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Cross ownership and firm performance

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Master project by Octavi Castells Pera, Jaime LΓ³pez Sastre, Berenice Ramirez Hart. Barcelona GSE Master's in Finance

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Cross ownership and firm performance

  1. 1. Cross ownership and firm performance Octavi Castells Pera, Jaime LΓ³pez Sastre, Berenice Ramirez Hart* Barcelona Graduate School of Economics Abstract: This paper assesses the impact of cross ownership on firm performance and industry competition through an analysis of shareholderΒ΄s networks in Spain using a panel regression model on a sample of non-financial listed companies between the years 2004 and 2012. The results show that there is a positive and significant effect of the number of connections a firm has with other industry rivals through the common ownership mechanism on its markup. Keywords: Institutional ownership, Firm performance, Industry competition. Classification: Empirical work – research * E-mail Addresses: octavi.castells@barcelongse.eu (O. Castells Pera), jaime.lopez@barcelonagse.eu (J. LΓ³pez Sastre), berenice.ramirez@barcelonagse.eu (B. Ramirez Hart)
  2. 2. 1. Introduction The analysis of cross ownership of companies within the same industry has been a topic of increasing interest for market regulators and researchers. As firms aim to maximize shareholdersΒ΄ value, this may incentivize them to compete less aggressively in the market, therefore creating negative externalities for consumers. Most of the literature in this area focuses on analyzing companies and investors in the U.S. In our case, we target the Spanish market, for which we believe our paper is innovative and provides important insights into the mechanics of cross ownership in this country. We focus on three specific sectors: Food, Pharmaceutical and Energy, for which we show the evolution of cross ownership concentration from 2004 to 2012, and the impact of the largest shareholders on this. Our main objective is to study the impact of common ownership on industry competition and firm performance. We measure them by firm-level markups and market share, respectively. However, we also acknowledge that markups contain information about individual performance, since companies with higher markups obtain profits above the equilibrium ones implied by perfect competitive markets. Thus, we will observe those firms outperforming their industry rivals. The chief hypothesis we present is that more connected firms coordinate their market strategies to compete less aggressively and perform better than their less connected rivals. Our results suggest that there has been an increase in the concentration of cross ownership among Spanish listed companies in the last ten years. The impact of the largest shareholders on this seems to vary a lot across the three industries under analysis. We find a positive and significant effect of our measure of cross ownership on markups, which is robust to several variations in our main specification. We do not find a significant effect on firmsΒ΄ market share. The rest of the paper is organized as follows. Section 2 provides an overview of related literature. Section 3 describes the theoretical base on which we base our empirical analysis. Section 4 includes information on the data used. In Section 5, we show the methodology applied for both the descriptive and inferential analyses. Section 6 and 7 present our main descriptive and regression results, respectively. Finally, Section 8 concludes.
  3. 3. 2. Literature review From the seminal work of Bain (1951) the industry concentration has been a topic that has generated much interest on the part of researchers, who have carried out several studies on the relationship between this, different measures of profit rates and other characteristics of the company. Consequently, one branch of studies derived from Bain’s work reflects an increasing interest on the effects of the growing interconnection of businesses through common stock ownership and how this can affect corporate performance (Schmalensee, 1989; Demsetz and Villalonga, 2001; Bhattacharya and Graham, 2009; Azar, 2012). In the cases described in the literature, researchers study partial control scenarios, in which the acquiring company does not own 100% of the acquired company, presenting a situation in which the interest of the first may or may not have an influence on the corporate decisions of the second one changing the strategic interaction between the companies in the same industry. On the one hand, the cross-holder has incentives to make the companies in the portfolio compete less aggressively with the intention to maximize the aggregate profit of these ones. Also, he may assist implicit or explicit coordination between the firms in the portfolio. On the other hand, the acquiring firm might have some constraints to influence the acquired one, for example the influence of other shareholders, corporate governance structure and corporate law, leading to a scenario in which every firm maximizes its own profit by taking care of the interest of all its shareholders. Within the assessment of the effect of cross-ownership on market power and firm performance, we can identify studies that evaluate two different types of cross ownership: Partial Ownership and Institutional Shareholders. The first ones stress the competitive effect of Partial ownership, defined as the acquisition of some percentage of a rivalΒ΄s firm stock (O'Brien, D. P., and Salop, S. C., 2000). The second ones evaluate how the presence of institutional shareholders in companies of the same industry affects these companies behavior. In both cases the presence of cross-ownership is expected to provide an effective framework of incentives to influence corporate performance leading to a positive correlation between cross-ownership and firm performance (Azar, J., 2012; He, J., and Huang, J.,2014) , this last one measured as profit rate, Tobin’s Q and market share, among others. Nonetheless, the authors find mixed results, showing that cross-ownership does not have a positive correlation with firm performance (Demsetz, H. and Villalonga, B., 2001; Bhattacharya, P. S., and Graham, M. A., 2009).
