Dividend payout ratio follows a Tweedie distribution: International evidence

Dragotă, Victor; Pele, Daniel Traian; Yaseen, Hanaan Article Dividend payout ratio follows a Tweedie distribution: International evidence Economics: The Open-Access, Open-Assessment E-Journal Provided in Cooperation with: Kiel Institute for the World Economy – Leibniz Center for Research on Global Economic Challenges Suggested Citation: Dragotă, Victor; Pele, Daniel Traian; Yaseen, Hanaan (2019) : Dividend payout ratio follows a Tweedie distribution: International evidence, Economics: The Open-Access, OpenAssessment E-Journal, ISSN 1864-6042, Kiel Institute for the World Economy (IfW), Kiel, Vol. 13, Iss. 2019-45, pp. 1-35, https://doi.org/10.5018/economics-ejournal.ja.2019-45 This Version is available at: https://hdl.handle.net/10419/206401 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Vol. 13, 2019-45 | October 29, 2019 | http://dx.doi.org/10.5018/economics-ejournal.ja.2019-45 Dividend payout ratio follows a Tweedie distribution: international evidence Victor Dragotă, Daniel Traian Pele, and Hanaan Yaseen Abstract Dividend policy is still a largely discussed issue in corporate finance literature. One of the main indicators used in analysing the dividend policy is the dividend payout ratio. Using a database consisting of 12,085 companies operating in 73 countries, for the period 2008– 2014, the authors found that the dividend payout ratio follows a Tweedie distribution, and not a normal one. This distribution is stable over time for the entire analysed period. In addition, it describes the case of almost all the countries included in the sample. Thus, a better estimation of the probability that dividend payout ratio is lower or higher than a benchmark can be provided. Also, an analysis of dividend policy, distinctly considering payer versus non-payer companies, can offer additional important information for both practitioners and academics. JEL G35 C01 C51 C55 Keywords Dividend policy; dividend payout ratio; Tweedie distribution Authors Victor Dragotă, Bucharest University of Economic Studies, Romania, [email protected] Daniel Traian Pele, Bucharest University of Economic Studies, Romania Hanaan Yaseen, Bucharest University of Economic Studies, Romania Citation Victor Dragotă, Daniel Traian Pele, and Hanaan Yaseen (2019). Dividend payout ratio follows a Tweedie distribution: international evidence. Economics: The Open-Access, Open-Assessment E-Journal, 13 (2019-45): 1–34. http://dx.doi.org/10.5018/economics-ejournal.ja.2019-45 Received June 4, 2019 Published as Economics Discussion Paper July 2, 2019 Revised October 16, 2019 Accepted October 17, 2019 Published October 29, 2019 © Author(s) 2019. Licensed under the Creative Commons License - Attribution 4.0 International (CC BY 4.0) Economics:TheOpen‐Access,Open‐AssessmentE‐Journal13(2019–45) www.economics‐ejournal.org2 1 Introduction Corporate finance literature discusses dividend policy from different perspectives (Lintner 1956, Miller and Modigliani 1961, Bhattacharya 1979, Easterbrook 1984, Holder et al. 1998, La Porta et al. 2000, Fama and French 2001, Fidrmuc and Jacob, 2010, Floyd et al, 2015, Jiang et al. 2017, etc.). Different viewpoints on dividend policy are contradictory, from its neutral impact on firms’ value (Miller and Modigliani 1961) to normative advices to increase (Graham and Dodd 1951) or to decrease (Walter 1956) the amount paid to shareholders, or to explain this financial decision through agency problems, asymmetrical information, socio-cultural or institutional factors, etc. (Bhattacharya 1979, Easterbrook 1984, La Porta et al. 2000, Fidrmuc and Jacob 2010, etc.). Among them, the studies concerning the factors determining dividend policy are an important direction (see, among others, La Porta et al. 2000, Fidrmuc and Jacob 2010, Nicolosi 2013, Ye et al. 2019). Different indicators are used for modelling dividend policy, each of them with its informative power. One of the most important is the dividend payout ratio (Holder et al. 1998, La Porta et al. 2000, Ye et al. 2019) (hereafter, DPR), respectively the part of the net earnings paid to shareholders, as dividends, considering the firms which record net profits (and excluding those which record losses).1 In a sense, DPR reflects exactly the interest expressed by one profitable company for paying dividends to its shareholders. Analysed for one period, a DPR equal to 100% reflects a totally dedicated policy to pay dividends to