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The effect of multi-nationality on foreign institutional
ownership and firm performance
Master thesis International Financial Management
Dirk van Woerden
S2331624
d.van.woerden.1@student.rug.nl June 7th 2019 Faculty of Economics & Business University of Groningen First assessor: prof. dr. C. L. M. Hermes Second assessor: dr. E. Kamarziene
Abstract
This study looks at the relationship between foreign institutional ownership (FIO) and firm performance of U.S. firms as well as the effect of firm multi-nationality on this relationship. The initial results provide some that supports a positive relationship between FIO and performance as high levels of FIO did show a significant positive relationship with firm performance. Some evidence was found that high levels of multi-nationality weaken this relationship. However, those results are less convincing and need further research.
JEL classification: G15; G23; G23; F30
Keywords: Institutional ownership, foreign institutional ownership, Monitoring,
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Table of Content
1. Introduction ... 3
2. Literature Review ... 5
2.1 Institutional ownership and firm performance ... 5
2.2 Independent institutional investors ... 7
2.3 Foreign institutional ownership ... 9
2.4 Firm multi-nationality ... 11 3. Methodology ... 14 3.2 Main Variables ... 15 3.4 Top 20 investors ... 16 3.3 FIO Dummy ... 16 3.4 Multi-nationality ... 16 3.5 Control Variables ... 17
3.8 Additional Robustness check ... 18
3.6 Regression techniques ... 20
3.7 Discriptive statistics ... 21
3.8 Correlation tables ... 23
4 Results ... 23
4.1 Results basic regression ... 23
4.2 Results top 20 investors ... 26
4.3 Results FIO dummy ... 28
4.7 Results multi-nationality ... 30
4.8 Results multi-nationality dummy ... 31
4.9 Additional Robustness check ... 32
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1. Introduction
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monitors and more active than domestic institutional investors (Ferreira and Matos, 2008). Hence, it is likely that the way how firms are monitored depends for a major part on the fact that institutional investors are domestic or foreign. Still, research on the impact of foreign institutional ownership and their monitoring characteristics on firm performance remains limited. Therefore, the relationship between foreign institutional ownership and performance is an interesting topic to explore further.
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2. Literature Review
2.1 Institutional ownership and firm performance
Within the scope of financial economic research institutional ownership is a concept that is often connected with board monitoring, shareholder activism and performance (Smith, 1996; Gillan and Starks, 2000). Through monitoring, institutional investors are able to influence management in their decision making which can affect the firms market and accounting performance (Chiganti and Damanpour, 1991). Different studies have tried to find a link between institutional ownership and management decision making, providing mixed outcomes. Brickley, Lease and Smith (1988) found that institutional owners have a significant influence on voting for corporate take overs. Moreover, Bushee (1998) and Baysinger et al (1991) show in their papers that institutional ownership positively affects R&D spending. Additionally, it is found that institutional ownership has a positive effect on managerial efficiency (Baghdadi et al, 2018). Furthermore, Almazan, Hartzell and Starks (2005) and Khan et al (2005) find that institutional investors play a significant role in CEO compensation.
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more recent papers have found positive results. Benamraoui et al (2019) find that ‘outside’ blockholders such as institutional investors, are related to an increase in performance. Similarly, Lin and Fu (2017) find a positive relationship between institutional ownership and firm performance of Chinese firms. Additionally, Bhattacharya and Graham (2009) find a positive relationship between institutional ownership and the performance of Finnish firms when the voting power is equally distributed among large institutional owners.
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The theory that institutional investors can be myopic is brought forward by Bushee (1998) who finds that institutional investors’ investment horizons may have a different impact on R&D spending and the performance of the firm. Myopic behavior would have a negative effect on performance as investors would pressure management to make decisions which are beneficial for short term gains but detrimental to longer term performance. Such myopic behavior leads to more sensitivity to earnings news which causes instability and misvaluations (Bushee, 2001). Therefore, Bushee (1998) classifies institutional investors into three categories; ‘transient’, ‘dedicated’ and ‘quasi-indexers’ indicating that some are more focused on short term (transient) returns and some are more focused on long term returns (dedicated/quasi-indexers). He indicates that transient institutional investors are less engaged with the firm and react more quickly to future value changes which can harm the company value and hence performance. Therefore, the proportion of transient institutional investors can be important in explaining the relationship between institutional ownership and firm performance. Bushee (2004) states that transient institutional investors in firms remain a minority as most institutional investors are engaged with firms for a longer period of time including passive index trackers. Therefore, the impact of transient institutional ownership on performance is often limited by the majority of investors that is engaged with the firm for the long-term. Still, like in Bushee’s theory, firms often contain both types of institutional investors which could mean that one group dominates the other. This makes it difficult to find a direct relationship between institutional ownership as a whole and firm performance as groups of institutional investors might compromise each other. In this respect, literature shows both a positive and negative link between certain groups of institutional investors and performance.
2.2 Independent institutional investors
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pressure-insensitive transient investors are more active monitors and impose more influence on acquisition decisions. These findings indicate that independent institutional investors have a positive effect on monitoring and performance.
2.3 Foreign institutional ownership
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find similar results for emerging markets. Bena, Ferreira, Matos and Pires (2017) support this theory showing that foreign institutional ownership increases long-term investments and leads to an increase in innovation. Their explanation is that domestic institutional owners are more risk-averse and less diversified because of ties with corporate insiders. Because of their international portfolios foreign institutional investors are better able to diversify risks and are more willing to engage in high risk investments with high growth opportunities and better performance for the long-term.