  4. 4. 3. Theoretical review and development of hypothesis In this section, we provide a brief overview of a theoretical model developed by Azar, Schmalz, and Tecu (2015), in which they show the relationship between cross ownership among industry participants and their profit maximization mechanism in a Cournot setting. In this way, we illustrate the intuition behind our main hypothesis, which we explain next. In a given industry with N firms and M investors companies maximize their shareholderΒ΄s value. Ξ²ij is defined as the share of equity of investor i on firm j, and Ο€j are the profits of company j. Thus, an investorΒ΄s total profits from his portfolio of companies are given by πœ‹ 𝑖 = βˆ‘ π›½π‘–π‘˜ πœ‹ π‘˜π‘˜ . Consequently, each firm maximizes a weighted average of its shareholders portfolio profits, where the weights are given by Ξ²ij 1 , and where xj is the strategy of firm j. max π‘₯ 𝑗 πœ‹π‘—Μ‚ = βˆ‘ 𝛽𝑖𝑗 𝑀 𝑖=1 βˆ‘ π›½π‘–π‘˜ πœ‹ π‘˜ 𝑁 π‘˜=1 By rearranging the formula and dividing by βˆ‘ 𝛽𝑖𝑗𝑖 , we can rewrite the objective function as max π‘₯ 𝑗 πœ‹π‘—Μ‚ = πœ‹π‘— + βˆ‘ βˆ‘ π›½π‘–π‘˜π‘– βˆ‘ 𝛽𝑖𝑗𝑖 π‘˜β‰ π‘— πœ‹ π‘˜ This formula shows that companies are interested in maximizing their own profits plus a linear combination of the profits of other companies where its shareholders have equity stakes. The weight each company puts in the profits of the rest of the firms is given by βˆ‘ π›½π‘–π‘˜π‘– βˆ‘ 𝛽𝑖𝑗𝑖 , which can be seen as a measure of the degree of connectivity between two companies as a result of their cross ownership. The closer this ratio is to one, the more interest company j has on companyΒ΄s k profits, and therefore its shareholders will compare the potential gains obtained from taking certain strategic actions (for example, increasing the quantity of goods offered in the market) to increase jΒ΄s performance, with the losses from the effect of these actions on firm k. Note that investors realize these potential losses on the 1 Azar, Schamlz, and Tecu (2015) use control rights to define the weight of each shareholder in the profit maximization function of each firm. They argue that control rights may differ from ownership share in practice, as different types of shares may provide different levels of power to make decisions within the company. However, because for our empirical analysis we are not able to obtain data on control rights, we assume that control of the firm is given by the share of ownership each shareholder has inside a firm, measured as the percentage of total shares held by that investor.
  5. 5. entire portfolio of companies belonging to the same industry, thus inducing them to compete less aggressively. Based on this model, we would expect companies which are more connected in terms of sharing major shareholders to perform better and have higher markups. Thus, our main hypothesis is that there exists a positive effect of our measure of cross ownership on markups and market share for Spanish listed companies. 4. Sample Selection and Data We use quarterly accounting data from Spanish listed companies and obtain ownership data from these. Information from the first is obtained from the database Compustat Global. We remove companies from the Financial Services industry2 (SIC 60), and SICAVs (β€œSociedades de InversiΓ³n de Capital Variable”). In the first case, we believe that this sector is very different from others in many aspects, such as leverage, profitability levels, and regulation and therefore if we were to include them, this could bias our results. In the case of the SICAVs, the main reason is that they do not have a proper economic activity other than investing their funds, thus we cannot treat them as regular companies. We also drop those firms with missing values in their Operating Revenues or Operating Costs. At the investorΒ΄s level, data is obtained from Thomson Reuters institutional holdings. By merging both samples, we end with 250,977 observations to perform our descriptive analysis, and 809 firm-quarterly observations for the regressions. In this last analysis, we only include companies belonging to the Food (SIC 20), Pharmaceutical (SIC 28), and Energy sectors (SIC 49), as they are the industries with more complete data and we believe that there is enough heterogeneity between them to obtain a good insight into the cross ownership environment of Spain. 5. Methodology Our analysis of common ownership networks in Spain is divided into two main sections. Firstly, we perform a descriptive study of the evolution of networks for our sample period, reporting changes in their density over time as well as measuring the impact of the largest shareholders on the total number of connections generated. Secondly, we develop a 2 We use the first two digits of the Standard Industry Classification (SIC) system to classify industries from our data.
