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Abstract

Human capital is crucial for sustainable competitive advantage, and certain workforce groups have a greater impact than others. The top management team, responsible for directing firm strategy and structure, is one such group, and executive turnover can be detrimental for a firm. While existing literature often suggests that executives change jobs primarily for higher pay, this is not always true. Drawing on human capital research and the geographic preference theory, we examine how an executive’s human capital and geographic preferences affect changes in compensation during job transitions. Using a sample of 351 executives who moved among S&P 500 companies from 2000 to 2015 (378 inter-organizational moves), we find that education (a proxy for human capital) and area desirability (a proxy for geographic desirability) correlate with changes in executive compensation. Additionally, a higher density of geographically proximal executive pay packages increases an executive’s compensation premium when changing jobs.

Keywords

executive compensation geographic preference human capital executive mobility top management teams

Introduction

The rich literature on executive compensation has largely focused on either the relationships between (a) executive pay and firm performance (e.g., Carpenter & Sanders, 2002; Leonard, 1990) or (b) executive compensation packages, as in amounts and types (e.g., stock options, bonuses, etc.) and executive experience or profiles (e.g., Sabanci & Elvira, 2024). Only recently has scholarship began to explore the role of geography in these relationships, largely starting with the work of Yonker (2017a). This lack of prior interest is surprising given the well-recognized importance of executives to firm value creation (Becker et al., 2009; Delery & Shaw, 2001), and given assumptions in both compensation and human capital literature that the labor market for top executives is perfectly competitive whereby executives will change employers when offered higher compensation (Campbell et al., 2012; Mackey et al., 2014). In fact, finance models of executive and firm matching historically included no role for geography, and assume that “the best managers” go “to the largest,” and presumably best paying, firms (Yonker, 2017a, p. 611). And yet common knowledge suggests this is clearly not always the case—raising the question of what is the role of geographic preference in executive compensation?

Under the differentiation of the workforce rationale (Osterman, 1987), specific workforce groups have relatively greater impact on a firm’s value creation process, particularly the top management team (Castanias & Helfat, 1991; Chandler, 1962). This aligns with other theoretical approaches such as Upper Echelons Theory (Hambrick, 2007; Hambrick & Mason, 1984) and Child’s (1972) “strategic choice” perspectives, highlighting the role top executives play for directing and defining a firm’s strategic initiatives and organizational structures. In addition, top executives’ human capital has a direct effect on organizational outcomes because, as Castanias and Helfat (1991) underline, executives possess a rare portfolio of superior management skills as well as other generic, industry-specific, and firm-specific knowledge, skills, and abilities (KSAs). Given the importance of top executives, their movement from one employer to another can be very impactful for the performance of both firms.

Historically, it has been recognized that factors other than just compensation influence employee mobility decisions, including, as relevant here, geography. For instance, Nelson (1959) criticizes the historical “money income model” whereby individuals maximize the monetary gains of changing jobs and argues that individual “tastes” (such as being near family and friends) and information influence employee mobility beyond simple wage differentials. Similarly, Sjaastad (1962) introduced the concepts of psychic cost of being near or close to family and friends, as well as the role of other geographically related (personal) preferences such as climate. Empirically, Clemens (2013) explicitly identified the role that geography plays on compensation by discussing the significant wage gap among software workers located in the United States and India who work for the same firm, finding that individuals differences (human capital, etc.) explain less than half of this gap. Thus, geography does significantly impact both employee mobility and employee compensation.

In addition, individuals frequently have geographic preferences, and these preferences can affect compensation. Yonker (2017a) introduced the idea of “geographical preference theory” by examining the role of the geography in the labor market for chief executive officers (CEOs), specifically, the idea that CEOs prefer living and working close to their home origins. He found that firms were over five times more likely to hire a locally born CEO, and that both voluntary turnover and compensation are lower for local CEOs. Bick and Flugum (2022) similarly found that executives who attended university near their firm’s headquarters are paid 4.40% to 11.01% less than non-local peers. Geographic ties may also affect executive (and thereby firm) behavior and performance. For instance, Yonker (2017b) found that employee favoritism was geographically biased in favor of business locations near CEO’s childhood homes, while Lai et al. (2020) found a “CEO locality effect” that was “highly effective in curbing managerial short-termism” (p. 229).

