maintain macroeconomic stability and uphold a decent standard of living for all citizens, .... activity, thereby decreasing tax revenue (Laffer, 2004). ..... 24. CHAPTER 3. DATA AND METHODS. 3.1. Introduction. The objective of this chapter was ...
The Impact of Taxation on Economic Growth. Evidence from the Federation of St. Kitts and Nevis from 1990-2015
Akim A Galloway 119040513
Subject Area: Finance and Economics Supervisor: Dr. Georgios Sermpinis
Submitted: December 2016
Dissertation submitted to the University of Leicester in partial fulfillment of the requirements of the degree of Masters of Science in Finance.
TABLE OF CONTENTS ACKNOWLEDGMENTS ...................................................................................................... 1 KEY TO ABBREVIATIONS ................................................................................................. 2 EXECUTIVE SUMMARY .................................................................................................... 3 CHAPTER 1. INTRODUCTION ........................................................................................... 4 1.1. Background of Study ................................................................................................... 4 1.2. Research Objective and Research Questions ............................................................... 5 1.3. Scope ............................................................................................................................ 7 1.4. Contribution of the Study ............................................................................................. 7 1.5. Structure of the Dissertation......................................................................................... 8 CHAPTER 2. LITERATURE REVIEW AND THEORY ..................................................... 9 2.1. Theoretical framework ................................................................................................. 9 2.1.1. Laffer Curve .......................................................................................................... 9 2.2. Empirical Review ....................................................................................................... 12 2.2.1. Taxes on Domestic Goods and Services and Economic Growth ........................ 12 2.2.2. Taxes on Income and Economic Growth ............................................................ 17 2.2.3. Taxes on Domestic Goods and Services and Revenue Generation ..................... 19 2.2.4. Taxes on Income and Revenue Generation ......................................................... 21 CHAPTER 3. DATA AND METHODS .............................................................................. 24 3.1. Introduction ................................................................................................................ 24
3.2. Sources of Data .......................................................................................................... 24 3.3. Definition and Measurement of Variables. ................................................................ 24 3.3.1. Dependent Variables. .......................................................................................... 24 3.3.2. Independent Variables ......................................................................................... 25 3.3.3. Control Variable .................................................................................................. 25 3.4. Econometric Analysis ................................................................................................ 26 3.5. Methods ...................................................................................................................... 26 3.6. Econometric Models .................................................................................................. 27 3.6.1. GDP Model .......................................................................................................... 27 3.6.2. Revenue Generation Model ................................................................................. 28 3.7. Research Hypothesis .................................................................................................. 29 CHAPTER 4. ANALYSIS AND RESULTS ....................................................................... 31 4.1. Descriptive Statistics .................................................................................................. 31 4.2. Diagnostic Test........................................................................................................... 33 4.2.1 Casewise Outliers ................................................................................................. 33 4.2.2 Multicollinearity Test ........................................................................................... 33 4.2.3. Normality, Linearity and Heteroscedasticity Tests. ............................................ 34 4.3. Regression Analysis and Results of the GDP Model ................................................. 36 4.3.1. Assessing the Goodness of Fit of the GDP model. ............................................. 36 4.3.2. Analysis of Variance (ANOVA) of the GDP Model........................................... 37
4.4. Evaluating Each Independent Variable in the GDP model. ....................................... 38 4.4.1. The Impact of Taxes on Domestic Goods and Services on Economic Growth .. 39 4.4.2. The Impact of Taxes on Income on Economic Growth ...................................... 41 4.5. Regression Analysis and Results of the Revenue Generation Model ........................ 43 4.5.1. Assessing the Goodness of Fit of the Revenue Generation Model ..................... 44 4.5.2 Analysis of Variance (ANOVA) of the Revenue Generation Model ................... 44 4.6. Evaluating Each Independent Variable in the Revenue Generation Model. .............. 45 4.6.1. The Impact of Taxes on Domestic Goods and Services on Revenue Generation ....................................................................................................................................... 46 4.6.2. The Impact of Taxes on Income on Revenue Generation ................................... 48 CHAPTER 5. DISCUSSION AND CONCLUSIONS ......................................................... 51 5.1. Summary .................................................................................................................... 51 5.1.1. To what extent does taxes on domestic goods and services impact economic growth? .......................................................................................................................... 52 5.1.2. To what extent does taxes on income impact economic growth? ....................... 53 5.1.3. To what extent does taxes on domestic goods and services impact revenue generation? .................................................................................................................... 54 5.1.4. To what extent does taxes on income impact revenue generation?..................... 54 5.2. Theoretical Implications............................................................................................. 55 5.3. Practical Implications ................................................................................................. 56 5.4. Limitations ................................................................................................................. 57
5.5. Directions for future research..................................................................................... 58 5.6. Reflections.................................................................................................................. 59 REFERENCES ..................................................................................................................... 62 APPENDIX A: DATA SOURCES AND DESCRIPTION .................................................. 67 APPENDIX B: CORRELATION MATRIX ........................................................................ 68 APPENDIX C: NORMALITY TEST .................................................................................. 69 APPENDIX D: LINEARITY TEST GDP MODEL............................................................. 70 APPENDIX D1: LINEARITY TEST FOR REVENUE GENERATION MODEL ............ 72 APPENDIX E: HETEROSCEDASTICITY TEST .............................................................. 74 APPENDIX F: SUMMARY OF EMPIRICAL LITERATURE .......................................... 75 APPENDIX G: PROPOSAL ................................................................................................ 78
ACKNOWLEDGMENTS I would like to express my deepest appreciation to the following persons. To my brother Ernest Smithen for encouraging me to undertake a Master’s Degree and supporting me throughout the duration, to my first and second dissertation supervisors Dr. Panayiotis Savvas and Dr. Georgios Sermpinis for their valuable guidance throughout the dissertation, to my other brothers Trevor Saunders and Kamal Moore for their constant encouragement, to my mother, sister and Toya for their generous support and understanding throughout my studies, to my two fellow students Bushan and Yannic (the only University of Leicester students I ever interacted with) for their support throughout my studies, to the staff of the ECCB statistics department for providing the secondary data for the dissertation. I am indebted to all the above.
Akim A Galloway
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KEY TO ABBREVIATIONS ANOVA: Analysis of Variance CED: Customs and Excise Duties ECCB: Eastern Caribbean Central Bank GDP: Economic Growth GST: Goods and Services Tax INCTAX: Taxes on Income IRD: Inland Revenue Department PPMC: Pearson Product Moment Correlation POP: Population Growth PPT: Petroleum Profit Tax PTI: Petroleum Tax Income PIT: Personal Income Tax RGDP: Real Gross Domestic Product TDGS: Taxes on Domestic Goods and Services TOTREV: Revenue Generation VAT: Value Added Tax VIF: Variance Inflation Factor
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EXECUTIVE SUMMARY The federation of St. Kitts and Nevis has experienced significant growth due to a change from an economy largely dependent on the sugar industry to a serviced-based economy driven by the tourism and financial services industries. However, for the government to maintain macroeconomic stability and uphold a decent standard of living for all citizens, various tax reforms were implemented. As such, the main objective of this study was to examine the impact of taxation on economic growth and revenue generation in the federation of St. Kitts and Nevis. Specifically, the study answered four research questions. First, to what extent does taxes on domestic goods and services (TDGS) impact economic growth (GDP)? Second, to what extent does taxes on income (INCTAX) impact GDP? Third, to what extent does TDGS impact revenue generation (TOTREV)? Fourth, to what extent does INCTAX impact TOTREV? The study was grounded by the Laffer curve which recognizes a positive impact on revenue generation and economic growth if taxes are set to an optimal rate but a negative impact if in the reverse. The study utilized twenty-six yearly observations from 1990 to 2015. The data was collected from the Eastern Caribbean Central Bank (ECCB) and covered the key variables of this study which were TDGS, GDP, INCTAX, TOTREV and population growth (POP).The study employed the multiple regression technique and revealed four key findings. Firstly, TDGS positively and significantly impact GDP. Secondly, INCTAX positively and significantly impact GDP. Third, TDGS positively and significantly impacts TOTREV. Last, INCTAX positively and significantly impact TOTREV. Furthermore, from a practical standpoint, the findings of this study can be useful for the decision makers of the government of St. Kitts and Nevis and other stakeholders as it relates to fiscal policy and economic growth. 3
CHAPTER 1. INTRODUCTION This chapter is intended to provide an overview of the dissertation. The sections of this chapter describe the background of the study, research objectives, research questions, scope, contribution of study, and the structure of the dissertation. 1.1. Background of Study Taxation is an integral part of a country’s national income and economic growth. As such, the political, economic and social developments of a country is highly dependent upon an efficient tax system to generate revenue to discharge the government’s pressing obligations. Tax is a compulsory levy imposed on an individual’s income, business profits, goods and services, imports, and property. The proceeds from taxes are used to provide security, defense, justice, social amenities and to create conditions for the economic well-being of citizens (Chigbu and Njoku, 2015). Edame and Okoi (2014) explained that taxation is an effective way for a government to mobilize internal resources and create conditions necessary for economic growth. Tosun and Abizadeh (2005) agrees and summarized five possible ways of how taxation can affect economic growth. First, taxes can discourage investment rate via corporate, personal and capital gains taxes. Second, taxes can hinder growth in labor supply by discouraging the workforce. Third, tax policy can have an effect on productivity growth by discouraging research and development expenditures. Last, exuberant tax rates on labor supply can have an effect on the government’s ability to collect sufficient revenue due to a decrease in the tax base. Musgrave and Musgrave (2004) shared similar sentiments and found that there are two significant economic effects of taxation. First, the author claims that there are
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micro effects on the distribution of income and the efficiency of resources. Second, there are macro effects on the amount of capacity output, employment, prices and growth. Economic growth is an increase in the amount of goods and services a country produces from one period to another (Salami et al. 2015). Jhingan (2005) explained that economic growth is a gradual and steady change in the long-run and is created by an increase in the rate of savings, investments and population. Similarly, Salami et al. (2015) added that economic growth is the catalyst for raising the standard of living for nationals in a country and that it contributes to the reduction of the inequalities of income distribution. The federation of St. Kitts and Nevis has made significant improvements from an economy predominantly dependent upon the sugar industry to a serviced-based economy driven by tourism and financial services (Tax Reform Unit, 2010). However, in order to keep pace with the development of the economy and to achieve macroeconomic stability to maintain a decent standard of living for all citizens, the government undertook various tax reforms. The consumption tax act of 1995 was replaced by the Value Added Tax (VAT) act of 2010 which is a broad-based tax on the consumption of goods and services. Similarly, the income tax act amendment revision of 2010 was also aimed at creating a larger tax base for revenue yield (Tax Reform Unit, 2010). 1.2. Research Objective and Research Questions The main objective of this study is to examine the impact of taxation on revenue generation and economic growth in the federation of St. Kitts and Nevis from 1990-2015. In order to meet this objective, this study explored the following research questions.
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1. To what extent does taxes on domestic goods and services impact economic growth? 2. To what extent does taxes on income impact economic growth? 3. To what extent does taxes on domestic goods and services impact revenue generation? 4. To what extent does taxes on income impact revenue generation? Many authors examined the impact of taxation on revenue generation and economic growth and found conflicting results. Several authors tested various fiscal and macroeconomic variables to examine the extent to which economic growth and revenue generation is impacted by taxation. Some found that taxation had a positive and significant impact on revenue generation and economic growth (Hassan, 2015; Hakim, Karia and Bujang, 2016; Kolahi and Noor, 2016; Lawrence and Victor, 2016). Conversely, other authors found that taxation had no significant impact on economic growth and revenue generation (Afolayan and Okoli, 2015; Mudugba, Ekwe and Kalu, 2015). However, with an extensive review of the existing empirical literature, there were several significant observations. Firstly, although there were some authors who found positively significant results, there were some who did not. Secondly, most of the empirical studies were conducted in Nigeria with scarce empirical studies in other smaller developing countries. Thirdly, the majority of the empirical studies reviewed were conducted over a twenty year period from 1994-2012 with twenty observations tested. The abundance of studies conducted in Nigeria, the conflicting conclusions in existing empirical findings, and the lack of empirical studies conducted on other smaller developing countries, have caused several gaps in the existing body of knowledge. In spite of this, this 6
study addressed these gaps, by investigating the impact of taxation on economic growth and revenue generation in St. Kitts and Nevis. Moreover, this study separates itself by covering a period that is more extensive and relevant from 1990 to 2015. This amounts to twenty-six observations unlike the majority of empirical studies that covered the period from 1994 to 2012 in Nigeria (Chigbu and Njoku, 2015; Afolayan and Okoli, 2015; Ofishie, 2015, Madugba and Joseph, 2016). 1.3. Scope Although there are several factors that impact economic growth and revenue generation, this study is limited to the impact of taxation with a focus on several macroeconomic variables similar to existing studies (Immanuela, 2016; Etale and Bingilar, 2016; Hakim, Karia and Bujang, 2016). In order to meet this objective, this study employed time series data from 1990-2015. This study was grounded by the Laffer curve theory, which hypothesizes that there is a positive impact on revenue generation and economic growth if taxes are at an optimal rate but would have a negative impact if it is in the reverse. In this regard, there were several empirical studies that revealed that taxation positively and significantly impacts revenue generation and economic growth which supports the Laffer curve theory (Hakim, Karia and Bujang, 2016). Furthermore, this study employed the multiple regression statistical method in order to examine the extent to which the independent variables of this study accounts for the variation in revenue generation and economic growth. 1.4. Contribution of the Study This study provided evidence to support the Laffer curve theory, which hypothesizes that there would be a positive impact on revenue generation and economic growth if taxes are at 7
an optimal rate but would have a negative impact if it is in the reverse (Laffer, 2004). This study provided a new contribution to the existing body of knowledge by documenting evidence of the findings of the impact of taxation on revenue generation and economic growth in St. Kitts and Nevis. The decision makers of the government of St. Kitts and Nevis may find these findings useful when defining appropriate levels of taxation for fiscal policy. Also, local and foreign risk-averse investors may find this information convenient for strategic planning as it relates to their return on investment. Moreover, this study can be a catalyst for future researchers to investigate other fiscal and macroeconomic variables to determine their impact on revenue generation and economic growth. 1.5. Structure of the Dissertation The remaining chapters of this dissertation are structured as follows: Chapter two, is the literature review which is a discussion of the theoretical framework along with existing empirical studies on this phenomena; Chapter three outlines the data and methods employed in the study along with a presentation of the variables, statistical method and the model specifications; Chapter four, presents an evaluation of the results from the statistical analysis; Chapter five presents a summary of the analysis and results, the theoretical and practical implications, limitations, directions for future research and a reflection of the research process.
