Do public and internal debt cause income inequality? Evidence from Kenya

Obiero, Wilkista; Topuz, Seher Gulsah Article Do public and internal debt cause income inequality? Evidence from Kenya Journal of Economics, Finance and Administrative Science Provided in Cooperation with: Universidad ESAN, Lima Suggested Citation: Obiero, Wilkista; Topuz, Seher Gulsah (2021) : Do public and internal debt cause income inequality? Evidence from Kenya, Journal of Economics, Finance and Administrative Science, ISSN 2218-0648, Emerald Publishing Limited, Bingley, Vol. 27, Iss. 53, pp. 124-138, https://doi.org/10.1108/JEFAS-05-2021-0049 This Version is available at: https://hdl.handle.net/10419/289647 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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Evidence from Kenya Wilkista Lore Obiero and Seher G€ uls ¸ah Topuz Department of Economics, Eskisehir Osmangazi University, Eskisehir, Turkey Abstract Purpose –This study aims to determine whether there is an effect of internal and public debt on income inequality in Kenya for the period 1970–2018. Design/methodology/approach –The relationship is examined by using the Autoregressive Distributed Lag (ARDL) model by Pesaran et al. (2001) and Toda Yamamoto causality by Toda and Yamamoto (1995). Findings –Our findings suggest that both internal and public debt harm inequality in Kenya in the long term. Furthermore, a one-way causality from internal debt to income inequality is also obtained while no causality relationship is found to exist between public debt and income inequality. Based on these findings, the study recommends that to reduce income inequality levels in Kenya, other methods of financing other than debt financing should be preferred because debt financing is not pro-poor. Originality/value –This study is unique based on the fact that no previous paper has analysed the debt and inequality relationship in Kenya. To the best of our knowledge, this will be the first study to analyse the applicability of redistribution effect of debt in Kenya. The study is also different in that it provides separate analysis for public debt and internal debt on their effects on income inequality. Keywords Internal debt, Public debt, Income inequality, Income redistribution Paper type Research paper 1. Introduction The question of how income should be distributed, and what level of inequality is acceptable in society has been pondered upon by many economists. Some argue that income ought to be distributed according to the contribution provided by the income earner so that more productive people earn higher than less productive ones (Byrns and Stone, 1989, p. 591). Another argument that is put forward by Karl Marx (1818–1883) is that distribution should be done according to people’s needs although this view has received sharp criticism for its tendency to encourage laziness. Others yet believe that income should be distributed equally among all individuals, and this too has been criticized as being likely to reduce productivity in the society (Conrad, 2016). The Greek philosopher Plato argues that income distribution should ensure that the income of the richest person should not exceed four times the income of the poorest person in society (Byrns and Stone, 1989). This is however not the case with our societies today where some people are extremely wealthy while others cannot even afford the necessities of life like proper food and shelter. Some form of inequality, whether in income or labour, exists in every economy. This may result from the ability of some people to perform some tasks better than others, work longer, take risks, warranting higher payments (Schmidt et al., 2015 and Checchi et al., 2017). JEFAS 27,53 124 JEL Classification —D31, E24, H63 © Wilkista Lore Obiero and Seher G€ uls ¸ah Topuz. Published in Journal of Economics, Finance and Administrative Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http:// creativecommons.org/licences/by/4.0/legalcode The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2077-1886.htm Received 10 May 2021 Revised 18 July 2021 Accepted 10 September 2021 Journal of Economics, Finance and Administrative Science Vol. 27 No. 53, 2022 pp. 124-138 Emerald Publishing Limited e-ISSN: 2218-0648 p-ISSN: 2077-1886 DOI 10.1108/JEFAS-05-2021-0049 Differences in education and skills also qualify people into different job groups (van Damme, 2014), in addition, some people inherit wealth