11 Hypothesis 1: Foreign institutional ownership has a positive effect on firm performance
Figure 1. The effect of foreign institutional ownership on performance
2.4 Firm multi-nationality
As firms and financial markets become more globally integrated, the effects of global diversification (multi-nationality) on monitoring and performance can be relevant to consider (Bekeart and Mehl, 2019). Going further into the international aspect of finance, firm multi-nationality can be an important aspect between foreign institutional ownership and performance because it affects how well firms can be monitored (O’Donnell, 2000). Looking at multi-nationality from a firm perspective, findings with respect to the direct relationship between multi-nationality and performance are mixed. There is evidence that multi-nationality can harm the performance of a firm but there is also evidence that it can increase performance. One theory is that the relationship between multi-nationality and performance is U-shaped, where the effect on performance is negative but over time the effect becomes positive due to gained experience in the local markets (Thomas, 2006). Shin, Mendoza, Hawkins and Choi (2017) find a similar U-shape for capital intensive firms when looking at MNE’s operating in the services industry. Some have found a horizontal S-shaped relationship between multi-nationality and firm performance, where the positive relationship increases with firm size (Benito-Osorio et al, 2016; Xiao et al, 2013). Another theory is provided by Tallman and Li (1996), who find a positive relationship between multi-nationality and performance. They indicate that MNE’s are able to gain greater returns on resources and are better able to diversify market risks than
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domestic companies because of their scale advantages. Goerzen and Beamish (2003) support this theory and add that multi-nationality leads to an internalization of markets and transactions which creates performance benefits. As such Bodnar, Tang and Weintrop (1997) find a positive relationship between geographic and industry diversification and performance. It is also found that global diversification of core-related foreign direct investments is related to an increase in firm value and long-term performance supporting the internalization theory (Doukas and Lang, 2003). Furthermore, Freund, Tahan and Vasudevan (2007) see a positive relationship between multi-nationality and firm value with foreign acquired companies by U.S. firms. Finally, Gande, Schenzler and Senbet (2009) find strong evidence showing that global diversification enhances firm value. In their paper they find a positive relationship between foreign sales and firm value (Tobin’s q) and argue that in previous literature the ‘financial dimension’ is overlooked, indicating that MNE’s help to make incomplete markets complete by giving investors of globally diversified firms international diversification opportunities. This would increase the value of multinational firms compared to domestic firms. However, there are many papers that state the contrary. It is shown that firms operating abroad lack the knowledge and experience of local markets (Johanson and Valhne, 1977). Additionally, Michel and Shaked (1989) find that domestic firms perform better than MNE’s because they do not have the political and fiscal disadvantages that MNE’s might have.
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dispersed monitoring will be more difficult and more expensive leading to higher agency costs as well (Ghoshal and Bartlett, 1990; Doukas and Pantzalis, 2001). Likewise, Kim and Mathur (2008) find evidence that geographic diversification is associated with a decrease in firm value. As firms differ in levels of multi-nationality and foreign institutional ownership, the question is how this variation influences the relationship between foreign institutional ownership and performance. In literature high foreign institutional ownership is associated with more independent monitoring and improved corporate governance. At the same time, high multi-nationality is associated with a higher level of firm complexity due to information asymmetry, which would make monitoring more difficult (Denis et al 2002; Kim and Mathur 2008). Based on the previous findings in literature, the hypothesis is that when multi-nationality of firms increases it will weaken the relationship between foreign institutional ownership and firm performance. Although there is evidence for both a positive and negative direct effect of nationality on firm performance, literature shows there is convincing evidence that multi-nationality has a negative effect on the monitoring of firms. As monitoring is the key element in the relationship between institutional ownership and firm performance it is expected that multi-nationality weakens the relationship between foreign institutional ownership and firm performance. Therefore, the following hypothesis is developed:
Hypothesis 2: Multi-nationality weakens the relationship between foreign institutional ownership and firm performance
14 Figure 2. The effect of multi-nationality
3. Methodology
3.1 Sample data collection
The sample data used for this research is based on US domestic and multinational firms from the S&P500 over a time period of 5 years form 2013 until 2018. This time period is chosen as the data must be recent to reflect the current developments in the global business environment and institutional investors’ characteristics. These characteristics include the proportion of institutional investors and the extent of internationalization on both the investment and the firm side as they develop quickly (Chen et al, 2007). Moreover, a period of five years is chosen to sufficiently assess the relationship between institutional ownership and performance over a longer period of time. A U.S. sample is chosen to maximize the ability to collect a large dataset and include specific variables that more widely available for U.S. firms. In total, a sample of 499 firms is used as a starting point for the analyses performed in this research. The included firms’ ownership data are retrieved and analyzed with Thomson Reuters EIKON. In the United States (foreign) institutional investors need to be registered with a separate filing (13-F). With this filing investors can be identified as institutional owners in the U.S.. Previous studies have used 13-F filings to find out the involvement of institutional investors in a firm. For this research a similar approach has been used (Elyasiani and Jia, 2017). Financial performance indicators
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and control variables which are needed to analyze firm performance are retrieved trough Warthon Research Data Services (WRDS) Compustat. Information on multi-nationality is collected on Thomson Reuters Datastream. Moreover, a time lag of one year will be implemented for the independent variables and control variables to avoid endogeneity problems between foreign institutional ownership, multi-nationality and performance. The variables are lagged with one year (𝑡 − 1). Additionally, industry and year fixed effects will be controlled for. The data variables are winsorized at 1% to control for possible outliers
3.2 Main Variables
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will be tested separately with firm performance. Among those variables are institutional ownership (IO), independent institutional ownership (IIO) independent foreign institutional ownership (IFIO).
3.4 Top 20 investors
Additionally, a method is used to incorporate the blockholder effect of major institutional investors. It is shown that blockholders play an important role in the monitoring of firms (Alvarez et al, 2018; Kang et al, 2018). With this method the size of institutional investors is taken into account as larger institutional investors might be able to have a greater influence on monitoring (Benaraoui et al, 2019). This is done by taking the top 20 owners of the sample firms and look at the absolute percentage of foreign institutional ownership in this top 20 (FIO20), as well as the relative percentage of foreign institutional ownership in this top 20 (FIOP20).
3.3 FIO Dummy
Thereafter, different techniques are used to capture the effect of high or low levels of foreign institutional ownership on performance. This is done by using dummies to distinguish high levels of foreign institutional ownership (HFIOdummy) and low levels of foreign institutional ownership (LFIOdummy). This technique is used because the variance within the variable of foreign institutional ownership is quite low (Table 2), which makes the effect of foreign institutional ownership as a whole more difficult to capture.
3.4 Multi-nationality
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created. One group is labelled as high firm multi-nationality (MULTFDH) and the other as low firm multi-nationality (MULTFDL). These classifications are made through taking the median of foreign sales ratio and incorporate a dummy for ‘high’ when the foreign sales ration reaches above the median and low when below. The dummies will then be interacted with the different types of foreign institutional ownership.