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CHAPTER 2. LITERATURE REVIEW AND THEORY This chapter consists of two main components. The first section outlines a theoretical framework with the theoretical approach of the Laffer curve whilst the second section evaluates existing literature on the impact of taxation on economic growth and revenue generation. 2.1. Theoretical framework The Laffer curve was used to explain the impact of taxation on economic growth and revenue generation. It was necessary to use this theory to explain this phenomenon because it has an aspect that was tested by the four research questions of this study. 2.1.1. Laffer Curve The Laffer curve provides a theoretical representation of the relationship between government revenue raised by taxation and its effects on economic growth (Laffer, 2004). This theory was tendered by Professor Arthur Laffer in 2004 and is one of the main constructs of supply-side economics theory which argues that economic growth can be achieved by investing in capital, and lowering the barriers to the production of goods and services (Laffer, 2004). However, Professor Arthur Laffer gives credit to the Muslim philosopher Ibn Khaldun and John Maynard Keynes for the idea. The Laffer curve holds that, as taxes increase, tax revenue would also increase. At the same time, the theory claims that when tax rates pass the optimum rate, it discourages economic activity, thereby decreasing tax revenue (Laffer, 2004). However, if the tax rate increases to 100%, passing the optimum revenue maximizing point, it would be unfavorable to revenue generation and economic growth as workers, producers and entrepreneurs become discouraged to work (Laffer, 2004). In spite of this, the theory finds that governments could 9
gain more revenue and positively impact economic growth by lowering tax rates to an optimum rate. This would create an atmosphere conducive for maximum revenue generation while maintaining an economy where workers are desirous of working, investing, saving and producing. The Laffer curve explains the impact of taxation on economic growth by two effects. Such effects are the arithmetic effect and the economic effect. The arithmetic effect holds that if tax rates are decreased, the revenue collected per dollar of the tax base would also decrease in relation to the decreased tax rate and vice versa (Laffer, 2004). The second aspect is the economic effect which was tested by the four research questions of this study. The economic effect recognizes the positive impact that lower tax rates have on economic variables such as work, output, and the tax base. This allows the government to collect more revenue and replace the foregone revenue from tax cuts. The economic effect also recognizes a negative impact when the tax rate is increased beyond an optimum rate. This creates the opposite of the economic effect by causing fewer producers and businessmen to work (Laffer, 2004). Importantly, there are two implications of the Laffer curve. Firstly, not all tax cuts are made equal. This is so because the revenue feedback is dependent upon the period, location, size and how the taxes are cut (Laffer, 2004). For example, supply-side tax cuts like income tax rate reductions, capital gains tax rates reductions and dividends tax rate reductions will generate more revenue because they reduce the tax penalty on productive behavior. Thus, when workers respond by working more, saving more and investing more the result is more taxable income. However, other tax cuts such as expanded credits deductions and exemptions are unlikely to have any significant impact on the incentive to engage in 10
productive behavior. This is because the marginal tax rates on additional increments of work, saving and investments remain unchanged and does not lead to significant changes in taxable income causing insignificant revenue feedback (Laffer, 2004). The second implication of the Laffer curve is that not all tax cuts pay for themselves. Although there is evidence from the Reagan administration where tax collections from the upper class increased when the top tax rate declined from 70% to 28%, in Laffer’s view, this was a rare case (Laffer, 2004). As such, notwithstanding the rare cases, Laffer (2004) argues that they do not pay for themselves in the majority of cases. The author argues that the economy is often on the first partition of the Laffer curve between 0% to the maximizing point. So, although taxes can be lowered during these two points and cause taxable income to increase and improve the economy, the author argues that this increase will not be enough to offset the effects of the lower tax rate (Laffer, 2004). In essence, the government could generate more revenue by gradually moving upward the first part of the curve between the first two points but will be very costly and result in lost economic growth and lower pre-tax income for workers. Thus, there would be revenue feedback but not enough to make a tax cut self-financing. The Laffer curve is appropriate for explaining the impact of taxation on revenue generation and economic growth (Lawrence and Victor, 2016). However, it must be noted that there are other theories that explain this phenomenon but it was the decision of the researcher to be selective. Moreover, a comprehensive review of empirical studies will suffice to support the theory and to evaluate the conclusions of other researchers. In summary, the Laffer curve gives a conditional prediction that there would be a positive impact on revenue
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generation and economic growth if taxes are set to an optimum rate but would have a negative impact if it is in the reverse. 2.2. Empirical Review The next four sections provided empirical literature in relation to the four research questions of this study. The first section provided empirical evidence on the impact of TDGS on GDP. The second section provided empirical evidence on the impact of INCTAX on GDP. The third section provided empirical evidence on the impact of TDGS on revenue generation. The fourth section provided empirical evidence on the impact of INCTAX on revenue generation. 2.2.1. Taxes on Domestic Goods and Services and Economic Growth Immanuella (2016) examined the contribution of VAT to the economic growth of Nigeria. The author utilized secondary time series data on GDP, VAT, total tax revenue and total federal government revenue from 2000-2012. To analyze the effect of VAT on GDP, the study utilized the multiple regression statistical technique and revealed that VAT had a positive and significant impact on GDP in Nigeria. The study also found that VAT had a positive and significant relationship with the total tax revenue in Nigeria.
In a similar study, Hakim, Karia and Bujang (2016) analyzed the impact of goods and service tax (GST) on the economic growth in various developed countries from 2005 to 2012. The researchers adopted time series secondary data on GDP, GST, population growth, inflation rate, trade openness, personal income tax (PIT) and government expenditure. The study found that GST was statistically significant and positively correlated with economic growth in developed countries. This study separated itself from
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the previous study (Immanuella, 2016) by using the Arellano-Bond Dynamic estimation method. However, although both studies used different statistical techniques, the findings were consistent as it revealed that GST had a positive and significant impact on economic growth in developed countries.
In the same breath, Emanuel (2013) studied the effects of VAT on economic growth and total tax revenue in Nigeria. The authors employed the simple regression method to analyze time series data on VAT, GDP and total revenue from 1994-2010. The findings revealed that VAT had a significant effect on both GDP and total tax revenue. However, in my view, the multiple regression statistical method would have been the appropriate statistical method. Moreover, the study recommended that the government should increase the tax rate but not without sensitizing the public. In my view, this recommendation should be taken with caution according to the claims of the Laffer curve (Laffer, 2004), which argues that raising taxes may not be a viable option since it can impact GDP negatively.
Similar to Emanuel (2013), Izedonmi and Okunbar (2014) conducted a study on the contribution of VAT to the development of the Nigerian economy. The authors utilized time series data on GDP, VAT, total tax revenue and total federal government revenue from 1994-2010 using the simple regression statistical method. However, different to the previous studies mentioned, it was found that VAT revenue had positive but statistically insignificant effects on economic growth. In this regard, the authors argued that this may have derived from the poor management of VAT collection in Nigeria. In my view, I concur with the views of the author since poor administration can be unfavorable to the tax system.
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The same positive but statistically insignificant impact on economic growth was found in a study conducted by Afolayan and Okoli (2015). This conclusion supported the previous author’s notion on poor administration of the VAT system. The authors analyzed macroeconomic variables on real gross domestic product (RGDP), VAT, company income tax (CIT), petroleum profit tax(PPT) and customs and excise duties (CED) using the multiple regression technique. This analysis was used to examine the impact of VAT on the Nigerian economic growth from 1994-2012. The findings revealed that VAT, as well as the other variables, were insignificant due to administrative issues that hinder the full potential of the system. Fredrick and Okeke (2013) examined the impact of VAT on investment growth in Nigeria. The author employed time series data on investment, government expenditure, real exchange rate, real interest rate and trade openness from 1994-2013. Similar to the previous authors, the author utilized the multiple regression statistical method but used different macroeconomic variables. However, although different macroeconomic variables were employed, similar conclusions were reached. In my view, this is indicative of the practicality of taxes on goods and services for economic growth. As a matter of fact, VAT was the only statistically significant variable in the model. From the findings, it could be argued that VAT can be an effective tax for economic growth. In similar fashion, a study by Hassan (2015) investigated the relationship between VAT revenue and economic growth in Pakistan. The author utilized time series data on macroeconomic variables such as GDP, income tax revenue, VAT and CED collection from 1991-1992 and 2011-2012. The author utilized the multiple regression technique and found that there was a strong and positive relationship between VAT revenue and the
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economic growth of Pakistan. Interestingly, this author utilized the same statistical method and similar variables as Afolayan & Okoli (2015) who found VAT to be insignificant towards GDP in Nigeria. However, this suggests that VAT can be a viable option for economic growth across different countries. In like manner, Onwuchekwa and Aruwa (2014) explored the impact of VAT on economic growth of Nigeria. The study used secondary data on VAT, GDP and total tax revenue and employed the multiple regression technique. The study revealed that VAT significantly contributed to the total tax revenue of the government as well as the economic growth of Nigeria. The results also showed an oscillating growth pattern from 1994-2009 but showed a geometric increase from 2010-2011. I can concur with the findings of the authors but there are unanswered questions: What caused the VAT to oscillate from 1994 to 2009? Could this have been derived from poor administration of the VAT alluded to by Afolayan & Okoli (2015) and Izedonmi and Okunbar (2014)? In my view, the author’s argument would have been better warranted had they investigated upon the period of oscillation and provided the results. A study conducted by Ofishie (2015) agrees with the views of Onwuchekwa and Aruwa (2014) on the impact of VAT on economic growth. The author investigated the impact of VAT on economic growth in Nigeria from 1994-2012. Time series data was analyzed by the multiple regression technique using macroeconomic variables GDP and VAT. The results revealed that VAT had a positive and significant impact on revenue generation; and by extension on the economic growth of Nigeria. Further, it was recommended that the government should utilize VAT revenue for infrastructural and economic development and review tax incentives to become more attractable to investors. 15
Likewise, a recent study by Kolahi and Noor (2016) explored the effects of VAT on economic growth and its sources in developing countries. Panel data on 19 developing countries were analyzed from 1995 to 2010 using the generalized moment’s method (GMM). Variables that were analyzed were VAT, capital accumulation growth, productivity growth and GDP. The study revealed that VAT had a positive effect on economic growth but had a negative effect on capital accumulation growth.
Lawrence and Victor (2016) conducted an empirical study on the relationship between VAT and macroeconomic performance in Nigeria. The study covered time series data on RGDP, VAT, PPT and inflation rate from 1994 to 2014 and used the co-integration and error correction methodology techniques. Although the statistical methods in this study differed from previous studies, all test revealed that VAT was statistically significant in explaining the level of economic growth in Nigeria.
Madugba and Joseph (2016) examined the relationship between VAT and economic development in Nigeria. The study covered the period 1994-2012 using the multiple regression technique on economic variables such as VAT, GDP and total consolidated revenue. The study concluded that there was a negative significant relationship between VAT and GDP. This is quite surprising because this study used the same statistical method as Ofishie (2015) in the same Nigerian context as well as the same period but found conflicting results. In my view, this negative significant relationship could be due to a difference in data quality which caused differences in the results of both authors.
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2.2.2. Taxes on Income and Economic Growth Etale and Bingilar (2016) examined the impact of companies’ income tax on economic growth in Nigeria. Time series data was analyzed over the period 2005-2014. The study used three variables which were GDP, VAT and CIT. The multiple regression technique was used to analyze the data. The study revealed that CIT had a positive and significant impact on economic growth in Nigeria. Moreover, the study recommended that government should strengthen the tax administration system to broaden the tax income and also to ensure voluntary tax compliance. Ojong, Anthony and Arikpo (2016) explored the impact of tax revenue on the Nigerian economy. The study investigated the relationship between PPT and GDP, the impact of CIT on GDP and the impact of non-oil revenue on the GDP of Nigeria. The study utilized the multiple regression method and analyzed macroeconomic variables such as GDP, PPT, CIT and Non-oil Revenue from 1986-2010. Interestingly, the study revealed that there was no significant relationship between CIT and the GDP of Nigeria. The authors claim that this may have been derived from tax avoidance and evasions that may be present in the Nigerian economy. However, the study revealed that there is a significant relationship with PPT as well as non-oil revenue to the economic growth in Nigeria. Chigbu and Njoku (2015) investigated the impact of taxation on the Nigerian economy for the period 1994-2012. The study covered macroeconomic data on GDP, inflation rate, unemployment rate, PIT, VAT, PPT, CED, and CIT. The augmented dickey fuller unit root test and the co-integration test were both used to analyze the data. The study revealed that PIT, CIT, and PPT were not statistically significant having no significant impact on the
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economic growth. However, the study also revealed that VAT and CED are statistically significant and have significantly contributed to the economic growth in Nigeria. In the same way, Salami et al. (2015) investigated the impact of taxation on economic growth in Nigeria. The study analyzed macroeconomic variables such as RGDP, PPT, CIT, CED, and VAT. The study covered 1976-2006 and utilized both simple and multiple linear regression analysis of the ordinary least square method. Not surprisingly, it was found that all variables when individually tested on economic growth had a significant impact on GDP. This supports the notion that taxation is impactful on economic growth.