  6. 6. series of regressions to assess the effect of cross-ownership on firm performance and market competition among industry participants. 5.1 Descriptive Analysis Following Azar, Schmalz, and Tecu (2015) and Azar (2012), we define two companies as being connected in the network if they have at least one investor with a stake of equity3 above x% in both companies. There does not seem to be a theoretical base to establish a threshold for two firms to be connected in the network. For example, Azar (2012) uses thresholds of 3%, 5%, and 7% to analyze the evolution of cross-ownership in the United States, but does not explain the rationale for this decision. In our case, we use thresholds of 1% and 3% to build the network of all listed companies in the Spanish market, and 1% to construct the different industry sub-networks under analysis. The motivation for this decision is that the size of the Spanish stock market, in terms of the number of firms and investors, is considerably smaller than the US market. Thus, if we were to choose higher thresholds, we would observe almost no connections among firms in the sample. Additionally, it is important to consider that the size of the investments of American shareholders is in general much larger that their Spanish counterparts. Therefore, holding a stake of 3% in a listed Spanish company may convert an investor into the major shareholder of that company. We construct the adjacency matrix of each network at each point in time. For every pair of firms (nodes) in the matrix, a value of β€œ1” represents a connection (edge) generated by a common shareholder, whereas a β€œ0” represents no connection. Then, we calculate the density of each network as a measure of how connected firms are through the cross- ownership mechanism. A perfectly connected network would be one in which all nodes have edges to each other, thus the density measures the percentage of existing connections over the maximum number of potential connections. As reported by Azar (2012), the density of a network is calculated with the following formula: 𝐷𝑒𝑛𝑠𝑖𝑑𝑦 = βˆ‘ βˆ‘ 𝑦𝑖𝑗𝑗≠𝑖 π‘˜ 𝑖=1 π‘˜(π‘˜ βˆ’ 1)/2 3 Due to data availability, we do not make distinction among the different types of shares that investors own in the sample companies (common shares, preferred shares, convertible shares, etc.). However, we are aware that shares with different voting rights enable shareholders to a different degree of influence in the decision- making of firms.
  7. 7. where k is the number of companies in the network, and 𝑦𝑖𝑗 equals 1 if firm i and firm j are connected in the network. Note that by definition, the adjacency matrix is symmetric and contains zeros in the diagonal, since a firm is not said to be connected to itself. We also consider it useful to report the percentage of connections generated in each network by the largest shareholders, and their evolution over time. This relates to the concept of how concentrated the connections are within a network, as it is not the same that a high- density network is due to a lot of different investors holding few blockholdings, or a few number of large institutional investors holding a big number of blockholdings. First, we consider the largest shareholders to be the ones who have held the largest number of blockholdings during the period, where a blockholding is defined as a stake in a company above the thresholds previously mentioned. Note that we do not require the blockholdings to be from different companies, meaning that, for example, a shareholder with blockholdings in three companies during the entire sample period would have held 120 blockholdings (3*40 periods). We did not consider the investors with the highest portfolio value as the largest shareholders for the reason that there are some firms where the largest shareholder is an individual investor holding up to 90% of the shares of the company (normally the founder or CEO of the firm), but he/she does not hold stakes in any other company, and thus, does not generate any connections in the networks. 5.2 Inferential Analysis We perform a series of regressions to evaluate the effect of common ownership on market competition of the selected industries and firm performance. In the context of our paper, market competition and firm performance are very closely related in the following sense: less competitive industries indicate the presence of companies that obtain profits above the equilibrium profits implied by perfect competitive markets. Thus, we will observe those firms outperforming their industry rivals. We use markups as our measure of market competition and market power. We calculate firmsΒ΄ markups for every period for the companies in the three industries under study. They are calculated in the following way: π‘€π‘Žπ‘Ÿπ‘˜π‘’π‘π‘–,𝑑 = π‘‚π‘π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘›π‘” 𝑅𝑒𝑣𝑒𝑛𝑒𝑒𝑠𝑖,𝑑 π‘‚π‘π‘’π‘Ÿπ‘Žπ‘‘π‘–π‘›π‘” 𝐸π‘₯𝑝𝑒𝑛𝑠𝑒𝑠𝑖,𝑑 We differ from Azar (2012) in the way of calculating markups, as he uses Total Cost instead of Operating Costs. We believe that by using Operating Expenses we are able to