shareholders, and one of 0% a reflection of a non-interest to dividends (argued in many cases as the company’s interest for investing). DPR is used in a large variety of studies, as dependent variable (e.g., Holder et al. 1998, La Porta et al. 2000, Faccio et al. 2001, Fidrmuc and Jacob 2010, Jiang et al. 2017, etc.), but also as explanatory variable in different contexts (e.g., Lintner 1956, Lamont 1998, Lettau and Ludvigson 2001, Arnott and Asness 2003, Cincotti et al. 2010, Baker et al. 2012, He et al. 2017). In such studies, the average DPR is often considered representative, as in the case of a Gaussian distribution. Many papers analyse the determining factors of DPR using a classical regression (e.g., La Porta et al. 2000, Fidrmuc and Jacob 2010, Jiang et al 2017). Other papers analyse the propensity to pay dividends2 and its determinants (e.g., Denis and Osobov 2008, von Eije and Megginson 2008, Fatemi and Bildik 2012, Kuo et al. 2013, Banyi and Kahle 2014, Jiang et al 2017). One missing link between considering the averages DPR and the propensity to pay dividends in modelling dividend policy can be somehow intuited. DPR does not follow a normal distribution. Figure 1 depicts DPR distribution for a number of 12,085 companies from 73 countries, in the period 2008–2014. In this study, we show that this empirical distribution may _________________________ 1 DPR can be also calculated as ratio between dividend per share and earnings per share. This second expression is the most familiar for investors on capital market. At a macroeconomic level, Ferris et al. (2009) use in their analysis an aggregate DPR, respectively a ratio between the value of total dividends and the total earnings for a country. 2 Denis and Osobov (2008) define the propensity to pay dividends, respectively the characteristic of one company to be a dividend payer or not. If DPR = 0, the company is a dividend payer. If DPR > 0, the company is not a dividend payer. Economics:TheOpen‐Access,Open‐AssessmentE‐Journal13(2019–45) www.economics‐ejournal.org3  Figure 1: Dividend payout ratio for the companies included in the sample, in the period 2008–2014. All companies’ financials were collected from the Thomson Research Worldscope database. DPR is computed as:    .  be fitted at best by a Tweedie distribution (Tweedie 1984). Moreover, this distribution is stable in time for the entire analysed period. In addition, it describes the case of almost all the countries included in the sample and the most part of the years (some more detailed statistics are provided in Appendix 1). As far as we know, finding the distribution for DPR is a new contribution for financial literature. The distribution depicted in Figure 1 suggests that dividend policy is mainly an issue of “to be or not to be” a dividend payer, some authors suggesting the decrease in dividend payments in time (Fama and French 2001, Fatemi and Bildik 2012, Kuo et al. 2013), which can be modelled through the propensity to pay dividends (Fama and French 2001, Denis and Osobov 2008, von Eije and Megginson 2008, Fatemi and Bildik 2012, Kuo et al. 2013, Banyi and Kahle 2014, Floyd et al. 2015, Jiang et al. 2017, etc.). As practical implication, a proper analysis of DPR should consider both components of the distribution – the 0 inflated component and the distribution for DPR > 0. However, as Figure 1 suggests, this is not a 0% / 100% dividend payout ratio policy! An analysis concerned only about the decision to pay or not to pay dividend can miss some important information. The most appropriate distribution for modelling DPR is not the normal (Gaussian) one, but the less used Tweedie distribution, proposed by Maurice Tweedie (1984). Using a better fit for the distribution, a better estimation of the probability that the event to occur (e.g., DPR to be lower or higher than a benchmark) can be provided. This result can be useful for instance in the context of international portfolio management, especially if the investments are made in a large Economics:TheOpen‐Access,Open‐AssessmentE‐Journal13(2019–45) www.economics‐ejournal.org4 number of companies (with minority participations), and when forecasting of the future collected dividends is of interest. One important contribution, comparative to previous research, is the large number of countries considered (73). For instance, the database used by La Porta et al. (2000) includes 33 countries, the one of Ferris et al. (2009) 25, etc. Our sample is geographically diverse, including countries from Africa, North and South Americas, Asia, Australia, and Europe. From here, a large diversity regarding the cultural values, but also legal systems (our sample includes common law, but also civil law countries) can be noticed. The remainder of our paper is structured as follows. Some related studies are discussed in Section 2. Section 3 presents the methodology. Section 4 describes the data. Section 5 presents and examines the results. In Section 6, we conclude. 