Adudu and Ojonye (2016) evaluated the impact of tax policy on economic growth in Nigeria. The study used time series data from 1990 to 2011 applying the granger- causality co-integration technique on macroeconomic variables such as GDP, PIT, corporate taxes on income, social security contributions, payroll taxes, property tax, GST, international trade taxes and other taxes. The findings revealed that all tax components were statistically significant and were positively significant on the economic growth of Nigeria. Although this study used a different statistical method in comparison to the other studies reviewed, the consensus remains that taxation has an impact on economic growth.
Dehghan and Nonejad (2015) examined the impact of tax rates on economic growth of Iran from 1981 to 2010. This study covered several macroeconomic variables such as GDP, population growth, inflation rate, trade openness, corporate income tax, business tax revenue, and GST. The study analyzed the data using the auto regressive distributed lags technique and found that corporate income tax, business tax revenue and GST had a
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negative and significant impact on the economic growth of Iran. The study also revealed that population growth rate and trade openness rate had a positive impact while inflation rate had a negative impact on the economic growth of Iran.
Another recent study by Ibanichuka, Akani and Ikebujo (2016), looked at a time series analysis of the effect of tax revenue on economic development in Nigeria for the period 1995 to 2014. The study analyzed time series data on human development index, CIT, VAT and CED and used the multiple regression statistical method. The study revealed that CIT, VAT, and CED all had a positive impact on the economic development of Nigeria. This study differed from previous studies because human development index was used as the dependent variable for economic development instead of GDP. 2.2.3. Taxes on Domestic Goods and Services and Revenue Generation Abdul-Rahman, Aworemi and Ayorinde (2013) examined the impact of VAT on revenue generation in Nigeria. The study utilized the stepwise regression technique and used macroeconomic variables such as total federal collected revenue, VAT, PPT, CIT and education tax over the period 2001 to 2010. The study revealed that VAT had a positive and significant effect on revenue generation in Nigeria. The results of this study are not surprising as it is consistent and supports Laffer (2004) in that taxation can positively impact revenue generation according to the rate of the tax. Evidence from India also suggests that VAT impacts revenue generation positively. Rajeshwari (2015) conducted a study on the impact of VAT on revenue generation in India. Secondary data on VAT, Excise tax, vehicle tax, goods and passenger taxes, electricity tax and entertainment tax were collected from 2005-2014. The author used the stepwise 19
regression technique and found that VAT had a statistically significant effect on revenue generation in India. Further, it was found that VAT had contributed the most to total revenue in India amongst the variables tested. Interestingly, this study used the same statistical method with dissimilar variables to the previous author and revealed identical results. From the findings of this study, it can be concluded that VAT is impactful of revenue generation. Similarly, Okoye (2013) studied the influence of revenue generated through VAT on wealth creation in Nigeria. The study analyzed secondary data on GDP and VAT from 2001-2010 and used the Pearson product moment correlation (PPMC) and student’s t-test. Findings show that VAT revenue had a significant influence on wealth creation as there is a positive correlation between both variables. The study also revealed that there was a positive correlation between VAT and total tax revenue. Although this study used a different statistical method to the other studies, the results supported the Laffer curve theory as there is a relationship between taxation and revenue generation. In like fashion, Okwori and Ochinyabo (2014) assessed the effect of VAT on revenue generation for sustainable development in Nigeria. The study used time series data on federally collectible revenue, VAT revenue, PPT revenue, GDP, and the private consumption expenditure from 1993-2012 using the multiple regression technique. The study revealed that VAT had a positive and significant impact on the revenue generation of Nigeria.
In the same way, Okoli & Afolayan (2015) examined the extent to which VAT had contributed to Nigeria’s total federally collected revenue and its position among the other 20
variables. The macroeconomic variables tested were total revenue, VAT, CIT, CED and PPT. The data spanned from 1994-2012 and were analyzed using the multiple regression method to find the impact of the four variables on total federal collected revenue. Findings show both VAT and PPT contributed positively to federally collected revenue. The study also suggests that VAT is the second most long-term source of revenue in Nigeria.
Onaolapo and Fasina (2014), conducted an investigation on the effect of VAT on revenue profiles of South-Western Nigeria. The study analyzed data on total revenue, VAT, per capita income, population, and other sources of revenue from 2002-2011 using the panel regression statistical method. The result showed that VAT was positively and significantly related to revenue profile of Southwestern Nigeria. The study also revealed that per capita income was significantly related to revenue profile of southwest Nigeria although it was negative. 2.2.4. Taxes on Income and Revenue Generation Adejare (2015) looked at an analysis of the effect of corporate income tax on revenue profile in Nigeria from 1993 to 2013. The study analyzed macroeconomic variables such as GDP, revenue profile, corporate income tax, VAT, PPT and inflation and employed the multiple regression statistical technique. However, the study found that corporate income tax had a positive and significant impact on revenue generation in Nigeria. The study also revealed that corporate income tax raised significant revenue for the Nigerian government and was a significant indicator of economic growth.
Edame and Okoi (2014) looked at the impact of taxation on investment and economic development in Nigeria from 1980-2010. The study analyzed GDP, CIT, PIT and 21
investment level in Nigeria using the multiple regression statistical technique. Interestingly, the study found that there was an inverse relationship between both PIT and CIT and investment. The study further revealed that a one percent increase in CIT will result in a one percent decrease in the level of investment. The same goes for the impact of CIT and PIT on GDP where it was found to be negative. However, all variables were a statistically significant factor influencing the level of investment and GDP in Nigeria.
Oriakhi and Ahuru (2014) investigated the impact of tax reform on federal revenue generation in Nigeria from 1981 to 2011. The study collected and analyzed several time series data on federally collected revenue, CIT, PPT, VAT and CED. The study used the co-integration test and found that CIT was statistically significant and had a positive impact on federally collected revenue in Nigeria. The study also revealed that CED and VAT had a significant and positive impact on federally collected revenue in Nigeria.
Madugba, Ekwe and Kalu (2015) studied the impact of corporate tax on revenue generation in Nigeria from 1981 to 2014. The study analyzed macroeconomic variables such as petroleum tax income (PTI), total consolidated revenue and CIT using the simple regression statistical method. The study revealed that CIT had no significant impact on total consolidated revenue in Nigeria. The findings also revealed that PTI had no significant impact on total consolidated revenue. In my view, the simple regression analysis might have been inadequate to address the complexities of time series data. As such, it is my view that a statistical technique such as the multiple regression might have produced more statistically robust results.
22
This chapter presented theoretical and empirical evidence on the impact of taxation on revenue generation and economic growth. Generally, the empirical evidence indicates that taxation had a positive and significant impact on revenue generation and economic growth according to the empirical literature.(see appendix F for summary of empirical literature) However, issues concerning poor tax administration was found to have a negative effect on the revenue and economic growth potential in some instances as it relates to taxation. The next chapter specified the models used to analyze the impact of taxation on revenue generation and economic growth.
23
CHAPTER 3. DATA AND METHODS 3.1. Introduction The objective of this chapter was to describe the data and present the methods employed to answer the four research questions of this study. As such, the data sources, variables, econometric analysis, methods, model specifications and the proposed hypotheses will suffice. 3.2. Sources of Data This study utilized twenty-six yearly observations from 1990 to 2015. The data was collected from the Eastern Caribbean Central Bank (ECCB). (see appendix A for data and the respective data sources) These were annual reports, statistical bulletins, reviews and financial statements .They covered quantitative annual data on the key variables of this study which were TDGS, GDP, INCTAX, TOTREV from 1990-2015 similar to existing literature (Salami et al. 2015; Etale and Bingilar, 2016; Immanuella, 2016; Lawrence and Victor, 2016). Additionally, data was gathered on POP from 1990-2015 similar to available literature (Hakim, Karia and Bujang, 2016). The rationale for including this variable was to control the potential casual effects of the dependent and independent variables (Hakim, Karia and Bujang, 2016).
3.3. Definition and Measurement of Variables. 3.3.1. Dependent Variables. This study used GDP as the dependent variable for the GDP model and TOTREV as the dependent variable for the revenue generation model. It should be noted that economic growth is proxy by GDP and is used as the dependent variable for the first and second research questions of this study. GDP is expressed at current market value similar to a study 24
from Wulandari (2015). GDP at current market value is the value of goods and services produced by a country annually according to the prevailing prices during the year in question (Wulandari, 2015). TOTREV represents all revenue collected by taxes and other sources by the government of the federation of St. Kitts and Nevis and was used as the dependent variable for the second and third research questions of this study.
3.3.2. Independent Variables TDGS are taxes on goods and services (including VAT, stamp duties, hotel and guest tax, telecommunications tax, wheel tax\vehicle rental tax, entertainment tax\cable TV, gasoline levy, insurance fees, traders tax and consumption tax) collected by the Inland Revenue Department (IRD).
INCTAX are taxes on income (including housing & social development levy, withholding tax, personal income tax and company income tax) collected by the Inland Revenue Department (IRD).
3.3.3. Control Variable POP represents the annual increase in the population of a country expressed as a percentage (Hakim, Karia and Bujang, 2016). There is literature that suggest that there is a positive relationship between population growth and GDP. For instance, Hakim, Karia and Bujang (2016) finds when the labor force increase, productivity also increase which as a result causes economic performance to increase. As such this variable was used as a means of control.
25
3.4. Econometric Analysis For this study, quantitative analysis was undertaken .The software employed was the Statistical Package for Social Science (SPSS) software package. This study utilized the multiple regression statistical method to answer the four research questions of this study similar to several empirical studies (Immanuella, 2016; Etale and Bingilar, 2016; Madugba and Joseph, 2016; Ibanichuka, Akani and Ikebujo, 2016). 3.5. Methods Firstly, all the variables of this study were analyzed to produce descriptive statistics. The descriptive statistics were used to interpret the data sample to prepare for regression analysis. As such, the measure of central tendency and dispersion were determined for all variables of the study. Secondly, the results of a classical assumption test were explained. The classical assumption test was used to conduct normality test, multicollinearity test, and heteroscedasticity test. Furthermore, a Pearson correlation test was conducted. The rationale for these test was to meet the statistical requirements in order to conduct regression analysis (Wulandari, 2015). Third, multiple regression analysis were used to answer the four research questions of the study. The rationale for using this statistical method was of its appropriateness when addressing the complexities of GDP opposed to the simple linear regression analysis (Wulandari, 2015). The multiple regression analysis was performed via the SPSS software and determined the goodness-of-fit for the two regression models of this study. Moreover, the R-square was used to determine the amount of variance that was accounted for by the independent variables as a whole towards the dependent variable for both models. The two 26
models were also subject to a Durbin-Watson test in order to detect autocorrelation within the regression models. Also, the analysis of variance (ANOVA) was conducted in order to determine if the R square was significant in determining if all independent and control variables as a whole predicted the dependent variables for both models. Last, the coefficient of determination examined how well the variance in the models were best explained by the independent variables towards the dependent variables of this study. As such, the coefficients, t-statistics, f-statistics and significance value were used for both models. Last, the results were discussed and evaluated in relation to the hypotheses as well as the existing empirical literature discussed in the literature review. 3.6. Econometric Models 3.6.1. GDP Model The GDP model was formulated to answer the first and second research questions of this study. As such, the model examined the contribution of TDGS, INCTAX and POP towards the dependent variable GDP. A perceived functional relationship was developed drawing knowledge from similar studies (Adejare, 2015; Immanuella, 2016). However, this model was modified to meet the objectives of this study. From a microeconomic standpoint, the model assumed that GDP is dependent on TDGS, INCTAX and POP. The functional relationship is specified below:
𝐺𝐷𝑃 = 𝑓(𝑇𝐷𝐺𝑆, 𝐼𝑁𝐶𝑇𝐴𝑋, 𝑃𝑂𝑃) … … … … . . (1)
27
A stochastic model of the above functional relationship will suffice in logarithm form below. 𝐿𝑜𝑔 𝐺𝐷𝑃 = 𝛼₀ + 𝛼₁ 𝐿𝑜𝑔 (𝑇𝐷𝐺𝑆) + 𝛼₂ 𝐿𝑜𝑔 (𝐼𝑁𝐶𝑇𝐴𝑋) + 𝛼₃ 𝐿𝑜𝑔 (𝑃𝑂𝑃) + e … … … … … … … … … … … … … … … … … … … … … … … (2) Where, GDP = Gross domestic product, TDGS = Taxes on Domestic Goods and Services INCTAX = Taxes on Income POP = Population Growth α0, α1 = Model parameters e = error term. 3.6.2. Revenue Generation Model The revenue generation model was formulated to answer the third and fourth research questions of this study. Thus, the model examined the contribution of TDGS, INCTAX and POP towards the dependent variable TOTREV. As such, a perceived functional relationship was developed drawing knowledge from similar studies (Adejare, 2015; Salami et al. 2015). However, this model was modified to meet the objectives of this study. From a microeconomic standpoint, the model assumed that TOTREV is dependent on TDGS, INCTAX and POP. The functional relationship is specified below:
𝑇𝑂𝑇𝑅𝐸𝑉 = 𝑓(𝐼𝑁𝐶𝑇𝐴𝑋, 𝑇𝐷𝐺𝑆, 𝑃𝑂𝑃) … … … … … . . (1)
28
A stochastic model of the above functional relationship is presented in logarithm form below.