  8. 8. examine the price charged in excess of the marginal cost of producing goods for the company, whereas if we used Total Costs we would also be considering other costs, such as interest expenses that may differ a lot among different companies and thus, may bias our results. Another option would be to use Cost of Goods Sold, as it would allow us to examine the change in revenues for a company associated with an increase/decrease of the quantity of goods sold, and thus observe how prices evolve in response to these changes. In this sense, if revenues were to increase in a higher proportion than the cost of goods sold, we could be observing anti-competitive incentives in companies. However, we find that Cost of Goods Sold data from Spanish listed companies in Compustat varies a lot in the way firms report it because of the different accounting methods used by them. This lack of homogeneity in the reporting standards forces us to use an alternative measure, and thus decide to use Operating Expenses4 , which are reported in the same way for all companies under study. Our selected measure of firm performance is market share, which we define in the following way: π‘€π‘Žπ‘Ÿπ‘˜π‘’π‘‘ π‘†β„Žπ‘Žπ‘Ÿπ‘’π‘–,𝑑 = π‘†π‘Žπ‘™π‘’π‘ π‘–,𝑑 βˆ‘ π‘†π‘Žπ‘™π‘’π‘ π‘–,𝑑 π‘˜ 𝑖=1 where k is the number of firms in the industry. By using market share as our dependent variable in the regression, the parameter of interest will reflect the expected change in the market share of a given company due to a change in its cross-ownership measure. To assess the effect of cross-ownership on industry competition we build a panel regression using ordinary least squares (OLS) with the following specification: π‘€π‘Žπ‘Ÿπ‘˜π‘’π‘π‘–,𝑑 = 𝛼 + π›½π‘π‘π‘œπ‘›π‘›π‘’π‘π‘‘π‘–π‘œπ‘›π‘ π‘–,𝑑 + 𝛾𝑋𝑖,𝑑 + 𝛿𝑍𝑖,𝑑 + πΉπ‘–π‘Ÿπ‘šπ‘– + πœ€π‘–,𝑑 (1) where Markup is the dependent variable measuring competition. The main variable of interest is Nconnections, which is our measure of cross-ownership. It measures the number of edges each company has with other industry firms in the common ownership network, for every period. We normalized this measure by dividing it by the number of firms in the network in each quarter, since this changes over time. X is a vector of industry time-varying characteristics, which include Average Industry Markup and Density. Z is a vector of firm- 4 Operating Expenses in Compustat data include Cost of Goods Sold, Selling, General, and Administrative Expenses, and Other Operating Expenses.
  9. 9. specific control variables that may have an effect in a companyΒ΄s markup, such as Debt to Assets, Ln (Assets) and Sales Growth. Finally, the variable Firm captures firm fixed effects. As reported by He and Huang (2014), including firm fixed effects may help solve the problem of omitted unobservable time-invariant characteristics that can be correlated with both markup and connections, leading us to wrong interpretations of the results. We run a second OLS regression using panel data to analyze the effect of cross- ownership on firm performance: π‘€π‘Žπ‘Ÿπ‘˜π‘’π‘‘ π‘†β„Žπ‘Žπ‘Ÿπ‘’π‘–,𝑑 = 𝛼 + π›½π‘π‘π‘œπ‘›π‘›π‘’π‘π‘‘π‘–π‘œπ‘›π‘ π‘–,𝑑 + 𝛾𝑋𝑖,𝑑 + 𝛿𝑍𝑖,𝑑 + πΉπ‘–π‘Ÿπ‘šπ‘– + πœ€π‘–,𝑑 (2) where Market Share is the dependent variable measuring firm performance. The independent variables are the same ones used in the previous regression, also including firm fixed effects. 6. Descriptive analysis 6.1 Biggest shareholders in Spain based on total portfolio value In this section, we make an introduction about major shareholders in Spain. As we can see in Figures 1 and 2, there is a big decrease in all investorsΒ΄ portfolio value around 2008. This is due to the financial crisis, as the assets held by these investors decreased in value by large amounts. As we can observe, the major shareholders in Spain do not increase their position to 2007 levels. The principal shareholders, ranked by total portfolio value, are Spanish banks, such as La Caixa, Banco Santander, BBVA; international investment institutions like Nordea, Blackrock, and Fidelity; and finally, we can also find individuals or family groups with the majority of their shares in one company, such as Amancio Ortega (major shareholder of Inditex), or the Entrecanales family (major shareholders of Acciona). As we can observe in Figure 2, the major shareholder in Spain in 2012 is Amancio Ortega, who owns shares of Inditex. La Caixa ranks second for that year, with major positions in companies belonging to the energy sector such as Repsol or Gas natural, and holding shares of the insurance company Abertis. La Caixa also has minor positions in a wide range of other Spanish companies. Finally, Enel SPA, which bought the Spanish energy company Endesa, also has a big portfolio value, although this is not very relevant for our analysis as it only holds shares of this company. For what we have seen, the major shareholders in Spain have invested huge amounts, but only in a few companies. An exception to this would be La Caixa. As we are interested in those investors with considerably big positions in different companies, we decide to take
