DETERMINANTS OF ECONOMIC GROWTH IN OECD COUNTRIES
The objective of the thesis was to research the association between the rate of economic growth and other selected independent variables which comprises tax on personal income, inflation, saving rates, greenhouse gas emissions, and trade. The objectives of the thesis included: (1) to determine if the relationships between the listed variables; to find out whether the independent variables have a significant impact on growing an economy, and to assess the significance of the dependent variables as determinants growth. The data obtained from the OECD database ranged from 1986 to 2016. Factors determining economic growth concerning inflation, long-term interest rates, saving rates, greenhouse gas emissions, and saving rates had a positive correlation. On the other hand, both taxes on personal income and gross domestic spending on R&D showed a negative relationship to economic growth. Statistically significant determinants included the tax on personal income, trade in goods and services, savings rate, and greenhouse gas emissions. OECD member states should invest in small and medium enterprises to enhance savings. Further, OECD countries need to reform their judicial and legal institutions to allow for more robust economies.
Keywords: economic growth, tax, saving rates, trade in goods, and inflation.
The thesis aimed at exploring the determinants of the growing economy in OECD countries. All countries have targets which must be met and therefore, there is the need to focus on prudent fiscal management. The objectives of the thesis included to determine if the relationships between the variable are positive or negative; to find out whether the independent variables significantly impact the growth of an economy, and to assess the significance of the dependent variables as determinants of economic growth. The study involved nineteen OECD countries and examined the determinants of economic development. The analysis software used for the data Eviews 8. The information gathered for the years between 1986 and 2016. Moreover, data analysis was conducted using regression method to find out the relationship between the variables. The result from the investigation shows the variations in gross domestic product per capita across these countries are as a result of different institutional and policy settings. Moreover, the nations under the OECD framework should support the development of SMEs because they are integral to enhancing the economy. Future research should focus on the disparities among the member states and how to fill the gaps in the literature.
The concept of economic growth has elicited debate amongst scholars, policymakers, administrators, and students. Several economic growth theories and models highlight the way in which the present economic activities impact on future development. Also, these economic concepts assist in the identification of revenue sources (Boldeanu and Constantinescu 2015). In economics, ensuring the welfare of the human race; there needs to be some level of economic growth. As such, economic growth theories have evolved about the dynamics within a country. Moreover, the development of improved mathematical and statistical tools used for formulating new concepts has led to the critical analysis.
Economic growth concept refers to the rise in GDP per capita. The idea of GDP rise associated with the national product measured under constant prices. Factors influencing economic growth include active population, investment in human capital, natural resources, and improvement in technology (Boldeanu and Constantinescu 2015). Factors affecting economic growth include the size of average demand, the rates of savings and investments, financial system efficiency, fiscal policies, and movement of labor and capital.
The classical growth theory emphasized the balanced growth characterized by corresponding increases in per capita capital and per-capita production. Under the neo-classical theory, technological developments and population surges are considered as external factors and with no specific function allocated to the government (Boldeanu and Constantinescu 2015). The endogenous growth theories emphasize the role played by the state in the economy. Developed countries could not experience a recession if they could manage to maintain some semblance of sustainable economic growth (Sezer and Abasiz 2016). The state’s role according to this theory involves promotion of research and development, improved quality of health, protection of property rights, and the establishment of an open economic system (Sezer and Abasiz 2016). The endogenous growth theories utilize the concept of in-system as the agent powering the economic growth.
Several studies (Sezer and Abasiz 2016; Boldeanu and Constantinescu 2015) have focused on economic growth and development. For example, the Solow total production function method addresses the issue of whether per capita income growth rates in economies had the likelihood of converging in the long run. Further, Demurger (2001) mentioned public infrastructure, transportation, and foreign direct investments as determinants of economic growth. The study sought to identify the determinants of economic growth of nineteen OECD countries between 1986 and 2016. The study variables included a tax on personal income, trade in goods and services, long-term interest, spending on research and development, inflation, saving rate, and air and greenhouse gas emissions.
Economic growth amongst OECD countries from the 1990s faced differences due to cyclical impacts. The disparities originate from the persistent rise in economic growth which is higher than average in some countries trying to be on par (Bassanini and Scarpetta 2002). Examples of such countries include Korea and Ireland. Also, the disparity results from high rates of growth in wealthier nations such as the United States, and the Netherlands among others. However, the trends in GDP per capita growth rates have broadened during the 1990s as compared to the 1980s. The widening gap attributes to the rising contrasts in the utilization of labor; a factor associated to diffusion of information technology in some of the high productivity countries (Bassanini and Scarpetta 2002). For instance, the United States experienced an unprecedented rate of economic growth due to significant technological change resulting in an increase in output and productivity of labor during the 1990s (Colecchia and Shreyer 2002).
constant mode of accumulation, information, and technology has proved to be one of the crucial factors with an influence on the GDP (Sokolov-Mladenović et al. 2016). The data from the 1990s showed a steep rise in productivity in the industry producing most information and technology-related goods and services. Indeed, this had a substantial role to play in the rapid increase in labor and productivities of various factors of production. Furthermore, considerable investment in other industries from capital obtained from information and communication technologies increased the aggregate output and productivity, which has risen in recent years (Basu et al. 2001).
