Ludger Schuknecht
email: ludger.schuknecht@ecb.int.

# $ Ludger Schuknecht # António Afonso
email: antonio.afonso@ecb.int

Vito Tanzi
email: vitot@msn.com.

Public Sector Efficiency: Evidence for New EU Member States and Emerging Markets

Keywords: C14 -Semiparametric and Nonparametric Methods < C1 -Econometric government expenditure, efficiency, DEA, new member states, emerging markets government expenditure, efficiency, DEA, new EU member states, emerging markets. JEL Classification Numbers: C14, H40, H50

In this paper we analyse public sector efficiency in the new member states of the EU compared to that in emerging markets. After a conceptual discussion of expenditure efficiency measurement, we compute efficiency scores and rankings by applying a range of measurement techniques. The study finds that expenditure efficiency across new EU member states is rather diverse especially as compared to the group of top performing emerging markets in Asia. Econometric analysis shows that higher income, civil service competence and education levels as well as the security of property rights seem to facilitate the prevention of inefficiencies in the public sector.

Introduction

The importance of an efficient use of public resources and high-quality fiscal policies for economic growth and stability and for the well-being of individuals has been brought to the forefront by several developments over recent decades. Macroeconomic constraints limit countries' scope for expenditure increases. The member states of the European Union are bound to fiscal discipline through the Stability and Growth Pact. Globalisation makes capital and taxpayers more mobile and exerts pressure on governments' revenue base. New management and budgeting techniques have been developed and there is more scope for goods and service provision via markets. Transparency of government practices across the globe has increased, raising public pressure to use resources more efficiently (see [START_REF] Tanzi | Public Spending in the 20 th Century: A Global Perspective[END_REF], [START_REF] Heller | Who Will Pay? Coping with Aging Societies, Climate Change and Other Long Term Fiscal Challenges[END_REF], Joumard, Konsgrud, Nam and Price (2004)).

The adequate measurement of public sector efficiency is a difficult empirical issue and the literature on it is rather scarce. The measurement of the costs of public activities, the identification of goals and the assessment of efficiency via appropriate cost and outcome measures of public policies are thorny issues. Academics and economists in international organisations have made some progress in this regard by paying more attention to the costs of public activities via rising marginal tax burdens and by looking at the composition of public expenditure. Moreover, they have been shifting the focus of analysis from the amount of resources used by ministry or programme (inputs) to the services delivered or outcomes achieved (see, for instance, OECD (2003), and Afonso, [START_REF] Schuknecht | Reforming public expenditure in industrialised countries: are there tradeoffs[END_REF]).

Our contribution in this study is fourfold: first we discuss and survey conceptual and methodological issues related to the measurement and analysis of public sector efficiency. Second we construct Public Sector Performance and Efficiency composite indicators for the ten (plus two) new member states that adhered to the European Union (EU) on 1 May 2004 (on 2007) as F o r P e e r R e v i e w 3 compared to emerging markets from different regions, and some current EU member countries that show features of emerging markets and/or are undergoing a catching up process. Third we use Data Envelopment Analysis to compute input and output efficiency scores and country rankings, which we combine with a Tobit analysis to see whether exogenous, non-discretionary (and non-fiscal) factors play a role in explaining expenditure inefficiencies. Fourth, we use the information from the Tobit estimations to correct the efficiency scores for the effect of nondiscretionary factors. To our knowledge, such an efficiency analysis has not been applied before to this set of countries.

On the second and third objective, the study finds significant differences in expenditure efficiency across new member countries: the Asian newly industrialised economies perform best while the new member states show a very diverse picture. The two-step analysis shows that income, public sector competence and educational levels, as well as the security of property rights, seem to facilitate the prevention of inefficiencies in the public sector, and such factors can be used to correct country efficiency scores.

The paper is organised as follows. In section two we briefly discuss our motivation and review the related literature on public expenditure efficiency. In section three we present the methodologies used for the measurement of public expenditure efficiency. Section four reports stylised facts and our empirical analysis of efficiency via i) performance and efficiency analysis based on cross-country composite indicators, ii) a non-parametric efficiency analysis, and iii) an explanation of inefficiencies via non-discretionary factors. Section five concludes.

Motivation and related literature

Economists are concerned about the efficient use of scarce resources. The concept of efficiency finds a prominent place in the study of the spending and taxing activities of governments. Economists believe that these activities should generate optimal potential benefits for the population and they castigate governments when, in their view, they use public resources inefficiently. International organisations, such as the World Bank and the IMF, often express concern about governmental activities that they consider inefficient or unproductive.

There are many reasons why a society needs public expenditure (and consequently tax revenue).

The reasons have been spelled out, over the years, by economists such as Adam Smith, Pigou, Musgrave, Samuelson, and others. Public expenditure is needed to deal with (a) genuine public goods, (b) significant externalities, (c) the creation of social institutions and (d) the rule of law that protect individuals and property. When the focus of public expenditure is on these agreed activities, and is carried out in an efficient manner, the public sector remains small and efficient.

Problems arise when (a) public sector activity is carried out beyond the theoretically justified areas; and/or (b) when it is carried out at excessive costs. Of course, the more efficient is the working of the market economy, the smaller should be the scope of activities in which there is even a theoretical justification for public spending (see [START_REF] Tanzi | The Economic Role of the State in the 21 st Century[END_REF]. Thus, as market economies become more efficient, public spending should fall.

There has been a tendency among economists to measure the output or the benefit in public activities on the basis of the budgeted allocation: the higher the expenditure, the higher the benefit. For example calls to allocate a given, or a larger, share of national budgets to health and education assume such identity between expenditure and benefits. The larger the expenditure, the greater the benefits received by the intended recipients are assumed to be. But, as argued by [START_REF] Tanzi | Redistributing Income through the Budget in Latin America[END_REF] the two can be widely different. This difference is central to the concept of efficiency. Additionally, a relevant issue is the distinction between output and outcome. This distinction should be fundamental in the analysis of the efficiency of public spending. There is often much attention paid to the outputs of certain activities and too little to the outcomes. For example the outputs of educational spending may be school enrolments, or number of students completing a grade. The outputs of health expenditure may be the number of operations performed or days spent in a hospital bed. However, the outcomes should be based on how much students learned and how many patients got well enough to return to a productive life. Some recent papers have used non-parametric approaches for measuring relative expenditure efficiency across countries, notably Data Envelopment Analysis (DEA). This technique, which is applied later in this study, was originally developed and applied to firms that convert inputs into outputs. The term "firm", sometimes replaced by the more encompassing term "Decision Making Unit" (henceforth DMUs) may include non-profit or public organisations, such as hospitals, universities, local authorities, or countries.