𝐿𝑜𝑔 𝑇𝑂𝑇𝑅𝐸𝑉 = 𝛼₀ + 𝛼₁ 𝐿𝑜𝑔 (𝑇𝐷𝐺𝑆) + 𝛼₂ 𝐿𝑜𝑔 (𝐼𝑁𝐶𝑇𝐴𝑋) + 𝛼₃ 𝐿𝑜𝑔 (𝑃𝑂𝑃) + e … … … … … … … … … … … … … … … … … … … … … … … (2)
Where, TOTREV = Revenue Generation, TDGS = Taxes on Domestic Goods and Services INCTAX = Taxes on Income POP = Population Growth α0, α1 = are model parameters e = stochastic error term. 3.7. Research Hypothesis The study proposed four hypotheses that were tested by the two models of this study. As such the GDP model was formulated to test the first and second hypotheses and the revenue generation model was formulated to test the third and fourth hypotheses. The proposed hypotheses are specified below.
I.
H₀ (Null) Taxes on Domestic Goods and Services (TDGS) has not made any significant impact to the GDP of St. Kitts and Nevis.
29
II.
H1: (Null) Taxes on Income (INCTAX) has not made any significant impact on the GDP of St. Kitts and Nevis.
III.
H2 (Null) Taxes on Domestic Goods and Services (TDGS) has not made any significant impact on revenue generation (TOTREV) in St. Kitts and Nevis
IV.
H3 (Null) Taxes on Income (INCTAX) has not made any significant impact on revenue generation (TOTREV) in St. Kitts and Nevis.
30
CHAPTER 4. ANALYSIS AND RESULTS This chapter provides a discussion on the analysis and results and provides an evaluation of the findings for the two models of this study. There are four core components of this chapter: descriptive statistics, diagnostic tests, regression analysis and an interpretation of the results. 4.1. Descriptive Statistics Descriptive statistics were necessary to outline the measures of central tendency for the sample. Other basic characteristics of the sample were also outlined. Table 1 shows the descriptive statistics which includes the maximum and minimum, mean, standard deviation, variance, skewness and kurtosis of the dependent and independent variables.
Table 1: Descriptive statistics for the variables used in the analysis. Std. N
Minimum Maximum
Mean
Deviation
Variance
Skewness
Kurtosis
Std. Statistic
Statistic
Statistic
Statistic
Std. Error
Statistic
GDP
26
563.60
INCTAX
26
18.73
147.60
68.9900
7.55075
38.50142
TDGS
26
8.55
238.08
77.1600
14.41069
POP
26
.05
1.58
1.1985
.06432
TOTREV
26
93.23
900.50
388.0935
Valid N (listwise)
Statistic
2366.49 1376.6773 113.55619 579.02521 335270.191
Std.
Statistic Error Statistic Error .170
.456
-1.353
.887
1482.359
.393
.456
-.791
.887
73.48041
5399.370
1.288
.456
.240
.887
.32798
.108
-2.230
.456
6.127
.887
48.18224 245.68219
60359.737
.735
.456
-.397
.887
26
Table 1 shows the mean for GDP is 1,376.6773. This means GDP on average was one billion, three hundred and seventy-six million dollars from 1990-2015. Moreover, the standard deviation for GDP is 579.02521. This means the data points for GDP are reasonably close to the mean. Both the skewness and kurtosis values for the GDP dataset 31
fall in an acceptable range with a negative kurtosis value of -1.353 and a positive skew of .170. The negative kurtosis means the shape of the distribution is flatter than normal while a positive skewness indicates that there are a greater number of moderately smaller values around the mean of the data set for GDP. The mean for TOTREV is 388.09 which means the amount of revenue generated on average is three hundred and eighty-eight million. The standard deviation is close to the mean at 245.68 implying that the data points for TOTREV are situated close to the mean. The distribution is positively skewed at .735 with a negative kurtosis of -.397. This suggest that the shape of the distribution is moderately flat with smaller values near the mean. The datasets for the other independent variables POP, INCTAX, and TDGS have means of 1.1985, 68.99 and 77.16 and standard deviations of .32798, 38.50 and 73.48 respectively. All values are relatively close to their mean which indicates that there are a greater amount of the smaller values closer to their respective means. The distribution for the dataset of the independent variables, except POP are positively skewed. INCTAX and TDGS have skewness values of .393 and 1.288 respectively which are skewed to the right. POP is negatively skewed to the left with skewness value of -2.230. Furthermore, the kurtosis values for POP and TDGS both have kurtosis values of 6.127 and .240 respectively. This indicates that the dataset for both variables is generally highly peaked. On the other hand, INCTAX had a kurtosis value of -.791 which means the distribution is more flatly shaped at the top than normal.
32
4.2. Diagnostic Test Diagnostic test were conducted to satisfy the principal assumptions of regression analysis. As such, the diagnostic test included casewise diagnostics, multicollinearity test, normality test, linearity test, and the heteroscedasticity test. These tests would determine if the sample meet the requirements for regression analysis. 4.2.1 Casewise Outliers The casewise diagnostics were generated by the SPSS package and is presented in table 2 below. This table outlines outliers within the dataset of the dependent and independent variables of the study. According to Tabachnick and Fidell (2013), an outlier is defined as extreme values within a dataset that are greater or less than -3.3 or positive 3.3. As such, this table shows the largest errors of the dataset which can be considered outliers. Table 2 showing Casewise Diagnosticsa Std. Case Number 21
Residual 3.211
Predicted GDP 1903.54
Value
Residual
1600.6272 302.91283
a. Dependent Variable: GDP
The casewise diagnostics in table 2 above shows one chosen case. This case had a standard residual of 3.21 and was chosen because of a default setting in SPSS which targets standard residual values above 3. However, according to Tabachnick and Fidell (2013), this case should not be dropped from the dataset since it is below 3.3. 4.2.2 Multicollinearity Test According to Tabachnick and Fidell (2013), multicollinearity exists when there are two or more independent variables that are highly correlated and causes predictability issues when
33
estimating regression models. Some implications of multicollinearity are that it causes coefficients to change signs and creates issues in determining the exact impact of independent variables. Hence, the two models are tested for multicollinearity in two ways. The first test for multicollinearity is conducted via the Pearson correlation function in SPSS which shows a correlation matrix. According to Tabachnick and Fidell (2013), if there are bivariate correlations that exceed .7 then it can be concluded that there is multicollinearity among independent variables within the regression model. However, the values in both correlation matrixes (see appendix B) suggest that there are no signs of multicollinearity since all values of the independent variables are less than.7 (Tabachnick and Fidell, 2013). The second test for multicollinearity was conducted using the variance inflation factor (VIF). The VIF finds to what extent the variance of a coefficient is inflated due to linear dependence among other independent variables of a model (Tabachnick and Fidell, 2013). Pallant (2013) claims that a tolerance that is more than 0.1 and a VIF less than 10 indicate that there is no sign of multicollinearity among independent variables. A review of the VIF results (see Tables 6 and 11) shows that there are no VIF values greater than 10. As such, it can be concluded that there are no issues of multicollinearity. 4.2.3. Normality, Linearity and Heteroscedasticity Tests. The statistical tests utilized in this study requires that the dataset for dependent variables are normally distributed (Tabachnick and Fidell, 2013). The assumption of normality is that data is statistically significantly different than a normal distribution while the null hypothesis is that there is no statistical difference from a normal distribution (Tabachnick and Fidell, 2013). As such, the dependent variables of this study were tested to determine if the sample was drawn from a normally distributed population. The implication of the 34
violation of normality is that the results of the regression analysis may be unreliable. For the purpose of this study, the Kolmogorov-Smirnov test was used to test the assumption of normality. For this test, if the significance value is greater than 0.5 then the data is considered to be normally distributed (Tabachnick and Fidell, 2013). In this regard, the null hypothesis is accepted since the significance values of the dependent variables were greater than 0.5 (see appendix C). As such, it can be concluded that the dependent variables are drawn from a normal distribution. For regression analysis, it is imperative that independent and dependent variable are linear. The implication of linearity is that if the assumption is not met, then the results of regression analysis would be considered unreliable. However, in order to test for linearity between the independent and dependent variables, the partial regression plot was used. According to Tabachnick and Fidel (2013), there must be a visual linear trend between the x and y-axis in order to assume linearity. As such, by review of each diagram, (see appendix D and D1) it can be concluded that there is linearity among the dependent and independent variables for each model. Another assumption that must be met for regression analysis is the absence of heteroscedasticity. Heteroscedasticity is where standard errors are not constant across all observations (Tabachnick and Fidell, 2013). The implication of heteroscedasticity is that it can lead to invalid conclusions due to bias standard errors which create unreliable regression coefficients. As such, in order to test for heteroscedasticity, a scatterplot was used to satisfy this assumption. Tabachnick and Fidell (2013) claims that the scatterplot should not show signs of any systematic pattern in order to assume heteroscedasticity is
35
non-existent. However, with a look at the results, (see appendix E) there are no signs of systematic patterns. Hence, it can be concluded that the dataset is free of heteroscedasticity. 4.3. Regression Analysis and Results of the GDP Model Table 3 shows the independent variables (INCTAX, TDGS), the dependent variable (GDP), and the control variable (POP) that were entered in the GDP model via SPSS 22 software.
Table 3: Variables Entered for the GDP Model
Model 1
Variables
Variables
Entered
Removed
POP, TDGS, INCTAXb
Method . Enter
a. Dependent Variable: GDP b. All requested variables entered.
Over the following sections, three key tables are used to display the results of the regression analysis for the GDP model. These tables show the model summary, the analysis of variance, and the coefficients obtained from SPSS software package. At the end, the results of the test of the first and second hypothesis of this study are outlined. 4.3.1. Assessing the Goodness of Fit of the GDP model. The model summary shows the goodness-of-fit as it relates to the GDP model. This section examines the overall model to assess the percentage of variance in the dependent variables from the independent variables as a group. The model summary also shows the results of a Durbin-Watson test to detect the presence of autocorrelation within the model. As such, a 36
value close to 2 indicates that there is no autocorrelation within the model. The results are seen in table 9 below. The results are seen in table 4 below. Table 4: GDP Model Summaryb Change Statistics
Std. Adjusted Error of R Model 1
R .989a
R
Square Square .979
the
R Square
F
Sig. F Durbin-
Estimate Change Change df1 df2 Change Watson
.976 90.17116
.979 336.287
3
22
.000
2.011
a. Predictors: (Constant), POP, TDGS, INCTAX b. Dependent Variable: GDP
As seen in table 4, the R-square is .979. This means the combination of TDGS, INCTAX and POP accounts for 97.9% of the variance in GDP. Since all variables included in the model as a whole accounts for 97.9% of the variance in GDP, it can be concluded that the GDP model is a good fit and can be used to predict the variance in GDP. Last, a DurbinWatson of 2.011 indicates that there is no presence of autocorrelation within the model.
4.3.2. Analysis of Variance (ANOVA) of the GDP Model The analysis of variance (ANOVA) examines if the R-square (97.9%) in table 4 is statistically significant. In essence, the ANOVA was used to determine if the independent variables as a whole were able to account for a significant amount of variance in the dependent variable. The results of the ANOVA table are seen in Table 5.
37
Table 5: Analysis of Variance (ANOVA) Sum of Model 1
Squares Regression Residual Total
8202876.344
df
Mean Square 3 2734292.115
178878.436
22
8381754.780
25
F 336.287
Sig. .000b
8130.838
a. Dependent Variable: GDP b. Predictors: (Constant), POP, TDGS, INCTAX
As seen in table 5, the F-value is 336.287 and the significance value is .000. Since significance value is less than .05, this indicates that the GDP model is statistically significant. This means TDGS, INCTAX and POP as a whole are able to account for a significant amount of variance in GDP. As such, it can be concluded that the GDP model overall was statistically significant since TDGS, INCTAX and POP as a whole predicted GDP significantly. 4.4. Evaluating Each Independent Variable in the GDP model. This section examined the independent variables individually to determine their unique contributions. Also, the magnitude of each relationship was discussed via the standardized (BETA) and unstandardized coefficients (B) in table 6. Furthermore, the degree of impact was discussed as it relates to the sign of the BETA coefficient. Additionally, the significance value was used to measure the significance of the unique contributions of each independent variable within the model at an alpha of .05. Therefore, if the significance
38
value is less than 0.05 then it can be concluded that the independent variable made a unique contribution to the dependent variable. The coefficient table 6 is seem below. Table 6:Coefficientsa Unstandardized
Standardized
95.0% Confidence
Collinearity
Coefficients
Coefficients
Interval for B
Statistics
Std. Model 1
B
(Constant) 339.800
Error
Beta
t
Lower
Upper
Sig.