The increases in productivity resulted from ICT investment even though they typically start at a lower stage than observed in the United States (Basu et al. 2001). The spread of ICT networks witnessed in other counties and thus disrupting significantly the methods and procedures used by businesses in their operations. The phenomenon has created robust opportunities for potential growth (Spiezia 2013).
The determinant of a growing economy in a country links to an increase in productivity, an element dependent on a variety of factors. According to the growth model, the increase in output growth and the accumulation of capital is dependent on the progress of technology. However, the theory has its limitations associated with its inability to explain when and how the advancement of technology takes place. As such, the determinants of economic growth in OECD countries vary hence necessitating the utilization of a panel data approach in observing a set of potential influencing variables.
The goals of the thesis are to explore the link between GDP growth rate and other determinants which include a tax on personal income, trade in goods and services, inflation, saving rate, and greenhouse gas emissions. Therefore, the study sought to expound on the interrelationship between these variables and how countries can stabilize economic factors.
- To determine if the relationships between the variables concerning economic growth.
- To find out whether the independent variables have a significant impact on the rate at which an economy grows.
- To determine the significance of the dependent variables as determinants of economic growth.
- Is there a relationship between the listed determinants and the growth of an economy?
- Do the independent and dependent variables have collinearity?
- What is the significance of the dependent variables as determinants of economic growth?
There is a need to develop a suitable model and framework to address issues relating to the determinants of economic growth. Explanatory variables commonly used to explain the influence on GDP include the rate of population growth, interest rates, government expenditure, inflation, net natural resources, capital availability, foreign direct investment, and savings rate. Compared to the global determinants of economic growth, the ones for the OECD countries have matured but vary due to the differences in country political, economic and cultural environment. The adoption of comprehensive fiscal policies has a direct impact on the growth of the economy of the panel countries.
Osinubi (2005) established a relationship between the growth of the economy and the rate of inflation, money supply, unemployment, disputes in trade and the percentage of savings. Stephen (2012) used Private Domestic Investment, the level of unemployment, inflation and money supply as determinants of economic growth. The initial level of quality human capital, terms of trade, government consumption, fertility rate, the rule of law index, local variables and investment ratio as the determinants of economic growth (Barro 1996). Therefore, the findings of this thesis will fill in the gaps of other variables or factors contributing to the economic growth within the OECD countries and present a strong foundation for future research.
The thesis includes six chapters: One, the introduction- discusses the background information, significance, study objectives, aims, and problem statement. Second, the literature review focuses on the eight factors influencing economic growth. Third, methodology- details the model used in the thesis based on the data collected from the OECD countries between 1986 and 2016. The section entails a detailed description of the research methodology. Fourth – data analysis. The fifth section of the thesis delineates the discussion of findings and conclusion. In chapter six, recommendations and suggestions for further research.
The section reviews information on inflation, tax, long and short-term interest rates, trade in goods and services, saving pattern, and air and greenhouse gases emissions (GHG) among other. The wide range of the reviewed literature provides a broader scope for examining the determinants of growth of economies in OECD countries.
There is varied literature on economic growth spanning centuries. Studies have detailed the various direct and external factors to act as determinants of economic growth (Barro 1996; Sokolov-Mladenović et al. 2016). According to Palić, Žmuk, and Grofelnik (2017), taxes impede economic growth of a country. Taxes create distortions in a person’s behavior regarding savings, expenditure, and leisure. Moreover, taxes bring unnecessary loss to the economy by creating inefficiencies.
The two most commonly used macroeconomic theory models for understanding the impact of personal income taxes are endogenous and neoclassical growth model. According to the neoclassical paradigm, the rate of economic progress, after many years, refers to the technological advancement and population growth. The neoclassical model suggests a significant influence of personal income taxation on the rate of growth of the economy in short- run (Bleaney, Gemmell and Kneller 2001).
Sustaining high economic growth together with low inflation is one of the most vital objectives of developing as well as industrialized countries’ macroeconomic policies. For many years now, researchers have debated on the connection between inflation and economic growth. Evidence suggests an association exists between low inflation, which is the macroeconomic stability, and economic growth. Andrés and Hernando (1999) for industrialized economies to experience good growth, there is a need to maintain a low inflation rate. Therefore, stabilizing prices should be the central objective of the fiscal procedure. When inflation interferes with the economy of a country, it imposes a negative externality. For example, there is uncertainty about future investment projects profitability for an increase in price variability. According to Gregorio (1999), growth in the economy and inflation is positive positively related. Inflation rates indicate the overall capability of the government to manage the economy favorably (Fischer 1993).
