TNR2013 Ron Burt, Network Advantage on How the Network Was Built

469 views

Published on

Published in: Technology, Business
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
469
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
12
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide

TNR2013 Ron Burt, Network Advantage on How the Network Was Built

  1. 1. Path DependentNetwork Advantage A certain Ron Burt Jen Merluzzi To download a copy of the working paper, go to http://faculty.chicagobooth.edu/ronald.burt/research.
  2. 2. December February April June August October Network Survey 12 1 12 1 12 1 12 1 12 1 1 11 2 11 2 11 2 11 2 11 2 1110 3 10 3 10 3 10 3 10 3 3 Bob Bob Bob Bob Bob Bob 9 4 9 4 9 4 9 4 9 4 8 5 8 5 8 5 8 5 8 5 7 6 7 6 7 6 7 6 7 6 8 5 1 12 1 12 1 12 1 12 1 12 1 11 2 11 11 2 11 2 11 2 11 210 3 10 3 10 3 10 3 10 3 3 Deb Deb Deb Deb Deb Deb 9 4 9 4 9 4 9 4 9 4 8 5 8 5 8 5 8 5 8 5 8 5 7 6 7 6 7 6 7 6 7 6 Figure 5. NonRedundant Network Density Bob Is Contacts & Constraint Broker Network Can Always a Broker (thin solid line) (bold line is constraint) Result from Always Being a Broker,or from Serial Closure Deb Builds via Serial (Metrics oscillate through Closure reversals)
  3. 3. Path Dependent Network Advantage Puzzle: Individual Differencesin Returns to Network Advantage Volatility Treated as Noise Volatility Treated as Signal Conclusions & Serial Closure
  4. 4. Small World of Organizations & Markets A 1 B 7 3 2 James RobertCreating Value: The Social Capital of Brokerage (page 8) 5 4 6 Density Table C&D 85 Group A 5 25 Group B Network indicates 0 1 100 Group C Network Constraint distributionStrategic Leadership (C = Σj cij = Σj [pij + Σq piqpqj]2, i,j ≠ q) 0 0 29 0 Group D of sticky person 3: .402 = [.25+0]2 + [.25+.084]2 + [.25+.091]2 + [.25+.084]2 information, which defines Robert: .148 = [.077+0]2 + [.154+0]2 + [.154+0]2 + [.154+0]2 + [.154+0]2 + [.154+0]2 + [.154+0]2 advantage. From Figure 1.1 in Brokerage and Closure. For an HBR treatment of the network distinction between Robert and James, see in the course packet Kotters classic distinction between "leaders" versus "managers." Robert ideally corresponds to the image of a "T-shaped manager," nicely articulated in Hansens HBR paper in the course packet.
  5. 5. In Sum: Individuals Differ Greatly in Returns to Brokerage A. Achievement Scores Higher than Peers on Average B. Vary Widely between Individuals r = -.58, t = -6.78, n = 85 r = -.24, t = -9.98, n = 1,989 (heteroscedasticity c2 = 2.97, 1 d.f., P < .08) (heteroscedasticity c2 = 269.5, 1 d.f., P < .001) Z-Score Residual Achievement (evaluation, compensation, promotion)Creating Value: The Social Capital of Brokerage (page 23) Network Constraint many ——— Structural Holes ——— few Graph (A) shows achievement decreasing with less access to structural holes. Circles are z-score residual achievement for 1,986 observations averaged within five-point intervals of network constraint in each of six management populations (analysts, bankers,Strategic Leadership and managers in Asia, Europe, and North America; heteroscedasticity is negligible, c2 = 2.97, 1 d.f., P ~ .08; Burt, 2010:26, cf. Burt, 2005:56). Bold line is the vertical axis predicted by the natural logarithm of network constraint. Graph (B) shows the raw data averaged in Graph (A). Vertical axis is wider to accommodate wider achievement differences. Heteroscedasticity is high given wide achievement differences between brokers (c2 = 269.5, 1 d.f., P < .001), but returns to brokerage remain statistically significant when adjusted for hteroscedasticity (Huber-White, t = -8.49). Figure 1 in Burt, "Network-relevant personality and the agency question" (2012 AJS)
  6. 6. Network Brokers Tend To Be the Leaders Constraint and status are computed from work discussion networks around twelve hundred managers in four organizations. A. In the formal B. And in the informal 18% organization organization Most Senior Job Ranks (29.5 mean network constraint) Percent of People within Each Level of Job Ranks (Si = Σj zji Sj, divided by mean so average is 1.0) 1% Network Status (S) Next-Lower, Senior Ranks (41.9 mean constraint) r2 = .61Creating Value: The Social Capital of Brokerage (page 13) Next-Lower, Middle Ranks (56.4 mean constraint)Strategic Leadership Network Constraint Network Constraint many ——— Structural Holes ——— few many ——— Structural Holes ——— few Figure 2.4 in Burt, "Network structure of advantage" (2013 manuscript)