All of this research on the importance of geography goes against the dominating rationale among human capital scholars that “workers stay in their current firms because there is low external demand for their skills, and workers change firms because there is high external demand for their skills, independent of their desire to leave their current firm—that is, workers are not averse to changing firms and always choose the employer that offers the highest wage” (Campbell et al., 2012, p. 382). Compensation, as demonstrated by numerous studies in human resource management (e.g., Conroy et al., 2022; Shaw et al., 1998) is a very important element that affects the decisions individuals make about employment relationships, such as turnover decisions. Focusing specifically on top managers, Mackey et al. (2014) indicate that executives with rare human capital (defined as knowledge, skills, abilities, and other characteristics or KSAOs; Ployhart et al., 2014), are more likely to enter into employment relationships with firms that can offer them higher compensation and are, in general, resource-rich. As Campbell et al. (2012) argue, however, relying solely on compensation in predicting employee mobility/turnover is a rather simplistic approach since a number of other factors—such as human capital and geography—play a critical role.

We therefore seek to shed light on the factors that influence the decision of executives to change employers and join firms that offer higher or lower compensation packages than their current employer. Specifically, this study draws on human capital research and geographic preference theory (Yonker, 2017a) to suggest that differences in total compensation across firms of mobile executives are linked to both executive human capital and executive geographic preferences. The hypotheses are tested using a sample of executives who moved between Standard & Poor’s (S&P) 500 firms between 2000 and 2015. This unique data set of mobile executives allows us to avoid concerns associated with “pay for performance” as we are able to compare wages pre/post the executive mobility event, and therefore, the new compensation package directly reflects what a focal executive demanded in order to accept a new job offer. Using a sample of 351 executives who moved among S&P 500 companies during 2000 to 2015 (378 inter-organizational moves), our results provide support for the positive effect of human capital on the receipt of a compensation premium, while high area desirability is negatively associated with the receipt of such premiums when executives move from one company to another. In addition, we find that geographically proximal executive packages with higher density (a great concentration of firms that offer comparable executive compensation packages in relatively high proximity) appear to positively affect an executive’s compensation premium during inter-organizational moves.

We contribute to research on compensation by exploring whether compensation for top executives is affected by human capital attributes such as level of education, status of higher education institution, and experience (as proxied by age), along with geographic factors, such as the desirability of a location and the geographical concentration of comparable high executive compensation packages. As Devers et al. (2007) emphasize in their review of the executive compensation literature, most of the relevant literature that explores executives’ choices and compensation focus on the impact of compensation on executives’ choices (e.g., choices that impact their performance; Banker et al., 2000). In this study, we expand the relevant literature by focusing on executives’ choices (in the form of geographic preferences) on compensation. Moreover, we answer calls for exploring the impact of the context on executives’ compensation (e.g., Gomez-Mejia & Wiseman, 1997), by incorporating in our theorizing the location of the companies involved in the executives’ intra-organizational moves. Moreover, our study has important implications for practitioners as it highlights key components that influence executives’ compensation when moving from one organization to another.

Human Capital and Total Compensation Premiums

As mentioned above, most human capital-based literature assumes that executives are more likely to accept offers from employers offering higher compensation because such organizations use compensation packages to signal that they are resource-rich, thus enhancing the perception that the employment offer has the potential for resource complementarities (e.g., the new employer has both the financial and human capital resources necessary for pursuing new strategies) that will allow executives to generate more value for firms—and perhaps for themselves (Mackey et al., 2014). Moreover, a number of studies have indicated that the human capital (normally defined as the knowledge, skills, abilities, and other attributes, or “KSAOs,” Ployhart et al., 2014) of employees, in general, and managers, in particular, is associated with their compensation (Agarwal, 1981; Becker, 1964; Fisher & Govindarajan, 1992; Harris & Helfat, 1997; Mackey et al., 2014). Given that superior managerial skills are relatively scarce (Castanias & Helfat, 1991; Combs & Skill, 2003; Mackey et al., 2014), managers can seek to maximize their compensation when faced with alternative employment offers. Moreover, as Harris and Helfat (1997) argue when focusing on CEO succession, pay premiums are required in order to compensate for the forgone gains such mobile individuals have from leaving behind their firm-specific skills at their former employers as well as for the increased likelihood of failure due to the lack of firm-specific skills at their new employers. We refer to such premiums as a “total compensation premium” since we recognize that compensation, particularly for executives, includes varied components such as wages, bonuses, stock options, and the like. Based on the above, we hypothesize that:

The greater the human capital of an executive, the higher the total compensation premium they will receive when moving from one firm to another.

