Corporate boards, interorganizational ties and profitability: the case of Japan

The Japanese system of corporate boards used to be a very special one at least until the 1990s. Boards used to be large and had limited intend to care about international governance standards or even shareholder value. Really important decisions were taken within smaller groups of senior board members anyhow. The crisis of the 1990s lead to some change and influence from the US system. Following Sony, boards mostly shrank to a size of about 10 corporate executive officers plus 2 to 3 externals, including the auditor. An alternative system is the company with committees. In this system, additional to the board of directors, three committees would handle audit, nomination and remuneration duties (Buchanan and Deakin 2009). Hence, in our analysis, we look in the very large majority at cases where the board of directors consists of 6 to 15 corporate executive officers, 1–2 auditors and possibly 1 or 2 outside board members. Only few mainly very large corporations report up to 35 total board members.

For our analysis, we have collected data of all publicly listed companies in Japan. Most of these are listed at the Tokyo Stock Exchange (TSE). This means that our sample includes all the roughly 1700 firms of the so-called first section together with a similar amount of slightly smaller firms, totaling close to 4000 firms per year. We combine the data on the composition of corporate boards available from Toyo Keizai with financial data obtained from Nikkei Needs and Thompson Reuters Datastream. In particular, we use the information on market value, income, total assets, the business sector, largest shareholders and shareholder composition. This data is not available for the entirety of listed firms; hence, some parts of our analysis will be carried out using sub-samples of the data.

The information on the composition of the board is updated annually in the middle of the year. Besides the names of the board members, we have obtained information on the age, gender and role of the board member. The naming and numerical identifiers of board members are unanimous within each year, but not necessarily throughout the years. Hence, we have developed an algorithm to trace the destinies of board members over time based on parts of their names, date of birth and affiliations and have created our own identifier, which is unanimous for the entire sample period.¹ The financial data of the firms is matched using the same yearly frequency.

The basics of the treatment of the board composition data are simple. For each year, we observe a set of board members and a set of firms. Board members serve on the boards of one or more firms. This creates relationships (incidences) between the set of board members and the set of firms and resemble a bipartite graph. Incidences can be described by positive entries in a matrix I, where the dimensions of I are given by the number of firms and the number of board members within a year.

From the incidence matrix I, we can obtain an undirected network that describes the links between the boards of these firms by projecting \(I' I = A_B\). \(A_B\) is a square matrix with as many rows and columns as we have firms in our sample. A positive entry \(A_{ij}\) describes a connection between the firms i and j that is given by at least one shared board members. In the following, we will refer to this network as the board network.²

The network that we look at naturally shows some churning over time, caused by entry and exit of firms as well as retirement and replacement of board members. Nevertheless, the basic statistics provided in Table 1 shows that the number of firms varies steadily between 3532 and 3943. (The number of distinct firm for all years is 4505.) In the same time, the average number of board members is slightly declining from almost 12 in 2004 to 11.1 in 2013. The number of board members we observe per year lies around 40,000 (distinct: 95,192).

We can measure the survival of firms by looking at the stock identifier codes. We observe that the unconditional survival probability of the firms in our data set is very stable and lies around 96% (see also Table 7 in the “Appendix”). Slightly lower values are only observed around 2007, which is in line with the weak GDP growth at that time. The same exercise can be done for the board members. The survival rates for them are also rather stable and vary around 83%. Slightly lower figures are observed around 2007–2008 and slightly higher values are observed toward the end of the sample period (see also Table 8 in the “Appendix”).

The disparity between the average number of mandates and its maximum demands a short look at the distribution of the number of mandates. Figure 1 shows that this distribution is heavy-tailed. While around 40,000 board members have one mandate, only around 200 have three mandates, and only a handful of board members find themselves with eight or more. The degree distributions of the board networks are roughly in line with a power-law (see the left panel in Fig. 2). The estimated power-law exponents range from 3.2 to 3.6, which should be interpreted with caution given the low range of the degrees.

The middle part of Table 1 shows some statistics for the unweighted board network. The mean degree and the very low density reveal that in fact many firms do not share any board member. The correlation between a node’s degree and the average neighbor’s degree (i.e., the degree assortativity) is positive. The networks show transitivity. We find positive local clustering and a significantly positive (global) clustering coefficient (compared to a random network with identical degree sequence). To some extend, these effects result from ties between firm conglomerates, which can be confirmed by applying community detection algorithms to this network. We are not interested in the details of community structure in this study, yet one possible visualization of communities is provided in Fig. 10 in the “Appendix”.