  7. 7. Path Dependent Network Advantage Puzzle: Individual Differencesin Returns to Network Advantage Volatility Treated as Noise Volatility Treated as Signal Conclusions & Serial Closure
  8. 8. Network Volatility — Noise or Signal? Current Previous Network Subsequent AdvantageIllustra(ng  the  privilege  accorded  stability  in  network  analysis,  here  is  Laumann  &  Pappi  (1976:213)  on  community  elites:  "Despite  differences  in  nuance  associated  with  structure,  the  root  meaning  refers  to  a  persis(ng  order  or  paKern  of  rela(onships  among  units  of  sociological  analysis,  be  they  individual  actors,  classes  of  actors,  or  behavioral  paKerns.”    Nadel  (1957:9)  is  cited  as  precedent:  "We  iden(fy  the  mutual  ways  of  ac(ng  of  individuals  as  rela(onships  only  when  the  former  exhibit  some  consistency  and  constancy,  wince  without  these  aKributes  they  would  merely  be  single  or  disjointed  acts.”    Of  course,  implicit  in  Laumann  &  Pappi’s  emphasis  on  stability  is  Nadel’s  (1957:8)  conceptual  dis(nc(on  between  stable  structures  and  variable  parts:  "structure  indicates  an  ordered  arrangement  of  parts,  which  can  be  treated  as  transposable,  being  rela(vely  invariant,  while    the  parts  themselves  are  variable.”  
  9. 9. Data 1Four annual panels of compensation and network data on 346 investmentbankers employed by the study organization in each of the four panels. Network data are from annual 360 evaluation process (zji = 1 if j cited ior i cited j as a colleague with whom citer had frequent and substantivebusiness during previous year, 0 otherwise; summary colleague evaluation forbanker i is average across colleagues j of zji on 4-point scale). There are also some control variables (banker’s job rank, colleagueevaluation, years in organization, minority, works at headquarters).Measures of network advantage: Network eigenvector measures centrality/status (si = ∑j pjisj, where pji is proportion of i’s relations that are with j) Network constraint measures lack of access to structural holes (ci = ∑j cij, where cji = (pij + ∑k pikpkj)2, k ≠ i, j, and pij is the proportion of i’s relations that are with j).
  10. 10. Figure 1. Enduring Banker Relations Better Reveal Social Clusters A BLegend: Color indicates banker job rank: top (gold), senior (gray), or other (white). Shape indicates location: US (circle) or elsewhere (square).Lines in sociogram A connect bankers linked by a citation in any of the four years. Lines in sociogram B connect bankers linked by a citation inall four years.
  11. 11. Table 2. Compensation Returns to Network Advantage All Years Within Years Between Years I II III IV V VI Network Status .41 (.05) ** — .47 (.05) ** — .33 (.04) ** — Network Constraint — -.31 (.07) ** — -.41 (.08) ** — -.27 (.04) **Job Rank 2 .20 (.08) * .20 (.09) * .20 (.08) * .21 (.09) * .23 (.03) ** .24 (.03) **Job Rank 3 .48 (.09) ** .51 (.09) ** .50 (.08) ** .52 (.09) ** .59 (.06) ** .62 (.06) **Job Rank 4 1.48 (.10) ** 1.64 (.11) ** 1.37 (.10) ** 1.58 (.11) ** 1.55 (.10) ** 1.74 (.11) **Colleague Evaluation .17 (.04) ** .18 (.04) ** .13 (.04) ** .17 (.04) ** .12 (.02) ** .14 (.03) **Years with the Organization .004 (.01) .008 (.01) .001 (.01) .006 (.01) -.003 (.01) .002 (.01)Minority (gender or race) -.07 (.07) -.08 (.08) -.05 (.07) -.07 (.07) -.07 (.04) -.09 (.05)US Headquarters -.11 (.06) -.06 (.06) -.14 (.06) * -.07 (.06) -.09 (.05) -.04 (.05) Intercept -.91 .21 -.91 .78 -.81 .31Multiple Correlation Squared .71 .68 .74 .70 .71 .66Number of Observations 346 346 346 346 1038 1038NOTE — Unstandardized OLS regression coefficients are presented with standard errors in parentheses. Compensation is measuredas a z-score. Network status is an eigenvector score normalized to the average banker. Network constraint is the log of constraint.See Appendix B for control variables, means, standard deviations, and correlations. Models I and II predict compensation summedacross years from network indices computed from relations pooled over time (relation is 1 if it occurs in only one year, 2 if two years,etc.). Network status is correlated -.86 with log network constraint. Models III and IV predict annual compensation averaged acrossyears from network indices computed for each year then averaged across years. Network status is again correlated -.86 with lognetwork constraint. Models V and VI predict compensation next year from network indices this year (with standard errors adjusted forautocorrelation between repeated observations of the same bankers using the “cluster” option in STATA). * p < .05, ** p ≤ .001
