueckner, Slot-Based Approaches to Airport Congestion Management, May
2008
2303 Sören Blomquist, Vidar Christiansen and Luca Micheletto, Public Provision of Private
Goods and Nondistortionary Marginal Tax Rates, May 2008
2304 Dan Anderberg and Alessandro Balestrino, The Political Economy of Post-Compulsory
Education Policy with Endogenous Credit Constraints, May 2008
2305 Tomer Blumkin, Yoram Margalioth and Efraim Sadka, The Role of Stigma in the
Design of Welfare Programs, May 2008
2306 Vesa Kanniainen and Paolo M. Panteghini, Tax Neutrality: Illusion or Reality? The
Case of Entrepreneurship, May 2008
2307 Thomas Dohmen, Armin Falk, David Huffman and Uwe Sunde, The Intergenerational
Transmission of Risk and Trust Attitudes, May 2008
2308 Guglielmo Maria Caporale and Mario Cerrato, Using Chebyshev Polynomials to
Approximate Partial Differential Equations, May 2008
2309 Peter Egger and Doina Maria Radulescu, Labour Taxation and Foreign Direct
Investment, May 2008
2310 Laurent Linnemer, Dissipative Advertising Signals Quality even without Repeat
Purchases, May 2008
2311 Jordi Jofre-Monseny and Albert Solé-Ollé, Which Communities should be afraid of
Mobility? The Effects of Agglomeration Economies on the Sensitivity of Firm Location
to Local Taxes, May 2008
2312 Andreas Haufler and Ferdinand Mittermaier, Unionisation Triggers Tax Incentives to
Attract Foreign Direct Investment, May 2008
2313 Ronel Elul and Piero Gottardi, Bankruptcy: Is it enough to Forgive or must we also
Forget?, May 2008
2314 Andreas Irmen and Johanna Kuehnel, Productive Government Expenditure and
Economic Growth, May 2008
2315 Beate Henschel, Carsten Pohl and Marcel Thum, Demographic Change and Regional
Labour Markets: The Case of Eastern Germany, May 2008
2316 Gabriel Felbermayr, Wido Geis and Wilhelm Kohler, Restrictive Immigration Policy in
Germany: Pains and Gains Foregone?, May 2008
2317 Michael Hofmann, Gerhard Kempkes and Helmut Seitz, Demographic Change and
Public Sector Budgets in a Federal System, May 2008
2318 Paul De Grauwe, Macroeconomic Modeling when Agents are Imperfectly Informed,
June 2008
2319 Johann K. Brunner and Susanne Pech, Optimum Taxation of Inheritances, June 2008
2320 Thomas Eichner and Marco Runkel, Corporate Income Taxation of Multinationals in a
General Equilibrium Model, June 2008
2321 Rainald Borck and Matthias Wrede, Subsidies for Intracity and Intercity Commuting,
June 2008
2322 Patricia Apps and Ray Rees, Testing the Pareto Efficiency of Household Resource
Allocations, June 2008
2323 Amihai Glazer, Vesa Kanniainen and Panu Poutvaara, Firms’ Ethics, Consumer
Boycotts, and Signalling, June 2008
2324 Claudia M. Buch, Jörg Döpke and Kerstin Stahn, Great Moderation at the Firm Level?
Unconditional vs. Conditional Output Volatility, June 2008
2325 Helmuth Cremer, Philippe De Donder, Dario Maldonado and Pierre Pestieau, Forced
Saving, Redistribution and Nonlinear Social Security Schemes, June 2008
2326 M. Hashem Pesaran and Paolo Zaffaroni, Optimal Asset Allocation with Factor Models
for Large Portfolios, June 2008
2327 Harald Badinger and Peter Egger, Horizontal versus Vertical Interdependence in
Multinational Activity, June 2008
2328 Jan K. Brueckner and Harris Selod, A Theory of Urban Squatting and Land-Tenure
Formalization in Developing Countries, June 2008
2329 Paolo M. Panteghini, Corporate Debt, Hybrid Securities and the Effective Tax Rate,
June 2008
2330 Guglielmo Maria Caporale, Juncal Cuñado and Luis A. Gil-Alana, Modelling Long-Run
Trends and Cycles in Financial Time Series Data, June 2008
2331 Avi Ben-Bassat and Momi Dahan, Social Identity and Voter Turnout, June 2008
2332 Martin R. West and Ludger Wößmann, “Every Catholic Child in a Catholic School”:
Historical Resistance to State Schooling, Contemporary Private Competition, and
Student Achievement across Countries, June 2008
2333 Erkki Koskela and Panu Poutvaara, Outsourcing and Labor Taxation in Dual Labor
Markets, June 2008
2334 Philippe Choné and Laurent Linnemer, Optimal Litigation Strategies with Signaling and
Screening, June 2008
2335 Albert Solé-Ollé and Pilar Sorribas-Navarro, Does Partisan Alignment Affect the
Electoral Reward of Intergovernmental Transfers?, June 2008
2336 Antonio Cabrales and Piero Gottardi, Markets for Information: Of Inefficient Firewalls
and Efficient Monopolies, June 2008
2337 Sumon Majumdar and Sharun W. Mukand, The Leader as Catalyst – on Leadership and
the Mechanics of Institutional Change, June 2008
2338 Ulrich Hange, Tax Competition, Elastic Labor Supply, and Growth, June 2008
2339 Guy Laroque and Bernard Salanié, Does Fertility Respond to Financial Incentives?,
June 2008
2340 Adriano Paggiaro, Enrico Rettore and Ugo Trivellato, The Effect of Extending the
Duration of Eligibility in an Italian Labour Market Programme for Dismissed Workers,
June 2008
2341 Helmut Seitz, Minimum Standards, Fixed Costs and Taxing Autonomy of Subnational
Governments, June 2008
2342 Robert S. Chirinko, Leo de Haan and Elmer Sterken, Asset Price Shocks, Real
Expenditures, and Financial Structure: A Multi-Country Analysis, July 2008
2343 Wolfgang Leininger, Evolutionarily Stable Preferences in Contests, July 2008
2344 Hartmut Egger and Udo Kreickemeier, Fairness, Trade, and Inequality, July 2008
2345 Ngo Van Long and Bodhisattva Sengupta, Yardstick Competition, Corruption, and
Electoral Incentives, July 2008
2346 Florian Baumann, Employment Protection: The Case of Limited Enforceability, July
2008
2347 Alessandro Balestrino, Cinzia Ciardi and Claudio Mammini, On the Causes and
Consequences of Divorce, July 2008
2348 Dirk Schindler and Benjamin Weigert, Insuring Educational Risk: Opportunities versus
Income, July 2008
2349 Lammertjan Dam and Ben J. Heijdra, The Environmental and Macroeconomic Effects
of Socially Responsible Investment, July 2008
2350 Avner Greif, Contract Enforcement and Institutions among the Maghribi Traders:
Refuting Edwards and Ogilvie, July 2008
2351 Helmuth Cremer, Philippe De Donder, Dario Maldonado and Pierre Pestieau, Habit
Formation and Labor Supply, July 2008
2352 Francesco Menoncin and Paolo M. Panteghini, The Johansson-Samuelson Theorem in
General Equilibrium: A Rebuttal, July 2008
2353 Michael Kaganovich and Itzhak Zilcha, Alternative Social Security Systems and
Growth, July 2008
2354 Keith Blackburn, Kyriakos C. Neanidis and M. Emranul Haque, Corruption,
Seigniorage and Growth: Theory and Evidence, July 2008
2355 Edward Castronova, A Test of the Law of Demand in a Virtual World: Exploring the
Petri Dish Approach to Social Science, July 2008
2356 Harald Badinger and Peter Egger, GM Estimation of Higher-Order Spatial
Autoregressive Processes in Cross-Section Models with Heteroskedastic Disturbances,
July 2008
2357 Wolfgang Buchholz and Jan Schumacher, Discounting the Long-Distant Future: A
Simple Explanation for the Weitzman-Gollier-Puzzle, July 2008
2358 Luca Anderlini, Leonardo Felli and Alessandro Riboni, Statute Law or Case Law?, July
2008
2359 Guglielmo Maria Caporale, Davide Ciferri and Alessandro Girardi, Are the Baltic
Countries Ready to Adopt the Euro? A Generalised Purchasing Power Parity Approach,
July 2008
2360 Erkki Koskela and Ronnie Schöb, Outsourcing of Unionized Firms and the Impacts of
Labour Market Policy Reforms, July 2008
2361 Francisco Alvarez-Cuadrado and Ngo Van Long, A Permanent Income Version of the
Relative Income Hypothesis, July 2008
2362 Gabrielle Demange, Robert Fenge and Silke Uebelmesser, Financing Higher Education
and Labor Mobility, July 2008
2363 Alessandra Casarico and Alessandro Sommacal, Labor Income Taxation, Human
Capital and Growth: The Role of Child Care, August 2008
2364 Antonis Adam, Manthos D. Delis and Pantelis Kammas, Fiscal Decentralization and
Public Sector Efficiency: Evidence from OECD Countries, August 2008
EUROPEAN
CENTRAL
BANK
WO R K I N G PA P E R S E R I E S
WORKING PAPER NO. 242
PUBLIC SECTOR EFFICIENCY:
AN INTERNATIONAL
COMPARISON
BY ANTÓNIO AFONSO,
LUDGER SCHUKNECHT
AND VITO TANZI
July 2003
EUROPEAN
CENTRAL
BANK
WO R K I N G PA P E R S E R I E S
WORKING PAPER NO. 242
PUBLIC SECTOR EFFICIENCY:
AN INTERNATIONAL
COMPARISON1
BY ANTÓNIO AFONSO2,
LUDGER SCHUKNECHT3
AND VITO TANZI4
July 2003
1 We are grateful to Carlos Barros, Juergen von Hagen, José Marin, Pierre Pestieau, Philipp Rother, Miguel St. Aubyn, Rolf Strauch, an
anonymous referee, and participants at the ZEI Workshop, University of Bonn, at the 2003 European Public Choice Society conference
in Aarhus, at the 2003 French Economics Association conference in Lille, for helpful comments and Gerhard Schwab for valuable
research assistance. Any remaining errors are the responsibility of the authors.The opinions expressed herein are those of the author(s)
and do not necessarily represent those of the European Central Bank. This paper can be downloaded without charge from http://
www.ecb. int or from the Social Science Research Network electronic library at: http://ssrn.com/abstract_id=434002.
2 European Central Bank, Kaiserstrasse 29, D-60311 Frankfurt am Main, Germany, email: [email protected]
ISEG/UTL - Technical University of Lisbon, R. Miguel Lúpi 20, 1249-078 Lisbon, Portugal, email: [email protected]
3 European Central Bank, Kaiserstrasse 29, D-60311 Frankfurt am Main, Germany, email: [email protected]
4 Sottosegretario, Ministero del tesero,Via XX Settembre 97, 00187 Roma, Italy
© European Central Bank, 2003
Address
Postal address
Telephone
Internet
Fax
Telex
Kaiserstrasse 29
D-60311 Frankfurt am Main
Germany
Postfach 16 03 19
D-60066 Frankfurt am Main
Germany
+49 69 1344 0
http://www.ecb.int
+49 69 1344 6000
411 144 ecb d
All rights reserved by the author/s.
Reproduction for educational and non-commercial purposes is permitted provided that the source is acknowledged.
The views expressed in this paper do not necessarily reflect those of the European Central Bank.
ISSN 1561-0810 (print)
ISSN 1725-2806 (online)
Contents
Abstract
4
Non-technical summary
5
1.
Introduction
7
2.
Public sector performance indicators
8
3.
Public sector expenditure efficiency analysis
14
4.
Measuring input and output efficiency via an FDH analysis
4.1 The FDH analysis
4.2 FDH-based expenditure efficiency analysis
18
18
20
5.
Conclusion
23
Appendices
25
References
28
European Central Bank working paper series
35
ECB Working Paper No 242 July 2003
3
Abstract
We compute public sector performance (PSP) and efficiency (PSE) indicators,
comprising a composite and seven sub-indicators, for 23 industrialised countries. The
first four sub-indicators are “opportunity” indicators that take into account
administrative, education and health outcomes and the quality of public infrastructure
and that support the rule of law and a level playing-field in a market economy. Three
other indicators reflect the standard “Musgravian” tasks for government: allocation,
distribution, and stabilisation. The input and output efficiency of public sectors across
countries is then measured via a non-parametric production frontier technique.
Keywords: Government expenditure, Efficiency, Free Disposable Hull, Production
possibility frontier.
JEL Classification Numbers: C14, H50.
4
Non-technical summary
In this paper we study the performance and the efficiency of the public sectors
of 23 industrialised OECD countries. We compute public sector performance (PSP)
and efficiency indicators (PSE) for the government as whole and for its core
functions. When deriving performance indicators we distinguish the role of
government in providing “opportunities” and a level playing field in the market
process and the traditional “Musgravian” tasks of government. “Opportunity”
indicators look at administrative, education, health, and public infrastructure
outcomes. “Musgravian” indicators assess governments’ performance in allocation,
distribution, and stabilisation. A number of socio-economic indicators serve as
proxies for performance.
In assessing the efficiency of public sectors, we look at total public spending
and a number of spending categories as proxies for resource use. These are set in
relation to performance indicators as they can be seen as reflecting the opportunity
costs of public sector activities. The ratio of performance indicators and public
spending yields indicators of efficiency for each country.
Finally, we use a non-parametric framework to compute a so-called
production possibility frontier, and calculate input efficiency and output efficiency
scores in order to rank the sample countries in terms of public spending efficiency.
We find that the difference in overall performance is moderate across the
sample countries. Countries with “small” public sectors on average report the highest
scores for overall performance, and especially for administrative and economic
performance. Countries with large public sectors show more equal income
distribution. Some countries managed to deliver a significant relative improvement in
5
public sector performance over the last decade (notably, Greece, Portugal, Spain and
Ireland).
Regarding public sector efficiency, countries with small public sectors report
significantly higher indicators than countries with medium-sized or big public sectors.
Overall efficiency is highest in Japan, Luxembourg, Australia, the United States and
Switzerland. The results of the FDH analysis suggest that “average inefficiency” is
about 20%.
However, all the results have to be seen as indicative and need to be
interpreted with great care. Besides the occasional difficulty of data comparability, it
is also not easy to accurately identify the effects of public sector spending on
outcomes and separate the impact of spending from other influences. For instance, it
is difficult to assess to what extent does higher life expectancy reflect public
intervention rather than other factors such as climate, dietary habits, etc.
Robustness analysis that emulates the effect of different preferences as to the
role of government by giving different weights to sub-indicators suggested that the
overall results are not sensitive to moderate changes in the weights of sub-indicators.
Finally, the discussion focuses on the overall indicators, while the comparison of the
different sub-indicators across countries may provide further and more specific
insights and lessons.
6
1. Introduction
The debate on the role of the state has shifted in recent years towards empirical
assessments of the efficiency and usefulness of public sector activities. A growing
academic literature has been investigating the stabilisation, allocation and distribution
effects of public expenditure. It has also been assessing the role of rules and
institutions, and the scope for privatising public sector activities (see e.g., Mueller
(1997), Persson and Tabellini (2001), Shleifer and Vishny (1998), Strauch and Von
Hagen (2000), Tanzi and Schuknecht (1997, 2000), Rodrik (2000), Gwartney et al.
(2002)). Most studies conclude that public spending could be much smaller and more
efficient than today. However, for this to happen, governments should adopt better
institutions and should transfer many non-core activities to the private sector.
The measurement of public sector performance (defined as the outcome of public
sector activities) and efficiency (defined as the outcome relative to the resources
employed), however, is still very limited. The objective of this paper is to provide a
proxy for measuring public sector performance and efficiency. To do this we will put
together a number of performance indicators in the government’s core functions.
These include the summary functions defined by Musgrave (allocation, distribution,
stabilisation) and a number of specific indicators that promote equality of opportunity
in the market place. Economic philosophers from Adam Smith to Hayek and
Buchanan have stressed the importance of rules of law in promoting “good”
government and the “wealth of nations”. Naturally they assume that the rules are
“good” rules.
We will set these indicators in relation to the costs of achieving them. We will, hence,
derive simple performance and efficiency indicators for 1990 and 2000 for the public
sectors of 23 industrialised OECD countries. The performance index is then also used
in a Free Disposable Hull (FDH) analysis, a rarely used non-parametric production
frontier technique to estimate the extent of slack in government expenditures.
Note, however, that it is not only public expenditure but also tax and regulatory
policies that affect the efficiency of the public sector. While expenditure is also a
relatively good proxy of the tax burden, we ignore the composition of tax revenue and
7
other characteristics of tax systems.5 Public spending may be closely related to
regulation because large civil services, that often accompany large public spending,
are likely to generate much regulation and vice versa.6
The paper is organised as follows. In Sections two and three we discuss and compute
the public sector performance (PSP) and efficiency (PSE) indicators. Section four
extends the efficiency analysis with the help of an FDH analysis and section five
provides conclusions.
2. Public sector performance indicators
The study looks at 23 OECD countries for which we compiled data on various public
expenditure
categories
and
socio-economic
variables,
reflecting
the
effects/outputs/outcomes of government policies.7
Assume that public sector performance (PSP) depends on the values of certain
economic and social indicators (I). If there are i countries and j areas of government
performance which together determine overall performance in country i, PSPi, we can
then write
n
PSPi = ∑ PSPij ,
(1)
j =1
with PSPij = f ( I k ) .
Therefore, an improvement in public sector performance depends on an improvement
in the values of the relevant socioeconomic indicators:
5
For exemple, tax collection may impose significant welfare and compliance costs on taxpayers.
However, Brennan (2000) and Tanzi (1998) have argued that regulations and tax expenditures
can also become a substitute of public spending, and thereby be negatively correlated with the size
of the public sector as measured by the level of public spending.
7
One should be aware of the distinction between output and outcome. The number of hospital
days per 1000 people is an output but full recovery from illness or life expectancy is an outcome.
Even though we try to approximate outcomes rather than output (e.g. red tape, life expectance) the
distinction is not always possible and we use both terms in an interchangeable way.
6
8
n
∆PSPij = ∑
i =k
∂f
∆I k .
∂I k
(2)
Reasonably, the greater the positive effect of public expenditure on any of the selected
sub-indicators, the greater will be the envisaged improvement in the PSP indicator.
Accordingly, the changes that might occur in the economic and social indicators may
be seen as changes in public sector performance.
As a first step, we define 7 sub-indicators of public performance. The first four look at
administrative, education, health, and public infrastructure outcomes. A good public
administration, with a well-functioning judiciary and a healthy and well-educated
population, could be considered a prerequisite for a level playing field with wellfunctioning markets and secure property rights, where the rule of law applies, and
opportunities are plenty and in principle accessible to all. High-quality public
infrastructure is conducive to attaining the same objectives. These indicators, thereby,
try to reflect the quality of the interaction between fiscal policies and the market
process and the influence on individual opportunities this has. They could be called
“process” or “opportunity” indicators. We adopt the latter terminology in the
following.
The three other sub-indicators reflect the “Musgravian” tasks for government. These
try to measure the outcomes of the interaction with and reactions to the market
process by government. Income distribution is measured by the first of these
indicators. An economic stability indicator illustrates the achievement of the
stabilisation objective. The third indicator tries to assess allocative efficiency by
economic performance. The conceptual separation is of course somewhat artificial, as
for example health and education indicators could also be seen as indicators of
allocative efficiency. Finally all sub-indicators are put together in a public sector
performance indicator.
9
Figure 1. Total public sector performance (PSP) indicator
Opportunity indicators
Standard “Musgravian” indicators
Corruption
Distribution
Income share of
40% poorest
households
Stability of GDP
growth (coeff. of
variation)
Red tape
Administrative
Stability
Quality of
judiciary
Inflation (10 years
average)
Shadow
economy
GDP per capita
(PPP)
Secondary
school
enrolment
Economic
performance
GDP growth (10
years average)
Education
Education
achievement
Unemployment
(10 years average)
Infant mortality
Health
Life expectancy
Quality communication &
transport infrast.
Public
Infrastruc
-ture
Total public
sector
performance
Before showing the result it is worthwhile illustrating how we derive these
performance indicators. Figure 1 shows the socio-economic indices on which
government has a significant if not exclusive influence and which, therefore, reflect as
close as possible the outcomes of public policies (Annex Tables A and B provide
primary data). In as much as possible we provide data for 1990 and 2000 (or the
nearest available year), and in some instances, 10-year averages. This is because we
are not so much interested in annual fluctuations but in structural changes in public
sector performance. Many indices reflect “stocks” which change only very slowly