while others do not (Elinder et al., 2018). Economists have different views on how this existing inequality level should be handled [1]. According to theoretical views, one of the macroeconomic variables that explain income inequality is debt. This relationship, however, is not straightforward. Productive use of debt could lead to a reduction in inequality levels (You and Duttf, 1996) while high debt values could lead to volatility of income and as a result increase inequality (Azzimonti et al., 2014). The direction and impact of this relationship, therefore, varies from country to country depending on their macroeconomic policies (Anselmann and Kr€ amer, 2016). The redistributive theory states that an increase in internal debt will lead to an increase in inequality levels in an economy. Internal debts are held in the form of government securities, coupled with the fact that the government securities have relatively high prices, it is only the rich who can purchase the bonds. Consequently, when debt is serviced, it is the rich class of bondholders who again receive interest from the debt amounts. Being that debt servicing is achieved through taxation, resources end up being transferred from the poor to the rich bondholder class. The redistributive theory forms the main motivation of this study since there is little attention given to analysing the effect of debt on income inequality, a gap which this study seeks to fill. The main hypothesis of this study is to ascertain the impact of high values of debt on income inequality level as stated in the debt redistribution theory. There are limited studies on this topic, but to the best of our knowledge, none of them examine the Kenyan economy. Debt-inequality nexus is a peculiar phenomenon for every country and the current study will be specific to Kenya. In this study, the effect of internal and public debt on income inequality is examined by using ARDL Method for the 1970–2018 period in Kenya. After determining the long-term relationships between variables, Toda Yamamoto Causality tests are also conducted to ascertain the existence of causality relationships between debt and inequality. The rest of the study will be divided as follows: the next section will provide theoretical and empirical background on the study, followed by the methodology and data section while the last section is where the results, conclusion and policy recommendations will be provided. 2. Literature review 2.1 Theoretical framework In a bid to finance expenditures, the government may resort to one or both of two options debt financing and/or an increase in taxes. The impacts of these forms of financing, most especially debt financing on the economy have been analysed by various economists. David Ricardo argues that there is no change in the national output of the overall economy when either form of financing is adopted, commonly known as Ricardian equivalence. This term was formally used by Barro (1989). The term has since been argued by economists as one of the theoretical views on public debt and inequality relationship. A decline in government budget deficit is offset by an increase in private savings implying no change in the national savings amount (Ricardo, 1817). This is because if there is an increase in government debt currently, the forward-looking consumer increases their savings as opposed to increasing their consumption to cater for possible future increases in taxes. The increase in savings can then be spent in the bonds market further increasing government debt. It is the rich in the society who often save as compared to the poor who are likely to channel the increase in disposable income to consumption. The implication is that government borrows from the rich but taxes both rich and poor to pay those debts. Therefore, government financing decisions may impact inequality position even though it may not impact output as postulated by Ricardo. Another explanation for the theoretical relationships between domestic debt and inequality is that domestic debt causes income redistribution. According to this theory, Internal and public debt on income inequality 125 internal debt causes income redistribution in the economy since the people who purchase government bonds and treasury bills are the rich while during repayment, the burden of repayment lies on the entire tax base. This implies that during the debt repayment process, although rich people also pay tax, they