Top executives, like all employees, possess a portfolio of human capital (Campbell et al., 2012; Castanias & Helfat, 1991) that is comprised of generic (transferable across industries and firms with a mobile executive), industry-specific (transferable across firms only within the same industry), and firm-specific human capital (that is not readily transferable to other firms or other industries) (e.g., Delery & Roumpi, 2019). A widely held assumption across human capital research is that the firm-specificity of human capital creates (or increases) barriers to employee mobility (Hatch & Dyer, 2004; Seo & Somaya, 2022; Williamson, 1988). Thus, the perception an individual holds regarding the extent to which they possess firm-, industry-specific, or generic human capital influences their perceptions regarding their employability and potential mobility (Coff & Raffiee, 2015). In particular, when employees perceive they have high levels of firm-specific human capital, they may feel a poor sense of fit with a focal firm which may lead them to consider a job (Akinsanmi Oyedeji & Coff, 2024). Alternatively, a hiring or destination firm’s perceptions of an employee’s firm-specific capital at a prior employer may serve as a signal of the employee’s future ability to develop firm-specific human capital at the new employer—thus leading to recruitment actions (Morris et al., 2017). Given that in the case of executives, it is difficult to capture perceptions of firm-specificity, we focus here on industry-specific human capital which is more readily perceivable by both executives and firms. Industry-specific human capital has been defined as “the KSAOs that are relevant and valuable only within a specific (or at least very similar) industry or sector of the economy” (Delery & Roumpi, 2019, p. 174). It can be expected, therefore, that when an alternative job offer comes from a firm (the hiring or destination firm; Mawdsley & Somaya, 2016) operating within the same industry as the current employer (the source form) of such an individual, that employee’s total human capital is more transferable than when the source and destination firms operate in different industries. Such firms are deemed to be high on human capital relatedness (Bergh, 2001; Buchholtz et al., 2003); thus, an executive moving among such firms should demand (and receive) greater compensation due to the transferability of their industry-specific human capital (Sturman et al., 2008). In line with this, Sabanci and Elvira (2024) argue and find support that executive mobility across industries (i.e., when the source and destination firms are in different industries) is associated with lower compensation. We therefore hypothesize that:

H2a

The greater the human capital relatedness among source and destination firms, the higher the total compensation premium executives will receive when moving from one firm to another.

H2b

The human capital relatedness among source and destination firms will moderate the relationship between executives’ human capital and their total compensation premium.

Geographic Preference Theory and Total Compensation Premiums

A common underlying assumption in the executive compensation literature has been that “labor markets for top executives are likely to be nationally segmented rather than geographically segmented” (Kedia & Rajgopal, 2009, p. 110). However, recent research has instead found that geography matters. Specifically, geographic preference theory was proposed by Yonker (2017a) to explain the trade-off between CEO compensation and CEO geographic preference. Yonker (2017a) asserts that executives who consider moving to less geographically desirable locations will request wage premiums, compared to other executives of equivalent skills and knowledge who regard the same geographic locations more preferably (defined in that paper as the executive being from that area). As Yonker (2017a) notes, this theory is only a few steps away from what Adam Smith claimed so many years ago: “jobs with disagreeable characteristics will command higher wages, other things equal” (Smith, 1979, p. 339). Similarly, Bhabra and Hossain (2018) found that rural firms pay their executives 13% greater incentive-based equity pay versus size-matched urban firms, although total compensation was not significant after controlling for cost-of-living differentials.

An area’s desirability can be affected by multiple factors, ranging from tax incentives to local amenities and aesthetics, and these factors can influence both an individual’s, as well as a firm’s, location decisions (e.g., Gottlieb, 1994). On these grounds, when an executive accepts a new job offer, we expect that they will be willing to forgo a compensation premium if the change in employers also corresponds to an increase in area desirability. We therefore hypothesize that:

The higher the area desirability of a location, the lower the total compensation premium they will receive when moving from one firm to another.