  10. 10. a different approach to define and rank the largest shareholders in Spain. Specifically, we will consider the major shareholders to be the ones who have held the largest number of block holdings during the period under study, where a blockholding is defined as a stake in a company’s equity above thresholds of 1% and 3%, as described in Section 4. 6.2 Biggest shareholders in Spain based on total number of blockholdings As we can see in Tables 1 and 2, the biggest shareholder in Spain on a blockholding basis is Bestinver. Bestinver is a fund company that invests a large part of its capital in Spain. The company is owned by Acciona, which is held by the aforementioned Entrecanales family. One of the other major shareholders is La Caixa, also mentioned previously, as one of the biggest shareholders in a total portfolio value basis. Finally, we can see NBIM, which is a Norwegian investment bank, the fund manager Fidelity, and the American investment advisor Dimensional Fund Advisors. At the individual industry level, we can observe that Bestinver is the major investor in the food sector (see Table 3), and also the second biggest in the pharmaceutical sector (see Table 4), but it does not have a big position in the energy sector (see Table 5). We can also see some of the major players at the entire Spanish market level such as Fidelity and NBIM having important positions in the energy and pharmaceutical industries, respectively. 6.3 Density analysis In Figures 3, 4, and 5, we provide a picture of the cross ownership networks of the three industries (SIC 20, 28, and 49) at a specific point in time; in this case the last quarter of 2012. In this way, we visually show the level of interconnections among companies belonging to the same industry. With respect to the evolution of densities, we observe how, overall, the density has increased during the reported period for the three industries under analysis (see Figure 6). We can see a decrease from 2005-2006 for the three sectors. However, we generally observe an increasing density over time. It can be seen how the density moves from 5%-10% on the first years to 20% in the last ones. The pharmaceutical sector is the one that has experienced a higher increasing trend as we can notice a network density growth from approximately 0% to 70%. The percentage of connections generated by the largest shareholders is very volatile throughout our sample period for the three industries reported (see Figure 7). We can observe how the Food and Energy sectors follow similar patterns, reaching minimum levels
  11. 11. in 2006. After this, they increase in 2007 to then fall again from 2008 until the end of the period, being around 0% and 10%, respectively. In the case of the Pharmaceutical industry, the percentage of connections generated by the major shareholders is 0% from 2004 until the fourth quarter of 2009, when they experience a sharp increase, reaching a maximum of around 85% during 2011. Finally, they decrease during 2012, as also happened with the Food and Energy industries. It is interesting to compare the evolution of densities and connections generated by the largest shareholders between Spanish and American listed companies, which are analyzed by Azar (2012). In both countries industry densities have had an increasing trend over time, indicating a higher degree of connectivity among firms through their cross ownership. Whereas in the U.S. the largest shareholders generate a big percentage of the connections5 (between 80% and 90%), and they stay flat from 2004 to 2012, in the case of Spain they vary a lot over time. This indicates that institutional investors have a very high influence and presence in American listed companies, while in Spain this is more ambiguous. 7. Empirical results In this section, we present the results of our regression on the effect of cross ownership in industry competition and firm performance. Table 6 shows the estimation results from our two main panel regressions using equations (1) and (2) described in Section 4. With respect to our main variable of interest, Nconnections, we observe a positive and significant effect on Markup, although only at a 10% significance level. This implies that companies which share investors with large stakes in their equity with other industry rivals tend to have higher markups, suggesting the presence of anti-competitive incentives of cross ownership. To show the economic magnitude of this effect, the coefficient of Nconnections on Markup (0.613) conveys that a firm which is connected to 10% of its within-industry rivals, will have a markup higher by 0.0613 points than a company with no connections in the network. We do not find significant effect of the overall industry density of the network on markups. These results differ from the ones obtained by Azar (2012), where he shows a significantly negative effect of the average number of connections of firms in the same industry on average (industry) markups, and a significantly positive effect of common shareholder density on the same dependent variable. This suggests that in the U.S., cross ownership concentration has an effect on markups at 5 See Chapter 4 of Azar (2012).
