Empirical Research Methods for Business

Question: Write a report on "Empirical Research Methods for Business". Answer: Introduction Health Care Expenditure in the OECD countries have been rising on an increasing rate such that the maximum pressure has been imposed on the public budgets of these countries which is not only increasing the spending on the social programs which is estimated to a increase by 1% in 2015, according to the 2015 outlook ("Health Expenditure And Financing" 2016). The main issue lies in the health expenditure that is started following a reverse trend in comparison to the economic growth such that economic growth levels in health expenditure have grown due to the reforms in the sector that has led to health funding as being a demoralizing task for the government. However, according to Baumols theory health sector is representative of low productivity growth. However, according to the present conditioning, the government has to formulate policies so that corrective measures could be taken with proper time and in efficient manner. According to the preliminary analysis, it has been stated that the concern of low and middle countries can easily be manifested by the changes that has been based on the analysis of the technologies, demographic factors as well as income level of the respondents followed by institutional characteristics ("OECD Reviews Of Health Care Quality - OECD Ilibrary" 2016). However, the research needs to be based on the research aims that can be manifested in analysing the drivers factor that are essential to study health expenditure in the OECD countries. On the other hand, GDP will be even considered as one of the drivers of health expenditure as well. Data In this study, all the OECD countries are considered. However, all the data is taken from major 34 countries from the year 2009 to 2014. Current and public health expenditure is taken from OECD Health Stats. The data taken was complemented with data on demographics and mortality. However, according to the research question stated above, two hypotheses were generated (Xu and Saksena 2011). Hypothesis 1 Ho: There is no significant impact of variables like current health expenditure, public expenditure and out of pocket on GDP. H1: There is significant impact of variables like current health expenditure, public expenditure and out of pocket on GDP. Hypothesis 2 Ho: There is no significant impact of variables like out of pocket, mortality rate and demographic characteristics on current health expenditure. H1: There is significant impact of variables like public expenditure, mortality rate and demographic characteristics on current health expenditure. All the expenditure variables are taken US $ except GDP which is turned into log to give the instantaneous change made in the US dollars. However adding to the study, summary statistics were also presented Methods The various concepts have been adopted for the research that it is helped in conduct of the study to give a critical explanation provided with the secondary data taken from OECD Health Statistics. Research Paradigm The research paradigm selected for the study is to alleviate the theoretical process and procedures. Although, the study is based on certain assumptions like GDP is impacted by health expenditure and public expenditure which deals with the knowledge and result in following epistemology paradigm for the enquiry of results (Shajahan 2015). Research Philosophy The research process validates a post-positivism approach as its is based on facts and figures such as interrelationship is established between different variables (Cohen, Louis, Manion and Morrison 2011). However, the background of health expenditure is calculated by the help of calculations. Research Approach The research approach taken into consideration is deductive in nature as it is based on the hypothesis testing as certain propositions have been made between different variables and relevant methods like multivariate regression analysis is taken for accepting or rejecting the theory so that appropriate modifications can be devised. The relationship criterion helps in analysing the clarity and reliability of the data (Harlow 2014). Research Design The research design is correlational in nature as different drivers are tested on the health expenditure that does not elaborates on the interconnection between variables but also helps in physical conduct of the data through the meta-analysis procedure. Data Collection Methods The interpretation as well as gathering of information is tested by building a research framework on the variables. The secondary data collection method was adopted such that the data for different variables were taken from OECD Health Statistics and the data is quantitative in nature. However, the data collected from secondary sources is helpful in creating an ideal scenario on the health expenditure condition of the OECD countries. The quantitative method is adopted because it gives an experimental as well as descriptive method through variables structured on the sample size (Gorard 2013). Sampling Method and Sampling Size The random method adopted is cluster random sampling as the average data of all the 34 countries listed in OECD is taken into consideration to avoid errors on the degree of attainment. When analysed, the sample size is undertaken is 6 that is from the year 2009 to 2014 to analyse the impact of variables on GDP and health expenditure. Data Analysis Data analysis is the process of analysing the information based on the decision making exercise that has been carried out to study the figures with statistical as well as structural technique. Although, the data is structured in nature but it is closely linked with quantitative data analysis using SPSS statistical tool for data processing and matching files (Brymen and Bell 2015). Results The results and discussion can be based on the analysis of all the variables considered. The data results can be divided into two divisions, one in the summary statistics gained on mean, standard deviation and quartile on each variable. Section 1 - Summary Statistics According to the summary statistics calculated for each variable as it is based on the original data taken without the log values. The explanation of each variable can be described below. Mortality Rate The average of mortality rate that have achieved from 2009 to 2014 years is 79.18 with the deviation from mean achieved is 2 years