Gross Domestic Production and Infant Mortality Rate

Wealth and health are two inseparable entities. While one class may be able to access the low quality of health services, less frequently or even none because of a lack of finance, another class may access a higher level of health services or even regularly due to their stable financial status. Therefore, lifespan is attached to the financial status of an individual notwithstanding that some may also develop lifestyle diseases because of a higher living standard as Carrin et al. (2010) argued that when wealth increases, diseases abound even the more. The quality and duration of life can also be studied and demonstrated across wider populations such as community, national or global level where common factors include catastrophes, finances, hunger, and plagues are common. At the national level, given the burden of the government to subsidize basic needs, a low economic status will result in low service delivery while the same is contrary in cases where the economy of the nation is stable. In infants, the trait is marked where infants in developing countries face higher mortality rate as compared to their counterparts in economically stable nations. The measure of the economic status of a nation is provided regarding its gross domestic product per capita while the deaths of infants are measured in terms of infant mortality rate per 1000 infants. 

Keywords: infant mortality rate (IMR), gross domestic production (GDP) 

Literature Review 

Defining Gross Domestic Production (GDP) and its Economic Relevance 

GDP is the tally of nation's expenditure in a given year; the expenditure, in this case, involves every single item bought, sold, exported, or imported by the people, or the government (Brezina, 2011). GDP is often estimated regarding percentage, therefore, a change in GDP by a percentage of 3% (Brezina, 2011). GDP is an indication that the economy has changed by 3%. GDP has been used traditionally as a measure to estimate the productivity of a nation (Lepenies, 2016). Besides other factors such as tax and wealth declarations and records, GDP has proved to be more effective in estimating production per head. Information derived from the GDP is applied in the determination of best options to be pursued in social, political and physical sciences such as medicine (Lepenies, 2016). Therefore, the calculation of GDP is not only an indicator but also a power that can be used effectively for policymaking. The historical development of the concept of GDP locates at a period of political turmoil that plagued the continent of Europe in countries such as England and France (Lepenies, 2016). The age of revolutions was marked by periods of hunger, increased death, and calamities such as diseases. The economic production was low seeing men engage in war and less productivity; the concept served to indicate the nation's economic productivity (Lepenies, 2016). Therefore, an increase or a fall of a nation’s GDP indicates an increase in economic growth or a fall in economic growth respectively. 

Defining Infant Mortality Rate (IMR) 

Infants are at greater risk of dying given the underdeveloped immunity. As such, the care administered to the young will determine whether they will stay longer or die as infants. Research has indicated that there is a myriad list of factors contributing to the infant mortality rate; such factors include socioeconomic, health and demographic factors (Mondal, Hossain & Ali, 2009). Research by Mondal, Hossain and Ali (2009) indicates that infant mortality rate deferrers by the parents level of education, quality of sanitation at homes, fathers occupations, mothers age at birth, mother’s breastfeeding patterns, and the birth gap between one child and the next (Mondal, Hossain & Ali, 2009). The research found that there was a high mortality rate among families with low education level, poor sanitation, low income, older parents, poor breastfeeding, and small gaps between children. Besides, the research proved that mortality rate was influenced by accessibility to health facilities whereby families that could not access adequate health care such as immunization for their children had higher infant mortality rate as compared to those who accessed health services adequately and in time (Mondal, Hossain & Ali, 2009). Therefore, according to this study, the infant mortality rate is increased by poor socioeconomic, health and demographic factors while the same is lower in those cases where the factors mentioned above are readily available to all. 

Linking a fall or a rise of GDP to IMR 

As pointed out, GDP is the measure of the economic growth of a country regarding expenditure, import, and export by the government and individuals. As a value calculated per head, the figure is also a demonstration of the economic strength of citizens of a nation. Therefore, a higher GDP indicates that the citizens have a higher economic power while a lower GDP is an indicator of low economic power among the citizens. Concerning IMR, socioeconomic, demographic and health factors contribute to the infant mortality rate. These factors depend on the economy of a country and the economic potential of the citizens. Finance is needed by the government to ensure that health services are availed to all and that other basic needs are subsidized so that all can afford them. 

Moreover, families need finance to take care of their basic needs as well as luxurious needs. A lower GDP will inhibit the government's and families ability to meet the basic demands such as health care, food, education and so on which will result in low care for infants increasing the is mortality rate. Therefore, an observation is drawn that the higher the GDP, the lower the IMR while the lower the GDP, the higher the IMR. From this principle, those countries with a lower GDP, the IMR will be high as compared to those with a higher GDP. 

Methodology 

Type of data and sample size 

The research study involved collection of quantitative data on the IMR and GDP in 30 countries including Switzerland, United States, Germany, Japan, China, Australia, India, Canada, United Kingdom , France, Brazil, Italy, Russia, South Korea, Spain, Mexico, Jamaica, Israel, Singapore, Ireland, Denmark, Philippines, Indonesia, Afghanistan, Haiti, Chad, Burkina Faso, Nigeria, Pakistan, and Ethiopia. The samples are widely distributed representation of the global representation of the first world, second world, and third world countries. 

Data analysis 

The data was compiled in table format and analyzed through excel whereby 95% confidence intervals, mean, median, mode, standard deviation, and variances were calculated. 

Data presentation 

The data was presented on a table and graphically. The IMR values were plotted on the Y-axis against the GDP on the x-axis. The graphical plot resulted in a straight line falling towards the right. The graphical presentation produced a visual image of the impact of GDP on the IMR as compared to raw data compiled in the table. 