10
over time so that observations every 10 years suffice to reflect such structural
changes. A case in point is for example per capita GDP and secondary school
enrolment. Other indices, such as inflation or GDP growth, vary strongly and a 10year average seems the best way to capture long-term trends and structural changes.8
Figure 1 also displays the composition of PSP indicators. As to the “opportunity
indicators”, administrative performance of government is measured as a composite of
the following indices: corruption, red tape, quality of the judiciary, and the size of the
shadow economy. The education indicator contains secondary school enrolment and
the OECD educational attainment indicators in order to measure both the quantity and
quality of education. The health performance indicator contains infant mortality and
life expectancy.
The public infrastructure indicator contains a measure of the
communication and transport infrastructure quality. All these indicators change
slowly so that observations every 10 years provide a good impression of changes over
time except in the case of public infrastructure where period averages have been used.
As to the standard “Musgravian” general indicators, income distribution is proxied by
the income share of the poorest 40 per cent of the households. Economic stability is
measured by the stability of output growth (coefficient of variation) and average
inflation (10-year average). Economic performance comprises per-capita GDP (PPP),
GDP growth (10-year average), and unemployment (10-year average). The total PSP
indicator combines the seven sub-indicators. Note that some indices also capture the
effect of regulation rather than expenditure policies and some indices are only partly
the result of government policies (for example, private provision and financing of
health and education play an important role in some countries).
We compile the performance indicators from the various indices giving equal weight
to each of them. For example, red tape, efficiency of the judiciary, corruption and
size of the shadow economy each contribute 25 per cent to the administrative
performance indicator. This of course introduces a strong assumption. For those
indicators where higher numbers are less favourable (e.g., infant mortality, inflation),
8
There are few instances where actual and trend growth deviate by 0.4/0.5% for the 10-year
averages. However, when using trend rather than actual growth in the calculation of indices,
results change very little even for the economic performance indicator.
11
we use the inverse of the original values. In order to facilitate the compilation, we
normalised the values and set the average for all indices equal to 1. The values for
each country are then recalculated relative to the average. Table 1 presents the results
for the constructed PSP indicators for the year 2000.
Table 1. Public sector performance (PSP) indicators (2000)
Standard “Musgravian”
Total public
indicators
sector
Adminis- Education Health
Infra- Distribu- Stability Economic performance
(equal
Country
tration
structure
tion
perform.
weights 1/)
Australia
1.17
1.02
0.94
1.00
0.87
1.31
1.00
1.04
Austria
1.21
1.00
0.98
1.10
1.22
1.28
1.01
1.12
Belgium
0.73
1.00
0.94
0.91
1.17
1.10
0.83
0.95
Canada
1.11
1.05
0.95
1.16
0.92
1.00
0.92
1.02
Denmark
1.16
1.00
1.03
1.03
1.19
1.10
0.91
1.06
Finland
1.26
1.07
1.04
1.18
0.75
0.73
1.01
France
0.72
1.03
1.03
1.01
0.90
1.12
0.70
0.93
Germany
1.02
0.98
1.01
1.01
0.98
0.91
0.81
0.96
Greece
0.60
0.94
0.93
0.81
0.97
0.55
0.69
0.78
Iceland
1.02
0.98
1.25
0.59
1.29
1.03
Ireland
1.06
0.94
0.88
1.00
0.89
1.22
1.40
1.05
Italy
0.52
0.96
0.93
0.84
1.10
0.76
0.69
0.83
Japan
0.87
1.09
1.12
1.09
1.20
1.40
1.18
1.14
Luxembourg
1.05
0.81
0.95
1.22
2.04
1.21
Netherlands
1.16
1.04
0.97
1.09
1.00
1.42
1.06
1.11
New Zealand
1.18
1.03
0.89
0.62
0.99
0.84
0.93
Norway
0.97
1.04
1.09
0.94
1.17
1.45
1.26
1.13
Portugal
0.54
0.94
0.90
0.75
0.92
0.64
0.92
0.80
Spain
0.77
1.00
1.10
0.86
1.02
0.82
0.67
0.89
Sweden
1.16
1.07
1.19
1.10
1.17
0.69
0.91
1.04
Switzerland
1.32
0.97
1.14
1.23
0.95
0.79
1.09
1.07
United Kingdom
1.00
1.05
0.91
0.99
0.79
0.78
0.84
0.91
United States
1.15
1.00
0.82
1.08
0.76
1.14
1.20
1.02
Average
0.99
1.00
1.00
1.00
1.00
1.00
1.00
1.00
Small govs 2/
1.11
1.01
0.98
1.08
0.94
1.17
1.17
1.07
Medium govs
0.93
0.98
1.00
0.93
0.92
0.89
1.03
0.97
Big govs
0.99
1.02
1.01
1.01
1.12
1.03
0.85
1.01
EU 15 3/
0.88
1.00
0.99
0.98
0.98
0.93
0.80
0.94
Euro area 3/
0.84
0.99
1.00
0.97
1.00
0.96
0.78
0.93
1/ Each sub-indicator contributes 1/7 to total indicator.
2/ Small governments: public spending <40% of GDP in 2000. Big governments: public spending
>50% of GDP in 2000. Medium governments: 40%< public spending <50% of GDP in 2000.
3/ Weighted averages according to the share of each country GDP in the relevant group.
Indicators suggest notable but not extremely large differences in public sector
performance across countries (with a few exceptions). Countries with the highest
values for sub-indicators include Switzerland (administration and infrastructure),
Japan (education), Iceland (health), Austria (distribution), Norway (economic
stability) and Luxembourg (economic performance). Countries such as Luxembourg,
12
Japan, Norway, Austria, and the Netherlands report high total PSP indicators. The
latter is true both for a PSP indicator with equal weights for the sub-indicators and for
different weighting, suggesting that the findings are relatively robust to moderate
changes in weighting.9
Looking at country groups, small governments (industrialised countries with public
spending below 40 % of GDP in 2000) on balance report better economic
performance than big governments (public spending above 50 % of GDP) or medium
sized governments (spending between 40 and 50 percent of GDP). Big governments
feature more even income distribution whereas small governments perform better
especially in the administrative, stability and economic performance domains. These
results are consistent with those found in Tanzi and Schuknecht (2000).
When comparing the main economic “players” of today, it is noteworthy that the US
and particularly Japan report above-average performance in most sub-indices and for
the total PSP measure. By contrast, the EU (weighted average) performs below
average.
Taking advantage of the data set available, we performed a comparison between the
PSP for 2000 and for 1990, in order to assess how public sector performance has
changed over time10 and the results are presented in Figure 2.
9
For example, giving alternative weights to the sub-indicators does not change much the results in
most cases. In the Appendix (Table A1) we present alternative weighting schemes. Rank
correlations for PSP indicators with the tested changes in weights are in the [0.95 0.99] range.
This weigthing of the variables is quite straightforward and economically intuitive (even though it
is still somewhat ad hoc). It avoids the problem of lack of economic justification of a more
complex statistical approach such as principal component analysis that might come to mind in this
context.
10
One should bear in mind that data are not fully comparable. E.g., some data are not available for
some countries. For example the OECD PISA report on education achievement only covers 2000.
13
Figure 2. Public sector performance: 1990 and 2000
1.50
Improvement
(+)
1.25
Luxembourg
Ireland
Japan
Switzerland
1.00
2000
Spain
US
0.75
Portugal
Greece
Euro area
Italy UK
0.50
0.25
0.25
Worsening
(-)
0.50
0.75
1.00
1.25
1.50
1990
One can easily see that while some countries managed to deliver a relative
improvement in public sector performance (all the countries located above and to the
left of the diagonal line), some other countries showed a decrease in public sector
performance (countries below and to right of the diagonal). Examples of the first
group of countries are Greece, Portugal, Spain and Ireland. However, only Ireland
succeeded in placing itself above the average of the 23 OECD country sample. Some
countries experienced reductions in public sector performance. Especially Japan and
Switzerland saw their performance fall in 2000 compared to 1990. This is also true for
the EU and the euro area as a whole. However, one should be aware that progress in
public sector performance made by the different countries over time is measured
relative to other countries and not relative to its own past performance.
3. Public sector expenditure efficiency analysis
Public expenditure, expressed as a share of GDP, can be assumed to reflect the
opportunity costs of achieving the public sector performance estimated in the previous
14
section.11 In addition to total public spending we looked at average spending on goods
and services, transfers, functional spending on education and health, and public
investment. Data for 1990 and 2000 for these categories across countries are reported
in Annex Table C. Public expenditures differ considerably across countries. Average
total spending in the 1990s ranged from around 35 percent of GDP in the US to 64
percent of GDP in Sweden. The difference is mainly due to more or less extensive
welfare programs. Public spending on health and education and on goods and services
differs much less strongly across countries.
Based on the framework of equations (1) and (2), we now compute indicators of
Public Sector Efficiency (PSE). We weigh performance (as measured by the PSP
indicators) by the amount of relevant public expenditure, PEX, that is used to achieve
a given performance level. The overall PSE indicator for any country i, is given by:
PSEi =
PSPi
,
PEX i
(3)
with
n PSP
PSPi
ij
=∑
.
PEX i
j =1 PEX ij
(4)
Positive but declining marginal productivity of public expenditure would imply:
∂PSEij
∂PEX ij
> 0,
∂ 2 PSEij
∂PEX ij2
< 0.
(5)
In order to compute efficiency indicators, public spending was normalised across
countries, with the average taking the value of one for each of the six categories
specified above. We focus on average expenditure over the 1990s, as we would
assume a lagged effect from spending on performance. For example, public spending
11
Proceeds from the sale of UMTS mobile telephone licences have been excluded from total
expenditure since they were recorded as a temporary decline in expenditure.
15
on education (at least) over the previous decade, is assumed to affect educational
achievement in the late 2000.
Before putting public sector performance and expenditure together it is worth
stressing that not all expenditure categories are equally suitable indices for measuring
the efficiency with which a certain performance is achieved. Goods and services
spending are a rather crude approximation for what is needed to achieve
administrative efficiency. Health and education spending seem better measures of the
public sector inputs in these domains.12 Similarly, transfers (social payments only)
are probably suitable approximations for government spending to promote income
equality, and public investment is likely to be closely connected with infrastructure
quality.13 Total spending may be a useful proxy for government stabilisation efforts
because automatic stabilisers are larger in countries with “big governments” (Van den
Noord (2000), Bouthevillain et al (2001)). Total spending is generally financed by
distortive taxation. It can, hence, be used as a proxy for the efficiency (or
inefficiency) of the state in affecting economic performance.
Before turning to Table 2, which reports the ratio of performance and expenditure
indices as so-called Public Sector Efficiency (PSE) indicators it is worthwhile
stressing a few caveats. Public spending across countries is not always fully
comparable even though much progress has been achieved in this regard.
For
example, some countries’ transfer payments are taxed, thereby overstating public
spending compared to countries where such benefits are not taxed. Nevertheless, it is
not possible to systematically assess and correct such problems. Moreover, comparing
expenditure ratios across countries implicitly assumes that production costs for public
services are proportionate to GDP per capita. While this approximation is likely to be
quite good for labour intensive services (such as education or administrative
efficiency) it is likely to be less so for infrastructure quality. In the absence of cross12
Notice however, that it is not easy to accurately identify the effects of public sector spending on
outcomes and separate the impact of spending from other influences. For instance, it is difficult to
assess to what extent does higher life expectancy reflect public intervention rather than other
factors such as climate, dietary habits, etc. The same argument could be made regarding infant
mortality. On that line of reasoning, adverse geographical conditions may also impair on the
quality and cost of a country communications infrastrucutre.
13
Income distribution and stabilisation is also affected by the progressivity of the tax system, but
this effect is very difficult to assess due to the lack of comparable and detailed enough data.
16
country data of different public service sector costs, this is nevertheless the best
possible approximation.
Table 2. Public sector efficiency (PSE) indicators (2000) 1/
Standard “Musgravian”
Total public
indicators
sector
Adminis- Education Health
Infra- Distribu- Stability Economic efficiency
(equal
Country
tration
structure
tion
perform.
weights 2/)
Australia
1.21
1.06
1.05
1.05
1.80
1.59
1.22
1.28
Austria
1.22
0.93
1.07
0.98
0.93
1.17
0.92
1.03
Belgium
0.64
0.96
0.85
1.11
0.71
0.87
0.65
0.83
Canada
1.00
0.84
0.86
1.27
1.39
1.01
0.93
1.04
Denmark
0.86
0.74
0.76
1.62
1.05
0.89
0.74
0.95
Finland
1.22
1.07
1.03
1.19
0.79
0.77
1.01
France
0.61
0.99
0.90
1.00
0.64
1.01
0.63
0.83
Germany
1.01
1.09
0.93
1.27
0.85
0.88
0.78
0.97
Greece
0.79
2.25
1.05
0.87
1.04
0.61
0.78
1.06
Iceland
1.06
1.12
0.65
1.42
0.85
Ireland
1.10
0.90
0.88
0.96
0.90
1.20
1.38
1.05
Italy
0.54
1.11
0.93
0.75
0.95
0.68
0.62
0.80
Japan
1.25
1.12
1.34
0.68
1.60
1.99
1.68
1.38
Luxembourg
1.10
0.88
0.98
1.19
1.99
1.23
Netherlands
0.90
0.85
0.95
1.52
0.56
1.15
0.85
0.97
New Zealand
1.20
1.02
0.85
0.00
0.68
0.97
0.82
0.93
Norway
0.95
0.86
0.96
0.88
1.32
1.40
1.22
1.09
Portugal
0.74
1.31
1.46
0.66
1.28
0.73
1.05
1.03
Spain
0.97
1.49
1.33
0.81
1.12
0.95
0.78
1.06
Sweden
0.81
0.75
0.83
1.19
0.94
0.51
0.68
0.82
Switzerland
1.86
1.01
1.21
1.07
1.68
1.05
1.45
1.33
United Kingdom
0.94
1.10
1.01
1.68
0.98
0.84
0.91
1.06
United States
1.30
0.92
1.05
1.40
1.15
1.46
1.55
1.26
Average
1.01
1.06
1.01
1.09
1.08
1.03
1.04
1.04
Small govs 3/
1.34
1.00
1.11
1.03
1.43
1.46
1.45
1.26
Medium govs
0.98
1.19
1.05
1.06
1.08
0.92
1.07
1.03
Big govs
0.85
0.93
0.92
1.17
0.87
0.88
0.73
0.90
EU 15 4/
0.84
1.09
0.97
1.18
0.87
0.88
0.77
0.94
Euro area 4/
0.82
1.11
0.97
1.06
0.84
0.90
0.74
0.92
1/ These indicators are the expenditure weighted “counterparts” of the indicators of Table 1.
2/ Each sub-indicator contributes 1/7 to total indicator.
3/ Small governments: public spending <40% of GDP in 2000. Big governments: public spending
>50% of GDP in 2000. Medium governments: 40%< public spending <50% of GDP in 2000.
We find significant differences in public sector efficiency across countries. Japan,
Switzerland, Australia, the United States and Luxembourg show the best values for
overall efficiency. Looking at country groups, “small” governments post the highest
17
efficiency amongst industrialised countries. Differences are considerable as “small”
governments on average post a 40 percent higher scores than “big” governments.14
In summary, we find that differences in efficiency are much more pronounced than in
performance across countries, with “small” governments clearly outranking the
others. This illustrates that the size of government may be too large in many
industrialised countries, with declining marginal products being rather prevalent. But
given the non-extreme differences in performance as outlined above, the incidence of
“negative” marginal products of public spending may be more limited.
4. Measuring input and output efficiency via an FDH analysis
4.1 The FDH analysis
In a final step, we use the information from previous sections to measure the
“wastefulness” of public spending across countries, i.e. the input and output efficiency
of expenditure. To this end, we apply a so-called FDH analysis, which is a nonparametric technique that was first proposed by Deprins, Simar, and Tulkens (1984).15
In the FDH framework it is possible to rank the efficiency of producers by comparing
each individual performance with a production possibility frontier. Along this
production possibility frontier one can observe the highest possible level of
output/outcome for a given level of input. Conversely, it is possible to determine the
lowest level of input necessary to attain a given level of output/outcome. This allows
identifying inefficient producers both in terms of input efficiency and in terms of
output/outcome efficiency.
A few other studies that apply FDH analysis to assess public spending efficiency
include Vanden Eeckhaut, Tulkens and Jamar (1993) who studied the efficiency of
public spending in Belgian municipalities, and Fakin and Crombrugghe (1997) who
assessed the efficiency of government expenditures as regards some specific public
14
The PSE indicators are also quite robust to different weightings as can be seen in the Appendix
(Table A2).
15
For an overview of the FDH analysis see for instance Tulkens (1993). Another non-parametric
approach that might be used to assess public expenditure efficiency would be Data Envelopment
Analysis (DEA). This technique, developed by Charnes, Cooper and Rhodes (1978), implies a
convex production frontier, an hypothesis which is not required in the FDH approach. For an
overview of non-parametric approaches see for instance Simar and Wilson (2003).
18
services in OECD and Central Europe countries. Gupta and Verhoeven (2001) use
FDH analysis to measure the efficiency of government expenditure on education and
health in a set of countries in Africa. Clements (2002) assessed the efficiency of
education spending in the European Union. St. Aubyn (2002) reports results of FDH
analysis applied to education and health spending in OECD countries. The FDH
methodology can be well illustrated graphically (Figure 3).
Figure 3. Production possibility frontier
Assume four countries, A, B, C and D that use a certain amount of public
expenditures, measured on the horizontal axis in monetary units. The countries are
then assumed to achieve a certain level of public spending performance, measured on
the vertical axis.
The efficiency of the four countries is obviously different. For instance, country B
uses more input than country A [X(B)>X(A)], but produces less output [Y(B)<Y(A)].
Therefore country B is relatively inefficient in comparison with country A. On the
other hand, country A is efficient in relation to country B, and it is placed on the
production possibility frontier. This means there are no other countries besides
country A that deliver the same level of output with a lower level of input. Similarly,
19
countries C and D are efficient and are also on the production possibility frontier. No
other country is inefficient compared to them.16
This framework allows the calculation of the production possibility frontier, and input
efficiency and output efficiency scores in order to rank the several countries in terms
of public spending efficiency. These efficiency scores will be set between 0 and 1,
and all the countries placed on the production possibility frontier will be assigned the
maximum score of 1. Note that this approach is likely to underestimate inefficiencies,
as the countries on the production possibility frontier are efficient by definition (even
though they too may have scope for savings). The input efficiency score of a given
country indicates how much less input this country could use to achieve the same
level of output. Additionally, the output efficiency score of a given country would tell
how much more output the country should be able to produce with the same amount
of resources that it is currently using. 17
4.2. FDH-based expenditure efficiency analysis
We now conduct an FDH efficiency analysis of public expenditure to our sample of
23 OECD countries. Public spending as a percentage of GDP in 2000 measures the
input and as output we use the public sector performance indicator already determined
in section 2. The production possibility frontier for our set of countries is presented in
Figure 4.18 One can see that the most efficient countries, positioned on the production
possibility frontier, are the US, Japan, and Luxembourg. Australia, Ireland and
16
Gupta and Verhoeven (2001) would call countries such as C and D “independently efficient”,
and country A “not independently efficient.”
17
Figure 3 illustrates that country B’s input efficiency score is given by X(A)/X(B), which is 0.5,
smaller than one, since B is the interior of the production possibility frontier. This implies that the
excess use of inputs by inefficient country B is 50 per cent of the necessary inputs to achieve the
same level of performance of country A. Country B’s output efficiency score is Y(B)/Y(A). In this
case, the loss of output of country B relative to the most efficient country turns out to be also 50
per cent (since for country B one can calculate Y(B)/Y(A)=5/10=0.5). The production possibility
frontier for the example in Figure 3 is as
0, X < 100
10, 100 ≤ X < X (C )
follows: Y = f ( X ) = 
.