2 Theoretical background. Modelling dividend policy through dividend payout ratio Different indicators are used for modelling dividend policy, in various contexts (see Table 1). Of course, each of these indicators expresses something else, but all of them can be used in analysing dividend policy. Two indicators are extensively used in studies on dividend policy, respectively dividend payout ratio (DPR) and the propensity to pay dividends (hereafter, PPD). They are somehow related, in the sense that PPD can be modelled as a particular case of DPR. Thus, PPD can be defined as: PPD = DPR if DPR = 0, and PPD = 1 if DPR > 0. DPR is defined as the ratio between net dividend paid to shareholders and net earnings (for instance, DPR in the year t, as a ratio between dividend per share and earnings per share, both recorded in the year t) and calculated only if the company records profit, and not loss (La Porta et al. 2000, Fidrmuc and Jacob 2010). Dividend is considered usually as total cash dividend paid to common and preferred shareholders (La Porta et al. 2000, Fidrmuc and Jacob 2010).3 DPR can be considered as explaining the interest of the shareholders for receiving dividends (or, in some cases, the interest of managers to protect the shareholders’ interests). Share repurchases can be considered as an alternative to dividend payments (La Porta et al. 2000, von Eije and Megginson 2008, Fidrmuc and Jacob 2010, Banyi and Kahle 2014, Baker and Weigand 2015), and some studies correct DPR for accounting for this type of shareholders’ remuneration (e.g., Renneboog and Trojanowski 2007, Floyd et al 2015, Ye et al. 2019). However, share repurchases imply the termination of the role as shareholder for the receiver of the payment, and this is why it should be analyzed independently by dividend payments. Undoubtedly, as most of the financial indicators, DPR has certain limits. Net earnings depend on the countries’ accounting conventions and are not always comparable from one country to another, being also easily manipulated by “accounting tricks”. Also, “diversion of _________________________ 3 In some cases, supplementary adjustments are made. For instance, “Earnings are measured after taxes and interest but before extraordinary items” (e.g., La Porta et al. 2000, Faccio et al. 2001). Economics:TheOpen‐Access,Open‐AssessmentE‐Journal13(2019–45) www.economics‐ejournal.org5 Table 1: Indicators used in modelling dividend policy Indicator Studies Dividend payout ratio (dividend-toearnings ratio) Lintner (1956), Holder et al. (1998), La Porta et al. (2000), Faccio et al. (2001), Aivazian et al. (2003), Renneboog and Trojanowski (2007), Fidrmuc and Jacob (2010), Fatemi and Bildik (2012), Floyd et al. (2015), He et al. (2017), Jiang et al. (2017), Chen et al. (2017), Yaseen and Dragotă (2019), Ye et al. (2019), Yaseen (2019) Propensity to pay dividends (dummy variable, reflecting the quality of dividend payer / non-payer) Fama and French (2001), Denis and Osobov (2008), von Eije and Megginson (2008), Bena and Hanousek (2008), Ferris et al. (2009), Fatemi and Bildik (2012), Kuo et al. (2013), Banyi and Kahle (2014), Zheng and Ashraf (2014), Floyd et al. (2015), He et al. (2017), Jiang et al. (2017), Yaseen and Dragotă (2019), Ye et al. (2019) Dividends / sales La Porta et al. (2000), Faccio et al. (2001), Shao et al. (2010), Fidrmuc and Jacob (2010), Chen et al. (2017) Dividends / cash flow La Porta et al. (2000), Faccio et al. (2001), Fidrmuc and Jacob (2010), Jiang et al. (2017) Dividend / earnings before interest and taxes Renneboog and Trojanowski (2007) Dividends / market capitalization (Dividend yield) Faccio et al. (2001), Aivazian et al. (2003), Nicolosi (2013), Arnott and Asness (2003), Desai and Jin (2011), Nicolosi (2013), He et al. (2017), Yaseen (2019) Dividends / total assets Shao et al. (2010), Zheng and Ashraf (2014), Chen et al. (2017), Ye et al. (2019) Dividend / equity Yaseen (2019) Dividend initiation DeAngelo and DeAngelo (1990), Ferris et al. (2009), Huang et