Knocking out the Supply and Sorting in Decentralized Job Markets - - PowerPoint PPT Presentation

Knocking out the Supply and Sorting in Decentralized Job Markets

Guillaume Haeringer Universitat Aut`

• noma de Barcelona

Vincent Iehl´ e Universit´ e Paris Dauphine N´ uria Rodriguez-Planas Universitat Aut`

• noma de Barcelona

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Introduction

Many-to-one markets using Deferred Acceptance with the “many side” proposing: students and schools (e.g., NYC, Boston); students and universities (Spain); job candidates and academic departments (France!!!!!).

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Introduction

Strategic behavior: Proposing side: not an issue, incentive to be truthful (really?) Receiving side: the game is manipulable. Some theoretical characterizations of optimal play has been proposed (Roth and Rothblum (1999), Ehlers (2004), Coles (2009)). In practice? Little is known.

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Introduction

Questions: How do actors on the “receiving side” behave? How does this behavior affect market outcomes?

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Introduction

This paper: Describes the French Academic job market (junior and senior levels), link it to the Deferred Acceptance game. Documents on the behavior (and the market outcome)

• f the job market for junior mathematicians.

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The French job market

Since 1984 the French job market is a centralized market. A year before the market:

• 1. Departments negociate with the universities about the
• penings
• 2. Universities negociate with the Ministry of higher

education

• 3. Ministry makes the final decision.

⇒ Departments have little control over the number of

• penings.

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The French job market

The job market is organized into “sections”, one per field (or subfield). There are 74 different sections (122 if including medical sub-sub-sections): 5 = economics; 32 = organic, mineral and industrial chemistry 25 = (pure) mathematics, 26 = applied mathematics. Most positions are advertised for 1 section, but some positions are open to different sections.

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The French job market

December-January: Candidates (with a PhD degree) apply to a national committee for the “right” to enter the market (valid for 4 years). Candidate OK for section X can apply to section Y (but usually not considered). February: job openings are anounced. Candidates send their package to the departments March-April: Departments announce the list of candidates to be interviewed.

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The French job market

May: Interviews take place (3 weeks period). Departments rank the candidates (up to 5 per position). June: Candidates submit their “preferences”. End of June-early July: final assignment published.

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The French job market

The “matching algorithm” used by the French Ministry combines three algorithms:

• 1. Top-top match:

Match a candidate and a department if they are each

• ther’s first choice.

Update candidates’ submitted preferences and Departments’ rankings, and repeat.

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The French job market

If “top-top” does not produce a complete matching:

• 2. “Clean the lists”:

Delete the vows that will never be expressed. Example: Candidate i ranked k-th at university X, and

X is i’s first choice.

Delete the candidates ranked after i at X in X’s ranking. Delete X from those candidates’ preferences. Do “top-top” again.

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The French job market

If “top-top” + “clean-the-lists” do not produce a complete matching:

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The French job market

If “top-top” + “clean-the-lists” do not produce a complete matching:

• 3. Roth and Sotomayor (1990), page 27

(with candidates-proposing).

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The French job market

If “top-top” + “clean-the-lists” do not produce a complete matching:

• 3. Roth and Sotomayor (1990), page 27

(with candidates-proposing). In the last 25 years, across all 74 sections, step 3 was activated about 10 times!

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The French job market

Quick summary: The job market is very “costly” Not filling a position is the worst outcome for the departments; Participants have little understanding of the assignment procedure (i.e., the algorithm).

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The French mathematicians

In 1998 a group of mathematicians set up a web-side: “opération postes”. It records in real-time: Job openings (with field label) + list of candidates with the right to enter the market; interview schedules; candidates’ rankings by the departments.

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Our data

Our data covers the period 1999-2007. It also includes: Year and origin of PhD of all ranked candidates (some candidates are interviewed but not ranked). All publications (as from 1990) by: all ranked candidates; all hiring institutions;

→ About 50’000 publications.

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Our data

Publication records include: year; journal (matched to its impact factor); AMS (ordered) subfields (in order of importance). (“JEL codes” for mathematicians)

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Our data

We also have: The final match; But we do not have: Candidates’ preferences.

