1. Locus of decision-making and the
efficiency of the education sector
Marie Le Mouel, Alexander Schiersch, Marting Gornig
June 17, 2015

2. Introduction Data Data Envelopment Analysis Truncated regression Conclusion
Motivation
Education spending in the EU averages 6% of GDP, 90% of which
is public. How efficiently are these funds being spent?
In sum, the general pattern of the cross-country analyses suggests
that quantitative measures of school inputs such as expenditure
and class size cannot account for the cross-country variation in
educational achievement. By contrast, several studies tend to find
positive associations of student achievement with the quality of
instructional material and the quality of the teaching force.
Hanushek & Woessmann (2011)
SPINTAN Project: funded from the EU 7th Framework Programme. Grant agreement no: 612774.

3. Introduction Data Data Envelopment Analysis Truncated regression Conclusion
Research question and Methodology
Research questions
How are resources related to outputs in the education sector?
Is centralisation of decision-making related to the efficiency of
the education sector?
2-stage procedure `a la Simar and Wilson (2007)
Stage 1: Non-parametric estimation (DEA) of the production
function of education
2-input, 2-output framework for education sector as a whole
Input-oriented Sheppard inefficiency scores
Stage 2: Truncated regression:
Dependent variable: inefficiency scores
Explanatory variables: proxies for teacher quality and for locus
of decision-making (Woessmann, 2003)
SPINTAN Project: funded from the EU 7th Framework Programme. Grant agreement no: 612774.

4. Introduction Data Data Envelopment Analysis Truncated regression Conclusion
Summary of results
Proxy for teacher quality is consistently associated with lower
inefficiency in the education sector
Indicator of overall centralisation shows no significant
relationship to inefficiency
Breakdown by type of decision reveals that centralisation of
decisions relating to a) personnel management are associated
with higher inefficiency and to b) planning and structures (e.g.
programme design and accreditation) with lower inefficiency.
SPINTAN Project: funded from the EU 7th Framework Programme. Grant agreement no: 612774.

5. Introduction Data Data Envelopment Analysis Truncated regression Conclusion
Outline
1 Introduction
Motivation
Research question and Methodology
Summary of results
2 Data
Education output
Education inputs and environmental variables
3 Data Envelopment Analysis
Input-oriented Sheppard inefficiency score
4 Truncated regression
Estimation equations
Preliminary Results
5 Conclusion
Summary of the results
Robustness checks and next steps
SPINTAN Project: funded from the EU 7th Framework Programme. Grant agreement no: 612774.

6. Introduction Data Data Envelopment Analysis Truncated regression Conclusion
Overview
We construct a cross-country unbalanced panel:
23 countries: selection of EU countries, United States &
Japan
Period 2002-2010
Number of observations:
First stage: 158 observations
Second stage: 95 observations
Sources: Eurostat, Education at a Glance (OECD, 2011,
2012), World Input-Output Database (WIOD), OECD STAN
Database
SPINTAN Project: funded from the EU 7th Framework Programme. Grant agreement no: 612774.

7. Introduction Data Data Envelopment Analysis Truncated regression Conclusion
Education output
Education output
Quality-adjusted measure of the number of students going through
the education system
O’Mahony & Stevens (2009), Schreyer (2010), INDICSER (2012)
Compulsory Education (ISCED 0 to 2):
YCE = PISA ∗ (N0 + N1 + N2) (1)
Post-compulsory Education (ISCED 3 to 6):
YPCE =
ISCED6
ISCED3
Ni ∗ Wi (2)
where
Wi = pG
i pE
i Wi + (1 − pG
i )pE
i−1Wi−1 (3)
SPINTAN Project: funded from the EU 7th Framework Programme. Grant agreement no: 612774.

8. Introduction Data Data Envelopment Analysis Truncated regression Conclusion
Education inputs and environmental variables
Education inputs
1 Number of employees in the Education sector
2 Capital stock in the Education sector
Environmental variables
Teacher quality: relative teacher pay compared to GDP per
capita
Locus of decision making: % of decisions taken at each level
Central Intermediate School Sum
Personnel xp ... ... 100%
Resource xr 100%
Org. of instruction xi 100%
Planning & structures xs 100%
TOTAL
(xp+...+xs )
4 100%
SPINTAN Project: funded from the EU 7th Framework Programme. Grant agreement no: 612774.

9. Introduction Data Data Envelopment Analysis Truncated regression Conclusion
Input-oriented Sheppard inefficiency score
Bias-corrected input inefficiency scores, average over 2002-2010
0.511.52
Inefficiencyscores
SVN
SVK
USA
SWE
JPN
IRL
ESP
POL
ITA
GRC
NOR
BEL
FIN
GBR
PRT
DNK
AUT
NLD
CZE
HUN
DEU
FRA
EST
Inefficiency scores Bias-corrected inefficiency scores
Source: Authors calculations based on data from Eurostat, OECD and WIOD
SPINTAN Project: funded from the EU 7th Framework Programme. Grant agreement no: 612774.

10. Introduction Data Data Envelopment Analysis Truncated regression Conclusion
Estimation equation
Truncated Regression
θit = α + β1TeachSalariesit + β2j Decisionsjit + uit (4)
θit ≥ 1 (5)
SPINTAN Project: funded from the EU 7th Framework Programme. Grant agreement no: 612774.