Kormendi and Meguire (1985) analyzed the association between inflation and economic growth. The finding of their study helps to shed light on how inflation impacts economic growth (Hernando 1997). The study findings suggest the sudden rise in general prices affects economic growth negatively. Some inquiries have explored how inflation and growth are associated (Fischer 1993; Gregorio 1996; and Barro 1996; Sarel (1995). According to Barro, the relationship between the two may not be linear. On the other hand, Levine and Zervos (1993) argued inflation as a determinant is not robust in the growth of an economy. The studies found out the output could be reduced to between 0.5 and 2.5 percent if inflation reduces by one percent.
In a cross-country study by Guellec and van Pottelsberghe (1997), the impacts of tax credits and direct subsidies have a different pattern of time. Tax credits significantly affect expenditure only in the short run while subsidies substantially affect spending after an extended period (Guellec and van Pottelsberghe 1997). The most concrete outcome on expense occurs when subsidization rates are 11-19 percent. For any rates above thirty percent, the public funds substitute for private funds.
A study conducted by Griffith, Redding, and Reenen (2004) suggested research and development expenditure play a crucial part in integrating cross-country ideologies and fostering innovation. Research and development determine the spillover size and is also a driver of economic growth through the generation of a string of technologies (Griffith, Redding, and Reenen 2004).
The investigation by Pottelsberghe and Lichtenberg (2010) focuses on whether technology transfer can occur across borders through Foreign Direct Investment (FDI). The research concludes: One, technology transfer can happen through FDI. Second, larger countries have a high ratio of different benefits of R&D expressed by outward FDI to international benefits of R&D expressed by imports compared to small states. Third, the existence of an upwardly biased output elasticity estimates of the domestic research and development capital stock because of the failure to be responsible for spillovers of international R&D. Fourth, substantial technology transferred from America to Japan and not the vice versa.
Research conducted by Zietz and Fayissa (1994) on 360 manufacturing firms in the United States over between 1975 and 1987 to determine the association between R&D spending and exchange rates changes. Firms which reacted to an appreciation of exchange rate with increased research and development are those with average spending of research and development of at least three percent of net sales (Zietz and Fayissa 1994).
Griffith and Reenen (2002) investigated whether there is a relationship between fiscal incentives and research and development. Tax incentives increase the intensity of research and development (Griffith and Reenen 2002). Further, one percent rise in the R&D level is stimulated by a 10% fall in the research and development cost shortly while stimulating a 10 percent increase in research and development after an extended period.
There is significant empirical evidence pointing to global trade stimulating economic growth, both for developing and industrialized nations. Opening up markets with the aim of attaining integration of universal markets has stimulated the development trade policies (Sandri, Alshyab and Ghazo 2016). Moreover, international trade has undergone has transformed with emphasis on the importance of such a business. The development of IT and the internet have made it easy for cross-border trade in services to take place seamlessly. Improved telecommunication and transportation facilities, liberalization of the majority of the financial transactions and the increased demand for the services related to tourism and travel have expanded the scope international trade (Sandri, Alshyab and Ghazo 2016).
Services predominantly feature in the liberalization plan of trade. After the latest Canada- EU Trade Agreement (CETA), based on the European Commission a half of the total GDP gains made by the EU would only be possible after liberalizing trade and services. The current negotiated trade arrangement between the EU and the US on the Transatlantic Trade and Investment Partnership (TTIP) places services at the center of the trade (Ariu et al. 2017). The statistics on international trade have a significant part in the monitoring, projections, and analysis undertaken by OECD countries on the macroeconomic developments in both world and individual economies. While responding to these needs, the secretariat of OECD manages to trade in goods and services, and the highest percentage constitutes the merchandise database (Lindner et al. 2001).
The records of customs serve as the most common source of tracking and measuring how goods move physically across the borders. OECD gets its merchandise data from the statistical offices or the national customs authorities. For the case of the European Union, there are some exceptions because the formation of the Single Market in 1993, the two systems have co-existed, and necessitated individual countries in the EU to remove the internal customs (Lindner et al. 2001). The measurement of trade occurs in the services sector may be cumbersome compared to tracking the trade of goods. Services intangibility renders them difficult to define and quantify. However, the physical functions of some services make it possible to identify such services, for example, the hotel or transport services (Lindner et al. 2001). Some services such as education or consultancy are more abstract. Trade in services does not entail any package moving across the customs frontier with documents accompanying it describing the contents, showing the invoice, origin, and place of destination. After defining the service if it is applicable, getting the required information on the type of the service will rely on the degree of the mutual intercontinental understanding of the statistical concepts and the data provider (Lindner et al. 2001).
When the LTIRs in the U.S increase substantially, it is accompanied by reduced levels of gross capital inflows brought about by foreign investors. Once the capital arrives in the country, these foreign investors inject it into the domestic emerging market economies (EMCs) (Olaberría 2015).
The factors influencing the cycles of capital flows include external to the economies which receive the money flow and are known as the push or global factors. Second, the domestic elements which are internal to the economy and also referred to as the pull factors. However, various extensive studies show the push factors serve as the most vital factors drive capital flows into the emerging economies (Fratzscher 2012; Blanchard et al. 2010). There are also studies which underscore the role of domestic factors and the impact they have as determinants of capital flows (Olaberría 2015; Griffith and Reenen 2002).
