Lesson 5. Education Production Function
J. Oriol Escardíbul
Welfare Economics – Economics of Education
Master in Economics (UB) - 2015
Index
1. The production function. Definition
2. The explained variable: measuring the output
3. Inputs: observed and unobserved
4. Observed inputs. Individual & family level
5. Observed inputs. School level
6. Observed inputs. Education system level
7. Observed inputs. Others
1. The production function. Definition
•A
set of variables (inputs) determine the output of the
educational process.
• The
effect of each input is isolated from the other inputs by
means of multivariate techniques.
• Multivariate
techniques: OLS
& IV regression, multilevel
regression, logit/probit, regression discontinuity, dif-in-dif,
propensity score matching....
2. The explained variable: measuring the output (I)
• Years of schooling completed
• Level
• In
of schooling achieved
recent years, key competences (reading, mathematics and
science) are considered a good proxy of the output in primary
and secondary education.
• Universities:
multi-output process (research should be taken
into account).
2. The explained variable: measuring the output (II)
• External evaluations vs.
internal evaluations.
• Importance of evaluations
• IEA:
implemented by:
International Association for the Evaluation of
Educational Achievement (PIRLS and TIMMS)
• OECD
(PISA, PIAAC).
• Proliferation
of external evaluations in the educational system.
The example of the USA after 2003 No Children Left Behind.
3. Inputs: observed and unobserved
•
Observed inputs: by means of surveys linked to competence
evaluation. Information about the individual, the family and
the school.
•
Unobserved inputs: genetic differences? Psychological
characteristics? No information because:
•
Not accessible with current knowledge
•
Too complex and/or expensive to access.
4. Observed inputs. Individual & family level
Personal characteristics
•
Gender (girls better in reading; boys better in science & mathematics)
•
Previous educational trajectories (e.g. importance of early childhood
education: 0-3 years).
•
Origin: natives better than immigrants (especially first generation)
•
Course repetition (-)
•
Truancy (-)
Family characteristics
•
Human capital (parent’s education)
•
Physical resources at home (books, computers, Internet)
•
Aggregated indexes: ESCS index in PISA (index of economic, social and
cultural status).
5. Observed inputs. School level (I)
•
Ownership (public/private). The role of parents’ ESCS
•
Resources:
•
Size: Pupil/teacher ratios. Class size.
•
Teachers: Qualifications, Skills, Training, Experience, Gender,
Wage (general/ pay-performance).
•
Physical resources:
No average effect but an equitable distribution of
physical resources between centers is + (OECD, 2013).
•
ICT resources. International evidence inconclusive.
•
Peer effects (by parent’s education, immigrant density…).
•
School management: repetition, streaming, autonomy…
5. Observed inputs. School level (II)
Teachers: characteristics (qualitative)
- Skills (test results USA; PIAAC+PISA)
+
- Diploma/certificates (USA)
+
- Labour experiencie (decreasing)
+
- Wages
+
- Pay-performance wages
+
- Teacher distribution among centers (better/worse teachers to
better/worse centers)
+
Teachers: Quantivative
- Class-size/student-teacher ratio (worse cost/benefit)
relationship (better outcomes below 20 students: STAR- USA)
+
Principals
- Wage, experience & seniority
+
5. Observed inputs. School level (III)
6. Observed inputs. Education system level
•
School competition / School evaluation / School autonomy.
In Spain, little evidence that greater autonomy, competition between centers or
accountability (advertising results) improve educational performance.
International evidence exaggerates positive relationship:
•
•
•
Positive evidence not in all countries and in all types of autonomy.
Positive relationship between autonomy if there are external evaluation
systems/results are published. Otherwise the effect of autonomy is negative
(Woessmann et al., 2007; Hanushek et al., 2011). No effect when centers are divided
by ownership (Benton, 2014).