Beyond an area’s overall desirability, we also expect that the location of the new employer will also affect an executive’s compensation premium in one additional manner. We posit that the number of other comparable compensation pay packages (from other comparable firms) and the distance of those pay packages to the focal (destination) firm, which we define as “density of geographically proximal executive pay packages,” will affect an executive’s receipt of a compensation premium during an inter-organizational move. While this concept is related to the opportunity cost of leaving a current position—since executives evaluate potential gains and losses—it specifically captures the influence of the external compensation landscape in the local market of the destination firm. This density of geographically proximal executive packages reflects (a) the neighborhood effects created by leading firms that set the pay standards in their immediate environment (Glaeser et al., 1996; Kedia & Rajgopal, 2009), (b) the local labor market competition for top executives (Kennan & Walker. 2011; Vietorisz & Harrison, 1973), and (c) an individual’s perceptions and expectations of compensation in a particular locality (Adams, 1963; Clark & Oswald, 1996; Elster, 1991). In essence, while opportunity cost involves a broader calculation of what an executive might lose by leaving their current firm, the density of geographically proximal pay packages offers a more focused view on the immediate, external compensation options available, thereby influencing their mobility decision. This is in line with other studies investigating the impact of geographical density of other practices on organizational decisions. For instance, Husted et al. (2016) focus on corporate social responsibility density and Roumpi et al. (2020) focus on LGBT-friendliness density. We therefore hypothesize that:

The higher the density of geographically proximal executive packages, the higher the total compensation premium they will receive when moving from one firm to another.

Methodology

Sample

Our sample consists of 378 inter-organizational moves of 351 executives who changed employers among S&P 500 companies from 2000 to 2015. Researchers typically identify top executives as those individuals who possess job titles that reflect great influence in firms’ strategic choices (Carpenter et al., 2004) or who also serve on a focal firm’s board of directors (e.g., Finkelstein & Hambrick, 1990). Since the availability of compensation data is critical and given that the Securities and Exchange Commission requires the incentive data for the CEOs and the four next highest paid executives be provided in annual reports (Wright et al., 2007), we follow the example of extant literature (e.g., Carpenter et al., 2003; Carpenter et al., 2001), and identify top executives as the five most highly paid executives at each firm. Top executives who moved from one firm to another were identified from the COMPUSTAT database and compensation data for each individual employee was retrieved from the ExecuComp database. The mean age of the executives who took the decision to move to a new firm was around 50 years, and they received a mean of 1.5 million dollars in terms of compensation premiums when they changed jobs.

Measures

Total Compensation Premium

The dependent variable is the difference in total compensation an executive agrees to when they decide to move jobs. To calculate the total compensation premium variable (in thousands of dollars), we measured the difference between the total compensation packages at each firm, by subtracting the total compensation received in the last year of employment with the employer they moved from (the source firm) from the total compensation received in the first year of employment with the new employer they moved to (the destination firm). This was calculated for each mobility event using the “total compensation including option grant” variable from COMPUSTAT’s ExecuComp database, where total compensation includes base salary, bonuses, the total value of restricted stock granted, the total value of stock options granted (using Black-Scholes), long-term incentive payouts, and “all other compensation” such as perquisites, personal benefits, termination payments, change-in-control payments, contributions to 401K and/or other defined contribution plans, life insurance premiums, tax reimbursements, etc.

Human Capital

Due to the multi-dimensional nature of the human capital construct, we decompose this construct into three different measures. First, we collected information regarding the higher education of each executive using data provided by Bloomberg and calculated the levels of higher education that each executive completed. In total, the executive mobility events in our sample featured moves by executives with 111 bachelor’s degrees, 257 master’s degrees, and 11 PhDs. Second, similar to other scholars (e.g., Hitt et al., 2001; Hitt et al., 2006), we obtained the ranking (from the US News website) of the university from which the focal executive received their highest degree. According to Hitt et al. (2006), the quality of an educational institution can be viewed as a proxy for both the prestige and articulable knowledge of an individual who successfully completed a degree. Moreover, managers who attend high quality educational institutions are often promoted more quickly (Useem & Karabel, 1986) and receive overall greater compensation (Falato et al., 2012). Bick and Flugum (2022) additionally find that being from a “top tier” university eliminated the compensation discount attributed to geographic preference, which they assume is due to the greater bargaining power or ability of the executive. Finally, we retrieved the age of each executive at the time when they joined their new organization from the COMPUSTAT database. Age, according to Buchholtz et al. (2003), constitutes a proxy for human capital because it indicates accumulated knowledge and expertise.