3.5 Control Variables
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Here, similarly to Aggarwal et al (2011) and Cornett et al (2007) log of total assets was chosen to as a metric for firm size. Dividend yield is another measure that form a link between management decisions and the market performance of the firm similarly which is also used by Ferreira and Matos (2008), Chen et al (2007) and Elyasiani and Jia (2010). Institutional investors seem to have different preferences regarding dividend yield. For example, foreign institutional investors seem to avoid high dividend paying stocks because of tax withholding concerns (Ferreira and Matos, 2008). Like Cornett et al (2007) board size is used to control for the monitoring effect and its relation on performance. It is argued that large boards are more difficult to monitor, which indirectly has an effect on performance (Yermack, 1996). This is also supported by Eisenberg et al (1998), who see a negative relationship between board size and performance.
3.8 Additional Robustness check
As an additional robustness check, the same analysis will be performed with an alternative proxy for firm performance; return on assets (ROA). This corresponds with the methods of Chiganti and Damanpour (1991), and Ferreira et al (2008), who either use ROA as main dependent variable or alternative proxy firm performance. All regressions will be repeated with ROA as main dependent variable. The results are presented in Appendix 1.2.
Table 1. Variable Overview
Variable Symbol Explanation
Dependent variable
Firm performance FP Tobin’s Q = market value of firm / book value total assets
Alternative proxy firm performance
ROA Return on assets = net income / total assets
Main independent variables
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Independent Institutional ownership
IIO Percentage of shares held by independent
‘pressure-insensitive’ institutional investors
Foreign institutional ownership FIO Percentage of shares held by foreign institutions
Top 20 investors independent variables
Institutional ownership top 20 investors
IO20 Percentage of shares held by institutions in top 20
investors Independent institutional
ownership top 20 investors
IIO20 Percentage of shares held by independent institutions in
top 20 investors Foreign institutional ownership
top 20 investors
FIO20 Percentage of shares held by foreign institutions in top
20 investors Relative foreign institutional
ownership top 20 investors
FIOP20 Relative percentage of shares held by foreign institutions
in top 20
Dummies
Dummy high foreign institutional ownership
HFIOdummy Dummy (=1) when foreign institutional ownership is in the highest quartile
Dummy low foreign institutional ownership
LFIOdummy Dummy (=1) when foreign institutional ownership is in the lowest quartile
Interaction dummies:
Firm multi-nationality MULTF Foreign sales ratio (Foreign sales / sales)
High firm multi-nationality dummy
MULTFDH Dummy variable foreign sales ratio larger than median
(=1) Low firm multi-nationality
dummy
MULTFDL Dummy variable foreign sales ratio lower than median (=1)
Controls:
Firm Size SIZE Log of total assets
Leverage LEV (Market) Debt Ratio (total debt / total assets)
Capital Expenditures Cash Dividend Yield CAPEX CASH DIV
Ratio of capital expenditures (capital expenditures / total assets)
Ratio of cash holdings (cash holdings / total assets) Dividend yield % over the year (dividend / share price)
20 3.6 Regression techniques
For this analysis a panel data sample is used with 𝑖 indicating the different firms in the sample and 𝑡 − 1 indicating the time dimension with a one-year time lag. When incorporating the different variables in the OLS regression the following regression equations are stated as follows:
1) H1: 𝐹𝑃𝑖,𝑡 = 𝛼0+ 𝛽1𝐹𝐼𝑂𝑖,𝑡−1+ 𝑆𝐼𝑍𝐸𝑖,𝑡−1+ 𝐿𝐸𝑉𝑖,𝑡−1+ 𝐶𝐴𝑃𝐸𝑋𝑖,𝑡−1+ 𝐶𝐴𝑆𝐻𝑖,𝑡−1+ 𝐷𝐼𝑉𝑖,𝑡−1+ 𝐵𝑆𝑖,𝑡−1+ 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝐹𝑋 + 𝑌𝑒𝑎𝑟𝐹𝑋 + 𝜀𝑖,𝑡
The first OLS regression equation is used to measure the relationship between foreign institutional ownership (𝐹𝐼𝑂) and firm performance (𝐹𝑃) with the control variables individually identified. From equation 2 until 6, the control variables will be mentioned under the term (𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠). Moreover, the industry and year fixed effects are stated (𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐹𝑋, 𝑌𝑒𝑎𝑟𝐹𝑋). This is the basic equation on which the regressions are based. The same regressions will be performed with other independent variables, such as institutional ownership and independent institutional ownership (𝐼𝑂, 𝐼𝐼𝑂). The institutional investors taken from the top 20 investors (𝐼𝑂20, 𝐼𝐼𝑂20, 𝐹𝐼𝑂20, 𝐹𝐼𝑂𝑃20) are regressed with firm performance in the same manner. Moreover, the robustness check regressions equations are identical except that they have ROA as main dependent variable.