While the board network shows some similarities with a social network, we also have to stress the differences. For example, the giant component never exceeds 50 percent of all the nodes in the network. This is caused by the fact that the size of a corporation influences its ability to create ties in both the board and the ownership network, because human resources and capital act as limiting factors. The size of the giant component in board networks therefore partly depends on the size of the dataset.

Links in the board network are to some extend of course the result of business relationships between firms. One example are relationships between producers and suppliers. For studies of such networks, we refer the reader to Krichene et al. (2019) and Chakraborty et al. (2018). For our purposes, the analysis of this level of connectivity would however delve too deeply into a very specific topic. However, business relationships that are more elaborate often result in some kind of shareholding or even cross-shareholding relationship. We have obtained data that reports the five largest shareholders and the percentage of shareholding for all of the firms in our sample. (This data is based on reporting required by law that is limited to the top five owners.) This might at first seem a little restrictive, yet in practice significant influence onto a company is unlikely to be performed by more than five owners. Also since this data is reported from the viewpoint of the owned company, this still results in a rather complete picture of the ownership network.

In the following, we will refer to the weighted networks based on ownership simply as the ownership networks. The ownership networks differ from the board networks by the fact that they are directed networks. The densities are however comparable, the ownership network in 2004 contains 2574 directed links, and we see a steady increase until 2013 when the network has 3695 links. The out-degree distributions of these networks are more skewed (see the right panel of Fig. 2), and the power-law exponents lie around 2.2–2.3.

Links in the ownership network are (with negligible exceptions) not reciprocal and tendentially hierarchical. This has to do with the legal framework for reporting ownership relations.³ When calculating degree assortativity, it therefore makes sense to report the correlation of the average out-degree of neighbors with a node’s in-degree. We find that this value fluctuates around 0.3. Different from the board network, this behavior translates only into negligible clustering since short directed loops are rare. If we were to ignore directionality for a moment and calculate clustering based on undirected ownership, this changes and we observe similar local clustering as in the board network. The global clustering in this case is lower than local clustering, and this difference is likely due to the just mentioned hierarchical structures and the fact that some nodes in this network have a much higher degree (compared to the maximum in the board network). This behavior is related to the diversified shareholding of some corporations, which does not produce much clustering.

The big hubs in the ownership network are mostly insurers and securities companies. Their shareholdership in a firm is normally small, but their activity is very spread out. In fact, much of their holdings are likely on behalf of their clients. A visualization of the ownership network in 2013 is shown in Fig. 11 in the “Appendix”. It shows that communities in a classical sense do not exist, or vastly overlap. Not surprisingly, large corporations like Toyota, Mitsubishi, Fujitsu, Honda, Mitsui and Nippon Steel can be classified as smaller hubs in this network.

The ownership network is very stable, about 90 percent of the links survive from each one year to the next. However, the overlap between links in the firm network and the ownership network is with about 10 percent relatively small, as is shown in the left panel of Fig. 3.

One can condition this relationship on the level of shareholding. For this reason, we have binned pairs of firms with similar ownership percentage (\(N=30\)) and have calculated how many of those are also linked in the board network.⁴ Results are shown in the right panel of Fig. 3. We can observe that the probability of having a board link increases with ownership and passes the unconditional probability once we reach about 10 percent ownership. Interestingly, at the high end when ownership reaches 50 percent this relationship slows down and might even slightly drop. It is likely that since this represents a majority ownership, there is less demand for control by shared board members once we reach this level of ownership.

Since we have seen that some board members have multiple mandates, it is useful to investigate how the survival figures change when we condition the survival on the number of mandates that a board member has. These conditional survival probabilities are shown in Fig. 4. The probability for board members with one mandate differs only insignificantly from that of the entire population (83%). The likelihood to survive increases to around 93% with another mandate, further additional mandates only lead to marginal improvements. So even if directors with multiple mandates are of course more likely to survive, one can easily see that the losses of mandates are not independent.⁵

Based on these results about board member survival we can have a more detailed look at determinants of their destinies. Since the large number of board members prohibited us from collecting detailed information on each of their career paths, we have to confine ourselves to some of their basic characteristics together with details on the firms for which they work.

We can check if the role of a board member has an influence on his survival probability. We can further check if gender or the size of the company are important aspects of director survival, while we control for the number of mandates⁶ and age. Further, the performance of the firm should have an impact on survival. We can disentangle this effect into two parts. First, we introduce a dummy variable for the case that a board member works for a firm that ceases to exist. Second, we calculate the difference in the market value for two consecutive years to capture the influence of the performance of firms that remain in business.⁷

Since survival is a binary variable this demands for a logistic regression where the observed survival or death (in the sense of leaving the data set) depends on the above mentioned variables. To confirms the robustness of the results with respect to the firm performance variables, we have also estimated the model while omitting these variables, and results are given in Table 9 in the “Appendix”.