11. Introduction Data Data Envelopment Analysis Truncated regression Conclusion
Summary Statistics
Variable Mean Std. Dev. Min. Max. N
Educ3to6 2427.9 765.3 1011.2 4142.8 158
Educ0to2 68.4 10.5 46.7 85.7 158
Employment 30.3 8.3 10 49.9 158
CapitalStock 1696.7 1058.6 174.5 4146 158
Inefficiency 1.2 0.2 1 2 158
BCInefficiency 1.3 0.3 1 2.4 158
Planning 40.9 30.9 0 100 114
Instruction 6.1 7.1 0 25 114
Resources 7.5 18.6 0 66.7 114
PersManag 18.1 19.5 0 70.8 114
Decisions central 18.2 14.5 0 61.1 114
Decisions school 47.4 19.6 21.4 96.4 114
Teacher salaries 1.2 0.2 0.6 1.7 132
SPINTAN Project: funded from the EU 7th Framework Programme. Grant agreement no: 612774.

12. Introduction Data Data Envelopment Analysis Truncated regression Conclusion
Preliminary Results
Summary decision-making index
Inefficiency BCInefficiency
(1) (2)
Teacher-salaries -.980 -1.341
(.308)∗∗∗ (.535)∗∗
Decisions-central .004 -.001
(.006) (.009)
Decisions-school .007 .009
(.004)∗ (.006)
cons 1.659 1.717
(.284)∗∗∗ (.475)∗∗∗
SPINTAN Project: funded from the EU 7th Framework Programme. Grant agreement no: 612774.

13. Introduction Data Data Envelopment Analysis Truncated regression Conclusion
Preliminary Results
Looking at decision areas
Decisions PersManag Res. Inst. Plan.
(1) (2) (3) (4) (5)
Teacher-salaries -1.484 -1.782 -1.528 -1.596 -1.152
(.620)∗∗ (.649)∗∗∗ (.650)∗∗ (.674)∗∗ (.413)∗∗∗
Decisions-central -.009
(.009)
PersManag .014
(.006)∗∗
Resources -.012
(.012)
Instruction .006
(.017)
Planning -.009
(.004)∗∗∗
cons 2.430 2.481 2.358 2.336 2.511
(.441)∗∗∗ (.499)∗∗∗ (.488)∗∗∗ (.509)∗∗∗ (.349)∗∗∗
SPINTAN Project: funded from the EU 7th Framework Programme. Grant agreement no: 612774.

14. Introduction Data Data Envelopment Analysis Truncated regression Conclusion
Preliminary Results
Looking at decision areas
PersPlan ResPlan InstPlan All
(1) (2) (3) (4)
Teacher-salaries -1.125 -1.152 -1.003 -1.030
(.341)∗∗∗ (.415)∗∗∗ (.408)∗∗ (.379)∗∗∗
PersManag .016 .016
(.004)∗∗∗ (.005)∗∗∗
Resources -.002 -.010
(.009) (.006)∗
Instruction .022 .004
(.012)∗ (.012)
Planning -.012 -.009 -.011 -.010
(.003)∗∗∗ (.003)∗∗∗ (.004)∗∗∗ (.003)∗∗∗
cons 2.485 2.507 2.307 2.360
(.312)∗∗∗ (.351)∗∗∗ (.373)∗∗∗ (.381)∗∗∗
SPINTAN Project: funded from the EU 7th Framework Programme. Grant agreement no: 612774.

15. Introduction Data Data Envelopment Analysis Truncated regression Conclusion
Summary of the results
Relative teacher pay is consistently associated with lower
school inefficiency
No significant relationship between inefficiency and aggregate
centralised decision-making
Breakdown by type of decisions supports microeconomic
theory: making personnel management decisions at
decentralised level improves performance if there are external
standards to assess the quality of instruction (e.g. external
exams and curricula).
SPINTAN Project: funded from the EU 7th Framework Programme. Grant agreement no: 612774.

16. Introduction Data Data Envelopment Analysis Truncated regression Conclusion
Robustness checks and next steps
1 Bootstrapped confidence intervals - not reported here
2 Test for frontier shift
Conditional DEA
Semi-parametric approaches (e.g. SFA)
3 Include Intangible capital stock as a third input
SPINTAN Project: funded from the EU 7th Framework Programme. Grant agreement no: 612774.

17. Thank you for your attention
DIW Berlin – Deutsches Institut
f¨ur Wirtschaftsforschung e.V.
Mohrenstraße 58, 10117 Berlin
www.diw.de

18. Introduction Data Data Envelopment Analysis Truncated regression Conclusion
References
Barslund, M. & M. O’Mahony (2012). Output and Productivity in the Education Sector. INDICSER Discussion
paper 39
Hanushek, E. & L. Woessmann (2011). The Economics of International Differences in Educational Achievement. In
Eric A. Hanushek, Stephen Machin, and Ludger Woessmann, Handbooks in Economics, Vol. 3, The Netherlands.
Maimaiti, Y.& M. O’Mahony (2012). Quality adjusted education output in the EU. INDICSER Discussion paper 37
O’Mahony, M., J. Pastor, F. Peng, L. Serrano & L. Hernandez (2012). Output growth in the post-compulsory
education sector: the European Experience. INDICSER Discussion paper 32
O’Mahony, M. & P. Stevens (2009). Output and productivity growth in the education sector: comparison for the
US and the UK. Journal of Productivity Analysis, 31:177-194
Schreyer, P (2010). Towards Measuring the Volume Output of Education and Health Services: A Handbook.
OECD Statistics Working Papers 2010/02
Simar, L. & P. Wilson (2007). Estimation and inference in two-stage, semi-parametric models of production
processes. Journal of Econometrics 136(2007) 31-64
Woessmann, L (2003). Schooling Resources, Educational Institutions and Student Performance: the International
Evidence. Oxford Bulletin of Economics and Statistics, 65:2
SPINTAN Project: funded from the EU 7th Framework Programme. Grant agreement no: 612774.