F

Previous research on the performance and efficiency of the public sector and its functions that applied non-parametric methods found significant inefficiencies in countries. Studies include notably [START_REF] Gupta | The efficiency of government expenditure Experiences from Africa[END_REF] for education and health in Africa, [START_REF] Clements | How Efficient is Education Spending in Europe[END_REF] for education in Europe, [START_REF] St | Evaluating Efficiency in the Portuguese Education Sector[END_REF] for education spending in the OECD, [START_REF] Afonso | Public Sector Efficiency: An International Comparison[END_REF] for public sector performance expenditure in the OECD, Afonso andSt. Aubyn (2005, 2006) for efficiency in providing health and education in OECD countries.

De [START_REF] Borger | Explaining differences in productive efficiency: An application to Belgian Municipalities[END_REF], [START_REF] Borger | Cost efficiency of Belgian local governments: A comparative analysis of FDH, DEA, and econometric approaches[END_REF], and [START_REF] Afonso | Local Government Spending Efficiency: DEA Evidence for the Lisbon Region[END_REF] find evidence of spending inefficiencies for the local government sector. Most studies apply the DEA method while [START_REF] Afonso | Cross-country Efficiency of Secondary Education Provision: a Semiparametric Analysis with Non-discretionary Inputs[END_REF] undertook a two-step DEA/Tobit analysis, in the context of a cross-country analysis of secondary education efficiency. In recent years various attempts have been made at measuring the efficiency of public expenditure via composite indicators. These attempts are of two broad types: macro measurements, and micro measurements. Macro measurements have as their aim an evaluation of public spending in its entirety. In other words they attempt to measure, or rather to get some ideas of, the benefits from higher public spending. When, for example, Sweden spends 1 ½ times as much in terms of GDP shares as Switzerland, what does it get in return? Micro measurements attempt to determine the relationship between spending and benefits in a particular budgetary function or even sub-function (i.e. health spending or the efficiency of spending in hospitals, or spending for protection against malaria, aids, etc.).

A first macro measurement attempt was made by Tanzi andSchuknecht (1997, 2000) in trying to assess the benefits from total public spending in eighteen industrialized countries. The approach attempts to determine whether larger public spending in these industrialized countries provided returns, in terms of some identifiable benefits, that could justify the additional costs (including the reduction in individual economic freedom) associated with higher tax burdens. The key question that it tries to address is whether there is a positive, identifiable relationship between higher public spending and higher social welfare. The application of this method led the authors to conclude that additional public expenditure had not been particularly productive in recent decades. The group of countries with lower levels of public spending had socio-economic indicators that were as good as or at times better than that of the countries with much higher spending levels. [START_REF] Afonso | Public Sector Efficiency: An International Comparison[END_REF] built composite indicators of public sector performance.

They distinguished public sector performance (PSP), defined as the outcome of public policies, is also the first method we apply in our country sample analysis later in the paper.

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, PSP i , we can then write

∑ = = n j ij i PSP PSP 1 , (1) with
) ( k ij I f PSP = .
Therefore, an improvement in public sector performance depends on an improvement in the values of the relevant socio-economic indicators:

∑ = ∆ ∂ ∂ = ∆ n k i k k ij I I f PSP . ( 2
)
The performance indicators are of two kinds: process or opportunity indicators, and traditional or Musgravian indicators. As a first step, they define seven sub-indicators of public performance.

The first four look at administrative, education, health and public infrastructure outcomes. Each of these sub-indicators can contain several elements. For example, "administrative" includes indicators for corruption, red tape, quality of judiciary, and the size of the shadow economy.

These are averaged to give the value for "administrative" performance. Health includes infant mortality and life expectancy. A good public administration, a healthy and well-educated population, and a sound infrastructure could be considered a prerequisite for a level playing field with well-functioning markets and secure property rights, where the rule of law applies, and opportunities are plenty and in principle accessible to all. These indicators thereby try to reflect the quality of the interaction between fiscal policies and the market process and the influence this has on individual opportunities. However, these performances reflect outcomes without taking into account the level of public spending. They ignore the costs in terms of public expenditure. We weigh performance (as measured by the PSP indicators) by the amount of relevant public expenditure that is used to achieve a given performance level. In order to compute these so-called efficiency indicators, public spending was normalised across countries, taking the average value of one for each of the six categories specified above. To get some values of public sector efficiency (PSE) the public sector performance (PSP) is weighted by the public expenditures as follows:

i i i PEX PSP PSE = , (3)
with 1 The conceptual separation between "opportunity" and standard "Musgravian" indicators is of course somewhat artificial as, for example, health and education indicators could also be seen as indicators of allocative efficiency.

The input measures for opportunity indicators are: public consumption as proxy for input to produce administrative outcomes; health expenditure for health performance/outcome indicators;

and education expenditure for education performance. Our earlier study also included a measure of the outcome of public investment, but due to a lack of comparable data, this measure is not used in this study.

Inputs for the standard or "Musgravian indicators" are: transfers and subsidies as proxies for input to affect the income distribution; total spending as proxy for the input to affect economic stabilization (given that larger public sectors are claimed to make economies more stable);2 and total spending also as a proxy input for economic efficiency and the distortions of taxation needed to finance total expenditure.

However, there are some caveats: it is not easy to accurately identify the effects of public sector spending on outcomes and separate the impact of public spending from other influences.

Moreover, comparing expenditure ratios across countries implicitly assumes that production costs for public services are proportionate to GDP per capita.3

Non-parametric analysis of performance and efficiency

The DEA methodology, originating from [START_REF] Farrell | The Measurement of Productive Efficiency[END_REF] seminal work and popularised by [START_REF] Charnes | Measuring the efficiency of decision making units[END_REF], assumes the existence of a convex production frontier. The production frontier in the DEA approach is constructed using linear programming methods.4 Regarding public sector efficiency, the general relationship that we expect to test can be given by the following function for each country i:

) ( i i X f Y = , i=1,…,n (5)
where we have Y i -a composite indicator reflecting our output measure (the PSP indicator in our case); X i -spending or other relevant inputs in country i (government spending will be used

ahead). If ) ( i i x f Y <
, it is said that country i exhibits inefficiency. For the observed input level, the actual output is smaller than the best attainable one and inefficiency can then be measured by computing the distance to the theoretical efficiency frontier.

The analytical description of the linear programming problem, in the variable-returns to scale hypothesis, is sketched below for an input-oriented specification. Suppose there are k inputs and m outputs for n DMUs. For the i-th DMU, y i is the column vector of the outputs and x i is the column vector of the intputs. We can also define X as the (k× n) input matrix and Y as the (m× n)

output matrix. The DEA model is then specified with the following mathematical programming problem, for a given i-th DMU, and to be solved for each DMU:5 , s. to 0 0 1' 1

0 i i Min y Y x X n δ λ δ λ δ λ λ λ -+ ≥ - ≥ = ≥ . (6)
In problem (6), δ is a scalar (that satisfies δ ≤ 1), more specifically it is the efficiency score that measures technical efficiency. With δ<1, the country is inside the frontier (i.e. it is inefficient), while δ=1 implies that the country is on the frontier (i.e. it is efficient). The vector λ is a (n× 1) vector of constants that measures the weights used to compute the location of an inefficient DMU if it were to become efficient. The inefficient DMU would be projected on the production frontier as a linear combination of those weights, related to the peers of the inefficient DMU. 1 n is a n-dimensional vector of ones. The restriction 1 ' 1 = λ n imposes convexity of the frontier, accounting for variable returns to scale. Dropping this restriction would amount to admit that returns to scale were constant.

