employee mobility and interfirm relationship transfer ...and specialized human capital, this study...

Strategic Management JournalStrat. Mgmt. J., 38: 2019–2040 (2017)

Published online EarlyView 15 February 2017 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/smj.2634Received 28 December 2015; Final revision received 21 December 2016

Employee Mobility and Interfirm RelationshipTransfer: Evidence from the Mobility and ClientAttachments of United States Federal Lobbyists,1998–2014Joseph Raffiee*Department of Management and Organization, Marshall School of Business,University of Southern California, Los Angeles, California

Research summary: Employee mobility can erode competitive advantage by facilitating interfirmknowledge and relationship transfer. This study investigates the latter and identifies factors thatinfluence the likelihood of its occurrence. Using a novel database that tracks the employmentand client attachments of U.S. federal lobbyists, I show that repeated exchange with employees(firms) increases (decreases) the likelihood clients follow employees who switch firms. Structurally,multiplexity reduces the likelihood of client transfer and weakens the effect of employee–clientrepeated exchange, with the multiplexity effect strongest when team members have specializedexpertise. By examining the main and interactive effects of repeated exchange, multiplexity,and specialized human capital, this study extends prior work by demonstrating how individual,organizational, and structural relationship characteristics affect client transfer and retentionex-post employee mobility.

Managerial summary: When do clients follow employees who switch firms? What can firms doto guard against it? These questions are important in service-based industries where clients maybecome loyal to individual employees within the firm rather than to the firm itself. This studyprovides evidence that helps practicing managers: (a) identify which clients are most at risk ofdefecting if employees exit, and (b) structure relationships in ways that mitigate the likelihoodthat employee exit results in client loss. Findings suggest that a client is more likely to defectwhen she has extensive history working with the exiting employee, particularly if the employeewas the sole link between the client and firm. Managers, however, can reduce the risk of client lossfollowing employee exit by structuring relationships so that clients work with teams of employeesrather than exclusively with an individual and by increasing the degree of specialization withinthese teams. Copyright © 2017 John Wiley & Sons, Ltd.

Introduction

Human assets have long been recognized asa promising source of competitive advantage(Campbell, Coff, & Kryscynski, 2012; Hatch& Dyer, 2004). Yet sustaining these advantages

Keywords: client relationships; employee mobility;human capital; multiplexity; specialization*Correspondence to: Joseph Raffiee, Department of Managementand Organization, Marshall School of Business, University ofSouthern California, 701 Exposition Blvd., HOH 512, Los Ange-les, CA 90089. E-mail: [email protected]

Copyright © 2017 John Wiley & Sons, Ltd.

is challenging (Coff, 1997), because employeemobility often results in the interfirm transferof valuable knowledge to rival firms (Agarwal,Ganco, & Ziedonis, 2009). Employee mobilitymay also lead to the interfirm transfer of valuablestakeholder relationships, for example, relation-ships with clients, customers, suppliers, and othermarket actors (Carnahan & Somaya, 2013; Dokko& Rosenkopf, 2010). However, while employeemobility and interfirm knowledge transfer hasreceived significant scholarly attention (e.g., Agar-wal et al., 2009), we know much less about the

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conditions under which employee mobility leadsto the interfirm transfer of relationships or what (ifanything) firms can do to reduce the likelihood ofits occurrence (Sorenson & Rogan, 2014).

In this article, I contribute to this literature byexamining when employee mobility leads to inter-firm client transfer in the context of professionalservices firms. Client transfer differs fundamentallyfrom knowledge transfer in that the latter is oftennonrivalrous and nonexcludable, while the formerhas agency and makes transfer choices. Accord-ingly, prior work examining the conditions thatlead to knowledge transfer may not easily translateto client transfer, as client transfer presents its ownunique set of challenges.1 I begin by theoreticallyarguing that employee–client and firm–client rela-tionships are interrelated but distinct (cf. Broschak& Block, 2014) and empirically show that the like-lihood a client follows an employee who switchesfirms increases with repeated exchange between theemployee and client, but decreases with repeatedexchange between the client and firm. Whenrelationships are structured as multiplex (clientsserved by a team), however, clients will developmultiple employee–client relationships within thefirm, reducing reliance on any specific employee.Accordingly, I hypothesize and find that multiplex-ity significantly reduces the likelihood that mobilityleads to client transfer, while also weakening theeffect associated with employee–client repeatedexchange. By doing so, my study responds torecent calls urging scholars to investigate “therole that multiplex ties play in the retention ofclients” (Rogan, 2014, p. 580). I extend this logicfurther to argue that the efficacy of multiplexrelationship structures will vary with the humancapital characteristics of multiplex team members.Drawing from the labor market specializationliterature (Ferguson & Hasan, 2013), I theorize andfind that the strength of the multiplexity main effectincreases when teams are comprised of specialistsrather than generalists, consistent with the theo-retical argument that increased specialization and

1 Prior work has also shown that managerial mobility canlead to the general dissolution of client ties (Broschak, 2004;Broschak & Block, 2014). However, it thus far remains unclearif (or when) defecting clients “follow the individuals leaving theorganizations” (Sorenson & Rogan, 2014: p. 272), in part due toempirical difficulties disaggregating employee–lient relationshipsfrom firm–client relationships. This limitation has importanttheoretical and practical implications because the competitiveramifications of resource transfer are distinct from resourcedepletion (Agarwal, Campbell, Franco, & Ganco, 2016).

division of labor in team production of productsand services increases the difficulty and perceivedability for an individual employee to replicatethe production of value without his or her team(Liebeskind, 1996).

I test my hypotheses using data on the inter-firm mobility of U.S. federal lobbyists. To over-come the inherent data challenges that have plaguedscholars conducting empirical work in this area, Iexploit the stringent reporting requirements man-dated by the Lobbying Disclosure Act of 1995and Honest Leadership and Open Government Actof 2007 that require all lobbying activity to bereported via lobbying disclosure reports to the Sen-ate Office of Public Records. I use these reports toconstruct a novel database that tracks the employ-ment and client attachments of all registered lob-byists between 1998 and 2014. Analysis of thesedata—including econometric corrections for selec-tion and numerous fixed-effects regressions thathelp account for lobbyist, firm, client, and des-tination firm unobserved heterogeneity—providerobust support for my theory.

This article contributes to the growing litera-ture on strategic human capital (Bidwell, Won,Barbulescu, & Mollick, 2015; Lecuona & Reitzig,2014; Raffiee & Coff, 2016) and complementsextensive work on interfirm knowledge transfer(e.g., Franco & Filson, 2006) by theoreticallyidentifying conditions that facilitate interfirmrelationship transfer. By doing so, this study addsempirical insights to the employee mobility litera-ture by disentangling what is transferred throughmobility (i.e., clients) from who is transferredthrough mobility (i.e., the employee[s]), a rarityin extant work, despite the prevalence of theoret-ical arguments in the mobility literature alludingto client transfer and its strategic importance(Campbell, Ganco, Franco, & Agarwal, 2012;Phillips, 2002; Wezel, Cattani, & Pennings, 2006).My findings have significant and widespreadeconomic implications, particularly in the contextof professional services, a crucial and substantialcomponent of the U.S. economy that contributedmore than $2.2 trillion (∼20%) to the U.S. grossdomestic product and was the leading source ofU.S. private sector employment in 2011 (USITCPublication 4412). By examining the main andinteractive effects of repeated exchange, multi-plexity, and specialized human capital, this studyempirically shows how individual, organizational,and structural relationship characteristics affect

Copyright © 2017 John Wiley & Sons, Ltd. Strat. Mgmt. J., 38: 2019–2040 (2017)DOI: 10.1002/smj

Employee Mobility and Interfirm Relationship Transfer 2021

the likelihood that employee mobility results inclient transfer, thereby contributing to our theo-retical understanding of the multilevel nature ofrelationship attachment (Bermiss & Greenbaum,2016) and the sustainability of human-asset–basedcompetitive advantages (Coff, 1997).

Theory and Hypotheses

Human assets are crucial components for value cre-ation and the emergence of competitive advantage,particularly in knowledge-intensive and profes-sional services industries (Coff, 1997). The poten-tial for employee mobility, however, may threatenthese advantages because mobility can facilitatethe interfirm transfer of knowledge (Aime, John-son, Ridge, & Hill, 2010) and relationships (Car-nahan & Somaya, 2013). While scholars have iden-tified conditions that facilitate interfirm knowledgetransfer (Agarwal et al., 2009) and outlined waysfirms can reduce it (Liebeskind, 1996), the poten-tial for client transfer presents unique challenges.Unlike knowledge, clients have free-will and there-fore must decide whether or not they should followan employee who engages in mobility to a differ-ent firm. Theoretically, I adopt the assumption thatin cases of mobility both the employee and the firmprefer to retain the client relationship. As a result,relationship “ownership by a focal actor thereforedepends on the orientation of the alter in each rela-tionship” (Sorenson & Rogan, 2014, p. 263), withthe alter being the client and the focal actor beingthe firm or employee.

My theory focuses on three sets of factors thatI argue will influence a client’s choice to followan employee who switches firms and therefore thelikelihood that mobility results in interfirm clienttransfer: (a) the strength of the client’s relationshipwith the employee, (b) the strength of the client’srelationship with the firm, and (c) structural char-acteristics regarding how the relationship is orga-nized within the firm. In what follows, I developarguments to explain how these factors directly andjointly affect client choice and therefore the likeli-hood mobility leads to client transfer.2

2 Other factors may influence the odds of client transfer, forexample, characteristics of the client firm (Broschak & Block,2014) or the destination of where the mobile employee goes(Sorenson & Rogan, 2014). These factors are outside the scope ofthis article, but I attempt to empirically account for them in various

Relationship Strength: Employee–client andFirm-client Repeated Exchange

In this section, I focus on the strength of a client’srelationship with the exiting employee and thestrength of the client’s relationship with the firm.Specifically, I examine the degree to which therehas been repeated exchange, as theory suggests thatrepeated exchange generates a number of benefitsthat can strengthen and embed exchange relation-ships (Elfenbein & Zenger, 2013).

Importantly, I depart from prior work and treat aclient’s repeated exchange with the firm as distinctfrom repeated exchange with an employee (cf.Rogan, 2013). That is, when a client engagesin repeated exchange with a firm, the sameemployee(s) may or may not be involved in theinteraction. Therefore, repeated exchange occursdistinctly at the individual and organizationallevels, and, as Elfenbein and Zenger (2013, p. 222)note, “a pattern of repeated interorganizationalexchange generates both efficiency benefits to thefirm and private benefits to individuals within thefirm.”