  12. 12. the industry level, whereas in Spain this relationship occurs at the individual (firm) level. The fact that the number of listed companies in each industry in the U.S. is higher than in Spain may also be driving these differences. With respect to firm performance, measured by market share, we do not observe any significant effect of the normalized number of connections on the above variable, as reported in Column A of Table 6. In this sense, we differ from He and Huang (2014), which found a positive and significant effect of different measures of cross ownership on market share growth, for a sample of U.S. public companies. Finally, we conduct a series of robustness checks to assess the quality of our main regression results, which are presented in Tables 7 and 8, corresponding to variations in specifications (1) and (2), respectively. In Column A of both tables, we remove company fixed effects and perform a Generalised Least Squares (GLS) regression allowing for random effects. Several reasons lead us to this decision. Firstly, we cannot assume that company time variance is not present in our data. These time variances are existent in unobservable factors on which we cannot have data. For example, manager efficiency can vary over time and firms. Moreover, it is an unobservable factor that, because we include a 10-year sample, it cannot be assumed to be constant over this period. Another example is the location of the activity. Some firms during this 10-year period may move their production centers across autonomic communities or even outside of the country. This can highly affect markups due to the regulatory differences across Spanish regions. Moreover, each community has their own grant programs that can have an effect as well. The level of exports of each firm may also play an important role. Some firms in the same industry may have a different level of exposure to international markets and currency fluctuations. For example, as a result of exchange rates, firms competing in the same sector with higher exports can become more competitive and get better results if exchange rates vary favorably. These changes (which happened during our 10-year period) are not observable and affect the results over time. We observe how the coefficient of Nconnections is still positive and significant (now at 5% significance level) for the effect on Markup. In addition, the coefficient of Density now is significant at a 10% level, indicating that the higher the overall concentration of cross ownership at an industry level, the lower the individual markup of companies. With respect to the Market Share regression, both parameters remain negative and statistically not significant.
  13. 13. In Column B, we also use random effects but include industry dummies to account for the potential existence of differences across industries not included in the control variables. The effect of Nconnections on Markup is still positive and significant, now at a 1% level; and remains negative and not significant for the case of Market Share. Column C reports the same GLS regression, but without industry dummies and accounting for potential seasonality effects, for which we included quarter dummies. Again, the parameter of our cross ownership measure remains positive and significant for the case of Markup (at a 5% level), and negative and not significant for Market Share. Overall, our main result from these regressions is that cross ownership of within- industry companies by the same institutional investors increases markups of those firms, which is consistent with our main hypothesis that common shareholders tend to relax competition of their portfolio companies in order to obtain more gains and limit their losses. 8. Conclusions In this paper, we provide both a descriptive and empirical analysis on the effects of cross ownership on the strategic interaction of firms within an industry. We find a positive and significant relationship between our measure of cross ownership and firm markups, which provides evidence of collusive behavior among natural competitors, creating a deadweight loss for consumers. However, we do not find a significant effect on firmsΒ΄ market share. Our results have an important implication on the regulation of the ownership structure of firms. For this reason, we suggest that further empirical research is conducted with respect to this topic, in order to assess regulators in setting up a new policy framework that considers more carefully the implications that cross ownership may have in market competition. 9. References Azar, J. (2011). A new look at oligopoly: Implicit collusion through portfolio diversification. Available at SSRN 1993364. Azar, J., Schmalz, M. C., and Tecu, I. (2014). Anti-competitive effects of common ownership. Ross School of Business Paper, (1235). Bain, J. S. (1951). Relation of profit rate to industry concentration: American manufacturing, 1936-1940. The Quarterly Journal of Economics, 293-324.
  14. 14. Bhattacharya, P. S., and Graham, M. A. (2009). On institutional ownership and firm performance: A disaggregated view. Journal of Multinational Financial Management, 19(5), 370-394. Demsetz, H., and Villalonga, B. (2001). Ownership structure and corporate performance. Journal of Corporate Finance, 7(3), 209-233. Edmans, A., Levit, D., and Reilly, D. (2014). Governance and Comovement Under Common Ownership. He, J., and Huang, J. (2014). Product market competition in a world of cross ownership: Evidence from institutional blockholdings. Available at SSRN 2380426. O'Brien, D. P., and Salop, S. C. (2000). Competitive effects of partial ownership: Financial interest and corporate control. Antitrust Law Journal, 559-614. Schmalensee, R. (1989). Inter-industry studies of structure and performance. Handbook of Industrial Organization, 2.