The results in Table 2 indicate that most of the variables have a significant influence on the destinies of board members, yet the explanatory power is not very large, which hints at additional unobserved effects on the level of the individual board members. In general, the results are a bit more volatile in the first 4 years where we observe more changes in the sample composition, including the financial crisis, from 2009 on the results are remarkably stable.

The delisting of a firm and the number of previously held mandates are of course the most important influences in this analysis. The impact of age is mostly negative or insignificant, as expected, with some exceptions in the first years. Only very few women are serving as board members, less than 2%, and only at the end of our sample period, we see a slight tendency of higher survival. The market value (and thus also size) of a firm has a slight negative influence on survival; hence, smaller firms have slightly fewer changes in their boards. The influences of firm performance are mixed. Of course board members mostly disappear when a firm ceases to exist, but this influence is admittedly there mostly by construction. We cannot answer the question if the firm ceased to exist because of poor performance of the board or if other influences like mergers or restructuring are responsible. The influence of a change in market value is significant, yet the effect is small. Hence, the performance does influence the board in general, but admittedly our dataset does not identify specific responsibilities within the board which could explain these effects in more detail.

Rather clear however are the effects for board members who are not executives. Outside board members are being dropped with a higher likelihood throughout the sample period. This intuitively make sense since they are a more dispensable part of the board. On the other hand, it is common practice to stick to an auditing company once relations are established and thus outside auditors stay on the board longer than executives. The outlier in 2006/07 for the survival of auditors is at first sight puzzling, but is in fact easily explained by the ChuoAoyama PricewaterhouseCoopers accounting scandal (Skinner and Srinivasam 2012) that lead to a temporary increase in auditor replacement.⁸ In addition to the results presented in the table, we have checked if the existence of ownership ties increases the probability of board member survival. We could not find proof of such a relationship.

Another question is where the persistence of the network structure in the board network comes from. If it were just a matter of board members with multiple mandates at highly capitalized firms, then we should see higher survival rates of executives at these companies. We have however seen that this is not the case. This implies that there must be mechanism of upkeep of board network ties that go beyond the existence of central board members.

For this reason, we use the same method as in Raddant et al. (2017) and compute how many links between companies are being kept from one year to the next and look into how these links are maintained if board members retire. The findings are summarized in Table 3. We observe that only very few links are being lost because firms disappear. About 76% of the links survive from one year to the next and about 73% do so because at least one of the board members who was bridging the two boards is still there. Another 3% of links however is being kept because a new board member is replacing this function, in more than half of the cases he/she was already member of one of the boards. In any case, the board member was already a central player in the board network, with typically 2–3 mandates in the year before, much more than the average. We can thus conclude that one source of the persistence of the board network comes from the fact that board members with multiple mandates are being replaced by other board members that also hold multiple mandates.

Our findings are thus in line with those of Raddant et al. (2017) and Stearns and Mizruchi (1986) who find that about 14% of broken ties are being reconstituted by new directors.⁹ We cannot analyze the determinants of the reconstruction in this setting. Yet, we can contribute to the debate about link reconstitution (Westphal et al. 2006) by confirming that in fact only a fraction of interlocks are being reconstructed. We can add that nevertheless this fraction contributes dis-proportionally to the overall connectivity, the persistence of the network structure over time and the tightly knitted networks between the most central board members.

Over the ten years that our sample covers, some firms have, as discussed earlier, ceased to exist. Many others have seen profound changes in their business models or have for other reasons gained or lost in influence and size. We will therefore follow the 1798 firms that have survived and for which consistent data is available on their performance. We have calculated the percentage growth in total assets over the course of the 10 years and have grouped the firms into the top 25%, middle 50% and bottom 25% according to this criterion.

In Fig. 6, we show how these different groups develop. We observe that the firms from the weakest performing group do not only loose in terms of total assets but that these firms also loose connections in the board network and the ownership network (top left and bottom right panel). The top 25% on the other side have a high connectivity in both networks which is further growing, partially by hiring more outside board members and other board members with multiple mandates.¹³

Arguably, some of these developments are natural consequences of the growth and shrinkage of firms. (For more details, see also Fig. 14 in the “Appendix”, which shows the relationship of connectivity with other firm characteristics.) The fact however that the worst performing 25% of firms had a connectivity above average in the board network and the ownership network in 2004, which then successively dropped, suggests that some conditional feedback might exist from connectivity to profitability and growth. Those firms who performed worst lost more ties during the financial crisis than everybody else. This suggests that not the amount of ties but their quality is related to growth and profitability. We will elaborate on this in the following section.