Bound
Bound
68.994
4.925
.000
196.716
482.885
Tolerance
VIF
INCTAX
8.364
.623
.556 13.426
.000
7.072
9.656
.565 1.769
TDGS
4.157
.307
.528 13.535
.000
3.520
4.794
.639 1.566
116.070
59.345
.066
.063
-7.003
239.143
.858 1.165
POP
1.956
a. Dependent Variable: GDP
The interpretation of the findings in table 6 is presented in the following sub-sections with a focus on the economic implications, statistical significance, theoretical implications and practical implications of the relationships found. As such, the findings were evaluated in relation to the null hypotheses and theoretical framework and explained in subsection 4.4.1 and 4.4.2. 4.4.1. The Impact of Taxes on Domestic Goods and Services on Economic Growth Hypothesis 1 suggest that TDGS has no significant impact on GDP Table 6 shows that TDGS has a standardized BETA coefficient of .528. This is indicative of a strong positive relationship between TDGS and GDP. The economic implication of this result is for every unit increase for TDGS will result in a one unit increase in GDP by .556. Moreover, TDGS has a t-value of 13.535 with a significance value of .000. Since TDGS is significant, it accounts for a unique amount of variance in GDP. Moreover, the unique contribution of TDGS to GDP is statistically significant since the significance value is less 39
than the alpha amount of .001. Thus, the null hypothesis is rejected at the 95% confidence level and concludes that TDGS has made a positive and statistically significant impact on GDP in the federation of St. Kitts and Nevis. This highly significant and positive relationship between TDGS and GDP suggest that TDGS are such that encourage economic activity by allowing consumers and entrepreneurs to spend, save and invest since a significant amount of income is not depleted by taxes. When this occurs, the savings can be invested into home ownership, real estate and other financial instruments. Savings can also enable financial institutions to finance business investments according to the number of deposits received from consumers and entrepreneurs. Also, when consumers are encouraged to spend, businesses are better able to perform, expand and invest in other businesses. The findings confirm the claims of the Laffer curve which is the theoretical framework of this study. The Laffer curve states that there is an impact of taxation on economic activity according to the rates of taxes. This is so because the rates of taxation can either be an incentive that causes individuals to work, spend, save and invest, or can create a disincentive to economic activity if in the reverse (Laffer, 2004). All in all, this finding confirms the Laffer curve in the positive sense since TDGS impacts GDP positively and significantly. There is no surprise that TDGS has a positive and statistically significant impact on GDP in St. Kitts & Nevis from 1990-2015. This is so because the findings agree with several exemplary studies that investigated upon this phenomena and found that TDGS had a positive and significant impact on GDP (Hassan, 2015; Hakim, Karia and Bujang, 2016; Immanuella, 2016; Kolahi and Noor, 2016; Lawrence and Victor, 2016). (see appendix F) 40
Conversely, studies by Afolayan and Okoli (2015) and Izedonmi and Okunbar (2014) contradicts the findings of this study. (see appendix F) As such, they found that VAT had a positive but insignificant impact on GDP. The difference in findings also speaks to the assumptions of the Laffer curve in the reverse where taxes may be exuberant and discourages economic activity. As such, the negative and insignificant impact on GDP in those cases could have been derived from extreme tax rates on consumption of goods and services which causes consumers to not spend (Laffer, 2004). This finding can be used as a benchmark for taxation in St. Kitts & Nevis as it relates to GDP. This will allow the decision makers to be aware of the optimum levels of taxation when introducing new taxes as well as their impact on GDP. Therefore, the findings of this study suggest that the decision makers should continue to create taxes that lower barriers on consumption, improve disposable income, and encourage consumer savings to stimulate GDP. 4.4.2. The Impact of Taxes on Income on Economic Growth Hypothesis 2 suggest that INCTAX has no significant impact on GDP. Table 6 shows that INCTAX has a standardized BETA coefficient of .556. This shows that INCTAX had a stronger relationship towards GDP than TDGS. However, the economic implication of this result is that for every one unit of variation increase in standard deviation of INCTAX will result in one unit of variation increase in the standard deviation of GDP. Further, INCTAX had a T-value of 13.426 and a significance value of.000. This means that INCTAX is statistically significant and accounts for a unique amount of variance in GDP. Therefore the null hypothesis is rejected and it can be concluded that
41
INCTAX had made a positive and statistically significant impact on GDP in the federation of St. Kitts and Nevis. This highly significant and positive relationship between INCTAX and GDP suggest that INCTAX is optimally set to allow more individuals to be desirous of working and allows business owners to be desirous of expanding since their incomes are not heavily taxed. With more individuals desirous of working, employment and production are increased which as a result impact GDP positively. Also, with more business owners desirous of expanding and investing in other businesses, more employment opportunities are also made available for individuals which also positively impact GDP. In this instance, the findings confirm the claims of the Laffer curve which is the theoretical framework of this study. The Laffer curve states that there is an impact of taxation on economic activity according to the rates of taxes. This is so because the rates of taxation can either be an incentive that causes workers and entrepreneurs to work and invest (Laffer, 2004). Hence, this finding confirms the Laffer curve since INCTAX impacts GDP positively and significantly. The findings of this study in this instance fit well within the existing body of literature on the impact of taxation on GDP. This study which confirms a positive and significant impact of INCTAX on GDP is consistent with the findings of several existing empirical studies (Salami et al. 2015; Etale and Bingilar, 2016; Adudu and Ojonye, 2016; Ibanichuka, Akani and Ikebujo, 2016). (see appendix F) Conversely, this contradicts the findings of authors who found that taxes on income had an insignificant impact on GDP (Chigbu and Njoku, 2015; Madugba and Joseph, 2016; Ojong, Anthony and Arikpo, 2016).
42
This information can be useful to potential risk-averse foreign investors and entrepreneurs interested in investing in the federation of St. Kitts and Nevis. The information provided in this study indicates that the cost of doing business as it relates to taxes on income in St. Kitts are moderate. Since this tax type would affect their incomes and profits, the results according to this study can be incorporated into their investment decisions in order to strategically plan for favorable return on investments. These sections looked at the results of the GDP model on the impact of INCTAX and TDGS on GDP. The followings sections looked at the results of revenue generation model which examined the impact of INCTAX, TDGS and POP on revenue generation (TOTREV). 4.5. Regression Analysis and Results of the Revenue Generation Model Table 8 below shows the independent variables (INCTAX, TDGS), dependent variable (TOTREV), and the control variable (POP) that were entered in the revenue generation model via SPSS 22 software. Table 8: Variables Entered for the Revenue Generation Model
Model 1
Variables
Variables
Entered
Removed
POP, TDGS, INCTAXb
Method . Enter
a. Dependent Variable: TOTREV b. All requested variables entered.
Over the next three sections, three key tables are used to display the results of the regression analysis for the revenue generation model. These tables show the model 43
summary, the analysis of variance, and the coefficients obtained from SPSS. At the end, the results of the test of the third and fourth hypotheses of this study were outlined. 4.5.1. Assessing the Goodness of Fit of the Revenue Generation Model The model summary shows the goodness-of-fit for the revenue generation model. This section examines the overall model to assess the percentage of variance in the dependent variables to that of independent variables. The model summary also shows the results of a Durbin-Watson test to detect the presence of autocorrelation within the model. As such, a value close to 2 indicates that there is no autocorrelation within the model. The results are seen in table 9 below. Table 9 : Revenue Generation Model Summaryb Change Statistics
Std. Adjusted Error of
Model 1
R .991a
R
R
Square
Square
.982
the
R Square
F
Estimate Change Change
.980 35.16080
.982 399.530
df1 3
df2
Sig. F
Durbin-
Change
Watson
.000
1.786
22
a. Predictors: (Constant), POP, TDGS, INCTAX b. Dependent Variable: TOTREV
Table 9 shows that the R-square is .982. This means the combination of TDGS, INCTAX and POP as a whole accounts for 98.2% of the variance in TOTREV. Since all variables included in the model as a whole accounts for 98.2% of the variance in TOTREV, it can be concluded that the revenue generation model is a good fit. Last, with a Durbin-Watson of 1.786 this means that there is no presence of autocorrelation within the model. 4.5.2 Analysis of Variance (ANOVA) of the Revenue Generation Model The analysis of variance examined if the R-Square in table 9 was statistically significant. The results of the ANOVA table are displayed in table 10. 44
Table 10: Revenue Generation model ANOVA a
Model 1
Sum of Squares Regression Residual Total
df
Mean Square
1481795.214
3
493931.738
27198.201
22
1236.282
1508993.415
25
F
Sig.
399.530
.000b
a. Dependent Variable: TOTREV b. Predictors: (Constant), POP, TDGS, INCTAX
As seen in table 10, the F value is 399.530 and the significance value is .000. Since the significance value is less than .05, this indicates that the revenue generation model is statistically significant. This means that TDGS, INCTAX and POP as a whole are able to account for a significant amount of variance in TOTREV. As such, it can be concluded that the revenue generation model was statistically significant since the three independent variables combined as a whole predicts TOTREV significantly over this period. 4.6. Evaluating Each Independent Variable in the Revenue Generation Model. This section examined the independent variables individually to determine their unique contributions. Also, the magnitude of each relationship was discussed via the standardized (BETA) and unstandardized coefficients (B) in table 11. Furthermore, the degree of impact is discussed as it relates to the sign of the BETA coefficient. Additionally, the sig is used to measure the significance of the unique contributions of each independent variable within the model at an alpha of .05. Therefore, if the sig value is less than 0.05 then the independent variable made a unique contribution to the dependent variable. The coefficient table 11 is seen below.
45
Table 11: Revenue generation model Coefficientsa Unstandardized
Standardized
95.0% Confidence
Collinearity
Coefficients
Coefficients
Interval for B
Statistics
Std. Model 1
Error
Beta
Upper
Sig.
Bound
Bound
1.010
.323
-28.613
82.974
t
Tolerance
VIF
(Constant)
27.181
26.903
INCTAX
2.348
.243
.368
9.668
.000
1.845
2.852
.565 1.769
TDGS
2.417
.120
.723 20.180
.000
2.168
2.665
.639 1.566
10.365
23.140
.014
.659
-37.626
58.355
.858 1.165
POP a.
B
Lower
.448
Dependent Variable: TOTREV
The interpretation of the findings in table 11 is presented in the following subsections with a focus on economic implications, statistical significance, theoretical implications and practical implications that stem from the relationships found between the dependent and the individual independent variables. As such, the findings were evaluated in relation to the null hypotheses and theoretical framework and explained in subsections 4.6.1 and 4.6.2.
4.6.1. The Impact of Taxes on Domestic Goods and Services on Revenue Generation Hypothesis 3 suggest that TDGS has no significant impact on TOTREV. Table 11 shows TDGS had a standardized BETA coefficient of .723. This is indicative of a strong positive relationship between TDGS and TOTREV. The economic implication of this relationship is for every unit variation increase in the standard deviation of TDGS will result in a one variation unit increase in TOTREV by .723. Moreover, TDGS has a t-value of 20.180 with a significance value of .000. Since TDGS is significant, it accounts for a unique amount of variance in TOTREV. As such the unique contribution from TDGS to TOTREV is statistically significant since the significance value is less than the alpha 46
amount of .001. Thus, the null hypothesis is rejected at the 95% confidence level which concludes that TDGS has a positive and statistically significant impact on TOTREV. From an economic standpoint, this positively significant relationship between TDGS and TOTREV is very important. This is so because it suggests that TDGS encourages consumer spending, increases business revenues, and allows for business to expand and employ more workers to meet the increased consumer demand. The increased economic activity increases the amount of revenue that can be collected by the government. Furthermore, this significant and positive relationship between TDGS and TOTREV could have been attributed to a period of tax reform that started in 2010 where consumption tax as well as the other taxes on domestic goods and services were replaced by VAT which was implemented to be a broad-based tax charged on the markup of domestic goods and services from one business to another business or to the final consumer (Tax Reform Unit, 2010). This finding fits well with the existing empirical studies on this phenomena. (see appendix F for summary of empirical literature)This is so because the findings agree with several exemplary studies that investigated upon the same phenomena and found that taxes on goods and services had a positive and significant impact on revenue generation (AbdulRahman, Aworemi and Ayorinde, 2013; Okoye, 2013; Okwori and Ochinyabo, 2014; Onaolapo and Fasina, 2014; Okoli & Afolayan, 2015;Rajeshwari, 2015). Since this study found that TDGS impacts positively and significant on TOTREV, this supports the hypothesis of the Laffer curve which recognizes the positive impact that taxation has on revenue generation and GDP according to the tax rates set.
