Banker directors and firm performance: Are family firms different?

Ghosh, Saibal Article Banker directors and firm performance: Are family firms different? Future Business Journal Provided in Cooperation with: Faculty of Commerce and Business Administration, Future University Suggested Citation: Ghosh, Saibal (2018) : Banker directors and firm performance: Are family firms different?, Future Business Journal, ISSN 2314-7210, Elsevier, Amsterdam, Vol. 4, Iss. 1, pp. 1-15, https://doi.org/10.1016/j.fbj.2017.11.002 This Version is available at: https://hdl.handle.net/10419/187964 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Saibal Ghosh 1 Centre for Advanced Financial Research and Learning, Fort, Mumbai 400001, India Received 8 July 2017; accepted 6 November 2017 Available online 28 December 2017 Abstract Employing data on publicly listed Indian manufacturing firms covering the period 1996–2012, we investigate the impact of the presence of banker-director on the board of family firms. We posit several hypotheses that highlight the pros and cons of the presence of banker-directors. The findings provide support to the industry expertise hypothesis which suggests that bankers are less likely on boards on family firms that operate in industries where the possibilities of knowledge spillovers can significantly influence profits. A disaggregated analysis suggests that the performance of these firms varies depending on the nature of equity and ownership interlocking. &2017 Faculty of Commerce and Business Administration, Future University. Production and Hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). JEL classification: G34; L26; P52 Keywords: Family firms; Banker-director; Corporate governance 1. Introduction The growth and emergence of family firms has been a widely debated topic in recent years. According to La Porta, Lopez-de-Silanes, and Shleifer (1999), 65% of the 20 largest firms in Argentina had a family stake of at least 20%; in Japan, this was 5%. Anderson and Reeb (2003a,2003b) document that in the US, 35% of the S&P500 firms are those with family ownership. A research report by Credit Suisse (2011) finds that family-owned companies controlled 50% of the over 3500 publicly listed companies in ten major Asian economies: the share was the highest for India at over 65% and the lowest for China at 13% (See also, Claessens, Djankov & Lang, 2000;Claessens, Djankov, Fan & Lang, 2002). The presence of family firms has raised important questions as to whether it is an efficient organizational form. On one side of the debate, it has been argued that having a large minority shareholder can ensure effective monitoring and thereby ameliorate agency problems (Shleifer & Vishny, 1986). Following from this argument, several studies adduce evidence in support of this contention (Anderson & Reeb, 2003a,2003b;Barontini & Caprio, 2005;Villalonga & Amit, 2006;Ghosh, 2010). Focusing on a sample of Western European family firms, Maury (2006) for example find that family firms exhibit better performance than firms controlled by non-family block holders. Critics of this argument contend that by putting their own interests before minority shareholders, family ownership might end up www.elsevier.com/locate/fbj https://doi.org/10.1016/j.fbj.2017.11.002 2314-7210/&2017 Faculty of Commerce and Business Administration, Future University. Production and Hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). E-mail address: [email protected] 1 The views expressed and the approach pursued in the paper are strictly personal. Peer review under responsibility of Faculty of Commerce and Business Administration, Future University aggravating agency problems and impede performance (Faccio, Lang & Young, 2001;Dyer, 2003;Perez-Gonzalez, 2006;Morck & Yeung, 2003). Another strand of the literature highlights the importance of bankers in enhancing corporate governance in firms. Research based on advanced economies indicate that bankers not only play the role of expertise provider (Booth & Deli, 1999), but in several instances, improve the bank's business opportunities (Dittmann, Maug & Schneider, 2010). Earlier, Kroszner and Strahan (2001) had demonstrated that bankers are less likely to be represented on boards of firms when the monitoring costs overwhelm the benefits. 