such that the inter quartile range is one year and 8 months across the years amongst the different OECD countries (Refer Appendix). According to the normal, curve, the data is more skewed to the age of 79-80 years as given by life expectancy of 79-80 years which has not changes much since 2005 (Refer Appendix)..General Demographics General Demographics includes the income level achieved of the countries across years. According to the calculation of the data, the change has seen to be efficient as there is vast gap in the inter quartile range that is 7.14 (000) US Dollars and the mean received has been 367.03 which has shown to increase in the last year of the analysis that is 2013-2014 (Refer Appendix). Out of Pocket Payments Out of pocket payments is the general consultation fees, medication and other hospital bills that have recently showed to be increasing with each passing year such that the change manifested has shown positive skewness with rising changes in the values as it poses a standard deviation 97.16 (000) US Dollars (Refer Appendix).. Current Health expenditures According to the summary statistics, the health expenditure includes both the spending made by the public as well as private external funds through the channels to national health system. However, the data shows a deviation of 417.21 (000) US Dollars that is increased to a twofold as depicted on normally distributed histogram as well (Refer Appendix). Public Expenditure Public expenditure elaborates the government spending of the country on OECD countries across years that has averaged to 2462.98 (000) US Dollars with a change of 221.952 (000) US Dollars (Refer Appendix).. GDP According to GDP results, GDP has been growing in each country and across each year but its log form has not shown any drastic change as compared to last years GDP ("Gross Domestic Product (GDP)" 2016). Section 2 Regression Analysis The regression analysis is based on two hypotheses as mentioned. a) Impact on GDP The impact of GDP as seen from the regression results has been significant. However, in this case long form of GDP was considered. That makes it a log-linear form of equation. The regression equation comes out to be as LnGDP = 16.4 +0.003Current_health_expenditure 0.003public_expenditure 0.002out of pocket. However, this states that with the change in current health expenditure, the positive changes of 0.3% will be made in GDP followed by negative change of 0.3% on public expenditure and 0.2% on out of pocket expenses made by the individuals. However, adjusted R square with 85.6% makes the model a good fit (Refer Appendix).. Moreover, as depicted on F statistics, the changes have been significant on the 95% level which depicts that analysis dealt with accurate results across the years. On the whole, with residual value of 0.001 on ANOVA table that mostly the values taken are estimated and none of them is affected by the errors made in the data (Riff, Lacy and Fico 2014). However, we reject the null hypothesis and state that there is significant impact of variables like current health expenditure, public expenditure and out of pocket on GDP. b) Impact on Current Health Expenditure LnCurrent_Health_Expenditure = 7.81 -0.009General_demographics + 0.3morality_rate + 0.002out of pocket. However, this states that with the change in general demographics, the negative changes of 0.9% will be made in GDP followed by positive change of 3% on morality rate and 0.2% on out of pocket expenses made by the individuals. However, adjusted R square with 98.6% makes the model a good fit (Refer Appendix). Moreover, as depicted on F statistics, the changes have been significant on the 95% level with value 120.461 which depicts that analysis dealt with accurate results across the years. On the whole, with residual value of 0.00 on ANOVA has only been estimated and none of them is affected by the errors made in the data. However, we reject the null hypothesis and state that there is significant impact of variables like public expenditure, mortality rate and demographic characteristics on current health expenditure. Discussion Research Limitations The basic research limitations taken across in the model is a period of 6 years depicting only the changes made after the global financial crisis to analyse the gearing up of GDP and public expenditure in the OECD countries. The research limitations are the taken countries according to the OECD Statistics is only 34 countries followed by only 6 years of analysis. Secondly, the variables included in the study were only important variables and rest other variables were missing. The third reasoning can be stated that the GDP is country specific but healthy expenditure is not necessarily predicted on GDP (Xu and Saksena 2011). Comparison with Previous research According to the previous research done, it can be explained that the study is based on cross section studies and no such evidence has been provided but health has not been considered as an luxury good as opined by Friedman (Xu and Saksena 2011). However, the cross elasticity in earlier researches were based on the time series and panel; data models through the distinction in the transitory and permanent income. Only demographic factor have been considered at micro as well as micro level. Demographic factors were considered using inconclusive results being ageing and health expenditure, but here mortality rate has been taken (Astolfi, Lorenzoni, and Oderkirk 2012). Conclusion To conclude, it can be elaborated major impact has been experienced by GDP resulting in marginal change in the GDP of 34 countries across 6 years. Moreover, globally health expenditure has been growing with the excessive spending on health activities and less of investments achieved in comparison to fixed budget that is followed in each country. Lastly, irrespective of key driver, most of the factors should be considered to give more generalized results. References "Gross Domestic Product (GDP)". 2016.OECD Statistics. "OECD Health Statistics". 2016. doi:10.1787/health-data-en. "OECD Health Statistics". 2016. doi:10.1787/health-data-en. "OECD Reviews Of Health Care Quality - OECD Ilibrary". 2016.Oecd-Ilibrary.Org. Astolfi, Roberto, Luca Lorenzoni, and Jillian Oderkirk. "Informing policy makers about future health spending: a comparative analysis of forecasting methods in OECD countries."Health Policy107, no. 1 (2012): 1-10. 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