Sometimes, the LTIRs are low investors develop a bad economic outlook and, in the process, anticipate low inflation rates and the persistence of output growth. The foreign policy on monetary issues or higher demand for treasuries from the international partners also contributes to pushing down the LTIRs (Bauer and Rudebusch 2016). The decrease in the long-term rates may be significant in the short-term but could assist in raising the aggregate demand and signify a positive development for the economy in the long-run. Gaining insights into the low rates requires a critical look at the long-term interest rates based on first, focus on the average of anticipated future short-term rates. The second, the premium- recompensing the investors for the risk of being in possession of long-term bonds. However, it is an element for all the factors exceed the expectation component to impact the long-term rates (Bauer and Rudebusch 2016).
International trade impacts firms’ activities of R&D. Free trade can influence firms’ R&D activities according to a study conducted by Matsushima and Yamamoto (2008). Moreover, the costs of innovation and fixed trade costs fall under the assumption of intermediate values (Fischer 1993). Trade openness reduces the firms’ R&D activities for relatively high fixed trade costs and lo/w innovation cost.
In an economy, saving rates is the money in ratio or percentage form, deducted by a person from his income for a specific objective such as retirement security. Investors keep the cash produced at a minimum risk investment depending on factors including waiting time before the owner retires. Some of these investments are individual retirement account (IRA), bonds, stocks or money market fund (OECD 2017). For years, the saving rate is not equal for different countries and has continuously declined. However, in rare circumstances, it is a positive response. According to data from the OECD (2017), Australia has had a significant change in the household saving rates, which reduced from a ratio of 7.026 in 2012 to 4.943 in 2016; moreover, it is expected to project to 2.443 in 2018, and 2.023 by 2019 (OECD 2017).
In the U.S between 1970 and 1980, personal saving rates ranged between five and seven percent but reduced to one and three percent in the 21st century. However, as from 2008, the saving rate increased to 8%, but it later declined (FRED Economic data 2017). By 2018, the saving rates stood at 3.7%, but the country is trying to put measures aimed at raising savings to 4.655% by 2019 (OECD 2018). For the United States, the central government began to track the saving rates and the highest recorded data was 17% in 1975. On the contrary, the saving rate for Chinese was 30%.
Economic factors affect the economy significantly; for instance, saving rates and income can have a positive relationship, and this happens as households believe they can earn more in future as compared to what they gain presently. Moreover, people can decide to spend less money, a phenomenon known as the substitution effect. The level of income for the citizens is a critical factor in the determination of the saving rates; saving rates and the per capita GDP are related positively. Low-income earners spend most of their resource on necessities while the wealthy use it on luxuries and high saving rates (Khan et al. 2017). However, the relationship does not rise indefinitely but levels off at some point. According to the Ricardian theory of equivalence, private savings increases as public saving decrease, and therefore, increasing the federal debt makes people save as they prepare for tax increment to finance the deficit (Hüfner and Koske 2010). Thus, increased saving impacts positively on the economy OECD states.
GHG focuses on a total of seven gases directly affecting the climate (carbon dioxide (CO2), chlorofluorocarbons (CFCs), methane (CH4), perfluorocarbons (PFCs), nitrous oxide (N2O), hydrofluorocarbons (HFCs), nitrogen trifluoride (NF3) and sulfur hexafluoride (SF6)) (OECD Data, 2015). Researchers express all these data from all gases in CO2 equivalent. Other gas emissions include nitrogen oxide and Sulphur oxide among others (OECD 2015). Governments and other responsible agents such as World Health Organization measures air and these greenhouse gases in the form of thousands of tonnes, kilogram or tonnes per capita, but they weigh carbon dioxide in tonnes per capita or million tonnes (OECD 2015).
Due to increased manufacturing and energy industries, most of the OECD countries have tried to reduce the gas emissions, but few have succeeded. For instance, in 1990 Estonia released 40,402.74 CO2 equivalents, but the state has managed to maintain a reduction trend, and by 2015, it record 18,040.49 (OECD 2018). However, most countries have a constant pattern where the emissions are reduced at a slow pace or increased significantly. For example, France’s discharge has remained between 540 and 570 thousand tonnes between 1990 and 2005. The country has reduced this amount to 463 thousand tonnes of CO2 by 2015 (OECD 2017).
GHG has adverse effects on the economy as it causes high temperatures during working hours hence triggering work discomfort which eventually leads to low production rates. Moreover, the health of the workforce can be affected negatively leading to an increase in absenteeism and low return rate. Th/e economic damage caused by the emission of carbon dioxide is estimated to be at $220 per tonne, a figure six times above what the U.S government records ($37) (Moore and Diaz 2015). Beside health issue, other impacts emanating from the emission of Green House Gases include the destruction of properties through flooding among other environmental problems.
Reducing GHG emissions should be a typical role for all stakeholders to help in eliminating the adverse effects. An increase in greenhouses emission would lead to more economic benefits without taking into account the diminishing return then the advantage is unnecessary and therefore economically inefficient to add more GHG to the environment, and implying the emissions are financially optimal (Zhang and Cheng 2009).