The benefits accrued from repeated exchange areboth economic and relational (Kuwabara, 2011).First, logic from an economic perspective suggeststhat exchange partners will be driven by self-interestand make choices in a manner they believe willmaximize (or at least maintain) their individualbenefits. Accordingly, actors tend to remain in rela-tionships when the perceived benefits are greaterthan alternative outside options (Emerson, 1981).Repeated exchange facilitates the accumulation ofprivate information regarding client-specific needs,preferences, and behavioral norms (Bidwell &Fernandez-Mateo, 2010; Chatain, 2011), much ofwhich may be tacit and difficult to codify (Mayer,Somaya, & Williamson, 2012). Over time, thisfacilitates shared organizational routines, commonlanguage, and relationship-specific investment(Dyer & Singh, 1998). These investments can beattributed to individuals (e.g., Anand, Gardner,& Morris, 2007) or firms (e.g., Biong & Ulvnes,2011) and economically embed relationships bycreating uncertainty regarding if (or how quickly)the client’s needs could be met by outside optionsshould the focal relationship end.

Second, logic from a relational perspective sug-gests that repeated exchange builds “mutual liking,

ways through my primary analysis and subsequent robustnesschecks.


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trust, and the quality of a dyadic relationship”(Curhan, Neale, Ross, & Rosencranz-Engelmann,2008, p. 192). This can lead actors to perceivethe relationship as holding its own value aboveand beyond the economic and uncertainty reduc-tion properties (Lawler & Thye, 1999). Like eco-nomic benefits, relational benefits can developwith individuals (Rosenkopf, Metiu, & George,2001) and firms (Gulati, 1995). These benefits—thedevelopment of relational capital, goodwill, andtrust—work to socially embed client relationships,thereby increasing the client’s perceived relationalcost associated with relationship severance.

Together, these arguments suggest that a pat-tern of repeated exchange generates economic andrelational benefits that simultaneously strengthenemployee–client and firm–client relationships. Thestrength of these relationships, in turn, should influ-ence the locus of client attachment and thereforea client’s decision to follow an employee whoswitches firms, albeit in different directions.

Hypothesis 1: The greater the employee–clientrepeated exchange, the more likely the clientwill follow the employee if the employee exits,controlling for firm–client repeated exchange.

Hypothesis 2: The greater the firm–clientrepeated exchange, the less likely the clientwill follow the employee if the employeeexits, controlling for employee-client repeatedexchange.

Relationship Structure: The Degree ofMultiplexity

As I have argued above, client relationships developat multiple levels—clients build relationships withindividual employees and relationships with thefirm itself. However, from a structural standpoint,not all client relationships are organized in thesame way—relationships vary in the degree towhich they are structured as multiplex. Multiplex-ity, both at the interpersonal and interfirm lev-els, broadly refers to situations in which two ormore relationships simultaneously occur (Shipilov,2012). In this study, I follow Rogan (2014) andconsider a client relationship to be multiplex ifmore than one employee works with the client. Ido not require relationships to have multiple iden-tities or functions (cf. Wasserman & Faust, 1994).

Thus, multiplexity exists with a single identity(e.g., buyer–seller) but multiple individual ties (i.e.,multiple employee–client relationships) (Shipilov,Gulati, Kilduff, Li, & Tsai, 2014).

In terms of determining where client loyaltyresides, the degree of multiplexity complicates theallocation process because clients will develop indi-vidual relationships—each carrying their own eco-nomic and relational benefits—with each employeewith whom they work. These relationships canbe conceptualized as being aggregated toward thefirm, thereby forming the micro-foundations offirm–client relationships. That is, multiplexity pitsthe employee not only against the firm in termsof competing for client loyalty, but also againstthe other employees with whom the client works.As multiplexity rises, this becomes increasinglyproblematic for employees who wish to switchfirms and take clients, because larger teams servingclients means that clients are likely to be econom-ically and socially embedded with other employ-ees who will remain with the firm. As a result,for a mobile employee to convince a client to fol-low, the client’s perceived benefits of following themobile employee must outweigh the collective ben-efits from the client’s relationships with the otherteam members, a conclusion a client is less likelyto reach as the size of the team serving the clientgrows.

In addition, this issue is exacerbated by the factthat team production of services often creates aproblem of nonseparability, where individual con-tributions to the creation of value can be difficult forclients to gauge (Alchian & Demsetz, 1972). Theseproblems are particularly relevant in professionalservices where knowledge asymmetries make thequality of production opaque and difficult for clientsto observe. Accordingly, issues of nonseparability,which arise from high levels of multiplexity cancreate further uncertainty and doubt regarding themobile employee’s ability to meet the client’s needswithout her team members. Together, these reasonssuggest that the greater the degree to which a rela-tionship is structured as multiplex (i.e., the size ofthe team serving the client), the more concerns aclient is likely to have about following an employeewho switches firms.

Hypothesis 3: The greater the multiplexity, theless likely the client will follow the employee ifthe employee exits.



While the arguments above point to a mul-tiplexity main effect, similar logic can be usedto argue for a moderating effect with respect toemployee–client repeated exchange. Of course,even with high levels of multiplexity, the ben-efits associated with employee–client repeatedexchange—specific knowledge and relationalcapital—will still be developed. However, whenrelationships are multiplex the client will alsobuild relationships with other employees, whichshould decrease the relative degree to which theclient becomes loyal to a specific employee, evenif the client and employee have extensive repeatedexchange. Again, this would be particularly truewhen the client simultaneously develops a highnumber of individual relationships with employees(i.e., large team). Therefore, while I still expecta positive relationship between employee–clientrepeated exchange and client transfer, the degreeto which repeated exchange increases the rela-tive strength of an employee–client relationshipand therefore client loyalty should weaken asmultiplexity increases.

Hypothesis 4: Multiplexity moderates the rela-tionship between employee–client repeatedexchange and client transfer such that the posi-tive effect of employee–client repeated exchangeon the likelihood a client follows an employeeif the employee exits decreases (becomes lesspositive) as multiplexity increases.

Relationship Structure: Multiplexity andSpecialized Expertise

Thus far I have argued that multiplexity can reducethe risk of client transfer and dampen the effectof employee–client repeated exchange. While mymultiplexity arguments focused on mechanismsrelated to the quantity of employees serving a client,not all employees have the same expertise. And sowhile greater multiplexity should decrease clienttransfer for the reasons above, its effectivenessshould increase when multiplex teams are com-prised of employees whose expertise structures castfurther doubt on a client’s belief that his or her needscan be met outside of the multiplex relationship.Accordingly, I focus on the degree to which theteam has specialized versus generalized expertise.

The literature on labor market specialization dif-ferentiates between individuals who are specialistsfrom those who are generalists (S. Rosen, 1983). To

wit, some employees—specialists —have highlyspecialized knowledge and expertise in a singlearea (i.e., high depth and low breadth), whereasother employees—generalists—have a broader setof knowledge and expertise that touches upon amultitude of different areas (i.e., low depth andhigh breadth) (Ferguson & Hasan, 2013). On theone hand, multiplex teams can be comprised ofemployees who are specialists in different areas,which typically implies greater division of labor(S. Rosen, 1983). When specialization is used todisaggregate production in this way, it makes itunlikely that any individual employee will possessthe knowledge needed to replicate team produc-tion on her own (Liebeskind, 1996). This, in turn,should translate into client concerns about follow-ing a mobile employee, as the employee’s abilityto effectively meet the client’s needs without herteam members would be in question. On the otherhand, multiplex teams can be comprised of special-ists in the same area, a composition that may implyless-explicit division of labor. When relationshipsare structured this way, it should also raise clientconcerns regarding an individual employee’s abilityto effectively serve his or her needs because teamsof same-area specialists can amplify the problem ofnonseparability. That is, determining an employee’sunique contribution to the production of value canbecome more difficult for clients to discern if theemployee is part of a large team who all specializein the same area. Thus, when teams are comprisedof specialists rather than generalists, on average, itshould increase client concerns and create uncer-tainty about the choice to follow an employee whoengages in mobility.

The logic above is further strengthened by thefact that specialists are often perceived by clientsas being scarcer than generalists in professionalservices—even if objectively this is not the case.The perception of scarcity, regardless of objectivereality, is in part a product of employee and firmactions. For example, R. E. Rosen (2010) detailshow specialization is used by law firms and attor-neys as a marketing device to attract and retainclients. Similarly, Dunn and Mayhew (2004) notethat auditors in public accounting often empha-size specialized expertise as a way to differentiatethemselves from rivals. Indeed, Rosen (9, empha-sis added) describes how specialists in the legalsector historically emerged as “a means to holdonto a client already in its stable.” The perceptionof scarcity suggests that when a client works with


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specialist employees, it can create further doubt anduncertainty regarding the client’s ability to havetheir needs effectively met outside of the focal rela-tionship. As I elaborate below, the way in which thismechanism influences client transfer will largelydepend on relationship structure—the degree towhich the relationship is multiplex.

To begin, consider a scenario where a clientrelationship is not multiplex (i.e., it is exclusive).Because the employee is the sole link between thefirm and client, increased employee specializationshould increase the likelihood of client transferpostmobility. In other words, because the employeehas specialized expertise and specialized expertiseis often perceived as scarce (Wholey, 1985), itincreases the uncertainty regarding whether or notthe focal firm will be able to effectively replicate theemployee’s skills internally if the employee exits.In contrast, if the employee is a generalist, then theability for the focal firm to internally replicate theemployee’s expertise would be less in question.

Now consider a scenario in which a client rela-tionship is multiplex and team members have ahigh average level of specialized expertise. The per-ception of specialist scarcity suggests that as thenumber of employees involved in the relationshipincreases, the uncertainty from the client’s perspec-tive will become less about the focal firm replicat-ing the employee’s expertise internally and moreabout the mobile employee’s ability to replicate theexpertise of his or her team members externally ather new firm. Indeed, even if the mobile employeeis a specialist, if he or she was part of a highlyspecialized team, it can cast doubt regarding theemployee’s ability to create the same amount ofvalue for the client at the new firm—due both toissues of nonseparability and because the client mayperceive that few firms will have the level of busi-ness required to support a similarly large stable ofspecialists (Wholey, 1985). And so while addingemployees to a client team should have the maineffect of reducing client transfer (i.e., Hypothesis3), the logic here suggests that the relative size ofthis effect should increase when teams have higheraverage levels of specialized expertise.

Importantly, the perceptual logic of scarcityworks regardless of the areas the individualemployees specialize. That is, if a team of employ-ees are all same-area specialists, then replicatingthis expertise externally would be perceived asmore difficult than replicating a team of generalists.The same holds true for teams in which all the

employees are different-area specialists. Again,replicating this portfolio of (diverse) specializedexpertise would be perceived as more difficultthan replicating the expertise of a portfolio ofgeneralists, which clients may perceive as lessscarce.

Hypothesis 5: Specialization moderates the rela-tionship between multiplexity and client transfersuch that the negative effect of multiplexity onthe likelihood a client follows an employee if theemployee exits increases (becomes more nega-tive) when the team has higher average levels ofspecialized expertise.

