Research ArticleNETWORK SCIENCES

Systematic inequality and hierarchy in faculty hiring networks

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Science Advances  12 Feb 2015:
Vol. 1, no. 1, e1400005
DOI: 10.1126/sciadv.1400005
  • Fig. 1 Prestige hierarchies in faculty hiring networks.

    (Top) Placements for 267 computer science faculty among 10 universities, with placements from one particular university highlighted. Each arc (u,v) has a width proportional to the number of current faculty at university v who received their doctorate at university u (≠v). (Bottom) Prestige hierarchy on these institutions that minimizes the total weight of “upward” arcs, that is, arcs where v is more highly ranked than u.

  • Fig. 2 Inequality in faculty production.

    (A) Lorenz curves showing the fraction of all faculty produced as a function of producing institutions. (B and C) Complementary cumulative distributions for institution out-degree (faculty produced) and in-degree (faculty hired). The means of these distributions are 21 for computer science, 70 for business, and 29 for history.

  • Fig. 3 Faculty placement distributions.

    (A) Network visualizations for computer science, business, and history (top to bottom) showing central positions for institutions in the top 15% of prestige ranks (highlighted; vertex size proportional to ko). (B and C) Estimated probability density functions for relative change in prestige (doctoral to faculty institution) for (B) the top 15% and (C) the remaining institutions, showing a common but right-skewed structure.

  • Fig. 4 Core-periphery patterns.

    (A to C) For several institutions within each disciplinary hiring network, we highlight the tree of shortest paths rooted at each u within this network (black) for (A) computer science, (B) business, and (C) history (vertex size is proportional to out-degree, and lighter colors indicate higher prestige). As prestige increases (left), the paths in these trees contract, reflecting a more central network position, increased faculty production, and better faculty placement.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/ content/full/1/1/e1400005/DC1

    Table S1. Data summary for collected tenure-track faculty from each discipline.

    Table S2. Statistical measures of inequality by discipline.

    Fig. S1. An example graph A, the two minimum violation rankings (MVRs) on these vertices, both with S[π(A)] = 3, and a “consensus” hierarchy, in which the position of each u is the average of all positions that u takes in the MVRs.

    Fig. S2. Bootstrap distributions (smoothed) for the fraction of unviolated edges ρ in the empirical data (filled) and in a null model (dashed), in which the in- and out-degree sequences are preserved but with the connections between them otherwise randomized, and those for the empirical data.

    Fig. S3. Prestige uncertainty versus prestige, shown as the SD of the estimated distribution versus the distribution mean, for (A) computer science, (B) business, and (C) history.

    Fig. S4. Changes in rank from doctoral institution u to faculty institution v, for each edge (u, v) in (A) computer science, (B) business, and (C) history.

    Fig. S5. Changes in rank from doctoral institution u to faculty institution v, for each edge (u, v) in (A) computer science, (B) business, and (C) history, divided by male versus female faculty for u in the top 15% of institutions (top panels) or in the remaining institutions (bottom panels).

    Fig. S6. Ratio of the median change-in-rank, from doctoral institution u to faculty institution v, for men versus women, for faculty receiving their doctorate from the “most prestige” institutions, showing that elite women tend to place below their male counterparts in computer science and business (ratio < 1).

    Fig. S7. Changes in rank from doctoral institution u to faculty institution v, for each edge (u, v) in (A) computer science, (B) business, and (C) history, divided by faculty who have held one or more postdoctoral positions versus those that held none, for u in the top 15% of institutions (top panels) or in the remaining institutions (bottom panels).

    Fig. S8. Centrality measures versus prestige rank.

    Fig. S9. Placement accuracy for assistant professors.

    Fig. S10. Prestige scores for the top 60 institutions for (A) computer science, (B) business, and (C) history.

    Fig. S11. Centrality versus prestige rank for (A) computer science, (B) business, and (C) history departments, where centrality is defined as the mean geodesic distance (also known as closeness) divided by the maximum geodesic distance (diameter).

    Fig. S12. Relative change in rank from doctoral to current institution for all Full, Associate, and Assistant Professors in (A) computer science, (B) business, and (C) history.

    Fig. S13. Geographic structure of faculty hiring.

    Dataset 1: Business Faculty-Hiring Network Edges

    Dataset 2: Business Faculty-Hiring Network Vertex Attributes

    Dataset 3: Computer Science Faculty-Hiring Network Edges

    Dataset 4: Computer Science Faculty-Hiring Network Vertex Attributes

    Dataset 5: History Faculty-Hiring Network Edges

    Dataset 6: History Faculty-Hiring Network Vertex Attributes

    References (3449)

  • Supplementary Materials

    This PDF file includes:

    • Table S1. Data summary for collected tenure-track faculty from each discipline.
    • Table S2. Statistical measures of inequality by discipline.
    • Fig. S1. An example graph A, the two minimum violation rankings (MVRs) on these vertices, both with Sp(A) = 3, and a “consensus” hierarchy, in which the position of each u is the average of all positions that u takes in the MVRs.
    • Fig. S2. Bootstrap distributions (smoothed) for the fraction of unviolated edges r in the empirical data (filled) and in a null model (dashed), in which the in- and out-degree sequences are preserved but with the connections between them otherwise randomized, and those for the empirical data.
    • Fig. S3. Prestige uncertainty versus prestige, shown as the SD of the estimated distribution versus the distribution mean, for (A) computer science, (B) business, and (C) history.
    • Fig. S4. Changes in rank from doctoral institution u to faculty institution v, for each edge (u, v) in (A) computer science, (B) business, and (C) history.
    • Fig. S5. Changes in rank from doctoral institution u to faculty institution v, for each edge (u, v) in (A) computer science, (B) business, and (C) history, divided by male versus female faculty for u in the top 15% of institutions (top panels) or in the remaining institutions (bottom panels).
    • Fig. S6. Ratio of the median change-in-rank, from doctoral institution u to faculty institution v, for men versus women, for faculty receiving their doctorate from the “most prestige” institutions, showing that elite women tend to place below their male counterparts in computer science and business (ratio < 1).
    • Fig. S7. Changes in rank from doctoral institution u to faculty institution v, for each edge (u, v) in (A) computer science, (B) business, and (C) history, divided by faculty who have held one or more postdoctoral positions versus those that held none, for u in the top 15% of institutions (top panels) or in the remaining institutions (bottom panels).
    • Fig. S8. Centrality measures versus prestige rank.
    • Fig. S9. Placement accuracy for assistant professors.
    • Fig. S10. Prestige scores for the top 60 institutions for (A) computer science, (B) business, and (C) history.
    • Fig. S11. Centrality versus prestige rank for (A) computer science, (B) business, and (C) history departments, where centrality is defined as the mean geodesic distance (also known as closeness) divided by the maximum geodesic distance (diameter).
    • Fig. S12. Relative change in rank from doctoral to current institution for all Full, Associate, and Assistant Professors in (A) computer science, (B) business, and (C) history.
    • Fig. S13. Geographic structure of faculty hiring.
    • References (34–49)

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    Other Supplementary Material for this manuscript includes the following:

    • Dataset 1: Business Faculty-Hiring Network Edges.
    • Dataset 2: Business Faculty-Hiring Network Vertex Attributes.
    • Dataset 3: Computer Science Faculty-Hiring Network Edges.
    • Dataset 4: Computer Science Faculty-Hiring Network Vertex Attributes.
    • Dataset 5: History Faculty-Hiring Network Edges.
    • Dataset 6: History Faculty-Hiring Network Vertex Attributes.

    Download Datasets

    Files in this Data Supplement:

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