47
This particular information can be useful in the practical sense. This finding suggest that the government may be in a good position to tend to health care, civil servant salaries and other public necessities. As such, the information derived from the relationship between TDGS and TOTREV can also be incorporated into the government’s strategic budgetary planning as it relates to government revenue. 4.6.2. The Impact of Taxes on Income on Revenue Generation Hypothesis 4 suggest that INCTAX has no significant impact on TOTREV. Table 11 shows INCTAX had a standardized BETA coefficient of .368. This means there is a strong positive relationship between INCTAX and TOTREV. The economic implication is for every one unit variation increase in the standard deviation of INCTAX will equate to one variation unit increase in TOTREV by .368. Moreover, INCTAX has a t-value of 9.668 with a significance of .000. This means that INCTAX is statistically significant and accounts for a unique amount of variance in TOTREV. This confirms that INCTAX makes a unique contribution to TOTREV with a significance value less than the alpha amount .001. Thus, the null hypothesis is rejected at the 95% confidence level which concludes that INCTAX has made a positive and statistically significant impact on TOTREV in the federation of St. Kitts and Nevis. The rationale for this relationship is that a moderate INCTAX can contribute to a person’s willingness to work and pay taxes. When the willingness to work increases, the tax base also increases and enables the government to collect more taxes due to the increase in the tax base. Also, INCTAX may be such that provides adequate disposable income to taxpayers after income taxes are paid. When this money is spent, a boost in business activity is created to meet the demands of consumers. As a result, the increase in demand 48
enables businesses to expand and employ more workers who will, in turn, work, spend and pay taxes because of the money left at their disposal. This increase in employment creates a larger tax base and also allows the government to increase the amount of revenue from income taxes. This finding agrees with several exemplary studies that found that taxes on income has a positive and significant impact on revenue generation (Oriakhi and Ahuru, 2014; Adejare, 2015). (see appendix F) Conversely, Madugba, Ekwe and Kalu (2015) found that taxes on income had no significant impact on revenue generation which was similar to the findings of Edame and Okoi (2014) who found that taxes on income had a negative impact on investments. In my view, this could have been derived from differences in statistical methods or country specific issues with regards to tax administration. Nevertheless, the findings support the claims of the Laffer curve. The Laffer curve claims that government revenue can be maximized if the tax rates are such that encourages citizens to work, produce and spend (Laffer, 2004). Since this particular finding provides empirical evidence that INCTAX positively and significantly impacts revenue generation, it can be concluded that it supports the Laffer curve. The information derived from this study is useful for the decision makers of the government of St. Kitts and Nevis as far taxation and revenue generation. This information suggest that the government is in a good position to invest in capital projects, repay debt, and improve public infrastructures. Therefore, this information can be incorporated into the government’s strategic budgetary planning as it relates to government revenue generation.
49
This chapter displayed the descriptive statistics and evaluated the research findings of the two models as a whole as well as the individual contributions of each independent variable. From this, several theoretical and practical implications were deduced. The next chapter answered the research questions of this study and positioned the findings in relation to the existing body of theoretical and empirical literature. Furthermore, theoretical implications, practical implications, limitations, directions for future research and reflections were outlined.
50
CHAPTER 5. DISCUSSION AND CONCLUSIONS This chapter summarizes the keys findings of the analysis and results in chapter four. Furthermore, this chapter outlined the theoretical implications, practical implications, limitations, directions for future research and a reflection of the research process. 5.1. Summary This section outlines the key findings of the regression analysis in chapter four in order to provide answers to the four research questions of this study. The results of each model as a whole were summarized as well as the individual contributions of the independent variables to the dependent variables. The GDP and revenue generation models were formulated to test the four hypotheses of this study. The GDP model summary revealed that the independent variables combined accounted for 97.9% of the variance in GDP. This means that the combination of TDGS, INCTAX, and POP accounted for a significant amount of variance in GDP. The ANOVA for the GDP model revealed that the model is statistically significant. This means the independent variables combined accounts for a significant amount of variance in GDP and indicates that the model is a good fit. The revenue generation model summary revealed that the independent variables combined accounted for 98.2% of the variance in the TOTREV. This implies that the combination of TDGS, INCTAX, and POP accounted for a significant amount of variance in TOTREV. The ANOVA for the revenue generation model revealed that the model is statistically significant. This means the independent variables combined accounts for a significant amount of variance in TOTREV and indicates that the model as a whole is a good fit.
51
The model summary and the ANOVA explained the goodness of fit by looking at both models as a whole. However, the following 4 subsections explained the influence of each independent variable towards the dependent variable while providing answers to the four research questions of the study. 5.1.1. To what extent does taxes on domestic goods and services impact economic growth? Based on the regression results, it can be concluded that TDGS had a positive and significant impact on GDP in the federation of St. Kitts and Nevis. The economic implication of this result is that a one-unit increase in TDGS will result in a one-unit increase in GDP. The rationale behind this relationship is that TDGS boost economic activity by encouraging consumers to spend. As a result, when consumers spend, businesses collect more revenue, expand, and employ more workers to meet the increased consumer demand. Therefore, when this type of activity exist, the GDP can be positively and significantly impacted. Moreover, this finding is consistent with other existing empirical literature such as (Hassan, 2015; Hakim, Karia and Bujang, 2016; Immanuella, 2016; Kolahi and Noor, 2016; Lawrence, 2016) who found that taxes on domestic goods and services has a positive and significant impact on GDP. Conversely, the findings of Afolayan and Okoli, (2015) and Izedonmi and Okunbar (2014) contradicts this finding as they found that VAT had an insignificant impact on GDP in Nigeria. Nevertheless, this finding is consistent with the theoretical framework of this study which postulates that a positive impact on GDP is recognized when the tax rates are such that creates an incentive for individuals to work, spend and produce (Laffer, 2004). Moreover, government officials
52
may find this information useful when defining appropriate levels of taxation for fiscal policy. 5.1.2. To what extent does taxes on income impact economic growth? The regression results revealed that INCTAX had a positive and significant impact on GDP in the federation of St. Kitts and Nevis. The economic implication of this result is that one unit increase in INCTAX will lead to a one-unit increase in GDP. The rationale behind this strong relationship is that INCTAX is such that encourages individuals to work and allows business owners to be desirous of expanding. Therefore when individuals are encouraged to work and business create more job opportunities by expanding, the GDP can be positively and significantly impacted. Further, this finding is consistent with other existing empirical literature who found that taxes on income positively and significantly impact GDP (Salami et al. 2015; Etale and Bingilar, 2016; Adudu and Ojonye, 2016; Ibanichuka, Akani and Ikebujo, 2016). On the other hand, there were some authors who found that taxes on income had no significant impact on GDP (Chigbu and Njoku, 2015; Madugba and Joseph, 2016; Ojong, Anthony and Arikpo, 2016). However, this finding appears to provide support to the Laffer curve which hypotheses that a positive impact is recognized when taxes are such that encourage economic activity according to the tax rate (Laffer curve, 2004). From a practical standpoint, this information can be used as a guide for potential risk-averse local and foreign investors and entrepreneurs who would be concerned with their return on investment when investing in the federation of St. Kitts and Nevis.
53
5.1.3. To what extent does taxes on domestic goods and services impact revenue generation? The regression results of this study indicate that TDGS had a highly significant and positive impact on TOTREV in the federation of St Kits and Nevis. The economic implication of this result is that a one-unit increase in TDGS will lead to a one-unit increase in TOTREV. From an economic standpoint, the positively significant relationship between TDGS and revenue generation is very important. This is so because it suggest that TDGS in the federation of St Kitts and Nevis encourages consumer spending, increases business revenues, and allows for business to expand and employ more workers to meet the increased consumer demand. As such, the increased economic activity increases the amount of revenue that can be collected by the government. This finding is in line with several empirical studies who found that taxes on goods and services had a positive impact on revenue generation (Abdul-Rahman, Aworemi and Ayorinde, 2013; Onaolapo and Fasina, 2014; Rajeshwari, 2015; Okoli & Afolayan, 2015). Moreover, this finding appears to support the Laffer curve which claims that the government can increase the amount of revenue collected by taxes if the tax rate is moderate and encourages economic activity (Laffer, 2004). This particular information can be useful in the practical sense. Government officials will find this information useful for decision-making with regards to fiscal policy since the revenue collected via taxation is integral to government functioning. 5.1.4. To what extent does taxes on income impact revenue generation? Based on the regression results, INCTAX had a positive and significant impact on TOREV. The economic implication of this result is that a one-unit increase in INCTAX will lead to a 54
one-unit increase in TOTREV. The rationale for this relationship is that a moderate INCTAX can contribute to a person’s willingness to work and pay taxes. When the willingness to work increases, the tax base also increases which as a results enables the government to collect more taxes due to the increase in the tax base. Further, this finding fits well within the existing body of knowledge as it is consistent with several studies who found that taxes on income positively and significantly impact revenue generation (Oriakhi and Ahuru, 2014; Adejare, 2015). Conversely, empirical studies by Madugba, Ekwe and Kalu (2015) and Edame and Okoi (2014) found that taxes on income had no significant impact on revenue generation in Nigeria. However, this finding supports the Laffer curve which postulates that lower tax rates can increase the tax base and allow the government to maximize revenue (Laffer, 2004). The information derived from this study can be useful for the decision makers of the government of St. Kitts and Nevis as far taxation and revenue generation. Therefore, this information can be incorporated into the governments strategic budgetary planning as it relates to government revenue generation. 5.2. Theoretical Implications A vast majority of authors contributed several empirical studies on the impact of taxation on revenue generation and economic growth. However, this study makes several new contributions to the existing body of knowledge and understanding of this phenomena. As such, this study provides four new contributions to knowledge and understanding. Firstly, various studies were conducted on this phenomena but the majority were conducted in Nigeria (Lawrence and Victor, 2016; Etale and Bingilar, 2016; Kolahi and Noor, 2016; Immanuella, 2016). As such, according to my knowledge, this was the first investigation on the impact of taxation on revenue generation and economic growth in the federation of St.
55
Kitts and Nevis. Secondly, from observation, the majority of sample sizes that were used to investigate this phenomena covered a period only as recent as 2012 (Chigbu and Njoku, 2015; Afolayan and Okoli, 2015; Ofishie, 2015, Madugba and Joseph, 2016). As such, according to my knowledge, this was the first study that test a sample that covers a period as recent as 2015. Thirdly, according to my knowledge, the majority of studies under this phenomena analyzed twenty observations or less (Onaolapo and Fasina, 2014; Edame and Okoi, 2015; Lawrence and Victor, 2016; Hakim, Karia and Bujang, 2016; Ibanichuka, Akani and Ikebujo, 2016). However, this study separates itself and utilizes twenty-six observations which is of twenty six years from 1990 to 2015. Last, from observation, there were several influential studies who disregarded the use of control variables needed to address complexities of economic growth (Adudu and Ojonye, 2015; Chigbu and Njoku, 2015; Ofishie, 2015; Ibanichuka, Akani and Ikebujo, 2016). However, with the exception of Hakim, Karia and Bujang (2016), this study was one of the few studies on the impact of taxation on economic growth and revenue generation that utilized a macroeconomic variable (POP) to isolate the causal effects of the dependent and independent variables. 5.3. Practical Implications The findings of this study have practical implications for two types of practitioners. First, the findings can be used as a benchmark for taxation in St. Kitts & Nevis. Government officials can use this information for decision-making as it relates to fiscal policy and the economic growth. Thus, the government will be made aware of appropriate levels of taxation in order to stimulate consumer spending, investments, employment and the production of goods and services in St. Kitts and Nevis. Secondly, the findings of this study can also be useful for potential foreign and local investors who are interested in investing in
56
the federation of St. Kitts and Nevis. The findings provided in this study can be incorporated into their strategic planning as it relates to business cost. This is so because taxes that would affect them such as taxes on domestic goods and services and taxes on income in St. Kitts can have an impact on their return on investment. 5.4. Limitations This study is not without limitations. In spite of this, there are several limitations that can constrain the generalizability of my findings. For instance, there are other tax types such as non-tax revenue, property tax and CED in the federation of St. Kitts and Nevis which may have some influence on economic growth and were not considered for the regression models for this study. Secondly, another limitation that may constrain the generalization of the findings of this study would be the unavailability of data beyond 1990. Therefore, this study considered time series annual data over the period 1990-2015 which limits the yearly observations to twenty-six. Due to the unavailability of previous data, the sample size could not be increased which may have produced more robust results. Third, this study considered POP as a control variable to isolate the causal effects of the other independent and dependent variables. However, there are other macroeconomic variables such as trade openness, consumer price index, and money supply which were not considered which may have had an influenced on the revenue generation and economic growth of the federation of St. Kitts and Nevis. Last, another limitation that may constrain the generalizability of my finding is that most of the empirical studies considered for the literature review were conducted in Nigeria. This
57
created a sense of country bias and did not provide a balanced view of the phenomena under investigation. Therefore, if other countries were considered for review, then the country bias would have been eliminated and the generalizability increased. 5.5. Directions for future research While this dissertation utilized several variables to analyze the impact of taxation on economic growth and revenue generation, there remain several opportunities for further research. In spite of this, this section presents some of these directions. Firstly, non-tax revenue and taxes on international trade should be investigated in the context of St. Kitts & Nevis as well as other small countries. Since this study did not consider these independent variables, this can be an opportunity for prospective researchers to investigate the impact of such variables on economic growth and revenue generation which may produce more robust results. Secondly, this study considered POP as a macroeconomic control variable. However, there are several other macroeconomic indicators of GDP that can be used. Such control variables are inflation rate, money supply, and trade openness. The addition of these variables to the regression models might be an opportunity for future researchers to achieve more robust results in the context of St. Kitts and Nevis as well as other developing countries. Thirdly, this study covered the period 1990-2015. If possible, further research should be investigated on a period that is earlier than 1990 in order to achieve more generalized results on the impact of taxation on economic growth and revenue generation. As such, this can also be an opportunity for further research in this regard.