receive interest rates from their treasury bills and bonds thus gaining more income. Through this process, the rich lenders become richer while the poor become poorer thereby increasing the inequality gap (Alesina and Tabellini, 1987; Elmendorf and Mankiw, 1998,p.8;Mishkin, 2014, p. 438; Salti, 2015;Bohoslavsky, 2016, p. 189). This effect, however, may not be experienced in the short run because most rich people are highly dependent on capital income while the poor rely mostly on income from labour. When a debt crisis occurs due to a high amount of debt in the economy, a decline in output is likely to be experienced implying a reduction in both capital and labour incomes. In the long run, however, the capital income owners receive compensation for their capital making them richer while the poor are not compensated and tend to become poorer. The direction of the relationship between debt and income inequality can also be from inequality to debt (Kumhof, 2015;Bohoslavsky, 2016, p. 183). With inequality, there is an existing possibility of reduced future consumption and so private investors seeking to maintain their present consumption into the future will purchase government securities when they are issued. The demand for government bonds thus increases. Through elections and exercising of democratic rights, the government is forced to issue more bonds implying higher public debts. Inequality thus triggers both the demand and supply of bonds. The rich vote for the bonds and treasury bills because it is a safe way of keeping money and ensuring continued consumption. The poor keep voting because of reduced international interest rates which are attractive to them. 2.2 Debt and income inequality Much of the current literature on inequality pays particular attention to the relationship between inequality and economic growth. Similarly, a considerable number of empirical studies on debt and economic growth have been conducted. However, the studies on the relationship between debt, both public and internal, and income inequality are quite limited. These studies are summarized in Table 1. Sakkas and Varthalitis (2019) and Tung (2020) ascertain that public debt harms inequality for countries in the Euro Area and Asia–pacific region respectively. These studies taken together suggest that governments may use public debt as a means of reducing inequality. However, Akram and Hamid (2016) analyse the impact of internal debt and external debt separately. The study concludes that only internal debt reduces inequality, whereas external debt has no impact on inequality. The study finds no statistically significant difference in the impact of external debt on the rich and the poor in South Asian economies. Country-specific studies include Akram (2013) and Farid et al. (2016) who provide an analysis on how external debt impacts inequality levels in Pakistan. The former study uses the OLS method while the latter uses the ARDL method of analysis. Both studies find that external debt is not pro-poor as they prove the existence of a positive relationship between external debt and income inequality in Pakistan. Sayed (2020) and Topuz (2021) find a positive impact of domestic debt on income inequality in Lebanon and Turkey respectively. In a study conducted for Turkey, Arslan (2019) proves the applicability of the redistribution effect in Turkey. The results from this study indicate that there is an improvement in income inequality levels in the country when public borrowing reduces. Some of the panel studies that have considered the inequality and debt relationship for both developing and developed economies belong to Prechel (1985),Arawatari and Ono (2015),Salti (2015),Tibi (2015),Detzer (2016) and Sezgenç (2019).Detzer (2016) uses financialization to explain the differences in debt and inequality for developed and developing economies while JEFAS 27,53 126 Prechel (1985) explains that the insignificant debt and inequality relationship in these economies is due to the differences in export and investment strategies. Sezgenç (2019) on the other hand attributes the differences in debt and inequality relationships to the political social setup of the countries and Tibi (2015) states that the initial level of income and development level of an economy highly influences the debt and inequality relationship. Arawatari and Ono (2015) find that countries with high inequality tend to have higher debt amounts compared to countries with lower inequality. The study emphasizes the role played by loose fiscal policies in causing high debt levels and high inequality. Salti (2015) concludes that internal debt is Author(s) Sample (country) and period Methodology Main findings Prechel (1985) Panel data. 1960–1975 Panel OLS The impact of debt on inequality is positive for some countries, negative for others and non-significant for all Akram (2013) Pakistan. 