2) H1: 𝐹𝑃𝑖,𝑡 = 𝛼0+ 𝛽1𝐹𝐼𝑂𝑖,𝑡−1 + 𝛽3𝐻𝐹𝐼𝑂𝑑𝑢𝑚𝑚𝑦𝑖,𝑡−1+
𝛽2𝐹𝐼𝑂𝑖,𝑡−1𝑥 𝐻𝐹𝐼𝑂𝑑𝑢𝑚𝑚𝑦𝑖,𝑡−1+ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡−1+ 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝐹𝑋 + 𝑌𝑒𝑎𝑟𝐹𝑋 + 𝜀𝑖,𝑡 3) H1: 𝐹𝑃𝑖,𝑡 = 𝛼0+ 𝛽1𝐹𝐼𝑂𝑖,𝑡−1 + 𝛽3𝐿𝐹𝐼𝑂𝑑𝑢𝑚𝑚𝑦𝑖,𝑡−1+
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(FIO). The dummies are generated through taking the highest and lowest quartile of foreign institutional ownership. 4) H2: 𝐹𝑃𝑖,𝑡 = 𝛼0+ 𝛽1𝐹𝐼𝑂𝑖,𝑡−1 + 𝛽3𝑀𝑈𝐿𝑇𝐹𝑖,𝑡−1+ 𝛽2𝐹𝐼𝑂𝑖,𝑡−1𝑥 𝑀𝑈𝐿𝑇𝐹𝑖,𝑡−1+ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡−1+ 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝐹𝑋 + 𝑌𝑒𝑎𝑟𝐹𝑋 + 𝜀𝑖,𝑡 5) H2: 𝐹𝑃𝑖,𝑡 = 𝛼0+ 𝛽1𝐹𝐼𝑂𝑖,𝑡−1 + 𝛽3𝑀𝑈𝐿𝑇𝐹𝐷𝐻𝑖,𝑡−1+ 𝛽2𝐹𝐼𝑂𝑖,𝑡−1𝑥 𝑀𝑈𝐿𝑇𝐹𝐷𝐻𝑖,𝑡−1+ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡−1+ 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝐹𝑋 + 𝑌𝑒𝑎𝑟𝐹𝑋 + 𝜀𝑖,𝑡 6) H2: 𝐹𝑃𝑖,𝑡 = 𝛼0+ 𝛽1𝐹𝐼𝑂𝑖,𝑡−1 + 𝛽3𝑀𝑈𝐿𝑇𝐹𝐷𝐿𝑖,𝑡−1+ 𝛽2𝐹𝐼𝑂𝑖,𝑡−1𝑥 𝑀𝑈𝐿𝑇𝐹𝐷𝐿𝑖,𝑡−1+ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡−1+ 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝐹𝑋 + 𝑌𝑒𝑎𝑟𝐹𝑋 + 𝜀𝑖,𝑡
In equation 4 the the interaction of multi-nationality (𝐹𝐼𝑂𝑖,𝑡−1𝑥𝑀𝑈𝐿𝑇𝐹𝑖,𝑡−1) with foreign institutional ownership is incorporated to measure the effect of multi-nationality on the relationship between foreign institutional ownership and firm performance. This is done using two techniques. First, multi-nationality as a percentage (𝑀𝑈𝐿𝑇𝐹) is interacted with foreign institutional ownership. Subsequently, a dummy is used for high and low levels of multi- nationality (𝑀𝑈𝐿𝑇𝐹𝐷𝐻, 𝑀𝑈𝐿𝑇𝐹𝐿) to see if there is a distinguishable effect between the two groups. Finally, different types of institutional investors are regressed separately with firm performance to see if there is a distinct difference in effect between the several subgroups; IO, IIO, FIO, IO20, IIO20 FIO20 and FIOP20. Again, the different types of institutional investors will be regressed with the firm performance and interacted with multi-nationality to see the separate effect of multi-nationality per sub-group. Industry- and year- fixed effects (𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝐹𝑋, 𝑌𝑒𝑎𝑟𝐹𝑋) are incorporated to control for collinearity among the variables between industries or fiscal years.
3.7 Descriptive statistics
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regressions is visible. First, the main dependent variable is listed. Thereafter, the main independent variables, with the different forms of foreign institutional ownership and the dummies. Then, the multi-nationality variables are listed, followed by the control variables used for this research.
Table 2. Descriptive Statistics.
Variable Obs Mean Std.Dev. Min Max
Dependent variable: FP 2436 1.568 1.52 0.000 8.341 ROA 2436 0.062 0.062 -0.144 0.245 Main independent variables: IO 2868 0.822 0.113 0.589 0.99 IIO 2997 0.716 0.194 0.000 0.99 FIO 2868 0.119 0.042 0.035 0.281 Top 20 variables: IO20 2994 0.386 0.108 0.16 0.594 IIO20 2994 0.362 0.107 0.146 0.567 FIO20 2875 0.019 0.024 0.000 0.125 FIOP20 2875 0.043 0.047 0.000 0.225 FIO dummies: HFIOdummy 2868 0.238 0.426 0 1 LFIOdummy 2868 0.239 0.427 0 1 Multi-nationality MULTF 2635 0.274 0.267 0.000 1 MULTFDH 2635 0.486 0.5 0 1 MULTFDL 2635 0.46 0.498 0 1 Control variables: SIZE 2930 9.865 1.34 7.055 13.694 LEV 2930 0.249 0.191 0.000 0.847 CAPEX 2930 0.037 0.042 0.000 0.606 CASH 2930 0.084 0.093 0.000 0.749 DIV 2943 0.019 0.019 0.000 0.329 BOARD 2808 10.902 2.174 5 29
23 3.8 Correlation tables
In Appendix 1.1 the correlation tables of the variables used for this research are listed. As there are many different variables used for analysis the correlation matrices are split up into the groups that are used in the regressions. In general the correlation coefficients of the control variables are quite low. This indicates that there is not much multicollinearity between the variables. Still, there is quite a strong correlation (around 0.5) between firm size and board size. This could indicate that board size would be positively associated with firm size. However, there is no clear academic evidence that supports this relationship. Moreover, there is little variance in board size as 98% of boards in the sample lie between 6 and 14. Additionally, board size does not have strong correlation coefficients with the other control variables in this group. Therefore, it is expected that this will not cause any multicollinearity issues for the regression analyses.
4 Results
4.1 Results basic regression
24 Table 3. OLS Regression results for initial analysis institutional ownership and firm
performance. (1) (2) (3) VARIABLES FP FP FP IO -2.056*** (0.257) IIO -0.510*** (0.152) FIO -1.416** (0.627) SIZE -0.464*** -0.423*** -0.401*** (0.026) (0.025) (0.025) LEV -0.719*** -0.821*** -0.795*** (0.151) (0.150) (0.153) CAPEX 3.287*** 3.956*** 3.744*** (0.722) (0.717) (0.730) CASH 3.411*** 3.563*** 3.660*** (0.308) (0.304) (0.311) DIV -7.518*** -6.251*** -6.180*** (1.316) (1.304) (1.321) BOARD -0.009 0.001 0.002 (0.013) (0.013) (0.013) Constant 7.689*** 5.879*** 5.432*** (0.390) (0.301) (0.260) Observations 2,274 2,336 2,274 R-squared 0.292 0.280 0.273
Year FE Yes Yes Yes
Industry FE Yes Yes Yes
The main dependent variable is firm performance (FP) which is measured with Tobin’s Q. Institutional ownership (IO), independent institutional ownership (IIO) and foreign institutional ownership (FIO) are measured with the amount of shares held by investors with a 13-F filing. The control variables such size (SIZE) which is taken by the logarithm of total assets, leverage (LEV) which is calculated through dividing the total debt by total assets, capital expenditures (CAPEX) which is the ratio of the yearly capital expenditures, cash holdings (CASH) ratio of total amount of cash held, and dividend yield (DIV) dividend yield in that specific year. Boardsize (BOARD) is measured through amount of board members per year. Stars indicate the significance with * p < 0.05, ** p < 0.01, *** p < 0.001.