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5.6. Reflections In retrospect, before conducting this study, I was never exposed to research of this magnitude and so my secondary research skills were basic. However, after conducting this study, those skills have immensely improved and would have a positive impact on my career. Thus, this section will provide an appraisal of the enhanced competencies and lessons learned, the successes and failures, and the application of the new knowledge. There were a few competencies gained during the research process such as prioritizing the secondary data, critically analyzing secondary data, testing the data and interpreting the results. Firstly, during the literature review stage, I found a plethora of empirical studies on taxation and economic growth. I was overwhelmed by the volume of information located in books, the internet and academic journals and often wondered how the large amounts of information would fit in the literature review without breaking the word limit. However, undertaking this research taught me that I should be selective and focus on the most influential studies, the most relevant studies and also peer-reviewed academic journals. This allowed me to narrow my focus and be selective. Secondly, prior to conducting this research, I was not critical of information published in books and other sources. I often thought that such information would be factual. However, after conducting this research, I have obtained the competence to critically evaluate information by identifying weaknesses and strengths, looking at alternative viewpoints and creating a balanced conclusion with the facts available.
59
Thirdly, although I had a research topic of interest, I was unsure of the statistical method that would best meet the objectives of the study as well as the skills of properly interpreting the findings. After paying keen attention to the statistical methods informed by the most influential empirical studies, as well as the interpretation of their results, I learned how to use the appropriate statistical methods and techniques as well as the proper way to interpret the findings. During the research process, there was one significant difficulty that I had to overcome. Unfortunately, I was made aware during the later stages of my research that the independent variables that I proposed were not available although my proposal had already been approved. To resolve this, I quickly investigated on the type of data available from several sources and was lucky to acquire data from the ECCB. However, since the newly acquired independent variables were different to what was initially proposed, it caused me to make changes to the title, research questions, theoretical framework and the study period. I communicated my issues and solutions to the dissertation supervisor and was advised to carry on with my research. In hindsight, had I known what I know now, I would better prepare myself in the early stages of my research to avoid losing time in the later stages. However, from this, I learned a very valuable lesson that researchers should always investigate upon the availability of data during the proposal stage and not assume data is always available. All in all, I learned valuable concepts, techniques and lessons throughout the research process that will benefit me greatly. Moreover, my problem solving, critical thinking and synthesizing skills have all been significantly enhanced by this research process. The new
60
knowledge gained from this experience will now be transferred to my professional career and beyond.
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APPENDIX A: DATA SOURCES AND DESCRIPTION Data and the respective data sources VARIABLE TYPE
SOURCE DESCRIPTION
GDP
Dependent Variable ECCB for GDP Model
Economic Growth is Proxy for GDP
TOTREV
Dependent Variable for Revenue Generation Model
ECCB
Represents all revenue collected by taxes and other sources by the government of the federation of St. Kitts and Nevis
TDGS
Independent Variable for GDP and Revenue Generation Models
ECCB
Stamp Duties, Hotel and Guest Tax, Telecommunications Tax, Wheel Tax\Vehicle Rental Tax, Entertainment Tax\Cable TV, Gasoline Levy, Value Added Tax, Insurance Fees, Traders Tax and Consumption Tax
INCTAX
Independent Variable For GDP and Revenue Generation Models
ECCB
Housing & Social Development Levy, Withholding Tax, and Company Income Tax collected by the Inland Revenue Department.
POP
Control Variable for GDP and Revenue Generation Models
ECCB
Average Annual increase of the population. This is used as a Control to Isolate Casual Effects of Independent Variables
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APPENDIX B: CORRELATION MATRIX Correlation Matrix GDP Model Table showing Correlations for model 1 GDP Pearson Correlation
Sig. (1-tailed)
N
GDP
INCTAX
Comptax
PopG
1.000
.895
.869
.346
INCTAX
.895
1.000
.596
.365
TDGS
.869
.596
1.000
.146
POP
.346
.365
.146
1.000
GDP
.
.000
.000
.042
INCTAX
.000
.
.001
.033
TDGS
.000
.001
.
.238
POP
.042
.033
.238
.
GDP
26
26
26
26
INCTAX
26
26
26
26
TDGS
26
26
26
26
POP
26
26
26
26
Correlation Matrix Revenue Generation Model Table showing Correlations for Model 2 Totrev Pearson Correlation
Sig. (1-tailed)
N
INCTAX
Comptax
PopG
TOTREV
1.000
.804
.944
.254
INCTAX
.804
1.000
.596
.365
TDGS
.944
.596
1.000
.146
POP
.254
.365
.146
1.000
.
.000
.000
.105
INCTAX
.000
.
.001
.033
TDGS
.000
.001
.
.238
POP
.105
.033
.238
.
Totrev
26
26
26
26
INCTAX
26
26
26
26
TDGS
26
26
26
26
POP
26
26
26
26
Totrev
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APPENDIX C: NORMALITY TEST Normality Test for the two dependent variables (TOTREV, GDP) using KolmogorovSmirnov Test
Tests of Normality Kolmogorov-Smirnova Statistic TOTREV GDP
.150 .126
df
Shapiro-Wilk
Sig.
Statistic
df
Sig.
26
.136
.911
26
.028
26
.200*
.931
26
.081
*. This is a lower bound of the true significance. a. Lilliefors Significance Correction
69
APPENDIX D: LINEARITY TEST GDP MODEL Linearity Test GDP Model
70
71
APPENDIX D1: LINEARITY TEST FOR REVENUE GENERATION MODEL Linear Test for Revenue Generation Model
72
73
APPENDIX E: HETEROSCEDASTICITY TEST Heteroscedasticity test for GDP model
Heteroscedasticity test for Revenue Generation model
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APPENDIX F: SUMMARY OF EMPIRICAL LITERATURE
Author, Year and Location
Statistical Method and Period
Immanuella (2016), Nigeria
multiple regression, 20002012
Hakim, Karia and Bujang (2016), Developed Countries
Arellano-Bond Dynamic estimation method, 2005- 2012
Emanuel (2013), Nigeria
VAT had a significant effect on both GDP and total tax revenue Simple Regression, GDP, VAT, total tax VAT positive 1994-2010 revenue and total federal and Insignificant government revenue impact on GDP Multiple RGDP, VAT, CIT, PPT VAT had a Regression ,1994- and CED positive but 2012 statistically insignificant impact on GDP multiple investment, government VAT had a regression, 1994expenditure, real exchange positive and 2013 rate, real interest rate and significant trade openness impact on Investment Multiple GDP, income tax revenue, VAT had a regression, 1991VAT and CED positive and 1992 and 2011significant 2012 impact on GDP Multiple VAT, GDP and total tax VAT had a regression, 1994revenue positive and 2009, 2010-2011 significant impact on GDP Multiple GDP and VAT VAT had a regression, 1994positive and 2012 significant impact on GDP generalized VAT, capital VAT had a moment’s method accumulation growth, positive and
Izedonmi and Okunbar (2014), Nigeria Afolayan and Okoli (2015), Nigeria
Frederick and Okeke (2013), Nigeria
Hassan, (2015), Pakistan
Onwuchekwa and Aruwa (2014), Nigeria Ofishie, (2015), Nigeria
Kolahi and Noor, (2016),
Variables
GDP, VAT, total tax revenue, total federal government revenue,
GDP, GST, population growth, inflation rate, trade openness, personal income tax (PIT) and government expenditure Simple Regression, VAT, GDP and total 1994-2010 revenue
75
Results
VAT had a positive and Significant Impact on GDP GST had a positive and Significant Impact on GDP
Developing Countries Lawrence and Victor (2016), Nigeria
(GMM), 1995 to 2010 co-integration technique ,1994 to 2014
productivity growth and GDP RGDP, VAT, PPT and inflation rate
Madugba and Joseph (2016), Nigeria
multiple regression technique ,19942012
VAT, GDP and total consolidated revenue
Etale and Bingilar (2016), Nigeria
Multiple regression,
GDP, VAT, CIT and GDP
Ojong, Anthony and Arikpo (2016), Nigeria Chigbu and Njoku (2015), Nigeria
Multiple regression, 1986-2010
GDP, PPT, CIT and Nonoil Revenue
co-integration test, 1994-2012
Salami et al. (2015), Nigeria
Multiple regression,
GDP, inflation rate, unemployment rate PIT, VAT, PPT, CED, and CIT RGDP, PPT, CIT, CED, and VAT
Adudu and Ojonye (2016), Nigeria
granger- causality co-integration technique,1990 to 2011
Dehghan and Nonejad (2015), Nigeria
auto regressive distributed lags technique,1981 to 2010
Ibanichuka, Akani and Ikebujo (2016),Nigeria
multiple regression statistical method,1995 to 2014
Abdul-Rahman, Aworemi and Ayorinde (2013), Nigeria
Stepwise Regression, 2001 to
Rajeshwari (2015), India
stepwise regression, 2005-2014
2005-2014
1976-2006
2010
GDP, PIT, corporate taxes on income, social security contributions, payroll taxes, property tax, GST, international trade taxes and other taxes GDP, population growth, inflation rate, trade openness, corporate income tax, business tax revenue, and GST human development index, CIT, VAT and CED total federal collected revenue, VAT, PPT, CIT and education tax VAT, Excise tax, vehicle tax, goods and passenger taxes, electricity tax and entertainment tax 76
significant impact on GDP VAT had a positive and significant impact on GDP VAT had a negative and significant impact on GDP CIT had a positive and significant impact on GDP CIT had a positive and Insignificant impact on GDP CIT had a positive and Insignificant impact on GDP CIT had a positive and significant impact on GDP Corporate Taxes on income had a positive and significant impact on GDP Corporate income tax had a negative and significant impact on GDP CIT had a positive and significant impact on Economic development VAT had a positive and significant impact on Revenue Generation VAT had a positive and significant impact on
Revenue Generation
Okoye, (2013), Nigeria
PPMC, 2001-2010
GDP and VAT
VAT revenue had a significant impact
Okwori and Ochinyabo (2014), Nigeria
Multiple regression,19932012
Okoli & Afolayan, (2015, Nigeria
Multiple regression,19942012
federally collectible revenue, VAT revenue, PPT revenue, GDP, and the private consumption expenditure total revenue, VAT, CIT, CED and PPT
Onaolapo and Fasina (2014), Nigeria
Panel regression statistical method.
total revenue, VAT, per capita income, population, and other sources of revenue
Adejare (2015), Nigeria
Multiple regression,19932013
GDP, revenue profile, CIT, VAT, PPT and inflation
Edame and Okoi (2014), Nigeria
Multiple regression, 1980- 2010.
GDP, CIT, PIT and investment level
Oriakhi and Ahuru (2014), Nigeria
co-integration test, 1981-2011
federally collected revenue, CIT, PPT, VAT and CED
VAT had a positive and significant impact on Revenue Generation VAT had a positive and significant impact on Revenue Generation VAT had a positive and significant impact on Revenue Generation CIT had a positive and significant impact on Revenue generation CIT had a negative and significant impact on revenue generation CIT had a positive and significant impact on Revenue generation
Madugba , Ekwe and Kalu (2015), Nigeria
simple regression statistical method
petroleum tax income (PTI), total consolidated revenue and CIT
77
CIT had no significant impact on revenue generation
APPENDIX G: PROPOSAL Author Guidelines for the Proposal (May2013) Please note; failure to follow these guidelines outlined below may result in the marker being unable to mark your work. Before the Project Proposal can be submitted, students have to seek ethical approval from their supervisors. This is done by submitting an application via https://wads2.le.ac.uk/ethics/Ethics.aspx. Once student have gained ethics approval for their project, all Project Proposals should be submitted online, via Blackboard; Instructions on how to do this are provided on Blackboard. Students must save their work in a Microsoft Word compatible format (i.e. doc or docx). There is a maximum file size limit of 5 MB, and students are encouraged to keep their file size to a minimum by avoiding the insertion of unnecessary image files.
Page Setup A Proposal template is provided below and students are required to use that template. The coursework should be typed and be formatted for A4 (portrait). A left-hand margin of 1.25cm to 1.5cm should be used. Line spacing of 2 should be used for typescript, except for indented quotations where single spacing may be used. A font size of 12 is required, and you are encouraged to use a clear font design such as Arial, Times New Roman or Courier New. Pages must be numbered consecutively throughout the text, with numbers located centrally at the bottom of each page. Any abbreviations used should be those in normal use. Where necessary a key to abbreviations should be provided. Type your full name, student registration number, programme title, module title, assignment question, registration expiry date and word count at the start of the coursework. You must provide your word count immediately below the assignment, as well. The word count includes everything except the references and appendices.
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Student to Complete
Student Name: Akim Galloway
Registration Number: 119040513
Programme Title: MSC Finance
Module Title: Research Methods
Proposed Dissertation Title: The Effects of Value Added Tax on Economic Growth. Evidence from St.Kitts & Nevis during 2011-2015
Registration Expiration Date: 31/7/2016
Word Count: 1691
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The Proposal Template Specialism MBA students only: If you have elected to specialize please identify your specialism (i.e. Finance/Marketing/Managing Quality etc …
Click here to enter text
Ethical Approval reference number (you will receive this once your Supervisor approves your ethical approval request) Please insert your reference number here
5077-ag327-schoolofmanagement
Dissertation Supervisor Please identify any University of Leicester Tutors with whom you have discussed your proposal and the forum you used (e.g. workshops/Blackboard)
Dr. Panayiotis Savvas/Blackboard
Title Note on Content:
(max. 25 words – recommended 15 words)
A title should summarise the main idea of the proposal simply and, if possible, with style. You may want to use a title and a subtitle, separated by a colon (e.g. ‘Brown Eggs: What they are Made of and How to Eat Them’)
“The Effects of Value Added Tax on Economic Growth: Evidence from St.Kitts & Nevis during 2011-2015.”