1975–2008 ARDL External debt has a positive and significant impact on inequality Salti (2015) Panel data Fixed effects model Domestic debt contributes more to inequality than public debt Arawatari and Ono (2015) Panel data of developed and developing countries. 1980– 2010 Panel regression methods An increase in inequality leads to an increase in public debt. Low inequality leads to lower debts Jabło nski et al. (2015) 34 OECD countries. 1995–2010 Multiple regression High levels of inequality contribute to rising debt values Tibi (2015) Panel data of 34 countries. 1980–2010 Fixed effects panel regression Income inequality has a positive impact on debt in developing countries but a negative impact on debt for developed countries Farid et al. (2016) Pakistan. 1973–2013 OLS Augmented Engle- Granger test External debt has a positive impact on inequality Detzer (2016) Developed and developing countries Stock flow Inequality and debt relationships are different across countries Akram and Hamid (2016) Selected South Asian countries. 1975–2010 Fixed Effects model Public debt has no significant relationship with inequality Domestic debt has a negative relationship with inequality Aksman (2017) A panel study of EU countries. 1995–2015 Dynamic panel data model Income inequality is not a significant predictor of the public debt to GDP ratio Karlin (2018) OECD countries. 1980–2015 Fixed Effects model, Random-Effects model Both external debt and domestic debt harm inequality, but the effect is stronger on external debt Sezgenç (2019) Panel data. 1990–2016 OLS Public external debt has a negative impact on income distribution Luo (2019) OECD countries. 1970–2010 Fixed effects model Labour income inequality has a positive impact on debt while capital income inequality has a negative impact on debt Sakkas and Varthalitis (2019) Euro Area. 2001–2015 Closed economy dynamic general equilibrium model Debt favours rich households Arslan (2019) Turkey. 2005–2015 Income decomposition method Decreased borrowing leads to an improvement in income distribution Sayed (2020) Lebanon. 1990–2015 ARDL and ECM Domestic debt has a positive impact on inequality Tung (2020) A panel study of 17 developing and emerging countries in the Asia Pacific region. 1980–2018 Fixed Effects model, Random-effects model Public debt has a negative impact on inequality Topuz (2021) Turkey. 1987–2018 VAR Unidirectional causality from domestic debt to income inequality exists An increase in public domestic debt increases inequality Source(s): Own elaboration Table 1. Empirical literature Internal and public debt on income inequality 127 responsible for the increased inequality in different economies. Governments should adopt alternative sources of financing to help reduce inequality. The relationship between debt and inequality for OECD countries is analysed by Jabło nski et al. (2015),Karlin (2018) and Luo (2019). The studies are conducted for different periods. While Jabło nski et al. (2015) claims that rising inequality contributes to an increase in public debt, Karlin (2018) states that there is a negative impact of external and internal debt on inequality for OECD countries. In this study, the impact of external debt is found to be stronger in reducing income inequality compared to internal debt. Unlike the other studies, Luo (2019) introduces labour and capital inequality. Inequality in labour contributes to higher debts in these economies while inequality in capital leads to a reduction of debt levels in these economies. Finally, a remarkable study due to the results obtained belongs to Aksman (2017). The author analyses the impact of inequality and poverty on public debt to GDP ratio for European countries. This study finds out that inequality and poverty are not very significant in explaining changes in public debt. Looking at the studies in the literature, especially on the relationship between internal debt and income inequality, it can be said that they are quite limited. To the best of our knowledge, no study has examined the debt and income relationship for Kenya. The gap in the literature that arises due to an undefined relationship between these variables in Kenya forms the basis for this research. 