There is a gap relating to the emerging public sector reforms. The implementation of robust economic growth variables drives both external and domestic factors. The advent of international agreements and globalization has altered the way countries trade. Much of the existing literature does not consider the significant in socio-demography of human lives. Moreover, the changing geopolitical arrangements influence partnerships and development of economic policies. Therefore, the thesis focused on the analysis of all the economic growth determinants within the socio-political context of each country.
The literature review showed the tax on personal income has a negative correlation to economic growth. Despite the expectation of inflation having a negative relationship to GDP, some studies say otherwise. Gross domestic spending on R&D increases innovation and production while trade in goods and services boost economic activity. The long-term interest rates, savings rates, and greenhouse gas emissions also have positive relationships to the growth in GDP. Increased production correlates to increased pollution.
The methodology section comprises the methods used in the thesis through a topical description of each process. As such, the various topics covered in the methodology section include the research design of the study and sampling technique using Eviews software.
The thesis focused on time-series data obtained from various countries within the OECD framework. The study collected additional data from the World Bank’s database. The World Bank Indicators helped in getting the required data. Since the focus of the paper was on OECD countries, some of the missing data gap fillings occurred through collecting the necessary information from the OECD’s statistical electronic database and the database of the World Economic Outlook. The range of data collection was from 1986 to 2016 from nineteen OECD countries, namely: Austria, Belgium, Canada, Denmark, France, Germany, Greece, Iceland, Ireland, Italy, and Luxemburg. Others include the Netherlands. Norway, Portugal, Sweden, Spain, Switzerland, United Kingdom and the United States.
The arrangement of the data followed an annual basis with each country contributing a total of thirty-one observations for the recorded variable. For this thesis, the regression analysis involved the dependent variable which functions as a proxy for the economic growth is the GDP Growth Rate measured regarding the change in the annual percentage. The trade in goods and services, long-term interest rates, inflation, savings rate, and Air and GHG emissions (CO2, in tonnes/capita).
A panel data in which countries formed the groups assisted in investigating the determinants of a set of variables on the economic growth requires longitudinal research design (Angrist and Pischke 2009). The model works best with a panel data approach and helps in addressing the issue of unit heterogeneity and temporal instability (Blossfeld et al. 2009). The unit heterogeneity refers to the existence of a difference in the factors, data or variables under comparison. On the other hand, the threat posed by temporal instability is associated with the changes in exogenous variables observed and can provide different explanations concerning exploring how explanatory s cause changes in the dependent variable. In locating different units, a researcher can come across temporal stability, and proper use can deal with the threat posed by temporal instability which presents a problem for longitudinal data. Numerous works on economics have applied this approach using various econometric methods and panel data regressions.
Data from a variety of countries to enhance generalization and thus employed the panel data approach. The period for the study was from 1986 to 2016 and explained the influence of various explanatory variables on economic growth rate using GDP as a proxy since the data needed for the regression analysis was adequate.
The least squares linear regression models analysis design was employed. A regression analysis approach is appropriate for exploring the relationship between two or more variables. The study used a multiple linear regression model.
Y= α + β1X1 + β2X2 + … + βnXn + ε …………………………………………………… (1)
Where: Y = dependent variable
α = constant term
β = coefficient
X = dependent or explanatory variable
n = number of variables
ε = error term (capturing any other factor outside the model influencing Y)
Another conventional method used in many statistical analyses is the use of the ordinary least square (OLS) technique to carry out a regression analysis process. The objective of conducting a linear regression using independent and dependent variables is to fit a line through some obvious points. The obtained fit line indicates the point at where if we have the minimum value of the square the deviations of the observed data, which is the fundamental definition of the OLS technique.
Through the process of carrying out a regression analysis, the objective was to find out whether the selected determinants of economic growth variables had an impact or influence on the dependent variable and if a relationship exists between these the independent and dependent variables, whether the link is a positive or a negative one. Moreover, pooled regression analysis followed by fixed effect and random effect regressions dominated the analysis section. The method used is the OLS method since the data was in time-series form and it also functions a fundamental statistical approach. The independent variables include taxes on personal income, trade on goods and services, long-term interest rates, gross domestic spending on R&D, inflation rate, savings rate, and greenhouse gas emission while the dependent variable is the GDP, a proxy for economic growth. The right-hand side of the regression formula contains the following variables: gross domestic savings, tax on personal income, inflation, and long-term interest rates.
On the other hand, OLS has its applications in a variety of settings and includes political science and economics. The OLS can function as a linear model in modeling a single dependent variable recorded in an interval manner. The method can work in more than one variable and even category explanatory variables with proper coding and arrangement. Since this study has multiple explanatory variables, it will take multiple linear forms of regression as shown below:
Y= α + β1X1 + β2X2 + β3X3 + β4X4 + β5X5 + β6X6 + β7X7 + ε …………………………. (2)
Transforming equation (2) to reflect the actual variables as results in the equation below.