Human Capital Relatedness

In order to capture the human capital relatedness between each pair of source/destination firms, following the examples of Bergh (2001) and Harris and Helfat (1997), we compared the four-digit SIC codes of each organization. The first two digits of the SIC code identify a firm’s primary industry, while the third and fourth digits reflect, respectively, the broader industry group and the industry sector. We thus categorized the SIC codes among each pair of firms by determining which digits they shared (from one to four). If a pair of firms shared all four digits, they were deemed to be in the same industry and thus have the highest level of human capital relatedness.

Area Desirability

Using the COMPUSTAT database we retrieved the geographical location of the headquarters of each organization. To identify the area desirability of each location, we utilized the Internal Revenue Service (IRS) reports that provide the number of people migrating in and out of each state every year. We then calculated the overall migration rate for each state and each year, by subtracting the number of people moving out of a state from the people who moved in during the year of each mobility event.

Geographically Proximal Executive Packages

We used the geographic location of all S&P 500 companies to operationalize the local labor market competition for each destination firm. We achieved that by utilizing the geographic distance power law

\sum_{j} \frac{1}{1 + d_{i, j}}

where d_i,j denotes the distance of the focal organization i to every other organization j, that dictates that the autocorrelating neighboring effects decrease as the distance among the neighbors increases.

Finally, control variables that were included in our models were the gender of the executives, due to research indicating a relationship between executives’ gender and their compensation (e.g., Cook et al., 2019), and the return of investment (ROI) of the destination firm the year before they hired an executive as a proxy of firm performance that has often been associated with executive compensation (Carpenter & Sanders, 2002). The mobility events in the sample were done by females 31 times versus 348 times for males. Our last control variable was the median income for each state (of the destination firm) and each year in our sample in thousands of dollars. This control variable serves as a proxy for the expected cost of living in the new state, while also controlling for any expectations that an executive might have, compensation wise, when moving to a specific state. This information was collected from the IRS.

Statistical Model

To analyze our dataset, we employed a generalized linear regression, and the produced residuals were checked to identify if our model was biased by the effects of spatial autocorrelation. Spatial autocorrelation is the phenomenon where the values sampled at nearby locations are not independent (Tobler, 1970), creating significant issues. For instance, as Unwin and Hepple (1974) showed, a positive spatial autocorrelation might cause the t and F statistics to have falsely diminished denominators creating erroneously large test statistics. These tests indicated that our results were not biased by spatial autocorrelation.

Results

Descriptive statistics are reported in Table 1. Figure 1 maps the areas where the executives of our sample relocated and whether they received a total compensation increase (green color) or decrease (red color).

Table 1.

Summary Statistics and Correlations.

Variables	Mean	S.D.	1.	2.	3.	4.	5.	6.	7.
1. Total compensation premium	1533,201	7876,65
2. State migration rate	−22.83	88.75	−0.167**
3. Density of geographically proximal executive packages	0.46	0.53	0.24**	−0.13*
4. University ranking	68.55	20.83	0.03	−0.08	−0.11**
5. Age when joined the new firm	50.82	6.25	0.03	−0.04	0.06	−0.07
6. Relatedness	1.27	1.49	0.01	0.02	−0.04	−0.04	0.01
7. ROI	0.03	0.39	0.07	0.01	0.1†	0.09†	0.05	0.01
8. State median income	21.67	7.03	−0.04	0.09†	−0.01	−0.06	0.06	0.08	0.05

N = 378.

**p < .01.

*p < .05.

†p < .10.

Note. Total Compensation Premium and State Median Income are in thousands of dollars.

Figure 1.

Differences in executive compensation by destination.

Table 2 includes the results of our regression models. The results provide mixed support for our first hypothesis, depending on how “human capital” is measured. In our results, executives who held a PhD degree receive a greater compensation premium (β = 8674.99, p < .01), in line with Hypothesis 1. However, we did not find support for the ranking of the executive’s university (β = 10.50, p > .05) or the executive’s age at hire (β = 34.78, p > .05). We also did not find support for our second set of hypotheses, as the human capital relatedness measure did not appear to have a statistically significant relationship with an executive’s compensation premium when moving to a new firm (β = 82.78, p > .05). The same result was found for the interactions between relatedness and our human capital variables (relatedness with ranking: β = 1.45, p > .05, relatedness with Master’s degree: β = 70.45, p > .05, relatedness with PhD: β = 2371.28, p > .05, and relatedness with age: β = 6.87, p > .05). We did find support for our third hypothesis, where a high area desirability as captured by a positive state migration rate appeared to have a negative statistically significant relationship with the compensation package an executive might receive when moving to a new firm (β = −15.14, p < .01). Finally, we also found support for our fourth hypothesis, where the density of geographically proximal executive pay packages had a statistically significant positive relationship on an executive’s compensation premium when moving to a new firm and location (β = 3497.45, p < .01).