3. Method 3.1 Data and Variables This study uses data for the period 1970–2018 for empirical analysis. The dependent variable is the Gini coefficient which represents income inequality. This data is obtained from the Standardized World Income Inequality Database (SWIID) published by Solt (2020). The internal and public debt data are sourced from the Kenya National Bureau of Statistics (KNBS) while the remaining data is from the World Development Indicator (WDI). Other control variables like military expenditure, human capital, per capita GDP, trade openness and investment are also included. The choice of the control variables is based on their consistent association with inequality as suggested in the studies by Akram (2013) and Salti (2015). Based on economic theory, a positive relationship between internal debt, public debt, military expenditure and investment on income inequality is expected. The variables expected to harm income inequality include GDP per capita, trade openness and secondary school enrolment. Table 2 shows the descriptive statistics and source of the variables. 3.2 Analytical procedures To demonstrate the effect of debt on income inequality in the long run, we used Autoregressive Distributed Lag (ARDL) based boundary test developed by Pesaran et al. (2001). To determine whether cointegration exists between the variables, the following equation is specified [2]: ΔGinit¼β0þX m i¼0 β1iΔGinit−iþX n i¼0 β2iΔDebtt−iþX p i¼0 β3iΔMEXPt−iþX q i¼0 β4iΔSSEt−i þX r i¼0 β5iΔGPCt−iþX s i¼0 β6iΔINVt−iþX v i¼0 β7iΔTOt−iþβ8Ginit−1þβ9PDt−1 þβ10IDt−1þβ11Mexpt−1þβ12SSEt−1þβ13GPCt−1þβ14Invt−1þβ15TOt−1þ μ t (1) where β0is the constant term, Δis a difference of variables, β1i...β7iand β8...β15 are the variable coefficients, m,n,p,q,r,s,v, represent the optimal lag length and μ tis the error term. JEFAS 27,53 128 The optimal lag is chosen based on the Akaike information criterion. To determine the existence of a long-run relationship, we derive the hypothesis below from equation (1): Ho. β9¼β10 ¼β11 ¼β12 ¼β13 ¼β14 ¼β15 ¼β16 ¼0 (no cointegration) H1. β9≠β10 ≠β11 ≠β12 ≠β13 ≠β14 ≠β15 ≠β16 ≠0 (cointegration) The test results are obtained by comparing the Fstatistic with the upper and lower bound critical values as suggested by Pesaran et al. (2001). The rejection of the null hypothesis is done when the calculated Fstatistic is greater than the upper bound value implying the presence of a cointegrating relationship between the variables. To represent the short-run equation, the error correction model used is as shown below: ΔGinit¼β0þX m i¼0 β1iΔGinit−iþX n i¼0 β2iΔDebtt−iþX p i¼0 β3iΔMEXPt−iþX q i¼0 β4iΔSSEt−i þX r i¼0 β5iΔGPCt−iþX s i¼0 β6iΔInvt−iþX v i¼0 β7iΔTOt−iþβ8ECMt−iþ μ t (2) where β8is the speed of adjustment coefficient and shows the speed of adjustment in the long run. After examining the long-term relationships between the variables using the ARDL method, the Toda Yamamoto causality test is applied. The conventional approach for testing the causality relationship was put forward by Granger (1969), however, it is limited as it may lead to spurious results if the variables are non-stationary or cointegrated (Wolde-Rufael, 2005). The Toda Yamamoto test is preferable as it produces reliable results as long as the order of integration does not exceed the lag length (Toda and Yamamoto, 1995). The Toda Yamamoto causality test is implemented in stages with the first step being fitting a VAR equation with knumber of lags based on the different information criteria, AIC or SC, similarly, the maximum order of integration (dmax) is made known in this step. The second step is based on the two values (kand dmax) where a new VAR of order (kþdmax) is fitted. Variable Data definition and source Obs Mean Std dev Min Max Gini Gini coefficient for Kenya 49 46.18 17.90 21.38 95.82 SWIID ID Internal debt (%GDP) 49 22.58 6.42 12.49 39.49 KNBS PD Public debt (%GDP) 49 53.42 20.32 26.81 120.70 KNBS MEXP Military expenditure (% GDP) 49 2.17 1.14 1.05 5.50 WDI SSE Secondary school enrolment (%gross enrolment) 49 40.98 15.21 16.42 70.3 WDI GPC Annual growth in GDP per capita (%) 49 1.988 4.304 3.95 17.88 WDI INV Investment (%GDP) 49 20.61 3.36 15 29.78 WDI TO Trade openness (sum of exports and imports % GDP) 49 56.64 8.38 36.15 74.57 WDI