GDP = α + β1TPI + β2TGS + β3LT_IR + β4GDS + β5I+ β6SR + β7GHG
Where: Y = GDP = Gross Domestic Product (Constant price)
X1= TPI = Tax on Personal Income
X2= TGS = Trade in Goods and Services
X3= LT_IR = Long-Term Interest Rates
X4= GDS = Gross Domestic Spending on R&D
X5= I = Inflation (Consumer Price Index)
X6 = SR = Savings Rate
X7 = GHG= Greenhouse Gas Emissions (Tonnes/Capita)
Eviews has established itself as a proprietary program for solving econometrics regressions for the personal computer. The software estimates the macroeconomic data points, makes financial analyses, conducts the simulation, makes an evaluation of data through specific analysis, does a cost analysis and predicts sales. As an economic and statistical tool, it has a broad range of uses from classroom teaching to being used in several locations on the internet. Eviews has some forecasting tools which examine data and conduct regression analyses on the Windows operating system. In the same line, this study has used Eviews 8 to analyze the
The study obtained the required for the analysis from the databases of OECD and World Bank. The sampled countries were nineteen and are members of OECD. A panel of cross-country technique was adopted to address the challenge of temporal instability. The data collected reflects various economic indicators of the sampled countries to enhance the generalization of the findings.
The data collected from nineteen OECD countries existed in excel format in the website where irrelevant variables such as data code and state code eliminated. The next step involved importing the data into Eviews 8 and conversion to panel data with attributes (1986 2016 x 19 – 589). The final pre-analysis correction made was discarding NA observations. The three models were used to analyze the relationship between the seven variables (gross domestic product, trade in goods and services, long-term interest rates, inflation, savings rate, and Air and GHG emissions (CO2, in tonnes/capita)). The models include pooled regression analysis, fixed effect or LSDV model and Random effect model. Hausman test was used to select the most appropriate model. The significance of a model accepted at 5% (0.05) or less. Further, multicollinearity and normality test on the residuals concludes the chapter.
The regression involves the pooling of the 598 observations together and performs a least squares linear model without considering times series and cross-sectional nature of the OECD data. The assumption requires none heterogeneity among the nineteen countries. Table 1 shows the summarised regression results.
|Table 1: Summary of Pooled Regression Results|
|Tax on Personal Income||-0.012253||0.5680|
|Trade in Goods and Services||0.293723||0.0000***|
|Long-Term Interest Rates||-0.063053||0.1003|
|Gross Domestic Spending on R&D||-0.357490||0.0146*|
|Greenhouse gas Emissions||0.066488||0.0020**|
|R-squared = 0.5432
Adjusted R-squared = 0.5360
|F-statistic = 74.7563, p-value < 0.0000
Sources: Authors (2018)
The results show at 5% level of significance, trade in goods and services, gross domestic spending on research and development, savings rate and greenhouse gas emission are significant determinants of the gross domestic product of the OECD countries. However, F statistic of 74.7563 with p-value < 0.000 shows all independent variables jointly influence gross domestic product. An indication the model is generally significant. Besides R-squared of 0.5432 implies the pooled regression model explains 54.32% of the variability of the response data around its mean. Also, adjusted R-squared of 0.5360 implies 53.60% of the variation are explained by only those independent variable (trade in goods and services, gross domestic spending on research and development, savings rate and greenhouse gas emission) in reality affect the gross domestic product.
The model allows for individuality or heterogeneity between all the countries by allowing each to have its distinct constant value. However, the constant term varies across different countries it is fixed over the period 1986 to 2016. Thus the name fixed effect. Table 2 shows the summarised LSDV model results.
|Table 2: Summary of LSDV Model Results|
|Tax on Personal Income||0.0263||0.7367|
|Trade in Goods and Services||0.2665||0.0000***|
|Long-Term Interest Rates||-0.0984||0.0087**|
|Gross Domestic Spending on R&D||-0.9121||0.0031**|
|Greenhouse gas Emissions||0.0756||0.3554|
|R-squared = 0.6545
Adjusted R-squared = 0.6340
|F-statistic = 31.9722, p-value < 0.0000
Sources: Authors (2018)
With the non-homogeneity among countries, at 5% significance level, trade in goods and services, long-term interest rates, gross domestic spending on research and development, and savings rate are four significant determinants of the gross domestic product of the OECD countries. Besides, F statistic of 31.9722 with p-value < 0.000 shows all the independent variables jointly influence gross domestic product. The fixed effect model is generally significant. Besides R-squared of 0.6545 implies the LSDV model explains 65.45% of the variability of the response data around its mean. Also, adjusted R-squared of 0.6340 implies 63.40% of the variation are explained by only those independent variable (trade in goods and services, long-term interest rates, gross domestic spending on research and development, and savings rate) in reality affect the gross domestic product. An R-square greater than 60% indicates a good model estimation.