Table 2.

Regression Models.

	1a	1b	1c	2	2a	2b	2c	3	4	Comprehensive model
Intercept	2881.01 (2427.77)	3927.96† (2108.51)	1876.73 (4060.09)	3547.21 (2002.75)	2934.38 (2669.89)	4018.56† (2180.18)	1379.12 (4975.34)	2866.605 (1980.12)	1772.71 (1981.27)	1584.69 (4189.16)
Ranking	10.50 (20.23)				8.75 (26.64)					10.88 (21.58)
Masters		−728.37 (906.2)				−830.06 (1203.08)				−1634.87† (986.73)
PhD		8674.99** (2496.60)				5209.16 (3545.91)				7028.30** (2480.41)
Age			34.78 (70.71)				42.95 (89.03)			−6.69 (67.29)
Relatedness				82.78 (281.24)	−11.88 (933.27)	−62.46 (481.34)	449.06 (2434.58)			152.94 (278.62)
Relatedness * ranking					1.45 (13.11)
Relatedness * masters						70.45 (592.12)
Relatedness * PhD						2371.28 (1727.39)
Relatedness * Age							−6.87 (47.72)
Area desirability								−15.15** (4.7)		−13.67** (4.81)
Density									3497.45** (768.06)	3688.47** (803.33)
Gender: Male	−1179.45 (1506.86)	−1481.06 (1488.21)	−1102.10 (1572.03)	−1154.87 (1512.27)	−1503.04 (1493.82)	−1503.04 (1493.82)	−1127.46 (1579.5)	−1182.59 (1489.49)	−961.98 (1469.6)	−1495.42 (1498.85)
ROI	1346.97 (1082.55)	1593.57 (1063.87)	1447.09 (1119.15)	1398.73 (1081.14)	1349.49 (1089.98)	1442.69 (1071.53)	1431.39 (1123.59)	1396.06 (1066.08)	923.33 (1056.76)	1055.46 (1071.14)
State median income	−45.27 (59.58)	−37.28 (58.44)	−45.88 (62.48)	−48.85 (59.68)	−46.31 (60.12)	−36.17 (58.97)	−47.07 (62.97)	−28.03 (58.94)	−45.12 (57.83)	−14.40 (59.78)
$R^{2}$	.01	.05	.01	.01	.01	.01	.01	.04	.06	.14
AIC	7645	7632.3	7265	7645.2	7648.9	7636.3	7268.9	7634.9	7624.9	7230

N = 378; standard errors in parentheses.

**p < .01.

*p < .05.

†p < .10.

Discussion

Challenging the traditional notion that typically employees choose employers on the basis of financial incentives and in line with other scholars (e.g., Campbell et al., 2012) suggesting that employee mobility is not solely predicted by compensation, we draw on human capital theory and geographic preferences theory to investigate factors that influence the decision of firm executives to change employers and join firms that offer higher or lower compensation packages than their current employer. Analyzing a sample of 351 executives who moved among S&P 500 companies from 2000 to 2015 (378 inter-organizational moves), we find that human capital (in the form of the highest degree earned) has a positive impact on the receipt of a compensation premium, while high area desirability is negatively associated with the receipt of such premiums when executives move from one company to another. We moreover find that geographically proximal executive packages with higher density increase the value of an executive’s compensation premium during inter-organizational moves.

Our study contributes to the extant literature on executive compensation in several meaningful ways. First, we answer calls for greater emphasis on the impact of context on executive compensation (e.g., Gomez-Mejia & Wiseman, 1997). As Devers et al. (2007) highlight in their review of the executive compensation literature: “We believe that taken together, these results indicate the value in recognizing that pay is not simply a function of size, but can be affected by factors both endogenous and exogenous to the firm and we encourage research that continues to explore the influences of executive-specific characteristics, market forces, and social comparisons on executive pay” (p. 1036). Our study is a direct response to such calls, as at the core of our theorizing is the notion of the area desirability (captured as the migration ratio) of the location of the company where an executive moves. Second, we advance the (limited) research that explores the impact of geography on executive compensation. Unlike Bick and Flugum (2022) who found that the executives who have received their education from elite institutions have higher human capital, or, at least, greater bargaining power, and receive higher compensation regardless of geographic preferences, we find support for both the human capital and the geographic preferences arguments but find that educational prestige was not significant. Third, we contribute to the literature on envy among geographically proximal executives (Bouwman, 2012) by showing that the density of geographically proximal executive pay packages positively increase an executive’s compensation premium when moving to a new firm and location.