Using non-discretionary factors to explain inefficiencies

The analysis via composite performance indicators and DEA analysis have assumed tacitly that expenditure efficiency is purely the result of discretionary (policy and spending) inputs. They do not take into account the presence of "environmental" factors, also known as non-discretionary or "exogenous" inputs. However, such factors may play a relevant role in determining heterogeneity across countries and influence performance and efficiency. Exogenous or non-discretionary factors can have an economic and non-economic origin.

As non-discretionary and discretionary factors jointly contribute to country performance and efficiency, there are in the literature several proposals on how to deal with this issue, implying usually the use of two-stage and even three-stage models. 6 Using the DEA output efficiency scores, we will evaluate the importance of non-discretionary factors below in the context of our new member and emerging market sample. We will undertake Tobit regressions by regressing the output efficiency scores, δ ι , on a set of possible non-discretionary inputs, Z, as as follows the two EU new member states as from 2007 (Bulgaria, and Romania); three "old" member countries that underwent a catching up process after entering the EU, (Greece, Ireland and Portugal); and finally nine countries that can also be considered as emerging markets, (Brazil, Chile, Korea, Mauritius, Mexico, Singapore, South Africa, Thailand, and Turkey). This country selection was determined by the search for a sufficient number of countries which can be compared with the new EU members and for which reasonably good quality data is available so that an expenditure efficiency analysis becomes meaningful. In addition, we will make occasional references to comparative indicators for OECD or EU countries and country averages.

i i i Z f ε δ + = ) ( . (7
Table 1 illustrates total expenditure and the public expenditure composition across the sample countries, on an average basis for the period 1999-2003 (or within this period according to data availability). First, it is striking that the new EU member countries on average report similar total spending as the "old" EU members and much higher spending than most other emerging markets.

When looking for relatively small governments with spending ratios of less than 40% of GDP, we only find the Baltic countries belonging to this group. Second, the divergence in expenditure ratios is enormous ranging from about 18% to 50% of GDP. The Baltics' relatively low spending ratio is about one quarter less than that of the central European countries but it is significantly higher than the average for the Asian emerging economies (Korea, Singapore, and Thailand).

[ As regards public sector performance we have deviated in a few respects from our earlier study.

In the absence of reasonable data on public infrastructure we in particular focus on three opportunity indicators and the three respective Musgravian indicators (Annex Tables provide primary data). 7We compile performance indicators from the various indices giving an equal weight to each of them and the results are reported in Table 2. 8 The results for public sector performance show Starting with the overall PSP indicator, the best performers seem to be Singapore, Cyprus and Ireland. Other Asian emerging economies and Malta follow this group of top performers while most new EU member countries and Portugal and Greece post a broadly average performance.

Brazil, Bulgaria and Turkey are placed at the bottom end. The size of government per se appears to be a too crude instrument of differentiation, when looking at the score for large public sector countries.

[Insert Table 2] When comparing the results for the best performers in this study with those from our earlier study on industrialised OECD countries, it is noteworthy that Ireland was "only" an average performer. 3 where, due to data limitations for the pre-1998 period in many countries, averages of the corresponding expenditure item were used for the relatively short period of 1998-2003 (see Annex for precise dates and primary data).

[Insert Table 3]

The results for measuring public sector efficiency show an accentuation of the findings for public sector performance. This suggests that more public spending often has relatively low returns as regards improved performance (which is consistent with the findings of our earlier study for industrialised countries). Most low performers, including most new EU member states range between 0.8 and 0.9 and Cyprus is the only new member country with an average PSE score.

Countries with a small government sector post a higher PSE score than the average (and hence even more so than the countries with "big" governments). The emerging countries of Asia plus

Mauritius have most of the highest scores as their good performance is achieved with low public spending.

When looking at sub-indices, the new member states efficiency scores are near average on human capital and on income distribution. In all other areas, PSE scores are well below average for the new EU member states. Note also that the income distribution efficiency score is highest in the countries with smaller welfare states. This confirms findings elsewhere that welfare programmes in (rich and) poor countries are often poorly targeted and benefit those with special interests rather than those in need [START_REF] Alesina | The Political Economy of Macroeconomic Stabilization and Income Inequality: Myths and Reality[END_REF] and [START_REF] Schuknecht | Reforming public expenditure in industrialised countries: are there tradeoffs[END_REF]). All in all the results suggest that efficiency differs enormously across countries. In the new member states, a relatively average performance (PSP scores) in most countries is "bought" with too many inputs so that efficiency (PSE) is low. In the next section, we will analyse whether these findings are confirmed by using a DEA approach.

Relative efficiency analysis via a DEA approach

We used a DEA approach as described above, using as our output measure the PSP composite indicator reported in Table 2 and as an input measure, the total government spending as a ratio of GDP. Table 4 presents both the input and the output oriented efficiency coefficients of the variable returns to scale analysis while the constant returns to scale coefficients are also reported for completeness. We opted for not including Singapore since its economic indicators give it characteristics of an outlier in the country sample we use. 9

[Insert Table 4]

The results largely confirm the findings of the earlier "macro" approach of determining efficiency of the public sector. New member states are ranked between 9 and 23 on input scores and between 6 and 21 on output scores, hence reflecting rather diverse and often below average efficiency. Two countries that also had amongst the top PSE scores are located on the frontier:

Cyprus and Thailand, while Ireland, Korea, Chile and Mauritius come next. From an input oriented perspective, Brazil, Greece and Hungary find themselves at the bottom of the list while most new member states fill the middle ranks. The highest-ranking country uses 1/3 of the input that the bottom ranking one uses to attain a certain PSP score. The average input score of 0.59 hints to the possibility that, for the level of output they are attaining, countries could in theory use around 41 per cent less resources.

9 Similar overall results are obtained with Singapore, as shown in Afonso et al. (2006).

Page From an output perspective, the top performer achieves twice as much output as the least efficient country with the same input. The average output score of 0.78 implies that on average, for the level of input they are using, the countries are only obtaining around 78 percent of the output they should deliver if they were deemed efficient. 10 Figure 1 illustrates the production possibility frontier associated with the aforementioned set of DEA results.