Methods

Empirical Context

I test my hypotheses in the context of the U.S. fed-eral lobbying industry. Lobbying is defined as tar-geted activity geared toward shaping public policyoutcomes (Drutman, 2015). Clients, mostly cor-porations and interest groups, hire lobbying firmsto advocate for their interests in Washington, DC,arguing for specific legislation to be passed or forlawmakers to maintain the status quo (Baumgartner,Berry, Hojnacki, Leech, & Kimball, 2009). Lobby-ists do so by providing issue expertise and politi-cal access (Bertrand, Bombardini, & Trebbi, 2014).Because public policy has become increasinglycomplex, lobbyists frequently function as bidi-rectional information filters—educating lawmak-ers regarding the implications of policy changes,while also informing clients about developmentsin Washington, helping clients make sense of pol-icy, and advising clients about what they should dopolitically (Drutman, 2015). Indeed, when asked todescribe what it is that lobbyists do, Nick Allardof the prominent lobbying firm Patton Boggs notedthat the first thing lobbyists do is provide informa-tion to the government (e.g., pros and cons of leg-islation), but “second, and even more important, isthat lobbyists provide information to their clients”(Leech, 2013, p. 38). Accordingly, lobbyists are infrequent contact with clients, to act as a conduit ofinformation but also because successful advocacyrequires a deep understanding of the client’s busi-ness and political needs (Drutman, 2015).

The service-based nature of the lobbying indus-try is high in knowledge-intensity and low in



capital-intensity, two characteristics that broadlydefine professional services firms, including but notlimited to law (Phillips, 2002), accounting (Wezelet al., 2006), consulting (Anand et al., 2007), andadvertising (Rogan, 2014). Within lobbying firms,lobbyists are compensated much like legal part-ners, with compensation largely tied to the valueof the clients the lobbyist serves (Drutman, 2015).One difference is that lobbying firms tend to workfor fixed fees rather than billable hours (Becker,2011). Like other professional services, there isa high degree of employee–client interaction andmaintaining client relationships is crucial to firmhealth and performance (Greenwood, Li, Prakash,& Deephouse, 2005).

The process of client acquisition for lobbyingfirms occurs both passively and actively. Forinstance, when asked how lobbying firms getclients, Bob Walker of Wexler & Walker PublicPolicy Associates responded that, “A lot of it iswhat we call ‘over the transom’. It’s just peoplewho know the reputation of the firm and have anissue they think would fit, and they come to us,”but also noted that “New clients also come from theoutreach we do” (Leech, 2013, p. 19). Consistentwith the focus of this article, lobbying firms alsoacquire clients though inbound lobbyist mobility(Wilson, 2014). Qualitative evidence from myinformal interviews with lobbyists are consistentwith the notion that clients are acquired through amixture of passive and active recruitment strategies.

Once lobbying firms secure clients, they workto figure out how they can best serve the client’spolitical needs. As Allard of Patton Boggs put it,a lobbyist’s job is to problem solve—“a good lob-byist looks at the problem or challenge, or what-ever it is that a client wants to accomplish, andthe lobbyist analyzes it and comes up with a solu-tion” (Leech, 2013, p. 37). Much like developinga case for litigation in the legal sector or creatinga media campaign in the advertising industry, thisprocess involves assessing feasibility, developing astrategy, and assembling the proper team to executethat strategy (Levine, 2009). Thus, a key benefit ofhiring a lobbying firm, even if a client has its ownin-house lobbyists, is that “Many independent lob-bying firms—in fact, practically all—feature a fullroster of legislative pros,” some of whom have thedeep expertise required to effectively lobby evenperipheral policy areas (Levine, 2009: 64)—thefirm value add is its lobbyists. As Holland andKnight partner Rich Gold explained, “The work we

do for clients, we don’t get hired for a specific issue.… They hire our whole team as their outside team”(Wilson, 2014).

The process of lobbying is a long-term endeavoras there are rarely quick-fixes in Washington(Levine, 2009). Accordingly, Drutman (2015)provides empirical evidence that once a clienthires a lobbying firm it tends to continue to lobbyfor extended periods of time—both due to thelengthiness of the legislative process but alsobecause clients quickly get accustomed to theperceived benefits of having a political presence inWashington. For example, Bob Walker recalled that“many of our clients here have been here for a long,long time,” and reflected that there are “a couple ofclients here that have been here ever since the firmopened back in 1981” (Leech, 2013, p. 19). Thus,retaining existing clients is a key objective forlobbying firms because existing clients representa steady stream of recurring revenue. Speakingdirectly to this point, Rob Smith of Venable’slegislative practice group explained that while hisfirm is constantly looking for new clients, they areprimarily concerned with cultivating the relation-ships with his firm’s existing clients. Indeed, inSmith’s own words, “If you’re not focused on yourcurrent clients — they have options…You have toprotect your own slice of pie before you reach foranother slice” (Wilson, 2014).

The characteristics and dynamics of the federallobbying industry provide a unique and appropriatecontext to examine client relationship transfer forseveral reasons. First, as detailed above, federallobbying is a knowledge-intensive industry thatis heavily reliant on lobbyist human and socialcapital (Bertrand et al., 2014). Second, clientsrepresent a key source of value and client retentionis a significant priority for lobbying firms. Third,employee mobility is not uncommon, and, despitethe potential for noncompete and nonsolicitationenforcement, “lobbyists moving from one firm toanother often take their clients with them” (Wilson,2014). Fourth, following the passing of the Lobby-ing Disclosure Act of 1995 (henceforth LDA) andsubsequent amendment via the Honest Leadershipand Open Government Act of 2007 (henceforthHLOGA), all organizations are required to filelobbying disclosure reports with the Senate Officeof Public Records (SOPR) that provide details onlobbying activity. As I detail below, the reportingrequirements imposed by the LDA and HLOGAoffer a unique opportunity to longitudinally link


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individual employees with clients, a rarity in extantempirical work (Sorenson & Rogan, 2014).

Data and Sample

The data used in this study are compiled directlyfrom lobbying disclosure reports, which are legallymandated to be filed with the SOPR as stipulated bythe LDA and amended by HLOGA. I obtained thedata from the Center for Responsive Politics (CRP),a nonprofit and nonpartisan organization dedicatedto promoting political transparency. The CRP col-lects data directly from the SOPR and maintains adatabase of all lobbying activity reported between1998 and the present.3 Each lobbying report filedcontains, among other things, information on thelobbying firm, the client, and the individual lob-byists involved in lobbying activity. The HLOGAamended the LDA, increasing the filing frequencyfor lobbying reports (semiannually to quarterly). Anexample of a lobbying report filed under the LDA isprovided in Appendix S1 and an example of a lob-bying report filed under the HLOGA is provided inAppendix S2. For reports filed under the HLOGA, Icollapse the quarterly reports biannually to maintainconsistency with the reports filed under the LDA.This results in an initial longitudinal data structurethat has a maximum of 32 semiannual observationalperiods.

Because my hypotheses predict the likelihoodof client transfer following employee mobility, Irestrict my sample to situations in which employeemobility is observed. I begin with all lobbying firmsthat have at least two registered lobbyists (Wezelet al., 2006). I identify employee mobility if a givenlobbyist l in period p occurs on a lobbying disclo-sure report as a registered lobbyist employed by

3 The CRP research team dedicates a significant amount of timeand resources to maintain the accuracy of the data. For example,CRP researchers manually comb the data searching for errorsand/or inconsistencies. In situations in which it appears the datacontains errors, CRP researchers contact the SOPR and/or thereport filer for clarification. The CRP also standardizes variantsof individual lobbyist names, lobbying firm names, client firmnames, and assigns each lobbyist a unique identifier. The uniqueidentifier facilitates the identification of lobbyists over time. Withrespect to firm names, to guard against type I errors with regardto mobility, with the help of two research assistants, I manuallysearched every firm in my sample to identify name changes,mergers, and acquisitions, many of which are not reflectedconsistently in the CRP’s standardized names. The raw data (priorto my manual corrections) used in this study was downloaded onApril 11, 2014, from the CRP website https://www.opensecrets.org/.

lobbying firm f , but appears on a lobbying disclo-sure report in period p+ 1 as registered lobbyist lfor a firm other than firm f . Given the fluid natureof labor market transitions, in some cases lobbyistl will appear as registered to lobby for firm f and afirm other than firm f in period p+ 1. In these cases,I require that lobbyist l registers to lobby for the newfirm and only the new firm in period p+ 2 (i.e., lob-byist no longer registers to lobby for firm f ) in orderto ensure a clear mobility event.4 Because lobby-ists tend to work for multiple clients, I predict thelikelihood that a specific client will follow a specificlobbyist who engages in mobility. The unit of anal-ysis is the lobbyist-firm-period-client. Thus, I havean observation for each client a lobbyist works withwhen they engage in mobility and I estimate thelikelihood that that specific client follows the lobby-ist to the new firm. In my primary analysis I analyzethe mobility of 1,859 unique individual lobby-ists representing 18,305 lobbyist-firm-period-clientobservations. As detailed below, in my estima-tion strategy and robustness checks, I attempt toaccount for selection and unobserved heterogene-ity in several ways, including a Heckman (1979)correction and a series of conservative fixed-effectsestimations.

A potential limitation of the data is that I do notobserve lobbyists who do not meet the requirementsto register as federal lobbyists or those who meet thecriteria but choose not to register (Drutman, 2015).5

Despite this limitation, the use of lobbying disclo-sure reports to identify lobbyist mobility comparesfavorably with other common practices of identi-fying employee mobility in the strategy literature,

4 There are multiple ways in which to identify mobilityevents—particularly given the presence of lobbyists who regis-ter to lobby at multiple firms simultaneously. As detailed in therobustness checks, my results are robust to a number of mobilitycoding schemes—both restrictive and relaxed—which increasesthe reliability of the results and robustness of the theory developed(see Ge, Huang, & Png, 2016).5 According to the LDA, lobbying activity is defined as “lobby-ing contacts and efforts in support of such contacts, includingpreparation and planning activities, research and other back-ground work that is intended, at the time it is performed, for usein contacts, and coordination with the lobbying activities of oth-ers,” where a lobbying contact is defined as, “any oral or writtencommunication (including an electronic communication) to a cov-ered executive branch official or a covered legislative branch offi-cial.” Registration according to the LDA is required if a registrantemploys a lobbyist who lobbies for a client and “he or she makesmore than one lobbying contact and his or her ‘lobbying activi-ties’ constitute at least 20% of the individual’s time in services forthat client.” Detailed information on the LDA and amendments bythe HLOGA can be found here: http://lobbyingdisclosure.house.gov/amended_lda_guide.html.


https://www.opensecrets.org/

https://www.opensecrets.org/

http://lobbyingdisclosure.house.gov/amended_lda_guide.html

http://lobbyingdisclosure.house.gov/amended_lda_guide.html


such as the use of patents, a technique where mobil-ity is only observed for the fraction of employeeswho patent at multiple firms (Ge et al., 2016). Thatsaid, there is a strong incentive for actors to complywith the LDA, as whoever knowingly and corruptlyfails to comply with any provision of the LDA (e.g.,reporting) may be fined up to $200,000 and impris-oned for up to 5 years under Title 18 if the UnitedStates Code. Indeed, Jack Burkman, founder of thelobbying shop JM Burkman and Associates, alludedto this when discussing new client registrations,“We come from the school of thought that it pays tobe squeaky clean, because lots of folks watch theseregistrations… So, if it’s close, you file” (Wilson,2014). Lobbying reports filed with the SOPR arecommonly used by national political websites (e.g.,Politico, Roll Call, The Hill) and political watchdogorganizations (e.g., Factcheck.org) to track lobby-ing activity and money in politics.