The method considers the heterogeneity among countries with the constant mean for all the intercepts. Table 3 shows the summarised random effect model results.
|Table 3: Summary of Random Effect Model Results|
|Tax on Personal Income||-0.0033||0.9163|
|Trade in Goods and Services||0.2790||0.0000***|
|Long-Term Interest Rates||-0.0764||0.0300*|
|Gross Domestic Spending on R&D||-0.5799||0.0020**|
|Greenhouse gas Emissions||0.0656||0.0302*|
|R-squared = 0.5535
Adjusted R-squared = 0.5464
|F-statistic = 77.9072, p-value < 0.0000
Sources: Authors (2018)
The intercepts are random and among the countries. At 5% level of significance, trade in goods and services, long-term interest rates, gross domestic spending on research and development, savings rate and greenhouse gas emission are significant determinants of the gross domestic product of the OECD countries. Moreover, F statistic of 77.9072 with p-value < 0.000 shows all the independent variables jointly influence gross domestic product. The model is generally significant. Besides R-squared of 0.5535 implies the pooled regression model explains 55.35% of the variability of the response data around its mean. Also, adjusted R-squared of 0.5464 implies 54.64% of the variation are explained by trade in goods and services, long-term interest rates, gross domestic spending on research and development, savings rate and greenhouse gas emission, in reality, affect the gross domestic product.
The three models based on F-statics are appropriate for modeling the OECD data. The question of which model most appropriate model the data remained unsolved without performing a Hausman test. The data is time series with within-country variations: therefore, the pooled regression model eliminated on times series and panel basis. Hausman test is used to test which model between fixed and random effect is the most appropriate. The table 4 below shows Correlated Random Effects – Hausman Test results.
|Table 4: Correlated Random Effects – Hausman Test|
|Test Summary||Chi-Sq. Statistic||Df||P-value|
Sources: Authors (2018)
The hypothesis for the test are:
H0: Random effect model is the most appropriate model
H0: Fixed effect or LSDV model is the most appropriate
From the table Chi-sq. Statistics of 52.763 with a corresponding p-value less than 0.000 indicate at 5% level of significance; there is sufficient evidence to reject the null hypotheses. Therefore, conclude the fixed effect or LSDV model is the most appropriate model for estimation of the gross domestic product of OECD countries.
The values from table 2 are used to present the estimated model followed by a test of normality, multicollinearity, and heteroscedasticity.
GDP = 0.4347 + 0.0263TPI + 0.2665TGS – 0.0984LTIR – 0.9121GDS – 0.0354I+ 0.2611SR + 0.0756GHG …(4)
Where: GDP – Gross Domestic Product (Constant price)
TPI – Tax on Personal Income
TGS – Trade in Goods and Services
LT_IR – Long-Term Interest Rates
GDS – Gross Domestic Spending on R&D
I – Inflation (Consumer Price Index)
SR – Savings Rate
GHG – Greenhouse Gas Emissions (Tonnes/Capita)
The observations for a linear model are assumed to have originated from a normally distributed population. Therefore, Jarque-Bera test was used, with the following test hypothesis:
H0: The residuals follows a normally distributed population
Ha: The residuals do not follow a normally distributed population
The test gave a Jarque-Bera statistic of 1110.625 with a p-value less than 0.0000. Thus, reject the null hypothesis and conclude the residuals do not follow a normally distributed population. The observed results may have originated from the small sample size (19 countries).
The second objective was to test for the existence of collinearity among the six independent variables. The fixed model regression results produced Durbin-Watson statistic of 1.074 (Appendix 3). The rule of thumb for D-W test requires a value between 0 to 2 to represent positive autocorrelation, between 2 to 4 represent negative autocorrelation and a value of 2 no autocorrelation. Therefore, there exist a positive autocorrelation between the variables used.
The flow of this methodology section presented the whole process starting with a short introduction of the outline. The next part involved a description of the research design used and an explanation of the OLS method. The next section was the sampling technique used with a description of the Eviews 8 software for carrying out the regression analyses. The final part was the data analysis detailing the various data examinations methods used.
The section details a discussion of the analyzed results to address the study objectives. Further, the section focuses on the conclusion.
From chapter four Fixed effect model was used to estimate equation (3) the following estimated equation was obtained:
GDP = 0.4347 + 0.0263TPI + 0.2665TGS – 0.0984LT_IR – 0.9121GDS – 0.0354I+ 0.2611SR + 0.0756GHG
Moreover, trade in goods and services (TGS), long-term interest rates (LT_IR), gross domestic spending on research and development (GDS), and savings rate (SR) significant. Therefore, the discussion focused on significant variables. A constant term of 0.4347 implies without the intervention of the seven independent variables the GDP of any OECD country will be 0.4347 units. Implying other factors not included in the model contribute to the GDP by 0.4347.
The TPI has an insignificant coefficient of 0.0263 showing a positive relationship between gross domestic and taxes on personal income. A unit increase (decrease) in the taxes on personal income increases (decrease) a countries GDP by 0.0263 units. The results are in line with those obtained by Gale, Krupkin, and Reuben (2015, p.16), there is no significant relationship between growth and taxes on personal income. The general economic theory on the relationship between TPI and GDP is inconclusive with varying results.