≤
<
(
),
(
)
(
)
Y
C
X
C
X
X
D

Y ( D), X ≥ X ( D)
18
One must be aware of the scaling when interpreting the chart. A doubling in PSP is not
necessarily a doubling of welfare or utility.
20
Switzerland come very close to the frontier while the other countries are further
removed and therefore less “efficient”.
Figure 4. Production possibility frontier, 23 OECD countries, 2000
Total public sector performance
(PSP index)
1.50
Luxembourg
1.25
Austria
Japan
Sweden
Switzerland
US
1.00
Australia
Germany
UK
Italy
0.75
Greece
Production possibility frontier
0.50
25
30
35
40
45
50
55
60
Total public expenditures/GDP (%)
The figure shows that the EU countries are mostly well inside the production
possibility frontier. They mostly report a much higher ratio of public expenditure-toGDP than the US, but nevertheless often report lower public sector performance
indicators.
The results both for input efficiency and output efficiency are presented in Table 3,
where we report the respective efficiency scores along with each country’s ranking.
21
Table 3. Efficiency scores: public expenditures as a % of GDP in 2000 and Public
Sector Performance indicator (see Table 1)
Country
Australia
Austria
Belgium
Canada
Denmark
Finland
France
Germany
Greece
Iceland
Ireland
Italy
Japan
Luxembourg
Netherlands
New Zealand
Norway
Portugal
Spain
Sweden
Switzerland
United Kingdom
United States
Average
EU15 average
Non-EU15 average
Small governments 1/
Medium governments 1/
Big governments 1/
EU 15 2/
Euro area 2/
Input efficiency
Score
Rank
0.99
4
0.67
17
0.66
19
0.75
12
0.62
21
0.61
22
0.64
20
0.72
16
0.73
14
0.87
7
0.96
5
0.66
18
1.00
1
1.00
1
0.72
15
0.83
9
0.73
13
0.79
11
0.80
10
0.57
23
0.95
6
0.84
8
1.00
1
0.79
0.73
0.89
0.98
0.81
0.65
0.72
0.70
Output efficiency
Score
Rank
0.92
7
0.92
8
0.79
18
0.84
13
0.87
11
0.83
14
0.77
20
0.79
17
0.65
23
0.90
10
0.93
6
0.68
22
1.00
1
1.00
1
0.91
9
0.81
15
0.93
5
0.70
21
0.78
19
0.86
12
0.94
4
0.80
16
1.00
1
0.85
0.82
0.92
0.96
0.82
0.83
0.78
0.78
The values in bold signal the countries located on the production possibility frontier.
1/ See notes of Tables 1 and 2.
The Table shows that input efficiency scores start at 0.57 and output efficiency scores
at 0.65. The average input efficiency of the 15 EU countries is 0.73 meaning that they
should be able to attain the same level output using only 73 per cent of the inputs they
are currently using (or about 35% of GDP rather than close to 50%). The output
efficiency score implies that with given public expenditures, public sector
performance is 82 percent (or 18 percent less) of what it could be if the EU was on the
production possibility frontier (and more if the countries on the production possibility
frontier also have scope for expenditure savings). By contrast, the non-EU OECD
22
countries report more public expenditure efficiency. An average input efficiency score
of 0.89 implies only roughly 11 percent “waste”.
It is also now possible to focus on some specific interesting cases, such as Sweden. It
reports a PSP indicator of 1.04, above the average of the country sample. High public
spending pushes down the PSE indicator to a value of only 0.82, well below the
average. The input efficiency score of 0.57 suggests that little more than half the
current spending would be sufficient to achieve the same public sector performance.
The situation is similar in some of the other countries with “big governments”,
namely France, Germany and Italy where public expenditures account for around 50
per cent of GDP. Indeed, with the exception of Luxembourg, all two other countries
located on or near the production possibility frontier belong to the group of “small
government” countries, with a public expenditures-to-GDP ratio below the 40 per cent
threshold.
5. Conclusion
We developed indicators of public sector performance for 23 industrialised countries.
For that purpose we used a number of socio-economic indicators as proxies for
performance, and total spending and a number of spending categories as proxies for
resource use. We find moderate differences in the public sector performance (PSP)
indicators across industrialised countries. Unsurprisingly, countries with small public
sectors report the “best” economic performance while countries with large public
sectors show more equal income distribution.
When weighing performance by the resources used to achieve it, i.e. public
expenditure, there are important differences across countries in the resulting public
sector efficiency (PSE) indicators. Countries with small public sectors report
significantly higher PSE indicators than countries with medium-sized or big public
sectors. All these findings suggest diminishing marginal products of higher public
spending.
The results that we get from the production-frontier-related FDH analysis, which uses
the PSP indicators, are also in line with the aforementioned conclusions. Small
23
governments tend to show better results. Spending in big governments could be, on
average, about 35 per cent lower to attain the same public sector performance. The
calculations also point out that EU 15 countries show relatively low public sector
efficiency when compared with the US and also the average of the other OECD
countries in the sample. EU 15 countries are using 27 per cent more public spending
than the “most efficient” countries with similar PSP indicators. Spending for the
average of the other OECD countries is “only” 11 percent higher than necessary.
However, all the results have to be seen as indicative and need to be interpreted with
great care for the reasons outlined above. In our interpretation, we mainly focussed on
the overall PSP and PSE indicators to which we also applied the FDH analysis. This is
appropriate to gain an overall impression. The comparison of the different opportunity
and standard “Musgravian” sub-indicators across countries and the detailed
assessment of differences may provide further and more specific insights and lessons.
Finally, it seems important to bear in mind that by using a non-parametric approach,
and in spite of FDH being an established and valid methodology, differences across
countries are not statistically assessed, which can be considered as a limitation of such
methodology. Additionally, scale economies may also play a role in public sector
policies being able to deliver better outcomes.
24
Appendix
In order to assess the sensitivity of the results for public sector performance and
efficiency, we used alternative weighting schemes. We computed PSP and PSE
indicators that can give more weight to, inter alia, opportunity, equality, stability and
economic performance sub-indicators. One could argue that these indicators emulate
people with different intensities of preferences. The results, presented in Table A1 and
in Table A2, confirm that the conclusions presented in the main text are generally not
changed. Rank correlations with the tested changes in weights are in the [0.95 0.99]
range for PSP indicators and in the [0.96 0.99] range for PSE indicators.
25
Table A1 – Total public sector performance (PSP), 2000, different weights
Country
Australia
Austria
Belgium
Canada
Denmark
Finland
France
Germany
Greece
Iceland
Ireland
Italy
Japan
Luxembourg
Netherlands
New Zealand
Norway
Portugal
Spain
Sweden
Switzerland
United Kingdom
United States
Average
Small govs
Medium govs
Big govs
EU 15 *
Euro area *
Baseline 1)
1.04
1.12
0.95
1.02
1.06
1.01
0.93
0.96
0.78
1.03
1.05
0.83
1.14
1.21
1.11
0.93
1.13
0.80
0.89
1.04
1.07
0.91
1.02
1.00
1.07
0.97
1.01
0.94
0.93
Weighting of sub-indicators with emphasis on:
Opportunity 2) Equality 3)
Stability 4)
Economic
performance 5)
1.04
1.01
1.10
1.03
1.11
1.14
1.15
1.09
0.94
1.00
0.99
0.93
1.03
1.00
1.01
1.00
1.06
1.09
1.07
1.03
1.05
1.04
0.96
0.95
0.93
0.92
0.97
0.88
0.97
0.96
0.95
0.92
0.79
0.82
0.73
0.76
1.04
1.03
0.95
1.07
1.04
1.02
1.09
1.13
0.83
0.89
0.81
0.80
1.12
1.15
1.20
1.15
1.17
1.21
1.22
1.35
1.10
1.08
1.18
1.09
0.96
0.86
0.94
0.91
1.11
1.14
1.20
1.16
0.80
0.83
0.76
0.83
0.90
0.92
0.87
0.84
1.06
1.07
0.96
1.01
1.09
1.04
1.01
1.07
0.93
0.88
0.88
0.89
1.02
0.96
1.05
1.06
1.00
1.00
1.00
1.00
1.06
1.04
1.09
1.09
0.97
0.97
0.95
0.97
1.01
1.03
1.01
0.97
0.94
0.95
0.93
0.91
0.94
0.95
0.94
0.90
1) Equal weights assigned to each sub-indicator (1/7), as in Table 1.
2) 2/3 assigned to opportunity indicators and 1/3 to "Musgravian indicators". This means 1/6 assigned to
each of the 4 opportunity indicators and 1/9 to each of the 3 "Musgravian indicators".
3) 1/3 assigned to the distribution indicator and 2/3 to the other indicators. This means that each of the
other 6 indicators will have a weight of 1/9.
4) 1/3 assigned to the stability indicator and 2/3 to the other indicators. This means that each of the other
6 indicators will have a weight of 1/9.
5) 1/3 assigned to the economic performance indicator and 2/3 to the other indicators. This means that
each of the other 6 indicators will have a weight of 1/9.
* Weighted averages according to the share of each country GDP in the relevant group.
26
Table A2 – Total public sector efficiency (PSE), 2000, different weights
Country
Australia
Austria
Belgium
Canada
Denmark
Finland
France
Germany
Greece
Iceland
Ireland
Italy
Japan
Luxembourg
Netherlands
New Zealand
Norway
Portugal
Spain
Sweden
Switzerland
United Kingdom
United States
Average
Small govs
Medium govs
Big govs
EU 15 *
Euro area *
Baseline 1)
1.28
1.03
0.83
1.04
0.95
1.01
0.83
0.97
1.06
0.85
1.05
0.80
1.38
1.23
0.97
0.93
1.09
1.03
1.06
0.82
1.33
1.06
1.26
1.04
1.26
1.03
0.90
0.94
0.92
Weighting of sub-indicators with emphasis on:
Opportunity 2) Equality 3)
Stability 4)
Economic
performance 5)
1.24
1.40
1.35
1.27
1.04
1.01
1.06
1.01
0.84
0.80
0.84
0.79
1.03
1.12
1.04
1.02
0.96
0.97
0.94
0.90
1.04
1.05
0.97
0.96
0.84
0.79
0.87
0.78
0.99
0.94
0.95
0.93
1.10
1.05
0.96
0.99
0.83
0.85
0.82
0.95
1.03
1.01
1.08
1.12
0.80
0.83
0.77
0.76
1.32
1.43
1.52
1.45
1.19
1.23
1.22
1.35
0.99
0.88
1.01
0.94
0.96
0.88
0.93
0.91
1.05
1.14
1.16
1.12
1.04
1.09
0.97
1.04
1.08
1.08
1.04
1.00
0.84
0.84
0.75
0.79
1.32
1.41
1.27
1.36
1.09
1.05
1.01
1.03
1.24
1.24
1.31
1.33
1.04
1.05
1.04
1.03
1.23
1.30
1.30
1.30
1.04
1.04
1.01
1.03
0.92
0.90
0.90
0.87
0.96
0.93
0.93
0.90
0.93
0.90
0.91
0.88
1) Equal weights assigned to each sub-indicator (1/7), as in Table 2.
2) 2/3 assigned to opportunity indicators and 1/3 to "Musgravian indicators". This means 1/6 assigned to
each of the 4 opportunity indicators and 1/9 to each of the 3 "Musgravian indicators".
3) 1/3 assigned to the distribution indicator and 2/3 to the other indicators. This means that each of the
other 6 indicators will have a weight of 1/9.
4) 1/3 assigned to the stability indicator and 2/3 to the other indicators. This means that each of the other
6 indicators will have a weight of 1/9.
5) 1/3 assigned to the economic performance indicator and 2/3 to the other indicators. This means that
each of the other 6 indicators will have a weight of 1/9.
* Weighted averages according to the share of each country GDP in the relevant group.
27
References
Bouthevillain, C.; Cour-Thimann, P.; Van Den Dool, G; Hernandez de Cos, P.;
Langenus, G.; Mohr, M.; Momigliano, S. and Tujula, M. (2001). “Cyclically Adjusted
Budget Balances: An Alternative Approach,” ECB Working Paper 77.
Brennan, G. (2000). “The Political Economy of Regulation,” Australian National
University, mimeo.
Charnes, A.; Cooper, W. and Rhodes, E. (1978). “Measuring the efficiency of
decision making units,” European Journal of Operational Research, 2 (6), 429–444.
Clements, B. (2002). “ How Efficient is Education Spending in Europe?” European
Review of Economics and Finance, 1 (1), 3-26.
Deprins, D., Simar, L., and Tulkens, H. (1984). “Measuring labor-efficiency in post
offices,” in: Marchand, M.; Pestieau, P. and Tulkens, H. (Eds.), The performance of
public enterprises: concepts and measurement. Amsterdam: North-Holland.
Fakin, B. and de Crombrugghe, A. (1997). “Fiscal adjustment in transition economies:
social transfers and the efficiency of public spending: a comparison with OECD
countries,” Policy Research Working Paper 1803). Washington, DC: World Bank.
Gwartney, J.; Lawson, R.; Park, W.; Wagh, S.; Edwards, C. and de Rugy, V. (2002).
Economic Freedom of the World: 2002 Annual Report. Vancouver, the Fraser
Institute.
Gupta, S. and Verhoeven, M. (2001). “The efficiency of government expenditure
Experiences from Africa,” Journal of Policy Modelling 23, 433– 467.
Mueller, D. (1997). (ed.) Perspectives on Public Choice: A Handbook, Cambridge:
Cambridge University Press.
Persson, T. and Tabellini, G. (2001). “Political Institutions and Policy Outcomes:
What are the Stylized Facts?” Mimeo.
Rodrik, D. (2000). “Institutions for High-Quality Growth: What they are and how to
Acquire them,” NBER Working Paper 7540.
Schneider, F. (2002). “The Size and Development of the Shadow Economies and
Shadow Labor Force of 22 Transition and 21 OECD Countries: What do We Really
Know?” mimeo, prepared for the Round Table Conference: “On the Informal
Economy,” Sofia, Bulgaria, April 2002.
Shleifer, A. and Vishny, R. (1998). The Grabbing Hand: Government Pathologies
and their Cures, Cambridge: Harvard University Press.
Simar, L. and Wilson, P. (2003). Efficiency analysis: the statistical approach, lecture
notes, January.
28
St. Aubyn, M. (2002). “Evaluating efficiency in the Portuguese health and education
sectors,” mimeo presented at the conference on Desenvolvimento Económico
Português no Espaço Europeu: Determinantes e Políticas organised by Bank of
Portugal, 24-25 May 2002.
Strauch, R. and Hagen, J. (2000). Institutions, Politics and Fiscal Policy, Boston:
Kluwer Academic Publishers.
Tanzi, V. (1998). “Government Role and the Efficiency of Policy Instruments,” in
Sorenson. P. (ed.) Public Finance in a Changing World, Macmillan Press, 51-79.
Tanzi, V. and Schuknecht, L. (1997). “Reconsidering the Fiscal Role of Government:
The International Perspective,” American Economic Review, 87 (2), 164-168.
Tanzi. V. and Schuknecht, L. (2000). Public Spending in the 20th Century: A Global
Perspective, Cambridge: Cambridge University Press.
Tulkens, H. (1993). “On FDH analysis: some methodological issues and applications
to retail banking, courts and urban transit,” Journal of Productivity Analysis 4, 183–
210.
Vanden Eeckhaut, P., Tulkens, H., and Jamar, M.-A. (1993). “Cost-efficiency in
Belgian municipalities,” in Fried, H.; Lovell, C. and Schmidt, S. (eds.), The
Measurement of Productive Efficiency: Techniques and Applications. New York:
Oxford Univ. Press.
Van den Noord, P. (2000). “The Size and Role of Automatic Fiscal Stabilisers in the
1990s and Beyond,” OECD Working Paper 230.
Data references
International Institute for Management
Competitiveness Yearbook 2001.
Development
(2001).
The
World
OECD (2001a). Economic Outlook Database.
OECD (2001b). Education at a Glance 2001.
OECD (2001c). Knowledge and Skills for Life – First Results from Pisa 2000, Paris.
OECD (2001d). Social Expenditure Database.
OECD (2001e). Main Economic Indicators Database.
World Bank, several years. World Development Report.
World Bank (2001). World Development Indicators 2001.
World Economic Forum (1990). The World Competitiveness Report 1990.
29
30
Red tape 1/
Quality of
judiciary 1/
Shadow Economy 2/
School
enrolment 3/
Education
achievement
1990
78.8
77.9
78.0
79.0
75.9
77.3
78.5
77.0
77.9
79.2
76.1
78.3
80.6
76.9
77.7
77.4
78.5
75.4
77.9
79.3
79.6
77.2
76.9
76.2
2000
78.9
78.2
78.2
78.9
76.4
77.5
78.9
77.4
77.9
79.5
76.3
78.7
80.7
77.0
77.9
78.2
78.6
75.6
78.2
79.6
79.7
77.3
77.1
78.1
Infant mortality Life expectancy
1990
2001
1990
2001 1990
2001 1989/90 1999/2000 1990
1998
1995
2000
1990
2000
Australia
6.57
8.21
4.13
4.94
7.84
8.51
10.1
14.3
78.6
88.9
519
530
8.0
5.3
Austria
5.24
6.92
4.86
4.12
7.33
9.04
6.9
9.8
91.1
88.2
514
7.8
4.8
Belgium
5.52
5.22
3.76
2.78
6.18
5.70
19.3
22.2
87.7
88.0
550
508
7.9
5.3
Canada
7.50
7.78
4.59
4.63
8.44
8.49
12.8
16.0
88.7
93.7
521
532
6.8
5.2
Denmark
9.16
9.03
4.74
5.04
8.42
8.59
10.8
18.0
86.8
89.5
497
7.5
4.3
Finland
7.79
9.53
5.46
6.38
8.42
8.70
13.4
18.1
93.0
94.8
540
5.6
4.2
France
6.03
4.22
4.09
1.76
6.20
5.85
9.0
15.2
85.8
94.2
507
7.3
4.4
Germany
7.58
6.91
4.81
3.87
8.17
8.23
11.8
16.0
87.8
487
7.0
4.5
Greece
2.82
3.00
1.90
2.34
5.00
6.15
22.6
28.7
82.7
86.4
460
9.7
5.4
Iceland
9.03
6.28
8.28
85.4
506
5.9
3.1
Ireland
6.98
5.54
5.30
5.64
8.09
7.57
11.0
15.9
79.9
77.0
514
8.2
5.9
Italy
2.56
3.53
2.76
1.97
3.16
3.56
22.8
27.1
88.3
491
473
8.2
5.3
Japan
5.46
4.27
5.32
2.62
7.76
6.25
8.8
11.2
96.8
98.6
581
543
4.6
3.8
Luxembourg
5.52
7.37
3.76
4.11
6.18
7.47
67.6
436
7.3
5.0
Netherlands
8.13
7.97
5.42
4.69
8.13
8.28
11.9
13.1
83.6
92.6
529
7.1
4.9
New Zealand
8.43
8.76
6.27
4.34
7.89
8.26
9.2
12.8
85.0
90.3
501
531
8.3
5.9
Norway
7.35
8.07
4.00
3.03
8.20
8.30
14.8
19.1
87.7
96.4
501
6.9
3.9
Portugal
4.51
3.89
3.32
2.22
8.03
2.70
15.9
22.7
87.6
456
10.9
5.5
Spain
3.78
5.57
3.18
3.97
2.89
4.43
16.1
22.7
91.6
487
7.6
3.9
Sweden
7.63
8.61
4.63
5.58
7.06
8.52
15.8
19.2
85.3
99.5
513
6.0
3.4
Switzerland
7.89
7.16
6.11
5.36
8.70
8.02
6.7
8.6
79.8
83.1
506
6.8
3.7
United Kingdom 8.00
6.83
5.97
3.14
7.51
7.40
9.6
12.7
79.1
93.7
498
528
7.9
5.6
United States
6.53
6.55
5.31
3.73
7.61
7.07
6.7
8.7
85.8
90.2
492
499
9.4
7.1
Average
6.4
6.7
4.5
4.0
7.1
7.2
12.7
16.7
85.7
89.3
520.2 518.2
7.5
4.8
1/ Scale 1-10.
2/ In percentage of GDP.
3/ Ratio of the number of children of official school age enrolled in school, to the population of the corresponding official school age.
Corruption 1/
Annex Table A - Opportunity indicators
Annex – Data and sources
3.3
3.4
2.8
2.2
2.6
3.3
3.7
3.0
3.3
3.0
3.0
2.5
3.3
2.8
2.3
3.5
3.2
2.0
2.3
3.3
3.6
2.9
3.8
3.0
3.0
3.0
2.4
2.8
3.4
2.6
Public Communic.
and transports
quality
1980-89 1990-95
3.5
3.0
3.3
2.8
2.7
3.1
3.5
3.1
3.1
31
Coefficient of
Average inflation Per capita income 2/ Average economic
variation of
growth
growth
1980s 3/ 1990s 3/ 1980s
1990s
1980s
1990s
1990
2000
1980s
1990s
Australia
15.50
17.90
1.2
2.5
8.4
2.5
15530
25420
3.1
3.6
Austria
25.20
1.6
2.3
3.8
2.4
15710
24690
2.4
2.4
Belgium
21.60
24.10
1.3
1.6
4.9
2.1
15530
24910
2.0
2.2
Canada
18.95
1.1
1.3
6.5
2.2
17400
27320
2.9
2.9
Denmark
17.40
24.50
0.8
1.5
6.9
2.1
15820
27070
1.6
2.3
Finland
18.40
24.20
2.1
0.5
7.2
2.2
15220
23200
3.1
2.1
France
18.60
2.2
1.4
7.4
1.9
15970
21980
2.5
1.9
Germany
20.10
1.2
1.3
2.9
2.6
17010
23630
2.2
1.9
Greece
19.90
0.3
1.3
19.5
11.1
8680
15250
0.7
2.3
Iceland
0.9
0.9
39.2
4.3
16210
27070
2.8
2.7
Ireland
18.30
1.5
2.1
9.3
2.3
10940
26610
3.6
7.3
Italy
18.80
22.70
2.1
1.4
11.2
4.2
15180
22890
2.3
1.6
Japan
21.90
24.80
3.1
1.0
2.5
1.2
16950
24920
4.1
1.5
Luxembourg
1.4
1.9
4.8
2.2
22320
43110
5.0
5.4
Netherlands
20.65
1.2
2.8
2.9
2.4
15390
26310
2.3
2.9
New Zealand
15.90
12.70
1.0
1.2
11.9
2.1
12360
18740
1.9
2.8
Norway
19.00
24.00
1.2
2.9
8.3
2.4
16220
30730
2.4
3.6
Portugal
18.90
1.1
1.3
17.6
6.0
9120
16590
3.3
2.8
Spain
21.05
1.5
1.6
10.2
4.2
11320
18230
2.9
2.7
Sweden
21.20
24.10
1.7
1.0
8.0
3.5
16320
22940
2.2
2.3
Switzerland
16.90
19.60
1.2
0.7
3.3
2.3
19670
28360
2.1
0.9
United Kingdom
16.35
1.4
1.3
7.4
3.7
14860
23290
2.7
2.3
United States
15.70
15.70
1.4
2.2
5.6
3.0
21340
35030
3.2
3.2
Average
18.4
20.6
1.4
1.6
9.1
3.2
15438
25143
2.7
2.8
1/ Share of 40% poorest.2/ GDP at current market prices per head of population (in 1000 PPS).
2/ GDP at market prices per head of population (in 1000PPS).
3/ Or nearest available year. Precise year varies and depends on data availability.
Income distribution 1/
Annex Table B – Standard “Musgravian” indicators
1980s
7.5
3.3
9.5
9.4
7.1
4.9
9.0
6.8
6.6
0.8
14.2
8.4
2.5
1.4
8.0
4.3
2.8
7.7
17.5
2.5
0.7
9.6
7.3
6.6
1990s
8.9
5.2
8.7
9.5
7.4
11.9
11.2
7.7
9.5
3.3
12.0
10.7
3.0
2.5
5.8
7.9
4.8
5.6
19.6
6.2
3.4
7.9
5.8
7.8
Average
Unemployment
Annex Table C – Expenditures categories (% of GDP)
Total expenditure Goods and
Education
1/
services
1980s
1990s 1980s 1990s 1980s 1990s
Australia
37.4
36.7
19.1 18.6
5.1
5.1
Austria
49.7
53.8
19.4 19.9
5.6
5.6
Belgium
57.9
52.5
22.6 21.2
5.5
4.6
Canada
45.1
45.9
21.7 21.2
6.6
6.7
Denmark
56.3
58.3
26.6 25.9
7.1
7.8
Finland
43.4
56.3
20.3 23.0
5.2
7.1
France
50.3
53.6
23.0 23.6
5.5
5.8
Germany
47.1
48.2
19.8 19.5
4.7
4.7
Greece
40.5
47.3
15.0 14.7
2.2
2.7
Iceland
41.2
41.7
18.8 22.0
4.6
5.5
Ireland
46.1
37.7
18.9 16.0
5.5
5.1
Italy
50.6
52.2
18.9 18.8
4.5
4.4
Japan
31.9
36.2
13.7 15.0
5.1
3.6
Luxembourg
46.6
44.0
18.8 17.7
4.8
3.5
Netherlands
56.3
50.1
25.5 23.5
6.4
5.1
New Zealand
46.4
41.7
19.2 18.5
5.3
6.9
Norway
46.8
49.3
20.1 21.5
6.4
7.7
Portugal
39.5
43.7
14.5 18.9
3.8
5.2
Spain
39.0
43.4
15.6 17.9
3.5
4.5
Sweden
60.8
63.5
28.0 27.8
7.4
7.6
Switzerland
34.1
38.2
13.9 15.1
5.0
5.6
United Kingdom 42.3
40.9
20.9 19.5
5.0
5.2
United States
35.3
34.5
17.4 15.4
5.7
5.1
Average
45.4
46.5
19.6 19.8
5.2
5.4
Health
1980s
5.0
5.1
6.1
6.2
7.5
5.6
6.4
6.1
4.9
5.6
5.6
4.7
5.4
5.7
5.8
6.3
3.4
4.6
8.0
5.3
5.0
4.4
5.6
1990s
5.6
5.8
6.6
6.7
6.9
6.1
7.3
7.7
4.7
6.8
5.2
5.9
5.3
5.7
6.3
6.1
6.8
4.7
5.5
7.1
7.0
5.7
6.0
6.2
Social transfers
1980s
7.2
19.6
24.6
9.8
16.9
14.7
21.0
17.0
13.8
7.0
14.6
17.3
11.2
20.5
26.7
13.4
13.1
10.7
13.6
18.5
8.4
12.0
9.9
14.8
1990s
8.6
19.6
19.3
12.0
19.2
20.8
20.0
18.4
15.4
7.5
11.8
17.9
10.0
15.4
18.7
13.6
15.3
12.7
14.1
20.4
11.2
13.7
11.3
15.1
Public
investment
1980s 1990s
3.0
2.5
3.6
2.6
2.6
1.6
2.9
2.5
2.0
1.8
3.7
3.0
3.2
3.2
2.5
2.3
3.0
3.4
4.3
4.1
3.3
2.5
3.5
2.5
5.1
5.7
4.7
4.5
2.3
2.6
2.1
2.1
3.4
3.3
3.6
3.9
3.4
3.6
2.9
2.8
3.7
3.1
1.9
1.6
2.5
2.6
3.2
3.0
1/ All general government, averages for the period.
32
Annex Table D – Variables and series
Variable
Corruption
Sources, notes
Series
World Economic Forum: The World Values divided by 10 for better comparison.
Competitiveness Report 1990, item
"10.22 Corruption (for 1990)
World Economic Forum, The World Competitiveness Yearbook 2001, item 2.3.16 Bribing and
corruption (for 2001).
Red tape
"6.21 Regulatory environment (for
1990)
World Economic Forum, The World Competitiveness Yearbook 2001, "Bureaucracy" (for 2001).
Efficient judiciary
"10.04 Confidence in administration
o justice" (for 1990)
World Economic Forum, The World Competitiveness Yearbook 2001, "Justice" (for 2001).
Size shadow
economy
Schneider (2002)
Currency demand approach, (in % of official GDP),
reciprocal value (1/x).
Secondary school
enrolment
based on WDI 2001
Secondary school enrolment
Education
achievement
OECD, Education at a glance, 2001
Mathematical achievement, grade eight (page 309).
PISA report, 2000
Simple average of reading, mathematics and science scores.
Infant mortality
WDI 2001
Mortality rate, infant (per 1,000 live births), reciprocal value
(1/x).
Life expectancy
WDI 2001
Life expectancy at birth, total (years).
Communications
and transport
quality
Center for Institutional Reform and the Informal Sector (IRIS) based on reports from Business
Environmental Risk Intelligence (BERI).
Income distribution Worldbank: World Development
Report 1995, 2000/2001
Poorest 40 % (when two surveys within the time range of
86-98 were available the average was calculated).
2000 Annual Report (for 1990), 2002 Annual Report (for 2000).
Coefficient of
European Commission, Ameco
variation of growth
Based on GDP at constant market prices (1.1.0.0.ovgd),
reciprocal value (1/x).
Standard deviation
of inflation
OECD, Main Economic Indicators
Based on "CPI, all items" (CPALTT01.IXOB), reciprocal
value (1/x).
Per capita income
Ameco, GDP at current market prices per head of
population (in 1000 PPS) (1.0.212.0.hvgdp).
Average economic
growth
Based on GDP at constant market prices (1.1.0.0.ovgd).
33
Unemployment
OECD, Economic Outlook
Unemployment rate (UNR), reciprocal value (1/x).
Total public
expenditure
Total expenditure; general government (UUTG/UUTGF).
Goods and services European Commission, Ameco
Final consumption expenditure of general government at
current prices (UCTG).
Public education
Based on WDI 2001
Public spending on education, total (% of GNI, UNESCO).
Public health
OECD, Social Expenditure database
Public expenditure on health (item 11) (for 1980 - 1999).
Transfers and
subsidies
Social transfers other than in kind (UYTGH/UYTGHF)
Public investment
Gross fixed capital formation at current prices; general
government (UIGG).
34
(XURSHDQ&HQWUDO%DQNZRUNLQJSDSHUVHULHV
)RUDFRPSOHWHOLVWRI:RUNLQJ3DSHUVSXEOLVKHGE\WKH(&%SOHDVHYLVLWWKH(&%·VZHEVLWH
KWWSZZZHFELQW
´$JJUHJDWHORDQVWRWKHHXURDUHDSULYDWHVHFWRUµE\$&DO]D00DQULTXHDQG-6RXVD
-DQXDU\
´0\RSLFORVVDYHUVLRQGLVDSSRLQWPHQWDYHUVLRQDQGWKHHTXLW\SUHPLXPSX]]OHµE\
')LHOGLQJDQG/6WUDFFD-DQXDU\
´$V\PPHWULFG\QDPLFVLQWKHFRUUHODWLRQVRIJOREDOHTXLW\DQGERQGUHWXUQVµE\
/&DSSLHOOR5)(QJOHDQG.6KHSSDUG-DQXDU\
´5HDOH[FKDQJHUDWHLQDQLQWHUWHPSRUDOQFRXQWU\PRGHOZLWKLQFRPSOHWHPDUNHWVµE\
%0HUFHUHDX-DQXDU\
´(PSLULFDOHVWLPDWHVRIUHDFWLRQIXQFWLRQVIRUWKHHXURDUHDµE\'*HUGHVPHLHUDQG
%5RIILD-DQXDU\
´$FRPSUHKHQVLYHPRGHORQWKHHXURRYHUQLJKWUDWHµE\)5:UW]-DQXDU\
´'RGHPRJUDSKLFFKDQJHVDIIHFWULVNSUHPLXPV"(YLGHQFHIURPLQWHUQDWLRQDOGDWDµE\
$$QJDQG$0DGGDORQL-DQXDU\
´$IUDPHZRUNIRUFROODWHUDOULVNFRQWUROGHWHUPLQDWLRQµE\'&RVVLQ=+XDQJ
'$XQRQ1HULQDQG)*RQ]iOH]-DQXDU\
´$QWLFLSDWHG5DPVH\UHIRUPVDQGWKHXQLIRUPWD[DWLRQSULQFLSOHWKHUROHRILQWHUQDWLRQDO
ILQDQFLDOPDUNHWVµE\66FKPLWW*URKpDQG08ULEH-DQXDU\
´6HOIFRQWURODQGVDYLQJVµE\30LFKHODQG-39LGDO-DQXDU\
´0RGHOOLQJWKHLPSOLHGSUREDELOLW\RIVWRFNPDUNHWPRYHPHQWVµE\(*ODW]HUDQG
06FKHLFKHU-DQXDU\
´$JJUHJDWLRQDQGHXURDUHD3KLOOLSVFXUYHVµE\6)DELDQLDQG-0RUJDQ)HEUXDU\
´2QWKHVHOHFWLRQRIIRUHFDVWLQJPRGHOVµE\$,QRXHDQG/.LOLDQ)HEUXDU\
´%XGJHWLQVWLWXWLRQVDQGILVFDOSHUIRUPDQFHLQ&HQWUDODQG(DVWHUQ(XURSHDQFRXQWULHVµE\
+*OHLFK)HEUXDU\
´7KHDGPLVVLRQRIDFFHVVLRQFRXQWULHVWRDQHQODUJHGPRQHWDU\XQLRQDWHQWDWLYH
DVVHVVPHQWµE\0&D·=RU]LDQG5$'H6DQWLV)HEUXDU\