2 More broadly, using data on non-listed Spanish family firms, Arosa, Iturralde, and Maseda (2010) document a negative impact of outside directors on performance. In this context, using an extended sample of Indian firms for the period 1996–2012, the article investigates three major hypotheses. First, are family firms more likely to have banker nominee on their boards? In India, 70% of the firms are family-controlled (Piramal, 1996). This lowers the likelihood of principal-agent conflict (Carney, 2005), in turn, reducing the importance of the monitoring role of the board. That being the case, the importance of bankerdirectors in family firms could actually be much less significant. Second, how does non-bank debt influence firm capital structure? Besides provision of debt, banks have other channels of influence over firms, such as through interlocking directorates. We examine whether this channel matters for firm behaviour. Finally, how important are banker-director in family firms in influencing performance, especially given their varying intensity of involvement? There are several reasons as to why these are important questions and India presents a compelling laboratory for examining these issues. First, in emerging economies such as India, the presence of family firms spans several industries and product lines. van der Molen (2005) found that Indian families operate in an average of 5.4 industries. A second reason is that although the foundations of the corporate governance model in India are based on the AngloSaxon model, the investor base is distinctly at variance with those observed under such a framework. For instance, in the Indian case, investors comprise largely of the company founders, their respective family members and the government. This contrasts with the UK evidence wherein companies are less concentrated towards certain groups, are geographically dispersed and largely held by professional investors. Third, India has a rich longitudinal database on corporates, which permits rigorous statistical analysis. The findings obtained from the analysis may offer useful implications for the role of these firms during periods of financial distress in other emerging markets. Our study contributes to the literature in three distinct ways. First, this is one of the earliest studies for an emerging economy to systemically investigate the role of banker-director in family firms. Given the limited likelihood of principalagent conflict in these firms, this raises the question as to what purpose the banker-director serves in such firms. Second, it is well recognised that banks play an important role in enhancing corporate governance in firms (Booth & Deli, 1999;Kroszner & Strahan, 2001). In the Indian case, based on cross-section data for 2003, Nachane, Ghosh, and Ray (2005) find that banker nominees primarily act as expertise providers. More recently, Dittmann et al. (2010) show that in Germany, banker-directors on the board of non-financial firms help firms tide over funding difficulties. The present analysis augments these findings by exploring the involvement of bankers on boards for family firms across varying degree of equity and ownership interlocks. Finally and more broadly, our paper is related to the literature that focused on the corporate governance of firms (Shleifer and Vishny, 1997). In the case of Japan, Morck and Nakamura (2007) find that bankers are represented on boards of poorly performing firms to ensure prompt repayment of their debt. In contrast, Dittman et al. (2010) find that bankers represented on boards of German firms also promote their own business interests. Unlike these papers, we focus on an emerging market, where the weak institutions and greater likelihood of cronyism make the role of banks in corporate governance potentially far more critical. The remainder of this paper continues as follows. In Section 2 we provide an overview of the relevant literature and derives several testable hypotheses. Section 3 discusses the institutional environment, followed by the database (Section 4) and empirical strategy (Section 5), results (Section 6), robustness checks (Section 7 and 8) and the concluding remarks in the final section. 