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26 Table 4. OLS regression results for top20 investors.
(1) (2) (3) (4) VARIABLES FP FP FP FP IO20 0.341 (0.264) IIO20 0.240 (0.267) FIO20 0.913 (1.055) FIOP20 0.843 (0.514) SIZE -0.395*** -0.396*** -0.397*** -0.396*** (0.025) (0.025) (0.025) (0.025) LEV -0.855*** -0.847*** -0.851*** -0.863*** (0.151) (0.151) (0.154) (0.154) CAPEX 3.994*** 3.979*** 3.937*** 3.965*** (0.719) (0.718) (0.728) (0.728) CASH 3.658*** 3.654*** 3.660*** 3.665*** (0.304) (0.305) (0.310) (0.310) DIV -5.956*** -5.952*** -6.100*** -6.111*** (1.304) (1.305) (1.319) (1.319) BOARD 0.003 0.003 0.002 0.002 (0.013) (0.013) (0.013) (0.013) Constant 5.053*** 5.119*** 5.203*** 5.177*** (0.302) (0.305) (0.254) (0.251) Observations 2,336 2,336 2,278 2,278 R-squared 0.277 0.277 0.275 0.275
Year FE Yes Yes Yes Yes
Industry FE Yes Yes Yes Yes
The main dependent variable is firm performance (FP) which is measured with Tobin’s Q. Institutional ownership (IO), independent institutional ownership (IIO) and foreign institutional ownership (FIO) are measured with the amount of shares held by investors with a F filing. IO20 ,IIO20 and FIO20 are calculated by taking the top 20 investors of each firm with 13-F filings. 13-FIOP20 is the relative foreign institutional ownership to 20 top investors. The control variables such size (SIZE) which is taken by the logarithm of total assets, leverage (LEV) which is calculated through dividing the total debt by total assets, capital expenditures (CAPEX) which is the ratio of the yearly capital expenditures, cash holdings (CASH) ratio of total amount of cash held, and dividend yield (DIV) dividend yield in that specific year. Boardsize (BOARD) is measured through amount of board members per year. Stars indicate the significance with * p < 0.05, ** p < 0.01, *** p < 0.001.
4.2 Results top 20 investors
27
percentage of foreign institutional ownership of the top 20 investors. Although all coefficients of dependent variables are positive, they are all insignificant. Table 4 illustrates the regression results with FIO20 and IFIO20 having insignificant coefficients of 0.913 and 0.843 respectively. These results are not in line with H1 as they do not indicate that foreign institutional ownership has a positive effect on firm performance.
Table 5. OLS Regression results for FIO dummy analysis.
(1) (2) VARIABLES FP FP FIO -1.727** -1.603 (0.814) (1.208) HFIOdummy -0.746*** (0.275) HFIOdummy*FIO 10.17*** (3.419) LFIOdummy -0.108 (0.289) LFIOdummy*FIO 0.675 (1.908) SIZE -0.411*** -0.401*** (0.025) (0.025) LEV -0.772*** -0.794*** (0.153) (0.154) CAPEX 3.973*** 3.733*** (0.734) (0.733) CASH 3.656*** 3.652*** (0.311) (0.312) DIV -6.483*** -6.166*** (1.324) (1.324) BOARD 0.002 0.002 (0.013) (0.013) Constant 5.551*** 5.449*** (0.281) (0.270) Observations 2,274 2,274 R-squared 0.276 0.273
Year FE Yes Yes
28 The main dependent variable is firm performance (FP) which is measured with Tobin’s Q. Institutional ownership (IO), independent institutional ownership (IIO) and foreign institutional ownership (FIO) are measured with the amount of shares held by investors with a 13-F filing. The control variables such size (SIZE) which is taken by the logarithm of total assets, leverage (LEV) which is calculated through dividing the total debt by total assets, capital expenditures (CAPEX) which is the ratio of the yearly capital expenditures, cash holdings (CASH) ratio of total amount of cash held, and dividend yield (DIV) dividend yield in that specific year. Boardsize (BOARD) is measured through amount of board members per year. Stars indicate the significance with * p < 0.05, ** p < 0.01, *** p < 0.001.
4.3 Results FIO dummy
In the third analysis dummy variables are created to look at high and low levels of foreign institutional ownership by taking the highest and lowest quartile. The analysis with the FIO dummies shows some interesting results which are illustrated in table 5. The coefficient of the interaction between high foreign institutional ownership and foreign institutional ownership (HFIOdummy*FIO) is positive (10.17) and significant at a 0,1% level. This is in accordance with H1 as it is indicates that high levels of foreign institutional ownership might have a positive effect on firm performance. Additionally, it would make the result stronger if the coefficient of low levels of foreign institutional ownership would also be significant and less positive or negative. However, the coefficient of the interaction between low foreign institutional ownership and foreign institutional ownership (LFIOdummy*FIO) is positive and insignificant.
29 Table 7. OLS Regression results for multi-national interactions.
(1) (2) (3) VARIABLES FP FP FP FIO -1.226 (0.895) FIO*MULTF 0.497 (2.158) FIO20 0.743 (1.674) FIO20*MULTF -1.329 (3.733) FIOP20 0.735 (0.807) FIOP20*MULTF -0.196 (1.857) MULTF -0.132 -0.0381 -0.0623 (0.269) (0.121) (0.127) SIZE -0.375*** -0.374*** -0.371*** (0.025) (0.025) (0.025) LEV -0.673*** -0.733*** -0.749*** (0.154) (0.156) (0.155) CAPEX 3.399*** 3.562*** 3.585*** (0.736) (0.734) (0.733) CASH 3.354*** 3.358*** 3.365*** (0.314) (0.314) (0.313) DIV -5.424*** -5.273*** -5.292*** (1.298) (1.294) (1.293) BOARD 0.004 0.006 0.005 (0.0130) (0.0130) (0.0130) Constant 5.116*** 4.934*** 4.897*** (0.269) (0.256) (0.253) Observations 2,043 2,047 2,047 R-squared 0.265 0.267 0.267
Year FE Yes Yes Yes
Industry FE Yes Yes Yes
30 4.7 Results multi-nationality
For the impact of multi-nationality on this relationship there are two different analyses performed. With the first analysis the multi-nationality (foreign sales) is interacted directly with the three different types of foreign institutional ownership (FIO, FIO20, FIOP20). In the second analysis multi-nationality is separated into two groups through a dummy; high multi-nationality and low multi-nationality for the three different groups of foreign institutional ownership. Unfortunately, the results are not in line with H2. The results are visible in table 7. They show that the interaction terms of the three types of foreign institutional ownership are insignificant. For FIO the coefficient of the interaction term (FIO*MULTF) is positive and insignificant (0.497). For FIO20 the interaction is negative but insignificant (-1.329) and for FIOP20 the coefficient is negative and insignificant as well (-0.196). The results in this first analysis suggest that there is no evidence to support H2, as there is no significant negative effect of multi-nationality on firm performance. This does not correspond with the findings of Denis et al (2002) and Kim and Yost (2008) who find a negative relationship between multi-nationality, monitoring and performance.