al. (2015), Chen et al. (2017), He et al. (2017) Dividend omission DeAngelo and DeAngelo (1990), Huang et al. (2015), He et al. (2017) Dividend re-initiation Chen et al. (2017) Dividend per share Bena and Hanousek (2008), Chen et al. (2017) Dividend payments (total amount paid as dividend) Lintner (1956), Renneboog and Trojanowski (2007)   resources may occur before earnings are reported” (in this case, dividend payout ratio “overestimates the share of true earnings that is paid as dividends” (La Porta et al. 2000).4 It can be stated that DPR is also a classical, traditional indicator. It expresses the share of profit paid to shareholders. In this vision, profit is somehow considered having “a cash flow essence”. As signalling theory notices (Bhattacharya 1979, Kalay 1980), in practice, one company can record profits, but having not enough cash for paying dividends. Also, if one company pays dividends from previous years earnings (from reserves), DPR can be higher than 100%. This nonsynchronicity between dividends (an amount paid from the cash existent in one financial exercise) and net earnings (the result in previous year) can complicate also the financial interpretation of DPR. _________________________ 4 These problems are solved somehow using dividend-to-sales or dividend-to-cash flow ratios (La Porta et al. 2000, Faccio et al. 2001, Fidrmuc and Jacob 2010, etc.). However, these indicators do not reflect a portion from net earnings paid as dividend, dividends being defined as a part of the earnings distributed to shareholders. Economics:TheOpen‐Access,Open‐AssessmentE‐Journal13(2019–45) www.economics‐ejournal.org6 DPR does not reflect a return (like dividend yield); it is a share of profit paid to shareholders. If dividends and retained earnings are considered as expressing opposite interests (see the literature regarding minority shareholders’ protection, e.g., La Porta et al. 2000), DPR would reflect a higher interest for one issue or another or, maybe, a power in negotiation. However, the interpretation of the indicator should be made cautiously. If one company records 100 monetary units (m.u.) as earnings and pays 50 m.u. as dividends, it records only a 50% DPR, comparatively with another, which pays 100% as dividends from its 1 m.u. earnings. Looking only to DPR, the second one seems to be more oriented to shareholders; however, it does not mean that its shareholders would be more satisfied. DPR is used as dependent variable in regressions (La Porta et al. 2000, Fidrmuc and Jacob 2010, Jiang et al 2017). Different factors are considered as determinants of DPR, some of them – financial (e.g., size, return of assets, leverage, sales growth, in Fidrmuc and Jacob 2010, Jiang et al 2017, Ye et al. 2019), other – legal (legal system, mandatory dividends, tax advantages, etc., as in La Porta et al. 2000, Fidrmuc and Jacob 2010, Ye et al. 2019, etc.), cultural (individualism, power distance, uncertainty avoidance,5 in Fidrmuc and Jacob 2010), related to ownership structure (Jiang et al 2017, Ye et al. 2019), board gender diversity (Chen et al. 2017, Ye et al. 2019), etc. DPR is also used as independent variable in some studies (e.g., Lintner 1956, Lamont 1998, Lettau and Ludvigson 2001, Arnott and Asness 2003, Cincotti et al 2010, Baker et al. 2012, He et al. 2017, Chen and Desiderio 2018). Based on empirical evidences, different papers found that the presence of non-paying dividends companies is significant (Fama and French 2001, von Eije and Megginson 2008, Fatemi and Bildik 2012, Kuo et al. 2013). Maybe for this reason, many papers prefer to analyse the propensity to pay dividends and its determinants, along with dividend payout ratio or not (e.g., Denis and Osobov 2008, von Eije and Megginson 2008, Fatemi and Bildik 2012, Kuo et al. 2013, Banyi and Kahle 2014, Jiang et al. 2017, Ye et al. 2019) (see also Table 1). Even PPD is a less sensitive indicator, it has the same purpose as DPR in reflecting the company’s interest for paying dividends for shareholders. One missing link between considering averages DPR and propensity to pay dividends in modelling dividend policy can be somehow intuited. On the one hand, the use of the average DPR can be misleading, as long as DPR is 0% in many cases. An average DPR should be interpreted cautiously; it is as if you would say that in average you feel all right if one part of you is kept in frozen water and the other one in boiling water. On the other hand, neglecting the distribution of DPR in the