Tracking. Students’ early separation/selection increases educational inequality and does not
improve outcomes (Hanushek & Woessmann, 2006). Partial positive effect (some subjects) of
introduction of a comprehensive school, system in Finland (Pekkarinen et al., 2009).
7. Observed inputs. Others (I)
•
Expenditure on education
•
Residence:
•
Municipality (population)
•
Region
7. Observed inputs. Others (II)
•
Expenditure on education
7. Observed inputs. Others (III)
Public expenditure
Internat.
large
samples
Hanushek & Kimko (2000), Lee & Barro
(2001)
No
Chaudhuri & Maitra (2008), McMahon
(1999)
Yes
MetaAnalysis
Hanushek (1986, 1997, 2003)
No
Hedges et al. (1994), Dewey et al.
(2000), Krueger (2003)
Yes
Donato y Ferrer (2012): PISA-2009.
Mora, Escardíbul & Espasa (2010).
Drop-out
Morales y Pérez (2011). Drop-out
Levacic et al. (2005), Jenkins et al.
(2006), Holmlund et al. (2010), Gibbons
et al. (2011), Machin et al. (2010)
Progresa en México, Bolsa Escola de
Brasil. Israel, some US cities & states
Dynarski (2003), Dynarski & ScottClayton (2013), Mealli & Rampichini
(2012), Mediavilla (2013)
Regional
expend.
(SPAIN)
Lowperform.
schools
(UK)
Demandside
(families)
Scholarships
Yes
Yes
Yes
Yes
Some references
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Angrist, J.D. & Lavy, V. (1999). Using Maimonides’ rule to estimate the effect of class size on scholastic
achievement. Quarterly Journal of Economics, 114, 533–75.
Calero, J. & Escardíbul, J.O. (2007). Evaluación de servicios educativos: el rendimiento en los centros
públicos y privados medido en PISA-2003. Hacienda Pública Española, 183(4), 33-66.
Chingos, M. M. (2013). Class Size and Student Outcomes: Research and Policy Implications. Journal of Policy
Analysis and Management, 32(2), 411–438.
Coleman, J.S., Campbell, E.Q., Hobson, C.J., McPartland, J., Mood, A.M., Weinfeld, F.D. &York, R.L. (1966).
Equality of Educational Opportunity. Washington, DC: US Government Printing Office.
Dolton, P. & Marcenaro-Gutierrez, O. D. (2010). If you pay peanuts do you get monkeys? A cross country
analysis of teacher pay and pupil performance. Economic Policy, 26(65), 5-55.
Fuchs, T. & Woessmann, L. (2007). What accounts for international differences in student performance? A
reexamination using PISA data. Empirical Economics, 32(2-3), 433-462.
Hanushek, E.A. (1979). Conceptual and empirical issues in the estimation of educational production
functions. Journal of Human Resources, 14, 351–88.
Hanushek, E.A. (2003). The failure of input-based schooling policies. Economic Journal, 113, F64–F98.
Hoxby C. M. (2000). The Effects of Class Size on Student Achievement: New Evidence from Population
Variation.” Quarterly Journal of Economics, 115(4), 1239-1285.
Krueger, A.B. (1999). Experimental estimates of education production functions. Quarterly Journal of
Economics 114, 497–532.
Krueger, A. B. (2003). Economic considerations and class size. Economic Journal, 113(485), F34-63.
Rivkin, S.G., Hanushek, E.A. & Kain, J.F. (2005). Teachers, schools, and academic achievement. Econometrica
73, 417–58.
Sacerdote, B. I. (2001). Peer effects with random assignment. Quarterly Journal of Economics, 116(2), 681704.
Vignoles, A., Levacic, R., Walker, J., Machin, S. & Reynolds, D. (2000). The relationship between resource
allocation and pupil attainment: a review. Working Paper CEEDP, 2. CEE. London: LSE.
Woessmann, L. (2003). Schooling resources, educational institutions, and student performance: The
international evidence. Oxford Bulletin of Economics and Statistics, 65(2), 117-170.