  15. 15. Appendix Figure 1: Top 25 Shareholders in Spain ranked by Total Portfolio Value.
  16. 16. Figure 2: Top 7 Shareholders in Spain ranked by Total Portfolio Value. $0.00 $10,000,000,000.00 $20,000,000,000.00 $30,000,000,000.00 $40,000,000,000.00 $50,000,000,000.00 $60,000,000,000.00 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Banco Bilbao Vizcaya Argentaria S.A. Banco Santander SA BlackRock Institutional Trust Company, N.A. Caja de Ahorros y Pensiones de Barcelona (La Caixa) Capital Research Global Investors
  17. 17. Figure 3: Network of Food companies in 2012Q4. 1 ES0110047919 DEOLEO SA 2 ES0112501012 EBRO FOODS SA 3 ES0114297015 BARON DE LEY SA 4 ES0114930011 "bodegas bilbainas" 5 ES0115002018 BODEGAS RIOJANAS 6 ES0121501318 CAMPOFRIO FOOD GROUP SA 7 ES0125690513 DAMMSA 8 ES0158746026 "lumar seafood" 9 ES0165359011 "lab reig jofre" 10 ES0165515117 NATRA SA 11 ES0184140210 VINICOLA DEL NORTE DE ESPANA SIC 20
  18. 18. Figure 4: Network of Pharmaceutical companies in 2012Q4. 1 ES0109659013 AB-BIOTICS SA 2 ES0125140A14 ERCROS SA 3 ES0134950F36 FAES FARMA SA 4 ES0157097017 ALMIRALL SA 5 ES0157261019 LABORATORIOS FARMACEUTOCOS 6 ES0163960018 7 ES0165380017 SNIACE SA 8 ES0171996004 GRIFOLS SA B class 9 ES0171996012 GRIFOLS SA 10 ES0172233118 BIOSEARCH SA SIC 28
  19. 19. Figure 5: Network of Energy companies in 2012Q4. 1 ES0107350011 AIGUES DE SABADELL 2 ES0116494016 MONTEBALITO SA 3 ES0116870314 GAS NATURAL FENOSA 4 ES0125220311 ACCIONA SA 5 ES0127797019 EDP RENOVAVEIS SA 6 ES0130670112 ENDESA SA 7 ES0130960018 ENAGAS SA 8 ES0136463017 FERSA ENERGIAS RENOVABLES 9 ES0143328005 GRINO ECOLOGIC SA 10 ES0144580Y14 IBERDROLA SA 11 ES0173093115 RED ELECTRICA CORP SA SIC 49
  20. 20. Figure 6: Network density evolution for the reported industries at 1% threshold. Figure 7: Percentage of connections generated by the largest shareholders at 1% threshold.
  21. 21. Table 1: Major shareholders by block holdings all sectors at 1%. All Companies at 1% Total number of blockholdings for the period Bestinver GestiΓ³n S.G.I.I.C. S.A. 726 Norges Bank Investment Management (NBIM) 550 Fidelity Worldwide Investment (UK) Ltd. 305 Caja de Ahorros y Pensiones de Barcelona (La Caixa) 257 Dimensional Fund Advisors, LP 231 Table 2: Major shareholders by block holdings all sectors at 3%. All Companies at 3% Total number of blockholdings for the period Bestinver GestiΓ³n S.G.I.I.C. S.A. 503 Caja de Ahorros y Pensiones de Barcelona (La Caixa) 257 Banco Bilbao Vizcaya Argentaria S.A. 216 Kutxabank Gestion, SGIIC, S.A.U. 168 Caixanova, Caixa de Aforros de Vigo, Ourense e Pontevedra 162 Table 3: Major shareholders by block holdings SIC 20. SIC 20 Total number of blockholdings for the period Bestinver GestiΓ³n S.G.I.I.C. S.A. 78 Kutxabank Gestion, SGIIC, S.A.U. 49 Libertas 7 SA 45 Lafuente Family 42 Boag de Valores, S.L. 40 Table 4: Major shareholders by block holdings SIC 28. SIC 28 Total number of blockholdings for the period Norges Bank Investment Management (NBIM) 56 Bestinver GestiΓ³n S.G.I.I.C. S.A. 51 Promociones Arier, S.A. 40 BBVA Patrimonios Gestora S.G.I.I.C., S.A. 39 Ebro Foods SA 38 Table 5: Major shareholders by block holdings SIC 49. SIC 49 Total number of blockholdings for the period Fidelity Worldwide Investment (UK) Ltd. 65 Sociedad Estatal de Participaciones Industriales 59 Caja de Ahorros y Pensiones de Barcelona (La Caixa) 58 Kutxabank Gestion, SGIIC, S.A.U. 53 Gdf Suez SA 49
