Economics And Financial Modelling For Health Technologies

Review of Previous Literature

Describe about the Economics and Financial Modelling for Health Technologies.

Income inequality is one of the causal factors, which has been associated to a large number of impact analysis throughout the globe in the areas of Economics and Social Science. Association of income inequality with mortality rate is one such area which has substantial social impact. Determination of mortality rate and change in mortality rate in less developed countries or developing countries are challenging. A number of initiatives like education, community health development program using improved health technologies etc. have positive impact on the life expectancy in last few decades.

Various societal benefits like superior sanitation, clean drinking water and modern sewage system etc. have substantial impact on the mortality and have been associated to the income potential as well. These environment variables are, in particular, have close association in developing countries like Brazil rather than developed countries like Australia, USA, and Canada etc. In order to identify the impact factors, which are closely related to health and mortality is important for any country for the purpose of formulation of economic and social policies. These specific variables are associated to the overall economic status of an individual and can influence the health through nutrition pattern, which is closely related to the income potential. Hence, individuals of higher income group of a developing country could become a precondition for availing better health service and for ensuring a healthier living environment. Questions remain over how to interpret the available information from various domains and from various countries and communities from policy making perspective, with reduction of inequalities in the areas of health services. In terms of econometric analysis perspective, this article proposes a causal analysis to examine the correlation between income and mortality with the help a number of variables  (Schipp, 2016).

Wide range of literature mostly in electronic have been considered for bringing up correlation analysis and to collate data from various sources of information. In developing this analysis, data published in the journals of British Health, national Statistical Abstracts of Australia, and national Statistical abstract from Brazil etc. A number of papers in various journals have been published on the similar subject to provide analysis on the social and economic aspects of income inequality (Sunderland & Andrews, 2013). These papers have served valuable references to baseline this piece of work. 

Literature survey and the data, which have been represented in various articles provides generic correlation between income inequality and health at various level. However, from policy making perspective, the interpretation of any given set of data is more important for any civic body rather than establishing a generic correlation. An attempt has been made in this article to bring out the method of interpretation of the outcome from policy decision perspective. This is the uniqueness of this article. It will serve as a pioneer article to refer for carrying out additional research and publications on the same field  (Sunderland & Andrews, 2013).

Specification of the Model

A number of considerations have been considered in establishing the scenario with the interpretation of individual income. General perception on individual income is the sum total of earning, which is associated to income inequality and health reflecting the overall societal offering and individual economic status. It has been observed that the relationship between mortality rate or life expectancy and individual income follows an asymptotic trend in most of the developed and industrially superior countries. Hence a curvilinear correlation exists between individual income and mortality. This can be considered as a necessary and sufficient condition to substantiate difference of health parameters between the similar income groups but different distribution patterns in terms of income potential. Due to non-linear relationship between income and mortality, it is possible that very high income might actually reduce life expectancy. Few observations also indicate that a proportional change in individual income will be required for change in life expectancy (The conversation, 2014). If this type of observation becomes true, we will tend to suggest the use of the logarithmic series for explanatory variable. Regression analysis is one such primary model for this econometric analysis  (Theconversation, 2014).

There are many powerful tools in statistical analysis that permit simultaneously analysing the factors that determine a phenomenon such as income. One such tool is regression analysis. Regression analysis gives a general idea of how one or more variables are associated with another variable. For example, if we did a regression analysis of income using the variables mentioned in the above paragraph, we would find that, on average, an additional year of life leads to higher income. Regression also helps us know if the association between mortality and income is purely due to chance and if that association is positive or negative. For discussion, we would expect to see a positive association between mortality and income.

The relationship in the diagram (Diagram 1) below can be seen as two sets of income observations x₁ and x₄. If the scattering is controlled by raising x₁ to the value x₂and by reducing x₄ to the value x₃ while the mean remains the same, mean life expectancy is raised.

The model for describing the scenario is given as

X = α + f(X) + bβ + €    …………………………… (1)

Where

X= mortality or life expectancy

f(X) = function representing mean income

β = Measurement of distributed income

€ = The error term

This simplistic model explains income with one variable. The hypothesized claim for the slope of the regression line, F1, is that it is expected to be positive. This claim is based on theory and common sense. One expects higher income with more life expectancy, since health is a kind of investment dependant on income. A healthy person is more agile and hence, more productive, and thus, deserves higher income per unit of time than someone with poor health

Data Description

Diagram 1: Representative diagram for life expectancy vs. distributed income

The function and the associated equation described in previous section, has been estimated based on the data collected from a number of developed countries. Availability of income distribution data have been the major criteria for the selection of the countries.  For the analysis perspective the data have been considered from over 500 metropolitan areas across five developed countries in 1991 with minimum population size of 50,000. Calculation of income inequality has been done as the share of net household income by the lower strata of households (below 50%) in each of the selected metropolitan areas.  The data for income inequality measurement for Australia were derived from census conducted in 1991 and from house hold expenditure survey conducted in 1993. Similarly, data for Japan, Germany and UK was derived based on 1991 census and based on a separate project initiative during 1991. Total enumeration income survey conducted in 1991 was the primary source of data for measuring income inequality for UK  (Jonsson, Mood, & Bihagen, 2013).