2. Literature and testable hypotheses The prevalence of family firms is quite pervasive in both developed and emerging economies (Leff, 1976;Caves & Uekusa, 1976;Chang & Choi, 1988;Ghemawat and Khanna, 1998;Khanna & Rivkin, 1999; Morck, 2005; Morck, 2 We employ the terms bank nominee, banker-director and banker on board interchangeably. S. Ghosh / Future Business Journal 4 (2018) 1–152 Percy, Tian & Yeung, 2005), although their exact structures differ markedly across countries (Cuervo-Cazurra, 2006). In India for example, family firms operate somewhat like large, diversified conglomerates, with significant resource sharing across the affiliated-firms. The managing director of one family firm might serve as the board chairman of another affiliated firm, leading to commonality of board members. These characteristics of family firms make it easy for the majority owners to expropriate firm value at the expense of minority owners. Research points to the fact that business groups resort to tunnelling resources through intra-group transactions (Johnson, La Porta, Lopez-de-Silanes & Shleifer, 2000; Bertrand, Mehta & Mullainathan, 2002;Bae, Kang & Kim, 2002;Friedman, Johnson & Mitton, 2003;La Porta, Lopez-de-Silanes & Shleifer, 2000,2002) and even the provision of equity capital (Gopalan, Nanda & Seru, 2007). Banks can influence corporate governance in several ways. In their capacity as shareholders, banks improve the likelihood for selection of profitable investment projects (Morck, Stangeland & Yeung, 2000;Kang, Kumar & Lee, 2006). As well, firms with bank-shareholders might be better placed to raise external resources (Hoshi, Kashyap & Scharfstein, 1991) at a lower cost (Petersen & Rajan, 1994). The more pervasive role of banks is in their capacity as nominees on the boards of firms. We posit several channels that are germane for the role of banker nominee in affecting the performance of non-financial firms. According to the information channel, bank nominees gather proprietary information about the firm and transmit it to the lender. This lowers the degree of information asymmetry between the borrower and lender and thereby eases the access to external (bank) finance. That being the case, it appears less likely that bank nominee would be on family firms, in order to reduce the spillover resulting from leakage of confidential information (Bhattacharya & Chiesa, 1995). Combining this with the fact that age and size are proxies for creditworthiness (Petersen & Rajan, 1997), this would suggest that bankers are less likely to be present on boards of large and older family firms where the knowledge repository is likely to be higher. This leads us to our first hypothesis: H1. Under the information hypothesis, bankers are less likely to be on board of large and older family firms. The debt monitoring channel contends that bank nominees are involved in monitoring management on behalf of external financial markets so as to safeguard their lending. As a result, bankers would be more likely on the boards of firms that have a higher debt burden. In case of family firms, given the convergence of ownership and management, agency costs are lower. In addition, given that their long-term reputation and credibility are tied to the image of the firm, they are often willing to provide ‘patient capital’(James, 1999) and less concerned about loss of control. All these considerations might entail family firms to have lower leverage ratios. Empirical evidence on this aspect is however mixed, with some studies reporting a positive relationship (Ellul, 2008), whereas others report a negative (Anderson et al., 2011) or no such relationship (Anderson & Reeb, 2003a,2003b). That being the case, bankers are less likely on boards of family firms with lower debt burden. H2. Under debt monitoring hypothesis, bankers are less likely to be on board of family firms with lower burden of debt. The equity monitoring channel observes that banker-directors pursue their interests as shareholders. In that case, banker-directors are more likely to be present in firms with lower valuation, which necessitate stronger intervention. Earlier studies (Holderness & Sheehan, 1988) found higher valuation for non-family firms as compared to family firms, although subsequent research based on US Fortune 500 companies show that family firms exhibit higher market valuation, as measured by Tobin's