Introduction 80
Note on Content:
(max. 200 words)
A statement of your research question, possibly including a central question and three or four aspects or subquestions (approx. 30–100 words depending on number of research questions).
Explain why this question is interesting (approx. 100 words).
Insert text The government of St. Kitts and Nevis found that due to trade liberalization, the opening of boarders and a transition from a sugar industry to an economy driven by tourism and a financial sector, that the previous tax system (Consumption Tax) would be ineffective (Tax Reform Unit, 2010). To mitigate this, the government of St. Kitts and Nevis introduced a value added tax (VAT) system to meet modern demands.
This implementation may lead an individual to wonder if such a system is viable for the economy. Hence, since there is no known investigation on the impact of VAT on the economic growth in St. Kitts & Nevis, this study will seek to examine the implications of VAT on the economic growth.
With the purpose of examining how a VAT system would impact on the economic growth of St. Kitts & Nevis, this study aims to explore the following research questions.
1. To what extent does VAT impacts on economic growth (GDP)?
2. To what extent does VAT revenue impact on government revenue generation?
81
Relation to previous research (Theoretical Framework) Note on Content:
(max. 500 words).
Discussion of the relation between your proposed research and previous research. When expanded in the dissertation this will be referred to as a Literature Review.
Insert text This study is based on Kendrick’s ability to pay theory (1939) where citizens pay taxes dependent upon their ability to pay. Since the taxes paid are used by governments to foster economic growth (Kendrick, 1939), this study will be used to theorize the effects of VAT on economic growth and utilize the underlining approaches of the ability to pay theory with the use of influential empirical evidence to meet the objects of this paper.
The ability to pay theory holds that public expenditures should come from “him that hath” and not from “him that hath not” Kendrick, (1939). This theory is used to justify progressive taxation as it is usually interpreted on terms of sacrifice. This is so because the citizen has to surrender his money as taxes paid which would have been used for personal use (Adebisi and George, 2013). According to Kendrick, (1939), the terms of sacrifice have three interpretations which are equal, equal-proportional, and least-sacrifice but both the equal and equal-proportional theories taxes both rich and poor citizens.
82
However, it is unknown whether the ability to pay theory has the ability to answer the phenomenon under investigation. As such, questions tend to linger. What is the impact of the economy due to the ability to pay theory? Does it consider economic stability? What good has it has done for GDP? If the ability to pay theory is such an efficient tax system with a progressive approach, is government revenue generation improved?
The theoretical discussions would be supported by review of influential empirical literature on the impact of VAT on economic growth (GDP) and revenue generation. Adreti et al., (2011) studied VAT and economic growth in Nigeria. Time series data on GDP, VAT, total tax revenue and total federal government revenue were analyzed from 1994-2008. The data was analyzed using the simple regression analysis. Results show there is a positive and significant correlation that exists between Vat and GDP. Also, the ratio of VAT revenue to GDP averaged 1.3% which is considered low when compared to 4.5% in Indonesia. Further, there is no casualty that exists between GDP and VAT revenue. In my view, the simple regression analysis may be inadequate when analyzing the complexities of economic growth and so I would take such results with caution.
Another pivotal study by Jalata, (2014) analyzed the role of VAT on the economic growth of Ethiopia from 2003 to 2012 using time series data on GDP, VAT, total tax revenue, non –tax revenue and foreign revenue. This study employed multiple regressions. Results shows VAT boost the general economic growth of Ethiopia positively at a significance of 5%. All variables except foreign revenue were significant at 5% but all positively contributed to GDP.
83
The reviews show that different researchers utilized various methodologies and may concur that VAT generally contributes to economic growth but with mixed results. This study seeks to test the ability to pay theory as well as the empirical assumptions in the context of St. Kitts & Nevis.
Proposed methods Note on Content:
(max. 500 words).
A precise statement of the methods you propose to use.
Justify the choices you make. Explain why this method is being used in preference to others.
Discuss the specifics of the method(s) you will use. Be clear about data sources and what will count as data in your research project.
(In your methods section you may need to make some reference to other exemplary studies and will certainly need to refer to the literature on research methods.)
Insert text Type and Sources of Data
The study uses secondary data collected from various government offices. This data would be time series data similar to (Basila, 2010; Adreti et al., 2011; Emanuel, 2013; Onaolapo et al., 2013; Jalata, 2014). Further, the data would be collected from Inland Revenue Department, Ministry of Finance, and the Eastern Caribbean Central Bank. Such data are annual reports, statistical bulletins, reviews and financial statements. (see appendix for data 84
sources). These would cover quantitative yearly data on GDP, VAT, Non-Tax Revenue, Total Tax Revenue, and Foreign Revenue from 2011 – 2015. This period is warranted because VAT was only recently implemented in November of 2010. Hence, the period under investigation is from 2011 to 2015 which is 5 full years of data.
Methods of Data Analysis
To meet the objectives of this research, inferential analysis would be undertaken which is similar to Adreti, 2011 and Jalata, 2014. However, in order to decide on what variables would be used, various exemplar studies were assessed. Such exemplar studies were (Basila, 2010; Adreti et al., 2011; Emanuel, 2013; Onaolapo et al., 2013; Jalata, 2014).
From an assessment of the relevant literature mentioned, this study would conduct analysis based on five economic variables similar to Adreti et al., 2011 and Jalata, 2014 of which are gross domestic product (GDP), VAT, total tax revenue (TTR), non-tax revenue (NTR) and foreign revenue (FR) (Jalata, 2014). For the purposes of this study, GDP is the dependent variable whilst VAT, TTR, NTR and FR are independent variables. Additionally, money supply (MS), imports (IMP), exports (EXP) and inflation (INF) would be used as control variables in order to isolate the casual effects of the other mentioned independent variables. (see appendix for variables)
85
The software that would be employed is the Statistical Package for Social Science (SPSS) software package. In line with Jalata, (2014), this study will employ the multiple regression analysis. This method predicts the relationships that exist between the dependent variable and the other independent variables (Jalata, 2014). Additionally, descriptive statistics would be utilized in order to describe the measure of dispersion of the sample with regards to the mean and standard deviation whilst descriptive analysis would be used to prepare the data for the regression analysis.
Specifications of the Model
With the aim of analyzing the effect of VAT on economic growth, this study would adopt the models similar to Jalata (2014). However; the models have been modified in order to suit the aims of the research. The equations mentioned below are a representation of the multiple regression analysis. Thus the functional relationships are shown below. 𝐺𝐷𝑃 = 𝑓(𝑉𝐴𝑇, 𝑇𝑇𝑅, 𝑁𝑇𝑅, 𝐹𝑅) … … … … … . . (1)
For the purpose of control variables, other macroeconomic factors namely (IMP, EXP, INF and MS) would be considered (Biswas and Saha, 2014). These control variables are necessary in order to isolate the casual effects of the independent variables namely VAT, TTR, NTR and FR. As such, the model would be modified with IMP for imports; EXP for exports; INF for inflation and MS for money supply. The model modified with control variables is seen below. 𝐺𝐷𝑃 = 𝑓(𝑉𝐴𝑇, 𝑇𝑇𝑅, 𝑁𝑇𝑅, 𝐹𝑅, 𝐼𝑀𝑃, 𝐸𝑋𝑃, 𝐼𝑁𝐹, 𝑀𝑆) … … … … . . (2)
86
Reflections Note on Content:
(max. 500 words).
Include reflections on: o
Potential practical and empirical obstacles (e.g. access).
o
Conceptual and theoretical problems and difficulties.
o
Ethics (both in the narrow and the broader senses).
o
Your position as a researcher in a political field, and reflection on how this will impact on your study.
Insert text There are always challenges and obstacles when conducting research. As such, this study titled “The Effect of Value Added Tax on Economic Growth: Evidence from St. Kitts & Nevis during 2011-2015” is no different. Firstly, the researcher foresees that data collection from the various sources may be a tedious process. As such, although they are publicly available, it is imperative to inquire about the necessary policies and procedures ahead of schedule in order to acquire the information.
Secondly, the theoretical framework was somewhat of a challenge to construct. This was so due to the researchers’ lack of knowledge towards the theories of taxation. Also, the issue of choosing which theory or theories would best suit the phenomenon under investigation. For the researcher, this can be a painstaking process and so more research will need to be done as to choose the best theory or theories to meet the objectives of this study.
Third, another challenge would be the lack of extensive empirical studies in this area of study reflected in academic journals. With the exception of Nigeria, there seem to be very little research done on other countries. This could be derived from the fact that it may be a 87
fairly new phenomenon especially in the Caribbean and other developing countries. As such, the study has the potential to be a catalyst for more research in the region and provide strong analysis for government administrations involved.
This study is guided by the ethical standards of the University of Leicester. As such, the study would not indulge in any form of academic dishonesty and will be strictly independent work with sources properly cited accordingly. Further, the data comes from secondary sources and interviews and other methods aimed at human participants would not be utilized. At the same breath, the results of this research will not be manipulated in order to portray bias views as data samples would be presented. The study is deemed to further research and scholarship in this area and contribute to social good.
The study under investigation has some bearing on my work as an officer at the St. Kitts & Nevis Customs & Excise Department where VAT is one of the tax types that are collected. This study should provide me with more insight of the usefulness of taxation with regards to economic growth and also help to guide the organization to be more aware of the importance of efficient revenue collection and the implications involved. The analysis may also help management to better advice government officials when making economic decisions with regards to VAT and other related tax types.
88
Timetable Note on Content:
(max. 100 words, or a one page diagram)
Provide dates and major steps or milestones.
This should be presented in bullet points or as a pictorial diagram.
Make sure that you include other commitments such as holidays, and allowing time for tutors to approve your research proposal.
Insert text
Schedule
FEBR 2016 Week 1
2
3
MARCH 2016 Week 4
1
2
3
APRIL 2016 Week 4
1
Await the results of the proposal and produce draft literature review (Chapter 2) Data and Methods (Chapter 3) Analysis & Results (Chapter4) Discussions & Conclusions (Chapter 5) Introduction (Chapter 1) Add Executive Summary/Submit first draft for review Submit Second draft for review
89
2
3
MAY 2016 Week 4
1
2
3
4
JUNE 2016 Week 1 2 3
Submission of Thesis
References Note on Content:
A full list of works referred to in the text referenced correctly.
Quality is more important than quantity, demonstrating engagement with relevant literature.
The Internet should not be the only source of references.
Insert text
Adebis, J .F and Gbegi, D.O (2013). Effect of Tax Avoidance and Tax Evasion on Personal Income Tax Administration in Nigeria. American Journal of Humanities and Social Sciences. 1(3), 125 -134.
Adereti, S. A, Sanni, M.R and Adesina, J.A (2011). Value Added Tax and Economic Growth of Nigeria. European Journal of Humanities and Social Sciences. 10 (1), 456 – 471.
Basila, D., 2010. Investigating the Relationship between VAT and GDP in Nigerian Economy. Journal of Management and Corporate Governance, 2 (1)
Biswas, S., and Saha, A., 2014. Macroeconomic Determinants of Economic Growth in India. SOP Transactions on Economic Research, 1 (2), pp. 54-72.
Emmanuel, U. C., 2013. The Effects of Value Added Tax (V.A.T) on Economic Growth. Journal of Economics and Sustainable Development, 4 (6) 90
Jalata, D. M., 2014. The Role of Value Added Tax on Economic Growth of Ethiopia. Science, Technology and Arts Research Journal, 3 (1), pp. 156-161
Kendrick, M. S., (1939). The Ability-to-Pay Theory of Taxation. The American Economic Review, 29(1), pp. 92–101. Retrieved from http://www.jstor.org.ezproxy4.lib.le.ac.uk/stable/1806989
Onaolapo, A., Aworemi, R., and Ajala, A., 2013. Assessment of Value Added Tax and Its Effects on Revenue Generation in Nigeria. International Journal of Business and Social Science, 4 (1)
Tax Reform Unit (2010) Value Added Tax White Paper 2010. [Online]. St.Kitts, The VAT Office. Available from: www.sknvat.com/phocadownload/white_paper_final_20100420.pdf [Accessed 27 December 2015].
Appendices (optional) Note on Content:
Containing materials distracting from, but relevant to, the body of the proposal, for example, draft questionnaires, interview questions, other tables, lists, etc.
Do not overdo it. Only include things that really are relevant. You won’t get extra marks for this.
Insert text Independent and Dependent Variables 91
VARIABLE GDP VAT Total Tax Revenue
TYPE Dependent Independent Independent
SOURCE MOF/ECCB IRD MOF
Non-Tax Revenue
Independent MOF
Foreign Revenue Imports
Independent MOF
Exports
Independent MOF
Money Supply Inflation
Independent ECCB
Independent MOF
Independent ECCB
DESCRIPTION Economic Growth is Proxy for GDP Value Added Tax Revenue Collected by the Government, Both Direct and Indirect Taxes Excluding VAT Summations of fees, Fines, Forfeitures, Rent of Government Property, Water Services, Post Office, Interest, Dividends & Profits, Stone Crusher, Hospital Fees, Citizenship by Investment, Maritime Fees, Other Revenue External Assistance , Loans and Grants Control: Isolate Casual Effects of Independent Variables Control: Isolate Casual Effects of Independent Variables Control: Isolate Casual Effects of Independent Variables Control: Isolate Casual Effects of Independent Variables
Word Count: 1691
92