[Insert Figure 1]

Explaining inefficiencies via non-discretionary factors

As an additional step, we extend our analysis to exogenous factors that explain expenditure efficiency (see section three for methodical issues). It is probably reasonable to conjecture that expenditure efficiency depends on the "technology" applied in the public sector, on factors that influence the ability of private agents to protect their resources from public claims, on the monitoring capacities of public and private agents, and on international constraint. The variables and underlying hypotheses we test are the following: i) Secondary school enrolment. This variable aims to proxy the level of education of the population in a given country. More educated people are hypothesized to be better able to monitor the activities of politicians and bureaucrats and ultimately sanction crass inefficiency.

But more education is also likely to imply better educated and trained (and hence more efficient) civil servants.

ii) The competence of the civil servants (survey results presented in the Global Competitiveness Report, see Annex for sources and explanations). This variable aims to measure greater 10 Such calculations are an approximation of potential direct costs of inefficiency. However, indirect costs, implying a higher loss for consumer welfare should also be taken into account. This is outside the scope of our paper, but [START_REF] Afonso | Dupuit, Pigou and cost of inefficiency in public services provision[END_REF] address this issue. iii) Per capita GDP. This variable aims to proxy the physical capital stock which facilitates an efficient production of public goods and services but which may also facilitate monitoring of policy makers.

iv) An indicator of property rights. Secure property rights make it more difficult for governments to extract wealth/rents from the private sector. They also facilitate holding governments accountable for their actions.

v) Trade openness (exports and imports as a share of GDP). This indicator proxies the degree of international competition over labour and capital that would penalise public inefficiency disproportionately.

vi) Transparency in public policy. This is another indicator that should measure the ease of monitoring public officials.

vii) Other more direct indicators of political accountability (such as civil liberty, political rights or checks and balances) do not show much variation for this country group as almost all of them are in the top group.

Exogenous factors could also include other factors that could be detrimental or favourable to efficiency (such as the climate, the cultural background) for which economically meaningful hypotheses are less readily available. We do not include such variables in our analysis.

Using the DEA output efficiency scores computed in the previous subsection, we now evaluate the importance of non-discretionary inputs via censored Tobit regressions where output efficiency scores are regressed on our choice of exogenous, non-discretionary factors. Table 5 confirms the relevance of several of our hypotheses and the variables chosen to test them.

[Insert The Tobit analysis suggests that the security of property rights, per capita GDP, the competence of civil servants, and the education level of people positively affect expenditure efficiency. Due to significant correlation, however, the two competence/education variables are only significant in separate regressions while the other two variables are robust over all specifications.

International trade openness, trust in politicians and transparency of the political system have not been found to display a significant influence on expenditure efficiency (even though only the coefficient for public trust in politicians had the wrong sign). The regressions' standard deviation also points to a reasonable model fit.

In Table 6 we report output efficiency score corrections from model 1 in Table 5 for the environmental variables detected as statistically significant in the Tobit analysis: GDP, property rights, competence of civil servants. We computed these corrections by considering that the nondiscretionary factors varied to the sample average in each country. The output scores corrected for environmental effects (truncated to one when necessary) are presented in column five of Table 6 as a result of the sum of the previous four columns. One should also notice that the number of countries considered decreased from twenty-three in the DEA calculations to twenty in the two-step analysis, since property rights, and competence of public civil servants data was not available for Cyprus, Malta, and Thailand.11

[Insert Table 6]

Comparing the corrected scores in Table 6, resulting from corrections for environmental variables with the scores from the standard DEA analysis, we can observe some changes. Some countries decreased their respective distances to the production possibility frontier, for instance this is the case of Hungary, Slovenia, Mauritius and South Africa. In the case of these two last countries, for such improvement contributed positively the above average positioning in terms of property rights, which compensate the negative impact from GDP and competence of civil servants variables. On the other hand, other countries see a decline in their efficiency scores after taking into account the non-discretionary factors. For instance, the aforementioned three factors all contribute negatively to distance away from the frontier Mexico, Romania, and Turkey.

When we compare the results of our composite indicator analysis of performance and efficiency with those of the standard DEA analysis, and the two-step analysis, we notice rather similar results as reflected in very high correlation coefficients for scores and ranks across methods (see Table 7). This is evidence for a certain robustness of our results.

[Insert Table 7]

Conclusion

In this paper we analysed public sector efficiency in the new member states of the European Union as compared to emerging markets. We start with a conceptual discussion of expenditure efficiency measurement issues where challenges regarding the measurement of costs, the definition of goals and the measurement of outcomes are significant. Taking these challenges into account, we calculate efficiency scores and rankings by applying a range of measurement techniques to the new EU member countries and a selection of emerging markets, catch-up economies, and EU candidate countries.

The results of our analysis show that expenditure efficiency across new EU member states is rather diverse, especially compared to the group of top performing emerging markets in Asia.

From the analysis of composite public sector performance (PSP) and efficiency (PSE) scores we 21 find that countries with lean public sectors and public expenditure ratios not far from 30% of GDP tend to be most efficient. PSE scores of the most efficient countries are more than twice as high as those of the poorest performers.

From the DEA results we see that a small set of countries define or are very close to the theoretical production possibility frontier: Thailand, Cyprus, Korea, and Ireland. From an input perspective the highest ranking country uses 1/3 of the input that the bottom ranking one uses to attain a certain PSP score. The average input scores suggest that countries could use around 45 per cent less resources to attain the same outcomes if they were fully efficient. Average output scores suggest that countries are only delivering around 2/3 of the output they could deliver if they were on the efficiency frontier.

Annex -Data and sources

Annex Table A

  indicators for measuring public sector performance and efficiency

  efficiency, defined as the outcome in relation to the resources employed. This

  sub-indicators reflect the "Musgravian" tasks for government.1 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. Once again each of these traditional indicators may be made up of various elements. For example stability is made up of variation in output around a trend and inflation. Finally all sub-indicators are used to compute a composite public sector performance indicator by giving the sub-indicators equal weights. The values are normalized and the average is set equal to one. Then the PSP of each country is related to this average and deviations from this average provide an indication of the public sector performance of each of country.

  , with an overall very diverse picture for the new EU member states.

  in the public sector through better training etc. It is expected to be correlated with the education variable.

Finally

  we examine via Tobit analysis the influence of non-discretionary factors, notably nonfiscal variables, on expenditure efficiency. The study shows that per-capita income, public sector competence and education levels as well as the security of property rights seem to facilitate the prevention of inefficiencies in the public sector. Additionally, we corrected output scores by considering the effects of non-discretionary factors. The country rankings and output scores resulting from such correction, even if not considerably different from the DEA results, allow decomposing the specific non-discretionary factor contribution to the change in the efficiency scores.From a policy perspective, one should be careful to draw overly strong conclusions and we have referred to a number of caveats in the course of the paper. Nevertheless, it is apparent that many new members states and other emerging markets can still considerably increase the efficiency of public spending by improving the outcomes and by restraining the resource use. The final suggests that high education levels, a competent civil service and the security of property rights seem to provide an "extra boost" to public expenditure efficiency. P, Tulkens H, Jamar M-A. Cost-efficiency in Belgian municipalities. In Fried H, Lovell C, Schmidt S (Eds), The Measurement of Productive Efficiency: Techniques and Applications. New York: Oxford Univ. Press; 1993.