Dependent Variable

Client Transfer is measured with a dummy variableset to 1 if a client that a lobbyist works for inperiod p follows them to their new firm in periodp+ 1. To ensure that the client is following theemployee and not simply returning to a firm inwhich they have prior ties, I also require that theclient has not enlisted the services of the lobbyist’snew firm in prior periods. In professional serviceindustries, clients often employ numerous servicefirms simultaneously. My results are robust if Irequire the client to sever the relationship with theemployee’s prior firm or allow split business.

Independent Variables

Employee–Client Repeated Exchange(Employee–client RE) is measured as the numberof periods a given lobbyist l lobbied for a givenclient c for lobbying firm f , prior to mobility. Thus,this measure captures the employee–client jointtenure at a focal firm or the number of periods theclient has enlisted the services of the given lobbyistat the focal firm.

Firm–Client Repeated Exchange (Firm–clientRE) is measured as the number of periods a givenlobbying firm f lobbied for a given client c, priorto the mobility of lobbyist l. Thus, this measurecaptures the firm–client joint tenure or the numberof periods the client has enlisted the services of thegiven lobbying firm.

Multiplexity is measured as the total number oflobbyists at a given lobbying firm that are registeredto lobby for a particular client in a given period.Therefore, a value of 1 indicates that the client isexclusively tied to a single lobbyist, whereas a valueof 6 indicates that a total of six individual lobbyists(the focal lobbyist and five others) jointly lobby forthe client in a given period.

Specialization (specialized expertise) is mea-sured with an issue-based Herfindahl-HirschmanIndex (HHI) as used by Bertrand et al. (2014). HHIsare a commonly used measure to capture labor mar-ket specialization (Ferguson & Hasan, 2013). TheLDA and HLOGA require that all lobbying reportslist the issues lobbied. There are 79 distinct issuecodes (see Appendix S3). These issues, along withthe dollar value associated with the lobbying report,allow me to determine the degree to which a givenlobbyist specializes in a particular issue or lobbiesacross issues more generally. Given that multipleissues and multiple lobbyists can be listed on a sin-gle report, I follow Bertrand et al and divide the dol-lar amount by the number of lobbyists listed on thatreport. I then divide this amount by the number ofissues listed on the report. The result is an issue- andlobbyist-weighted dollar value for each lobbyingreport. Summing these values across issues by lob-byist allows me to create the total amount of lobby-ing revenue a lobbyist earned for a particular issue,adjusted by the number of lobbyists and issues ona given report. I use these amounts to calculate anissue-based HHI for each lobbyist based on theirlobbying history using the following formula:

Specialization =N∑

i=1

s2i .

where si is the share of lobbyist revenue of issuei and N is the number of issues the lobbyist haslobbied. This value is bound between 0 and 1.Higher values indicate specialists and lower val-ues represent generalists. I take the average ofthese values across all lobbyists who work witha given client in a given period to capture thedegree to which the lobbyist team has specializedexpertise.

Control Variables

To control for alternative explanations, I includea number of controls at the firm, lobbyist,


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2028 J. Raffiee

client, and lobbyist–client levels. To estab-lish time-precedence, if the employee reportslobbying for a new firm in period p+ 1, thecontrol variables are calculated based on datain period p.

At the lobbying firm, I control for lobbyingfirm experience (F.experience) as the total num-ber of periods the lobbying firm has actively lob-bied, the total number of clients (F.clients) thelobbying firm is registered to lobby for, the totalnumber of lobbyists (F.lobbyists) the lobbying firmemploys, and the logged revenue generated per lob-byist (F.performance). With regard to the individ-ual lobbyists, I control for lobbyist experience (L.experience) as the number of periods the lobby-ist has actively lobbied, lobbyist tenure (L.tenure)as the number of periods the lobbyist has lob-bied for the focal firm, and lobbyist client portfolio(L.clients) as the total number of clients the lob-byist has. To control for potential resource depen-dence, I control for the percentage of total clientspend the individual lobbying contract represents(LC.percent.client) and the amount the lobbyingcontract represents as a percentage of total revenuethe lobbyist generates (LC.percent.lobbyist). I alsocontrol for whether the lobbyist exited individu-ally or collectively (L.co-mobility). Specifically, Iinclude a count variable of the number of lobbyistswho work for a specific client while registered tolobby for lobbying firm f in period p who jointlyregistered to lobby for a firm other than firm f inperiod p+ 1. Therefore, a value of 1 indicates thatthe lobbyist switched firms alone and a value of6 indicates that a total of six lobbyists (the focallobbyist and five others) jointly moved. For eachclient, I focus on whether or not lobbyists whojointly worked with that specific client engage inco-mobility. Thus, when a lobbyist works with mul-tiple clients, it is possible that the lobbyist engagesin co-mobility with respect to a particular client andengages in individual mobility with respect to otherclients.

At the client level, I control for the numberof in-house lobbyists the client firm employs(C.in-house), the number of external lobbyists theclient firm employs (C.lobbyists), the number oflobbying firms the client has hired (C.firms), andthe logged total amount of dollars the client spendson lobbying activities for external firms (C.spend).Finally, I include period fixed effects in all modelsto absorb changes in the macro economy, politicalclimate, and political lifecycles.

Estimation Strategy

Given the binary nature of my dependent variable,I test my hypotheses using probit regression(Hoetker, 2007). Because most lobbyists workwith multiple clients per period and because somelobbyists experience multiple mobility events, Iestimate a cluster corrected covariance matrix thatadjusts standard errors for intragroup correlationwithin lobbyists. Interpretation of interactioneffects in probit models is not straightforwardbecause the estimated coefficient for the interactionterm does not necessarily convey meaningfulinformation given that the magnitude, sign, andsignificance of the interaction is conditional on thevalues of all other variables in the model (Hoetker,2007). Accordingly, I follow the best-practice rec-ommendation of Hoetker (2007) and use graphicalpresentations to discuss and interpret interactionhypotheses.

My sample consists only of employees whoengage in mobility and is therefore not random. Iaddress this econometrically by employing Heck-man’s (1979) two-stage sample selection correc-tion. Specifically, I first estimate a probit model inwhich I predict employee mobility (i.e., selectionmodel). From this I calculate the inverse-mills ratio(𝜆) and insert it into my primary estimations where Iexamine the hypotheses developed above. The iden-tifying assumption of the Heckman model is that theselection model contains at least one covariate—aninstrument—which is correlated with mobility, butnot correlated with the likelihood that a given clientwill follow the mobile employee. I follow Kimand Marschke (2005) and Agarwal et al. (2009) anduse gender (L.gender) as an instrument for mobil-ity. L.gender is a dummy variable that takes thevalue of “1” for females and “0” for males. Asdetailed by Kim and Marschke (2005), the under-lying logic for this instrument is that empiricalpatterns suggest that women have higher overallrates of turnover (mobility) relative to men (Light& Ureta, 1992), particularly in the context of pro-fessional services (Campbell, Ganco, et al., 2012).However, there should not be any unobserved differ-ences across gender (e.g., ability, quality) that, con-ditional on the controls and explanatory variables,would influence client transfer.6 Lobbyist gender

6 Approximately 27% of lobbyists in the data are female (32%of mobile lobbyists). Empirically, L.gender is positively relatedto employee mobility in the selection model but insignificant



Table 1Database Summary Statistics1

Totallobbyists2

Mobilelobbyists

Mobilityevents

Mobility events w/oany client transfer

Mobility eventsw/ client transfer

19,608 1,859 2,145 1,247 (58.14%) 898 (41.86%)

Clients ofmobile lobbyists

Exclusiveclient relationships

Multiplexclient relationships

Clients who do nottransfer

Clients whotransfer

18,305 1,248 (6.82%) 17,057 (93.18%) 15,756 (86.07%) 2,549 (13.93%)

1 These statistics will vary depending on the way in which mobility is operationalized, as described above. However, the general patternsare similar with alternative mobility coding schemes.2 This number refers to external lobbyists who work at lobbying firms. In total, there are 42,851 unique lobbyists in the data, includingin-house lobbyists who are employed directly by client firms.

was coded by matching the first name of lobbyistslisted on the lobbying reports with common maleand female first names collected from the censusof Social Security card applications filed with theUnited States Social Security Administration.7

Results

Table 1 presents general summary statistics of thedata. As detailed in Table 1, the data contain 19,608unique lobbyists working at lobbying firms. Ofthese lobbyists, 1,859 engage in mobility at somepoint during the sample window, resulting in 2,145mobility events. These mobility events generate18,305 lobbyist-firm-period-client observations, anobservation for each client a lobbyist works withat the time of mobility. The majority (93.18%)of these client relationships are multiplex withapproximately 64% involving multiplex teams offive lobbyists or less.

A potential concern with my empirical designis the possibility that lobbyists move only afterreceiving assurance that clients will follow. The

when included in the model that predicts client transfer. That said,I acknowledge that my instrument may have some limitations,and so in my robustness checks I estimate a series of fixedeffects regressions—including a stringent fixed-effects structurewith firm-period-lobbyist fixed effects—which help at least partlyalleviate some of these concerns.7 For unisex names, two research assistants determined lobbyistgender by examining various internet sources (e.g., LinkedIn,lobbying firm websites, news articles). The name of the lobbyistand lobbying firm name were used in tandem during searchesto ensure the precise lobbyist was located and not a randomperson who happens to have the same first and last name.Gender was determined by examining photos when available andgender-specific language (e.g., “she,” “her”) used to describe thelobbyist in biographies, news articles, press releases, and so on.

patterns in Table 1 provide some descriptiveevidence suggesting that this does not appearto be the norm. That is, approximately 58% ofmobility events do not result in any client transferand approximately 86% of clients at the time ofmobility do not transfer. While unobservables makeit difficult to rule out the pre-assurance possibilitycompletely, the fact that most clients do not transferhelps alleviate some of this concern.

Figure 1 provides kernel-density estimates forthe distribution of lobbyist levels of specialization.In Figure 1a, density estimates are provided at theindividual lobbyist level using data on all 2,145mobility events. The vast majority of mobile lobby-ists are generalists, however, there is a slight bumpin density as specialization approaches 1, indicat-ing that some are highly specialized in a single issuearea. Similar patterns hold for the overall distribu-tion of lobbyists in any given period, not just formobile lobbyists at the time of mobility. Figure 1bprovides density estimates for specialization at theteam level for all 18,305 lobbyist-firm-period-clientobservations. The distribution in Figure 1b is sim-ilar to the distribution in Figure 1a, albeit with aless pronounced increase in density as specializa-tion approaches 1. Together, these figures suggestthat the supply of specialists tends to be more scarcethan the supply of generalists in the lobbying con-text (Figure 1a), and that highly specialized teamsappear to be even more scarce (Figure 1b).8

8 Note that the distribution of specialists as shown in thekernel-density estimates is only suggestive of specialist scarcitybecause I am unable to observe actual client demand for special-ists. Accordingly, it is possible that the market for specialists isclearing. Note also that my results remain robust when I use thenatural log for specialization.