An important limitation of this study is that the availability of data regarding compensation and inter-organizational movements of executives restricts our sample to a limited group of some of the highest performing firms that have their headquarters in the US. Taking into consideration that modern-day labor markets have become increasingly global, it would be interesting for researchers to track the movements of executives between companies that have their headquarters in different countries. Such a study would provide richness to our assertions regarding the importance of geographical preferences on changes in compensation packages.

Another important limitation of this study is the use of proxies for capturing human capital and geographical preferences. Even though efforts were made to use multiple operationalizations that align with the existing literature, these proxies are only some possible estimates of human capital and geographical preferences. For instance, even though area desirability as captured by state emigration rates offers an overall estimation of the desirability of a location, there are personal aspects, such as where the executive grew up or where their extended family resides, that could influence an individual’s perceived area desirability. Thus, a study that captures directly the perceptions of individuals regarding their human capital and their geographical preferences could offer more insights into executives’ movement decisions.

Conclusion

Our results provided support for the effect of executives’ human capital and geographical preferences on the receipt of a compensation premium when changing employers. Our findings indicate that when the area desirability is high, executives moving from one company to another receive a decrease in their total compensation. Thus, it can be argued that compensation, even though it is an important factor, it is not the only factor that employees consider when making decisions regarding changing employers.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Dorothea Roumpi

Lauren E. Aydinliyim

Author Biographies

Panagiotis Giannakis is an Assistant Teaching Professor in the Department of Geography and the Dutton e-Education Institute within the College of Earth & Mineral Sciences. His research interests lie at the intersection of Economic Geography, Strategic Management, and Social Networks. More specifically, Panagiotis’ research focuses on the relationship between organizational and physical and/or social network space. His work has been published in leading journals, such as Human Resource Management Journal.

Dorothea Roumpi is the LER Alumni Endowed Early Career Professor and Assistant Professor of Human Resource Management in the School of Labor and Employment Relations at The Pennsylvania State University. Her research focuses on strategic human resource management, strategic human capital, factors that influence employee mobility, and inclusive human resource management practices and policies. She has published her work in various internationally recognized journals including Journal of Management, Personnel Psychology, Human Resource Management, and Human Resource Management Journal.

Lauren E. Aydinliyim is an Assistant Professor of Strategic Management in the Narendra P. Loomba Department of Management, ZIcklin School of Business, Baruch College, CUNY. Her research focuses on the legal and ethical dimensions of strategic human capital, particularly the creation and sustainability of competitive advantages through human capital resources. She leverages her background, which combines her formal education in strategy, law, and applied mathematics with her professional experiences as a human resources executive and in-house legal counsel. In addition to media interviews, Lauren's research has appeared in leading academic outlets including the Journal of Business Ethics. She received her Ph.D. from Rutgers Business School , her J.D. from Vanderbilt University Law School, and her B.S. in applied mathematics and economics from the College of Wiliam & Mary.

John E. Delery is the Raymond F. Orr Professor of Management in the Sam M. Walton College of the University of Arkansas. He received his M.S. in Psychology from Memphis State University and his Ph.D. in Business Administration from Texas A&M University. His current research interests include the strategic management of human capital, the structure of human resource management systems, and human capital flows. Specifically, he is interested in how the management of human capital influences employee turnover, organizational performance, and profitability. He is a past Chair of the Human Resources Division of the Academy of Management and currently editor-in-chief of Human Resource Management Review.

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Executive Compensation: The Role of Human Capital and Geographic Preferences

Abstract

Keywords

Introduction

Human Capital and Total Compensation Premiums

Geographic Preference Theory and Total Compensation Premiums

Methodology

Sample

Measures

Total Compensation Premium

Human Capital

Human Capital Relatedness

Area Desirability

Geographically Proximal Executive Packages

Statistical Model

Results

Discussion

Conclusion

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iDs

Author Biographies

References