Q and higher operational performance, as measured by return on asset (Anderson & Reeb, 2003a,2003b). This leads us to conclude the following: H3. Under equity monitoring hypothesis, bankers are less likely to be on board of family firms with lower profitability and lower market valuation. The capital market hypothesis postulates that banker nominees are more likely to be represented on boards of firms with higher funding requirements. To the extent that family firms display higher growth, their funding requirements are likely to be high. With banks being the major source of external finance for firms in India (RBI, 2014), this would suggest that such firms are more likely to have banker-directors. On the other hand, family firms are more likely to have an internal capital market. Such a practice improves these firms’access to external finance (Khanna & Palepu, 1999;Shin and Park, 1999), increasing the likelihood of bankerdirectors on their boards. Internal capital markets also provide liquidity and funding support to member firms. To S. Ghosh / Future Business Journal 4 (2018) 1–15 3 exemplify, Khanna and Yafeh (2005a, 2005b) show that family firms in India use intra-group loans to smooth liquidity across firms. Likewise, Gopalan et al. (2007) find that the internal capital market for Indian family firms is used to support member firms in trouble. That being the case, notwithstanding their slow growth, dependence on external bank finance, are likely to be lower. This leads us to the following hypothesis: H4. Under capital markets hypothesis, it is not clear whether family firms with slow growth are more or less likely to have banker-director on their board. Finally, the industry expertise hypothesis posits that bankers might be present on firm boards in order to gather industry expertise. In the case of family firms, while the pressures to conform to good governance standards are higher, the family might also want to exercise tight control over some of the affiliated firms (Almeida & Wolfenzon, 2006). To the extent that such firms belong to industries which account for a significant proportion of revenue, the possibility of banker-directors on such firms are less likely. As compared to this, provided such firms belong to more diversified industries but where knowledge spillovers are likely to be significant, the possibility of banker-directors on such firms are even less likely. We measure diversification as the ratio of segment sales to total sales, where the segment considered is the one generating the maximum revenues. Knowledge spillovers are measured as the ratio of R&D to sales. Our final hypothesis therefore reads as follows: H5. Under industry expertise hypothesis, less diversified family firms with high knowledge spillovers are less likely to have banker-directors. 3. Institutional background Prior to the inception of financial sector reforms in the 1990s, the financial sector in India broadly comprised of banks and government-owned development finance institutions (DFIs). Banks provided shortto medium-term credit to industry and agriculture, whereas long-term finance was provided primarily by DFIs. In addition, both banks and DFIs could also invest in the equity of companies. In order to protect their investments, the founding Act of Parliament of the DFIs entailed two specific clauses: (a) a convertibility clause to ensure that a loan can be converted into equity in case of default and (b) a nominee director clause which imparted flexibility to the DFI to appoint one or more nominee directors on the firm board. The economic reforms of the 1990s altered the financial landscape for these entities. Competition in the financial marketplace increased manifold with the liberal entry of foreign banks as well as de novo private banks. However, it was the DFIs that experienced the biggest change. With gradual lowering of concessional finance from the government alongside increased competition, particularly for access to low-cost resources, several of these entities metamorphosed into banks. More importantly, they began competing with commercial banks for credit extension, besides making large equity investments in firms. In March 1984, the Ministry of Finance issued guidelines focusing on the issue of nominee directors. More specifically, it