Source(s): Own elaboration Table 2. Descriptive statistics and data source Internal and public debt on income inequality 129 To understand the existence of a causality relationship, a modified Wald (MWALD) test is applied. The resulting parameter has asymptotic χ 2distribution which is important for inferencing. Equations (3–8) are used to test for the Toda Yamamoto causality relationship in the internal debt, public debt and income inequality models. IDt¼ α 0þX k i¼1 δ1iIDt−iþX kþdmax j¼kþ1 δ2jIDt−jþX k i¼1 γ1iGinit−i þX kþdmax j¼kþ1 γ2jGinit−jþ μ 1t (3) Ginit¼w0þX k i¼1 ∅1iGinit−iþX kþdmax j¼kþ1 ∅2jGinit−jþX k i¼1 θ1iIDt−i þX kþdmax j¼kþ1 θ2jIDt−iþ μ 2t (4) PDt¼a0þX k i¼1 ω 1iPDt−iþX kþdmax j¼kþ1 ω 2jPDt−jþX k i¼1 σ 1iGinit−i þX kþdmax j¼kþ1 σ 2jGinit−jþ ε 1t (5) Ginit¼c0þX k i¼1 ρ 1iGinit−iþX kþdmax j¼kþ1 ρ 2jGinit−jþX k i¼1 τ 1iPDt−i þX kþdmax j¼kþ1 τ 2iPDt−jþ ε 2t (6) IDt¼r0þX k i¼1 d1iIDt−iþX kþdmax j¼kþ1 d2jIDt−jþX k i¼1 b1iPDt−i þX kþdmax j¼kþ1 b2jPDt−jþλ1t (7) PDt¼w0þX k i¼1 p1iPDt−iþX kþdmax j¼kþ1 p2jPDt−jþX k i¼1 n1iIDt−i þX kþdmax j¼kþ1 n2jIDt−jþλ2t (8) Granger causality from Gini to internal debt (ID), implies that γ1i≠0∀i;granger causality from ID to Gini implies that θ1i≠0∀i;granger causality from Gini to Public debt (PD) implies that σ 1i≠0∀i;while granger causality from public debt to Gini implies that τ 1i≠0∀i; similarly granger causality from public debt to internal debt implies that b1i≠0∀i, and granger causality from internal debt to public debt implies that n1i≠0∀i. The error terms μ 1t, μ 2t, ε 1t, ε 2t,λ1tand λ2tare normal (0, δ2). JEFAS 27,53 130 4. Results and discussion This section presents the results of the ARDL model estimation and Toda Yamamoto causality tests in Kenya for the period 1970–2018. The results of Augmented Dickey–Fuller (ADF), Phillips–Perron (PP) and Zivot–Andrews (ZA), unit root tests are shown in Table 3. After determining that the stationarity level of the variables are I(0) and I(1), the ARDL model can be estimated. Variables Test statistic Level First difference Constant Constant and trend Constant Constant and trend Gini ADF 2.3129 2.5706 6.3098*** 6.4041*** PP 2.4285 2.4200 10.266*** 10.555*** ZA 2.5948 (1999) 2.6373 (1995) 9.7247*** (1992) 9.76*** (1992) PD ADF 1.8694 1.7732 6.8598*** 4.5442*** PP 1.8457 1.7407 6.8598*** 6.8357*** ZA 2.8908 (2000) 4.2129 (1995) 8.2614*** (1995) 8.1749*** (1994) ID ADF 2.4524 2.4317 8.1951*** 8.1158*** PP 2.3979 2.3897 8.2171*** 8.1158*** ZA 5.1864** (1995) 6.9746*** (1995) 9.3019*** (1995) 9.4139*** (1995) MEXP ADF 1.3921 2.5559 5.2406*** 5.2979*** PP 1.6280 2.5123 3.6200*** 3.6250*** ZA 4.1329 (1991) 3.5536 (2007) 5.5354*** (1980) 8.8388*** (1980) SSE ADF 0.0549 1.7534 7.6540*** 7.6074*** PP 0.0549 1.7600 7.6511*** 7.6047*** ZA 3.0624 (2008) 3.1028 (1994) 8.2547*** (2003) 8.1922*** (2003) GPC ADF 4.8251*** 4.7758*** 7.3163*** 7.4968*** PP 4.6197*** 4.5704*** 12.036*** 13.297*** ZA 5.1894** (2004) 5.2054** (1992) 6.1541*** (1992) 6.6684*** (1992) TO ADF 2.2899 3.2297* 7.9714*** 7.9425*** PP 2.2278 3.2767* 8.4435*** 8.7830*** ZA 4.1750 (1993) 4.2652 (2011) 6.5844*** (1989) 6.5722*** (1988) INV ADF 3.7947*** 4.4163*** 10.081*** 9.9712*** PP 3.6658*** 4.3827*** 28.227*** 29.410*** ZA 6.2331*** (1992) 6.4041*** (1992) 7.0395*** (2000) 6.9665*** (2000) Note(s): The values demonstrate tstatistic for the test. *, **, *** represent significance at 10%, 5% and 1% level, respectively. The values in the parenthesis represent the break dates obtained in ZA Source(s): Own elaboration Test H 0 ID model Dependent variable: Gini ARDL (1, 1, 1, 0, 1, 0, 0) PD model Dependent variable: Gini ARDL (1,2,2,0, 2,1,0) Normality Series are normal 0.3570 0.6311 Autocorrelation Autocorrelation does not exist 0.1034 0.1107 Heteroscedasticity Heteroscedasticity does not exist 0.2689 0.1005 Ramsey Reset Test The model is correctly specified 0.0826 0.1181 Source(s): Own elaboration Table 3. Unit root tests Table 4. Diagnostic test results Internal and public debt on income inequality 131 Prechel, H. (1985), “The effects of exports, public debt, and development on income inequality”, Sociological Quarterly, Vol. 26 No. 2, pp. 213-234, doi: 10.1111/j.1533-8525.1985.tb00225.x. Ravinthirakumaran, K. and Ravinthirakumaran, N. 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Corresponding author Seher G€ uls ¸ah Topuz can be contacted at: [email protected] For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: [email protected] JEFAS 27,53 138