2030 J. Raffiee

(a) (b)

N = 2,145 N = 18,305

0.5

11.

52

2.5

0 .2 .4 .6 .8 1Specialization (HHI)

kernel = epanechnikov, bandwidth = 0.0513

01

23

4

0 .2 .4 .6 .8 1Specialization (Mean HHI)

kernel = epanechnikov, bandwidth = 0.0238

Figure 1. Kernel-density estimates of specialization. (a) Specialization at the individual level and (b) specialization atthe team level.

Table 2 displays descriptive statistics and bivari-ate correlations. Visual inspection of Table 2reveals some high correlations, so I checked formulticollinearity by calculating variance infla-tion factors (VIFs). Two control variables hadVIFs that surpassed the general threshold of 10(C.lobbyists VIF= 14.00; C.firms VIF= 12.61), butthe mean VIF was within acceptable range (meanVIF= 3.46). Removal of control variables with highVIFs does not alter the results, and, more impor-tantly, the VIFs for explanatory and moderatorvariables are below the more stringent criteria of 5(mean VIF= 2.29), indicating that multicollinearityis not a major concern (Allison, 2012).

Table 3 displays the estimated regression coeffi-cients from my probit regressions. Model 1 beginswith a controls-only model and Model 6 includes allcontrols and independent variables. Of the controlvariables, it is worth highlighting that L.co-mobilityin Model 1 of Table 3 has a strong positive effecton client transfer (𝛽 = 0.32; p= .000; ConfidenceInterval 95% [0.25; 0.38]). This result is robust andholds across all models. I return to the implica-tions of this finding in the discussion section. Withrespect to my hypotheses, Hypothesis 1 predictedthat employee–client repeated exchange wouldbe positively related to client transfer, control-ling for firm–client repeated exchange. Model 2of Table 3 provides support for this hypothesis(𝛽 = 0.10; p= .000; Confidence Interval 95% [0.08;0.11]). The average marginal effect is 0.018 (1.8%),which is a nontrivial increase given the 14% base-line probability of client transfer. Hypothesis 2

predicted that firm–client repeated exchange wouldbe negatively related to client transfer, controllingfor employee–client repeated exchange. Model2 of Table 3 provides support for this hypothe-sis (𝛽 =−0.05; p= .000; Confidence Interval 95%[−0.07; −0.04]).9 The average marginal effect is−0.01 (a decrease of 1%). Importantly, Hypothe-sis 1 and 2 maintain support in the fully saturatedModel 6 of Table 3.

Hypothesis 3 predicted that multiplexity woulddecrease the likelihood of client transfer. Model3 of Table 3 provides support for this hypothe-sis (𝛽 =−0.14; p= .000; Confidence Interval 95%[−0.16, −0.12]). The average marginal effect is−0.025 (2.5%), representing an approximate 18%decrease to the baseline probability. Similar resultsare confirmed in Model 6. Hypothesis 4 predictedthat multiplexity would moderate the relationshipbetween employee–client repeated exchange andclient transfer. As shown in Table 3, the coefficientfor the interaction terms in Model 4 (𝛽 = 0.0002;p= .86; Confidence Interval 95% [−0.003, 0.003])and Model 6 (𝛽 =−0.0001; p= .92; ConfidenceInterval 95% [−0.003, 0.002]) are imprecisely esti-mated. However, because the interaction dependson the other variables in the model, I plotted it(using the “margins” command in STATA 14.1)

9 A test of coefficients (using the “nlcom” command in STATA14.1) indicates that the absolute value of the Employee-client REcoefficient is significantly greater than the absolute value of thecoefficient for Firm-client RE (z= 10.00; p= .000), suggestingthat the benefits of repeated exchange with respect to clienttransfer may accrue faster at the individual level.



Tabl

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Stat

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csan

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Cor

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7.66

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49.4

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0.19

1.00

(4)

F.lo

bbyi

sts

21.9

522

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0.08

0.03

0.82

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(5)F

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11.9

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0.23

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(6)

L.e

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417.

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510.

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0.01

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(7)

L.te

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7.11

6.16

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0.50

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0.80

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0.64

0.92

1.00

(12)

C.e

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3.58

0.03

0.06

0.08

0.07

0.26

0.05

0.03

0.01

0.30

0.39

0.38

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(13)

LC

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cent

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nt0.

500.

390.

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0.31

−0.

38−

0.43

0.19

1.00

(14)

LC

.per

cent

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yist

0.10

0.17

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110.

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Em

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ee-c

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3.99

3.63

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0.27

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0.07

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0.06

−0.

080.

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00(1

7)Fi

rm-c

lient

RE

6.15

5.30

0.03

0.37

0.06

0.00

0.08

0.26

0.33

−0.

030.

130.

100.

120.

13−

0.12

−0.

010.

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671.

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8)M

ultip

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0.16

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230.

330.

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100.

380.

100.

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0.15

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ecia

lizat

ion

0.28

0.22

0.07

−0.

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0.12

−0.

15−

0.19

−0.

06−

0.03

−0.

13−

0.06

−0.

13−

0.08

−0.

11−

0.01

0.15

0.13

0.02

0.00

−0.

201.

00

N=

18,3

05.


2032 J. Raffiee

Table 3Probit Regression Results (DV: Client Transfer)

(1) (2) (3) (4) (5) (6)

F.experience 0.01 0.02 0.02 0.02 0.02 0.02(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)

F.clients 0.00 0.00 0.00 0.00 0.00 0.00(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

F.lobbyists −0.02 −0.02 −0.01 −0.01 −0.01 −0.01(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

F.performance 0.06 0.04 0.03 0.03 0.01 0.01(0.04) (0.04) (0.04) (0.04) (0.04) (0.04)

L.experience 0.02 0.02 0.02 0.02 0.02 0.02(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)

L.tenure −0.01 −0.02 −0.02 −0.02 −0.02 −0.02(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)

L.clients −0.02 −0.02 −0.01 −0.01 −0.01 −0.01(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

C.in-house 0.00 0.00 0.00 0.00 0.00 0.00(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

C.lobbyists −0.02 −0.02 0.00 0.00 0.00 0.00(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

C.firms 0.07 0.07 −0.02 −0.02 −0.01 −0.01(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)

C.expense 0.02 0.02 0.03 0.03 0.03 0.03(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)

LC.percent.client −0.09 −0.08 −0.05 −0.05 −0.05 −0.05(0.05) (0.05) (0.05) (0.05) (0.05) (0.05)

LC.percent.lobbyist 0.29 0.26 0.40 0.40 0.46 0.46(0.09) (0.09) (0.09) (0.09) (0.10) (0.10)

Co-mobility 0.32 0.34 0.46 0.46 0.55 0.55(0.03) (0.03) (0.03) (0.03) (0.03) (0.03)

Employee-client RE 0.10 0.09 0.09 0.09 0.09(0.01) (0.01) (0.01) (0.01) (0.01)

Firm-client RE −0.05 −0.05 −0.05 −0.04 −0.04(0.01) (0.01) (0.01) (0.01) (0.01)

Multiplexity −0.14 −0.14 −0.10 −0.10(0.01) (0.01) (0.01) (0.01)

Employee-client RE×multiplexity 0.00 −0.00(0.00) (0.00)

Specialization 0.55 0.55(0.17) (0.17)

Multiplexity× specialization −0.27 −0.27(0.05) (0.05)

Inverse mills (𝜆) 1.54 1.37 1.01 1.01 0.92 0.92(0.75) (0.75) (0.75) (0.75) (0.77) (0.77)

Constant −5.81 −5.25 −4.19 −4.19 −3.98 −3.98(2.22) (2.22) (2.19) (2.19) (2.27) (2.27)

Period fixed effects Yes Yes Yes Yes Yes YesObservations 18,305 18,305 18,305 18,305 18,305 18,305Log pseudo likelihood −6,416 −6,265 −6,008 −6,008 −5,925 −5,925Pseudo R-squared 0.132 0.152 0.187 0.187 0.198 0.198

Notes. Robust standard errors in parenthesis clustered by lobbyist. Asterisks for significance levels omitted per the policy of the StrategicManagement Journal.

from the estimates in Model 6. The plot is dis-played in Figure 2. The three lines in Figure 2 rep-resent multiplexity of 5 (approximate mean), mul-tiplexity of 1 (exclusive), and multiplexity of 10

(approximately 1 standard deviation above themean), with other covariates held at mean val-ues. As shown in Figure 2, the effect of repeatedexchange on the probability of client transfer is



0.1

.2.3

.4

Pr(

Clie

nt tr

ansf

er=

1)

1 2 3 4 5 6 7 8Employee–client repeated exchange

Multiplexity=1 Multiplexity=5 Multiplexity=10

Predictive Margins with 95% Confidence Intervals

Figure 2. Employee-client RE×multiplexity interac-tion.

weaker (stronger) when multiplexity is high (low),providing general support for Hypothesis 4.

Hypothesis 5 predicted that specialization wouldmoderate the relationship between multiplexity andclient transfer. The coefficient for the interactionterm in Model 5 (𝛽 =−0.27; p= .000; Confi-dence Interval 95% [−0.37, −0.17]) and Model6 (𝛽 =−0.27; p= .000; Confidence Interval 95%[−0.37, −0.17]) of Table 3 are negative and nearlyidentical. Again, to interpret these effects I plottedthe interaction from the estimates in Model 6, asdisplayed in Figure 3. The three lines in Figure 3represent the mean level of specialized expertise(specialization= 0.28), highly generalized exper-tise (specialization= 0), and highly specializedexpertise (specialization= 1). When multiplexity isequal to 1 (i.e., the relationship is exclusive), thepoint estimates for the probability of client transferrise with the level of specialization. This suggeststhat when a relationship is exclusive, specializationmay increase the likelihood of client transfer,although the point estimate 95% confidence inter-vals slightly overlap. As multiplexity rises, theprobability of client transfer decreases at all levelsof specialization, but, as shown in Figure 3, the rateof decrease is much stronger for teams that havehigh levels of specialized rather than generalizedexpertise.10 Thus, Hypothesis 5 receives support.

10 Results are similar when specialization excluded the mobilelobbyist’s specialization, which was separately controlled for,when I exclude observations where the client relationship isexclusive, and when I include measures of within-team variationin specialization as additional controls. Note also that the rateof decrease for specialized teams dampens when multiplexityapproaches a size of seven because at this point the probabilityof client transfer begins to approach zero.

0.1

.2.3

.4P

r(C

lient

tran

sfer

=1)

1 2 3 4 5 6 7 8 9 10Multiplexity

Specialization=0 Specialization=.28 Specialization=1

Predictive Margins with 95% Confidence Intervals

Figure 3. Multiplexity× specialization interaction.