The structure of the top layer management of a corporation and how it is connected with the management of other corporations has implications for the long run success of a company. Executives or other board members who can bring in experience from outside can be very valuable to navigate economic downturns or restructuring processes. On the other hand, board members who serve as a mere transmission channel for the needs of affiliated companies can slow down the effective management of a company (see also Davis and Cobb 2010; Carpenter and Westphal 2001). Similar effects occur in the case of ownership or equity ties between corporations. Connected firms can profit from these connections if they support a chain of value generating activities that would be hard to achieve without them. When ties exist for the mere sake of diversification of business activities the effects are often ambiguous (see also Anjos and Fracassi 2015).

In the case of Japan corporate ties however have a special history. Firm conglomerates, often refereed to as keiretsu, used to have a huge influence on the economic system until the middle of the 20th century. Traces of them are still visible today, even if many argue that the economic downturn of the 1990s dissolved most. Studies on the long-run success of these conglomerates find that these structures go at hand with within-group interventions and risk sharing, a process that in general has been found to significantly lower the return on assets of conglomerate members. We will follow up on this finding and analyze if there are effects from firm connectivity on profitability by using variables from the board and ownership network together with some control variables. Similar to the work by Lincoln and Gerlach (2004), we measure profitability by the return on assets (ROA) and we use the total assets and the ratio of loans to total assets as controls.¹⁴ Data on these key financials is not consistently available for all the firms in our sample. This limits the sample for this part of the analysis to around 2000 firms in each year.¹⁵

Since the number of potential variables that describe network relationships, centrality, local connectivity or clustering is almost endless, we choose to break this analysis down into two steps. First, we employ a machine learning algorithm that we feed with data of the potential variables. From this process, we learn which variables seem to have an impact on the ROA. It also helps to identify variables that might interact. In any case, the results from the machine learning algorithm are then used to construct dummy variables which significance can be analyzed within a regression analysis.

We found that a regression tree model delivers satisfactory results in the first step. The output of this model consists in a regression tree, a hierarchical structure where at each branch the dataset is split into two parts depending on the value or state of the most important variable. An example of such a tree is shown in Fig. 13 in the “Appendix”. We have run the tree model separately for all the ten years of our dataset, and we have evaluated which variables, thresholds of variables, and combinations of variables repeatedly appear in the regression trees for all the ten years. We have supported this by calculating the importance scores for all the variables for all ten years. These results are shown in Fig. 7.

We have tested the following variables: log total assets, loans to total assets, number of outside directors, log eigencentrality in board network, sector dummies, log eigencentrality in ownership network, log eigencentrality in undirected ownership network, total ownership of other companies, fraction of company owner by other companies, average ROA of company linked to in board network, ROA of largest owner (min 3.8% ownership), average ROA of companies owned (min 2% ownership),¹⁶ degree in board network, in- and out-degree in ownership network, local clustering in board and ownership network, and foreign share ownership.

From this analysis, we learn that the ROA of owned firms as well as the ROA of the main owner have most influence on the firms’ ROAs. The regression trees show us in fact even more, namely that these two variables often appear as two successive branches in the tree with predicted ROAs significantly different from the mean when both, the ROA of the owner as well as the average ROA of owned firms are greater than 3.5 percent. Also other variables from the ownership network have some importance, namely total ownership and the total share owned by other firms as well as centrality (which are of course related). The degree itself is almost never important in any network. What is further interesting is that also the ROA of a firm which is linked by a shared board member is sometimes a significant influence.

In a second step, we can now test the significance of the influence of links in the ownership and board network by estimating the determinants of ROA. Since only some of the firms have a connection in the board and ownership network, we cannot use the ROA of connected firms as a variable directly. Also, in the case of the ownership network, both directions of ownership seem to be important. Hence, we set up two dummy variables. The board link dummy is 1 if the mean of the ROA of connected firms is larger than the mean of all firms plus 0.2 times the standard deviation. The dummy for the ownership network is 1 if both, the average ROA of owners and of owned firms is larger than the mean ROA minus 0.3 times the standard deviation.¹⁷ Hence, it signals that a firm is not sandwiched in between a badly performing owner and badly performing partly owned firms. We further test for the influence of the position of a firm in the ownership network by adding the variable total ownership, which describes the cumulative percentage shares held by other companies in the ownership network and degree/total assets, the number of companies of which a firms holds shares of divided by total assets. These specifications are chosen to make sure that these two variables do not correlate with total assets but give a measures of ownership relative to the size of a firm.