´7KHUROHRISURGXFWPDUNHWUHJXODWLRQVLQWKHSURFHVVRIVWUXFWXUDOFKDQJHµE\-0HVVLQD
0DUFK
35
´7KH]HURLQWHUHVWUDWHERXQGDQGWKHUROHRIWKHH[FKDQJHUDWHIRUPRQHWDU\SROLF\LQ
-DSDQµE\*&RHQHQDQG9:LHODQG0DUFK
´([WUDHXURDUHDPDQXIDFWXULQJLPSRUWSULFHVDQGH[FKDQJHUDWHSDVVWKURXJKµE\
%$QGHUWRQ0DUFK
´7KHDOORFDWLRQRIFRPSHWHQFLHVLQDQLQWHUQDWLRQDOXQLRQDSRVLWLYHDQDO\VLVµE\05XWD
$SULO
´(VWLPDWLQJULVNSUHPLDLQPRQH\PDUNHWUDWHVµE\$'XUUp6(YMHQDQG53LOHJDDUG
$SULO
´,QIODWLRQG\QDPLFVDQGVXEMHFWLYHH[SHFWDWLRQVLQWKH8QLWHG6WDWHVµE\.$GDPDQG
03DGXOD$SULO
´2SWLPDOPRQHWDU\SROLF\ZLWKLPSHUIHFWFRPPRQNQRZOHGJHµE\.$GDP$SULO
´7KHULVHRIWKH\HQYLVjYLVWKH´V\QWKHWLFµHXURLVLWVXSSRUWHGE\HFRQRPLF
IXQGDPHQWDOV"µE\&2VEDW55IIHUDQG%6FKQDW]$SULO
´3URGXFWLYLW\DQGWKH´V\QWKHWLFµHXURGROODUH[FKDQJHUDWHµE\&2VEDW)9LMVHODDUDQG
%6FKQDW]$SULO
´7KHFHQWUDOEDQNHUDVDULVNPDQDJHUTXDQWLI\LQJDQGIRUHFDVWLQJLQIODWLRQULVNVµE\
/.LOLDQDQG60DQJDQHOOL$SULO
´0RQHWDU\SROLF\LQDORZSDVVWKURXJKHQYLURQPHQWµE\70RQDFHOOL$SULO
´0RQHWDU\SROLF\VKRFNV²DQRQIXQGDPHQWDOORRNDWWKHGDWDµE\0.ODHIILQJ0D\
´+RZGRHVWKH(&%WDUJHWLQIODWLRQ"µE\36XULFR0D\
´7KHHXURDUHDILQDQFLDOV\VWHPVWUXFWXUHLQWHJUDWLRQDQGSROLF\LQLWLDWLYHVµE\
3+DUWPDQQ$0DGGDORQLDQG60DQJDQHOOL0D\
´3ULFHVWDELOLW\DQGPRQHWDU\SROLF\HIIHFWLYHQHVVZKHQQRPLQDOLQWHUHVWUDWHVDUHERXQGHG
DW]HURµE\*&RHQHQ$2USKDQLGHVDQG9:LHODQG0D\
´'HVFULELQJWKH)HG·VFRQGXFWZLWK7D\ORUUXOHVLVLQWHUHVWUDWHVPRRWKLQJLPSRUWDQW"µE\
(&DVWHOQXRYR0D\
´7KHQDWXUDOUHDOUDWHRILQWHUHVWLQWKHHXURDUHDµE\1*LDPPDULROLDQG19DOOD
0D\
´8QHPSOR\PHQWK\VWHUHVLVDQGWUDQVLWLRQµE\0/HyQ/HGHVPDDQG30F$GDP
0D\
´9RODWLOLW\RILQWHUHVWUDWHVLQWKHHXURDUHDHYLGHQFHIURPKLJKIUHTXHQF\GDWDµE\
1&DVVRODDQG&0RUDQD-XQH
36
´6ZLVVPRQHWDU\WDUJHWLQJWKHUROHRILQWHUQDOSROLF\DQDO\VLVµE\*5LFK-XQH
´*URZWKH[SHFWDWLRQVFDSLWDOIORZVDQGLQWHUQDWLRQDOULVNVKDULQJµE\2&DVWUpQ00LOOHU
DQG56WLHJHUW-XQH
´7KHLPSDFWRIPRQHWDU\XQLRQRQWUDGHSULFHVµE\5$QGHUWRQ5(%DOGZLQDQG
'7DJOLRQL-XQH
´7HPSRUDU\VKRFNVDQGXQDYRLGDEOHWUDQVLWLRQVWRDKLJKXQHPSOR\PHQWUHJLPHµE\
:-'HQKDDQ-XQH
´0RQHWDU\SROLF\WUDQVPLVVLRQLQWKHHXURDUHDDQ\FKDQJHVDIWHU(08"µE\,$QJHORQLDQG
0(KUPDQQ-XO\
0DLQWDLQLQJSULFHVWDELOLW\XQGHUIUHHIORDWLQJDIHDUOHVVZD\RXWRIWKHFRUQHU"µE\
&'HWNHQDQG9*DVSDU-XO\
´3XEOLFVHFWRUHIILFLHQF\DQLQWHUQDWLRQDOFRPSDULVRQµE\$$IRQVR/6FKXNQHFKWDQG
97DQ]L-XO\
37
Does public sector efficiency matter? Revisiting the relation between fiscal size
and economic growth in a world sample
Konstantinos Angelopoulos
University of Glasgow
Apostolis Philippopoulos
Athens University of Economics & Business, University of Glasgow, and CESifo
Efthymios Tsionas
Athens University of Economics & Business
January 2008
Abstract: This paper revisits the relationship between fiscal size and economic growth. Our work
differs from the empirical growth literature because this relationship depends explicitly on the
efficiency of the public sector. We use a sample of 64 countries, both developed and developing, in
four 5-year time-periods over 1980-2000. Building on the work of Afonso, Schuknecht and Tanzi
(2005), we construct a measure of public sector efficiency in each country and each time-period by
calculating an output-to-input ratio. In addition, we get an estimate of technical efficiency of public
spending for 52 countries for the time-period 1995-2000 by employing a stochastic frontier
analysis. Using these two measures, we find evidence of a non-monotonic relation between fiscal
size and economic growth that depends critically on the size-efficiency mix.
Keywords: Fiscal policy, government efficiency, growth.
JEL classification: H1, E6, 04.
Corresponding author: Apostolis Philippopoulos, Department of Economics, Athens University
of Economics & Business, 76 Patission Street, Athens 10434, Greece. Tel. +30-210-8203357. Fax:
+30-210-8203301. Email: [email protected]
Acknowledgements: We thank S. Kalyvitis, P. Kammas, M. Katsimi, J. Malley, M. Ntelis, H.
Park, E. Tzavalis and seminar participants at the University of Stirling and the 2007 Scottish
Economics Society Conference for discussions and comments. Any errors are ours. The first coauthor is grateful to the "Foundation Propondis" for their support.
1. Introduction
The relationship between government size and economic growth is not expected to be monotonic.
On one hand, governments provide public goods and services and correct market failures. On the
other hand, policy intervention generates its own distortions, as it requires taxes and distorts
incentives. There is thus a tradeoff depending on the size-efficiency mix of the public sector. By
efficiency, we mean the ability of the government to transform its revenues into public goods and
services that benefit the economy and promote growth. After a critically large size, or a critically
low efficiency, the costs of a larger public sector outweigh the benefits.1
This paper revisits the relation between fiscal size and economic growth. Our work differs
from the empirical growth literature because this relation depends explicitly on the efficiency of the
public sector. We use a sample of 64 countries, both developed and developing, in four 5-year
periods over 1980-2000.
To obtain a measure of government efficiency, we follow the methodology of Afonso,
Schuknecht and Tanzi (2005) for the OECD and construct measures of public sector efficiency
(PSE). This index measures the efficiency of public sector in reaching a range of objectives of
government intervention. It is basically the ratio of performance indicators (output) to a measure of
public expenditure related to those indicators (input), based on the assumption that the input is used
to achieve that output. We construct such indexes of public sector efficiency for four policy areas:
administration, stabilization, infrastructure and education. In addition to this measure, focusing on
52 countries for the sub-period 1995-2000 during which more data are available, we also obtain an
estimate of the so-called technical efficiency (TE) of the public sector by applying a stochastic
production frontier analysis (see e.g. Kumbhakar and Lovell, 2000, and Greene, 2005). The ranking
of countries according to the TE measure does not differ substantially from that implied by the PSE
measure.
We then incorporate these two measures (PSE or TE) into a simple econometric model in
which the size-growth relationship is non-monotonic depending on the size-efficiency mix. This
novel feature is included into an otherwise standard growth regression (see e.g. Barro and Sala-iMartin, 2004, chapter 12).
Our main finding is that, when the fiscal size is measured by the government consumption
share in GDP, the size-efficiency mix is significant in explaining the size-growth relationship. The
latter is indeed non-monotonic as discussed above. This result holds for both efficiency measures
1
A simple and popular conceptual framework is provided by Barro’s (1990) model, where there is a trade-off between
growth-promoting public goods and the distorting taxes required to finance them. When the government size and its
associated tax burden are high (resp. small) relative to the productivity of public sector, a larger size is bad (resp. good)
for growth. See also Hillman (2003) and Mueller (2003) on the market failures vs policy distortions trade-off.
1
constructed and is robust to a number of changes in the econometric specification, as well as to
dividing the world sample into two sub-samples consisting of “high-income” and “developing”
countries. Among other things, the model provides an endogenously determined efficiency
threshold below (resp. above) which the size-growth relationship is negative (resp. positive). In
general, this relationship is found to be negative in most countries and time periods. When we use,
for instance, the PSE as a measure of efficiency in our world sample for all four 5-year periods, our
estimates imply that only in 34 out of 159 observations (different countries in different periods) the
size-growth relationship is positive.2
Our results imply that what really matters to growth is not the government size per se, but
the size-efficiency mix. They can also help to explain why the evidence on the growth effects of the
overall fiscal size has so far been mixed (see e.g. Levine and Renelt, 1992, Tanzi and Zee, 1997,
Gemmel and Kneller, 2001, and Mueller, 2003, chapter 22). Essentially, our results suggest that it is
difficult to obtain a “robust” effect of the overall fiscal size on economic growth when important
elements that shape the size-growth relationship (in our case, the efficiency of the public sector) are
omitted from the analysis.3 In sum, as Levine and Renelt (1992, p. 951) point out, “using simple
expenditure data without accounting for government efficiency may yield inaccurate measures of
the actual delivery of public services”.
The rest of the paper is as follows. Section 2 develops measures of government efficiency.
Section 3 studies the growth effects of the size-efficiency mix. Conclusions are in Section 4.
2. Measures of government efficiency
In this section, we present two measures of government efficiency.
2.1 Public sector efficiency
Following Afonso et al. (2005, 2006), we construct sub-indices of relative Public Sector Efficiency
(PSE) in certain policy areas in each country and each time period, and then take the average of
these sub-indices to obtain an index of aggregate government efficiency in each country and each
time period.
2
Regarding the causal effect of fiscal size on economic growth, a concern has been the potential endogeneity of fiscal
size. The literature so far has not provided a “credible” identification of fiscal size in growth regressions (see e.g. Agell
et al., 2006). Although the aim of our paper is not to resolve the causality issue, we also provide some evidence that it
can be easier to find a credible identification of the size-efficiency mix, rather than of size alone, in growth regressions.
3
An additional potential explanation that has received a lot of empirical support is that the overall size of government
cannot capture the different implications of different government activities. As has been shown (see e.g. Devarajan et
al., 1996, Kneller et al., 1999, and Angelopoulos et al., 2007), the growth effects of the different components of
government expenditure, as well as of the various types of tax instruments, are not the same. See also Angelopoulos and
Philippopoulos (2007) for a single country, time-series study that also supports the result that both the composition and
efficiency of the government matter.
2
Afonso et al. have constructed PSEs for seven policy areas for OECD countries over the
eighties and nineties. Here, we focus on four policy areas (education, administration, infrastructure
and stabilization) for 64 countries, both industrialized and developing, and four 5-year time-periods,
over 1980-2000 (obviously, due to data availability, there is a trade-off between the number of
countries and the number of policy areas).4 We keep only those observations for which indexes of
government efficiency in all four areas are available.
Since the methodology is in Afonso et al. (2005, 2006), here we only discuss the basic
insight and point out where we differ. The basic insight of this methodology is to compare the
performance of government in certain areas of economic activity (where these areas are influenced
directly by government intervention) to the associated expenditure that the government allocates to
achieve this particular performance. Thus, to construct a PSE index, we need a measure of Public
Sector Performance (PSP) and a measure of the associated Public Sector Expenditure (PEX) for
each country in each policy area and each time-period. Then, the PSE will be the ratio of PSP to
PEX. More details about the construction of PSP and PSE indexes in each policy area are in our
Appendix.
To make these PSP and PEX measures (expressed in different units of measurement)
comparable across countries, we follow Afonso et al. by expressing each country’s PSP and PEX
relative to the average PSP and PEX of all countries in each period, and this is done for all periods
and indexes. In other words, each country’s PSP and PEX are expressed as percentages of the
respective average (normalized to be 1), and in turn the PSE is obtained as the ratio of these relative
PSP and PEX.5 Therefore, the resulting PSE is an index that measures the efficiency of a country’s
government relative to governments in other countries in each period in a particular policy area. The
larger the value, the more efficient the country’s government is. This is the notion of relative
efficiency in Afonso et al.
Table A.1 in the Appendix reports the relative PSPs, and the resulting PSEs, in the four
policy areas for the countries and the time-periods that data are available. The order of countries is
alphabetical. The second-from-the-end column in Table A.1 reports the (relative) aggregate
efficiency of a country’s government obtained as the average of the four (relative) sub-indices. As
expected, high-income OECD countries get on average better scores, although the public sectors in
economies like Korea, Thailand or Malaysia appear to be particularly efficient. The most efficient
governments during 1995-2000 are those of Korea (2.221), Canada (2.039), the USA (1.938) and
4
Greene (2005) has measured the efficiency of public spending in developing countries focusing on the areas of health
and education. Afonso et al. (2006) have also constructed measures of public sector efficiency for a group of 24 uppermiddle income countries for the late nineties.
5
Since the averages of PSP and PEX are both normalized to be 1, the resulting PSE has an average around 1
(specifically, the PSEs in education and stabilization have an average of about 1.1, whereas the PSEs in infrastructure
and administration have an average of about 1.25).
3
Switzerland (1.813) that are twice as efficient as the average countries, e.g. United Kingdom or
France. At the bottom end, Namibia (0.483), Nicaragua (0.447) and Yemen (0.35) score about half
of the average score.6
Of course, we have to be cautious with these estimates. For instance, in rich countries, like
Finland or Sweden, the cost of resources used for providing public education or capital is higher
than in say Uruguay or Lebanon, and this may result in an overestimation of relative efficiency in
the latter group of countries. In addition, government performance in a certain policy area may be
overestimated when private resources are used to complement government policy; this is especially
the case of education in many countries (e.g. Greece).
In sum, the main advantage of the above output-to-input approach is its simplicity and
logical coherence, which allow a meaningful comparison across countries. Its main weakness is that
several assumptions have to be made to calculate such a composite index (for a critical assessment
of different methodologies and measures of public sector efficiency, see e.g. Afonso et al., 2005 and
2006, as well as the special issue of European Economy, no. 3, 2004, on “Public finances in EMU
2004”).
2.2 A stochastic production frontier methodology
As an alternative approach to measuring government efficiency, we estimate a stochastic production
frontier for the public sector and then obtain an estimate of the so-called Technical Efficiency
( TE ) of this sector. For a review of this methodology, see Kumbhakar and Lovell (2000).
Our stochastic frontier model is of the form:
ln y i = β 0 + β 1 ln x i + v i − u i
(1)
where y i is a measure of public sector output in country i , xi is a measure of public sector input,
ui
is the nonnegative technical inefficiency component of the error term, and v i is the noise
component assumed to be distributed normally and independently of u i . Both error components are
assumed to be independent of the regressors.
6
Two countries score suspiciously high in this Table. Paraguay, which seems to be the most efficient country in the
world, and Argentina, which seems to be the second most efficient country in the last time-period. Regarding Paraguay,
this result is driven by a very high score in the variable Electric Power Transmission and Distribution Losses (see the
Appendix), which results in a very high PSP in infrastructure. This score may reflect measurement errors or unusual
circumstances, so we drop Paraguay from our regressions in the next section. Regarding Argentina, the high efficiency
score for 1995-2000 is probably due to the extended stabilization program implemented by the country in this period.
We also choose not to include Argentina in our analysis in the next section. We report, however, that including these
two countries does not have a significant effect on the econometric results presented later.
4
After estimating equation (1) by maximum likelihood, a measure of technical efficiency for
each country i ( T E i ) is defined as:
T E i = E [ ex p { − u i } / ε i ]
(2)
where ε i = vi − ui (see Kumbhakar and Lovell, 2000, chapter 3, for details). This efficiency score is
bounded between zero and one.
To apply the above, we need to measure public sector outputs and inputs ( y i and xi ,
respectively). We use the average of the PSP indices as a measure of y i . As a measure of xi , we
use Total Government Expenditure (as a share of GDP) which is available from the World
Development Indicators. We estimate (1)-(2) under the assumption that u i is characterized by a
nonnegative half-normal distribution (we have also examined the case where u i is assumed to
follow a truncated normal distribution but, since this gives very similar results, we discuss only the
nonnegative half-normal case).
Results for each country’s technical government efficiency ( TE i ) during the 1995-2000
sub-period (where we again look at a 5-year period average, as we did with the PSE measure above)
are reported in Table A.2.7 The ranking results look sensible again. In this cross-section world
sample during 1995-2000, Switzerland’s government scores the best being followed by Sweden and
Finland. Again, as probably expected, governments in OECD countries are more efficient than
those in developing countries, although public sectors in fast-growing economies like Thailand,
Malaysia, Cyprus and especially Korea get high scores. Algeria, Nicaragua and Yemen have now
the least efficient governments. Therefore, the ranking of countries using the PSE measure does not
differ substantially from that using the TE measure (recall that this refers to the 1995-2000 period
during which both measures are available) with the correlation coefficient being 0.75.
In this sample, an LR test of the null that σ u2 = 0 gives a value of 5.64, which rejects the
null (the respective p-value of the test is 0.009).8 This implies that government technical efficiency
differs significantly across countries during 1995-2000. We report that we have also estimated
government TE during the three time-periods before 1995 (i.e. the three 5-year periods between
1980 and 1995). However, there are significantly less data available for these earlier years
7
To examine whether the TE i estimates in Table A.2 are not biased due to heteroskedasticity in either v i or u i (see
Kumbhakar and Lovell, 2000), we have tested whether the variance functions of vi or u i depend (linearly) on govexp.
Since this is rejected, we can have some faith in the homoskedasticity assumption.
The limiting distribution of the LR test statistic is a mixture of a chi-square with zero degrees of freedom, i.e. a point
mass at zero, and a chi-square with 1 degree of freedom (see e.g. Kumbhakar and Lovell, 2000). The p-value of the test
reported here takes this into account.
8
5
(especially in the eighties when the sample size drops to around 25-30, i.e. it mainly consists of the
OECD countries). Not surprisingly, we have not been able to reject the null σ u2 = 0 for any of these
early periods. Hence, concerning the TE measure, we concentrate on the 1995-2000 period.
In sum, the TE measure has obvious advantages but, on the other hand, it depends on the
assumptions made about the error term. The assumption that government expenditure is
uncorrelated with the error term may be strong when governments respond to negative shocks by
increasing their expenditures. In any case, the TE measure of government efficiency provides a
useful alternative measure also used below to check the importance of the size-efficiency mix.
3. The size-efficiency nexus matters to growth
This section tests whether there is a non-monotonic relationship between government size and
economic growth with this relationship driven by the size-efficiency mix.
3.1 Econometric model
We use the above constructed measures of government efficiency (PSE or TE) in a growth
regression of the following form (see Dutt and Mitra, 2002, for a similar specification in a trade
policy context):
growthit = α 0 + α 1 sizeit + α 2 sizeit * eff it + X it β + ε it
(3)
where growthit is the growth rate of country i at time t , sizeit is a measure of government size,
eff it is a measure of government efficiency (PSE or TE) and X it includes control variables usually
included in growth regressions (see below).
The partial derivative with respect to sizeit is simply:
∂growthit
= α 1 + α 2 eff it
∂sizeit
(4)
where we expect α 2 to be positive in the sense that the more efficient the public sector, the larger
the positive effect of government on growth. We also expect α 1 to be negative to catch the adverse
effects of government size on growth.
As long as the estimated coefficients α 1 and α 2 in (3) are statistically significant and have
the right signs, so that the size-efficiency nexus matters to growth, the above specification can also
6
give an estimate of a (common to all countries) critical level of efficiency, eff * , where
eff * ≡ −(α 1 / α 2 ) > 0 makes the partial in (4) equal to zero. When an individual country’s
efficiency, eff it , is higher (resp. lower) than eff * , the positive (resp. negative) effects dominate and
the country is placed on the positively (resp. negatively) sloped part of the size-growth curve; this,
of course, requires eff * to lie within the range of values of eff it in the data. Note that (3)-(4) imply
that the growth effects of fiscal size can differ among countries and time-periods.9
3.2 Data and variables used in the regressions
For the eff variable, we use the two measures of government efficiency (TE and PSE) constructed in
section 2 above. The rest of the variables are as in most of the literature. We work with 5-year
period averages as we did with our eff measures (5-year periods are also used in the growth
literature, especially the literature on the growth effects of fiscal policy, see e.g. Folster and
Henrekson, 2001, and Kneller et al, 1999). The main datasets used are the Penn World Tables
(PWT) version 6.1 (see Heston et al., 2002) and the World Development Indicators (WDI)
developed by the World Bank.
Our dependent variable, the growth rate of per capita GDP, is from the PWT. In particular,
the PWT dataset provides us with the real GDP per capita in constant prices, which is then used to
obtain the five-year average of annual growth rates (denoted as growth in our regressions). The
PWT also provides us with consumption of the general government as a share of GDP in constant
prices, which is averaged over 5-year periods to give a variable denoted as govshare in our
regressions. This will be our primary measure of government size.10 An alternative measure of
government size, which is also used below, is total expenditures of the central government as a
share of GDP (denoted as govexp in our regressions and obtained from WDI). This variable
includes transfers and interest payments on public debt, in addition to government consumption
(note that to avoid double counting, we do not include government investment in our govexp
measure, as government investment is included in the investment share in GDP used as a separate
regressor (see below).
We have also examined a specification like growthit = α 0 + α 11 size it + α 12 size it 2 + X it β + ε it , which gives a partial as
a function of size, so that an “optimal” size can be calculated given the estimated coefficients irrespectively of
efficiency. We report that estimation of this equation does not give meaningful results (coefficients are not significant
and in some regressions they have wrong signs).
10
This is the general government consumption component of GDP. It does not include public investment, interest
payments, subsidies and other transfers. Public investment is included in PWT in the variable “investment share in
GDP” (see below). Note however that a large part of government spending on goods and services, included in govshare,
has investment features (e.g. salaries of teachers, professors and doctors and spending on police or the judiciary
system). The variable govshare is closer to what Tanzi and Schuknecht (2000) refer to as a measure of “real
government expenditure”.
9
7
Concerning the above two measures of fiscal size, an advantage of govshare over govexp is
that it refers to the general government and can thus capture better the full trust of fiscal size on
economic growth; moreover, it is PPP adjusted and therefore more suitable for international
comparisons. The advantage of govexp, on the other hand, is that it allows us to examine whether
including more types of government expenditure (at the disadvantage of using data at the central
level only) gives different results regarding the effect of fiscal size on growth. Ideally, we would
like to have a measure of general government spending for all types of government expenditure, but