Robustness checks

I conducted several robustness checks to ensurethe robustness of the results reported. First, I tookadditional steps to help ensure my results are notbeing entirely driven by unobserved heterogeneityor self-selection by estimating a series of condi-tional fixed-effects logistic (logit) regressions. Notethat I use logit rather than probit to allow for fixedeffects. The conditional fixed-effects logit modeluses within-subject variation (e.g., lobbyists, lob-bying firms), thereby allowing subjects to serve astheir own controls. The benefit of the fixed-effectsestimations is that it allows me to control forsources of unobserved heterogeneity. The downsideis that covariates without within-subject variationand subjects that do not experience variation onthe outcome are dropped. Table 4 provides regres-sion results for a number of different conditionalfixed-effects logit specifications.

Starting with Model 1 in Table 4, I use individuallobbyist fixed effects, thereby allowing me tocontrol for sources of unobserved lobbyist hetero-geneity that may explain individual differences andpreferences resulting in self-selection. In Model2 of Table 4, I estimate a conditional fixed-effectsregression with fixed effects for lobbying firms.In Model 3 of Table 4, I estimate a conditionalfixed-effects logit with fixed effects for clientfirms. These models capture firm-specific andclient-specific factors that may influence clienttransfer. All models include period fixed effects.The pattern of results in Table 4 are generallyconsistent with the results reported in Table 3.

In Models 1–3 of Table 4, coefficients areestimated for variables that are time invariant atthe time of mobility, such as firm and lobbyistcharacteristics. This is because some lobbyists


2034 J. Raffiee

Table 4Conditional Fixed Effects Logistic Regression Results (DV: Client Transfer)

(1) (2) (3) (4)

F.experience 0.04 0.17 0.03(0.03) (0.21) (0.01)

F.clients 0.00 0.01 0.01(0.01) (0.00) (0.00)

F.lobbyists −0.00 −0.01 −0.02(0.02) (0.01) (0.01)

F.performance −0.07 0.30 0.14(0.26) (0.19) (0.09)

L.experience 0.04 0.03 0.03(0.55) (0.01) (0.01)

L.tenure −0.03 −0.02 −0.02(0.03) (0.01) (0.01)

L.clients −0.01 −0.02 −0.01(0.01) (0.01) (0.01)

C.in-house 0.00 0.00 0.01 0.00(0.01) (0.01) (0.02) (0.01)

C.lobbyists 0.00 0.00 0.01 0.00(0.01) (0.01) (0.01) (0.01)

C.firms −0.01 −0.02 −0.05 −0.01(0.02) (0.02) (0.04) (0.02)

C.expense 0.08 0.07 −0.03 0.09(0.01) (0.01) (0.03) (0.01)

LC.percent.client −0.05 −0.14 0.26 −0.08(0.10) (0.10) (0.23) (0.11)

LC.percent.lobbyist 0.78 1.05 1.30 0.79(0.32) (0.26) (0.30) (0.35)

Co-mobility 0.82 0.98 1.29 0.71(0.08) (0.15) (0.13) (0.09)

Employee-client RE 0.12 0.15 0.12 0.13(0.02) (0.02) (0.02) (0.02)

Firm-client RE −0.06 −0.07 −0.07 −0.06(0.01) (0.01) (0.01) (0.01)

Multiplexity −0.21 −0.18 −0.21 −0.22(0.03) (0.04) (0.05) (0.03)

Employee-client RE×multiplexity 0.00 0.00 0.00 0.00(0.00) (0.00) (0.00) (0.00)

Specialization 1.01 1.22 1.47 0.95(0.66) (0.44) (0.57) (0.70)

Multiplexity× specialization −0.48 −0.59 −0.54 −0.43(0.13) (0.10) (0.19) (0.14)

Period fixed effects Yes Yes Yes NoLobbyist fixed effects Yes No No NoFirm fixed effects No Yes No NoClient fixed effects No No Yes NoLobbyist-firm-period fixed effects No No No YesObservations 10,179 16,099 5,701 8,895Log pseudo likelihood −3,134 −4,492 −1,446 −2,883Pseudo R-squared 0.116 0.147 0.313 0.078

Notes. Robust standard errors in parentheses. Model 1 clustered by lobbyist, Model 2 clustered by firm, Model 3 clustered by client, andModel 4 clustered by lobbyist-firm-period. Asterisks for significance levels omitted per the policy of the Strategic Management Journal.

engage in multiple mobility events. As a result, theconditional fixed-effects model uses the variationfrom multiple movers to estimate coefficients forvariables that are time-invariant at the time of

mobility (Allison, 2009). In Model 4 of Table 4,however, I estimate an extremely conservative con-ditional fixed-effects logit that uses a fixed effectat the lobbyist-firm-period level. Accordingly, only



data on lobbyists who (a) have more than a singleclient at the time of mobility, (b) have variation withrespect to the explanatory variables between clients,and (c) have variation on the dependent variable,is used in this specification. In essence, this modelforces variation to be primarily confined to theexplanatory variables, holding all else constant andusing the lobbyist-firm-period combination as itsown control. This is noteworthy for several reasons.First, it allows me to control for unobserved char-acteristics, such as potential political connectionsthe lobbyist may have at the time of mobility,which may influence client transfer. Second, it alsoprovides evidence that, even if mobility is partlydetermined ex-ante by assurance that some clientswill transfer, the clients who do transfer (even ifthey agreed ex-ante) exhibit characteristics consis-tent with the theoretical arguments above. Third,by holding the lobbyist-firm-period constant, thisestimation implicitly controls for the characteristicsof the firm that the lobbyist joins (which is absorbedby the fixed effect). In doing so, this model alsohelps alleviate lingering concerns regarding thestrength of the mobility instrument (L.gender),particularly the concern that mobile women maysystematically join different types of firms relativeto mobile men. It also helps rule out the potentialthat destination-firm characteristics are drivingclient transfer. No material differences manifestfrom this test, providing added evidence againstthe potential for selection bias or unobservedheterogeneity strongly influencing the results.

Next, some lobbyists register to lobby for mul-tiple lobbying firms, making it unclear if theselobbyists are employees or independent contractors(Bidwell & Briscoe, 2009). Accordingly, I exam-ined a number of alternative ways to code mobilityevents to ensure that the results are not sensitive tothe measurement of mobility (see Ge et al., 2016).I relaxed my requirements for mobility and treateda lobbyist as mobile even if they did not sever theiremployment with their prior lobbying firm in periodp+ 1 or p+ 2. I also restricted my sample to eventswhere a lobbyist registered to lobby for the sourcefirm (and source firm only) for at least two peri-ods premobility and remained registered to lobbyfor the destination firm (and only the destinationfirm) for a minimum of two periods postmobil-ity. In addition, I focused only on the lobbyist’s“dominant employer” as the firm where the lob-byist generates the most revenue (e.g., Campbell,Ganco, et al., 2012). Neither these restrictions nor

controlling for “moonlighters” materially alteredthe results reported above.

Furthermore, because reporting is done in semi-annual periods, it is possible that my dependentvariable may be capturing the continuation ofthe same lobbying assignment/contract rather thantrue interfirm client transfer, especially if lobby-ist mobility is sudden and unexpected. To investi-gate this, I examined how long clients who followmobile lobbyists stay with the new lobbying firm. Ifthe majority of these clients stay with the new firmfor only a single period, then it raises concern asto whether or not these events represent relation-ship transfer. Of the clients who followed lobby-ists, more than 80% remained with the new firmfor at least 1 year (two periods) and more than 60%remained with the new firm for 2 years (four peri-ods) or greater. The mean duration is just over sixperiods (3 years), consistent with the overall samplemean for firm–client repeated exchange. Accord-ingly, while I cannot fully rule out the possibilitythat my dependent variable captures some continu-ation of lobbying assignments, the fact that the vastmajority of clients who follow mobile employeesstay with the new lobbying firm for a considerableamount of time indicates that my results are indeedcapturing true interfirm relationship transfer.

I also took steps to ensure that instances ofco-mobility and subsequent client transfer werenot being influenced by unobserved factors drivingmobility, such as a potential scandal. First, the fixedeffects estimations—particularly the model withlobbyist-firm-period fixed effects—help rule outthis possibility. Second, using the covariates andspecification in Model 3 of Table 3, I included thenumber of employees an employee jointly exitedwith who did not share the client and the totalnumber of employees exiting a firm (i.e., totalturnover). The effect of co-mobility with respectto employees who worked jointly together for aclient remained positive and statistically significant.Interestingly, the coefficients for co-mobility withrespect to employees who did not jointly work witha client and total mobility were both negative, butfailed to reach conventional statistical significancethresholds. This suggests the effect of co-mobilitywith respect to client transfer is conditional onco-mobile employees jointly working directly withthe client, thereby highlighting a potentially serioushazard of multiplex relationship structures.

Finally, I checked the robustness of my resultswith respect to subsamples and range restrictions


2036 J. Raffiee

on various explanatory variables. This includedrestricting my sample to larger lobbying firms (e.g.,at least 10 lobbyists), increasing my sample toinclude firms with a single registered lobbyist, andeliminating observations with excessive levels ofmultiplexity (e.g., greater than 10). In addition,given that the LDA was amended in 2007 with theHLOGA, I used subsamples for lobbying activitypre and post the passing of the HLOGA. Results arematerially consistent.

Discussion

The strategic management literature has long identi-fied employee mobility as a salient threat to sustain-able competitive advantage (Coff, 1997), in largepart due to its role in facilitating knowledge transferand spillover (Agarwal et al., 2016). In this study, Iinvestigated the conditions under which employeemobility may lead to interfirm relationship trans-fer, a potentially serious concern that has receivedmuch less scholarly attention (Sorenson & Rogan,2014). By isolating a key mechanism through whichmobility may influence firm outcomes (i.e., rela-tionship transfer), this study adds insights to theemployee mobility literature, a body of work wherethe transfer of resources (i.e., what is transferred)is rarely disaggregated from mobility itself (Camp-bell, Ganco, et al., 2012; Phillips, 2002).

Utilizing a novel database that allowed me totrack the mobility and client attachments of U.S.federal lobbyists with uncharacteristically highlevels of precision, I found that the likelihood aclient follows a mobile employee to his or her newfirm increased with the strength of the relationship(repeated exchange) between the employee andclient (Hypothesis 1), but decreased with strengthof the relationship (repeated exchange) between theclient and firm (Hypothesis 2). These findings areconsistent with the theoretical predictions deducedfrom prior literature (Broschak, 2004; Somaya,Williamson, & Lorinkova, 2008) and provide robustempirical evidence that repeated exchange simulta-neously generates economically significant benefitsfor both individuals and firms (Elfenbein & Zenger,2013). However, not all relationships are structuredin the same way, and I found strong evidence thatstructural characteristics of client relationships,specifically the degree to which relationshipsare organized as multiplex, have significantimplications for client retention. I found consistent

evidence that multiplexity reduced the likelihoodthat mobility leads to client transfer (Hypothesis3) and also diminished the effects associated withemployee–client repeated exchange (Hypothesis4). The theory developed suggests that multiplexityworks by reducing the degree to which clients relyon any single employee and creating ambiguitywith regard to the unique contributions of eachteam member (Alchian & Demsetz, 1972). Inkeeping with this logic, I further argued and foundthat the effect of multiplexity would be strongestwhen multiplex team members had high averagelevels of specialized expertise (Hypothesis 5). Thisfinding complements classic management workthat suggests firms can protect knowledge throughspecialization and division of labor as a means toincrease the difficultly and perceived ability forindividual employees to replicate the productionprocess (Liebeskind, 1996). Taken together, thisstudy highlights the precarious position firms arein with respect to employee mobility and clienttransfer, but also identifies several characteristicsof relationships that increase the odds of clientretention.