absence of DPR = 0 (considering 1% DPR to be as such important as a 100% DPR) can determine missing some information. For all these reasons, finding if probability distribution of DPR can be modelled can provide a useful result for both academics and practitioners. Finding the probability distribution for one variable is studied in literature (e.g., Clauset et al. 2009). In general, in is accepted that the selection procedure is following some subsequent steps, respectively (Clauset et al. 2009): (1) choose a suitable theoretical model; (2) estimate the model parameters; (3) determine the significance level and use a goodness-of-fit test in order to determine the most appropriate theoretical distribution. As far as we know, finding the distribution for DPR is a new contribution for financial literature. _________________________ 5 These indicators are proposed by Hofstede (2001), as proxies for the national culture. Economics:TheOpen‐Access,Open‐AssessmentE‐Journal13(2019–45) www.economics‐ejournal.org7 3 Methodology Our methodology is focused on finding the most appropriate distribution for DPR. In order to fit the probability distribution for DPR, we follow the methodology recommended by Clauset et al. (2009), and we applied it for the analysed variable. Thus, the fitting problem can be split in three main tasks (Clauset et al. 2009). First, we have chosen a suitable theoretical model. Descriptive statistics like histogram and skewness are useful in this step. Based on the shape of the empirical distribution, we have decided to estimate a range of theoretical distribution that may fit the data: Tweedie, Scaled Tweedie, Lognormal, Burr, Weibull, Inverse Gaussian, Exponential, Generalized Pareto Distribution, Pareto and Gamma distribution. The Tweedie distribution, as a model for zero-inflated data (see Gilchrist and Drinkwater, 1999), has been previously used in other areas, such as healthcare data (Kurz 2017), modelling insurance claims (Renshaw 1994; Jørgensen et al. 1994), etc. Secondly, we have estimated the model parameters. In order to estimate the parameters of the theoretical distributions, the Maximum Likelihood method was used. Finally, we have determined the significance level and we have used a goodness-of-fit test in order to determine the most appropriate theoretical distribution. For finding the most appropriate distribution for the data, we have used the Anderson-Darling test (Anderson and Darling 1954). This is one alternative used to test and to find the distribution of experimental data that follows a theoretical distribution. The conclusion of the Anderson–Darling test is usually drawn by comparing the obtained statistics with the available critical value. This test is one of the most frequent tests used to find the best distribution for the data, generally called „goodness-of-fit tests” (Pearson 1895, Anderson and Darling 1954, Stephens 1974, Jäntschi and Bolboacă 2018). This methodology has the advantage of allowing a more sensitive test (Scholz and Stephens 1987). By minimizing the statistics obtained from the Anderson Darling test, we have chosen the most appropriate distribution for our data. 4 Data All companies’ financials were collected from the Thomson Research Worldscope database.6 We have included in our database only those countries with minimum 10 companies available for the entire period (for this reason, we have excluded from the initial database some countries). In addition, we have not considered the financial institutions because of the difference in the accounting standards for financial reporting, as La Porta et al. (2000), Fidrmuc and Jacob (2010), Jiang et al. (2017), etc.7, 8 Furthermore, we have imported from the original database only companies with data available for the entire analysed period. Also, we have _________________________ 6 The access to the Thomson Research Worldscope Database was granted by Deloitte Romania. 