Table 8. OLS regression results for multi-national interactions (high/low)
31 (0.753) (0.661) FIOP20*MULTFDH -1.719* (1.011) FIOP20*MULTFDL 1.826* (1.014) MULTFDH 0.130 0.059 0.0715 (0.150) (0.064) (0.067) MULTFDL -0.003 -0.087 -0.090 (0.150) (0.064) (0.067) SIZE -0.402*** -0.402*** -0.396*** -0.395*** -0.394*** -0.395*** (0.025) (0.025) (0.025) (0.025) (0.025) (0.025) LEV -0.791*** -0.797*** -0.852*** -0.855*** -0.864*** -0.863*** (0.153) (0.154) (0.154) (0.155) (0.154) (0.154) CAPEX 3.818*** 3.745*** 4.029*** 4.048*** 4.064*** 4.064*** (0.735) (0.736) (0.732) (0.731) (0.731) (0.730) CASH 3.667*** 3.653*** 3.690*** 3.688*** 3.691*** 3.685*** (0.312) (0.312) (0.312) (0.312) (0.311) (0.312) DIV -6.169*** -6.183*** -6.062*** -6.068*** -6.081*** -6.087*** (1.322) (1.322) (1.319) (1.319) (1.318) (1.318) BOARD 0.002 0.002 0.003 0.003 0.002 0.002 (0.013) (0.013) (0.013) (0.013) (0.013) (0.013) Constant 5.371*** 5.438*** 5.147*** 5.214*** 5.121*** 5.199*** (0.268) (0.272) (0.257) (0.257) (0.254) (0.255) Observations 2,274 2,274 2,278 2,278 2,278 2,278 R-squared 0.274 0.273 0.275 0.276 0.276 0.276 Number of industryn 11 11 11 11 11 11
Year FE Yes Yes Yes Yes Yes Yes
Industry FE Yes Yes Yes Yes Yes Yes
The main dependent variable is firm performance (FP) which is measured with Tobin’s Q. Institutional ownership (IO), independent institutional ownership (IIO) and foreign institutional ownership (FIO) are measured with the amount of shares held by investors with a F filing. IO20 ,IIO20 and FIO20 are calculated by taking the top 20 investors of each firm with 13-F filings. 13-FIOP20 is the relative foreign institutional ownership to 20 top investors. H13-FIOdummy and L13-FIOdummy represent the dummies for high and low foreign institutional ownership respectively. Multintaionality (MULTF) is measured through proportion of foreign sales of total sales. Both dummies for high and low multi-nationality (MULTFDH, MULTFDL). The control variables such size (SIZE) which is taken by the logarithm of total assets, leverage (LEV) which is calculated through dividing the total debt by total assets, capital expenditures (CAPEX) which is the ratio of the yearly capital expenditures, cash holdings (CASH) ratio of total amount of cash held, and dividend yield (DIV) dividend yield in that specific year. Boardsize (BOARD) is measured through amount of board members per year. Stars indicate the significance with * p < 0.05, ** p < 0.01, *** p < 0.001.
4.8 Results multi-nationality dummy
32
where the interactions with low levels of multi-nationality (MULTFDL) are expected to be less negative (or positive). The results in table 8 show some significant coefficients for the interaction terms between the different types of foreign institutional ownership and high and low multi-nationality. For FIO the interaction is negative for thigh multi-nationality (-1.197) and negative for low multi-nationality (-0.066) but both coefficients are insignificant. In model 3 and 4 the coefficient of the interaction between FIO20 and high multi-nationality is negative for high multi-nationality (-3.127) but insignificant, where the coefficient is positive and significant for low multi-nationality (3.827) at 5%. For FIOP20 the results are most in line with H2 as the coefficient of the interaction with high multi-nationality in model 5 and 6 is negative and significant at 5% (-1.719), where the coefficient of the interaction with low nationality is positive and significant at 5% (1.826). This indicates that high levels of multi-nationality might have a negative effect on the relationship between foreign institutional ownership and firm performance and that this effect is less negative or even positive with low levels of multi-nationality. These results do correspond with the literature as Doukas and Pantzalis (2003) and Ghoshall and Bartlett (1990) show that multi-nationality increases agency costs which has a negative effect on monitoring and performance. As this is in line with H2, this is some evidence that supports H2.
4.9 Robustness check
33
table 3, illustrate that the coefficients of the interactions with the FIO dummies are positive at high levels of foreign institutional ownership (0.482) and negative at low levels of foreign institutional ownership (-0.251), both significant at 0.01%. These results are much more in line with H1 and the corresponding literature as different measures of foreign institutional ownership show a positive relationship with the proxy for firm performance; ROA. However, it has to be mentioned the R-squared coefficients of those tables are considerably lower (around 0.10) than the R-squared coefficients (around 0.27) of the primary analysis. This might mean these OLS models are less explanatory than in the models with tobin’s q as main dependent variable. Nevertheless, the two proxies do correspond when high levels and low levels of foreign institutional ownership were tested, which gives some evidence to support H1.
The regressions where multi-nationality is added as interaction term do not yield any significant effects (Appendix, 1.2, table 4 and table 5). This does not correspond with the analysis where tobin’s q is the main dependent variable in section 4.8. This makes the results of that OLS regression less convincing evidence to support H2. Additionally, the difference between the two proxies also indicates that results are inconsistent and that there are grounds for further research on the effect of multi-nationality.
