Statistical Descriptive Incentives and Disincentives of Profit S

59 corporate tax rate difference with time, � � − � × ; and interaction between effectiveness of anti- avoidance rule with time, �� � × � � × . Where t is 2005 = 1, and so forth, to 2013 = 9. Table 4.2 – Variable Specification Variable Source Format Measurement Dependent variables Earnings before interest and expense EBIT ORBIS Natural log ln � Pre-tax profitability EBT ORBIS Natural log ln � Predictor variables  Tax rate difference KPMG, EY, Deloitte, and PwC Percentage � � − � Effectiveness of anti-avoidance rule IBFD, KPMG, EY, Deloitte, PwC and Global Insight Scale availability of anti-avoidance rule times regulatory quality - Transfer pricing rule � × � � - Interest limitation rule � � × � � Control variables Fixed asset capital ORBIS Natural log ln � Cost of employees labour ORBIS Natural log ln � GDP per capita World Development Indicators World Bank Scale ‘000 USD � Robustness variables Total asset capital ORBIS Natural log ln � Availability of transfer pricing rules IBFD, KPMG, EY, Deloitte and PwC Binary variable 1 = available, 0 = otherwise � Availability of interest limitation rules � � Tax effectiveness Variable of regulatory quality from Global Insight Scale 0 - 1 � � Tax rate differencetime KPMG, EY, Deloitte, and PwC Percentage � � − � × Effectiveness of anti-avoidance ruletime IBFD, KPMG, EY, Deloitte, PwC and Global Insight Scale � × � � × ; � � × � � × Control of corruption Worldwide Governance Indicator World Bank Scale -2.5 to + 2.5 �

4.6. Statistical Descriptive

Based on preliminary search on ORBIS database, there are 28,679 potential samples; however to include them as my final sample I also make second stage of filtering process based on availability of data for each firm. Since information of EBIT, pre-tax profitability, fixed asset, cost of employees, and also –the important thing- information of parent location are primary for the basic regression model; then I excluded any firm which have no information for those variables during 2005 to 2013. Thus, if firm have at least has one single year of complete data, I will include as my final sample. With STATA software, I found only 8,602 firms that survived to be the final sample. These data are unequally distributed in 29 developing countries see Table 4.3. Furthermore, most of subsidiaries in 60 those developing countries have parent in advanced economies 65 and tax havens 26. Whereas, the rest 9 have their parents located in another developing countries. Furthermore, I build dataset for regression analysis containing eleven variables. There are 40,394 to 43,821 observations for 8,602 sample firms, meanings that on average each firm have 4.7 to 5.0 years of analysis. Mean from both variables lnEBIT and lnpre-tax profitability are in similar amount, indicating that they have small difference Table 4.4. This is also supported by high correlation between them 0.949 as shown by Table 4.5. Table 4.3 – Number of Samples, based on Location of Their Parents Developing Country Location of Parent Total Subsidiaries N Developing Countries Tax Havens Advanced Economies Undescribed Argentina - - 1.00 - 2 Bangladesh 0.50 - 0.50 - 2 Bosnia and Herzegovina 0.19 0.11 0.70 - 391 Brazil - 0.50 0.50 - 2 Bulgaria 0.07 0.27 0.66 - 599 China - 1.00 - - 2 Colombia - - 1.00 - 1 Ecuador 0.33 0.17 0.50 - 6 Egypt - 1.00 - - 1 El Salvador - - 1.00 - 1 Ghana - - 1.00 - 1 Hungary 0.01 0.10 0.89 - 597 India 0.03 0.35 0.62 - 113 Indonesia 0.11 0.44 0.44 - 27 Macedonia - 1.00 - - 1 Malaysia 0.05 0.32 0.63 - 131 Montenegro - 0.33 0.67 - 3 Nepal 1.00 - - - 1 Nigeria 0.50 - 0.50 - 2 Pakistan 0.06 0.12 0.82 - 17 Papua New Guinea - - 1.00 - 1 Philippines - 0.50 0.50 - 6 Romania 0.08 0.28 0.63 0.00 4,158 Serbia 0.17 0.19 0.64 - 1,277 Sri Lanka 0.10 0.60 0.30 - 10 Tanzania - 0.50 0.50 - 2 Thailand 0.05 0.20 0.75 - 129 Ukraine 0.04 0.38 0.57 - 1,117 Zambia - 0.50 0.50 - 2 Total 0.09 0.26 0.65 0.00 8,602 On the other hand, tax rate difference ranging from -45 to 34, with average on -10.5. At first sight, it can indicate that corporate tax rate in parent’s location is relative high. However, detailed information on samples showed that this caused by that almost 95 of sample comes from developing countries in European region, which have relative low corporate tax rates. Moreover, effectiveness of transfer pricing rules is 0.67 on average, higher than effectiveness of interest limitation rules, 0.58. 61 Both of variables have minimum value of 0 anti-avoidance rule is not available to 0.88 anti- avoidance rule is available but not optimal to enforce. This data support explanation on Chapter 3 that argued that application of anti-avoidance rule would be depending on capability of tax administration. Table 4.4 – Summary Statistics Obs. Mean Std dev Min Median Max ln EBIT 43821 5.25 2.20 -8.12 5.28 14.31 ln pre-tax profitability 40394 5.10 2.28 -5.77 5.14 14.33 Tax rate difference 43821 -9.71 9.60 -45.00 -10.55 34.00 GDP per capita 000 USD 43821 7.12 2.92 0.32 8.07 15.60 Fixed asset ln 43821 5.92 2.69 -8.10 6.01 15.32 Total asset ln 43820 7.74 1.98 -2.60 7.70 15.36 Cost of employees ln 43821 5.80 1.99 -5.37 5.87 12.67 Transfer pricing rule availability 43821 0.99 0.11 1 1 Interest limitation rule availability 43810 0.81 0.39 1 1 Tax effectiveness 43821 0.67 0.12 0.25 0.75 0.88 Effectiveness of interest limitation rule 43821 0.67 0.14 0.75 0.88 Effectiveness of transfer pricing rule 43810 0.58 0.29 0.75 0.88 Control of corruption 43821 -0.25 0.29 -1.42 -0.22 0.61 Control variables, such as fixed asset, cost of employees, total asset or fixed asset are having high correlation 0.3 with both EBIT and pre-tax profitability Table 4.5. Positive relationship between them indicates that those factors are drivers for profitability of firms. Control of corruption data is ranging between -1.42 and 0.62, with average on -0.22 which means that most of developing countries still struggling with corruption problem.

4.7. Conclusion