  12. 12. Path Dependent Network Advantage Puzzle: Individual Differencesin Returns to Network Advantage Volatility Treated as Noise Volatility Treated as Signal Conclusions & Serial Closure
  13. 13. Data 2Four annual panels of compensation and network data on 346 investment bankers employed bythe study organization in each of the four panels. Network data are from annual 360 evaluationprocess (zji = 1 if j cited i or i cited j as a colleague with whom citer had frequent and substantivebusiness during previous year, 0 otherwise; colleague evaluation is average of zji on 4-pointscale). There are also some control variables (banker’s job rank, colleague evaluation, years inorganization, minority, works at headquarters).Measures of network advantage: Network eigenvector measuring centrality/status (si = ∑j zjisj) Network constraint measuring lack of access to structural holes (ci = ∑j cij, where cji = (pij + ∑k pikpkj)2, k ≠ i, j and pij is the proportion of i’s relations that are with j).Measures of network volatility: Positive churn dummy (1 if middle third of percent change in contacts in 4 years) Wide variation dummy (1 if above-median standard deviation in network score over time) Trend up dummy (1 if positive slope on banker’s over time) Negative trend dummy (1 if negative slope on banker’s over time) Reversals dummy (1 if slope of change in banker’s scores reverses during the 4 years)
  14. 14. Figure 4. Illustrative Variation, Trend, and ReversalReversal refers to change this year contradicted next year, as when banker status decreases this yearafter increasing last year (e.g., bankers C and D). No reversals means that banker status was stablefrom year to year (e.g., banker E), or changed in one direction (e.g., banker A’s increasing status). 3.0 A. Trending-Positive Banker, initially Average Banker Status across the Annual Networks on periphery (1.86 mean over time, 1.19 SD) 2.5 Banker Status within Annual Network B. One-Reversal Banker (1.98 mean status over time, .48 SD) 2.0 DB A C. Two-Reversal Banker (1.20 mean over time, .73 SD) 1.5 D. Two-Reversal Banker (1.97 mean over time, .64 SD) C 1.0 G E. No-Change Banker (.74 mean over E time, .07 SD) F 0.5 F. One-Reversal Banker (.62 mean over time, .20 SD) 0.0 G. Trending-Negative Banker, initially central (.84 mean over time, .78 SD) Year Year Year Year One Two Three Four
  15. 15. Table 4. Compensation Returns to Network Advantage and Volatility Network Status Predictions Network Constraint Predictions III VII VIII IV IX X Volatility Level Adjustments(at median network advantage) Positive Churn in Contacts -.00 (.01) .10 (.10) Wide Variation in Advantage .07 (.08) .10 (.10) Positive Trend in Advantage .02 (.08) -.03 (.12) Negative Trend in Advantage -.07 (.10) -.19 (.11) Reversal in Advantage -.00 (.08) -.00 (.06) .07 (.08) .11 (.06)Network Advantage Slopes (at low volatility) Network Status .47 (.05)** .06 (.14) .25 (.07)** — — — — — — Network Constraint — — — — — — -.41 (.08)** -.25 (.13)* -.28 (.09)** Volatility Slope Adjustments (Positive Churn)*(Network-Median) -.02 (.16) -.16 (.18) (Wide Variation)*(Network-Median) .15 (.12) .11 (.18) (Positive Trend)*(Network-Median) .13 (.18) -.10 (.22) (Negative Trend)*(Network-Median) .22 (.15) .20 (.19) (Reversal)*(Network-Median) .38 (.14)** .31 (.08)** -.31 (.15)* -.33 (.12)** Intercept -.91 -.67 -.79 .78 .33 .33R2 .74 .76 .76 .70 .71 .71NOTE — OLS regression coefficients are presented with standard errors in parentheses. Z-score compensation, network status, and networkconstraint are average annual scores as in Models III and IV in Table 2. Means, standard deviations, and correlations for Models VII and VIII aregiven in Appendix B, Tables B2 and B3 respectively. All models include the seven Table 2 control variables for job rank, colleague evaluation, yearswith organization, minority, and US headquarters. Volatility variable “Positive Churn” equals one for bankers outside the shaded cells in Table 3indicating stability traps. Level adjustments show change in compensation associated with the five binary volatility measures defined in the text.Slope adjustments are interaction terms between volatility variables and network advantage as a deviation from its median. * p < .05, ** p ≤ .01