We now estimate three versions of this model.¹⁸ The first version uses only the just mentioned variables without any further differentiation for sectors or years. Since the thresholds for our ownership and board link ROA variables depend on the yearly averages of the ROA, a pooling for all 10 years should in principal be possible. The results for this model are shown in the left column of Table 6. Since the financial crisis of 2008 has probably let to more than just minor fluctuations it makes of course sense to employ individual constants for each year. As we can see, this does not change the estimation results much, yet it improves the explained variance quite a bit. Finally, we can add variables to classify the most populated Topix sectors. This should help to explain differences in ROA which are caused by sector-specific effects. We use 12 dummy variables for the sectors, yet in the table, we only show the most important ones. We note that the financial sector is a merged category that contains banks, insurances, and firms offering other financial services.

Interestingly, our analysis shows that ownership relations generally lead to slightly (yet significantly) lower ROAs. There are however two exceptions from this. Positive effects can be found when both the owner and the (partly) owned firms are profitable (signaled by the variable ROA owner net). Significantly positive effects can also be found from links in the board network (ROA board net), if connected firms have above average profitability.¹⁹

At last, the results on the effects of ownership deserve a closer look. We want to investigate if instead of referencing to connected firms’ ROA structural features could be responsible for our results.

First of all, one could argue that in some cases we might see effects from minority shareholder relationships. Some authors claim that minority shareholders can be disadvantaged against controlling shareholder who enjoys private access. This can impact firm valuation and profitability (see, e.g., Guedes and Loureiro 2006; Claessens et al. 2002).

Second, our general result on negative effects from ownership relations is in fact in line with many studies on diversification. Although diversification as an instrument of risk management is often successful, many studies show that corporations which invest into companies that operate outside of their own area of expertise are likely to negatively influence their own profitability (Berger and Ofek 1995; Schommer et al. 2019; Kawakami 2017).

In order to judge if any of these effects are related to our findings, we will look at subsamples of companies from the five most populated sectors, namely construction, chemicals, machinery, electrical appliances and IT and communications. We will compare firms with regard to the above-mentioned effects based on their ROA, which we normalize by the yearly group averages.

The results are presented in Fig. 8. We show box plots for three different comparisons for firms from five different sectors. The top panel shows the differences in the normalized ROA for our good ownership dummy variable as defined in our regression analysis and serves as a reference point. The averages of the bar plots labeled as ‘no’ and ‘yes’ are almost always significantly different, thus confirming that the results of our regression analysis can in principal be confirmed without further control variables.

We can now look at differences in between the same firms that might be caused by minority shareholdership. The middle panel show a comparison where the firms are now grouped by the criterion whether they have an owner that controls at least 20 percent of the company. The results for this comparison are ambiguous. Firms from the sectors chemicals and electrical appliances do in fact have a significantly lower normalized ROA when they have one very large owner, for firms from other sectors this effect cannot be confirmed.

Finally, we group the firms by asking whether they own at least one percent of some other firm that is active in a sector different from their own. This aims at checking effects from diversification. At a first glance the results, presented on the bottom panel, appear unsystematic. For some sectors the difference is positive, for others negative. However, a pattern can be found once we go back to the sector-based dummy variables from our regression analysis in Table 6. In those sectors, where the average ROA is above or similar to the economy average (chemicals, machinery, IT) the average normalized ROA is lower for corporations which diversify to other sectors. For the two sectors where the average ROA is below economy average (construction, electrical appliances) diversifying into other sectors yields a slightly higher normalized ROA. These results are admittedly not in all cases significant, yet they show an interesting tendency. The benefits of diversification might depend on what the heritage of a corporation is and whether they can in the long run divert funding into activities in more lucrative fields.

Coming back to the question if our previous regression results are possibly remnants of minority shareholding or diversification effects, we can conclude that these do not explain our findings. The network effects on profitability that we have identified in the ownership network and through corporate board interlocks are of a more general kind and are caused by interdependencies of profitability rather than ownership relations alone. Our results can thus be viewed as complementary to that of Zona et al. (2018). While in their study they have found effects of board interlocks that depend on characteristics of the ego firm in a network, we have found effects that mainly depend on characteristics of the alter firms.