5 Conclusion
34
positive significant relationship between high levels of foreign institutional ownership and firm performance. Although more significant positive results were expected with foreign institutional ownership, this is minor evidence that supports H1. Additionally, when ROA was used as a proxy for firm performance, results appeared that indicated a positive relationship between foreign institutional ownership and firm performance. The two proxies corresponded for the analysis with high and low levels of foreign institutional ownership. Although these results are in line with H1, the difference between FP and ROA in the other analyses indicates that there is little consistency between the two models which makes the results with respect to H1 less strong and robust.
35
foreign institutional ownership on firm performance. Still, the analysis for H2 provided mixed and weak evidence that confirms H2 and corresponds with the literature studied for this paper. This indicates that more research needs to be done to investigate the effect foreign institutional ownership on firm performance and the effect of multi-nationality on this relationship.
6 Discussion
36
37
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8 Appendix
Appendix 1.1
Correlation Table 1.
FP IO IIO FIO SIZE LEV CAPEX CASH DIV BOARD
FP 1 IO 0.0472* 1 IIO 0.0708*** 0.914*** 1 FIO 0.00254 0.358*** 0.310*** 1 SIZE -0.525*** -0.381*** -0.399*** -0.0719*** 1 LEV 0.0454* 0.185*** 0.150*** 0.100*** -0.191*** 1 CAPEX 0.0905*** -0.0952*** -0.0485* -0.155*** -0.130*** -0.0203 1 CASH 0.454*** 0.0847*** 0.0994*** 0.0530* -0.384*** 0.0151 -0.0279 1 DIV -0.247*** -0.176*** -0.185*** -0.0182 0.185*** 0.0701*** -0.0188 -0.134*** 1 BOARD -0.266*** -0.330*** -0.350*** -0.0599** 0.501*** -0.146*** -0.100*** -0.225*** 0.153*** 1
45 Correlation table 2.
FP IO20 IIO20 FIO20 FIOP20 SIZE LEV CAPEX CASH DIV SIZE
FP 1 IO20 0.0597** 1 IIO20 0.0830*** 0.954*** 1 FIO20 0.0188 0.340*** 0.285*** 1 FIOP20 0.0110 0.153*** 0.106*** 0.945*** 1 SIZE -0.528*** -0.263*** -0.308*** -0.117*** -0.0537* 1 LEV 0.0426* 0.102*** 0.0761*** 0.0957*** 0.0548** -0.191*** 1 CAPEX 0.0936*** -0.104*** -0.0821*** -0.133*** -0.108*** -0.134*** -0.0173 1 CASH 0.455*** 0.0258 0.0394 0.00987 -0.0143 -0.384*** 0.0130 -0.0263 1 DIV -0.245*** -0.0724*** -0.0987*** -0.00574 0.0165 0.184*** 0.0740*** -0.0182 -0.134*** 1 BOARD -0.261*** -0.223*** -0.257*** -0.0525* 0.00618 0.500*** -0.148*** -0.100*** -0.222*** 0.149*** 1
46 Table 5. Correlation table 3.
FP FIO HFIO
dummy
LFIO dummy
SIZE LEV CAPEX CASH DIV BOARD
FP 1 FIO 0.00254 1 HFIO dummy 0.0547** -0.626*** 1 LFIO dummy 0.0369 0.760*** -0.324*** 1 SIZE -0.525*** -0.0719*** -0.0371 -0.144*** 1 LEV 0.0454* 0.100*** -0.0505* 0.138*** -0.191*** 1 CAPEX 0.0905*** -0.155*** 0.143*** -0.0880*** -0.130*** -0.0203 1 CASH 0.454*** 0.0530* 0.0233 0.0452* -0.384*** 0.0151 -0.0279 1 DIV -0.247*** -0.0182 -0.0277 -0.0386 0.185*** 0.0701*** -0.0188 -0.134*** 1 BOARD -0.266*** -0.0599** 0.0210 -0.0892*** 0.501*** -0.146*** -0.100*** -0.225*** 0.153*** 1
47 Table 6. Correlation table 4.
FP FIO FIO20 FIOP20 MULTF SIZE LEV CAPEX CASH DIV BOARD
FP 1 FIO 0.00185 1 FIO20 -0.00609 0.599*** 1 FIOP20 -0.00956 0.597*** 0.937*** 1 MULTF 0.0723** -0.000876 0.00901 0.0328 1 SIZE -0.526*** -0.0839*** -0.104*** -0.0196 -0.0135 1 LEV 0.0532* 0.115*** 0.109*** 0.0516* 0.0554* -0.198*** 1 CAPEX 0.104*** -0.152*** -0.143*** -0.116*** -0.0854*** -0.138*** -0.0294 1 CASH 0.446*** 0.0474* -0.00753 -0.0188 0.143*** -0.381*** 0.0111 -0.0156 1 DIV -0.225*** -0.0222 -0.00281 0.0126 -0.109*** 0.182*** 0.0951*** -0.0290 -0.112*** 1 BOARD -0.252*** -0.0737*** -0.0465* 0.0217 -0.0240 0.500*** -0.159*** -0.105*** -0.206*** 0.144*** 1
48 Table 7. Correlation table 5.
(1)
FP FIOwin FIO20 FIOP20 MULTF
DH
MULTF DL
SIZE LEV CAPEX CASH DIV BOARD
49 Appendix 1.2
The main dependent variable is firm performance (ROA) which is measured with return on assets. Institutional ownership (IO), independent institutional ownership (IIO) and foreign institutional ownership (FIO) are measured with the amount of shares held by investors with a 13-F filing. The control variables such size (SIZE) which is taken by the logarithm of total assets, leverage (LEV) which is calculated through dividing the total debt by total assets, capital expenditures (CAPEX) which is the ratio of the yearly capital expenditures, cash holdings (CASH) ratio of total amount of cash held, and dividend yield (DIV) dividend yield in that specific year. Boardsize (BOARD) is measured through amount of board members per year. Stars indicate the significance with * p < 0.05, ** p < 0.01, *** p < 0.001.
Table 2.