Limitations and Future Research

This study is not without limitations, many of whichcreate promising pathways for future research. First,it is not clear how generalizable my findings areto contexts outside of federal lobbying. However,my findings should be fairly generalizable to otherprofessional services firms (PSFs), a sector that isan increasingly important component of the over-all economy (Greenwood et al., 2005). That said,my findings may also have implications that extendbeyond service industries, particularly to firms thatemploy sales representatives, client-facing employ-ees, or have extensive buyer–supplier and alliancenetworks. Indeed, employees interface across firmboundaries in contexts beyond PSFs and so futureresearch could examine relationship transfer innon-PSF settings.

Second, while the LDA/HLOGA offers me aunique way to observe the dynamics of lobbyistand client mobility, I am unable to observe lob-byists who do not meet the registration thresholdsspecified in the LDA/HLOGA or lobbyists whoexceed these thresholds but choose not to regis-ter (Drutman, 2015). Although the limitations asso-ciated with using lobbying registrations to trackmobility are similar to the limitations in prior work



(Ge et al., 2016) and my fixed-effects estimationsshow that the theoretical mechanisms proposedwork with respect to within-lobbyist variation, evi-dence suggests the unreported lobbying activitymay be substantial (Drutman, 2015).

Third, lobbyists are not randomly assigned toclients and so I am unable to empirically disentan-gle to what degree initial match quality betweenlobbyists and clients leads to greater levels ofrepeated exchange and therefore a higher likelihoodof client transfer, or, if greater levels of repeatedexchange between lobbyists and clients leads tohigher match quality, which leads to a higher like-lihood of client transfer. While it is theoreticallypossible that pure initial match quality drives theresults, it is somewhat unlikely because establishingmatch quality takes time, during which the benefitsof repeated exchange continually accrue (Bidwell &Fernandez-Mateo, 2010). Future work could teaseapart these factors and establish the relative impor-tance of each, perhaps through an experiment whereresearchers can manipulate initial match quality andutilize random assignment.

Fourth, I am also unable to differentiate volun-tary from involuntary employee mobility. Althoughinvoluntary mobility is unlikely given the researchdesign, and interviews with practicing lobbyistssupport this view, future research that examines dif-ferences in voluntary and involuntary turnover asit relates to interfirm knowledge and relationshiptransfer would be useful.

Finally, employee mobility is not random. There-fore, while I attempted to econometrically accountfor selection, utilized stringent fixed-effect struc-tures, and presented descriptive evidence showingthat the majority of employees who switch firms donot take clients, the general nature of my researchquestion means that my results should be inter-preted with at least some caution.

Contributions and Conclusion

Notwithstanding the above limitations, this studymakes several contributions. This study contributesto the strategy and mobility literatures by respond-ing to recent calls urging researchers to investigatethe micro-foundations of strategic human capital(Campbell, Coff, et al., 2012; Coff & Raffiee,2015). Surprisingly, despite decades of researchon employee turnover and firm performance, littlework has examined the potential role of relationshiptransfer (cf. Somaya et al., 2008), despite calls to

examine how relationships between employeesand customers influence mobility processes andoutcomes (Holtom, Mitchell, Lee, & Eberly, 2008).By isolating client transfer, this study furtherreinforces the need to theoretically and empiricallyaccount for relationship transfer when examiningmechanisms through which mobility may impactfirm performance (Carnahan & Somaya, 2013).The research design and granularity of the datain this study add further empirical contribution byfacilitating the measurement of client transfer asdistinct from employee mobility (cf. Campbell,Ganco, et al., 2012).

By doing so, this study extends prior work exam-ining the role of employee departure on the sta-bility of interfirm relationships (Broschak, 2004).My ability to observe client attachments at theemployee level allowed me to add nuances to exist-ing work by theorizing about how characteristicsof individuals and characteristics of firms uniquelyand jointly influence client transfer (cf. Broschak& Block, 2014). For example, this study highlightsthe unique and opposing effects of employee–clientand firm–client repeated exchange, effects thatwould and could be confounded absent separatemeasurement of employee and firm client attach-ment. These results also contribute to the litera-ture on repeated exchange, “as few studies havebeen able to measure or infer directly the eco-nomic value that accrues within these relation-ships of repeated exchange” (Elfenbein & Zenger,2013, p. 222). The finding that repeated exchangehas robust effects on client transfer—the primarysource of economic value for firms—suggests thevalue of repeated exchange, particularly in profes-sional services industries, is extremely high andmay have long-term economic implications.

This study also extends research on labor marketspecialization. Prior work has shown that special-ization and the division of labor can increase firmproductivity (S. Rosen, 1983) and protect intangibleknowledge assets (Liebeskind, 1996). This studybuilds on this work to show how specializationcan embed important tangible assets—clientrelationships—with the firm. The theoreticalcontribution is rooted in the nuanced relation-ship between multiplexity, specialization, andclient transfer uncovered in this study—increasedspecialization can benefit the firm when clientrelationships are multiplex, but potentially harmthe firm when the relationship is exclusive. Myfindings also raise the question of who captures


2038 J. Raffiee

value created from multiplex client relationships,particularly when clients are served by highlyspecialized teams. Future work could examinehow specialization influences value creation versuscapture in client relationships (Ethiraj & Garg,2012).

Beyond the literature on labor market specializa-tion, this study contributes to the broader literatureon multiplexity and embeddedness by showing thatthe client retention benefits of multiplexity dependnot just on the quantity of employees involved inmultiplex ties (Bermiss & Greenbaum, 2016), butalso on their human capital characteristics (i.e., spe-cialization). At the same time, this study adds to theliterature by drawing attention to the fact that theclient retention benefits associated with multiplex-ity do not come without potential hazards. Althoughnot hypothesized, I found strong evidence that col-lective employee exit increased the likelihood ofclient transfer, specifically when co-mobile employ-ees jointly served the client. This finding adds to thenascent stream of research on co-mobility (Camp-bell, Saxton, & Banerjee, 2014), an understudiedphenomena estimated to account for up to 11%of all interfirm job moves (Marx & Timmermans,2014). Within this emerging body of work, scholarshave shown that co-mobility is associated withlower performance declines (Groysberg & Lee,2009), greater increases in individual compensation(Marx & Timmermans, 2014), and greater adverseeffects on source firm performance (Agarwalet al., 2016). This article adds to this conversationby providing robust evidence that co-mobility ispositively associated with relationship transfer.One implication is that relationship transfer—notsolely the preservation of coworker-specific humancapital, co-specialized skills, and tacit knowledgeembedded in routines—may explain findingsin prior work, particularly since many studiesexamining co-mobility test theory in professionalservices industries, but lack empirical measures ofclient relationships and client transfer (e.g., Wezelet al., 2006).

Lastly, the above discussion broadly raises anumber of interesting questions regarding the strate-gic use of restrictive covenants in employmentcontracts. Indeed, my interviews with lobbyistsrevealed an interesting pattern—lobbying firmsseemed reluctant to enforce restrictive clauses toprevent client transfer because they viewed the lossof clients as a repeated games scenario and believedthat by not enforcing the contract it increased

their chances of reacquiring the client at a laterdate. In contrast, lobbying firms were concernedthat enforcing the contract could be viewed asrestricting the client’s market choices, thereby cre-ating bad-will and essentially eliminating the firm’schances of reacquiring the client down the road.As these anecdotes suggest, the decision to enforcerestrictive covenants can have both short-term andlong-term consequences, and firms must balancethese tradeoffs when making enforcement deci-sions. Future work examining these nuances couldyield interesting insights regarding how restrictivecovenants can aide firms in retaining employees,interfirm relationships, and ultimately sustaining acompetitive advantage.

In sum, this article investigated when employeemobility leads to interfirm relationship transferand identified how individual, organizational, andstructural relationship characteristics influence thisprocess. Analysis of a novel database tracking theemployment and client attachments of registeredU.S. federal lobbyists provided support for mytheoretical predictions while unearthing numerousareas for future research on mobility, relationshipportability, and competitive advantage.

Acknowledgments

I thank SMJ co-editor Sendil Ethiraj and two anony-mous SMJ reviewers for insightful and construc-tive comments that greatly improved and refined theideas in this article. I am also grateful to RajshreeAgarwal, Zhi Cao, Russ Coff, Paul Davis, J.P.Eggers, April Franco, Martin Ganco, Barry Ger-hart, Hart Posen, Violina Rindova, Sarada, TerryShelton, John Surdyk, Tiffany Trzebiatowski, SusanYackee, seminar participants at the 2015 Consor-tium on Competitiveness and Cooperation (“CCC”)doctoral conference, and seminar participants at theWisconsin MHR brown bag. Finally, I thank themany lobbyists who generously agreed to speakwith me and share their thoughts regarding this arti-cle and the lobbying industry. Any errors remainmy own.

References

Agarwal, R., Campbell, B. A., Franco, A., & Ganco, M.(2016). What do I take with me?: The mediating effectof spin-out team size and tenure on the founder-firmperformance relationship. Academy of ManagementJournal, 59(3), 1060–1087.



Agarwal, R., Ganco, M., & Ziedonis, R. H. (2009). Reputa-tions for toughness in patent enforcement: Implicationsfor knowledge spillovers via inventor mobility. Strate-gic Management Journal, 30(13), 1349–1374.

Aime, F., Johnson, S., Ridge, J. W., & Hill, A. D. (2010).The routine may be stable but the advantage is not:Competitive implications of key employee mobility.Strategic Management Journal, 31(1), 75–87.

Alchian, A. A., & Demsetz, H. (1972). Production, infor-mation costs, and economic organization. AmericanEconomic Review, 62(5), 777–795.

Allison, P. D. (2009). Fixed effects regression models. LosAngeles, CA: SAGE.

Allison, P. D. (2012). When can you safely ignoremulticollinearity? Statistical Horizons. Retrieved fromhttp://statisticalhorizons.com/multicollinearity

Anand, N., Gardner, H. K., & Morris, T. (2007).Knowledge-based innovation: Emergence and embed-ding of new practice areas in management consultingfirms. Academy of Management Journal, 50(2),406–428.

Baumgartner, F. R., Berry, J. M., Hojnacki, M., Leech,B. L., & Kimball, D. C. (2009). Lobbying and policychange: Who wins, who loses, and why. Chicago andLondon, England: University of Chicago Press.