  22. 22. Table 6: Panel OLS Regressions of Cross Ownership on Markup and Market Share This table shows regression results from the specifications shown in equation (1) and (2), using a panel of Spanish listed companies belonging to the industries of Energy (SIC 49), Food Products (SIC 20), and Chemicals (SIC 28), across time. The dependent variables are individual firmΒ΄s markup and market share. Independent variables are: normalized connections, which refer to the number of edges each company has in the cross ownership network, divided by the total number of firms in the network; density of the industry network to which each firm belongs; debt to assets ratio; natural logarithm of total assets; sales growth and average industry markup. These regressions also include company fixed effects. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. (A) (B) Markup Market Share Normalized connections 0,613* -0,021 (0,330) (0,025) Density -0,542 -0,032 (0,449) (0,034) Debt to Assets -0,079 0,036 (0,374) (0,026) Ln (Assets) -0,127** 0,008** (0,053) (0,004) Sales Growth -0,001 0,004*** (0,018) (0,001) Avg Industry Markup 0,800*** 0,009 (0,090) (0,007) Constant 0,942*** 0,042* (0,326) (0,024) Number of observations 787 809 R-squared 0,105 0,032
  23. 23. Table 7: Panel GLS Regression of Cross Ownership on Markup – Robustness Tests Dependent variable: Markup (A) (B) (C) Normalized connections 0,782** 0,830*** 0,787** (0,314) (0,315) (0,314) Density -0,751* -0,757* -0,753* (0,440) (0,441) (0,446) Debt to Assets -0,339 -0,299 -0,338 (0,344) (0,346) (0,344) Ln (Assets) -0,030 -0,068 -0,031 (0,029) (0,044) (0,030) Sales Growth 0,001 0,002 0,002 (0,018) (0,018) (0,018) Avg Industry Markup 0,821*** 0,800*** 0,822*** (0,089) (0,091) (0,091) SIC20 0,023 (0,282) SIC49 0,425 (0,376) Quarter2 -0,027 (0,081) Quarter3 -0,015 (0,081) Quarter4 -0,045 (0,081) Constant 0,515** 0,517* 0,539** (0,253) (0,292) (0,264) Number of observations 787 787 787 This table reports robustness test results for the effect of the cross ownership measure on markups. In the three regressions included in this table, we use a GLS regression with random effects to account for unobserved differences among companies. Column A includes the same independent variables used in Table 1. Column B also includes industry dummies, which are SIC20 and SIC49. Finally, column C includes quarter dummies to account for potential seasonality effects (Quarter2, Quarter3, and Quarter4). Robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
  24. 24. Table 8: Panel GLS Regression of Cross Ownership on Market Share – Robustness Tests Dependent variable: Market Share (A) (B) (C) Normalized connections -0,014 -0,016 -0,016 (0,025) (0,025) (0,025) Density -0,035 -0,039 -0,042 (0,034) (0,034) (0,034) Debt to Assets 0,044* 0,039 0,042* (0,025) (0,025) (0,025) Ln (Assets) 0,006* 0,011*** 0,007** (0,003) (0,004) (0,003) Sales Growth 0,004*** 0,004*** 0,003** (0,001) (0,001) (0,001) Avg Industry Markup 0,007 0,009 0,005 (0,007) (0,007) (0,007) SIC20 -0,013 (0,055) SIC49 -0,144** (0,056) Quarter2 0,005 (0,006) Quarter3 -0,002 (0,006) Quarter4 0,019*** (0,006) Constant 0,049* 0,092** 0,042 (0,030) (0,043) (0,031) Number of observations 809 809 809 This table reports robustness test results for the effect of the cross ownership measure on market share. In all columns, we use a GLS regression with random effects to account for unobserved differences among companies. Column A includes the same independent variables used in Table 1. Column B also includes industry dummies, which are SIC20 and SIC49. Finally, column C includes quarter dummies to account for potential seasonality effects (Quarter2, Quarter3, and Quarter4). Robust standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

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