  Figure 1 -Production possibility frontier: one input, one output

quantitive assessment of public sector performance and expenditure efficiency 4.1. Some stylised facts for the EU new member states and comparative countries

  )Our country sample includes the ten EU new member states as from 2004 (Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovak Republic, and Slovenia);

	4. A
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. Public sector performance and efficiency via composite indicators 4.2.1. Public sector performance (PSP)

  spend equally as much, or even significantly more, on productive spending such as investment and education as the rest of the sample countries. New members report public consumption around 20% of GDP, twice as much as Asian emerging economies, with the reverse relation holding for public investment where new members spend roughly 3% of GDP while the Asian countries report an average above 6% of GDP. Data on transfers and subsidies are sketchier but huge differences are noteworthy: large welfare states of similar size as in the old EU members predominate in many of the new member countries (with the Baltics' featuring somewhat lower expenditure) while such spending in Asian emerging economies is only fractional. When looking at education, differences across country groups are much smaller than for total spending. New members, old EU members and other emerging markets are not far apart from each other. In health, differences are again very significant where central European countries spend almost two and half times as much in % of GDP as the Asian emerging economies.

	countries tend to 4.2
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Insert Table 1 here]

When looking at the expenditure composition, there are further major differences. But these differences are much more pronounced for less productive spending categories. Small government

  Portugal and Greece, which are near average in this group, were amongst the weakest in the former study. The results hence show that public sector performance is on average still somewhat lower in most new EU member countries and emerging markets than in the "old" industrialised countries but a few of them (notably the new member island countries and Asian Emerging economies) have broadly caught up.Public sector performance must be set in relation to the inputs used in order to gauge the efficiency of the state. We compute indicators of Public Sector Efficiency (PSE), taking into account the expenditure related to each sub-indicator as described in section three. PSE indicators are presented in Table

	4.2.2. Public sector efficiency (PSE)
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With regard to sub-indicators, it is interesting to see that the relatively strong performance of the new EU member states on human capital/education and income distribution contrasts with a relatively weak one for economic performance and stability. There is no clear pattern of distinction between Baltics and Central European countries while the two island countries post strong values for all indicators for which data is available. Asian Emerging economies performed very strongly on administration, human capital and economic stability and growth. Overall performance was very equal as regards health indicators.

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Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Table 5 ]

 5

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  -Primary data for performance sub-indicators

			Annex Table B -Primary data for the non-discretionary factors
			GDP per	Property	Competence	Secondary	Degree of	Public trust	Transparency
			capita	rights	of public	school		openness		of		7/
			1/		2/		officials	enrolment	5/	politicians
							3/		4/				6/
	Brazil		7360		5.0		2.4		71.3		29.15		2.2		4.51
	Bulgaria		6890		3.2		3.3		87.6		116.20		2.3
	Chile		9190		5.6		2.1		74.5		69.15		2.9		6.64
	Cyprus	1/	2/ 21190	3/		4/	5/	6/	7/ 88.3	8/	9/ 95.53	10/	11/	12/	13/	14/
	Brazil Czech Republic	4.6	2.8 14720	3.9	4.4	4.6	3.3 68.3 31.0 60.7 1.41 220.9 2.3 87.1 126.64	2.6 1.9	7.6 7360 71.3 3.60
	Bulgaria Estonia	5.5	2.5 10170	2.7	4.8	5.2	5.0 71.7 14.0 26.4 0.14 139.0 3.0 82.8 156.22	0.7 14.6 6890 87.6 2.8 5.96
	Chile Greece	6.3	3.1 17440	4.6	5.0	2.4	3.6 75.8 10.0 56.7 1.43 1.8 87.4 48.59	5.5	4.6 2.5	7.9 9190 74.5 3.45
	Cyprus Hungary		12340		5.3		2.8	78.0	5.0 87.2		2.31 131.49	3.0	4.0 2.6	3.2 21190 88.3 3.50
	Czech Republic Ireland	5.2	2.7 32410	4.2	6.1	2.6	5.5 74.9 3.6	4.0 25.4 0.97 85.8 151.31	6.0	2.1 3.2	7.0 14720 87.1 5.47
	Estonia Korea	5.9	4.2 15090	5.3	4.7	2.1	5.5 70.6 11.0 37.6 1.26 13.7 3.0 90.9 73.51	4.3 10.6 10170 82.8 2.1 4.21
	Greece Latvia Hungary Lithuania Ireland Malta Korea Mauritius Latvia Mexico Lithuania Poland Malta Portugal Mauritius Romania Mexico Slovak Republic Poland Slovenia Portugal South Africa Romania Thailand Singapore Turkey	4.8 5.8 6.0 5.3 4.9 5.5 6.1 4.6 5.0 4.8 5.8 3.6 6.7	2.4 7730 2.7 8470 3.4 13160 3.2 9860 3.7 8430 2.8 9450 2.9 18150 2.2 5830 2.3 11960 2.8 17130 2.8 11290 2.0 6400 5.1 5890	4.7 4.9 5.2 4.1 4.2 3.3 5.3 4.4 3.3 3.9 5.7 2.4 5.2	4.3 4.2 5.4 4.6 4.6 5.3 4.5 5.2 4.8 5.3 4.2	3.5 2.3 2.3 2.8 3.6 2.4 3.0 3.3 5.0 3.7 3.0 5.5 1.4	4.6 78.0 3.1 5.7 71.5 3.4 5.3 76.6 4.7 73.6 2.6 4.8 70.4 17.0 32.4 1.66 10.4 5.0 32.7 3.91 5.4 74.4 97.51 8.0 24.4 2.64 14.2 88.6 109.46 6.0 35.9 2.53 3.1 79.2 163.55 5.0 31.6 1.08 4.1 64.2 115.24 2.6 59.7 57.30 5.2 72.7 8.0 32.4 0.63 15.4 2.7 90.9 71.28 4.9 78.2 5.0 1.47 2.7 2.2 85.2 66.59 4.2 72.1 17.0 3.26 6.3 2.6 79.6 80.38 3.1 73.4 24.0 53.1 0.70 15.5 2.0 74.9 156.87 4.7 73.5 8.0 31.6 2.10 13.2 3.4 88.6 112.97 3.2 75.8 5.0 38.5 1.53 3.3 1.9 57.2 53.69 5.9 69.9 19.0 31.1 0.46 58.5 2.6 79.8 124.31 6.5 78.4 3.0 42.5 1.06 1.1 2.1 51.3 58.05	2.3 3.3 10.3 17440 87.4 3.5 1.9 7.8 12340 87.2 7.9 7.8 32410 85.8 5.4 2.6 3.7 15090 90.9 2.5 4.53 4.7 12.9 7730 74.4 3.4 2.4 2.21 8.4 8470 88.6 3.8 3.2 5.09 5.2 13160 79.2 4.8 3.1 3.23 7.3 9860 64.2 2.7 2.8 4.28 3.1 8430 59.7 3.0 3.70 4.3 13.7 9450 90.9 2.6 2.9 6.05 5.7 18150 85.2 2.1 2.8 5.66 9.3 5830 79.6 5.1 3.2 22680 74.3 1.9 4.43
	Slovak Republic	5.2	2.2	3.2		1.6	5.6 73.2	8.0 19.5 2.58	8.4	4.2 15.7 11960 74.9
	Slovenia 1/ GDP per capita PPP, 2001, USD. 5.8 2.8 4.3	2.0	5.3 75.6	4.0 28.4 3.61	9.7	4.1	7.3 17130 88.6
	South Africa Thailand Turkey Average 2/ Financial assets and wealth are (1=poorly delineated and not protected by law, 7=clearly delineated and 4.9 2.9 5.6 4.5 2.8 47.1 56.0 59.3 2.90 7.3 2.8 25.3 11290 57.2 5.1 3.2 4.8 3.7 4.5 69.0 24.0 41.4 0.58 3.6 3.4 3.0 6400 79.8 4.1 2.5 3.7 5.7 4.0 69.8 36.0 41.5 0.46 69.8 2.8 7.2 5890 51.3 5.3 2.9 4.3 3.3 4.7 72.4 13.9 37.3 1.7 26.7 3.7 8.7 12635 78.8 protected by law), 2001-02. F 3/ The competence of personnel in the public sector is (1=lower than the private sector, 7=higher than the o private sector).
	4/ Secondary school enrolment, 2001 or latest. r 5/ Degree of openness = (Imports+Exports)/GDP, 2003.
	6/ Public trust in the honesty of politicians is (1=very low, 7=very high) 7/ Transparency, highest is best, 2003 data. P e Sources: 1/, 4/ -World Bank, WDI 2003. e 2/, 3/, 6/ -World Economic Forum: Global Competitiveness Report 2001-2002. r 5/ -IMF World Economic Outlook (WEO database).
	7/ -IMD World Competitiveness Yearbook 2004.	R
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Table 1 -