unfortunately, such a measure does not, as far as we know, exist for all the countries and time
periods in our world sample. Finally, the fiscal size of government can be also measured by tax
revenue or the budget balance, both as shares of GDP (see e.g. Tanzi and Zee, 1997, and Persson
and Tabellini, 2003); see below in subsection 3.4 for details.11
In our choice of the control variables included in X in equation (3) above, we will follow
most of the literature (see e.g. Barro and Sala-i-Martin, 2004, chapter 12, and the review papers
mentioned above). Thus, we use the logarithm of the initial level of GDP per capita (denoted as
lgdp), obtained from PWT, to control for convergence effects; the initial (or the value closest to the
beginning of the period) secondary school enrolment rate (denoted as enrol), obtained from WDI, to
proxy for human capital;12 the investment share of GDP (denoted as investment), obtained from
PWT and averaged over the 5-year period; the logarithm of the fertility ratio (denoted as fertility),
obtained from WDI; a measure of openness (denoted as openness), obtained from PWT and defined
as the sum of exports and imports over GDP.13 Finally, we include in our regressions time
dummies, as well as regional dummies for countries in Sub-Saharan Africa, East Asia, Latin
America and the economies in transition.
3.3 Basic results
Results using the PSE measure of efficiency for the sample of 64 countries over 1980-2000 are
presented in Table 1. We report standard errors obtained under the assumption of spherical errors
and standard errors that are robust to arbitrary heteroskedasticity and arbitrary intra-country serial
11
The tax revenue-to-GDP ratio is generally not preferred to fiscal spending measures, mainly because of tax evasion
problems (see e.g. Tanzi and Zee, 1997). The same can be said about the budget-to-GDP ratio since it includes tax
revenue.
12
A better proxy for human capital could be a measure of the average years of schooling (see e.g. Barro and Sala-iMartin, 2004). However, such measures are not available for all the countries in our sample and we do not want to
restrict our sample for any other reasons than the requirements for the efficiency measure. Hence, we use the enrol
variable, also used by Levine and Renelt (1992).
13
We have also used the average annual growth rate of the labour force, obtained from the WDI, in the growth
regressions, but it is always insignificant.
8
correlation (see e.g. Wooldridge, 2002). The first three columns report estimates when using
govshare as a measure of fiscal size and the last three when using govexp.14
Table 1 around here
In column 1 of Table 1, we start with a standard growth regression: the coefficient of
govshare is significantly negative. In column 2, we add the PSE measure of government efficiency,
which is positive but marginally significant, while the coefficient of govshare remains significantly
negative. To examine whether it is government efficiency that shapes the size-growth relationship,
we move to column 3, which presents results for our key equation (3) above.15 Both estimates of
govshare and govshare*eff are significant with the expected sign (negative and positive
respectively), indicating a heterogeneous across countries size-growth relationship depending on
government efficiency. Actually, the estimates imply a threshold of eff * = 1.358 , which means that
only in 34 out of 159 observations (different countries in different time periods), the size-growth
relationship is positive.
The estimated coefficients α 1 and α 2 also allow us to calculate the growth effect of fiscal
size in each country and each time period, as implied by equation (4). Results are reported in the
last column of Table A.1. As can be seen, the estimated effect differs substantially across countries.
There is a small group of countries where public sectors are efficient meaning a positive growth
effect from fiscal size. This group includes Canada, Japan, Korea and Switzerland in all time
periods we have data for; and Australia, Finland and the USA in most time periods (here we report
those countries with more than one observation/time period; see Table A.1 for all countries).
However, for most countries and time periods, this effect is negative. Therefore, the general picture
that emerges is that fiscal sizes have grown too much - relative to public sector efficiency - in the
last decades. This finding is similar to the arguments made in e.g. Gwartney et al. (1998) and Tanzi
and Schuknecht (2000) although these papers do not take account of efficiency explicitly.
Regarding the control variables that enter significantly, lgdp is negative, implying
(conditional) convergence, while investment and openness are positive. The effect of fertility is
negative (this is as in Barro and Sala-i-Martin, 2004, chapter 12) but not robustly significant. The
effect of enrolment is positive but not significant. Regarding the regional dummies, those for the
economies in transition are significantly negative, while those for Latin American countries are
14
We do not include a dummy for each country (and thus we do not estimate fixed effects regressions) as this would
result in losing all cross-country variation. This is important because the measure of efficiency developed here is a
relative one across countries. It would make little sense to use this variation to explain differences within countries only.
9
negative but not significant when we use robust standard errors. An interesting result is the negative
dummy for East Asian countries, as this variable usually has a positive effect in similar regressions
(see e.g. Barro and Sala-i-Martin, 2004, chapter 12). However, East Asian countries, in general, are
ranked highly in our efficiency measures (see Table A.1), so that a large part of the positive
regional effect has been already controlled for by our fiscal measure.
The results are less clear when we use the other widely used measure of fiscal size, govexp
(see the last three columns in Table 1). The coefficient of govexp is negative but not robustly so (see
column 5 that includes pse). More importantly, in column 6, there is no significant evidence of a
non-linear relationship like the one found in column 3; namely, the coefficient of govexp*pse is not
significant (although it has the right sign). Recall that the key difference between govshare and
govexp is that the latter includes redistributive transfers and interest payments on public debt. Both
items (i.e. transfers and interest payments) do not involve a direct use of real resources by the state
sector (recall the economy’s resource constraint). We thus do not find it surprising that govexp does
not give as clear results as govshare. In a sense, these new results indicate that both the sizeefficiency mix and the composition of government expenditure matter to growth.16
3.4 Robustness of basic results
We now examine the robustness of the basic results above by extending the empirical specification
in two dimensions. First, we test whether our results - regarding the importance of the sizeefficiency mix on growth - are sensitive to the financing assumption of government spending (see
e.g. Miller and Russek, 1997, and Kneller et al., 1999). Given that we do not have detailed tax and
spending data for all the countries and time periods in our sample, we use a general form of
government budget that equates aggregate spending to tax revenue and deficit (see e.g. Miller and
Russek, 1997). In principle, in the absence of Ricardian equivalence, the effect of spending on
growth can be different depending on whether higher spending is financed by more tax revenues or
by a larger budget deficit (higher debt). If, for instance, we include a measure of taxation, together
with spending, in a growth regression, we would expect the effect of the tax measure to be negative
15
We do not include eff together with size * eff in the same regression, as they are highly correlated and as a result both
eff and size * eff become insignificant. In this specification, the growth effect of government efficiency takes place only
via government size, assuming that efficiency is independent of size.
16
We have also used another potential measure of the extent of government involvement in the economy, the so-called
Economic Freedom index as developed by the Fraser Institute (see e.g. Gwartney et al., 2006). The Economic Freedom
(EF) index is a rather general measure of government involvement than includes the size of government; the degree of
regulation of credit, labor and business by the government; the legal structure; the security of property rights; the
freedom to trade; etc. We report that, when we use the EF index as a measure of fiscal size in our regressions for the
world sample (i.e. instead of govshare and govexp), then (a) it has a negative growth effect (see also De Haan et al.,
2006) although this effect is not always significant (b) the estimated α 2 is not significant in equations (3)-(4) above.
We believe this is not surprising given that this index contains more variables than the size of the government, while
equations like (3) test whether the growth effect of size depends on the size-efficiency mix. Besides, the EF index may
be correlated with government efficiency.
10
capturing the adverse implications of a larger fiscal size, whereas the effect of the spending measure
to be positive capturing the positive effects of e.g. more public good provision. It is therefore
interesting to see whether our results are robust to the inclusion of a finance instrument (obviously,
because of multi-collinearity problems, we cannot include both tax revenues and public deficits in
the regressions).
For our sample, we obtain data for tax revenues, as a share of GDP, from the WDI database
(we denote the respective measure, which is again expressed in 5-year period averages, as tax). We
then rerun the basic regressions of Table 1 by including tax as an additional explanatory variable.
Results for the main variables are shown in Table 2 (since the estimates for the control variables are
not generally affected, we do not include them in Table 2 to save on space - these results are
available upon request). As can be seen, the results of Table 1 remain essentially unchanged when
we include tax, which, itself, is not significant. We report that these results again do not change if
we use deficits instead of taxes.17
Tables 2 and 3 around here
Second, we also test whether the inclusion of lagged growth rates changes our results.
Although our basic specification (see Table 1) is common in the empirical growth-policy literature
working with 5-year averages (see e.g. Kneller et al., 1999, and Folster and Henrekson, 2001),
dynamic effects from past growth may persist even after five years. Therefore, we now examine
whether the size-efficiency mix retains its significance in explaining economic growth, even after
controlling for lagged growth rates (see also Miller and Russek, 1997). Results obtained from
including the lagged-once growth rate (denoted as grolag in our regressions) as an explanatory
variable in the regressions of Table 1 are reported in Table 3 (again, we present results for the main
variables only to save on space). Note that the sample size drops from 159 to 98 observations (there
are now 46 instead of 62 countries). The lagged-once growth rate is generally significant, but the
results for the main variables of interest are not qualitatively affected. Actually, in column (3),
where we present our key results by using govshare, grolag is not found to be significant.
17
Notice, when we compare Tables 1 and 2, that the inclusion of tax does not alter the negative effects of govshare and
govexp in columns (1) and (4) respectively. Thus, the effect of government size itself, as measured by govshare or
govexp, remains negative even if we add a measure of the tax burden, tax. This is probably because tax revenues, as an
ex post measure, is not an ideal proxy for the distortions imposed by the tax system; higher tax revenue may e.g. reflect
less tax evasion and better institutions (see Tanzi and Zee, 1997 and Angelopoulos et al., 2007, for discussion and
references). Thus, the basic size-efficiency specification in Table 1 appears to be good enough to capture the trade-offs
in fiscal policy at least in our sample. In other words, to the extent that we allow the effect of the fiscal size to depend
on the size-efficiency mix, we view our basic specification as an alternative to including both spending (see positive
effects) and taxation (see negative effects) to capture the trade-off in fiscal policy.
11
Finally, in Table 4, we present results for the main variables by including both grolag and
tax in our regressions. As can be seen, the previous results and analysis remain robust to this
specification as well.
Table 4 around here
3.5 High-income and developing countries
So far - although we allowed for the effect of fiscal size to differ across countries depending on the
efficiency of the public sector in each country - we have studied rich and developing countries
jointly in a single sample. We now divide countries into two subgroups to study whether the sizeefficiency mix matters differently in high income and developing countries (where we classify
countries as high income following the classification in the WDI dataset). For each group, we first
calculate the measure of public sector efficiency (PSE) separately, repeating the steps described in
sub-section 2.1 above (since the efficiency measure is re-constructed for more homogeneous groups
of countries, this can provide an additional robustness test).
Tables 5 and 6 around here
Using these new PSE measures, Tables 5 and 6 rerun the basic regressions of Table 1 for
high income and developing countries respectively (again, we present results for the main variables
only to save on space). As can be seen, the results remain practically unchanged for the subgroup of
high-income countries in Table 5. For the subgroup of developing countries in Table 6, the main
story, regarding the importance of the size-efficiency mix, is again supported when we use govshare
as a measure of fiscal size (see column (3) in Table 6), which is as in the world sample above. It is
interesting to note that, for developing countries, public expenditure is not significantly related to
economic growth in the first two columns, but significance is restored in column (3) that explicitly
allows for the size-efficiency mix. All this suggests that in both subgroups, our story - that the sizeefficiency mix matters - is confirmed by the data.
We finally report that these results are robust to the inclusion of tax as an explanatory
variable (see subsection 3.4 above). On the other hand, including grolag reduces the sample size in
both subgroups too much to give any reliable results.
3.6 Can the size-efficiency mix help with endogeneity?
When looking for a causal effect from fiscal policy in a growth regression, a usual concern is that
there might be a reverse causality when e.g. governments respond to negative shocks by increasing
12
their expenditure (see e.g. Tanzi and Zee, 1997, and Agell et al., 2006). Although this problem is to
some extent mitigated here since we work with 5-year averages, such reverse causality cannot be
excluded. In addition, our fiscal size variables, and especially the measure of government
efficiency, may be correlated with the error term due to omitted variables or measurement error.
The natural approach to dealing with such an endogeneity is to use instruments for the
endogenous variables in IV methods. A fundamental concern with IV regression methods, however,
is whether the instruments are valid and relevant. As far as we know, the relevant literature has not
yet provided a credible identification of fiscal policy so that the instruments used are both
exogenous and strongly correlated with the endogenous variables (see e.g. Agell et al., 2006). We
now investigate whether accounting for the size-efficiency mix can help in this direction. We will
build upon the basic specification of subsection 3.3.
We need instruments for size and size*eff in 2SLS regressions. As such instruments, we use
variables usually considered as potential determinants of fiscal policy (see e.g. Person and Tabellini,
2003, chapter 3). In particular, we use the age dependency ratio (agedep) and two measures of
country size (population and surface, denoted respectively as pop and surface). All these three
variables are obtained from WDI and, except for surface, are averaged over the 5-year periods. In
Table 2, we present results for the core variables when we re-estimate the basic regressions of Table
1 by using these instruments in 2SLS methods (the results for the control variables do not change
significantly, so we do not present them to save on space).
Table 7 around here
We start again with the govshare variable. When we do not account for efficiency (column 1
in Table 7), the Sargan over-identifying restrictions test rejects the null that the instruments are
uncorrelated with the error term. However, when efficiency is included as an endogenous variable,
either on its own (column 2) or multiplicatively with govshare (column 3), the null clearly cannot
be rejected (the p-value is very low in both cases). Therefore, in this sample, the instruments affect
growth only indirectly through the size-efficiency mix. Note also that the Anderson (1984)
canonical correlations, and the Cragg and Donald (1993) tests of whether the equation is underidentified, reject the null thus lending some support to the relevance of the instruments.18 More
importantly, the first-stage F-statistic is very high for the govshare*eff variable, which indicates that
the instruments are strongly correlated with this variable. Although the first-stage F-statistic for
govshare is not as high, it is clear that the diagnostics favor the key regression in column 3 that
controls for the size-efficiency mix. In this regression (in column 3), the critical eff * = 1.238
18
These tests have been implemented using the routines written by Baum et al. (2006).
13
implies that in 46 countries/periods there is a positive effect on growth from govshare. The fact that
the critical efficiency level is lower in the 2SLS regressions indicates that the estimate of fiscal size
is biased downwards when endogeneity is not accounted for, so that the “true” effect of fiscal size
may in fact be less negative (or more positive) than implied in Table 1 for many countries.
As in Table 1 above, the results are not so promising when we use the govexp variable as a
measure of government size. Although the Sargan test does not reject the validity of the
instruments, the Anderson (1984) canonical correlations and the Cragg and Donald (1993) tests
cannot reject the null that the equation in column 6 of Table 7 is under-identified.
Therefore, although further research is clearly required concerning the issue of causality in
the fiscal policy-growth relation in cross-country growth regressions, our results suggest that taking
account of the size-efficiency mix can help in identifying the growth effects of fiscal policy.
3.7 An alternative measure of government efficiency
To further examine the robustness of our results, we also use the TE measure of efficiency
instead of PSE. Again, we will build upon the basic specification of subsection 3.3.
As explained in section 2, we have been able to obtain the TE measure for the 1995-2000
period only. In Table 8, we present results focusing on this period. Actually, in this table, we report
results for both the PSE and TE indices of government efficiency, and both the govshare and
govexp measures of fiscal size. This has the additional advantage of checking whether there has
been a structural break in the size-efficiency-growth relationship of equation (3). The regressions in
Table 8 are the same as those in Table 1, except that now we do not include time dummies.
Table 8 around here
We start again with govshare (columns 1-3). The average effect of govshare is negative
(column 1), while the size efficiency mix (when we use the PSE measure for efficiency) is
important (column 2). Thus, the non-monotonic relationship holds for both the whole period and the
1995-2000 sub-period. The critical level of efficiency is now eff * = 1.216 , which implies that for
24 out of 51 countries in this period the size-growth relationship is positive. Note also that the
regression with the size-efficiency mix is much better that the regression without it, as can be seen
by both the increase in R 2 and the fact that the coefficients of lgdp, openness and East Asia become
significant. Regarding lgdp, in particular, this implies that the size-efficiency mix is an important
long-run determinant of economic growth that has to be conditioned upon so that convergence can
be captured in the data (see e.g. Barro and Sala-i-Martin, 2004, chapter 12, for conditional
convergence).
14
Then, we estimate equation (3) for the 1995-2000 sample by using TE as the efficiency
measure. Results are in column (3). The coefficients are again significant with the right signs. The
critical efficiency is now eff * = 0.889 , which implies that only in 8 out of 51 countries in this
period the size-growth relationship is positive (see the last column in Table A.2 for the estimated
growth effect in each country in this case). These are Finland, Korea, Sweden and Switzerland, as
well as (but only marginally) Canada, Germany, Iceland and Uruguay. Note, however, that the
regression with the PSE measure in the size-efficiency mix explains about 10% more of the
variation in the growth rate than the regression with the TE measure.
In columns 4-6 of Table 8, we repeat the same regressions by using govexp as a measure of
government size. As before, govexp is negative and significant, while the size*eff variables have a
positive sign but are not significant.
As we did in Table 7, we have also run 2SLS regressions for the equations in Table 8 by
using the same set of instruments for the size-efficiency mix. The estimated coefficients are again
supportive of the importance of the size-efficiency nexus, at least for the govshare measure, but the
first stage regression diagnostics reveal that the instruments are not strongly correlated with the
endogenous variables. Since the small sample size does not help us to draw any safe conclusions,
we find the results of Table 7 to be more reliable. In any case, as discussed above, the identification
of fiscal policy remains a challenge in this literature. Finally, we report that with the TE measure of
efficiency, we cannot divide countries into rich and developing, as we did in subsection 3.5 (the
sub-samples are now too small). Concerning the addition of tax in the regressions (as we did in
subsection 3.4 above), we report that once more the main results are not affected.
Therefore, the main result from this subsection is that the relationship between the sizeefficiency mix and economic growth is robust to the time period and the measure of government
efficiency used.
4. Concluding remarks
We revisited the relationship between fiscal size and economic growth and provided evidence that
this relationship depends on the size-efficiency mix of the public sector. The policy implication is
that what matters to growth is not the size per se, but the size-efficiency mix. Of course, improving
the efficiency of the public sector is not an easy task. It requires, among other things, the
reallocation of government resources, as well as the effective and efficient use of those resources
towards identified and transparent strategic priorities.
The measurement of government efficiency is still an open issue. The measures developed
here, although plausible, cannot be treated as definitive. Future research may provide alternative
15
measures to test the robustness of our results. Further research is also needed to investigate the
causal effects of fiscal policy on growth in cross-country regressions. We nevertheless believe that
we have contributed to these important policy issues.
16
TABLE 1: Growth regressions using PSE: 62 countries, 1980-2000
Dep. Variable:
growth rate
govshare
(1)
(2)
(3)
(4)
(5)
(6)
-0.054
[0.022]**
(0.026)**
-
-
-
-
-
-
govexp
-
-
-0.106
[0.031]**
(0.037)**
0.078
[0.030]**
(0.039)**
-
-
govshare*pse
-0.052
[0.023]**
(0.027)*
-
govexp*pse
-
-
-
-0.049
[0.023]**
(0.025)*
-
-0.037
[0.028]
(0.028)
-
Pse
-
-
-
Lgdp
-2.108
[0.503]**
(0.720)**
0.119
[0.039]**
(0.045)**
0.015
[0.014]
(0.020)
-1.677
[0.813]**
(1.012)
0.011
[0.004]**
(0.005)**
-0.227
[0.996]
(0.706)
-0.838
[0.720]
(0.916)
-0.873
[0.523]*
(0.751)
-3.601
[0.742]**
(0.974)**
19.327
[4.878]**
(6.248)**
0.919
[0.477]*
(0.567)
-2.392
[0.519]**
(0.671)**
0.107
[0.039]**
(0.047)**
0.016
[0.014]
(0.019)
-1.522
[0.810]*
(1.003)
0.013
[0.004]**
(0.005)**
-0.204
[0.987]
(0.690)
-1.794
[0.869]**
(1.101)
-0.994
[0.522]*
(0.739)
-3.478
[0.738]**
(1.011)**
20.898
[4.902]**
(5.908)**
-2.325
[0.501]**
(0.636)**
0.118
[0.038]**
(0.047)**
0.019
[0.014]
(0.017)
-1.275
[0.814]
(0.985)
0.012
[0.004]**
(0.005)**
-0.051
[0.980]
(0.683)
-1.629
[0.773]**
(0.883)*
-0.956
[0.515]*
(0.678)
-3.329
[0.736]**
(0.984)**
20.269
[6.609]**
(5.685)**
-1.736
[0.479]**
(0.686)**
0.109
[0.039]**
(0.049)**
0.025
[0.015]
(0.021)
-2.136
[0.810]**
(1.041)**
0.011
[0.004]**
(0.005)**
-0.372
[0.992]
(0.708)
-1.156
[0.784]
(0.944)
-0.801
[0.519]
(0.737)
-3.280
[(0.756]**
(1.191)**
16.342
[4.592]**
(5.898)**
0.471
[0.576]
(0.596)
-1.879
[0.576]**
(0.664)**
0.106
[0.040]**
(0.049)**
0.024
[0.015]
(0.021)
-2.017
[0.824]**