stipulated that term-lending institutions should constitute a separate Cell, with the avowed objective of representing the DFIs on the board of financially-assisted companies. In 2000, a Committee appointed by the Securities and Exchange Board of India recommended that institutions should appoint nominee directors on firm boards on a selective basis, where such appointment is pursuant to a right under loan agreements or where such appointments is considered necessary to protect the interest of the institution. In addition, it was recommended that the nominee director should be subject to the same discipline and responsibility and should be equally accountable to the shareholders as the other directors of the company. Taking these considerations on board, the new Companies Act 2013 has excluded nominee directors from the list of independent directors and incorporated it as a separate category. Accordingly, it has been stated in Section 161 of the Act that the Board may appoint any person as a director nominated by any institution or by any Government (Central or State) by virtue of their shareholding. For the purposes of our analysis, we treat nominees of all financial institutions, whether DFI or bank as ‘bank’ nominees. The appointment of nominee directors in the Indian case is driven more by statutory obligation and is therefore less susceptible to endogeneity concerns that plague research on director appointments in other countries. S. Ghosh / Future Business Journal 4 (2018) 1–154 4. The dataset and variables The data employed is extracted from the Prowess database, generated and maintained by the Centre for Monitoring the Indian Economy (CMIE), a leading private think-tank in India. The Prowess is a firm-level database, akin to the Compustat database for US firms and the Financial Analysis Made Easy (FAME) database for UK and Irish public and private limited companies. This database is being increasingly employed in the literature for firm-level analysis on Indian industry (Khanna & Palepu, 1997;Gupta, 2005;Ghosh, 2006;Gopalan et al., 2007;Goldberg, Khandelwal, Pavcnik & Topalova, 2010). Our sample spans the period 1996–2012 and focus on publicly listed firms, since it is primarily these firms that report information on banker-directors as part of their corporate governance statements. We cull out information on all listed manufacturing firms, yielding a total of 1532 firms. We subsequently delete several firms from the sample. First, we delete firms with extremely misrecorded data. In step two, we delete firms with negative observations on some of the relevant variables, such as profits and leverage, further reducing the sample. These exclusions reduce the final sample to 1418 firms. Table 1 provides the description of the sample. Over one-third are family firms; the remaining are stand-alone firms across other ownership groups. Taken together, these firms account for, on average, nearly 75% of asset and over 80% of the market capitalization of all manufacturing companies on which information is reported in the Prowess database. 5. Empirical strategy We first characterize the firms that have bank nominees on their boards. Our focus is specifically on family firms. Accordingly, we estimate the following panel regression model: Bank no min ee ðdummy=f raction=numberÞi;t¼αþβ1Familyi;tþβ2Xi;tþηtþμindus þεi;tð1Þ where Bank nominee i,t is a dummy variable that takes value one if firm ihas a bank nominee in year t, else zero. Xis a vector of firm characteristics such as size, age, profitability, leverage and investment opportunities; η t and η indus are yearand industry fixed effects and єis the error term. Our coefficient of interest is Family, the dummy variable signifying family affiliation. Provided family firms have higher likelihood of bankers on their boards, the coefficient on this variable would be positive. We estimate Eq. (1) by logit model. The logit model does not take into account the size of the board. Ceteris paribus, a banker is more likely to be on boards of larger firms as compared to smaller ones. Therefore, to adjust for the size of the board and to account for the incidence of multiple bankers on bigger boards, we also employ a Tobit model. The dependent variable is reformulated as the number of bankers on the board of a