14th european workshop on efficiency and productivity analysis (ewepa)

Locus of decision-making and theefficiency 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 whichis public. How efficiently are these funds being spent?

In sum, the general pattern of the cross-country analyses suggeststhat quantitative measures of school inputs such as expenditureand class size cannot account for the cross-country variation in

educational achievement. By contrast, several studies tend to findpositive 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.


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 ofthe education sector?

2-stage procedure a la Simar and Wilson (2007)

Stage 1 : Non-parametric estimation (DEA) of the productionfunction of education

2-input, 2-output framework for education sector as a wholeInput-oriented Sheppard inefficiency scores

Stage 2 : Truncated regression:

Dependent variable: inefficiency scoresExplanatory variables: proxies for teacher quality and for locusof decision-making (Woessmann, 2003)



Summary of results

Proxy for teacher quality is consistently associated with lowerinefficiency in the education sector

Indicator of overall centralisation shows no significantrelationship to inefficiency

Breakdown by type of decision reveals that centralisation ofdecisions relating to a) personnel management are associatedwith higher inefficiency and to b) planning and structures (e.g.programme design and accreditation) with lower inefficiency.



Outline

1 IntroductionMotivationResearch question and MethodologySummary of results

2 DataEducation outputEducation inputs and environmental variables

3 Data Envelopment AnalysisInput-oriented Sheppard inefficiency score

4 Truncated regressionEstimation equationsPreliminary Results

5 ConclusionSummary of the resultsRobustness checks and next steps



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 observationsSecond stage: 95 observations

Sources: Eurostat, Education at a Glance (OECD, 2011,2012), World Input-Output Database (WIOD), OECD STANDatabase



Education output

Education output

Quality-adjusted measure of the number of students going throughthe 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)

whereWi = pGi p

Ei Wi + (1 − pGi )pEi−1Wi−1 (3)



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 percapita

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%



Input-oriented Sheppard inefficiency score

Bias-corrected input inefficiency scores, average over 2002-2010

0.5

11.

52

Inef

ficie

ncy

scor

es

SV

NS

VK

US

AS

WE

JPN

IRL

ES

PP

OL

ITA

GR

CN

OR

BE

LF

ING

BR

PR

TD

NK

AU

TN

LDC

ZE

HU

ND

EU

FR

AE

ST

Inefficiency scores Bias-corrected inefficiency scores

Source: Authors calculations based on data from Eurostat, OECD and WIOD



Estimation equation

Truncated Regression

θit = α + β1TeachSalariesit + β2jDecisionsjit + uit (4)

θit ≥ 1 (5)



Summary Statistics

Variable Mean Std. Dev. Min. Max. NEduc3to6 2427.9 765.3 1011.2 4142.8 158Educ0to2 68.4 10.5 46.7 85.7 158Employment 30.3 8.3 10 49.9 158CapitalStock 1696.7 1058.6 174.5 4146 158

Inefficiency 1.2 0.2 1 2 158BCInefficiency 1.3 0.3 1 2.4 158

Planning 40.9 30.9 0 100 114Instruction 6.1 7.1 0 25 114Resources 7.5 18.6 0 66.7 114PersManag 18.1 19.5 0 70.8 114Decisions central 18.2 14.5 0 61.1 114Decisions school 47.4 19.6 21.4 96.4 114Teacher salaries 1.2 0.2 0.6 1.7 132



Preliminary ResultsSummary 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)∗∗∗



Preliminary ResultsLooking 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)∗∗∗



Preliminary ResultsLooking 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)∗∗∗



Summary of the results

Relative teacher pay is consistently associated with lowerschool inefficiency

No significant relationship between inefficiency and aggregatecentralised decision-making

Breakdown by type of decisions supports microeconomictheory: making personnel management decisions atdecentralised level improves performance if there are externalstandards to assess the quality of instruction (e.g. externalexams and curricula).



Robustness checks and next steps

1 Bootstrapped confidence intervals - not reported here

2 Test for frontier shift

Conditional DEASemi-parametric approaches (e.g. SFA)

3 Include Intangible capital stock as a third input


Thank you for your attention

DIW Berlin – Deutsches Institutfur Wirtschaftsforschung e.V.Mohrenstraße 58, 10117 Berlinwww.diw.de


References

Barslund, M. & M. O’Mahony (2012). Output and Productivity in the Education Sector. INDICSER Discussionpaper 39

Hanushek, E. & L. Woessmann (2011). The Economics of International Differences in Educational Achievement. InEric 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-compulsoryeducation sector: the European Experience. INDICSER Discussion paper 32

O’Mahony, M. & P. Stevens (2009). Output and productivity growth in the education sector: comparison for theUS 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 productionprocesses. 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


14th european workshop on efficiency and productivity analysis (ewepa)

Economy & Finance