Abstract
Using financial data from a large sample of U.S. conglomerates, we find a significant and positive association between conglomerate informational advantage through internal information markets and resource allocation efficiency. Our finding suggests that the internal informational environment of conglomerates plays an important role in top management internal resource allocation decisions. Additional evidence shows that this positive association is more pronounced when internal competition for resources is higher, suggesting that internal competition elevates the importance of conglomerate informational advantage in internal resource allocation efficiency. Finally, this association is weaker when top management is more powerful, suggesting that powerful CEOs have access to alternative channels of information, which limits the usefulness of conglomerate informational advantage for their internal resource allocation decisions.
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Notes
The BEA published input–output tables at the detailed level in 1997, 2002, and 2007, respectively. At the detailed level, industries are reclassified over time. For example, Anjos and Fracassi (2015) note that there are 409 (470) industries in 2002 (1997). Similar to Anjos and Fracassi (2015), Ahern and Harford (2014) use the 1997 input–output data to conduct their analyses. According to the statistics in Ahern and Harford (2014), customer–supplier relations remain stable over time from 1982 to 2002. Therefore, we use data from the 1997 input–output table to proxy for information structure for our sample period of 1998–2011. Structural shifts of industry networks during our sample period could create noise if we had used data from the 2002 or 2007 input–output tables. In a robustness check (untabulated), we find that our results hold when we narrow the sample period between 1998 and 2002 based on the 1997 input–output data.
The Dijkstra’s algorithm is available from MatlabBGI at https://nl.mathworks.com/matlabcentral/fileexchange/10922-matlabbgl. We thank Professors Fernando Anjos and Cesare Fracassi for providing us with the code required to construct the distance measure.
The segment’s Tobin’s Q is the median q of single-segment firms operating in the same industry, calculated as (total assets – book value of equity + market capitalization)/book value of total assets.
We estimate Eq. (2) on an annual basis in order to provide robust results. The Cho (2015) model includes both firm and year fixed effects. We do not include a firm fixed effect because our centrality measure is computed based on the 1997 input–output data and the measure exhibits little variation for most of our sample firms unless their operating segments change significantly during the sample period.
The G-index seeks to measure the power-sharing relation between managers and shareholders and is constructed based on the total number of corporate governance provisions that restrict shareholder rights. The higher the G-index, the weaker the shareholder rights, which in turn implies that management possesses greater power.
We do not report the descriptive statistics for competition, which is simply defined as concentration ratio multiplied by − 1.
P-values are based on one-tailed t-tests of the coefficients on the main variables of interest with directional predictions.
We extend the sample period for three more years for the following reasons. First, by including the sample period until 2014, we want to highlight that our results based on the original centrality (1997 input–output tables) are aligned with the additional evidence we provided by using the aggregated time-varying network. Second, while COMPUSTAT database provides industry classification data based on the NAICS codes, the BEA uses I-O industry classification systems. Therefore, we need to manually use the mapping tables between NAICS codes and the input–output codes to construct the centrality measure at the conglomerate level.
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Appendices
Appendix A
Variable definitions
Variable | Definition |
---|---|
CAE | Capital allocation efficiency. Please refer to the text, 3.2, for detailed calculation |
CC | Conglomerate centrality (proxy for conglomerate informational advantage). In accordance with Anjos and Fracassi (2015), closeness centrality of conglomerate is calculated as the inverse of the average minimum distance between industries within a conglomerate and all the other industries in the economy but not in a conglomerate. Please refer to the text, 3.1, for detailed calculation |
Betav | Beta value obtained from CRSP database, computed using the methods developed by Scholes and Williams (1977) |
Log(Mktval) | Log of market value of equity |
MTB | Market-to-book ratio, calculated as market value of equity divided by book value of equity |
Cash Flow | Cash flows from operating activities deflated by the beginning-of-period total assets |
CAPEX | Capital expenditures deflated by property, plant, and equipment (net) |
CAPEX change | Percentage change in capital expenditures from prior-year to current year |
NonCAPEX | Indicator variable that takes the value of 1, if a firm reports positive amount of R&Ds or intangibles, and 0 otherwise |
Tangibility | Property, plant, and equipment (net) deflated by the total assets |
External financing | Net external financing deflated by the capital expenditures. Net external financing is calculated as [(sale of preferred and common stock-purchase of preferred and common stock-dividend) + (issuance of long-term debt-repayment of long-term debt + changes in current debt)] |
Cash | Cash and cash equivalents deflated by the total assets |
Leverage | Total liabilities divided by the total assets |
Dividend | Indicator variable that takes the value of 1, if a firm reports positive amount of dividends for common stocks, and 0 otherwise |
Number of segments | Number of segments of a conglomerate |
Segment profit variability | Segment profit variability, calculated as the range of segment return on assets |
Segment industry diversity | Segment industry diversity, calculated as the ratio of the number of segments with unique two-digit SIC codes to the total number of segments |
Speed of profit adjustment | Speed of profit adjustments, calculated as the asset-weighted average of the speed of profit adjustments in the industries in which the firm’s segments operate. The speed is equal to \({\beta }_{2}\), estimated in the following equation for each industry over the prior 20-year period: \({ROA}_{ijt}-\overline{{ROA }_{jt}}={\beta }_{0j}+{\beta }_{1j}\left( {D}_{n}\times ({ROA}_{ijt-1}-\overline{{ROA }_{jt-1}}\right)+ {\beta }_{2j} \left( {D}_{p}\times ({ROA}_{ijt-1}-\overline{{ROA }_{jt-1}} \right)+ {\varepsilon }_{ijt}\) , where Dn = 1, if the difference between firm i’s return on assets (ROA) and the median ROA for its two-digit industry j in year t is less than zero, or = 0 otherwise; Dp = 1, if the difference between firm i’s ROA and the median ROA for its two-digit industry j in year t is greater than zero, or = 0 otherwise |
Concentration ratio/competition | Concentration ratio, calculated as a decile rank of the asset-weighted average of the Herfindahl index of the industries in which the firm’s segments operate (based on segment two-digit SIC code). Competition is measured as concentration × (-1) |
Segment earnings persistence | Segment earnings persistence, calculated as the asset-weighted average of the persistence of abnormal earnings in the industries in which the firm’s segments operate (based on segment two-digit SIC code). The persistence of abnormal earnings in industry j is equal to \({\beta }_{1}\), estimated in the following equation for each industry over the prior 20-year period: \({ROA}_{ijt}-\overline{{ROA }_{jt}}={\beta }_{0j}+{\beta }_{1j} \left( ({ROA}_{ijt-1}-\overline{{ROA }_{jt-1}} \right)+ {\varepsilon }_{ijt}\) , |
CEOpower | A composite score ranging between 0 and 5, based on five variables: (1) CEO compensation over the sum of top five executives combined, (2) CEO age, (3) percentage of shares owned by CEO, (4) governance index (G-index) (Gompers et al. 2003), and (5) a dummy variable that equals 1 if the CEO also serves as the director, 0 otherwise. Each of the first four variables is transformed into an indicator variable that equals 1 if the value is greater than the median in full sample, otherwise 0 |
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Chou, SC., Natarajan, R. & Zheng, K. Conglomerate internal informational advantage and resource allocation efficiency. Rev Quant Finan Acc 59, 717–748 (2022). https://doi.org/10.1007/s11156-022-01056-w
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DOI: https://doi.org/10.1007/s11156-022-01056-w
Keywords
- Conglomerate information environment
- Resource allocation efficiency
- Inter-industry information transfer
- Internal competition