The performance of the public sector Pierre Pestieau CREPP, University of Liège, CORE, PSE and CEPR

2 Outline 1. Introduction 2.The performance approach and the concept of best practice 3. Measuring productive efficiency 4. The performance of social protection 5. Conclusion

3 1. Introduction Measuring and ranking: a must  People do it anyway but badly  Transparency and governance  Yardstick competition – Open Method of Coordination (OMC) Important distinction between the public sector as a whole and its components  Problem of aggregation  Technical link between outcomes (outputs) and resources (inputs) The performance is to be measured by the extent to which the preassigned objectives are achieved.

4 2. The performance approach and the concept of best practice The public sector is a set of more or less aggregated production units (social security administration, railways, health care, education, national defence, social protection,…) Each unit is supposed to use a number of resources, within a particular setting, to produce a number of outputs Those outputs are related to the objectives that have been assigned to the production unit by the principal, the authority in charge Approach used here: productive efficiency and to measure it, the efficiency frontier technique is going to be used

5 Productive efficiency is just a part of an overall performance analysis. It has two advantages:  It can be measured  It is a necessary condition for any other type of objectives Main drawback: it is relative  Based on a comparison among a number of rather similar production units  Its quality depends on the quality of the observation units.

6 Set of observations Best practice frontier  Non parametric method: DEA (data envelopment analysis)  Parametric method Comparative advantage Illustration with one input/one output

7 output input Set of comparable observations Figure 1

8 output input Parametric Figure 2

9 output input Non Parametric Figure 3

10 output input   b c  AB a t+1 t Figure 4

11 Technical progress:  Efficiency in t: in t + 1: Change in efficiency: ca - 

12  Motivation of efficiency study: performance improvement  Factors of inefficiency:  Exogenous (location)  Endogenous (low effort)  Policy related (ownership, competition)

13 3. Measuring productive efficiency. Conceptual and data problems Two problems.  Weak link between the inputs used and the expected outcomes  Confusion between lack of data and conceptual difficulties Research strategy. Two areas quite typical of public spending: education and railways transports; how performance should be measured if data availability were not a constraint? More precisely, when listing the outputs and the inputs, assume that the best evidence one can dream of is available.

The best evidence Inter-country comparison. Importance of institutional, political and geographical factors.

15 Railways

16 High schools

Actual studies Most qualitative variables are missing. Difference between developed and less developed countries. Focus on financial variables.

18 Note: v = OK; ~ = more or less; – = unavailable Railways

19 Note: v = OK; ~ = more or less; – = unavailable Education

20 Sector and authors Number of units Type and period of data Number of outputs and inputsMethod Mean efficiency degrees Remarks and other findings Education Rhodes and South-wick (1988) 64 public and 57 private universities in US Panel annual 1971, 1974, outputs 5 inputs Non parametric About 88% a year - Private universities have slightly hither efficiency scores, for everyyear considered Railways Oum & Yu (1991) 21 railways companies Annual data1 output Parametric1 each year- Limited evidence has been found for a relationship between the share of state in capital and cost efficiency - Positive correlation appears between cost efficiency and the importance of the cantons’s participation in the deficit of firms Filippini & Maggi (1991) 57 railways under mixed ownership Annual data output 3 inputs +2 network characteristics Non parametric 81%- Tendered services have higher efficiency scores that non-tendered ones. Productive efficiency comparative studies of public and private firms

21 Is it worth the amount of time? Yes, but with caution  Technical efficiency is just one aspect of efficiency.  Lack of quantitative variables may distort the results.  For education importance of employability.

22 4. Measuring the performance of the public sector as a whole  Ideally:  Data on happiness (average and distribution) with and without social protection or at least on how the welfare state fulfils its objectives: health, education, employment, poverty alleviation, inequality reduction;  Data on inputs.

23  Actually :  Data on indicators of social inclusion (or exclusion);  Data on social spending.

24  Three issues :  Aggregation: DEA or SPI,  Scaling: (0,1) or average or goalposts,  Use of inputs: performance versus inefficiency.

25 Table 1: Indicators of exclusion. Definition and correlations Definition POVAt-risk-of-poverty rate INEInequality UNELong term unemployed EDUEarly school leavers EXPLife expectancy Correlation POVINEUNEEDUEXP POV1.000 INE UNE EDU EXP Source: The five indicators are taken from the Eurostat database on Laeken indicators (2007).

26 Table 2: HDI normalization and SPI

27 Difference in shadow prices SPI1SPI2 POV INE UNE EDU EXP Correlation: 0.9 Dependent on irrelevant alternatives. SPI1 and SPI2

28 DEA with same input: - DEA1: DEA2: DEA is not invariant to non linear transformation. - DEA3: 0.992

29 Figure 1: DEA1 frontier q1q1 q2q2 0 C A B D E D* E* F* F

30 Note: DEA1, DEA2 and DEA3 results correspond to HDI, Afonso et al. and “goalspot” normalization data respectively. Table 3: DEA efficiency scores. 2004

31 Table 4: Correlations between indexes

32 Measuring performance or efficiency Problem: weak link between social spending and education, health, unemployment. Ranking modified

33 Table 5 DEA efficiency scores without and with social expenditures as input. 2004

34 Race to the bottom? Test of convergence SPI1 and Malmquist decomposition

35 SE FI DK DE NL AT FR LU BE GR UK IT IE PT ES y = x R 2 = % 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 0,10,20,30,40,50,60,70,80,91,0 SPI Growth rate of SPI1 ( ) Figure 6: Convergence of SPI1

36 IT ES GRUK BE FRDE NL IE AT DK FI LUPTSE y = x R 2 = % 0% 1% 2% 3% 4% 5% 0,40,50,60,70,80,91,01,1 DEA Average Effciciency change Figure 7: Convergence of DEA1 according to “technical efficiency” change

37 5. Conclusion  Yes for efficiency measures when the production technology is well understood.  Caution when the technology is unclear and environmental variables are missing.  For the welfare states, ranking performance is preferable.  DEA is to be preferred over SPI.  No clear guidelines on the choice of scaling.