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Overview

The French job market for (junior) mathematicians is about: Tenure positions. Low salary, high teaching load, little room for negociation. 100 positions per year (min = 73, max = 123). Candidates (per year):

≥ 500 on the market

330–450 candidates interviewed (average: 4 times the number of positions) 200–250 candiates ranked (about twice the number

• f positions).

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Issue & Observation

Departments are quite good at “targeting” their candidates (coordination?) Distribution of ranks of matched candidates 1 2 3 4 5 70 % 18 % 7.3% 2.9 % 1.8%

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Issue & Observation

In general, we need both sides’ preferences to compute the

• match. But in some cases we don’t:

Candidate A ranked first by some department X. Only department X ranked A. A’s only possible choice is whether A acceptable or not. If acceptable (always is), A takes the job at X. Candidates ranked by X below A are thus ranked one time less.

• Repeat. . .

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Issue & Observation

Positions filled without needing candidates’ preferences: year

• pos. filled

% of all positions 1999 39 32.5 2000 45 50.5 2001 33 38.4 2002 40 57.1 2003 49 50.5 2004 36 49.3 2005 37 41.5 2006 41 33.6 2007 52 45.2

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Who’s the culprit?

Candidates ranked first at some date not ranked at a later date: Their obtained their most preferred department, no need to attend more interviews (and thus are not ranked). Departments consider them as matched, so do not rank them (or rank them in ineligible position).

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Who’s the culprit?

For each matched candidate, consider interviews and succesful interviews that happen after the interview with the hiring dept. All ranked 1st only

• Comp. sub-market

.481 .458 Non-comp. sub-market .456 .457 In the non-comp. sub-market, departments are more likely to fill their ranking with “rubish.”

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Sorting

Ranking departments and candidates presents some hurdles: Impact factors can be “polluted” by non-pure math journals (e.g., Nature ≈ 10 × Annals of Mathematics)

⇒ Consider subfields.

Most candidates do not have (or have too few) publications to obtain meaningful rankings.

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Sorting

Departements and candidates classified into quartiles. Ranking of departments is quite “stable” over the years, not too “subfielf sensitive.” For candidates, considered the publications at years y-2, y-1, y, y+1. Used the squares of impact factors. Outliers (i.e., publishing in Nature/Science/PNAS/. . . ) deleted (about 30 observations).

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Sorting

More “good” candidates in the competitive sub-market. Quartile 1st 2nd 3rd 4th comp. 22 24.7 27.2 26.2 non-comp. 31.6 27.1 22.4 18.9

t-stat

3.09 .79 1.55 2.43

p-value

.001 .215 .0608 .0076

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Sorting

Relative vs. absolute ranking Absolute ranking: The department’s ranking across all departments in France. Relative ranking: The department’s ranking during the year. Some departments may not be hiring: relative ranking ≥ absolute ranking.

⇒ Abs. rank − rel. rank ≈ measure of competitiveness

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Sorting

Competiveness w.r.t. hiring a “better” candidate Non-comp. comp. No 5.09 4.27 Yes 25.83 15.74 Competiveness w.r.t. hiring a “worse” candidate Non-comp. comp. No 16.84 11.22 Yes 9.06 5.25

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Sorting

• Comp. market more assortative than non-comp. market

Worse cand. Better cand Same quality Comp. 28.33 54.46 25.7 Non-comp. 31.82 64.25 18

t-stat

2.4225 .8573 2.6166

p-value

.0078 .1958 .0045

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Sorting

Comparing candidates publication after and during the job market: candidates hired in the comp. market: Publish more after the job market Have a higher increase in publications when hired by top half departments. For hires by low half departments, publication increase in non-comp. market is much higher than in comp. market.

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Conclusion

Risky market: not hiring is the worst outcome Market divide: one part does not compete, one part competes. Competing sub-market: Hires better candidates Hiring more assortative Non-competitive sub-market: Low progress candidates if top-tier departement High progress candidates if low-tier departement.

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