The coefficient obtained for TGS is 0.2665 which show a unit rise (fall) on the taxes on goods and services cause a rise (fall) in the GDP by 0.2665 units. There exists a negative relationship between TGS and GDP. The coefficient is statistically significant and but contradicts general economic theory which states high taxes on goods and service contribute to slow growth in an economy. The coefficient ought to be negative.
The slope of LTIR is -0.0984 which show a unit rise (fall) on the long-term interest rates cause a fall (rise) in the GDP by 0.0984 units. There exists a negative relationship between long-term interest rates and GDP. The results are in line with a general economic theory which states; high-interest rates contribute to slow growth in an economy. The rates discourage borrowing translating to low investment levels in a country.
The GDS slope is -0.9121 and significantly influence the GDP. It implies a unit increase (decrease) in the gross domestic spending in R&D will decrease (decrease) growth by 0.9121 units. However, countries spending more on R&D have reported positive growth in all sectors attributed to the R&D. These results though significant contradict existing literature on spending in R&D and Growth. For example, in a study by Gumus and Celikay (2015, p.206) utilizing data from 52 countries from 1996 to 2010 using a dynamic panel data model, a significant positive relationship exists between spending on R&D and economic growth for all countries in the long run.
The slope coefficient for inflation (I) is -0.03541 and insignificant. However, the slope shows a unit improvement(decline) in inflation rate reduces (raise) economic growth by a factor of 0.03541. In economic models, single digit inflation has a positive relationship with growth while two digits and above have an inverse link with growth. The negative relationship obtained show OECD countries are experiencing two or more-digit inflation rates.
Savings rates (SR) slope is 0.2611 showing a significant positive direct link between the level of savings and growth. Savings avail money for borrowing b investors in an economy. The higher the rate, the higher the growth should be a proposal confirmed by the results. A unit change in SR contributes to economic growth (GDP) by a factor of 0.2611. The findings are in line with economic theories.
A positive relationship exists between the emission of greenhouse gases emission and economic growth. However, the link is weak and insignificant based on the results obtained in the estimation using a fixed-effect model. The coefficient is 0.0756 implying a unit change in the amount of GHG causes a change in growth by 0.0756 units. The results are in line with those of Cederborg and Snobohn (2016, p.21), which conclude higher GDP associated with high levels of greenhouses gas emission. Also, OECD member countries are developed implying they contribute greatly to the emission of greenhouse gases.
The factors significantly affect economic growth as captured by GDP are taxes on goods and services, gross domestic spending on research and development, long-term interest rates and savings rate in a country. Therefore, OECD countries to control the observed growth attention should focus on the manipulation of TGS, LT-IR, GDS, and SR).
The section delineates the recommendations of the inquiry based on the analyzed data. Furthermore, the need for future research is underscored to improve economic growth and development among OECD countries.
The OECD countries are striving to attain sustained economic growth through the implementation of a raft of measures. The data collected have revealed wider disparities resulting from different policies and the implementation criteria. There should be increased interactions in developing essential econometric indicators for standardization. The study makes the following recommendations to foster economic growth among the member states and minimize disparities. The suggestions include:
The study recommends the OECD member states should invest in viable economic programmes to increase their gross domestic product and reduce inflation. Future research needs to incorporate data from other countries outside OECD to enable comparisons of different economic conditions.
Countries under the OECD framework should support the development of SMEs because they are integral in enhancing an economy. From the study, the results have shown inefficient tax policies hinder meaningful production. Therefore, countries should rethink tax administration and regulation.
The judicial and legal institutions influence investment decisions. The study recommends to countries under OECD should reform TGS, GDS, LTIR, and GHG to increase the rate and the levels of cross-country engagement. Trade restrictions ought to be removed to foster the movement of goods and services for rapid development.
One of the limitations of the study involves the differences in membership in the OECD framework which affected the availability of some of the statistical data of these countries during those years without data. However, the study undertook an unbalanced panel analysis. There was limited time for conducting a comprehensive data collection for the study. Moreover, the finding of the research cannot be generalized to all other countries outside OECD. The data used was exclusively drawn from the OECD database with minimal information taken from the World Bank database.
For future research, there should be an increased variety of data sources apart from depending on data from the OECD database. There is the need for future studies to focus on the challenges affect the full integration of the economies of OECD countries. Moreover, the reasons for the continued disparities between nations ought to be analyzed and reported towards improving economic growth.
Moreover, future studies should focus on exploring the effects of social inequalities in individual states on the framework of OECD. All the member states have unique challenges ought to be addressed independently before making a generalized perception of the achievements of OECD.
For more academic help please check a wide range of services our Economics Writing Helpteam offers:
– Economics Assignment Writing Services
– Economics Essay Writing Services
– Economics Dissertation Writing Services
– Buy An Economics Research Paper
Here you can check some of our dissertation services:
– Dissertation Writing Services
– Write My Dissertation
– Buy Dissertation Online
– Dissertation Editing Services
– Custom Dissertation Writing Help Service
– Dissertation Proposal Services
– Dissertation Literature Review Writing
– Dissertation Consultation Services
– Dissertation Survey Help