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

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the performance of the public sector

The performanceof the public sector

Pierre Pestieau

CREPP, University of Liège,

CORE, PSE and CEPR

outline
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

1 introduction
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.
2 the performance approach and the concept of best practice
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
slide5
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.
slide6

Illustration with one input/one output

  • Set of observations
  • Best practice frontier
    • Non parametric method: DEA (data envelopment analysis)
    • Parametric method
  • Comparative advantage
slide8

Parametric

output

input

Figure 2

slide9

Non Parametric

output

input

Figure 3

slide10

t+1

output

b

g

b

t

c

a

a

A

B

input

Figure 4

slide11

Technical progress: ga

Efficiency in t:

in t + 1:

Change in efficiency: ca - ga

slide12

Motivation of efficiency study: performance improvement

  • Factors of inefficiency:
    • Exogenous (location)
    • Endogenous (low effort)
    • Policy related (ownership, competition)
3 measuring productive efficiency conceptual and data problems
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.

slide14

3.1. The best evidence

Inter-country comparison.

Importance of institutional, political and geographical factors.

slide17

3.2. Actual studies

Most qualitative variables are missing.

Difference between developed and less developed countries.

Focus on financial variables.

slide18

Railways

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

slide19

Education

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

slide21

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.
slide22

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.
slide23

Actually:

    • Data on indicators of social inclusion (or exclusion);
    • Data on social spending.
slide24

Three issues:

    • Aggregation: DEA or SPI,
    • Scaling: (0,1) or average or goalposts,
    • Use of inputs: performance versus inefficiency.
table 1 indicators of exclusion definition and correlations
Table 1: Indicators of exclusion. Definition and correlations

Source: The five indicators are taken from the Eurostat database on Laeken indicators (2007).

difference in shadow prices
Difference in shadow prices

SPI1 and SPI2

Correlation: 0.9

Dependent on irrelevant alternatives.

slide28

DEA with same input:

- DEA1: 0.921

- DEA2: 0.990

DEA is not invariant to non linear transformation.

- DEA3: 0.992

slide29

q1

A

D*

B

E*

D

E

F*

C

F

q2

0

Figure 1: DEA1 frontier

slide30

Table 3: DEA efficiency scores. 2004

Note: DEA1, DEA2 and DEA3 results correspond to HDI, Afonso et al. and “goalspot” normalization data respectively.

measuring performance or efficiency
Measuring performance or efficiency
  • Problem: weak link between social spending and education, health, unemployment.
  • Ranking modified
slide33

Table 5

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

race to the bottom
Race to the bottom?
  • Test of convergence
  • SPI1 and Malmquist decomposition
slide35

9%

y = -1.2741x + 1.0326

2

8%

ES

R

= 0.8024

7%

PT

IE

6%

IT

UK

5%

4%

Growth rate of SPI1 (1995-2004)

LU

GR

BE

3%

FR

2%

AT

NL

DE

1%

DK

FI

SE

0%

-1%

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

SPI1 - 1995

Figure 6: Convergence of SPI1

slide36

5%

y = -0.0862x + 0.0853

IT

ES

2

R

= 0.9468

4%

3%

GR

UK

Average Effciciency change 1995-2004

2%

BE

FR

DE

1%

NL

IE

FI

0%

PT

LU

SE

AT

DK

-1%

0,4

0,5

0,6

0,7

0,8

0,9

1,0

1,1

DEA1 1995

Figure 7: Convergence of DEA1 according to “technical efficiency” change

5 conclusion
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.