 1 Public expenditure in sample countries and country groups, % of GDP

				Transfers
		Total	Government	and	Interest	Public	Education	Health
	spending	consumption	subsidies	payments	investment
		1/	2/	3/	4/	5/	6/	7/
	Brazil	46.6	19.5	17.1	8.2	1.9	4.6	3.3
	Bulgaria	38.6	17.3	15.2	3.2	3.4	3.4	4.0
	Chile	24.4	12.6	7.9	1.2	2.7	3.8	2.4
	Cyprus	40.0	18.0	11.0	3.3	3.0	5.6	2.5
	Czech Republic	40.6	22.7	15.0	1.2	3.4	4.0	6.2
	Estonia	36.4	19.7	10.7	0.3	4.1	6.2	4.4
	Greece	48.6	16.8	17.0	7.2	3.8	3.7	5.1
	Hungary	50.2	22.4	15.0	4.6	3.8	4.8	5.3
	Ireland	33.0	14.8	9.3	1.7	3.8	4.4	4.9
	Korea	24.4	12.7			5.4	3.8	2.4
	Latvia	36.6	21.4	12.7	0.9	1.3	5.8	3.5
	Lithuania	33.3	20.3	11.1	1.5	2.6	5.9	4.5
	Malta	45.0	20.7	14.5	3.8	4.4	4.8	6.2
	Mauritius	24.7	12.9		3.8	7.5	3.8	2.1
	Mexico	25.3	11.7	5.2	4.6	3.8	4.6	2.6
	Poland	43.2	17.9	17.9	2.8	3.3	5.1	4.2
	Portugal	46.2	20.7	14.3	3.1	3.7	5.7	6.2
	Romania	33.7	15.7	13.7	2.3	1.9	3.4	3.8
	Singapore	21.0	11.4	8.7	0.8			1.4
	Slovak Republic	43.8	20.0	14.2	3.5	2.9	4.1	5.2
	Slovenia	42.1	20.2	18.6	2.3	2.9		6.0
	South Africa	26.3	18.4		4.5	2.7	5.7	3.6
	Thailand	17.8	11.2			7.7	5.3	2.3
	Turkey	42.7	13.8		21.3	4.6	3.5	4.0
	Average	36.0	17.2	13.1	3.9	3.7	4.6	4.0
	Max	50.2	22.7	18.6	21.3	7.7	6.2	6.2
	Min	17.8	11.2	5.2	0.3	1.3	3.4	1.4
	New EU members	41.1	20.3	14.1	2.4	3.2	5.2	4.8
	Baltic countries	35.4	20.5	11.5	0.9	2.7	6.0	4.1
	Other new EU	43.5	20.3	15.2	3.1	3.4	4.7	5.1
	Asian NIC	21.0	11.8	8.7	0.8	6.6	4.6	2.0
	Other NIC	32.8	15.2	11.8	6.1	3.5	4.1	3.2
	OECD 1990s 8/	46.5	19.8	15.1		3.0	5.4	6.2
	1/, 2/, 3/, 4/, 5/ -Average for 1999-2003, source: IMF World Economic Outlook, and European Commission
	AMECO database.
	6/ Average for 1998-2001, source: World Bank, WDI 2003.
	7/ Average for 1998-2002, source: World Bank, WDI 2003.
	8/ Source: Afonso, Schuknecht and Tanzi (2005) for OECD 1990s.
	Note: columns 2 through 5 report economic expenditure categories, and that the last two columns report functional
	expenditure categories.

