Date: April 12, 2013 Topic: The Shrinking ... - Georgetown Law

Independent variables
Technology use (document download): In models predicting new tie formation, we coded
technology use at the dyad level, based on whether a given M had downloaded a given P’s
document. For this variable, downloading was coded 1, and otherwise the variable was coded 0.
In order to use include technology use in our models predicting value creation, we aggregated
document download activity at the dyad level to create a variable which is the count of P’s from
whom each M had downloaded documents. This count variable ranged across individual M’s and
years from 0 to 49.
Indirect ties: Indirect ties exist between an M and a P if there is a “two step” connection
between them, but no direct tie. We are able to observe indirect two-step ties in our cross-nested
data because individual managers work on client projects for multiple partners. Hence M1 has an
indirect tie to P2 if there is a proximal M2 who works on projects for the same P1 as he does,
who also works on projects for P2. In this case, although M1 is not directly working for P2,
information could flow between them through proximal colleague M2. We considered direct and
indirect ties to be mutually exclusive; hence if there was a direct tie between an M and a P (i.e.
M did not bill any of P’s clients), we did not consider that as an indirect tie.
Direct ties. We operationalize direct ties based on a prior connection between M and P.
Analytic approach
New tie formation: To analyze new tie formation, we conducted a dyad-level analysis,
using a risk of all possible dyads between the managers and partners across all possible years of
data. Thus the unit of analysis was the dyad-year. We employed multilevel logistic regression
models, using STATA’s xtmelogit command, to predict the formation of a new tie in a given
year. These models include two-way crossed random effects to account for unmeasured variance
associated with both individual managers and individual partners. The two way crossed effects
design (also sometimes referred to as cross-nesting) is appropriate given that managers are often
nested within more than one partner’s projects, meaning that although the data are multilevel,
traditional approaches that assume full nesting of lower level units within higher level units
would be in appropriate. A common analogy for this type of data structure is raters and subjects,
where each rater evaluates multiple subjects, and each subject is also evaluated by multiple raters.
The final models included 8326 dyad-years.
Value creation. For our value creation analysis, we shifted to the individual level. We
used person-year data to predict yearly value creation outcomes for M’s. We employed OLS
regression models with individual fixed effects, using STATA’s xtreg command. The final
models included 253 person-years. In all value creation and new tie formation models, the
independent variables are lagged one year. Year dummies are also included in all models to
control for unobserved variation associated with changes taking place over time in the case firm.
Because of our fixed and random effects model designs, we did not include other controls for
individual differences in our analyses.
RESULTS
Descriptive statistics are presented in Table 2. The left-hand column of Table 2 provides
information related to the dyad-year sample, and the right-hand column provides information
related to the person-year sample. Table 3 presents the results for models predicting new tie
formation, and Table 4 presents the result for models predicting value creation.
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