firm divided by board size. Table 1 Distribution of sample firms by industry and ownership. Source: Computed from the Prowess database. Industry GROUP Number of Firms Percent Ownership Family Indian private Foreign Food, Sugar and Beverages 95 6.7 55 36 4 Textile and textile products 131 9.2 62 68 1 Chemicals and Pharmaceuticals 187 13.2 95 77 15 Electrical and Machinery 259 18.3 126 107 26 Metal and metal products 139 9.8 78 60 1 Cement 29 2.0 23 4 2 Rubber and plastic products 61 4.3 34 26 1 Miscellaneous manufacturing 87 6.1 47 33 7 Others 430 40.4 195 220 15 Total 1418 100 715 631 72 S. Ghosh / Future Business Journal 4 (2018) 1–15 5 Table 2 Variable definition and summary statistics. Variable Unit Description Mean (SD) p.75 (p.25) Board level Board Number Total number of board members 8.81 (3.04) 11 (7) D.Banker Dummy¼1ifafirm has a banker-director on the board, else zero 0.123 (0.329) 0 (0) S.Banker Share Number of banker-directors on the board/Total number of board of directors 0.042 (0.088) 0 (0) N.Bankers Number Number of bankers on the board 0.201 (0.639) 0 (0) Firm level Family Dummy¼1ifafirm is family-owned, else zero 0.504 (0.499) 1 (0) Sales .. Log Sales 2.192 (0.769) 2.683 (1.786) Sales growth % Growth rate of sales, defined as Sales (t) –Sales (t-1)/Sales (t-1) 0.181 (0.684) 0.256 (0.002) SIZE Log Asset 2.309 (0.704) 2.748 (1.867) Capex Ratio Capital expenditure/Total asset 0.009 (0.011) 0.014 (0.002) Profits .. Profits before depreciation, interest and taxes/Asset 0.043 (0.083) 0.083 (0.010) Age Year Ln (1þnumber of years since incorporation) 2.953 (0.849) 3.526 (2.485) RoA Profit before depreciation and taxes/Asset 0.048 (0.082) 0.083 (0.010) R&D % Research and development expenses/ Total sales 0.085 (0.285) 0 (0) Tobin's Q (Market capitalizationþtotal assets-common equity)/Total asset, where Market capitalization¼Shares outstanding*Closing share price 0.414 (0.237) 0.527 (0.257) ICR Interest coverage ratio, defined as Profits before interest and taxes/Interest expense BKDEBT Ratio Bank borrowings/Total asset 0.245 (0.227) 0.388 (0.045) Segment_sales Ratio Segment sales/Total sales, where segment sales refers to the segment that accounts for the maximum sales during the year 0.116 (0.472) 0 (0) S. Ghosh / Future Business Journal 4 (2018) 1–156 Finally, we also explore whether the robustness of the results when the dependent variable is the number of bankers on the firm's board. Employing the number as a dependent variable in similar setup has been employed in prior research, such as analysing the role of board connections in bank lending (Kroszner & Strahan, 2001), the impact of the number of bankers on banking relationships (Berger, Klapper, Martinez Peria & Zaidi, 2008) and examining the incentives of firm directors to attend board meetings (Adams & Ferriera, 2012). Furthermore, it is inappropriate to assume that firm observations over time are independent. Consequently, the standard errors are adjusted for clustering at the firm level. In the second set of tests, we estimate the effect of a banker-director on firm performance. As above, our focus is primarily on family firms. To do this, we estimate variants of the following model: yi;t¼αþβ1Bank no min eei;t−1þβ2Bankno min eei;t−1Familyi;tþβ3Xi;tþηtþμindus þεi;tð2Þ where yis the performance measure. We consider four measures of performance: Sales growth, capital expenditures (Capex), Leverage and R&D. The specific control variables include, among others, one or more of the following variables: industry sales, industry capital expenditure, industry leverage, Tobin's Q, profits (lagged) and leverage (lagged). 5.1. Summary statistics In Table 2, we provide a description of the variables employed. The descriptive statistics suggest that, among board-level characteristics, 12% of the firms have at least one banker on their board and there is on average less than one banker on the board. Among firm-level variables, over fifty percent are family firms; the remaining are stand-alone firms across other ownership groups. To moderate the effect of outliers, all variables of empirical interest are winsorized at the 1% level. The average total assets translates into a book value of INR 200 billion. We measure firm age as the number of age since incorporation and the average age of the