The collated data is given in the table (Table 1) below.

Section	Data Sample Taken From	Sample size	Population	Median
mortality rate (per 100,000)
1	All	500
Average		541,440	0.02	358
Min		49,762	0.15	194
Max		17,087,259	0.27	569
SD (standard deviation)		13,821,26	0.02	68
2	Australia	30
Average		722,667	0.24	289
Min		60,157	0.2	229
Max		3,672,345	0	415
SD		1,071,349	0.01	49
3	Japan	70
Average		356,447	0.24	300
Min		49,100	0.22	256
Max		3,893,046	0.25	400
SD		696,621	0.01	36
4	Germany	115
Average		416,343	0.23	382
Min		49,717	0.2	237
Max		12,823,841	0.25	571
SD		1,712,578	0.01	62
5	UK	285
Average		687,410	0.209	366
Min		56,735	0.154	253
Max		18,087,251	0.249	528
SD		1,643,829	0.016	54

Table 1: Collated data table

Choice of independent variables influence the decision to be made based on the data analysis. The choice of the parameters for the regression analysis depends on the associated econometric theory. The variables have been chosen in a particular manner as they are considered to be related causally to X (refer to equation 1).Selection criteria of dependent and independent variables are crucial to make informed decisions. To establish relationship between income inequality and mortality rate, either weighted or un-weighted bivariate linear regression analysis or analysis of variance (ANOVA) can be used. Weighted bivariate regression analysis is calculated as square root of total population. The equation has been used to analyse the correlation of mortality rate and inequality of income. In this case the square root of total population (in case of weighted method), becomes inversely proportional to variance of individual observation.

Statistically significant relationship is observed between the median values of income and mortality rate for all metropolitan areas across all the five countries. Additionally, a hypothetical condition like increasing 1% in the share of income to the bottom part of population could result in a declining trend in terms of mortality rate. The median variable contributed around 34% of the variation in mortality rates across all the metropolitan areas from where the data were collected across five countries  (Ross, Nobrega, & Dunn, 2002). The computed outcome is given in table (Table 2) below.

	Metropolitan	Population	Median	R2	Median share	R2
	area grouping		coefficient (weighted)		coefficient (unweighted)
	All	500	−21.2	0.33	−19.5	0.3
	Australia	30	-2.3	0.01	-2.3	0.01
	Japan	70	-1.9	0	-1.80	0
	Germany	115	−18.3	0.12	−16.1	0.1
	UK	285	0.03	0	0.2	0.03

Empirical Results and Analysis

Table 2: Outcome of the Regression Analysis

In ANOVA models of mortality, it is observed that significant main effects for income inequality and metropolitan areas are population. We can also observe a fairly significant impact for the interaction between income inequality and population size, which indicates that the impact of income inequality on mortality is significant for the cities with high population.

Overall relationship was driven by around 80% of metropolitan areas which were from the two specific countries, e.g. Germany and UK. The correlation between share of income and mortality rate is significant for both the countries although no relationship between metropolitan median share of income and mortality within each of the five countries are observed  (Queensu, 2001).

Conclusions and Recommendations

The most significant outcome of the analysis is the statistical significance of distribution of income as a variable. The indication that statistically significant inequality is linked to higher mortality is evident throughout. From econometric and social science stand point, an aggregation of individual income and associated interpretations is a good fit with reference to the collected data points on income inequality and health. The result and data pointsare comprehensive in nature and have enough potential to provide interventions. From policy formulation perspective, it is strategic investments through higher distribution of resources are likely to have the significant impact on reducing health related inequality. This will help to adopt a comprehensive policy to improve public health in both developed and developing countries in coming years. Finally, it is worth mentioning that the negligence in the areas of health, social and environment related factors in many developing countries do not indicate that these are not important parameters. Overall it is globally accepted that inequality of income has a deeper social and economic impact.

List of References

Jonsson, J. O., Mood, C., & Bihagen, E. (2013, August 02). Income Inequality and Poverty during Economic Recession and Growth: Sweden 1991-2007 . Retrieved September 23, 2016, from gini-research: https://www.gini-research.org/system/uploads/517/original/60.pdf?1380552301

Queensu. (2001, July). S-PLUS 6 for Windows Guide to Statistics,. Retrieved September 23, 2016, from Queensu: https://www.mast.queensu.ca/~stat462/resources/statman2.pdf

Ross, N., Nobrega, K., & Dunn, J. (2002). Economic segregation and mortality in North American metropolitan areas. Geojournal , 1-53.

Schipp, D. (2016, June 22). Income inequality means we’re no longer the land of the middle class. Retrieved September 23, 2016, from news: https://www.news.com./national/income-inequality-means-were-no-longer-the-land-of-the-middle-class/news-story/90821b0b0b013babd29d2ac4c5dfd304

Sunderland, M., & Andrews, G. (2013, January). Health anxiety in Australia: prevalence, comorbidity, disability and service use. Retrieved September 23, 2016, from Bjp: https://bjp.rcpsych.org/content/202/1/56

Theconversation. (2014, March 04). Income and wealth inequality: how is Australia faring? Retrieved September 23, 2016, from Theconversation: https://theconversation.com/income-and-wealth-inequality-how-is-australia-faring-23483