Admission to Selective Schools

1. Admission to Selective Schools Štěpán Jurajda and Daniel Münich CERGE-EI ALPHABETICALLY CentER, 2008

2. Introduction • ‘Alphabetical order’ is omnipresent. Makes people ask about advantage to ‘A’ individuals. (The Economist 2001) • Sorting of applicants is important for allocating a prize even when allocation is based on performance (ability). (van Ours and Ginsburgh, 2003) • We ask whether ‘Z’ students face lower chances of being admitted to oversubscribed Czech schools than ‘A’ ones. • Anecdotal evidence: • Sorted lists with multiple characteristics • Oral exams • Breaking ties • Repeated use of alphabetical sorting (the same lottery ticket) at entry to both secondary and tertiary education can lead to efficiency losses. • We find evidence consistent with substantial effects, even though no one seems to be aware of this.

3. ‘Names’ Research • Alphabetical sorting in academia and citation bias: • Einav and Yariv (2006), Praag and Praag (2008) Katuscak et al. (2005). • Racial attributes of first names: Bertrand and Mullainathan (2003) and Fryer and Levitt (2003) • Last names and marriage: Golding and Shim (2004)

4. Hypotheses and tests • ‘Z’s face lower chances of admission to selective schools. • Hence, ability-alphabet sorting arises within school types. • Assume ability and alphabetical position are independent. • Ability • ‘A’HighMarginal Low • ‘Z’HighMarginal Low • Admission/ Rejection • 3. Formalization with noisy admission tests suggests the ability gap between ‘Zs’ and ‘As’ is larger in more selective schools. • 4. A natural check: do first-name initials matter?

5. Some formalization • Admissions based on lexicographic order on S (score) and N (alphabet position): • discrete admission test Sreflects ability a ~ N(0,1) • ST - admission test score threshold for Directly Admitted • selection among Marginal applicants based alphabet; their share is m. • NT- last-name initial of the last marginal student admitted. • ability gap between ‘Z’ and ‘A’ students grows with admission selectivity:

6. The Czech Education System in our Data • A selective education system: • academic secondary programs cover only 15% of cohort • tertiary attainment rate very low at 12% • Admission rate: 63% for academic secondary schools; • 30% of applications (50% of applicants) admitted to colleges • Population data: • 1999 national high-school leaving examination ‘Maturita’ + administrative register of all applications to universities, but we don’t see the college admission test. • Can’t differentiate schools that actually use the alphabet: • department-specific procedures, but faculty-level code, • there are no records of 1999 practices, • it is difficult to ask directly.

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9. Test Score Analysis • Assume that ‘Maturita’ test scores reflect ability and look across schools displaying different degree of selection (admission rates). • Except for 40% in short apprenticeship programs with no hope of further education, everyone takes the test => some alphabet-ability sorting across entire districts. • Selective schools have higher admission standards (even within academic ones) as reflected in share of applicants admitted to college, in ‘Maturita’ test scores, and in grades at primary school of last 3 admitted (SET96). • Density at margin is higher in academic programs (PISA) (m does not shrink).

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11. So, what do we do? • Test for predictions of the alphabet-based admissions: • ability-alphabet sorting across secondary schools • direct evidence on college admissions 10

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16. Secondary School Test Score Analysis • Students with surnames sorted low in the alphabet do achieve higher test scores on average. • This is fully robust to including school fixed effects. • This sorting “effect” is stronger in more over-subscribed schools, as predicted. • => Ability-alphabet sorting suggestive of alphabet-based admissions at the secondary school level. • Esp. given some evidence of no alphabet-ability sorting among students graduating from primary-level programs: 10k 2005 commercial practice test scores of 9th graders.

17. College Admission Analysis • Want to test whether marginal applications are affected. But do not observe school-specific admission test scores. • identify school-specific marginal applications from data. • Assign applications percentile rankings based on school-specific admission regressions using Maturita test scores and secondary school avg. success rate. • Look at those close to the median predicted admission probability. Control for faculty/college excess demand.

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19. LAST-NAME INITIAL COEFF. ACROSS PREDICTED ADMISSION RANGE

20. LAST-NAME INITIAL STEP-FUNCTION SPECIFICATION for the 40-60 range

21. Size of Estimated Effects • In a very selective secondary school (50% admission rate), “moving” from ‘A’ to ‘Z’ corresponds to a move from the median mathematics test score to the 60th percentile, • about 10% of standard deviation in academic programs. • So, among marginal college applicants (a group with indistinguishable admission test scores), ‘Zs’ are smarter. In presence of noisy entrance exams, colleges should choose ‘Z’ over ‘A’ among marginal applicants. • But college admission estimates suggest “moving” from ‘A’ to ‘Z’ reduces admission chances by 2 percent. Reflects mix of schools with and without “alphabet” admissions.

22. Calibrating the Efficiency Loss • Simulate population graduating from secondary schools: • Generate 9th graders’ ability, take them through our model admission procedure (70% admission rate). • Assume schools don’t change ability gap. • Run test score regressions -- they match Table 2. • Next, simulate college admissions (50% admission rate). • => Should colleges apply reverse alphabetical order, they would improve matches for 52% of marginal applicants from academic secondary programs. • Accounting for the share of this group on all admitted, the repeated use of the alphabetical order may lead to inefficient school-student matches for about 5% of students admitted to Czech universities. • In reality, those not admitted to one school may enroll in another. Czech universities have some of the lowest program completion rates in the EU: CR 63% , OECD 70%, Germany 75%, Portugal 66%, Turkey 76 %.

23. Could There Be a Wage Effect? • Ability-alphabet sorting suggest a wage effect within school type, to the extent that wages reflect ability. • If wages rise with ability the same way for workers with different education, there would be no effect of the alphabet on wages on average. • Use a 1996 household sample and look at male wages.

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25. Conclusion • Sorting may affect allocation of rationed public services. We have a unique opportunity to study a sorting name-based mechanism affecting entire cohorts. • We never document how treatment works and whether it exists at all, but support for the hypothesis comes from different data and school types. • It is also reassuring that first name initial is not siggy. • The use of the seemingly non-discriminatory alphabetical order may have not only distributional but also efficiency consequences. • FRIN on the use of alphabet in public decision making.

26. Gender Gap in Performance under Competitive Pressure • Gneezy et al. (2003): experimental evidence that women are less effective in competitive environments, even if they perform similarly well in non-competitive settings. • Field tests using competitive nature of education process: Örs et al. (2008): applicants to a French business school. Show that within this group, women outperform their male colleagues in non-competitive comprehensive tests, but lag behind men in the competitive admission process. • We perform similar analysis using entire cohort of Czech secondary-school graduates applying to all universities. • ? admission chances at a given university of men and women with similar non-competitive test scores • ? gender gap in performance under a varying degree of competition • + can control for field-specific unobservable ability.

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