Table 2 -

 2 Public Sector Performance (PSP) indicators(2001/2003)

			Opportunity			"Musgravian"		Total public
			Indicators			Indicators		sector
		Adminis-	Human	Health	Distribu-	Stability Economic	performance
	Country	tration	capital		tion		perform.	(equal
								weights 1/)
	Brazil	0.88	0.80	0.96	0.63	0.43	0.77	0.75
	Bulgaria	0.80	1.09	0.99	1.17	0.06	0.31	0.74
	Chile	1.12	0.86	1.03	0.69	0.92	1.02	0.94
	Cyprus		1.12	1.04		1.59	1.54	1.33
	Czech Republic	1.00	1.14	1.02	1.19	0.74	0.74	0.97
	Estonia	1.25	1.11	0.99	1.00	0.57	0.88	0.97
	Greece	0.95	1.04	1.04	1.07	1.67	0.76	1.09
	Hungary	1.09	1.16	1.00	1.21	0.97	0.88	1.05
	Ireland	1.17	1.11	1.03	1.02	1.64	1.47	1.24
	Korea	1.04	1.08	1.01	1.09	1.00	1.60	1.14
	Latvia	1.03	0.98	0.98	1.08	0.76	0.88	0.95
	Lithuania	0.98	1.12	1.00	1.08	0.37	0.84	0.90
	Malta	1.11	1.03	1.04		1.45	1.12	1.15
	Mauritius	0.91	0.86	1.00		1.40	1.08	1.05
	Mexico	0.80	0.71	1.00	0.75	0.38	1.41	0.84
	Poland	0.92	1.08	1.01	1.09	0.83	0.81	0.96
	Portugal	1.11	0.88	1.03	0.98	1.30	0.91	1.04
	Romania	0.63	1.13	0.98	1.10	0.18	0.63	0.78
	Singapore	1.39	1.16	1.05	0.92	2.94	1.71	1.53
	Slovak Republic	0.95	1.07	1.01	1.28	1.09	0.77	1.03
	Slovenia	1.07	1.13	1.03	1.14	1.35	0.99	1.12
	South Africa	1.00	0.66	0.80	0.65	1.23	0.50	0.81
	Thailand	1.03	0.99	0.97	0.93	0.94	1.54	1.07
	Turkey	0.77	0.75	0.97	0.93	0.17	0.82	0.74
	Average 2/	1.00	1.00	1.00	1.00	1.00	1.00	1.00
	Max	1.39	1.16	1.05	1.28	2.94	1.71	1.53
	Min	0.63	0.66	0.80	0.63	0.06	0.31	0.74
	New EU countries	0.99	1.06	1.00	1.09	0.74	0.86	0.96
	Baltics	1.06	1.10	1.02	1.14	0.93	0.95	1.03
	Other new EU	0.95	1.05	1.00	1.08	0.66	0.82	0.93
	Asian NIC	1.11	1.00	1.00	0.93	1.76	1.44	1.21
	Other NIC	0.97	0.91	0.98	0.87	0.96	1.08	0.98
	1/ Each sub-indicator contributes 1/6 to total indicator. 2/ Simple averages.

Table 3 -

 3 Public sector efficiency (PSE) indicators (2001/2003) 1/ These indicators are the expenditure weighted "counterparts" of the indicators of Table 1. 2/ Each sub-indicator contributes equally to the total indicator. 3/ Simple averages.

			Opportunity			"Musgravian"		Total public
			Indicators			Indicators		sector
		Adminis-	Human	Health	Distribution Stability	Economic	efficiency
	Country	tration	capital				perform.	(equal
								weights 2/)
	Brazil	0.78	0.81	1.15	0.48	0.33	0.59	0.69
	Bulgaria	0.79	1.49	1.00	1.01	0.06	0.29	0.77
	Chile	1.53	1.04	1.70	1.15	1.37	1.51	1.38
	Cyprus		0.92	1.66		1.44	1.39	1.08
	Czech Republic	0.76	1.31	0.66	1.04	0.66	0.66	0.85
	Estonia	1.09	0.83	0.91	1.21	0.57	0.87	0.91
	Greece	0.97	1.32	0.83	0.83	1.23	0.56	0.96
	Hungary	0.83	1.12	0.75	1.05	0.70	0.63	0.85
	Ireland	1.36	1.18	0.84	1.44	1.79	1.61	1.37
	Korea	1.40	1.31	1.72		1.47	2.36	1.65
	Latvia	0.82	0.79	1.14	1.11	0.75	0.87	0.91
	Lithuania	0.83	0.88	0.90	1.27	0.40	0.90	0.86
	Malta	0.92	0.99	0.68		1.16	0.90	0.78
	Mauritius	1.21	1.04	1.91		2.04	1.58	1.56
	Mexico	1.18	0.72	1.52	1.90	0.55	2.01	1.31
	Poland	0.89	0.98	0.97	0.80	0.69	0.68	0.83
	Portugal	0.92	0.71	0.66	0.90	1.01	0.71	0.82
	Romania	0.69	1.53	1.03	1.05	0.20	0.68	0.86
	Singapore	2.09		2.90	1.38	5.05	2.94	2.39
	Slovak Republic	0.82	1.23	0.77	1.18	0.90	0.64	0.92
	Slovenia	0.91		0.68	0.81	1.15	0.84	0.88
	South Africa	0.93	0.54	0.89		1.69	0.68	0.95
	Thailand	1.58	0.86	1.68		1.91	3.11	1.83
	Turkey	0.96	0.99	0.98		0.15	0.69	0.63
	Average 3/	1.06	1.03	1.16	1.03	1.14	1.15	1.09
	Max	2.09	1.53	2.90	1.90	5.05	3.11	2.39
	Min	0.69	0.54	0.66	0.48	0.06	0.29	0.63
	New EU countries	0.87	1.05	0.87	1.04	0.64	0.77	0.84
	Baltics	0.86	1.00	0.78	1.16	0.75	0.81	0.83
	Other new EU	0.88	1.07	0.91	1.00	0.59	0.76	0.84
	Asian NIC	1.63	0.95	2.16	1.38	3.00	2.54	1.93
	Other NIC	1.10	0.95	1.32	0.96	1.11	1.29	1.13
	1/

Table 4 -

 4 DEA results: one input, one output

	Country	Input oriented	Output oriented
		VRS TE	Rank	VRS TE	Rank
	Brazil	0.381	22	0.562	22
	Bulgaria	0.461	15	0.564	21
	Chile	0.730	5	0.823	8
	Cyprus	1.000	1	1.000	1
	Czech Republic	0.439	16	0.735	15
	Estonia	0.489	13	0.753	13
	Greece	0.407	19	0.822	9
	Hungary	0.355	23	0.792	10
	Ireland	0.997	3	0.999	3
	Korea	0.976	4	0.994	4
	Latvia	0.486	14	0.742	14
	Lithuania	0.535	10	0.720	18
	Malta	0.555	9	0.868	6
	Mauritius	0.721	6	0.914	5
	Mexico	0.703	7	0.730	16
	Poland	0.412	18	0.723	17
	Portugal	0.385	21	0.782	11
	Romania	0.528	11	0.621	20
	Slovak Republic	0.406	20	0.777	12
	Slovenia	0.526	12	0.843	7
	South Africa	0.676	8	0.693	19
	Thailand	1.000	1	1.000	1
	Turkey	0.416	17	0.555	23
	Average	0.591		0.783

Table 5 -

 5 Censored normal Tobit results (dependent variable: output efficiency scores from Table4)The z statistics are in brackets. *, **, *** -Significant at the 10, 5 and 1 per cent level respectively.