(1) (2) (3) (4)
VARIABLES ROA ROA ROA ROA
IO20 -0.0174* (0.0103) IIO20 -0.0281*** (0.0105) FIO20 0.112** (0.0496) Table 1. (1) (2) (3)
VARIABLES ROA ROA ROA
IO -0.0810*** (0.0109) IIO -0.0335*** (0.00719) FIO 0.0836*** (0.0295) SIZE -0.0126*** -0.0115*** -0.00987*** (0.00120) (0.00117) (0.00116) LEV -0.0337*** -0.0348*** -0.0385*** (0.00715) (0.00710) (0.00721) CAPEX 0.0749** 0.106*** 0.106*** (0.0340) (0.0338) (0.0344) CASH 0.0716*** 0.0774*** 0.0795*** (0.0145) (0.0143) (0.0146) DIV -0.0714 -0.0181 -0.0123 (0.0620) (0.0615) (0.0622) BOARD 0.000130 0.000361 0.000583 (0.000618) (0.000606) (0.000621) Constant 0.252*** 0.196*** 0.145*** (0.0173) (0.0142) (0.0123) Observations 2,274 2,336 2,274 R-squared 0.116 0.105 0.098
Year FE Yes Yes Yes
50 FIOP20 0.0795*** (0.0241) SIZE -0.0106*** -0.0109*** -0.00980*** -0.00977*** (0.00117) (0.00117) (0.00117) (0.00116) LEV -0.0358*** -0.0360*** -0.0397*** -0.0403*** (0.00713) (0.00711) (0.00726) (0.00724) CAPEX 0.105*** 0.105*** 0.102*** 0.103*** (0.0340) (0.0339) (0.0342) (0.0342) CASH 0.0806*** 0.0793*** 0.0819*** 0.0823*** (0.0144) (0.0144) (0.0146) (0.0146) DIV -0.00262 -0.00562 -0.0106 -0.0124 (0.0617) (0.0616) (0.0620) (0.0619) BOARD 0.000466 0.000440 0.000574 0.000531 (0.000608) (0.000607) (0.000617) (0.000617) Constant 0.169*** 0.177*** 0.150*** 0.150*** (0.0135) (0.0137) (0.0119) (0.0118) Observations 2,336 2,336 2,278 2,278 R-squared 0.097 0.099 0.099 0.101
Year FE Yes Yes Yes Yes
Industry FE Yes Yes Yes Yes
The main dependent variable is firm performance (ROA) which is measured with return on assets. Institutional ownership (IO), independent institutional ownership (IIO) and foreign institutional ownership (FIO) are measured with the amount of shares held by investors with a F filing. IO20 ,IIO20 and FIO20 are calculated by taking the top 20 investors of each firm with 13-F filings. 13-FIOP20 is the relative foreign institutional ownership to 20 top investors. The control variables such size (SIZE) which is taken by the logarithm of total assets, leverage (LEV) which is calculated through dividing the total debt by total assets, capital expenditures (CAPEX) which is the ratio of the yearly capital expenditures, cash holdings (CASH) ratio of total amount of cash held, and dividend yield (DIV) dividend yield in that specific year. Boardsize (BOARD) is measured through amount of board members per year. Stars indicate the significance with * p < 0.05, ** p < 0.01, *** p < 0.001.
Table 3.
(1) (2)
VARIABLES ROA ROA
51 CASH 0.0805*** 0.0814*** (0.0146) (0.0146) DIV -0.0296 -0.0198 (0.0623) (0.0622) BOARD 0.000675 0.000627 (0.000620) (0.000620) Constant 0.161*** 0.137*** (0.0132) (0.0127) Observations 2,274 2,274 R-squared 0.103 0.101
Year FE Yes Yes
Industry FE Yes Yes
The main dependent variable is firm performance (ROA) which is measured with return on assets. Institutional ownership (IO), independent institutional ownership (IIO) and foreign institutional ownership (FIO) are measured with the amount of shares held by investors with a 13-F filing. The control variables such size (SIZE) which is taken by the logarithm of total assets, leverage (LEV) which is calculated through dividing the total debt by total assets, capital expenditures (CAPEX) which is the ratio of the yearly capital expenditures, cash holdings (CASH) ratio of total amount of cash held, and dividend yield (DIV) dividend yield in that specific year. Boardsize (BOARD) is measured through amount of board members per year. Stars indicate the significance with * p < 0.05, ** p < 0.01, *** p < 0.001.
Table 4.
(1) (2) (3)
VARIABLES ROA ROA ROA
52 (0.000635) (0.000630) (0.000629) Constant 0.142*** 0.147*** 0.146*** (0.0131) (0.0124) (0.0123) Observations 2,043 2,047 2,047 R-squared 0.098 0.101 0.103
Year FE Yes Yes Yes
Industry FE Yes Yes Yes
The main dependent variable is firm performance (ROA) which is measured with return on assets. Institutional ownership (IO), independent institutional ownership (IIO) and foreign institutional ownership (FIO) are measured with the amount of shares held by investors with a F filing. IO20 ,IIO20 and FIO20 are calculated by taking the top 20 investors of each firm with 13-F filings. 13-FIOP20 is the relative foreign institutional ownership to 20 top investors. Multintaionality (MULT13-F) is measured through proportion of foreign sales of total sales. The control variables such size (SIZE) which is taken by the logarithm of total assets, leverage (LEV) which is calculated through dividing the total debt by total assets, capital expenditures (CAPEX) which is the ratio of the yearly capital expenditures, cash holdings (CASH) ratio of total amount of cash held, and dividend yield (DIV) dividend yield in that specific year. Boardsize (BOARD) is measured through amount of board members per year. Stars indicate the significance with * p < 0.05, ** p < 0.01, *** p < 0.001.
Table 5.
(1) (2) (3) (4) (5) (6)
VARIABLES ROA ROA ROA ROA ROA ROA
53 CASH 0.0812*** 0.0807*** 0.0845*** 0.0833*** 0.0846*** 0.0836*** (0.0147) (0.0147) (0.0147) (0.0147) (0.0146) (0.0146) DIV -0.0110 -0.0116 -0.00861 -0.0100 -0.0107 -0.0119 (0.0622) (0.0622) (0.0620) (0.0620) (0.0619) (0.0620) BOARD 0.000564 0.000558 0.000563 0.000557 0.000513 0.000512 (0.000621) (0.000621) (0.000617) (0.000617) (0.000617) (0.000617) Constant 0.143*** 0.140*** 0.149*** 0.148*** 0.149*** 0.147*** (0.0126) (0.0128) (0.0121) (0.0121) (0.0119) (0.0120) Observations 2,274 2,274 2,278 2,278 2,278 2,278 R-squared 0.099 0.098 0.100 0.099 0.102 0.101
Year FE Yes Yes Yes Yes Yes Yes
Industry FE Yes Yes Yes Yes Yes Yes