Results 

The data from the 30 countries are presented in table 1 below. The results are not arranged systematically but randomly and therefore are not an indication of the rank from ascending or descending order as per a country’s GDP. Given the amount of data, the data will be classified based on range. The GDP and IMR for thirty countries are shown in the table below. The results indicate a higher IMR in countries with low GDP and low IMR in countries with higher GDP. 

Table 1 Countries and their GDP and IMR data 

Observation #	Country	Year	x [GDP per capita in USD]	y [IMR]
1.	Switzerland	2017	$76,667.44	3.60
2.	United States	2017	$53,128.54	5.80
3.	Germany	2017	$46,747.19	3.40
4.	Japan	2017	$48,556.93	2.00
5.	China	2017	$7,329.09	12.00
6.	Australia	2017	$55,925.93	4.30
7.	India	2017	$1,963.55	39.10
8.	Canada	2017	$5,1315.89	4.50
9.	United Kingdom	2017	$42,514.49	4.30
10.	France	2017	$42,567.74	3.20
11.	Brazil	2017	$10,888.98	17.50
12.	Italy	2017	$34,877.03	3.30
13.	Russia	2017	$11,441.00	6.80
14.	South Korea	2017	$26,152.03	3.00
15.	Spain	2017	$32,405.75	3.30
16.	Mexico	2017	$9,946.16	11.60
17.	Jamaica	2017	$4,798.21	12.80
18.	Israel	2017	$34,134.81	3.40
19.	Singapore	2017	$55,235.51	2.40
20.	Ireland	2017	$74,433.46	3.60
21.	Denmark	2017	$61,582.17	4.00
22.	Philippines	2017	$2,891.36	21.40
23.	Indonesia	2017	$4,130.66	22.70
24.	Afghanistan	2017	$618.30	110.60
25.	Haiti	2017	$728.92	46.80
26.	Chad	2017	$823.43	85.40
27.	Burkina Faso	2017	$688.53	72.20
28.	Nigeria	2017	$2412.41	69.80
29.	Pakistan	2017	$1,222.52	52.10
30.	Ethiopia	2017	$549.80	49.60

The graphical representation of the above data is contained in figure 1 below 

Figure 1 GDP per capita vs. IMR 

Interpretation 

From the data, countries with a higher GDP have low IMR while countries with low GDP have a high IMR. For example, counties with GDP of more than $20,000 have the IMR as low as 3.0 while countries with low GDP of lower than $20,000 have the IMR above 3.0 with some as high as above 50.0. The graphical representation indicates that as the GDP increases, the IMR reduces. 

From the 95% confidence intervals calculations for the GDP and the IMR, one has a 95% that most of the GDP lies within a range of 17534.74 -35577.08 while IMR lies with the range of 12.33 - 33.29. This is a rough estimate of the global GDP and IMR indicating that countries with GDP lower than 17534.74 B are on the extreme end are likely to face a higher IMR while countries whose GDP lies above 35577.08 are likely to face even lower IMR. Further, the confidence intervals locate countries outside this bracket to be within a special bracket of either very poor economic growth or more stable economic growth. Therefore, higher GDP is reflected in low IMR while low GDPs are reflected in high IMR, which further implies that the two elements are inseparable. 

Limitations of the study 

The limitation of this study is that it consulted available data as opposed to conducting actual research on the field where other factors affecting IMR could be ascertained. However, the study is reliable since the results used are from a good number of countries indicating an associative pattern between GDP and IMR. 

Recommendations 

Countries with low GDP and high IMR should make a wise investment in those areas that will ensure a high survival rate for the infants. Notably, the sustainable population will form the labor of tomorrow and therefore neglecting to sustain the infants through their growth, education, and maturity will cripple the economy of such countries and bequeath to the nation a labor force comprised of older generations with fewer younger generation to steer the economy. Therefore, future studies should focus on other factors that affect the IMR other than GDP. 

Conclusions 

GDP is a significant measure of the economic strength of a nation and its citizens at large. A lower GDP is an indicator of low economic growth and financially challenged citizens. Similarly, a higher GDP is an indicator of positive economic growth. The value of the GDP is reflected in the lifestyle of the nation regarding the government's ability to subsidize basic needs and provide services such as security, education, and health. Therefore, a country with a low GDP will face challenging times in providing basic needs and services as compared to countries with stable GDP. The same pattern is expected in the citizens' ability to purchase goods and cater for services. Since GDP affects health services such as infant health care, countries with lower GDP have poor health services, over dependency and difficulty facing the government to provide basic needs and services, which translate to high mortality rate among infants. Countries with low GDP and high IMR should invest heavily in health services and education to ensure that the younger generation is protected and guided as future labor force whilst improving the GDP. 

References 

Brezina, C. (2011).  Understanding the gross domestic product and the gross national product . New York City, NY: The Rosen Publishing Group. 

Carrin, G., Buse, K., Heggenhougen, K., & Quah, S. R. (Eds.). (2010).  Health systems policy, finance, and organization . Cambridge, MA: Academic Press. 

Lepenies, P. (2016).  The power of a single number: A political history of GDP . New York City, NY: Columbia University Press. 

Mondal, M. N. I., Hossain, M. K., & Ali, M. K. (2009). Factors influencing infant and child mortality: A case study of Rajshahi District, Bangladesh.  Journal of Human Ecology, 26 (1), 31-39. 

Roberts, J. L., & Ibitoye, I. (2012).  The big divide: A ten-year report of small island developing states and the millennium development goals . London: Commonwealth Secretariat.