7 Different studies, after the exclusion of companies with missing values, eliminate from their databases: (i) utility companies (Fidrmuc and Jacob, 2010); (ii) companies from Luxembourg (La Porta et al. 2000, Fidrmuc and Jacob 2010); (iii) companies completely or partially owned by the governments (La Porta et al. 2000); (iv) companies from Economics:TheOpen‐Access,Open‐AssessmentE‐Journal13(2019–45) www.economics‐ejournal.org8 excluded from our database those companies which recorded negative net income (as in La Porta et al. 2000, Fidrmuc and Jacob 2010). The inclusion of this kind of data is incoherent with the financial logic of the indicator – dividend payout ratio is defined as a share of profit paid to shareholders. Another criterion for the imported data from Thomson Research Worldscope was that dividend DPR ≥ 0 (to eliminate possible negative dividend payout ratio) (Jiang et al. 2017). We considered dividends, but not other forms of shareholders’ remuneration (such as shares repurchases) (as Floyd et al. 2015, among others) (due to data availability, but also because share repurchases determine the end of the quality of company’s shareholder for their receiver). Also, have considered only cash dividends, and no other “cosmetically” (non-cash) operations (e.g., dividends in stocks). The final database consists of 12,085 companies operating in 73 countries in the period 2008–2014. As such, our database covers a crisis, but also a post-crisis period. The data are winsorized to 2% and DPR is limited to 100%.9 We have considered each company as being a different and sole company, in the case of a group of companies, which activates in more than one country.10 Appendix 2 presents the descriptive statistics for DPR for the analysed countries. The number of companies per country is constant for the entire period analysed and the average number of companies per country is 168. Table 2 provides much more details about the process of building the final sample. Table 2: Final sample construction Description Companies Total number of companies imported from the database 14,071 Banks and investment trust 1,540 Companies without a specific industry (not mentioned in the database) 30 National Banks 2 Negative Assets, Negative Sales, Negative Income or other aberrant financial data 336 Companies from countries with less than 10 companies 78 Final Sample 12,085 _________________________ socialist or former socialist countries (La Porta et al. 2000). We included these categories for assuring a larger perspective on DPR. As observation, in our database, inclusion of Luxembourg does not have an important impact, as long it counts only with 56 records. In addition, even some particularities persist for the economies of socialist or former socialist countries, we do not consider them significant for the purpose of our study. 8 Zheng and Ashraf (2014) analyze the banks’ dividend policy in an international context. 9 In some cases, the rough data is questionable per se. In this category can be mentioned companies with negative dividends (reported also in Fidrmuc and Jacob 2010), or with dividends exceeding sales (reported in La Porta et al. 2000, Fidrmuc and Jacob 2010). The quality of the databases used can be a problem. For instance, Fidrmuc and Jacob (2010) use as main source of data “Standard & Poor’s Capital IQ database, which provides data covering company information for 58,670 public companies”. From this total number of companies, the authors exclude 37,109 companies (that means approximately 63.25%!!!), because they have missing dividend data, negative dividends or dividends exceeding sales. DPR can be greater than 100% if dividends are paid from reserves. We did not consider this case for the reasons explained in Section 2. 10 Relatively the same database was used in Yaseen and Dragotă (2019) and Yaseen (2019). Economics:TheOpen‐Access,Open‐AssessmentE‐Journal13(2019–45) www.economics‐ejournal.org15 6 Conclusions Dividend policy is still a largely discussed issue in corporate finance literature. For its analysis, dividend payout ratio has certain advantages and is extensively used. Using a database consisting of 12,085 companies operating in 73 countries, for the period 2008–2014, we found that this indicator does not follow a normal distribution, but a zero-inflated one. The most appropriate distribution for modelling dividend payout ratio is the Tweedie distribution (Tweedie 1984) and its version Scaled Tweedie Distribution (Dunn and Smyth 2005). Thus, a better estimation of the probability that dividend payout ratio is lower or higher than a benchmark can be provided. Also, an analysis of dividend policy, distinctly considering payer versus non-payer companies, can offer additional important information for practitioners and, also, for academics. The use of the average levels of dividend payout ratio can determine misleading results. As far as we know, finding the distribution for DPR is a new contribution for financial literature. Another contribution, comparative to previous research, is the large