		1	2	3	4	5
	Per-capita GDP	7.08E-06 ***	6.68E-06 **	6.75E-06 **	7.08E-06 **	1.33E-05 **
		(2.18)	(2.01)	(2.04)	(2.25)	(2.12)
	Property rights	0.102 ***	0.095 ***	0.101 ***	0.127 ***	0.063 *
		(6.57)	(5.07)	(6.60)	(4.54)	(1.76)
	Competence of civil	0.069 ***		0.062 **	0.075 ***	0.109 ***
	service	(2.80)		(2.12)	(3.06)	(3.02)
	Secondary school		0.003 ***
	enrolment		(2.60)
	Trade openness			2.46E-04
				(0.46)
	Public trust in				-0.055
	politicians				(-1.08)
	Transparency in					0.010
	government ε σˆ	0.081	0.086	0.083	0.081	(0.42) 0.083
	Nº of observations	20	20	20	20	16
	ε σˆ -Estimated standard deviation of ε.

Table 6 -

 6 Corrected output efficiency scores (for Model 1) Note: the corrected scores do not always add up to the indicated sum since for the cases were the result was above one it was truncated to the unity.

		DEA	GDP	Property	Civil	Corrected	Corrected
		scores	correction	rights	servants	scores	Rank
				correction	correction	(5)=(1)+(2)+
		(1)	(2)	(3)	(4)	(3)+(4)
	Brazil	0.562	-0.033	0.018	-0.017	0.530	18
	Bulgaria	0.564	-0.036	-0.165	0.045	0.408	20
	Chile	0.823	-0.020	0.079	-0.037	0.844	6
	Czech Republic	0.735	0.019	-0.043	-0.024	0.687	12
	Estonia	0.753	-0.013	-0.003	0.024	0.762	10
	Greece	0.822	0.039	0.018	-0.058	0.820	8
	Hungary	0.792	0.002	0.048	0.011	0.853	5
	Ireland	0.999	0.145	0.130	0.066	1.000	1
	Korea	0.994	0.022	-0.013	0.024	1.000	1
	Latvia	0.742	-0.030	-0.053	0.031	0.690	11
	Lithuania	0.720	-0.025	-0.063	0.052	0.684	15
	Mauritius	0.914	-0.015	0.058	-0.003	0.954	3
	Mexico	0.730	-0.025	-0.023	-0.003	0.679	16
	Poland	0.723	-0.018	-0.023	0.004	0.686	13
	Portugal	0.782	0.044	0.048	-0.031	0.843	7
	Romania	0.621	-0.044	-0.033	-0.003	0.541	17
	Slovak Republic	0.777	0.000	0.038	-0.044	0.771	9
	Slovenia	0.843	0.036	-0.003	0.052	0.929	4
	South Africa	0.693	-0.005	0.048	-0.051	0.685	14
	Turkey	0.555	-0.043	-0.063	-0.037	0.411	19
	Average	0.757	0.000	0.000	0.000	0.739

Table 7 -

 7 Comparison of country scores and ranks across methods

			DEA Analyis		Public Sector	Two-step
						Efficiency (PSE)	correction
	Country	Input oriented	Output oriented
		Score	Rank	Score	Rank	Score	Rank	Score	Rank
	Brazil	0.381	22	0.488	22	0.69	23	0.530	18
	Bulgaria	0.461	14	0.483	23	0.77	22	0.408	20
	Chile	0.73	4	0.615	17	1.38	5	0.844	6
	Cyprus	0.489	11	0.867	3	1.08	8	-	-
	Czech Republic	0.439	15	0.637	13	0.85	17	0.687	12
	Estonia	0.489	12	0.632	14	0.91	12	0.762	10
	Greece	0.369	23	0.713	8	0.96	9	0.820	8
	Hungary	0.355	24	0.687	9	0.85	17	0.853	5
	Ireland	0.576	8	0.813	4	1.37	6	1.000	1
	Korea	0.749	3	0.743	6	1.65	3	1.000	1
	Latvia	0.486	13	0.624	16	0.91	12	0.690	11
	Lithuania	0.535	9	0.588	18	0.86	15	0.684	15
	Malta	0.408	19	0.753	5	0.78	21	-	-
	Mauritius	0.721	5	0.686	10	1.56	4	0.954	3
	Mexico	0.703	6	0.551	19	1.31	7	0.679	16
	Poland	0.412	18	0.627	15	0.83	19	0.686	13
	Portugal	0.385	21	0.678	11	0.82	20	0.843	7
	Romania	0.528	10	0.509	21	0.86	15	0.541	17
	Singapore	1	1	1	1	2.39	1	-	-
	Slovak Republic	0.406	20	0.674	12	0.92	11	0.771	9
	Slovenia	0.431	16	0.731	7	0.88	14	0.929	4
	South Africa	0.676	7	0.529	20	0.95	10	0.685	14
	Thailand	1	1	1	1	1.83	2	-	-
	Turkey	0.416	17	0.482	24	0.63	24	0.411	19
	Correlation	Score	Rank	Score	Rank	Score	Rank
	(DEA input, PSE)	0.91	0.77
	(DEA output, PSE)			0.71	0.56
	(DEA output, two-step)			0.92	0.93
	(PSE, two-step)					0.69	0.65

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

For a differing view on the limits of the stabilising effect of growing government, seeCuaresma, Reitschuler and Sillgoner (2005) and[START_REF] Buti | What is the Impact of Tax and Welfare Reforms on Fiscal Stabilisers? A Simple Model and an Application to EMU[END_REF].

See Afonso, Schuknecht, and Tanzi (2005) for a discussion of the several caveats of such approach.

Coelli et al. (1998) and[START_REF] Thanassoulis | Introduction to the Theory and Application of Data Envelopment Analysis[END_REF] offer introductions to DEA, while[START_REF] Simar | Efficiency analysis: the statistical approach[END_REF] and[START_REF] Murillo-Zamorano | Economic Efficiency and Frontier Techniques[END_REF] are good references for an overview of frontier techniques.

We present here the equivalent envelopment form, derived by[START_REF] Charnes | Measuring the efficiency of decision making units[END_REF], using the duality property of the multiplier form of the original programming model.

The choice of indicators is slightly different from that used in[START_REF] Afonso | Public Sector Efficiency: An International Comparison[END_REF]. In addtion to omitting public infrastructure, education is reflected only by a qualitative measure of education achievement (leaving out secondary school enrolment) and economic performance excludes the level of per-capita GDP (which in this sample would strongly bias in favour of the rich countries).

The relevant time period for the several sub-indicators varies a little according to the availability of data but is essentially reported to 2001/2003 with some variables being used as an average of longer time spans (see the Annex for the precise periods).

We did a similar correction exercise for model 2, where secondary school enrolment is statistically significant, but since the results were not very diferent, this is not reported, being worthwhile noticing that such factor has a positive contribution to the efficiency score of most new EU member states.

UECE is supported by FCT (Fundação para a Ciência e a Tecnologia, Portugal), financed by ERDF and Portuguese funds. , Washington, D. C.