(1.064)*
0.011
[0.004]**
(0.005)**
-0.414
[0.995]
(0.720)
-1.449
[0.863]
(1.085)
-0.797
[0.520]
(0.738)
-3.289
[(0.757]**
(1.170)**
16.826
[4.635]**
(5.763)**
-0.053
[0.025]**
(0.028)*
0.009
[0.028]
(0.027)
-
-1.804
[0.521]**
(0.670)**
0.108
[0.040]**
(0.050)**
0.025
[0.015]
(0.021)
-2.078
[0.830]**
(1.067)*
0.011
[0.004]**
(0.005)**
-0.365
[0.995]
(0.704)
-1.207
[0.801]
(0.978)
-0.729
[0.522]
(0.729)
-3.240
[(0.768]**
(1.211)**
16.801
[4.807]**
(5.840)**
0.378
0.394
0.405
0.374
0.377
0.375
investment
enrolment
fertility
openness
Sub-Saharan Africa
East Asia
Latin America
Transition
Economies
constant
R2
Notes: 1. The estimation method is Least Squares. The sample consists of 62 countries, in 5-year periods over
1980-2000. There is a total of 159 observations. All regressions include time dummies. 2. Standard errors obtained
under the assumption of spherical errors are shown in brackets below the estimated coefficients. Standard errors that
are robust to arbitrary heteroskedasticity and arbitrary intra-country serial correlation are shown in parentheses. 3. An
asterisk denotes significance at the 10% level and two asterisks at the 5% level.
17
TABLE 2: Growth regressions using PSE (controlling for tax): 62 countries, 1980-2000
Dep. Variable:
(1)
(2)
(3)
(4)
(5)
(6)
growth rate
-0.057
-0.056
-0.103
govshare
govshare*pse
[0.023]**
(0.026)*
-
[0.023]**
(0.026)**
-
govexp
-
-
[0.032]**
(0.041)**
0.072
[0.035]**
(0.048)
-
govexp*pse
-
-
pse
-
tax
-0.039
[0.026]
(0.030)
0.748
[0.594]
(0.699)
-0.015
[0.032]
(0.038)
0.388
0.395
R2
-
-
-
-
-0.067
[0.037]*
(0.038)*
-
-0.060
[0.037]
(0.037)
-
-
-
-0.011
[0.029]
(0.036)
0.024
[0.040]
(0.050)
0.656
[0.609]
(0.693)
0.039
[0.042]
(0.055)
-0.078
[0.042]*
(0.049)
0.016
[0.029]
(0.032)
-
0.405
0.376
0.381
0.032
[0.042]
(0.056)
0.377
1980-2000. There is a total of 159 observations. All regressions include time dummies, regional dummies and the
control variables of the regressions in Table 1. 2. Standard errors obtained under the assumption of spherical errors
are shown in brackets below the estimated coefficients. Standard errors that are robust to arbitrary heteroskedasticity
and arbitrary intra-country serial correlation are shown in parentheses. 3. An asterisk denotes significance at the 10%
level and two asterisks at the 5% level.
TABLE 3: Growth regressions using PSE (controlling for grolag): 46 countries, 1985-2000
(1)
(2)
(3)
(4)
(5)
(6)
Dep. Variable:
growth rate
-0.071
-0.082
-0.165
govshare
govshare*pse
[0.033]**
(0.038)*
-
[0.033]**
(0.036)**
-
govexp
-
-
[0.053]**
(0.049)**
0.103
[0.0474]**
(0.057)
-
govexp*pse
-
-
pse
-
grolag
0.198
[0.104]*
(0.139)
1.120
[0.679]
(0.674)
0.182
[0.103]*
(0.135)
0.458
0.475
R2
-
-
-
-
-0.054
[0.031]*
(0.024)**
-
-0.047
[0.036]
(0.031)
-
-
-
0.131
[0.106]
(0.124)
0.225
[0.104]**
(0.132)*
0.282
[0.796]
(0.837)
0.223
[0.105]**
(0.131)*
-0.055
[0.035]
(0.030)*
0.001
[0.034]
(0.039)
-
0.225
[0.105]**
(0.137)
0.487
0.448
0.448
0.448
1985-2000. There is a total of 98 observations. All regressions time dummies, regional dummies and the control
variables of the regressions in Table 1. 2. Standard errors obtained under the assumption of spherical errors are shown
in brackets below the estimated coefficients. Standard errors that are robust to arbitrary heteroskedasticity and
arbitrary intra-country serial correlation are shown in parentheses. 3. An asterisk denotes significance at the 10% level
and two asterisks at the 5% level.
18
TABLE 4: Growth regressions using PSE (controlling for tax and grolag): 46 countries, 19852000
(1)
(2)
(3)
(4)
(5)
(6)
Dep. Variable:
growth rate
-0.074
-0.079
-0.190
govshare
govshare*pse
[0.034]**
(0.036)**
-
[0.033]**
(0.036)**
-
govexp
-
-
[0.058]**
(0.061)**
0.140
[0.057]**
(0.077)*
-
govexp*pse
-
-
pse
-
tax
-0.011
[0.033]
(0.030)
0.198
[0.104]*
(0.139)
1.585
[0.868]
(1.026)
-0.035
[0.041]
(0.046)
0.176
[0.104]*
(0.138)
0.458
0.479
grolag
R2
-
-
-
-
-0.151
[0.049]**
(0.056)**
-
-0.150
[0.049]**
(0.056)**
-
-
-
0.043
[0.039]
(0.043)
0.107
[0.108]
(0.125)
0.127
[0.051]**
(0.063)**
0.208
[0.101]**
(0.128)
1.373
[0.847]
(0.940)
0.168
[0.057]**
(0.069)**
0.191
[0.101]*
(0.129)
-0.207
[0.063]**
(0.072)**
0.054
[0.034]
(0.049
-
0.168
[0.059]**
(0.077)**
0.213
[0.101]**
(0.135)
0.495
0.485
0.501
0.497
1985-2000. There is a total of 98 observations. All regressions include time dummies, regional dummies and the control
and two asterisks at the 5% level.
19
TABLE 5: Growth regressions using PSE (high income countries): 26 countries, 1980-2000
Dep. Variable:
(1)
(2)
(3)
(4)
(5)
(6)
growth rate
-0.065
-0.057
-0.173
govshare
govshare*pse
[0.028]**
(0.038)*
-
[0.027]**
(0.032)*
-
govexp
-
-
[0.042]**
(0.050)**
0.124
[0.038]**
(0.040)**
-
govexp*pse
-
-
pse
-
0.599
R2
-
-
-
-
-0.079
[0.016]**
(0.020)**
-
-0.072
[0.022]**
(0.024)**
-
1.161
[0.358]**
(0.345)**
-
-
0.211
[0.465]
(0.314)
-0.080
[0.016]**
(0.020)**
0.004
[0.023]
(0.019)
-
0.649
0.649
0.673
0.674
0.674
1980-2000. There is a total of 85 observations. All regressions include time dummies and the control variables of the
regressions in Table 1. 2. Standard errors obtained under the assumption of spherical errors are shown in brackets
below the estimated coefficients. Standard errors that are robust to arbitrary heteroskedasticity and arbitrary intracountry serial correlation are shown in parentheses. 3. An asterisk denotes significance at the 10% level and two
asterisks at the 5% level.
TABLE 6: Growth regressions using PSE (developing countries): 36 countries, 1980-2000
Dep. Variable:
(1)
(2)
(3)
(4)
(5)
(6)
growth rate
-0.022
-0.010
-0.069
govshare
govshare*pse
[0.035]
(0.037)
-
[0.036]
(0.038)
-
govexp
-
-
[0.041]*
(0.049)
0.078
[0.037]**
(0.044)*
-
govexp*pse
-
-
pse
-
0.191
R2
-
-
-
-
-0.026
[0.036]
(0.040)
-
0.012
[0.047]
(0.045)
-
1.127
[0.848]
(1.131)
-
-
1.362
[1.072]
(1.327)
-0.056
[0.039]
(0.046)
0.090
[0.050]*
(0.057)
-
0.213
0.243
0.192
0.212
0.238
1980-2000. There is a total of 74 observations. All regressions include time dummies and the control variables of the
regressions in Table 1. 2. Standard errors obtained under the assumption of spherical errors are shown in brackets
below the estimated coefficients. Standard errors that are robust to arbitrary heteroskedasticity and arbitrary intracountry serial correlation are shown in parentheses. 3. An asterisk denotes significance at the 10% level and two
asterisks at the 5% level.
20
TABLE 7: Growth regressions using PSE: 2SLS for 62 countries, 1980-2000
Dep. variable:
growth rate
govshare
(1)
(2)
(3)
(4)
(5)
(6)
-0.160
[0.082]*
(0.096)*
-
-
-
-
-
-
govexp
-
-
-0.208
[0.071]**
(0.081)**
0.168
[0.053]**
(0.054)**
-
-
govshare*pse
-0.126
[0.067]*
(0.088)
-
govexp*pse
-
-
-
-0.096
[0.050]*
(0.059)
-
0.028
[0.094]
(0.123)
-
pse
-
4.826
[1.902]**
(1.775)**
χ (21) = 0.110
-
-
χ (21) = 0.029
2
χ (2)
= 9.894
4.880
[2.848]
(2.718)
χ (21) = 4.144
-0.225
[0.106]**
(0.107)**
0.314
[0.198]
(0.205)
-
χ (21) = 2.902
(0.739)
(0.865)
(0.007)
χ (23) = 42.17
(0.041)
(0.088)
Sargan overidentification test
Cragg-Donald
Underidentification
Anderson canonical
correlations
First-stage
F (gov)
First-stage
F (pse)
First-stage
F (gov*pse)
2
χ (2)
= 9.513
(0.008)
χ (23) = 20.78
χ
2
( 2) =
15.12
χ
2
( 2) =
19.88
χ
2
( 2) =
8.96
χ (22) = 5.56
(0.000)
(0.000)
(0.000)
(0.000)
(0.011)
(0.061)
χ (23) = 19.53
χ (22 ) = 14.44
χ (22) = 18.73
χ (23) = 37.40
χ (22) = 8.72
χ (22) = 5.469
(0.000)
(0.000)
(0.000)
(0.000)
(0.012)
(0.064)
F (3,143) =
F (3,143) =
F (3,143) =
F (3,143) =
F (3,143) =
F (3,143) =
6.23
-
6.23
12.64
-
12.64
F (3,143) =
6.23
-
F (3,143) =
12.64
-
-
4.91
-
F (3,143) =
-
4.91
-
F (3,143) =
11.95
3.59
Notes: Notes: 1. The estimation method is 2SLS. The sample consists of 62 countries, in 5-year periods over 19802000. There is a total of 159 observations. All regressions include time dummies, regional dummies and the control
and two asterisks at the 5% level. 4. The instruments used are: agedep, pop, surface. 5. The Sargan test is a test of overidentifying restrictions. Under the null, the test statistic is distributed as chi-squared in the number of over-identifying
restrictions (the p-value is reported in parenthesis). 6. The Anderson (1984) canonical correlation is a likelihood-ratio
test of whether the equation is identified. The Cragg and Donald (1993) test statistic is also a chi-squared test of
whether the equation is identified. Under the null of underidentification, the statistics are distributed as chi-squared
with degrees of freedom=(L-K+1) where L=number of instruments (included + excluded) and K is the number of
regressors (the p-values are reported in parentheses). 7. The 1st stage F-statistic tests the hypothesis that the
coefficients on all the excluded instruments are zero in the 1st stage regression of the endogenous regressor on all
instruments (the p-value is reported in parenthesis).
21
TABLE 8: Growth regressions using PSE and TE: OLS for 51 countries, 1995-2000
Dep. variable:
Growth rate
govshare
(1)
(2)
(3)
(4)
(5)
(6)
-0.225
[0.088]**
(0.083)**
-
-
-
-
-
-
govshare*te
-
-0.202
[0.055]**
(0.055)**
0.166
[0.050]**
(0.046)**
-
-
govshare*pse
-0.088
[0.049]*
(0.048)*
-
-
-
-
govexp
-
-
0.253
[0.137]*
(0.130)*
-
govexp*pse
-
-
-
-0.138
[0.055]**
(0.049)**
-
-0.187
[0.089]**
(0.088)**
-
govexp*te
-
-
-
-
-0.148
[0.047]**
(0.056)**
0.047
[0.060]
(0.045)
-
lgdp
-1.143
[1.027]
(1.344)
0.040
[0.076]
(0.093)
0.015
[0.027]
(0.034)
0.025
[1.679]
(1.359)
0.012
[0.008]
(0.010)
-0.929
[1.803]
(0.960)
-1.903
[1.709]
(1.814)
-1.191
[0.980]
(1.039)
-3.617
[1.608]**
(1.705)**
12.004
[9.003]
(10.020)
0.291
-1.185
[0.943]*
(1.094)*
0.040
[0.068]
(0.089)
0.029
[0.025]
(0.028)
0.502
[1.508]
(1.260)
0.019
[0.008]**
(0.009)**
-0.535
[1.617]
(1.000)
-3.923
[1.645]**
(1.893)**
-1.470
[0.881]
(0.903)
-3.854
[1.440]**
(1.590)**
16.093
[8.146]*
(7.823)
0.446
-1.899
[1.079]*
(1.275)
0.024
[0.074]
(0.091)
0.014
[0.026]
(0.033)
-0.069
[1.632]
(1.384)
0.012
[0.008]
(0.011)
-1.262
[1.761]
(0.974)
-2.774
[1.726]
(1.934)
-1.341
[0.956]
(1.056)
-3.738
[1.564]**
(1.697)**
19.240
[9.588]
(9.638)
0.347
-0.676
[0.908]
(0.992)
0.035
[0.073]
(0.095)
0.046
[0.030]
(0.030)
-1.388
[1.528]
(1.105)
0.018
[0.009]**
(0.010)*
-0.348
[1.776]
(0.877)
-4.179
[2.020]**
(1.768)**
-1.316
[0.942]
(0.958)
-4.104
[1.551]**
(2.063)**
8.906
[7.980]
(7.423)
0.335
-0.940
[0.972]
(1.078)
0.031
[0.074]
(0.093)
0.039
[0.031]
(0.032)
-1.087
[1.582]
(1.158)
0.017
[0.009]*
(0.010)*
-0.347
[1.785]
(0.905)
-3.895
[2.061]*
(1.875)**
-1.181
[0.962]
(0.922)
-3.871
[1.586]**
(2.025)*
10.497
[8.257]
(7.951)*
0.346
investment
enrolment
fertility
openness
Sub-Saharan
Africa
East Asia
Latin America
Transition
Economies
Constant
R2
0.081
[0.113]
(0.098)
-0.994
[1.015]
(1.091)
0.026
[0.075]
(0.094)
0.043
[0.304]
(0.031)
-1.409
[1.538]
(1.122)
0.018
[0.009]**
(0.011)*
-0.247
[1.793]
(0.906)
-4.122
[2.034]**
(1.966)**
-1.199
[0.976]
(0.950)
-3.768
[1.629]**
(2.029)*
12.026
[8.986]
(8.143)*
0.344
Notes: 1. The estimation method is OLS. The sample consists of 51 countries, 1985-2000. There are 51
observations. 2.-3. As in Table 1.
22
APPENDIX: construction of PSE
We construct measures of public sector efficiency (PSE) for 64 countries, in four 5-year
periods, over 1980-2000, as output-to-input ratios by working as in Afonso et al. (2005).
Afonso et al. have focused on OECD countries, where the available data cover both
government performance and the associated public expenditure. Although we have tried to
follow Afonso et al. in the choice of policy areas and variables used, the construction of such
a rich PSE for a broader group of countries runs into data limitations, especially when
looking for decomposed public expenditure data. Thus, some deviations from the variables
used by Afonso et al. are inevitable. Nevertheless, the variables used here are the same in
spirit.
In the policy area of education, the PSP can be measured by the variable Secondary
School Enrollment, while the associated PEX is the average of the variable Public Spending
in Education as a percentage of GDP (both variables are available from the World
Development Indicators, WDI), where we use the end of period values (or the closest to the
end available) of Secondary School Enrollment.19 The resulting PSE is then a measure of
government efficiency in the policy area of education.
In the policy area of administration, the PSP is measured by the end of period values
of the variables Corruption in Government and Bureaucratic Quality (both obtained from
the IRIS-3 dataset)20 with higher scores denoting better outcomes, while the PSE is obtained
as in Afonso et al. (2005) by dividing this variable by the average public spending on goods
and services (available from WDI).
In the policy area of infrastructure, the PSP is measured by the average of Diesel
Locomotives in Use as a percentage of total locomotives, and the average of the inverse of
Electric Power Transmission and Distribution Losses (both variables are available from
WDI). These measures have also been used by Tanzi and Davoodi (1998) as indicators of
the quality of infrastructure (see also Angelopoulos and Philippopoulos, 2007). A problem
here is that the relevant PEX for infrastructure quality, which has been used by Afonso et al.
for the OECD countries, is not available for the larger group of countries we work with. We
19
Afonso et al. (2005) use the same PEX, but they also include a measure of the quality of education when they
construct the PSP.
20
Afonso et al. (2005) have used very similar variables (measures of corruption, red tape, quality of judiciary
and shadow economy). We prefer the IRIS-3 indexes because they are available for the counties and time
periods we work with.
23
therefore choose to use Total Government Expenditure (as a percentage of GDP) for PEX
(this is also available from WDI), again averaged over the 5-year period.
Finally, in the policy area of stabilization, the PSP is measured by the average of the
inverse of the variables Inflation Rate and Unemployment Rate (obtained from WDI), while
the relevant PSE is calculated by dividing this PSP by Total Government Expenditure (as a
percentage of GDP), averaged over the 5-year period. Afonso et al. also use total
government spending as a measure of public sector expenditures that are associated with
stabilization and economic performance indicators, such as inflation and unemployment.
24
Table A.1: Public Sector Efficiency (PSE) in 64 countries, 1980-2000
Country
Period
Algeria
19901995
19952000
19952000
19801985
19851990
19901995
19952000
19851990
19901995
19952000
19801985
19851990
19901995
19851990
19901995
19901995
19952000
19801985
19851990
19901995
19952000
19851990
19901995
19952000
19901995
19851990
19901995
Algeria
Argentina
Australia
Australia
Australia
Australia
Austria
Austria
Austria
Belgium
Belgium
Belgium
Bolivia
Brazil
Bulgaria
Bulgaria
Canada
Canada
Canada
Canada
Chile
Chile
Chile
Colombia
Costa Rica
Costa Rica
PSPAdmin
0.557
PSPEduc
0.793
PSPInfrast
0.797
PSPStabil
0.258
PSEAdmin
0.466
PSEEduc
0.648
PSEInfrast
0.749
PSEStabil
0.242
PSEaverage
0.526
Growth
effect
-0.065
0.586
0.803
0.452
0.233
0.522
0.73
0.459
0.237
0.487
-0.068
0.592
1.013
0.528
2.092
1.763
1.366
1.043
4.136
2.077
-
1.266
1.108
0.972
0.78
2.374
1.048
1.357
1.089
1.467
0.008
1.309
1.169
1.015
0.552
2.053
1.079
1.296
0.705
1.283
-0.006
1.224
1.226
0.997
1.316
1.754
1.122
1.243
1.64
1.44
0.006
1.306
1.222
1.26
1.149
1.954
1.218
1.603
1.462
1.559
0.016
1.309
1.354
1.214
1.409
1.257
1.074
0.981
1.139
1.113
-0.019
1.224
1.255
1.043
1.701
1.196
1.083
0.831
1.355
1.116
-0.019
1.257
1.212
1.247
1.709
1.197
1.042
0.956
1.31
1.127
-0.018
1.384
1.254
1.332
1.22
1.181
1.069
0.814
0.745
0.952
-0.032
1.285
1.303
1.279
1.108
1.243
1.12
0.791
0.686
0.96
-0.031
1.156
1.24
1.155
1.154
1.242
1.114
0.735
0.734
0.956
-0.031
0.359
0.437
0.691
0.23
0.404
0.935
1.584
0.527
0.863
-0.039
0.778
0.275
0.42
0.553
1.599
0.78
0.41
0.539
0.832
-0.041
0.78
1.056
0.724
0.24
0.565
0.934
0.499
0.166
0.541
-0.064
0.836
1.111
0.618
0.239
0.792
1.606
0.502
0.194
0.773
-0.046
1.384
1.247
1.107
0.728
2.715
0.921
1.559
1.026
1.555
0.015
1.429
1.318
1.105
0.686
2.712
0.924
1.432
0.889
1.489
0.010
1.336
1.284
1.037
1.379
2.457
0.873
1.197
1.592
1.53
0.013
1.428
1.288
1.187
1.266
3.654
1.13
1.631
1.741
2.039
0.053
0.715
0.811
0.638
0.374
0.936
1.08
0.783
0.459
0.815
-0.042
0.668
0.773
0.575
0.756
1.08
1.363
0.856
1.126
1.106
-0.020
0.952
0.966
0.962
0.748
1.5
1.411
1.377
1.071
1.34
-0.002
0.778
0.643
0.302
0.451
2.23
1.09
0.719
1.075
1.278
-0.006
0.956
0.531
0.917
0.524
0.757
0.568
1.145
0.655
0.781
-0.045
0.892
0.575
0.698
0.832
0.751
0.743
1.001
1.193
0.922
-0.034
25
Costa Rica
Cyprus
Cyprus
Cyprus
Cyprus
Czech Rep.
Czech Rep.
Denmark
Denmark
Denmark
Denmark
Dominican
Rep.
Dominican
Rep.
Egypt
Egypt
El Salvador
Finland
Finland
Finland
France
France
France
France
Germany
Germany
Greece
Greece
Greece
Greece
19952000
19801985
19851990
19901995
19952000
19901995
19952000
19801985
19851990
19901995
19952000
19901995
19952000
19901995
19952000
19952000
19851990
19901995
19952000
19801985
19851990
19901995
19952000
19901995
19952000
19801985
19851990
19901995
19952000
0.959
0.563
0.868
0.672
0.892
0.613
1.216
0.941
0.915
-0.035
0.805
1.116
1.248
1.251
0.565
1.481
1.326
1.328
1.175
-0.014
0.832
1.182
1.247
1.306
0.663
1.472
1.289
1.35
1.193
-0.013
1.113
1.204
1.139
1.845
0.897
1.438
1.084
1.757
1.294
-0.005
1.19
1.002
1.491
1.619
0.941
0.977
1.314
1.427
1.165
-0.015
0.89
1.228
0.995
1.244
1.043
1.148
0.827
1.033
1.013
-0.027
0.952
1.089
1.086
0.777
1.806
1.119
0.954
0.683
1.14
-0.017
1.384
1.179
0.952
0.705
1.579
0.829
0.758
0.562
0.932
-0.033
1.429
1.291
1.073
0.756
1.755
0.839
0.88
0.62
1.023
-0.026
1.336
1.236
1.093
1.791
1.58
0.77
0.837
1.371
1.139
-0.017
1.428
1.23
1.62
1.234
1.801
0.758
1.31
0.998
1.217
-0.011
0.668
0.405
0.239
0.29
1.302
1.169
0.528
0.639
0.909
-0.035
0.836
0.725
0.32
0.397
1.172
1.783
0.616
0.764
1.084
-0.021
0.757
0.919
0.8
0.521
0.61
1.011
0.723
0.471
0.704
-0.051
0.592
0.927
0.701
0.563
0.465
0.978
0.677
0.543
0.666
-0.054
0.647
0.515
0.648
0.694
0.539
1.089
1.287
1.378
1.073
-0.022
1.429
1.383
1.257
0.868
2.288
1.186
1.318
0.911
1.426
0.005
1.336
1.305
1.248
1.22
1.786
0.909
0.99
0.967
1.163
-0.015
1.312
1.304
1.773
1.598
1.911
0.905
1.485
1.338
1.41
0.004
1.384
1.156
1.163
0.667
1.092
1.067
0.91
0.521
0.898
-0.036
1.309
1.275
1.168
0.742
1.108
1.056
0.859
0.545
0.892
-0.036
1.112
1.331
1.113
1.477
0.953
1.117
0.782
1.038
0.972
-0.030
1.068
1.295
1.289
1.438
0.945
1.075
0.869
0.969
0.965
-0.031
1.336
1.255
1.592
1.33
1.356
1.272
1.588
1.326
1.386
0.002
1.306
1.207
1.547
1.467
1.234
1.272
1.445
1.371
1.331
-0.002
0.739
1.142
0.86
0.559
0.477
2.826
0.77
0.5
1.143
-0.017
0.98
1.229
0.875
0.41
0.555
2.431
0.64
0.3
0.981
-0.029
1.002
1.215
0.729
0.555
0.839
2.303
0.626
0.477
1.061
-0.023
1.074
1.188
1.172
0.577
1.058
1.971
1.144
0.563
1.184
-0.014
26
Hungary
Hungary
Hungary
Hungary
Iceland
Iceland
Iceland
India
Indonesia
Indonesia
Iran
Ireland
Ireland
Ireland
Ireland
Israel
Italy
Jamaica
Jamaica
Jamaica
Jamaica
Japan
Japan
Japan
Jordan
Jordan
Jordan
Korea, Rep
Korea, Rep
19801985
19851990
19901995
19952000
19851990
19901995
19952000
19952000
19851990
19901995
19901995
19801985
19851990
19901995
19952000
19952000
19952000
19801985
19851990
19901995
19952000
19801985
19851990
19901995
19851990
19901995
19952000
19801985
19851990
0.922
0.987
0.79
1.289
0.861
0.98
0.474
0.773
0.772
-0.046
1.049
1.111
0.777
0.342
0.964
0.93
0.438
0.193
0.631
-0.057
1.102
1.212
0.766
0.472
0.963
0.905
0.439
0.27
0.644
-0.056
1.19
1.166
0.72
0.456
1.545
1.224
0.498
0.315
0.895
-0.036
1.429
1.268
0.83
2.745
0.842
1.201
0.862
2.854
1.44
0.006
1.336
1.228
0.904
1.258
0.787
1.117
0.858
1.193
0.989
-0.029
1.428
1.174
1.228
1.638
0.881
1.003
1.238
1.652
1.193
-0.013
0.83
0.534
0.779
0.324
2.335
0.87
1.602
0.667
1.369
0.001
0.154
0.563
0.679
0.536
0.315
3.07
1.051
0.829
1.316
-0.003
0.668
0.597
0.721
0.703
1.487
2.196
1.3
1.268
1.563
0.016
0.89
1.003
0.621
0.293
0.744
1.038
0.898
0.424
0.776
-0.045
1.153
1.151
0.794
0.459
1.281
0.998
0.552
0.319
0.788
-0.045
1.191
1.188
0.934
0.632
1.429
0.986
0.656
0.443
0.878
-0.037
1.224
1.219
0.803
1.335
1.597
1.11
0.639
1.062
1.102
-0.020
1.183
1.058
1.013
1.083
1.878
1.079
0.905
0.968
1.207
-0.012
1.153
1.163
1.616
0.604
0.702
0.748
1.058
0.395
0.726
-0.049
1.068
1.214
1.089
0.771
1.269
1.272
0.738
0.522
0.95
-0.032
0.574
0.814
0.551
0.342
0.381
0.646
0.463
0.287
0.444
-0.071
0.594
0.945
0.408
0.24
0.282
0.851
0.334
0.196
0.416
-0.074
0.778
0.9
0.4
0.265
0.593
0.974
0.587
0.389
0.636
-0.056
0.83
1.085
0.819
0.309
0.451
0.905
0.721
0.272
0.587
-0.060
1.266
1.349
1.479
1.993
5.247
1.232
2.676
3.606
3.19
0.143
1.309
1.439
1.475
2.489
5.594
1.352
2.816
4.753
3.629
0.177
1.224
1.39
1.371
2.784
5.47
1.816
2.128
4.322
3.434
0.162
0.715
0.488
0.627
0.523
0.346
0.46
0.56
0.467
0.458
-0.070
0.89
0.584
0.85
0.859
0.455
0.368
0.807
0.816
0.611
-0.058
0.952
0.818
0.992
0.682
0.465
0.551
0.956
0.657
0.658
-0.055
0.687
1.191
1.193
0.811
0.89
1.54
2.266
1.539
1.559
0.016
0.711
1.275
1.263
1.152
1.131
1.542
2.542
2.319
1.883
0.041
27
Korea, Rep
Korea, Rep
Lebanon
Luxembourg
Luxembourg
Luxembourg
Luxembourg
Malaysia
Mexico
Mexico
Mexico
Mexico
Namibia
Namibia
Netherlands
Netherlands
Netherlands
Netherlands
New Zealand
New Zealand
New Zealand
New Zealand
Nicaragua
Norway
Norway
Norway
Norway
Panama
Panama
19901995
19952000
19952000
19801985
19851990
19901995
19952000
19952000
19801985
19851990
19901995
19952000
19901995
19952000
19801985
19851990
19901995
19952000
19801985
19851990
19901995
19952000
19952000
19801985
19851990
19901995
19952000
19801985
19851990
1.113
1.353
1.176
1.645
1.96
1.653
2.225
3.112
2.237
0.069
1.068
1.333
1.512
1.165
2.254
1.757
2.753
2.121
2.221
0.067
0.354
1.039
0.548
0.771
0.311
2.071
0.46
0.648
0.872
-0.038
1.372
0.936
1.136
1.704
1.418
0.8
0.867
1.3
1.096
-0.020
1.429
0.953
1.044
2.003
1.585
0.958
0.871
1.671
1.271
-0.007
1.336
0.944
0.418
2.34
1.426
1.396
0.331
1.85
1.251
-0.008
1.342
0.929
0.299
2.274
1.441
1.12
0.238
1.81
1.152
-0.016
0.952
1.277
0.933
1.559
1.012
1.357
1.378
2.301
1.512
0.012
0.624
0.649
0.755
0.165
1.003
0.741
1.165
0.254
0.791
-0.044
0.715
0.666
0.731
0.94
1.325
0.888
0.91
1.17
1.074
-0.022
0.668
0.722
0.725
1.077
1.319
0.811
1.467
2.18
1.444
0.007
0.592
0.771
0.726
0.964
1.51
0.764
1.455
1.931
1.415
0.004
1.113
0.504
1.128
0.403
0.417
0.269
0.991
0.354
0.508
-0.066
1.068
0.432
1.115
0.343
0.425
0.254
0.959
0.295
0.483
-0.068
1.384
1.254
1.501
1.114
1.65
0.907
0.912
0.677
1.037
-0.025
1.429
1.243
1.47
2.888
1.762
0.901
0.873
1.714
1.313
-0.004
1.336
1.279
1.291
1.528
1.744
1.115
0.799
0.945
1.15
-0.016
1.428
1.273
1.561
1.312
1.947
1.271
1.037
0.871
1.281
-0.006
1.384
1.185
0.86
0.744
1.235
1.165
0.7
0.605
0.926
-0.034
1.429
1.264
0.924
0.599
1.269
1.042
0.684
0.443
0.86
-0.039
1.336
1.256
0.875
1.583
0.893
0.885
0.704
1.273
0.939
-0.033
1.306
1.241
0.71
1.355
0.784
0.872
0.677
1.293
0.907
-0.035
0.721
0.449
0.315
0.355
0.588
0.626
0.271
0.305
0.447
-0.071
1.293
1.217
0.978
1.207
1.716
0.993
0.92
1.135
1.191
-0.013
1.312
1.304
1.017
1.06
1.763
0.916
0.853
0.889
1.105
-0.020
1.336
1.346
0.998
1.649
1.537
0.833
0.738
1.221
1.082
-0.022
1.306
1.325
1.129
1.488
1.698
0.849
0.949
1.251
1.187
-0.013
0.348
0.677
0.525
1.003
0.205
0.787
0.541
1.034
0.642
-0.056
0.359
0.754
0.363
3.949
0.209
0.712
0.419
4.555
1.474
0.009
28
Paraguay
Paraguay
Peru
Peru
Peru
Philippines
Philippines
Philippines
Poland
Portugal
Portugal
Portugal
Romania
Romania
South Africa
South Africa
South Africa
Spain
Sweden
Sweden
Sweden
Switzerland
Switzerland
Switzerland
Syria
Thailand
Trinidad &
Tobago
Trinidad &
Tobago
Tunisia
19851990
19901995
19801985
19901995
19952000
19801985
19851990
19901995
19901995
19851990
19901995
19952000
19901995
19952000
19851990
19901995
19952000
19952000
19851990
19901995
19952000
19801985
19901995
19952000
19801985
19952000
19901995
19952000
19851990
0.117
0.384
3.504
0.489
0.228
1.539
12.52
1.747
4.008
-
0.556
0.503
10.84
0.698
0.822
1.043
28.17
1.812
7.96
-
0.579
0.693
0.717
0.11
0.633
1.202
1.252
0.192
0.82
-0.042
0.557
0.749
0.356
0.356
0.97
1.137
0.603
0.603
0.828
-0.041
0.598
0.845
0.587
0.606
0.84
1.317
0.993
1.025
1.044
-0.025
0.192
0.701
1.417
0.604
0.259
1.952
3.714
1.583
1.877
0.040
0.359
0.842
0.449
0.485
0.467
1.768
0.946
1.021
1.05
-0.024
0.557
0.829
0.449
0.622
0.649
1.459
0.733
1.015
0.964
-0.031
1.058
1.191
0.731
0.271
0.93
1.127
0.547
0.203
0.702
-0.051
0.98
1.038
0.84
0.472
0.825
1.246
0.709
0.398
0.794
-0.044
0.946
1.095
0.849
0.926
0.577
1.058
0.643
0.701
0.744
-0.048
1.074
1.205
1.015
1.033
0.661
1.094
0.797
0.811
0.841
-0.040
0.669
1.029
0.736
0.419
0.58
1.495
0.663
0.377
0.779
-0.045
0.598
1.038
0.845
0.512
0.546
1.282
0.805
0.487
0.78
-0.045
1.309
0.751
1.302
0.234
0.805
0.626
1.361
0.244
0.759
-0.047
1.069
0.812
1.02
0.403
0.673
0.615
1.015
0.401
0.676
-0.053
1.074
0.768
1.17
0.352
1.381
0.605
1.186
0.357
0.882
-0.037
1.19
1.259
1.02
0.667
2.132
1.337
0.929
0.607
1.251
-0.008
1.429
1.267
1.105
1.299
2.461
0.824
0.863
1.015
1.291
-0.005
1.336
1.388
0.99
1.031
1.979
0.869
0.694
0.723
1.066
-0.023
1.428
1.368
1.234
2.248
2.129
0.877
0.894
1.628
1.382
0.002
1.384
1.119
1.239
4.532
2.379
1.128
2.089
7.642
3.31
0.152
1.336
1.178
1.099
1.95
1.672
1.009
1.346
2.389
1.604
0.019
1.306
1.142
1.41
2.735
1.575
1.006
1.59
3.083
1.813
0.035
0.461
0.72
0.767
0.8
0.224
0.642
0.577
0.602
0.511
-0.066
0.83
0.759
0.999
2.025
0.849
0.837
1.551
3.145
1.596
0.018
0.668
0.917
0.631
0.525
0.431
1.138
0.7
0.583
0.713
-0.050
0.714
0.996
0.984
0.557
0.488
1.51
1.089
0.616
0.926
-0.034
0.715
0.632
0.647
0.502
0.567
0.498
0.567
0.441
0.518
-0.066
29
Tunisia
Turkey
Turkey
Turkey
Turkey
United
Kingdom
United
Kingdom
United
Kingdom
United