number of countries considered (73) and covering a crisis and a post-crisis period (2008–2014). Our sample included countries from Africa, North and South Americas, Asia, Australia, and Europe. From here, conclusions are validated for countries with a large diversity regarding the cultural values, but also legal systems (our sample includes common law, but also civil law countries). This outcome could be useful in the future research where a more appropriate distribution could be used for modelling the influencing factors of the DPR. Based on our knowledge, our paper is the first one that tried to investigate which would be the most appropriate distribution function for DPR. Our result can be useful in the context of international portfolio management, especially when we discuss about investments made in a large number of companies (with minority participations), and when forecasting of the future collected dividends is of interest. As a limitation of the study, our analysis and results are made on only one financial indicator that describe dividend policy - dividend payout ratio. One interesting extension can be made analyzing other indicators reflecting the dividend policy, too. Also, accounting rules are different from country to country (Chui et al. 2002; Dragotă et al. 2018) and from sector to sector (Short et al 2002). Fiscal systems are also different and they can have an impact on financial decisions (Chui et al. 2002, Dragotă et al. 2018), including dividend policy (Short et al 2002, Fidrmuc and Jacob 2010). These issues can have an impact on earnings (La Porta et al. 2000), and, from here, on dividend payout ratio.  Acknowledgements We would like to thank the anonymous referee of this study, who provided very valuable observations. Also, we wish to thank to Dan Anghel, Andreea Curmei-Semenescu, Bogdan Negrea, and Mădălina Ecaterina Popescu, for their useful comments. We also wish to thank to the participants to the 19th Annual Conference on Finance and Accounting organised by the Faculty of Finance and Accounting, University of Economics, Prague (25 May 2018), and especially to David Prochazka, Marcio Fernandes Gabrielli, and Jan Hospodka. 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All companies’ financials were collected from the Thomson Research Worldscope database. DPR is computed as:    .   Country 1 – Argentina Country 2 – Australia Country 3 – Austria Country 4 – Bahrein Country 5 – Bangladesh Country 6 – Belgium Economics:TheOpen‐Access,Open‐AssessmentE‐Journal13(2019–45) www.economics‐ejournal.org22 Country 7 – Brazil Country 8 – Bulgaria Country 9 – Canada Country 10 – Chile Country 11 – China Country 12 - Cote d’Ivoire Country 13 – Croatia Country 14 – Czech Republic Economics:TheOpen‐Access,Open‐AssessmentE‐Journal13(2019–45) www.economics‐ejournal.org23 Country 15 – Denmark Country 16 – Egypt Country 17 – Estonia Country 18 – Finland Country 19 – France Country 20 – Germany Country 21 – Ghana Country 22 – Greece Economics:TheOpen‐Access,Open‐AssessmentE‐Journal13(2019–45) www.economics‐ejournal.org24 Country 23 – Hong Kong Country 24 – Hungary Country 25 – India Country 26 – Indonesia Country 27 – Ireland Country 28 – Israel Country 29 – Italy Country 30 – Japan Economics:TheOpen‐Access,Open‐AssessmentE‐Journal13(2019–45) www.economics‐ejournal.org31 Table A.3.1: Countries with zero-inflated distributions versus countries with other distributions of Dividend payout ratio Zero-inflated distribution Other distributions Austria Belgium Argentina Bulgaria Australia Chile Bahrein Cote d’Ivoire Bangladesh Former Yugoslav Republic of Macedonia Brazil France Bulgaria Germany Canada Hong-Kong China Japan Croatia Kazakhstan Czech Republic Kenya Denmark Latvia Egypt Luxembourg Estonia Mauritius Finland Slovenia Greece Spain Holland Sweden Hungary Switzerland India United Kingdom Indonesia Ireland Israel Italy Kuwait Lithuania Malaysia Malta Mexico Morocco New Zeeland Norway Pakistan Palestine Peru Philippines Poland Portugal Romania Russia Saudi Arabia Serbia South Africa South Korea Sri Lanka Thailand Tunisia Turkey Ukraine United Arad Emirates United States of America Venezuela Zambia Zimbabwe Note: the results for Oman are not conclusive. Economics:TheOpen‐Access,Open‐AssessmentE‐Journal13(2019–45) www.economics‐ejournal.org32 It can be suspected that the situation from Table A.3.1 can be related to the capital market development (see market capitalization as proxy). However, from the first 10 countries ranked function of market capitalization,15 four present a zero-inflated distribution (US, China, Canada, India). Considering the value of stocks traded as percent in GDP,16 six present a zero-inflated distribution.17 One interesting future direction for analysis is to consider some cultural determinants for explaining this zero-inflated distribution for DPR. These similarities can be explained by similar cultural dimensions or people behaviour. For example, similar harmony index (Yaseen and Dragotă, 2019) or similar life standards (Yaseen, 2019) in those countries may lead to similar decisions regarding paying dividends or not.  