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Industry co-agglomeration, executive mobility and compensation


We find evidence of geographic segmentation in the market for top executives and identify industry co-agglomeration as the primary driver. When top executives move from one firm to another, nearly 40% of the moves are between local firms, which is more than five times greater than predicted by available employment opportunities. Furthermore, these local moves are dominated by moves among firms in co-agglomerated industries. While the strong local move bias is also accompanied by local co-movement in the compensation of top executives, the co-movement is driven by local peers in co-agglomerated industries only but not by other local peers.

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  1. This view also seems to be supported by market research reports (such as from IBISWorld), which indicate that the market size of executive search companies is growing steadily. In 2022 the market size, as measured by revenue, is $8.8bn, and it is projected to growth by 4.8% compared to 2021.

  2. We also use the size of the local population as proxy for local job opportunities. The results for local move bias are not materially different.

  3. Firms in the financial and utility industries are excluded from our sample. We also exclude job relocations that take place in the year of an M&A or spin-off involving either of the two firms or if the M&A/spin-off event occurs between the ending year of the executive’s previous job and the starting year at the new firm.

  4. This proxy is also used by Yonker (2016) in a different context. In addition, we also use an alternative proxy based on the local population in each MSA as a fraction of the country’s population. The results are not materially different if we use this alternative proxy for the expected level of local job relocations.

  5. Note that related industries are reciprocal relationships: if industry X is a related industry for industry Y, then industry Y is also a related industry for industry X. We also experiment with other cutoff point (e.g., 5%) for defining related industries. The main results are not materially affected.

  6. We also perform a separate analysis just for CEOs in order to see if segmentation is strong enough to even extend to the market for CEOs. The results are not materially different.

  7. Bouwman (2014) uses a similar regression approach to examine geographic variations in CEO compensation.

  8. For robustness, we also include local peers in the industry-size peer group and re-run the regressions. The results are not materially different.

  9. The data for the firm’s self-selected peers are identical to those used in Faulkender and Yang (2010). We thank Jun Yang for providing the peer group data which were manually collected from the companies’ SEC DEF-14A filings available on EDGAR.


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We thank conference participants at the 2015 annual meetings of the Northern Finance Association in Lake Louise, the Financial Management Association in Orlando, the 2016 Society for Financial Studies Finance Cavalcade in Toronto, the 2019 International Symposium of Quantitative Economics, Changchun, China, and seminar participants at Brandeis University, University of Massachusetts at Boston, and Wilfrid Laurier University for helpful comments and suggestions. Financial support provided by the Social Sciences and Humanities Research Council of Canada is gratefully acknowledged.

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Correspondence to Debarshi K. Nandy.

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Appendix: Variable definitions

Appendix: Variable definitions

Executive compensation

Total annual pay

Total compensation comprised of the following: Salary, Bonus, Other Annual, Total Value of Restricted Stock Granted, Total Value of Stock Options Granted, Long-Term Incentive Payouts, and All Other Total

Equity pay

Stock and option grants

Cash pay

Salary and bonus

Control variables in pay regressions

Firm size

The logarithm of total sales

Growth opportunities

The market-to-book ratio

Cash-flow shortfall

The three-year average of the sum of common and preferred dividends plus the cash flow used in investing activities minus the cash flow generated from operations, normalized by total assets

Interest burden

The three-year average of interest expense scaled by operating income before depreciation


The three-year average of research and development expense scaled by sales

Stock performance

Stock return over the prior year

Stock return volatility

Stock return volatility over the prior year


Age of the executive


the number of years the executive has worked in the company as a top executive

Number of local S&P 1500 firms

The logarithm of the number of S&P1500 firms in the same MSA or within 60 miles of the firm’s HQ

Firm policy or performance variables


Capital expenditure over lagged total assets


Research and development expenditure over lagged total assets


Long-term debt plus debt in current liabilities over the market value of assets

Interest coverage

The natural logarithm of the ratio of earnings before depreciation, interest, and tax over interest expenses

Cash holdings

Cash and short-term investments over lagged total assets

Working capital

Current assets minus current liabilities over lagged total assets


Sum of common and preferred dividends over earnings before depreciation, interest, and tax


Earnings before depreciation, interest and tax over lagged total assets

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Broman, M., Nandy, D.K. & Tian, Y.S. Industry co-agglomeration, executive mobility and compensation. Rev Quant Finan Acc 61, 817–854 (2023).

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