  16. 16. Path Dependent Network Advantage Puzzle: Individual Differencesin Returns to Network Advantage Volatility Treated as Noise Volatility Treated as Signal Conclusions & Serial Closure
  17. 17. Three Conclusions and an Inference(1) Network volatility does not affect performance directly. Compensation is neither highernor lower for bankers in volatile networks — holding constant level of network advantageand allowing for slope adjustments.(2) Volatility has its effect by enhancing a person’s returns to level of network advantage.Adding volatility to the predictions did not strengthen the network effect. It disaggregatedthe effect into a portion due to level of advantage and a portion due to volatility. Justholding constant the corrosive effect of stability traps, the split is about equal betweennetwork advantage level versus volatility.(3) The network volatility that protects against stability traps is a specific kind. It is notmaking new contacts in place of old. Churn should be moderate, about what is typical forthe median person. It is not a matter of trend. Trend has no association with performancebeyond level of network advantage. The variation associated with compensation isreversal: a pattern in which advantage is lost then regained, or gained then lost. Bankerswho go through network reversals enjoy significantly higher returns to their level of networkadvantage. In fact, compensation has no association with level of network advantage forthe bankers who failed to experience a reversal during the four years.Our inference from the analysis is that a substantial portion of network advantage is pathdependent — advantage is about level of advantage, but it is also about how one’s level ofadvantage developed. In contrast to the positive image of continuous access to structuralholes, the “serial closure” hypothesis is that advantage depends on discontinuous access.
  18. 18. December February April June August October Network Survey 12 1 12 1 12 1 12 1 12 1 1 11 2 11 2 11 2 11 2 11 2 1110 3 10 3 10 3 10 3 10 3 3 Bob Bob Bob Bob Bob Bob 9 4 9 4 9 4 9 4 9 4 8 5 8 5 8 5 8 5 8 5 7 6 7 6 7 6 7 6 7 6 8 5 1 12 1 12 1 12 1 12 1 12 1 11 2 11 11 2 11 2 11 2 11 210 3 10 3 10 3 10 3 10 3 3 Deb Deb Deb Deb Deb Deb 9 4 9 4 9 4 9 4 9 4 8 5 8 5 8 5 8 5 8 5 8 5 7 6 7 6 7 6 7 6 7 6 Figure 5. NonRedundant Network Density Bob Is Contacts & Constraint Broker Network Can Always a Broker (thin solid line) (bold line is constraint) Result from Always Being a Broker,or from Serial Closure Deb Builds via Serial (Metrics oscillate through Closure reversals)
  19. 19. Table 6. Returns to Brokerage Are Higher for Bankers Who Experienced Network Reversals Access to Structural Holes Network Reversals High (Brokers) Average Low Yes 1.11** -.16 -.25 (banker reversals in status ANDaccess to structural holes, n = 76) (25) (27) (24) Probably .34* -.13 -.11 (banker reversal in status ORaccess to structural holes, n = 143) (47) (48) (48) No -.39 -.02 -.11 (no reversals, n = 127) (39) (41) (47) .26 -.10 -.14 All Bankers (111) (116) (119)NOTE — Rows distinguish bankers by reversals in network status and constraint using the two dummy “reversal” variablesin Table 4. Columns distinguish the bankers by network constraint. The third of bankers with the lowest average constraintscores over time are in the “High” access column. The third with the highest average constraint scores are in the “Low”access column. Cell entries are mean z-score compensation adjusted for the seven control variables in Table 2 (Model IV).Number of bankers is given in parentheses. Asterisks indicate compensation averages in the nine table cells that aresignificantly high or low (log network constraint in Model IV in Table 2 was replaced by 8 dummy variables distinguishing thenine cells using the middle cell as a reference category).* P < .01 ** P < .001

×