Appendix 4: Parameters of the Tweedie and Scaled Tweedie distribution, by country Table A.4.1: Estimated Parameters of the Tweedie and Scaled Tweedie distribution, by country Country Distribution of DPR (20082014) Theta θ P p Mu μ Phi  Lambda λ Côte d'Ivoire Burr Luxembourg Burr Latvia Exp South Africa STweedie 0.029 1.139 2.150 Argentina STweedie 0.204 1.478 0.727 Australia STweedie 0.052 1.143 1.376 Austria STweedie 0.022 1.063 1.009 Canada STweedie 0.148 1.344 1.031 Denmark STweedie 0.105 1.283 0.653 Egypt STweedie 0.094 1.214 1.094 Switzerland STweedie 0.004 1.038 3.542 Estonia STweedie 0.101 1.258 0.928 Germany STweedie 0.021 1.144 3.000 Ghana STweedie 0.059 1.297 3.124 Greece STweedie 0.083 1.248 1.213 Hong Kong STweedie 0.010 1.099 3.954 Indonesia STweedie 0.109 1.288 0.502 Italy STweedie 0.018 1.070 1.699 Kenya STweedie 0.034 1.140 2.586 _________________________ 15 Top 10 countries, as market capitalization (2019), according to: https://www.indexmundi.com/facts/indicators/CM.MKT.LCAP.CD/rankings, is: 1. US. 2. China. 3. Japan. 4. HongKong. 5. France. 6. Canada. 7. UK. 8. Germany. 9. India. 10. Switzerland. 16 Top 10 countries, as value of stocks traded as percent in GDP (2019), according to: https://www.indexmundi.com/facts/indicators/CM.MKT.TRAD.GD.ZS/rankings, is: 1. Hong-Kong. 2. US. 3. China. 4. South Africa. 5. Switzerland. 6. South Korea. 7. Japan. 8. Finland. 9. Italy. 10. Iceland. 17 We did not include in our study Iceland because of lack of data. Economics:TheOpen‐Access,Open‐AssessmentE‐Journal13(2019–45) www.economics‐ejournal.org33 Country Distribution of DPR (20082014) Theta θ P p Mu μ Phi  Lambda λ Malaysia STweedie 0.031 1.129 1.868 Malta STweedie 0.041 1.149 2.219 Mauritius STweedie 0.022 1.179 3.945 Mexico STweedie 0.142 1.404 1.156 Morocco STweedie 0.031 1.120 2.340 Norway STweedie 0.140 1.295 0.476 Oman STweedie 0.033 1.139 2.105 Palestine STweedie 0.071 1.269 2.697 Philippines STweedie 0.053 1.261 1.711 Poland STweedie 0.125 1.259 0.335 Portugal STweedie 0.145 1.282 0.660 Russian Federation STweedie 0.112 1.432 1.285 Saudi Arabia STweedie 0.080 1.172 0.984 Rep. of Korea STweedie 0.057 1.336 1.662 Spain STweedie 0.007 1.053 3.594 Sri Lanka STweedie 0.109 1.356 1.212 Sweden STweedie 0.003 1.027 4.111 Thailand STweedie 0.061 1.157 1.161 Ukraine STweedie 0.212 1.631 1.739 United Kingdom STweedie 0.021 1.144 3.525 United States STweedie 0.094 1.326 1.103 Venezuela STweedie 0.190 1.537 2.805 Bahrain Tweedie 1.050 0.430 0.210 Bangladesh Tweedie 1.375 0.286 0.600 Belgium Tweedie 1.054 0.442 0.181 Brazil Tweedie 1.267 0.334 0.351 Bulgaria Tweedie 1.703 0.322 0.677 Czech Republic Tweedie 1.487 0.290 1.454 Chile Tweedie 1.181 0.417 0.238 China Tweedie 1.218 0.202 0.507 Croatia Tweedie 1.407 0.257 0.742 Finland Tweedie 1.144 0.413 0.394 France Tweedie 1.095 0.419 0.145 Hungary Tweedie 1.408 0.159 0.933 India Tweedie 1.268 0.233 0.191 Ireland Tweedie 1.160 0.168 0.411 Israel Tweedie 1.255 0.293 0.530 Japan Tweedie 1.124 0.305 0.133 Kazakhstan Tweedie 1.695 0.382 0.593 Kuwait Tweedie 1.083 0.318 0.492 Lithuania Tweedie 1.315 0.233 0.641 Economics:TheOpen‐Access,Open‐AssessmentE‐Journal13(2019–45) www.economics‐ejournal.org34 Country Distribution of DPR (20082014) Theta θ P p Mu μ Phi  Lambda λ FYR of Macedonia Tweedie 2.639 0.390 0.534 Netherlands Tweedie 1.097 0.381 0.203 New Zealand Tweedie 1.030 0.587 0.183 Pakistan Tweedie 1.234 0.395 0.356 Peru Tweedie 1.073 0.274 0.271 Romania Tweedie 1.186 0.280 0.607 Serbia Tweedie 1.595 0.322 0.516 Slovenia Tweedie 1.583 0.249 0.478 Tunisia Tweedie 1.136 0.389 0.379 Turkey Tweedie 1.325 0.230 0.714 United Arab Emirates Tweedie 1.193 0.266 0.708 Zambia Tweedie 1.336 0.402 0.447 Zimbabwe Tweedie 1.620 0.049 1.828  Please note: You are most sincerely encouraged to participate in the open assessment of this article. 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