sample firms is roughly 17 years. The profitability of the sample firms is close to 5% with average Tobin's Q of 0.41. Taken together, the firms account for, on average, nearly 75% of asset and over 80% of the market capitalisation of all listed non-manufacturing entities reported in Prowess. In Table 3, we report the year-wise difference in the major variables for family and non-family firms. Family firms have larger board size and a larger share of banker-directors as compared to non-family firms. For the period as a Table 3 Family vs. non-family firms –average values. Year Family firms Non-family firms Normalised difference Board size Banker Dummy Banker Share Board size Banker Dummy Banker Share Board size Banker Dummy Banker Share 1996 9.07 0.21 0.084 8.14 0.01 0.030 0.19 0.24 0.17 1997 8.93 0.23 0.087 8.10 0.009 0.032 0.15 0.19 0.24 1998 8.48 0.21 0.085 8.51 0.02 0.043 0.06 0.24 0.21 1999 8.47 0.19 0.079 7.07 0.01 0.022 0.25 0.21 0.21 2000 8.10 0.18 0.081 6.92 0.02 0.029 0.22 0.24 0.22 2001 8.16 0.18 0.084 7.24 0.02 0.032 0.17 0.18 0.22 2002 8.76 0.19 0.079 7.79 0.05 0.029 0.18 0.21 0.23 2003 8.83 0.22 0.075 7.35 0.08 0.024 0.23 0.18 0.23 2004 9.63 0.27 0.072 7.74 0.11 0.030 0.20 0.20 0.19 2005 9.71 0.25 0.065 7.72 0.11 0.027 0.24 0.24 0.17 2006 9.68 0.22 0.052 7.99 0.09 0.021 0.23 0.25 0.15 2007 9.56 0.19 0.045 8.19 0.08 0.017 0.24 0.23 0.14 2008 9.70 0.18 0.041 8.44 0.08 0.015 0.21 0.22 0.24 2009 9.87 0.15 0.034 8.48 0.07 0.014 0.18 0.18 0.15 2010 9.76 0.12 0.025 8.26 0.06 0.012 0.24 0.14 0.16 2011 9.58 0.13 0.025 8.29 0.05 0.012 0.24 0.18 0.15 2012 9.58 0.12 0.023 8.17 0.05 0.011 0.22 0.17 0.14 Average 9.31 0.19 0.056 8.07 0.05 0.019 0.21 0.19 0.13 S. Ghosh / Future Business Journal 4 (2018) 1–15 7 Table 4 Determinants of bankers on family firms. Dependent variable D.BANKER S.BANKER N.BANKER Estimation method Logit Tobit Poisson Negative binomial (1) (2) (3) (4) (5) (6) (7) (8) FAMILY 1.135 * (0.674) 1.071 * (0.615) 1.857 * (0.987) 0.123 * (0.068) 0.138 * (0.082) 0.182 * (0.108) 1.426 *** (0.427) 1.229 *** (0.471) Board size 0.139 *** (0.021) 0.113 *** (0.026) 0.141 *** (0.021) 0.099 *** (0.005) 0.119 *** (0.007) SIZE 0.605 *** (0.167) 0.579 *** (0.186) 0.609 * (0.349) 0.099 *** (0.018) 0.098 *** (0.022) 0.092 *** (0.036) 0.552 *** (0.089) 0.482 *** (0.098) AGE 0.079 (0.116) 0.241 ** (0.127) 0.116 (0.223) 0.017 (0.013) 0.038 *** (0.015) 0.023 (0.025) 0.072 (0.081) 0.056 (0.092) BKDEBT 0.959 *** (0.283) 1.529 *** (0.341) 1.091 ** (0.4999) 0.112 *** (0.032) 0.198 *** (0.043) 0.119 ** (0.057) 0.712 *** (0.209) 0.836 *** (0.231) TOBIN'S Q −0.182 * (0.104) −0.083 (0.108) −0.023 (0.160) −0.017 (0.011) −0.006 (0.012) −0.009 (0.016) −0.085 (0.057) −0.039 (0.057) RoA −0.061 *** (0.008) −0.045 *** (0.011) −0.054 *** (0.014) −0.007 *** (0.0009) −0.006 *** (0.001) −0.006 *** (0.002) −0.048 *** (0.005) −0.048 *** (0.006) Gr_SALES −0.112 (0.072) −0.113 (0.095) −0.092 (0.152) 0.012 (0.008) 0.019 (0.012) −0.002 (0.017) 0.033 (0.117) 0.027 (0.137) R&D 0.019 (0.242) 0.015 (0.275) 0.749 ** (0.332) −0.0002 (0.026) 0.006 (0.032) 0.085 ** (0.036) 0.569 *** (0.103) 0.581 *** (0.118) Sales_segment 0.022 (0.163) 0.004 (0.021) SIZE*Family −0.004 (0.377) −0.011 (0.041) −0.005 (0.095) 0.047 (0.106) AGE*Family −0.062 (0.259) −0.008 (0.029) −0.077 (0.086) −0.023 (0.099) BKDEBT *Family −0.164 (0.606) −0.009 (0.068) −0.050 (0.228) −0.149 (0.258) TOBIN'S Q*Family −0.198 (0.201) −0.023 (0.020) −0.122 * (0.065) −0.159 *** (0.067) RoA*Family 0.009 (0.017) −0.099 (0.191) 0.008 ** (0.004) 0.004 (0.007) Gr_SALES*Family 0.262 (0.173) 0.018 (0.019) 0.071 (0.125) 0.080 (0.149) R&D*Family −1.161 *** (0.449) −0.129 *** (0.049) −0.849 *** (0.130) −0.896 *** (0.146) Industry FE YES YES YES YES YES YES YES YES Year FE YES YES YES YES YES YES YES YES Observations 7995 5075 7995 7993 5073 7993 7986 7986 Censored, Left 5912 4205 5912 Log pseudo likelihood −3618 −1972 −3593 −1954 −1217 −1929 −6116 −5977 Pseudo R-squared 0.211 0.152 0.216 0.314 0.216 0.323 0.207 0.145 Prob 4χ 2 (p-Value) 0.00 0.00 0.00 0.00 Over-dispersion parameter 0.59 χ 2 (1) p-Value 0.00 Standard errors (clustered by firm) are in parentheses. *** denote statistical significance at 1%. ** denote statistical significance at 5. * denote statistical significance at 10%. 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