Kingdom
Uruguay
USA
USA
USA
USA
Venezuela
Venezuela
Venezuela
Venezuela
Yemen
19952000
19801985
19851990
19901995
19952000
19801985
19851990
19901995
19952000
19952000
19801985
19851990
19901995
19952000
19801985
19851990
19901995
19952000
19952000
0.714
0.754
0.863
0.739
0.551
0.536
0.836
0.716
0.66
-0.055
0.692
0.51
0.728
0.258
0.798
1.076
1.169
0.413
0.864
-0.039
0.594
0.615
0.731
0.28
0.877
1.895
1.327
0.509
1.152
-0.016
0.846
0.723
0.728
0.419
0.772
1.201
1.058
0.609
0.91
-0.035
0.707
0.705
0.705
0.518
0.702
1.544
0.736
0.541
0.881
-0.037
1.384
1.132
0.963
0.673
1.074
1.06
0.782
0.547
0.866
-0.038
1.309
1.175
0.936
0.582
1.117
1.093
0.798
0.497
0.876
-0.038
1.224
1.29
0.894
0.991
0.995
1.17
0.677
0.751
0.898
-0.036
1.232
1.288
1.048
0.975
1.09
1.278
0.844
0.785
0.999
-0.028
0.83
0.901
0.472
0.382
0.89
1.662
0.484
0.392
0.857
0.001
1.266
1.291
1.044
0.851
1.778
1.001
1.494
1.218
1.373
0.006
1.309
1.275
1.161
0.878
1.821
1.093
1.612
1.219
1.436
0.014
1.224
1.268
0.962
1.287
1.955
1.154
1.307
1.748
1.541
0.045
1.183
1.24
1.288
1.282
2.637
1.218
1.954
1.944
1.938
-0.039
0.692
0.224
0.617
0.585
0.684
0.213
0.92
0.872
0.672
-0.054
0.715
0.276
0.462
0.283
0.996
0.265
0.72
0.44
0.606
-0.059
0.668
0.271
0.341
0.424
1.042
0.287
0.539
0.669
0.634
-0.057
0.714
0.307
0.603
0.305
1.534
0.302
0.978
0.494
0.827
-0.041
0.714
0.484
0.332
0.115
0.479
0.446
0.351
0.122
0.35
-0.079
Key:
PSP: Public Sector Performance
PSE: Public Sector Efficiency
Admin: Administration
Educ: Education
Infrast: Infrastructure
Stabil: Stabilization
* See footnote 6
30
Table A.2: Technical Efficiency (TE) of public spending in 52 countries, 1995-2000
Growth
effect
-0.133
-
Country
Algeria
Argentina (see fn 6)
Australia
Austria
Bulgaria
Canada
Chile
TE
0.363355
0.830471
0.875214
0.867984
0.466058
0.910333
0.672651
Costa Rica
Cyprus
Czech Republic
Denmark
Dominican Rep.
Egypt
El Salvador
Finland
France
Germany
Greece
Hungary
Iceland
India
Ireland
Israel
Italy
Jamaica
Jordan
0.56748
0.872052
0.653997
0.885186
0.453656
0.47918
0.500757
0.928959
0.802157
0.903573
0.686213
0.565051
0.906244
0.496827
0.727425
0.713245
0.657281
0.513118
0.589832
-0.081
-0.004
-0.060
-0.001
-0.110
-0.104
-0.098
0.010
-0.022
0.004
-0.051
-0.082
0.004
-0.099
-0.041
-0.045
-0.059
-0.095
-0.076
Korea, Rep
Lebanon
Luxembourg
Malaysia
Mexico
Namibia
0.927815
0.454145
0.791004
0.866256
0.608441
0.496673
0.010
-0.110
-0.025
-0.006
-0.071
-0.099
Netherlands
0.866055
-0.006
-0.004
-0.005
-0.107
0.005
-0.055
31
Country
New Zealand
Nicaragua
Norway
Peru
Portugal
Romania
South Africa
TE
0.7823
0.312448
0.858392
0.509566
0.706914
0.513156
0.582557
Growth
effect
-0.027
-0.146
-0.008
-0.096
-0.046
-0.095
-0.078
Spain
Sweden
Switzerland
Thailand
Trinidad & Tobago
Tunisia
Turkey
United Kingdom
Uruguay
USA
Venezuela
Yemen
0.697047
0.934942
0.965281
0.857647
0.573398
0.527729
0.461675
0.745181
0.451386
0.903279
0.372336
0.292314
-0.049
0.012
0.019
-0.008
-0.080
-0.091
-0.108
-0.036
0.004
-0.111
-0.131
-0.151
References
Afonso A., L. Schuknecht and V. Tanzi (2005): Public sector efficiency: An international
comparison, Public Choice, 123, 321-347.
Afonso A., L. Schuknecht and V. Tanzi (2006): Public sector efficiency: Evidence for new EU
member states and emerging markets, ECB Working Paper, no. 581.
Agell J., H. Ohlsson and P. S. Thoursie (2006): Growth effects of government expenditure and
taxation in rich countries: A comment, European Economic Review, 50, 211-218.
Anderson T.W. (1984): Introduction to Multivariate Statistical Analysis, 2d edition, New York:
John Wiley & Sons.
Angelopoulos K., G. Economides and P. Kammas (2007): Tax-spending policies and economic
growth: Theoretical predictions and evidence from the OECD, European Journal of Political
Economy, 23, 885-902.
Angelopoulos K. and A. Philippopoulos (2007): The growth effects of fiscal policy in Greece 19602000, Public Choice, 131, 157-175.
Baum C. F., M. E. Schaffer and S. Stillman (2006): Stata module to Extended instrumental
variables/2SLS,
GMM
and
AC/HAC,
LIML
and
k-class
regression,
http://ideas.repec.org/c/boc/bocode/s425401.html
Barro R. (1990): Government spending in a simple model of economic growth, Journal of Political
Economy, 98, S103-S125.
Barro R. and X. Sala-i-Martin (2004): Economic Growth, Second edition, The MIT Press, Cambridge,
Mass.
Cragg J.G. and S.G. Donald (1993): Testing identfiability and specification in instrumental
variables models, Econometric Theory, 9, 222-240.
De Haan J., S. Lundstrom and J.-E. Sturm (2006): Market-oriented institutions and policies and
economic growth: a critical survey, Journal of Economic Surveys, 20, 157-191.
Devarajan S., Swaroop, V., Zoo H. (1996): The composition of public expenditure and economic
growth, Journal of Monetary Economics, 37, 313-344.
Dutt P. and D. Mitra (2002): Endogenous trade policy through majority voting: An empirical
investigation, Journal of International Economics, 58, 107-133.
Folster S. and M. Henrekson (2001): Growth effects of government expenditure and taxation in rich
countries, European Economic Review, 45, 1501-20.
Gemmel N. and R. Kneller (2001): The impact of fiscal policy on long-run growth, European
Economy, 1, 98-129.
Greene W. H. (2005): Efficiency of public spending in developing countries: A stochastic frontier
approach, May 2005, mimeo.
32
Gwartney J., R. Holcombe and R. Lawson (1998): The scope of government and the wealth of
nations, Cato Journal, 18, 163-190.
Heston A., R. Summers and B. Aten (2002): Penn World Table Version 6.1, Center for
International Comparisons at the University of Pennsylvania (CICUP).
Hillman A. (2003): Public Finance and Public Policy: Responsibilities and Limitations of
Government, Cambridge University Press, Cambridge.
Kneller R., M. Bleaney and N. Gemmel (1999): Public policy and the government budget
constraint, Journal of Public Economics, 74, 171-190.
Kumbhakar S. C. and C. A. K. Lovell (2000): Stochastic Frontier Analysis, Cambridge University
Press, Cambridge
Levine R. and D. Renelt (1992): A sensitivity analysis of cross-country growth regressions,
American Economic Review, 82, 942-63.
Miller S. and F. Russek (1997): Fiscal structures and economic growth: international evidence,
Economic Inquiry, XXXV, 603-613.
Mueller D. (2003): Public Choice III, Cambridge University Press, Cambridge.
Persson T. and G. Tabellini (2003): The Economic Effects of Constitutions. The MIT Press,
Cambridge, Mass.
Tanzi V. and H. R. Davoodi (1998): Corruption, public investment and growth, in The Welfare
State, Public Investment and Growth, edited by H. Shibata and T. Ihori, Springer-Verlag, Tokyo.
Tanzi V. and L. Schuknecht (2000): Public Spending in the 20th Century: A Global Perspective,
Cambridge University Press, Cambridge.
Tanzi V. and H. Zee (1997): Fiscal policy and long-run growth, IMF Staff Papers, 44, 179-209.
Wooldridge J. W. (2002): Econometric Analysis of Cross Section and Panel Data, The MIT Press,
Cambridge, Mass.
33
Economia Aplicada, v. 14, n. 1, 2010, pp. 51-66
EFICIÊNCIA DO SETOR HOSPITALAR NOS
MUNICÍPIOS PAULISTAS
Igor Viveiros Souza*
Marislei Nishijima†
Fabiana Rocha‡
Resumo
O objetivo deste artigo é avaliar o grau de eficiência produtiva do setor público hospitalar em 366 municípios do estado de São Paulo entre os
anos de 1998 e 2003. Para tanto é utilizado o método de fronteira estocástica de produção. O modelo estimado com a forma flexível de Fourier usa
o complemento da taxa de mortalidade hospitalar como produto e o gasto
público com profissionais e o número de leitos por município como insumos. Os resultados sugerem que os municípios mais eficientes são aqueles
que contratam mais leitos de hospitais privados, que realizam o maior número de internações (efeito de economia de escala), que possuem menor
população (efeito congestionamento) e que apresentam menor tempo médio de internação.
Palavras-chave: hospitais, eficiência, fronteira estocástica, municípios.
Abstract
The purpose of this article is to assess the degree of productive efficiency of public sector hospitals in 366 municipalities of the state in São
Paulo between the years 1998 and 2003. In2003. In order to do so we use
the stochastic frontier of production approach. The model is estimated
using the flexible form of Fourier and uses the complement of the rate
of hospital mortality as output and public spending with professionals
and the number of beds per municipality as inputs. The results suggest
that the most efficient municipalities are those that hire more beds in private hospitals, the ones which perform the highest number of admissions
(economies of scale effect), the ones with smaller population (congestion
effect), and the ones which show lower average time of hospitalization.
Keywords: hospitals, efficiency, stochastic frontier, municipalities.
JEL classification: C23, H51, I10
* Fundação João Pinheiro
† EACH/USP
‡ FEA/USP
Recebido em 14 de dezembro de 2007 . Aceito em 8 de março de 2010.
52
1
Souza, Nishijima e Rocha
Economia Aplicada, v.14, n.1
Introdução
O setor de saúde brasileiro é um dos grandes demandantes de recursos públicos. Para se ter uma idéia o Governo despendeu uma média anual de 3,23%
de seu PIB em saúde entre os anos de 1999 e 2006, conforme estatísticas do
Banco Mundial. Em 2003, somente o estado de São Paulo teve um orçamento
para o setor de saúde, considerando as três esferas de Governo, de aproximadamente 13,4 bilhões de reais, o que implica um gasto per capita em torno de
R$ 346,211 .
Apesar da Constituição de 1988 ter estabelecido como modelo a descentralização na provisão e no financiameanto das ações de saúde, por meio do
Sistema Único de Saúde (SUS), visando maior eficiência no uso dos recursos,
poucos trabalhos empíricos foram realizados para avaliar o desempenho dos
municípios na gestão de tais recursos.
Deste modo, este estudo busca contribuir para a redução desta lacuna através da investigação da eficiência produtiva dos gastos públicos no setor hospitalar dos municípios do estado de São Paulo. O período de análise vai de
1998 a 2003 e abrange somente os municípios de São Paulo por questão de
disponibilidade e confiabilidade dos dados. São avaliados 366 municípios o
que corresponde a 89% do total de municípios que possuem rede hospitalar
pública ou privada contratada. Somente para esses tem-se informação disponível para todas as variáveis invetisgadas em 2003. Além disso, em 2003
a população desses 366 municípios representava 94% da população total do
Estado.
A literatura sobre fronteira estocástica aplicada especificamente a hospitais em geral trata de estimar fronteiras de eficiência de custos, em que são
utilizados grandes volumes de informação sobre preços e quantidades de insumos e produtos, além de informações específicas sobre os pacientes para
estimar relações microeconômicas. Bradford & Kleit (2001) utilizam dados
de um hospital para comparar a eficiência produtiva de dois tipos de tratamentos alternativos em pacientes cardíacos. Smet (2007) compara a eficiência entre hospitais na Bélgica, usando uma função multi-produto para avaliar seus desempenhos na presença de uma demanda estocástica (dada a sua
grande variabilidade). Bernet et al. (2008) verificam como o acesso ao financiamento de investimentos produtivos de hospitais (equipamentos e instalações) altera o grau de eficiência produtiva de uma amostra de hospitais. Finalmente, Brown III & Pagán (2006) avaliam como o sistema de saúde norte
americano Managed Care impacta sobre o custo de uma amostra de hospitais
em diferentes localidades.
Considerando-se, contudo, a ausência de informações detalhadas sobre insumos utilizados pelos hospitais públicos brasileiros a alternativa adotada foi
estimar modelos de fronteira estocástica de produção, seguindo a literatura de
eficiência produtiva de gastos públicos baseada em dados agregados, usando
informações hospitalares por município.
A literatura sobre eficiência de gastos públicos é farta em estudos que comparam a eficiência relativa de diferentes países na provisão de saúde (Evans
et al. (2000), Gupta & Verhoeven (2001),Jayasuriya & Wodon (2002), Greene (2003a), Afonso & St. Aubyn (2004), Herrera & Pang (n.d.), Herrera &
Worldwide (2005)). Afonso et al. (2003), por sua vez, constrõem um indi1 Valores de 2003, informação retirada do DATASUS.
Eficiência do setor hospitalar
53
cador de desempenho para o setor público como um todo para 23 países da
OCDE. Esse indicador é composto por 7 sub-indicadores que captam a qualidade das funções administrativas, os resultados em educação, os resultados
em saúde, a qualidade da infra-estrutura, o grau de desigualdade, a estabilidade econômica e o desempenho econômico.
Especificamente para o Brasil, Sousa et al. (2005) que buscam calcular scores de eficiência sobre os serviços gerais dos municípios brasileiros e de Marinho (2001), que analisa a eficiência da prestação de serviços de saúde em
municípios do Rio de Janeiro.
A fim de estimar a eficiência dos hospitais será utilizado o método de Fronteira Estocástica (FE), mais especificamente o modelo implementado por Battese & Coelli (1995). Esse método permite a decomposição das variações do
desempenho dos municípios na provisão de serviços hospitalares em relação
à fronteira em variações na eficiência técnica e em choques puramente aleatórios. Além disso, permite a investigação empírica objetiva de prováveis
variáveis explicativas do termo ineficiência. O uso desse método se constitui
num diferencial analítico em relação aos trabalhos anteriormente listados que
majoritariamente utilizam metodologias não paramétricas, quais sejam, o Free
Disposable Hull (FDH) e a Data Envelopment Analysis (DEA).2
Além desta introdução, o artigo apresenta três seções. Na segunda seção,
apresenta-se a base teórica e quantitativa sobre a qual este estudo repousa. Na
terceira seção discute-se a base de dados e os principais resultados obtidos das
estimativas. Por fim, na quarta seção resumem-se as principais conclusões.
2
Metodologia
Para medir eficiência produtiva a teoria utiliza uma medida de distância entre
o ponto de operação da unidade tomadora de decisão (neste caso, o município)
e a fronteira tecnológica, medida que pode ser dividida em eficiência técnica e
alocativa.3 O conceito de eficiência técnica, que remonta a Debreu (1951), foi
consolidado por Farrel (1957), que, adicionalmente desenvolveu o conceito de
eficiência alocativa. O conceito de eficiência técnica diz respeito estritamente
às relações entre as quantidades produzidas de produto e as quantidades de
fatores utilizados na produção. Quando a quantidade produzida por uma
firma, dada uma combinação de fatores, fica aquém do máximo possível de
ser atingido com aquela combinação tem-se a caracterização da ineficiência
técnica.
A eficiência alocativa - que se refere à escolha ótima da proporção de insumos dado o vetor de preços - não será mensurada neste estudo. De acordo
com Farrel (1957), a ineficiência alocativa deve ser estudada quando se objetiva verificar se as firmas agem dentro de um arcabouço lucro-maximizador
ou custo-minimizador. Como o objetivo aqui é apenas a obtenção de uma relação técnica da função de produção e, além disso, não se dispõe de informações
2 Gupta & Verhoeven (2001), por exemplo, usam uma abordagem FDH. Afonso & St. Aubyn
(2004), Herrera & Pang (n.d.) e Herrera & Worldwide (2005) usam tanto DEA quanto FDH. Uma
exceção do ponto de vista metodológico é Greene (2003b) que estima uma fronteira estocástica
para avaliar a eficiência dos gastos em saúde usando os dados do World Health Organization
(WHO).
3 A literatura que trata da eficiência do setor público seguiu de perto a literatura que trata da
eficiência do setor privado e, assim, países, estados e municípios são considerados analiticamente
como iguais às firmas.
54
sobre preços, pois se trata de serviços prestados pelo setor público, estima-se
apenas um modelo de fronteira de produção. 4
Para mensurar a eficiência técnica, a literatura nos apresenta técnicas paramétricas e não paramétricas. Dentre as técnicas paramétricas, a mais difundida, e com propriedades amplamente pesquisadas, é a fronteira estocástica
de produção. Já dentre as técnicas não paramétricas, tem-se o Free Disposal
Hull (FDH) e Data Envelopment Analisys (DEA). Neste estudo opta-se pelo uso
de da fronteira estocástica, pois ela apresenta algumas propriedades que não
estão disponíveis nos métodos não paramétricos de acordo com Coelli et all
(2005). São elas: (1) a possibilidade de se realizar testes de hipóteses sobre
os parâmetros das variáveis explicativas, (2) a possibilidade de se incluir variáveis de controle para explicar a ineficiência técnica em apenas um estágio
e (3) permite a presença de ruídos aleatórios no ambiente em que a unidade
tomadora de decisão opera5 .
Embora as fronteiras estocásticas apresentem as vantagens descritas anteriormente, exigem a necessidade de imposição de uma forma funcional a
priori e de hipóteses acerca da distribuição do termo de ineficiência6 . Estes
são custos que a metodologia DEA não impõe uma vez que tal técnica simplesmente mapeia os pontos dados existentes e traça, sobre os mesmos, um
envoltório convexo que contém todos os pontos observados. Dessa forma, a
única restrição que o DEA impõe é a convexidade dos conjuntos de produção.
Já o FDH, assim como o DEA, não apresenta as propriedades descritas para
as fronteiras estocásticas. Adicionalmente, esta técnica descarta a hipótese de
convexidade dos conjuntos de produção.
As características positivas supracitadas das fronteiras estocásticas de produção fundamentam a escolha de tal metodologia. No entanto, na tentativa de
atenuar os efeitos da imposição de uma forma funcional a priori que a escolha
de tal método traz consigo, optou-se pelo uso da forma funcional Flexível de
Fourrier7 .
Não existe na literatura um consenso acerca de qual método é superior.
Evidências apontam que ambos produzem, em muitos casos, resultados pouco
robustos quando comparados entre métodos. Uma dessas evidências pode
ser vista em Jacobs (2000). Em tal estudo, examinam-se as propriedades das
fronteiras estocásticas e do DEA, em diversas especificações, para o cálculo
de eficiência hospitalar no Reino Unido. Seus resultados apontam para uma
robustez interna dos métodos, no tocante aos rankings de eficiência, mas não
entre métodos.
Conforme exposto, as fronteiras estocásticas de produção permitem a decomposição do resíduo em dois componentes: um relativo à ineficiência e
outro relativo ao choque puramente aleatório, chamado de erro idiossincrático. Esse método foi introduzido na literatura econômica simultaneamente
por Aigner et al. (1977) e Meeusen & Van de Broeck (1977) numa versão crosssection e sua especificação para dados em painel feita por Pitt & Lee (1981) é
descrita em (1).
4 Apesar disso, a metodologia aqui aplicada permite afirmar que se uma unidade tomadora
de decisão é tecnicamente ineficiente, então, ela não maximiza seu retorno (Kumbhakar e Lovell,
2000).
5 Métodos não paramétricos incorporam esses ruídos no valor da ineficiência técnica.
6 Maiores detalhes serão dados a seguir.
7 Propriedades da forma Flexível de Fourrier serão apresentadas mais adiante.
k
ln yit = β0 + f (βk , xit
) + vit − uit ,
i = 1, . . . , I; k = 1, . . . , n; t = 1, . . . , T
55
(1)
em que
ln yit é o logaritmo da quantidade produzida pelo município i no período t;
β0 é o intercepto da equação;
f (βk , xik ) é a forma funcional adequada;
βk é o vetor de coeficientes tecnológicos;
k
é o vetor de insumos utilizado na produção pelo município i no período t;
xit
k
e uit e com distribuição
vit é o choque aleatório não correlacionado com xit
2
N (0, σv );
uit é o termo de ineficiência não negativo do município i no período t também
k
.
não correlacionado com xit
Para a estimação dos coeficientes tecnológicos das fronteiras estocásticas
de produção são empregados os estimadores de máxima verossimilhança8 ,
sendo necessário assumir hipóteses explícitas sobre a distribuição assimétrica
do termo de ineficiência, uit .
Aqui se utiliza o modelo proposto por Battese & Coelli (1995) que desenvolveram uma metodologia de estimação para painéis desbalanceados utilizando uma distribuição normal-truncada. A escolha desta distribuição resulta do fato de ser mais flexível do que outras formas funcionais mais simples
como a semi-normal ou a exponencial.9
O modelo especificado por Battese & Coelli (1995) também permite a variação da ineficiência técnica ao longo do tempo, que pode ser modelada usandose características dos municípios que variam ao longo do tempo. A equação
(2), em que o termo de ineficiência segue uma distribuição normal truncada
uit ∼ N + (zit δ, σu2 ), mostra o termo zit δ que representa a média da ineficiência, sendo esta composta pelo vetor de variáveis específicas dos municípios
zit e δo vetor de coeficientes associados a essas variáveis. Assim, o termo ineficiência é modelado como uma média condicional de um conjunto linear de
covariadas pré-especificadas. O termo wit é uma variável correspondente à
truncagem de uma normal com média zero e variância σu2 no ponto (− zit δ),
ou seja, wit ≥ (− zit δ).
uit = zit δ + wit
(2)
Conforme, já explicitado, escolheu-se a forma funcional Flexível de Fourier,
que consiste numa aproximação global que inclui os termos padrões de uma
trans-log mais os termos trigonométricos de Fourier, sua especificação é dada
pela equação (3) abaixo.10
8 Sobre metodologia de estimação de fronteiras estocásticas ver Kumbhakar & Lovell (2000).
9 Para estimações com distribuição semi-normal ou exponencial ver Aigner, Lovell e Schmidt
(1977) e para estimações com distribuições gama ver Greene (2000).
10 Foram feitos testes estatísticos para justificar a escolha da forma flexível de Fourier que serão
apresentados e discutidos na seção 3 que trata dos resultados das estimativas.
56
ln yit =β0 + βt t + βtt t 2
n
X
k
βk ln xit
+
k=1
+
+
n
X
k
βkt ln xit
t+
k=1
n X
n
X
k=1 q=k
n
XX
j
k
βjk ln xit ln xit
j≤k k=1
n
X
[φ k cos(hk ) + ωk sen(hk )]
k=1
(3)
[φ kq cos(hk + hq ) + ωkq sen(hk + hq )] + vit − uit ,
i = 1, . . . , I; k = 1, . . . , n; t = 1, . . . , T .
Sendo assim é possível, a partir da estimação da equação acima, testar a
adequação tanto de uma trans-log, quanto de uma Cobb-Douglas.
De acordo com Berger & Mester (1997), esta forma funcional é uma aproximação global pelo fato dos termos cos(hk ), sen(hk ), cos(hk + hq ) e sen(hk +
hq )serem mutuamente ortogonais no intervalo [0;2π], aproximando a função
a ser estimada de seu verdadeiro caminho.11 Segue-se a sugestão dos autores para o corte de 10% de cada cauda do intervalo [0, 2π] evitando-se, desta
maneira, problemas de aproximação nas fronteiras do intervalo. Como conseqüência, os termos hk e hq são calculados por (4).
hk = 0, 2 × π − µ × a + µ × xk .
(4)
intervalo12
Sendo [a,b] o
transformado em radianos e µ ≡ (0, 9 × 2π − 0, 1 ×
2π)/(b − a). Portanto, para obter as estimativas dos parâmetros da equação
acima por máxima verossimilhança, basta maximizar a função de verossimilhança deεit . Para tal, é preciso conhecer a distribuição de εit , sendo εit =
vit − uit .
Supondo vit e uit independentes e com suas respectivas distribuições conhecidas, a distribuição conjunta vit e uit é dada por f (vit , uit ) = f (uit ) × f (vit )
e desde que εit = vit − uit tem-se quef (vit , uit ) = f (uit + εit , uit ) = f (εit , uit ). A
função de distribuição conjunta assume a expressão dada em (5).
f (ε, u) =
e
− 21
(
(u−µ∗ )2
σ∗2
"
+
(ε−zδ)2
σv2 +σu2
(
)
2πσu σv Φ (zδ/σu )
#)
(5)
onde:
µ∗ =
σv2 zδ − σu2 ε
σv2 + σu2
σ2σ2
σ∗2 = 2u v 2
σv + σu
e Φ(zδ/σu ) é a função de distribuição acumulada da normal padrão avaliada
no ponto (z δ/σu ).
Para obter a distribuição
R ∞ do erro idiossincrático ε, integra-se a função (5)
com respeito a u: f (ε) = 0 f (ε, u)du, para obter (6).
11 O que lhe confere a flexibilidade destacada anteriormente.
12 São respectivamente os valores mínimo e máximo de xk.
f (ε) = q
e
(
− 21
(ε+zδ)2
σv2 +σu2
(
)
)
2π σv2 + σu2 [Φ (zδ/σu ) Φ (µ∗ /σ∗ )]
−∞ ≤ ε ≤ ∞
,
57
(6)
As estimativas dos parâmetros são obtidas a partir maximização do logaritmo da função de verossimilhança dada por (7).
N
L(β, δ, σu , σv ) = −
N
T
i
yit − xit β + zit δ
1 XX
ln 2π + ln σs2 X
Ti −
2
2
σs2
i=1
i=1 t=1
−
Ti h
N X
X
i=1 t=1
!2
i
ln Φ(dit ) − ln Φ(dit∗ )
(7)
onde:
dit =
zit δ
√
σs2 γ
µ∗it = (1 − γ)zit δ − γ(yit − xit β)
γ≡
σu2
.
σs2
dit∗ =
µ∗
p it
σs γ(1 − γ)
σs2 ≡ σu2 + σv2
A parametrização (7) permite verificar a relevância do termo de ineficiência. Se o termo γ converge para 1 o termo de ineficiência predomina o erro idiossincrático e se γ converge para zero a ineficiência torna-se irrelevante para
explicar o termo ε. No último caso o emprego da técnica de FE não traz ganhos em relação ao método de mínimos quadrados ordinários13 . Deste modo,
caso não se rejeite a hipótese de ineficiência técnica, obtêm-se as estimativas
destas a partir da distribuição condicional de uit em εit dada pela expressão
(8).
µ Φ σ∗ − σ∗
2
∗ µ E(e uit |εit ) = e (−µ∗ +σ∗ /2)
(8)
Φ σ∗
∗
A partir da estimação da ineficiência é possível calcular os scores de eficiência que permitem ordenar os municípios de acordo com seu desempenho
relativo. A preocupação, contudo, aqui é com os determinantes de ineficiência
e não com a distância dos mesmos em relação à fronteira.
3
Dados e resultados econométricos
A amostra é composta por 366 municípios paulistas em painel abrangendo os
anos de 1998 a 2003, o que corresponde a 89% do total de municípios com
rede hospitalar disponível. Além disso, a população desses 366 municípios
corresponde a 94% dos residentes do Estado de São Paulo em 2003.
13 A aplicação de mínimos quadrados parte do pressuposto de que não existe correlação entre
