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Locus of decision-making and the
efficiency of the education sector
Marie Le Mouel, Alexander Schiersch, Marting Gornig
June 17, 2015
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Thank you for your attention
DIW Berlin – Deutsches Institut
f¨ur Wirtschaftsforschung e.V.
Mohrenstraße 58, 10117 Berlin
www.diw.de
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.

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14th European Workshop on Efficiency and Productivity Analysis (EWEPA)

  • 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.