Becker, A. (2011). Marriage of lobby shops and lawfirms is uneasy but lucrative. The Washington Post.Retrieved from http://www.washingtonpost.com/wp-dyn/content/article/2011/03/04/AR2011030405688.html

Bermiss, Y. S., & Greenbaum, B. E. (2016). Loyal towhom? The effect of relational embeddedness and man-agers’ mobility on market tie dissolution. Administra-tive Science Quarterly, 6(2), 254–290.

Bertrand, M., Bombardini, M., & Trebbi, F. (2014). Isit whom you know or what you know? An empiri-cal assessment of the lobbying process. American Eco-nomic Review, 104(12), 3885–3920.

Bidwell, M. J., & Briscoe, F. (2009). Who contracts?Determinants of the decision to work as an independentcontractor among information technology workers.Academy of Management Journal, 52(6), 1148–1168.

Bidwell, M. J., & Fernandez-Mateo, I. (2010). Relation-ship duration and returns to brokerage in the staffingsector. Organization Science, 21(6), 1141–1158.

Bidwell, M., Won, S., Barbulescu, R., & Mollick, E.(2015). I used to work at Goldman Sachs! How firmsbenefit from organizational status in the market forhuman capital. Strategic Management Journal, 36(8),1164–1173.

Biong, H., & Ulvnes, A. M. (2011). If the supplier’shuman capital walks away, where would the customergo? Journal of Business-to-Business Marketing, 18(3),223–252.

Broschak, J. P. (2004). Managers’ mobility and marketinterface: The effect of managers’ career mobility onthe dissolution of market ties. Administrative ScienceQuarterly, 49(4), 608–640.

Broschak, J. P., & Block, E. S. (2014). With or without you:When does managerial exit matter for the dissolution ofdyadic market ties? Academy of Management Journal,57(3), 743–765.

Campbell, B. A., Coff, R. W., & Kryscynski, D. (2012).Re-thinking competitive advantage from human capi-tal. Academy of Management Review, 37(3), 376–395.

Campbell, B. A., Ganco, M., Franco, A. M., & Agar-wal, R. (2012). Who leaves, where to, and why worry?Employee mobility, entrepreneurship and effects onsource firm performance. Strategic Management Jour-nal, 33(2), 65–87.

Campbell, B. A., Saxton, B. M., & Banerjee, P. M. (2014).Resetting the shot clock: The effect of comobilityon human capital. Journal of Management, 40(2),531–556.

Carnahan, S., & Somaya, D. (2013). Alumni effects andrelational advantage: The impact on outsourcing whena buyer hires employees from a supplier’s competitors.Academy of Management Journal, 56(6), 1578–1600.

Chatain, O. (2011). Value creation, competition, andperformance in buyer-supplier relationships. StrategicManagement Journal, 32(1), 76–102.

Coff, R. W. (1997). Human assets and managementdilemmas: Coping with hazards on the road toresource-based theory. Academy of ManagementReview, 22(2), 374–402.

Coff, R. W., & Raffiee, J. (2015). Toward a theory ofperceived firm-specific human capital. Academy ofManagement Perspectives, 29(3), 326–341.

Curhan, J. R., Neale, M. A., Ross, L., &Rosencranz-Engelmann, J. (2008). Relationalaccommodation in negotiation: Effects of egalitarian-ism and gender on economic efficiency and relationalcapital. Organizational Behavior and Human DecisionProcesses, 107(2), 192–205.

Dokko, G., & Rosenkopf, L. (2010). Social capital forhire? Mobility of technical professionals and firm influ-ence in wireless standards committees. OrganizationScience, 21(3), 677–695.

Drutman, L. (2015). The business of America is lobby-ing: How corporations became poliicized and politicsbecame more corporate. New York, NY: Oxford Uni-versity Press.

Dunn, K. A., & Mayhew, B. W. (2004). Audit firm industryspecialization and client disclosure quality. Review ofAccounting Studies, 9(1), 35–58.

Dyer, J. H., & Singh, H. (1998). The relational view: Coop-erative strategy and sources of interorganizational com-petitive advantage. Academy of Management Review,23(4), 660–680.

Elfenbein, D. W., & Zenger, T. R. (2013). What is a rela-tionship worth? Repeated exchange and the develop-ment and deployment of relational capital. Organiza-tion Science, 25(1), 222–244.

Emerson, R. M. (1981). Social exchange theory. In M.Rosenberg & R. H. Tyrner (Eds.), Social psychology:Social perspectives (pp. 30–65). New York, NY: BasicBooks.

Ethiraj, S. K., & Garg, P. (2012). The division of gains fromcomplementarities in human-capital-intensive activity.Organization Science, 23(3), 725–742.

Ferguson, J.-P., & Hasan, S. (2013). Specialization andcareer dynamics: Evidence from the Indian administra-tive service. Administrative Science Quarterly, 58(2),233–256.


http://statisticalhorizons.com/multicollinearity:

http://www.washingtonpost.com/wp-dyn/content/article/2011/03/04/AR2011030405688.html:



2040 J. Raffiee

Franco, A. M., & Filson, D. (2006). Spin-outs: Knowledgediffusion through employee mobility. RAND Journal ofEconomics, 37(4), 841–860.

Ge, C., Huang, K. W., & Png, I. P. (2016). Engi-neer/scientist careers: Patents, online profiles, andmisclassification bias. Strategic Management Journal,37(1), 232–253.

Greenwood, R., Li, S. X., Prakash, R., & Deephouse, D. L.(2005). Reputation, diversification, and organizationalexplanations of performance in professional servicefirms. Organization Science, 16(6), 661–673.

Groysberg, B., & Lee, L. (2009). Hiring stars and their col-leagues: Exploration and exploitation in professionalservice firms. Organization Science, 20(4), 740–758.

Gulati, R. (1995). Social structure and alliance formationpatterns: A longitudinal analysis. Administrative Sci-ence Quarterly, 40(4), 619.

Hatch, N. W., & Dyer, J. H. (2004). Human capital andlearning as a source of sustainable competitive advan-tage. Strategic Management Journal, 25(12), 1155.

Heckman, J. (1979). Sample selection bias as a specficationerror. Econometrica, 47(1), 153–161.

Hoetker, G. (2007). The use of logit and probit models instrategic management research: Critical issues. Strate-gic Management Journal, 28(4), 331–343.

Holtom, B. C., Mitchell, T. R., Lee, T. W., & Eberly, M.B. (2008). Turnover and retention research: A glance atthe past, a closer review of the present, and a ventureinto the future. Academy of Management Annals, 2(1),231–274.

Kim, J., & Marschke, G. (2005). Labor mobility of scien-tists, technological diffusion, and the firm’s patentingdecision. RAND Journal of Economics, 298–317.

Kuwabara, K. (2011). Cohesion, cooperation, and the valueof doing things together: How economic exchange cre-ates relational bonds. American Sociological Review,76(4), 560–580.

Lawler, E. J., & Thye, S. R. (1999). Bringing emotions intosocial exchange theory. Annual Review of Sociology,25, 217–244.

Lecuona, J. R., & Reitzig, M. (2014). Knowledge worthhaving in ‘excess’: The value of tacit and firm-specifichuman resource slack. Strategic Management Journal,35(7), 954–973.

Leech, B. L. (2013). Lobbyists at work. New York, NY:Apress.

Levine, B. J. (2009). The art of lobbying: Building trustand selling policy. Washington, DC: CQ Press.

Liebeskind, J. P. (1996). Knowledge, strategy, and thetheory of the firm. Strategic Management Journal,17(S2), 93–107.

Light, A., & Ureta, M. (1992). Panel estimates of male andfemale job turnover behavior: Can female nonquittersbe identified? Journal of Labor Economics, 156–181.

Marx, M., & Timmermans, B. (2014). Comobil-ity. Retrieved from SSRN: https://ssrn.com/abstract=2576981 or http://dx.doi.org/10.2139/ssrn.2576981

Mayer, K., Somaya, D., & Williamson, I. (2012).Firm-specific, industry-specific and occupationalhuman capital, and the sourcing of knowledge work.Organization Science, 23, 1311–1329.

Phillips, D. J. (2002). A genealogical approach to organiza-tional life chances: The parent-progeny transfer amongSilicon Valley law firms, 1946-1996. AdministrativeScience Quarterly, 47(3), 474–506.

Raffiee, J., & Coff, R. (2016). Micro-foundations offirm-specific human capital: When do employees per-ceive their skills to be firm-specific? Academy of Man-agement Journal, 59(3), 766–790.

Rogan, M. (2013). Too close for comfort? The effect ofembeddedness and competitive overlap on client rela-tionship retention following an acquisition. Organiza-tion Science, 25(1), 185–203.

Rogan, M. (2014). Executive departures without clientlosses: The role of multiplex ties in exchange partnerretention. Academy of Management Journal, 57(2),563–584.

Rosen, S. (1983). Specialization and human capital. Jour-nal of Labor Economics, 1(1), 43–49.

Rosen, R. E. (2010). Lawyers in corporate decision-making. New Orleans, LA: Quid Pro Books.

Rosenkopf, L., Metiu, A., & George, V. P. (2001). From thebottom up? Technical committee activity and allianceformation. Administrative Science Quarterly, 46(4),748–772.

Shipilov, A. (2012). Strategic multiplexity. Strategic Orga-nization, 10(3), 215–222.

Shipilov, A., Gulati, R., Kilduff, M., Li, S., & Tsai,W. (2014). Relational pluralism within and betweenorganizations. Academy of Management Journal, 57(2),449–459.

Somaya, D., Williamson, I., & Lorinkova, N. (2008).Gone but not Lost: The different performance impactsof employee mobility between cooperators versuscompetitors. Academy of Management Journal, 51(5),936–953.

Sorenson, O., & Rogan, M. (2014). (When) do organiza-tions have social capital? Annual Review of Sociology,40, 261–280.

Wasserman, S., & Faust, K. (1994). Social network anal-ysis: Methods and applications. Cambridge, Englandand New York, NY: Cambridge University Press.

Wezel, F. C., Cattani, G., & Pennings, J. M. (2006). Com-petitive implications of interfirm mobility. Organiza-tion Science, 17(6), 691–709.

Wholey, D. R. (1985). Determinants of firm internal labormarkets in large law firms. Administrative ScienceQuarterly, 318–335.

Wilson, K. (2014). Lobby firms sweep up new clients.The Hill. Retrieved from http://thehill.com/business-a-lobbying/196401-lobby-firms-sweep-up-new-clients

Supporting Information

Additional supporting information may be foundin the online version of this article:

Appendix S1. LDA lobbying disclosure report.Appendix S2. HLOGA lobbying disclosure report.Appendix S3. Issue codes and issues listed onlobbying disclosure reports.


https://ssrn.com/abstract=2576981

https://ssrn.com/abstract=2576981

http://dx.doi.org/10.2139/ssrn.2576981

http://dx.doi.org/10.2139/ssrn.2576981

http://thehill.com/business-a-lobbying/196401-lobby-firms-sweep-up-new-clients:

http://thehill.com/business-a-lobbying/196401-lobby-firms-sweep-up-new-clients:

employee mobility and interfirm relationship transfer ...and specialized human capital, this study...

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