o termo residual e a matriz de variáveis independentes X. Se esta hipótese não puder ser sustentada, então torna-se necessário o uso de estimadores de efeito fixo ou mínimos quadrados de dois
estágios.
58
Tabela 1: Variáveis utilizadas
Variáveis
Obs.
Média
Desvio
padrão
Mín.
Máx.
Complemento mortalidade hospitalar
Pessoal
Leitos
Tendência (T)
População
Internações
Gestão
PMDB
PSDB
PT
PFL
PTB
Demais partidos
Internações hospitais públicos
Internações hospitais privados
Internações hospitais universitários
Internações cirurgia clínica
Internações obstetrícia
Internações clínica médica
Internações pediatria
Outras internações
Gasto público
Permanência
2135
2129
2135
2135
2135
2135
2135
2135
2135
2135
2135
2135
2135
2135
2135
2135
2135
2135
2135
2135
2135
2134
2129
97.2988
440191
274.87
3.46042
95696.8
6202.09
0.32758
0.16815
0.27681
0.06464
0.13068
0.10258
0.25714
0.21261
0.76802
0.01937
0.15145
0.19290
0.49633
0.12503
0.03429
268.59
6.75514
1.85975
2886995
1345.44
1.70676
558698
28076.5
0.44198
0.37409
0.44753
0.24594
0.33713
0.30348
0.43716
0.39081
0.39903
0.10904
0.11261
0.13439
0.20742
0.08572
0.11224
510.56
23.2544
76.92
23.57
6
1
3320
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
63.36
1.3
100
6, 77 × 107
28091.6
6
1, 07 × 107
561530
1
1
1
1
1
1
1
1
1
0.93467
0.61576
1
1
0.75521
1
20722.0
956.4
Fonte: DATASUS e IBGE
O critério de seleção foi a existência de rede hospitalar no município ( SUS
e particular contratado14 ) e a disponibilidade de informações. Supõe-se que
pacientes de localidades onde não exista rede hospitalar se desloquem para
aqueles municípios que possuem hospital, fenômeno este que também pode
ocorrer devido às especialidades disponíveis em cada município.
Para estimação da função de produção hospitalar dos municípios paulistas considerou-se como produto (ly) o logaritmo da variável complemento da
taxa de mortalidade hospitalar por município (100 - taxa de mortalidade hospitalar)15 . Ainda que por questões de indisponibilidade de dados siga-se a
literatura de eficiência de gastos públicos em saúde, esta utiliza como medida
de produto a mortalidade infantil e a expectativa de vida. Como aqui a preocupação é com a eficiência dos hospitais de procura-se utilizar uma medida
que capte a qualidade do atendimento hospitalar do SUS. Quanto menos pessoas morrem nos hospitais teoricamente melhor é a qualidade do atendimento
hospitalar.
Para mensurar as variáveis explicativas, trabalho (lw) e capital (lk), foram
consideradas como proxies respectivamente, o valor dos serviços de profissionais empregados nas internações hospitalares e o número de leitos contratados pelo SUS em cada município. A variável de natureza monetária (lw) foi
deflacionada pelo IPCA e está expressa em reais de 1998.
Todas as informações sobre hospitais foram obtidas junto ao Datasus e as
informações complementares foram obtidas junto ao IBGE e estão resumidas
na Tabela 1.
14 O SUS aluga a capacidade produtiva do setor privado.
15 Usou-se o completo para tornar mais fácil a leitura das estimativas da função de produção.
59
A Tabela 1 apresenta inicialmente as variáveis utilizadas na função de produção: taxa de sobrevivência, que corresponde ao complemento da variável
taxa de mortalidade hospitalar; o valor de gastos com profissionais ligados diretamente às internações como proxy do insumo trabalho, o número de leitos
(do SUS e contratados) como proxy do insumo capital e uma variável que descreve uma tendência no tempo (T). As demais variáveis são usadas como “controle”16 e , assim, aparecem como variáveis explicativas do termo ineficiência
estimado. Foram usadas as seguintes variáveis explicativas por município:
população; número de internações; gestão, variável com valores compreendidos entre 0 e 1 que descreve o percentual de internações realizadas sob gestão
plena do município17 ; dummies de partidos que estavam à frente da prefeitura
durante os anos de 1998 a 2003; variáveis que descrevem o percentual das internações que ocorreu em leitos públicos (do próprio SUS), privados contratados ou em hospitais universitários, portanto, variando entre 0 e 1; variáveis
que descrevem os percentuais de internações realizadas em cirurgia clínica,
obstetrícia, clínica médica, pediatria e demais tipos de internação18 ; o valor
médio do gasto público por internação; e o tempo médio de permanência na
internação.
Considerando que as variáveis usadas como insumos não estão em valores
per capita ou por quantidade de internações é preciso controlar pelo tamanho da população e pelo número de internações para tornar compatíveis os
resultados.
As dummies de partido e a variável de gestão municipal plena buscam captar efeitos da forma de gestão sobre a eficiência produtiva do setor, ou seja,
verificar se a gestão descentralizada nas prefeituras é mais eficiente que a gestão mais central por parte dos Estados.
As variáveis que descrevem os percentuais de internações em leitos públicos, privados e universitários buscam inferir se existem efeitos diferenciados
entre a provisão de internações públicas diretamente pelo setor público e a
produzida pelo setor privado. Já as variáveis de percentuais de tipos de internação buscam reduzir o grau de homogeneidade da informação sobre taxa de
mortalidade hospitalar, uma vez que traz informações sobre o tipo de tratamento recebido na internação, controlando assim por diferenças possíveis de
tratamentos envolvidos na internação, como por exemplo diferentes graus de
complexidade.
A variável valor médio da internação busca controlar gastos diferenciados
sobre a taxa de sobrevivência hospitalar e a variável tempo médio de internação os tempos diferenciados dos tratamentos. Além disso, um termo de
tendência foi incluído no vetor Z a fim de verificar como a média da eficiência técnica se comporta no tempo, isso é, se os municípios estão, em média,
convergindo ou divergindo da fronteira.
A Tabela 2 mostra as estimativas19 da fronteira estocástica para os hospi16 Colocamos entre aspas para lembrar que estas variáveis não entram diretamente na função
de produção que em nosso caso é utilizada a forma flexível de Fourier. A especificação colocandoas como controle, entretanto, não alterou a direção (sinais dos coeficientes) dos valores das estimativas.
17 A alternativa compreende internações realizadas sob gestão estadual ou composição de gestão em que o município gere apenas a atenção básica em saúde.
18 Note que tanto estas variáveis como as anteriores equivalem a um conjunto de dummies
centradas, ou seja, seus valores somam 1, mas não são compostas exclusivamente de zeros e uns.
19 As estimativas foram obtidas utilizando o pacote Stata.
60
tais paulistas.
O modelo (1) descreve o modelo inicialmente considerado para a análise.
Os modelos de (2) a (5) atestam a robustez dos resultados obtidos, uma vez
que consistem nas estimativas do modelo (1) com a retirada consecutiva dos
“controles” da ineficiência.
Na primeira parte da tabela estão os resultados das estimativas da função de produção usando a forma flexível de Fourier. Assim, além das variáveis de trabalho (Pessoal) e capital (Leitos) tem-se o produto do logaritmo do
capital e do trabalho (Pessoal×leitos), o tempo ao quadrado (Tendência2 ), o
quadrado do logaritmo do trabalho (Pessoal2 ) , o quadrado do logaritmo do
capital (Leitos2 ), o tempo vezes o logaritmo do trabalho (Tempo×pessoal), o
tempo vezes o logaritmo do capital (Tempo×leitos) que são os termos tradicionais de uma função trans-log. Os termos f_cos_w, f_cos_k, f_sen_w, f_sen_k,
f_cos_2w, f_cos_2k, f_cos_wk, f_sen_2w, f_sen_2k e f_sen_wk são os termos
de Fourier.
Os resultados da Tabela 2 sugerem, como esperado, que capital e trabalho
afetam positivamente a produção de sobrevivência hospitalar. Atestam também que o capital ao quadrado, que mede uma grande capacidade produtiva
instalada, proporciona menor taxa de mortalidade hospitalar. As significâncias estatísticas dos vários termos de Fourier atestam a escolha adequada desta
forma funcional.
Na Tabela 3 são apresentados os coeficientes estimados das variáveis explicativas da ineficiência. Embora as estimativas da função de produção e
da ineficiência tenham sido feitas conjuntamente, optou-se por apresentar os
resultados separadamente para facilitar a análise.
Inicialmente é importante observar que a hipótese de inexistência da ineficiência pode ser rejeitada. A estimativa gama é muito próxima da unidade,
indicando a predominância do termo ineficiência20 sobre a variância do erro
idiossincrático.
Testes de adequação das formas funcionais também foram realizados, indicando que a escolha da forma funcional Flexível de Fourier é adequada (Tabela 4). Outro teste reportado nessa mesma tabela é o teste conjunto nos coeficientes que explicam a ineficiência. O objetivo de tal teste é verificar se a
ineficiência é melhor descrita por uma constante do que condicionada a características individuais que poderiam explicá-las. Assim, as diferenças observadas entre firmas na ineficiência, seriam aleatórias. Esse teste também
apresenta o resultado favorável do teste à modelagem da ineficiência. Assim
sendo o conjunto de características descrito anteriormente parece ser eficaz
na determinação do valor da ineficiência técnica.
Os resultados das estimativas do termo ineficiência mostram que quanto
maior a população do município maior é a ineficiência do setor hospitalar.
Esse resultado pode refletir um efeito congestionamento. As indivisibilidades
características da produção de certos serviços de saúde (por exemplo, serviços
de alta complexidade) restringiriam a provisão desses serviços aos grandes
centros urbanos, implicando um custo de congestionamento. Já com relação
ao número de internações realizadas por um município quanto maior é este
20 A estatística LR não pode ser calculada para o teste conjunto de todos os coeficientes que
compõem o termo de ineficiência porque não houve convergência dados os valores iniciais. Seus
valores críticos estão em Koode e Palm (1986). Entretanto, estimando por MQO, o teste de Coelli
comprova a ineficiência dos resíduos.
61
Tabela 2: Estimativas da função de produção
Pessoal
Leitos
Tendência
Tendência2
Pessoal×leitos
Pessoal2
Leitos2
Tempo×pessoal
Tempo×Leitos
F_cos_w
f_sen_w
f_cos_k
f_sen_k
f_cos_wk
f_sen_wk
f_cos_2w
f_sen_2k
Constante
(1)
(2)
(3)
(4)
(5)
0.0135∗∗∗
(0.00453)
0.0189∗∗∗
(0.00453)
0.0147∗∗∗
(0.00452)
0.0187∗∗∗
(0.00452)
0.0156∗∗∗
(0.00453)
0.0179∗∗∗
(0.00453)
0.0170∗∗∗
(0.00448)
0.0178∗∗∗
(0.00448)
0.0196∗∗∗
(0.00437)
0.0188∗∗∗
(0.00437)
0.000234
(0.00157)
0.000409
0.00058
(0.00156)
(0.00155)
−0.000144
−0.000158∗
(0.00009)
−0.00576∗∗∗
(0.00092)
9.83 × 10−5
(0.00033)
0.00517∗∗∗
(0.00074)
−0.000148
(0.00009)
−0.00578∗∗∗
(0.00093)
(0.00009)
−0.00572∗∗∗
(0.00093)
0.000274
0.000198
0.000149
(0.00033)
0.00506∗∗∗
(0.00075)
0.000139
(0.00021)
(0.00033)
0.00504∗∗∗
(0.00075)
0.000179
(0.00021)
(0.00033)
0.00503∗∗∗
(0.00074)
0.000158
(0.00021)
(0.00027)
−1.017∗∗∗
(0.29700)
1.088∗∗∗
(0.31000)
0.0273∗∗
(0.01300)
−0.000513∗
(0.00027)
−1.063∗∗∗
(0.29500)
1.136∗∗∗
(0.30800)
0.0297∗∗
(0.01280)
−0.000487∗
(0.00027)
−1.082∗∗∗
(0.29300)
1.156∗∗∗
(0.30600)
0.0308∗∗
(0.01270)
−0.0322
−0.0335
−0.0342
−0.000391
(0.02130)
−0.0522∗
(0.03020)
−0.0784∗∗∗
(0.02580)
−0.0699∗∗∗
(0.02460)
0.0668∗∗∗
(0.02480)
4.564∗∗∗
(0.02530)
0.000884
(0.00157)
−0.000154∗
(0.00009)
−0.00566∗∗∗
(0.00093)
−0.00015
0.000758
(0.00157)
(0.02110)
−0.0533∗
(0.03000)
−0.0816∗∗∗
(0.02550)
−0.0727∗∗∗
(0.02440)
0.0702∗∗∗
(0.02460)
4.559∗∗∗
(0.02510)
(0.02100)
−0.0543∗
(0.02980)
−0.0833∗∗∗
(0.02530)
−0.0739∗∗∗
(0.02410)
0.0719∗∗∗
(0.02430)
4.557∗∗∗
(0.02500)
0.000134
(0.00020)
−0.000480∗
(0.00026)
−1.167∗∗∗
(0.29300)
1.249∗∗∗
(0.30600)
0.0341∗∗∗
(0.01260)
−0.0379∗
(0.02100)
−0.0594∗∗
(0.02960)
−0.0879∗∗∗
(0.02520)
−0.0796∗∗∗
(0.02410)
0.0778∗∗∗
(0.02410)
4.552∗∗∗
(0.02500)
(0.00009)
−0.00536∗∗∗
(0.00090)
−0.000106
(0.00032)
0.00459∗∗∗
(0.00073)
0.000117
(0.00020)
−0.000479∗
(0.00026)
−1.278∗∗∗
(0.29300)
1.368∗∗∗
(0.30600)
0.0311∗∗
(0.01260)
−0.0386∗
(0.02190)
−0.0615∗∗
(0.03010)
−0.0929∗∗∗
(0.02580)
−0.0849∗∗∗
(0.02460)
0.0775∗∗∗
(0.02440)
4.547∗∗∗
(0.02520)
Obs: ***, ** e * implicam significância estatística aos níves de 1%, 5% e 10%,
respectivamente.
número menor é a ineficiência hospitalar, sugerindo que municípios que possuem maior escala de produção são mais eficientes.21
Os coeficientes negativos das variáveis internações em leitos privados e internações em leitos universitários significam que municípios onde predominam hospitais com estas formas de gestão são mais eficientes (o sinal negativo
implica que a variável tem um impacto de reduzir a ineficiência). Em relação
à significância dos leitos em hospitais privados contratados pelo SUS, pode-se
pensar que a provisão dos bens de saúde pelo setor privado tende a ser mais
eficiente do que a oferta direta pelo setor público.
Como discutido anteriormente, a justificativa para a inclusão de dummies
de partido está no fato da saúde, atualmente, ser um serviço público descentralizado. Dessa forma, as prefeituras administram boa parte dos recursos repassados pelo SUS. Acredita-se que prefeituras administradas por um mesmo
partido partilhem de práticas comuns de gestão resultantes, entre outras coisas, de programas partidários de Governo. Adicionalmente, para uma melhor
inferência sobre gestão é necessário considerar se a gestão é municipal plena
ou estadual. A insignificância estatística de todas estas variáveis para expli21 Para uma avaliação do papel do congestionamento e da presença de economias de escala
na provisão de serviços públicos municipais de saúde e educação ver Mendes e Sousa (2006).
O trabalho de Mendes e Sousa avalia essas questões olhando a função de demanda por serviços
públicos e neste sentido é complementar ao presente estudo que enfatiza o lado da oferta.
62
Tabela 3: Determinantes da ineficiência
População
Internações
Tendência
Internações hospitais
púbicos
Internações hospitais
universitários
PMDB
PT
PFL
PTB
Demais
Gestão
Internações obstetrícia
Internações clínica
médica
Internações pediatria
Outras internações
Gastos públicos
Permanência
Constante
ilgtgamma Cons
lnsigma2 Cons
σ2
γ
σu2
σv2
Observações
(1)
(2)
(3)
(4)
(5)
0.0960∗∗∗
(0.02090)
−0.113∗∗∗
(0.02140)
−0.0151∗∗
(0.00767)
−0.181∗∗∗
(0.05230)
−0.561∗
(0.32600)
0.0922∗∗∗
(0.02540)
−0.103∗∗∗
(0.02500)
0.103∗∗∗
(0.02550)
−0.123∗∗∗
(0.02530)
0.0840∗∗∗
(0.02060)
−0.111∗∗∗
(0.02110)
0.101∗∗∗
(0.02200)
−0.126∗∗∗
(0.02230)
−0.0032
(0.00692)
−0.00371
(0.00845)
−0.00717
−0.00718
(0.07070)
(0.07280)
−0.223∗∗∗
(0.06710)
− 686
(0.45700)
0.00389
−0.199∗∗∗
− 608
(0.38100)
0.0131
(0.04620)
− 66
(0.03600)
−0.0829∗
(0.04830)
0.0186
24
(0.03270)
(0.03690)
0.000294
(0.03770)
0.00543
(0.03990)
0.0148
(0.03980)
23
−0.242∗∗∗
(0.46400)
−0.0879
(0.05940)
0.0128
(0.04750)
0.0306
(0.04780)
0.0128
(0.03250)
(0.03640)
0.0278
0.0253
(0.02660)
− 177
(0.13200)
− 164
(0.12700)
−0.471∗∗∗
(0.16000)
−0.670∗∗∗
(0.21700)
− 678
0.0187
0.0374
− 124
(0.00711)
(0.04340)
(0.02870)
(0.02560)
(0.00767)
(0.03190)
(0.13700)
− 134
(0.12300)
−0.469∗∗∗
(0.17500)
−0.0724
(0.14000)
−0.00468
(0.04170)
0.120∗∗∗
(0.03630)
− 384
− 324
(0.28100)
4.036∗∗∗
(0.25200)
−5.556∗∗∗
(0.23200)
(0.20700)
4.075∗∗∗
(0.33600)
−5.522∗∗∗
(0.31600)
0.0038625
0.9826323
0.0037954
0.0000671
2129
0.003996
0.9832849
0.003929
0.0000668
2129
−0.547∗∗∗
−0.385∗∗
−0.516∗∗∗
0.0051577
0.9870321
0.0050908
0.0000669
2129
0.0046625
0.9854348
0.0045946
0.0000679
2190
0.0042371
0.9848355
0.0041729
0.0000643
2190
(0.20100)
4.332∗∗∗
(0.26700)
−5.267∗∗∗
(0.24800)
(0.15800)
4.214∗∗∗
(0.27000)
−5.368∗∗∗
(0.24900)
(0.17100)
4.174∗∗∗
(0.24400)
−5.464∗∗∗
(0.22100)
Obs: ***, ** e * implicam significância estatística aos níves de 1%, 5% e 10%, respectivamente.
car o termo de ineficiência sugere que os resultados eleitorais não alteram o
padrão de eficiência produtiva do setor hospitalar para o período estudado.
Além disso, para o caso particular da variável de gestão municipal plena, parece não existirem ganhos de eficiência decorrente da descentralização da provisão de bens de saúde.
O termo de tendência nos coeficientes de ineficiência indica que os municípios ao longo do tempo vão melhorando sua eficiência, isto é, vão convergindo
para a fronteira.
As variáveis que descrevem os percentuais por tipos de internação - clínica
cirúrgica, que é a categoria base, obstetrícia, clínica médica, pediatria e outros
(cuidados crônicos prolongados, psiquiatria, tisiologia e reabilitação) indicam
internações de pediatria e outros motivos de internação são estatisticamente
significantes para redução da ineficiência em relação à clínica cirúrgica. Este
resultado era esperado uma vez que a pediatria atende somente crianças en-
63
Tabela 4: Testes para a forma funcional da função de produção e modelagem
da ineficiência
Testes para a Forma Funcional
Estimativas por
MQO
Cobb× Translog
Douglas
Translog × Fourier
MV
Conclusão:
LR
p-valor
LR
p-valor
502,35
0,00
126,77
0,00
LR
p-valor
LR
p-valor
188,17
0,00
51,44
0,00
teste est.
p-valor
820.5996
0,000
Teste para a modelagem de eficiência*
Rejeita Cobb-Douglas em
favor da Translog
Rejeita Translog em favor
de Fourier
Rejeita não modelagem
de eficiência
* Distribuição Qui-quadrado mista. Valores críticos em Koode e Palm (1986)
quanto as demais modalidades atende a todas as idades, o que aumenta a probabilidade de morte durante o tratamento, pois esta aumenta com o avanço da
idade. Pela composição da categoria outras internações também se explica o
fato de estas terem menos impacto sobre a mortalidade hospitalar, resultando
em maior eficiência para a produção.
Por fim, a variável gasto médio por internação não se mostra estatisticamente significante para explicar o termo de ineficiência e a variável tempo
médio de internação sugere que quanto maior o tempo de internação mais ineficiente é o município. Este último resultado, num primeiro momento poderia
sugerir que uma redução no tempo de internação poderia ser ideal, entretanto,
deve ser tomado com cautela, pois se deve ter em conta que um tempo maior
de internação pode indicar um estoque de saúde muito baixo, de modo que o
risco de morte durante a internação é alto.
4
Conclusões e considerações
O objetivo deste trabalho é avaliar a eficiência técnica produtiva do setor hospitalar nos municípios do Estado de São Paulo utilizando como variável dependente a taxa de sobrevivência hospitalar (o complemento da taxa de mortalidade hospitalar) e os gastos com profissionais de saúde empregados nestas
internações e o número de leitos disponíveis em cada município como variáveis explicativas/insumos.
Os resultados obtidos, entretanto, devem ser analisados considerando que
as informações hospitalares utilizadas são definidas por local de internação e
não por moradia. Além disso, deve-se ter em conta o alto grau de agregação
das informações e o fato de os modelos estimados não estabelecerem uma
relação de causalidade entre as variáveis e a ineficiência.
As estimativas indicam que os municípios com maior população são mais
ineficientes, neste caso a população é a residente e as demais informações são
de local de internação. Também indicam que são mais eficientes os municípios que: contratam um maior percentual de leitos de hospitais privados e
de leitos universitários; possuem maior escala de atendimentos, ou seja, com
64
maior número de internações por ano; atendem internação por pediatria e
por uma categoria denominada outras internações (cuidados crônicos prolongados, psiquiatria, tisiologia e reabilitação); e que apresentam menor tempo
médio de internação.
Como o produto considerado é a taxa de sobrevivência hospitalar era esperado que internações por pediatria e outras internações se mostrassem mais
eficientes, pois têm impactos diretos sobre a taxa de mortalidade. Sendo assim, estas variáveis podem ser vistas como controles para o grau de complexidade do tratamento oferecido na internação. Com relação à significância
dos leitos privados e universitários pode-se pensar que a provisão pelo setor
privado parece mais eficiente do que a oferta direta pelo setor público. A
redução do tempo médio de internação, por sua vez, não deve ser vista como
promotora da eficiência, pois pacientes que ficam internados por muito tempo
devem na média apresentar capital-saúde baixo e alto risco de morte.
O fato de o número de internações estar correlacionado com o termo ineficiência com sinal negativo sugere existirem ganhos de escala na produção de
qualidade de atendimento hospitalar medida pela taxa de sobrevivência hospitalar. De fato, os municípios que se mostraram menos ineficientes possuem
número de internações anuais muito baixos comparados com os demais.
O fato de a população estar correlacionado com o termo de ineficiência
com sinal positivo sugere existirem custos de congestionamento.
Por último, variáveis de gestão (partidos políticos e percentual de gestão
municipal plena) foram testadas e se mostraram não significantes. Essa ausência de correlação sugere que a qualidade dos produtos de internação hospitalar independe do ciclo político e que a gestão municipal não apresenta
ganhos quando comparada com a estadual.
Referências Bibliográficas
Afonso, A., Schuknecht, L. & Tanzi, V. (2003), Public sector efficiency : an
international comparison, Working Paper 242, European Central Bank.
Afonso, A. & St. Aubyn, M. (2004), Non-parametric approaches to education
and health expenditure efficiency in oecd countries, Economics Working Paper 1/2004/DE/CISEP/UECE, ISEG-UTL.
Aigner, D., Lovell, C. A. K. & Schmidt, P. (1977), ‘Formulation and estimation
of stochastic frontier production function models’, Journal of Econometrics
6, 21–37.
Battese, G. & Coelli, T. (1995), ‘A model for technical inefficiency effects in a
stochastic frontier production function for panel data’, Empirical Economics
20(2), 325–332.
Berger, A. & Mester, L. (1997), ‘Inside the black box: What explains differences in the efficiencies of financial institutions’, Journal of Banking & Finance
21, 895–947.
Bernet, P. M., Rosko, M. D. & Valdmanis, V. G. (2008), ‘Hospital efficiency
and debt’, Journal of Health Care Finance 34(4), 66–88.
65
Bradford, W. D. & Kleit, A. N. (2001), ‘Stochastic frontier estimation of cost
models within the hospital’, The Review of Economics and Statistics 83(2), 302–
309.
Brown III, H. S. & Pagán, J. A. (2006), ‘Managed care and the scale efficiency
of us hospitals’, International Journal of Health Care Finance and Economics
6, 278–289.
Debreu, G. (1951), ‘The coefficient of resource utilization’, Econometrica
19(3), 273–292.
Evans, D., Tandon, A. & e J.A. Lauer, C. J. M. (2000), The comparative efficiency of national health systems in producing health : an analysis of 191
countries, GPE Discussion Paper Series 29, World Health Organization.
Farrel, M. J. (1957), ‘The measurement of productive efficiency’, Journal of
the Royal Statistical Society 120, 253–281. Series A, General.
Greene, W. (2003a), Distinguishing between heterogeneity and inefficiency :
stochastic frontier analysis of the world health organization´s panel data on
national health care systems, NYU Working Paper EC-03-10, NYU.
Greene, W. H. (2003b), Econometric Analysis, Prentice-hall.
Gupta, S. & Verhoeven, M. (2001), ‘The efficiency of government expenditures experiences from África’, Journal of Policy Modeling 23, 433–467.
Herrera, S. & Pang, G. (n.d.), Efficiency of public spending in developing
countries : an efficiency frontier approach. Mimeo.
Herrera, S. & Worldwide, W. W. (2005), Efficiency of public spending in developing countries : an efficiency frontier approach, Policy Research Working Paper 3645, The World Bank.
Jacobs, R. (2000), Alternative methods to examine hospital efficiency: Data
envelopment analysis and stochastic frontier analysis, Working papers
177chedp, Centre for Health Economics, University of York.
Jayasuriya, R. & Wodon, Q. (2002), Measuring and explaining country efficiency in improving health and education indicators, Technical report, The
World Bank.
Kumbhakar, S. & Lovell, K. (2000), Sthocastic Frontier Analysis, Cambridge
University Press.
Marinho, A. (2001), Avaliação da eficiência técnica nos serviços de saúde dos
municípios do estado do rio de janeiro, Texto para discussão 842, IPEA.
Meeusen, W. & Van de Broeck, J. (1977), ‘Efficiency estimation from cobbdouglas production functions with composed error’, International Economic
Review 18, 435–444.
Pitt, M. & Lee, L. (1981), ‘The measurement and sources of technical inefficiency in indonesian weaving industry’, Journal of Development Economics
9, 43–64.
66
Smet, M. (2007), ‘Measuring performance in the presence of stochastic demand for hospital services: an analysis of belgian general care hospitals’,
Journal of Productivity Analysis 27, 13–29.
Sousa, M. C. S., Cribari-Neto, F. & Stosic, B. D. (2005), ‘Explaining dea technical efficiency scores in an outlier corrected environment: The case of
public services in brazilian municipalities’, Brazilian Review of Econometrics
25(2), 287–313.

I Escola de Verão em Economia do Desenvolvimento – FEA/USP

Transcrição

Documentos relacionados

É tempo de analisar e fazer uma completa reflexão sobre a vida

Mas Que Nada Notes (01-02-15) 1. MIDIs for Individual parts are

SET LIST | INTERNATIONAL Hotel California

Passei do ponto lua virei em mil noites ,contei eu, na procura, em

Pedro Campos Costa, Paula Sousa, Louis Mariette, Benoit

The Emperor`s New Clothes As novas roupas do

COUNTABLE AND UNCOUNTABLE NOUNS

Sentinela - Zelia Fonseca

AES TIETÊ S

Cabo Verde águas de terra engulo em seco várias vezes é a saliva

TS 120S - Mobya

Untitled - Travelingua

WRITTEN QUESTION by Carlos Coelho (EPP) to the

Fiscal Monitor - Jornal de Negócios

sterilization monitoring products